[ { "key": "CQYGHYCE", "version": 8, "library": { "type": "group", "id": 470430, "name": "Brain Connectivity", "links": { "alternate": { "href": "https://www.zotero.org/groups/brain_connectivity", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/470430/items/CQYGHYCE", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/brain_connectivity/items/CQYGHYCE", "type": "text/html" } }, "meta": { "createdByUser": { "id": 177899, "username": "drkitty28", "name": "Catherine Hanson", "links": { "alternate": { "href": "https://www.zotero.org/drkitty28", "type": "text/html" } } }, "creatorSummary": "Xu et al.", "parsedDate": "2023-07", "numChildren": 0 }, "data": { "key": "CQYGHYCE", "version": 8, "itemType": "journalArticle", "title": "Interacting spiral wave patterns underlie complex brain dynamics and are related to cognitive processing", "creators": [ { "creatorType": "author", "firstName": "Yiben", "lastName": "Xu" }, { "creatorType": "author", "firstName": "Xian", "lastName": "Long" }, { "creatorType": "author", "firstName": "Jianfeng", "lastName": "Feng" }, { "creatorType": "author", "firstName": "Pulin", "lastName": "Gong" } ], "abstractNote": "The large-scale activity of the human brain exhibits rich and complex patterns, but the spatiotemporal dynamics of these patterns and their functional roles in cognition remain unclear. Here by characterizing moment-by-moment fluctuations of human cortical functional magnetic resonance imaging signals, we show that spiral-like, rotational wave patterns (brain spirals) are widespread during both resting and cognitive task states. These brain spirals propagate across the cortex while rotating around their phase singularity centres, giving rise to spatiotemporal activity dynamics with non-stationary features. The properties of these brain spirals, such as their rotational directions and locations, are task relevant and can be used to classify different cognitive tasks. We also demonstrate that multiple, interacting brain spirals are involved in coordinating the correlated activations and de-activations of distributed functional regions; this mechanism enables flexible reconfiguration of task-driven activity flow between bottom-up and top-down directions during cognitive processing. Our findings suggest that brain spirals organize complex spatiotemporal dynamics of the human brain and have functional correlates to cognitive processing.", "publicationTitle": "Nature Human Behaviour", "publisher": "", "place": "", "date": "2023-07", "volume": "7", "issue": "7", "section": "", "partNumber": "", "partTitle": "", "pages": "1196-1215", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Nat Hum Behav", "DOI": "10.1038/s41562-023-01626-5", "citationKey": "", "url": "https://www.nature.com/articles/s41562-023-01626-5", "accessDate": "2023-09-30T13:55:55Z", "PMID": "", "PMCID": "", "ISSN": "2397-3374", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "en", "libraryCatalog": "www.nature.com", "callNumber": "", "rights": "2023 The Author(s), under exclusive licence to Springer Nature Limited", "extra": "Number: 7\nPublisher: Nature Publishing Group", "tags": [ { "tag": "Cognitive neuroscience", "type": 1 }, { "tag": "Computational neuroscience", "type": 1 } ], "collections": [], "relations": {}, "dateAdded": "2023-09-30T13:55:55Z", "dateModified": "2023-09-30T13:55:55Z" } }, { "key": "ZC2MFJD5", "version": 6, "library": { "type": "group", "id": 470430, "name": "Brain Connectivity", "links": { "alternate": { "href": "https://www.zotero.org/groups/brain_connectivity", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/470430/items/ZC2MFJD5", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/brain_connectivity/items/ZC2MFJD5", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/470430/items/2Z69TPL9", "type": "application/json" } }, "meta": { "createdByUser": { "id": 177899, "username": "drkitty28", "name": "Catherine Hanson", "links": { "alternate": { "href": "https://www.zotero.org/drkitty28", "type": "text/html" } } }, "numChildren": 0 }, "data": { "key": "ZC2MFJD5", "version": 6, "parentItem": "2Z69TPL9", "itemType": "note", "note": "Comment: 23 pages, 8 figures", "tags": [], "relations": {}, "dateAdded": "2018-06-04T13:09:40Z", "dateModified": "2018-06-04T13:09:40Z" } }, { "key": "2Z69TPL9", "version": 6, "library": { "type": "group", "id": 470430, "name": "Brain Connectivity", "links": { "alternate": { "href": "https://www.zotero.org/groups/brain_connectivity", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/470430/items/2Z69TPL9", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/brain_connectivity/items/2Z69TPL9", "type": "text/html" } }, "meta": { "createdByUser": { "id": 177899, "username": "drkitty28", "name": "Catherine Hanson", "links": { "alternate": { "href": "https://www.zotero.org/drkitty28", "type": "text/html" } } }, "creatorSummary": "Hanson et al.", "parsedDate": "2016-05-30", "numChildren": 1 }, "data": { "key": "2Z69TPL9", "version": 6, "itemType": "journalArticle", "title": "Scale-Free Exponents of Resting State provide a Biomarker for Typical and Atypical Brain Activity", "creators": [ { "creatorType": "author", "firstName": "S. J.", "lastName": "Hanson" }, { "creatorType": "author", "firstName": "D.", "lastName": "Mastrovito" }, { "creatorType": "author", "firstName": "C.", "lastName": "Hanson" }, { "creatorType": "author", "firstName": "J.", "lastName": "Ramsey" }, { "creatorType": "author", "firstName": "C.", "lastName": "Glymour" } ], "abstractNote": "Scale-free networks (SFN) arise from simple growth processes, which can encourage efficient, centralized and fault tolerant communication (1). Recently its been shown that stable network hub structure is governed by a phase transition at exponents (>2.0) causing a dramatic change in network structure including a loss of global connectivity, an increasing minimum dominating node set, and a shift towards increasing connectivity growth compared to node growth. Is this SFN shift identifiable in atypical brain activity? The Pareto Distribution (P(D)~D^-\\b{eta}) on the hub Degree (D) is a signature of scale-free networks. During resting-state, we assess Degree exponents across a large range of neurotypical and atypical subjects. We use graph complexity theory to provide a predictive theory of the brain network structure. Results. We show that neurotypical resting-state fMRI brain activity possess scale-free Pareto exponents (1.8 se .01) in a single individual scanned over 66 days as well as in 60 different individuals (1.8 se .02). We also show that 60 individuals with Autistic Spectrum Disorder, and 60 individuals with Schizophrenia have significantly higher (>2.0) scale-free exponents (2.4 se .03, 2.3 se .04), indicating more fractionated and less controllable dynamics in the brain networks revealed in resting state. Finally we show that the exponent values vary with phenotypic measures of atypical disease severity indicating that the global topology of the network itself can provide specific diagnostic biomarkers for atypical brain activity.", "publicationTitle": "arXiv:1605.09282 [q-bio]", "publisher": "", "place": "", "date": "2016-05-30", "volume": "", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "", "citationKey": "", "url": "http://arxiv.org/abs/1605.09282", "accessDate": "2018-06-04T13:09:03Z", "PMID": "", "PMCID": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "arXiv.org", "callNumber": "", "rights": "", "extra": "arXiv: 1605.09282", "tags": [ { "tag": "Quantitative Biology - Neurons and Cognition", "type": 1 } ], "collections": [], "relations": {}, "dateAdded": "2018-06-04T13:09:40Z", "dateModified": "2018-06-04T13:09:40Z" } }, { "key": "K9EQEAPC", "version": 2, "library": { "type": "group", "id": 470430, "name": "Brain Connectivity", "links": { "alternate": { "href": "https://www.zotero.org/groups/brain_connectivity", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/470430/items/K9EQEAPC", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/brain_connectivity/items/K9EQEAPC", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/470430/items/JXR6VS85", "type": "application/json" } }, "meta": { "createdByUser": { "id": 177899, "username": "drkitty28", "name": "Catherine Hanson", "links": { "alternate": { "href": "https://www.zotero.org/drkitty28", "type": "text/html" } } } }, "data": { "key": "K9EQEAPC", "version": 2, "parentItem": "JXR6VS85", "itemType": "attachment", "linkMode": "linked_url", "title": "PubMed