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Relating Structure and Function in the Human Brain: Relative Contributions of Anatomy, Stationary Dynamics, and Non-stationarities

* E-mail: [email protected]

Affiliation Laboratoire d'Imagerie Fonctionnelle, UMR678, Inserm/UPMC Univ Paris 06, Paris, France

• Arnaud Messé, 
• David Rudrauf, 
• Habib Benali, 
• Guillaume Marrelec

• Published: March 20, 2014
• https://doi.org/10.1371/journal.pcbi.1003530
• Reader Comments

Investigating the relationship between brain structure and function is a central endeavor for neuroscience research. Yet, the mechanisms shaping this relationship largely remain to be elucidated and are highly debated. In particular, the existence and relative contributions of anatomical constraints and dynamical physiological mechanisms of different types remain to be established. We addressed this issue by systematically comparing functional connectivity (FC) from resting-state functional magnetic resonance imaging data with simulations from increasingly complex computational models, and by manipulating anatomical connectivity obtained from fiber tractography based on diffusion-weighted imaging. We hypothesized that FC reflects the interplay of at least three types of components: (i) a backbone of anatomical connectivity, (ii) a stationary dynamical regime directly driven by the underlying anatomy, and (iii) other stationary and non-stationary dynamics not directly related to the anatomy. We showed that anatomical connectivity alone accounts for up to 15% of FC variance; that there is a stationary regime accounting for up to an additional 20% of variance and that this regime can be associated to a stationary FC; that a simple stationary model of FC better explains FC than more complex models; and that there is a large remaining variance (around 65%), which must contain the non-stationarities of FC evidenced in the literature. We also show that homotopic connections across cerebral hemispheres, which are typically improperly estimated, play a strong role in shaping all aspects of FC, notably indirect connections and the topographic organization of brain networks.

Author Summary

By analogy with the road network, the human brain is defined both by its anatomy (the ‘roads’), that is, the way neurons are shaped, clustered together and connected to each others and its dynamics (the ‘traffic’): electrical and chemical signals of various types, shapes and strength constantly propagate through the brain to support its sensorimotor and cognitive functions, its capacity to learn and adapt to disease, and to create consciousness. While anatomy and dynamics are organically intertwined (anatomy contributes to shape dynamics), the nature and strength of this relation remain largely mysterious. Various hypotheses have been proposed and tested using modern neuroimaging techniques combined with mathematical models of brain activity. In this study, we demonstrate the existence (and quantify the contribution) of a dynamical regime in the brain, coined ‘stationary’, that appears to be largely induced and shaped by the underlying anatomy. We also reveal the critical importance of specific anatomical connections in shaping the global anatomo-functional structure of this dynamical regime, notably connections between hemispheres.

Citation: Messé A, Rudrauf D, Benali H, Marrelec G (2014) Relating Structure and Function in the Human Brain: Relative Contributions of Anatomy, Stationary Dynamics, and Non-stationarities. PLoS Comput Biol 10(3): e1003530. https://doi.org/10.1371/journal.pcbi.1003530

Editor: Claus C. Hilgetag, Hamburg University, Germany

Received: August 14, 2013; Accepted: February 8, 2014; Published: March 20, 2014

Copyright: © 2014 Messé et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: This work is supported by the Inserm and the University Pierre et Marie Curie (Paris, France). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Coherent behavior and cognition involve synergies between neuronal populations in interaction [1] – [3] . Even at rest, in the absence of direct environmental stimulations, these interactions drive the synchronization of spontaneous activity across brain systems, shedding light on the large-scale anatomo-functional organization of the brain [4] . The study of such patterns of synchronization has known important developments due to recent methodological advances in brain imaging data acquisition and analysis. These advances have enabled investigators to estimate interactions in the brain by measuring functional connectivity (FC) from resting-state functional MRI (rs-fMRI). Analyses of FC at rest have supported the hypothesis that the brain is spatially organized into large-scale intrinsic networks [5] – [7] , e.g. the so-called resting-state networks [8] , [9] , such as the default mode network, which have been linked to central integrative cognitive functions [10] – [13] . The study of large-scale intrinsic networks from rs-fMRI has become a central and active area for neuroscience research. However, the mechanisms and factors driving FC, as well as their relative contribution to empirical data, are still highly debated [14] and remain to be elucidated.

Theoretical rationale and empirical findings support the hypothesis that FC is driven and shaped by structural connectivity (SC) between brain systems, i.e., by the actual bundles of white matter fiber connecting neurons [15] . As a first approximation, SC can be inferred from fiber tractography based on diffusion-weighted imaging (DWI) [16] – [19] . A recent study [20] , which focused on a small subset of robustly estimated structural connections, demonstrated the existence of a statistical, yet complex, correspondence between FC and specific features of SC (e.g., low vs. high fiber density, short vs. long fibers, intra vs. interhemispheric connections). However, a large part of FC cannot be explained by SC alone [21] . There appears that FC is the result of at least two main contributing factors: (i) the underlying anatomical structure of connectivity, and (ii) the dynamics of neuronal populations emerging from their physiology [3] . A key issue is to better understand the relative contributions of these two components to FC. Besides, recent studies using windowed analyses have suggested that FC estimated over an entire acquisition session (referred to as ‘stationary FC’ in the literature) breaks down into a variety of reliable correlation patterns (also referred to as ‘dynamic FC’ or ‘non-stationarities’) when estimated over short time windows (30 s) [14] , [22] . The authors advocated that FC estimated over short time windows (or windowed FC, for short) mostly reflects recurrent transitory patterns that are aggregated when estimating FC over a whole session. They further suggested that whole-session FC may only be an epiphenomenon without clear physiological underpinning, and not the reflection of an actual process with stationary FC [14] . This perspective remains to be reconciled with the fact that whole-session FC has been found to be highly reproducible, functionally meaningful and a useful biomarker in many pathological contexts [23] , [24] . Note that, in the recent literature of fMRI data analysis, stationarity implicitely refers to a stationary FC (i.e., the invariance of FC over time), to be contrasted with the more general notion of (strong) stationarity, where a model or process is stationary if its parameters remain constant over time [25] , [26] . SC being temporally stable at the scale of a whole resting state fMRI session (typically 10 min), we could expect SC to drive a stationary process (in the strong sense). Since SC is furthermore expected to drive FC, we can hypothesize that this stationary process contributes to generate a stationary FC.

In order to bring together the structural and dynamical components underlying FC, some studies have used computational models that incorporate SC together with biophysical models of neuronal activity to generate coherent brain dynamics [27] – [32] . This approach has yielded promising results for the understanding of the relationship between structure and function [17] , [33] , [34] . Here, we used a testbed of well-established generative models simulating neuronal dynamics combined with empirical measures, to investigate the relative contributions of anatomical connections, stationary dynamics, and non-stationarities to the emergence of empirical functional connectivity. In particular, we considered the following hypotheses: (H1) part of FC directly reflects SC; (H2) models of physiological mechanisms added to SC increase predictive power all the more as they are complex; (H3) part of the variance of FC that is unexplained by models is due to issues in the estimation of SC, e.g., problems with measuring homotopic connections; (H4) there is an actual stationary process reflected in whole-session FC that is not merely an artifact but substantially reflects the driving of the dynamics by SC.

In order to test these hypotheses and estimate the relative contribution of anatomy, stationary dynamics and non-stationarities to FC, we relied on the following approach. After T 1 -weighted MRI based parcellation of the brain ( N  = 160 regions), SC was estimated using the proportion of white matter fibers connecting pairs of regions, based on probabilistic tractography of DWI data [35] . FC was measured on rs-fMRI data using Pearson correlation between the time courses of brain regions. We quantified the correlation between SC alone and FC as a reference, and also fed SC to generative neurodynamical models of increasing complexity: a spatial autoregressive (SAR) model [36] , analytic models with or without conduction delays [28] – [31] , [37] , and biologically constrained models [29] , [32] . Importantly, all these models were used in their stationary regime in the strong sense, since their parameters were not changed during the simulations. Of these models, only the SAR is explicitely associated with a stationary FC; other, more complex models, generate dynamics that are compatible with a non-stationary FC. We computed FC from data simulated by these models and compared the results to empirical FC. For each model, performance was quantified using predictive power [29] , for each subject as well as on the ‘average subject’ (obtained by averaging SC and empirical FC across subjects). Values for the model parameters were based on the literature, except for the structural coupling strength that was optimized in order to maximize each model's performance.

Predictive power of models

In agreement with H1, SC explained a significant amount of the variance of whole-session FC for all subjects, as did all generative models (permutation test, p <0.05 corrected) ( Figure 1 , panel A). Generative models predicted FC better than SC alone (paired permutation test, p <0.05 corrected). Predictive power obtained with the average subject ranged from 0.32 for SC alone to 0.43 for the SAR model ( Table 1 ). For a given model, predictive power was reproducible across subjects. Contrary to our hypothesis H2, generative models had similar performance, and complexity was not predictive of performance. The results remained unchanged when no global signal regression was applied ( Figure S1 ). Also, findings were found to be similar for SC alone and the SAR model at finer spatial scales ( N  = 461 and N  = 825 regions, Figure S2 ) and consistent with a replication dataset ( Figure S3 ). Most importantly, a large part of the variance ( R 2 ) in the empirical data (at least 82%) remained unexplained by this first round of simulations.

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(A) Predictive power for all connections and when restricted to intra/interhemispheric, direct/indirect connections. For each type of connections and each model, we represented the individual predictive powers (bar chart representing means and associated standard deviations), as well as the predictive power for the average subject computed using the original SC (diamonds), or after adding homotopic connections (circles). Of note, SC alone has no predictive power (zero) for the subset of indirect connections, by definition. (B) Patterns of SC, empirical FC and FC simulated from the SAR model for the average subject and associated scatter plot of simulated versus empirical FC (solid line represents perfect match). SARh stands for the SAR model with added homotopic connections. Matrices were rearranged such that network structure is highlighted. Homologous regions were arranged symmetrically with respect to the center of the matrix; for instance, the first and last regions are homologous. (C) Similarity of functional brain networks across subjects (bar chart with means and associated standard deviations), for the average subject (diamonds), and when adding homotopic connections (circles) (left). Network maps for the average subject and empirical FC, as well as for FC simulated using either the SAR model with original SC or the SARh.

https://doi.org/10.1371/journal.pcbi.1003530.g001

https://doi.org/10.1371/journal.pcbi.1003530.t001

Role of homotopic connections

We reasoned (see hypothesis H3) that part of the unexplained variance could reflect issues with the estimation of SC from DWI, which can be expected because of limitations in current fiber tracking algorithms and the problem of crossing fibers [38] . We know for instance that many fibers passing through the corpus callosum are poorly estimated in diffusion imaging, in particular those connecting more lateral parts of the cerebral cortex [39] . Yet, the corpus callosum is the main interhemispheric commissure of the mammal brain, see [40] . It systematically connects homologous sectors of the cerebral cortex across the two hemispheres in a topographically organized manner, with an antero-posterior gradient, through a system of myelinated homotopic fibers or ‘homotopic connections’. The hypothesis of an impact of SC estimation problems on FC unexplained variance was supported by the observation that, in our results, intrahemispheric connections yielded on average a much higher predictive power (e.g., 0.59 for the SAR model) than interhemispheric connections (0.16 for the SAR model).

In order to further test the role of white matter connections in driving FC, we artificially set all homotopic connections to a constant SC value (0.5) for the average subject and reran all simulations. As a result, the predictive power strongly increased for all models ( Figure 1 , panels A and B), ranging from 0.39 for SC alone to 0.61 for the SAR model ( Table 1 ). Thus the variance unexplained (1- R 2 ) was reduced to 63%. Moreover, predictive power for intra and interhemispheric connections became equivalent (0.60 and 0.62, respectively). Interestingly, adding homotopic connections also led to a substantial increase in predictive power for indirect connections, that is, pairs of regions for which SC is zero (increasing from 0.07 to 0.45). The effect of adding interhemispheric anatomical connections on increasing predictive power was highly specific to homotopic connections. When applying the SAR model to the SC matrix with added homotopic connections and randomly permuting (10 000 permutations) the 80 corresponding interhemispheric connections (one region in one hemisphere was connected to one and only one region in the other hemisphere), the predictive power strongly decreased, even compared to results with the original SC ( Figure 2 , panel A). Moreover, we further assessed the specificity of this result by systematically manipulating SC. In three different simulations, we randomly removed, added, and permuted structural connections (10 000 times). In all cases, the predictive power decreased as a function of the proportion of connections manipulated ( Figure 2 , panel B). Moreover, changes induced by these manipulations remained small (<0.05), far below the changes that we were able to induce by adding homotopic connections. All in all, these results suggest that homotopic connections play a key role in shaping the network dynamics, in a complex and non-trivial manner.

(A) Predictive power of the SAR model with original SC (green), when adding homotopic connections (‘SARh’, red), or with shuffled homotopic connections (black). (B) Predictive power of the SAR model with original SC (red) and when SC values were randomly permuted, removed or added (from left to right). For each graph, predictive power was quantified as a function of the percentage of connections manipulated.

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Predicting functional brain networks

Beyond predicting the overall pattern of FC, we also assessed whether models could predict the empirical organization of FC into a set of intrinsic networks. Connectivity matrices were clustered into groups of non-overlapping brain regions showing high within-group correlation and low between-group correlation, and the resulting partitions into functional brain networks were compared between empirical and simulated FC using the adjusted Rand index (see Methods ). Again, the SAR model tended to perform best among all computational models ( Figure 1 , panel C).

