{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T05:03:41Z","timestamp":1789621421950,"version":"build-2803163510"},"reference-count":34,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T00:00:00Z","timestamp":1789603200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurol."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>\n                      Evaluating novel therapies for multiple sclerosis requires robust, non-invasive biomarkers. In the experimental autoimmune encephalomyelitis (EAE) model, standard disease evaluations rely heavily on subjective clinical motor scoring and terminal histology, which frequently suffer from a mathematical dilution effect. To address this, we developed an automated, spatially targeted multiparametric magnetic resonance imaging (MRI) pipeline to extract highly specific\n                      <jats:italic>in vivo<\/jats:italic>\n                      biophysical markers and benchmarked its performance against a potent positive control immunomodulator, fingolimod.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      EAE was induced in mice using a myelin oligodendrocyte glycoprotein peptide, followed by treatment with either vehicle or fingolimod. At 28\u202fdays post-induction,\n                      <jats:italic>in vivo<\/jats:italic>\n                      T2-weighted and diffusion tensor imaging maps of the lumbar spinal cord were acquired and segmented into specific gray and white matter columns. The extracted MRI parameters were cross-correlated with clinical scores, spatial histopathology, and flow cytometry.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Vehicle-treated mice exhibited severe elevations in gray matter T2 relaxation times and precipitous drops in white matter fractional anisotropy, structurally mapping to localized edema, demyelination, and dense cellular infiltrates. Fingolimod treatment successfully rescued these structural deficits, alongside clinical motor deficits and T-cell infiltration. Localized metrics, specifically ventral white matter fractional anisotropy and a composite fractional anisotropy to T2 ratio, demonstrated treatment effect sizes that exceeded those of the gross clinical paralysis score and corresponding lumbar histopathology. Multivariate predictive modeling showed that the combined MRI panel predicted up to 87.2% of the variance in regional spinal cord pathology and 80.1% of the variance in clinical paralysis. Furthermore, unsupervised principal component analysis extracted an MRI disease score that strongly correlated with clinical disability, global histopathology, and total central nervous system T-cells.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Spatially targeted, multiparametric MRI serves as a highly sensitive, objective surrogate biomarker signature. By overcoming the dilution effect, this non-invasive approach provides an objective, continuous metric that complements traditional tissue assays, enhancing the evaluation of multiple sclerosis therapeutics while advancing the reduction of animal use.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fneur.2026.1850801","type":"journal-article","created":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T04:29:24Z","timestamp":1789619364000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Cross-modality network phenotyping: a multivariate MRI biomarker signature predicts spatial pathology and clinical severity in EAE"],"prefix":"10.3389","volume":"17","author":[{"given":"Rohan S.","family":"Virgincar","sequence":"first","affiliation":[{"name":"Department of Translational Imaging, Genentech","place":["South San Francisco, CA, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Man Kin","family":"Choy","sequence":"additional","affiliation":[{"name":"Department of Translational Imaging, Genentech","place":["South San Francisco, CA, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Surinder","family":"Jeet","sequence":"additional","affiliation":[{"name":"Department of Translational Immunology, Genentech","place":["South San Francisco, CA, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hunter","family":"Martinez","sequence":"additional","affiliation":[{"name":"Department of Immunology, Genentech","place":["South San Francisco, CA, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luca","family":"Ghita","sequence":"additional","affiliation":[{"name":"Department of Immunology, Genentech","place":["South San Francisco, CA, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ronal","family":"Peralta","sequence":"additional","affiliation":[{"name":"Department of Immunology, Genentech","place":["South San Francisco, CA, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hannah","family":"Bender","sequence":"additional","affiliation":[{"name":"Department of Research Pathology, Genentech","place":["South San Francisco, CA, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joshua D.","family":"Webster","sequence":"additional","affiliation":[{"name":"Department of Research Pathology, Genentech","place":["South San Francisco, CA, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kai","family":"Barck","sequence":"additional","affiliation":[{"name":"Department of Translational Imaging, Genentech","place":["South San Francisco, CA, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hye-Jung","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Immunology, Genentech","place":["South San Francisco, CA, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Subba","family":"Chintalacharuvu","sequence":"additional","affiliation":[{"name":"Department of Translational Immunology, Genentech","place":["South San Francisco, CA, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luke","family":"Xie","sequence":"additional","affiliation":[{"name":"Department of Translational Imaging, Genentech","place":["South San Francisco, CA, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,17]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"399","DOI":"10.1038\/s41582-021-00498-5","article-title":"B cells in multiple sclerosis - 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