Resting state fMRI scanner instabilities revealed by longitudinal phantom scans in a multi-center study

Kayvanrad, Aras; Arnott, Stephen R.; Churchill, Nathan W.; Hassel, Stefanie; Chemparathy, Aditi; Fan, Dashan; Zamyadi, Mojdeh; Gee, Tom; Bartha, Robert; Black, Sandra E.; Lawrence‐Dewar, Jane M.; Scott, Christopher J.; Symons, Sean; Davis, Andrew D.; Hall, Geoffrey B.; Harris, Jacqueline; Lobaugh, Nancy J.; MacQueen, Glenda; Woo, Cindy; Strother, Stephen C.

doi:10.1016/j.neuroimage.2021.118197

Cited by 5 publications

(6 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Here, we observed statistically significant differences in the FWHM metric across sites that were not explained by voxel size as good as by the scanner type. In fact, we noticed that GE (Center B) and Philips scanners (centers A and D) exhibited greater FWHM values compared to a Siemens scanner (Center C), in line with previous works [ 66 , 67 ]. A major cause of site differences in FWHM could be the presence (and type) or absence of spatial filtering during the image reconstruction process in k-space; in fact, Friedman and colleagues hypothesized in their work (a multicenter study where GE and Siemens scanners are included) that the key reason why images from GE scanners are smoother than those from Siemens scanners is the different k-space filtering algorithm employed [ 66 ].…”

Section: Discussionsupporting

confidence: 92%

Resting-state functional MRI in multicenter studies on multiple sclerosis: a report on raw data quality and functional connectivity features from the Italian Neuroimaging Network Initiative

et al. 2022

View full text Add to dashboard Cite

The Italian Neuroimaging Network Initiative (INNI) is an expanding repository of brain MRI data from multiple sclerosis (MS) patients recruited at four Italian MRI research sites. We describe the raw data quality of resting-state functional MRI (RS-fMRI) time-series in INNI and the inter-site variability in functional connectivity (FC) features after unified automated data preprocessing. MRI datasets from 489 MS patients and 246 healthy control (HC) subjects were retrieved from the INNI database. Raw data quality metrics included temporal signal-to-noise ratio (tSNR), spatial smoothness (FWHM), framewise displacement (FD), and differential variation in signals (DVARS). Automated preprocessing integrated white-matter lesion segmentation (SAMSEG) into a standard fMRI pipeline (fMRIPrep). FC features were calculated on pre-processed data and harmonized between sites (Combat) prior to assessing general MS-related alterations. Across centers (both groups), median tSNR and FWHM ranged from 47 to 84 and from 2.0 to 2.5, and median FD and DVARS ranged from 0.08 to 0.24 and from 1.06 to 1.22. After preprocessing, only global FC-related features were significantly correlated with FD or DVARS. Across large-scale networks, age/sex/FD-adjusted and harmonized FC features exhibited both inter-site and site-specific inter-group effects. Significant general reductions were obtained for somatomotor and limbic networks in MS patients (vs. HC). The implemented procedures provide technical information on raw data quality and outcome of fully automated preprocessing that might serve as reference in future RS-fMRI studies within INNI. The unified pipeline introduced little bias across sites and appears suitable for multisite FC analyses on harmonized network estimates.

show abstract

Section: Discussionsupporting

confidence: 92%

Resting-state functional MRI in multicenter studies on multiple sclerosis: a report on raw data quality and functional connectivity features from the Italian Neuroimaging Network Initiative

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The FBIRN project led to the creation of an open-access data repository (44), which is designed to advance both clinical and research applications of MRI to neuroanatomy. The FBIRN model is still used to guide clinical fMRI studies (45). Similarly, the THEMES DAM could become the basis for efforts to harmonize multicenter studies that use pediatric CPET.…”

Section: Discussionmentioning

confidence: 99%

Pediatric Cardiopulmonary Exercise Testing: Interoperability Through Domain Analysis Modeling and a National Survey

