TY  - JOUR
PY  - 2024//
TI  - Predicting suicidality in late-life depression by 3D convolutional neural network and cross-sample entropy analysis of resting-state fMRI
JO  - Brain and behavior
A1  - Lin, Chemin
A1  - Huang, Chih-Mao
A1  - Chang, Wei
A1  - Chang, You-Xun
A1  - Liu, Ho-Ling
A1  - Ng, Shu-Hang
A1  - Lin, Huang-Li
A1  - Lee, Tatia Mei-Chun
A1  - Lee, Shwu-Hua
A1  - Wu, Shun-Chi
SP  - e3348
EP  - e3348
VL  - 14
IS  - 1
N2  - BACKGROUND: Predicting suicide is a pressing issue among older adults; however, predicting its risk is difficult. Capitalizing on the recent development of machine learning, considerable progress has been made in predicting complex behavior such as suicide. As depression remained the strongest risk for suicide, we aimed to apply deep learning algorithms to identify suicidality in a group with late-life depression (LLD). <br><br>METHODS: We enrolled 83 patients with LLD, 35 of which were non-suicidal and 48 were suicidal, including 26 with only suicidal ideation and 22 with past suicide attempts, for resting-state functional magnetic resonance imaging (MRI). Cross-sample entropy (CSE) analysis was conducted to examine the complexity of MRI signals among brain regions. Three-dimensional (3D) convolutional neural networks (CNNs) were used, and the classification accuracy in each brain region was averaged to predict suicidality after sixfold cross-validation. <br><br>RESULTS: We found brain regions with a mean accuracy above 75% to predict suicidality located mostly in default mode, fronto-parietal, and cingulo-opercular resting-state networks. The models with right amygdala and left caudate provided the most reliable accuracy in all cross-validation folds, indicating their neurobiological importance in late-life suicide. <br><br>CONCLUSION: Combining CSE analysis and the 3D CNN, several brain regions were found to be associated with suicidality.<p />  <p>Language: en</p>
LA  - en
SN  - 2162-3279
UR  - http://dx.doi.org/10.1002/brb3.3348
ID  - ref1
ER  -