entry", "accessDate": "2013-07-18T19:55:42Z", "url": "http://www.ncbi.nlm.nih.gov/pubmed/20879304", "note": "", "contentType": "text/html", "charset": "", "tags": [], "relations": {}, "dateAdded": "2016-01-29T13:07:12Z", "dateModified": "2016-01-29T13:07:12Z" } }, { "key": "WQDQI9UK", "version": 2, "library": { "type": "group", "id": 470430, "name": "Brain Connectivity", "links": { "alternate": { "href": "https://www.zotero.org/groups/brain_connectivity", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/470430/items/WQDQI9UK", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/brain_connectivity/items/WQDQI9UK", "type": "text/html" } }, "meta": { "createdByUser": { "id": 177899, "username": "drkitty28", "name": "Catherine Hanson", "links": { "alternate": { "href": "https://www.zotero.org/drkitty28", "type": "text/html" } } }, "creatorSummary": "Ramasubramanian and Mosse", "parsedDate": "2002", "numChildren": 0 }, "data": { "key": "WQDQI9UK", "version": 2, "itemType": "conferencePaper", "title": "Statistical analysis of connectivity in unidirectional ad hoc networks", "creators": [ { "creatorType": "author", "firstName": "Venugopalan", "lastName": "Ramasubramanian" }, { "creatorType": "author", "firstName": "Daniel", "lastName": "Mosse" } ], "abstractNote": "", "proceedingsTitle": "Parallel Processing Workshops, 2002. Proceedings. International Conference on", "conferenceName": "", "publisher": "IEEE", "place": "", "date": "2002", "eventPlace": "", "volume": "", "issue": "", "numberOfVolumes": "", "pages": "109–115", "series": "", "seriesNumber": "", "DOI": "", "ISBN": "", "citationKey": "", "url": "http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1039719", "accessDate": "2016-01-29T12:16:41Z", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "Google Scholar", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [ "EPW2IIX9" ], "relations": {}, "dateAdded": "2016-01-29T13:07:12Z", "dateModified": "2016-01-29T13:07:12Z" } }, { "key": "TTZEIE32", "version": 2, "library": { "type": "group", "id": 470430, "name": "Brain Connectivity", "links": { "alternate": { "href": "https://www.zotero.org/groups/brain_connectivity", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/470430/items/TTZEIE32", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/brain_connectivity/items/TTZEIE32", "type": "text/html" } }, "meta": { "createdByUser": { "id": 177899, "username": "drkitty28", "name": "Catherine Hanson", "links": { "alternate": { "href": "https://www.zotero.org/drkitty28", "type": "text/html" } } }, "creatorSummary": "Toppi et al.", "parsedDate": "2012", "numChildren": 0 }, "data": { "key": "TTZEIE32", "version": 2, "itemType": "journalArticle", "title": "How the Statistical Validation of Functional Connectivity Patterns Can Prevent Erroneous Definition of Small-World Properties of a Brain Connectivity Network", "creators": [ { "creatorType": "author", "firstName": "J.", "lastName": "Toppi" }, { "creatorType": "author", "firstName": "F.", "lastName": "De Vico Fallani" }, { "creatorType": "author", "firstName": "G.", "lastName": "Vecchiato" }, { "creatorType": "author", "firstName": "A. G.", "lastName": "Maglione" }, { "creatorType": "author", "firstName": "F.", "lastName": "Cincotti" }, { "creatorType": "author", "firstName": "D.", "lastName": "Mattia" }, { "creatorType": "author", "firstName": "S.", "lastName": "Salinari" }, { "creatorType": "author", "firstName": "F.", "lastName": "Babiloni" }, { "creatorType": "author", "firstName": "L.", "lastName": "Astolfi" } ], "abstractNote": "", "publicationTitle": "Computational and Mathematical Methods in Medicine", "publisher": "", "place": "", "date": "2012", "volume": "2012", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "1-13", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1155/2012/130985", "citationKey": "", "url": "http://www.hindawi.com/journals/cmmm/2012/130985/", "accessDate": "2016-01-28T23:11:20Z", "PMID": "", "PMCID": "", "ISSN": "1748-670X, 1748-6718", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "en", "libraryCatalog": "CrossRef", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [ "EPW2IIX9" ], "relations": {}, "dateAdded": "2016-01-29T13:07:12Z", "dateModified": "2016-01-29T13:07:12Z" } }, { "key": "JXR6VS85", "version": 2, "library": { "type": "group", "id": 470430, "name": "Brain Connectivity", "links": { "alternate": { "href": "https://www.zotero.org/groups/brain_connectivity", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/470430/items/JXR6VS85", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/brain_connectivity/items/JXR6VS85", "type": "text/html" } }, "meta": { "createdByUser": { "id": 177899, "username": "drkitty28", "name": "Catherine Hanson", "links": { "alternate": { "href": "https://www.zotero.org/drkitty28", "type": "text/html" } } }, "creatorSummary": "de Boer et al.", "parsedDate": "2010", "numChildren": 1 }, "data": { "key": "JXR6VS85", "version": 2, "itemType": "journalArticle", "title": "Statistical analysis of structural brain connectivity", "creators": [ { "creatorType": "author", "firstName": "Renske", "lastName": "de Boer" }, { "creatorType": "author", "firstName": "Michiel", "lastName": "Schaap" }, { "creatorType": "author", "firstName": "Fedde", "lastName": "van der Lijn" }, { "creatorType": "author", "firstName": "Henri A", "lastName": "Vrooman" }, { "creatorType": "author", "firstName": "Marius", "lastName": "de Groot" }, { "creatorType": "author", "firstName": "Meike W", "lastName": "Vernooij" }, { "creatorType": "author", "firstName": "M Arfan", "lastName": "Ikram" }, { "creatorType": "author", "firstName": "Evert F S", "lastName": "van Velsen" }, { "creatorType": "author", "firstName": "Aad", "lastName": "van der Lugt" }, { "creatorType": "author", "firstName": "Monique M B", "lastName": "Breteler" }, { "creatorType": "author", "firstName": "Wiro J", "lastName": "Niessen" } ], "abstractNote": "We present a framework for statistical analysis in large cohorts of structural brain connectivity, derived from diffusion weighted MRI. A brain network is defined between subcortical gray matter structures and a cortical parcellation obtained with FreeSurfer. Connectivity is established through minimum cost paths with an anisotropic local cost function and is quantified per connection. The connectivity network potentially encodes important information about brain structure, and can be analyzed using multivariate regression methods. The proposed framework can be used to study the relation between connectivity and e.g. brain function or neurodegenerative disease. As a proof of principle, we perform principal component regression in order to predict age and gender, based on the connectivity networks of 979 middle-aged and elderly subjects, in a 10-fold cross-validation. The results are compared to predictions based on fractional anisotropy and mean diffusivity averaged over the white matter and over the corpus callosum. Additionally, the predictions are performed based on the best predicting connection in the network. Principal component regression outperformed all other prediction models, demonstrating the age and gender information encoded in the connectivity network.", "publicationTitle": "Medical image computing and computer-assisted intervention: MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention", "publisher": "", "place": "", "date": "2010", "volume": "13", "issue": "Pt 2", "section": "", "partNumber": "", "partTitle": "", "pages": "101-108", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Med Image Comput Comput Assist Interv", "DOI": "", "citationKey": "", "url": "", "accessDate": "", "PMID": "", "PMCID": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "eng", "libraryCatalog": "NCBI PubMed", "callNumber": "", "rights": "", "extra": "PMID: 20879304", "tags": [ { "tag": "Algorithms", "type": 1 }, { "tag": "Brain", "type": 1 }, { "tag": "Data Interpretation, Statistical", "type": 1 }, { "tag": "Diffusion tensor imaging", "type": 1 }, { "tag": "Humans", "type": 1 }, { "tag": "Image Enhancement", "type": 1 }, { "tag": "Image Interpretation, Computer-Assisted", "type": 1 }, { "tag": "Imaging, Three-Dimensional", "type": 1 }, { "tag": "Neural Pathways", "type": 1 }, { "tag": "Reproducibility of Results", "type": 1 }, { "tag": "Sensitivity and Specificity", "type": 1 } ], "collections": [ "EPW2IIX9" ], "relations": {}, "dateAdded": "2016-01-29T13:07:12Z", "dateModified": "2016-01-29T13:07:12Z" } }, { "key": "R7W4XCAF", "version": 