Improved estimation of the risk of manic relapse by combining clinical and brain scan data.

INTRODUCTION: Estimating the risk of manic relapse could help the psychiatrist individually adjust the treatment to the risk. Some authors have attempted to estimate this risk from baseline clinical data. Still, no studies have assessed whether the estimation could improve by adding structural magnetic resonance imaging (MRI) data. We aimed to evaluate it. MATERIAL AND METHODS: We followed a cohort of 78 patients with a manic episode without mixed symptoms (bipolar type I or schizoaffective disorder) at 2-4-6-9-12-15-18 months and up to 10 years. Within a cross-validation scheme, we created and evaluated a Cox lasso model to estimate the risk of manic relapse using both clinical and MRI data. RESULTS: The model successfully estimated the risk of manic relapse (Cox regression of the time to relapse as a function of the estimated risk: hazard ratio (HR)=2.35, p=0.027; area under the curve (AUC)=0.65, expected calibration error (ECE)<0.2). The most relevant variables included in the model were the diagnosis of schizoaffective disorder, poor impulse control, unusual thought content, and cerebellum volume decrease. The estimations were poorer when we used clinical or MRI data separately. CONCLUSION: Combining clinical and MRI data may improve the risk of manic relapse estimation after a manic episode. We provide a website that estimates the risk according to the model to facilitate replication by independent groups before translation to clinical settings.

Brain correlates of impaired goal management in bipolar mania.

BACKGROUND: Although executive impairment has been reported in mania, its brain functional correlates have been relatively little studied. This study examined goal management, believed to be more closely related to executive impairment in daily life than other executive tasks, using a novel functional magnetic resonance imaging (fMRI) paradigm in patients in this illness phase. METHODS: Twenty-one currently manic patients with bipolar disorder and 30 matched healthy controls were scanned while performing the Computerized Multiple Elements Test (CMET). This requires participants to sequentially play four simple games, with transition between games being made either voluntarily (executive condition) or automatically (control condition). RESULTS: CMET performance was impaired in the manic patients compared to the healthy controls. Manic patients failed to increase activation in the lateral frontal, cingulate and inferior parietal cortex when the executive demands of the task increased, while this increase was observed in the healthy controls. Activity in these regions was associated with task performance. CONCLUSIONS: Manic patients show evidence of impaired goal management, which is associated with a pattern of reduced medial and lateral frontal and parietal activity.

Structural abnormality in schizophrenia versus bipolar disorder: A whole brain cortical thickness, surface area, volume and gyrification analyses.

OBJECTIVES: The profiles of cortical abnormalities in schizophrenia and bipolar disorder, and how far they resemble each other, have only been studied to a limited extent. The aim of this study was to identify and compare the changes in cortical morphology associated with these pathologies. METHODS: A total of 384 subjects, including 128 patients with schizophrenia, 128 patients with bipolar disorder and 127 sex-age-matched healthy subjects, were examined using cortical surface-based morphology. Four cortical structural measures were studied: cortical volume (CV), cortical thickness (CT), surface area (SA) and gyrification index (GI). Group comparisons for each separate cortical measure were conducted. RESULTS: At a threshold of P = 0.05 corrected, both patient groups showed significant widespread CV and CT reductions in similar areas compared to healthy subjects. However, the changes in schizophrenia were more pronounced. While CV decrease in bipolar disorder was exclusively explained by cortical thinning, in schizophrenia it was driven by changes in CT and partially by SA. Reduced GI was only found in schizophrenia. The direct comparison between both disorders showed significant reductions in all measures in patients with schizophrenia. CONCLUSIONS: Cortical volume and cortical thickness deficits are shared between patients with schizophrenia and bipolar disorder, suggesting that both pathologies may be affected by similar environmental and neurodegenerative factors. However, the exclusive alteration in schizophrenia of metrics related to the geometry and curvature of the brain cortical surface (SA, GI) suggests that this group is influenced by additional neurodevelopmental and genetic factors.

Longitudinal brain functional changes between mania and euthymia in bipolar disorder.

