Association of White Matter With Core Cognitive Deficits in Patients With Schizophrenia

Kochunov, Peter; Coyle, Thomas R.; Rowland, Laura M.; Jahanshad, Neda; Thompson, Paul M.; Kelly, Sinéad; Du, Xiaoming; Sampath, Hemalatha; Bruce, Heather; Chiappelli, Joshua; Ryan, Meghann; Fisseha, Feven; Savransky, Anya; Adhikari, Bhim M.; Chen, Shuo; Paciga, Sara A.; Whelan, Christopher D.; Xie, Zhiyong; Hyde, Craig; Chen, Xing; Schubert, Christian; O’Donnell, Patricio; Hong, L. Elliot

doi:10.1001/jamapsychiatry.2017.2228

Cited by 127 publications

(108 citation statements)

References 49 publications

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“…The details of DTI data preprocessing were described in Supplementary Material and our previous studies(35, 43). Based on a tract-based spatial statistics method(44), fractional anisotropy(FA) images were obtained, spatially normalized to the JHU atlas(45) which separated white matter into different tracts.…”

Section: Methodsmentioning

confidence: 99%

Aberrant Middle Prefrontal-Motor Cortex Connectivity Mediates Motor Inhibitory Biomarker in Schizophrenia

Choa

Chiappelli

et al. 2019

Biological Psychiatry

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Higher resting-state left prefrontal-motor cortex FC, accompanied by a higher fractional anisotropy of left corona radiata, predicted fewer inhibitory deficits, suggesting that the inhibitory deficits in motor cortex in schizophrenia may in part be mediated by a top-down prefrontal influence. SICI may serve as a robust biomarker indexing inhibitory dysfunction at anatomic as well as circuitry levels in schizophrenia.

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Section: Methodsmentioning

confidence: 99%

Aberrant Middle Prefrontal-Motor Cortex Connectivity Mediates Motor Inhibitory Biomarker in Schizophrenia

Choa

Chiappelli

et al. 2019

Biological Psychiatry

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“…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 2013;Ellison-Wright & Bullmore 2009;Friedman et al, 2008;Glahn et al, 2013;Kelly et al, 2017;Kochunov & Hong 2014;Kubicki et al, 2007;Nazeri et al, 2012;Perez-Iglesias et al, 2011;Phillips, Rogers, Barrett, Glahn, & Kochunov, 2012;Wright et al, 2015). FA deficits are hypothesized to be prominent in the associative WM fibers and responsible for neuropsychological deficits association *Peter Kochunov, Erin W. Dickie, and Joseph D. Viviano contributed equally to this study.…”

Section: Introductionmentioning

confidence: 96%

“…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%

“…FA deficits are hypothesized to be prominent in the associative WM fibers and responsible for neuropsychological deficits association *Peter Kochunov, Erin W. Dickie, and Joseph D. Viviano contributed equally to this study. with this disorder (Ellison-Wright & Bullmore 2009;Friedman et al, 2008;Kochunov et al, 2016Kochunov et al, , 2017Kubicki et al, 2007;Nazeri et al, 2012;Perez-Iglesias et al, 2011). 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 2014;Jahanshad et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

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

Kochunov

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.

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“…One of those studies has found that deficits in working memory and reduced speed in information processing, two of the fundamental cognitive deficits in schizophrenia, are associated with impaired whole-brain white matter microstructure [51]. In deficit syndrome schizophrenia, characterized by prominent negative symptoms and poor functional outcome [52], impairments in specific white matter tracts have been found, particularly in the uncinate fasciculus, inferior longitudinal fasciculus, and arcuate fasciculus [53].…”

Section: Structural Connectivitymentioning

confidence: 99%

The quest for biomarkers in Schizophrenia: from neuroimaging to machine learning

Bajouco¹,

Mota²,

Coroa³

et al. 2017

IJCNMH

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Schizophrenia is a severe mental disorder and one of the leading causes of disease burden worldwide. It represents a source of significant suffering and disability to the affected individuals, and is associated with substantial societal and economical costs.The diagnosis of schizophrenia still depends exclusively on the detection of symptoms that are also present in other mental disorders. This situation causes overlapping of the boundaries of the diagnostic categories and constitutes a source of diagnostic errors. Moreover, current treatment algorithms do not take into account the substantial interindividual variability in response to antipsychotic drugs. As a result, around one-third of patients are treatment-resistant to first line antipsychotic drugs. This deleterious consequence is associated with poor individual outcomes and elevated healthcare costs.Neuroimaging research in schizophrenia has shed some light in a vast array of structural and functional connectivity abnormalities and neurochemical (dopamine and glutamate) imbalances, which may constitute 'organic surrogates' of this disorder. However, the neuroimaging field, so far, has not been able to identify biomarkers that could facilitate early detection and allow individualised treatment management. This paper reviews neuroimaging studies from different modalities that may provide relevant biomarkers for schizophrenia. We discuss how the current application of novel Machine Learning methods to the analyses of imaging data is allowing the translation of such findings into potential biomarkers enabling the prediction of clinical outcomes at the individual level, towards the development of innovative and personalised treatment strategies.

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Association of White Matter With Core Cognitive Deficits in Patients With Schizophrenia

Cited by 127 publications

References 49 publications

Aberrant Middle Prefrontal-Motor Cortex Connectivity Mediates Motor Inhibitory Biomarker in Schizophrenia

Aberrant Middle Prefrontal-Motor Cortex Connectivity Mediates Motor Inhibitory Biomarker in Schizophrenia

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

The quest for biomarkers in Schizophrenia: from neuroimaging to machine learning

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