With the proliferation of multi-site neuroimaging studies, there is a greater need for handling non-biological variance introduced by differences in MRI scanners and acquisition protocols. Such ...unwanted sources of variation, which we refer to as “scanner effects”, can hinder the detection of imaging features associated with clinical covariates of interest and cause spurious findings. In this paper, we investigate scanner effects in two large multi-site studies on cortical thickness measurements across a total of 11 scanners. We propose a set of tools for visualizing and identifying scanner effects that are generalizable to other modalities. We then propose to use ComBat, a technique adopted from the genomics literature and recently applied to diffusion tensor imaging data, to combine and harmonize cortical thickness values across scanners. We show that ComBat removes unwanted sources of scan variability while simultaneously increasing the power and reproducibility of subsequent statistical analyses. We also show that ComBat is useful for combining imaging data with the goal of studying life-span trajectories in the brain.
•Cortical thickness (CT) measurements are highly scanner specific.•Identifying scanner effects is crucial for inference and biomarker development.•We propose to use ComBat to harmonize cortical thickness values across scanners.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
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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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Recruitment and retention of interventional radiologists for rural and smaller community hospital practices is a serious physician staffing issue. This article explores rural interventional radiology ...and perspectives of various stakeholders, such as rural radiology group practices, rural hospitals, interventional radiologists, public and private academic institutions, and urban health care providers, and considers the unique health care needs of rural patients.
Some early solutions are evident. Collaboration among all stakeholders will be necessary to properly address the challenges.
While aggregation of neuroimaging datasets from multiple sites and scanners can yield increased statistical power, it also presents challenges due to systematic scanner effects. This unwanted ...technical variability can introduce noise and bias into estimation of biological variability of interest. We propose a method for harmonizing longitudinal multi-scanner imaging data based on ComBat, a method originally developed for genomics and later adapted to cross-sectional neuroimaging data. Using longitudinal cortical thickness measurements from 663 participants in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study, we demonstrate the presence of additive and multiplicative scanner effects in various brain regions. We compare estimates of the association between diagnosis and change in cortical thickness over time using three versions of the ADNI data: unharmonized data, data harmonized using cross-sectional ComBat, and data harmonized using longitudinal ComBat. In simulation studies, we show that longitudinal ComBat is more powerful for detecting longitudinal change than cross-sectional ComBat and controls the type I error rate better than unharmonized data with scanner included as a covariate. The proposed method would be useful for other types of longitudinal data requiring harmonization, such as genomic data, or neuroimaging studies of neurodevelopment, psychiatric disorders, or other neurological diseases.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The United States National Institutes of Health (NIH) commit significant support to open-source data and software resources in order to foment reproducibility in the biomedical imaging sciences. ...Here, we report and evaluate a recent product of this commitment: Advanced Neuroimaging Tools (ANTs), which is approaching its 2.0 release. The ANTs open source software library consists of a suite of state-of-the-art image registration, segmentation and template building tools for quantitative morphometric analysis. In this work, we use ANTs to quantify, for the first time, the impact of similarity metrics on the affine and deformable components of a template-based normalization study. We detail the ANTs implementation of three similarity metrics: squared intensity difference, a new and faster cross-correlation, and voxel-wise mutual information. We then use two-fold cross-validation to compare their performance on openly available, manually labeled, T1-weighted MRI brain image data of 40 subjects (UCLA's LPBA40 dataset). We report evaluation results on cortical and whole brain labels for both the affine and deformable components of the registration. Results indicate that the best ANTs methods are competitive with existing brain extraction results (Jaccard=0.958) and cortical labeling approaches. Mutual information affine mapping combined with cross-correlation diffeomorphic mapping gave the best cortical labeling results (Jaccard=0.669±0.022). Furthermore, our two-fold cross-validation allows us to quantify the similarity of templates derived from different subgroups. Our open code, data and evaluation scripts set performance benchmark parameters for this state-of-the-art toolkit. This is the first study to use a consistent transformation framework to provide a reproducible evaluation of the isolated effect of the similarity metric on optimal template construction and brain labeling.
►A new, fast implementation of the cross-correlation that increases computational efficiency by a factor of 4 to 5 and allows larger correlation windows to be used for registration without excessive increase in computation time. ►Open-source implementation of the mutual information for symmetric diffeomorphic registration. ►A reproducible system for performance evaluation of the mean squares metric, cross-correlation metric and mutual information metric on optimal template-based brain extraction and regional brain labeling. The full evaluation system is documented in a bash script that is also released and available. The script is also being translated to python. ►Quantification of the similarity between optimal templates derived from different population subsets and with different similarity metrics.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
For every fatal shooting in the United States, detailed information from reports of coroners or medical examiners, police departments, and other sources is recorded in the National Violent Death ...Reporting System. There is no such system in place for nonfatal shootings, which far outnumber fatalities. Hospital data systems are in place that could, with some improvements, provide access to reliable local, state and national estimates of firearm injuries. Such estimates are possible because most firearms injuries are treated in hospitals, and hospitals routinely assign “external cause of injury” codes to all injury encounters. Federal health agencies supervise a number of data systems that centralize hospital data. Challenges currently being addressed are public access, timeliness, and accuracy of coding of intent. (Hospitals misclassify many firearm assaults as accidents.)
