# The Precuneus Region Drives Brain Network Changes in Tremor‐Dominant Parkinson's Disease: Insights from a Morphological Causal Analysis

## TL;DR

This study identifies a brain region, the precuneus, as central to structural changes in tremor-dominant Parkinson's disease and develops a machine learning model to predict DBS treatment outcomes.

## Contribution

The study reveals a causal morphological progression pattern in tremor-dominant Parkinson's disease and introduces a predictive model for DBS outcomes.

## Key findings

- The bilateral precuneus shows significant gray matter volume reduction in tremor-dominant Parkinson's patients.
- The precuneus regulates structural covariance with regions like the supplementary motor area and temporal lobe.
- A machine learning model using cortical volume and clinical data accurately predicts DBS treatment outcomes.

## Abstract

The tremor‐dominant (TD) subtype of Parkinson's disease (PD) is characterized by prominent tremor symptoms. However, the temporal and causal relationships between brain structural alterations in TD patients remain unexplored. A total of 61 TD patients and 61 matched healthy controls (HCs) were included in this study. The gray matter volume (GMV) of the bilateral precuneus (PCUN) was significantly reduced in TD patients. A structural covariance network analysis seeded with the left pallidum (PAL.L), which had the most significant differences, revealed a substantial reduction in covariance with precentral gyrus in TD patients. We performed a causal structural covariance network analysis using the TD duration as a pseudotime series. The PCUN, with the highest out‐degree in the cortex, regulates numerous regions, including the supplementary motor area and the extensive temporal lobe. Machine learning was utilized to construct a model that accurately assesses the surgical prognosis based on the above cortical volume and clinical scale, with the aim of assisting in clinical deep brain stimulation (DBS) treatment. These findings suggested a progressive pattern of GMV changes extending from the PAL.L to the PCUN region and continuing to other brain regions, providing insights into the progression of TD and enhancing DBS treatment strategies.

We uncovered a unique morphological progression pattern in tremor‐dominant Parkinson's disease. Based on this, a machine‐learning model combining cortical volume features with clinical assessments was developed to reliably predict deep brain stimulation (DBS) outcomes.