Without adding homotopic connections in the SC matrix, the simulated networks highly differed from the empirical networks. In particular, most networks were found to be lateralized. After adding homotopic connections, the resemblence between simulated and empirical networks greatly improved. Networks were more often bilateral and overall consistent with the topography of empirical functional networks, including somatosensory, motor, visual, and associative networks. High FC between the amygdala and ventral-lateral sectors of the prefrontal cortex was also correctly predicted by the simulations. There were nevertheless some notable differences. First, the clustering of empirical FC yielded a long-range fronto-parieto-temporal association network ( Figure 1 , panel C, cyan) that was not observed in simulated FC as such. Second, a parieto-temporal cluster ( Figure 1 , panel C, red), which was associated with thalamo-striatal networks, was predicted by simulations but was not present in the empirical data. Third, a cluster encompassing the entire cingulate gyrus and precuneus ( Figure 1 , panel C, green) was predicted by simulations but was broken down into more clusters in the empirical data.

Stationary FC, non-stationary FC, and non-stationarities

The results above show that SC plays a causal role in FC, but one can still wonder what aspects of the underlying dynamics are the most directly related to this influence. A hypothesis is that SC, in combination with stable physiological processes (e.g., overall gain in synaptic transmission), drives a stationary regime of the dynamics. This hypothesis is supported by the finding that all models tested in this study, which were used in a stationary regime (in the strong sense), could explain significantly more variance than SC alone. Furthermore, the fact that the SAR could predict FC significantly better than all other models is evidence that this stationary regime is associated with stationary FC (paired permutation test, p <0.05 corrected).

But, clearly, many variations in the dynamical patterns of brain activity, be it in the process of spontaneous cognition, physiological regulation, or context-dependent changes, cannot be expected to be associated with a purely stationary FC. Modeling how the brain dynamics deal with endogenous and environmental contexts should require more complex models, either stationary or non-stationary, that are able to generate non-stationary (i.e., time-varying) patterns of FC. Given that at best 37% of the variance could be explained by the model of a purely stationary FC (the SAR), we can wonder why the models of higher complexity used in our simulation testbed did not perform better in predicting FC. One possible hypothesis is that the SAR model was favored in the simulations, because we estimated FC over about 10 minutes of actual brain dynamics. In such configuration, we can imagine that the non-stationarities of FC cancel out, the estimation effectively keeping the stationary part of FC. We thus wondered whether the more complex models would better perform when non-stationary FC had the potential of being more strongly reflected in the data. We approached this question by computing predictive power on windowed FC as a function of the length of the time-window used [22] , for all possible time-windows over which FC could be estimated and for all models. We also investigated the effect of simulation duration (see Methods ). We found that the relative performance of more complex models was still lower than that of the SAR model ( Figures 3 and S4 ). The average predictive power was lower for shorter time-windows and increased towards a limit for longer time-windows. The SAR model behaved like an ‘upper-bound’ for predictive power. The performance of all other models, irrespective of the size of the time-window, was between that of SC alone and that of the SAR model.

Predictive power as a function of time-window length across subjects (left) and of duration of simulated runs on the average subject (right). For color code see Figure 1 .

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A straightforward explanation is that the non-stationary patterns of FC, as generated by the simulation models, did not match the non-stationary patterns of the empirical FC as they unfolded during the acquisition in the brain of the participants. Context-dependent and transient dynamics are likely to be missed by models of the dynamics that cannot be contextually constrained in the absence of further information. It is thus difficult to infer how much of the 63% of unexplained variance remaining in whole-session FC actually reflect physiologically meaningful non-stationary FC, and more broadly, non-stationary dynamics.

In the present study, we investigated the respective contributions of anatomical connections, stationary dynamics, and non-stationarities to the emergence of empirical functional connectivity. We compared the performance of computational models in modeling FC and manipulated SC in order to analyze the impact of SC on FC, with and without the filter of combined physiological models of the dynamics.

The importance of white matter fiber pathways in shaping functional brain networks is a known fact, for a review, see [15] , [17] , [21] , [23] . Previous modeling studies have supported the importance of the underlying anatomical connections, i.e., SC, in shaping functional relationships among brain systems [16] , [41] , [42] . In agreement with our hypothesis H1, we showed that functional connectivity could at least in part be explained by structural connectivity alone. Adding homotopic connections in the matrix of SC, we found a slight increase in explained variance when considering the prediction of whole-session FC from SC alone (+4% of explained variance). In agreement with H2, adding models of physiological interactions above and beyond SC alone increased the explained variance in whole-session FC, by 8% for the best performing model, the SAR model, when no homotopic connections were added, and by 22% when homotopic connections were added. This latter fact, which strongly supports H3, suggests a complex interplay between anatomy as reflected by SC and physiological mechanisms in generating FC. This impact of SC manipulations on predicted FC pertained not only to direct but also to indirect connections. For indirect connections, whole-session FC was much better predicted after adding homotopic connections to SC than before adding them (0.45 versus 0.07 in predictive power). The problem of limited predictive power for FC based on SC when considering indirect connections has puzzled the field [43] . For this reasons many studies only assess the performance of models on direct connections. Here, we showed that a major factor in driving FC for indirect anatomical connections (+20% in explained variance) is the interplay between a subset of anatomical connections, i.e., homotopic connections (which are typically underestimated by DWI), and physiological parameters that generate the dynamics underlying FC, themselves conditioned by the possible interactions defined by SC.

Contrary to our expectation (see hypothesis H2), all models tended to perform similarly, irrespective of model complexity. The best performing model in most cases was the SAR model, a model of stationary FC driven by SC, with 63% of the variance remaining unexplained. It is likely that, above and beyond problems with the estimation of SC from DWI, and other incompressible sources of irrelevant noise, much of the unexplained variance in FC relates to non-stationary patterns in FC, and more generally to non-stationarities in the strong-sense. Such non-stationarities are difficult to model in the experimentally unconstrained resting-state and in the absence of further information regarding the specific parameters shaping FC. Irrespective of their complexity, computational models are only capable of generating prototypical brain activities, and not the subject-dependent activity that took place in the brain of the participants during scanning. The scientific necessity of modeling brain dynamics is hindered by such uncertainty and it will be a challenge to find solutions to approach this problem [26] , [44] . Even though one objective for neuroscience is to propose generative models that are capable of generating detailed neuronal dynamics, generative models cannot be informed by this unknown context and, as a consequence, cannot generate context-dependent activity in a manner that would be predictive of empirical data, in the absence of additional measures and experimental controls. Nevertheless, and perhaps for that very reason, the study of non-stationarity in FC should become of central interest for the field, as such non-stationarities could explain much of FC (up to 63% according to our simulation results), and thus reflect critical mechanisms for neurocognitive processing.

In the absence of adequate modeling principles, determining the precise contribution of non-stationarities to the unexplained variance in FC is impossible, as other confounding sources of unexplained variance are expected. As we showed, even naive manipulations aimed at estimating the impact of the known errors in DWI-based reconstruction of homotopic connections showed that such errors could cause 20% of the unexplained variance in predicting empirical FC. How DWI and fiber tracking should be used for an optimal estimation of structural connectivity is still a topic of intense debates [45] – [48] . It is likely that part of the unexplained variance in predicting FC will be reduced as better estimates of SC become available.

The model showing the best results, the SAR model, explicitly modeled a stationary process with a stationary FC. In line with our hypothesis H4, empirical FC is likely to incorporate stationary components driven by SC. Further knowledge about this stationary process might be gained by analyzing FC computed over much longer periods of time than is commonly performed (e.g., hours versus minutes). This stationary process is itself likely to be only locally stationary, as it might be expected that slow physiological cycles, from nycthemeral cycles to hormonal cycles, development, learning and aging, will modify the parameters controlling it.

In the present study, we did not take into account the statistical fluctuations induced by the fact that the time series were of finite length. Such a finiteness entails that even a model that is stationary in the strong sense could generate sample moments that fluctuate over time. For instance, the sample sum of square of a multivariate normal model with covariance matrix Σ computed from time series of size N is not equal to N Σ but is Wishart distributed with N -1 degrees of freedom and scale matrix Σ. This phenomenon will artificially increase the part of variance that cannot be accounted for by stationary models and, hence, play against stationary models. Since it is conversely very unlikely for a non-stationary model to generate sample moments that are constant over time, statistical fluctuations cannot at the same time artificially increase the part of variance that can be accounted for by stationary models. As a consequence, not considering these statistical fluctuations made us underestimate the part of variance that can be accounted for by models that are stationary in the strong sense. In other words, our estimate of the part of variance accounted for by a stationary model is a lower bound for the true value. We can therefore be confident that taking statistical fluctuations into account will only strengthen H4.

Our goal here was to investigate how current generative models of brain acticity fare in predicting the relationship between structure and function. The complexity of some of these models was such that the simulations included here were only possible thanks to a computer cluster. The behavior of all these models depends on the values of some parameters and, in the present study, we set these parameters in agreement with the literature. In what measure this choice affects how well models predict FC is unclear. Yet a full investigation of this issue remains beyond the scope of this study, since parameter optimization through extensive exploration of the parameter space for all models is at this stage unrealistic. Nevertheless, in order to get a sense of the sensitivity of our results to parameter values in a way that is compatible with the computational power available, we explored the behavior of the Fitzhugh-Nagumo, Wilson and Kuramoto models over a subset of the parameter space (see Figures S5 and S6 ). We found that parameter values had little influence on predictive power, which, in all cases, remained below that of the SAR, the simplest model tested.

We formulated H2 to test for the existence of a relationship between complexity and realism in the models that we used. Indeed, there should exist a very tight connection between the two, since the more complex generative models in our study have been designed to take biophysical mechanisms into account, with parameters that are physiologically relevant and values often chosen based on prior experimental results. Now, realism usually comes at the cost of complexity. As a consequence, it is often (implicitely) assumed that, among the models we selected, the more complex a model is, the more realistic it will also be and the better it will fit the data. This is the reason why we stated H2, based on such rationale inspired from the literature, in order to put such hypothesis to the test. The results show that for the models we used, with their sets of parameters, an increase in complexity was not associated with an increase in performance. This suggests that, for these models, complexity and realism are not quite as tightly connected as expected.

Given that the SAR model is the only model that does not include a step of hemodynamic modeling (Balloon-Windkessel), it cannot be ruled out that the superiority of the SAR reflects issues with this step. In order to check that this is not the case, we computed predictive power for all models without the hemodynamic model. The predictive power was largely insensitive to the presence of the hemodynamic model (see Figure S7 ). In particular, the SAR model remained overall an upper bound in terms of predictive power.

Finally, we should note that we relied on a definition of SC restricted to the white matter compartment. Although this is standard in the field, in reality, local intrinsic SC exists in the gray matter. However, current models generally make prior assumptions about such SC. Moreover, intrinsic SC currently remains impossible to measure reliably for the entire brain.

In spite of the complexity of the problems and the limitations of current modeling approaches, computational modeling of large-scale brain dynamics remains an essential scientific endeavor. It is key to better understand generative mechanisms and make progress in brain physiology, physiopathology and, more generally, theoretical neuroscience. It is also central to the endeavor of searching for accurate and meaningful biomarkers in aging and disease [49] . Moreover, computational modeling of FC opens the possibility of making inference on specific biophysical parameters, including inference about the underlying anatomical connectivity itself. In spite of their limited predictive powers, simpler models can be useful in this context. The SAR model, introduced in [36] , may appear well-suited to model essential stationary aspects of the generative mechanisms of FC. One interest of such a simple and analytically tractable model is that, beyond its very low computational burden, it could be the basis for straightforward estimation of the model parameters that can be used to compare clinical populations, and could constitute a potentially important biomarker of disease.

Ethics statement

All participants gave written informed consent and the protocol was approved by the local Ethics Committee of the Pitié-Salpêtrière Hospital (number: 44-08; Paris, France).

Twenty-one right-handed healthy volunteers were recruited within local community (11 males, mean age 22±2.4 years). Data were acquired using a 3 T Siemens Trio TIM MRI scanner (CENIR, Paris, France). For acquisition and preprocessing details, see Text S1 . For each subject, the preprocessing yielded three matrices: one of SC, one with the average fiber lengths, and one of empirical FC. These matrices were also averaged across subjects (‘average subject’).

Simulations

We used eight generative models with various levels of complexity: the SAR model, a purely spatial model with no dynamics that expresses BOLD fluctuations within one region as a linear combination of the fluctuations in other regions; the Wilson-Cowan system, a model expressing excitatory and inhibitory neuronal populations activity; the two rate models (with or without conduction delays), simplified versions of the Wilson-Cowan system obtained by considering exclusively the excitatory population; the Kuramoto model, which simulates neuronal activity using oscillators; the Fitzhugh-Nagumo model, which aims at reproducing complex behaviors such as those observed in conductance-based models; the neural-mass model, also based on conductance and with strong biophysiological constraints; and finally, the model of spiking neurons, the most constrained model in the current study which models neuron populations as attractors. For more details, see Text S2 .

