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This work considers a continuous framework to characterize the population-level variability of structural connectivity. Our framework assumes the observed white matter fiber tract endpoints are driven by a latent random function defined…

统计计算 · 统计学 2022-07-19 William Consagra , Martin Cole , Zhengwu Zhang

Anatomy of the human brain constrains the formation of large-scale functional networks. Here, given measured brain activity in gray matter, we interpolate these functional signals into the white matter on a structurally-informed…

神经元与认知 · 定量生物学 2019-08-27 Anjali Tarun , Hamid Behjat , David Abramian , Dimitri Van De Ville

Neurons in the brain are often finely tuned for specific task variables. Moreover, such disentangled representations are highly sought after in machine learning. Here we mathematically prove that simple biological constraints on neurons,…

神经元与认知 · 定量生物学 2023-04-04 James C. R. Whittington , Will Dorrell , Surya Ganguli , Timothy E. J. Behrens

Spontaneous brain activity, as observed in functional neuroimaging, has been shown to display reproducible structure that expresses brain architecture and carries markers of brain pathologies. An important view of modern neuroscience is…

机器学习 · 统计学 2010-11-15 Gaël Varoquaux , Alexandre Gramfort , Jean Baptiste Poline , Bertrand Thirion

The frequency-specific coupling mechanism of the functional human brain networks underpins its complex cognitive and behavioral functions. Nevertheless, it is not well unveiled what are the frequency-specific subdivisions and network…

神经元与认知 · 定量生物学 2021-03-30 Zhiguo Luo , Ling-Li Zeng , Hui Shen , Dewen Hu

In order to understand the complex cognitive functions of the human brain, it is essential to study the structural connectome, i.e., the wiring of different brain regions to each other through axonal pathways. However, the high degree of…

神经元与认知 · 定量生物学 2021-01-25 Anand Pathak , Shakti N. Menon , Sitabhra Sinha

Cognitive function is driven by dynamic interactions between large-scale neural circuits or networks, enabling behavior. Fundamental principles constraining these dynamic network processes have remained elusive. Here we use network control…

神经元与认知 · 定量生物学 2015-10-28 Shi Gu , Fabio Pasqualetti , Matthew Cieslak , Scott T. Grafton , Danielle S. Bassett

Modeling the behavior of coupled networks is challenging due to their intricate dynamics. For example in neuroscience, it is of critical importance to understand the relationship between the functional neural processes and anatomical…

机器学习 · 计算机科学 2021-04-20 Hongyuan You , Sikun Lin , Ambuj K. Singh

Human learning is a complex process in which future behavior is altered via the reorganization of brain activity and connectivity. It remains unknown whether activity and connectivity differentially reorganize during learning, and, if so,…

MRI-based modeling of brain networks has been widely used to understand functional and structural interactions and connections among brain regions, and factors that affect them, such as brain development and disease. Graph mining on brain…

机器学习 · 计算机科学 2022-05-18 Haoteng Tang , Xiyao Fu , Lei Guo , Yalin Wang , Scott Mackin , Olusola Ajilore , Alex Leow , Paul Thompson , Heng Huang , Liang Zhan

A critical mystery in neuroscience lies in determining how anatomical structure impacts the complex functional dynamics of human thought. How does large-scale brain circuitry constrain states of neuronal activity and transitions between…

神经元与认知 · 定量生物学 2016-07-08 Shi Gu , Matthew Cieslak , Benjamin Baird , Sarah F. Muldoon , Scott T. Grafton , Fabio Pasqualetti , Danielle S. Bassett

Objective: Brain is a fantastic organ that helps creature adapting to the environment. Network is the most essential structure of brain, but the capability of a simple network is still not very clear. In this study, we try to expound some…

神经元与认知 · 定量生物学 2019-11-05 Xiang Zou , Lie Yao , Donghua Zhao , Liang Chen , Ying Mao

Human brain structural networks contain sets of centrally embedded hub regions that enable efficient information communication. However, it remains largely unknown about categories of structural brain hubs and their microstructural,…

神经元与认知 · 定量生物学 2016-09-13 Xindi Wang , Qixiang Lin , Mingrui Xia , Yong He

The human brain is organized as a complex network, where connections between regions are characterized by both functional connectivity (FC) and structural connectivity (SC). While previous studies have primarily focused on network-level…

Many recent efforts in computational modeling of macro-scale brain dynamics have begun to take a data-driven approach by incorporating structural and/or functional information derived from subject data. Here, we discuss recent work using…

神经元与认知 · 定量生物学 2018-11-15 Kanika Bansal , Johan Nakuci , Sarah Feldt Muldoon

Recent experimental advances are producing an avalanche of data on both neural connectivity and neural activity. To take full advantage of these two emerging datasets we need a framework that links them, revealing how collective neural…

神经元与认知 · 定量生物学 2020-02-27 Gabriel Koch Ocker , Krešimir Josić , Eric Shea-Brown , Michael A. Buice

Functional differentiation in the brain emerges as distinct regions specialize and is key to understanding brain function as a complex system. Previous research has modeled this process using artificial neural networks with specific…

神经元与认知 · 定量生物学 2025-11-17 Yuki Tomoda , Ichiro Tsuda , Yutaka Yamaguti

The cerebral cortex is composed of multiple cortical areas that exert a wide variety of brain functions. Although human brain neurons are genetically and areally mosaic, the three-dimensional structural differences between neurons in…

Human cognition is supported by brain structural connectivity wherein weak connectivity with weights several orders of magnitude smaller than those of strong connectivity, has been treated as noise and ignored from analysis over a long…

Many fMRI analyses examine functional connectivity, or statistical dependencies among remote brain regions. Yet popular methods for studying whole-brain functional connectivity often yield results that are difficult to interpret. Factor…

统计方法学 · 统计学 2024-09-24 Kyle Stanley , Nicole Lazar , Matthew Reimherr