ArticleScientific reports2023
A low dimensional embedding of brain dynamics enhances diagnostic accuracy and behavioral prediction in stroke.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed, 17 citations in OpenAlex.
- Mapping the psychosis spectrum - imaging neurosubtypes from multiscale functional network connectivity.Molecular psychiatry · 2026Article
- TVB C++: A Fast and Flexible Back-End for The Virtual Brain.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Neuroprosthetic closed-loop strategy for sustained blood pressure reduction via simultaneous stimulation and recording from the spinal cord.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2025Article
- Magnetoencephalography Dimensionality Reduction Informed by Dynamic Brain States.The European journal of neuroscience · 2025Article
- A mechanism for the emergence of low-dimensional structures in brain dynamics.NPJ systems biology and applications · 2025Article
- Article
- Connectomic stroke lesion measures provide no benefit over basic spatial lesion features in the prognosis of global stroke outcome measures.Brain communications · 2025Article
- Changes in cortical manifold structure following stroke and its relation to behavioral recovery in the male macaque.Nature communications · 2024Article
- Analyzing asymmetry in brain hierarchies with a linear state-space model of resting-state fMRI data.Network neuroscience (Cambridge, Mass.) · 2024Article
- Functional hierarchies in brain dynamics characterized by signal reversibility in ferret cortex.PLoS computational biology · 2024Article
- Generative whole-brain dynamics models from healthy subjects predict functional alterations in stroke at the level of individual patients.Brain communications · 2024Article
- Turbulent dynamics and whole-brain modeling: toward new clinical applications for traumatic brain injury.Frontiers in neuroinformatics · 2024Review
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Authors and funding
8 authors at 5 institutions in 6 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Large-scale brain networks reveal structural connections as well as functional synchronization between distinct regions of the brain. The latter, referred to as functional connectivity (FC), can be derived from neuroimaging techniques such as functional magnetic resonance imaging (fMRI). FC studies have shown that brain networks are severely disrupted by stroke. However, since FC data are usually large and high-dimensional, extracting clinically useful information from this vast amount of data is still a great challenge, and our understanding of the functional consequences of stroke remains limited. Here, we propose a dimensionality reduction approach to simplify the analysis of this complex neural data. By using autoencoders, we find a low-dimensional representation encoding the fMRI data which preserves the typical FC anomalies known to be present in stroke patients. By employing the latent representations emerging from the autoencoders, we enhanced patients' diagnostics and severity classification. Furthermore, we showed how low-dimensional representation increased the accuracy of recovery prediction.
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