All models took an SC matrix as input, and all but the SAR were taken as models of neuronal (rather than BOLD) activity. Simulated fMRI BOLD signal was obtained from simulated neuronal activity by means of the Balloon-Windkessel hemodynamic model [50] , [51] . Global mean signal was then regressed out from each region's time series. Finally, simulated FC was computed as Pearson correlation between simulated time series. For the SAR model, we directly computed simulated FC from the analytical expression of the covariance, see Equation (2) in Text S2 . All models had a parameter that represents the coupling strength between regions. This parameter was optimized separately for each model on the average subject to limit computational burden ( Text S2 ). After optimization, we generated three runs of 8 min BOLD activity and averaged the corresponding FCs to obtain the simulated FC for each dynamical model and each subject. For the average subject, simulated FC was obtained by feeding the average SC matrix to the different models.

Performance

Modeling performance was assessed using predictive power and similarity of spatial patterns. Predictive power was quantified for each subject and for the average subject by means of Pearson correlation between simulated and empirical FC [29] . Regarding the similarity of functional brain networks, SC, empirical FC and simulated FC were decomposed into 10 networks using agglomerative hierarchical clustering and generalized Ward criterion [52] . The resulting networks from SC and simulated FC were compared to the ones resulting from empirical FC using the adjusted Rand index [53] , [54] . The Rand index quantifies the similarity between two partitions of the brain into networks by computing the proportion of pairs of regions for which the two partitions are consistent (i.e., they are either in the same network for both partitions, or in a different network for both partitions). The adjustment accounts for the level of similarity that would be expected by chance only.

Analysis of dynamics

Empirical and simulated windowed FC were computed on individual subjects using sliding time-windows (increment of 20 s) of varying length (from 20 to 420 s by step of 20 s). Predictive power was computed as the correlation between any pair of time-windows of equal length corresponding to simulated and empirical windowed FC, respectively. This approach was only applied to the dynamical models; for SC alone and the SAR model, simulated FC remained, by definition, constant through time and, as a consequence, windowed FC was equaled to whole-session FC. The influence of simulated run duration on predictive power was also investigated. For each model, three runs of one hour were simulated on the average subject. Predictive power was then computed as a function of simulated run duration. For the same reason as above, SC alone and the SAR model did not depend on simulation duration.

Supporting Information

Performance of computational models when no global signal regression was performed. (A) Predictive power for all connections and when restricted to intra/interhemispheric, direct/indirect connections. For each type of connections and each model, we represented the individual predictive powers (bar chart representing means and associated standard deviations), as well as the predictive power for the average subject computed using the original SC (diamonds), or after adding homotopic connections (circles). Of note, SC alone has no predictive power (zero) for the subset of indirect connections, by definition. (B) Patterns of SC, empirical FC and FC simulated from the SAR model for the average subject and associated scatter plot of simulated versus empirical FC (solid line represents perfect match). SARh stands for the SAR model with added homotopic connections. Matrices were rearranged such that network structure is highlighted. Homologous regions were arranged symmetrically with respect to the center of the matrix; for instance, the first and last regions are homologous. (C) Similarity of functional brain networks across subjects (bar chart with means and associated standard deviations), for the average subject (diamonds), and when adding homotopic connections (circles) (left). Network maps for the average subject and empirical FC, as well as for FC simulated using either the SAR model with original SC or the SARh.

https://doi.org/10.1371/journal.pcbi.1003530.s001

Performance of SC alone and the SAR model at finer spatial scales. Predictive power for all connections and when restricted to intra/interhemispheric, direct/indirect connections. For each type of connections and each model, we represented the individual predictive powers (bar chart representing mean and associated standard deviation), as well as the predictive power of the average subject computed using the original SC (diamonds), or after adding homotopic connections (circles).

https://doi.org/10.1371/journal.pcbi.1003530.s002

Performance of computational models on the replication dataset. The replication dataset was from the study of Hagmann and colleagues [55] . Brain network was defined at low anatomical granularity (N = 66 regions), and connectivity measures were averaged over five healthy volunteer subjects. (A) Predictive power for all connections and when restricted to intra/interhemispheric, direct/indirect connections. For each type of connections and each model, we represented the individual predictive powers (bar chart representing means and associated standard deviations), as well as the predictive power for the average subject computed using the original SC (diamonds), or after adding homotopic connections (circles). Of note, SC alone has no predictive power (zero) for the subset of indirect connections, by definition. (B) Patterns of SC, empirical FC and FC simulated from the SAR model for the average subject and associated scatter plot of simulated versus empirical FC (solid line represents perfect match). SARh stands for the SAR model with added homotopic connections. Matrices were rearranged such that network structure is highlighted. Homologous regions were arranged symmetrically with respect to the center of the matrix; for instance, the first and last regions are homologous. (C) Similarity of functional brain networks across subjects (bar chart with means and associated standard deviations), for the average subject (diamonds), and when adding homotopic connections (circles) (left). Network maps for the average subject and empirical FC, as well as for FC simulated using either the SAR model with original SC or the SARh.

https://doi.org/10.1371/journal.pcbi.1003530.s003

Effect of time on performance. Predictive power of computational models as a function of the time-window length for each subject (graphs) and model (color).

https://doi.org/10.1371/journal.pcbi.1003530.s004

Exploration of the parameter space for the Fitzhugh-Nagumo model. (Left) Phase diagrams (i.e., x - y plane) for an uncoupled model ( k  = 0) over various parameter values of α and β . The model operate mostly in an oscillatory regime for the range of parameter values investigated. (Right) Predictive power as a function of α and β . The black dot represents the parameter set used in our simulations, while the black square corresponds to the values from [28] . The values used in our simulations gave rise to higher predictive power than the parameters values from [28] . In any case, for the range of parameters considered, the predictive power always remained lower than that obtained with a SAR model.

https://doi.org/10.1371/journal.pcbi.1003530.s005

Effect of velocity on predictive power. Predictive power as a function of the coupling strength and velocity values in generative models. Black dots represent values used for subsequent simulations. These simulations show that the predictive power is little influenced by velocity. In any case, for the range of parameters considered, the predictive power also always remained lower than that obtained with a SAR model.

https://doi.org/10.1371/journal.pcbi.1003530.s006

Effect of the hemodynamic model. Predictive power for all connections and when restricted to intra/interhemispheric, direct/indirect connections. For each type of connections and each model, we represented the predictive power for the average subject computed using the BOLD signal (diamonds, solid line) or using the neuronal activity (circles, dashed line). Of note, the prediction differs slightly from that of the Figure 1 due to the stochastic component of most models at each run.

https://doi.org/10.1371/journal.pcbi.1003530.s007

Data and preprocessing.

https://doi.org/10.1371/journal.pcbi.1003530.s008

Computational models.

https://doi.org/10.1371/journal.pcbi.1003530.s009

Acknowledgments

The authors are thankful to Olaf Sporns (Department of Psychology, Princeton University, Princeton, USA) and Christopher J. Honey (Department of Psychological and Brain Sciences, Indiana University, Bloomington, USA) for providing the neural-mass model; to Gustavo Deco, Étienne Hugues and Joanna Cabral (Computational Neuroscience Group, Department of Technology, Universitat pompeu Fabra, Barcelona, Spain) for providing the Kuramoto and rate models as well as the spike model; and to Olaf Sporns and Patric Hagmann (Department of Radiology, University Hospital Center and University of Lausanne, Lausanne, Switzerland) for sharing their data for replication. We would also like to thank them for fruitful discussions. The authors are grateful to Stéphane Lehéricy and his team (Center for Neuroimaging Research, Paris, France) for providing them with the data, and especially to Romain Valabrègue for his help in handling coarse-grained distributed parallelization of computational tasks.

Author Contributions

Conceived and designed the experiments: AM DR HB GM. Performed the experiments: AM DR GM. Analyzed the data: AM. Wrote the paper: AM DR HB GM.

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• 40. Schmahmann JD, Pandya DN (2006) Fiber pathways of the brain. Oxford University Press.
• 52. Batageli V (1988) Generalized ward and related clustering problems. In: Bock HH, editor. Classification and Related Methods of Data Analysis. North-Holland. pp. 67–74.

March 25, 2019

The Adult Brain Does Grow New Neurons After All, Study Says

Study points toward lifelong neuron formation in the human brain’s hippocampus, with implications for memory and disease

By Karen Weintraub

Cerebral cortical neuron.

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If the memory center of the human brain can grow new cells, it might help people recover from depression and post-traumatic stress disorder (PTSD), delay the onset of Alzheimer’s, deepen our understanding of epilepsy and offer new insights into memory and learning. If not, well then, it’s just one other way people are different from rodents and birds.

For decades, scientists have debated whether the birth of new neurons—called neurogenesis—was possible in an area of the brain that is responsible for learning, memory and mood regulation. A growing body of research suggested they could, but then a Nature paper last year raised doubts.

Now, a new study published in March in another of the Nature family of journals— Nature Medicine —tips the balance back toward “yes.” In light of the new study, “I would say that there is an overwhelming case for the neurogenesis throughout life in humans,” Jonas Frisén, a professor at the Karolinska Institute in Sweden, said in an e-mail. Frisén, who was not involved in the new research, wrote a News and Views about the study in the March issue of Nature Medicine .

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Not everyone was convinced. Arturo Alvarez-Buylla was the senior author on last year’s Nature paper, which questioned the existence of neurogenesis. Alvarez-Buylla, a professor of neurological surgery at the University of California, San Francisco, says he still doubts that new neurons develop in the brain’s hippocampus after toddlerhood.

“I don’t think this at all settles things out,” he says. “I’ve been studying adult neurogenesis all my life. I wish I could find a place [in humans] where it does happen convincingly.”

For decades, some researchers have thought that the brain circuits of primates—including humans—would be too disrupted by the growth of substantial numbers of new neurons. Alvarez-Buylla says he thinks the scientific debate over the existence of neurogenesis should continue. “Basic knowledge is fundamental. Just knowing whether adult neurons get replaced is a fascinating basic problem,” he says.

New technologies that can locate cells in the living brain and measure the cells’ individual activity, none of which were used in the Nature Medicine study, may eventually put to rest any lingering questions.

A number of researchers praised the new study as thoughtful and carefully conducted. It’s a “technical tour de force,” and addresses the concerns raised by last year’s paper, says Michael Bonaguidi, an assistant professor at the University of Southern California Keck School of Medicine.

The researchers, from Spain, tested a variety of methods of preserving brain tissue from 58 newly deceased people. They found that different methods of preservation led to different conclusions about whether new neurons could develop in the adult and aging brain.

Brain tissue has to be preserved within a few hours after death, and specific chemicals used to preserve the tissue, or the proteins that identify newly developing cells will be destroyed, said María Llorens-Martín, the paper’s senior author. Other researchers have missed the presence of these cells, because their brain tissue was not as precisely preserved, says Llorens-Martín, a neuroscientist at the Autonomous University of Madrid in Spain.

Jenny Hsieh, a professor at the University of Texas San Antonio who was not involved in the new research, said the study provides a lesson for all scientists who rely on the generosity of brain donations. “If and when we go and look at something in human postmortem, we have to be very cautious about these technical issues.”

Llorens-Martín said she began carefully collecting and preserving brain samples in 2010, when she realized that many brains stored in brain banks were not adequately preserved for this kind of research. In their study, she and her colleagues examined the brains of people who died with their memories intact, and those who died at different stages of Alzheimer’s disease. She found that the brains of people with Alzheimer’s showed few if any signs of new neurons in the hippocampus—with less signal the further along the people were in the course of the disease. This suggests that the loss of new neurons—if it could be detected in the living brain—would be an early indicator of the onset of Alzheimer’s, and that promoting new neuronal growth could delay or prevent the disease that now affects more than 5.5 million Americans.

Rusty Gage, president of the Salk Institute for Biological Studies and a neuroscientist and professor there, says he was impressed by the researchers’ attention to detail. “Methodologically, it sets the bar for future studies,” says Gage, who was not involved in the new research but was the senior author in 1998 of a paper that found the first evidence for neurogenesis. Gage says this new study addresses the concerns raised by Alvarez-Buylla’s research. “From my view, this puts to rest that one blip that occurred,” he says. “This paper in a very nice way… systematically evaluates all the issues that we all feel are very important.”

Neurogenesis in the hippocampus matters, Gage says, because evidence in animals shows that it is essential for pattern separation, “allowing an animal to distinguish between two events that are closely associated with each other.” In people, Gage says, the inability to distinguish between two similar events could explain why patients with PTSD keep reliving the same experiences, even though their circumstances have changed. Also, many deficits seen in the early stages of cognitive decline are similar to those seen in animals whose neurogenesis has been halted, he says.

In healthy animals, neurogenesis promotes resilience in stressful situations, Gage says. Mood disorders, including depression, have also been linked to neurogenesis.

Hsieh says her research on epilepsy has found that newborn neurons get miswired, disrupting brain circuits and causing seizures and potential memory loss. In rodents with epilepsy, if researchers prevent the abnormal growth of new neurons, they prevent seizures, Hsieh says, giving her hope that something similar could someday help human patients. Epilepsy increases someone’s risk of Alzheimer’s as well as depression and anxiety, she says. “So, it’s all connected somehow. We believe that the new neurons play a vital role connecting all of these pieces,” Hsieh says.

In mice and rats, researchers can stimulate the growth of new neurons by getting the rodents to exercise more or by providing them with environments that are more cognitively or socially stimulating, Llorens-Martín says. “This could not be applied to advanced stages of Alzheimer’s disease. But if we could act at earlier stages where mobility is not yet compromised,” she says, “who knows, maybe we could slow down or prevent some of the loss of plasticity [in the brain].”

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Biology library

Course: biology library   >   unit 33, anatomy of a neuron, overview of neuron structure and function.

