Simultaneous analysis and quality assurance for diffusion tensor imaging

Assessing measurement consistency of a diffusion tensor imaging (DTI) quality control (QC) anisotropy phantom

OBJECTIVES

We evaluated a quality control (QC) phantom designed to mimic diffusion characteristics and white matter fiber tracts in the brain. We hypothesized that acquisition of diffusion tensor imaging (DTI) data on different vendors and over multiple repeated measures would not contribute to significant variability in calculated diffusion tensor scalar metrics such as fractional anisotropy (FA) and mean diffusivity (MD).

MATERIALS AND METHODS

The DTI QC phantom was scanned using a 32-direction DTI sequence on General Electric (GE), Siemens, and Philips 3 Tesla scanners. Motion probing gradients (MPGs) were investigated as a source of variance in our statistical design, and data were acquired on GE and Siemens scanners using GE, Siemens, and Philips vendor MPGs for 32 directions. In total, 8 repeated scans were made for each GE/Siemens combination of vendor and MPGs with 8 repeated scans on a Philips machine using its stock DTI sequence. Data were analyzed using 2-way ANOVAs to investigate repeat scan and vendor variances and 3-way ANOVAs with repeat, MPG, and vendor as factors.

RESULTS

No statistical differences (i.e., P > 0.05) were found in any DTI scalar metrics (FA, MD) or for any factor, suggesting system constancy across imaging platforms and the specified phantom's reliability and reproducibility across vendors and conditions.

DISCUSSION

A DTI QC phantom demonstrates that DTI measurements maintain their consistency across different MRI systems and can contribute to a standard that is more reliable for quantitative MRI analyses.

Longitudinal patterns of brain aging and neurodegeneration among older adults with dual decline in memory and gait

INTRODUCTION

Dual cognitive and mobility decline is more strongly associated with dementia risk than cognitive decline only. It remains unknown whether this syndrome is associated with brain atrophy patterns, white matter (WM) damage, or pathology.

METHODS

In the Baltimore Longitudinal Study of Aging participants with and without dual decline, we used linear mixed-effects models to compare up to 13-year longitudinal changes in MRI-derived atrophy patterns, WM hyperintensities (n = 339), microstructure (n = 307), plasma glial fibrillary acidic protein (GFAP), neurofilament light chain (NfL), amyloid beta 42/40 (Aβ42/40) ratio (n = 349), and phosphorylated tau 181 (pTau181) (n = 258).

RESULTS

Those experiencing dual decline showed accelerated atrophy in medial temporal (p = .004), parietotemporal (p = .029), and perisylvian regions (p = .028), whereas gait decline only showed accelerated parietotemporal atrophy (p = .035) and memory decline only showed perisylvian atrophy (p = .021). Dual decline was also associated with unique microstructural deterioration in several WM tracts (p < .05), a greater decrease in Aβ42/40 ratio (p = .015), and greater increases in GFAP (p = .009) and NfL (p < .001).

DISCUSSION

Individuals experiencing dual decline are at an increased risk for regional brain atrophy, microstructural degradation, and biomarker-defined molecular changes underlying dementia.

HIGHLIGHTS

Those experiencing dual decline showed several accelerated brain atrophy patterns. Those experiencing dual decline showed unique microstructural deterioration. Dual decline showed a greater decline in plasma Aβ42/40 ratio. Dual decline showed greater increases in plasma GFAP and NfL. Dual decline may indicate brain and blood markers underlying dementia.

Higher skeletal muscle mitochondrial oxidative capacity is associated with preserved brain structure up to over a decade

Impaired muscle mitochondrial oxidative capacity is associated with future cognitive impairment, and higher levels of PET and blood biomarkers of Alzheimer's disease and neurodegeneration. Here, we examine its associations with up to over a decade-long changes in brain atrophy and microstructure. Higher in vivo skeletal muscle oxidative capacity via MR spectroscopy (post-exercise recovery rate, kPCr) is associated with less ventricular enlargement and brain aging progression, and less atrophy in specific regions, notably primary sensorimotor cortex, temporal white and gray matter, thalamus, occipital areas, cingulate cortex, and cerebellum white matter. Higher kPCr is also associated with less microstructural integrity decline in white matter around cingulate, including superior longitudinal fasciculus, corpus callosum, and cingulum. Higher in vivo muscle oxidative capacity is associated with preserved brain structure up to over a decade, particularly in areas important for cognition, motor function, and sensorimotor integration.

Assessing biological self-organization patterns using statistical complexity characteristics: a tool for diffusion tensor imaging analysis

OBJECT

Diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI) are well-known and powerful imaging techniques for MRI. Although DTI evaluation has evolved continually in recent years, there are still struggles regarding quantitative measurements that can benefit brain areas that are consistently difficult to measure via diffusion-based methods, e.g., gray matter (GM). The present study proposes a new image processing technique based on diffusion distribution evaluation of López-Ruiz, Mancini and Calbet (LMC) complexity called diffusion complexity (DC).

MATERIALS AND METHODS

The OASIS-3 and TractoInferno open-science databases for healthy individuals were used, and all the codes are provided as open-source materials.

RESULTS

The DC map showed relevant signal characterization in brain tissues and structures, achieving contrast-to-noise ratio (CNR) gains of approximately 39% and 93%, respectively, compared to those of the FA and ADC maps.

DISCUSSION

In the special case of GM tissue, the DC map obtains its maximum signal level, showing the possibility of studying cortical and subcortical structures challenging for classical DTI quantitative formalism. The ability to apply the DC technique, which requires the same imaging acquisition for DTI and its potential to provide complementary information to study the brain's GM structures, can be a rich source of information for further neuroscience research and clinical practice.

Field-of-view extension for brain diffusion MRI via deep generative models

Purpose

In brain diffusion magnetic resonance imaging (dMRI), the volumetric and bundle analyses of whole-brain tissue microstructure and connectivity can be severely impeded by an incomplete field of view (FOV). We aim to develop a method for imputing the missing slices directly from existing dMRI scans with an incomplete FOV. We hypothesize that the imputed image with a complete FOV can improve whole-brain tractography for corrupted data with an incomplete FOV. Therefore, our approach provides a desirable alternative to discarding the valuable brain dMRI data, enabling subsequent tractography analyses that would otherwise be challenging or unattainable with corrupted data.

Approach

We propose a framework based on a deep generative model that estimates the absent brain regions in dMRI scans with an incomplete FOV. The model is capable of learning both the diffusion characteristics in diffusion-weighted images (DWIs) and the anatomical features evident in the corresponding structural images for efficiently imputing missing slices of DWIs in the incomplete part of the FOV.

Results

For evaluating the imputed slices, on the Wisconsin Registry for Alzheimer's Prevention (WRAP) dataset, the proposed framework achievedPSNR b 0 = 22.397 ,SSIM b 0 = 0.905 ,PSNR b 1300 = 22.479 , andSSIM b 1300 = 0.893 ; on the National Alzheimer's Coordinating Center (NACC) dataset, it achievedPSNR b 0 = 21.304 ,SSIM b 0 = 0.892 ,PSNR b 1300 = 21.599 , andSSIM b 1300 = 0.877 . The proposed framework improved the tractography accuracy, as demonstrated by an increased average Dice score for 72 tracts ( p < 0.001 ) on both the WRAP and NACC datasets.

Conclusions

Results suggest that the proposed framework achieved sufficient imputation performance in brain dMRI data with an incomplete FOV for improving whole-brain tractography, thereby repairing the corrupted data. Our approach achieved more accurate whole-brain tractography results with an extended and complete FOV and reduced the uncertainty when analyzing bundles associated with Alzheimer's disease.

Graph theoretical approach to brain remodeling in multiple sclerosis

Multiple sclerosis (MS) is a neuroinflammatory disorder damaging structural connectivity. Natural remodeling processes of the nervous system can, to some extent, restore the damage caused. However, there is a lack of biomarkers to evaluate remodeling in MS. Our objective is to evaluate graph theory metrics (especially modularity) as a biomarker of remodeling and cognition in MS. We recruited 60 relapsing-remitting MS and 26 healthy controls. Structural and diffusion MRI, plus cognitive and disability evaluations, were done. We calculated modularity and global efficiency from the tractography-derived connectivity matrices. Association of graph metrics with T2 lesion load, cognition, and disability was evaluated using general linear models adjusting for age, gender, and disease duration wherever applicable. We showed that MS subjects had higher modularity and lower global efficiency compared with controls. In the MS group, modularity was inversely associated with cognitive performance but positively associated with T2 lesion load. Our results indicate that modularity increase is due to the disruption of intermodular connections in MS because of the lesions, with no improvement or preserving of cognitive functions.

Longitudinal associations of absolute versus relative moderate-to-vigorous physical activity with brain microstructural decline in aging

Higher moderate-to-vigorous intensity (MVPA) may preserve brain structural integrity, but evidence is mostly cross-sectional and relies on absolute PA measures. We examined longitudinal associations of absolute MVPA using population-level activity count thresholds and relative MVPA using individual heart rate reserve (HRR) via Actiheart with subsequent changes in brain diffusion tensor imaging (DTI) over average of 3.8 years in 248 initially cognitively normal individuals (56-91 years). DTI markers included areas important for memory (temporal areas), executive (prefrontal cortex, superior longitudinal fasciculus), and motor function (precentral gyrus, putamen, caudate, body of corpus callosum). Associations of MVPA with changes in DTI markers were examined using linear mixed-effects models, adjusted for demographics and apolipoprotein e4 carrier status. Each additional 22 min of relative MVPA per day was significantly associated with less decline in fractional anisotropy of uncinate fasciculus and cingulum-hippocampal part and with less increase in mean diffusivity of entorhinal cortex and parahippocampal gyrus. Absolute MVPA was not associated with DTI changes. More time spent in relative MVPA by HRR may prevent brain microstructural decline in selected temporal areas.

