Brain networks in bipolar disorder II: A resting-state fMRI study

Bipolar disorder II (BD-II) is characterized by hypomanic and depressive episodes, accompanied by mild cognitive deficits, which can be postulated to be due to emotional and cognitive dyscontrol, with attention as the binding factor. Structural and functional magnetic resonance imaging (fMRI) have implicated several brain regions across the BD spectrum, including the prefrontal cortex (PFC), cingulate cortex and amygdala. Newer studies also look at whole brain networks using resting-state fMRI (RS-fMRI). A notable RS network is the default-mode network (DMN), typically activated at rest and associated with mind wandering. The aim of the current study was to characterize functional brain networks specifically in BD-II patients (n = 32) by assessing of within and between network connectivity against healthy controls (n = 35) through RS-fMRI (age 18-50). We also assessed the subjects on working memory measures using the RAVLT and BVMT-R. Independent component analysis and dual regression was used for within-network analysis, and FSLNets was used for between-network connectivity and network modeling. Based on earlier findings, we predicted aberrant connectivity within the DMN, increased connectivity within the anterior cingulate cortex, decreased connectivity between the ventrolateral PFC and amygdala, and decreased connectivity between the posterior cingulate cortex and DMN. We also expected visual networks to display increased connectivity to the amygdala. Decreased test performance was observed on the BVMT-R, and decreased delayed recall on the RAVLT. We found no statistically significant changes in connectivity within or between networks, indicating that brain networks in BD-II are not significantly different from healthy individuals. Keywords: rs-fMRI, BD-II, resting-state networks, DMN, PFC, ACC, amygdala, ICA, dual regression, network modeling, within-network connectivity, between-network connectivity, clustering hierarch

Elucidating depression heterogeneity using clinical, neuroimaging and genetic data

The present thesis contributes to the understanding of depression and elucidating its heterogeneous nature. In paper I we identified five subgroups based on a data-driven clustering of depression and anxiety symptoms, which differed in terms of unique symptom profiles, which were supported by resting-state fMRI brain functional connectivity differences. We did not find any significant association between brain functional connectivity patterns and depression case-control status, nor sum scores of depression or anxiety symptoms, illustrating the heterogeneity of depression. In paper II, using a machine learning approach, we were not able to predict mental health traits relevant for depression, nor polygenic scores using several conceptualizations of brain functional connectivity in the UK Biobank. In contrast, we were able to predict age and sex with high accuracy, and robustly predict years of educational attainment and fluid intelligence. In paper III, we found no significant association with conventional categorical and dimensional measures of depression with brain components based on a multimodal fusing approach. Overall, these the thesis and these three papers shows the need for precise stratification of depression at the individual level is required to disentangle its heterogeneous nature

Attentional bias modification is associated with fMRI response toward negative stimuli in individuals with residual depression: a randomized controlled trial

Background: Attentional bias modification (ABM) may lead to more adaptive emotion perception and emotion regulation. Understanding the neural basis of these effects may lead to greater precision for the development of future treatments. Task-related functional MRI (fMRI) after ABM training has not been investigated in depression so far. The main aim of this randomized controlled trial was to explore differences in brain activity after ABM training, in response to emotional stimuli. Methods: A total of 134 people with previous depression, who had been treated for depression and had various degrees of residual symptoms, were randomized to 14 days of active ABM or a closely matched placebo training, followed by an fMRI emotion regulation task. The training procedure was a classical dot–probe task with emotional face stimuli. In the active ABM condition, the probes replaced the more positively valenced face of a given pair. As participants implicitly learned to predict the probe location, this would be likely to induce a more positive attentional bias. The placebo condition was identical, except for the contingency of the probe, which appeared equally behind positive and negative stimuli. We compared depression symptoms and subjective ratings of perceived negativity during fMRI between the training groups. We explored brain activation in predefined regions of interest and across the whole brain. We explored activation in areas associated with changes in attentional bias and degree of depression. Results: Compared with the placebo group, the ABM group showed reduced activation in the amygdala and the anterior cingulate cortex when passively viewing negative images. We found no group differences in predefined regions of interest associated with emotion regulation strategies. Response in the temporal cortices was associated with the degree of change in attentional bias and the degree of depressive symptoms in ABM versus placebo. Limitations: These findings should be replicated in other samples of patients with depression, and in studies using fMRI designs that allow analyses of within-group variability from baseline to follow-up. Conclusion: Attentional bias modification training has an effect on brain function in the circuitry associated with emotional appraisal and the generation of affective states. Clinicaltrials.gov identifier: NCT02931487