• The membrane potential
• Electrotonic and action potentials
• Saltatory conduction in neurons
• Neuronal synapses (chemical)
• The synapse
• Neurotransmitters and receptors
• Q & A: Neuron depolarization, hyperpolarization, and action potentials
• Overview of the functions of the cerebral cortex

How do you know where you are right now?

The human nervous system.

• The central nervous system ( CNS ) consists of the brain and the spinal cord. It is in the CNS that all of the analysis of information takes place.
• The peripheral nervous system ( PNS ), which consists of the neurons and parts of neurons found outside of the CNS, includes sensory neurons and motor neurons. Sensory neurons bring signals into the CNS, and motor neurons carry signals out of the CNS.

Classes of neurons

Sensory neurons, motor neurons, interneurons, the basic functions of a neuron.

• Receive signals (or information).
• Integrate incoming signals (to determine whether or not the information should be passed along).
• Communicate signals to target cells (other neurons or muscles or glands).
• The dendrites tend to taper and are often covered with little bumps called spines. In contrast, the axon tends to stay the same diameter for most of its length and doesn't have spines.
• The axon arises from the cell body at a specialized area called the axon hillock .
• Finally, many axons are covered with a special insulating substance called myelin , which helps them convey the nerve impulse rapidly. Myelin is never found on dendrites.

Variations on the neuronal theme

Neurons form networks, the knee-jerk reflex.

• Motor neuron innervating the quadriceps muscle. The sensory neuron activates the motor neuron, causing the quadriceps muscle to contract.
• Interneuron. The sensory neuron activates the interneuron. However, this interneuron is itself inhibitory, and the target it inhibits is a motor neuron traveling to the hamstring muscle on the back of the thigh. Thus, the activation of the sensory neuron serves to inhibit contraction in the hamstring muscle. The hamstring muscle thus relaxes, facilitating contraction of the quadriceps muscle (which is antagonized by the hamstring muscle).

Glial cells

Types of glia and their functions, references:, want to join the conversation.

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Parkinson’s warning in skin biopsy.

Medical office procedure identifies key biomarker that may lead to more reliable diagnosis of neurodegenerative disorders

Jacqueline Mitchell

BIDMC Communications

Illustration shows neurons containing deposits of biomarker alpha-synuclein (indicated with small red spheres) that have accumulated in brain cells.

A simple skin biopsy test has shown a high accuracy rate in detecting an abnormal form of alpha-synuclein , the pathological hallmark of Parkinson’s disease, according to neurologists at Harvard-affiliated Beth Israel Deaconess Medical Center.

In a paper published in the Journal of the American Medical Association, results from the landmark study, sponsored by the National Institutes of Health, validate this cutaneous method as a reliable and convenient tool to help physicians make more accurate diagnoses of Parkinson’s and the subgroup of neurodegenerative disorders known as synucleinopathies.

“While we have been aware of the presence of alpha-synuclein in cutaneous nerves for many years, we were thrilled with the accuracy of this diagnostic test.” Roy Freeman, professor of neurology

“Each year, there are nearly 200,000 people in the U.S. who face a diagnosis of Parkinson’s disease, dementia with Lewy bodies, and related disorders,” said lead author Christopher Gibbons, a professor of neurology at Harvard Medical School and a neurologist at BIDMC. “Too often patients experience delays in diagnosis or are misdiagnosed due to the complexity of these diseases. With a simple, minimally-invasive skin biopsy test, this blinded multicenter study demonstrated how we can more objectively identify the underlying pathology of synucleinopathies and offer better diagnostic answers and care for patients.”

Affecting an estimated 2.5 million people in the United States, the synucleinopathies include Parkinson’s disease, dementia with Lewy bodies (DLB), multiple system atrophy (MSA), and pure autonomic failure (PAF). While the four progressive neurodegenerative diseases have varying prognoses and do not respond to the same therapies, they do share some overlapping clinical features such as tremors and cognitive changes. Additionally, all are characterized by the presence of an abnormal protein present in the nerve fibers in the skin called phosphorylated α-synuclein (P-SYN).

In this investigation, titled the Synuclein-One Study, Gibbons and colleagues at 30 academic and community-based neurology practices enrolled 428 people, ages 40-99 years, with a clinical diagnosis of one of the four synucleinopathies based on clinical criteria and confirmed by an expert panel or were healthy control subjects with no history of neurodegenerative disease. Participants underwent three 3-millimeter skin punch biopsies taken from the neck, the knee, and the ankle.

“These are systemic disorders that impact the peripheral and central nervous systems in profound ways,” said senior author Roy Freeman, director of the Center for Autonomic and Peripheral Nerve Disorders at BIDMC and professor of neurology at HMS. “While we have been aware of the presence of alpha-synuclein in cutaneous nerves for many years, we were thrilled with the accuracy of this diagnostic test.”

Among the participants with clinically confirmed Parkinson’s disease, 93 percent demonstrated a positive skin biopsy for P-SYN. Participants with DLB and MSA tested 96 percent and 98 percent positive, respectively. One hundred percent of participants with PAF were positive for the abnormal protein. Among the controls, just over 3 percent tested positive for P-SYN — an error rate the authors suspect may indicate some of the healthy controls are at risk for a synucleinopathy.

“Parkinson’s disease and its subgroup of progressive neurodegenerative diseases show gradual progression, but alpha-synuclein is present in the skin even at the earliest stages,” noted Freeman.

The team’s findings are built on earlier work by Freeman and Gibbons. The pair, together with immunohistochemist, Ningshan Wang, a research scientist at BIDMC and an assistant professor of neurology at HMS, have been focused on finding a reliable biomarker for synucleinopathies since 2009. Developing the research around alpha-synuclein in the skin is part of a licensing collaboration with CND Life Sciences , a neurodiagnostics company.

In 2023, the BIDMC researchers demonstrated and published in the journal Neurology that this technique could reliably distinguish between Parkinson’s and MSA, a differentiation that is critical to properly managing the diseases that appear clinically similar but have very different prognoses.

The authors anticipate that this research will play a role in accelerating drug development for synucleinopathies.

“Enrolling the right patients in clinical trials for these complex diseases is of utmost importance,” said Freeman. “Identifying the relevant biomarker in a patient and tracking it over the course of a clinical trial is an essential component of drug development in the neurodegeneration field.”

This work is supported by the National Institutes of Health (grants NIH R44NS117214) and sponsored by CND Life Sciences.

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Abstract 5521: Nicotine promotes perineural brain metastasis of lung cancer by activating GABAergic neurons

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Shih-Ying Wu , Abhishek Tyagi , Ravindra Pramod Deshpande , Kerui Wu , Eleanor Cecile Smith , Kounosuke Watabe; Abstract 5521: Nicotine promotes perineural brain metastasis of lung cancer by activating GABAergic neurons. Cancer Res 15 March 2024; 84 (6_Supplement): 5521. https://doi.org/10.1158/1538-7445.AM2024-5521

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Tobacco smoking significantly increases the incidence of brain metastasis. However, the pathological mechanism by which smoking promotes brain metastasis through modulating brain microenvironment is yet poorly understood. We previously showed that the incidence of brain metastasis is associated with nicotine intake. Nicotine enhanced polarization of M2 pro-tumor microglia, which promoted brain metastasis. In addition to the immune surveillance role, microglial cells also control neuronal synapse formation. Microglial cells promote neuron synapse formation in the developing brain, whereas microglia depletion suppresses synapse density. Furthermore, microglia-secreted microRNAs (miRNAs) have been shown to promote synaptic formation by targeting synapse-related genes of the DKK-Wnt family. Neurons are one of the most abundant cell types in the brain, and they are highly specialized for cell-to-cell signal activation. However, little is known about the roles of neurons in brain metastasis of lung cancer. We hypothesize that nicotine stimulates microglia to secrete exosomal miR-32-3p, which promotes brain metastasis by augmenting GABAergic synaptic formation and hence releasing GABA that serves as a metabolic substrate to fuel tumor cell growth. We also hypothesize that inhibiting the GABA transporter of tumor cells suppresses brain metastasis by blocking the GABA shunt. We found that lung cancer patients with a smoking history had significantly higher synaptic formation and the expression of GABA transporter in brain metastasis. To investigate the effect of smoking and nicotine intake on brain metastasis in vivo, mice were intracardially transplanted with lung cancer brain metastasis cells (H2030BrM and PC9BrM) followed by administration of nicotine or cigarette smoke exposure in a smoking chamber. We found that smoking and nicotine increased M2 microglia polarization, synaptic formation, and the expression of GABA in mouse brain metastasis. Furthermore, nicotine increased the secretion of miR-32-3p from nicotine-pretreated microglia, which promoted GABA release from GABAergic neurons. Treating H2030BrM and PC9BrM cells with conditioned medium (CM) obtained from neurons exposed to CM derived from nicotine-treated microglia exosomes resulted in increased tumor growth through the activation of the GABA shunt pathway. Blocking GABA transporter 1 (GAT1) by CRISPR/Cas9 or a small molecule of GAT1 inhibitor suppressed the GABAergic neuron-induced tumor progression. Our results indicate that nicotine-activated microglia enhanced GABA release from neurons followed by the promotion of GABA shunt in tumor cells and that a GAT inhibitor serves as a promising therapeutic tool for the treatment of lung cancer patients with brain metastasis.

Citation Format: Shih-Ying Wu, Abhishek Tyagi, Ravindra Pramod Deshpande, Kerui Wu, Eleanor Cecile Smith, Kounosuke Watabe. Nicotine promotes perineural brain metastasis of lung cancer by activating GABAergic neurons [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5521.

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Research News

Scientists studied how cicadas pee. their insights could shed light on fluid dynamics.

A cicada perches on a picnic table in front of Nolde Mansion in Cumru Township, PA in May 2021. New research shows that these insects urinate in a surprising way. Ben Hasty / MediaNews Group/Reading Eagle via Getty Images hide caption

A cicada perches on a picnic table in front of Nolde Mansion in Cumru Township, PA in May 2021. New research shows that these insects urinate in a surprising way.

This spring and summer, across the Midwest and Southeast United States, cicadas will crawl out of their underground burrows by the trillions to mate — due to two different broods of these wingèd insects emerging at about the same time, one on a 13-year cycle and one on a 17-year cycle.

In their brief several weeks aboveground, their mission will be to reproduce. Each male will attempt to attract females by producing a buzzing noise as loud as a lawnmower.

But beyond their prodigious numbers and raucous noise, new research published in PNAS reveals that cicadas are special in yet another way — their urination. Based on their size and diet, scientists suspected they'd urinate in droplets, but it turns out that these insects produce jets of pee.

This often-overlooked sea creature may be quietly protecting the planet's coral reefs

The surprising results have numerous applications when it comes to manipulating fluids at small scales — including 3D printing, drug delivery, disease diagnostics, and even testing compounds in outer space.

It's a striking discovery in a realm we know relatively little about, scientists say.

"Excretion in general is not very well understood," says lead author Elio Challita , a bio-inspired roboticist at Harvard University. "Cicadas are some of the smallest insects, to the best of our knowledge, that can form such jets at this small scale."

"Insects are just the perfect laboratory for exploring handling fluids at the micro-scale," says Anne Staples , a fluid dynamicist at Virginia Tech who studies the mechanics of insect respiration and wasn't involved in the research. "They fly through air, they drink water, they handle nectar. As this paper shows, they urinate — they excrete."

Be that as it may, ask Challita what motivated the study, and he says it was simple curiosity. "I think people should understand science doesn't have to be very serious," he says. "It can be fun, too."

A spectrum of pee

Not all animals pee in the same way. On the one hand, there are larger animals like humans and elephants. "They rely on the forces of inertia and gravity to pull down the fluids from their bladder," says Challita.

This results in a stream or jet of urine, like the one that might hit your toilet bowl on a regular basis. In fact, a 2014 Georgia Tech study entitled " Duration of urination does not change with body size " found that all mammals more than six or seven pounds take an average of 21 seconds (plus or minus 13 seconds) to empty their bladders. The researchers referred to this as the Law of Urination .

But when an organism is small (like the size of an insect), then fluids care less about gravity. Instead, surface tension and friction dominate — forces that are negligible for larger organisms like us.

"Surface tension is an invisible kind of force that is very significant for small insects," says Challita. "Just pushing a fluid at a small scale is challenging."

The result is that most insects and most small mammals like mice and bats urinate in droplets through smaller orifices. In fact, while in grad school at Georgia Tech, Challita studied a kind of insect called a sharpshooter, which sucks low-nutrient sap from plants (sap that's 95% water). "And then we calculated what is the energy required to form a jet versus a droplet," Challita says.

California sea otters nearly went extinct. Now they're rescuing their coastal habitat

It wasn't even a contest. Droplet urination used way less energy. So that seemed to be the general rule: if you're big, you pee in a jet. If you're small — and especially if you're feeding on nutrient-poor sap — you pee in droplets.

But Challita knew that the world rarely divides so cleanly. "We try to create theories that can explain things in nature," he says, "but nature is always finding surprises and exceptions for us."

A splash of insight

Based on a handful of YouTube videos he'd seen, Challita had a hunch that cicadas might just prove to be the exception to the rule. The only trouble is that they're hard to observe.

"They're usually very high up on trees," he says. "And even if you find them, it's hard to not disturb them and then they would fly away."

But later, on a different project in the Peruvian Amazon, a stroke of luck: Challita and a couple colleagues had wrapped up their field work and were taking the six-hour boat ride back to town when their driver made an early pit stop for lunch.

