Search

Data from multisite magnetic resonance imaging (MRI) studies contain variance attributable to the scanner that can reduce statistical power and potentially bias results if not appropriately managed. The Adolescent Cognitive Brain Development (ABCD) study is an ongoing, longitudinal neuroimaging study acquiring data from over 11,000 children starting at 9-10 years of age. These scans are acquired on 29 different scanners of 5 different model types manufactured by 3 different vendors. Publicly available data from the ABCD study include structural MRI (sMRI) measures such as cortical thickness and diffusion MRI (dMRI) measures such as fractional anisotropy. In this work, we 1) quantify the variance attributable to scanner effects in the sMRI and dMRI datasets, 2) demonstrate the effectiveness of the data harmonization approach called ComBat to address scanner effects, and 3) present a simple, open-source tool for investigators to harmonize image features from the ABCD study. Scanner-induced variance was present in every image feature and varied in magnitude by feature type and brain location. For almost all features, scanner variance exceeded variability attributable to age and sex. ComBat harmonization was shown to effectively remove scanner induced variance from all image features while preserving the biological variability in the data. Moreover, we show that for studies examining relatively small subsamples of the ABCD dataset, the use of ComBat harmonized data provides more accurate estimates of effect sizes compared to controlling for scanner effects using ordinary least squares regression., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Iron concentration in the human brain plays a crucial role in several neurodegenerative diseases and can be monitored noninvasively using quantitative susceptibility mapping (QSM) and effective transverse relaxation rate (R 2 *) mapping from multiecho T 2 *-weighted images. Large population studies enable better understanding of pathologies and can benefit from pooling multisite data. However, reproducibility may be compromised between sites and studies using different hardware and sequence protocols. This work investigates QSM and R 2 * reproducibility at 3 T using locally optimized sequences from three centers and two vendors, and investigates possible reduction of cross-site variability through postprocessing approaches. Twenty-four healthy subjects traveled between three sites and were scanned twice at each site. Scan-rescan measurements from seven deep gray matter regions were used for assessing within-site and cross-site reproducibility using intraclass correlation coefficient (ICC) and within-subject standard deviation (SDw) measures. In addition, multiple QSM and R 2 * postprocessing options were investigated with the aim to minimize cross-site sequence-related variations, including: mask generation approach, echo-timing selection, harmonizing spatial resolution, field map estimation, susceptibility inversion method, and linear field correction for magnitude images. The same-subject cross-site region of interest measurements for QSM and R 2 * were highly correlated (R 2  ≥ 0.94) and reproducible (mean ICC of 0.89 and 0.82 for QSM and R 2 *, respectively). The mean cross-site SDw was 4.16 parts per billion (ppb) for QSM and 1.27 s -1 for R 2 *. For within-site measurements of QSM and R 2 *, the mean ICC was 0.97 and 0.87 and mean SDw was 2.36 ppb and 0.97 s -1 , respectively. The precision level is regionally dependent and is reduced in the frontal lobe, near brain edges, and in white matter regions. Cross-site QSM variability (mean SDw) was reduced up to 46% through postprocessing approaches, such as masking out less reliable regions, matching available echo timings and spatial resolution, avoiding the use of the nonconsistent magnitude contrast between scans in field estimation, and minimizing streaking artifacts., (© 2022 John Wiley & Sons Ltd.)

Background: Magnetic resonance imaging (MRI) with hyperpolarized [1- 13 C]pyruvate allows real-time and pathway specific clinical detection of otherwise unimageable in vivo metabolism. However, the comparability between sites and protocols is unknown. Here, we provide initial experiences on the agreement of hyperpolarized MRI between sites and protocols by repeated imaging of same healthy volunteers in Europe and the US., Methods: Three healthy volunteers traveled for repeated multicenter brain MRI exams with hyperpolarized [1- 13 C]pyruvate within one year. First, multisite agreement was assessed with the same echo-planar imaging protocol at both sites. Then, this was compared to a variable resolution echo-planar imaging protocol. In total, 12 examinations were performed. Common metrics of 13 C-pyruvate to 13 C-lactate conversion were calculated, including the k PL , a model-based kinetic rate constant, and its model-free equivalents. Repeatability was evaluated with intraclass correlation coefficients (ICC) for absolute agreement computed using two-way random effects models., Results: The mean k across all examinations in the multisite comparison was 0.024 ± 0.0016 s PL across all examinations in the multisite comparison was 0.024 ± 0.0016 s -1 . The ICC of the k PL was 0.83 ( p = 0.14) between sites and 0.7 ( p = 0.09) between examinations of the same volunteer at any of the two sites. For the model-free metrics, the lactate Z-score had similar site-to-site ICC, while it was considerably lower for the lactate-to-pyruvate ratio., Conclusions: Estimation of metabolic conversion from hyperpolarized [1- 13 C]pyruvate to lactate using model-based metrics such as k PL suggests close agreement between sites and examinations in volunteers. Our initial results support harmonization of protocols, support multicenter studies, and inform their design.

