On Tuesday, 7/6/2026 at 13.00 Vasileios Klearchos Chatzitolios, a graduate
student of the program “Data Science and Information Technologies,
Biomedical Data Science – Bioinformatics” will present their MSc thesis
titled:

Fine-Tuning a Pretrained Actigraphy Transformer for Mild Cognitive
Impairment Classification

Abstract

Mild Cognitive Impairment (MCI) represents a prodromal stage of dementia,
impacting approximately 15–20% of adults over the age of 60. Currently,
scalable and low-burden assessment tools remain scarce. Wrist actigraphy
emerges as a promising modality, offering continuous and unobtrusive
measurement of rest–activity rhythms in real-world environments. Although
disrupted circadian rhythms have been linked to cognitive decline,
classical machine learning approaches that use human-selected actigraphy
features have generally demonstrated only moderate efficacy in
distinguishing cognitively normal (NC) individuals from those with MCI.

This thesis investigates whether a pretrained actigraphy foundation model
can be transferred to MCI classification without the need for human
feature engineering, and whether its learned representations can be
interpreted in terms of circadian patterns relevant to cognitive decline.
The study involves fine-tuning the Pretrained Actigraphy Transformer
(PAT-Large), which was pretrained via masked patch reconstruction on
29,307 NHANES participants, for binary classification between normal
control (NC) and MCI. It employs six-day wrist actigraphy recordings from
150 clinically characterized participants in the ALBION Greek cohort (98
NC, 52 MCI).

Through a systematic configuration search, two Pareto-optimal fine-tuning
strategies were identified: a fully frozen encoder configuration
(Head-Only) and a ULMFiT-style two-phase partial adaptation strategy.
These approaches were evaluated against deep learning baselines trained
from scratch and classical machine learning models utilizing feature
engineering, all using identical stratified cross-validation splits.

Among models trained exclusively on actigraphy data, PAT fine-tuned
solutions demonstrated leading overall performance. PAT ULMFiT achieved
the highest Matthews Correlation Coefficient (MCC ≈ 0.49), F1 score, and
balanced accuracy. It outperformed both raw-sequence deep learning
baselines and feature-engineered machine learning pipelines on these
summary metrics. Performance was also competitive with age-augmented
machine learning models, despite PAT-based solutions not receiving
participant age, suggesting that pretrained actigraphy representations
capture discriminative temporal structure beyond what circadian summaries
alone can.

A multi-method interpretability framework that integrates Integrated
Gradients, learned attention pooling, and occlusion analysis has
identified a circadian sensitivity pattern characterized by nocturnal
activity and daytime hypoactivity. The model attributes MCI-supporting
evidence to heightened nocturnal activity and persistent daytime
hypoactivity, with the attribution polarity shifting near the sleep–wake
boundary. Furthermore, occlusion analysis independently underscores the
importance of nocturnal periods and complements the observed phase lag
between peak model sensitivity and the peak of MCI-related nocturnal
fragmentation.
As an initial proof of concept in a single cohort, these findings indicate
that pretrained actigraphy representations can be transferred to the
classification of clinician-diagnosed MCI and produce interpretable
circadian signatures consistent with established rest–activity
disturbances associated with cognitive decline.

Examination Committee:

Prof. Em. Elias S. Manolakos (advisor), Dept. of Informatics and
Telecommunications, NKUA

Prof. Nikolaos Scarmeas, Dept. of Neurology, NKUA Medical School

Dr. Kostas Vekrellis, Research Director, Biomedical Research Foundation of
the Academy of Athens

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Topic: Vassilis Chatzitolios Msc Thesis presentation
Time: Jul 7, 2026 01:00 PM Athens

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