Law enforcement agencies provide detailed data on shootings in criminal circumstances, including shootings that are not treated in a hospital. The FBI's Uniform Crime Reports (UCR) system aggregates data from agencies. The FBI instituted a radical reform of this system beginning in 2021, resulting in a sharp agency participation drop that prevents valid national estimates. The reform requires agencies to report incident-level data instead of summary counts, which is all that was required for the previous 90 years. There are ongoing efforts to increase participation in the new system and restore its former status as the leading source of national crime estimates. In the meantime, data on nonfatal gunshot cases are available from a number of police departments.
We discuss additional reforms needed to generate timely, accurate, publicly accessible data from hospitals and police.
•Firearm suicides outnumber homicides, but most firearms injuries result from criminal assault and are not fatal.•The United States currently has no system that accurately tracks nonfatal firearms injuries.•Reforms are underway that will provide several reliable systems based on hospital data for surveilling firearms injuries.•Police data are also a promising source of state and national estimates of nonfatal firearms injuries from assault.•Current agency participation in the FBI's Uniform Crime Reports is too low to generate valid national estimates.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Concepts bind together the features commonly associated with objects and events to form networks in long-term semantic memory. These conceptual networks are the basis of human knowledge and underlie ...perception, imagination, and the ability to communicate about experiences and the contents of the environment. Although it is often assumed that this distributed semantic information is integrated in higher-level heteromodal association cortices, open questions remain about the role and anatomic basis of heteromodal representations in semantic memory. Here we used combined neuroimaging evidence from functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) to characterize the cortical networks underlying concept representation. Using a lexical decision task, we examined the processing of concepts in four semantic categories that varied on their sensory–motor feature associations (sight, sound, manipulation, and abstract). We found that the angular gyrus was activated across all categories regardless of their modality-specific feature associations, consistent with a heteromodal account for the angular gyrus. Exploratory analyses suggested that categories with weighted sensory–motor features additionally recruited modality-specific association cortices. Furthermore, DTI tractography identified white matter tracts connecting these regions of modality-specific functional activation with the angular gyrus. These findings are consistent with a distributed semantic network that includes a heteromodal, integrative component in the angular gyrus in combination with sensory–motor feature representations in modality-specific association cortices.
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► We use fMRI and DTI of a lexical decision task to examine semantic memory networks. ► We examine heteromodal and modality-specific components of semantic memory. ► All word categories recruit heteromodal association cortex of the angular gyrus. ► Modality-weighted categories recruit distinct sensory–motor association regions. ► DTI shows heteromodal convergence of white matter connectivity in the angular gyrus.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Participant motion during functional magnetic resonance image (fMRI) acquisition produces spurious signal fluctuations that can confound measures of functional connectivity. Without mitigation, ...motion artifact can bias statistical inferences about relationships between connectivity and individual differences. To counteract motion artifact, this protocol describes the implementation of a validated, high-performance denoising strategy that combines a set of model features, including physiological signals, motion estimates, and mathematical expansions, to target both widespread and focal effects of subject movement. This protocol can be used to reduce motion-related variance to near zero in studies of functional connectivity, providing up to a 100-fold improvement over minimal-processing approaches in large datasets. Image denoising requires 40 min to 4 h of computing per image, depending on model specifications and data dimensionality. The protocol additionally includes instructions for assessing the performance of a denoising strategy. Associated software implements all denoising and diagnostic procedures, using a combination of established image-processing libraries and the eXtensible Connectivity Pipeline (XCP) software.
Neurobiological abnormalities associated with psychiatric disorders do not map well to existing diagnostic categories. High co-morbidity suggests dimensional circuit-level abnormalities that cross ...diagnoses. Here we seek to identify brain-based dimensions of psychopathology using sparse canonical correlation analysis in a sample of 663 youths. This analysis reveals correlated patterns of functional connectivity and psychiatric symptoms. We find that four dimensions of psychopathology - mood, psychosis, fear, and externalizing behavior - are associated (r = 0.68-0.71) with distinct patterns of connectivity. Loss of network segregation between the default mode network and executive networks emerges as a common feature across all dimensions. Connectivity linked to mood and psychosis becomes more prominent with development, and sex differences are present for connectivity related to mood and fear. Critically, findings largely replicate in an independent dataset (n = 336). These results delineate connectivity-guided dimensions of psychopathology that cross clinical diagnostic categories, which could serve as a foundation for developing network-based biomarkers in psychiatry.
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