"So we started walking around," Challita recalls, "and then one of our colleagues, he felt this little sprinkle on his head. And then we looked up — and then we saw a lot of cicadas."

Challita and his colleagues couldn't believe it. There were 20 or 30 cicadas low down in the trees, feeding and peeing with abandon. The team leapt into action, rushing to collect data before their driver's lunch break ended. They climbed one of the trees. They grabbed a table to stand on. And they filmed the cicadas using the high-speed video setting on their phones.

"All the villagers over there, they were just staring at us, like, 'What the hell's wrong with these guys?'" says Challita with a chuckle.

It was a rush.

And the experiment turned out well, too.

The researchers saw cicadas defying expectations. They are insects feeding on low-nutrient sap, but there they were — peeing in jets . It was one of those streams that had splashed against Challita's colleague that had made them all look up in the first place.

Here's what Challita thinks is going on: Cicadas are big insects with a wider gut, so they're not under the exact same size constraint as, say, a sharpshooter. Plus, they have to process a huge quantity of sap to extract enough energy to power their bodies.

"Peeing one droplet at a time takes too long and it's not very efficient," says Challita. "So they have to get rid of that fluid in jets."

This means that in addition to large animals that pee in jets, and small animals that pee in droplets, Challita's found a third category: small organisms that also pee in jets.

Staples says that while the research would have benefited from studying a larger number of cicadas, it still pushes the limits of our understanding.

"They've extended the scale into the lower reaches of the animal kingdom and showed some surprising results that are counterintuitive," says Staples. "You wouldn't think that this would be the most efficient exploitation of fundamental physics to urinate at that size."

And yet it is. It's the latest leap in the development of what Challita hopes could become a kind of Grand Urinating Theory.

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‘You Transformed the World,’ NVIDIA CEO Tells Researchers Behind Landmark AI Paper

Of GTC ’s 900+ sessions, the most wildly popular was a conversation hosted by NVIDIA founder and CEO Jensen Huang with seven of the authors of the legendary research paper that introduced the aptly named transformer — a neural network architecture that went on to change the deep learning landscape and enable today’s era of generative AI.

“Everything that we’re enjoying today can be traced back to that moment,” Huang said to a packed room with hundreds of attendees, who heard him speak with the authors of “ Attention Is All You Need .”

Sharing the stage for the first time, the research luminaries reflected on the factors that led to their original paper, which has been cited more than 100,000 times since it was first published and presented at the NeurIPS AI conference. They also discussed their latest projects and offered insights into future directions for the field of generative AI.

While they started as Google researchers, the collaborators are now spread across the industry, most as founders of their own AI companies.

“We have a whole industry that is grateful for the work that you guys did,” Huang said.

Origins of the Transformer Model

The research team initially sought to overcome the limitations of recurrent neural networks , or RNNs, which were then the state of the art for processing language data.

Noam Shazeer, cofounder and CEO of Character.AI, compared RNNs to the steam engine and transformers to the improved efficiency of internal combustion.

“We could have done the industrial revolution on the steam engine, but it would just have been a pain,” he said. “Things went way, way better with internal combustion.”

“Now we’re just waiting for the fusion,” quipped Illia Polosukhin, cofounder of blockchain company NEAR Protocol.

The paper’s title came from a realization that attention mechanisms — an element of neural networks that enable them to determine the relationship between different parts of input data — were the most critical component of their model’s performance.

“We had very recently started throwing bits of the model away, just to see how much worse it would get. And to our surprise it started getting better,” said Llion Jones, cofounder and chief technology officer at Sakana AI.

Having a name as general as “transformers” spoke to the team’s ambitions to build AI models that could process and transform every data type — including text, images, audio, tensors and biological data.

“That North Star, it was there on day zero, and so it’s been really exciting and gratifying to watch that come to fruition,” said Aidan Gomez, cofounder and CEO of Cohere. “We’re actually seeing it happen now.”

Envisioning the Road Ahead 

Adaptive computation, where a model adjusts how much computing power is used based on the complexity of a given problem, is a key factor the researchers see improving in future AI models.

“It’s really about spending the right amount of effort and ultimately energy on a given problem,” said Jakob Uszkoreit, cofounder and CEO of biological software company Inceptive. “You don’t want to spend too much on a problem that’s easy or too little on a problem that’s hard.”

A math problem like two plus two, for example, shouldn’t be run through a trillion-parameter transformer model — it should run on a basic calculator, the group agreed.

They’re also looking forward to the next generation of AI models.

“I think the world needs something better than the transformer,” said Gomez. “I think all of us here hope it gets succeeded by something that will carry us to a new plateau of performance.”

“You don’t want to miss these next 10 years,” Huang said. “Unbelievable new capabilities will be invented.”

The conversation concluded with Huang presenting each researcher with a framed cover plate of the NVIDIA DGX-1 AI supercomputer, signed with the message, “You transformed the world.”

There’s still time to catch the session replay by registering for a virtual GTC pass — it’s free.

To discover the latest in generative AI, watch Huang’s GTC keynote address:

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Development of the nervous system articles within Nature Neuroscience

Article 12 March 2024 | Open Access

Liprin-α proteins are master regulators of human presynapse assembly

This paper identifies the evolutionarily conserved liprin-α protein family as key mediators of presynaptic assembly in human neurons. Their recruitment to sites formed by contacting neurons is the critical initial step that triggers presynaptic differentiation.

• Berta Marcó de la Cruz
• , Joaquín Campos
•  &  Fredrik H. Sterky

Research Highlight | 07 March 2024

Slowing human neurons

• Shari Wiseman

Article 17 January 2024 | Open Access

The developmental timing of spinal touch processing alterations predicts behavioral changes in genetic mouse models of autism spectrum disorders

Tasnim et al. show that ASD-associated genes act in different compartments of somatosensory circuits and that differences in developmental timing of ASD gene function and circuit maturation contribute to phenotypic heterogeneity across ASD models.

• Aniqa Tasnim
• , Ilayda Alkislar
•  &  David D. Ginty

Research Briefing | 23 November 2023

Discovery of a non-canonical function of SYNGAP1 at early stages of human cortical neurogenesis

We discovered expression of SYNGAP1 , which encodes the ‘synaptic’ protein SYNGAP1, within human cortical progenitors. In an organoid model of SYNGAP1 haploinsufficiency, cortical neurogenesis and neuronal network activity were disrupted. This finding reveals an unknown function for SYNGAP1 at early stages of development, providing a new framework for understanding the pathophysiology of autism spectrum disorder.

Resource 23 November 2023 | Open Access

A global multicohort study to map subcortical brain development and cognition in infancy and early childhood

The ENIGMA-ORIGINs group presents a large and globally diverse pediatric neuroimaging dataset from birth to age 6. They use this resource to study the effects of sociodemographics and adverse birth outcomes on trajectories of brain volumes and cognition.

• Ann M. Alex
• , Fernando Aguate
•  &  Rebecca C. Knickmeyer

Article | 31 August 2023

Prominent in vivo influence of single interneurons in the developing barrel cortex

Spontaneous population activity bursts promote the development of brain circuits. This study shows that single GABAergic hub neurons exert a strong influence on spontaneous and sensory-evoked population bursts in the mouse barrel cortex.

• Yannick Bollmann
• , Laura Modol
•  &  Rosa Cossart

Article | 10 August 2023

Modeling idiopathic autism in forebrain organoids reveals an imbalance of excitatory cortical neuron subtypes during early neurogenesis

Organoid modeling of human forebrain development reveals an opposite imbalance in excitatory neuron number in macrocephalic and normocephalic autistic probands, stemming from divergent expression of transcription factors driving cell fate.

• Alexandre Jourdon
• , Feinan Wu
•  &  Flora M. Vaccarino

Article | 17 July 2023

Identification of ligand–receptor pairs that drive human astrocyte development

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• Anna J. Voss
• , Samantha N. Lanjewar
•  &  Steven A. Sloan

Article | 08 June 2023

Large-scale encoding of emotion concepts becomes increasingly similar between individuals from childhood to adolescence

Camacho et al. show that emotion concepts are represented throughout the brain, giving insight to how the brain perceives real-world emotions. These patterns are present before children enter school and become more standardized across adolescence.

• M. Catalina Camacho
• , Ashley N. Nielsen
•  &  Deanna M. Barch

Article 11 May 2023 | Open Access

ARG1-expressing microglia show a distinct molecular signature and modulate postnatal development and function of the mouse brain

The molecular diversity of microglia has been described. Here the authors show that ARG1-expressing microglia are enriched in phagocytic inclusions and are involved in hippocampal innervation and spine maturation in mice. ARG1-expressing microglia also modulate cognition in a sex-dependent manner.

• Vassilis Stratoulias
• , Rocío Ruiz
•  &  Bertrand Joseph

Resource 24 April 2023 | Open Access

Profiling spatiotemporal gene expression of the developing human spinal cord and implications for ependymoma origin

The authors created a comprehensive developmental cell atlas for spatiotemporal gene expression of the human spinal cord, revealed species-specific regulation during development and used the atlas to infer novel markers for pediatric ependymomas.

• , Zaneta Andrusivova
•  &  Erik Sundström

Resource | 24 April 2023

Single-cell transcriptomic landscape of the developing human spinal cord

Andersen, Thom and colleagues reveal the single-cell-resolution transcriptome of the midgestation human spinal cord and discover remarkable heterogeneity across and within cell types.

• Jimena Andersen
• , Nicholas Thom
•  &  Sergiu P. Pașca

Article | 27 March 2023

Intrinsic activity development unfolds along a sensorimotor–association cortical axis in youth

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• Valerie J. Sydnor
• , Bart Larsen
•  &  Theodore D. Satterthwaite

Brief Communication 09 March 2023 | Open Access

Developmental trajectory of transmission speed in the human brain

This study mapped the developmental trajectory of transmission speed in the human brain by using electrical pulses and intracranial recordings. The authors found that these pulses travel with increasing speeds up to at least the age of 30.

• Dorien van Blooijs
• , Max A. van den Boom
•  &  Dora Hermes

Research Highlight | 29 November 2022

The impact of ambient RNA

• Elisa Floriddia

Resource | 27 October 2022

A developmental atlas of somatosensory diversification and maturation in the dorsal root ganglia by single-cell mass cytometry

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• Austin B. Keeler
• , Amy L. Van Deusen
•  &  Eli R. Zunder

Resource 30 September 2022 | Open Access

A tool for mapping microglial morphology, morphOMICs, reveals brain-region and sex-dependent phenotypes

Colombo et al. build a morphological spectrum of over 40,000 microglia across development and disease with a topological data analysis approach that allows mapping of new conditions along these sex-region-specific and brain-region-specific atlases.

• Gloria Colombo
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Brief Communication 28 September 2022 | Open Access

Oligodendrocyte precursor cells engulf synapses during circuit remodeling in mice

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Resource | 28 September 2022

Developmental dynamics of RNA translation in the human brain

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Article | 30 August 2022

Aberrant astrocyte protein secretion contributes to altered neuronal development in multiple models of neurodevelopmental disorders

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Article | 22 August 2022

Preconfigured dynamics in the hippocampus are guided by embryonic birthdate and rate of neurogenesis

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• Roman Huszár
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Brief Communication | 18 August 2022

A multifaceted gradient in human cerebellum of structural and functional development

The structural and functional development of the human cerebellum is not well known. The cerebellum shows a gradient of tissue properties across its lobules, each of which develops at a unique rate and closely follows changes in function across childhood.

• , Federico d’Oleire Uquillas
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Research Highlight | 01 August 2022

Transcriptomics predicts activity

Article 01 August 2022 | Open Access

Temporally divergent regulatory mechanisms govern neuronal diversification and maturation in the mouse and marmoset neocortex

The mechanisms underlying neuron specification and maturation are unclear. Here the authors provide an integrated epigenomic and transcriptomic analysis of mouse and marmoset neocortical neuronal classes. Pan-neuronal programs active during early development are more evolutionary conserved but not neuron-specific, whereas pan-neuronal programs active during later stages of maturation are more neuron- and species-specific.

•  &  Paola Arlotta

News & Views | 04 July 2022

Neurogenic–angiogenic synchrony via lactate

During cortical development, the generation of neurons from progenitors coincides with increasing vascularization and oxygen levels in the fetal brain and the transition from anaerobic to aerobic metabolism. Dong et al. identify lactate, a metabolite of anaerobic glycolysis, as a multifunctional regulator that coordinates synchrony of cortical neurogenesis and angiogenesis.

• Daniel Z. Doyle
•  &  Kenneth Y. Kwan

Article | 16 June 2022

The embryonic zebrafish brain is seeded by a lymphatic-dependent population of mrc1 + microglia precursors

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• Lauren A. Green
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•  &  Cody J. Smith

Article | 19 May 2022

Adolescent thalamic inhibition leads to long-lasting impairments in prefrontal cortex function

Benoit et al. show that inhibition of the thalamus during adolescence leads to long-lasting changes in prefrontal cortex function and behavior, demonstrating the importance of adolescent thalamic activity for prefrontal circuit maturation.

• Laura J. Benoit
• , Emma S. Holt
•  &  Christoph Kellendonk

Technical Report | 06 May 2022

Trans-Seq maps a selective mammalian retinotectal synapse instructed by Nephronectin

The authors present a circuit tracing method, Trans-Seq, which determines the targets of a given neuron type through anterograde tracing combined with single-cell RNA sequencing. Applying Trans-Seq to retinotectal synapses, the authors find a selective connection assembled by Nephronectin.

