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 is 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 six 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 the 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).…”

Focality-Oriented Selection of Current Dose for Transcranial Direct Current Stimulation

“…We have shown how DTDI can be used for a target ROI. However, a user may be interested in stimulating multiple target ROIs, or a network of ROIs specific to a particular functionality [82][83][84][85][86][87][88]. This may be because studies have shown that more than one brain area can be involved in neuropsychiatric disorders like depression [89,90].…”

Detection of functional and structural brain alterations in female schizophrenia using elastic net logistic regression

“…Thus more cognitive and behavioral assessments should be involved to explain the impact of SZ pathology, such as the Montreal Cognitive Assessment (MOCA) and Mini-Cog test (Tsoi et al, 2015 ). Lastly, some additional parameters, such as smoking and antisocial personality, may play an important role in analyzing resting-state brain patterns (Kashyap et al, 2020 ), which need be considered in selecting healthy control populations in the future studies.…”