What's new and what's next in diffusion MRI preprocessing

Diffusion MRI (dMRI) provides invaluable information for the study of tissue microstructure and brain connectivity, but suffers from a range of imaging artifacts that greatly challenge the analysis of results and their interpretability if not appropriately accounted for. This review will cover dMRI artifacts and preprocessing steps, some of which have not typically been considered in existing pipelines or reviews, or have only gained attention in recent years: brain/skull extraction, B-matrix incompatibilities w.r.t the imaging data, signal drift, Gibbs ringing, noise distribution bias, denoising, between- and within-volumes motion, eddy currents, outliers, susceptibility distortions, EPI Nyquist ghosts, gradient deviations, B1 bias fields, and spatial normalization. The focus will be on "what's new" since the notable advances prior to and brought by the Human Connectome Project (HCP), as presented in the predecessing issue on "Mapping the Connectome" in 2013. In addition to the development of novel strategies for dMRI preprocessing, exciting progress has been made in the availability of open source tools and reproducible pipelines, databases and simulation tools for the evaluation of preprocessing steps, and automated quality control frameworks, amongst others. Finally, this review will consider practical considerations and our view on "what's next" in dMRI preprocessing.

Accelerated decline in white matter microstructure in subsequently impaired older adults and its relationship with cognitive decline

Little is known about a longitudinal decline in white matter microstructure and its associations with cognition in preclinical dementia. Longitudinal diffusion tensor imaging and neuropsychological testing were performed in 50 older adults who subsequently developed mild cognitive impairment or dementia (subsequently impaired) and 200 cognitively normal controls. Rates of white matter microstructural decline were compared between groups using voxel-wise linear mixed-effects models. Associations between change in white matter microstructure and cognition were examined. Subsequently impaired individuals had a faster decline in fractional anisotropy in the right inferior fronto-occipital fasciculus and bilateral splenium of the corpus callosum. A decline in right inferior fronto-occipital fasciculus fractional anisotropy was related to a decline in verbal memory, visuospatial ability, processing speed and mini-mental state examination. A decline in bilateral splenium fractional anisotropy was related to a decline in verbal fluency, processing speed and mini-mental state examination. Accelerated regional white matter microstructural decline is evident during the preclinical phase of mild cognitive impairment/dementia and is related to domain-specific cognitive decline.

Left and Right Arcuate Fasciculi Are Uniquely Related to Word Reading Skills in Chinese-English Bilingual Children

Whether reading in different writing systems recruits language-unique or language-universal neural processes is a long-standing debate. Many studies have shown the left arcuate fasciculus (AF) to be involved in phonological and reading processes. In contrast, little is known about the role of the right AF in reading, but some have suggested that it may play a role in visual spatial aspects of reading or the prosodic components of language. The right AF may be more important for reading in Chinese due to its logographic and tonal properties, but this hypothesis has yet to be tested. We recruited a group of Chinese-English bilingual children (8.2 to 12.0 years old) to explore the common and unique relation of reading skill in English and Chinese to fractional anisotropy (FA) in the bilateral AF. We found that both English and Chinese reading skills were positively correlated with FA in the rostral part of the left AF-direct segment. Additionally, English reading skill was positively correlated with FA in the caudal part of the left AF-direct segment, which was also positively correlated with phonological awareness. In contrast, Chinese reading skill was positively correlated with FA in certain segments of the right AF, which was positively correlated with visual spatial ability, but not tone discrimination ability. Our results suggest that there are language universal substrates of reading across languages, but that certain left AF nodes support phonological mechanisms important for reading in English, whereas certain right AF nodes support visual spatial mechanisms important for reading in Chinese.

Incorporating outlier information into diffusion-weighted MRI modeling for robust microstructural imaging and structural brain connectivity analyses

The white matter structures of the human brain can be represented using diffusion-weighted MRI tractography. Unfortunately, tractography is prone to find false-positive streamlines causing a severe decline in its specificity and limiting its feasibility in accurate structural brain connectivity analyses. Filtering algorithms have been proposed to reduce the number of invalid streamlines but the currently available filtering algorithms are not suitable to process data that contains motion artefacts which are typical in clinical research. We augmented the Convex Optimization Modelling for Microstructure Informed Tractography (COMMIT) algorithm to adjust for these signals drop-out motion artefacts. We demonstrate with comprehensive Monte-Carlo whole brain simulations and in vivo infant data that our robust algorithm is capable of properly filtering tractography reconstructions despite these artefacts. We evaluated the results using parametric and non-parametric statistics and our results demonstrate that if not accounted for, motion artefacts can have severe adverse effects in human brain structural connectivity analyses as well as in microstructural property mappings. In conclusion, the usage of robust filtering methods to mitigate motion related errors in tractogram filtering is highly beneficial, especially in clinical studies with uncooperative patient groups such as infants. With our presented robust augmentation and open-source implementation, robust tractogram filtering is readily available.

Contrast-Free Detection of Focused Ultrasound-Induced Blood-Brain Barrier Opening Using Diffusion Tensor Imaging

OBJECTIVE

Focused ultrasound (FUS) has emerged as a non-invasive technique to locally and reversibly disrupt the blood-brain barrier (BBB). Here, we investigate the use of diffusion tensor imaging (DTI) as a means of detecting FUS-induced BBB opening at the absence of an MRI contrast agent. A non-human primate (NHP) was repeatedly treated with FUS and preformed circulating microbubbles to transiently disrupt the BBB (n = 4). T1- and diffusion-weighted MRI scans were acquired after the ultrasound treatment, with and without gadolinium-based contrast agent, respectively. Both scans were registered with a high-resolution T1-weighted scan of the NHP to investigate signal correlations. DTI detected an increase in fractional anisotropy from 0.21 ± 0.02 to 0.38 ± 0.03 (82.6 ± 5.2% change) within the targeted area one hour after BBB opening. Enhanced DTI contrast overlapped by 77.22 ± 9.2% with hyper-intense areas of gadolinium-enhanced T1-weighted scans, indicating diffusion anisotropy enhancement only within the BBB opening volume. Diffusion was highly anisotropic and unidirectional within the treated brain region, as indicated by the direction of the principal diffusion eigenvectors. Polar and azimuthal angle ranges decreased by 35.6% and 82.4%, respectively, following BBB opening. Evaluation of the detection methodology on a second NHP (n = 1) confirmed the across-animal feasibility of the technique. In conclusion, DTI may be used as a contrast-free MR imaging modality in lieu of contrast-enhanced T1 mapping for detecting BBB opening during focused-ultrasound treatment or evaluating BBB integrity in brain-related pathologies.

PreQual: An automated pipeline for integrated preprocessing and quality assurance of diffusion weighted MRI images

PURPOSE

Diffusion weighted MRI imaging (DWI) is often subject to low signal-to-noise ratios (SNRs) and artifacts. Recent work has produced software tools that can correct individual problems, but these tools have not been combined with each other and with quality assurance (QA). A single integrated pipeline is proposed to perform DWI preprocessing with a spectrum of tools and produce an intuitive QA document.

METHODS

The proposed pipeline, built around the FSL, MRTrix3, and ANTs software packages, performs DWI denoising; inter-scan intensity normalization; susceptibility-, eddy current-, and motion-induced artifact correction; and slice-wise signal drop-out imputation. To perform QA on the raw and preprocessed data and each preprocessing operation, the pipeline documents qualitative visualizations, quantitative plots, gradient verifications, and tensor goodness-of-fit and fractional anisotropy analyses.

RESULTS

Raw DWI data were preprocessed and quality checked with the proposed pipeline and demonstrated improved SNRs; physiologic intensity ratios; corrected susceptibility-, eddy current-, and motion-induced artifacts; imputed signal-lost slices; and improved tensor fits. The pipeline identified incorrect gradient configurations and file-type conversion errors and was shown to be effective on externally available datasets.

CONCLUSIONS

The proposed pipeline is a single integrated pipeline that combines established diffusion preprocessing tools from major MRI-focused software packages with intuitive QA.

Association of Poorer Hearing With Longitudinal Change in Cerebral White Matter Microstructure

Importance

There is a dearth of studies that examine the association between poorer hearing and change in cerebral white matter (WM) microstructure.

Objective

To examine the association of poorer hearing with baseline and change in WM microstructure among older adults.

Design, Setting, and Participants

This was a prospective cohort study that evaluated speech-in-noise, pure-tone audiometry, and WM microstructure, as measured by mean diffusivity (MD) and fractional anisotropy (FA), both of which were evaluated by diffusion tensor imaging (DTI) in 17 WM regions. Data were collected between October 2012 and December 2018 and analyzed between March 2019 and August 2019 with a mean follow-up time of 1.7 years. The study evaluated responses to the Baltimore Longitudinal Study of Aging among 356 cognitively normal adults who had at least 1 hearing assessment and DTI session. Excluded were those with baseline cognitive impairment, stroke, head injuries, Parkinson disease, and/or bipolar disorder.

