Resting-state functional connectivity predictors of treatment response in schizophrenia – A systematic review and meta-analysis

2021 ◽  
Vol 237 ◽  
pp. 153-165
Author(s):  
Urvakhsh Meherwan Mehta ◽  
Ferose Azeez Ibrahim ◽  
Manu S. Sharma ◽  
Ganesan Venkatasubramanian ◽  
Jagadisha Thirthalli ◽  
...  
2021 ◽  
Vol 14 ◽  
Author(s):  
Preeti Sinha ◽  
Himanshu Joshi ◽  
Dhruva Ithal

Introduction: Electroconvulsive therapy (ECT) is a commonly used brain stimulation treatment for treatment-resistant or severe depression. This study was planned to find the effects of ECT on brain connectivity by conducting a systematic review and coordinate-based meta-analysis of the studies performing resting state fMRI (rsfMRI) in patients with depression receiving ECT.Methods: We systematically searched the databases published up to July 31, 2020, for studies in patients having depression that compared resting-state functional connectivity (rsFC) before and after a course of pulse wave ECT. Meta-analysis was performed using the activation likelihood estimation method after extracting details about coordinates, voxel size, and method for correction of multiple comparisons corresponding to the significant clusters and the respective rsFC analysis measure with its method of extraction.Results: Among 41 articles selected for full-text review, 31 articles were included in the systematic review. Among them, 13 articles were included in the meta-analysis, and a total of 73 foci of 21 experiments were examined using activation likelihood estimation in 10 sets. Using the cluster-level interference method, one voxel-wise analysis with the measure of amplitude of low frequency fluctuations and one seed-voxel analysis with the right hippocampus showed a significant reduction (p < 0.0001) in the left cingulate gyrus (dorsal anterior cingulate cortex) and a significant increase (p < 0.0001) in the right hippocampus with the right parahippocampal gyrus, respectively. Another analysis with the studies implementing network-wise (posterior default mode network: dorsomedial prefrontal cortex) resting state functional connectivity showed a significant increase (p < 0.001) in bilateral posterior cingulate cortex. There was considerable variability as well as a few key deficits in the preprocessing and analysis of the neuroimages and the reporting of results in the included studies. Due to lesser studies, we could not do further analysis to address the neuroimaging variability and subject-related differences.Conclusion: The brain regions noted in this meta-analysis are reasonably specific and distinguished, and they had significant changes in resting state functional connectivity after a course of ECT for depression. More studies with better neuroimaging standards should be conducted in the future to confirm these results in different subgroups of depression and with varied aspects of ECT.


2021 ◽  
Vol 124 ◽  
pp. 108336
Author(s):  
Anita L. Dharan ◽  
Stephen C. Bowden ◽  
Alan Lai ◽  
Andre D.H. Peterson ◽  
Mike W.-L. Cheung ◽  
...  

2019 ◽  
Vol 211 ◽  
pp. 10-20 ◽  
Author(s):  
Nathan K. Chan ◽  
Julia Kim ◽  
Parita Shah ◽  
Eric E. Brown ◽  
Eric Plitman ◽  
...  

2018 ◽  
Vol 43 (5) ◽  
pp. 298-316 ◽  
Author(s):  
Sabrina K. Syan ◽  
Mara Smith ◽  
Benicio N. Frey ◽  
Raheem Remtulla ◽  
Flavio Kapczinski ◽  
...  

2013 ◽  
Vol 214 (3) ◽  
pp. 313-321 ◽  
Author(s):  
Carmen Andreescu ◽  
Dana L. Tudorascu ◽  
Meryl A. Butters ◽  
Erica Tamburo ◽  
Meenal Patel ◽  
...  

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Seoyeon Kwak ◽  
Minah Kim ◽  
Taekwan Kim ◽  
Yoobin Kwak ◽  
Sanghoon Oh ◽  
...  

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.


2021 ◽  
Vol 12 ◽  
Author(s):  
Joseph J. Taylor ◽  
Hatice Guncu Kurt ◽  
Amit Anand

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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