2, "library": { "type": "group", "id": 470430, "name": "Brain Connectivity", "links": { "alternate": { "href": "https://www.zotero.org/groups/brain_connectivity", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/470430/items/R7W4XCAF", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/brain_connectivity/items/R7W4XCAF", "type": "text/html" } }, "meta": { "createdByUser": { "id": 177899, "username": "drkitty28", "name": "Catherine Hanson", "links": { "alternate": { "href": "https://www.zotero.org/drkitty28", "type": "text/html" } } }, "creatorSummary": "Lang et al.", "parsedDate": "2012", "numChildren": 0 }, "data": { "key": "R7W4XCAF", "version": 2, "itemType": "journalArticle", "title": "Brain Connectivity Analysis: A Short Survey", "creators": [ { "creatorType": "author", "firstName": "E. W.", "lastName": "Lang" }, { "creatorType": "author", "firstName": "A. M.", "lastName": "Tomé" }, { "creatorType": "author", "firstName": "I. R.", "lastName": "Keck" }, { "creatorType": "author", "firstName": "J. M.", "lastName": "Górriz-Sáez" }, { "creatorType": "author", "firstName": "C. G.", "lastName": "Puntonet" } ], "abstractNote": "", "publicationTitle": "Computational Intelligence and Neuroscience", "publisher": "", "place": "", "date": "2012", "volume": "2012", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "1-21", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1155/2012/412512", "citationKey": "", "url": "http://www.hindawi.com/journals/cin/2012/412512/", "accessDate": "2016-01-28T23:15:00Z", "PMID": "", "PMCID": "", "ISSN": "1687-5265, 1687-5273", "archive": "", "archiveLocation": "", "shortTitle": "Brain Connectivity Analysis", "language": "en", "libraryCatalog": "CrossRef", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [ "EPW2IIX9" ], "relations": {}, "dateAdded": "2016-01-29T13:07:12Z", "dateModified": "2016-01-29T13:07:12Z" } }, { "key": "VPZH2EDP", "version": 2, "library": { "type": "group", "id": 470430, "name": "Brain Connectivity", "links": { "alternate": { "href": "https://www.zotero.org/groups/brain_connectivity", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/470430/items/VPZH2EDP", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/brain_connectivity/items/VPZH2EDP", "type": "text/html" } }, "meta": { "createdByUser": { "id": 177899, "username": "drkitty28", "name": "Catherine Hanson", "links": { "alternate": { "href": "https://www.zotero.org/drkitty28", "type": "text/html" } } }, "creatorSummary": "Sporns", "parsedDate": "2003", "numChildren": 0 }, "data": { "key": "VPZH2EDP", "version": 2, "itemType": "bookSection", "title": "Graph theory methods for the analysis of neural connectivity patterns", "creators": [ { "creatorType": "author", "firstName": "Olaf", "lastName": "Sporns" } ], "abstractNote": "", "bookTitle": "Neuroscience databases", "series": "", "seriesNumber": "", "volume": "", "numberOfVolumes": "", "edition": "", "date": "2003", "publisher": "Springer", "place": "", "originalDate": "", "originalPublisher": "", "originalPlace": "", "format": "", "pages": "171–185", "ISBN": "", "DOI": "", "citationKey": "", "url": "http://link.springer.com/chapter/10.1007/978-1-4615-1079-6_12", "accessDate": "2016-01-28T23:11:57Z", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "Google Scholar", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [ "EPW2IIX9" ], "relations": {}, "dateAdded": "2016-01-29T13:07:12Z", "dateModified": "2016-01-29T13:07:12Z" } }, { "key": "F3HZGWU3", "version": 2, "library": { "type": "group", "id": 470430, "name": "Brain Connectivity", "links": { "alternate": { "href": "https://www.zotero.org/groups/brain_connectivity", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/470430/items/F3HZGWU3", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/brain_connectivity/items/F3HZGWU3", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/470430/items/B34CMJXF", "type": "application/json" } }, "meta": { "createdByUser": { "id": 177899, "username": "drkitty28", "name": "Catherine Hanson", "links": { "alternate": { "href": "https://www.zotero.org/drkitty28", "type": "text/html" } } } }, "data": { "key": "F3HZGWU3", "version": 2, "parentItem": "B34CMJXF", "itemType": "attachment", "linkMode": "linked_url", "title": "PubMed entry", "accessDate": "2014-09-11T13:03:26Z", "url": "http://www.ncbi.nlm.nih.gov/pubmed/24093627", "note": "", "contentType": "text/html", "charset": "", "tags": [], "relations": {}, "dateAdded": "2016-01-29T13:06:58Z", "dateModified": "2016-01-29T13:06:58Z" } }, { "key": "BTIHXQI3", "version": 2, "library": { "type": "group", "id": 470430, "name": "Brain Connectivity", "links": { "alternate": { "href": "https://www.zotero.org/groups/brain_connectivity", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/470430/items/BTIHXQI3", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/brain_connectivity/items/BTIHXQI3", "type": "text/html" } }, "meta": { "createdByUser": { "id": 177899, "username": "drkitty28", "name": "Catherine Hanson", "links": { "alternate": { "href": "https://www.zotero.org/drkitty28", "type": "text/html" } } }, "creatorSummary": "de Vos et al.", "numChildren": 0 }, "data": { "key": "BTIHXQI3", "version": 2, "itemType": "document", "title": "Effective connectivity analysis: ROI selection with Activated Region Fitting.", "creators": [ { "creatorType": "author", "firstName": "Frank", "lastName": "de Vos" }, { "creatorType": "author", "firstName": "Wouter", "lastName": "Weeda" }, { "creatorType": "author", "firstName": "Hilda", "lastName": "Huizenga" }, { "creatorType": "author", "firstName": "Lourens", "lastName": "Waldorp" }, { "creatorType": "author", "firstName": "Russel", "lastName": "Poldrack" } ], "abstractNote": "An effective connectivity analysis involves two steps: first locating Regions Of Interest\n(ROIs) and second estimating effective connectivity. In a simulation study with a TETRAD effective\nconnectivity analysis (Ramsey et al., 2009) we show that network connectivity can only be estimated\nadequately if all relevant ROIs are incorporated. If not all relevant ROIs are incorporated, this will\nresult in true connections that remain undetected or in spurious connections. We argue that Activated\nRegion Fitting (ARF, Weeda et al. (2011)) is suited for ROI selection, for it exhibits high power and\nyields ready to use ROIs. We illustrate this by analyzing empirical data, gathered in a rhyming task\n(N=6). We show that ARF is more sensitive (i.e. detects more active regions) and is more specific (i.e.\ndetects functionally distinct regions), as compared to a standard Random Field Theory (RFT)\nclusterwise correction. We argue that it is therefore likely that ARF based connectivity analyses detect\nmore true and less spurious connections.", "type": "", "date": "", "publisher": "", "place": "", "DOI": "", "citationKey": "", "url": "", "accessDate": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [ "CFQS2MAT" ], "relations": {}, "dateAdded": "2016-01-29T13:06:58Z", "dateModified": "2016-01-29T13:06:58Z" } }, { "key": "AUMAURHA", "version": 2, "library": { "type": "group", "id": 470430, "name": "Brain Connectivity", "links": { "alternate": { "href": "https://www.zotero.org/groups/brain_connectivity", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/470430/items/AUMAURHA", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/brain_connectivity/items/AUMAURHA", "type": "text/html" } }, "meta": { "createdByUser": { "id": 177899, "username": "drkitty28", "name": "Catherine Hanson", "links": { "alternate": { "href": "https://www.zotero.org/drkitty28", "type": "text/html" } } }, "creatorSummary": "Manelis and Reder", "parsedDate": "2014", "numChildren": 0 }, "data": { "key": "AUMAURHA", "version": 2, "itemType": "journalArticle", "title": "Effective connectivity among the working memory regions during preparation for and during performance of the n-back task", "creators": [ { "creatorType": "author", "firstName": "A.a", "lastName": "Manelis" }, { "creatorType": "author", "firstName": "L.M.b", "lastName": "Reder" } ], "abstractNote": "Recent neuroimaging studies have shown that working memory (WM) task difficulty can be decoded from patterns of brain activation in the WM network during preparation to perform those tasks. The inter-regional connectivity among the WM regions during task preparation has not yet been investigated. We examined this question using the graph modeling methods IMaGES and LOFS, applied to the previously published fMRI data of Manelis and Reder (2013). In that study, subjects performed 1-, 2-, and 3-back tasks. Each block of n-back was preceded by a preparation period and followed by a rest period. The analyses of task-related brain activity identified a network of 18 regions that increased in activation from 1to 3-back (Increase network) and a network of 17 regions that decreased in activation from 1to 3-back (Decrease network). The graph analyses revealed two types of connectivity sub-networks within the Increase and Decrease networks: \"default\" and \"preparation-related.