Abnormal resting-state cerebellar–cerebral functional connectivity in treatment-resistant depression and treatment sensitive depression

Guo, Wenbin; Liu, Feng; Zhang, Xue; Gao, Keming; Liu, Zhening; Xiao, Changfu; Chen, Huafu; Zhao, Jingping

doi:10.1016/j.pnpbp.2013.01.010

Cited by 165 publications

(105 citation statements)

References 46 publications

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“…Of note, a number of findings highlight altered connectivity with middle temporal gyrus, a brain area that is involved in imitation, social cognition, and mentalizing, implicating brain regions that code for awareness, salience, and cognitive control of mental states in the pathophysiology of MDD. Additionally, pretreatment connectivity of the anterior insula and intraparietal sulcus predicted response to psychotherapy, a pattern of results consistent with other reports of pretreatment anterior insula metabolism and connectivity (Downar et al, 2013) as predictors of response to psychotherapy and parietal lobe connectivity predicting response to antidepressant medication treatment (Guo et al, 2013a;Wu et al, 2011). More generally, the present study adds to the nascent but growing body of evidence linking pretreatment neuroimaging endophenotypes to antidepressant treatment outcomes .…”

Section: Discussionsupporting

confidence: 88%

Resting-State Connectivity Predictors of Response to Psychotherapy in Major Depressive Disorder

Crowther

Smoski

Minkel

et al. 2015

Neuropsychopharmacol

117

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Despite the heterogeneous symptom presentation and complex etiology of major depressive disorder (MDD), functional neuroimaging studies have shown with remarkable consistency that dysfunction in mesocorticolimbic brain systems are central to the disorder. Relatively less research has focused on the identification of biological markers of response to antidepressant treatment that would serve to improve the personalized delivery of empirically supported antidepressant interventions. In the present study, we investigated whether resting-state functional brain connectivity (rs-fcMRI) predicted response to Behavioral Activation Treatment for Depression, an empirically validated psychotherapy modality designed to increase engagement with rewarding stimuli and reduce avoidance behaviors. Twenty-three unmedicated outpatients with MDD and 20 matched nondepressed controls completed rs-fcMRI scans after which the MDD group received an average of 12 sessions of psychotherapy. The mean change in Beck Depression Inventory-II scores after psychotherapy was 12.04 points, a clinically meaningful response. Resting-state neuroimaging data were analyzed with a seed-based approach to investigate functional connectivity with four canonical resting-state networks: the default mode network, the dorsal attention network, the executive control network, and the salience network. At baseline, the MDD group was characterized by relative hyperconnectivity of multiple regions with precuneus, anterior insula, dorsal anterior cingulate cortex (dACC), and left dorsolateral prefrontal cortex seeds and by relative hypoconnectivity with intraparietal sulcus, anterior insula, and dACC seeds. Additionally, connectivity of the precuneus with the left middle temporal gyrus and connectivity of the dACC with the parahippocampal gyrus predicted the magnitude of pretreatment MDD symptoms. Hierarchical linear modeling revealed that response to psychotherapy in the MDD group was predicted by pretreatment connectivity of the right insula with the right middle temporal gyrus and the left intraparietal sulcus with the orbital frontal cortex. These results add to the nascent body of literature investigating pretreatment rs-fcMRI predictors of antidepressant treatment response and is the first study to examine rs-fcMRI predictors of response to psychotherapy.

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Section: Discussionsupporting

confidence: 88%

Resting-State Connectivity Predictors of Response to Psychotherapy in Major Depressive Disorder

Crowther

Smoski

Minkel

et al. 2015

Neuropsychopharmacol

117

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“…In MDD, increased connectivity between the DMN and both the subgenual anterior cingulate cortex (sgACC) and thalamus has been observed (Davey et al, 2012; Greicius et al, 2007; Li et al, 2013; Posner et al, 2013). Decreased connectivity between the DMN and the cerebellum and prefrontal cortex, and increased connectivity between the DMN and the visual system and parahippocampal gyrus (Guo et al, 2013) have also been noted. Outside the DMN, corticolimbic connectivity may be impaired (Cao et al, 2012; Wang et al, 2012), and inter-hemispheric connectivity may be reduced (Guo et al, 2013; Wang et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

