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Multiscale Neural Modeling of Resting-State Fmri Reveals Executive-Limbic Malfunction As a Core Mechanism in Major Depressive Disorder

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Multiscale Neural Modeling of Resting-State Fmri Reveals Executive-Limbic Malfunction As a Core Mechanism in Major Depressive Disorder medRxiv preprint doi: https://doi.org/10.1101/2020.04.29.20084855; this version posted May 5, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license . 1 Multiscale Neural Modeling of Resting-state fMRI Reveals Executive-Limbic Malfunction as a Core Mechanism in Major Depressive Disorder Guoshi Li1, Yujie Liu1,2, Yanting Zheng1,2, Ye Wu1, Danian Li3, Xinyu Liang2, Yaoping Chen2, Ying Cui4, Pew-Thian Yap1, Shijun Qiu*5, Han Zhang*1, Dinggang Shen*1,6 1 Department of Radiology and BRIC University of North Carolina at Chapel Hill Chapel Hill, NC USA 2 The First School of Clinical Medicine Guangzhou University of Chinese Medicine Guangzhou, China 3 Cerebropathy Center The First Affiliated Hospital of Guangzhou University of Chinese Medicine Guangzhou, China 4 Cerebropathy Center The Third Affiliated Hospital of Guangzhou Medical University Guangzhou, China 5 Department of Radiology The First Affiliated Hospital of Guangzhou University of Chinese Medicine Guangzhou, China 6 Department of Brain and Cognitive Engineering Korea University Seoul, Korea *Correspondence Han Zhang ([email protected]) Shijun Qiu ([email protected]) Dinggang Shen ([email protected]) NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice. medRxiv preprint doi: https://doi.org/10.1101/2020.04.29.20084855; this version posted May 5, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license . 2 1 Abstract 2 Computational neuroimaging has played a central role in characterizing functional abnormalities in major 3 depressive disorder (MDD). However, most of existing non-invasive analysis tools based on functional 4 magnetic resonance imaging (fMRI) are largely descriptive and superficial, thus cannot offer a deep 5 mechanistic understanding of neural circuit dysfunction in MDD. To overcome this limitation, we 6 significantly improved a previously developed Multiscale Neural Model Inversion (MNMI) framework 7 that can link mesoscopic neural interaction with macroscale network dynamics and enable the estimation 8 of both intra-regional and inter-regional effective connectivity. We applied the improved MNMI 9 approach to test two well-established competing hypotheses of MDD pathophysiology (default mode- 10 salience network disruption vs. executive-limbic network malfunction) based on resting-state fMRI with a 11 relatively large sample size. Results indicate that MDD is primarily characterized by aberrant circuit 12 interactions within the executive-limbic network, rather than the default mode-salience network. 13 Specifically, we observed reduced frontoparietal effective connectivity that potentially contributes to 14 hypoactivity in the dorsolateral prefrontal cortex (dlPFC), and decreased intrinsic inhibition combined 15 with increased excitation from the superior parietal cortex (SPC) that potentially leads to amygdala 16 hyperactivity, together resulting in connectivity imbalance in the PFC-amygdala circuit that pervades in 17 MDD. Moreover, the model unravels reduced PFC-to-hippocampus excitation but decreased SPC-to- 18 thalamus inhibition in MDD population that potentially leads to hypoactivity in hippocampus and 19 hyperactivity in thalamus, consistent with previous experimental data. Overall, our findings provide 20 strong support for the long-standing limbic-cortical dysregulation model in major depression but also 21 offer novel insights into the multi-scale pathophysiology of this deliberating disease. 22 Keywords: Neural mass model, Effective connectivity, Resting-state fMRI, Major depressive disorder, 23 Genetic algorithm, Computational neuroscience, Brain networks medRxiv preprint doi: https://doi.org/10.1101/2020.04.29.20084855; this version posted May 5, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license . 3 24 Introduction 25 Major depressive disorder (MDD) is a serious mental illness that is characterized by depressed mood, 26 diminished interests and impaired cognitive function (Otte et al., 2016). It is a leading cause of chronic 27 disability worldwide with a lifetime prevalence of up to 17% (Kessler et al., 2005). Despite the advances 28 in treatment modalities, it is estimated that up to 30% of patients do not respond to standard intervention 29 (Raymaekers et al., 2017) and 10%–30% develop treatment-resistant symptoms with increased disability 30 and suicidal tendency as well as a higher risk of relapse (Al-Harbi, 2012; Delaloye and Holtzheimer, 31 2014). To improve diagnosis and treatment of MDD, a better understanding of the pathophysiological 32 mechanisms of MDD is urgently needed. 