Differentiating between bipolar and unipolar depression in functional and structural MRI studies

Specific Substantial Dysconnectivity in Schizophrenia: A Transdiagnostic Multimodal Meta-analysis of Resting-State Functional and Structural Magnetic Resonance Imaging Studies

Background: Major depressive disorder is associated with both structural and functional alterations in the emotion regulation network of the central nervous system. The relation between structural and functional changes is largely unknown. Therefore, we sought to determine the relation between structural differences and functional alterations during the recognition of emotional facial expressions. Methods: We examined 13 medication-free patients with major depression and 15 healthy controls by use of structural T1-weighted high-resolution magnetic resonance imaging (MRI) and functional MRI during 1 session. We set the statistical threshold for the analysis of imaging data to p < 0.001 (uncorrected). Results: As shown by voxel-based morphometry, depressed patients had reductions in orbitofrontal cortex volume and increases in cerebellar volume. Additionally, depressed patients showed increased activity during emotion recognition in the middle frontal cortex, caudate nucleus, precuneus and lingual gyrus. Within this cerebral network, the orbitofrontal volumes were negatively correlated in depressed patients but not in healthy controls with changes in blood oxygen level–dependent signal in the middle frontal gyrus, caudate nucleus, precuneus and supplementary motor area. Limitations: Our results are limited by the relatively small sample size. Conclusions: This combined functional and structural MRI study provides evidence that the orbitofrontal cortex is a key area in major depression and that structural changes result in functional alterations within the emotional circuit. Whether these alterations in the orbitofrontal cortex are also related to persistent emotional dysfunction in remitted mental states and, therefore, are related to the risk of depression needs further exploration.

Ultra-high field imaging in Major Depressive Disorder: a review of structural and functional studies

Structural magnetic resonance imaging (MRI) studies reported gray matter (GM) loss in bipolar disorder (BD) in cingulate cortices, key regions subserving emotional regulation and cognitive functions in humans. The aim of this study was to further explore cingulate GM volumes in a sizeable group of BD patients with respect to healthy controls, particularly investigating the impact of gender and clinical variables. 39 BD patients (mean Age = 48.6 ±9.7, 15 males and 24 females) and 39 demographically matched healthy subjects (mean Age = 47.9 ± 9.1, 15 males and 24 females) underwent a 1.5T MRI scan. GM volumes within the cingulate cortex were manually detected, including anterior and posterior regions. BD patients had decreased left anterior cingulate volumes compared with healthy controls (F = 6.7, p = 0.01). Additionally, a significant gender effect was observed, with male patients showing reduced left anterior cingulate cortex (ACC) volumes compared to healthy controls (F = 5.1, p = 0.03). Furthermore, a significant inverse correlation between right ACC volumes and number of hospitalizations were found in the whole group of BD patients (r = -0.51, p = 0.04) and in male BD patients (r = -0.88, p = 0.04). Finally, no statistically significant correlations were observed in female BD patients. Our findings further confirm the putative role of the ACC in the pathophysiology of BD. Interestingly, this study also suggested the presence of gender-specific GM volume reductions in ACC in BD, which may also be associated to poor outcome.

of a G72 susceptibility haplotype on fiber tract integrity in young healthy probands. They found clusters of increased fractional anisotropy in homozygous risk haplotype carriers in the right periinsular and inferior parietal region. Both regions have been hypothesized to be involved in the pathophysiology of bipolar disorder and schizophrenia, and alterations in fractional anisotropy may reflect changes in neuropil such as morphology of dendrites. In a functional magnetic resonance imaging (fMRI) study using a working memory task, Stegmayer et al. [4] found reduced functional interactions of the right amygdala with cortical regions supporting verbal working memory in 18 bipolar patients compared with 18 healthy controls. The results point to a disturbed right-hemispheric cognitive–emotional interaction between the amygdala and cortical regions, leading to deficits in working memory. In bipolar disorder, poorer prognosis is related to weight gain and obesity. Lackner et al. [5] investigated body mass index, obesity measures, lipometry, metabolic parameters and monoamines in a large sample of bipolar patients compared with healthy controls. In the patient group, they found increased abdominal fat accumulation and measures of the metabolic syndrome along with correlation with epinephrine levels. The glutamate system has been reported to be involved in the pathophysiology of both bipolar disorder and major depression. Quinolinic acid is produced by activated microglia and is an agonist at the glutamatergic N-methyl-d-aspartate (NMDA) receptor. In a postmortem study, Busse et al. [6] investigated immunohistochemistry of quinolinic acid in hippocampal subregions of 12 patients with depression, among them 6 unipolar and 6 bipolar, and 10 healthy controls. Quinolinic-positive microglia was reduced in unipolar and bipolar patients in the right CA1 subregion. A degradation of quinolinic acid may be involved in the In the new DSM-5 and ICD-11 classification systems, pure mania and mania with mild depression are subsumed under bipolar disorder. In a comprehensive review, Angst and Grobler [1] vote for improved differential diagnoses of unipolar mania. This is clinically relevant since the disorder has a prevalence of approximately 1.8 %, and clinical follow-up studies demonstrated good diagnostic stability. Moreover, mania is associated with a hyperthymic temperament, more psychotic symptoms and higher heritability compared with depression. In contrast, the concept of bipolar disorder has been widely investigated in neurogenetic studies. For example, the dopaminergic system has been suggested to be affected in bipolar disorder, but the impact of the genetic variants of the human dopamine transporter DAT1 (SLC6A3) is inconsistent. Huang et al. [2] investigated the association of 18 polymorphisms of the DAT1 gene with bipolar disorder and explored its influence on specific personality traits such as novelty seeking and harm avoidance. Several polymorphisms had a weak association with bipolar disorder, and the promotor G-A-C-G haplotype was overrepresented in their sample of 492 patients compared with 436 healthy controls. Bipolar II patients had the highest harm avoidance score and a significant association between rs40184 of DAT1 and this personality trait in patients with bipolar disorder. G72 (D-amino acid oxidase activator, DAOA) is a susceptibility gene for bipolar disorder and schizophrenia. In a diffusion tensor imaging (DTI) study, Nickl-Jockschat et al. [3] investigated the influence

