A wavelet method for modeling and despiking motion artifacts from resting-state fMRI time series.
about
Oscillatory activity in the medial prefrontal cortex and nucleus accumbens correlates with impulsivity and reward outcomeDefault mode network connectivity as a function of familial and environmental risk for psychotic disorderMotion-related artifacts in structural brain images revealed with independent estimates of in-scanner head motionThe Cambridge Centre for Ageing and Neuroscience (Cam-CAN) data repository: Structural and functional MRI, MEG, and cognitive data from a cross-sectional adult lifespan sampleAgeing increases reliance on sensorimotor prediction through structural and functional differences in frontostriatal circuitsGRETNA: a graph theoretical network analysis toolbox for imaging connectomicsAnnual research review: Growth connectomics--the organization and reorganization of brain networks during normal and abnormal development.Functional Magnetic Resonance Imaging Methods.Intrinsic Functional Connectivity in Attention-Deficit/Hyperactivity Disorder: A Science in Development.Methods for cleaning the BOLD fMRI signal.Unraveling the miswired connectome: a developmental perspectiveRecent progress and outstanding issues in motion correction in resting state fMRI.Semi-Metric Topology of the Human Connectome: Sensitivity and Specificity to Autism and Major Depressive Disorder.Head Motion and Correction Methods in Resting-state Functional MRIThe Contribution of Network Organization and Integration to the Development of Cognitive Control.Randomization and resilience of brain functional networks as systems-level endophenotypes of schizophrenia.Meta-connectomics: human brain network and connectivity meta-analyses.Whole-brain functional hypoconnectivity as an endophenotype of autism in adolescents.Choosing Wavelet Methods, Filters, and Lengths for Functional Brain Network Construction.Spatial Disassociation of Disrupted Functional Connectivity for the Default Mode Network in Patients with End-Stage Renal Disease.State and Trait Components of Functional Connectivity: Individual Differences Vary with Mental StateSemi-metric analysis of the functional brain network: Relationship with familial risk for psychotic disorderA Winding Road: Alzheimer's Disease Increases Circuitous Functional Connectivity PathwaysIdiosyncratic responding during movie-watching predicted by age differences in attentional control.The effect of ageing on fMRI: Correction for the confounding effects of vascular reactivity evaluated by joint fMRI and MEG in 335 adults.Extrinsic and Intrinsic Brain Network Connectivity Maintains Cognition across the Lifespan Despite Accelerated Decay of Regional Brain Activation.Robust preprocessing for stimulus-based functional MRI of the moving fetus.Atomoxetine Enhances Connectivity of Prefrontal Networks in Parkinson's Disease.Gene transcription profiles associated with inter-modular hubs and connection distance in human functional magnetic resonance imaging networks.Reduced specialized processing in psychotic disorder: a graph theoretical analysis of cerebral functional connectivity.The effects of hippocampal lesions on MRI measures of structural and functional connectivityLongitudinal Study of the Emerging Functional Connectivity Asymmetry of Primary Language Regions during Infancy.Subtle in-scanner motion biases automated measurement of brain anatomy from in vivo MRI.Disrupted brain network functional dynamics and hyper-correlation of structural and functional connectome topology in patients with breast cancer prior to