Use of an Individual-Level Approach to Identify Cortical Connectivity Biomarkers in Obsessive-Compulsive Disorder

Brennan, Brian P.; Wang, Danhong; Li, Meiling; Perriello, Chris; Ren, Jing; Elias, Jason A.; Kirk, Nathaniel P. Van; Krompinger, Jason W.; Pope, Harrison G.; Haber, Suzanne N.; Rauch, Scott L.; Baker, Justin T.; Liu, Hesheng

doi:10.1016/j.bpsc.2018.07.014

Cited by 40 publications

(39 citation statements)

References 75 publications

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“…Moreover, accurate qualification of between-network connectivity based on individualized ROIs will have particularly strong implications for clinical research, as recent studies have suggested that changes in between-network connectivity may signify normal brain development [63] as well as pathological changes [64]. The analytical framework developed in this study can be conveniently extended to the investigations of brain-behavior associations in clinical populations [65,66].…”

Section: Discussionmentioning

confidence: 99%

Performing group-level functional image analyses based on homologous functional regions mapped in individuals

et al. 2019

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Functional MRI (fMRI) studies have traditionally relied on intersubject normalization based on global brain morphology, which cannot establish proper functional correspondence between subjects due to substantial intersubject variability in functional organization. Here, we reliably identified a set of discrete, homologous functional regions in individuals to improve intersubject alignment of fMRI data. These functional regions demonstrated marked intersubject variability in size, position, and connectivity. We found that previously reported intersubject variability in functional connectivity maps could be partially explained by variability in size and position of the functional regions. Importantly, individual differences in network topography are associated with individual differences in task-evoked activations, suggesting that these individually specified regions may serve as the “localizer” to improve the alignment of task-fMRI data. We demonstrated that aligning task-fMRI data using the regions derived from resting state fMRI may lead to increased statistical power of task-fMRI analyses. In addition, resting state functional connectivity among these homologous regions is able to capture the idiosyncrasies of subjects and better predict fluid intelligence (gF) than connectivity measures derived from group-level brain atlases. Critically, we showed that not only the connectivity but also the size and position of functional regions are related to human behavior. Collectively, these findings suggest that identifying homologous functional regions across individuals can benefit a wide range of studies in the investigation of connectivity, task activation, and brain-behavior associations.

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Section: Discussionmentioning

confidence: 99%

Performing group-level functional image analyses based on homologous functional regions mapped in individuals

et al. 2019

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“…However, an individual-specific approach is critical in identifying biomarker and there is still need for implementing studies that detect functional brain circuit connections that track changes in global and dimension-specific symptom severity longitudinally, in the same patient. Recently, one study conducted in OCD patients [97] found that changes in connectivity pre-post treatment in the individual patient can predict YBOCS improvement, while connectivity was less predictive in template-based comparisons.…”

Section: Towards a Neurobiologically Based Approachmentioning

confidence: 99%

Diagnostic Issues in Early-Onset Obsessive-Compulsive Disorder and their Treatment Implications

Burchi

Pallanti

2019

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There is evidence that EO might be considered the neurodevelopmental subtype of OCD. Indeed there is evidence that different clusters of symptoms and dimensions at an early stage predict different trajectories in phenotype and that distinct neurocircuit patwhays underpin the progression of the disorder. Despite the development of high refractoriness in the course of the disorder, evidence suggests that EO may be particularly treatment responsive in the early stages, thus showing the need for early recognition and additional recovery oriented studies in this subgroup. Consistent with the neurodevelopmental perspective, immunity and glutamate neurotransmission are emerging as novel pathways for parsing out the neurobiology of OCD, the EO form in particular, supporting the implementation of new multisystemic models of the OCD phenotype. Brain connectivity patterns, immune and microbiome profiles are standing out as promising areas for biomarkers with the potential for targeted personalized therapies in EO.

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“…For the past few years, researchers have attempted to develop analytical methods that overcome such limitations, and machine-learning analyses have been one of those methods 31 , 32 . Owing to its advantages, few recent OCD studies have applied machine-learning algorithms with rsFC 17 , 33 , 34 . However, these studies have one or more of the following limitations: a relatively small sample size, involvement of medicated OCD patients in the analyses, or the absence of therapeutic response information.…”

Section: Introductionmentioning

confidence: 99%

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

Kwak

Kim

et al. 2020

Transl Psychiatry

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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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Use of an Individual-Level Approach to Identify Cortical Connectivity Biomarkers in Obsessive-Compulsive Disorder

Abstract: Our findings would seem to encourage the use of individual-level approaches to connectivity analyses to better delineate the cortical and subcortical networks underlying symptom severity and improvement at the dimensional level in OCD patients.

Cited by 40 publications

References 75 publications

Performing group-level functional image analyses based on homologous functional regions mapped in individuals

Performing group-level functional image analyses based on homologous functional regions mapped in individuals

Diagnostic Issues in Early-Onset Obsessive-Compulsive Disorder and their Treatment Implications

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

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