Dataset Information

A novel resting-state functional magnetic resonance imaging signature of resilience to recurrent depression.

ABSTRACT:

Background

A high proportion of patients with remitted major depressive disorder (MDD) will experience recurring episodes, whilst some develop resilience and remain in recovery. The neural basis of resilience to recurrence is elusive. Abnormal resting-state connectivity of the subgenual cingulate cortex (sgACC) was previously found in cross-sectional studies of MDD, suggesting its potential pathophysiological importance. The current study aimed to investigate whether resting-state connectivity to a left sgACC seed region distinguishes resilient patients from those developing recurring episodes.

Method

A total of 47 medication-free remitted MDD patients and 38 healthy controls underwent resting-state functional magnetic resonance imaging (fMRI) at baseline. Over 14 months, 30 patients remained resilient whilst 17 experienced a recurring episode.

Results

Attenuated interhemispheric left-to-right sgACC connectivity distinguished the resilient from the recurring-episode and control groups and was not correlated with residual depressive symptoms.

Conclusions

The current study revealed a neural signature of resilience to recurrence in MDD and thereby elucidates the role of compensatory adaptation in sgACC networks.

SUBMITTER: Workman CI

PROVIDER: S-EPMC5426313 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

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Publications

A novel resting-state functional magnetic resonance imaging signature of resilience to recurrent depression.

Workman C I CI Lythe K E KE McKie S S Moll J J Gethin J A JA Deakin J F W JF Elliott R R Zahn R R

Psychological medicine 20161108 4

<h4>Background</h4>A high proportion of patients with remitted major depressive disorder (MDD) will experience recurring episodes, whilst some develop resilience and remain in recovery. The neural basis of resilience to recurrence is elusive. Abnormal resting-state connectivity of the subgenual cingulate cortex (sgACC) was previously found in cross-sectional studies of MDD, suggesting its potential pathophysiological importance. The current study aimed to investigate whether resting-state connec ...[more]

PMID: 27821193

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Project description:Introduction: Functional movement disorder (FMD) is a type of functional neurological disorder characterized by abnormal movements that patients do not perceive as self-generated. Prior imaging studies show a complex pattern of altered activity, linking regions of the brain involved in emotional responses, motor control, and agency. This study aimed to better characterize these relationships by building a classifier using a support vector machine to accurately distinguish between 61 FMD patients and 59 healthy controls using features derived from resting-state functional magnetic resonance imaging. Materials and Methods: First, we selected 66 seed regions based on prior related studies, then we calculated the full correlation matrix between them before performing recursive feature elimination to winnow the feature set to the most predictive features and building the classifier. Results: We identified 29 features of interest that were highly predictive of the FMD condition, classifying patients and controls with 80% accuracy. Several key features included regions in the right sensorimotor cortex, left dorsolateral prefrontal cortex, left cerebellum, and left posterior insula. Conclusions: The features selected by the model highlight the importance of the interconnected relationship between areas associated with emotion, reward, and sensorimotor integration, potentially mediating communication between regions associated with motor function, attention, and executive function. Exploratory machine learning was able to identify this distinctive abnormal pattern, suggesting that alterations in functional linkages between these regions may be a consistent feature of the condition in many FMD patients. Clinical-Trials.gov ID: NCT00500994 Impact statement Our research presents novel results that further elucidate the pathophysiology of functional movement disorder (FMD) with a machine learning model that classifies FMD and healthy controls correctly 80% of the time. Herein, we demonstrate how known differences in resting-state functional magnetic resonance imaging connectivity in FMD patients can be leveraged to better understand the complex pattern of neural changes in these patients. Knowing that there are measurable predictable differences in brain activity in patients with FMD may help both clinicians and patients conceptualize and better understand the illness at the point of diagnosis and during treatment. Our methods demonstrate how an effective combination of machine learning and qualitative approaches to analyzing functional brain connectivity can enhance our understanding of abnormal patterns of brain activity in FMD patients.

Project description:Functional magnetic resonance imaging (fMRI) during a resting-state condition can reveal the co-activation of specific brain regions in distributed networks, called resting-state networks, which are selected by independent component analysis (ICA) of the fMRI data. One of the major difficulties with component analysis is the automatic selection of the ICA features related to brain activity. In this study we describe a method designed to automatically select networks of potential functional relevance, specifically, those regions known to be involved in motor function, visual processing, executive functioning, auditory processing, memory, and the default-mode network. To do this, image analysis was based on probabilistic ICA as implemented in FSL software. After decomposition, the optimal number of components was selected by applying a novel algorithm which takes into account, for each component, Pearson's median coefficient of skewness of the spatial maps generated by FSL, followed by clustering, segmentation, and spectral analysis. To evaluate the performance of the approach, we investigated the resting-state networks in 25 subjects. For each subject, three resting-state scans were obtained with a Siemens Allegra 3 T scanner (NYU data set). Comparison of the visually and the automatically identified neuronal networks showed that the algorithm had high accuracy (first scan: 95%, second scan: 95%, third scan: 93%) and precision (90%, 90%, 84%). The reproducibility of the networks for visual and automatic selection was very close: it was highly consistent in each subject for the default-mode network (?92%) and the occipital network, which includes the medial visual cortical areas (?94%), and consistent for the attention network (?80%), the right and/or left lateralized frontoparietal attention networks, and the temporal-motor network (?80%). The automatic selection method may be used to detect neural networks and reduce subjectivity in ICA component assessment.

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Dataset Information

A novel resting-state functional magnetic resonance imaging signature of resilience to recurrent depression.

Background

Method

Results

Conclusions

Publications

A novel resting-state functional magnetic resonance imaging signature of resilience to recurrent depression.

Similar Datasets

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