Multimodal fusion of structural and functional brain imaging in depression using linked independent component analysis

Previous structural and functional neuroimaging studies have implicated distributed brain regions and networks in depression. However, there are no robust imaging biomarkers that are specific to depression, which may be due to clinical heterogeneity and neurobiological complexity. A dimensional approach and fusion of imaging modalities may yield a more coherent view of the neuronal correlates of depression. We used linked independent component analysis to fuse cortical macrostructure (thickness, area, gray matter density), white matter diffusion properties and resting‐state functional magnetic resonance imaging default mode network amplitude in patients with a history of depression (n = 170) and controls (n = 71). We used univariate and machine learning approaches to assess the relationship between age, sex, case–control status, and symptom loads for depression and anxiety with the resulting brain components. Univariate analyses revealed strong associations between age and sex with mainly global but also regional specific brain components, with varying degrees of multimodal involvement. In contrast, there were no significant associations with case–control status, nor symptom loads for depression and anxiety with the brain components, nor any interaction effects with age and sex. Machine learning revealed low model performance for classifying patients from controls and predicting symptom loads for depression and anxiety, but high age prediction accuracy. Multimodal fusion of brain imaging data alone may not be sufficient for dissecting the clinical and neurobiological heterogeneity of depression. Precise clinical stratification and methods for brain phenotyping at the individual level based on large training samples may be needed to parse the neuroanatomy of depression

Data-driven clustering reveals a link between symptoms and functional brain connectivity in depression

Background: Depression is a complex disorder with large interindividual variability in symptom profiles that often occur alongside symptoms of other psychiatric domains, such as anxiety. A dimensional and symptom-based approach may help refine the characterization of depressive and anxiety disorders and thus aid in establishing robust biomarkers. We use resting-state functional magnetic resonance imaging to assess the brain functional connectivity correlates of a symptom-based clustering of individuals. Methods: We assessed symptoms using the Beck Depression and Beck Anxiety Inventories in individuals with or without a history of depression (N = 1084) and high-dimensional data clustering to form subgroups based on symptom profiles. We compared dynamic and static functional connectivity between subgroups in a subset of the total sample (n = 252). Results: We identified five subgroups with distinct symptom profiles, which cut across diagnostic boundaries with different total severity, symptom patterns, and centrality. For instance, inability to relax, fear of the worst, and feelings of guilt were among the most severe symptoms in subgroups 1, 2, and 3, respectively. The distribution of individuals was 32%, 25%, 22%, 10%, and 11% in subgroups 1 to 5, respectively. These subgroups showed evidence of differential static brain-connectivity patterns, in particular comprising a frontotemporal network. In contrast, we found no significant associations with clinical sum scores, dynamic functional connectivity, or global connectivity. Conclusions: Adding to the pursuit of individual-based treatment, subtyping based on a dimensional conceptualization and unique constellations of anxiety and depression symptoms is supported by distinct patterns of static functional connectivity in the brain

Exploring the Links between Specific Depression Symptoms and Brain Structure: A Network Study

Understanding the neural substrates of specific symptoms may provide important information about mechanisms underlying depression vulnerability. A growing body of research under the umbrella term ‘network approach’ has recently received considerable attention[5]; the approach understands and aims to model mental disorders as systems of causally interacting symptoms. So far, network studies have been based on symptoms and environmental factors, ignoring relevant neurobiological factors[6]. Here, we address this knowledge gap by modelling a joint network of depression-related brain structures and individual depression symptoms, using 21 symptoms and five regional brain measures. The sample is a mixed group of individuals that previously have been treated for one or more major depressive episodes (MDE) and never depressed individuals, with the goal to model a continuum of depression severity. Hippocampus was negatively associated with changes in appetite and sadness, and positively associated with loss of interest and irritability. Insula was negatively associated with loss of interest in sex and sadness. Cingulate had a negative association with sadness, and positive associations with crying and worthlessness. Fusiform gyrus had positive associations with crying and irritability

