Superior temporal gyrus functional connectivity predicts transcranial direct current stimulation response in Schizophrenia: A machine learning study
Transcranial direct current stimulation (tDCS) is a promising adjuvant treatment for persistent auditory verbal hallucinations (AVH) in Schizophrenia (SZ). Nonetheless, there is considerable inter-patient variability in the treatment response of AVH to tDCS in SZ. Machine-learned models have the pot...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-08-01
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| Series: | Frontiers in Psychiatry |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyt.2022.923938/full |
| _version_ | 1811315584253558784 |
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| author | Animesh Kumar Paul Animesh Kumar Paul Anushree Bose Anushree Bose Sunil Vasu Kalmady Sunil Vasu Kalmady Venkataram Shivakumar Venkataram Shivakumar Vanteemar S. Sreeraj Vanteemar S. Sreeraj Rujuta Parlikar Rujuta Parlikar Janardhanan C. Narayanaswamy Janardhanan C. Narayanaswamy Serdar M. Dursun Andrew J. Greenshaw Russell Greiner Russell Greiner Russell Greiner Ganesan Venkatasubramanian Ganesan Venkatasubramanian |
| author_facet | Animesh Kumar Paul Animesh Kumar Paul Anushree Bose Anushree Bose Sunil Vasu Kalmady Sunil Vasu Kalmady Venkataram Shivakumar Venkataram Shivakumar Vanteemar S. Sreeraj Vanteemar S. Sreeraj Rujuta Parlikar Rujuta Parlikar Janardhanan C. Narayanaswamy Janardhanan C. Narayanaswamy Serdar M. Dursun Andrew J. Greenshaw Russell Greiner Russell Greiner Russell Greiner Ganesan Venkatasubramanian Ganesan Venkatasubramanian |
| author_sort | Animesh Kumar Paul |
| collection | DOAJ |
| description | Transcranial direct current stimulation (tDCS) is a promising adjuvant treatment for persistent auditory verbal hallucinations (AVH) in Schizophrenia (SZ). Nonetheless, there is considerable inter-patient variability in the treatment response of AVH to tDCS in SZ. Machine-learned models have the potential to predict clinical response to tDCS in SZ. This study aims to examine the feasibility of identifying SZ patients with persistent AVH (SZ-AVH) who will respond to tDCS based on resting-state functional connectivity (rs-FC). Thirty-four SZ-AVH patients underwent resting-state functional MRI at baseline followed by add-on, twice-daily, 20-min sessions with tDCS (conventional/high-definition) for 5 days. A machine learning model was developed to identify tDCS treatment responders based on the rs-FC pattern, using the left superior temporal gyrus (LSTG) as the seed region. Functional connectivity between LSTG and brain regions involved in auditory and sensorimotor processing emerged as the important predictors of the tDCS treatment response. L1-regularized logistic regression model had an overall accuracy of 72.5% in classifying responders vs. non-responders. This model outperformed the state-of-the-art convolutional neural networks (CNN) model—both without (59.41%) and with pre-training (68.82%). It also outperformed the L1-logistic regression model trained with baseline demographic features and clinical scores of SZ patients. This study reports the first evidence that rs-fMRI-derived brain connectivity pattern can predict the clinical response of persistent AVH to add-on tDCS in SZ patients with 72.5% accuracy. |
| first_indexed | 2024-04-13T11:33:56Z |
| format | Article |
| id | doaj.art-613e01b71d484cbe807d0ecf82ae6bd5 |
| institution | Directory Open Access Journal |
| issn | 1664-0640 |
| language | English |
| last_indexed | 2024-04-13T11:33:56Z |
| publishDate | 2022-08-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Psychiatry |
| spelling | doaj.art-613e01b71d484cbe807d0ecf82ae6bd52022-12-22T02:48:30ZengFrontiers Media S.A.Frontiers in Psychiatry1664-06402022-08-011310.3389/fpsyt.2022.923938923938Superior temporal gyrus functional connectivity predicts transcranial direct current stimulation response in Schizophrenia: A machine learning studyAnimesh Kumar Paul0Animesh Kumar Paul1Anushree Bose2Anushree Bose3Sunil Vasu Kalmady4Sunil Vasu Kalmady5Venkataram Shivakumar6Venkataram Shivakumar7Vanteemar S. Sreeraj8Vanteemar S. Sreeraj9Rujuta Parlikar10Rujuta Parlikar11Janardhanan C. Narayanaswamy12Janardhanan C. Narayanaswamy13Serdar M. Dursun14Andrew J. Greenshaw15Russell Greiner16Russell Greiner17Russell Greiner18Ganesan Venkatasubramanian19Ganesan Venkatasubramanian20Alberta Machine Intelligence Institute, University of Alberta, Edmonton, AB, CanadaDepartment of Computing Science, University of Alberta, Edmonton, AB, CanadaSchizophrenia Clinic, Department of Psychiatry, National Institute of Mental Health and Neuro Sciences, Bengaluru, IndiaTranslational Psychiatry Laboratory, Neurobiology Research Centre, National Institute of Mental Health and Neuro Sciences, Bengaluru, IndiaAlberta Machine Intelligence Institute, University of Alberta, Edmonton, AB, CanadaCanadian VIGOUR Centre, University of Alberta, Edmonton, AB, CanadaSchizophrenia Clinic, Department of Psychiatry, National Institute of Mental Health and Neuro Sciences, Bengaluru, IndiaTranslational Psychiatry Laboratory, Neurobiology Research Centre, National Institute of Mental Health and Neuro Sciences, Bengaluru, IndiaSchizophrenia Clinic, Department of Psychiatry, National