Cooper

Bar‐Yoseph²,

Liem³

et al. 2022

Medicine &Amp; Science in Sports &Amp; Exercise

View full text Add to dashboard Cite

PurposeThe electronic health record, data science advances, and dynamic environmental and infectious threats to child health highlight the need for harmonized and interoperable approaches to pediatric cardiopulmonary exercise testing (CPET). Accordingly, we developed a terminology harmonization in exercise medicine and exercise science domain analysis model (THEMES DAM) to structure CPET data elements.MethodsTHEMES DAM identified 114 data elements, including participant information, calibration, equipment, protocols, laboratory personnel, encouragement strategies, and analysis procedures. We used the THEMES DAM, vetted by the international data standards organization HL7, to construct a current-state survey of pediatric CPET centers in the United States. Forty-eight of 101 centers responded to a questionnaire covering seven major topic areas (38 items).ResultsCenters predominantly performed between 100 and 500 tests annually. Cardiac disease represented 55% of referrals. Almost all centers calibrated gas concentrations and flow daily, but 42% never calibrated their treadmill or cycle ergometers. All centers measured V̇O2peakbut calculated differently. Centers used a variety of protocols (e.g., for treadmill: 61%, Bruce; 43%, modified Bruce; 59%, other); 44% calculated CPET slopes from submaximal portions of CPET (e.g., V̇O2-HR). All centers verbally encouraged participants, but only 40% used a standardized approach. The interpretation of CPET was done by physicians (60%), exercise physiologists (25%), exercise technicians (10%), nurses (1%), or others (4%). Ninety-one percent would agree to collaborate in multicenter research, 89% to establish dynamic reference values, and 83% to better interpret CPET.ConclusionsThe survey data and the implementation of THEMES DAM could accelerate interoperability across multiple centers. This would facilitate a nimble approach to create pediatric reference values responsive to the constantly changing health environment and stimulate novel approaches to CPET research and clinical application.

show abstract

“…This analysis estimated the number of parameters that are of interest for neuroimaging functional studies. It computed Signal to Noise Ratio (SNR), Signal to Fluctuation Noise Ratio (SFNR), Percentage Signal Change (PSC), Signal drift, temporal SNR (tSNR) ( 16 , 40 ). It performed even-odd analysis producing an output to visualize structured noise and spike detection to identify anomalous volumes.…”

Section: Methods and Analysismentioning

confidence: 99%

“…These initiatives are also important opportunities for sharing technical and scientific knowledge, new ideas, and available resources. Initiatives such as the Alzheimer's Disease Neuroimaging Initiative (ADNI, http://adni.loni.usc.edu/ ), ESR/EIBALL ( https://www.myesr.org/research/european-imaging-biomarkers-alliance-eiball ), Quantitative Imaging Biomarkers Alliance (QIBA) ( https://www.rsna.org/research/quantitative-imaging-biomarkers-alliance ) ( 14 ), or Biomedical Informatics Research Network (BIRN) ( 15 , 16 ) are successful examples of this intention.…”

Section: Introductionmentioning

confidence: 99%

Quantitative MRI Harmonization to Maximize Clinical Impact: The RIN–Neuroimaging Network

Nigri¹,

Ferraro²,

Wheeler-Kingshott³

et al. 2022

Front. Neurol.

View full text Add to dashboard Cite

Neuroimaging studies often lack reproducibility, one of the cardinal features of the scientific method. Multisite collaboration initiatives increase sample size and limit methodological flexibility, therefore providing the foundation for increased statistical power and generalizable results. However, multisite collaborative initiatives are inherently limited by hardware, software, and pulse and sequence design heterogeneities of both clinical and preclinical MRI scanners and the lack of benchmark for acquisition protocols, data analysis, and data sharing. We present the overarching vision that yielded to the constitution of RIN-Neuroimaging Network, a national consortium dedicated to identifying disease and subject-specific in-vivo neuroimaging biomarkers of diverse neurological and neuropsychiatric conditions. This ambitious goal needs efforts toward increasing the diagnostic and prognostic power of advanced MRI data. To this aim, 23 Italian Scientific Institutes of Hospitalization and Care (IRCCS), with technological and clinical specialization in the neurological and neuroimaging field, have gathered together. Each IRCCS is equipped with high- or ultra-high field MRI scanners (i.e., ≥3T) for clinical or preclinical research or has established expertise in MRI data analysis and infrastructure. The actions of this Network were defined across several work packages (WP). A clinical work package (WP1) defined the guidelines for a minimum standard clinical qualitative MRI assessment for the main neurological diseases. Two neuroimaging technical work packages (WP2 and WP3, for clinical and preclinical scanners) established Standard Operative Procedures for quality controls on phantoms as well as advanced harmonized quantitative MRI protocols for studying the brain of healthy human participants and wild type mice. Under FAIR principles, a web-based e-infrastructure to store and share data across sites was also implemented (WP4). Finally, the RIN translated all these efforts into a large-scale multimodal data collection in patients and animal models with dementia (i.e., case study). The RIN-Neuroimaging Network can maximize the impact of public investments in research and clinical practice acquiring data across institutes and pathologies with high-quality and highly-consistent acquisition protocols, optimizing the analysis pipeline and data sharing procedures.

show abstract

Resting state fMRI scanner instabilities revealed by longitudinal phantom scans in a multi-center study

Cited by 5 publications

References 32 publications

Resting-state functional MRI in multicenter studies on multiple sclerosis: a report on raw data quality and functional connectivity features from the Italian Neuroimaging Network Initiative

Resting-state functional MRI in multicenter studies on multiple sclerosis: a report on raw data quality and functional connectivity features from the Italian Neuroimaging Network Initiative

Pediatric Cardiopulmonary Exercise Testing: Interoperability Through Domain Analysis Modeling and a National Survey

Quantitative MRI Harmonization to Maximize Clinical Impact: The RIN–Neuroimaging Network

Contact Info

Product

Resources

About