Functional brain segmentation using inter‐subject correlation in fMRI

Abstract

The human brain continuously processes massive amounts of rich sensory information. To better understand such highly complex brain processes, modern neuroimaging studies are increasingly utilizing experimental setups that better mimic daily-life situations. A new exploratory data-analysis approach, functional segmentation inter-subject correlation analysis (FuSeISC), was proposed to facilitate the analysis of functional magnetic resonance (fMRI) data sets collected in these experiments. The method provides a new type of functional segmentation of brain areas, not only characterizing areas that display similar processing across subjects but also areas in which processing across subjects is highly variable. FuSeISC was tested using fMRI data sets collected during traditional block-design stimuli (37 subjects) as well as naturalistic auditory narratives (19 subjects). The method identified spatially local and/or bilaterally symmetric clusters in several cortical areas, many of which are known to be processing the types of stimuli used in the experiments. The method is not only useful for spatial exploration of large fMRI data sets obtained using naturalistic stimuli, but also has other potential applications, such as generation of a functional brain atlases including both lower- and higher-order processing areas. Finally, as a part of FuSeISC, a criterion-based sparsification of the shared nearest-neighbor graph was proposed for detecting clusters in noisy data. In the tests with synthetic data, this technique was superior to well-known clustering methods, such as Ward's method, affinity propagation, and K-means ++. Hum Brain Mapp 38:2643-2665, 2017. © 2017 Wiley Periodicals, Inc.

Measuring shared responses across subjects using intersubject correlation

“…The time course of each IC can then be examined and related to the paradigm . Second, if the ISC analysis identifies a large number of regions surviving a particular statistical criterion, one can submit the average time courses throughout the brain to a clustering algorithm to functionally parcellate the cortex or identify ROIs with similar time series (Kauppi et al, 2010b(Kauppi et al, , 2017Thomas et al, 2018). Alternatively, ISC can be computed using a sliding-window approach in order to identify epochs in which ISC was highest, which can then be related to the stimulus (window sizes in the literature range from 10 to 60 TRs; Nummenmaa et al, 2012;Simony et al, 2016).…”

Consistency and similarity of MEG- and fMRI-signal time courses during movie viewing

“…J€ a€ askel€ ainen et al, 2008;Golland et al, 2007;Kauppi et al, 2010Kauppi et al, , 2017Nummenmaa et al, 2012;Lahnakoski et al, 2012). Compared with the strong across-viewers correlations in BOLD signals (up to 0.78 in Kauppi et al, 2010), the correspondingly calculated intersubject correlations of MEG or EEG signals are usually weaker (typically less than 0.1) both at sensor (Bridwell et al, 2015) and source level (Suppanen, 2014;Chang et al, 2015).…”

Comparing fMRI inter-subject correlations between groups using permutation tests

“…While Chen et al (2016) demonstrated the importance of subject-wise rather than element-wise permutations in ISC group comparisons, they based their analysis on simple, purely synthetic data. However, in ISC analysis, this kind of synthetic data fails to reproduce some of the main characteristics of ISCs seen in actual fMRI experiments such as the increase of variance of subject-pair-wise ISCs with the increase of mean ISCs (Kauppi et al, 2017).…”

“…This is a biased variance estimate as it does not account for the dependencies between the elements of the correlation matrix z (i) j (n, m). On the other hand, it is an approximation, up to a multiplication by a constant, of the leave-one-subject-out variance defined by Kauppi et al (2017). We definev…”