Dataset Information

Prediction of depression in individuals at high familial risk of mood disorders using functional magnetic resonance imaging.

ABSTRACT:

Objective

Bipolar disorder is a highly heritable condition. First-degree relatives of affected individuals have a more than a ten-fold increased risk of developing bipolar disorder (BD), and a three-fold risk of developing major depressive disorder (MDD) than the general population. It is unclear however whether differences in brain activation reported in BD and MDD are present before the onset of illness.

Methods

We studied 98 young unaffected individuals at high familial risk of BD and 58 healthy controls using functional Magnetic Resonance Imaging (fMRI) scans and a task involving executive and language processing. Twenty of the high-risk subjects subsequently developed MDD after the baseline fMRI scan.

Results

At baseline the high-risk subjects who later developed MDD demonstrated relatively increased activation in the insula cortex, compared to controls and high risk subjects who remained well. In the healthy controls and high-risk group who remained well, this region demonstrated reduced engagement with increasing task difficulty. The high risk subjects who subsequently developed MDD did not demonstrate this normal disengagement. Activation in this region correlated positively with measures of cyclothymia and neuroticism at baseline, but not with measures of depression.

Conclusions

These results suggest that increased activation of the insula can differentiate individuals at high-risk of bipolar disorder who later develop MDD from healthy controls and those at familial risk who remain well. These findings offer the potential of future risk stratification in individuals at risk of mood disorder for familial reasons.

SUBMITTER: Whalley HC

PROVIDER: S-EPMC3590244 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Prediction of depression in individuals at high familial risk of mood disorders using functional magnetic resonance imaging.

Whalley Heather C HC Sussmann Jessika E JE Romaniuk Liana L Stewart Tiffany T Papmeyer Martina M Sprooten Emma E Hackett Suzanna S Hall Jeremy J Lawrie Stephen M SM McIntosh Andrew M AM

PloS one 20130306 3

<h4>Objective</h4>Bipolar disorder is a highly heritable condition. First-degree relatives of affected individuals have a more than a ten-fold increased risk of developing bipolar disorder (BD), and a three-fold risk of developing major depressive disorder (MDD) than the general population. It is unclear however whether differences in brain activation reported in BD and MDD are present before the onset of illness.<h4>Methods</h4>We studied 98 young unaffected individuals at high familial risk of ...[more]

PMID: 23483904

Similar Datasets

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:Background and objectivesKidney functional magnetic resonance imaging (MRI) requires further investigation to enhance the noninvasive identification of patients at high risk of CKD progression.Design, setting, participants, & measurementsIn this exploratory study, we obtained baseline diffusion-weighted and blood oxygen level-dependent MRI in 122 participants of the CKD Optimal Management with Binders and Nicotinamide trial, which was a multicenter, randomized, double-blinded, 12-month, four-group parallel trial of nicotinamide and lanthanum carbonate versus placebo conducted in individuals with eGFR 20-45 ml/min per 1.73 m2. Lower values of apparent diffusion coefficient (ADC) on diffusion-weighted MRI may indicate increased fibrosis, and higher values of relaxation rate (R2*) on blood oxygen level-dependent MRI may represent decreased oxygenation. Because there was no effect of active treatment on eGFR over 12 months, we tested whether baseline kidney functional MRI biomarkers were associated with eGFR decline in all 122 participants. In a subset of 87 participants with 12-month follow-up MRI data, we evaluated whether kidney functional MRI biomarkers change over time.ResultsMean baseline eGFR was 32±9 ml/min per 1.73 m2, and mean annual eGFR slope was -2.3 (95% confidence interval [95% CI], -3.4 to -1.1) ml/min per 1.73 m2 per year. After adjustment for baseline covariates, baseline ADC was associated with change in eGFR over time (difference in annual eGFR slope per 1 SD increase in ADC: 1.3 [95% CI, 0.1 to 2.5] ml/min per 1.73 m2 per year, ADC×time interaction P=0.04). This association was no longer significant after further adjustment for albuminuria (difference in annual eGFR slope per 1 SD increase in ADC: 1.0 (95% CI, -0.1 to 2.2) ml/min per 1.73 m2 per year, ADC×time interaction P=0.08). There was no significant association between baseline R2* and change in eGFR over time. In 87 participants with follow-up functional MRI, ADC and R2* values remained stable over 12 months (intraclass correlation: 0.71 and 0.68, respectively).ConclusionsBaseline cortical ADC was associated with change in eGFR over time, but this association was not independent of albuminuria. Kidney functional MRI biomarkers remained stable over 1 year.Clinical trial registry name and registration numberCKD Optimal Management with Binders and Nicotinamide (COMBINE), NCT02258074.

Dataset Information

Prediction of depression in individuals at high familial risk of mood disorders using functional magnetic resonance imaging.

Objective

Methods

Results

Conclusions

Publications

Prediction of depression in individuals at high familial risk of mood disorders using functional magnetic resonance imaging.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets