Task-based dynamic functional connectivity: Recent findings and open questions

There is ample evidence of atypical functional connectivity (FC) in autism spectrum disorders (ASDs). However, transient relationships between neural networks cannot be captured by conventional static FC analyses. Dynamic FC (dFC) approaches have been used to identify repeating, transient connectivity patterns (“states”), revealing spatiotemporal network properties not observable in static FC. Recent studies have found atypical dFC in ASDs, but questions remain about the nature of group differences in transient connectivity, and the degree to which states persist or change over time. This study aimed to: (a) describe and relate static and dynamic FC in typical development and ASDs, (b) describe group differences in transient states and compare them with static FC patterns, and (c) examine temporal stability and flexibility between identified states. Resting‐state functional magnetic resonance imaging (fMRI) data were collected from 62 ASD and 57 typically developing (TD) children and adolescents. Whole‐brain, data‐driven regions of interest were derived from group independent component analysis. Sliding window analysis and k‐means clustering were used to explore dFC and identify transient states. Across all regions, static overconnnectivity and increased variability over time in ASDs predominated. Furthermore, significant patterns of group differences emerged in two transient states that were not observed in the static FC matrix, with group differences in one state primarily involving sensory and motor networks, and in the other involving higher‐order cognition networks. Default mode network segregation was significantly reduced in ASDs in both states. Results highlight that dynamic approaches may reveal more nuanced transient patterns of atypical FC in ASDs.

Section: Discussionmentioning

confidence: 78%

Transient states of network connectivity are atypical in autism: A dynamic functional connectivity study

Mash

Linke

Olson

et al. 2019

“…The advantages of Yeo 7‐network model, relative to other similar models of brain parcellation, are that it is built on a large dataset ( n = 1,000), it involves an extensive range of validation efforts, including a split‐half replication, its topography corresponds closely to that of activation maps observed in subtraction imaging studies, and its usefulness as a frame of reference in interpreting task‐based activations has been repeatedly demonstrated (e.g., Benoit & Schacter, ; Fox, Spreng, Ellamil, Andrews‐Hanna, & Christoff, ; Kim, , ; Rosen, Stern, Michalka, Devaney, & Somers, ). However, any resting state‐based connectivity models are at best an approximate framework for interpreting task‐based fMRI activations, because functional coupling across dispersed brain regions differs between rest and task, although overall network configuration remains preserved (Buckner, Krienen, & Yeo, ; Gonzalez‐Castillo & Bandettini, ; Krienen, Yeo, & Buckner, ). Thus, the breakdown of WM‐phase maps as a function of Yeo 7 networks was meant to aid in interpreting meta‐analysis results rather than to test the model in a rigorously quantitative way.…”

Section: Methodsmentioning

confidence: 99%

“…According to Yeo et al (2011Yeo et al ( , p. 1138, "the violet ventral attention network is likely an aggregate of (or closely adjacent to) multiple networks in the literature variably referred to as the salience (Seeley et al, 2007) and cingulo-opercular networks (Dosenbach et al, 2007)." Based on this aggregation and relevant task-based activation and functional connectivity findings (Dosenbach et al, 2007;Gonzalez-Castillo & Bandettini, 2018;Krienen et al, 2014), the anterior insula and pmPFC components of the FPCN likely extend to adjacent frontal operculum and SMA regions during the WM task and also likely during other similar cognitively intensive states.…”

Section: Comparisons Between Different Phase Effectsmentioning

confidence: 99%

Neural activity during working memory encoding, maintenance, and retrieval: A network‐based model and meta‐analysis

Kim

2019

It remains unclear whether and to what extent working memory (WM) temporal subprocesses (i.e., encoding, maintenance, and retrieval) involve shared or distinct intrinsic networks. To address this issue, I constructed a model of intrinsic network contributions to different WM phases and then evaluated the validity of the model by performing a quantitative meta‐analysis of relevant functional neuroimaging data. The model suggests that the transition from the encoding to maintenance and to retrieval stages involves progressively decreasing involvement of the dorsal attention network (DAN), but progressively increasing involvement of the frontoparietal control network (FPCN). Separate meta‐analysis of each phase effect and direct comparisons between them yielded results that were largely consistent with the model. This evidence included between‐phase double dissociations that were consistent with the model, such as encoding > maintenance contrast showing some DAN, but no FPCN, regions, and maintenance > encoding contrast showing the reverse, that is, some FPCN, but no DAN, regions. Two closely juxtaposed regions that are members of the DAN and FPCN, such as inferior frontal junction versus caudal prefrontal cortex and superior versus inferior intraparietal sulcus, showed a high degree of functional differentiation. Although all regions identified in the present study were already identified in previous WM studies, this study uniquely enhances our understating of their roles by clarifying their network membership and specific associations with different WM phases.

“…For the past few years, task-FC has been increasingly used in fMRI studies (Cole et al, 2013;Gonzalez-Castillo & Bandettini, 2017), and there has also been some discussion regarding its advantages and characteristics (Arbabshirani, Havlicek, Kiehl, Pearlson, & Calhoun, 2013;Cisler, Bush, & Steele, 2014). For the past few years, task-FC has been increasingly used in fMRI studies (Cole et al, 2013;Gonzalez-Castillo & Bandettini, 2017), and there has also been some discussion regarding its advantages and characteristics (Arbabshirani, Havlicek, Kiehl, Pearlson, & Calhoun, 2013;Cisler, Bush, & Steele, 2014).…”

Section: Multivariate Methods Task-state Network and Distributed Rmentioning

confidence: 99%

Brain connection pattern under interoceptive attention state predict interoceptive intensity and subjective anxiety feeling

Shi

Wei

et al. 2018

Interoception involves the processing of a variety of different types of information ascending from the body. Accumulating evidence has indicated that interoception plays a fundamental role in cognitive and emotional processes, such as anxiety, but how different functional connectivity patterns contribute to emotions and visceral feelings during an interoceptive attention state is still unclear. In the present study, an interoceptive attention task was performed during functional magnetic resonance imaging of healthy subjects, and the participants' subjective ratings of the intensity of interoception and feelings of anxiety were recorded. Several network nodes were selected, based on previous studies, to construct task‐dependent functional connectivity patterns, which were processed by support vector regression to predict the corresponding feeling scores. The results showed that for interoception, the cingulo‐opercular task control network provided the greatest contribution, whereas the most important feature for anxiety was the connections between the sensorimotor area (SSM) and the salience network (SN). There existed four overlapping connections between the two predictions: two negative connections between the default mode network (DMN) and the SSM, one negative connection between the DMN and the SN, and one positive connection between the ventral attention network and the SN; this overlap might suggest common bodily attention processing that is involved in both interoception and anxiety. This study remediates the lack of network‐level biomarkers of interoception and provides a reference at the level of the brain for further understanding anxiety from an interoceptive perspective.