Social physics

Physics Reports, Journal Year: 2022, Volume and Issue: 948, P. 1 - 148

Published: Jan. 11, 2022

Language: Английский

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Environmental influences on the pace of brain development

Nature reviews. Neuroscience, Journal Year: 2021, Volume and Issue: 22(6), P. 372 - 384

Published: April 28, 2021

Childhood socio-economic status (SES), a measure of the availability material and social resources, is one strongest predictors lifelong well-being. Here we review evidence that experiences associated with childhood SES affect not only outcome but also pace brain development. We argue higher protracted structural development prolonged trajectory functional network segregation, ultimately leading to more efficient cortical networks in adulthood. hypothesize greater exposure chronic stress accelerates maturation, whereas access novel positive decelerates maturation. discuss impact variation on plasticity learning. provide generative theoretical framework catalyse future basic science translational research environmental influences Evidence suggests can its rate. Tooley, Bassett Mackey this suggest valence frequency early interact influence

Language: Английский

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The physics of brain network structure, function and control

Nature Reviews Physics, Journal Year: 2019, Volume and Issue: 1(5), P. 318 - 332

Published: March 27, 2019

Language: Английский

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Understanding the dynamics of biological and neural oscillator networks through exact mean-field reductions: a review

The Journal of Mathematical Neuroscience, Journal Year: 2020, Volume and Issue: 10(1)

Published: May 27, 2020

Abstract Many biological and neural systems can be seen as networks of interacting periodic processes. Importantly, their functionality, i.e., whether these perform function or not, depends on the emerging collective dynamics network. Synchrony oscillations is one most prominent examples such behavior has been associated both with dysfunction. Understanding how network structure interactions, well microscopic properties individual units, shape critical to find factors that lead malfunction. However, many brain consist a large number dynamical units. Hence, analysis either relied simplified heuristic models coarse scale, comes at huge computational cost. Here we review recently introduced approaches, known Ott–Antonsen Watanabe–Strogatz reductions, allowing simplify by bridging small scales. Thus, reduced model equations are obtained exactly describe for each subpopulation in oscillator via few variables only. The resulting next-generation models: Rather than being heuristic, they link macroscopic descriptions therefore accurately capture underlying system. At same time, sufficiently simple analyze without great effort. In last decade, reduction methods have become instrumental understanding interactions emergence synchrony. We this progress based concrete outline possible limitations. Finally, discuss linking experimental data guide way towards development new treatment example, neurological disease.

Language: Английский

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Training deep neural density estimators to identify mechanistic models of neural dynamics

eLife, Journal Year: 2020, Volume and Issue: 9

Published: Sept. 17, 2020

Mechanistic modeling in neuroscience aims to explain observed phenomena terms of underlying causes. However, determining which model parameters agree with complex and stochastic neural data presents a significant challenge. We address this challenge machine learning tool uses deep density estimators—trained using simulations—to carry out Bayesian inference retrieve the full space compatible raw or selected features. Our method is scalable features can rapidly analyze new after initial training. demonstrate power flexibility our approach on receptive fields, ion channels, Hodgkin–Huxley models. also characterize circuit configurations giving rise rhythmic activity crustacean stomatogastric ganglion, use these results derive hypotheses for compensation mechanisms. will help close gap between data-driven theory-driven models dynamics.

Language: Английский

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Integrative Benchmarking to Advance Neurally Mechanistic Models of Human Intelligence

Neuron, Journal Year: 2020, Volume and Issue: 108(3), P. 413 - 423

Published: Sept. 11, 2020

A potentially organizing goal of the brain and cognitive sciences is to accurately explain domains human intelligence as executable, neurally mechanistic models. Years research have led models that capture experimental results in individual behavioral tasks regions. We here advocate for taking next step: integrating from many laboratories into suites benchmarks that, when considered together, push toward explaining entire intelligence, such vision, language, motor control. Given recent successes surging availability neural, anatomical, data, we believe now time create integrative benchmarking platforms incentivize ambitious, unified This perspective discusses advantages challenges this approach proposes specific steps achieve domain visual with case study an platform called Brain-Score.

Language: Английский

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The human connectome in Alzheimer disease — relationship to biomarkers and genetics

Nature Reviews Neurology, Journal Year: 2021, Volume and Issue: 17(9), P. 545 - 563

Published: July 20, 2021

Language: Английский

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Neuronal excitation/inhibition imbalance: core element of a translational perspective on Alzheimer pathophysiology

Ageing Research Reviews, Journal Year: 2021, Volume and Issue: 69, P. 101372 - 101372

Published: May 21, 2021

Our incomplete understanding of the link between Alzheimer's Disease pathology and symptomatology is a crucial obstacle for therapeutic success. Recently, translational studies have begun to connect dots protein alterations deposition, brain network dysfunction cognitive deficits. Disturbance neuronal activity, in particular an imbalance underlying excitation/inhibition (E/I), appears early AD, can be regarded as forming central structural dysfunction. While there are emerging (non-)pharmacological options influence this imbalance, complexity human dynamics has hindered identification optimal approach. We suggest that focusing on integration neurophysiological aspects AD at micro-, meso- macroscale, with support computational modeling, unite fundamental clinical knowledge, provide general framework, rational targets.

Language: Английский

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Multimodal network dynamics underpinning working memory

Nature Communications, Journal Year: 2020, Volume and Issue: 11(1)

Published: June 15, 2020

Working memory (WM) allows information to be stored and manipulated over short time scales. Performance on WM tasks is thought supported by the frontoparietal system (FPS), default mode (DMS), interactions between them. Yet little known about how these systems their relate individual differences in performance. We address this gap knowledge using functional MRI data acquired during performance of a 2-back task, as well diffusion tensor imaging collected same individuals. show that strength FPS DMS task engagement inversely correlated with performance, modulated activation regions but not regions. Next, we use clustering algorithm identify two distinct subnetworks FPS, find display distinguishable patterns gene expression. Activity one subnetwork positively associated FPS-DMS interactions, while activity second negatively associated. Further, pattern structural linkages explains differential capacity influence interactions. To determine whether observations could provide mechanistic account large-scale neural underpinnings WM, build computational model composed coupled oscillators. Modulating amplitude causes expected change thereby offering support for mechanism which tunes Broadly, our study presents holistic regional activity, together humans.

Language: Английский

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Connectome-based modelling of neurodegenerative diseases: towards precision medicine and mechanistic insight

Nature reviews. Neuroscience, Journal Year: 2023, Volume and Issue: 24(10), P. 620 - 639

Published: Aug. 24, 2023

Language: Английский

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