Reversal Learning as a Neuropsychological Indicator for the Neuropathology of Obsessive Compulsive Disorder? A Behavioral Study

DISC1 gene and affective psychopathology: A combined structural and functional MRI study

Objective: We conducted meta-analyses of functional and structural neuroimaging studies comparing adolescent and adult individuals with a history of suicidal behavior and a psychiatric disorder to psychiatric controls in order to objectify changes in brain structure and function in association with a vulnerability to suicidal behavior.Methods: Magnetic resonance imaging studies published up to July 2013 investigating structural or functional brain correlates of suicidal behavior were identified through computerized and manual literature searches. Activation foci from 12 studies encompassing 475 individuals, i.e., 213 suicide attempters and 262 psychiatric controls were subjected to meta-analytical study using anatomic or activation likelihood estimation (ALE).Result: Activation likelihood estimation revealed structural deficits and functional changes in association with a history of suicidal behavior. Structural findings included reduced volumes of the rectal gyrus, superior temporal gyrus and caudate nucleus. Functional differences between study groups included an increased reactivity of the anterior and posterior cingulate cortices.Discussion: A history of suicidal behavior appears to be associated with (probably interrelated) structural deficits and functional overactivation in brain areas, which contribute to a decision-making network. The findings suggest that a vulnerability to suicidal behavior can be defined in terms of a reduced motivational control over the intentional behavioral reaction to salient negative stimuli.

Major depressive disorder (MDD) is one of the most common mental health disorders that can affect sleep, mood, appetite, and behavior of people. Multimodal neuroimaging data, such as functional and structural magnetic resonance imaging (MRI) scans, have been widely used in computer-aided detection of MDD. However, previous studies usually treat these two modalities separately, without considering their potentially complementary information. Even though a few studies propose integrating these two modalities, they usually suffer from significant inter-modality data heterogeneity. In this paper, we propose an adaptive multimodal neuroimage integration (AMNI) framework for automated MDD detection based on functional and structural MRIs. The AMNI framework consists of four major components: (1) a graph convolutional network to learn feature representations of functional connectivity networks derived from functional MRIs, (2) a convolutional neural network to learn features of T1-weighted structural MRIs, (3) a feature adaptation module to alleviate inter-modality difference, and (4) a feature fusion module to integrate feature representations extracted from two modalities for classification. To the best of our knowledge, this is among the first attempts to adaptively integrate functional and structural MRIs for neuroimaging-based MDD analysis by explicitly alleviating inter-modality heterogeneity. Extensive evaluations are performed on 533 subjects with resting-state functional MRI and T1-weighted MRI, with results suggesting the efficacy of the proposed method.

Objective To provide a new approach to further elucidate the underlying pathogenesis of major depressive disorder by using resting-state functional connectivity strengths(FCS) in unmedicated patients with major depressive disorder(MDD). Methods Thirty-three unmedicated patients with MDD and thirty-three normal controls underwent the resting-state functional magnetic resonance imaging(rs-fMRI).After preprocessing of rs-fMRI, the whole brain FCS values were constructed. Two-sample t-test was used to compare the FCS values of both groups to locate the regions with significant change. And correlation analysis was performed between the FCS values extracted from significantly different brain regions and Hamilton Depression Scale (HAMD24) scores. Results One sample t test had shown that high FCS values brain areas (hubs) of MDD patients and normal controls were mainly located in the default mode network (the prefrontal cortex, anterior cingulate gyrus, posterior cingulate gyrus, precuneus/cuneus, angular gyrus) and parietal cortex, lateral temporal cortex as well as occipital cortex. In comparison to the normal controls, the MDD patients had FCS values significantly decreased in the bilateral posterior cerebellar lobe (right posterior cerebellar t=-4.57, P<0.05; left posterior cerebellar t=-3.40,P<0.05), and FCS values increased in the right superior temporal gyrus (t=3.71,P<0.05) and left anterior insular lobe (t=3.91, P<0.05). There were no significant correlations between the HAMD24 scores and the FCS values in right/left posterior cerebellar (r=0.081, P=0.654; r=-0.028, P=0.877), right superior temporal gyrus (r=0.211, P=0.239) and left anterior insular lobe (r=-0.017,P=0.924). Conclusions These findings suggest abnormal functional connectivity in the bilateral posterior cerebellar lobe, right temporal lobe and left insular lobe in MDD. The FCS analysis may be used as a new research method to explore the pathogenesis of MDD. Key words: Depressive disorder; Resting state; Functional connectivity strengths

