Cognitive Effort and Schizophrenia Modulate Large-Scale Functional Brain Connectivity

Cumulative Effect of Psychosis and Aging on Cognitive Function in Patients Diagnosed With Schizophrenia Spectrum Disorders: A Cognitive Domain Approach

BACKGROUND

Schizophrenia spectrum disorders are characterized by cognitive decline, which is evident even in the prodromal phase. Aging is a complex gradual procedure that affects, among other organs, the central nervous system, resulting in age-related cognitive decline.

OBJECTIVE

The objective of this study is to assess the cognitive function of patients diagnosed with psychotic disorders, in comparison with healthy controls, along the age spectrum.

METHODS

Sixty patients diagnosed with schizophrenia spectrum disorders in remission, 20-59 years old, and 60 healthy controls, matched by age and educational level, from the region of Thessaly in Central Greece, were evaluated, with respect to their cognitive performance, using the Greek version of the Montreal Cognitive Assessment (MoCA). Correlations between age and MoCA total and cognitive domains' scores, as well as statistical analysis of variance (ANOVA) and t-test among age groups, were performed using Statistical Product and Service Solutions (SPSS, version 23; IBM SPSS Statistics for Windows, Armonk, NY).

RESULTS

The MoCA score was negatively correlated with age, both in the patients' group (p<0.001) and in the control group (p=0.001). A significant statistical difference in mean MoCA scores between patients and healthy controls was observed, not only in the total sample (p<0.001) but also in all age groups (20-29: p=0.006, 40-49: p=0.024, 50-59: p<0.001), except for age group 30-39 (30-39: p=0.356). Statistically significant differences were also found between patients and healthy controls in the total sample, regarding specific cognitive domains, in the visuospatial and executive function domain (p=0.01), attention domain (p<0.001), language domain (p<0.001), and orientation domain (p<0.005). Interestingly, different deterioration patterns in cognitive domains were observed in each age group. Specifically, in the age group 20-29, statistically significant differences were found between patients and healthy controls in the language domain (p<0.014) and orientation domain (p<0.041). No difference was found in the age group 30-39, while statistically significant differences were found between patients and healthy controls in the age group 40-49 in the attention domain (p<0.001) and language domain (p<0.001). Finally, in the age group 50-59, such differences were found in the visuospatial and executive function domain (p=0.041), attention domain (p=0.006), and language domain (p=0.001). Statistically significant cognitive decline occurs in a shorter period in the patients' group, suggesting an accelerated cognitive decline in psychotic patients after middle age.

CONCLUSIONS

Age-related cognitive decline in psychotic patients occurs at an accelerated rate in relation to the control sample, with age-specific cognitive domain decline patterns, due to the cumulative effect of aging and psychosis on cognition. Further, larger, multicenter research should focus on establishing these results and designing relevant procognitive interventions.

Boosting Schizophrenia Genetics by Utilizing Genetic Overlap With Brain Morphology

BACKGROUND

Schizophrenia is a complex polygenic disorder with subtle, distributed abnormalities in brain morphology. There are indications of shared genetic architecture between schizophrenia and brain measures despite low genetic correlations. Through the use of analytical methods that allow for mixed directions of effects, this overlap may be leveraged to improve our understanding of underlying mechanisms of schizophrenia and enrich polygenic risk prediction outcome.

METHODS

We ran a multivariate genome-wide analysis of 175 brain morphology measures using data from 33,735 participants of the UK Biobank and analyzed the results in a conditional false discovery rate together with schizophrenia genome-wide association study summary statistics of the Psychiatric Genomics Consortium (PGC) Wave 3. We subsequently created a pleiotropy-enriched polygenic score based on the loci identified through the conditional false discovery rate approach and used this to predict schizophrenia in a nonoverlapping sample of 743 individuals with schizophrenia and 1074 healthy controls.

RESULTS

We found that 20% of the loci and 50% of the genes significantly associated with schizophrenia were also associated with brain morphology. The conditional false discovery rate analysis identified 428 loci, including 267 novel loci, significantly associated with brain-linked schizophrenia risk, with functional annotation indicating high relevance for brain tissue. The pleiotropy-enriched polygenic score explained more variance in liability than conventional polygenic scores across several scenarios.

CONCLUSIONS

Our results indicate strong genetic overlap between schizophrenia and brain morphology with mixed directions of effect. The results also illustrate the potential of exploiting polygenetic overlap between brain morphology and mental disorders to boost discovery of brain tissue-specific genetic variants and its use in polygenic risk frameworks.

