Object Detection in Neuroimaging: Enhancing Early Diagnosis of Neurological Disorders through Big Data Analysis

Yuting Pan^*

• Xinjiang University of Finance and Economics, Ürümqi, China

The accurate interpretation of neuroimaging data is critical for the early diagnosis of neurological disorders, which is increasingly feasible due to advancements in brain scanning technologies. Neurological diseases often involve subtle and widespread alterations in brain connectivity. These patterns require robust methodologies capable of extracting clinically relevant features from high-dimensional data. Traditional approaches, including graph theory metrics and convolutional neural networks, face challenges in simultaneously capturing localized disruptions and global structural changes, while also struggling to provide statistically interpretable outputs under varying data quality. To tackle the aforementioned problems, we design a hybrid framework that merges deep neural graph representations with statistical analysis, specifically designed for brain network analysis. The NeuroGraph Anomaly Detector (N-GAD) employs a multi-scale graph encoder that incorporates motif-augmented message passing, jump-aware feature aggregation, and spectral cross-convolution to capture both localized and distributed anomalies. This encoding is coupled with Contrastive Structural Differentiation (CSD), an inference strategy that formulates anomaly detection as a topological hypothesis testing problem using graphlet statistics, spectral, and Wasserstein-based distributional metrics. Experimental evaluations demonstrate that this framework improves sensitivity to pathological alterations while maintaining interpretability and statistical rigor, making it particularly suitable for large-scale neuroimaging applications aimed at early disorder diagnosis. This approach contributes to computational neurodiagnostics by enhancing both accuracy and transparency in clinical decision-making.