\" The \"default\" connectivity was present not only during task performance, but also during task preparation and during rest. We propose that this sub-network may serve as a core system that allows one to quickly activate cognitive, perceptual and motor systems in response to the relevant stimuli. The \"preparation-related\" connectivity was present during task preparation and task performance, but not at rest, and depended on the n-back condition. The role of this sub-network may be to pre-activate a connectivity \"road map\" in order to establish a top-down and bottom-up regulation of attention prior to performance on WM tasks. © 2014 Manelis and Reder.", "publicationTitle": "Frontiers in Human Neuroscience", "publisher": "", "place": "", "date": "2014", "volume": "8", "issue": "AUG", "section": "", "partNumber": "", "partTitle": "", "pages": "", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.3389/fnhum.2014.00593", "citationKey": "", "url": "http://www.scopus.com/inward/record.url?eid=2-s2.0-84905594077&partnerID=40&md5=e1f0a7ec6a247f2c5846d36bf2a59485", "accessDate": "", "PMID": "", "PMCID": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "", "callNumber": "", "rights": "", "extra": "cited By 2", "tags": [ { "tag": "Adult" }, { "tag": "Article" }, { "tag": "Cognition" }, { "tag": "Connectivity", "type": 1 }, { "tag": "Female" }, { "tag": "IMaGES", "type": 1 }, { "tag": "LOFS", "type": 1 }, { "tag": "Male" }, { "tag": "Rest" }, { "tag": "brain depth stimulation" }, { "tag": "brain electrophysiology" }, { "tag": "controlled study" }, { "tag": "default mode network" }, { "tag": "evoked muscle response" }, { "tag": "fMRI", "type": 1 }, { "tag": "functional assessment" }, { "tag": "functional magnetic resonance imaging" }, { "tag": "graph modeling", "type": 1 }, { "tag": "human" }, { "tag": "human experiment" }, { "tag": "mental capacity" }, { "tag": "mental performance" }, { "tag": "mental task" }, { "tag": "motor performance" }, { "tag": "nerve cell network" }, { "tag": "normal human" }, { "tag": "perception" }, { "tag": "sensory stimulation" }, { "tag": "task performance" }, { "tag": "task preparation", "type": 1 }, { "tag": "working memory" }, { "tag": "working memory", "type": 1 }, { "tag": "working memory network", "type": 1 } ], "collections": [ "CFQS2MAT" ], "relations": {}, "dateAdded": "2016-01-29T13:06:58Z", "dateModified": "2016-01-29T13:06:58Z" } }, { "key": "BHJT8VA4", "version": 2, "library": { "type": "group", "id": 470430, "name": "Brain Connectivity", "links": { "alternate": { "href": "https://www.zotero.org/groups/brain_connectivity", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/470430/items/BHJT8VA4", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/brain_connectivity/items/BHJT8VA4", "type": "text/html" } }, "meta": { "createdByUser": { "id": 177899, "username": "drkitty28", "name": "Catherine Hanson", "links": { "alternate": { "href": "https://www.zotero.org/drkitty28", "type": "text/html" } } }, "creatorSummary": "Sun et al.", "parsedDate": "2012", "numChildren": 0 }, "data": { "key": "BHJT8VA4", "version": 2, "itemType": "journalArticle", "title": "Inferring consistent functional interaction patterns from natural stimulus FMRI data", "creators": [ { "creatorType": "author", "firstName": "Jiehuan", "lastName": "Sun" }, { "creatorType": "author", "firstName": "Xintao", "lastName": "Hu" }, { "creatorType": "author", "firstName": "Xiu", "lastName": "Huang" }, { "creatorType": "author", "firstName": "Yang", "lastName": "Liu" }, { "creatorType": "author", "firstName": "Kaiming", "lastName": "Li" }, { "creatorType": "author", "firstName": "Xiang", "lastName": "Li" }, { "creatorType": "author", "firstName": "Junwei", "lastName": "Han" }, { "creatorType": "author", "firstName": "Lei", "lastName": "Guo" }, { "creatorType": "author", "firstName": "Tianming", "lastName": "Liu" }, { "creatorType": "author", "firstName": "Jing", "lastName": "Zhang" } ], "abstractNote": "", "publicationTitle": "NeuroImage", "publisher": "", "place": "", "date": "7/2012", "volume": "61", "issue": "4", "section": "", "partNumber": "", "partTitle": "", "pages": "987-999", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1016/j.neuroimage.2012.01.142", "citationKey": "", "url": "http://linkinghub.elsevier.com/retrieve/pii/S1053811912002868", "accessDate": "2013-07-31T13:50:37Z", "PMID": "", "PMCID": "", "ISSN": "10538119", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "CrossRef", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Algorithms" }, { "tag": "Article" }, { "tag": "Bayes Theorem" }, { "tag": "Brain" }, { "tag": "Brain Mapping" }, { "tag": "Computer-Assisted" }, { "tag": "DTI", "type": 1 }, { "tag": "Functional interaction", "type": 1 }, { "tag": "Granger causality analysis" }, { "tag": "Humans" }, { "tag": "Image Interpretation" }, { "tag": "Magnetic Resonance Imaging" }, { "tag": "Models" }, { "tag": "Natural stimulus fMRI", "type": 1 }, { "tag": "Neural Pathways" }, { "tag": "Neurological" }, { "tag": "Peter and Clark analysis" }, { "tag": "Photic Stimulation" }, { "tag": "algorithm" }, { "tag": "brain function" }, { "tag": "brain region" }, { "tag": "controlled study" }, { "tag": "diffusion tensor imaging" }, { "tag": "electroencephalogram" }, { "tag": "functional magnetic resonance imaging" }, { "tag": "greedy equivalence search" }, { "tag": "human" }, { "tag": "human experiment" }, { "tag": "image analysis" }, { "tag": "image display" }, { "tag": "independent multiple greedy equivalence search" }, { "tag": "nerve stimulation" }, { "tag": "neuroimaging" }, { "tag": "neuroscience" }, { "tag": "normal human" }, { "tag": "priority journal" }, { "tag": "statistical analysis" }, { "tag": "videorecording" } ], "collections": [ "CFQS2MAT" ], "relations": {}, "dateAdded": "2016-01-29T13:06:58Z", "dateModified": "2016-01-29T13:06:58Z" } }, { "key": "8W48842K", "version": 2, "library": { "type": "group", "id": 470430, "name": "Brain Connectivity", "links": { "alternate": { "href": "https://www.zotero.org/groups/brain_connectivity", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/470430/items/8W48842K", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/brain_connectivity/items/8W48842K", "type": "text/html" } }, "meta": { "createdByUser": { "id": 177899, "username": "drkitty28", "name": "Catherine Hanson", "links": { "alternate": { "href": "https://www.zotero.org/drkitty28", "type": "text/html" } } }, "creatorSummary": "Boukrina and Graves", "parsedDate": "2013", "numChildren": 0 }, "data": { "key": "8W48842K", "version": 2, "itemType": "journalArticle", "title": "Neural networks underlying contributions from semantics in reading aloud", "creators": [ { "creatorType": "author", "firstName": "Olga", "lastName": "Boukrina" }, { "creatorType": "author", "firstName": "William W.", "lastName": "Graves" } ], "abstractNote": "", "publicationTitle": "Frontiers in Human Neuroscience", "publisher": "", "place": "", "date": "2013", "volume": "7", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.3389/fnhum.2013.00518", "citationKey": "", "url": "http://www.frontiersin.org/Human_Neuroscience/10.3389/fnhum.2013.00518/abstract", "accessDate": "2014-01-08T16:44:25Z", "PMID": "", "PMCID": "", "ISSN": "1662-5161", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "CrossRef", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Adult" }, { "tag": "Article" }, { "tag": "Female" }, { "tag": "IMaGES" }, { "tag": "Male" }, { "tag": "Temporal Lobe" }, { "tag": "algorithm" }, { "tag": "angular gyrus" }, { "tag": "controlled study" }, { "tag": "functional magnetic resonance imaging" }, { "tag": "human" }, { "tag": "human experiment" }, { "tag": "inferior temporal sulcus" }, { "tag": "left hemisphere" }, { "tag": "middle temporal gyrus" }, { "tag": "nerve cell network" }, { "tag": "normal