Resting state functional connectivity and treatment response in late-life depression

Background: Problem solving therapy (PST) and “Engage”, a reward-exposure” based therapy, are important treatment options for late-life depression, given modest efficacy of antidepressants in this disorder. Abnormal function of the reward and default mode networks has been observed during depressive episodes. This study examined whether resting state functional connectivity (rsFC) of reward and DMN circuitries is associated with treatment outcomes. Methods: Thirty-two older adults with major depression (mean age = 72.7) were randomized to 9-weeks of either PST or “Engage”. We assessed rsFC at baseline and Week 6. We placed seeds in three a priori regions of interest: subgenual cingulate (sgACC), dorsal anterior cingulate cortex (dACC), and nucleus accumbens (NAcc). Outcome measures included the Hamilton Depression Rating Scale (HAMD) and the Behavioral Activation for Depression Scale (BADS).Results: In both PST and “Engage”, higher rsFC between the sgACC and middle temporal gyrus at baseline was associated with greater improvement in depression severity (HAMD). Preliminary findings suggested that in “Engage” treated participants, lower rsFC between the dACC and DMPFC at baseline was associated with HAM-D improvement. Finally, in Engage only, increased rsFC from baseline to Week 6 between NAcc and Superior Parietal Cortex was associated with increased BADS scores.Conclusion: The results suggest that patients who present with higher rsFC between the sgACC and a structure within the DMN may benefit from behavioral psychotherapies for late life depression. ‘Engage’ may lead to increased rsFC within the reward system reflecting a reconditioning of the reward systems by reward exposure.

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Defining data-driven subgroups of obsessive–compulsive disorder with different treatment responses based on resting-state functional connectivity

Translational Psychiatry ◽

10.1038/s41398-020-01045-4 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Seoyeon Kwak ◽

Minah Kim ◽

Taekwan Kim ◽

Yoobin Kwak ◽

Sanghoon Oh ◽

...

Keyword(s):

Functional Connectivity ◽

Treatment Response ◽

Obsessive Compulsive Disorder ◽

Resting State ◽

Predictive Biomarkers ◽

Support Vector ◽

Resting State Functional Connectivity ◽

Obsessive Compulsive ◽

Compulsive Disorder ◽

Treatment Responses

Abstract Characterization of obsessive–compulsive disorder (OCD), like other psychiatric disorders, suffers from heterogeneities in its symptoms and therapeutic responses, and identification of more homogeneous subgroups may help to resolve the heterogeneity. We aimed to identify the OCD subgroups based on resting-state functional connectivity (rsFC) and to explore their differences in treatment responses via a multivariate approach. From the resting-state functional MRI data of 107 medication-free OCD patients and 110 healthy controls (HCs), we selected rsFC features, which discriminated OCD patients from HCs via support vector machine (SVM) analyses. With the selected brain features, we subdivided OCD patients into subgroups using hierarchical clustering analyses. We identified 35 rsFC features that achieved a high sensitivity (82.74%) and specificity (76.29%) in SVM analyses. The OCD patients were subdivided into two subgroups, which did not show significant differences in their demographic and clinical backgrounds. However, one of the OCD subgroups demonstrated more impaired rsFC that was involved either within the default mode network (DMN) or between DMN brain regions and other network regions. This subgroup also showed both lower improvements in symptom severity in the 16-week follow-up visit and lower responder percentage than the other subgroup. Our results highlight that not only abnormalities within the DMN but also aberrant rsFC between the DMN and other networks may contribute to the treatment response and support the importance of these neurobiological alterations in OCD patients. We suggest that abnormalities in these connectivity may play predictive biomarkers of treatment response, and aid to build more optimal treatment strategies.

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Functional Connectivity in Late-Life Depression Using Resting-State Functional Magnetic Resonance Imaging

American Journal of Geriatric Psychiatry ◽

10.1097/jgp.0b013e3181cabd0e ◽

2010 ◽

Vol 18 (7) ◽

pp. 643-651 ◽

Cited By ~ 63

Author(s):

Eva R. Kenny ◽

John T. O’Brien ◽

David A. Cousins ◽

Jonathan Richardson ◽

Alan J. Thomas ◽

...

Keyword(s):

Magnetic Resonance Imaging ◽

Magnetic Resonance ◽

Functional Connectivity ◽

Functional Magnetic Resonance Imaging ◽

Resting State ◽

Late Life ◽

Functional Magnetic Resonance ◽

Resonance Imaging ◽

Late Life Depression

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Resting State Functional Connectivity Biomarkers of Treatment Response in Mood Disorders: A Review

Frontiers in Psychiatry ◽

10.3389/fpsyt.2021.565136 ◽

2021 ◽

Vol 12 ◽

Author(s):

Joseph J. Taylor ◽

Hatice Guncu Kurt ◽

Amit Anand

Keyword(s):

Functional Connectivity ◽

Treatment Response ◽

Mood Disorders ◽

Resting State ◽

Network Models ◽

Evidence Base ◽

Brain Network ◽

Treatment Modalities ◽

Resting State Functional Connectivity ◽

Graph Theory Analysis

There are currently no validated treatment biomarkers in psychiatry. Resting State Functional Connectivity (RSFC) is a popular method for investigating the neural correlates of mood disorders, but the breadth of the field makes it difficult to assess progress toward treatment response biomarkers. In this review, we followed general PRISMA guidelines to evaluate the evidence base for mood disorder treatment biomarkers across diagnoses, brain network models, and treatment modalities. We hypothesized that no treatment biomarker would be validated across these domains or with independent datasets. Results are organized, interpreted, and discussed in the context of four popular analytic techniques: (1) reference region (seed-based) analysis, (2) independent component analysis, (3) graph theory analysis, and (4) other methods. Cortico-limbic connectivity is implicated across studies, but there is no single biomarker that spans analyses or that has been replicated in multiple independent datasets. We discuss RSFC limitations and future directions in biomarker development.

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