Greater white matter hyperintensities and the association with executive function in suicide attempters with late-life depression

Lin, Chia Wei; Huang, Chih-Mao; Karim, Helmet T.; Liu, Ho Ling; Lee, Tatia Mei Chun; Wu, Changwei; Toh, Cheng Hong; Tsai, Yun Fang; Yen, Tzung Hai; Lee, Shwu-Hua

doi:10.1016/j.neurobiolaging.2020.12.016

Cited by 10 publications

(6 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Cognitive impairment is one of the best characterised traits of LLD ( Masse et al , 2022 ) that has been associated with response and attrition during antidepressant treatment, as well as with suicidal behaviour ( Cristancho et al , 2018 ; Jordan et al , 2020 ; Lin et al , 2021 ; Pan et al , 2022 ). The degree of cognitive dysfunction was suggested to be higher in LOD in comparison with the EOD subtype ( Ly et al ., 2021 ).…”

Section: Discussionmentioning

confidence: 99%

Age or age of onset: which is the best criterion to classify late-life depression?

Olgiati

Fanelli

Serretti

2023

International Clinical Psychopharmacology

View full text Add to dashboard Cite

In late-life depression (LLD), several differences between patients whose first episode is reported after age 65 (late-onset depression, LOD) and those with early-onset depression (EOD) might reflect the effects of brain ageing. To test this hypothesis, we analysed the impact of current age and age at illness onset on a number of clinical and cognitive manifestations in 438 outpatients with major depressive disorder aged >60 years, treated with venlafaxine for 12 weeks. When compared to the EOD group, patients with LOD were older (P < 0.00001) and associated with lower depression severity (P = 0.0029), lower global cognitive functioning [Mini-Mental State Examination (MMSE): P = 0.0001; Repeatable Battery for the Assessment of Neuropsychological Status: immediate memory, P = 0.0009, and delayed memory, P < 0.00001; Delis-Kaplan Executive Function System measuring executive functions: Trail-Making Test (TMT) -P = 0.0004 and Colour-Word Interference Test, Inhibition -P = 0.0063], and more dyskinesias (Abnormal Involuntary Movement Scale: P = 0.0006). After controlling for its interactions with age of onset, current age was inversely correlated with Montgomery Åsberg Depression Rating Scale scores at baseline (P < 0.00001) and week 12 (P = 0.0066), MMSE (P < 0.00001), delayed memory (P < 0.00001), and TMT (P = 0.0021). Age of onset predicted impairment in immediate (P = 0.023) and delayed memory (P = 0.0181), and dyskinesias (P = 0.0006). Although most features of LLD are related to ageing rather than to late-onset, LOD is a possible separate diagnostic entity characterised by memory dysfunction and increased liability to movement disorders.

show abstract

Section: Discussionmentioning

confidence: 99%

Age or age of onset: which is the best criterion to classify late-life depression?

Olgiati

Fanelli

Serretti

2023

International Clinical Psychopharmacology

View full text Add to dashboard Cite

show abstract

“…All the study participants provided written informed consent. The participants were merged from two consecutive projects in order to gain adequate power in the model building (Lin et al, 2021 ; Lin et al, 2019 ).…”

Section: Methodsmentioning

confidence: 99%

Automatic diagnosis of late-life depression by 3D convolutional neural networks and cross-sample Entropy analysis from resting-state fMRI

Lin

Lee

Huang

et al. 2022

Brain Imaging and Behavior

Self Cite

View full text Add to dashboard Cite

Resting-state fMRI has been widely used in investigating the pathophysiology of late-life depression (LLD). Unlike the conventional linear approach, cross-sample entropy (CSE) analysis shows the nonlinear property in fMRI signals between brain regions. Moreover, recent advances in deep learning, such as convolutional neural networks (CNNs), provide a timely application for understanding LLD. Accurate and prompt diagnosis is essential in LLD; hence, this study aimed to combine CNN and CSE analysis to discriminate LLD patients and non-depressed comparison older adults based on brain resting-state fMRI signals. Seventy-seven older adults, including 49 patients and 28 comparison older adults, were included for fMRI scans. Three-dimensional CSEs with volumes corresponding to 90 seed regions of interest of each participant were developed and fed into models for disease classification and depression severity prediction. We obtained a diagnostic accuracy > 85% in the superior frontal gyrus (left dorsolateral and right orbital parts), left insula, and right middle occipital gyrus. With a mean root-mean-square error (RMSE) of 2.41, three separate models were required to predict depressive symptoms in the severe, moderate, and mild depression groups. The CSE volumes in the left inferior parietal lobule, left parahippocampal gyrus, and left postcentral gyrus performed best in each respective model. Combined complexity analysis and deep learning algorithms can classify patients with LLD from comparison older adults and predict symptom severity based on fMRI data. Such application can be utilized in precision medicine for disease detection and symptom monitoring in LLD.

show abstract

Section: Introductionmentioning

confidence: 99%

“…Depression symptoms include sadness, loss of interest, guilt, sleep problems, tiredness, poor concentration, and a loss of interest in day-to-day activities ( Lin et al, 2021 ; Lu et al, 2014 ). More severe cases of depression can lead to increased risk of death ( Cuijpers and Smit, 2002 ) and suicide ( Choo et al, 2014 ; Large, 2016 ; Lin et al, 2021 ). Depression is a chronic disorder that affects an individual's quality of life ( Daly et al, 2010 ; Mayor, 2015 ), social and emotional functioning ( Huang et al, 2019 ; Huang and Huang, 2019 ; Wong et al, 2016 ), and ability to work ( Hirschfeld et al, 2000 ; Lerner and Henke, 2008 ; Stewart et al, 2003 ).…”

Section: Introductionmentioning

confidence: 99%

Review of EEG-based neurofeedback as a therapeutic intervention to treat depression

Patil

Lin

Lee

et al. 2023

Psychiatry Research: Neuroimaging

Self Cite

View full text Add to dashboard Cite

Greater white matter hyperintensities and the association with executive function in suicide attempters with late-life depression

Cited by 10 publications

References 63 publications

Age or age of onset: which is the best criterion to classify late-life depression?

Age or age of onset: which is the best criterion to classify late-life depression?

Automatic diagnosis of late-life depression by 3D convolutional neural networks and cross-sample Entropy analysis from resting-state fMRI

Review of EEG-based neurofeedback as a therapeutic intervention to treat depression

Contact Info

Product

Resources

About