• Nicole Y. Tsai
•  &  Xin Duan

Research Highlight | 08 March 2022

Poverty impacts infant brains

Brief Communication 03 March 2022 | Open Access

Oligodendrocyte precursor cells sculpt the visual system by regulating axonal remodeling

Oligodendrocyte precursor cells exist in abundance throughout the brain lifelong, with unclear functions. Xiao et al. show that, in zebrafish, these cells regulate the precise formation of retinal ganglion cell arbors and fine-tune visual processing.

• , Luigi Petrucco
•  &  Tim Czopka

Article 24 February 2022 | Open Access

Clonal relations in the mouse brain revealed by single-cell and spatial transcriptomics

Ratz et al. present an easy-to-use method to barcode progenitor cells, enabling profiling of cell phenotypes and clonal relations using single-cell and spatial transcriptomics, providing an integrated approach for understanding brain architecture.

• Michael Ratz
• , Leonie von Berlin
•  &  Jonas Frisén

Research Highlight | 07 February 2022

Senescence in Down syndrome NPCs

Review Article | 29 November 2021

Cortical interneurons in autism

This Review discusses evidence from human studies and mouse models that cortical interneurons are involved in the pathophysiology of autism and that parvalbumin cell hypofunction may be a primary driver of circuit dysfunction in autism.

• Anis Contractor
• , Iryna M. Ethell
•  &  Carlos Portera-Cailliau

Technical Report | 23 August 2021

Identification of neural oscillations and epileptiform changes in human brain organoids

This paper explores neural network and cellular complexity within human cortical and subcortical fusion organoids. The platform is used to model network dysfunction associated with Rett syndrome and to identify new therapeutic candidates.

• Ranmal A. Samarasinghe
• , Osvaldo A. Miranda
•  &  Bennett G. Novitch

Article | 19 August 2021

A human forebrain organoid model of fragile X syndrome exhibits altered neurogenesis and highlights new treatment strategies

Using a human forebrain organoid model of fragile X syndrome, Kang et al. reveal a critical role of FMRP in human brain development and identify a large number of human-specific mRNAs that could be regulated by FMRP.

• Yunhee Kang
• , Ying Zhou
•  &  Zhexing Wen

Article 16 August 2021 | Open Access

Placental endocrine function shapes cerebellar development and social behavior

Placental dysfunction has been implicated in abnormal neurodevelopment. Vacher et al. found that loss of a neuroactive hormone from the placenta alters brain development in a regional and sex-linked manner, resulting in autism-like behaviors in male offspring.

• Claire-Marie Vacher
• , Helene Lacaille
•  &  Anna A. Penn

Article | 22 July 2021

X-chromosome influences on neuroanatomical variation in humans

Mallard et al. study the often overlooked X-chromosome’s influences on the human brain. They find that X-chromosome influences on cortical surface area are sex biased and concentrated in specific cortical systems.

• Travis T. Mallard
• , Siyuan Liu
•  &  Armin Raznahan

Resource | 17 June 2021

Spatial and cell type transcriptional landscape of human cerebellar development

SPLiT-seq single-nucleus RNA sequencing of the developing human cerebellum reveals cell-type complexities and prolonged maturation compared to mouse with important disease implications.

• Kimberly A. Aldinger
• , Zachary Thomson
•  &  Kathleen J. Millen

Resource 15 March 2021 | Open Access

Single-cell atlas of early human brain development highlights heterogeneity of human neuroepithelial cells and early radial glia

Eze et al. use single-cell sequencing and immunohistochemical validation to create an atlas of early human brain development. In the telencephalon, they discover a diversity of progenitor subtypes, including two that are enriched in humans.

• Ugomma C. Eze
• , Aparna Bhaduri
•  &  Arnold R. Kriegstein

Article | 22 February 2021

Long-term maturation of human cortical organoids matches key early postnatal transitions

Gordon et al. use genome-wide unbiased approaches to show that human cerebral cortical organoids, when cultured for many months, start to resemble stages of postnatal brain development, with a timeline that parallels in vivo development.

• Aaron Gordon
• , Se-Jin Yoon
•  &  Daniel H. Geschwind

Article | 11 January 2021

Machine learning reveals bilateral distribution of somatic L1 insertions in human neurons and glia

Zhu et al. discover that in human brain there is widespread anatomic distribution of low-frequency somatic, mosaic L1 insertions, using deep whole-genome sequencing of neuronal and glial fractions and machine-learning analysis.

• Xiaowei Zhu
•  &  Alexander E. Urban

Article | 22 December 2020

Overexpression of schizophrenia susceptibility factor human complement C4A promotes excessive synaptic loss and behavioral changes in mice

Overexpression of complement C4A is associated with schizophrenia risk. Using a novel mouse model, Yilmaz et al. find that increased expression of C4A leads to abnormal synaptic pruning and behavior, suggesting its importance as a therapeutic target.

• Melis Yilmaz
• , Esra Yalcin
•  &  Michael C. Carroll

News & Views | 10 December 2020

Revealing the complexity of the gut’s brain

A new study proposes an exciting new model of neuronal diversification in the developing enteric nervous system (ENS) and establishes a detailed molecular taxonomy for enteric neurons. Their findings open new horizons for ENS research and for developing cell-based therapies for ENS disorders.

Article | 07 December 2020

Diversification of molecularly defined myenteric neuron classes revealed by single-cell RNA sequencing

Imaging and transcriptomic approaches to investigate mouse enteric nervous system diversity and development reveal a new classification of intestinal myenteric neurons and a novel principle of neuronal diversification by postmitotic transitions.

• Khomgrit Morarach
• , Anastassia Mikhailova
•  &  Ulrika Marklund

Review Article | 02 November 2020

Brain organoids for the study of human neurobiology at the interface of in vitro and in vivo

Chiaradia and Lancaster review applications and limitations of brain organoids, placing them in context with other technologies and describing how these methods are heavily informed by in vivo development.

• Ilaria Chiaradia
•  &  Madeline A. Lancaster

News & Views | 07 August 2020

CRISPR Rube Goldberg machines for visualizing cell lineage

A new technique developed by Garcia-Marques and colleagues uses CRISPR–Cas9 editing to activate an ordered sequence of fluorescent markers in stem cells and their progeny. These tools represent a new way to probe the spatial and temporal patterns of cell lineage progression.

• Clayton M. Carey
•  &  James A. Gagnon

Technical Report | 27 July 2020

A programmable sequence of reporters for lineage analysis

Garcia-Marques et al. present CLADES, an innovative approach to study neuronal lineages based on CRISPR. Inspired by synthetic biology, CLADES relies on a system of genetic switches to activate and inactivate reporter genes in a predetermined order.

• Jorge Garcia-Marques
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•  &  Tzumin Lee

Article | 13 July 2020

The endogenous neuronal complement inhibitor SRPX2 protects against complement-mediated synapse elimination during development

The complement–microglia pathway is a key mediator of synapse elimination in development and disease. Cong et al. show that neurons endogenously express a complement inhibitor, SRPX2, that regulates synapse elimination in development.

• , Breeanne M. Soteros
•  &  Gek-Ming Sia

Article | 06 July 2020

Microglia phagocytose myelin sheaths to modify developmental myelination

Microglia refine the developing CNS by engulfing excess neurons and synapses. Hughes and Appel here show that microglia also prune myelin sheaths in a neuronal activity-regulated manner to sculpt developmental myelination.

• Alexandria N. Hughes
•  &  Bruce Appel

Brief Communication | 08 June 2020

Conservation of brain connectivity and wiring across the mammalian class

Brain connectivity and normalized wiring costs are conserved across mammals independently of brain size according to a common principle: species with fewer interhemispheric connections exhibit better intrahemispheric connectivity.

• Yaniv Assaf
• , Arieli Bouznach
•  &  Yossi Yovel

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Reverend Kristin Michael Hayter blurs lines between reality, performance and research with ‘Saved!’

Reverend Kristin Michael Hayter is taking the idea of a concept album to a new level. Hayter combined her interest in religion and her education in art and linguistics, to embark on a kind of anthropological experiment in her latest album, “Saved!,” which explores a fictionalized conversion to Pentecostalism. Hayter previously recorded under the moniker Lingua Ignota, a Latin phrase which means “unknown language.” (March 21)

Singer/songwriter Kristin Hayter poses for a portrait before a concert at The Masonic Lodge at Hollywood Forever, Friday, Feb. 16, 2024, in Los Angeles. (AP Photo/Chris Pizzello)

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LOS ANGELES (AP) — The bones that embody an album can take many shapes. They may tell a story, follow a genre or soundtrack a film .

But thanks to her interest in religion and her education in art, literature and linguistics, Kristin Hayter found herself in a unique position to embark on a kind of anthropological experiment through her latest album.

Released under the name Reverend Kristin Michael Hayter, “Saved!” is a concept album which explores a fictionalized conversion to Pentecostalism.

“When people ask me like, ‘What is it?’ I’m like — I honestly don’t know what to say,” she says of her album, ahead of the second of two recent performances at the Masonic Lodge at Los Angeles’ Hollywood Forever Cemetery . “It’s supposed to sound kind of like found sound, field recordings, that kind of thing.”

Although not attempting to portray a genuine conversion or create a piece of historical research, Hayter, who previously recorded under the moniker Lingua Ignota, used the album to meditate on how people tell stories about their perceived realities. As she made it, she found herself thinking about the concept of documentary storytelling and “what is edited out and what we choose to leave in.”

“Saved!” is made up of a combination of recognizable Christian hymns, including “Nothing but the Blood of Jesus” and “How Can I Keep from Singing,” as well as original and sometimes more subversive tracks like “All of My Friends Are Going to Hell.”

That range reflects Hayter’s following, from devout Christians — including a snake handler from West Virginia who extended to her an open invitation — to those vehemently opposed to religion.

“I was expecting more outrage,” she said plainly. “But I think there’s enough ambiguity in it and the ambiguity is pretty intentional, where I’m not requesting or requiring people to have any kind of particular response. Your experience is going to dictate what you hear.”

To emphasize that “found sound” approach, Hayter recorded in a lo-fi style, often abruptly ending or fading in and out of a song. Hayter’s powerful voice, accompanied by her prepared piano, vacillates between beautiful and terrifying in a manner not unlike the way in which she thinks about religion.

“A lot of the language surrounding Christianity really is quite beautiful and poetic but is also, or can also be, pretty horrifying,” she said.

But Hayter doesn’t just utilize her voice for singing on “Saved!” Woven throughout is her attempt at glossolalia — speaking in tongues — a defining feature of Pentecostalism, according to Grant Wacker, a historian at Duke Divinity School who specializes in the denomination.

“It’s important to understand how fundamental speaking in tongues is to the identity of historic Pentecostals,” Wacker said, recalling the pressure he himself witnessed to speak in tongues growing up in the church.

That Hayter turns such a sacred and integral aspect of the faith into a performance, while cultivating conditions that could bring the act of speaking in tongues about, could be taken as disingenuous. But Wacker says similar tactics are frequently employed within the Pentecostal church.

“The pastor would encourage young people — usually teenagers — they’d say ‘Well, just start talking faster and faster, and before long, your tongue will just fall into it,’” he said.

Wacker explained that as long as attempts at glossolalia are done in a “worshipful context,” tactics can be employed to achieve it. For Hayter, those included sleep deprivation, fasting and listening to others speak in tongues, an idea from her producer and recording engineer, Seth Manchester.

“He was like, ‘Well, let’s put you in the studio and blast other people speaking in tongues at you for 90 minutes and see what happens,’” she recalled. “What you hear on the record is actually like one unedited portion of maybe two hours total of speaking in tongues.”

As is often the case with art, the line between performance and reality is a blurry one for Hayter. While she would by no means describe herself as a Pentecostal, the preparation and research required for the project raised the age-old question in the study of religion: whether an insider, outsider or both can study a tradition.

She spent much of the pandemic researching the denomination as a clear outsider, meticulously procuring and sifting through countless Gospel tracts and attending Pentecostal worship services behind a distant screen on Zoom . But her research bled into practice when it came time to record the album and experiment with what can be considered spiritual disciplines.

“It was really pretty dissociative. I was able to just kind of let my brain go and let language and the brain kind of act independently or something. I’m not entirely sure what happened. But it definitely felt like releasing something,” she said.

Hayter attended parochial school as a kid and sang as a cantor in the Catholic Church. Though she was for years a devout atheist after denouncing her faith as a teenager, Hayter has long been drawn to religious concepts, imagery and iconography.

“I think the ideas of things that are absolutely evil or absolutely good are really interesting to me,” she said.

Across her chest, she is tattooed with the name “Caligula” — the notorious first century Roman emperor who — though the veracity of historical accounts is questionable — is often associated with religious persecution and sexual deviancy. Hayter’s previous recording name was an ode to the 12th century Benedictine mystic and saint, Hildegard Von Bingen, an epochal figure in the history of glossolalia.

Hayter is hardly the first musician in recent memory to commit to religious extremes for the sake of art — Grimes, also inspired by Von Bingen, famously locked herself in her room for weeks to make the album “Visions.” But Hayter is also cognizant of the ways in which her academic background — she has a master of fine arts from Brown University — make her distinct.

“It’s just the way that my brain works,” she said. “I do like this period of research and then this period of doing the thing and being kind of like an insurgent within the thing. And it becomes like a weird life-filling situation, an obsession.”

