A novel DTI-QA tool: Automated metric extraction exploiting the sphericity of an agar filled phantom

Self Cite

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.

Section: Discussionmentioning

confidence: 98%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

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

Self Cite

“…A few studies have attempted to mitigate site effects by standardizing protocols and image acquisition parameters (Friedman et al, 2008; Glover et al, 2012; Shinohara et al, 2017; Oh et al, 2017; Kochunov et al, 2018; Chavez et al, 2018). However, it has been shown that scanner-to-scanner variation arising from the use of scanners from different manufacturers is not eliminated completely by the standardization of acquisition parameters (Jovicich et al, 2016; Noble et al, 2017), for instance, by use of phantom-based imaging acquisitions (Delaparte et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

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

Linn

Cook

et al. 2018

372

268

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.

The robustness of persistent homology of brain networks to data acquisition‐related non‐neural variability in resting state fMRI

Kumar

Shovon

Deshpande

2023

There is increasing interest in investigating brain function based on functional connectivity networks (FCN) obtained from resting‐state functional magnetic resonance imaging (fMRI). FCNs, typically obtained using measures of time series association such as Pearson's correlation, are sensitive to data acquisition parameters such as sampling period. This introduces non‐neural variability in data pooled from different acquisition protocols and MRI scanners, negating the advantages of larger sample sizes in pooled data. To address this, we hypothesize that the topology or shape of brain networks must be preserved irrespective of how densely it is sampled, and metrics which capture this topology may be statistically similar across sampling periods, thereby alleviating this source of non‐neural variability. Accordingly, we present an end‐to‐end pipeline that uses persistent homology (PH), a branch of topological data analysis, to demonstrate similarity across FCNs acquired at different temporal sampling periods. PH, as a technique, extracts topological features by capturing the network organization across all continuous threshold values, as opposed to graph theoretic methods, which fix a discrete network topology by thresholding the connectivity matrix. The extracted topological features are encoded in the form of persistent diagrams that can be compared against one another using the earth‐moving metric, also popularly known as the Wasserstein distance. We extract topological features from three data cohorts, each acquired at different temporal sampling periods and demonstrate that these features are statistically the same, hence, empirically showing that PH may be robust to changes in data acquisition parameters such as sampling period.