Multisite magnetic resonance imaging (MRI) is increasingly used in clinical research and development. Measurement biases-caused by site differences in scanner/image-acquisition protocols-negatively influence the reliability and reproducibility of image-analysis methods. Harmonization can reduce bias and improve the reproducibility of multisite datasets. Herein, a traveling-subject (TS) dataset including 56 T1-weighted MRI scans of 20 healthy participants in three different MRI procedures-20, 19, and 17 subjects in Procedures 1, 2, and 3, respectively-was considered to compare the reproducibility of TS-GLM, ComBat, and TS-ComBat harmonization methods. The minimum participant count required for harmonization was determined, and the Cohen's d between different MRI procedures was evaluated as a measurement-bias indicator. The measurement-bias reduction realized with different methods was evaluated by comparing test-retest scans for 20 healthy participants. Moreover, the minimum subject count for harmonization was determined by comparing test-retest datasets. The results revealed that TS-GLM and TS-ComBat reduced measurement bias by up to 85 and 81.3%, respectively. Meanwhile, ComBat showed a reduction of only 59.0%. At least 6 TSs were required to harmonize data obtained from different MRI scanners, complying with the imaging protocol predetermined for multisite investigations and operated with similar scan parameters. The results indicate that TS-based harmonization outperforms ComBat for measurement-bias reduction and is optimal for MRI data in well-prepared multisite investigations. One drawback is the small sample size used, potentially limiting the applicability of ComBat. Investigation on the number of subjects needed for a large-scale study is an interesting future problem., (© 2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Background: Multisite studies can considerably increase the pool of normally aging individuals with neurodegenerative disorders and thereby expedite the associated research. Understanding the reproducibility of the parameters of related brain structures-including the hippocampus, amygdala, and entorhinal cortex-in multisite studies is crucial in determining the impact of healthy aging or neurodegenerative diseases., Purpose: To estimate the reproducibility of the fascinating structures by automatic (FreeSurfer) and manual segmentation methods in a well-controlled multisite dataset., Material and Methods: Three traveling individuals were scanned at 10 sites, which were equipped with the same equipment (3T Prisma Siemens). They used the same scan protocol (two inversion-contrast magnetization-prepared rapid gradient echo sequences) and operators. Validity coefficients (intraclass correlations coefficient [ICC]) and spatial overlap measures (Dice Similarity Coefficient [DSC]) were used to estimate the reproducibility of multisite data., Results: ICC and DSC values varied substantially among structures and segmentation methods, and values of manual tracing were relatively higher than the automated method. ICC and DSC values of structural parameters were greater than 0.80 and 0.60 across sites, as determined by manual tracing. Low reproducibility was observed in the amygdala parameters by automatic segmentation method (ICC = 0.349-0.529, DSC = 0.380-0.873). However, ICC and DSC scores of the hippocampus were higher than 0.60 and 0.65 by two segmentation methods., Conclusion: This study suggests that a well-controlled multisite study could provide a reliable MRI dataset. Manual tracing of volume assessments is recommended for low reproducibility structures that require high levels of precision in multisite studies.

