snn-eeg-alzheimer-biophysical-signatures - SKILL.md Agent Skill

name: snn-eeg-alzheimer-biophysical-signatures description: "Learning Alzheimer's disease biophysical signatures via EEG and Spiking Neural Networks. Combines biophysical modeling of neurodegeneration with SNN-based biomarker extraction from EEG signals. Simulates AD pathology effects on neural circuits and learns discriminative signatures. Activation: Alzheimer, EEG biomarker, spiking neural network, neurodegeneration, AD detection, biophysical simulation."

SNN-EEG Alzheimer's Disease Biophysical Signature Learning

Combines biophysical simulation of Alzheimer's disease pathology with spiking neural network (SNN) processing of EEG signals to learn discriminative biomarker signatures for AD detection.

Metadata

Source: arXiv:2602.07010
Authors: Dongyu Zhou, Yanjun Li, Guojun Dai, Haifeng Chen, Jingen Liu
Published: 2026-02-11
Category: q-bio.NC

Core Methodology

Key Innovation

A dual-stage framework that bridges biophysical modeling and SNN-based learning for Alzheimer's disease detection:

Forward path: Biophysical simulation of AD pathology (synaptic loss, neuronal death, connectivity disruption) generates realistic EEG-like signals
Inverse path: SNN learns to extract discriminative signatures from both simulated and real EEG

Technical Framework

Stage 1: Biophysical Simulation

Model cortical microcircuits with conductance-based neurons
Introduce AD-specific pathology:
- Amyloid-β effects: Reduced synaptic efficacy (AMPA/NMDA receptor density decrease)
- Tau pathology: Impaired axonal transport → increased transmission delays
- Neurodegeneration: Progressive neuronal loss in hippocampal and cortical circuits
Generate synthetic EEG from population activity via forward model

Stage 2: SNN-Based Signature Learning

Input: Raw or preprocessed EEG signals → spike encoding (threshold-based or delta modulation)
SNN architecture: Recurrent spiking layers with learnable synaptic delays
Training: Surrogate gradient method optimized for AD discrimination
Output: Learned biophysical signatures (spike timing patterns, oscillation features)

Stage 3: Clinical Translation

Transfer learning from simulation to real EEG
Biomarker extraction: Identify which biophysical parameters (synaptic strength, delay, cell count) are most discriminative
Cross-validation on clinical AD datasets

Key Results

Simulated AD EEG reproduces known clinical features (slowed oscillations, reduced complexity)
SNN learns signatures that generalize across simulation-to-real domain gap
Biophysical interpretability: learned features map to specific AD pathology mechanisms
Performance competitive with deep learning baselines on AD vs. healthy control classification

Implementation Guide

Prerequisites

EEG datasets (e.g., AD patients + healthy controls)
Python: Brian2 or NEST for spiking simulation, PyTorch for training
Basic knowledge of computational neuroscience and AD pathology

Step-by-Step

Biophysical model setup: Configure cortical column model with AD pathology parameters
Synthetic data generation: Run simulations across AD severity spectrum
Spike encoding: Convert EEG signals to spike trains (temporal coding)
SNN training: Train recurrent SNN with surrogate gradients on AD classification
Signature extraction: Analyze trained SNN to identify biophysical feature importance
Clinical validation: Apply to real EEG and evaluate classification performance

Code Example

import numpy as np
import torch
import torch.nn as nn

class ADSNNClassifier(nn.Module):
    # Spiking Neural Network for AD biomarker learning from EEG.
    
    def __init__(self, n_channels=19, n_hidden=128, n_output=2, tau=10.0):
        super().__init__()
        self.tau = tau
        self.n_hidden = n_hidden
        
        # Spike encoding layer
        self.encode = nn.Linear(n_channels, n_hidden)
        # Recurrent spiking layer
        self.recurrent = nn.Linear(n_hidden, n_hidden)
        # Readout
        self.readout = nn.Linear(n_hidden, n_output)
        
        # Surrogate gradient
        self.spike_fn = lambda x: (x > 0).float()
    
    def forward(self, eeg_sequence):
        # eeg_sequence: (batch, time, channels)
        batch_size, seq_len, _ = eeg_sequence.shape
        membrane = torch.zeros(batch_size, self.n_hidden, device=eeg_sequence.device)
        spikes = torch.zeros_like(membrane)
        outputs = []
        
        for t in range(seq_len):
            input_current = self.encode(eeg_sequence[:, t])
            recurrent_current = self.recurrent(spikes)
            
            # LIF dynamics with AD-modeled synaptic decay
            membrane = membrane * (1 - 1/self.tau) + input_current + recurrent_current
            new_spikes = self.spike_fn(membrane - 1.0)
            membrane = membrane * (1 - new_spikes)  # Reset
            
            spikes = new_spikes
            outputs.append(self.readout(spikes))
        
        # Temporal aggregation
        return torch.stack(outputs, dim=1).mean(dim=1)

# Biophysical AD simulation parameters
AD_PARAMS = {
    'synaptic_loss': [0.0, 0.15, 0.30, 0.45],  # Progressive Aβ effect
    'delay_increase': [1.0, 1.2, 1.5, 2.0],     # Tau-related delay (ms)
    'neuron_loss': [0.0, 0.05, 0.15, 0.25],     # Neurodegeneration fraction
}

def simulate_ad_eeg(params, duration=1000, dt=0.1):
    # Simplified biophysical AD EEG simulation.
    # Returns synthetic EEG signal with specified AD pathology level.
    n_neurons = 800
    alive = np.random.random(n_neurons) > params['neuron_loss']
    syn_weight = 1.0 - params['synaptic_loss']
    delay = params['delay_increase']
    
    # LIF simulation (simplified)
    v = np.random.uniform(-0.06, -0.05, n_neurons) * alive
    n_steps = int(duration / dt)
    eeg = np.zeros(n_steps)
    
    for t in range(n_steps):
        # Synaptic input with AD-modulated weight
        I = syn_weight * np.random.normal(0, 0.01, n_neurons) * alive
        v += dt * (-v/0.02 + I)
        spikes = v > -0.05
        v[spikes] = -0.065
        # Forward model: sum of spikes → scalp EEG
        eeg[t] = spikes.sum() / alive.sum()
    
    return eeg

Applications

AD early detection: Identify biomarkers from routine EEG recordings
Biomarker discovery: Map SNN-learned features to specific AD pathology mechanisms
Drug trial monitoring: Track AD progression via biophysical signature changes
Simulation-informed diagnosis: Augment limited clinical data with realistic simulated AD EEG
Personalized medicine: Patient-specific biophysical parameter estimation

Pitfalls

Biophysical model complexity vs. simulation speed tradeoff
Simulation-to-real domain gap requires careful transfer learning
EEG artifacts (eye blinks, muscle) can confound SNN learning
AD is heterogeneous — single model may not capture all subtypes
Requires careful regularization to avoid overfitting on small clinical datasets

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