Neuroimaging findings from childhood onset schizophrenia patients and their non-psychotic siblings

Diagnostic Instability: How Much Is Too Much?

ObjectiveWe aimed to investigate changes in whole-brain gray matter volumes (GMVs) before and after electroconvulsive therapy (ECT) in adolescents with major depressive disorder (MDD) and suicidal ideation (SI).MethodsThirty adolescents with MDD and SI were observed, and structural magnetic resonance imaging (sMRI) was performed at baseline and after ECT for each patient. But Twenty-five healthy controls (HCs) were scanned only at baseline. The voxel-based morphometry (VBM) techniques were used to examine GMVs.ResultsCompared with HCs, MDDs at baseline showed decreased GMVs in the left middle temporal gyrus, right superior temporal gyrus, right middle temporal gyrus, left precuneus, right precuneus, and left superior frontal gyrus. After ECT, MDDs showed increased GMVs in the right superior frontal gyrus and right superior temporal gyrus. Pearson’s correlation found that Beck Scale for Suicide Ideation (BSSI) scores at baseline were negatively correlated with GMVs in the left superior frontal gyrus and HAMD and BSSI scores after ECT were negatively correlated with GMVs in the right superior temporal gyrus.ConclusionFrontal–temporal–precuneus structure changes may be a potential cause of depressive and suicidal symptoms in adolescents. ECT may improve depressive and suicidal symptoms in adolescents by regulating brain structures to compensate original defects.

ObjectivesRecently, pattern recognition approaches have been used to classify patterns of brain activity elicited by sensory or cognitive processes. In the clinical context, these approaches have been mainly applied to classify groups of individuals based on structural magnetic resonance imaging (MRI) data. Only a few studies have applied similar methods to functional MRI (fMRI) data.MethodsWe used a novel analytic framework to examine the extent to which unipolar and bipolar depressed individuals differed on discrimination between patterns of neural activity for happy and neutral faces. We used data from 18 currently depressed individuals with bipolar I disorder (BD) and 18 currently depressed individuals with recurrent unipolar depression (UD), matched on depression severity, age, and illness duration, and 18 age- and gender ratio-matched healthy comparison subjects (HC). fMRI data were analyzed using a general linear model and Gaussian process classifiers.ResultsThe accuracy for discriminating between patterns of neural activity for happy versus neutral faces overall was lower in both patient groups relative to HC. The predictive probabilities for intense and mild happy faces were higher in HC than in BD, and for mild happy faces were higher in HC than UD (all p < 0.001). Interestingly, the predictive probability for intense happy faces was significantly higher in UD than BD (p = 0.03).ConclusionsThese results indicate that patterns of whole-brain neural activity to intense happy faces were significantly less distinct from those for neutral faces in BD than in either HC or UD. These findings indicate that pattern recognition approaches can be used to identify abnormal brain activity patterns in patient populations and have promising clinical utility as techniques that can help to discriminate between patients with different psychiatric illnesses.

There is a huge heterogeneity of magnetic resonance imaging findings in Parkinson's disease (PD) with impulse control disorders (ICDs) studies. Here, we hypothesized that brain regions identified by structural and functional imaging studies of PD with ICDs could be reconciled in a common network. In this study, an initial systematic literature review was conducted to collect and evaluate whole-brain functional and structural magnetic resonance imaging studies related to PD with ICDs. We subsequently utilized the Human Connectome Project (HCP) dataset (n = 1,093) and a novel functional connectivity network mapping (FCNM) technique to identify a common brain network affected in PD with ICDs. A total of 19 studies with 25 contrasts, incorporating 345 individuals with PD and ICDs, and 787 individuals with PD without ICDs were included in the analysis. By using the HCP dataset and a novel FCNM technique, we ultimately identified that the aberrant neural networks predominantly involve the default mode network (middle and inferior temporal gyrus, anterior cingulate cortex, angular gyrus) and subcortical network (caudate nucleus). This study suggests that the heterogeneous neuroimaging findings in PD with ICDs can be attributed to shared abnormalities in the default mode and subcortical networks. These dysfunctions are associated with impaired self-regulation, decision-making, and heightened impulsivity in PD with ICDs. Our findings integrate diverse neuroimaging results from previous studies, providing a clearer understanding of the neurobiological mechanisms underlying PD with ICDs at a network level.