Assessing dynamic brain graphs of time-varying connectivity in fMRI data: application to healthy controls and patients with schizophrenia.
about
Connectomics in psychiatric research: advances and applicationsGRETNA: a graph theoretical network analysis toolbox for imaging connectomicsStructure and Topology Dynamics of Hyper-Frequency Networks during Rest and Auditory Oddball PerformanceFunctional Magnetic Resonance Imaging Methods.Noise and non-neuronal contributions to the BOLD signal: applications to and insights from animal studies.Age-Related Decline in the Variation of Dynamic Functional Connectivity: A Resting State AnalysisHigher Dimensional Meta-State Analysis Reveals Reduced Resting fMRI Connectivity Dynamism in Schizophrenia Patients.Temporal Changes in Local Functional Connectivity Density Reflect the Temporal Variability of the Amplitude of Low Frequency Fluctuations in Gray MatterInteraction among subsystems within default mode network diminished in schizophrenia patients: A dynamic connectivity approach.Resting-State Time-Varying Analysis Reveals Aberrant Variations of Functional Connectivity in Autism.Building an EEG-fMRI Multi-Modal Brain Graph: A Concurrent EEG-fMRI Study.Dynamic Default Mode Network across Different Brain States.Multiple functional networks modeling for autism spectrum disorder diagnosis.Comparing brain graphs in which nodes are regions of interest or independent components: A simulation study.Sample entropy reveals an age-related reduction in the complexity of dynamic brain.Intrinsic functional connectivity variance and state-specific under-connectivity in autism.BRAPH: A graph theory software for the analysis of brain connectivity.Altered dynamic functional connectivity in the default mode network in patients with cirrhosis and minimal hepatic encephalopathy.Identifying dynamic functional connectivity biomarkers using GIG-ICA: Application to schizophrenia, schizoaffective disorder, and psychotic bipolar disorder.Neural, electrophysiological and anatomical basis of brain-network variability and its characteristic changes in mental disorders.Dynamic regional phase synchrony (DRePS): An Instantaneous Measure of Local fMRI Connectivity Within Spatially Clustered Brain Areas.Spectral properties of the temporal evolution of brain network structure.Dynamic functional connectivity reveals altered variability in functional connectivity among patients with major depressive disorderBrain Functional Plasticity Driven by Career Experience: A Resting-State fMRI Study of the Seafarer.Dynamic functional connectivity impairments in early schizophrenia and clinical high-risk for psychosis.An Effective Method to Identify Adolescent Generalized Anxiety Disorder by Temporal Features of Dynamic Functional Connectivity.Schizophrenia Shows Disrupted Links between Brain Volume and Dynamic Functional Connectivity.Altered topological patterns of brain functional networks in Crohn's disease.Abnormal intrinsic brain functional network dynamics in Parkinson's disease.Methods and Considerations for Dynamic Analysis of Functional MR Imaging Data.Characterizing dynamic amplitude of low-frequency fluctuation and its relationship with dynamic functional connectivity: An application to schizophrenia.Replicability of time-varying connectivity patterns in large resting state fMRI samples.Time-Resolved Resting-State Functional Magnetic Resonance Imaging Analysis: Current Status, Challenges, and New Directions.Incorporating spatial constraint in co-activation pattern analysis to explore the dynamics of resting-state networks: An application to