“…Decreased connectivity between the DMN and the cerebellum and prefrontal cortex, and increased connectivity between the DMN and the visual system and parahippocampal gyrus (Guo et al, 2013) have also been noted. Outside the DMN, corticolimbic connectivity may be impaired (Cao et al, 2012; Wang et al, 2012), and inter-hemispheric connectivity may be reduced (Guo et al, 2013; Wang et al, 2013). …”

Section: Introductionmentioning

confidence: 99%

Group differences in MEG-ICA derived resting state networks: Application to major depressive disorder

et al. 2015

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Functional magnetic resonance imaging (fMRI) studies have revealed the existence of robust, interconnected brain networks exhibiting correlated low frequency fluctuations during rest, which can be derived by examining inherent spatio-temporal patterns in functional scans independent of any a priori model. In order to explore the electrophysiological underpinnings of these networks, analogous techniques have recently been applied to magnetoencephalography (MEG) data, revealing similar networks that exhibit correlated low frequency fluctuations in the power envelope of beta band (14–30Hz) power. However, studies to date using this technique have concentrated on healthy subjects, and no method has yet been presented for group comparisons. We extended the ICA resting state MEG method to enable group comparisons, and demonstrate the technique in a sample of subjects with major depressive disorder (MDD). We found that the intrinsic resting state networks evident in fMRI appeared to be disrupted in individuals with MDD compared to healthy participants, particularly in the subgenual cingulate, although the electrophysiological correlates of this are unknown. Networks extracted from a combined group of healthy and MDD participants were examined for differences between groups. Individuals with MDD showed reduced correlations between the subgenual anterior cingulate (sgACC) and hippocampus in a network with primary nodes in the precentral and middle frontal gyri. Individuals with MDD also showed increased correlations between insulo-temporal nodes and amygdala compared to healthy controls. To further support our methods and findings, we present test/re-test reliability on independent recordings acquired within the same session. Our results demonstrate that group analyses are possible with the resting state MEG-independent components analysis (ICA) technique, highlighting a new pathway for analysis and discovery. This study also provides the first evidence of altered sgACC connectivity with a motor network. This finding, reliable across multiple sessions, suggests that the sgACC may partially mediate the psychomotor symptoms of MDD via synchronized changes in beta-band power, and expands the idea of the sgACC as a hub region mediating cognitive and emotional symptomatic domains in MDD. Findings of increased connectivity between the amygdala and cortical nodes further supports the role of amygdalar networks in mediated depressive symptomatology.

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“…Other studies have documented decreased intrinsic corticolimbic connectivity, including between the pregenual ACC and the thalamus, amygdala, and pallidostriatum, in depressive and bipolar mood disorders (Anand, Li, Wang, Lowe, & Dzemidzic, 2009). These findings highlight the possibility that mood-state and treatment-related factors such as treatment status (Anand, Li, Wang, Gardner, & Lowe, 2007) and treatment response (Guo et al, 2013), and exposure to early life stress (Cisler et al, 2013), influence the nature of the relation between MDD and neural activation. Despite the specific directional discrepancies, these findings collectively suggest that depression is a disorder of disrupted global and focal connectivity.…”

Section: How the Neuroscience Of Depression Can Inform Assessmentmentioning

confidence: 83%

The neuroscience of depression: Implications for assessment and intervention

Singh

Gotlib

2014

Behaviour Research and Therapy

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Major Depressive Disorder (MDD) is among the most prevalent of all psychiatric disorders and is the single most burdensome disease worldwide. In attempting to understand the profound deficits that characterize MDD across multiple domains of functioning, researchers have identified aberrations in brain structure and function in individuals diagnosed with this disorder. In this review we synthesize recent data from human neuroimaging studies in presenting an integrated neural network framework for understanding the impairments experienced by individuals with MDD. We discuss the implications of these findings for assessment of and intervention for MDD. We conclude by offering directions for future research that we believe will advance our understanding of neural factors that contribute to the etiology and course of depression, and to recovery from this debilitating disorder.

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Abnormal resting-state cerebellar–cerebral functional connectivity in treatment-resistant depression and treatment sensitive depression

Cited by 165 publications

References 46 publications

Resting-State Connectivity Predictors of Response to Psychotherapy in Major Depressive Disorder

Resting-State Connectivity Predictors of Response to Psychotherapy in Major Depressive Disorder

Group differences in MEG-ICA derived resting state networks: Application to major depressive disorder

The neuroscience of depression: Implications for assessment and intervention

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