It has been reported that resting state fluctuation amplitude (RSFA) exhibits extremely large inter-site variability, which limits its application in multisite studies. Although global normalization (GN) based approaches are efficient in reducing the site effects, they may cause spurious results. In this study, our purpose was to find alternative strategies to minimize the substantial site effects for RSFA, without the risk of introducing artificial findings. We firstly modified the ALFF algorithm so that it is conceptually validated and insensitive to data length, then found that (a) global mean amplitude of low-frequency fluctuation (ALFF) covaried only with BOLD signal intensity, while global mean fractional ALFF (fALFF) was significantly correlated with TRs across different sites; (b) The inter-site variations in raw RSFA values were significant across the entire brain and exhibited similar trends between gray matter and white matter; (c) For ALFF, signal intensity rescaling could dramatically reduce inter-site variability by several orders, but could not fully removed the globally distributed inter-site variability. For fALFF, the global site effects could be completely removed by TR controlling; (d) Meanwhile, the magnitude of the inter-site variability of fALFF could also be reduced to an acceptable level, as indicated by the detection power of fALFF in multisite data quite close to that in monosite data. Thus our findings suggest GN based harmonization methods could be replaced with only controlling for confounding factors including signal scaling, TR and full-band power.

Studies using resting-state functional magnetic resonance imaging (rsfMRI) are increasingly collecting data at multiple sites in order to speed up recruitment or increase sample size. The main objective of this study was to assess the long-term consistency of rsfMRI connectivity maps derived at multiple sites and vendors using the Canadian Dementia Imaging Protocol (CDIP, www.cdip-pcid.ca). Nine to 10 min of functional BOLD images were acquired from an adult cognitively healthy volunteer scanned repeatedly at 13 Canadian sites on three scanner makes (General Electric, Philips and Siemens) over the course of 2.5 years. The consistency (spatial Pearson's correlation) of rsfMRI connectivity maps for seven canonical networks ranged from 0.3 to 0.8, with a negligible effect of time, but significant site and vendor effects. We noted systematic differences in data quality (i.e. head motion, number of useable time frames, temporal signal-to-noise ratio) across vendors, which may also confound some of these results, and could not be disentangled in this sample. We also pooled the long-term longitudinal data with a single-site, short-term (1 month) data sample acquired on 26 subjects (10 scans per subject), called HNU1. Using randomly selected pairs of scans from each subject, we quantified the ability of a data-driven unsupervised cluster analysis to match two scans of the same subjects. In this "fingerprinting" experiment, we found that scans from the Canadian subject (Csub) could be matched with high accuracy intra-site (>95% for some networks), but that the accuracy decreased substantially for scans drawn from different sites and vendors, even falling outside of the range of accuracies observed in HNU1. Overall, our results demonstrate good multivariate stability of rsfMRI measures over several years, but substantial impact of scanning site and vendors. How detrimental these effects are will depend on the application, yet our results demonstrate that new methods for harmonizing multisite analysis represent an important area for future work., (Copyright © 2019. Published by Elsevier Inc.)

Background: MRI is the imaging modality of choice for diagnosis and intervention assessment in neurological disease. Its full potential has not been realized due in part to challenges in harmonizing advanced techniques across multiple sites., Purpose: To develop a method for the assessment of reliability and repeatability of advanced multisite-multisession neuroimaging studies and specifically to assess the reliability of an advanced MRI protocol, including multiband fMRI and diffusion tensor MRI, in a multisite setting., Study Type: Prospective., Population: Twice repeated measurement of a single subject with stable relapsing-remitting multiple sclerosis (MS) at seven institutions., Field Strength/sequence: A 3 T MRI protocol included higher spatial resolution anatomical scans, a variable flip-angle longitudinal relaxation rate constant (R 1 ≡ 1/T 1 ) measurement, quantitative magnetization transfer imaging, diffusion tensor imaging, and a resting-state fMRI (rsFMRI) series., Assessment: Multiple methods of assessing intrasite repeatability and intersite reliability were evaluated for imaging metrics derived from each sequence., Statistical Tests: Student's t-test, Pearson's r, and intraclass correlation coefficient (ICC) (2,1) were employed to assess repeatability and reliability. Two new statistical metrics are introduced that frame reliability and repeatability in the respective units of the measurements themselves., Results: Intrasite repeatability was excellent for quantitative R 1 , magnetization transfer ratio (MTR), and diffusion-weighted imaging (DWI) based metrics (r > 0.95). rsFMRI metrics were less repeatable (r = 0.8). Intersite reliability was excellent for R 1 , MTR, and DWI (ICC >0.9), and moderate for rsFMRI metrics (ICC∼0.4)., Data Conclusion: From most reliable to least, using a new reliability metric introduced here, MTR > R 1 > DWI > rsFMRI; for repeatability, MTR > DWI > R 1 > rsFMRI. A graphical method for at-a-glance assessment of reliability and repeatability, effect sizes, and outlier identification in multisite-multisession neuroimaging studies is introduced., Level of Evidence: 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:878-888., (© 2019 International Society for Magnetic Resonance in Medicine.)

