Neuroanatomical and functional substrates of the hypomanic personality trait and its prediction on aggression

Zhu, Wenwei; Chen, Xiongying; Wang, Jie; Li, Zixi; Im, Hohjin; Chen, Shuning; Deng, Kun; Zhang, Bin; Wei, C.; Feng, Junjiao; Zhang, Manman; Yang, Shaofeng; He, Wei; Wang, Qiang

doi:10.1016/j.ijchp.2023.100397

International Journal of Clinical and Health Psychology

2023

DOI: 10.1016/j.ijchp.2023.100397

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Neuroanatomical and functional substrates of the hypomanic personality trait and its prediction on aggression

Wenwei Zhu

Xiongying Chen

Jie Wang

et al.

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2024

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Cited by 3 publications

References 87 publications

(97 reference statements)

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The computational and neural substrates of individual differences in impulsivity under loss framework

Jiang,

Zhao,

Feng

et al. 2024

Human Brain Mapping

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Numerous neuroimaging studies have identified significant individual variability in intertemporal choice, often attributed to three neural mechanisms: (1) increased reward circuit activity, (2) decreased cognitive control, and (3) prospection ability. These mechanisms that explain impulsivity, however, have been primarily studied in the gain domain. This study extends this investigation to the loss domain. We employed a hierarchical Bayesian drift‐diffusion model (DDM) and the inter‐subject representational similarity approach (IS‐RSA) to investigate the potential computational neural substrates underlying impulsivity in loss domain across two experiments (n = 155). These experiments utilized a revised intertemporal task that independently manipulated the amounts of immediate and delayed‐loss options. Behavioral results demonstrated positive correlations between the drift rate, measured by the DDM, and the impulsivity index K in Exp. 1 (n = 97) and were replicated in Exp. 2 (n = 58). Imaging analyses further revealed that the drift rate significantly mediated the relations between brain properties (e.g., prefrontal cortex activations and gray matter volume in the orbitofrontal cortex and precuneus) and K in Exp. 1. IS‐RSA analyses indicated that variability in the drift rate also mediated the associations between inter‐subject variations in activation patterns and individual differences in K. These findings suggest that individuals with similar impulsivity levels are likely to exhibit similar value processing patterns, providing a potential explanation for individual differences in impulsivity within a loss framework.

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The computational and neural substrates of individual differences in impulsivity under loss framework

Jiang,

Zhao,

Feng

et al. 2024

Human Brain Mapping

View full text Add to dashboard Cite

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Happy people are always similar: The evidence from brain morphological and functional inter-subject correlations

Li,

Jiang,

Zhu

et al. 2024

NeuroImage

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Individualized prediction of anxiety and depressive symptoms using gray matter volume in a non-clinical population

Zhang,

Chen,

Jiang

et al. 2024

Cerebral Cortex

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Machine learning is an emerging tool in clinical psychology and neuroscience for the individualized prediction of psychiatric symptoms. However, its application in non-clinical populations is still in its infancy. Given the widespread morphological changes observed in psychiatric disorders, our study applies five supervised machine learning regression algorithms—ridge regression, support vector regression, partial least squares regression, least absolute shrinkage and selection operator regression, and Elastic-Net regression—to predict anxiety and depressive symptom scores. We base these predictions on the whole-brain gray matter volume in a large non-clinical sample (n = 425). Our results demonstrate that machine learning algorithms can effectively predict individual variability in anxiety and depressive symptoms, as measured by the Mood and Anxiety Symptoms Questionnaire. The most discriminative features contributing to the prediction models were primarily located in the prefrontal-parietal, temporal, visual, and sub-cortical regions (e.g. amygdala, hippocampus, and putamen). These regions showed distinct patterns for anxious arousal and high positive affect in three of the five models (partial least squares regression, support vector regression, and ridge regression). Importantly, these predictions were consistent across genders and robust to demographic variability (e.g. age, parental education, etc.). Our findings offer critical insights into the distinct brain morphological patterns underlying specific components of anxiety and depressive symptoms, supporting the existing tripartite theory from a neuroimaging perspective.

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Neuroanatomical and functional substrates of the hypomanic personality trait and its prediction on aggression

Cited by 3 publications

References 87 publications

The computational and neural substrates of individual differences in impulsivity under loss framework

The computational and neural substrates of individual differences in impulsivity under loss framework

Happy people are always similar: The evidence from brain morphological and functional inter-subject correlations

Individualized prediction of anxiety and depressive symptoms using gray matter volume in a non-clinical population

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