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Optimal trajectories of brain state transitions

Shi Gu, Richard F. Betzel, Marcelo G. Mattar, Matthew Cieslak, Philip R. Delio, Scott T. Grafton, Fabio Pasqualetti, Danielle S. Bassett
2017 NeuroImage  
Here we address this question by drawing on recent advances in network control theory to model the underlying mechanisms of brain state transitions as elicited by the collective control of region sets.  ...  Yet how white matter structure constrains how the brain transitions from one cognitive state to another remains unknown.  ...  The content is solely the responsibility of the authors and does not necessarily represent the official views of any of the funding agencies.  ... 
doi:10.1016/j.neuroimage.2017.01.003 pmid:28088484 pmcid:PMC5489344 fatcat:pwabw2gevna5jbnsfakxxzmmee

Optimal Trajectories of Brain State Transitions [article]

Shi Gu, Richard F. Betzel, Matthew Cieslak, Philip R. Delio, Scott T. Grafton, Fabio Pasqualetti, Danielle S. Bassett
2017 arXiv   pre-print
Yet how the organization of white matter architecture constrains how the brain transitions from one cognitive state to another remains unknown.  ...  Drawing on recent advances in network control theory, we model the underlying mechanisms of brain state transitions as elicited by the collective control of region sets.  ...  of how the activity in individual brain regions affects the trajectory of the brain as it transitions between states.  ... 
arXiv:1607.01706v3 fatcat:mx7meaktizeanfjjrdhgbd2rte

Brain network dynamics during working memory are modulated by dopamine and diminished in schizophrenia

Urs Braun, Anais Harneit, Giulio Pergola, Tommaso Menara, Axel Schäfer, Richard F. Betzel, Zhenxiang Zang, Janina I. Schweiger, Xiaolong Zhang, Kristina Schwarz, Junfang Chen, Giuseppe Blasi (+7 others)
2021 Nature Communications  
AbstractDynamical brain state transitions are critical for flexible working memory but the network mechanisms are incompletely understood.  ...  The stability of states relates to dopamine D1 receptor gene expression while state transitions are influenced by D2 receptor expression and pharmacological modulation.  ...  There was no involvement by the funding bodies at any stage of the study.  ... 
doi:10.1038/s41467-021-23694-9 pmid:34108456 fatcat:7hlyjkgo65hbzn2ebwibzm6gzy

Developmental increases in white matter network controllability support a growing diversity of brain dynamics

Evelyn Tang, Chad Giusti, Graham L. Baum, Shi Gu, Eli Pollock, Ari E. Kahn, David R. Roalf, Tyler M. Moore, Kosha Ruparel, Ruben C. Gur, Raquel E. Gur, Theodore D. Satterthwaite (+1 others)
2017 Nature Communications  
This work reveals a possible mechanism of human brain development that preferentially optimizes dynamic network control over static network architecture.  ...  We use a network representation of diffusion imaging data from 882 youth ages 8 to 22 to show that white matter connectivity becomes increasingly optimized for a diverse range of predicted dynamics in  ...  The content is solely the responsibility of the authors and does not necessarily represent the official views of any of the funding agencies.  ... 
doi:10.1038/s41467-017-01254-4 pmid:29093441 pmcid:PMC5665937 fatcat:echxtaar2rablfmcotiq5jhqly

White Matter Network Architecture Guides Direct Electrical Stimulation Through Optimal State Transitions [article]

Jennifer Stiso, Ankit N. Khambhati, Tommaso Menara, Ari E. Kahn, Joel M. Stein, Sandihitsu R. Das, Richard Gorniak, Joseph Tracy, Brian Litt, Kathryn A. Davis, Fabio Pasqualetti, Timothy Lucas, Danielle S. Bassett
2018 arXiv   pre-print
We then use a targeted optimal control framework to solve for the optimal energy required to drive the brain to a given state.  ...  In a first validation of our model, we find that the true pattern of white matter tracts can be used to more accurately predict the state transitions induced by direct electrical stimulation than the artificial  ...  Author Declaration The authors declare no conflicts of interest.  ... 
arXiv:1805.01260v1 fatcat:wlz7mebpere5xan272vvrz7ohy

