Dataset Information

Reproducibility of tract-based white matter microstructural measures using the ENIGMA-DTI protocol.

ABSTRACT: BACKGROUND:In preparation for longitudinal analyses of white matter development in youths with family histories of substance use disorders (FH+) or without such histories (FH-), we examined the reproducibility and reliability of global and regional measures of fractional anisotropy (FA) values, measured using the Enhancing Neuro Imaging Genetics Through Meta Analysis (ENIGMA)-diffusion tensor imaging (DTI) protocol. Highly reliable measures are necessary to detect any subtle differences in brain development. METHODS:First, we analyzed reproducibility data in a sample of 12 healthy young adults (ages 20-28) imaged three times within a week. Next, we calculated the same metrics in data collected 1-year apart in the sample of 68 FH+ and 21 FH- adolescents. This is a timeframe where within subject changes in white matter microstructure are small compared to between subject variance. Reproducibility was estimated by examining mean coefficients of variation (MCV), mean absolute differences (MAD), and intraclass correlations (ICC) for global and tract-specific FA values. RESULTS:We found excellent reproducibility for whole-brain DTI-FA values and most of the white matter tracts, except for the corticospinal tract and the fornix in both adults and youths. There was no significant effect of FH-group on reproducibility (p = .4). Reproducibility metrics were not significantly different between adolescents and adults (all p > .2). In post hoc analyses, the reproducibility metrics for regional FA values showed a strong positive correlation (r = .6) with the regional FA heritability measures previously reported by ENIGMA-DTI. CONCLUSION:Overall, this study demonstrated an excellent reproducibility of ENIGMA-DTI FA, positing it as viable analysis tools for longitudinal studies and other protocols that repeatedly assess white matter microstructure.

SUBMITTER: Acheson A

PROVIDER: S-EPMC5318368 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature

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Reproducibility of tract-based white matter microstructural measures using the ENIGMA-DTI protocol.

Acheson Ashley A Wijtenburg S Andrea SA Rowland Laura M LM Winkler Anderson A Mathias Charles W CW Hong L Elliot LE Jahanshad Neda N Patel Binish B Thompson Paul M PM McGuire Stephen A SA Sherman Paul M PM Kochunov Peter P Dougherty Donald M DM

Brain and behavior 20170114 2

<h4>Background</h4>In preparation for longitudinal analyses of white matter development in youths with family histories of substance use disorders (FH+) or without such histories (FH-), we examined the reproducibility and reliability of global and regional measures of fractional anisotropy (FA) values, measured using the Enhancing Neuro Imaging Genetics Through Meta Analysis (ENIGMA)-diffusion tensor imaging (DTI) protocol. Highly reliable measures are necessary to detect any subtle differences ...[more]

PMID: 28239525

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Project description:Diffusion tensor imaging (DTI) based assessment of white matter fiber tract integrity can support the diagnosis of Alzheimer's disease (AD). The use of DTI as a biomarker, however, depends on its applicability in a multicenter setting accounting for effects of different MRI scanners. We applied multivariate machine learning (ML) to a large multicenter sample from the recently created framework of the European DTI study on Dementia (EDSD). We hypothesized that ML approaches may amend effects of multicenter acquisition. We included a sample of 137 patients with clinically probable AD (MMSE 20.6±5.3) and 143 healthy elderly controls, scanned in nine different scanners. For diagnostic classification we used the DTI indices fractional anisotropy (FA) and mean diffusivity (MD) and, for comparison, gray matter and white matter density maps from anatomical MRI. Data were classified using a Support Vector Machine (SVM) and a Naïve Bayes (NB) classifier. We used two cross-validation approaches, (i) test and training samples randomly drawn from the entire data set (pooled cross-validation) and (ii) data from each scanner as test set, and the data from the remaining scanners as training set (scanner-specific cross-validation). In the pooled cross-validation, SVM achieved an accuracy of 80% for FA and 83% for MD. Accuracies for NB were significantly lower, ranging between 68% and 75%. Removing variance components arising from scanners using principal component analysis did not significantly change the classification results for both classifiers. For the scanner-specific cross-validation, the classification accuracy was reduced for both SVM and NB. After mean correction, classification accuracy reached a level comparable to the results obtained from the pooled cross-validation. Our findings support the notion that machine learning classification allows robust classification of DTI data sets arising from multiple scanners, even if a new data set comes from a scanner that was not part of the training sample.

Project description:ObjectivesThis study explored group-wise quantitative measures of tract-specific white matter (WM) microstructure and functional default mode network (DMN) connectivity to establish an initial indication of their clinical applicability for early-stage and follow-up differential diagnosis of Alzheimer's disease (AD) and behavioural variant frontotemporal dementia (bvFTD).MethodsEleven AD and 12 bvFTD early-stage patients and 18 controls underwent diffusion tensor imaging and resting state functional magnetic resonance imaging at 3 T. All AD and 6 bvFTD patients underwent the same protocol at 1-year follow-up. Functional connectivity measures of DMN and WM tract-specific diffusivity measures were determined for all groups. Exploratory analyses were performed to compare all measures between the three groups at baseline and between patients at follow-up. Additionally, the difference between baseline and follow-up diffusivity measures in AD and bvFTD patients was compared.ResultsFunctional connectivity of the DMN was not different between groups at baseline and at follow-up. Diffusion abnormalities were observed widely in bvFTD and regionally in the hippocampal cingulum in AD. The extent of the differences between bvFTD and AD was diminished at follow-up, yet abnormalities were still more pronounced in bvFTD. The rate of change was similar in bvFTD and AD.ConclusionsThis study provides a tentative indication that quantitative tract-specific microstructural WM abnormalities, but not quantitative functional connectivity of the DMN, may aid early-stage and follow-up differential diagnosis of bvFTD and AD. Specifically, pronounced microstructural changes in anterior WM tracts may characterise bvFTD, whereas microstructural abnormalities of the hippocampal cingulum may characterise AD.Key points• The clinical applicability of quantitative brain imaging measures for early-stage and follow-up differential diagnosis of dementia subtypes was explored using a group-wise approach. • Quantitative tract-specific microstructural white matter abnormalities, but not quantitative functional connectivity of the default mode network, may aid early-stage and follow-up differential diagnosis of behavioural variant frontotemporal dementia and Alzheimer's disease. • Pronounced microstructural white matter (WM) changes in anterior WM tracts characterise behavioural variant frontotemporal dementia, whereas microstructural WM abnormalities of the hippocampal cingulum in the absence of other WM changes characterise Alzheimer's disease.

Dataset Information

Reproducibility of tract-based white matter microstructural measures using the ENIGMA-DTI protocol.

Publications

Reproducibility of tract-based white matter microstructural measures using the ENIGMA-DTI protocol.

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