2024
DOI: 10.1002/brb3.3348
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Predicting suicidality in late‐life depression by 3D convolutional neural network and cross‐sample entropy analysis of resting‐state fMRI

Chemin Lin,
Chih‐Mao Huang,
Wei Chang
et al.

Abstract: 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).Methods: We enrolled 83 patients with LLD, 35 of which were non‐suicidal and 48 were suicida… Show more

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“…Regarding the left caudate hypoactivation we observed in LLD, this region has been associated with psychomotor dysfunction linked to reduced volume in LLD [ 107 ], suggesting a crucial involvement of the caudate nucleus in the motor aspects of depression during the elderly. The left caudate resting-state function has been also involved in first-episode treatment-naive LLD [ 19 ] and in late-life suicidality among LLD patients [ 108 ].…”
Section: Discussionmentioning
confidence: 99%
“…Regarding the left caudate hypoactivation we observed in LLD, this region has been associated with psychomotor dysfunction linked to reduced volume in LLD [ 107 ], suggesting a crucial involvement of the caudate nucleus in the motor aspects of depression during the elderly. The left caudate resting-state function has been also involved in first-episode treatment-naive LLD [ 19 ] and in late-life suicidality among LLD patients [ 108 ].…”
Section: Discussionmentioning
confidence: 99%