scholarly journals An evaluation of the efficacy, reliability, and sensitivity of motion correction strategies for resting-state functional MRI

2017 ◽  
Author(s):  
Linden Parkes ◽  
Ben Fulcher ◽  
Murat Yücel ◽  
Alex Fornito
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Degrees Of Freedom ◽  
Best Practice ◽  
Low Cost ◽  
Head Motion ◽  
Healthy Controls ◽  
Obsessive Compulsive ◽  
Compulsive Disorder ◽  
Motion Parameters

AbstractEstimates of functional connectivity derived from resting-state functional magnetic resonance imaging (rs-fMRI) are sensitive to artefacts caused by in-scanner head motion. This susceptibility has motivated the development of numerous denoising methods designed to mitigate motion-related artefacts. Here, we compare popular retrospective rs-fMRI denoising methods, such as regression of head motion parameters and mean white matter (WM) and cerebrospinal fluid (CSF) (with and without expansion terms), aCompCor, volume censoring (e.g., scrubbing and spike regression), global signal regression and ICA-AROMA, combined into 19 different pipelines. These pipelines were evaluated across five different quality control benchmarks in four independent datasets associated with varying levels of motion. Pipelines were benchmarked by examining the residual relationship between in-scanner movement and functional connectivity after denoising; the effect of distance on this residual relationship; whole-brain differences in functional connectivity between high- and low-motion healthy controls (HC); the temporal degrees of freedom lost during denoising; and the test-retest reliability of functional connectivity estimates. We also compared the sensitivity of each pipeline to clinical differences in functional connectivity in independent samples of schizophrenia and obsessive-compulsive disorder. Our results indicate that (1) simple linear regression of regional fMRI time series against head motion parameters and WM/CSF signals (with or without expansion terms) is not sufficient to remove head motion artefacts; (2) aCompCor pipelines may only be viable in low-motion data; (3) volume censoring performs well at minimising motion-related artefact but a major benefit of this approach derives from the exclusion of high-motion individuals; (4) while not as effective as volume censoring, ICA-AROMA performed well across our benchmarks for relatively low cost in terms of data loss; and (5) group comparisons in functional connectivity between healthy controls and schizophrenia patients are highly dependent on preprocessing strategy. We offer some recommendations for best practice and outline some simple analyses to facilitate transparent reporting of the degree to which a given set of findings may be affected by motion-related artefact.

The impact of childhood trauma on thalamic functional connectivity in patients with obsessive–compulsive disorder

2020 ◽  
pp. 1-10
Author(s):  
Minyi Chu ◽  
Tingting Xu ◽  
Yi Wang ◽  
Pei Wang ◽  
Qiumeng Gu ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Obsessive Compulsive Disorder ◽  
Childhood Trauma ◽  
Resting State ◽  
Healthy Controls ◽  
Resting State Functional Connectivity ◽  
Obsessive Compulsive ◽  
Predisposing Factor ◽  
Compulsive Disorder ◽  
The Impact

Abstract Background Childhood trauma is a vulnerability factor for the development of obsessive–compulsive disorder (OCD). Empirical findings suggest that trauma-related alterations in brain networks, especially in thalamus-related regions, have been observed in OCD patients. However, the relationship between childhood trauma and thalamic connectivity in patients with OCD remains unclear. The present study aimed to examine the impact of childhood trauma on thalamic functional connectivity in OCD patients. Methods Magnetic resonance imaging resting-state scans were acquired in 79 patients with OCD, including 22 patients with a high level of childhood trauma (OCD_HCT), 57 patients with a low level of childhood trauma (OCD_LCT) and 47 healthy controls. Seven thalamic subdivisions were chosen as regions of interest (ROIs) to examine the group difference in thalamic ROIs and whole-brain resting-state functional connectivity (rsFC). Results We found significantly decreased caudate-thalamic rsFC in OCD patients as a whole group and also in OCD_LCT patients, compared with healthy controls. However, OCD_HCT patients exhibited increased thalamic rsFC with the prefrontal cortex when compared with both OCD_LCT patients and healthy controls. Conclusions Taken together, OCD patients with high and low levels of childhood trauma exhibit different pathological alterations in thalamic rsFC, suggesting that childhood trauma may be a predisposing factor for some OCD patients.

Frontostriatal functional connectivity correlates with repetitive behaviour across autism spectrum disorder and obsessive–compulsive disorder

2018 ◽  
Vol 49 (13) ◽  
pp. 2247-2255 ◽  
Author(s):  
Sophie E.A. Akkermans ◽  
Nicole Rheinheimer ◽  
Muriel M.K. Bruchhage ◽  
Sarah Durston ◽  
Daniel Brandeis ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Functional Connectivity ◽  
Resting State ◽  
Premotor Cortex ◽  
Autism Spectrum ◽  
Healthy Controls ◽  
Resting State Functional Connectivity ◽  
Obsessive Compulsive ◽  
Repetitive Behaviour ◽  
Compulsive Disorder

AbstractBackgroundAutism spectrum disorder (ASD) and obsessive–compulsive disorder (OCD) are neurodevelopmental disorders with considerable overlap in terms of their defining symptoms of compulsivity/repetitive behaviour. Little is known about the extent to which ASD and OCD have common versus distinct neural correlates of compulsivity. Previous research points to potentially common dysfunction in frontostriatal connectivity, but direct comparisons in one study are lacking. Here, we assessed frontostriatal resting-state functional connectivity in youth with ASD or OCD, and healthy controls. In addition, we applied a cross-disorder approach to examine whether repetitive behaviour across ASD and OCD has common neural substrates.MethodsA sample of 78 children and adolescents aged 8–16 years was used (ASD n = 24; OCD n = 25; healthy controls n = 29), originating from the multicentre study COMPULS. We tested whether diagnostic group, repetitive behaviour (measured with the Repetitive Behavior Scale-Revised) or their interaction was associated with resting-state functional connectivity of striatal seed regions.ResultsNo diagnosis-specific differences were detected. The cross-disorder analysis, on the other hand, showed that increased functional connectivity between the left nucleus accumbens (NAcc) and a cluster in the right premotor cortex/middle frontal gyrus was related to more severe symptoms of repetitive behaviour.ConclusionsWe demonstrate the fruitfulness of applying a cross-disorder approach to investigate the neural underpinnings of compulsivity/repetitive behaviour, by revealing a shared alteration in functional connectivity in ASD and OCD. We argue that this alteration might reflect aberrant reward or motivational processing of the NAcc with excessive connectivity to the premotor cortex implementing learned action patterns.

Alterations in resting-state local functional connectivity in obsessive-compulsive disorder

2019 ◽  
Vol 245 ◽  
pp. 113-119 ◽  
Author(s):  
HuiHui Hao ◽  
Chuang Chen ◽  
WeiBing Mao ◽  
Wei Xia ◽  
ZhongQuan Yi ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Obsessive Compulsive Disorder ◽  
Resting State ◽  
Obsessive Compulsive ◽  
Compulsive Disorder ◽  
Local Functional

Functional connectivity in fronto-subcortical circuitry during the resting state in obsessive-compulsive disorder

2010 ◽  
Vol 474 (3) ◽  
pp. 158-162 ◽  
Author(s):  
Joon Hwan Jang ◽  
Jae-Hun Kim ◽  
Wi Hoon Jung ◽  
Jung-Seok Choi ◽  
Myung Hun Jung ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Obsessive Compulsive Disorder ◽  
Resting State ◽  
Obsessive Compulsive ◽  
Compulsive Disorder

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.

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