A Set of Functional Brain Networks for the Comprehensive Evaluation of Human Characteristics

Abstract

Many human characteristics must be evaluated to comprehensively understand an individual, and measurements of the corresponding cognition/behavior are required. Brain imaging by functional MRI (fMRI) has been widely used to examine brain function related to human cognition/behavior. However, few aspects of cognition/behavior of individuals or experimental groups can be examined through task-based fMRI. Recently, resting state fMRI (rs-fMRI) signals have been shown to represent functional infrastructure in the brain that is highly involved in processing information related to cognition/behavior. Using rs-fMRI may allow diverse information about the brain through a single MRI scan to be obtained, as rs-fMRI does not require stimulus tasks. In this study, we attempted to identify a set of functional networks representing cognition/behavior that are related to a wide variety of human characteristics and to evaluate these characteristics using rs-fMRI data. If possible, these findings would support the potential of rs-fMRI to provide diverse information about the brain. We used resting-state fMRI and a set of 130 psychometric parameters that cover most human characteristics, including those related to intelligence and emotional quotients and social ability/skill. We identified 163 brain regions by VBM analysis using regression analysis with 130 psychometric parameters. Next, using a 163 × 163 correlation matrix, we identified functional networks related to 111 of the 130 psychometric parameters. Finally, we made an 8-class support vector machine classifiers corresponding to these 111 functional networks. Our results demonstrate that rs-fMRI signals contain intrinsic information about brain function related to cognition/behaviors and that this set of 111 networks/classifiers can be used to comprehensively evaluate human characteristics.

Relationship of self‐transcendence traits with in vivo dopamine D2/3 receptor availability and functional connectivity: An [18F]fallypride PET and fMRI study

“…We speculate that our findings imply that self‐transcendence is enhanced by lower prefrontal control of dopaminergic signaling in the insula, although we cannot clearly distinguish whether higher D 2/3 receptor availability results from increased D 2/3 receptor expression or lesser competition from endogenous dopamine. The present study also supports previous findings that rs‐fMRI signals can be linked to inherent human characteristics or personality traits (Dubois, Galdi, Han, Paul, & Adolphs, ; Sung et al, ). However, given the highly exploratory nature of the BP ND seed‐based rs‐fMRI analysis in our study, future confirmatory studies are warranted.…”

Hierarchical complexity of the adult human structural connectome

“…), an understanding of which is required to help solve what has been termed the clinico-radiological paradox (Chard and Trip, 2017). Evidence shows that there are specific white matter pathways that have greater impact on clinical and functional outcome regardless of the lesion size (Valdés Hernández et al., 2015) whilst other tracts offer routes for functional reorganisation (Sung et al., 2018; Tomassini et al., 2016; Powell et al., 2018). Future studies applying the hierarchical complexity measure to health and disease may help to uncover subtler but still significant differences in brain network topology that will add to our understanding of this topic.…”

DeepParcellation: a novel deep learning method for robust brain magnetic resonance imaging parcellation in older East Asians

“…Decreased cortical thickness in temporal regions was found in patients with PD [ 24 ] and reduced volumes in cortical regions were reported in patients with AD [ 2 ]. Since aging affects certain brain regions differently [ 31 ], accurate and precise structural measurements are critical for monitoring neurodegenerative processes. Our robust and reliable parcellation method of the brain of older individuals can guarantee higher prediction accuracy and help disease diagnosis.…”

“…The predicted parcellation derived from our method can be extended to neurodevelopmental and clinical studies, such as brain age prediction or biomarker development for neurodegenerative diseases. Parcellated subcortical regions, including the putamen, caudate, and hippocampus, predict brain age with good accuracy Since aging affects certain brain regions differently[31], accurate and precise structural measurements are critical for monitoring neurodegenerative processes. Our robust and reliable parcellation method of the brain of older individuals can guarantee higher prediction accuracy and help disease diagnosis.The fast and robust parcellation achieved by our proposed model can accelerate big brain MRI data analysis.…”