Impaired attentional modulation of sensorimotor control and cortical excitability in schizophrenia

Impairments in attentional, working memory and sensorimotor processing have been consistently reported in schizophrenia. However, the interaction between cognitive and sensorimotor impairments and the underlying neural mechanisms remains largely uncharted. We hypothesized that altered attentional processing in patients with schizophrenia, probed through saccadic inhibition, would partly explain impaired sensorimotor control and would be reflected as altered task-dependent modulation of cortical excitability and inhibition. Twenty-five stabilized patients with schizophrenia, 17 unaffected siblings and 25 healthy control subjects were recruited. Subjects performed visuomotor grip force-tracking alone (single-task condition) and with increased cognitive load (dual-task condition). In the dual-task condition, two types of trials were randomly presented: trials with visual distractors (requiring inhibition of saccades) or trials with addition of numbers (requiring saccades and addition). Both dual-task trial types required divided visual attention to the force-tracking target and to the distractor or number. Gaze was measured during force-tracking tasks, and task-dependent modulation of cortical excitability and inhibition were assessed using transcranial magnetic stimulation. In the single-task, patients with schizophrenia showed increased force-tracking error. In dual-task distraction trials, force-tracking error increased further in patients, but not in the other two groups. Patients inhibited fewer saccades to distractors, and the capacity to inhibit saccades explained group differences in force-tracking performance. Cortical excitability at rest was not different between groups and increased for all groups during single-task force-tracking, although, to a greater extent in patients (80%) compared to controls (40%). Compared to single-task force-tracking, the dual-task increased cortical excitability in control subjects, whereas patients showed decreased excitability. Again, the group differences in cortical excitability were no longer significant when failure to inhibit saccades was included as a covariate. Cortical inhibition was reduced in patients in all conditions, and only healthy controls increased inhibition in the dual-task. Siblings had similar force-tracking and gaze performance as controls but showed altered task-related modulation of cortical excitability and inhibition in dual-task conditions. In patients, neuropsychological scores of attention correlated with visuomotor performance and with task-dependant modulation of cortical excitability. Disorganization symptoms were greatest in patients with weakest task-dependent modulation of cortical excitability. This study provides insights into neurobiological mechanisms of impaired sensorimotor control in schizophrenia showing that deficient divided visual attention contributes to impaired visuomotor performance and is reflected in impaired modulation of cortical excitability and inhibition. In siblings, altered modulation of cortical excitability and inhibition is consistent with a genetic risk for cortical abnormality.

Common vs. Distinct Visuomotor Control Deficits in Autism Spectrum Disorder and Schizophrenia

Autism spectrum disorder (ASD) and schizophrenia (SCZ) are neurodevelopmental disorders with partly overlapping clinical phenotypes including sensorimotor impairments. However, direct comparative studies on sensorimotor control across these two disorders are lacking. We set out to compare visuomotor upper limb impairment, quantitatively, in ASD and SCZ. Patients with ASD (N = 24) were compared to previously published data from healthy control participants (N = 24) and patients with SCZ (N = 24). All participants performed a visuomotor grip force-tracking task in single and dual-task conditions. The dual-task (high cognitive load) presented either visual distractors or required mental addition during grip force-tracking. Motor inhibition was measured by duration of force release and from principal component analysis (PCA) of the participant's force-trajectory. Common impairments in patients with ASD and SCZ included increased force-tracking error in single-task condition compared to controls, a further increase in error in dual-task conditions, and prolonged duration of force release. These three sensorimotor impairments were found in both patient groups. In contrast, distinct impairments in patients with ASD included greater error under high cognitive load and delayed onset of force release compared to SCZ. The PCA inhibition component was higher in ASD than SCZ and controls, correlated to duration of force release, and explained group differences in tracking error. In conclusion, sensorimotor impairments related to motor inhibition are common to ASD and SCZ, but more severe in ASD, consistent with enhanced neurodevelopmental load in ASD. Furthermore, impaired motor anticipation may represent a further specific impairment in ASD. Autism Res 2020, 13: 885-896. © 2020 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Autism spectrum disorder (ASD) and schizophrenia (SCZ) are neurodevelopmental disorders with partly overlapping and partly distinct clinical symptoms. Sensorimotor impairments rank among these symptoms, but it is less clear whether they are shared or distinct. In this study, we showed using a grip force task that sensorimotor impairments related to motor inhibition are common to ASD and SCZ, but more severe in ASD. Impaired motor anticipation may represent a further specific impairment in ASD.

Consistent Functional Connectivity Alterations in Schizophrenia Spectrum Disorder: A Multisite Study

Schizophrenia (SZ) is a severe mental illness with high heritability and complex etiology. Mounting evidence from neuroimaging has implicated disrupted brain network connectivity in the pathophysiology. However, previous findings are inconsistent, likely due to a combination of methodological and clinical variability and relatively small sample sizes. Few studies have used a data-driven approach for characterizing pathological interactions between regions in the whole brain and evaluated the generalizability across independent samples. To overcome this issue, we collected resting-state functional magnetic resonance imaging data from 3 independent samples (1 from Norway and 2 from Sweden) consisting of 182 persons with a SZ spectrum diagnosis and 348 healthy controls. We used a whole-brain data-driven definition of network nodes and regularized partial correlations to evaluate and compare putatively direct brain network node interactions between groups. The clinical utility of the functional connectivity features and the generalizability of effects across samples were evaluated by training and testing multivariate classifiers in the independent samples using machine learning. Univariate analyses revealed 14 network edges with consistent reductions in functional connectivity encompassing frontal, somatomotor, visual, auditory, and subcortical brain nodes in patients with SZ. We found a high overall accuracy in classifying patients and controls (up to 80%) using independent training and test samples, strongly supporting the generalizability of connectivity alterations across different scanners and heterogeneous samples. Overall, our findings demonstrate robust reductions in functional connectivity in SZ spectrum disorders, indicating disrupted information flow in sensory, subcortical, and frontal brain regions.