Exposures

Peripheral auditory function was measured by pure-tone average in the better-hearing ear. Central auditory function was measured by signal-to-noise ratio score from a speech-in-noise task and adjusted by pure-tone average.

Main Outcomes and Measures

Linear mixed-effects models with random intercepts and slopes were used to examine the association of poorer peripheral and central auditory function with baseline and longitudinal DTI metrics in 17 WM regions, adjusting for baseline characteristics (age, sex, race, hypertension, elevated total cholesterol, and obesity).

Results

Of 356 cognitively normal adults included in the study, the mean (SD) age was 73.5 (8.8) years, and 204 (57.3%) were women. There were no baseline associations between hearing and DTI measures. Longitudinally, poorer peripheral hearing was associated with increases in MD in the inferior fronto-occipital fasciculus (β = 0.025; 95% CI, 0.008-0.042) and the body (β = 0.050; 95% CI, 0.015-0.085) of the corpus callosum, but there were no associations of peripheral hearing with FA changes in these tracts. Poorer central auditory function was associated with longitudinal MD increases (β = 0.031; 95% CI, 0.010-0.052) and FA declines (β = -1.624; 95% CI, -2.511 to -0.738) in the uncinate fasciculus.

Conclusions and Relevance

Findings of this cohort study suggest that poorer hearing is related to change in integrity of specific WM regions involved with auditory processing.

Microstructural Neuroimaging of Frailty in Cognitively Normal Older Adults

Physical frailty is an age-related clinical syndrome that is associated with multiple adverse health outcomes, including cognitive impairment and dementia. Recent studies have shown that frailty is associated with specific volumetric neuroimaging characteristics. Whether brain microstructural characteristics, particularly gray matter, associated with frailty exist and what their spatial distribution is have not been explored. We identified 670 participants of the Baltimore Longitudinal Study of Aging who were aged 60 and older and cognitively normal and who had concurrent data on frailty and regional microstructural neuroimaging markers by diffusion tensor imaging (DTI), including mean diffusivity (MD) of gray matter and fractional anisotropy (FA) of white matter. We identified neuroimaging markers that were associated with frailty status (non-frail, pre-frail, frail) and further examined differences between three groups using multivariate linear regression (non-frail = reference). Models were adjusted for age, sex, race, years of education, body mass index, scanner type, and Apolipoprotein E e4 carrier status. Compared to the non-frail participants, those who were frail had higher MD in the medial frontal cortex, several subcortical regions (putamen, caudate, thalamus), anterior cingulate cortex, and a trend of lower FA in the body of the corpus callosum. Those who were pre-frail also had higher MD in the putamen and a trend of lower FA in the body of the corpus callosum. Our study demonstrates for the first time that the microstructure of both gray and white matter differs by frailty status in cognitively normal older adults. Brain areas were not widespread but mostly localized in frontal and subcortical motor areas and the body of the corpus callosum. Whether changes in brain microstructure precede future frailty development warrants further investigation.

Thalamocortical Anatomical Connectivity in Schizophrenia and Psychotic Bipolar Disorder

BACKGROUND

Anatomical connectivity between the thalamus and cortex, including the prefrontal cortex (PFC), is abnormal in schizophrenia. Overlapping phenotypes, including deficits in executive cognitive abilities dependent on PFC-thalamic circuitry, suggest dysrupted thalamocortical anatomical connectivity may extend to psychotic bipolar disorder. We tested this hypothesis and examined the impact of illness stage to inform when in the illness course thalamocortical dysconnectivity emerges.

METHODS

Diffusion-weighted imaging data were collected on 70 healthy individuals and 124 people with a psychotic disorder (schizophrenia spectrum = 75; psychotic bipolar disorder = 49), including 62 individuals in the early stage of psychosis. Anatomical connectivity between major divisions of the cortex and thalamus was quantified using probabilistic tractography and compared between groups. Associations between PFC-thalamic anatomical connectivity and executive cognitive abilities were examined using regression analysis.

RESULTS

Psychosis was associated with lower PFC-thalamic and elevated somatosensory-thalamic anatomical connectivity. Follow-up analyses established that lower PFC-thalamic and elevated somatosensory-thalamic anatomical connectivity were present in both schizophrenia and psychotic bipolar disorder. Lower PFC-thalamic anatomical connectivity was also present in early-stage and chronic psychosis. Contrary to expectations, lower PFC-thalamic anatomical connectivity was not associated with impaired executive cognitive abilities.

CONCLUSIONS

Altered thalamocortical anatomical connectivity, especially reduced PFC-thalamic connectivity, is a transdiagnostic feature of psychosis detectable in the early stage of illness. Further work is required to elucidate the functional consequences of the full spectrum of thalamocortical connectivity abnormalities in psychosis.

Lasting consequences of concussion on the aging brain: Findings from the Baltimore Longitudinal Study of Aging

Studies suggest that concussions may be related to increased risk of neurodegenerative diseases, such as Chronic Traumatic Encephalopathy and Alzheimer’s Disease. Most neuroimaging studies show effects of concussionsin frontal and temporal lobes of the brain, yet the long-term impacts of concussions on the aging brain have not been well studied. We examined neuroimaging data from 51 participants (mean age at first imaging visit = 65.1±11.23) in the Baltimore Longitudinal Study of Aging (BLSA) who reported a concussion in their medical history an average of 23 years prior to the first imaging visit, and compared them to 150 participants (mean age at first imaging visit = 66.6 ± 10.97) with no history of concussion. Participants underwent serial structural MRI overa mean of 5.17 ± 6.14 years and DTI over a mean of 2.92 ± 2.22 years to measure brain structure, as well as ¹⁵O-water PET over a mean of 5.33 ± 2.19 years to measure brain function. A battery of neuropsychological tests was also administered over a mean of 11.62 ± 7.41 years. Analyses of frontal and temporal lobe regions were performed to examine differences in these measures between the concussion and control groups at first imaging visit and in change over time. Compared to those without concussion, participants with a prior concussion had greater brain atrophy in temporal lobe white matter and hippocampus at first imaging visit, which remained stable throughout the follow-up visits. Those with prior concussion also showed differences in white matter microstructure using DTI, including increased radial and axial diffusivity in the fornix/stria terminalis, anterior corona radiata, and superior longitudinal fasciculus at first imaging visit. In ¹⁵O-water PET, higher resting cerebral blood flow was seen at first imaging visit in orbitofrontal and lateral temporal regions, and both increases and decreases were seen in prefrontal, cingulate, insular, hippocampal, and ventral temporal regions with longitudinal follow-up. There were no significant differences in neuropsychological performance between groups. Most of the differences observed between the concussed and non-concussed groups were seen at the first imaging visit, suggesting that concussions can produce long-lasting structural and functional alterations in temporal and frontal regions of the brain in older individuals. These results also suggest that many of the reported short-term effects of concussion may still be apparent later in life.

Sex differences in associations of socioemotional dispositions measured in childhood and adolescence with brain white matter microstructure 12 years later

Predictive associations were estimated between socioemotional dispositions measured at 10-17 years using the Child and Adolescent Dispositions Scale (CADS) and future individual differences in white matter microstructure measured at 22-31 years of age. Participants were 410 twins (48.3% monozygotic) selected for later neuroimaging by oversampling on risk for psychopathology from a representative sample of child and adolescent twins. Controlling for demographic covariates and total intracranial volume (TICV), each CADS disposition (negative emotionality, prosociality, and daring) rated by one of the informants (parent or youth) significantly predicted global fractional anisotropy (FA) averaged across the major white matter tracts in brain in adulthood, but did so through significant interactions with sex after false discovery rate (FDR) correction. In females, each 1 SD difference in greater parent-rated prosociality was associated with 0.43 SD greater FA (p < 0.0008). In males, each 1 SD difference in greater parent-rated daring was associated with 0.24 SD lower FA (p < 0.0008), and each 1 SD difference in greater youth-rated negative emotionality was associated with 0.18 SD greater average FA (p < 0.0040). These findings suggest that CADS dispositions are associated with FA, but associations differ by sex. Exploratory analyses suggest that FA may mediate the associations between dispositions and psychopathology in some cases. These associations over 12 years could reflect enduring brain-behavior associations in spite of transactions with the environment, but could equally reflect processes in which dispositional differences in behavior influence the development of white matter. Future longitudinal studies are needed to resolve the causal nature of these sex-moderated associations.

A longitudinal neuroimaging dataset on multisensory lexical processing in school-aged children

Here we describe the open access dataset entitled “Longitudinal Brain Correlates of Multisensory Lexical Processing in Children” hosted on OpenNeuro.org. This dataset examines reading development through a longitudinal multimodal neuroimaging and behavioral approach, including diffusion-weighted and T1-weighted structural magnetic resonance imaging (MRI), task based functional MRI, and a battery of psycho-educational assessments and parental questionnaires. Neuroimaging, psycho-educational testing, and functional task behavioral data were collected from 188 typically developing children when they were approximately 10.5 years old (session T1). Seventy children returned approximately 2.5 years later (session T2), of which all completed longitudinal follow-ups of psycho-educational testing, and 49 completed neuroimaging and functional tasks. At session T1 participants completed auditory, visual, and audio-visual word and pseudo-word rhyming judgment tasks in the scanner. At session T2 participants completed visual word and pseudo-word rhyming judgement tasks in the scanner.

