Association of White Matter Integrity With Executive Function and Antidepressant Treatment Outcome in Patients With Late-Life Depression

He, Xiaofu; Pueraro, Elena; Kim, Yoojean; Garcia, Carolina Montes; Maas, Benjamin; Choi, Jongwoo; Egglefield, Dakota A.; Schiff, Sophie; Sneed, Joel R.; Brown, Patrick J.; Brickman, Adam M.; Roose, Steven P.; Rutherford, Bret R.

doi:10.1016/j.jagp.2021.01.004

Cited by 17 publications

(13 citation statements)

References 39 publications

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“…As reported in previous studies 50 using DTI model, we found alterations in UF, anterior parts of CC, ATR, SLF and striato-premotor fasciculus in LLD compared to HC group, confirming previous diffusion MRI studies in LLD 51 . The anterior CC plays a central role in depression pathogenesis 52 and is associated with many clinical features of LLD such as depression duration, cognitive symptoms, recurrence of depression episodes and late-onset depression 53 .…”

Section: Discussionsupporting

confidence: 92%

Microstructural brain assessment in late-life depression and apathy using diffusion MRI multi-compartments models associated with tractometry

Hedouin,

Roy,

Desmidt

et al. 2024

Preprint

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Late-life depression is a common disorder in the elderly, difficult to treat, where apathy contributes to a poor prognosis. Despite its severity and frequency, the pathophysiology of LLD remains complex and its exploration challenging. While white matter (WM) damages have been assessed using diffusion tensor imaging, this model cannot correctly represent the WM microstructure. We hypothesized that using a more complex multi-compartment model, never used on LLD, would better describe the WM microstructure. In this article, we performed a tract-based approach to investigate novel diffusion-model biomarkers of LLD and apathy, by interpolating the microstructural metrics directly along the fibers. We performed a multivariate statistical analysis along the fiber, combined with a principal component analysis for dimensional data reduction. Then, we tested the utility of our framework by showing classical modifications in LDD. Finally, we aimed to investigate the relationship between apathy and microstructure in different fibers. Our study suggests that new tracts, such as striato-premotor, may be involved in LLD and apathy, which has not been observed in previous studies. We also identified modifications of inflammation metrics in 5 different tracts, already reported in dementia, which may contribute to the cognitive decline observed with apathy.

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Section: Discussionsupporting

confidence: 92%

Microstructural brain assessment in late-life depression and apathy using diffusion MRI multi-compartments models associated with tractometry

Hedouin,

Roy,

Desmidt

et al. 2024

Preprint

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“…Compared with HCs, the anatomical connection of patients with MDD was significantly enhanced, indicating that the integrity of white matter fibers in patients with MDD may be damaged in the early stage of the disease. The CC and VIS are the main fiber bundles that connect the contralateral homologous brain system 35,36 . We inferred that local information processing between the hemispheres of patients with MDD may be interrupted.…”

Section: Discussionmentioning

confidence: 99%

“…The CC and VIS are the main fiber bundles that connect the contralateral homologous brain system. 35,36 We inferred that local information processing between the hemispheres of patients with MDD may be interrupted. However, whether this conjecture is correct requires further investigation.…”

Section: Discussionmentioning

confidence: 99%

Diagnosis of Major Depressive Disorder Using Machine Learning Based on Multisequence MRI Neuroimaging Features

