Ruiyang Ge1, Daniel M Blumberger2, Jonathan Downar3, Zafiris J Daskalakis2, Adam A Dipinto1, Joseph C W Tham4, Raymond Lam5, Fidel Vila-Rodriguez6. 1. Non-Invasive Neurostimulation Therapies (NINET) Laboratory, Department of Psychiatry, University of British Columbia, Vancouver, Canada. 2. Temerty Centre for Therapeutic Brain Intervention and Campbell Family Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada. 3. MRI-Guided rTMS Clinic and Krembil Research Institute, University Health Network, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada. 4. BC Neuropsychiatry Program, University of British Columbia, 2255 Wesbrook Mall, Vancouver, BC, Canada. 5. Mood Disorders Centre, University of British Columbia, 2255 Wesbrook Mall, Vancouver, BC, Canada. 6. Non-Invasive Neurostimulation Therapies (NINET) Laboratory, Department of Psychiatry, University of British Columbia, Vancouver, Canada. Electronic address: fidelvil@mail.ubc.ca.
Abstract
BACKGROUND: Treatment resistant depression (TRD) remains a clinical challenge, and finding biomarkers that predict treatment response are a long sought goal to precisely indicate treatments. This pilot study aims to characterize brain dysfunction in TRD patients who underwent rTMS to define neuroimaging biomarkers that discriminate non-responders (NR) from responders (R). METHODS: 20 TRD patients who underwent a course of rTMS to the left DLPFC were categorized into R and NR groups based on a >50% reduction in HRSD scores. Utilizing resting-state fMRI and ICA techniques, this study compared baseline RSNs of R vs. NR as well as TRD vs. healthy volunteer group. Regression analysis was conducted to link regions with clinical improvements. ROC analysis was further conducted to confirm the utility of the identified regions in classifying the patients. RESULTS: Prior to treatment, non-responders displayed hyper-connectivity in ACC/VMPFC, PCC/pC, dACC and insula within RSNs that have been associated with MDD pathology. Regression results showed that regions associated with clinical improvements overlapped largely with regions that showed aberrant connectivity. ACC/VMPFC, dACC and left insula, which are hub regions of DMN and SN, exhibited excellent performance (highest sensitivity=100% and highest specificity=82%) in discriminating the response status of the patients. LIMITATIONS: Relatively small sample size. CONCLUSIONS: Our findings provide insight into fMRI predictive measures of treatment response to rTMS treatment, and demonstrate the potential of RSNs-based biomarkers in predicting response to rTMS treatment. Future studies are needed to validate the application of these measures to inform individual treatment indications.
BACKGROUND: Treatment resistant depression (TRD) remains a clinical challenge, and finding biomarkers that predict treatment response are a long sought goal to precisely indicate treatments. This pilot study aims to characterize brain dysfunction in TRD patients who underwent rTMS to define neuroimaging biomarkers that discriminate non-responders (NR) from responders (R). METHODS: 20 TRD patients who underwent a course of rTMS to the left DLPFC were categorized into R and NR groups based on a >50% reduction in HRSD scores. Utilizing resting-state fMRI and ICA techniques, this study compared baseline RSNs of R vs. NR as well as TRD vs. healthy volunteer group. Regression analysis was conducted to link regions with clinical improvements. ROC analysis was further conducted to confirm the utility of the identified regions in classifying the patients. RESULTS: Prior to treatment, non-responders displayed hyper-connectivity in ACC/VMPFC, PCC/pC, dACC and insula within RSNs that have been associated with MDD pathology. Regression results showed that regions associated with clinical improvements overlapped largely with regions that showed aberrant connectivity. ACC/VMPFC, dACC and left insula, which are hub regions of DMN and SN, exhibited excellent performance (highest sensitivity=100% and highest specificity=82%) in discriminating the response status of the patients. LIMITATIONS: Relatively small sample size. CONCLUSIONS: Our findings provide insight into fMRI predictive measures of treatment response to rTMS treatment, and demonstrate the potential of RSNs-based biomarkers in predicting response to rTMS treatment. Future studies are needed to validate the application of these measures to inform individual treatment indications.
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