Resting-State Functional Connectivity in Treatment-Resistant Depression

Lui, Su; Wu, Qisong; Qiu, Lihua; Yang, Xun; Kuang, Weihong; Chan, Raymond C.K.; Huang, Xiaoqi; Kemp, Graham J.; Mechelli, Andrea; Gong, Qiyong

doi:10.1176/appi.ajp.2010.10101419

Cited by 289 publications

(205 citation statements)

References 56 publications

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“…Our results indicate that the integrated measure of MCM serves as a proxy for EC in brain states. Our approach might be particularly useful for investigating other signaling hierarchies in higher cognition or in brain disorders involving, e.g., hippocampal-cortical circuits in Alzheimer's disease (28) or frontomidbrain interactions in major depression (29).…”

mentioning

confidence: 99%

Metabolic connectivity mapping reveals effective connectivity in the resting human brain

Riedl

Utz

Castrillón

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

103

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Directionality of signaling among brain regions provides essential information about human cognition and disease states. Assessing such effective connectivity (EC) across brain states using functional magnetic resonance imaging (fMRI) alone has proven difficult, however. We propose a novel measure of EC, termed metabolic connectivity mapping (MCM), that integrates undirected functional connectivity (FC) with local energy metabolism from fMRI and positron emission tomography (PET) data acquired simultaneously. This method is based on the concept that most energy required for neuronal communication is consumed postsynaptically, i.e., at the target neurons. We investigated MCM and possible changes in EC within the physiological range using “eyes open” versus “eyes closed” conditions in healthy subjects. Independent of condition, MCM reliably detected stable and bidirectional communication between early and higher visual regions. Moreover, we found stable top-down signaling from a frontoparietal network including frontal eye fields. In contrast, we found additional top-down signaling from all major clusters of the salience network to early visual cortex only in the eyes open condition. MCM revealed consistent bidirectional and unidirectional signaling across the entire cortex, along with prominent changes in network interactions across two simple brain states. We propose MCM as a novel approach for inferring EC from neuronal energy metabolism that is ideally suited to study signaling hierarchies in the brain and their defects in brain disorders.

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mentioning

confidence: 99%

Metabolic connectivity mapping reveals effective connectivity in the resting human brain

Riedl

Utz

Castrillón

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

103

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“…Much of the current literature suggests that lower connectivity between the amygdala and the prefrontal cortex indexes higher levels of anxiety in both healthy controls (Coombs et al, 2014;Kim et al, 2011;Loewenstern et al, 2014;Long et al, 2014) and patients (Dodhia et al, 2014;Keutmann et al, 2014). This lower connectivity was also observed in major depression (MDD) (Lui et al, 2011;Tang et al, 2013), BD (Anand et al, 2009;Chepenik et al, 2010) and obsessive-compulsive disorder (OCD) (Gottlich et al, 2014). Likewise, connectivity between prefrontal regions and striatal regions has been observed to either increase or decrease in several of the same disorders, for instance in OCD (Gottlich et al, 2014;Sakai et al 2011) and…”

Section: Accepted Manuscriptmentioning

confidence: 82%

Individual differences in common factors of emotional traits and executive functions predict functional connectivity of the amygdala

et al. 2015

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“…Thus our finding of decreased connectivity between a DAN network hub and orbitofrontal gyrus may reflect the well-replicated finding of decreased cognitive control over emotion processing in MDD and prevailing neural models of MDD that highlight decreased modulatory control of prefrontal cortical brain regions over limbic brain regions, particularly in the context of emotion processing and emotion regulation (Johnstone et al, 2007;Joormann and Gotlib, 2010;Ray et al, 2005). Additionally, recent studies investigating response to antidepressant medications in MDD highlight that treatment response is associated with increased connectivity between prefrontal cortical and limbic brain regions, possibly implicating greater inhibitory control over neural circuits that process emotions in positive treatment response (Alexopoulos et al, 2012;Lai and Wu, 2012;Lui et al, 2011;Wu et al, 2011;Yang et al, 2014). The somatic subscale as defined by Siegert et al (2009) encompasses mood (sadness), vegetative (eg, sleep, fatigue), attention/concentration, and motivation-related (interest, energy) symptoms and thus may be especially sensitive to disruptions in circuits that underlie both attention and goal-directed behaviors.…”

Section: Discussionmentioning

confidence: 99%

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

Crowther

Smoski

Minkel

et al. 2015

Neuropsychopharmacol

113

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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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Resting-State Functional Connectivity in Treatment-Resistant Depression

Cited by 289 publications

References 56 publications

Metabolic connectivity mapping reveals effective connectivity in the resting human brain

Metabolic connectivity mapping reveals effective connectivity in the resting human brain

Individual differences in common factors of emotional traits and executive functions predict functional connectivity of the amygdala

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

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