{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T21:48:31Z","timestamp":1775339311779,"version":"3.50.1"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1010653","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2022,11,28]],"date-time":"2022-11-28T00:00:00Z","timestamp":1669593600000}}],"reference-count":63,"publisher":"Public Library of Science (PLoS)","issue":"11","license":[{"start":{"date-parts":[[2022,11,14]],"date-time":"2022-11-14T00:00:00Z","timestamp":1668384000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["IIS-2113003"],"award-info":[{"award-number":["IIS-2113003"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Washington Research Fund"}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>The representation of the flow of information between neurons in the brain based on their activity is termed the<jats:italic>causal functional connectome<\/jats:italic>. Such representation incorporates the dynamic nature of neuronal activity and causal interactions between them. In contrast to connectome, the causal functional connectome is not directly observed and needs to be inferred from neural time series. A popular statistical framework for inferring causal connectivity from observations is the<jats:italic>directed probabilistic graphical modeling<\/jats:italic>. Its common formulation is not suitable for neural time series since it was developed for variables with independent and identically distributed static samples. In this work, we propose to model and estimate the causal functional connectivity from neural time series using a novel approach that adapts directed probabilistic graphical modeling to the time series scenario. In particular, we develop the<jats:italic>Time-Aware PC<\/jats:italic>(TPC) algorithm for estimating the causal functional connectivity, which adapts the PC algorithm\u2014a state-of-the-art method for statistical causal inference. We show that the model outcome of TPC has the properties of reflecting causality of neural interactions such as being non-parametric, exhibits the<jats:italic>directed Markov<\/jats:italic>property in a time-series setting, and is predictive of the consequence of counterfactual interventions on the time series. We demonstrate the utility of the methodology to obtain the causal functional connectome for several datasets including simulations, benchmark datasets, and recent multi-array electro-physiological recordings from the mouse visual cortex.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1010653","type":"journal-article","created":{"date-parts":[[2022,11,14]],"date-time":"2022-11-14T18:28:52Z","timestamp":1668450532000},"page":"e1010653","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":12,"title":["Statistical perspective on functional and causal neural connectomics: The Time-Aware PC algorithm"],"prefix":"10.1371","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3579-4790","authenticated-orcid":true,"given":"Rahul","family":"Biswas","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3136-4531","authenticated-orcid":true,"given":"Eli","family":"Shlizerman","sequence":"additional","affiliation":[]}],"member":"340","published-online":{"date-parts":[[2022,11,14]]},"reference":[{"issue":"2","key":"pcbi.1010653.ref001","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/j.neuron.2012.06.031","article-title":"From functional architecture to functional connectomics","volume":"75","author":"RC Reid","year":"2012","journal-title":"Neuron"},{"issue":"10","key":"pcbi.1010653.ref002","article-title":"Advancing functional connectivity research from association to causation","volume":"1","author":"AT Reid","year":"2019","journal-title":"Nature neuroscience"},{"key":"pcbi.1010653.ref003","article-title":"Functional connectivity drives stroke recovery: shifting the paradigm from correlation to causation","author":"JM Cassidy","year":"2021","journal-title":"Brain"},{"issue":"2","key":"pcbi.1010653.ref004","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1162\/jocn_a_01580","article-title":"Combining multiple functional connectivity methods to improve causal inferences","volume":"33","author":"R Sanchez-Romero","year":"2021","journal-title":"Journal of cognitive neuroscience"},{"issue":"11","key":"pcbi.1010653.ref005","doi-asserted-by":"crossref","first-page":"1664","DOI":"10.1038\/nn.4135","article-title":"Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity","volume":"18","author":"ES Finn","year":"2015","journal-title":"Nature neuroscience"},{"key":"pcbi.1010653.ref006","doi-asserted-by":"crossref","DOI":"10.3389\/fnsys.2022.817962","article-title":"Statistical Perspective on Functional and Causal Neural Connectomics: A Comparative Study","volume":"16","author":"R Biswas","year":"2022","journal-title":"Frontiers in Systems Neuroscience"},{"key":"pcbi.1010653.ref007","doi-asserted-by":"crossref","first-page":"14","DOI":"10.3389\/fnhum.2016.00014","article-title":"Effective connectivity within the default mode network: dynamic causal modeling of resting-state fMRI data","volume":"10","author":"MG Sharaev","year":"2016","journal-title":"Frontiers in human neuroscience"},{"key":"pcbi.1010653.ref008","first-page":"63","article-title":"Causal inference from graphical models","author":"SL Lauritzen","year":"2001","journal-title":"Complex stochastic systems"},{"key":"pcbi.1010653.ref009","doi-asserted-by":"crossref","DOI":"10.1201\/9780429463976","volume-title":"Handbook of graphical models","author":"M Maathuis","year":"2018"},{"key":"pcbi.1010653.ref010","first-page":"1","article-title":"Functional directed graphical models and applications in root-cause analysis and diagnosis","author":"AME G\u00f3mez","year":"2020","journal-title":"Journal of Quality Technology"},{"issue":"2","key":"pcbi.1010653.ref011","doi-asserted-by":"crossref","first-page":"1","DOI":"10.21314\/JNTF.2016.016","article-title":"The econometrics of Bayesian graphical models: a review with financial application","volume":"2","author":"DF Ahelegbey","year":"2016","journal-title":"Journal of Network Theory in Finance"},{"issue":"17","key":"pcbi.1010653.ref012","doi-asserted-by":"crossref","first-page":"5648","DOI":"10.1175\/JCLI-D-11-00387.1","article-title":"Causal discovery for climate research using graphical models","volume":"25","author":"I Ebert-Uphoff","year":"2012","journal-title":"Journal of Climate"},{"issue":"1","key":"pcbi.1010653.ref013","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1471-2288-10-14","article-title":"Understanding human functioning using graphical models","volume":"10","author":"M Kalisch","year":"2010","journal-title":"BMC Medical Research Methodology"},{"key":"pcbi.1010653.ref014","doi-asserted-by":"crossref","unstructured":"Deng K, Liu D, Gao S, Geng Z. 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