Language

English

Publication Date

3-1-2026

Journal

Journal of Neural Transmission

DOI

10.1007/s00702-025-03020-z

PMID

40944724

PMCID

PMC12999822

PubMedCentral® Posted Date

9-13-2025

PubMedCentral® Full Text Version

Post-print

Abstract

While considered a motor control structure, the cerebellum contributes to non-motor functions, including impulsivity. However, whether it contributes to impulsivity in a domain-specific manner remains unknown. Studies on cerebellar ataxia (CA), a common model for cerebellar dysfunction, typically have small sample sizes, limiting robustness. In a multicenter cross-sectional study, we investigated the cerebellum's role in various forms of impulsivity by comparing large cohorts of CA to age- and education-matched neurotypical healthy (NH) controls. Additionally, to examine the ability to identify individuals with CA using impulsivity features alone, we developed supervised machine learning (ML) models. In experiment 1 (CA = 140, NH = 136), impulsivity was assessed using the BIS-11 questionnaire. In experiment 2 (CA = 110, NH = 107), performance-based impulsivity was assessed using the MCQ-27, evaluating delay discounting in monetary decision-making. Two ML models-Logistic Regression and Random Forest-were utilized to classify disorder status (CA/NH). The CA group showed higher BIS-11 scores (p = 0.001), indicating higher impulsivity, driven by motor (p <  0.001) and attention (p = 0.002) impulsivity. However, the CA group exhibited lower non-planning impulsivity (p = 0.014). In the MCQ-27, the CA group showed lower k-values (p <  0.005), indicating reduced impulsivity in monetary decisions. Both ML models demonstrated strong classification performance (AUC ≥ 0.85) in independent datasets. This study highlights the cerebellum's selective role in impulsivity. We found higher motor and attentional impulsivity in CA alongside lower non-planning and decision-making impulsivity. This suggests a unique impulsivity profile in CA that may indicate a compensatory mechanism for future events. ML models demonstrated high classification performance, suggesting impulsivity is a core non-motor feature of CA.

Keywords

Humans, Impulsive Behavior, Male, Cerebellar Ataxia, Female, Cross-Sectional Studies, Middle Aged, Adult, Supervised Machine Learning, Delay Discounting, Aged, Young Adult, Cerebellum, Cerebellar ataxia, Cerebellum, Impulsivity, BIS-11, Delay discounting, Reward

Published Open-Access

yes

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