Altered Cellular White Matter But Not Extracellular Free Water on Diffusion MRI in Individuals at Clinical High Risk for Psychosis

Tang, Yingying; Pasternak, Ofer; Kubicki, Marek; Rathi, Yogesh; Zhang, Tianhong; Wang, Junjie; Li, Huijun; Woodberry, Kristen A.; Xu, Li-Hua; Qian, Zhenying; Zhu, Angela; Whitfield-Gabrieli, Susan; Keshavan, Matcheri S.; Niznikiewicz, Margaret; Stone, William S.; McCarley, Robert W.; Shenton, Martha E.; Wang, Jijun; Seidman, Larry J.

doi:10.1176/appi.ajp.2019.18091044

Cited by 34 publications

(55 citation statements)

References 52 publications

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“…These white matter abnormalities could be driven by changes in the microstructural organization of the cellular domain, such as alterations in cellular heterogeneity and/or alterations in cellular boundaries that cause restricted diffusion and that also affect anisotropy. These abnormalities are aligned with previous hypotheses suggesting that early pathologies, likely rooted in neurodevelopment, affect the composition of the white matter in CHR ( Bloemen et al, 2010 ; Clemm von Hohenberg et al, 2014 ; Karlsgodt et al, 2009 ; Peters et al, 2010 , 2009 ; Tang et al, 2019 ). Although, we note that our analyses here can not directly infer a neurodevelopmental source, also since much like other dMRI measures, MK is not specific to intracellular changes and may be affected by regions of complex fiber architecture and other partial volume effects.…”

Section: Discussionsupporting

confidence: 90%

“…However, DTI cannot identify the biological compartment responsible for the changes in FA, since FA is sensitive to multiple biological and geometrical effects that may not provide a good approximation of white matter integrity ( Jones et al, 2013 ). More recent studies that applied compartmental models suggested that the changes in FA in CHR likely originate from a cellular compartment, rather than from an extracellular compartment ( Tang et al, 2019 ; Wang et al, 2016 ). The widespread and overlapping non-gaussian (MK) and gaussian (FA) differences, that we found in the current study, further support the accumulating evidence of white matter abnormalities that precede psychosis.…”

Section: Discussionmentioning

confidence: 99%

“…On all subjects, and as part of the SIPS evaluation, the Global Assessment of Functioning Scale (GAF) was administered to evaluate a functioning score for the time of recruitment, and retrospectively for the 12 months prior to the initial assessment. Functional decline, which is required for the SIPS definition of CHR was calculated as the drop in the current GAF score at the time of assessment compared with the subject’s highest estimated GAF score in the 12 months prior to the initial assessment ( Tang et al, 2019 ; Zhang et al, 2015 , 2014 ). We also investigated additional symptom measures that are assessed as part of SIPS.…”

Section: Methodsmentioning

confidence: 99%

“…All individuals passed imaging quality control evaluation (see Section 2.2 ). We note that 39 of the CHR and 50 of the HC individuals were included in a previous dMRI study ( Tang et al, 2019 ). The study protocol and consent form were reviewed and approved by the local ethics committees.…”

Section: Methodsmentioning

confidence: 99%

“…DKI measures have been found useful for the identification and characterization of neurological changes in psychosis ( Cho et al, 2019 ; Kochunov et al, 2016 ; Pasternak et al, 2018 ; Ramani et al, 2007 ; Zhu et al, 2016 , 2015 ), but have not yet been applied to CHR. Previous dMRI studies in CHR repeatedly identified subtle white matter abnormalities ( Bloemen et al, 2010 ; Clemm von Hohenberg et al, 2014 ; Karlsgodt et al, 2009 ; Peters et al, 2010 , 2009 ; Tang et al, 2019 ), which were explained as likely having a neurodevelopmental root contributing to the etiology of psychosis. These studies help establish the presence of white matter abnormalities in CHR populations, but since they are mostly based on standard DTI analysis, they provide limited information about the microstructural properties underlying these abnormalities.…”

Section: Introductionmentioning

confidence: 97%

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MK-Curve improves sensitivity to identify white matter alterations in clinical high risk for psychosis