human" }, { "tag": "phonetics" }, { "tag": "reading" }, { "tag": "semantics" }, { "tag": "superior temporal gyrus" }, { "tag": "task performance" }, { "tag": "verbalization" }, { "tag": "word recognition" } ], "collections": [ "CFQS2MAT" ], "relations": {}, "dateAdded": "2016-01-29T13:06:58Z", "dateModified": "2016-01-29T13:06:58Z" } }, { "key": "F3CKN54T", "version": 2, "library": { "type": "group", "id": 470430, "name": "Brain Connectivity", "links": { "alternate": { "href": "https://www.zotero.org/groups/brain_connectivity", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/470430/items/F3CKN54T", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/brain_connectivity/items/F3CKN54T", "type": "text/html" } }, "meta": { "createdByUser": { "id": 177899, "username": "drkitty28", "name": "Catherine Hanson", "links": { "alternate": { "href": "https://www.zotero.org/drkitty28", "type": "text/html" } } }, "creatorSummary": "Dawson et al.", "parsedDate": "2013-02-15", "numChildren": 0 }, "data": { "key": "F3CKN54T", "version": 2, "itemType": "journalArticle", "title": "Evaluation and calibration of functional network modeling methods based on known anatomical connections", "creators": [ { "creatorType": "author", "firstName": "Debra Ann", "lastName": "Dawson" }, { "creatorType": "author", "firstName": "Kuwook", "lastName": "Cha" }, { "creatorType": "author", "firstName": "Lindsay B.", "lastName": "Lewis" }, { "creatorType": "author", "firstName": "Janine D.", "lastName": "Mendola" }, { "creatorType": "author", "firstName": "Amir", "lastName": "Shmuel" } ], "abstractNote": "Recent studies have identified large scale brain networks based on the spatio-temporal structure of spontaneous fluctuations in resting-state fMRI data. It is expected that functional connectivity based on resting-state data is reflective of — but not identical to — the underlying anatomical connectivity. However, which functional connectivity analysis methods reliably predict the network structure remains unclear. Here we tested and compared network connectivity analysis methods by applying them to fMRI resting-state time-series obtained from the human visual cortex. The methods evaluated here are those previously tested against simulated data in Smith et al. (Neuroimage, 2011).\n\nTo this end, we defined regions within retinotopic visual areas V1, V2, and V3 according to their eccentricity in the visual field, delineating central, intermediate, and peripheral eccentricity regions of interest (ROIs). These ROIs served as nodes in the models we study. We based our evaluation on the “ground-truth”, thoroughly studied retinotopically-organized anatomical connectivity in the monkey visual cortex. For each evaluated method, we computed the fractional rate of detecting connections known to exist (“c-sensitivity”), while using a threshold of the 95th percentile of the distribution of interaction magnitudes of those connections not expected to exist.\n\nUnder optimal conditions — including session duration of 68 min, a relatively small network consisting of 9 nodes and artifact-free regression of the global effect — each of the top methods predicted the expected connections with 67–85% c-sensitivity. Correlation methods, including Correlation (Corr; 85%), Regularized Inverse Covariance (ICOV; 84%) and Partial Correlation (PCorr; 81%) performed best, followed by Patel's Kappa (80%), Bayesian Network method PC (BayesNet; 77%), General Synchronization measures (67–77%), and Coherence (CohB; 74%). With decreased session duration, these top methods saw decreases in c-sensitivities, achieving 59–76% for 17 min sessions. With a short resting-state fMRI scan of 8.5 min, none of the methods predicted the real network well, with Corr (65%) performing best. With increased complexity of the network from 9 to 36 nodes, multivariate methods including PCorr and BayesNet saw a decrease in performance. Artifact-free regression of the global effect increased the c-sensitivity of the top-performing methods. In an overall evaluation across all tests we performed, correlation methods (Corr, ICOV, and PCorr), Patel's Kappa, and BayesNet method PC set themselves somewhat above all other methods.\n\nWe propose that data-based calibration based on known anatomical connections be integrated into future network studies, in order to maximize sensitivity and reduce false positives.", "publicationTitle": "NeuroImage", "publisher": "", "place": "", "date": "February 15, 2013", "volume": "67", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "331-343", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "NeuroImage", "DOI": "10.1016/j.neuroimage.2012.11.006", "citationKey": "", "url": "http://www.sciencedirect.com/science/article/pii/S1053811912011020", "accessDate": "2014-07-25T21:55:29Z", "PMID": "", "PMCID": "", "ISSN": "1053-8119", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "ScienceDirect", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Algorithms" }, { "tag": "Anatomic" }, { "tag": "Article" }, { "tag": "Bayesian network", "type": 1 }, { "tag": "Calibration" }, { "tag": "Coherence", "type": 1 }, { "tag": "Computer Simulation" }, { "tag": "Computer-Assisted" }, { "tag": "Correlation", "type": 1 }, { "tag": "Functional connectivity", "type": 1 }, { "tag": "Generalized synchronization", "type": 1 }, { "tag": "Granger causality", "type": 1 }, { "tag": "Haplorhini" }, { "tag": "Humans" }, { "tag": "Image Interpretation" }, { "tag": "Models" }, { "tag": "Neurological" }, { "tag": "Partial correlation", "type": 1 }, { "tag": "Patel's conditional dependence", "type": 1 }, { "tag": "Reproducibility of Results" }, { "tag": "Resting-state", "type": 1 }, { "tag": "Resting-state networks", "type": 1 }, { "tag": "Sensitivity and Specificity" }, { "tag": "Spontaneous activity", "type": 1 }, { "tag": "Visual Perception" }, { "tag": "animal tissue" }, { "tag": "connectome" }, { "tag": "controlled study" }, { "tag": "experimentation" }, { "tag": "false positive result" }, { "tag": "functional magnetic resonance imaging" }, { "tag": "human" }, { "tag": "human experiment" }, { "tag": "nerve cell network" }, { "tag": "neuroanatomy" }, { "tag": "nonhuman" }, { "tag": "normal human" }, { "tag": "priority journal" }, { "tag": "receiver operating characteristic" }, { "tag": "secondary visual cortex" }, { "tag": "session duration" }, { "tag": "spatiotemporal analysis" }, { "tag": "striate cortex" }, { "tag": "time series analysis" }, { "tag": "visual area V3" }, { "tag": "visual cortex" }, { "tag": "visual field" } ], "collections": [ "CFQS2MAT" ], "relations": {}, "dateAdded": "2016-01-29T13:06:58Z", "dateModified": "2016-01-29T13:06:58Z" } }, { "key": "DE9UCRF5", "version": 2, "library": { "type": "group", "id": 470430, "name": "Brain Connectivity", "links": { "alternate": { "href": "https://www.zotero.org/groups/brain_connectivity", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/470430/items/DE9UCRF5", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/brain_connectivity/items/DE9UCRF5", "type": "text/html" } }, "meta": { "createdByUser": { "id": 177899, "username": "drkitty28", "name": "Catherine Hanson", "links": { "alternate": { "href": "https://www.zotero.org/drkitty28", "type": "text/html" } } }, "creatorSummary": "Anna Manelis", "parsedDate": "2014", "numChildren": 0 }, "data": { "key": "DE9UCRF5", "version": 2, "itemType": "journalArticle", "title": "Frontiers Effective connectivity among the working memory regions during preparation for and during performance of the n-back task", "creators": [ { "creatorType": "author", "firstName": "Lynne M. Reder", "lastName": "Anna Manelis" } ], "abstractNote": "", "publicationTitle": "", "publisher": "", "place": "", "date": "2014", "volume": "", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "", "citationKey": "", "url": "", "accessDate": "", "PMID": "", "PMCID": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [ "CFQS2MAT" ], "relations": {}, "dateAdded": "2016-01-29T13:06:58Z", "dateModified": "2016-01-29T13:06:58Z" } }, { "key": "BMMUE3KV", "version": 2, "library": { "type": "group", "id": 470430, "name": "Brain Connectivity", "links": { "alternate": { "href": "https://www.zotero.org/groups/brain_connectivity", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/470430/items/BMMUE3KV", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/brain_connectivity/items/BMMUE3KV", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/470430/items/SE4CSNAM", "type": "application/json" } }, "meta": { "createdByUser": { "id": 