Hauptseite > Publikationsdatenbank > Machine learning based identification of structural brain alterations underlying suicide risk in adolescents > print

001     999190
005     20241023094534.0
024 7 _ |a 10.1007/s44192-023-00033-6
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024 7 _ |a 2128/33917
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024 7 _ |a 37861863
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024 7 _ |a WOS:001319058800001
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037 _ _ |a FZJ-2023-01219
100 1 _ |a Bajaj, Sahil
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245 _ _ |a Machine learning based identification of structural brain alterations underlying suicide risk in adolescents
260 _ _ |a [Cham]
|c 2023
|b Springer International Publishing
336 7 _ |a article
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520 _ _ |a 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.
536 _ _ |a 5252 - Brain Dysfunction and Plasticity (POF4-525)
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536 _ _ |a 5254 - Neuroscientific Data Analytics and AI (POF4-525)
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700 1 _ |a Blair, Karina S.
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700 1 _ |a Dobbertin, Matthew
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700 1 _ |a Patil, Kaustubh R.
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700 1 _ |a Tyler, Patrick M.
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700 1 _ |a Ringle, Jay L.
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700 1 _ |a Bashford-Largo, Johannah
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700 1 _ |a Mathur, Avantika
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700 1 _ |a Elowsky, Jaimie
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700 1 _ |a Dominguez, Ahria
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700 1 _ |a Schmaal, Lianne
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700 1 _ |a Blair, R. James R.
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773 _ _ |a 10.1007/s44192-023-00033-6
|g Vol. 3, no. 1, p. 6
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|t Discover mental health
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|y 2023
|x 2731-4383
856 4 _ |u https://juser.fz-juelich.de/record/999190/files/s44192-023-00033-6.pdf
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910 1 _ |a Multimodal Clinical Neuroimaging Laboratory (MCNL), Center for Neurobehavioral Research, Boys Town National Research Hospital, 14015 Flanagan Blvd. Suite #102, Boys Town, NE, USA
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910 1 _ |a Forschungszentrum Jülich
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910 1 _ |a HHU Düsseldorf
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|v Decoding Brain Organization and Dysfunction
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