The Untapped Potential of Dimension Reduction in Neuroimaging: Artificial Intelligence-Driven Multimodal Analysis of Long COVID Fatigue

Digital Biomarkers and AI for Remote Monitoring of Fatigue Progression in Neurological Disorders: Bridging Mechanisms to Clinical Applications

Digital biomarkers for fatigue monitoring in neurological disorders represent an innovative approach to bridge the gap between mechanistic understanding and clinical application. This perspective paper examines how smartphone-derived measures, analyzed through artificial intelligence methods, can transform fatigue assessment from subjective, episodic reporting to continuous, objective monitoring. The proposed framework for smartphone-based digital phenotyping captures passive data (movement patterns, device interactions, and sleep metrics) and active assessments (ecological momentary assessments, cognitive tests, and voice analysis). These digital biomarkers can be validated through a multimodal approach connecting them to neuroimaging markers, clinical assessments, performance measures, and patient-reported experiences. Building on the previous research on frontal-striatal metabolism in multiple sclerosis and Long-COVID-19 patients, digital biomarkers could enable early warning systems for fatigue episodes, objective treatment response monitoring, and personalized fatigue management strategies. Implementation considerations include privacy protection, equity concerns, and regulatory pathways. By integrating smartphone-derived digital biomarkers with AI analysis approaches, the future envisions fatigue in neurological disorders no longer as an invisible, subjective experience but rather as a quantifiable, treatable phenomenon with established neural correlates and effective interventions. This transformative approach has significant potential to enhance both clinical care and the research for millions affected by disabling fatigue symptoms.

Convergent Mechanisms in Virus-Induced Cancers: A Perspective on Classical Viruses, SARS-CoV-2, and AI-Driven Solutions

This perspective examines the potential oncogenic mechanisms of SARS-CoV-2 through comparative analysis with established cancer-causing viruses, integrating classical virological approaches with artificial intelligence (AI)-driven analysis. The paper explores four key themes: shared oncogenic mechanisms between classical viruses and SARS-CoV-2 (including cell cycle dysregulation, inflammatory signaling, immune evasion, and metabolic reprogramming); the application of AI in understanding viral oncogenesis; the integration of neuroimaging evidence; and future research directions. The author presents novel hypotheses regarding SARS-CoV-2's potential oncogenic mechanisms, supported by recent PET/FDG imaging studies showing persistent metabolic alterations. The manuscript emphasizes the transformative potential of combining traditional virological methods with advanced AI technologies for better understanding and preventing virus-induced cancers, while highlighting the importance of long-term monitoring of COVID-19 survivors for potential oncogenic developments.

Integration of multimodal imaging data with machine learning for improved diagnosis and prognosis in neuroimaging

Introduction

Combining many types of imaging data-especially structural MRI (sMRI) and functional MRI (fMRI)-may greatly assist in the diagnosis and treatment of brain disorders like Alzheimer's. Current approaches are less helpful for forecasting, however, as they do not always blend spatial and temporal patterns from different sources properly. This work presents a novel mixed deep learning (DL) method combining data from many sources using CNN, GRU, and attention techniques. This work introduces a novel hybrid deep learning method combining CNN, GRU, and a Dynamic Cross-Modality Attention Module to help more efficiently blend spatial and temporal brain data. Through working around issues with current multimodal fusion techniques, our approach increases the accuracy and readability of diagnoses.

Methods

Utilizing CNNs and models of temporal dynamics from fMRI connection measures utilizing GRUs, the proposed approach extracts spatial characteristics from sMRI. Strong multimodal integration is made possible by including an attention mechanism to give diagnostically important features top priority. Training and evaluation of the model took place using the Human Connectome Project (HCP) dataset including behavioral data, fMRI, and sMRI. Measures include accuracy, recall, precision and F1-score used to evaluate performance.

Results

It was correct 96.79% of the time using the combined structure. Regarding the identification of brain disorders, the proposed model was more successful than existing ones.

Discussion

These findings indicate that the hybrid strategy makes sense for using complimentary information from several kinds of photos. Attention to detail helped one choose which aspects to concentrate on, thereby enhancing the readability and diagnostic accuracy.

Conclusion

The proposed method offers a fresh benchmark for multimodal neuroimaging analysis and has great potential for use in real-world brain assessment and prediction. Researchers will investigate future applications of this technique to new picture kinds and clinical data.