Systemic Ammonia Toxicity: An Underestimated Driver of Cerebral Energy Crisis in Hepatic Encephalopathy.

Parkinson’s disease (PD) is the second most common age-related neurodegenerative disorder, and its prevalence is increasing markedly with population ageing. Although the diagnosis is based on a recognizable motor syndrome, patients differ widely in clinical presentation, disease progression, and the severity of non-motor and cognitive manifestations []. Patients are conventionally classified into motor phenotypes, most commonly the tremor-dominant (TD) and postural instability/gait difficulty (PIGD) subtypes []. These categories, however, overlap substantially, evolve over the disease course, and have limited prognostic value [,], indicating that such clinical classifications capture only part of the underlying disease biology. Neuropathologically, PD is characterized by the progressive spread of Lewy body pathology that ultimately reaches widespread cortical areas, and the degree of neocortical involvement parallels disease severity and the emergence of cognitive decline []. In line with these pathological findings, structural MRI studies have reported cortical thinning and gray matter loss involving temporal, frontal, parietal, and occipital regions [,,]. However, most studies have examined cortical regions in isolation, one morphometric feature at a time, an approach that does not capture the distributed and coordinated nature of cortical degeneration in PD []. To address this, the Morphometric INverse Divergence (MIND) framework quantifies structural similarity between cortical regions by integrating multiple vertex-wise morphometric features, generating individualized maps of cortical organization []. Because morphometrically similar regions tend to share developmental origin, gene expression profiles, and axonal connectivity, MIND provides a biologically informed measure of cortical architecture and has recently been applied across several neurological and psychiatric disorders [,,,]. Building on this multivariate representation, Heterogeneity Through Discriminative Analysis (HYDRA) provides a semi-supervised framework for identifying patient subgroups by modeling multivariate deviations from healthy controls [,,]. When combined with MIND-derived measures, this approach enables the investigation of disease heterogeneity beyond conventional clinical classifications.