et al. 2021

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Diffusion kurtosis imaging (DKI) is a diffusion MRI approach that enables the measurement of brain microstructural properties, reflecting molecular restrictions and tissue heterogeneity. DKI parameters such as mean kurtosis (MK) provide additional subtle information to that provided by popular diffusion tensor imaging (DTI) parameters, and thus have been considered useful to detect white matter abnormalities, especially in populations that are not expected to show severe brain pathologies. However, DKI parameters often yield artifactual output values that are outside of the biologically plausible range, which diminish sensitivity to identify true microstructural changes. Recently we have proposed the mean-kurtosis-curve (MK-Curve) method to correct voxels with implausible DKI parameters, and demonstrated its improved performance against other approaches that correct artifacts in DKI. In this work, we aimed to evaluate the utility of the MK-Curve method to improve the identification of white matter abnormalities in group comparisons. To do so, we compared group differences, with and without the MK-Curve correction, between 115 individuals at clinical high risk for psychosis (CHR) and 93 healthy controls (HCs). We also compared the correlation of the corrected and uncorrected DKI parameters with clinical characteristics. Following the MK-curve correction, the group differences had larger effect sizes and higher statistical significance (i.e., lower p-values), demonstrating increased sensitivity to detect group differences, in particular in MK. Furthermore, the MK-curve-corrected DKI parameters displayed stronger correlations with clinical variables in CHR individuals, demonstrating the clinical relevance of the corrected parameters. Overall, following the MK-curve correction our analyses found widespread lower MK in CHR that overlapped with lower fractional anisotropy (FA), and both measures were significantly correlated with a decline in functioning and with more severe symptoms. These observations further characterize white matter alterations in the CHR stage, demonstrating that MK and FA abnormalities are widespread, and mostly overlap. The improvement in group differences and stronger correlation with clinical variables suggest that applying MK-curve would be beneficial for the detection and characterization of subtle group differences in other experiments as well.

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Section: Discussionsupporting

confidence: 90%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

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MK-Curve improves sensitivity to identify white matter alterations in clinical high risk for psychosis

et al. 2021

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Improving the predictive potential of diffusion MRI in schizophrenia using normative models—Towards subject‐level classification

Elad

Cetin-Karayumak

Zhang

et al. 2021

Human Brain Mapping

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Diffusion MRI studies consistently report group differences in white matter between individuals diagnosed with schizophrenia and healthy controls. Nevertheless, the abnormalities found at the group‐level are often not observed at the individual level. Among the different approaches aiming to study white matter abnormalities at the subject level, normative modeling analysis takes a step towards subject‐level predictions by identifying affected brain locations in individual subjects based on extreme deviations from a normative range. Here, we leveraged a large harmonized diffusion MRI dataset from 512 healthy controls and 601 individuals diagnosed with schizophrenia, to study whether normative modeling can improve subject‐level predictions from a binary classifier. To this aim, individual deviations from a normative model of standard (fractional anisotropy) and advanced (free‐water) dMRI measures, were calculated by means of age and sex‐adjusted z‐scores relative to control data, in 18 white matter regions. Even though larger effect sizes are found when testing for group differences in z‐scores than are found with raw values (p < .001), predictions based on summary z‐score measures achieved low predictive power (AUC < 0.63). Instead, we find that combining information from the different white matter tracts, while using multiple imaging measures simultaneously, improves prediction performance (the best predictor achieved AUC = 0.726). Our findings suggest that extreme deviations from a normative model are not optimal features for prediction. However, including the complete distribution of deviations across multiple imaging measures improves prediction, and could aid in subject‐level classification.

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Comprehensive review of multidimensional biomarkers in the ShangHai At Risk for Psychosis (SHARP) program for early psychosis identification

Zhang,

Xu,

Tang

et al. 2023

PCN Reports

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Psychosis is recognized as one of the largest contributors to nonfatal health loss, and early identification can largely improve routine clinical activity by predicting the psychotic course and guiding treatment. Clinicians have used the clinical high‐risk for psychosis (CHR) paradigm to better understand the risk factors that contribute to the onset of psychotic disorders. Clinical factors have been widely applied to calculate the individualized risks for conversion to psychosis 1–2 years later. However, there is still a dearth of valid biomarkers to predict psychosis. Biomarkers, in the context of this paper, refer to measurable biological indicators that can provide valuable information about the early identification of individuals at risk for psychosis. The aim of this paper is to critically review studies assessing CHR and suggest possible biomarkers for application of prediction. We summarized the studies on biomarkers derived from the findings of the ShangHai at Risk for Psychosis (SHARP) program, including those that are considered to have the most potential. This comprehensive review was conducted based on expert opinions within the SHARP research team, and the selection of studies and results presented in this paper reflects the collective expertise of the team in the field of early psychosis identification. The three dimensions with potential candidates include neuroimaging dimension of brain structure and function, electrophysiological dimension of event‐related potentials (ERPs), and immune dimension of inflammatory cytokines and complement proteins, which proved to be useful in supporting the prediction of psychosis from the CHR state. We suggest that these three dimensions could be useful as risk biomarkers for treatment optimization. In the future, when available for the integration of multiple dimensions, clinicians may be able to obtain a comprehensive report with detailed information of psychosis risk and specific indications about preferred prevention.

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Altered Cellular White Matter But Not Extracellular Free Water on Diffusion MRI in Individuals at Clinical High Risk for Psychosis

Cited by 34 publications

References 52 publications

MK-Curve improves sensitivity to identify white matter alterations in clinical high risk for psychosis

MK-Curve improves sensitivity to identify white matter alterations in clinical high risk for psychosis

Improving the predictive potential of diffusion MRI in schizophrenia using normative models—Towards subject‐level classification

Comprehensive review of multidimensional biomarkers in the ShangHai At Risk for Psychosis (SHARP) program for early psychosis identification

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