Dong

Gai

et al. 2023

Magnetic Resonance Imaging

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BackgroundPrevious studies have found qualitative structural and functional brain changes in major depressive disorder (MDD) patients. However, most studies ignored the complementarity of multisequence MRI neuroimaging features and cannot determine accurate biomarkers.PurposeTo evaluate machine‐learning models combined with multisequence MRI neuroimaging features to diagnose patients with MDD.Study TypeProspective.SubjectsA training cohort including 111 patients and 90 healthy controls (HCs) and a test cohort including 28 patients and 22 HCs.Field Strength/SequenceA 3.0 T/T1‐weighted imaging, resting‐state functional MRI with echo‐planar sequence, and single‐shot echo‐planar diffusion tensor imaging.AssessmentRecruitment and integration were used to reflect the dynamic changes of functional networks, while gray matter volume and fractional anisotropy were used to reflect the changes in the morphological and anatomical network. We then fused features with significant differences in functional, morphological, and anatomical networks to evaluate a random forest (RF) classifier to diagnose patients with MDD. Furthermore, a support vector machine (SVM) classifier was used to verify the stability of neuroimaging features. Linear regression analyses were conducted to investigate the relationships among multisequence neuroimaging features and the suicide risk of patients.Statistical TestsThe comparison of functional network attributes between patients and controls by two‐sample t‐test. Network‐based statistical analysis was used to identify structural and anatomical connectivity changes between MDD and HCs. The performance of the model was evaluated by receiver operating characteristic (ROC) curves.ResultsThe performance of the RF model integrating multisequence neuroimaging features in the diagnosis of depression was significantly improved, with an AUC of 93.6%. In addition, we found that multisequence neuroimaging features could accurately predict suicide risk in patients with MDD (r = 0.691).Data ConclusionThe RF model fusing functional, morphological, and anatomical network features performed well in diagnosing patients with MDD and provided important insights into the pathological mechanisms of MDD.Evidence Level1.Technical EfficacyStage 2.

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“…Fractional anisotropy (FA), an index of WM integrity, was significantly associated with executive function in WM tracts in LLD cases (He et al 2021 ). Severity of depression in mid- and late life was associated with WM integrity in association fibers and thalamic radiations, while greater subject variance was associated with decreased WM microstructure within projection fibers (Shen et al 2019 ).…”

Section: Structural Brain Abnormalities In Lldmentioning

confidence: 99%

The heterogeneity of late-life depression and its pathobiology: a brain network dysfunction disorder

Ka¹

2023

J Neural Transm

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Depression is frequent in older individuals and is often associated with cognitive impairment and increasing risk of subsequent dementia. Late-life depression (LLD) has a negative impact on quality of life, yet the underlying pathobiology is still poorly understood. It is characterized by considerable heterogeneity in clinical manifestation, genetics, brain morphology, and function. Although its diagnosis is based on standard criteria, due to overlap with other age-related pathologies, the relationship between depression and dementia and the relevant structural and functional cerebral lesions are still controversial. LLD has been related to a variety of pathogenic mechanisms associated with the underlying age-related neurodegenerative and cerebrovascular processes. In addition to biochemical abnormalities, involving serotonergic and GABAergic systems, widespread disturbances of cortico-limbic, cortico-subcortical, and other essential brain networks, with disruption in the topological organization of mood- and cognition-related or other global connections are involved. Most recent lesion mapping has identified an altered network architecture with "depressive circuits" and "resilience tracts", thus confirming that depression is a brain network dysfunction disorder. Further pathogenic mechanisms including neuroinflammation, neuroimmune dysregulation, oxidative stress, neurotrophic and other pathogenic factors, such as β-amyloid (and tau) deposition are in discussion. Antidepressant therapies induce various changes in brain structure and function. Better insights into the complex pathobiology of LLD and new biomarkers will allow earlier and better diagnosis of this frequent and disabling psychopathological disorder, and further elucidation of its complex pathobiological basis is warranted in order to provide better prevention and treatment of depression in older individuals.

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Association of White Matter Integrity With Executive Function and Antidepressant Treatment Outcome in Patients With Late-Life Depression

Cited by 17 publications

References 39 publications

Microstructural brain assessment in late-life depression and apathy using diffusion MRI multi-compartments models associated with tractometry

Microstructural brain assessment in late-life depression and apathy using diffusion MRI multi-compartments models associated with tractometry

Diagnosis of Major Depressive Disorder Using Machine Learning Based on Multisequence MRI Neuroimaging Features

The heterogeneity of late-life depression and its pathobiology: a brain network dysfunction disorder

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