Heterochronicity of white matter development and aging explains regional patient control differences in schizophrenia

Proc. Natl. Acad. Sci. U.S.A.

Rowland

Fieremans

et al. 2016

Self Cite

Schizophrenia, a devastating psychiatric illness with onset in the late teens to early 20s, is thought to involve disrupted brain connectivity. Functional and structural disconnections of cortical networks may underlie various cognitive deficits, including a substantial reduction in the speed of information processing in schizophrenia patients compared with controls. Myelinated white matter supports the speed of electrical signal transmission in the brain. To examine possible neuroanatomical sources of cognitive deficits, we used a comprehensive diffusion-weighted imaging (DWI) protocol and characterized the white matter diffusion signals using diffusion kurtosis imaging (DKI) and permeability-diffusivity imaging (PDI) in patients (n = 74), their nonill siblings (n = 41), and healthy controls (n = 113). Diffusion parameters that showed significant patient-control differences also explained the patient-control differences in processing speed. This association was also found for the nonill siblings of the patients. The association was specific to processing-speed abnormality but not specific to working memory abnormality or psychiatric symptoms. Our findings show that advanced diffusion MRI in white matter may capture microstructural connectivity patterns and mechanisms that govern the association between a core neurocognitive measureprocessing speed-and neurobiological deficits in schizophrenia that are detectable with in vivo brain scans. These non-Gaussian diffusion white matter metrics are promising surrogate imaging markers for modeling cognitive deficits and perhaps, guiding treatment development in schizophrenia.diffusion-weighted imaging | schizophrenia | processing speed | cognitive deficits | endophenotypes A lthough pharmaceutical interventions alleviate clinical symptoms, such as delusions and hallucinations, for some patients, schizophrenia remains a debilitating illness, often times leading to long-term disabilities and severe cognitive deficits (1). Discovering the underlying neurobiology behind these core cognitive deficits in schizophrenia patients may present a viable strategy to identify biomarkers for supporting pathophysiology and comprehensive treatment research. Two decades of research have implicated impaired brain connectivity as a source of functional disability in schizophrenia (1, 2). Delayed information processing is one of the most robust cognitive deficits (3, 4) and may contribute to other cognitive impairments in working memory and executive function (4-6).Myelinated axons in the brain's white matter (WM) support its functionality by propagating electric signal transmissions through saltatory conductance (7,8). Reports of WM abnormalities in patients with schizophrenia are common and include reduced fractional anisotropy (FA) of water diffusion measured by diffusion tensor imaging (DTI) MRI (9-11) as well as reduced axonal myelin levels and glial cell density in postmortem brain studies (12)(13)(14). Identifying the key WM microstructural properties that explain the patient-control...

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Diffusion-weighted imaging uncovers likely sources of processing-speed deficits in schizophrenia

Proc. Natl. Acad. Sci. U.S.A.

Rowland

Fieremans

et al. 2016

Self Cite

“…Lower integrity of cerebral white matter (WM), quantified as reduced fractional anisotropy (FA) of water diffusion measured from diffusion tensor imaging (DTI), is a consistent finding in schizophrenia (Alba‐Ferrara & de Erausquin ; Ellison‐Wright & Bullmore ; Friedman et al, ; Glahn et al, ; Kelly et al, ; Kochunov & Hong ; Kubicki et al, ; Nazeri et al, ; Perez‐Iglesias et al, ; Phillips, Rogers, Barrett, Glahn, & Kochunov, ; Wright et al, ). FA deficits are hypothesized to be prominent in the associative WM fibers and responsible for neuropsychological deficits association with this disorder (Ellison‐Wright & Bullmore ; Friedman et al, ; Kochunov et al, ; Kubicki et al, ; Nazeri et al, ; Perez‐Iglesias et al, ). A challenge for evaluating regional WM deficits is the need for statistically powerful and representative samples that can be difficult to collect at a single site (Ioannidis ; Jahanshad et al, ).…”