OBJECTIVES: While widespread cortical and subcortical brain functional abnormalities have been found in bipolar disorder, the changes that take place between illness phases and recovery are less clearly documented. Only a small number of longitudinal studies of manic patients, in particular, have been carried out. METHODS: Twenty-six bipolar patients underwent fMRI during performance of the n-back working memory task when manic and again after recovery. Twenty-six matched healthy controls were also scanned on two occasions. Task-related activations and de-activations were examined. RESULTS: When manic, the patients showed clusters of significantly reduced activation in the left dorsolateral prefrontal cortex (DLPFC)/precentral cortex and the parietal cortex/superior precuneus bilaterally. They also showed failure of de-activation in the ventromedial frontal cortex (vmPFC). After recovery, activation in the left DLPFC/precentral cortex and in the bilateral parietal cortex/superior precuneus clusters increased significantly. However, failure of de-activation remained present in the vmPFC. CONCLUSIONS: Recovery from mania is associated with normalization of DLPFC and parietal hypoactivation, but not with vmPFC failure of de-activation, which accordingly appears to represent a trait abnormality in the disorder.

Evaluation of machine learning algorithms and structural features for optimal MRI-based diagnostic prediction in psychosis.

A relatively large number of studies have investigated the power of structural magnetic resonance imaging (sMRI) data to discriminate patients with schizophrenia from healthy controls. However, very few of them have also included patients with bipolar disorder, allowing the clinically relevant discrimination between both psychotic diagnostics. To assess the efficacy of sMRI data for diagnostic prediction in psychosis we objectively evaluated the discriminative power of a wide range of commonly used machine learning algorithms (ridge, lasso, elastic net and L0 norm regularized logistic regressions, a support vector classifier, regularized discriminant analysis, random forests and a Gaussian process classifier) on main sMRI features including grey and white matter voxel-based morphometry (VBM), vertex-based cortical thickness and volume, region of interest volumetric measures and wavelet-based morphometry (WBM) maps. All possible combinations of algorithms and data features were considered in pairwise classifications of matched samples of healthy controls (N = 127), patients with schizophrenia (N = 128) and patients with bipolar disorder (N = 128). Results show that the selection of feature type is important, with grey matter VBM (without data reduction) delivering the best diagnostic prediction rates (averaging over classifiers: schizophrenia vs. healthy 75%, bipolar disorder vs. healthy 63% and schizophrenia vs. bipolar disorder 62%) whereas algorithms usually yielded very similar results. Indeed, those grey matter VBM accuracy rates were not even improved by combining all feature types in a single prediction model. Further multi-class classifications considering the three groups simultaneously made evident a lack of predictive power for the bipolar group, probably due to its intermediate anatomical features, located between those observed in healthy controls and those found in patients with schizophrenia. Finally, we provide MRIPredict (https://www.nitrc.org/projects/mripredict/), a free tool for SPM, FSL and R, to easily carry out voxelwise predictions based on VBM images.

Structural and Functional Brain Correlates of Cognitive Impairment in Euthymic Patients with Bipolar Disorder.

INTRODUCTION: Cognitive impairment in the euthymic phase is a well-established finding in bipolar disorder. However, its brain structural and/or functional correlates are uncertain. METHODS: Thirty-three euthymic bipolar patients with preserved memory and executive function and 28 euthymic bipolar patients with significant memory and/or executive impairment, as defined using two test batteries, the Rivermead Behavioural Memory Test (RBMT) and the Behavioural Assessment of the Dysexecutive Syndrome (BADS), plus 28 healthy controls underwent structural MRI using voxel-based morphometry (VBM). Twenty-seven of the cognitively preserved patients, 23 of the cognitively impaired patients and 28 controls also underwent fMRI during performance of the n-back working memory task. RESULTS: No clusters of grey or white matter volume difference were found between the two patient groups. During n-back performance, the cognitively impaired patients showed hypoactivation compared to the cognitively preserved patients in a circumscribed region in the right dorsolateral prefrontal cortex. Both patient groups showed failure of de-activation in the medial frontal cortex compared to the healthy controls. CONCLUSIONS: Cognitive impairment in euthymic bipolar patients appears from this study to be unrelated to structural brain abnormality, but there was some evidence for an association with altered prefrontal function.

Prevalence of cavum vergae in psychosis and mood spectrum disorders.

BACKGROUND: Midline brain abnormalities might increase susceptibility to both first-episode and chronic mental disorder. Evidence of cavum vergae (CV) abnormality in mental disorders is scarce. METHODS: The presence of CV was assessed by a researcher blind to clinical information in a cross-disorder sample of 639 patients with mood and psychotic disorders and in 223 healthy controls. Homogeneous magnetic resonance imaging methods of acquisition and assessment were applied. RESULTS: Seven out of 639 patients with mood or psychotic disorders were detected with CV which corresponds to a prevalence of 1.1%. There were no concurrent cases of CV in the healthy control group. Identified cases which are briefly described were diagnosed from bipolar I disorder (n=2), delusional disorder (n=1), brief psychotic disorder (n=1) and schizoaffective disorder (n=3). Patients with CV had descriptively lower current IQ, executive functioning and memory scores in relation to patients without CV but this was not statistically significant. LIMITATIONS: Effects of medication and lack of statistical power of the CV patient group. CONCLUSIONS: Midline brain abnormalities, such as CV, might represent an unspecific risk factor for the development of severe mental disorders.

