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Identifying commonality and specificity across psychosis sub-groups via classification based on features from dynamic connectivity analysis.

ABSTRACT: It is difficult to distinguish schizophrenia (SZ), schizoaffective disorder (SAD), and bipolar disorder with psychosis (BPP) as their clinical diagnoses rely on symptoms that overlap. In this paper, we investigate if there is biological evidence to support the symptom-based clinical categories by looking across the three disorders using dynamic connectivity measures, and provide meaningful characteristics on which brain functional connectivity measures are commonly or uniquely impaired. Large-sample functional magnetic resonance image (fMRI) datasets from 623 subjects including 238 healthy controls (HCs), 113 SZ patients, 132 SAD patients, and 140 BPP patients were analyzed. First, we computed whole-brain dynamic functional connectivity (DFC) using a sliding-window technique, and then extracted the individual connectivity states by applying our previously proposed decomposition-based DFC analysis method. Next, with the features from the dominant connectivity state, we assessed the clinical categories by performing both four-group (SZ, SAD, BPP and healthy control groups) and pair-wise classification using a support vector machine within cross-validation. Furthermore, we comprehensively summarized the shared and unique connectivity alterations among the disorders. In terms of the classification performance, our method achieved 69% in the four-group classification and >80% in the between-group classifications for the mean overall accuracy; and yielded 66% in the four-group classification and >80% in the between-group classifications for the mean balanced accuracy. Through summarizing the features that were automatically selected in the classifications, we found that among the three symptom-related disorders, their disorder-common impairments primarily included the decreased connectivity strength between thalamus and cerebellum and the increased strength between postcentral gyrus and thalamus. The disorder-unique changes included more various brain regions, mainly in the temporal and frontal gyrus. Our work demonstrates that dynamic functional connectivity provides biological evidence that both common and unique impairments exist in psychosis sub-groups.

SUBMITTER: Du Y

PROVIDER: S-EPMC7306624 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The validity and reliability of diagnoses in psychiatry is a challenging topic in mental health. The current mental health categorization is based primarily on symptoms and clinical course and is not biologically validated. Among multiple ongoing efforts, neurological observations alongside clinical evaluations are considered to be potential solutions to address diagnostic problems. The Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP) has published multiple papers attempting to reclassify psychotic illnesses based on biological rather than symptomatic measures. However, the effort to investigate the relationship between this new categorization approach and other neuroimaging techniques, including resting-state fMRI data, is still limited. This study focused on investigating the relationship between different psychotic disorders categorization methods and resting-state fMRI-based measures called dynamic functional network connectivity (dFNC) using state-of-the-art artificial intelligence (AI) approaches. We applied our method to 613 subjects, including individuals with psychosis and healthy controls, which were classified using both the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) and the B-SNIP biomarker-based (Biotype) approach. Statistical group differences and cross-validated classifiers were performed within each framework to assess how different categories. Results highlight interesting differences in occupancy in both DSM-IV and Biotype categorizations compared to healthy individuals, which are distributed across specific transient connectivity states. Biotypes tended to show less distinctiveness in occupancy level and included fewer cellwise differences. Classification accuracy obtained by DSM-IV and Biotype categories were both well above chance. Results provided new insights and highlighted the benefits of both DSM-IV and biology-based categories while also emphasizing the importance of future work in this direction, including employing further data types.

Project description:BackgroundMany adiposity traits have been related to health complications and premature death. These adiposity traits are intercorrelated but their underlying structure has not been extensively investigated. We report on the degree of commonality and specificity among multiple adiposity traits in normal-weight and moderately overweight adult males and females (mean body mass index (BMI)=22.9 kg m(-2), s.d.=2.4).MethodsA total of 75 healthy participants were assessed for a panel of adiposity traits including leg, arm, trunk, total fat masses and visceral adipose tissue (VAT) derived from dual energy X-ray absorptiometry (DXA), hepatic and muscle lipids from proton magnetic resonance spectroscopy, fat cell volume from an abdominal subcutaneous adipose tissue biopsy (n=36) and conventional anthropometry (BMI and waist girth). Spearman's correlations were calculated and were subjected to factor analysis.ResultsArm, leg, trunk and total fat masses correlated positively (r=0.78-0.95) with each other. VAT correlated weakly with fat mass indicators (r=0.24-0.31). Intrahepatic lipids (IHL) correlated weakly with all fat mass traits (r=0.09-0.34), whereas correlations between DXA depots and intramyocellular lipids (IMCL) were inconsequential. The four DXA fat mass measures, VAT, IHL and IMCL depots segregated as four independent factors that accounted for 96% of the overall adiposity variance. BMI and waist girth were moderately correlated with the arm, leg, trunk and total fat and weakly with VAT, IHL and IMCL.ConclusionAdiposity traits share a substantial degree of commonality, but there is considerable specificity across the adiposity variance space. For instance, VAT, IHL and IMCL are typically poorly correlated with each other and are poorly to weakly associated with the other adiposity traits. The same is true for BMI and waist girth, commonly used anthropometric indicators of adiposity. These results do not support the view that it will be possible to identify adequate anthropometric indicators of visceral, hepatic and muscle lipid content in normal-weight and moderately overweight individuals.

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Identifying commonality and specificity across psychosis sub-groups via classification based on features from dynamic connectivity analysis.

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