177899, "username": "drkitty28", "name": "Catherine Hanson", "links": { "alternate": { "href": "https://www.zotero.org/drkitty28", "type": "text/html" } } } }, "data": { "key": "BMMUE3KV", "version": 2, "parentItem": "SE4CSNAM", "itemType": "attachment", "linkMode": "linked_url", "title": "PubMed entry", "accessDate": "2013-08-01T21:38:18Z", "url": "http://www.ncbi.nlm.nih.gov/pubmed/21745580", "note": "", "contentType": "", "charset": "", "tags": [], "relations": {}, "dateAdded": "2016-01-29T13:06:58Z", "dateModified": "2016-01-29T13:06:58Z" } }, { "key": "2AXW43N4", "version": 2, "library": { "type": "group", "id": 470430, "name": "Brain Connectivity", "links": { "alternate": { "href": "https://www.zotero.org/groups/brain_connectivity", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/470430/items/2AXW43N4", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/brain_connectivity/items/2AXW43N4", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/470430/items/B34CMJXF", "type": "application/json" } }, "meta": { "createdByUser": { "id": 177899, "username": "drkitty28", "name": "Catherine Hanson", "links": { "alternate": { "href": "https://www.zotero.org/drkitty28", "type": "text/html" } } } }, "data": { "key": "2AXW43N4", "version": 2, "parentItem": "B34CMJXF", "itemType": "attachment", "linkMode": "linked_url", "title": "PubMed entry", "accessDate": "2014-09-11T13:03:36Z", "url": "http://www.ncbi.nlm.nih.gov/pubmed/24093627", "note": "", "contentType": "text/html", "charset": "", "tags": [], "relations": {}, "dateAdded": "2016-01-29T13:06:58Z", "dateModified": "2016-01-29T13:06:58Z" } }, { "key": "DPZ2PB9D", "version": 2, "library": { "type": "group", "id": 470430, "name": "Brain Connectivity", "links": { "alternate": { "href": "https://www.zotero.org/groups/brain_connectivity", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/470430/items/DPZ2PB9D", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/brain_connectivity/items/DPZ2PB9D", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/470430/items/B34CMJXF", "type": "application/json" } }, "meta": { "createdByUser": { "id": 177899, "username": "drkitty28", "name": "Catherine Hanson", "links": { "alternate": { "href": "https://www.zotero.org/drkitty28", "type": "text/html" } } } }, "data": { "key": "DPZ2PB9D", "version": 2, "parentItem": "B34CMJXF", "itemType": "attachment", "linkMode": "linked_url", "title": "PubMed entry", "accessDate": "2014-01-08T16:49:46Z", "url": "http://www.ncbi.nlm.nih.gov/pubmed/24093627", "note": "", "contentType": "text/html", "charset": "", "tags": [], "relations": {}, "dateAdded": "2016-01-29T13:06:58Z", "dateModified": "2016-01-29T13:06:58Z" } }, { "key": "UPU7QRR9", "version": 2, "library": { "type": "group", "id": 470430, "name": "Brain Connectivity", "links": { "alternate": { "href": "https://www.zotero.org/groups/brain_connectivity", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/470430/items/UPU7QRR9", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/brain_connectivity/items/UPU7QRR9", "type": "text/html" } }, "meta": { "createdByUser": { "id": 177899, "username": "drkitty28", "name": "Catherine Hanson", "links": { "alternate": { "href": "https://www.zotero.org/drkitty28", "type": "text/html" } } }, "creatorSummary": "Ramsey et al.", "parsedDate": "2014-01-01", "numChildren": 0 }, "data": { "key": "UPU7QRR9", "version": 2, "itemType": "journalArticle", "title": "Non-Gaussian methods and high-pass filters in the estimation of effective connections", "creators": [ { "creatorType": "author", "firstName": "Joseph D.", "lastName": "Ramsey" }, { "creatorType": "author", "firstName": "Ruben", "lastName": "Sanchez-Romero" }, { "creatorType": "author", "firstName": "Clark", "lastName": "Glymour" } ], "abstractNote": "We consider several alternative ways of exploiting non-Gaussian distributional features, including some that can in principle identify direct, positive feedback relations (graphically, 2-cycles) and combinations of methods that can identify high dimensional graphs. All of the procedures are implemented in the TETRAD freeware (Ramsey et al., 2013). We show that in most cases the limited accuracy of the several non-Gaussian methods in the Smith et al. (2011) simulations can be attributed to the high-pass Butterworth filter used in that study. Without that filter, or with the filter in the widely used FSL program (Jenkinson et al., 2012), the directional accuracies of several of the non-Gaussian methods are at or near ceiling in many conditions of the Smith et al. simulation. We show that the improvement of an apparently Gaussian method (Patel et al., 2006) when filtering is removed is due to non-Gaussian features of that method introduced by the Smith et al. implementation. We also investigate some conditions in which multi-subject data help with causal structure identification using higher moments, notably with non-stationary time series or with 2-cycles. We illustrate the accuracy of the methods with more complex graphs with and without 2-cycles, and with a 500 node graph; to illustrate applicability and provide a further test we apply the methods to an empirical case for which aspects of the causal structure are known. Finally, we note a number of cautions and issues that remain to be investigated, and some outstanding problems for determining the structure of effective connections from fMRI data.", "publicationTitle": "NeuroImage", "publisher": "", "place": "", "date": "January 1, 2014", "volume": "84", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "986-1006", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "NeuroImage", "DOI": "10.1016/j.neuroimage.2013.09.062", "citationKey": "", "url": "http://www.sciencedirect.com/science/article/pii/S1053811913009981", "accessDate": "2015-07-21T20:29:17Z", "PMID": "", "PMCID": "", "ISSN": "1053-8119", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "ScienceDirect", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Algorithms" }, { "tag": "Article" }, { "tag": "Brain" }, { "tag": "Butterworth filter" }, { "tag": "Causal networks" }, { "tag": "Causal networks", "type": 1 }, { "tag": "Computer-Assisted" }, { "tag": "Gaussian method" }, { "tag": "High-pass filters" }, { "tag": "High-pass filters", "type": 1 }, { "tag": "Humans" }, { "tag": "Image Processing" }, { "tag": "Magnetic Resonance Imaging" }, { "tag": "Models" }, { "tag": "Neural Pathways" }, { "tag": "Neurological" }, { "tag": "Non-Gaussianity" }, { "tag": "Non-Gaussianity", "type": 1 }, { "tag": "Orientation" }, { "tag": "Orientation", "type": 1 }, { "tag": "accuracy" }, { "tag": "causal network" }, { "tag": "computer program" }, { "tag": "controlled study" }, { "tag": "data analysis" }, { "tag": "fMRI", "type": 1 }, { "tag": "fmri" }, { "tag": "functional magnetic resonance imaging" }, { "tag": "high pass filter" }, { "tag": "human" }, { "tag": "intermethod comparison" }, { "tag": "nerve cell network" }, { "tag": "neuroimaging" }, { "tag": "non Gaussian method" }, { "tag": "priority journal" }, { "tag": "simulation" }, { "tag": "statistical analysis" }, { "tag": "statistical parameters" } ], "collections": [ "CFQS2MAT" ], "relations": {}, "dateAdded": "2016-01-29T13:06:58Z", "dateModified": "2016-01-29T13:06:58Z" } }, { "key": "34EE2PQN", "version": 2, "library": { "type": "group", "id": 470430, "name": "Brain Connectivity", "links": { "alternate": { "href": "https://www.zotero.org/groups/brain_connectivity", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/470430/items/34EE2PQN", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/brain_connectivity/items/34EE2PQN", "type": "text/html" } }, "meta": { "createdByUser": { "id": 177899, "username": "drkitty28", "name": "Catherine Hanson", "links": { "alternate": { "href": "https://www.zotero.org/drkitty28", "type": "text/html" } } }, "creatorSummary": "Perez et al.", "parsedDate": "2010-07", "numChildren": 0 }, "data": { "key": "34EE2PQN", "version": 2, "itemType": "journalArticle", "title": "Discovering Effective Connectivity Among Brain Regions from Functional MRI Data", "creators": [ { "creatorType": "author", "firstName": "Carlos A.", "lastName": "Perez" }, { "creatorType": "author", "firstName": "Eman M.", "lastName": "El-Sheikh" }, { "creatorType": "author", "firstName": "Clark", "lastName": "Glymour" } ], "abstractNote": "Functional magnetic resonance imaging (fMRI) data have