Harmonization of multi-site diffusion tensor imaging data

Fortin, Jean‐Philippe; Parker, Drew; Tunç, Birkan; Watanabe, Takanori; Elliott, Mark A.; Ruparel, Kosha; Roalf, David R.; Satterthwaite, Theodore D.; Gur, Ruben C.; Gur, Raquel E.; Schultz, Robert T.; Verma, Ragini; Shinohara, Russell T.

doi:10.1016/j.neuroimage.2017.08.047

Cited by 897 publications

(872 citation statements)

References 58 publications

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“…Given the excellent performance of ComBat in DTI (Fortin et al, 2017), MRI-based cortical thickness (Fortin et al, 2018), and fMRI (current study) measurements, we conclude that this harmonization method is a reliable and powerful technique that can be widely applied to different neuroimaging modalities and summary measurements.…”

Section: Discussionmentioning

confidence: 75%

“…Based on the literature (Friedman et al, 2008; Feis et al, 2015; Rath et al, 2016; Dansereau et al, 2017), we speculated that measurements such as DTI fractional anisotropy (Fortin et al, 2017), MRI cortical thickness (Fortin et al, 2018), and fMRI functional connectivity (the present study) would differ among the four sites (CU, MGH, TX and UM) due to systematic bias and non-biological variability attributable to the use of different scanners and different imaging parameters.…”

Section: Methodsmentioning

confidence: 92%

“…Recently, our group adapted ComBat harmonization (Johnson et al, 2007) to model and remove site effects in multi-site DTI (Fortin et al, 2017) and cortical thickness (Fortin et al, 2018) measurements. ComBat was originally designed to correct so-called “batch effects” in genomic studies (Johnson et al, 2007) that arise due to processing high-throughput genomic data in different laboratories with different equipment at different times.…”

Section: Introductionmentioning

confidence: 99%

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Statistical harmonization corrects site effects in functional connectivity measurements from multi‐site fMRI data

Linn

Cook

et al. 2018

Human Brain Mapping

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268

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Acquiring resting-state functional magnetic resonance imaging (fMRI) datasets at multiple MRI scanners and clinical sites can improve statistical power and generalizability of results. However, multi-site neuroimaging studies have reported considerable nonbiological variability in fMRI measurements due to different scanner manufacturers and acquisition protocols. These undesirable sources of variability may limit power to detect effects of interest and may even result in erroneous findings. Until now, there has not been an approach that removes unwanted site effects. In this study, using a relatively large multi-site (4 sites) fMRI dataset, we investigated the impact of site effects on functional connectivity and network measures estimated by widely used connectivity metrics and brain parcellations. The protocols and image acquisition of the dataset used in this study had been homogenized using identical MRI phantom acquisitions from each of the neuroimaging sites; however, intersite acquisition effects were not completely eliminated. Indeed, in this study, we found that the magnitude of site effects depended on the choice of connectivity metric and brain atlas. Therefore, to further remove site effects, we applied ComBat, a harmonization technique previously shown to eliminate site effects in multi-site diffusion tensor imaging (DTI) and cortical thickness studies. In the current work, ComBat successfully removed site effects identified in connectivity and network measures and increased the power to detect age associations when using optimal combinations of connectivity metrics and brain atlases. Our proposed ComBat harmonization approach for fMRI-derived connectivity measures facilitates reliable and efficient analysis of retrospective and prospective multi-site fMRI neuroimaging studies.

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Section: Discussionmentioning

confidence: 75%

Section: Methodsmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

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Statistical harmonization corrects site effects in functional connectivity measurements from multi‐site fMRI data

Linn

Cook

et al. 2018

Human Brain Mapping

Self Cite

365

268

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“…Differences between demographic data were tested with Mann-Whitney-Wilcoxon’s U test for numerical variables and with Fisher’s exact test for categorical variables. To further check that data were not primarily driven by site effects, we checked that the results were reproduced using an independent data harmonization approach [22] (see the Results section in the online supplementary materials). Further details on model selection and assumptions are provided in the online supplementary materials.…”

