Evaluation of the Cortical Subplate in Autism Using Postmortem Tissue and Machine Learning as an Indicator of Long-Range Connectional Alterations in Development.

Abstract

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by challenges in social communication and repetitive behaviors. These behaviors have been suggested to arise from an imbalance in brain connectivity, specifically between long- and short-range connections in ASD. The development of these crucial connections begins with subplate neurons (SPns), which are vital for forming and maturing brain circuits from early development into adulthood. Located beneath the cortical plate, SPns are essential for organizing the cerebral cortex. Similarly, glial cells-including astrocytes, microglia, and oligodendrocytes-are critical for establishing healthy neural circuits and connections between different cortical regions. Studies on other neurodevelopmental and neurodegenerative disorders, such as Alzheimer's disease, have found abnormalities in both the number and shape of glial cells. Given the strong evidence for altered connectivity in autism, it is plausible that dysfunctions in both SPns and glial cells contribute to the abnormal neural networks seen in ASD. However, our understanding of these cells' specific roles is limited due to a lack of research on this particular brain region. This project contains three aims: Aim 1 assessed SPns changes in ASD and neurotypical (NT) control subjects in the subplate of the parietal cortex. Aim 2 evaluate glial subtypes and their differences between ASD subjects and NT subjects within the subplate of the parietal cortex. Aim 3 evaluate the efficacy of recently developed, and commercially available, machine learning techniques for quantitative classification of both neurons (Aim 1) and glial cells (Aim 2) relative to more traditional statistical analysis. Analysis for Aim 1 and 2 suggest cellular morphology and texture revealed preliminary differences in the subplate and layer VI between ASD and NT with a main effect in diagnosis for intensity and texture and in cell bodies across layers, but no significant layer-by-diagnosis interactions were found. Results for Aim 3 quantification and classification using machine learning software revealed a greater number of neurons in the NT subplate compared to the ASD group, and a significantly greater amount of glia in ASD for layer VI compared to the NT group. Cluster analysis attempts to distinguish cell types resulted in poorly rated and imbalanced clusters that did not align with expected glial/neuronal ratios, thus limiting comparison with machine learning classification results. This research, validated by cutting-edge machine learning analysis, delivers robust, initial quantitative evidence of a specific cellular pathology underlying the ASD parietal cortex. The discovery of diagnostic-specific alterations in neuron and glia counts within the subplate and layer VI establishes a critical, anatomical basis for the widely accepted etiology of cortical unbalance in ASD. These pivotal findings empower researchers with new, quantifiable data points, significantly improving our cellular understanding of this key etiology and accelerating future mechanistic investigations.