Precision neurodiversity: personalized brain network architecture as a window into cognitive variability

Precision neurodiversity marks a shift in neuroscience from pathological models to personalized frameworks that view neurological differences as adaptive variations. This review synthesizes current knowledge on the Personalized Brain Network architecture and its relationship with cognitive variability in both typical and neurodiverse populations. The study examines advancements in connectome-based prediction modeling, normative modeling, dynamic fingerprinting, and machine learning methods that characterize individual-specific neural networks. Recent findings indicate that the Personalized Brain Network profile reliably predicts cognitive, behavioral, and sensory phenomena. Additionally, deep generative models demonstrate high fidelity in synthesizing connective cells. Recent studies have identified distinct neurobiological subgroups in conditions such as attention-deficit hyperactivity disorder (ADHD) and autism spectrum disorder that were previously undetectable by conventional diagnostic criteria. However, research has revealed significant network-level differences among these subgroups. Researchers have identified age-resistant biomarkers in specific brain regions, and genetic mutations significantly influence the connectivity patterns of individuals. Clinical applications span a range of neurodevelopmental conditions, including autism, ADHD, dyslexia, and talent. Network variability predicts executive functioning, social perception, and sensory processing abilities. However, successful translation requires overcoming challenges related to statistical power, reproducibility, ethical implementation and community participation. The convergence of advanced neuroimaging, artificial intelligence, and personalized medicine offers unprecedented opportunities for tailored interventions, while celebrating neurological diversity as a source of human strength.