Altered temporal reachability highlights the role of sensory perception systems in major depressive disorder

Zhao, Ziyang; Zhang, Yinghui; Chen, Nan; Li, Yongchao; Guo, Hanning; Guo, Man; Yao, Zhijun; Hu, Bin

doi:10.1016/j.pnpbp.2021.110426

Cited by 11 publications

(13 citation statements)

References 55 publications

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“…(4) Temporal clustering coefficient [ 14 , 17 , 21 , 39 , 40 ]: This measure is also called “the temporal correlation coefficient”; it is derived from the definition of analogous metric (clustering coefficient) for conventional static networks. The temporal clustering coefficient estimates the tendency of a dynamic brain network to keep stable over time (its “temporal clustering”) by quantifying the average topological overlap of the network connections between any two consecutive layers (i.e., neighboring time windows).…”

Section: The Basic Concepts and Commonly Used Measures Of The Tempora...mentioning

confidence: 99%

“…Among these studies, Long et al [ 17 ] reported that the level of temporal variabilities of dFCs are significantly increased (indicating decreased temporal stability) in patients with major depressive disorder at both the whole-brain level and subnetwork level within the default-mode, sensorimotor, and subcortical networks. Using the measure of temporal clustering coefficient, Zhao et al [ 39 ] found that patients with major depressive disorder showed decreased temporal stability at the global level and in regions of sensory perception systems. Hou et al [ 71 ] and Ouyang et al [ 40 ] found that patients with major depressive disorder showed decreased temporal stability within the striatum and default-mode regions, respectively.…”

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

confidence: 99%

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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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Section: The Basic Concepts and Commonly Used Measures Of The Tempora...mentioning

confidence: 99%

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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show abstract

“…(4) The temporal clustering coefficient [14,17,21,39,40]: this measure is also called "the temporal correlation coefficient"; it is derived from the definition of analogous metric (clustering coefficient) for conventional static networks. The temporal clustering coefficient estimates the tendency of a dynamic brain network to keep stable over time (its "temporal clustering") by quantifying the average topological overlap of the network connections between any two consecutive layers (i.e., neighboring time windows).…”

Section: The Basic Concepts and Commonly Used Measures Of The Tempora...mentioning

confidence: 99%

“…Among these studies, Long et al [17] reported that the level of temporal variabilities of dFCs are significantly increased (indicating decreased temporal stability) in patients with major depressive disorder at both the whole-brain level and sub-network level within the default-mode, sensorimotor and subcortical networks. Using the measure of temporal clustering coefficient, Zhao et al [39] found that patients with major depressive disorder showed decreased temporal stability at the global level and in regions of sensory perception systems. Hou et al [69] and Ouyang et al [40] found that patients with major depressive disorder showed decreased temporal stability within the striatum and default-mode regions, respectively.…”

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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“…The global signal regression was not conducted avoiding the removal of the significant neuronal signals(47,48). Participants with poor image quality or excessive head motion [translation distances > 2 mm or rotation degree > 2 • or mean framewise displacement > 0.2 mm (49, 50)] were excluded(51), leaving 30 CBP patients and 29 healthy controls for further analysis. The demographics for patients with CBP and HC are shown in Table1.…”

mentioning

confidence: 99%

Temporal Grading Index of Functional Network Topology Predicts Pain Perception of Patients With Chronic Back Pain

Zhao

et al. 2022

Front. Neurol.

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Chronic back pain (CBP) is a maladaptive health problem affecting the brain function and behavior of the patient. Accumulating evidence has shown that CBP may alter the organization of functional brain networks; however, whether the severity of CBP is associated with changes in dynamics of functional network topology remains unclear. Here, we generated dynamic functional networks based on resting-state functional magnetic resonance imaging (rs-fMRI) of 34 patients with CBP and 34 age-matched healthy controls (HC) in the OpenPain database via a sliding window approach, and extracted nodal degree, clustering coefficient (CC), and participation coefficient (PC) of all windows as features to characterize changes of network topology at temporal scale. A novel feature, named temporal grading index (TGI), was proposed to quantify the temporal deviation of each network property of a patient with CBP to the normal oscillation of the HCs. The TGI of the three features achieved outstanding performance in predicting pain intensity on three commonly used regression models (i.e., SVR, Lasso, and elastic net) through a 5-fold cross-validation strategy, with the minimum mean square error of 0.25 ± 0.05; and the TGI was not related to depression symptoms of the patients. Furthermore, compared to the HCs, brain regions that contributed most to prediction showed significantly higher CC and lower PC across time windows in the CBP cohort. These results highlighted spatiotemporal changes in functional network topology in patients with CBP, which might serve as a valuable biomarker for assessing the sensation of pain in the brain and may facilitate the development of CBP management/therapy approaches.

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Altered temporal reachability highlights the role of sensory perception systems in major depressive disorder

Cited by 11 publications

References 55 publications

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: 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

Temporal Grading Index of Functional Network Topology Predicts Pain Perception of Patients With Chronic Back Pain

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