Quantifying brain state transition cost via Schrödinger Bridge

Genji Kawakita, Shunsuke Kamiya, Shuntaro Sasai, Jun Kitazono, Masafumi Oizumi
2021 Network Neuroscience  
We demonstrate correspondence between brain state transition cost and the difficulty of tasks.  ...  However, this approach does not capture the stochasticity of neural systems, which is important for accurately quantifying brain state transition cost.  ...  To quantify the efficiency of brain state transition, it would be interesting to compare empirical and optimal paths.  ... 
doi:10.1162/netn_a_00213 pmid:35356194 pmcid:PMC8959122 fatcat:nfpgvpx7mbf4reujsndno6ttt4

Quantifying brain state transition cost via Schrödinger's bridge [article]

Genji Kawakita, Shunsuke Kamiya, Shuntaro Sasai, Jun Kitazono, Masafumi Oizumi
2021 bioRxiv   pre-print
We demonstrate correspondence between brain state transition cost and the difficulty of tasks.  ...  However, this approach does not capture the stochasticity of neural systems, which is important for accurately quantifying brain state transition cost.  ...  To quantify the efficiency of brain state transition, it would be interesting to compare empirical and optimal paths.  ... 
doi:10.1101/2021.05.24.445394 fatcat:jijhmyrscveuhlf46o5krscemy

White Matter Network Architecture Guides Direct Electrical Stimulation Through Optimal State Transitions [article]

Jennifer Stiso, Ankit N Khambhati, Tommaso Menara, Ari E Kahn, Joel M Stein, Sandihitsu R Das, Richard Gorniak, Joseph Tracy, Brian Litt, Kathryn A Davis, Fabio Pasqualetti, Timothy H Lucas (+1 others)
2018 bioRxiv   pre-print
We then use a targeted optimal control framework to solve for the optimal energy required to drive the brain to a given state.  ...  In a first validation of our model, we find that the true pattern of white matter tracts can be used to more accurately predict the state transitions induced by direct electrical stimulation than the artificial  ...  The Principle of Optimal Control in Brain State Transitions By positing a model for optimal brain state transitions, we relate expected energy expenditures to a change in the probability with which a pattern  ... 
doi:10.1101/313304 fatcat:2zmd2cuidzbibgvktcbpwobt4m

Metastable Resting State Brain Dynamics

Peter beim Graben, Antonio Jimenez-Marin, Ibai Diez, Jesus M. Cortes, Mathieu Desroches, Serafim Rodrigues
2019 Frontiers in Computational Neuroscience  
of system's trajectories into metastable states using recurrence grammars.  ...  Metastability refers to the fact that the state of a dynamical system spends a large amount of time in a restricted region of its available phase space before a transition takes place, bringing the system  ...  and associates to optimal brain structures, thus resolving the function-structure of the so-called resting state networks (RSNs) (Raichle et al., 2001; Fox et al., 2005; Diez et al., 2015; Smitha et  ... 
doi:10.3389/fncom.2019.00062 pmid:31551744 pmcid:PMC6743347 fatcat:xotgseqzpbghxi7dk7erjc2csy

White Matter Network Architecture Guides Direct Electrical Stimulation through Optimal State Transitions

Jennifer Stiso, Ankit N. Khambhati, Tommaso Menara, Ari E. Kahn, Joel M. Stein, Sandihitsu R. Das, Richard Gorniak, Joseph Tracy, Brian Litt, Kathryn A. Davis, Fabio Pasqualetti, Timothy H. Lucas (+1 others)
2019 Cell Reports  
Optimizing direct electrical stimulation for the treatment of neurological disease remains difficult due to an incomplete understanding of its physical propagation through brain tissue.  ...  We find statistically significant shared variance between the predicted activity state transitions and the observed activity state transitions.  ...  The views, opinions, and/or findings contained in this material are those of the authors and should not be interpreted as representing the official views or policies of the Department of Defense or the  ... 
doi:10.1016/j.celrep.2019.08.008 pmid:31484068 pmcid:PMC6849479 fatcat:yc244nlscjf5zojszaynn5c6xq