Brain functional changes across the different phases of bipolar disorder.

BACKGROUND: Little is known about how functional imaging changes in bipolar disorder relate to different phases of the illness. AIMS: To compare cognitive task activation in participants with bipolar disorder examined in different phases of illness. METHOD: Participants with bipolar disorder in mania (n = 38), depression (n = 38) and euthymia (n = 38), as well as healthy controls (n = 38), underwent functional magnetic resonance imaging during performance of the n-back working memory task. Activations and de-activations were compared between the bipolar subgroups and the controls, and among the bipolar subgroups. All participants were also entered into a linear mixed-effects model. RESULTS: Compared with the controls, the mania and depression subgroups, but not the euthymia subgroup, showed reduced activation in the dorsolateral prefrontal cortex, the parietal cortex and other areas. Compared with the euthymia subgroup, the mania and depression subgroups showed hypoactivation in the parietal cortex. All three bipolar subgroups showed failure of de-activation in the ventromedial frontal cortex. Linear mixed-effects modelling revealed a further cluster of reduced activation in the left dorsolateral prefrontal cortex in the patients; this was significantly more marked in the mania than in the euthymia subgroup. CONCLUSIONS: Bipolar disorder is characterised by mood state-dependent hypoactivation in the parietal cortex. Reduced dorsolateral prefrontal activation is a further feature of mania and depression, which may improve partially in euthymia. Failure of de-activation in the medial frontal cortex shows trait-like characteristics.

Bipolar depressed patients show both failure to activate and failure to de-activate during performance of a working memory task.

BACKGROUND: Bipolar depression has been found to be associated with changes in prefrontal cortex activity during performance of cognitive tasks. However, the role of task-related de-activations has been little investigated. METHOD: Forty-one bipolar depressed patients and 41 matched normal controls underwent fMRI scanning while performing baseline, 1-back and 2-back versions of the n-back task. Linear models were used to obtain maps of within-group activations and areas of differential activation between the groups. RESULTS: The bipolar depressed patients showed reduced activation in the dorsolateral prefrontal cortex (DLPFC) bilaterally and several other regions. After controlling for differences in task performance only differences in the DLPFC and cerebellum remained. Left DLPFC activation was inversely correlated with Hamilton and MADRS scores. The patients showed failure to de-activate in the medial prefrontal cortex, an area corresponding to the anterior medial node of the default mode network. LIMITATIONS: To confirm default mode network dysfunction demonstration of resting-state connectivity abnormalities would also be required. The study was carried out on treated patients, and did not assess for presence of depressive symptoms in the healthy controls. CONCLUSIONS: Both prefrontal cortical and default mode network dysfunction appear to characterise bipolar depression. The former, but not the latter, is associated with symptom severity.

Failure of de-activation in the medial frontal cortex in mania: evidence for default mode network dysfunction in the disorder.

OBJECTIVES: Manic patients have been found to show reduced activation in the prefrontal cortex and other regions during performance of cognitive tasks. However, little is known about de-activations associated with the disorder. This study aimed to examine, at the whole-brain level, abnormal patterns of task-related activation and de-activation during performance of a working memory task. METHODS: Twenty-nine DSM-IV bipolar patients and 46 healthy controls underwent fMRI during performance of the n-back task. The patients were scanned while they were in a manic episode. Linear models were used to obtain maps of within-group activations and areas of differential activation between the groups. RESULTS: The manic patients showed reduced activation compared to the controls in the bilateral dorsolateral prefrontal cortex and the right parietal cortex. They also showed failure of de-activation in the medial frontal cortex, extending to the temporal poles and parts of the limbic system bilaterally. The failure of activation in the dorsolateral prefrontal cortex disappeared when differences in task performance were controlled for in the analysis. However, the medial frontal failure of de-activation survived controlling for this. CONCLUSIONS: This study suggests that, in addition to reduced prefrontal activation, failure of de-activation is an important functional imaging abnormality in mania. This, together with its location in the medial prefrontal cortex, implies default mode network dysfunction in the disorder.