arXiv Machine Learning By Aixa X. Andrade

Interpretable machine learning predicts Parkinson's disease severity using motion-corrected QSM MRI and multiband multiecho fMRI features

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arXiv:2607. 02553v1 Announce Type: cross Abstract: Introduction: Objective neuroimaging biomarkers may improve Parkinson's disease motor assessment by capturing brain variation not directly observable from clinical examination.

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arXiv Machine Learning
Aug 28

Quantitative mapping from conventional MRI using self-supervised physics-guided deep learning: applications to a large-scale, clinically heterogeneous dataset

This study introduces a self‑supervised, physics‑guided deep‑learning framework that converts standard clinical T1‑, T2‑, and FLAIR MRIs into quantitative T1, T2, and proton‑density maps. Trained on 4,121 scan sessions from four different 3 T scanners over six years, the method produces maps whose white‑ and gray‑matter values fall within literature ranges and shows minimal variation across scanner hardware and acquisition protocols (coefficients of variation ≤ 1.1 %). Voxel‑wise reproducibility is high, with Pearson and concordance correlation coefficients above 0.82 for T1 and T2 and mean relative differences below 6 % for T2.

By Jelmer van Lune, Stefano Mandija, Oscar van der Heide, Matteo Maspero, Martin B. Schilder, Jan Willem Dankbaar, Cornelis A. T. van den Berg, Alessandro Sbrizzi
arXiv Machine Learning
Aug 28

Robust Neural Stimulation Response Modeling Through Meta-Learning and Pretraining

The study introduces a meta-learning and pretraining approach to improve neural stimulation response modeling. By extending temporal basis function models with a MAML-based architecture, the authors demonstrate a significant reduction in catastrophic forecast failures and narrower prediction intervals across 40 optogenetic stimulation sessions in non-human primates. The method also cuts calibration requirements by 50–90%, making closed‑loop stimulation more feasible within clinical time constraints.

By Matthew J Bryan, Daniel C Muir, Felix Schwock, Azadeh Yazdan-Shahmorad, Rajesh P N Rao
arXiv Machine Learning
Sep 14

Machine Learning-Based Classification of Jhana Advanced Concentrative Absorption Meditation Using 7 Tesla Functional Magnetic Resonance Imaging

The study uses 7‑Tesla fMRI data from 20 advanced meditators to test whether regional homogeneity (ReHo) patterns can classify Jhana advanced concentrative absorption meditation (ACAM‑J) states with machine learning. Across 19 binary comparisons, an ensemble of six classifiers achieved an average accuracy of about 66 % and a Cohen’s κ of 0.24, with the highest discrimination between the most distinct states (ACAM‑J1 vs ACAM‑J6). Prefrontal and anterior cingulate regions were the most influential features in the models.

By Puneet Kumar, Winson F. Z. Yang, Alakhsimar Singh, Xiaobai Li, Matthew D. Sacchet