Connectivity studies using resting-state functional magnetic resonance imaging are increasingly pooling data acquired at multiple sites. While this may allow investigators to speed up recruitment or increase sample size, multisite studies also potentially introduce systematic biases in connectivity measures across sites. In this work, we measure the inter-site effect in connectivity and its impact on our ability to detect individual and group differences. Our study was based on real, as opposed to simulated, multisite fMRI datasets collected in N=345 young, healthy subjects across 8 scanning sites with 3T scanners and heterogeneous scanning protocols, drawn from the 1000 functional connectome project. We first empirically show that typical functional networks were reliably found at the group level in all sites, and that the amplitude of the inter-site effects was small to moderate, with a Cohen's effect size below 0.5 on average across brain connections. We then implemented a series of Monte-Carlo simulations, based on real data, to evaluate the impact of the multisite effects on detection power in statistical tests comparing two groups (with and without the effect) using a general linear model, as well as on the prediction of group labels with a support-vector machine. As a reference, we also implemented the same simulations with fMRI data collected at a single site using an identical sample size. Simulations revealed that using data from heterogeneous sites only slightly decreased our ability to detect changes compared to a monosite study with the GLM, and had a greater impact on prediction accuracy. However, the deleterious effect of multisite data pooling tended to decrease as the total sample size increased, to a point where differences between monosite and multisite simulations were small with N=120 subjects. Taken together, our results support the feasibility of multisite studies in rs-fMRI provided the sample size is large enough., (Copyright © 2017. Published by Elsevier Inc.)

Understanding how to reduce the influence of physiological noise in resting state fMRI data is important for the interpretation of functional brain connectivity. Limited data is currently available to assess the performance of physiological noise correction techniques, in particular when evaluating longitudinal changes in the default mode network (DMN) of healthy elderly participants. In this 3T harmonized multisite fMRI study, we investigated how different retrospective physiological noise correction (rPNC) methods influence the within-site test-retest reliability and the across-site reproducibility consistency of DMN-derived measurements across 13 MRI sites. Elderly participants were scanned twice at least a week apart (five participants per site). The rPNC methods were: none (NPC), Tissue-based regression, PESTICA and FSL-FIX. The DMN at the single subject level was robustly identified using ICA methods in all rPNC conditions. The methods significantly affected the mean z-scores and, albeit less markedly, the cluster-size in the DMN; in particular, FSL-FIX tended to increase the DMN z-scores compared to others. Within-site test-retest reliability was consistent across sites, with no differences across rPNC methods. The absolute percent errors were in the range of 5-11% for DMN z-scores and cluster-size reliability. DMN pattern overlap was in the range 60-65%. In particular, no rPNC method showed a significant reliability improvement relative to NPC. However, FSL-FIX and Tissue-based physiological correction methods showed both similar and significant improvements of reproducibility consistency across the consortium (ICC = 0.67) for the DMN z-scores relative to NPC. Overall these findings support the use of rPNC methods like tissue-based or FSL-FIX to characterize multisite longitudinal changes of intrinsic functional connectivity. Hum Brain Mapp 37:2114-2132, 2016. © 2016 Wiley Periodicals, Inc., (© 2016 Wiley Periodicals, Inc.)

Multisite neuroimaging studies can facilitate the investigation of brain-related changes in many contexts, including patient groups that are relatively rare in the general population. Though multisite studies have characterized the reliability of brain activation during working memory and motor functional magnetic resonance imaging tasks, emotion processing tasks, pertinent to many clinical populations, remain less explored. A traveling participants study was conducted with eight healthy volunteers scanned twice on consecutive days at each of the eight North American Longitudinal Prodrome Study sites. Tests derived from generalizability theory showed excellent reliability in the amygdala ( Eρ2 = 0.82), inferior frontal gyrus (IFG; Eρ2 = 0.83), anterior cingulate cortex (ACC; Eρ2 = 0.76), insula ( Eρ2 = 0.85), and fusiform gyrus ( Eρ2 = 0.91) for maximum activation and fair to excellent reliability in the amygdala ( Eρ2 = 0.44), IFG ( Eρ2 = 0.48), ACC ( Eρ2 = 0.55), insula ( Eρ2 = 0.42), and fusiform gyrus ( Eρ2 = 0.83) for mean activation across sites and test days. For the amygdala, habituation ( Eρ2 = 0.71) was more stable than mean activation. In a second investigation, data from 111 healthy individuals across sites were aggregated in a voxelwise, quantitative meta-analysis. When compared with a mixed effects model controlling for site, both approaches identified robust activation in regions consistent with expected results based on prior single-site research. Overall, regions central to emotion processing showed strong reliability in the traveling participants study and robust activation in the aggregation study. These results support the reliability of blood oxygen level-dependent signal in emotion processing areas across different sites and scanners and may inform future efforts to increase efficiency and enhance knowledge of rare conditions in the population through multisite neuroimaging paradigms., (© 2015 Wiley Periodicals, Inc.)