Effects of Attentional Bias Modification on residual symptoms in depression: A randomized controlled trial

Background Following treatment, many depressed patients have significant residual symptoms. However, large randomised controlled trials (RCT) in this population are lacking. When Attention bias modification training (ABM) leads to more positive emotional biases, associated changes in clinical symptoms have been reported. A broader and more transparent picture of the true advantage of ABM based on larger and more stringent clinical trials have been requested. The current study evaluates the early effect of two weeks ABM training on blinded clinician-rated and self-reported residual symptoms, and whether changes towards more positive attentional biases (AB) would be associated with symptom reduction. Method A total of 321 patients with a history of depression were included in a preregistered randomized controlled double-blinded trial. Patients were randomised to an emotional ABM paradigm over fourteen days or a closely matched control condition. Symptoms based on the Hamilton Rating Scale for Depression (HRSD) and Beck Depression Inventory II (BDI-II) were obtained at baseline and after ABM training. Results ABM training led to significantly greater decrease in clinician-rated symptoms of depression as compared to the control condition. No differences between ABM and placebo were found for self-reported symptoms. ABM induced a change of AB towards relatively more positive stimuli for participants that also showed greater symptom reduction. Conclusion The current study demonstrates that ABM produces early changes in blinded clinician-rated depressive symptoms and that changes in AB is linked to changes in symptoms. ABM may have practical potential in the treatment of residual depression

The genetic architecture of human brainstem structures and their involvement in common brain disorders

Brainstem regions support vital bodily functions, yet their genetic architectures and involvement in common brain disorders remain understudied. Here, using imaging-genetics data from a discovery sample of 27,034 individuals, we identify 45 brainstem-associated genetic loci, including the first linked to midbrain, pons, and medulla oblongata volumes, and map them to 305 genes. In a replication sample of 7432 participants most of the loci show the same effect direction and are significant at a nominal threshold. We detect genetic overlap between brainstem volumes and eight psychiatric and neurological disorders. In additional clinical data from 5062 individuals with common brain disorders and 11,257 healthy controls, we observe differential volume alterations in schizophrenia, bipolar disorder, multiple sclerosis, mild cognitive impairment, dementia, and Parkinson’s disease, supporting the relevance of brainstem regions and their genetic architectures in common brain disorders

The genetic architecture of human brainstem structures and their involvement in common brain disorders

Brainstem regions support vital bodily functions, yet their genetic architectures and involvement in common brain disorders remain understudied. Here, using imaging-genetics data from a discovery sample of 27,034 individuals, we identify 45 brainstem-associated genetic loci, including the first linked to midbrain, pons, and medulla oblongata volumes, and map them to 305 genes. In a replication sample of 7432 participants most of the loci show the same effect direction and are significant at a nominal threshold. We detect genetic overlap between brainstem volumes and eight psychiatric and neurological disorders. In additional clinical data from 5062 individuals with common brain disorders and 11,257 healthy controls, we observe differential volume alterations in schizophrenia, bipolar disorder, multiple sclerosis, mild cognitive impairment, dementia, and Parkinson’s disease, supporting the relevance of brainstem regions and their genetic architectures in common brain disorders

The genetic architecture of human brainstem structures and their involvement in common brain disorders

Brainstem regions support vital bodily functions, yet their genetic architectures and invol- vement in common brain disorders remain understudied. Here, using imaging-genetics data from a discovery sample of 27,034 individuals, we identify 45 brainstem-associated genetic loci, including the first linked to midbrain, pons, and medulla oblongata volumes, and map them to 305 genes. In a replication sample of 7432 participants most of the loci show the same effect direction and are significant at a nominal threshold. We detect genetic overlap between brainstem volumes and eight psychiatric and neurological disorders. In additional clinical data from 5062 individuals with common brain disorders and 11,257 healthy controls, we observe differential volume alterations in schizophrenia, bipolar disorder, multiple sclerosis, mild cognitive impairment, dementia, and Parkinson’s disease, supporting the relevance of brainstem regions and their genetic architectures in common brain disorders