Section: Methodsmentioning

confidence: 99%

Higher in vivo Cortical Intracellular Volume Fraction Associated with Lithium Therapy in Bipolar Disorder: A Multicenter NODDI Study

Sarrazin

Poupon

Teillac

et al. 2019

Psychother Psychosom

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Background: MRI studies in patients with bipolar disorder have suggested that lithium is associated with grey matter increases that may underlie its therapeutic effects. However, the relationship between grey matter volume and cellular microstructural changes is not straightforward, as modifications of different cellular compartments of grey matter may be involved. Objectives: Our aim was to test the hypothesis that dendritic density is higher in patients undergoing lithium therapy than in patients without lithium, using advanced modelling of water diffusion investigated with MRI. Method: We included 41 patients and 40 controls matched for age and gender from two sites. All subjects underwent 3T MRI with 3 shells of diffusion. We used neurite orientation dispersion and density imaging to compare the grey matter neurite density between patients undergoing lithium therapy or not and control subjects. Results: We found a significant group effect in the left prefrontal region (p = 0.001, Bonferroni corrected): patients without lithium had a lower frontal neurite density than controls (p = 0.009), while those on lithium had a higher mean neurite density than those without (p < 0.001). Patients on lithium were not different from controls (p = 0.08). Conclusions: This is the first study to report in vivo evidence of preserved neurite density of the prefrontal cortex in humans associated with lithium intake. Changes of intracellular volume fraction are thought to reflect changes of grey matter microstructural organization. This reinforces the hypothesis of lithium having a positive effect on the neuronal compartment in humans.

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“…or processing pipeline can be used if the model is modified using domain adaptation or fine‐tuning using a much smaller dataset of new data. Additionally, MRI harmonization approaches could be used to ensure image uniformity across various scanner sites …”

Section: Discussionmentioning

confidence: 99%

Simultaneous NODDI and GFA parameter map generation from subsampled q‐space imaging using deep learning

Gibbons

Hodgson

Chaudhari

et al. 2018

Magnetic Resonance in Med

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Purpose To develop a robust multidimensional deep‐learning based method to simultaneously generate accurate neurite orientation dispersion and density imaging (NODDI) and generalized fractional anisotropy (GFA) parameter maps from undersampled q‐space datasets for use in stroke imaging. Methods Traditional diffusion spectrum imaging (DSI) capable of producing accurate NODDI and GFA parameter maps requires hundreds of q‐space samples which renders the scan time clinically untenable. A convolutional neural network (CNN) was trained to generated NODDI and GFA parameter maps simultaneously from 10× undersampled q‐space data. A total of 48 DSI scans from 15 stroke patients and 14 normal subjects were acquired for training, validating, and testing this method. The proposed network was compared to previously proposed voxel‐wise machine learning based approaches for q‐space imaging. Network‐generated images were used to predict stroke functional outcome measures. Results The proposed network achieves significant performance advantages compared to previously proposed machine learning approaches, showing significant improvements across image quality metrics. Generating these parameter maps using CNNs also comes with the computational benefits of only needing to generate and train a single network instead of multiple networks for each parameter type. Post‐stroke outcome prediction metrics do not appreciably change when using images generated from this proposed technique. Over three test participants, the predicted stroke functional outcome scores were within 1–6% of the clinical evaluations. Conclusions Estimates of NODDI and GFA parameters estimated simultaneously with a deep learning network from highly undersampled q‐space data were improved compared to other state‐of‐the‐art methods providing a 10‐fold reduction scan time compared to conventional methods.

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Harmonization of multi-site diffusion tensor imaging data

Cited by 897 publications

References 58 publications

Statistical harmonization corrects site effects in functional connectivity measurements from multi‐site fMRI data

Statistical harmonization corrects site effects in functional connectivity measurements from multi‐site fMRI data

Higher in vivo Cortical Intracellular Volume Fraction Associated with Lithium Therapy in Bipolar Disorder: A Multicenter NODDI Study

Simultaneous NODDI and GFA parameter map generation from subsampled q‐space imaging using deep learning

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