BackgroundMagnetic resonance imaging (MRI) with hyperpolarized [1-13C]pyruvate allows real-time and pathway specific clinical detection of otherwise unimageable in vivo metabolism. However, the comparability between sites and protocols is unknown. Here, we provide initial experiences on the agreement of hyperpolarized MRI between sites and protocols by repeated imaging of same healthy volunteers in Europe and the US.MethodsThree healthy volunteers traveled for repeated multicenter brain MRI exams with hyperpolarized [1-13C]pyruvate within one year. First, multisite agreement was assessed with the same echo-planar imaging protocol at both sites. Then, this was compared to a variable resolution echo-planar imaging protocol. In total, 12 examinations were performed. Common metrics of 13C-pyruvate to 13C-lactate conversion were calculated, including the kPL, a model-based kinetic rate constant, and its model-free equivalents. Repeatability was evaluated with intraclass correlation coefficients (ICC) for absolute agreement computed using two-way random effects models.ResultsThe mean kPL across all examinations in the multisite comparison was 0.024 ± 0.0016 s-1. The ICC of the kPL was 0.83 (p = 0.14) between sites and 0.7 (p = 0.09) between examinations of the same volunteer at any of the two sites. For the model-free metrics, the lactate Z-score had similar site-to-site ICC, while it was considerably lower for the lactate-to-pyruvate ratio.ConclusionsEstimation of metabolic conversion from hyperpolarized [1-13C]pyruvate to lactate using model-based metrics such as kPL suggests close agreement between sites and examinations in volunteers. Our initial results support harmonization of protocols, support multicenter studies, and inform their design.

Cerebral venous oxygenation (Yv) is a valuable biomarker for a variety of brain diseases. T2 relaxation under spin tagging (TRUST) MRI is a widely used method for Yv quantification. In this work, there were two main objectives. The first was to evaluate the reproducibility of TRUST Yv measurements across MRI scanners from different vendors. The second was to examine the correlation between Yv and end‐tidal CO2 (EtCO2) in a multisite, multivendor setting and determine the usefulness of this correlation to account for variations in Yv caused by normal variations and physiological fluctuations. Standardized TRUST pulse sequences were implemented on three scanners from major MRI vendors (GE, Siemens, Philips). These scanners were located at two research institutions. Ten healthy subjects were scanned. On each scanner, the subject underwent two scan sessions, each of which included three TRUST scans, to evaluate the intrasession and intersession reproducibility of Yv. Each scanner was also equipped with a capnograph device to record the EtCO2 of the subject during the MRI scan. We found no significant bias in Yv measurements across the three scanners (P = 0.18). The measured Yv values on the three scanners were also strongly correlated with each other (intraclass correlation coefficients > 0.85, P < 0.001). The intrasession and intersession coefficients of variation of Yv were less than 4% and showed no significant difference among the scanners. In addition, our results revealed that (1) within the same subject, Yv increased with EtCO2 at a rate of 1.24 ± 0.17%/mmHg (P < 0.0001), and (2) across different subjects, individuals with a higher EtCO2 had a higher Yv, at a rate of 0.94 ± 0.36%/mmHg (P = 0.01). These results suggest that (1) the standardized TRUST sequences had similar accuracies and reproducibilities for the quantification of Yv across the scanners, and (2) recording of EtCO2 may be a useful complement to Yv measurement to account for CO2‐related physiological fluctuations in Yv in multisite, multivendor studies. [ABSTRACT FROM AUTHOR]

