Christine Lycke Brandt

Working memory networks and activation patterns in schizophrenia and bipolar disorder : comparison with healthy controls

BACKGROUND Schizophrenia and bipolar disorder are severe mental disorders with overlapping genetic and clinical characteristics, including cognitive impairments. An important question is whether these disorders also have overlapping neuronal deficits. AIMS To determine whether large-scale brain networks associated with working memory, as measured with functional magnetic resonance imaging (fMRI), are the same in both schizophrenia and bipolar disorder, and how they differ from those in healthy individuals. METHOD Patients with schizophrenia (n = 100) and bipolar disorder (n = 100) and a healthy control group (n = 100) performed a 2-back working memory task while fMRI data were acquired. The imaging data were analysed using independent component analysis to extract large-scale networks of task-related activations. RESULTS Similar working memory networks were activated in all groups. However, in three out of nine networks related to the experimental task there was a graded response difference in fMRI signal amplitudes, where patients with schizophrenia showed greater activation than those with bipolar disorder, who in turn showed more activation than healthy controls. Secondary analysis of the patient groups showed that these activation patterns were associated with history of psychosis and current elevated mood in bipolar disorder. CONCLUSIONS The same brain networks were related to working memory in schizophrenia, bipolar disorder and controls. However, some key networks showed a graded hyperactivation in the two patient groups, in line with a continuum of neuronal abnormalities across psychotic disorders.

Polygenic Risk for Schizophrenia Associated With Working Memory-related Prefrontal Brain Activation in Patients With Schizophrenia and Healthy Controls

Schizophrenia is a highly heritable and polygenic disease, and identified common genetic variants have shown weak individual effects. Many studies have reported altered working memory (WM)-related brain activation in schizophrenia, preferentially in the frontal lobe. Such differences in brain activations could reflect inherited alterations possibly involved in the disease etiology, or rather secondary disease-related mechanisms. The use of polygenic risk scores (PGRS) based on a large number of risk polymorphisms with small effects is a valuable approach to examine the effect of cumulative genetic risk on brain functioning. This study examined the impact of cumulative genetic risk for schizophrenia on WM-related brain activations, assessed with functional magnetic resonance imaging. For each participant (63 schizophrenia patients and 118 healthy controls), we calculated a PGRS for schizophrenia based on 18 862 single-nucleotide polymorphism in a large multicenter genome-wide association study including 9146 schizophrenia patients and 12 111 controls, performed by the Psychiatric Genomics Consortium. As expected, the PGRS was significantly higher in patients compared with healthy controls. Further, the PGRS was related to differences in frontal lobe brain activation between high and low WM demand. Specifically, even in absence of main effects of diagnosis, increased PGRS was associated with decreased activation difference in the right middle-superior prefrontal cortex (BA 10/11) and the right inferior frontal gyrus (BA 45). This effect was seen in both cases and controls, and was not influenced by sex, age, or task performance. The findings support the notion of dysregulation of frontal lobe functioning as an inherited vulnerability factor in schizophrenia.

Delayed stabilization and individualization in connectome development are related to psychiatric disorders

The brain functional connectome constitutes a unique fingerprint allowing identification of individuals among a pool of people. Here we establish that the connectome develops into a more stable, individual wiring pattern during adolescence and demonstrate that a delay in this network tuning process is associated with reduced mental health in the formative years of late neurodevelopment.

Reduced heart rate variability in schizophrenia and bipolar disorder compared to healthy controls.

Despite current diagnostic systems distinguishing schizophrenia (SZ) and bipolar disorder (BD) as separate diseases, emerging evidence suggests they share a number of clinical and epidemiological features, such as increased cardiovascular disease (CVD) risk. It is not well understood if poor cardiac autonomic nervous system regulation, which can be indexed non‐invasively by the calculation of heart rate variability (HRV), contributes to these common CVD risk factors in both diseases.

