PRIME AGING COGNITION
Mne01-002	Detection of Novel Acoustic Biomarkers for Parkinsons Disease through a Machine Learning-Based Composite Spectrogram Analysis
	Quote:	Conclusion The proposed explainable composite spectrogram approach dually enabled high classification performance and identification of a vowel biomarker. Concordance between ML and acoustic analyses highlights the translational potential of explainable ML in PD speech assessment and its ability to reveal underlying pathophysiological insights.
	Abstract:	https://www.medrxiv.org/content/10.64898/2026.01.01.25343300v2
	Full Text:	https://www.medrxiv.org/content/10.64898/2026.01.01.25343300v2.full-text
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	Search Authors:	Kotaro Tsutsumi Peter D. Chang Sanaz A. Isfahani,
PRIME AGING COGNITION
Mne01-022	Longitudinal progression of digital arm swing measures during free-living gait in early Parkinsons disease
	Quote:	In medicated participants, arm swing increased on the most affected side, which was associated with higher dopaminergic doses and dyskinesia. Overall, free-living arm swing measures are promising progression biomarkers in early, untreated PD.
	Abstract:	https://www.medrxiv.org/content/10.64898/2026.01.06.26343500v1
	Full Text:	https://www.medrxiv.org/content/10.64898/2026.01.06.26343500v1.full-text
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	Search Authors:	Erik Post Twan van Laarhoven Nienke A. Timmermans Yordan P. Raykov Max A. Little Jorik Nonnekes King Chung Ho Tom M. Heskes Bastiaan R. Bloem Luc J.W. Evers,
AGING COGNITION
Mne01-026	ATN Classification and Machine-Learned Plasma Biomarker Phenotypes Reveal Distinct Alzheimers Pathology in a Population-Based Cohort
	Quote:	Sensitivity analyses confirmed that k4 provides superior biological resolution over k3, and that rare Cluster 4 represents a stable non-AD phenotype. Both approaches predict cognitive decline with modest effect sizes R21.9-2.4 consistent with population-based studies. Integrating theory-driven and data-driven frameworks promises a more comprehensive characterization of AD-related pathology in population research.
	Abstract:	https://www.medrxiv.org/content/10.64898/2026.01.02.26343331v1
	Full Text:	https://www.medrxiv.org/content/10.64898/2026.01.02.26343331v1.full-text
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	Search Authors:	Emmanuel Fle Chea,
AGING COGNITION
Mne01-028	Machine Learning Based Digital Assessment of Mild Cognitive Impairment Using Hand Movements during the Trail Making Test
	Quote:	These results indicate that fine-grained hand-movement features from the cTMT provide useful information for classifying mild cognitive impairment and for predicting multiple neuropsychological scores.
	Abstract:	https://www.medrxiv.org/content/10.64898/2026.01.05.26343443v1
	Full Text:	https://www.medrxiv.org/content/10.64898/2026.01.05.26343443v1.full-text
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	Search Authors:	Gustavo E. Juantorena Gianluca Capelo Betsabe D. Leon Vallejos Agustin Ibez Agustin Petroni Waleska Berrios Mara Cecilia Fernndez Juan E. Kamienkowski,

Updated weekly. 01/09/2026-11:01:09 PM
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Mne01-002

Detection of Novel Acoustic Biomarkers for Parkinsons Disease through a Machine Learning-Based Composite Spectrogram Analysis

Mne01-022

Longitudinal progression of digital arm swing measures during free-living gait in early Parkinsons disease

Mne01-026

ATN Classification and Machine-Learned Plasma Biomarker Phenotypes Reveal Distinct Alzheimers Pathology in a Population-Based Cohort

Mne01-028

Machine Learning Based Digital Assessment of Mild Cognitive Impairment Using Hand Movements during the Trail Making Test