33 Although the exact mechanisms underlying MDD remain unclear, clinical, neuroimaging and animal 34 studies are starting to offer promising insights into the neurobiological basis of this debilitating disease. 35 Preclinical stress models in rodents and non-human primates have reported disrupted excitatory and 36 inhibitory neurotransmission along with neuronal atrophy in cortical and limbic regions, leading to 37 structural and dynamical abnormalities (Musazzi et al., 2011; Duman et al., 2019). Consistently, 38 structural magnetic resonance imaging (MRI) and functional MRI (fMRI) have revealed both structural 39 and functional brain alterations in MDD (Otte et al., 2016; Lener et al., 2017). Understanding the 40 impaired interactions between brain regions is of particular importance as it is generally believed that 41 MDD can be characterized as a network disorder with aberrant connections among various brain areas 42 within networks (Menon, 2011; Dutta et al., 2014; Mulders et al., 2015; Brakowski et al., 2017). Indeed, 43 both task-based and resting-state fMRI (rs-fMRI) indicate disrupted connectivity among several 44 functional networks in the large-scale brain pathophysiology of MDD (Kerestes et al., 2014; Kaiser et al., 45 2015; Yang et al., 2016; Drysdale et al., 2017; Zhou et al., 2019). The first highly implicated large-scale 46 brain network is the default mode-salience network. The default model network (DMN) is activated in 47 the resting brain and consists of ventral anterior cingulate cortex (vACC), posterior cingulate cortex medRxiv preprint doi: https://doi.org/10.1101/2020.04.29.20084855; this version posted May 5, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license . 4 48 (PCC)/precuneus, medial prefrontal cortex (mPFC), and lateral and inferior parietal cortex (Raichle et al., 49 2001; Dutta et al., 2014). The DMN has been implicated in rumination and self-referential processing 50 (Cooney et al., 2010; Liston et al., 2014) and increased intrinsic connectivity in the DMN is hypothesized 51 to underlie excessive self-focused rumination in MDD (Cooney et al., 2010; Hamilton et al., 2015). The 52 salience network, on the other hand, is activated in response to salient stimuli including emotional pain, 53 empathy, metabolic stress and social rejection (Seeley et al., 2007; Hermans et al., 2014). Anchored in 54 the dorsal anterior cingulate cortex (dACC) and anterior insula, the salience network is involved in 55 integrating highly processed sensory information with visceral, autonomic and hedonic states, making it 56 particularly important for interoceptive-autonomic processing (Seeley et al., 2007). Disruption of DMN- 57 SAL connectivity has been reported in a number of MDD studies. Manoliu et al. (2014) found that MDD 58 patients showed increased FC between DMN and SAL, consistent with greater resting-state FC between 59 insula and anterior DMN (Avery et al., 2014). It has also been observed that FC between anterior DMN 60 and SAL was positively correlated with depression severity in post-stroke depression (Balaev et al., 61 2017). Using dynamic causal modeling of resting-state fMRI (rs-fMRI), we recently demonstrated that 62 MDD was mainly associated with reduced effective connectivity (EC) within the DMN, and between the 63 DMN and SAL networks (Li et al., 2020). These neuroimaging connectome studies support the notion 64 that aberrant mapping or engagement of the DMN by SAL results in excessive rumination and abnormal 65 interoceptive-autonomic processing in depression (Menon, 2011). 66 Another highly implicated neural system in MDD is the executive-limbic network. Opposite to the 67 DMN, the executive control network (EXE) is most active during cognitive tasks (Seeley et al., 2007) 68 and consists mainly of the dorsolateral prefrontal cortex (dlPFC) and posterior parietal cortex (PPC) 69 (Corbetta and Shulman, 2002; Rogers et al., 2004; Seeley et al., 2007). The executive network plays a 70 critical role in working memory maintenance, rule-based problem solving and decision making during 71 goal-directed cognitive tasks (Miller and Cohen, 2001; Petrides, 2005; Koechlin and Summerfield, 2007)
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