Altered brain activation during emotional face processing in relation to both diagnosis and polygenic risk of bipolar disorder

Objectives Bipolar disorder (BD) is a highly heritable disorder with polygenic inheritance. Among the most consistent findings from functional magnetic imaging (fMRI) studies are limbic hyperactivation and dorsal hypoactivation. However, the relation between reported brain functional abnormalities and underlying genetic risk remains elusive. This is the first cross-sectional study applying a whole-brain explorative approach to investigate potential influence of BD case-control status and polygenic risk on brain activation. Methods A BD polygenic risk score (PGRS) was estimated from the Psychiatric Genomics Consortium BD case-control study, and assigned to each individual in our independent sample (N=85 BD cases and 121 healthy controls (HC)), all of whom participated in an fMRI emotional faces matching paradigm. Potential differences in BOLD response across diagnostic groups were explored at whole-brain level in addition to amygdala as a region of interest. Putative effects of BD PGRS on brain activation were also investigated. Results At whole-brain level, BD cases presented with significantly lower cuneus/precuneus activation than HC during negative face processing (Z-threshold=2.3 as cluster-level correction). The PGRS was associated positively with increased right inferior frontal gyrus (rIFG) activation during negative face processing. For amygdala activation, there were no correlations with diagnostic status or PGRS. Conclusions These findings are in line with previous reports of reduced precuneus and altered rIFG activation in BD. While these results demonstrate the ability of PGRS to reveal underlying genetic risk of altered brain activation in BD, the lack of convergence of effects at diagnostic and PGRS level suggests that this relation is a complex one.

Resting-state high-frequency heart rate variability is related to respiratory frequency in individuals with severe mental illness but not healthy controls

Heart rate variability (HRV) has become central to biobehavioral models of self-regulation and interpersonal interaction. While research on healthy populations suggests changes in respiratory frequency do not affect short-term HRV, thus negating the need to include respiratory frequency as a HRV covariate, the nature of the relationship between these two variables in psychiatric illness is poorly understood. Therefore, the aim of this study was to investigate the association between HRV and respiratory frequency in a sample of individuals with severe psychiatric illness (n = 55) and a healthy control comparison group (n = 149). While there was no significant correlation between HF-HRV and respiration in the control group, we observed a significant negative correlation in the psychiatric illness group, with a 94.1% probability that these two relationships are different. Thus, we provide preliminary evidence suggesting that HF-HRV is related to respiratory frequency in severe mental illness, but not in healthy controls, suggesting that HRV research in this population may need to account for respiratory frequency. Future work is required to better understand the complex relationship between respiration and HRV in other clinical samples with psychiatric diseases.

Reduced load-dependent default mode network deactivation across executive tasks in schizophrenia spectrum disorders

Background Schizophrenia is associated with cognitive impairment and brain network dysconnectivity. Recent efforts have explored brain circuits underlying cognitive dysfunction in schizophrenia and documented altered activation of large-scale brain networks, including the task-positive network (TPN) and the task-negative default mode network (DMN) in response to cognitive demands. However, to what extent TPN and DMN dysfunction reflect overlapping mechanisms and are dependent on cognitive state remain to be determined. Methods In the current study, we investigated the recruitment of TPN and DMN using independent component analysis in patients with schizophrenia spectrum disorders (n = 29) and healthy controls (n = 21) during two different executive tasks probing planning/problem-solving and spatial working memory. Results We found reduced load-dependent DMN deactivation across tasks in patients compared to controls. Furthermore, we observed only moderate associations between the TPN and DMN activation across groups, implying that the two networks reflect partly independent mechanisms. Additionally, whereas TPN activation was associated with task performance in both tasks, no such associations were found for DMN. Conclusion These results support a general load-dependent DMN dysfunction in schizophrenia spectrum disorder across two demanding executive tasks that is not merely an epiphenomenon of cognitive dysfunction.

Distinct multivariate brain morphological patterns and their added predictive value with cognitive and polygenic risk scores in mental disorders.

The brain underpinnings of schizophrenia and bipolar disorders are multidimensional, reflecting complex pathological processes and causal pathways, requiring multivariate techniques to disentangle. Furthermore, little is known about the complementary clinical value of brain structural phenotypes when combined with data on cognitive performance and genetic risk. Using data-driven fusion of cortical thickness, surface area, and gray matter density maps (GMD), we found six biologically meaningful patterns showing strong group effects, including four statistically independent multimodal patterns reflecting co-occurring alterations in thickness and GMD in patients, over and above two other independent patterns of widespread thickness and area reduction. Case-control classification using cognitive scores alone revealed high accuracy, and adding imaging features or polygenic risk scores increased performance, suggesting their complementary predictive value with cognitive scores being the most sensitive features. Multivariate pattern analyses reveal distinct patterns of brain morphology in mental disorders, provide insights on the relative importance between brain structure, cognitive and polygenetic risk score in classification of patients, and demonstrate the importance of multivariate approaches in studying the pathophysiological substrate of these complex disorders.