Measurement(s)	reading and spelling ability • intelligence • brain • brain physiology trait
Technology Type(s)	psychoeducational test administration • magnetic resonance imaging • functional magnetic resonance imaging • Diffusion Weighted Imaging
Factor Type(s)	age • reading disability • type of task • parental educational level
Sample Characteristic - Organism	Homo sapiens

Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.11298188

Comparison of quality control methods for automated diffusion tensor imaging analysis pipelines

The processing of brain diffusion tensor imaging (DTI) data for large cohort studies requires fully automatic pipelines to perform quality control (QC) and artifact/outlier removal procedures on the raw DTI data prior to calculation of diffusion parameters. In this study, three automatic DTI processing pipelines, each complying with the general ENIGMA framework, were designed by uniquely combining multiple image processing software tools. Different QC procedures based on the RESTORE algorithm, the DTIPrep protocol, and a combination of both methods were compared using simulated ground truth and artifact containing DTI datasets modeling eddy current induced distortions, various levels of motion artifacts, and thermal noise. Variability was also examined in 20 DTI datasets acquired in subjects with vascular cognitive impairment (VCI) from the multi-site Ontario Neurodegenerative Disease Research Initiative (ONDRI). The mean fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were calculated in global brain grey matter (GM) and white matter (WM) regions. For the simulated DTI datasets, the measure used to evaluate the performance of the pipelines was the normalized difference between the mean DTI metrics measured in GM and WM regions and the corresponding ground truth DTI value. The performance of the proposed pipelines was very similar, particularly in FA measurements. However, the pipeline based on the RESTORE algorithm was the most accurate when analyzing the artifact containing DTI datasets. The pipeline that combined the DTIPrep protocol and the RESTORE algorithm produced the lowest standard deviation in FA measurements in normal appearing WM across subjects. We concluded that this pipeline was the most robust and is preferred for automated analysis of multisite brain DTI data.

The LONI QC System: A Semi-Automated, Web-Based and Freely-Available Environment for the Comprehensive Quality Control of Neuroimaging Data

Quantifying, controlling, and monitoring image quality is an essential prerequisite for ensuring the validity and reproducibility of many types of neuroimaging data analyses. Implementation of quality control (QC) procedures is the key to ensuring that neuroimaging data are of high-quality and their validity in the subsequent analyses. We introduce the QC system of the Laboratory of Neuro Imaging (LONI): a web-based system featuring a workflow for the assessment of various modality and contrast brain imaging data. The design allows users to anonymously upload imaging data to the LONI-QC system. It then computes an exhaustive set of QC metrics which aids users to perform a standardized QC by generating a range of scalar and vector statistics. These procedures are performed in parallel using a large compute cluster. Finally, the system offers an automated QC procedure for structural MRI, which can flag each QC metric as being 'good' or 'bad.' Validation using various sets of data acquired from a single scanner and from multiple sites demonstrated the reproducibility of our QC metrics, and the sensitivity and specificity of the proposed Auto QC to 'bad' quality images in comparison to visual inspection. To the best of our knowledge, LONI-QC is the first online QC system that uniquely supports the variety of functionality where we compute numerous QC metrics and perform visual/automated image QC of multi-contrast and multi-modal brain imaging data. The LONI-QC system has been used to assess the quality of large neuroimaging datasets acquired as part of various multi-site studies such as the Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) Study and the Alzheimer's Disease Neuroimaging Initiative (ADNI). LONI-QC's functionality is freely available to users worldwide and its adoption by imaging researchers is likely to contribute substantially to upholding high standards of brain image data quality and to implementing these standards across the neuroimaging community.

Towards Portable Large-Scale Image Processing with High-Performance Computing

High-throughput, large-scale medical image computing demands tight integration of high-performance computing (HPC) infrastructure for data storage, job distribution, and image processing. The Vanderbilt University Institute for Imaging Science (VUIIS) Center for Computational Imaging (CCI) has constructed a large-scale image storage and processing infrastructure that is composed of (1) a large-scale image database using the eXtensible Neuroimaging Archive Toolkit (XNAT), (2) a content-aware job scheduling platform using the Distributed Automation for XNAT pipeline automation tool (DAX), and (3) a wide variety of encapsulated image processing pipelines called “spiders.” The VUIIS CCI medical image data storage and processing infrastructure have housed and processed nearly half-million medical image volumes with Vanderbilt Advanced Computing Center for Research and Education (ACCRE), which is the HPC facility at the Vanderbilt University. The initial deployment was natively deployed (i.e., direct installations on a bare-metal server) within the ACCRE hardware and software environments, which lead to issues of portability and sustainability. First, it could be laborious to deploy the entire VUIIS CCI medical image data storage and processing infrastructure to another HPC center with varying hardware infrastructure, library availability, and software permission policies. Second, the spiders were not developed in an isolated manner, which has led to software dependency issues during system upgrades or remote software installation. To address such issues, herein, we describe recent innovations using containerization techniques with XNAT/DAX which are used to isolate the VUIIS CCI medical image data storage and processing infrastructure from the underlying hardware and software environments. The newly presented XNAT/DAX solution has the following new features: (1) multi-level portability from system level to the application level, (2) flexible and dynamic software development and expansion, and (3) scalable spider deployment compatible with HPC clusters and local workstations.

International Cognition and Cancer Task Force Recommendations for Neuroimaging Methods in the Study of Cognitive Impairment in Non-CNS Cancer Patients

Cancer- and treatment-related cognitive changes have been a focus of increasing research since the early 1980s, with meta-analyses demonstrating poorer performance in cancer patients in cognitive domains including executive functions, processing speed, and memory. To facilitate collaborative efforts, in 2011 the International Cognition and Cancer Task Force (ICCTF) published consensus recommendations for core neuropsychological tests for studies of cancer populations. Over the past decade, studies have used neuroimaging techniques, including structural and functional magnetic resonance imaging (fMRI) and positron emission tomography, to examine the underlying brain basis for cancer- and treatment-related cognitive declines. As yet, however, there have been no consensus recommendations to guide researchers new to this field or to promote the ability to combine data sets. We first discuss important methodological issues with regard to neuroimaging study design, scanner considerations, and sequence selection, focusing on concerns relevant to cancer populations. We propose a minimum recommended set of sequences, including a high-resolution T1-weighted volume and a resting state fMRI scan. Additional advanced imaging sequences are discussed for consideration when feasible, including task-based fMRI and diffusion tensor imaging. Important image data processing and analytic considerations are also reviewed. These recommendations are offered to facilitate increased use of neuroimaging in studies of cancer- and treatment-related cognitive dysfunction. They are not intended to discourage investigator-initiated efforts to develop cutting-edge techniques, which will be helpful in advancing the state of the knowledge. Use of common imaging protocols will facilitate multicenter and data-pooling initiatives, which are needed to address critical mechanistic research questions.

Vascular burden and APOE ε4 are associated with white matter microstructural decline in cognitively normal older adults

White matter microstructure can be measured with diffusion tensor imaging (DTI). While increasing age is a predictor of white matter (WM) microstructure changes, roles of other possible modifiers, such as cardiovascular risk factors, APOE ε4 allele status and biological sex have not been clarified. We investigated 665 cognitively normal participants from the Baltimore Longitudinal Study of Aging (age 50-95, 56.7% female) with a total of 1384 DTI scans. WM microstructure was assessed by fractional anisotropy (FA) and mean diffusivity (MD). A vascular burden score was defined as the sum of five risk factors (hypertension, obesity, elevated cholesterol, diabetes and smoking status). Linear mixed effects models assessed the association of baseline vascular burden on baseline and on rates of change of FA and MD over a mean follow-up of 3.6 years, while controlling for age, race, and scanner type. We also compared DTI trajectories in APOE ε4 carriers vs. non-carriers and men vs. women. At baseline, higher vascular burden was associated with lower FA and higher MD in many WM structures including association, commissural, and projection fibers. Higher baseline vascular burden was also associated with greater longitudinal decline in FA in the hippocampal part of the cingulum and the fornix (crus)/stria terminalis and splenium of the corpus callosum, and with greater increases in MD in the splenium of the corpus callosum. APOE ε4 carriers did not differ from non-carriers in baseline DTI metrics but had greater decline in FA in the genu and splenium of the corpus callosum. Men had higher FA and lower MD in multiple WM regions at baseline but showed greater increase in MD in the genu of the corpus callosum. Women showed greater decreases over time in FA in the gyrus part of the cingulum, compared to men. Our findings show that modifiable vascular risk factors (1) have a negative impact on white matter microstructure and (2) are associated with faster microstructural deterioration of temporal WM regions and the splenium of the corpus callosum in cognitively normal adults. Reducing vascular burden in aging could modify the rate of WM deterioration and could decrease age-related cognitive decline and impairment.

