# A whole-brain structural connectivity signature in adult Niemann–Pick disease type C

## TL;DR

This study identifies brain connectivity patterns in adult Niemann–Pick disease type C, focusing on regions like the frontal, temporal, and subcortical areas, which could help with diagnosis and monitoring.

## Contribution

The study provides a whole-brain structural connectivity signature specific to adult Niemann–Pick disease type C using diffusion-weighted MRI.

## Key findings

- NPC shows diffusive disconnectivity, especially in left frontotemporal and subcortical networks.
- Reductions in connection strength and number are observed in the thalamus and dorsal striatum.
- These findings may aid in early diagnosis and monitoring of NPC.

## Abstract

Niemann–Pick disease type C (NPC) is a rare genetic lysosomal storage disorder with a clinically heterogeneous phenotype that primarily affects the brain, liver and spleen. Most cases of NPC are diagnosed in childhood, but a subset of patients who are diagnosed in adulthood present with psychiatric symptoms and are initially diagnosed as schizophrenia or a mood disorder. Neuroimaging studies in NPC show a predilection for neurodegeneration in the subcortical nuclei, the cerebellum and subcortical white matter. We aimed to explore how adult NPC affects the connectivity of networks and hypothesized a state of widespread disconnectivity, particularly in subcortical areas. This cross-sectional neuroimaging study used diffusion-weighted magnetic resonance imaging to perform whole-brain tractography in 9 adult patients with NPC and 70 matched healthy controls. Connections between 84 unique brain regions were modelled with streamlines and weighted according to fibre bundle capacity. Statistical testing by each connection allowed the identification of significantly affected networks and regions in NPC. We observed diffusive disconnectivity in NPC primarily affecting left frontotemporal and subcortical networks. Globally and regionally, NPC showed reductions in the strength and number of affected connections, particularly in the thalamus and dorsal striatum. We show that left frontotemporal and subcortical networks are markedly affected in NPC. These findings may facilitate early diagnostic differentiation, monitoring and prognostication and may represent the neural correlates of neuropsychiatric symptoms in NPC.

Reilly et al. model brain structural connectivity in Niemann–Pick disease type C using diffusion-weighted MRI. They demonstrate the extent and distribution of connection loss that particularly affects the frontal, temporal and subcortical brain regions. This foundational work may contribute to improving diagnosis, disease monitoring and prognostication.

Graphical Abstract