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

Area of Science:

• Neuroscience
• Personalized Medicine
• Artificial Intelligence

Background:

• Neurodiversity is reframed from pathology to adaptive variation.
• Personalized Brain Network (PBN) architecture is key to understanding cognitive variability.
• Current diagnostic criteria may miss distinct neurobiological subgroups.

Purpose of the Study:

• To synthesize knowledge on PBN architecture and its link to cognitive variability.
• To explore advancements in characterizing individual-specific neural networks.
• To discuss clinical applications and challenges in personalized neurodiversity research.

Main Methods:

• Connectome-based prediction modeling
• Normative modeling
• Dynamic fingerprinting
• Machine learning and deep generative models

Main Results:

• PBN profiles reliably predict cognitive, behavioral, and sensory phenomena.
• Distinct neurobiological subgroups identified in ADHD and autism spectrum disorder.
• Age-resistant biomarkers and genetic influences on brain connectivity found.
• Network variability predicts executive functioning, social perception, and sensory processing.

Conclusions:

• Personalized neurodiversity offers tailored interventions for conditions like autism and ADHD.
• Challenges include statistical power, reproducibility, ethics, and community engagement.
• Integrating neuroimaging, AI, and personalized medicine celebrates neurological diversity.