Data harmonization is a key step widely used in multisite neuroimaging studies to remove inter-site heterogeneity of data distribution. However, data harmonization may even introduce additional inter-site differences in neuroimaging data if outliers are present in the data of one or more sites. It remains unclear how the presence of outliers could affect the effectiveness of data harmonization and consequently the results of analyses using harmonized data. To address this question, we generated a normal simulation dataset without outliers and a series of simulation datasets with outliers of varying properties (e.g., outlier location, outlier quantity, and outlier score) based on a real large-sample neuroimaging dataset. We first verified the effectiveness of the most commonly used ComBat harmonization method in the removal of inter-site heterogeneity using the normal simulation data, and then characterized the effects of outliers on the effectiveness of ComBat harmonization and on the results of association analyses between brain imaging-derived phenotypes and a simulated behavioral variable using the simulation datasets with outliers. We found that, although ComBat harmonization effectively removed the inter-site heterogeneity in multisite data and consequently improved the detection of the true brain-behavior relationships, the presence of outliers could damage severely the effectiveness of ComBat harmonization in the removal of data heterogeneity or even introduce extra heterogeneity in the data. Moreover, we found that the effects of outliers on the improvement of the detection of brain-behavior associations by ComBat harmonization were dependent on how such associations were assessed (i.e., by Pearson correlation or Spearman correlation), and on the outlier location, quantity, and outlier score. These findings help us better understand the influences of outliers on data harmonization and highlight the importance of detecting and removing outliers prior to data harmonization in multisite neuroimaging studies. [ABSTRACT FROM AUTHOR]

Background: Magnetic resonance imaging (MRI) with hyperpolarized [1-13C]pyruvate allows real-time and pathway specific clinical detection of otherwise unimageable in vivo metabolism. However, the comparability between sites and protocols is unknown. Here, we provide initial experiences on the agreement of hyperpolarized MRI between sites and protocols by repeated imaging of same healthy volunteers in Europe and the US. Methods: Three healthy volunteers traveled for repeated multicenter brain MRI exams with hyperpolarized [1-13C]pyruvate within one year. First, multisite agreement was assessed with the same echo-planar imaging protocol at both sites. Then, this was compared to a variable resolution echo-planar imaging protocol. In total, 12 examinations were performed. Common metrics of 13C-pyruvate to 13C-lactate conversion were calculated, including the kPL, a model-based kinetic rate constant, and its model-free equivalents. Repeatability was evaluated with intraclass correlation coefficients (ICC) for absolute agreement computed using two-way random effects models. Results: The mean kPL across all examinations in the multisite comparison was 0.024 ± 0.0016 s−1. The ICC of the kPL was 0.83 (p = 0.14) between sites and 0.7 (p = 0.09) between examinations of the same volunteer at any of the two sites. For the model-free metrics, the lactate Z-score had similar site-to-site ICC, while it was considerably lower for the lactate-to-pyruvate ratio. Conclusions: Estimation of metabolic conversion from hyperpolarized [1-13C]pyruvate to lactate using model-based metrics such as kPL suggests close agreement between sites and examinations in volunteers. Our initial results support harmonization of protocols, support multicenter studies, and inform their design. [ABSTRACT FROM AUTHOR]

Age has a major effect on brain volume. However, the normative studies available are constrained by small sample sizes, restricted age coverage and significant methodological variability. These limitations introduce inconsistencies and may obscure or distort the lifespan trajectories of brain morphometry. In response, we capitalized on the resources of the Enhancing Neuroimaging Genetics through Meta‐Analysis (ENIGMA) Consortium to examine age‐related trajectories inferred from cross‐sectional measures of the ventricles, the basal ganglia (caudate, putamen, pallidum, and nucleus accumbens), the thalamus, hippocampus and amygdala using magnetic resonance imaging data obtained from 18,605 individuals aged 3–90 years. All subcortical structure volumes were at their maximum value early in life. The volume of the basal ganglia showed a monotonic negative association with age thereafter; there was no significant association between age and the volumes of the thalamus, amygdala and the hippocampus (with some degree of decline in thalamus) until the sixth decade of life after which they also showed a steep negative association with age. The lateral ventricles showed continuous enlargement throughout the lifespan. Age was positively associated with inter‐individual variability in the hippocampus and amygdala and the lateral ventricles. These results were robust to potential confounders and could be used to examine the functional significance of deviations from typical age‐related morphometric patterns. [ABSTRACT FROM AUTHOR]