Laterality of the frontal aslant tract (FAT) explains externalizing behaviors through its association with executive function

We investigated the development of a recently identified white matter pathway, the frontal aslant tract (FAT) and its association with executive function and externalizing behaviors in a sample of 129 neurotypical male and female human children ranging in age from 7 months to 19 years. We found that the FAT could be tracked in 92% of those children, and that the pathway showed age-related differences into adulthood. The change in white matter microstructure was very rapid until about 6 years, and then plateaued, only to show age-related increases again after the age of 11 years. In a subset of those children (5-18 years; n = 70), left laterality of the microstructural properties of the FAT was associated with greater attention problems as measured by the Child Behavior Checklist (CBCL). However, this relationship was fully mediated by higher executive dysfunction as measured by the Behavior Rating Inventory of Executive Function (BRIEF). This relationship was specific to the FAT-we found no relationship between laterality of a control pathway, or of the white matter of the brain in general, and attention and executive function. These findings suggest that the degree to which the developing brain favors a right lateralized structural dominance of the FAT is directly associated with executive function and attention. This novel finding provides a new potential structural biomarker to assess attention deficit hyperactivity disorder (ADHD) and associated executive dysfunction during development.

White matter microstructure correlates of general and specific second-order factors of psychopathology

Increasing data indicate that prevalent forms of psychopathology can be organized into second-order dimensions based on their correlations, including a general factor of psychopathology that explains the common variance among all disorders and specific second-order externalizing and internalizing factors. Nevertheless, most existing studies on the neural correlates of psychopathology employ case-control designs that treat diagnoses as independent categories, ignoring the highly correlated nature of psychopathology. Thus, for instance, although perturbations in white matter microstructure have been identified across a range of mental disorders, nearly all such studies used case-control designs, leaving it unclear whether observed relations reflect disorder-specific characteristics or transdiagnostic associations. Using a representative sample of 410 young adult twins oversampled for psychopathology risk, we tested the hypothesis that some previously observed relations between white matter microstructure properties in major tracts and specific disorders are related to second-order factors of psychopathology. We examined fractional anisotropy (FA), radial diffusivity (RD), and axial diffusivity (AD). White matter correlates of all second-order factors were identified after controlling for multiple statistical tests, including the general factor (FA in the body of the corpus callosum), specific internalizing (AD in the fornix), and specific externalizing (AD in the splenium of the corpus callosum, sagittal stratum, anterior corona radiata, and internal capsule). These findings suggest that some features of white matter within specific tracts may be transdiagnostically associated multiple forms of psychopathology through second-order factors of psychopathology rather with than individual mental disorders.

A fiber coherence index for quality control of B-table orientation in diffusion MRI scans

PURPOSE

The diffusion MRI "b-vector" table describing the diffusion sensitization direction can be flipped and permuted in dimension due to different orientation conventions used in scanners and incorrect or improperly utilized file formats. This can lead to incorrect fiber orientation estimates and subsequent tractography failure. Here, we present an automated quality control procedure to detect when the b-table is flipped and/or permuted incorrectly.

METHODS

We define a "fiber coherence index" to describe how well fibers are connected to each other, and use it to automatically detect the correct configuration of b-vectors. We examined the performance on 3981 research subject scans (Baltimore Longitudinal Study of Aging), 1065 normal subject scans of high image quality (Human Connectome Project), and 202 patient scans (Vanderbilt University Medical Center), as well as 9 in-vivo and 9 ex-vivo animal data.

RESULTS

The coherence index resulted in a 99.9% (3979/3981) and 100% (1065/1065) success rate in normal subject scans, 98% (198/202) in patient scans, and 100% (18/18) in both in-vivo and ex-vivo animal data in detecting the correct gradient table in datasets without severe image artifacts. The four failing cases (4/202) in patient scans, and two failures in healthy subject scans (2/3981), all showed prominent motion or signal dropout artifacts.

CONCLUSIONS

The fiber coherence measure can be used as an automatic quality assurance check in any diffusion analysis pipeline. Additionally, the success of this fiber coherence measure suggests potential broader applications, including evaluating data quality, or even providing diagnostic value as a biomarker of white matter integrity.

Automated quality control for within and between studies diffusion MRI data using a non-parametric framework for movement and distortion correction

Diffusion MRI data can be affected by hardware and subject-related artefacts that can adversely affect downstream analyses. Therefore, automated quality control (QC) is of great importance, especially in large population studies where visual QC is not practical. In this work, we introduce an automated diffusion MRI QC framework for single subject and group studies. The QC is based on a comprehensive, non-parametric approach for movement and distortion correction: FSL EDDY, which allows us to extract a rich set of QC metrics that are both sensitive and specific to different types of artefacts. Two different tools are presented: QUAD (QUality Assessment for DMRI), for single subject QC and SQUAD (Study-wise QUality Assessment for DMRI), which is designed to enable group QC and facilitate cross-studies harmonisation efforts.

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Two tools to automatically perform QC of diffusion MRI data.

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Automated generation of single subject reports for visual inspection and database.

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Group databases and reports allow to compare subjects within and between studies.

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Categorical and continuous variables can be used to update the reports.

Fast and accurate Slicewise OutLIer Detection (SOLID) with informed model estimation for diffusion MRI data

The accurate characterization of the diffusion process in tissue using diffusion MRI is greatly challenged by the presence of artefacts. Subject motion causes not only spatial misalignments between diffusion weighted images, but often also slicewise signal intensity errors. Voxelwise robust model estimation is commonly used to exclude intensity errors as outliers. Slicewise outliers, however, become distributed over multiple adjacent slices after image registration and transformation. This challenges outlier detection with voxelwise procedures due to partial volume effects. Detecting the outlier slices before any transformations are applied to diffusion weighted images is therefore required. In this work, we present i) an automated tool coined SOLID for slicewise outlier detection prior to geometrical image transformation, and ii) a framework to naturally interpret data uncertainty information from SOLID and include it as such in model estimators. SOLID uses a straightforward intensity metric, is independent of the choice of the diffusion MRI model, and can handle datasets with a few or irregularly distributed gradient directions. The SOLID-informed estimation framework prevents the need to completely reject diffusion weighted images or individual voxel measurements by downweighting measurements with their degree of uncertainty, thereby supporting convergence and well-conditioning of iterative estimation algorithms. In comprehensive simulation experiments, SOLID detects outliers with a high sensitivity and specificity, and can achieve higher or at least similar sensitivity and specificity compared to other tools that are based on more complex and time-consuming procedures for the scenarios investigated. SOLID was further validated on data from 54 neonatal subjects which were visually inspected for outlier slices with the interactive tool developed as part of this study, showing its potential to quickly highlight problematic volumes and slices in large population studies. The informed model estimation framework was evaluated both in simulations and in vivo human data.

Right Fronto-Subcortical White Matter Microstructure Predicts Cognitive Control Ability on the Go/No-go Task in a Community Sample

Go/no-go tasks are widely used to index cognitive control. This construct has been linked to white matter microstructure in a circuit connecting the right inferior frontal gyrus (IFG), subthalamic nucleus (STN), and pre-supplementary motor area. However, the specificity of this association has not been tested. A general factor of white matter has been identified that is related to processing speed. Given the strong processing speed component in successful performance on the go/no-go task, this general factor could contribute to task performance, but the general factor has often not been accounted for in past studies of cognitive control. Further, studies on cognitive control have generally employed small unrepresentative case-control designs. The present study examined the relationship between go/no-go performance and white matter microstructure in a large community sample of 378 subjects that included participants with a range of both clinical and subclinical nonpsychotic psychopathology. We found that white matter microstructure properties in the right IFG-STN tract significantly predicted task performance, and remained significant after controlling for dimensional psychopathology. The general factor of white matter only reached statistical significance when controlling for dimensional psychopathology. Although the IFG-STN and general factor tracts were highly correlated, when both were included in the model, only the IFG-STN remained a significant predictor of performance. Overall, these findings suggest that while a general factor of white matter can be identified in a young community sample, white matter microstructure properties in the right IFG-STN tract show a specific relationship to cognitive control. The findings highlight the importance of examining both specific and general correlates of cognition, especially in tasks with a speeded component.

Prefrontal-Thalamic Anatomical Connectivity and Executive Cognitive Function in Schizophrenia

BACKGROUND

Executive cognitive functions, including working memory, cognitive flexibility, and inhibition, are impaired in schizophrenia. Executive functions rely on coordinated information processing between the prefrontal cortex (PFC) and thalamus, particularly the mediodorsal nucleus. This raises the possibility that anatomical connectivity between the PFC and mediodorsal thalamus may be 1) reduced in schizophrenia and 2) related to deficits in executive function. The current investigation tested these hypotheses.

METHODS

Forty-five healthy subjects and 62 patients with a schizophrenia spectrum disorder completed a battery of tests of executive function and underwent diffusion-weighted imaging. Probabilistic tractography was used to quantify anatomical connectivity between six cortical regions, including PFC, and the thalamus. Thalamocortical anatomical connectivity was compared between healthy subjects and patients with schizophrenia using region-of-interest and voxelwise approaches, and the association between PFC-thalamic anatomical connectivity and severity of executive function impairment was examined in patients.

RESULTS

Anatomical connectivity between the thalamus and PFC was reduced in schizophrenia. Voxelwise analysis localized the reduction to areas of the mediodorsal thalamus connected to lateral PFC. Reduced PFC-thalamic connectivity in schizophrenia correlated with impaired working memory but not cognitive flexibility and inhibition. In contrast to reduced PFC-thalamic connectivity, thalamic connectivity with somatosensory and occipital cortices was increased in schizophrenia.

