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AI-Driven Integration of Multimodal Imaging and Clinical Data for Long COVID: Mapping Brain-Behavior Associations and Treatment Outcomes

This special issue belongs to the section “Neurotechnology and Neuroimaging“.

Special Issue Information

Dear Colleagues,

The emergence of Long COVID has fundamentally challenged our healthcare systems, with an estimated 10–30% of COVID-19 survivors experiencing persistent symptoms across multiple organ systems, including significant neurological manifestations such as brain fog, memory deficits, and cognitive dysfunction. This complex syndrome, which has been linked to neuroinflammation and disrupted neural circuitry, demands sophisticated approaches for accurate diagnosis, prognosis, and treatment optimization. While early research relied on single-modality imaging and conventional analyses, the field has rapidly evolved toward more integrated approaches that leverage artificial intelligence.

Recent technological advances have created unprecedented opportunities to transform Long COVID care. The convergence of sophisticated deep learning architectures, high-quality multimodal imaging, and enhanced clinical informatics now enables comprehensive patient assessments, including detailed mapping of neural connectivity patterns and cognitive performance metrics. These developments allow for the simultaneous analysis of multiple imaging modalities (MRI, CT, PET, ultrasound), clinical measurements, biomarkers, longitudinal health records, and neurocognitive testing results.

This Special Issue focuses on innovative approaches to integrating artificial intelligence with multimodal imaging and clinical data in Long COVID research, with particular emphasis on neurocognitive outcomes. We seek submissions that advance automated diagnosis, disease trajectory prediction, treatment response optimization, and patient stratification. Of particular interest are studies that demonstrate practical clinical applications, novel AI architectures for heterogeneous data integration, and investigations into the neurobiological mechanisms underlying persistent symptoms.

By bringing together cutting-edge research in this rapidly evolving field, we aim to accelerate the development of more-effective and personalized approaches to Long COVID care, especially in addressing its neurological and psychiatric manifestations. This Special Issue welcomes original research articles, comprehensive reviews, and perspective pieces that showcase the successful integration of multiple data streams and AI-driven approaches to improve patient outcomes.

Dr. Thorsten Rudroff
Dr. Riku Klén
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Brain Sciences is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence 
  • neuroinflammation 
  • cognitive dysfunction 
  • multimodal imaging 
  • neural connectivity 
  • deep learning 
  • neurocognitive assessment

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Brain Sci. - ISSN 2076-3425