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Chiara Camastra

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YNICL Journal 2026 Journal Article

Subregional thalamic atrophy in Progressive Supranuclear Palsy: A machine learning study

  • Camilla Calomino
  • Maria Giovanna Bianco
  • Chiara Camastra
  • Jolanda Buonocore
  • Pier Paolo Arcuri
  • Aldo Quattrone
  • Andrea Quattrone

BACKGROUND: Several MRI studies have documented thalamic atrophy in Progressive Supranuclear Palsy (PSP), but investigations to date have considered the whole thalami, without evaluating their subcomponents. This study aimed to assess atrophy of thalamic substructures in PSP and investigate their potential to support PSP differential diagnosis. METHODS: A total of 398 subjects were enrolled in the study, including 164 PSP patients, 180 Parkinson's disease (PD) patients and 64 healthy controls (HC), from two cohorts. An automatic probabilistic segmentation of thalamic nuclei was employed on T1-weighted MRI images using FreeSurfer 7.4 to investigate thalamic subregional atrophy. Subsequently, machine learning analysis (XGBoost) was employed to differentiate 97 PSP from 98 PD patients, and the results were validated in an independent international cohort (67 PSP, 82 PD patients). RESULTS: PSP patients showed widespread thalamic atrophy, most prominent in the lateral, paraventricular, and pulvinar nuclei in comparison with HC. Conversely, no significant differences were observed between PD patients and HC. The machine learning model based on the thalamic nuclei volumes achieved excellent performance in distinguishing PSP from PD, and the results were validated in the independent international cohort (AUC: 0.96 in both cohorts), outperforming the volume of the whole thalamus (De Long test, p < 0.05). CONCLUSION: This study investigated subregional thalamic atrophy in PSP patients and demonstrated that a machine learning based on thalamic nuclei volumes was able to accurately distinguish PSP from PD in two independent cohorts, highlighting the potential of subregional thalamic atrophy as diagnostic biomarker for PSP.

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YNICL Journal 2024 Journal Article

Corpus callosum damage in PSP and unsteady PD patients: A multimodal MRI study

  • Maria Eugenia Caligiuri
  • Andrea Quattrone
  • Maria Giovanna Bianco
  • Valerio Riccardo Aquila
  • Maria Celeste Bonacci
  • Camilla Calomino
  • Chiara Camastra
  • Jolanda Buonocore

INTRODUCTION: Postural instability (PI) is a common disabling symptom in Parkinson's disease (PD) patients, but the brain alterations underlying this sign are not fully understood yet. This study aimed to investigate the association between PI and callosal damage in PD and progressive supranuclear palsy (PSP) patients, using multimodal MR imaging. METHODS: One-hundred and two PD patients stratified according to the presence/absence of PI (PD-steady N=58; PD-unsteady N=44), 69 PSP patients, and 38 healthy controls (HC) underwent structural and diffusion 3T brain MRI. Thickness, fractional anisotropy (FA) and mean diffusivity (MD) were calculated over 50 equidistant points covering the whole midsagittal profile of the corpus callosum (CC) and compared among groups. Associations between imaging metrics and postural instability score were investigated using linear regression. RESULTS: Both PSP and PD-unsteady patient groups showed CC involvement in comparison with HC, while no difference was found between PD-steady patients and controls. The CC damage was more severe and widespread in PSP than in PD patients. The CC genu was the regions most damaged in PD-unsteady patients compared with PD-steady patients, showing significant microstructural alterations of MD and FA metrics. Linear regression analysis pointed at the MD in the CC genu as the main contributor to PI among the considered MRI metrics. CONCLUSION: This study identified callosal microstructural alterations associated with PI in unsteady PD and PSP patients, which provide new insights on PI pathophysiology and might serve as imaging biomarkers for assessing postural instability progression and treatment response.

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