CONCLUSIONS

The results are consistent with models implicating disrupted PFC-thalamic connectivity in the pathophysiology of schizophrenia and mechanisms of cognitive impairment. PFC-thalamic anatomical connectivity may be an important target for procognitive interventions. Further work is needed to determine the implications of increased thalamic connectivity with sensory cortex.

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

PURPOSE

To develop a quality assurance (QA) tool (acquisition guidelines and automated processing) for diffusion tensor imaging (DTI) data using a common agar-based phantom used for fMRI QA. The goal is to produce a comprehensive set of automated, sensitive and robust QA metrics.

METHODS

A readily available agar phantom was scanned with and without parallel imaging reconstruction. Other scanning parameters were matched to the human scans. A central slab made up of either a thick slice or an average of a few slices, was extracted and all processing was performed on that image. The proposed QA relies on the creation of two ROIs for processing: (i) a preset central circular region of interest (ccROI) and (ii) a signal mask for all images in the dataset. The ccROI enables computation of average signal for SNR calculations as well as average FA values. The production of the signal masks enables automated measurements of eddy current and B0 inhomogeneity induced distortions by exploiting the sphericity of the phantom. Also, the signal masks allow automated background localization to assess levels of Nyquist ghosting.

RESULTS

The proposed DTI-QA was shown to produce eleven metrics which are robust yet sensitive to image quality changes within site and differences across sites. It can be performed in a reasonable amount of scan time (~15min) and the code for automated processing has been made publicly available.

CONCLUSIONS

A novel DTI-QA tool has been proposed. It has been applied successfully on data from several scanners/platforms. The novelty lies in the exploitation of the sphericity of the phantom for distortion measurements. Other novel contributions are: the computation of an SNR value per gradient direction for the diffusion weighted images (DWIs) and an SNR value per non-DWI, an automated background detection for the Nyquist ghosting measurement and an error metric reflecting the contribution of EPI instability to the eddy current induced shape changes observed for DWIs.

Diffusion MRI of white matter microstructure development in childhood and adolescence: Methods, challenges and progress

Diffusion magnetic resonance imaging (dMRI) continues to grow in popularity as a useful neuroimaging method to study brain development, and longitudinal studies that track the same individuals over time are emerging. Over the last decade, seminal work using dMRI has provided new insights into the development of brain white matter (WM) microstructure, connections and networks throughout childhood and adolescence. This review provides an introduction to dMRI, both diffusion tensor imaging (DTI) and other dMRI models, as well as common acquisition and analysis approaches. We highlight the difficulties associated with ascribing these imaging measurements and their changes over time to specific underlying cellular and molecular events. We also discuss selected methodological challenges that are of particular relevance for studies of development, including critical choices related to image acquisition, image analysis, quality control assessment, and the within-subject and longitudinal reliability of dMRI measurements. Next, we review the exciting progress in the characterization and understanding of brain development that has resulted from dMRI studies in childhood and adolescence, including brief overviews and discussions of studies focusing on sex and individual differences. Finally, we outline future directions that will be beneficial to the field.

The effect of age and microstructural white matter integrity on lap time variation and fast-paced walking speed

Macrostructural white matter damage (WMD) is associated with less uniform and slower walking in older adults. The effect of age and subclinical microstructural WM degeneration (a potentially earlier phase of WM ischemic damage) on walking patterns and speed is less clear. This study examines the effect of age on the associations of regional microstructural WM integrity with walking variability and speed, independent of macrostructural WMD. This study involved 493 participants (n = 51 young; n = 209 young-old; n = 233 old-old) from the Baltimore Longitudinal Study of Aging. All completed a 400-meter walk test and underwent a concurrent brain MRI with diffusion tensor imaging. Microstructural WM integrity was measured as fractional anisotropy (FA). Walking variability was measured as trend-adjusted variation in time over ten 40-meter laps (lap time variation, LTV). Fast-paced walking speed was assessed as mean lap time (MLT). Multiple linear regression models of FA predicting LTV and MLT were adjusted for age, sex, height, weight, and WM hyperintensities. Independent of WM hyperintensities, lower FA in the body of the corpus callosum was associated with higher LTV and longer MLT only in the young-old. Lower FA in superior longitudinal, inferior fronto-occipital, and uncinate fasciculi, the anterior limb of the internal capsule, and the anterior corona radiate was associated with longer MLT only in the young-old. While macrostructural WMD is known to predict more variable and slower walking in older adults, microstructural WM disruption is independently associated with more variable and slower fast-paced walking only in the young-old. Disrupted regional WM integrity may be a subclinical contributor to abnormal walking at an earlier phase of aging.

Intrainsular connectivity and somatosensory responsiveness in young children with ASD

BACKGROUND

The human somatosensory system comprises dissociable paths for discriminative and affective touch, reflected in separate peripheral afferent populations and distinct cortical targets. Differences in behavioral and neural responses to affective touch may have an important developmental role in early social experiences, which are relevant for autism spectrum disorder (ASD).

METHODS

Using probabilistic tractography, we compared the structural integrity of white matter pathways for discriminative and affective touch in young children with ASD and their typically developing (TD) peers. We examined two tracts: (1) a tract linking the thalamus with the primary somatosensory cortex, which carries discriminative tactile information, and (2) a tract linking the posterior insula-the cortical projection target of unmyelinated tactile afferents mediating affective touch-with the anterior insula, which integrates sensory and visceral inputs to interpret emotional salience of sensory stimuli. We investigated associations between tract integrity and performance on a standardized observational assessment measuring tactile discrimination and affective responses to touch.

RESULTS

Both the thalamocortical and intrainsular tracts showed reduced integrity (higher mean diffusivity) in the ASD group compared to those in the TD group. Consistent with the previous findings, the ASD group exhibited impaired tactile discriminative ability, more tactile defensiveness, and more sensory seeking (e.g., enthusiastic play or repetitive engagement with a specific tactile stimulus). There was a significant relation between intrainsular tract integrity and tactile seeking. The direction of this relation differed between groups: higher intrainsular mean diffusivity (MD) (reflecting decreased tract integrity) was associated with increased tactile seeking in the TD group but with decreased tactile seeking in the ASD group. In the TD group, decreased tactile defensiveness was also associated with higher intrainsular MD, but there was no relation in the ASD group. Discriminative touch was not significantly associated with integrity of either tract in either group.

CONCLUSIONS

These results support previous findings suggesting a central role for the insula in affective response to touch. While both discriminative and affective touch and both somatosensory tracts are affected in ASD, the restriction of brain-behavior associations to the intrainsular tract and tactile seeking suggests more complex and perhaps higher-order influence on differences in tactile defensiveness and discrimination.

The Vanderbilt Memory & Aging Project: Study Design and Baseline Cohort Overview

BACKGROUND

Vascular health factors frequently co-occur with Alzheimer's disease (AD). A better understanding of how systemic vascular and cerebrovascular health intersects with clinical and pathological AD may inform prevention and treatment opportunities.

OBJECTIVE

To establish the Vanderbilt Memory & Aging Project, a case-control longitudinal study investigating vascular health and brain aging, and describe baseline methodology and participant characteristics.

METHODS

From September 2012 to November 2014, 335 participants age 60- 92 were enrolled, including 168 individuals with mild cognitive impairment (MCI, 73±8 years, 41% female) and 167 age-, sex-, and race-matched cognitively normal controls (NC, 72±7 years, 41% female). At baseline, participants completed a physical and frailty examination, fasting blood draw, neuropsychological assessment, echocardiogram, cardiac MRI, and brain MRI. A subset underwent 24-hour ambulatory blood pressure monitoring and lumbar puncture for cerebrospinal fluid (CSF) collection.

RESULTS

As designed, participant groups were comparable for age (p = 0.31), sex (p = 0.95), and race (p = 0.65). MCI participants had greater Framingham Stroke Risk Profile scores (p = 0.008), systolic blood pressure values (p = 0.008), and history of left ventricular hypertrophy (p = 0.04) than NC participants. As expected, MCI participants performed worse on all neuropsychological measures (p-values < 0.001), were more likely to be APOEɛ4 carriers (p = 0.02), and had enhanced CSF biomarkers, including lower Aβ42 (p = 0.02), higher total tau (p = 0.004), and higher p-tau (p = 0.02) compared to NC participants.

CONCLUSION

Diverse sources of baseline and longitudinal data will provide rich opportunities to investigate pathways linking vascular and cerebrovascular health, clinical and pathological AD, and neurodegeneration contributing to novel strategies to delay or prevent cognitive decline.

Heads in the Cloud: A Primer on Neuroimaging Applications of High Performance Computing

With larger data sets and more sophisticated analyses, it is becoming increasingly common for neuroimaging researchers to push (or exceed) the limitations of standalone computer workstations. Nonetheless, although high-performance computing platforms such as clusters, grids and clouds are already in routine use by a small handful of neuroimaging researchers to increase their storage and/or computational power, the adoption of such resources by the broader neuroimaging community remains relatively uncommon. Therefore, the goal of the current manuscript is to: 1) inform prospective users about the similarities and differences between computing clusters, grids and clouds; 2) highlight their main advantages; 3) discuss when it may (and may not) be advisable to use them; 4) review some of their potential problems and barriers to access; and finally 5) give a few practical suggestions for how interested new users can start analyzing their neuroimaging data using cloud resources. Although the aim of cloud computing is to hide most of the complexity of the infrastructure management from end-users, we recognize that this can still be an intimidating area for cognitive neuroscientists, psychologists, neurologists, radiologists, and other neuroimaging researchers lacking a strong computational background. Therefore, with this in mind, we have aimed to provide a basic introduction to cloud computing in general (including some of the basic terminology, computer architectures, infrastructure and service models, etc.), a practical overview of the benefits and drawbacks, and a specific focus on how cloud resources can be used for various neuroimaging applications.

