Related Experiment Video
Updated: Feb 9, 2026

Developing a Rat Model for Bipolar Disorder
Published on: May 2, 2025
Illness, at-risk and resilience neural markers of early-stage bipolar disorder
Kangguang Lin1, Robin Shao2, Xiujuan Geng3
1Department of Affective Disorders, Guangzhou Brain Hospital, The Affiliated Hospital of Guangzhou Medical University, 36 Mingxin Road, Guangzhou, Guangdong 510370, China; Laboratory of Emotion and Cognition, The Affiliated Hospital of Guangzhou Medical University, Guangzhou, China; GMH Institute of CNS Regeneration, Jinan University, Guangzhou, China; GMU-HKU Mood and Brain Science Center, Guangzhou, China.
Background:
Current knowledge on objective and specific neural markers for bipolar risk and resilience-related processes is lacking, partly due to not subdividing high-risk individuals manifesting different levels of subclinical symptoms who possibly possess different levels of resilience.
Methods:
We delineated grey matter markers for bipolar illness, genetic high risk (endophenotype) and resilience, through comparing across 42 young non-comorbid bipolar patients, 42 healthy controls, and 72 diagnosis-free, medication-naive high-genetic-risk individuals subdivided into a combined-high-risk group who additionally manifested bipolar risk-relevant subsyndromes (N = 38), and an asymptomatic high-risk group (N = 34). Complementary analyses assessed the additional predictive and classification values of grey matter markers beyond those of clinical scores, through using logistic regression and support vector machine analyses.
Results:
Illness-related effects manifested as reduced grey matter volumes of bilateral temporal limbic-striatal and cerebellar regions, which significantly differentiated bipolar patients from healthy controls and improved clinical classification specificity by 20%. Reduced bilateral cerebellar grey matter volume emerged as a potential endophenotype and (along with parieto-occipital grey matter changes) separated combined-high-risk individuals from healthy and high-risk individuals, and increased clinical classification specificity by approximately 10% and 27%, respectively, while the relatively normalized cerebellar grey matter volumes in the high-risk sample may confer resilience.
Limitations:
The cross-validation procedure was not performed on an independent sample using independently-derived features. The BD group had different age and sex distributions than some other groups which may not be fully addressable statistically.
Conclusions:
Our framework can be applied in other measurement domains to derive complete profiles for bipolar patients and at-risk individuals, towards forming strategies for promoting resilience and preclinical intervention.
More Related Videos
13:08Measurement of Fronto-limbic Activity Using an Emotional Oddball Task in Children with Familial High Risk for Schizophrenia
Published on: December 2, 2015
05:52Handwriting Analysis Indicates Spontaneous Dyskinesias in Neuroleptic Naïve Adolescents at High Risk for Psychosis
Published on: November 21, 2013
Related Concept Videos
Bipolar Disorder
Classification of Illness
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...
Factors Affecting Illness
For instance, risk factors are connected to illness,...
Concepts of Health and Illness
Dimensions of Health and Illness
Bipolar Junction Transistor