Multimodal analyses identify linked functional and white matter abnormalities within the working memory network in schizophrenia
ABSTRACT Dysconnectivity between brain regions is thought to underlie the cognitive abnormalities that characterise schizophrenia (SZ). Consistent with this notion functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) studies in SZ have reliably provided evidence of abnormalities in functional integration and in white matter connectivity. Yet little is known about how alterations at the functional level related to abnormalities in anatomical connectivity.
We obtained fMRI data during the 2-back working memory task from 25 patients with SZ and 19 healthy controls matched for age, sex and IQ. DTI data were also acquired in the same session. In addition to conventional unimodal analyses we extracted "features" [contrast maps for fMRI and fractional anisotropy (FA) for DTI] that were subjected to joint independent component analysis (JICA) in order to examine interactions between fMRI and DTI data sources.
Conventional unimodal analyses revealed both functional and structural deficits in patients with SZ. The JICA identified regions of joint, multimodal brain sources that differed in patients and controls. The fMRI source implicated regions within the anterior cingulate and ventrolateral prefrontal cortex and in the cuneus where patients showed relative hypoactivation and within the frontopolar cortex where patients showed relative hyperactivation. The DTI source localised reduced FA in patients in the splenium and posterior cingulum.
This study promotes our understanding of structure-function relationships in SZ by characterising linked functional and white matter changes that contribute to working memory dysfunction in this disorder.
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ABSTRACT: Background Diffusion tensor imaging (DTI) studies in schizophrenia report widespread aberrations in brain white matter (WM). These appear related to poorer neurocognitive performance and higher levels of negative and positive symptomatology. However, identification of the most salient WM aberrations to neurocognition and clinical symptoms is limited by relatively small samples with divergent results. Methods We examined 53 well-characterized patients with schizophrenia and 62 healthy controls. All participants were administered a computerized neurocognitive battery, which evaluated performance in several domains. Patients were assessed for negative and positive symptoms. Fractional anisotropy (FA) of WM cortical regions and WM fiber tracts were compared across the groups. FA values were also used to predict neurocognitive performance and symptoms. Results We confirm widespread aberrant WM microstructure in a relatively large sample of well-characterized patients with schizophrenia in comparison to healthy participants. Moreover, we illustrate the utility of FA measures in predicting global neurocognitive performance in healthy participants and schizophrenia patients, especially for reaction time. FA was less predictive of clinical symptomatology. Conclusions Using a standardized computerized neurocognitive battery and diffusion tensor imaging we show that behavioral performance is moderated by a particular constellation of WM microstructure in healthy individuals that differs in schizophrenia.Schizophrenia Research 10/2014; 161(1). DOI:10.1016/j.schres.2014.09.026 · 4.43 Impact Factor
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ABSTRACT: Background: While many diffusion tensor imaging (DTI) investigations have noted disruptions to white matter integrity in individuals with chronic psychotic disorders, fewer studies have been conducted in young people at the early stages of disease onset. Using whole tract reconstruction techniques, the aim of this study was to identify the white matter pathology associated with the common clinical symptoms and executive function impairments observed in young people with psychosis. Methods: We obtained MRI scans from young people with psychosis and healthy controls. Eighteen major white matter tracts were reconstructed to determine group differences in fractional anisotropy (FA), axial diffusivity (AD) and radial diffusivity (RD) and then were subsequently correlated with symptomatology and neurocognitive performance. Results: Our study included 42 young people with psychosis (mean age 23 yr) and 45 healthy controls (mean age 25 yr). Compared with the control group, the psychosis group had reduced FA and AD in the left inferior longitudinal fasciculus (ILF) and forceps major indicative of axonal disorganization, reduction and/or loss. These changes were associated with worse overall psychiatric symptom severity, increases in positive and negative symptoms, and worse current levels of depression. The psychosis group also showed FA reductions in the left superior longitudinal fasciculus that were associated with impaired neurocognitive performance in attention and semantic fluency. Limitations: Our analysis grouped 4 subcategories of psychosis together, and a larger follow-up study comparing affective and nonaffective psychoses is warranted. Conclusion: Our findings suggest that impaired axonal coherence in the left ILF and forceps major underpin psychiatric symptoms in young people in the early stages of psychosis.Journal of psychiatry & neuroscience: JPN 08/2014; 39(4):130280. DOI:10.1503/jpn.130280 · 7.49 Impact Factor
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ABSTRACT: Human brain anatomy and function display a combination of modular and hierarchical organization, suggesting the importance of both cohesive structures and variable resolutions in the facilitation of healthy cognitive processes. However, tools to simultaneously probe these features of brain architecture require further development. We propose and apply a set of methods to extract cohesive structures in network representations of brain connectivity using multi-resolution techniques. We employ a combination of soft thresholding, windowed thresholding, and resolution in community detection, that enable us to identify and isolate structures associated with different weights. One such mesoscale structure is bipartivity, which quantifies the extent to which the brain is divided into two partitions with high connectivity between partitions and low connectivity within partitions. A second, complementary mesoscale structure is modularity, which quantifies the extent to which the brain is divided into multiple communities with strong connectivity within each community and weak connectivity between communities. Our methods lead to multi-resolution curves of these network diagnostics over a range of spatial, geometric, and structural scales. For statistical comparison, we contrast our results with those obtained for several benchmark null models. Our work demonstrates that multi-resolution diagnostic curves capture complex organizational profiles in weighted graphs. We apply these methods to the identification of resolution-specific characteristics of healthy weighted graph architecture and altered connectivity profiles in psychiatric disease.PLoS Computational Biology 10/2014; 10(10):e1003712. DOI:10.1371/journal.pcbi.1003712 · 4.83 Impact Factor