Peripheral sphingolipids are associated with variation in white matter microstructure in older adults

Sphingolipids serve important structural and functional roles in cellular membranes and myelin sheaths. Plasma sphingolipids have been shown to predict cognitive decline and Alzheimer's disease. However, the association between plasma sphingolipid levels and brain white matter (WM) microstructure has not been examined. We investigated whether plasma sphingolipids (ceramides and sphingomyelins) were associated with magnetic resonance imaging-based diffusion measures, fractional anisotropy (FA), and mean diffusivity, 10.5 years later in 17 WM regions of 150 cognitively normal adults (mean age 67.2). Elevated ceramide species (C20:0, C22:0, C22:1, and C24:1) were associated with lower FA in multiple WM regions, including total cerebral WM, anterior corona radiata, and the cingulum of the cingulate gyrus. Higher sphingomyelins (C18:1 and C20:1) were associated with lower FA in regions such as the anterior corona radiata and body of the corpus callosum. Furthermore, lower sphingomyelin to ceramide ratios (C22:0, C24:0, and C24:1) were associated with lower FA or higher mean diffusivity in regions including the superior and posterior corona radiata. However, although these associations were significant at the a priori p < 0.05, only associations with some regional diffusion measures for ceramide C22:0 and sphingomyelin C18:1 survived correction for multiple comparisons. These findings suggest plasma sphingolipids are associated with variation in WM microstructure in cognitively normal aging.

Lower gray matter integrity is associated with greater lap time variation in high-functioning older adults

BACKGROUND

Lower integrity of cerebral gray matter is associated with higher gait variability. It is not known whether gray matter integrity is associated with higher lap time variation (LTV), a clinically accessible measure of gait variability, high levels of which have been associated with mortality. This study examines the cross-sectional association between gray matter mean diffusivity (MD) and LTV in community-dwelling older adults.

METHODS

Study participants consisted of 449 high-functioning adults aged 50 and older (56.8% female) in the Baltimore Longitudinal Study of Aging, free of overt neurological disease. The magnitude of MD in the gray matter, a measure of impaired tissue integrity, was assessed by diffusion tensor imaging in 16 regions of interest (ROIs) involved with executive function, sensorimotor function, and memory. LTV was assessed as variability in lap time based on individual trajectories over ten 40-m laps. Age, sex, height, and weight were covariates. The model additionally adjusted for mean lap time and health conditions that may affect LTV.

RESULTS

Higher levels of average MD across 16 ROIs were significantly associated with higher LTV after adjustment for covariates. Specifically, higher MD in the precuneus and the anterior and middle cingulate cortices was strongly associated with higher LTV, as compared to other ROIs. The association persisted after adjustment for mean lap time, hypertension, and diabetes.

CONCLUSIONS

Lower gray matter integrity in selected areas may underlie greater LTV in high-functioning community-dwelling older adults. Longitudinal studies are warranted to examine whether changes in gray matter integrity precede more variable gait.

The impact of quality assurance assessment on diffusion tensor imaging outcomes in a large-scale population-based cohort

BACKGROUND

Diffusion tensor imaging (DTI) is applied in investigation of brain biomarkers for neurodevelopmental and neurodegenerative disorders. However, the quality of DTI measurements, like other neuroimaging techniques, is susceptible to several confounding factors (e.g., motion, eddy currents), which have only recently come under scrutiny. These confounds are especially relevant in adolescent samples where data quality may be compromised in ways that confound interpretation of maturation parameters. The current study aims to leverage DTI data from the Philadelphia Neurodevelopmental Cohort (PNC), a sample of 1601 youths with ages of 8-21 who underwent neuroimaging, to: 1) establish quality assurance (QA) metrics for the automatic identification of poor DTI image quality; 2) examine the performance of these QA measures in an external validation sample; 3) document the influence of data quality on developmental patterns of typical DTI metrics.

METHODS

All diffusion-weighted images were acquired on the same scanner. Visual QA was performed on all subjects completing DTI; images were manually categorized as Poor, Good, or Excellent. Four image quality metrics were automatically computed and used to predict manual QA status: Mean voxel intensity outlier count (MEANVOX), Maximum voxel intensity outlier count (MAXVOX), mean relative motion (MOTION) and temporal signal-to-noise ratio (TSNR). Classification accuracy for each metric was calculated as the area under the receiver-operating characteristic curve (AUC). A threshold was generated for each measure that best differentiated visual QA status and applied in a validation sample. The effects of data quality on sensitivity to expected age effects in this developmental sample were then investigated using the traditional MRI diffusion metrics: fractional anisotropy (FA) and mean diffusivity (MD). Finally, our method of QA is compared with DTIPrep.

RESULTS

TSNR (AUC=0.94) best differentiated Poor data from Good and Excellent data. MAXVOX (AUC=0.88) best differentiated Good from Excellent DTI data. At the optimal threshold, 88% of Poor data and 91% Good/Excellent data were correctly identified. Use of these thresholds on a validation dataset (n=374) indicated high accuracy. In the validation sample 83% of Poor data and 94% of Excellent data was identified using thresholds derived from the training sample. Both FA and MD were affected by the inclusion of poor data in an analysis of an age, sex and race matched comparison sample. In addition, we show that the inclusion of poor data results in significant attenuation of the correlation between diffusion metrics (FA and MD) and age during a critical neurodevelopmental period. We find higher correspondence between our QA method and DTIPrep for Poor data, but we find our method to be more robust for apparently high-quality images.

CONCLUSION

Automated QA of DTI can facilitate large-scale, high-throughput quality assurance by reliably identifying both scanner and subject induced imaging artifacts. The results present a practical example of the confounding effects of artifacts on DTI analysis in a large population-based sample, and suggest that estimates of data quality should not only be reported but also accounted for in data analysis, especially in studies of development.

Insight from uncertainty: bootstrap-derived diffusion metrics differentially predict memory function among older adults

Diffusion tensor imaging suffers from an intrinsic low signal-to-noise ratio. Bootstrap algorithms have been introduced to provide a non-parametric method to estimate the uncertainty of the measured diffusion parameters. To quantify the variability of the principal diffusion direction, bootstrap-derived metrics such as the cone of uncertainty have been proposed. However, bootstrap-derived metrics are not independent of the underlying diffusion profile. A higher mean diffusivity causes a smaller signal-to-noise ratio and, thus, increases the measurement uncertainty. Moreover, the goodness of the tensor model, which relies strongly on the complexity of the underlying diffusion profile, influences bootstrap-derived metrics as well. The presented simulations clearly depict the cone of uncertainty as a function of the underlying diffusion profile. Since the relationship of the cone of uncertainty and common diffusion parameters, such as the mean diffusivity and the fractional anisotropy, is not linear, the cone of uncertainty has a different sensitivity. In vivo analysis of the fornix reveals the cone of uncertainty to be a predictor of memory function among older adults. No significant correlation occurs with the common diffusion parameters. The present work not only demonstrates the cone of uncertainty as a function of the actual diffusion profile, but also discloses the cone of uncertainty as a sensitive predictor of memory function. Future studies should incorporate bootstrap-derived metrics to provide more comprehensive analysis.

Widespread white matter but focal gray matter alterations in depressed individuals with thoughts of death

BACKGROUND

Past work demonstrates that depressed individuals with suicidal thoughts or behaviors exhibit specific neuroanatomical alterations. This may represent a distinct phenotype characterized by specific findings on neuroimaging, but it is unclear if these findings extend to individuals with milder thoughts of death. We examined this question in outpatients with recurrent Major Depressive Disorder not receiving antidepressant treatment.

METHODS

We examined 165 subjects: 53 depressed without thoughts of death, 21 depressed with thoughts of death, and 91 healthy comparison subjects. Participants completed 3T cranial MRI, including anatomical and diffusion tensor imaging acquisitions. Automated methods measured regional gray matter volumes in addition to cortical thickness. White matter analyses examined diffusion measures within specific fiber tracts and included voxelwise comparisons.

RESULTS

After adjustment for multiple comparisons, the depressed group with thoughts of death did not exhibit differences in regional gray matter volume, but did exhibit reduced cortical thickness in frontoparietal regions and the insula. This depressed group with thoughts of death also exhibited widespread white matter differences in fractional anisotropy and radial diffusivity. These differences were observed primarily in posterior parietal white matter regions and central white matter tracts adjacent to the basal ganglia and thalamus.

CONCLUSIONS

Mild thoughts of death are associated with structural alterations in regions of the salience network, default mode network, and thalamocortical circuits. Further work is needed to understand the pathological basis of these findings.

A multimodal MRI dataset of professional chess players

Chess is a good model to study high-level human brain functions such as spatial cognition, memory, planning, learning and problem solving. Recent studies have demonstrated that non-invasive MRI techniques are valuable for researchers to investigate the underlying neural mechanism of playing chess. For professional chess players (e.g., chess grand masters and masters or GM/Ms), what are the structural and functional alterations due to long-term professional practice, and how these alterations relate to behavior, are largely veiled. Here, we report a multimodal MRI dataset from 29 professional Chinese chess players (most of whom are GM/Ms), and 29 age matched novices. We hope that this dataset will provide researchers with new materials to further explore high-level human brain functions.

