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Joint resting state and structural networks characterize pediatric bipolar patients compared to healthy controls: a

Xiaoping Yi1, Mingzhao Ma2, Xueying Wang3

  • 1Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, Hunan, PR China; Clinical Research Center (CRC), Medical Pathology Center (MPC), Cancer Early Detection and Treatment Center (CEDTC) and Translational Medicine Research Center (TMRC), Chongqing University Three Gorges Hospital, Chongqing University, Chongqing 404000, PR China; School of Medicine, Chongqing University, Chongqing 400030, PR China; National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Xiangya Hospital, Changsha 410008, Hunan, PR China.

Neuroimage
|April 19, 2025
PubMed

Insights

Pediatric bipolar disorder (PBD) shows distinct brain differences. Functional and structural abnormalities in PBD patients were identified using MRI, offering new insights into neurobiology.

Area of Science:

  • Neuroscience
  • Psychiatry
  • Medical Imaging

Background:

  • Pediatric bipolar disorder (PBD) is a severe mental health condition with limited understanding of its neurobiological underpinnings.
  • Distinguishing between PBD type I and type II subtypes is challenging, yet crucial for targeted treatment.
  • Resting-state functional MRI and structural MRI offer potential biomarkers for PBD.

Purpose of the Study:

  • To investigate functional and structural brain abnormalities in pediatric bipolar disorder (PBD) using resting-state functional MRI and structural MRI.
  • To compare neurobiological differences between PBD patients and healthy controls (HC).
  • To explore variations between PBD type I and type II, and their correlation with depressive symptoms.

Main Methods:

  • Utilized transposed Independent Vector Analysis (tIVA), a data fusion unsupervised machine learning approach.
  • Analyzed Resting state Regional Homogeneity (ReHo) and grey matter concentration (GMC) data from 58 PBD patients and 21 healthy controls.
  • Employed MRI scans to assess brain activity and structure.

Main Results:

  • Identified two distinct brain networks differing between PBD patients and HC: one with higher ReHo/GMC in fronto-medial regions (PBD > HC), and another with lower ReHo/GMC in temporo-posterior regions, insula, caudate, and precuneus (PBD < HC).
  • Found two networks differentiating PBD type I from type II: an occipito-cerebellar network with increased ReHo/GMC in type I, and a fronto-parietal network with decreased ReHo/GMC in type I.
  • Observed a positive correlation between the fronto-medial network abnormalities and depression scores in PBD patients.

Conclusions:

  • The study reveals significant functional and structural brain alterations in pediatric bipolar disorder, differentiating it from healthy controls.
  • Neurobiological differences exist between PBD type I and type II, suggesting distinct underlying pathophysiology.
  • These findings provide novel insights into the neurobiology of PBD and its subtypes, potentially aiding in biomarker development.

Keywords:
Bipolar disorderNeuroimagingPediatric populationUnsupervised machine learningtIVA

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