Institute of Mental Health and Neuro Sciences, Bengaluru, IndiaTranslational Psychiatry Laboratory, Neurobiology Research Centre, National Institute of Mental Health and Neuro Sciences, Bengaluru, IndiaSchizophrenia Clinic, Department of Psychiatry, National Institute of Mental Health and Neuro Sciences, Bengaluru, IndiaTranslational Psychiatry Laboratory, Neurobiology Research Centre, National Institute of Mental Health and Neuro Sciences, Bengaluru, IndiaSchizophrenia Clinic, Department of Psychiatry, National Institute of Mental Health and Neuro Sciences, Bengaluru, IndiaTranslational Psychiatry Laboratory, Neurobiology Research Centre, National Institute of Mental Health and Neuro Sciences, Bengaluru, IndiaDepartment of Psychiatry, University of Alberta, Edmonton, AB, CanadaDepartment of Psychiatry, University of Alberta, Edmonton, AB, CanadaAlberta Machine Intelligence Institute, University of Alberta, Edmonton, AB, CanadaDepartment of Computing Science, University of Alberta, Edmonton, AB, CanadaDepartment of Psychiatry, University of Alberta, Edmonton, AB, CanadaSchizophrenia Clinic, Department of Psychiatry, National Institute of Mental Health and Neuro Sciences, Bengaluru, IndiaTranslational Psychiatry Laboratory, Neurobiology Research Centre, National Institute of Mental Health and Neuro Sciences, Bengaluru, IndiaTranscranial direct current stimulation (tDCS) is a promising adjuvant treatment for persistent auditory verbal hallucinations (AVH) in Schizophrenia (SZ). Nonetheless, there is considerable inter-patient variability in the treatment response of AVH to tDCS in SZ. Machine-learned models have the potential to predict clinical response to tDCS in SZ. This study aims to examine the feasibility of identifying SZ patients with persistent AVH (SZ-AVH) who will respond to tDCS based on resting-state functional connectivity (rs-FC). Thirty-four SZ-AVH patients underwent resting-state functional MRI at baseline followed by add-on, twice-daily, 20-min sessions with tDCS (conventional/high-definition) for 5 days. A machine learning model was developed to identify tDCS treatment responders based on the rs-FC pattern, using the left superior temporal gyrus (LSTG) as the seed region. Functional connectivity between LSTG and brain regions involved in auditory and sensorimotor processing emerged as the important predictors of the tDCS treatment response. L1-regularized logistic regression model had an overall accuracy of 72.5% in classifying responders vs. non-responders. This model outperformed the state-of-the-art convolutional neural networks (CNN) model—both without (59.41%) and with pre-training (68.82%). It also outperformed the L1-logistic regression model trained with baseline demographic features and clinical scores of SZ patients. This study reports the first evidence that rs-fMRI-derived brain connectivity pattern can predict the clinical response of persistent AVH to add-on tDCS in SZ patients with 72.5% accuracy.https://www.frontiersin.org/articles/10.3389/fpsyt.2022.923938/fulltranscranial direct current stimulation (tDCS)Schizophreniaauditory verbal hallucinationsresting-state functional connectivitymachine learningtreatment response |
| spellingShingle | Animesh Kumar Paul Animesh Kumar Paul Anushree Bose Anushree Bose Sunil Vasu Kalmady Sunil Vasu Kalmady Venkataram Shivakumar Venkataram Shivakumar Vanteemar S. Sreeraj Vanteemar S. Sreeraj Rujuta Parlikar Rujuta Parlikar Janardhanan C. Narayanaswamy Janardhanan C. Narayanaswamy Serdar M. Dursun Andrew J. Greenshaw Russell Greiner Russell Greiner Russell Greiner Ganesan Venkatasubramanian Ganesan Venkatasubramanian Superior temporal gyrus functional connectivity predicts transcranial direct current stimulation response in Schizophrenia: A machine learning study Frontiers in Psychiatry transcranial direct current stimulation (tDCS) Schizophrenia auditory verbal hallucinations resting-state functional connectivity machine learning treatment response |
| title | Superior temporal gyrus functional connectivity predicts transcranial direct current stimulation response in Schizophrenia: A machine learning study |
| title_full | Superior temporal gyrus functional connectivity predicts transcranial direct current stimulation response in Schizophrenia: A machine learning study |
| title_fullStr | Superior temporal gyrus functional connectivity predicts transcranial direct current stimulation response in Schizophrenia: A machine learning study |
| title_full_unstemmed | Superior temporal gyrus functional connectivity predicts transcranial direct current stimulation response in Schizophrenia: A machine learning study |
| title_short | Superior temporal gyrus functional connectivity predicts transcranial direct current stimulation response in Schizophrenia: A machine learning study |
| title_sort | superior temporal gyrus functional connectivity predicts transcranial direct current stimulation response in schizophrenia a machine learning study |
| topic | transcranial direct current stimulation (tDCS) Schizophrenia auditory verbal hallucinations resting-state functional connectivity machine learning treatment response |
| url | https://www.frontiersin.org/articles/10.3389/fpsyt.2022.923938/full |
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