Task modulations and clinical manifestations in the brain functional connectome in 1615 fMRI datasets.

Objective: An abundance of experimental studies have motivated a range of models concerning the cognitive underpinnings of severe mental disorders, yet the conception that cognitive and brain dysfunction is confined to specific cognitive domains and contexts has limited ecological validity. Schizophrenia and bipolar spectrum disorders have been conceptualized as disorders of brain connectivity; yet little is known about the pervasiveness across cognitive tasks. Methods: To address this outstanding issue of context specificity, we estimated functional network connectivity from fMRI data obtained during five cognitive tasks (0‐back, 2‐back, go/no‐go, recognition of positive faces, negative faces) in 90 patients with schizophrenia spectrum, 97 patients with bipolar spectrum disorder, and 136 healthy controls, including 1615 fMRI datasets in total. We tested for main effects of task and group, and their interactions, and used machine learning to classify task labels and predict cognitive domain scores from brain connectivity. Results: Connectivity profiles were positively correlated across tasks, supporting the existence of a core functional connectivity backbone common to all tasks. However, 76.2% of all network links also showed significant task‐related alterations, robust on the single subject level as evidenced by high machine‐learning performance when classifying task labels. Independent of such task‐specific modulations, 9.5% of all network links showed significant group effects, particularly including sensory (sensorimotor, visual, auditory) and cognitive (frontoparietal, default‐mode, dorsal attention) networks. A lack of group by task interactions revealed that the pathophysiological sensitivity remained across tasks. Such pervasiveness across tasks was further supported by significant predictions of cognitive domain scores from the connectivity backbone obtained across tasks. Conclusions: The high accuracies obtained when classifying cognitive tasks support that brain connectivity indices provide sensitive and specific measures of cognitive states. Importantly, we provide evidence that brain network dysfunction in severe mental disorders is not confined to specific cognitive tasks and show that the connectivity backbone common to all tasks is predictive of cognitive domain traits. Such pervasiveness across tasks may support a generalization of pathophysiological models from different domains, thereby reducing their complexity and increasing their ecological validity. Future research incorporating a wider range of cognitive tasks, involving other sensory modalities (auditory, somatosensory, motor) and requirements (learning, perceptual inference, decision making, etc.), is needed to assess if under certain circumstances, context dependent aberrations may evolve. Our results provide further evidence from a large sample that fMRI based functional network connectivity can be used to reveal both, state and trait effects in the connectome. HIGHLIGHTSWe obtained fMRI from 187 patients with severe mental illness and 136 controls.We assessed connectivity during five cognitive tasks, resulting in 1615 data sets.Tasks strongly altered networks, supporting sensitivity of fMRI to cognitive states.Clinical network dysfunction was pervasive across the studied tasks.We successfully predicted cognitive traits from connectivity.

Cognitive Effort and Schizophrenia Modulate Large-Scale Functional Brain Connectivity

Schizophrenia (SZ) is characterized by cognitive dysfunction and disorganized thought, in addition to hallucinations and delusions, and is regarded a disorder of brain connectivity. Recent efforts have been made to characterize the underlying brain network organization and interactions. However, to which degree connectivity alterations in SZ vary across different levels of cognitive effort is unknown. Utilizing independent component analysis (ICA) and methods for delineating functional connectivity measures from functional magnetic resonance imaging (fMRI) data, we investigated the effects of cognitive effort, SZ and their interactions on between-network functional connectivity during 2 levels of cognitive load in a large and well-characterized sample of SZ patients (n = 99) and healthy individuals (n = 143). Cognitive load influenced a majority of the functional connections, including but not limited to fronto-parietal and default-mode networks, reflecting both decreases and increases in between-network synchronization. Reduced connectivity in SZ was identified in 2 large-scale functional connections across load conditions, with a particular involvement of an insular network. The results document an important role of interactions between insular, default-mode, and visual networks in SZ pathophysiology. The interplay between brain networks was robustly modulated by cognitive effort, but the reduced functional connectivity in SZ, primarily related to an insular network, was independent of cognitive load, indicating a relatively general brain network-level dysfunction.