Region of interest correction factors improve reliability of diffusion imaging measures within and across scanners and field strengths

Diffusion tensor imaging (DTI) measures are commonly used as imaging markers to investigate individual differences in relation to behavioral and health-related characteristics. However, the ability to detect reliable associations in cross-sectional or longitudinal studies is limited by the reliability of the diffusion measures. Several studies have examined the reliability of diffusion measures within (i.e. intra-site) and across (i.e. inter-site) scanners with mixed results. Our study compares the test-retest reliability of diffusion measures within and across scanners and field strengths in cognitively normal older adults with a follow-up interval less than 2.25 years. Intra-class correlation (ICC) and coefficient of variation (CoV) of fractional anisotropy (FA) and mean diffusivity (MD) were evaluated in sixteen white matter and twenty-six gray matter bilateral regions. The ICC for intra-site reliability (0.32 to 0.96 for FA and 0.18 to 0.95 for MD in white matter regions; 0.27 to 0.89 for MD and 0.03 to 0.79 for FA in gray matter regions) and inter-site reliability (0.28 to 0.95 for FA in white matter regions, 0.02 to 0.86 for MD in gray matter regions) with longer follow-up intervals were similar to earlier studies using shorter follow-up intervals. The reliability of across field strengths comparisons was lower than intra- and inter-site reliabilities. Within and across scanner comparisons showed that diffusion measures were more stable in larger white matter regions (>1500 mm(3)). For gray matter regions, the MD measure showed stability in specific regions and was not dependent on region size. Linear correction factor estimated from cross-sectional or longitudinal data improved the reliability across field strengths. Our findings indicate that investigations relating diffusion measures to external variables must consider variable reliability across the distinct regions of interest and that correction factors can be used to improve consistency of measurement across field strengths. An important result of this work is that inter-scanner and field strength effects can be partially mitigated with linear correction factors specific to regions of interest. These data-driven linear correction techniques can be applied in cross-sectional or longitudinal studies.

Vanderbilt University Institute of Imaging Science Center for Computational Imaging XNAT: A multimodal data archive and processing environment

The Vanderbilt University Institute for Imaging Science (VUIIS) Center for Computational Imaging (CCI) has developed a database built on XNAT housing over a quarter of a million scans. The database provides framework for (1) rapid prototyping, (2) large scale batch processing of images and (3) scalable project management. The system uses the web-based interfaces of XNAT and REDCap to allow for graphical interaction. A python middleware layer, the Distributed Automation for XNAT (DAX) package, distributes computation across the Vanderbilt Advanced Computing Center for Research and Education high performance computing center. All software are made available in open source for use in combining portable batch scripting (PBS) grids and XNAT servers.

Diffusion-MRI in neurodegenerative disorders

The ability to image the whole brain through ever more subtle and specific methods/contrasts has come to play a key role in understanding the basis of brain abnormalities in several diseases. In magnetic resonance imaging (MRI), "diffusion" (i.e. the random, thermally-induced displacements of water molecules over time) represents an extraordinarily sensitive contrast mechanism, and the exquisite structural detail it affords has proven useful in a vast number of clinical as well as research applications. Since diffusion-MRI is a truly quantitative imaging technique, the indices it provides can serve as potential imaging biomarkers which could allow early detection of pathological alterations as well as tracking and possibly predicting subtle changes in follow-up examinations and clinical trials. Accordingly, diffusion-MRI has proven useful in obtaining information to better understand the microstructural changes and neurophysiological mechanisms underlying various neurodegenerative disorders. In this review article, we summarize and explore the main applications, findings, perspectives as well as challenges and future research of diffusion-MRI in various neurodegenerative disorders including Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, Huntington's disease and degenerative ataxias.

The neurobiology of eating disorders--a clinical perspective

OBJECTIVE

To provide a neurobiological basis of eating disorders for clinicians and to enlighten how comparing neurobiology and eating disorders with neurobiology of other psychiatric illnesses can improve treatment protocols.

METHOD

A selective review on the neurobiology of eating disorders. The article focuses on clinical research on humans with consideration of the anatomical, neural, and molecular basis of eating disorders.

RESULTS

The neurobiology of people with eating disorders is altered. Many of the neurobiological regions, receptors, and chemical substrates that are affected in other mental illnesses also play an important role in eating disorders. More knowledge about the neurobiological overlap between eating disorders and other psychiatric populations will help when developing treatment protocols not the least regarding that comorbidity is common in patients with EDs.

CONCLUSION

Knowledge about the underlying neurobiology of eating disorders will improve treatment intervention and will benefit from comparisons with other mental illnesses and their treatments.

A full-brain, bootstrapped analysis of diffusion tensor imaging robustly differentiates Parkinson disease from healthy controls

There is a compelling need for early, accurate diagnosis of Parkinson's disease (PD). Various magnetic resonance imaging modalities are being explored as an adjunct to diagnosis. A significant challenge in using MR imaging for diagnosis is developing appropriate algorithms for extracting diagnostically relevant information from brain images. In previous work, we have demonstrated that individual subject variability can have a substantial effect on identifying and determining the borders of regions of analysis, and that this variability may impact on prediction accuracy. In this paper we evaluate a new statistical algorithm to determine if we can improve accuracy of prediction using a subjects left-out validation of a DTI analysis. Twenty subjects with PD and 22 healthy controls were imaged to evaluate if a full brain diffusion tensor imaging-fractional anisotropy (DTI-FA) map might be capable of segregating PD from controls. In this paper, we present a new statistical algorithm based on bootstrapping. We compare the capacity of this algorithm to classify the identity of subjects left out of the analysis with the accuracy of other statistical techniques, including standard cluster-thresholding. The bootstrapped analysis approach was able to correctly discriminate the 20 subjects with PD from the 22 healthy controls (area under the receiver operator curve or AUROC 0.90); however the sensitivity and specificity of standard cluster-thresholding techniques at various voxel-specific thresholds were less effective (AUROC 0.72-0.75). Based on these results sufficient information to generate diagnostically relevant statistical maps may already be collected by current MRI scanners. We present one statistical technique that might be used to extract diagnostically relevant information from a full brain analysis.

White matter correlates of sensory processing in autism spectrum disorders

Autism spectrum disorder (ASD) has been characterized by atypical socio-communicative behavior, sensorimotor impairment and abnormal neurodevelopmental trajectories. DTI has been used to determine the presence and nature of abnormality in white matter integrity that may contribute to the behavioral phenomena that characterize ASD. Although atypical patterns of sensory responding in ASD are well documented in the behavioral literature, much less is known about the neural networks associated with aberrant sensory processing. To address the roles of basic sensory, sensory association and early attentional processes in sensory responsiveness in ASD, our investigation focused on five white matter fiber tracts known to be involved in these various stages of sensory processing: superior corona radiata, centrum semiovale, inferior longitudinal fasciculus, posterior limb of the internal capsule, and splenium. We acquired high angular resolution diffusion images from 32 children with ASD and 26 typically developing children between the ages of 5 and 8. We also administered sensory assessments to examine brain-behavior relationships between white matter integrity and sensory variables. Our findings suggest a modulatory role of the inferior longitudinal fasciculus and splenium in atypical sensorimotor and early attention processes in ASD. Increased tactile defensiveness was found to be related to reduced fractional anisotropy in the inferior longitudinal fasciculus, which may reflect an aberrant connection between limbic structures in the temporal lobe and the inferior parietal cortex. Our findings also corroborate the modulatory role of the splenium in attentional orienting, but suggest the possibility of a more diffuse or separable network for social orienting in ASD. Future investigation should consider the use of whole brain analyses for a more robust assessment of white matter microstructure.

Impact of the control for corrupted diffusion tensor imaging data in comparisons at the group level: an application in Huntington disease

Background

Corrupted gradient directions (GD) in diffusion weighted images may seriously affect reliability of diffusion tensor imaging (DTI)-based comparisons at the group level. In the present study we employed a quality control (QC) algorithm to eliminate corrupted gradient directions from DTI data. We then assessed effects of this procedure on comparisons between Huntington disease (HD) subjects and controls at the group level.

Methods

Sixty-one HD patients in early stages and forty matched healthy controls were studied in a longitudinal design (baseline and two follow-ups at three time points over 15 months), in a multicenter setting with similar acquisition protocols on four different MR scanners at four European study sites. A QC algorithm was used to identify corrupted GD in DTI data sets. Differences in fractional anisotropy (FA) maps at the group level with and without elimination of corrupted GD were analyzed.

Results

The elimination of corrupted GD had an impact on individual FA maps as well as on cross-sectional group comparisons between HD subjects and controls. Following application of the QC algorithm, less small clusters of FA changes were observed, compared to the analysis without QC. However, the main pattern of regional reductions and increases in FA values with and without QC-based elimination of corrupted GD was unchanged.

Conclusion

An impact on the result patterns of the comparison of FA maps between HD subjects and controls was observed depending on whether QC-based elimination of corrupted GD was performed. QC-based elimination of corrupted GD in DTI scans reduces the risk of type I and type II errors in cross-sectional group comparison of FA maps contributing to an increase in reliability and stability of group comparisons.