Disrupted intrinsic functional brain network in patients with late-life depression: Evidence from a multi-site dataset

Tan, Wenjian; Ouyang, Xuan; Huang, Dingming; Wu, Zhipeng; Liu, Zhening; He, Z. Y.; Long, Yicheng

doi:10.1016/j.jad.2022.12.019

Cited by 20 publications

(15 citation statements)

References 55 publications

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“…Changes in FC strength were found in both early- and late-onset (after age 50 years) patients, while altered topological metrics were obvious only in late-onset patients. These results may help our understanding of the underlying neuronal mechanisms and biological heterogeneity of LLD (Tan et al 2023 ), the latter already having been emphasized by others (Wen et al 2022 ). Lesion-symptom mapping was used to identify brain regions significantly associated with depression severity.…”

Section: Lld and Brain Circuit Disturbancessupporting

confidence: 53%

“…Recent evidence from a multi-site data set showed that LLD patients had alterations in nodal network metrics, which mainly involved the DMN and somatomotor network (SMN). The above changes were present in both first-episode drug-naive (FEDN) and non-FEDN patients and were correlated with depression severity, altered topologic changes were found only in late-onset patients (Tan et al 2023 ). These results may help to strengthen our understanding of the underlying neuronal mechanisms and biological heterogeneity of LLD (Tan et al 2023 ).…”

Section: Lld and Brain Circuit Disturbancesmentioning

confidence: 99%

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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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Section: Lld and Brain Circuit Disturbancessupporting

confidence: 53%

Section: Lld and Brain Circuit Disturbancesmentioning

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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“…First, we notice that the sample sizes in some studies are relatively small ( Table 2 ), which may lead to less reliable results [ 77 ]. Second, it is possible that major depressive disorder is a disorder with significant clinical and biological heterogeneity; for example, it has been suggested that major depressive disorder-related brain dysfunctions may differ in different age groups (e.g., early adulthood vs. late-life) of patients [ 78 ]. Therefore, subtypes with distinct dFC profiles (e.g., hyper- and hypo-stability) may exist in major depressive disorder, which can be investigated in future studies.…”

Section: Research Progress On Possible Relationships Between the Temp...mentioning

confidence: 99%

Temporal Stability of the Dynamic Resting-State Functional Brain Network: Current Measures, Clinical Research Progress, and Future Perspectives

Long

Liu

2023

Brain Sciences

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Based on functional magnetic resonance imaging and multilayer dynamic network model, the brain network’s quantified temporal stability has shown potential in predicting altered brain functions. This manuscript aims to summarize current knowledge, clinical research progress, and future perspectives on brain network’s temporal stability. There are a variety of widely used measures of temporal stability such as the variance/standard deviation of dynamic functional connectivity strengths, the temporal variability, the flexibility (switching rate), and the temporal clustering coefficient, while there is no consensus to date which measure is the best. The temporal stability of brain networks may be associated with several factors such as sex, age, cognitive functions, head motion, circadian rhythm, and data preprocessing/analyzing strategies, which should be considered in clinical studies. Multiple common psychiatric disorders such as schizophrenia, major depressive disorder, and bipolar disorder have been found to be related to altered temporal stability, especially during the resting state; generally, both excessively decreased and increased temporal stabilities were thought to reflect disorder-related brain dysfunctions. However, the measures of temporal stability are still far from applications in clinical diagnoses for neuropsychiatric disorders partly because of the divergent results. Further studies with larger samples and in transdiagnostic (including schizoaffective disorder) subjects are warranted.

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“…First, we notice that the sample sizes in some studies are relatively small (Table 2), which may lead to less reliable results [75]. Second, it is possible that major depressive disorder is a disease with significant clinical and biological heterogeneity; for example, it has been suggested that major depressive disorder-related brain dysfunctions may differ in different age groups (e.g., early adulthood vs late-life) of patients [76]. Therefore, subtypes with distinct dFC profiles (e.g., hyper-and hypo-stability) may exist in major depressive disorder, which can be investigated in future studies.…”

Section: Research Progress On Possible Relationships Between the Temp...mentioning

confidence: 99%

Temporal Stability of the Dynamic Resting-State Functional Brain Network: a Narrative Review of Current Measures and Clinical Research Progress

Long¹,

Li²,

Liu³

2023

Preprint

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Based on functional magnetic resonance imaging and multilayer dynamic network model, the quantified temporal stability of brain network has shown potentials in predicting altered brain functions. The present review focuses on summarizing current knowledge on the commonly-used measures of brain network’s temporal stability and the clinical research progress on them. There are a variety of widely used measures of temporal stability such as the variance/standard deviation of dynamic functional connectivity strengths, the temporal variability, the flexibility (switching rate), and the temporal clustering coefficient, while there is no consensus to date that which measure is the best. The temporal stability of human brain networks may be associated with several factors such as sex, age, cognitive functions, head motion, and data preprocessing/analyzing strategies, which should be considered in the clinical studies. Multiple common psychiatric diseases such as schizophrenia, major depressive disorder, and bipolar disorder have been found to be related to altered temporal stability, especially during the resting state; generally, both excessively decreased and increased temporal stabilities were thought to reflect disease-related brain dysfunctions. However, the measures of temporal stability are still far from applications in clinical diagnoses for neuropsychiatric diseases partly because of the divergent results, and further studies are warranted.

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Disrupted intrinsic functional brain network in patients with late-life depression: Evidence from a multi-site dataset

Cited by 20 publications

References 55 publications

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

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

Temporal Stability of the Dynamic Resting-State Functional Brain Network: Current Measures, Clinical Research Progress, and Future Perspectives

Temporal Stability of the Dynamic Resting-State Functional Brain Network: a Narrative Review of Current Measures and Clinical Research Progress

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