Brain state stability during working memory is explained by network control theory, modulated by dopamine D1/D2 receptor function, and diminished in schizophrenia [article]

Urs Braun, Anais Harneit, Giulio Pergola, Tommaso Menara, Axel Schaefer, Richard F. Betzel, Zhenxiang Zang, Janina I. Schweiger, Kristina Schwarz, Junfang Chen, Giuseppe Blasi, Alessandro Bertolino (+6 others)
2019 bioRxiv   pre-print
Dynamical brain state transitions are critical for flexible working memory but the network mechanisms are incompletely understood.  ...  The stability of states relates to dopamine D1 receptor gene expression while state transitions are influenced by D2 receptor expression and pharmacological modulation.  ...  There was no involvement by the funding bodies at any stage of the study. We thank Oliver  ... 
doi:10.1101/679670 fatcat:7l3r4bku45d7xdyhnvjwgrx23e

Delimiting subterritories of the human subthalamic nucleus by means of microelectrode recordings and a Hidden Markov Model

Adam Zaidel, Alexander Spivak, Lavi Shpigelman, Hagai Bergman, Zvi Israel
2009 Movement Disorders  
The sensorimotor region of the STN (seemingly the preferred location for STN DBS) lies dorsolaterally, in a region also marked by distinct beta (13-30 Hz) oscillations in the parkinsonian state.  ...  Fifty-six MER trajectories were used, obtained from 21 PD patients who underwent bilateral STN DBS implantation surgery.  ...  at the Hadassah (PATH) committee of London.  ... 
doi:10.1002/mds.22674 pmid:19533755 fatcat:swtvvymk4rh2zbjyfnsxsmk5wq

A practical guide to methodological considerations in the controllability of structural brain networks [article]

Teresa M. Karrer, Jason Z. Kim, Jennifer Stiso, Ari E. Kahn, Fabio Pasqualetti, Ute Habel, Danielle S. Bassett
2019 arXiv   pre-print
Predicting how the brain can be driven to specific states by means of internal or external control requires a fundamental understanding of the relationship between neural connectivity and activity.  ...  optimal control energy.  ...  The optimal control energy additionally constrains the size of the state trajectory. (Right) Control strategies potentially examining all possible state transitions (dashed arrows).  ... 
arXiv:1908.03514v1 fatcat:ovcrpjr2gvd3pa3vg4zm6h6gwy

Optimization of energy state transition trajectory supports the development of executive function during youth

Zaixu Cui, Jennifer Stiso, Graham L Baum, Jason Z Kim, David R Roalf, Richard F Betzel, Shi Gu, Zhixin Lu, Cedric H Xia, Xiaosong He, Rastko Ciric, Desmond J Oathes (+9 others)
2020 eLife  
Our results reveal a mechanism by which structural networks develop during adolescence to reduce the theoretical energetic costs of transitions to activation states necessary for executive function.  ...  activate the fronto-parietal system through the control of multiple brain regions given existing structural network topology.  ...  (a) The activation profiles of all 27 brain regions of the fronto-parietal system 1393 during an optimal trajectory from the baseline state to the final state.  ... 
doi:10.7554/elife.53060 pmid:32216874 pmcid:PMC7162657 fatcat:k2ouoi7wrjgklfjmyliaemqhxu

Temporal sequences of brain activity at rest are constrained by white matter structure and modulated by cognitive demands

Eli J. Cornblath, Arian Ashourvan, Jason Z. Kim, Richard F. Betzel, Rastko Ciric, Azeez Adebimpe, Graham L. Baum, Xiaosong He, Kosha Ruparel, Tyler M. Moore, Ruben C. Gur, Raquel E. Gur (+4 others)
2020 Communications Biology  
A diverse set of white matter connections supports seamless transitions between cognitive states.  ...  However, it remains unclear how these connections guide the temporal progression of large-scale brain activity patterns in different cognitive states.  ...  First, we hypothesized that the brain is optimized to support the observed brain states and state transitions with relatively little energy.  ... 
doi:10.1038/s42003-020-0961-x pmid:32444827 fatcat:rs7ldiqujvfn3i4kyha3vvciqe
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