Calling it an obsession might not be an exaggeration. She co-founded her current label, Perpetual Flame Ministries, ahead of the album’s release. And once she settled on adding “reverend,” Hayter decided she might as well get ordained in the Universal Life Church.

Her past work encapsulated avant-garde sounds that tackled dark topics, including her experience with domestic abuse and anorexia.

But more recently, her journey has been one of healing — even conceding she has a “much more open sense of what God is and what God can be at this point” — and so felt it was time to retire her old recording name and music.

“For the first time in my adult life, I have a normal life now. I have a really nice home life. And I have a lovely boyfriend and the cats and the house,” she said with a smile. “So I’m trying to really lean into that.”

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Inside the brain of a neuron

Kyriaki sidiropoulou.

1 Institute of Molecular Biology and Biotechnology (IMBB), Foundation for Research and Technology–Hellas (FORTH), Vassilika Vouton PO Box 1583, Heraklion GR71110, Crete, Greece

Eleftheria Kyriaki Pissadaki

2 Department of Biology, University of Crete, Vassilika Vouton, Heraklion GR71409, Crete, Greece

Panayiota Poirazi

For many decades, neurons were considered to be the elementary computational units of the brain and were assumed to summate incoming signals and elicit action potentials only in response to suprathreshold stimuli. Although modelling studies predicted that single neurons constitute a much more powerful computational entity, able to perform an array of nonlinear calculations, this possibility was not explored experimentally until the discovery of active mechanisms in the dendrites of most neuron types. Here, we review several modelling studies that have addressed information processing in single neurons, starting with those characterizing the arithmetic of different dendritic components, to those tackling neuronal integration at the cell body and, finally, those analysing the computational abilities of the axon. We present modelling predictions along with supporting experimental data in an effort to highlight the significant contribution of modelling work to enhancing our understanding of single-neuron arithmetic.

Introduction

Understanding how the brain works remains one of the most exciting and intricate challenges of modern biology. Despite the wealth of information that has accumulated during the past years about the molecular and biophysical mechanisms that underlie neuronal activity, similar advances have yet to be made in understanding the rules that govern information processing and the relationship between the structure and function of a neuron.

Computational models provide a theoretical framework together with a technological platform for enhancing our understanding of nervous system functions. Certain tools are suitable for efficiently analysing and interpreting complex data sets, such as multi-channel recordings from hundreds of neurons, whereas others are used to simulate the activity of single cells, neural networks or systems of networks at various levels of abstraction. The development and application of such modelling tools enable researchers to quantitatively investigate several hypotheses by using interactive models of the systems under study. When used in conjunction with experimental techniques, these models facilitate hypothesis testing and help to identify key follow-up experiments.

In this review, we discuss several computational studies in which realistic biophysical models have been used to elucidate the computational tasks performed by a neuron. We focus on single-neuron models that incorporate a significant level of detail and compare modelling predictions with experimental findings. Although a great amount of work has also been devoted to modelling neural components as well as neuronal assemblies at a more abstract level, reviewing these studies is not the purpose of this article.

Single-neuron computations

Whether incredibly simple as bipolar cells in the retina or immensely complex as Purkinje cells in the cerebellum ( Ramon y Cajal, 1933 ), most neurons are composed of three main structural units: the dendrites, the soma (cell body) and the axon. For the past few decades, axons and dendrites have been considered to be simple transmitting devices that communicate signals to and from the soma in which thresholded computations take place. As a result, neuronal cells were initially represented as spherical point neurons—consisting only of a cell body—and information transfer was thought to lie entirely in their average firing rates ( McCulloch & Pitts, 1943 ). However, primarily computational, and more recently physiological, studies have shown that variations in the morphology and ionic conductance composition of different neurons provide the cell with enhanced computational abilities far outreaching those captured by a point neuron.

Computing with dendrites: new roles for old structures

The old view that dendrites are merely passive cables that relay incoming signals to the cell body no longer holds true. In the light of accumulating evidence highlighting the active role of dendrites in signal integration, these structures seem able to perform a variety of computational tasks, including temporal integration, signal amplification and attenuation, and detection of coincident incoming inputs (for a recent review, see London & Hausser, 2005 ). In this section, we further elucidate the role of dendrites in the information processing capacity of the neuron by focusing on insights gained primarily from modelling studies and by using a bottom-up approach: starting from the smallest dendritic subunit—the spine—up to the effect of network activity on dendritic and, subsequently, neuronal function.

Computations carried out by excitable spines

Dendritic spines were anatomically identified by Ramon y Cajal in 1911, who referred to them as espinas due to their resemblance to thorns on flower stems (for a review, see Segal, 2002 ). Theoretical findings first indicated that the anatomical characteristics of spines, as well as the possible presence of voltage-gated ion channels, allow for compartmentalized gain modulation of synaptic inputs in spine heads—that is, information can be combined nonlinearly from two or more sources ( Segev & Rall, 1998 ). Models predicted that strong inputs are able to initiate local dendritic spikes ( Baer & Rinzel, 1991 ), which in turn could activate additional nearby spines ( Shepherd et al , 1985 ) and therefore locally amplify incoming inputs. As schematically depicted in Fig 1 , individual spines can perform nonlinear integration of incoming coincident signals, and their interaction results in a spatially restricted enhancement of dendritic events. By contrast, if synaptic stimulation is sparse, the added resistance and capacitance load provided by the spine membrane coupled with the narrow spine neck would act as a local filter, promoting linear integration ( Yuste & Urban, 2004 ). In a recent compartmental modelling study, spine geometry together with a high density of sodium ion (Na + ) channels on spines might explain the efficacy of somatic action potentials for invading apical dendrites in CA1 pyramids ( Tsay & Yuste, 2002 , 2004 ). This implies a role for these structures in controlling back-propagating signals and perhaps in coincidence detection ( Tsay & Yuste, 2002 , 2004 ). Taken together, these studies reveal the exciting possibility that dendritic sections containing small clusters of spines act as individual computational units. Interestingly, in a recent experimental study, short (around 40 μm) basal dendritic compartments in layer V pyramidal neurons were shown to summate local signals as individually thresholded sigmoidal units ( Polsky et al , 2004 ), which supports this hypothesis.

The multiplicative neuron. Sigmoidal functions indicate nonlinear computations performed by various parts of the cell: gain modulation in spine heads, thresholded computations in small dendritic branchlets, and supralinearities at the main apical trunk and the cell body. The response of the cell is tuned by the overall effect of the network.

Dendritic computations

If sparse and clustered synaptic inputs can be differentially sensed by a dendritic section, is it possible that different spatial arrangements of synaptic inputs are differentially perceived by the cell? Early neuron models incorporating passive dendritic properties and applying the cable theory—that is, how voltage changes are propagated along dendritic segments—indicated a possible linear summation of inputs arriving at separate parts of the dendritic tree ( Rall, 1959 ). This implied that location is not important. By contrast, inputs that are close together were thought to combine sublinearly due to the activation of a shunting current ( Rall et al , 1967 ). Despite the simplicity of the early models and the presence of various active membrane mechanisms that could in principle support supralinear dendritic integration, experimental evidence in various neuron types has mostly reinforced the linear or sublinear summation rule ( Cash & Yuste, 1999 ; Magee & Cook, 2000 ; Tamas et al , 2002 ). However, at least two studies have shown the presence of powerful thresholding events isolated in the thin dendrites of neocortical ( Schiller et al , 2000 ) and CA1 ( Wei et al , 2001 ) pyramidal cells.

This idea of spatial compartmentalization in the neuron and its role in information processing was explored further with the use of a detailed CA1 pyramidal neuron model ( Poirazi et al , 2003a , b ). According to the model, each apical oblique dendrite—or part of it—acts as an independent computational unit that summates inputs using a sigmoidal activation function. Different branch outputs are then linearly combined at the cell body. Both of these predictions were verified experimentally in a layer V pyramidal neuron ( Polsky et al , 2004 ), and a recent study confirmed that radial oblique dendrites of CA1 pyramidal neurons function as single integrative compartments ( Losonczy & Magee, 2006 ). As shown in Fig 2 , layer V neocortical neurons linearly summate between-branch excitatory postsynaptic potentials (EPSPs), but implement a sigmoidal activation function for within-branch EPSPs ( Fig 2B ), as predicted by models of CA1 neurons ( Fig 2A ). In other words, thin dendritic branches seem to be able to combine incoming signals according to a thresholding nonlinearity, in a similar way to a typical point neuron. Interestingly, when synaptic inputs vary in both their temporal and spatial distribution, the distal apical trunk of a CA1 pyramidal cell operates in two fundamentally distinct integration forms ( Gasparini & Magee, 2006 ). Asynchronous or spatially distributed synaptic inputs—similar to those occurring during theta oscillations—summate linearly. Synchronous and clustered inputs—similar to those occurring during sharp waves—summate according to a steep sigmoidal nonlinearity. This bimodal dendritic integration code allows a single cell to perform two different state-dependent computations: input strength encoding during theta states and feature detection during sharp waves ( Gasparini & Magee, 2006 ).

Nonlinear dendritic computations. ( A ) Summation of paired, single-pulse inputs in the apical dendrites of a CA1 pyramidal model cell. Simulations predict a sigmoidal modulation of combined excitatory postsynaptic potentials (EPSPs) within a branch (red symbols) and a linear summation of EPSPs between branches (green symbols). Red curves correspond to within-branch data for dendrites at 92 μm (short dashes), 232 μm (solid) and 301 μm (long dashes) from the soma. Due to differences in local compared with somatic responses, axis values for the red curves are scaled up by a factor of 10. ( B ) Experimental verification of predicted summation rules in basal dendrites of a layer V pyramidal neuron. Single-pulse stimulation of synapses in different branches results in linear summation at the cell body (green squares). When γ-aminobutyric acid (GABA)ergic inhibition is blocked, the summation of within-branch EPSPs is modulated by a sigmoidal nonlinearity (all other symbols). Reproduced with permission from Polsky et al (2004) . ( C ) Schematic representation of a pyramidal neuron as a two-layer neural network. Radial oblique dendrites provide the first layer of the network, each performing individually thresholded computations as shown in ( A ) and ( B ). The outputs of this layer feed into the cell body, which constitutes the second layer of the network model. Adapted from Poirazi et al (2003b) .

Computing with regenerative dendritic events

What would be the benefit of a cell consisting of dendrites that are able to isolate complex events? Early physiological studies identified the presence of active dendritic events in neurons ( Kandel & Spencer, 1961 ); however, they were not further investigated experimentally at that time. Modelling studies predicted a role for Na + -mediated dendritic spikes—initially modelled with H-H conductances—in allowing the back-propagation of action potentials into the dendritic tree ( Rall & Shepherd, 1968 ), as well as enabling coincidence detection between proximal and distal inputs ( Softky & Koch, 1993 ).

A closer evaluation of dendritic events revealed that these spikes can be mediated by Na + ( Golding & Spruston, 1998 ) or calcium ion (Ca 2+ ; Yuste et al , 1994 ) channels in the distal dendrites, as well as by N-methyl D-aspartate (NMDA) channels ( Schiller et al , 2000 ) or a combination of Na + and NMDA channels ( Ariav et al , 2003 ) in the basal dendrites of pyramidal neurons. The quest is now to reveal their role in neuronal function. Although these spikes can be confined to their dendritic site of origin ( Wei et al , 2001 ; Schiller et al , 2000 ; Zhu, 2000 ), physiologically relevant situations such as large, suprathreshold synaptic stimuli in the distal dendrites or the activation of several branches together allow these spikes to act globally and modulate neuronal output ( Zhu, 2000 ; Larkum et al , 2001 ). In a compartmental CA1 neuron model, dendritic spike initiation in response to perforant path (PP) stimulation, which is confined in the distal tuft, could be transferred to the soma by activation of the Schaffer collateral (SC) pathway ( Jarsky et al , 2005 ). Similarly, dendritic Ca 2+ conductances in the distal tuft of a layer V cortical neuron model, which are unable to initiate an action potential at the soma, could be amplified by coincident back-propagating action potentials ( Larkum et al , 2004 ); in addition, dendritic Ca 2+ spikes have been suggested to convert single spikes into bursts in CA3 pyramidal neuron models ( Traub et al , 1991 ), leading to an enhanced neuronal response.

Although the above studies indicate that dendritic events might amplify neuronal gain and facilitate coincident detection of inputs, simulated experiments in a detailed CA1 pyramidal neuron model indicate that distal dendritic activation could bidirectionally gate suprathreshold SC input (E.K.P. and P.P., unpublished data). In particular, PP theta-burst stimulation coincident with or slightly preceding regular SC input, initiates Ca 2+ spikes in the distal dendrites and transforms a previously regular firing response into bursting ( Fig 3A,B ). By contrast, when subthreshold PP stimulation precedes the SC input by a few hundreds of milliseconds, the distal dendrites are hyperpolarized due to enhanced γ-aminobutyric acid (GABA) transmission. This results in a reduced gain of neuronal output, as seen by the blocking of the spikes induced by SC input only ( Fig 3A,C ). This PP-induced ‘spike blocking' was previously reported experimentally ( Dvorak-Carbone & Schuman, 1999 ). Collectively, dendritic regenerative events provide a means by which several localized events are combined to modulate neuronal output, thus expanding the response flexibility of single neurons.

