Multiscale neural modeling of resting-state fMRI reveals executive-limbic malfunction as a core mechanism in major depressive disorder

Li, Guoshi; Liu, Yujie; Zheng, Yan; Wu, Ye; Li, Danian; Liang, Xinyu; Chen, Yaoping; Cui, Ying; Yap, Pew Thian; Qiu, Shijun; Zhang, Han; Shen, Dinggang

doi:10.1016/j.nicl.2021.102758

Cited by 15 publications

(27 citation statements)

References 133 publications

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“…In addition to DCM and BNM, the two major types of generative models for fMRI, there exist other modeling frameworks that estimate EC and study connectome in a generative fashion ( Havlicek et al, 2011 ; Olier et al, 2013 ; Fukushima et al, 2015 ; Becker et al, 2018 ; Singh et al, 2020 ; Li et al, 2021 ). One framework that shows promising application to cognitive and clinical neuroscience is Dynamic Neural Model (DNM) with direct parameterization.…”

Section: Dynamic Neural Model With Direct Parameterizationmentioning

confidence: 99%

“…Another example of DNM with direct parameterization is a framework termed Multiscale Neural Model Inversion (MNMI, Figure 3 ; Li et al, 2021 ). In MNMI, the neuronal activity is generated by a neural mass network model comprised of multiple brain regions.…”

Section: Dynamic Neural Model With Direct Parameterizationmentioning

confidence: 99%

“…as well as spontaneous input ( u ) are estimated using genetic algorithm to maximize the similarity between simulated and empirical FC. Adapted from Li et al (2021) under the Creative Commons Attribution License (CC BY).…”

Section: Dynamic Neural Model With Direct Parameterizationmentioning

confidence: 99%

“…For instance, MINDy and MNMI use one-state and two-state NMMs, respectively. The NMM in MNMI is described by the following differential equations ( Wilson and Cowan, 1972 ; Li et al, 2021 ):…”

Section: Dynamic Neural Model With Direct Parameterizationmentioning

confidence: 99%

“…The development steps of DNMs are generally similar to BNMs: (1) parcellating the brain into discrete regions; (2) selecting brain regions to model and extracting fMRI-BOLD timeseries; (3) calculating FC and/or SC if necessary; (4) representing each network node with a NMM of local neuronal populations; (5) linking individual network nodes with long-range connections with or without SC constraint; (6) deconvolving the empirical BOLD signals with a hemodynamic response function (HRF, Singh et al, 2020 ) or transforming the network activities to simulated BOLD signals via a hemodynamic model ( Li et al, 2021 ); and (7) fitting model parameters to deconvolved BOLD signals or FC via an optimization scheme. Due to the flexibility in neuronal model, SC utilization, choice of objective function and optimization algorithm, DNMs are generally implemented using customized scripts with MATLAB or other computing languages (e.g., C++).…”

Section: Dynamic Neural Model With Direct Parameterizationmentioning

confidence: 99%

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From descriptive connectome to mechanistic connectome: Generative modeling in functional magnetic resonance imaging analysis

Yap

2022

Front. Hum. Neurosci.

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As a newly emerging field, connectomics has greatly advanced our understanding of the wiring diagram and organizational features of the human brain. Generative modeling-based connectome analysis, in particular, plays a vital role in deciphering the neural mechanisms of cognitive functions in health and dysfunction in diseases. Here we review the foundation and development of major generative modeling approaches for functional magnetic resonance imaging (fMRI) and survey their applications to cognitive or clinical neuroscience problems. We argue that conventional structural and functional connectivity (FC) analysis alone is not sufficient to reveal the complex circuit interactions underlying observed neuroimaging data and should be supplemented with generative modeling-based effective connectivity and simulation, a fruitful practice that we term “mechanistic connectome.” The transformation from descriptive connectome to mechanistic connectome will open up promising avenues to gain mechanistic insights into the delicate operating principles of the human brain and their potential impairments in diseases, which facilitates the development of effective personalized treatments to curb neurological and psychiatric disorders.

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Section: Dynamic Neural Model With Direct Parameterizationmentioning

confidence: 99%

Section: Dynamic Neural Model With Direct Parameterizationmentioning

confidence: 99%

Section: Dynamic Neural Model With Direct Parameterizationmentioning

confidence: 99%

Section: Dynamic Neural Model With Direct Parameterizationmentioning

confidence: 99%

Section: Dynamic Neural Model With Direct Parameterizationmentioning

confidence: 99%

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From descriptive connectome to mechanistic connectome: Generative modeling in functional magnetic resonance imaging analysis

Yap

2022

Front. Hum. Neurosci.