Altered brain connectivity in patients with schizophrenia is consistent across cognitive contexts

BACKGROUND

Schizophrenia has been defined as a dysconnection syndrome characterized by aberrant functional brain connectivity. Using task-based fMRI, we assessed to what extent the nature of the cognitive context may further modulate abnormal functional brain connectivity.

METHODS

We analyzed data matched for motion in patients with schizophrenia and healthy controls who performed 3 different tasks. Tasks 1 and 2 both involved emotional processing and only slighlty differed (incidental encoding v. memory recognition), whereas task 3 was a much different mental rotation task. We conducted a connectome-wide general linear model analysis aimed at identifying context-dependent and independent functional brain connectivity alterations in patients with schizophrenia.

RESULTS

After matching for motion, we included 30 patients with schizophrenia and 30 healthy controls in our study. Abnormal connectivity in patients with schizophrenia followed similar patterns regardless of the degree of similarity between cognitive tasks. Decreased connectivity was most notable in the medial prefrontal cortex, the anterior and posterior cingulate, the temporal lobe, the lobule IX of the cerebellum and the premotor cortex.

LIMITATIONS

A more circumscribed yet significant context-dependent effect might be detected with larger sample sizes or cognitive domains other than emotional and visuomotor processing.

CONCLUSION

The context-independence of functional brain dysconnectivity in patients with schizophrenia provides a good justification for pooling data from multiple experiments in order to identify connectivity biomarkers of this mental illness.

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.

Global brain connectivity alterations in patients with schizophrenia and bipolar spectrum disorders

BACKGROUND

The human brain is organized into functionally distinct modules of which interactions constitute the human functional connectome. Accumulating evidence has implicated perturbations in the patterns of brain connectivity across a range of neurologic and neuropsychiatric disorders, but little is known about diagnostic specificity. Schizophrenia and bipolar disorders are severe mental disorders with partly overlapping symptomatology. Neuroimaging has demonstrated brain network disintegration in the pathophysiologies; however, to which degree the 2 diagnoses present with overlapping abnormalities remains unclear.

METHODS

We collected resting-state fMRI data from patients with schizophrenia or bipolar disorder and from healthy controls. Aiming to characterize connectivity differences across 2 severe mental disorders, we derived global functional connectivity using eigenvector centrality mapping, which allows for regional inference of centrality or importance in the brain network.

RESULTS

Seventy-one patients with schizophrenia, 43 with bipolar disorder and 196 healthy controls participated in our study. We found significant effects of diagnosis in 12 clusters, where pairwise comparisons showed decreased global connectivity in high-centrality clusters: sensory regions in patients with schizophrenia and subcortical regions in both patient groups. Increased connectivity occurred in frontal and parietal clusters in patients with schizophrenia, with intermediate effects in those with bipolar disorder. Patient groups differed in most cortical clusters, with the strongest effects in sensory regions.

LIMITATIONS

Methodological concerns of in-scanner motion and the use of full correlation measures may make analyses more vulnerable to noise.

CONCLUSION

Our results show decreased eigenvector centrality of limbic structures in both patient groups and in sensory regions in patients with schizophrenia as well as increased centrality in frontal and parietal regions in both groups, with stronger effects in patients with schizophrenia.

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.

•

SZ patients have reduced load-dependent DMN deactivation compared to controls.

•

TPN activation is associated with task performance, whereas DMN deactivation is not.

•

There are only moderate associations between the TPN and DMN activation.

Assessing brain structural associations with working-memory related brain patterns in schizophrenia and healthy controls using linked independent component analysis

Schizophrenia (SZ) is a psychotic disorder with significant cognitive dysfunction. Abnormal brain activation during cognitive processing has been reported, both in task-positive and task-negative networks. Further, structural cortical and subcortical brain abnormalities have been documented, but little is known about how task-related brain activation is associated with brain anatomy in SZ compared to healthy controls (HC). Utilizing linked independent component analysis (LICA), a data-driven multimodal analysis approach, we investigated structure-function associations in a large sample of SZ (n = 96) and HC (n = 142). We tested for associations between task-positive (fronto-parietal) and task-negative (default-mode) brain networks derived from fMRI activation during an n-back working memory task, and brain structural measures of surface area, cortical thickness, and gray matter volume, and to what extent these associations differed in SZ compared to HC. A significant association (p < .05, corrected for multiple comparisons) was found between a component reflecting the task-positive fronto-parietal network and another component reflecting cortical thickness in fronto-temporal brain regions in SZ, indicating increased activation with increased thickness. Other structure-function associations across, between and within groups were generally moderate and significant at a nominal p-level only, with more numerous and stronger associations in SZ compared to HC. These results indicate a complex pattern of moderate associations between brain activation during cognitive processing and brain morphometry, and extend previous findings of fronto-temporal brain abnormalities in SZ by suggesting a coupling between cortical thickness of these brain regions and working memory-related brain activation.