Gray matter atrophy and structural connectivity in Posterior Cortical Atrophy: A voxel-based meta-analysis

Posterior Cortical Atrophy (PCA) is a neurodegenerative syndrome most commonly associated with Alzheimer's disease, characterized by progressive visuospatial and visuoperceptual decline. Although voxel-based morphometry studies have described gray matter loss in PCA, a comprehensive and updated coordinate-based meta-analysis is still missing, and associated structural connectivity alterations remain unclear. We conducted a systematic review and meta-analysis of whole-brain voxel-based morphometry studies comparing patients with PCA and healthy controls (PROSPERO ID: CRD420251010673). Analyses were performed using Seed-based d Mapping with Permutation of Subject Images (SDM-PSI) with family-wise error correction, and meta-regressions assessed the impact of demographic and clinical variables. To investigate structural connectivity, deterministic tractography was carried out on a normative diffusion MRI template, using meta-analytic gray matter clusters as seeds. Eighteen studies were included (339 PCA; 577 healthy controls). The meta-analysis revealed consistent bilateral gray matter atrophy in the lateral occipital cortex, inferior parietal lobule, precuneus, and ventral occipitotemporal regions. Meta-regression highlighted an interaction between age and disease duration, associated with atrophy in the left superior temporal gyrus and right thalamus. Tractography demonstrated that affected clusters were embedded within major long-range pathways, including the superior and inferior longitudinal fasciculi, vertical occipital fasciculi, and parietal aslant tract. Regression-derived clusters additionally mapped onto the arcuate fasciculus, frontal aslant tract, and superior thalamic radiations. This is the first systematic review and voxel-based meta-analysis of PCA conducted after the establishment of consensus diagnostic criteria, providing a statistically robust characterization of gray and white matter alterations and identifying potential imaging biomarkers for diagnosis and treatment.