Section: Introductionmentioning

confidence: 99%

Integration of routine QA data into mega‐analysis may improve quality and sensitivity of multisite diffusion tensor imaging studies

Dickie

Viviano

et al. 2017

Human Brain Mapping

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A novel mega-analytical approach that reduced methodological variance was evaluated using a multisite diffusion tensor imaging (DTI) fractional anisotropy (FA) data by comparing white matter integrity in people with schizophrenia to controls. Methodological variance was reduced through regression of variance captured from quality assurance (QA) and by using Marchenko-Pastur Principal Component Analysis (MP-PCA) denoising. N = 192 (119 patients/73 controls) data sets were collected at three sites equipped with 3T MRI systems: GE MR750, GE HDx, and Siemens Trio. DTI protocol included five b = 0 and 60 diffusion-sensitized gradient directions (b = 1,000 s/mm ). In-house DTI QA protocol data was acquired weekly using a uniform phantom; factor analysis was used to distil into two orthogonal QA factors related to: SNR and FA. They were used as site-specific covariates to perform mega-analytic data aggregation. The effect size of patient-control differences was compared to these reported by the enhancing neuro imaging genetics meta-analysis (ENIGMA) consortium before and after regressing QA variance. Impact of MP-PCA filtering was evaluated likewise. QA-factors explained ∼3-4% variance in the whole-brain average FA values per site. Regression of QA factors improved the effect size of schizophrenia on whole brain average FA values-from Cohen's d = .53 to .57-and improved the agreement between the regional pattern of FA differences observed in this study versus ENIGMA from r = .54 to .70. Application of MP-PCA-denoising further improved the agreement to r = .81. Regression of methodological variances captured by routine QA and advanced denoising that led to a better agreement with a large mega-analytic study.

“…White matter abnormalities in patients with schizophrenia have been shown to mediate their processing speed deficit (Karbasforoushan et al 2015; Wright et al 2015). Furthermore, processing speed and white matter FA have been found to be phenotypically and genetically correlated (Kochunov et al 2016a). These findings have led to a search for genes that influence both white matter and processing speed in healthy adults (Giddaluru et al 2016).…”

Section: Introductionmentioning

confidence: 99%

Potassium channel gene associations with joint processing speed and white matter impairments in schizophrenia

Bruce

Genes Brain and Behavior

Paciga

et al. 2017

Patients with schizophrenia show decreased processing speed on neuropsychological testing and decreased white matter integrity as measured by diffusion tensor imaging, two traits shown to be both heritable and genetically associated indicating that there may be genes that influence both traits as well as schizophrenia disease risk. The potassium channel gene family is a reasonable candidate to harbor such a gene given the prominent role potassium channels play in the central nervous system in signal transduction, particularly in myelinated axons. We genotyped members of the large potassium channel gene family focusing on putatively functional single nucleotide polymorphisms (SNPs) in a population of 363 controls, 194 patients with schizophrenia spectrum disorder, and 28 patients with affective disorders with psychotic features who completed imaging and neuropsychological testing. We then performed three association analyses using three phenotypes- processing speed, whole-brain white matter fractional anisotropy, and schizophrenia spectrum diagnosis. We extracted SNPs showing an association at a nominal P value of <0.05 with all three phenotypes in the expected direction: decreased processing speed, decreased fractional anisotropy, and increased risk of schizophrenia spectrum disorder. A single SNP, rs8234, in the 3′ untranslated region (UTR) of voltage-gated potassium channel subfamily Q member 1 (KCNQ1) was identified. Rs8234 has been shown to affect KCNQ1 expression levels, and KCNQ1 levels have been shown to affect neuronal action potentials. This exploratory analysis provides preliminary data suggesting that KCNQ1 may contribute to the shared risk for diminished processing speed, diminished white mater integrity, and increased risk of schizophrenia.