This report provides practical recommendations for the design and execution of multicenter functional MRI (MC-fMRI) studies based on the collective experience of the Function Biomedical Informatics Research Network (FBIRN). The study was inspired by many requests from the fMRI community to FBIRN group members for advice on how to conduct MC-fMRI studies. The introduction briefly discusses the advantages and complexities of MC-fMRI studies. Prerequisites for MC-fMRI studies are addressed before delving into the practical aspects of carefully and efficiently setting up a MC-fMRI study. Practical multisite aspects include: (i) establishing and verifying scan parameters including scanner types and magnetic fields, (ii) establishing and monitoring of a scanner quality program, (iii) developing task paradigms and scan session documentation, (iv) establishing clinical and scanner training to ensure consistency over time, (v) developing means for uploading, storing, and monitoring of imaging and other data, (vi) the use of a traveling fMRI expert, and (vii) collectively analyzing imaging data and disseminating results. We conclude that when MC-fMRI studies are organized well with careful attention to unification of hardware, software and procedural aspects, the process can be a highly effective means for accessing a desired participant demographics while accelerating scientific discovery.

Our objective was to assess the generalizability, across sites and cognitive contexts, of schizophrenia classification based on functional brain connectivity. We tested different training-test scenarios combining fMRI data from 191 schizophrenia patients and 191 matched healthy controls obtained at 6 scanning sites and under different task conditions. Diagnosis classification accuracy generalized well to a novel site and cognitive context provided data from multiple sites were used for classifier training. By contrast, lower classification accuracy was achieved when data from a single distinct site was used for training. These findings indicate that it is beneficial to use multisite data to train fMRI-based classifiers intended for large-scale use in the clinical realm. [ABSTRACT FROM AUTHOR]

• Cross-validation framework for determining sample size for inter-site harmonization. • Regression of confounding variables, after harmonization is an important step. • Sample size to remove site-effects increases with the number of sites. • Strong correlation noted between mahalanobis distance and sample size requirement. • Mahalanobis distance as a proposed framework for inter-site harmonization. When data is pooled across multiple sites, the extracted features are confounded by site effects. Harmonization methods attempt to correct these site effects while preserving the biological variability within the features. However, little is known about the sample size requirement for effectively learning the harmonization parameters and their relationship with the increasing number of sites. In this study, we performed experiments to find the minimum sample size required to achieve multisite harmonization (using neuroHarmonize) using volumetric and surface features by leveraging the concept of learning curves. Our first two experiments show that site-effects are effectively removed in a univariate and multivariate manner; however, it is essential to regress the effect of covariates from the harmonized data additionally. Our following two experiments with actual and simulated data showed that the minimum sample size required for achieving harmonization grows with the increasing average Mahalanobis distances between the sites and their reference distribution. We conclude by positing a general framework to understand the site effects using the Mahalanobis distance. Further, we provide insights on the various factors in a cross-validation design to achieve optimal inter-site harmonization. [ABSTRACT FROM AUTHOR]

The article presents research on a method for quantifying scanner instability in functional magnetic resonance imaging (fMRI). It relates that the instability noise was computed from agar phantom data collected with two flip angles. It explains that instability noise is generally less than 10 percent of physiological noise in white matter and about 2 percent of it in cortex. It concludes that the method allows researchers to make informed decisions about the maximum instability level of an imaging scanner.

Abstract: Coimaging employing electron paramagnetic resonance (EPR) imaging and MRI is used for rapid in vivo oximetry conducted simultaneously across multiple organs of a mouse. A recently developed hybrid EPR–NMR coimaging instrument is used for both EPR and NMR measurements. Oxygen sensitive particulate EPR probe is implanted in small localized pockets, called sites, across multiple regions of a live mouse. Three dimensional MRI is used to generate anatomic visualization, providing precise locations of implant sites. The pO2 values, one for every site, are then estimated from EPR measurements. To account for radio frequency (RF) phase inhomogeneities inside a large resonator carrying a lossy sample, a generalization of an existing EPR data model is proposed. Utilization of known spectral lineshape, sparse distribution, and known site locations reduce the EPR data collection by more than an order of magnitude over a conventional spectral–spatial imaging, enhancing the feasibility of in vivo EPR oximetry for clinically relevant models. [ABSTRACT FROM AUTHOR]