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

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.

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

2013 ◽  
Vol 214 (3) ◽  
pp. 313-321 ◽  
Author(s):  
Carmen Andreescu ◽  
Dana L. Tudorascu ◽  
Meryl A. Butters ◽  
Erica Tamburo ◽  
Meenal Patel ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Treatment Response ◽  
Resting State ◽  
Late Life ◽  
Late Life Depression ◽  
Resting State Functional Connectivity

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

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.

2.36 RESTING-STATE FUNCTIONAL CONNECTIVITY OF THE AMYGDALA IN HEALTHY OFFSPRING OF PARENTS WITH MOOD DISORDERS

2016 ◽  
Vol 55 (10) ◽  
pp. S132
Author(s):  
Sara M. Leslie ◽  
Lara Foland-Ross ◽  
Kalpa Bhattacharjee ◽  
Laila Soudi ◽  
Alexander Onopa ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Mood Disorders ◽  
Resting State ◽  
Resting State Functional Connectivity

scholarly journals Advanced Meditation Alters Resting-State Brain Network Connectivity Correlating With Improved Mindfulness

2021 ◽  
Vol 12 ◽  
Author(s):  
Ramana V. Vishnubhotla ◽  
Rupa Radhakrishnan ◽  
Kestas Kveraga ◽  
Rachael Deardorff ◽  
Chithra Ram ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Network Connectivity ◽  
Blood Oxygen Level Dependent ◽  
Brain Network ◽  
Resting State Functional Connectivity ◽  
Blood Oxygen Level ◽  
Default Mode ◽  
Before And After ◽  
Brain Network Connectivity

Purpose: The purpose of this study was to investigate the effect of an intensive 8-day Samyama meditation program on the brain functional connectivity using resting-state functional MRI (rs-fMRI).Methods: Thirteen Samyama program participants (meditators) and 4 controls underwent fMRI brain scans before and after the 8-day residential meditation program. Subjects underwent fMRI with a blood oxygen level dependent (BOLD) contrast at rest and during focused breathing. Changes in network connectivity before and after Samyama program were evaluated. In addition, validated psychological metrics were correlated with changes in functional connectivity.Results: Meditators showed significantly increased network connectivity between the salience network (SN) and default mode network (DMN) after the Samyama program (p < 0.01). Increased connectivity within the SN correlated with an improvement in self-reported mindfulness scores (p < 0.01).Conclusion: Samyama, an intensive silent meditation program, favorably increased the resting-state functional connectivity between the salience and default mode networks. During focused breath watching, meditators had lower intra-network connectivity in specific networks. Furthermore, increased intra-network connectivity correlated with improved self-reported mindfulness after Samyama.Clinical Trials Registration: [https://clinicaltrials.gov], Identifier: [NCT04366544]. Registered on 4/17/2020.

S96. Peripheral Inflammation and Resting-State Functional Connectivity in Adolescents With Mood Disorders

2019 ◽  
Vol 85 (10) ◽  
pp. S334
Author(s):  
Erin Begnel ◽  
Michelle Thai ◽  
Nathan Horek ◽  
Xinyang Zhou ◽  
Anvita Singh ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Mood Disorders ◽  
Resting State ◽  
Peripheral Inflammation ◽  
Resting State Functional Connectivity

scholarly journals Resting state functional connectivity patterns as biomarkers of treatment response to escitalopram in patients with major depressive disorder

2021 ◽  
Author(s):  
Marieke A. G. Martens ◽  
Nicola Filippini ◽  
Catherine J. Harmer ◽  
Beata R. Godlewska
Keyword(s):  
Functional Connectivity ◽  
Treatment Response ◽  
Resting State ◽  
Rating Scale ◽  
Antidepressant Treatment ◽  
Resting State Functional Connectivity ◽  
The Right ◽  
Treatment Responsivity ◽  
Response Biomarkers

Abstract Rational With no available response biomarkers, matching an appropriate antidepressant to an individual can be a lengthy process. Improving understanding of processes underlying treatment responsivity in depression is crucial for facilitating work on response biomarkers. Objectives To identify differences in patterns of pre-treatment resting-state functional connectivity (rsFC) that may underlie response to antidepressant treatment. Methods After a baseline MRI scan, thirty-four drug-free patients with depression were treated with an SSRI escitalopram 10 mg daily for 6 weeks; response was defined as ≥ 50% decrease in Hamilton Depression Rating Scale (HAMD) score. Thirty-one healthy controls had a baseline clinical assessment and scan. Healthy participants did not receive treatment. Results Twenty-one (62%) of patients responded to escitalopram. Treatment responsivity was associated with enhanced rsFC of the right fronto-parietal network (FPN)—with the posterior DMN, somatomotor network (SMN) and somatosensory association cortex. The lack of treatment response was characterized by reduced rsFC: of the bilateral FPN with the contralateral SMN, of the right FPN with the posterior DMN, and of the extended sensorimotor auditory area with the inferior parietal lobule (IPL) and posterior DMN. Reduced rsFC of the posterior DMN with IPL was seen in treatment responders, although only when compared with HC. Conclusions The study supports the role of resting-state networks in response to antidepressant treatment, and in particular the central role of the frontoparietal and default mode networks.

Resting State Functional Connectivity as a Potential Biomarker of Depression Treatment Response Across Two Independent Samples

2020 ◽  
Vol 87 (9) ◽  
pp. S17
Author(s):  
Robin Aupperle ◽  
Timothy McDermott ◽  
Elisabeth Akeman ◽  
Namik Kirlic ◽  
Jessica Santiago ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Treatment Response ◽  
Resting State ◽  
Depression Treatment ◽  
Resting State Functional Connectivity ◽  
Potential Biomarker
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