Cross-cohort transfer of clinical Alzheimer's EEG signatures to cognitively healthy APOE-ε4 and PICALM rs3851179 risk carriers using differentiable logic gate networks

Abstract
Neural alterations associated with Alzheimer's disease (AD) may emerge years before clinical diagnosis, and genetic risk factors such as APOE-ε4 and PICALM rs3851179 may identify individuals vulnerable to these shifts. We tested whether electroencephalography (EEG) signatures learned from clinically diagnosed AD patients transfer to cognitively healthy middle-aged adults carrying these variants. Differentiable logic gate networks (Diff-Logic) and neural baselines were trained on log-relative power spectral density features from a clinical cohort of AD patients and cognitively normal (CN) controls. These models were then applied without retraining to an independent genetic-risk cohort of neutral-risk non-carriers (N), APOE-ε4-only carriers (A+P-), and dual APOE-ε4/PICALM G-allele carriers (A+P+), yielding a continuous AD-like EEG signature score per participant. Diff-Logic matched neural baselines in clinical classification (balanced accuracy 0.809±0.028; AUROC 0.845±0.023) while enabling edge inference with a 7.4× model-size reduction and 0.22 ms model-only latency on a 7 W single-core CPU. In the genetic-risk cohort, scores were highest in A+P+ participants (0.393±0.167) relative to N (0.296±0.154) and A+P- (0.285±0.143; subject-level mean ± SD). This elevation was nominally significant before correction (A+P+ vs. A+P-, punc=0.036) but did not survive Holm correction (pHolm=0.109), and the cohort carried no AD pathology or conversion ground truth; the association was therefore exploratory. Integrated gradients attribution of the trained model showed genetic-risk relevance in delta, theta, and alpha, partially overlapping clinical AD topography. These findings support the feasibility of zero-shot EEG transfer in genetically at-risk individuals and highlight the need for replication in larger cohorts. Code is publicly available at https://github.com/Shyamal-Dharia/ad-difflogic-transfer.
- Deep learning
- Resources: datasets, atlases and tools
- Alzheimer's disease
- AD-like EEG signature score
- EEG log-relative power spectral density
- Humans
The paper
The University of Winnipeg
Computers in Biology and Medicine, 15 Oct 2026



