Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 1910.05378

Classification of Resting-State fMRI using Evolutionary Algorithms: Towards a Brain Imaging Biomarker for Parkinson's Disease

Amir Dehsarvi·Stephen L. Smith
View original source ↗Read source document ↗Export metadata ↓

Abstract

Accurate early diagnosis and monitoring of neurodegenerative conditions is essential for effective disease management and delivery of medication and treatment. This research develops automatic methods for detecting brain imaging preclinical biomarkers for Parkinson's disease (PD) by considering the novel application of evolutionary algorithms. A fundamental novel element of this work is the use of evolutionary algorithms to both map and predict the functional connectivity in patients using resting state functional MRI data taken from the PPMI to identify PD progression biomarkers. Specifically, Cartesian Genetic Programming was used to classify DCM data as well as time-series data. The findings were validated using two other commonly used classification methods (Artificial Neural Networks and Support Vector Machines) and by employing k-fold cross-validation. Across DCM and time-series analyses, findings revealed maximum accuracies of 75.21% for early stage (prodromal) PD patients versus healthy controls, 85.87% for PD patients versus prodromal PD patients, and 92.09% for PD patients versus healthy controls. Prodromal PD patients were classified from healthy controls with high accuracy - this is notable and represents the key finding of this research since current methods of diagnosing prodromal PD have both low reliability and low accuracy. Furthermore, Cartesian Genetic Programming provided comparable performance accuracy relative to ANN and SVM. Evolutionary algorithms enable us to decode the classifier in terms of understanding the data inputs that are used, more easily than in ANN and SVM. Hence, these findings underscore the relevance of both DCM analyses for classification and CGP as a novel classification tool for brain imaging data with medical implications for disease diagnosis, particularly in early and asymptomatic stages.

Explore related subjects

cs.NEq-bio.NC

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Amir Dehsarvi, Stephen L. Smith. 2019-10-11. Classification of Resting-State fMRI using Evolutionary Algorithms: Towards a Brain Imaging Biomarker for Parkinson's Disease. https://arxiv.org/abs/1910.05378

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

A Memristive Synapse for Online STDP Learning and Inference in SNNs

This work presents a fully analog memristive synaptic circuit for online spike-timing-dependent plasticity (STDP) learning in spiking neural networks (SNNs). The proposed synapse integrates a local STDP circuit generating gradual timing-dependent conductance updates directly from pre- and post-synaptic spikes. Learning occurs during normal network operation without requiring external digital control or explicit STDP waveform synthesis. Post-layout simulations of the memristive synapse implemented in a 130 nm CMOS technology show spike-timing-dependent conductance adaptation during SNN operation. A 2x2 SNN simulation further illustrates online neuron specialization through unsupervised learning.

cs.NE↗
arXiv2026

Complete Suffix Prediction for Recommendation via Latent Retrieval over Process Graphs

Complete suffix prediction is challenging in sequential decision settings, where the same prefix can remain compatible with several plausible suffixes. We propose a graphbased metric-learning framework that reformulates complete suffix prediction as latent retrieval over process graphs. Prefixes and suffixes are represented as directed attributed graphs and encoded by edge-conditioned graph neural networks, allowing event-level activities and transition-level durations to be modelled jointly. Prefix representations are projected into the latent suffix space through a predictor trained with a joint reconstruction and contrastive objective strengthened using process-aware hard negatives. To stabilise the learned retrieval geometry, spectral normalisation, and retrieval robustness, spectral normalisation is applied to enforce a Lipschitz constraint on both encoders and predictor. Experiments on two real-life process datasets demonstrate that the proposed framework achieves the best overall results across nearly all evaluated criteria. It improves semantic suffix accuracy measured by normalized Damerau-Levenshtein distance, yields strong retrieval quality through Recall@1, Recall@5, and MRR@5, and maintains temporal plausibility according to Mean Absolute Error. These results show that graph-based latent retrieval is an effective alternative to sequential suffix prediction for recommendation-oriented process monitoring under structural and KPI-related constraints.

cs.NE↗
arXiv2026

Event-Native Symbolic-Temporal Spike Encoding Framework for Heterogeneous Cyber Streams

Spiking neural networks (SNNs) have shown promise for sparse, event-driven computation through stateful processing that is naturally compatible with low-power edge hardware. These properties align with cyber monitoring, where data arrives asynchronously, and malicious behavior often emerges through temporal patterns across event sequences. However, cyber streams are not composed solely of continuous numeric signals: their informative structure is also carried by categorical identifiers, irregular timing, and local behavioral context. Traditional rate- and population-based spike encodings are not naturally suited to these heterogeneous semantics, while conventional intrusion detection system (IDS) pipelines typically resolve the mismatch by converting raw events into flows, fixed aggregation windows, or dense tensors. Although useful for conventional classifiers, these transformations introduce buffering latency, obscure native temporal structure, and weaken the computational advantages of event-driven neuromorphic processing. We introduce an event-native symbolic-temporal spike encoding framework that maps heterogeneous cyber events directly into sparse, spike-compatible inputs. By assigning encoding roles to semantic identity, local frequency context, and inter-event timing, the framework preserves categorical semantics and temporal dynamics. We validate the approach on packet-level Network IDS and extend it to message-level CAN IDS, using both domains to evaluate whether the encoding exposes usable structure for recurrent SNNs operating directly on native event streams. Under edge-oriented, $μ$Caspian-aligned hardware constraints, compact recurrent SNNs achieve strong anomaly detection performance, with an operational hybrid metric ($J_{hybrid}$) of 0.987 on Network IDS and 0.980 on CAN IDS.

cs.NE↗