Parkinson's disease.Neural and metabolic basis of dynamic resting state fMRI.Variability in Resting State Network and Functional Network Connectivity Associated With Schizophrenia Genetic Risk: A Pilot Study.Changes in Dynamics Within and Between Resting-State Subnetworks in Juvenile Myoclonic Epilepsy Occur at Multiple Frequency Bands.Classification and Prediction of Brain Disorders Using Functional Connectivity: Promising but ChallengingClinical Applications of the Functional ConnectomeApplication of Graph Theory to Assess Static and Dynamic Brain Connectivity: Approaches for Building Brain Graphs
P2860
Q26775883-5E85BC8E-F715-4179-9A59-D351FDAAF814Q28647817-8592A787-E2D6-4364-BCAC-E30BAC4C957AQ30275549-931315D1-3103-4825-A5F9-936826428B55Q30985535-DD3E1A0C-BD77-41ED-A6E0-698F4D762625Q31151269-369DB197-D60E-4ADB-A38B-E58C46266153Q33854480-D5C4CB8D-FB6F-42F5-A72E-B9C02F73C3C6Q35959068-5B1C8E9D-94C1-43AF-B9CA-917E83D4A84DQ35999798-5CB85DA4-DDD4-461F-A611-5C6A0724C222Q36447767-369F2140-4377-4811-82AC-EABC2FF8C95EQ37260090-12E7FF35-4EFE-410D-B335-19F04B45D54FQ37290008-B128B137-5636-4278-8834-01662C73932EQ37739544-7AE6F5B9-57C5-476E-9E13-E930B854B02CQ38606003-780A6C8B-22FE-444B-9DAE-22BAFE405052Q38625094-84594F91-4D31-4810-A9A0-64F33333192CQ38628105-0AA1ADD6-B125-4783-A607-863264A262F5Q38632575-8BC9ACAD-B2CE-4867-973D-E9244FF24379Q38649276-ED64944D-6AE4-4A91-8A91-15AE913D5E4BQ38679730-1DB306E9-7EE3-4D69-850C-6CA3D201CF14Q38907454-7E466E67-BD5E-4FA4-9627-94AE66DDB114Q39597547-C36F0B21-C717-4BEE-AB26-2D21F709924EQ39885407-E4179FEE-C754-4D9E-A500-59C65701E91BQ40152670-EB1F2E4A-7B2E-4A3A-B911-045558318126Q41531278-0837191C-769E-45D4-A88A-0FB8A6068EDCQ42654460-62585C50-EAF4-4B56-8A62-EC477D9381E9Q42697238-80E187A6-4BC8-4DEF-A5A2-BEFFCDA888C5Q43120146-C386397D-4CA6-4B12-8F15-1E3FDF4AD61DQ47108562-8DFE877B-B6D9-44F0-A1FA-B4C590B3D300Q47203243-A6895426-D4A5-4126-BE74-C29E5B6EA2BCQ47592919-244523AC-4B7E-4A55-9112-EA941D109E3AQ47594134-B1B9B471-EE8C-4469-8C02-688671601718Q47616068-654F65FC-CB7C-4D74-815A-469DA0DFCBE6Q47669379-AAAB3AAB-DC75-4BF1-8148-8DC298A41BECQ47713919-CE6FCF5F-98EA-48AA-9712-F450C848339AQ47848288-9977A0B4-91A9-4C72-8F81-138283E5A098Q50128472-F5456584-B6EA-499A-9F3B-9163CE5276B5Q52653152-6CEC86A8-A1B1-4925-BEED-6E8729BBD8BEQ55396526-9E957B4A-DB40-4559-90C0-91E110E25FDBQ57170569-B65C19F2-5072-446E-8B7A-3E4F887B7D62Q57911652-FE344F94-7C71-4309-AE67-2810B6F61C00Q58574825-BEBE1CB6-0E2B-47B6-A156-6709235E0187
P2860
Assessing dynamic brain graphs of time-varying connectivity in fMRI data: application to healthy controls and patients with schizophrenia.
description
2014 nî lūn-bûn
@nan
2014 թուականի Դեկտեմբերին հրատարակուած գիտական յօդուած
@hyw
2014 թվականի դեկտեմբերին հրատարակված գիտական հոդված
@hy
2014年の論文
@ja
2014年学术文章
@wuu
2014年学术文章
@zh-cn
2014年学术文章
@zh-hans
2014年学术文章
@zh-my
2014年学术文章
@zh-sg
2014年學術文章
@yue
name
Assessing dynamic brain graphs ...... d patients with schizophrenia.
@ast
Assessing dynamic brain graphs ...... d patients with schizophrenia.
@en
type
label
Assessing dynamic brain graphs ...... d patients with schizophrenia.
@ast
Assessing dynamic brain graphs ...... d patients with schizophrenia.
@en
prefLabel
Assessing dynamic brain graphs ...... d patients with schizophrenia.
@ast
Assessing dynamic brain graphs ...... d patients with schizophrenia.
@en
P2093
P2860
P50
P1433
P1476
Assessing dynamic brain graphs ...... d patients with schizophrenia.
@en
P2093
Devon Hjelm
Erik B Erhardt
Godfrey Pearlson
Mustafa S Cetin
Robyn L Miller
Srinivas Rachakonda
P2860
P304
P356
10.1016/J.NEUROIMAGE.2014.12.020
P407
P577
2014-12-13T00:00:00Z