treatment.Cognitive Behavioral Therapy Lowers Elevated Functional Connectivity in Depressed Adolescents.Graph analysis of functional brain networks: practical issues in translational neuroscience.Real-time motion analytics during brain MRI improve data quality and reduce costs.Challenges in measuring individual differences in functional connectivity using fMRI: The case of healthy aging.Regional expression of the MAPT gene is associated with loss of hubs in brain networks and cognitive impairment in Parkinson disease and progressive supranuclear palsy.Functional connectivity and structural covariance between regions of interest can be measured more accurately using multivariate distance correlation
P2860
Q28543989-48BE7CBE-5F84-4E4C-98F6-86BA8E2CE563Q28544701-4DF598A7-BB87-43C7-9A60-0CCA01F8FA6BQ28583749-58DCD51D-8ADF-4783-A15C-528227D8B5E8Q28584653-0E65A39C-37CD-40EC-B228-1DE7D06D42FFQ28597588-2CA67883-E554-4B72-B610-283C9FA399D9Q28647817-375B4472-C5E4-4C49-B120-A7F74B19344DQ30872781-B681EA0F-6CED-4597-B700-B7D493810A5EQ30985535-858A52A0-8002-4AE8-A0F3-6EDA71FA4F0BQ31135377-719736EE-065A-47C6-8355-1491D6E66831Q31148361-F697FF4C-2CE4-45D2-85F7-EBBF621E9E83Q34213589-D6BA9C30-792B-4319-8159-CFF8B451F830Q35477398-64501834-CA69-43C8-8DCA-A2F3C1BA14FEQ35756042-2A07224E-E50E-43BB-891E-41476ACB4C26Q35877913-BEA0C27C-C1B6-4BCA-8183-178FECC83183Q35880599-214B30AB-DFAD-4629-9F3E-7F986DB75B59Q35895465-90602A83-3890-4D58-8CDC-E31749768AD0Q35904103-912A9672-0E87-4DEA-9274-A0072799FB13Q36017102-12B09A53-82C9-4B38-BFCE-EF782476E3E9Q36063748-F1B2CE9A-D042-4827-9B43-6A641167F465Q36112676-90EB482E-9705-45F1-9809-70EFD049906FQ36157776-A2E348D1-DD06-4505-9E29-88CBBAB926BBQ36278266-5FAB8C1E-B723-48C9-9AE7-7083005899D2Q36290188-AFE7ECCD-5185-4490-9B32-844514AC50C5Q36444731-C596093E-E8C0-485E-953F-130087D9CC32Q36513412-0AB4E6E0-2E80-49A7-97E1-5654E53787C0Q36690110-324DA777-CE54-4B2B-B28F-B98DDFA7D496Q36768329-3D1C10CD-210D-4576-9EF6-BD621B18FA0DQ36870256-2DB6B8C6-4874-452D-AC5C-26089DF36744Q37214987-87F99A2A-A07D-489C-802E-7FE3DE637FE0Q37283045-6458CCD5-C4D4-4A54-AC6C-F3803A55F8C7Q37370972-CD7C6D00-45D5-4B45-A6C1-20575375F6CCQ37372055-A6FB319A-3425-4FAC-AA61-80B8EEC240AAQ37415567-ED9F091C-3456-48A4-839C-E495D63982C1Q37694398-F6FDA647-4807-4324-A6FF-C3BF9E8A5CB1Q37714290-8BFDCB36-5E52-422E-985D-B85DB29B315EQ38245458-BE984B82-0990-4F52-8B53-7B2D541D1814Q38627073-4AED8501-BA15-4E22-9C13-373316EBBB5AQ38764140-5AF27E65-CE3D-4C56-8468-516FF88F5FF0Q39327310-227A40F6-FD07-4C0C-A88B-A2A1C4AEA211Q39822426-F738D90D-ACDE-4D9A-AF71-F0520997E311
P2860
A wavelet method for modeling and despiking motion artifacts from resting-state fMRI time series.
description
2014 nî lūn-bûn
@nan
2014 թուականի Մարտին հրատարակուած գիտական յօդուած
@hyw
2014 թվականի մարտին հրատարակված գիտական հոդված
@hy
2014年の論文
@ja
2014年論文
@yue
2014年論文
@zh-hant
2014年論文
@zh-hk
2014年論文
@zh-mo
2014年論文
@zh-tw
2014年论文
@wuu
name
A wavelet method for modeling ...... esting-state fMRI time series.
@ast
A wavelet method for modeling ...... esting-state fMRI time series.
@en
type
label
A wavelet method for modeling ...... esting-state fMRI time series.
@ast
A wavelet method for modeling ...... esting-state fMRI time series.
@en
prefLabel
A wavelet method for modeling ...... esting-state fMRI time series.
@ast
A wavelet method for modeling ...... esting-state fMRI time series.
@en
P2093
P2860
P50
P1433
P1476
A wavelet method for modeling ...... esting-state fMRI time series.
@en
P2093
Ameera X Patel
Karen D Ersche
Mikail Rubinov
P Simon Jones
P2860
P304
P356
10.1016/J.NEUROIMAGE.2014.03.012
P407
P577
2014-03-21T00:00:00Z