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Anxiety and depression are associated with heightened risk of incident deep vein thrombosis: Mediation through stress‐related neural mechanisms

Rosovsky,

Mezue,

Gharios

et al. 2024

American J Hematol

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Controversy exists as to whether anxiety and depression increase deep vein thrombosis (DVT) risk, and the mechanisms mediating potential links remain unknown. We aimed to evaluate the association between anxiety and depression and DVT risk and determine whether upregulated stress‐related neural activity (SNA), which promotes chronic inflammation, contributes to this link. Our retrospective study included adults (N = 118 871) enrolled in Mass General Brigham Biobank. A subset (N = 1520) underwent clinical 18F‐FDG‐PET/CT imaging. SNA was measured as the ratio of amygdalar to cortical activity (AmygAC). High‐sensitivity C‐reactive protein (hs‐CRP) and heart rate variability (HRV) were also obtained. Median age was 58 [interquartile range (IQR) 42–70] years with 57% female participants. DVT occurred in 1781 participants (1.5%) over median follow‐up of 3.6 years [IQR 2.1–5.2]. Both anxiety and depression independently predicted incident DVT risk after robust adjustment (HR [95% CI]: 1.53 [1.38–1.71], p < .001; and 1.48 [1.33–1.65], p < .001, respectively). Additionally, both anxiety and depression associated with increased AmygAC (standardized beta [95% CI]: 0.16 [0.04–0.27], p = .007, and 0.17 [0.05–0.29], p = .006, respectively). Furthermore, AmygAC associated with incident DVT (HR [95% CI]: 1.30 [1.07–1.59], p = .009). Mediation analysis demonstrated that the link between anxiety/depression and DVT was mediated by: (1) higher AmygAC, (2) higher hs‐CRP, and (3) lower HRV ( < .05 for each). Anxiety and depression confer an attributable risk of DVT similar to other traditional DVT risk factors. Mechanisms appear to involve increased SNA, autonomic system activity, and inflammation. Future studies are needed to determine whether treatment of anxiety and depression can reduce DVT risk.

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Compressed primary‐to‐transmodal gradient is accompanied with subcortical alterations and linked to neurotransmitters and cellular signatures in major depressive disorder

Xiao,

Zhao,

Zang

et al. 2023

Human Brain Mapping

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Major depressive disorder (MDD) has been shown to involve widespread changes in low‐level sensorimotor and higher‐level cognitive functions. Recent research found that a primary‐to‐transmodal gradient could capture a cortical hierarchical organization ranging from perception and action to cognition in healthy subjects, but a prominent gradient dysfunction in MDD patients. However, whether and how this cortical gradient is linked to subcortical impairments and whether it is reflected in the microscale neurotransmitter systems and cell type‐specific transcriptional signatures remain largely unknown. Data were acquired from 323 MDD patients and 328 sex‐ and age‐matched healthy controls derived from the REST‐meta‐MDD project, and the human brain neurotransmitter systems density maps and gene expression data were drawn from two publicly available datasets. We investigated alterations of the primary‐to‐transmodal gradient in MDD patients and their correlations with clinical symptoms of depression and anxiety, as well as their paralleled subcortical impairments. The correlations between MDD‐related gradient alterations and densities of the neurotransmitter systems and gene expression information were assessed, respectively. The results demonstrated that MDD patients had a compressed primary‐to‐transmodal gradient accompanied by paralleled alterations in subcortical regions including the caudate, amygdala, and thalamus. The case–control gradient differences were spatially correlated with the densities of the neurotransmitter systems including the serotonin and dopamine receptors, and meanwhile with gene expression enriched in astrocytes, excitatory and inhibitory neuronal cells. These findings mapped the paralleled subcortical impairments in cortical hierarchical organization and also helped us understand the possible molecular and cellular substrates of the co‐occurrence of high‐level cognitive impairments with low‐level sensorimotor abnormalities in MDD.

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Multiscale neural modeling of resting-state fMRI reveals executive-limbic malfunction as a core mechanism in major depressive disorder

Cited by 15 publications

References 133 publications

From descriptive connectome to mechanistic connectome: Generative modeling in functional magnetic resonance imaging analysis

From descriptive connectome to mechanistic connectome: Generative modeling in functional magnetic resonance imaging analysis

Anxiety and depression are associated with heightened risk of incident deep vein thrombosis: Mediation through stress‐related neural mechanisms

Compressed primary‐to‐transmodal gradient is accompanied with subcortical alterations and linked to neurotransmitters and cellular signatures in major depressive disorder

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