Optimizing Antidepressant Efficacy: Multimodal Neuroimaging Biomarkers for Prediction of Treatment Response.
| العنوان: | Optimizing Antidepressant Efficacy: Multimodal Neuroimaging Biomarkers for Prediction of Treatment Response. |
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| المؤلفون: | Tong X; Department of Bioengineering, Lehigh University, Bethlehem, PA, USA., Zhao K; Department of Bioengineering, Lehigh University, Bethlehem, PA, USA., Fonzo GA; Center for Psychedelic Research and Therapy, Department of Psychiatry and Behavioral Sciences, Dell Medical School, The University of Texas at Austin, Austin, TX, USA., Xie H; Center for Neuroscience Research, Children's National Hospital, Washington, DC, USA., Carlisle NB; Department of Psychology, Lehigh University, Bethlehem, PA, USA., Keller CJ; Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA.; Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA.; Sierra-Pacific Mental Illness Research, Education and Clinical Center (MIRECC), Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA., Oathes DJ; Center for Brain Imaging and Stimulation, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.; Center for Neuromodulation in Depression and Stress, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA., Sheline Y; Center for Neuromodulation in Depression and Stress, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA., Nemeroff CB; Center for Psychedelic Research and Therapy, Department of Psychiatry and Behavioral Sciences, Dell Medical School, The University of Texas at Austin, Austin, TX, USA., Williams LM; Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA.; Sierra-Pacific Mental Illness Research, Education and Clinical Center (MIRECC), Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA., Trivedi M; Department of Psychiatry, Center for Depression Research and Clinical Care, The University of Texas Southwestern Medical Center, Dallas, TX, USA., Etkin A; Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA.; Alto Neuroscience, Inc., Los Altos, CA, USA., Zhang Y; Department of Bioengineering, Lehigh University, Bethlehem, PA, USA.; Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, PA, USA. |
| المصدر: | MedRxiv : the preprint server for health sciences [medRxiv] 2024 Apr 12. Date of Electronic Publication: 2024 Apr 12. |
| نوع المنشور: | Preprint |
| اللغة: | English |
| بيانات الدورية: | Country of Publication: United States NLM ID: 101767986 Publication Model: Electronic Cited Medium: Internet NLM ISO Abbreviation: medRxiv Subsets: PubMed not MEDLINE |
| مستخلص: | Major depressive disorder (MDD) is a common and often severe condition that profoundly diminishes quality of life for individuals across ages and demographic groups. Unfortunately, current antidepressant and psychotherapeutic treatments exhibit limited efficacy and unsatisfactory response rates in a substantial number of patients. The development of effective therapies for MDD is hindered by the insufficiently understood heterogeneity within the disorder and its elusive underlying mechanisms. To address these challenges, we present a target-oriented multimodal fusion framework that robustly predicts antidepressant response by integrating structural and functional connectivity data (sertraline: R 2 = 0.31; placebo: R 2 = 0.22). Through the model, we identify multimodal neuroimaging biomarkers of antidepressant response and observe that sertraline and placebo show distinct predictive patterns. We further decompose the overall predictive patterns into constitutive network constellations with generalizable structural-functional co-variation, which exhibit treatment-specific association with personality traits and behavioral/cognitive task performance. Our innovative and interpretable multimodal framework provides novel insights into the intricate neuropsychopharmacology of antidepressant treatment and paves the way for advances in precision medicine and development of more targeted antidepressant therapeutics. |
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| معلومات مُعتمدة: | K23 MH114023 United States MH NIMH NIH HHS; R01 MH125886 United States MH NIMH NIH HHS; R21 AG080425 United States AG NIA NIH HHS; R21 MH130956 United States MH NIMH NIH HHS; R01 MH129694 United States MH NIMH NIH HHS |
| تواريخ الأحداث: | Date Created: 20240422 Latest Revision: 20240903 |
| رمز التحديث: | 20240903 |
| مُعرف محوري في PubMed: | PMC11030479 |
| DOI: | 10.1101/2024.04.11.24305583 |
| PMID: | 38645124 |
| قاعدة البيانات: | MEDLINE |
| DOI: | 10.1101/2024.04.11.24305583 |
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