Maximizing Dissimilarity in Resting State detects Heterogeneous Subtypes in Healthy population associated with High Substance-Use and Problems in Antisocial Personality

Abstract

Patterns in resting-state fMRI (rs-fMRI) are widely used to characterize the trait effects of brain function. In this aspect, multiple rs-fMRI scans from single subjects can provide interesting clues about the rs-fMRI patterns, though scan-to-scan variability pose challenges. Therefore, rs-fMRI's are either concatenated or the functional connectivity is averaged. This leads to loss of information. Here, we use an alternative way to extract the rs-fMRI features that are common across all the scans by applying Common-and-Orthogonal-Basis-Extraction (COBE) technique. To address this, we employed rs-fMRI of 788 subjects from the human connectome project and estimated the common-COBE-component of each subject from the four rs-fMRI runs. Since the common-COBE-component are specific to a subject, the pattern was used to classify the subjects based on the similarity/dissimilarity of the features. The subset of subjects (n=107) with maximal-COBE-Dissimilarity (MCD) was extracted and the remaining subjects (n = 681) formed the COBE-similarity (CS) group. The distribution of weights of the common-COBE-component for the two groups across rs-fMRI networks and subcortical regions was evaluated. We found the weights in the default mode network to be lower in the MCD compared to the CS. We compared the scores of 69 behavioral measures and found 6 behaviors related to the use of marijuana, illicit drugs, alcohol, and tobacco; and including a measure of antisocial personality to differentiate the two groups. Gender differences were also significant. Altogether findings suggested that subtypes exist even in healthy control population and comparison studies (Case vs Control) need to be mindful of it.

Differences in the amplitude of low‐frequency fluctuation between methamphetamine and heroin use disorder individuals: A resting‐state fMRI study

“…Second, all participants were males, and the effects of MA on sex cannot be assessed. Finally, Kashyap, Bhattacharjee, Yeo, and Chen (2020) have shown that the general categorization of subjects based only on external symptoms (e.g., healthy vs. diseased, control vs. patient) should also consider aspects of a healthy subject's lifestyle habits and psyche. We did not select the control group from open datasets, which would bring some bias to the results (Kashyap et al., 2020).…”

Effect of selective transcranial direct current stimulation on dorsal and ventral pathways of reading

“…Supporting the behavioural finding with neural markers, such as electroenchaphalography (EEG), functional near-infrared spectroscopy (fNIRS) , or functional magnetic resonance imaging (fMRI) could have validated the claim that whether a pathway is maximally utilized or not. A detailed analysis by constructing brain networks using data from multiple modalities can further aid our understanding of the mechanism of action of tDCS (Lauro et al, 2014;Sehatpour et al, 2020;Vecchio et al, 2021;Wang et al, 2020;Kashyap et al, 2016Kashyap et al, , 2019aKashyap et al, ,b, 2020Kashyap et al, , 2021b.…”