scholarly journals Validating dynamicity in resting state fMRI with activation‐informed temporal segmentation

2021 ◽  
Author(s):  
Marlena Duda ◽  
Danai Koutra ◽  
Chandra Sripada
Keyword(s):  
Resting State ◽  
Resting State Fmri ◽  
Temporal Segmentation

scholarly journals Validating Dynamicity in Resting State fMRI with Activation-Informed Temporal Segmentation

2020 ◽  
Author(s):  
Marlena Duda ◽  
Danai Koutra ◽  
Chandra Sripada
Keyword(s):  
Resting State ◽  
Block Design ◽  
Memory Task ◽  
Sliding Window ◽  
Resting State Fmri ◽  
Cognitive State ◽  
Time Varying ◽  
Temporal Segmentation ◽  
Whole Brain ◽  
Window Approach

AbstractConfirming the presence (or absence) of dynamic functional connectivity (dFC) states during rest is an important open question in the field of cognitive neuroscience. The prevailing dFC framework aims to identify dynamics directly from connectivity estimates with a sliding window approach, however this method suffers from several drawbacks including sensitivity to window size and poor test-retest reliability. We hypothesize that time-varying changes in functional connectivity are mirrored by significant temporal changes in functional activation, and that this coupling can be leveraged to study dFC without the need for a predefined sliding window. Here we introduce a straightforward data-driven dFC framework, which involves informed segmentation of fMRI time series at candidate FC state transition points estimated from changes in whole-brain functional activation, rather than a fixed-length sliding window. We show our approach reliably identifies true cognitive state change points when applied on block-design working memory task data and outperforms the standard sliding window approach in both accuracy and computational efficiency in this context. When applied to data from four resting state fMRI scanning sessions, our method consistently recovers five reliable FC states, and subject-specific features derived from these states show significant correlation with behavioral phenotypes of interest (cognitive ability, personality). Overall, these results suggest abrupt whole-brain changes in activation can be used as a marker for changes in connectivity states, and provides strong evidence for the existence of time-varying FC in rest.

Resting-state fMRI used for intraoperative neuronavigation in children

2013 ◽  
Vol 44 (S 01) ◽  
Author(s):  
C Dorfer ◽  
T Czech ◽  
G Kasprian ◽  
A Azizi ◽  
J Furtner ◽  
...  
Keyword(s):  
Resting State ◽  
Resting State Fmri

scholarly journals Development of the default-mode network during childhood and adolescence: A longitudinal resting-state fMRI study

NeuroImage ◽  
2021 ◽  
Vol 226 ◽  
pp. 117581
Author(s):  
Fengmei Fan ◽  
Xuhong Liao ◽  
Tianyuan Lei ◽  
Tengda Zhao ◽  
Mingrui Xia ◽  
...  
Keyword(s):  
Default Mode Network ◽  
Resting State ◽  
Resting State Fmri ◽  
Childhood And Adolescence ◽  
Default Mode ◽  
Fmri Study

A Critical, Event-Related Appraisal of Denoising in Resting-State fMRI Studies

2020 ◽  
Vol 30 (10) ◽  
pp. 5544-5559 ◽  
Author(s):  
Jonathan D Power ◽  
Charles J Lynch ◽  
Babatunde Adeyemo ◽  
Steven E Petersen
Keyword(s):  
Resting State ◽  
Resting State Fmri ◽  
Critical Event ◽  
Head Motion ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Decay Properties ◽  
Signal Decay ◽  
Biological Variables ◽  
Related Approach

Abstract This article advances two parallel lines of argument about resting-state functional magnetic resonance imaging (fMRI) signals, one empirical and one conceptual. The empirical line creates a four-part organization of the text: (1) head motion and respiration commonly cause distinct, major, unwanted influences (artifacts) in fMRI signals; (2) head motion and respiratory changes are, confoundingly, both related to psychological and clinical and biological variables of interest; (3) many fMRI denoising strategies fail to identify and remove one or the other kind of artifact; and (4) unremoved artifact, due to correlations of artifacts with variables of interest, renders studies susceptible to identifying variance of noninterest as variance of interest. Arising from these empirical observations is a conceptual argument: that an event-related approach to task-free scans, targeting common behaviors during scanning, enables fundamental distinctions among the kinds of signals present in the data, information which is vital to understanding the effects of denoising procedures. This event-related perspective permits statements like “Event X is associated with signals A, B, and C, each with particular spatial, temporal, and signal decay properties”. Denoising approaches can then be tailored, via performance in known events, to permit or suppress certain kinds of signals based on their desirability.

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