Top-down gain modulation in a CA1 pyramidal neuron model. In a CA1 pyramidal neuron, afferents from the entorhinal cortex (EC) synapse onto distal dendrites, whereas axons from CA3 neurons project to proximal dendrites. ( A ) Synaptic activation (1 Hz) of proximal dendrites induces a regular firing response at the cell body of the model cell. ( B ) Strong (theta-burst) stimulation of EC inputs coinciding with CA3 stimulation evokes calcium spikes in the distal dendrites, the occurrence of which initiates bursts of action potentials (APs), and facilitates the somatic response. ( C ) Temporally distant activation of EC with subthreshold bursts (400 ms after the CA3 input) prevents the initiation of somatic action potentials for several seconds, thus weakening the somatic response. Green dots, excitatory inputs; red dots, inhibitory inputs.

Normalizing effects on synaptic integration

Whereas dendritic regenerative events allow gain modulation of neuronal output, passive properties and K + conductances in the dendrites act to spatially normalize and temporally integrate inputs, which provides the neuron with a different mode of information processing.

These passive properties (such as membrane time constant, input resistance and dendritic length) allow for differential integration of synaptic inputs that arrive at distal or proximal parts of the dendritic tree owing to the ‘large voltage attenuation and significant temporal delay' of propagated signals ( Koch & Segev, 2000 ). At the same time, incorporation of a hyperpolarization-activated, potassium ion (K + ) conductance ( I h ) in a CA1 compartmental model was shown to account for the experimentally observed normalization of EPSPs that originate from different parts of the dendritic tree so that all inputs induce similar depolarizations at the cell body ( Golding et al , 2005 ; Magee & Cook, 2000 ). Additional modelling experiments indicated that I h might be involved in setting the temporal window for input summation around subthreshold levels, thus enabling coincidence detection and minimizing the effectiveness of non-synchronized inputs ( Migliore et al , 2004 ; Migliore, 2003 ). Other dendritic K + conductances could either support or counteract the effect of I h on temporal summation ( Day et al , 2005 ).

Finally, the long-standing but quite neglected effect of background noise due to network activity on neuronal information processing should be considered. According to the work of Bernander and colleagues (1991) , background activity in a passive neuron model dampens the effectiveness of asynchronous—but not synchronous—inputs in generating a somatic action potential. This in turn facilitates the distinction between ‘unimportant' and ‘meaningful' signals, respectively. In a more detailed cortical neuron model that incorporates active dendritic conductances, intense synaptic network activity was shown to increase the membrane conductance, promote the location-independent effect of inputs arriving onto different dendritic regions (but see London & Segev, 2001 ) and increase the probability for dendritic spike initiation and its forward propagation to the axon ( Rudolph & Destexhe, 2003 ). Thus, incoming background noise from network activity could greatly influence the integrative properties of a neuron—for example, by modulating the spatiotemporal window for dendritic nonlinearities ( Azouz, 2005 ).

In conclusion, the neuron does not behave as a point neuron, but it might consist of many different point neurons in the form of a cluster of spines or a stretch of dendrite, each of which has its own integration rules according to its spatial location and temporal architecture of incoming inputs. When locally induced signals manage to escape their subunit, they are affected by global cellular parameters and are set by the overall network activity the cell receives, promoting a quasi-linear interaction mode.

The overall picture emerging from this analysis is that a single neuron could be decomposed into a multi-layer neural network, able to perform all sorts of nonlinear computations ( London & Hausser, 2005 ). Interestingly, the average firing rate of a detailed CA1 model to hundreds of different input patterns was accurately predicted by a two-layer neural network abstraction ( Fig 2 ), in which individual oblique dendrites provided the first layer and the soma acted as the output layer ( Poirazi et al , 2003b ). This implies a much larger storage capacity than originally assumed for single neurons. According to another computational study, the pattern discrimination capacity of such a cell exceeds that of a point neuron by at least one order of magnitude ( Poirazi & Mel, 2001 ). An even more complex single-neuron unit proposed by Hausser & Mel (2003) entails a two-compartment model in which the distal tuft acts as one compartment and the thin branches of the perisoma act as the other. Both compartments act as two-layer networks whose outputs combine at the cell body, giving rise to an extra powerful, three-layer computing unit.

Information processing at the cell body

Dendrites contribute to nonlinear summation of inputs, whereas the soma might support a different kind of information processing—that of enabling a persistent firing mode in the absence of stimulation. Recent experimental and modelling studies have highlighted the importance of somatic intrinsic membrane mechanisms in generating and maintaining persistent activity, in addition to the traditional network mechanisms (for a review, see Major & Tank, 2004 ). In vitro work in the entorhinal cortex ( Egorov et al, 2002 ; Tahvildari & Alonso, 2005 ) showed that a single neuron is able to generate graded persistent activity under pharmacological stimulation of muscarinic acetylcholine receptors in response to somatic or synaptic stimulation, owing to activation of a slow Ca 2+ -dependent mixed ionic (CAN) conductance. Modelling work has emphasized a possible involvement of the slow temporal decay of the EPSP ( Lisman et al , 1998 ), the CAN conductance ( Tegner et al , 2002 ) or the Ca 2+ -induced Ca 2+ release mechanism ( Loewenstein & Sompolinsky, 2003 ) in the maintenance of a stable persistent state at low physiological frequencies (10–50 Hz).

Persistent activity in vivo has also been observed in the hippocampus ( Wirth et al , 2003 ), although the underlying mechanisms are unclear. Ongoing work in our laboratory shows that persistent activity in a detailed CA1 pyramidal neuron model can be induced in response to theta-burst synaptic stimulation as well as in response to somatic stimulation under the influence of cholinergic modulation. The model supports a role for a slow, Ca 2+ -dependent tail current in maintaining sustained activity in hippocampal neurons ( Poirazi, 2005 ). The ability of single cells to be persistently active further enhances their computational power by adding another mode to their repertoire of complex functions.

Computing with axons

Axons provide a medium through which information, in the form of action potentials, flows across neuronal assemblies. Several computational studies have provided insights into the ionic mechanism for action-potential generation, particularly the seminal work of Hodgkin and Huxley, as well as the action-potential initiation site (reviewed in Stuart et al , 1997 ). More recent studies focusing on the reliability and accuracy of action-potential generation and propagation indicate that these properties, which are crucial for normal information processing, could be modified by changes in axonal geometry and ionic conductance composition ( Segev & Schneidman, 1999 ; Debanne, 2004 ). The work of Goldstein & Rall (1974) in simulated axons with changing diameter and different branching patterns first implicated the above structural characteristics in modifying action-potential curve and propagation speed. After quantifying the effect of such morphological changes on action-potential propagation velocity, another computational study suggested that synaptic boutons in different terminals are activated asynchronously in a reconstructed axon of a layer V neuron in the somatosensory cortex ( Manor et al , 1991 ). The density and type of ionic channels along the axon and branching points has also been suggested to gate action-potential propagation. For example, activation of just a few clusters of A-type K + channels has been shown to gate axonal propagation of action potentials in CA3 neurons in both simulations and experiments ( Debanne et al , 1997 ; Kopysova & Debanne, 1998 ). When combined, these findings imply that axons are much more than simple conducting devices for action potentials. Enriched with a variety of computational abilities, axons seem to be complex transmitting devices whose role in neuronal information processing is worth investigating thoroughly.

Concluding remarks

For several years, neuroscientists believed that the brain's transistor or fundamental processing unit was the neuron itself, which collects and processes incoming signals from neighbouring cells. In this review, we suggest that the morphological and ionic properties of the dendrites, the soma and the axon provide these structures with an array of computational abilities that might enable them to contribute differentially to neuronal function.

Dendrites seem to be key players in functions such as binocular disparity ( Archie & Mel, 2000 ) and directional selectivity in the visual system of various species ( Single & Borst, 1998 ; Euler et al , 2002 ; Vaney & Taylor, 2002 ), as well as in improving sound localization ( Agmon-Snir et al , 1998 ) and in supporting the transition between encoding and retrieval modes of associative memory systems ( Hasselmo et al , 1996 ; Dvorak-Carbone & Schuman, 1999 ). Conversely, persistent activity maintained by somatic mechanisms has been suggested to represent a cellular correlate of working memory functions ( Goldman-Rakic, 1995 ). Finally, propagation delays of the action potential along the axon have been attributed a role in precise temporal coding in the auditory system of the barn owl ( Carr et al , 2001 ), whereas axonal Na + channels have been suggested to act as a memory reservoir for previous activity levels ( Segev & Schneidman, 1999 ).

Linking computational properties to behaviour is the ultimate challenge for both modelling and experimental studies of the future. Recent papers applying modelling, physiological, molecular, genetic and behavioural techniques in Drosophila and mice have shown the contribution of different voltage-dependent K + conductances in light processing by photoreceptors and in reversing age-induced impairments in learning and memory, respectively ( Vahasoyrinki et al , 2006 ; Murphy et al , 2004 ). Such multidisciplinary approaches—in which models are used to formulate experimentally testable predictions and experiments are used to verify the predictions and refine the models—will enable a more thorough investigation of how different neuronal components and the cell as a whole contribute to information processing capacity and behaviour.

The following open questions could provide fertile ground for collaborations among molecular biologists, geneticists, physiologists, modellers and behaviourists for further explorations of the mysteries of the brain. Do specific behaviours require certain neuronal computational tasks? Which parts of the neural circuit or the neuron itself are responsible for these tasks? What are the underlying molecular mechanisms for the distinct operating modes of neuronal integration? Such holistic approaches should lend support to the growing idea reinforced by this review: that something smaller than the cell lies at the heart of neural computation.

Eleftheria Kyriaki Pissadaki, Panayiota Poirazi (who is an EMBO Young Investigator) & Kyriaki Sidiropoulou

Acknowledgments

This work was supported by Alexander S. Onassis Public Benefit Foundation (K.S.), General Secretariat of Research and Technology, ΠENEΔ 01EΔ311 (E.K.P.) and the EMBO Young investigator Programme.

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Biden Signs Executive Order to Expand Research on Women’s Health

The president said that improving women’s health was crucial to ensuring a healthy, stable economy.

Biden Signs Executive Order to Boost Women’s Health Research

The executive order is aimed at addressing the underrepresentation of women in health research..

We’ve launched the first ever White House initiative on women’s health research to pioneer the next generation of scientific research and discovery in women’s health. Think of all the breakthroughs we’ve made in medicine across the board, but women have not been the focus. And today — [applause] today, we’re jumpstarting that investment by dedicating $200 million in the National Institutes of Health to tackle some of the most pressing health problems facing women today. With the executive order I’m about to sign, I’m directing the most comprehensive set of executive actions ever taken to improve women’s health — ever taken. And I’m going to ensure that women’s health is integrated and prioritized across the entire federal government. It’s not just in women’s health, not just at N.I.H., the National Science Foundation or the Defense Department, the Environmental Protection Agency. I mean, across the board. This is really serious.

By Zolan Kanno-Youngs

Reporting from Washington

President Biden on Monday signed an executive order to expand the federal government’s research into women’s health, including midlife conditions like menopause, arthritis and heart disease, as well as issues specifically affecting women in the military.

In what the White House described as the “most comprehensive” action by a president on women’s health research, Mr. Biden directed federal agencies to ensure that they are using federal funds to research health conditions and diseases that disproportionately affect women.

Standing alongside the first lady, Jill Biden, and Vice President Kamala Harris, Mr. Biden said improving women’s health was crucial to guaranteeing a healthy, stable economy.

“There’s not a damn thing a man can do a woman can’t do,” Mr. Biden said. “To state the obvious, if you want to have the strongest economy in the world, you can’t leave half of the country behind.”

Carolyn M. Mazure, a psychologist and a professor at the Yale School of Medicine, who is the chairwoman of the White House initiative on Women’s Health Research, told reporters on Sunday night that health conditions like heart disease, Alzheimer’s, menopause and fibroids would be a focus of the expanded research effort.

“I’m not even a betting woman,” said Maria Shriver, the former first lady of California, who also attended the event, “but I’ll bet today that this is the first time a president of the United States has ever signed an executive order that mentions the words ‘menopause’ and ‘women’s midlife health’ in it.”

After the U.S. Supreme Court overturned Roe v. Wade in 2022 and the Alabama Supreme Court ruled last month that frozen embryos should be considered children , threatening in vitro fertilization, the Biden campaign has increasingly accused Republicans of undermining women’s health. During his State of the Union address this month, Mr. Biden said such decisions would motivate women to vote in the November election, while also saying his White House would commit to investing in women’s health in the year ahead.

“You can’t lead America with old ideas and take us backwards,” Mr. Biden said, adding, “To lead the land of possibilities, you need a vision for the future laying out what we can and should do and what we’re going to do.”

Mr. Biden’s executive order will require agencies to report annually their investments in women’s health research and to study ways that artificial intelligence can be used to advance such research. The National Institutes of Health will increase by 50 percent investments in small businesses focused on women’s health. The Defense Department also plans to invest $10 million to learn more about cancers and mental health issues affecting women in active military service.

The White House has called on Congress to pass a plan to invest $12 billion to create a new fund for women’s health research at the National Institutes of Health. In the meantime, the executive order signed on Monday directed the N.I.H. to spend $200 million on women’s health research. Dr. Biden traveled to Cambridge, Mass., last month to announce the first step of the women’s health initiative: $100 million to support women’s health researchers and start-up companies.

Zolan Kanno-Youngs is a White House correspondent, covering President Biden and his administration. More about Zolan Kanno-Youngs