been used for identifying brain regions that activate when a subject is presented a stimulus or performs a task. Beyond identifying which regions of the brain are active during a task, it is also of interest to discover causal relationships among activity in those regions, that is, which regions of the brain influence, which other regions of the brain during a task. Two algorithms for causal discovery were applied to fMRI data, the greedy equivalence search (GES) algorithm and the independent multiple-sample greedy equivalence search (iMAGES). GES applies to individual datasets, and iMAGES to multiple datasets. We consider the stability of the GES results across subjects and experimental repetitions with the same subject. We find that some iMAGES connections agree with previous knowledge of the functional roles of the brain regions. The strengths and limitations of the research work and opportunities for future work are also discussed.", "publicationTitle": "Int. J. Comput. Healthc.", "publisher": "", "place": "", "date": "July 2010", "volume": "1", "issue": "1", "section": "", "partNumber": "", "partTitle": "", "pages": "86–102", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1504/IJCIH.2010.034132", "citationKey": "", "url": "http://dx.doi.org/10.1504/IJCIH.2010.034132", "accessDate": "2014-01-08T16:37:57Z", "PMID": "", "PMCID": "", "ISSN": "1755-3199", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "ACM Digital Library", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Effective connectivity", "type": 1 }, { "tag": "Functional MRI", "type": 1 }, { "tag": "Magnetic Resonance Imaging", "type": 1 }, { "tag": "artificial intelligence", "type": 1 }, { "tag": "brain regions", "type": 1 }, { "tag": "causal modelling", "type": 1 }, { "tag": "fMRI", "type": 1 }, { "tag": "healthcare technology", "type": 1 }, { "tag": "knowledge discovery", "type": 1 }, { "tag": "local search", "type": 1 } ], "collections": [ "CFQS2MAT" ], "relations": {}, "dateAdded": "2016-01-29T13:06:58Z", "dateModified": "2016-01-29T13:06:58Z" } }, { "key": "SE4CSNAM", "version": 2, "library": { "type": "group", "id": 470430, "name": "Brain Connectivity", "links": { "alternate": { "href": "https://www.zotero.org/groups/brain_connectivity", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/470430/items/SE4CSNAM", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/brain_connectivity/items/SE4CSNAM", "type": "text/html" } }, "meta": { "createdByUser": { "id": 177899, "username": "drkitty28", "name": "Catherine Hanson", "links": { "alternate": { "href": "https://www.zotero.org/drkitty28", "type": "text/html" } } }, "creatorSummary": "Ramsey et al.", "parsedDate": "2011-10-01", "numChildren": 1 }, "data": { "key": "SE4CSNAM", "version": 2, "itemType": "journalArticle", "title": "Multi-subject search correctly identifies causal connections and most causal directions in the DCM models of the Smith et al. simulation study", "creators": [ { "creatorType": "author", "firstName": "Joseph D.", "lastName": "Ramsey" }, { "creatorType": "author", "firstName": "Stephen José", "lastName": "Hanson" }, { "creatorType": "author", "firstName": "Clark", "lastName": "Glymour" } ], "abstractNote": "Smith et al. report a large study of the accuracy of 38 search procedures for recovering effective connections in simulations of DCM models under 28 different conditions. Their results are disappointing: no method reliably finds and directs connections without large false negatives, large false positives, or both. Using multiple subject inputs, we apply a previously published search algorithm, IMaGES, and novel orientation algorithms, LOFS, in tandem to all of the simulations of DCM models described by Smith et al. (2011). We find that the procedures accurately identify effective connections in almost all of the conditions that Smith et al. simulated and, in most conditions, direct causal connections with precision greater than 90% and recall greater than 80%.", "publicationTitle": "NeuroImage", "publisher": "", "place": "", "date": "October 1, 2011", "volume": "58", "issue": "3", "section": "", "partNumber": "", "partTitle": "", "pages": "838-848", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1016/j.neuroimage.2011.06.068", "citationKey": "", "url": "http://www.sciencedirect.com/science/article/pii/S1053811911007142", "accessDate": "2012-10-25T21:40:59Z", "PMID": "", "PMCID": "", "ISSN": "1053-8119", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "ScienceDirect", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Algorithms" }, { "tag": "Algorithms", "type": 1 }, { "tag": "Article" }, { "tag": "Brain" }, { "tag": "Brain", "type": 1 }, { "tag": "Brain Mapping", "type": 1 }, { "tag": "Brain Mapping" }, { "tag": "Brain Mapping: methods" }, { "tag": "Brain: anatomy & histology" }, { "tag": "Brain: physiology" }, { "tag": "Causal modeling", "type": 1 }, { "tag": "Computer-Assisted" }, { "tag": "Computer-Assisted: methods" }, { "tag": "Directionality", "type": 1 }, { "tag": "Effective connectivity", "type": 1 }, { "tag": "Group analysis", "type": 1 }, { "tag": "Humans" }, { "tag": "Humans", "type": 1 }, { "tag": "IMaGES", "type": 1 }, { "tag": "Image Interpretation" }, { "tag": "Image Interpretation, Computer-Assisted", "type": 1 }, { "tag": "Magnetic Resonance Imaging", "type": 1 }, { "tag": "Magnetic Resonance Imaging" }, { "tag": "Magnetic Resonance Imaging: methods" }, { "tag": "Models" }, { "tag": "Models, Neurological", "type": 1 }, { "tag": "Neurological" }, { "tag": "algorithm" }, { "tag": "causal modeling" }, { "tag": "fMRI", "type": 1 }, { "tag": "false negative result" }, { "tag": "false positive result" }, { "tag": "functional magnetic resonance imaging" }, { "tag": "neuroimaging" }, { "tag": "priority journal" }, { "tag": "simulation" }, { "tag": "statistical analysis" } ], "collections": [ "CFQS2MAT" ], "relations": {}, "dateAdded": "2016-01-29T13:06:58Z", "dateModified": "2016-01-29T13:06:58Z" } }, { "key": "D3XQAJCF", "version": 2, "library": { "type": "group", "id": 470430, "name": "Brain Connectivity", "links": { "alternate": { "href": "https://www.zotero.org/groups/brain_connectivity", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/470430/items/D3XQAJCF", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/brain_connectivity/items/D3XQAJCF", "type": "text/html" } }, "meta": { "createdByUser": { "id": 177899, "username": "drkitty28", "name": "Catherine Hanson", "links": { "alternate": { "href": "https://www.zotero.org/drkitty28", "type": "text/html" } } }, "creatorSummary": "Fourie et al.", "parsedDate": "2014", "numChildren": 0 }, "data": { "key": "D3XQAJCF", "version": 2, "itemType": "journalArticle", "title": "Neural correlates of experienced moral emotion: An fMRI investigation of emotion in response to prejudice feedback", "creators": [ { "creatorType": "author", "firstName": "Melike M.", "lastName": "Fourie" }, { "creatorType": "author", "firstName": "Kevin G. F.", "lastName": "Thomas" }, { "creatorType": "author", "firstName": "David M.", "lastName": "Amodio" }, { "creatorType": "author", "firstName": "Christopher M. R.", "lastName": "Warton" }, { "creatorType": "author", "firstName": "Ernesta M.", "lastName": "Meintjes" } ], "abstractNote": "Guilt, shame, and embarrassment are quintessential moral emotions with important regulatory functions for the individual and society. Moral emotions are, however, difficult to study with neuroimaging methods because their elicitation is more intricate than that of basic emotions. Here, using functional MRI (fMRI), we employed a novel social prejudice paradigm to examine specific brain regions associated with real-time moral emotion, focusing on guilt and related moral-negative emotions. The paradigm induced intense moral-negative emotion (primarily guilt) in 22 low-prejudice individuals through preprogrammed feedback indicating implicit prejudice against Black and disabled people. fMRI data indicated that this experience of moral-negative emotion was associated with increased activity in anterior paralimbic structures, including the anterior cingulate cortex (ACC) and anterior insula, in addition to areas associated with mentalizing, including the dorsomedial prefrontal cortex, posterior cingulate cortex, and precuneus. Of significance was prominent conflict-related activity in the supragenual ACC, which is consistent with theories proposing an association between acute guilt and behavioral inhibition. Finally, a