Machine learning based identification of structural brain alterations underlying suicide risk in adolescents
Abstract Suicide is the third leading cause of death for individuals between 15 and 19 years of age. The high suicide mortality rate and limited prior success in identifying neuroimaging biomarkers indicate that it is crucial to improve the accuracy of clinical neural signatures underlying suicide r...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2023-02-01
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| Series: | Discover Mental Health |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44192-023-00033-6 |
| _version_ | 1797865490695061504 |
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| author | Sahil Bajaj Karina S. Blair Matthew Dobbertin Kaustubh R. Patil Patrick M. Tyler Jay L. Ringle Johannah Bashford-Largo Avantika Mathur Jaimie Elowsky Ahria Dominguez Lianne Schmaal R. James R. Blair |
| author_facet | Sahil Bajaj Karina S. Blair Matthew Dobbertin Kaustubh R. Patil Patrick M. Tyler Jay L. Ringle Johannah Bashford-Largo Avantika Mathur Jaimie Elowsky Ahria Dominguez Lianne Schmaal R. James R. Blair |
| author_sort | Sahil Bajaj |
| collection | DOAJ |
| description | Abstract Suicide is the third leading cause of death for individuals between 15 and 19 years of age. The high suicide mortality rate and limited prior success in identifying neuroimaging biomarkers indicate that it is crucial to improve the accuracy of clinical neural signatures underlying suicide risk. The current study implements machine-learning (ML) algorithms to examine structural brain alterations in adolescents that can discriminate individuals with suicide risk from typically developing (TD) adolescents at the individual level. Structural MRI data were collected from 79 adolescents who demonstrated clinical levels of suicide risk and 79 demographically matched TD adolescents. Region-specific cortical/subcortical volume (CV/SCV) was evaluated following whole-brain parcellation into 1000 cortical and 12 subcortical regions. CV/SCV parameters were used as inputs for feature selection and three ML algorithms (i.e., support vector machine [SVM], K-nearest neighbors, and ensemble) to classify adolescents at suicide risk from TD adolescents. The highest classification accuracy of 74.79% (with sensitivity = 75.90%, specificity = 74.07%, and area under the receiver operating characteristic curve = 87.18%) was obtained for CV/SCV data using the SVM classifier. Identified bilateral regions that contributed to the classification mainly included reduced CV within the frontal and temporal cortices but increased volume within the cuneus/precuneus for adolescents at suicide risk relative to TD adolescents. The current data demonstrate an unbiased region-specific ML framework to effectively assess the structural biomarkers of suicide risk. Future studies with larger sample sizes and the inclusion of clinical controls and independent validation data sets are needed to confirm our findings. |
| first_indexed | 2024-04-09T23:08:39Z |
| format | Article |
| id | doaj.art-aef5cfb9ce0c458a9729a69addef4d6b |
| institution | Directory Open Access Journal |
| issn | 2731-4383 |
| language | English |
| last_indexed | 2024-04-09T23:08:39Z |
| publishDate | 2023-02-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Mental Health |
| spelling | doaj.art-aef5cfb9ce0c458a9729a69addef4d6b2023-03-22T10:32:44ZengSpringerDiscover Mental Health2731-43832023-02-013111310.1007/s44192-023-00033-6Machine learning based identification of structural brain alterations underlying suicide risk in adolescentsSahil Bajaj0Karina S. Blair1Matthew Dobbertin2Kaustubh R. Patil3Patrick M. Tyler4Jay L. Ringle5Johannah Bashford-Largo6Avantika Mathur7Jaimie Elowsky8Ahria Dominguez9Lianne Schmaal10R. James R. Blair11Multimodal Clinical Neuroimaging Laboratory (MCNL), Center for Neurobehavioral Research, Boys Town National Research HospitalMultimodal Clinical Neuroimaging Laboratory (MCNL), Center for Neurobehavioral Research, Boys Town National Research HospitalMultimodal Clinical Neuroimaging Laboratory (MCNL), Center for Neurobehavioral Research, Boys Town National Research HospitalInstitute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre JülichChild and Family Translational Research Center, Boys Town National Research HospitalChild and Family Translational Research Center, Boys Town National Research HospitalMultimodal Clinical Neuroimaging Laboratory (MCNL), Center for Neurobehavioral Research, Boys Town National Research HospitalMultimodal Clinical Neuroimaging Laboratory (MCNL), Center for Neurobehavioral Research, Boys Town National Research HospitalMultimodal Clinical Neuroimaging Laboratory (MCNL), Center for Neurobehavioral Research, Boys Town National Research HospitalMultimodal Clinical Neuroimaging Laboratory (MCNL), Center for Neurobehavioral Research, Boys Town National Research HospitalCenter for Youth Mental Health, University of MelbourneChild and Adolescent Mental Health Centre, Mental Health ServicesAbstract Suicide is the third leading cause of death for individuals between 15 and 19 years of age. The high suicide mortality rate and limited prior success in identifying neuroimaging biomarkers indicate that it is crucial to improve the accuracy of clinical neural signatures underlying suicide risk. The current study implements machine-learning (ML) algorithms to examine structural brain alterations in adolescents that can discriminate individuals with suicide risk from typically developing (TD) adolescents at the individual level. Structural MRI data were collected from 79 adolescents who demonstrated clinical levels of suicide risk and 79 demographically matched TD adolescents. Region-specific cortical/subcortical volume (CV/SCV) was evaluated following whole-brain parcellation into 1000 cortical and 12 subcortical regions. CV/SCV parameters were used as inputs for feature selection and three ML algorithms (i.e., support vector machine [SVM], K-nearest neighbors, and ensemble) to classify adolescents at suicide risk from TD adolescents. The highest classification accuracy of 74.79% (with sensitivity = 75.90%, specificity = 74.07%, and area under the receiver operating characteristic curve = 87.18%) was obtained for CV/SCV data using the SVM classifier. Identified bilateral regions that contributed to the classification mainly included reduced CV within the frontal and temporal cortices but increased volume within the cuneus/precuneus for adolescents at suicide risk relative to TD adolescents. The current data demonstrate an unbiased region-specific ML framework to effectively assess the structural biomarkers of suicide risk. Future studies with larger sample sizes and the inclusion of clinical controls and independent validation data sets are needed to confirm our findings.https://doi.org/10.1007/s44192-023-00033-6Cortical volumeMorphometryBrain parcellationSuicidal ideationEmotionsYouth |
| spellingShingle | Sahil Bajaj Karina S. Blair Matthew Dobbertin Kaustubh R. Patil Patrick M. Tyler Jay L. Ringle Johannah Bashford-Largo Avantika Mathur Jaimie Elowsky Ahria Dominguez Lianne Schmaal R. James R. Blair Machine learning based identification of structural brain alterations underlying suicide risk in adolescents Discover Mental Health Cortical volume Morphometry Brain parcellation Suicidal ideation Emotions Youth |
| title | Machine learning based identification of structural brain alterations underlying suicide risk in adolescents |
| title_full | Machine learning based identification of structural brain alterations underlying suicide risk in adolescents |
| title_fullStr | Machine learning based identification of structural brain alterations underlying suicide risk in adolescents |
| title_full_unstemmed | Machine learning based identification of structural brain alterations underlying suicide risk in adolescents |
| title_short | Machine learning based identification of structural brain alterations underlying suicide risk in adolescents |
| title_sort | machine learning based identification of structural brain alterations underlying suicide risk in adolescents |
| topic | Cortical volume Morphometry Brain parcellation Suicidal ideation Emotions Youth |
| url | https://doi.org/10.1007/s44192-023-00033-6 |
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