Abstract #0853

Multivariate Pattern Analysis Reveals Neurobiological Markers of Communication Impairments in Children with Bilateral Spastic Cerebral Palsy

Hu Jie^1,2, He Cheng³, Li Xianjun⁴, Nie Lisha⁵, Peng Ying¹, Jiang Haoxiang⁶, Liu Heng¹, and Zhang Tijiang¹

¹Department of Radiology, Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Engineering Research Center of Intelligent Medical Imaging in Guizhou Higher Education Institutions, Zunyi, China, ²Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China, ³Imaging Department, Chongqing Key Laboratory of Emergency Medical; Imaging Department, Chongqing University Central Hospital, Chongqing, China, ⁴Department of Radiology, the First Affliated Hospital of Xi’an Jiaotong University, Xi'an, China, ⁵GE HealthCare MR Research, Beijing, China, ⁶Department of Radiology, the Affiliated Children's Hospital of Jiangnan University, Wuxi, China

Synopsis

Keywords: Neuro, Diagnosis/Prediction, Cerebral palsy;Communication impairment

Motivation: Communication impairments in children with bilateral spastic cerebral palsy (BSCP) are linked to specific brain morphological and connectivity changes, which require individualized diagnostic approaches for effective assessment.

Goal(s): To develop a personalized diagnostic model that identifies neurobiological markers of communication impairments in BSCP, enabling targeted interventions.

Approach: A multivariate pattern analysis integrating cortical morphology and white matter connectivity metrics was applied to compare BSCP children with typically developing controls.

Results: The combined support vector classification (SVC) model, based on cortical and white matter features, achieved 80.7% accuracy and an AUC of 0.881, effectively distinguishing communication impairments in BSCP.

Impact: The MVPA-based model facilitates individualized diagnostics for communication impairments in BSCP, supporting personalized treatment and rehabilitation, and paving the way for precision neuroimaging applications in pediatric neurodevelopmental disorders.

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