significant negative association between self-reported guilt and neural activity in the pregenual ACC suggested a role of self-regulatory processes in response to moral-negative affect. These findings are consistent with the multifaceted self-regulatory functions of moral-negative emotions in social behavior.", "publicationTitle": "Social Neuroscience", "publisher": "", "place": "", "date": "2014", "volume": "9", "issue": "2", "section": "", "partNumber": "", "partTitle": "", "pages": "203-218", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1080/17470919.2013.878750", "citationKey": "", "url": "http://www.tandfonline.com/doi/abs/10.1080/17470919.2013.878750", "accessDate": "2014-02-16T22:57:50Z", "PMID": "", "PMCID": "", "ISSN": "1747-0919", "archive": "", "archiveLocation": "", "shortTitle": "Neural correlates of experienced moral emotion", "language": "", "libraryCatalog": "Taylor and Francis+NEJM", "callNumber": "", "rights": "", "extra": "PMID: 24450582", "tags": [], "collections": [ "CFQS2MAT" ], "relations": {}, "dateAdded": "2016-01-29T13:06:58Z", "dateModified": "2016-01-29T13:06:58Z" } }, { "key": "PUUIFW9S", "version": 2, "library": { "type": "group", "id": 470430, "name": "Brain Connectivity", "links": { "alternate": { "href": "https://www.zotero.org/groups/brain_connectivity", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/470430/items/PUUIFW9S", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/brain_connectivity/items/PUUIFW9S", "type": "text/html" } }, "meta": { "createdByUser": { "id": 177899, "username": "drkitty28", "name": "Catherine Hanson", "links": { "alternate": { "href": "https://www.zotero.org/drkitty28", "type": "text/html" } } }, "creatorSummary": "Ramsey et al.", "parsedDate": "2010-01-15", "numChildren": 0 }, "data": { "key": "PUUIFW9S", "version": 2, "itemType": "journalArticle", "title": "Six problems for causal inference from fMRI", "creators": [ { "creatorType": "author", "firstName": "J D", "lastName": "Ramsey" }, { "creatorType": "author", "firstName": "S J", "lastName": "Hanson" }, { "creatorType": "author", "firstName": "C", "lastName": "Hanson" }, { "creatorType": "author", "firstName": "Y O", "lastName": "Halchenko" }, { "creatorType": "author", "firstName": "R A", "lastName": "Poldrack" }, { "creatorType": "author", "firstName": "C", "lastName": "Glymour" } ], "abstractNote": "Neuroimaging (e.g. fMRI) data are increasingly used to attempt to identify not only brain regions of interest (ROIs) that are especially active during perception, cognition, and action, but also the qualitative causal relations among activity in these regions (known as effective connectivity; Friston, 1994). Previous investigations and anatomical and physiological knowledge may somewhat constrain the possible hypotheses, but there often remains a vast space of possible causal structures. To find actual effective connectivity relations, search methods must accommodate indirect measurements of nonlinear time series dependencies, feedback, multiple subjects possibly varying in identified regions of interest, and unknown possible location-dependent variations in BOLD response delays. We describe combinations of procedures that under these conditions find feed-forward sub-structure characteristic of a group of subjects. The method is illustrated with an empirical data set and confirmed with simulations of time series of non-linear, randomly generated, effective connectivities, with feedback, subject to random differences of BOLD delays, with regions of interest missing at random for some subjects, measured with noise approximating the signal to noise ratio of the empirical data.", "publicationTitle": "NeuroImage", "publisher": "", "place": "", "date": "Jan 15, 2010", "volume": "49", "issue": "2", "section": "", "partNumber": "", "partTitle": "", "pages": "1545-1558", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Neuroimage", "DOI": "10.1016/j.neuroimage.2009.08.065", "citationKey": "", "url": "http://www.ncbi.nlm.nih.gov/pubmed/19747552", "accessDate": "2011-03-24T16:59:04Z", "PMID": "", "PMCID": "", "ISSN": "1095-9572", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "NCBI PubMed", "callNumber": "0018", "rights": "", "extra": "PMID: 19747552", "tags": [ { "tag": "Brain", "type": 1 }, { "tag": "Computer Simulation", "type": 1 }, { "tag": "Databases as Topic", "type": 1 }, { "tag": "Feedback, Physiological", "type": 1 }, { "tag": "Magnetic Resonance Imaging", "type": 1 }, { "tag": "Models, Neurological", "type": 1 }, { "tag": "Nonlinear Dynamics", "type": 1 }, { "tag": "Oxygen", "type": 1 }, { "tag": "Time Factors", "type": 1 } ], "collections": [ "CFQS2MAT", "EPW2IIX9" ], "relations": {}, "dateAdded": "2016-01-29T13:06:58Z", "dateModified": "2016-01-29T13:06:58Z" } }, { "key": "B34CMJXF", "version": 2, "library": { "type": "group", "id": 470430, "name": "Brain Connectivity", "links": { "alternate": { "href": "https://www.zotero.org/groups/brain_connectivity", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/470430/items/B34CMJXF", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/brain_connectivity/items/B34CMJXF", "type": "text/html" } }, "meta": { "createdByUser": { "id": 177899, "username": "drkitty28", "name": "Catherine Hanson", "links": { "alternate": { "href": "https://www.zotero.org/drkitty28", "type": "text/html" } } }, "creatorSummary": "Hanson et al.", "numChildren": 3 }, "data": { "key": "B34CMJXF", "version": 2, "itemType": "journalArticle", "title": "Atypical Effective Connectivity of Social Brain Networks in Individuals with Autism.", "creators": [ { "creatorType": "author", "firstName": "Catherine", "lastName": "Hanson" }, { "creatorType": "author", "firstName": "Stephen", "lastName": "Hanson" }, { "creatorType": "author", "firstName": "Joseph", "lastName": "Ramsey" }, { "creatorType": "author", "firstName": "Clark", "lastName": "Glymour" } ], "abstractNote": "Failing to engage in joint attention is a strong marker of impaired social cognition associated with autism spectrum disorder (ASD). The goal of this study was to localize the source of impaired joint attention in individuals with ASD by examining both behavioral and fMRI data collected during various tasks involving eye gaze, directional cuing, and face processing. The tasks were designed to engage three brain networks associated with social cognition (face processing, Theory of Mind, and action understanding). The behavioral results indicate that even high functioning individuals with ASD perform less accurately and more slowly than neurotypical (NT) controls when processing eyes, but not when processing a directional cue (an arrow) that did not involve eyes. Behavioral differences between the neurotypical and ASD groups were consistent with differences in the effective connectivity of FACE, TOM, and ACTION networks. An Independent Multiple-sample Greedy Equivalence Search (IMaGES) was used to examine these social brain networks and found that whereas neurotypicals produced stable patterns of response across tasks designed to engage a given brain network, ASD participants did not. Moreover, ASD participants recruited all three networks in a manner highly dissimilar to that of neurotypicals. These results extend a growing literature describing disruptions in general brain connectivity in individuals with autism by targeting specific networks hypothesized to underlie the social cognitive impairments observed in these individuals.", "publicationTitle": "Brain Connectivity", "publisher": "", "place": "", "date": "", "volume": "", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "", "citationKey": "", "url": "", "accessDate": "", "PMID": "", "PMCID": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Adolescent", "type": 1 }, { "tag": "Adult", "type": 1 }, { "tag": "Attention", "type": 1 }, { "tag": "Autistic Disorder", "type": 1 }, { "tag": "Brain", "type": 1 }, { "tag": "Brain Mapping", "type": 1 }, { "tag": "Case-Control Studies", "type": 1 }, { "tag": "Cues", "type": 1 }, { "tag": "Eye", "type": 1 }, { "tag": "Facial Expression", "type": 1 }, { "tag": "Humans", "type": 1 }, { "tag": "Magnetic Resonance Imaging", "type": 1 }, { "tag": "Reaction Time", "type": 1 }, { "tag": "Social Behavior", "type": 1 }, { "tag": "Visual Cortex", "type": 1 }, { "tag": "Young Adult", "type": 1 } ], "collections": [ "CFQS2MAT" ], "relations": {}, "dateAdded": "2016-01-29T13:06:58Z", "dateModified": "2016-01-29T13:06:58Z" } } ]