Chase R. Figley

Effects of Body Mass Index and Body Fat Percent on Default Mode, Executive Control, and Salience Network Structure and Function

It is well established that obesity decreases overall life expectancy and increases the risk of several adverse health conditions. Mounting evidence indicates that body fat is likely also associated with structural and functional brain changes, reduced cognitive function, and greater impulsivity. However, previously reported differences in brain structure and function have been variable across studies and difficult to reconcile due to sample population and methodological differences. To clarify these issues, we correlated two independent measures of body composition—i.e., body mass index (BMI) and body fat percent (BFP)—with structural and functional neuroimaging data obtained from a cohort of 32 neurologically healthy adults. Whole-brain voxel-wise analyses indicated that higher BMI and BFP were associated with widespread decreases in gray matter volume, white matter volume, and white matter microstructure (including several regions, such as the striatum and orbitofrontal cortex, which may influence value assessment, habit formation, and decision-making). Moreover, closer examination of resting state functional connectivity, white matter volume, and white matter microstructure throughout the default mode network (DMN), executive control network (ECN), and salience network (SN) revealed that higher BMI and BFP were associated with increased SN functional connectivity and decreased white matter volumes throughout all three networks (i.e., the DMN, ECN, and SN). Taken together, these findings: (1) offer a biologically plausible explanation for reduced cognitive performance, greater impulsivity, and altered reward processing among overweight individuals, and (2) suggest neurobiological mechanisms (i.e., altered functional and structural brain connectivity) that may affect overweight individuals ability to establish and maintain healthy lifestyle choices.

Probabilistic atlases of default mode, executive control and salience network white matter tracts: an fMRI-guided diffusion tensor imaging and tractography study

Diffusion tensor imaging (DTI) is a powerful MRI technique that can be used to estimate both the microstructural integrity and the trajectories of white matter pathways throughout the central nervous system. This fiber tracking (aka, “tractography”) approach is often carried out using anatomically-defined seed points to identify white matter tracts that pass through one or more structures, but can also be performed using functionally-defined regions of interest (ROIs) that have been determined using functional MRI (fMRI) or other methods. In this study, we performed fMRI-guided DTI tractography between all of the previously defined nodes within each of six common resting-state brain networks, including the: dorsal Default Mode Network (dDMN), ventral Default Mode Network (vDMN), left Executive Control Network (lECN), right Executive Control Network (rECN), anterior Salience Network (aSN), and posterior Salience Network (pSN). By normalizing the data from 32 healthy control subjects to a standard template—using high-dimensional, non-linear warping methods—we were able to create probabilistic white matter atlases for each tract in stereotaxic coordinates. By investigating all 198 ROI-to-ROI combinations within the aforementioned resting-state networks (for a total of 6336 independent DTI tractography analyses), the resulting probabilistic atlases represent a comprehensive cohort of functionally-defined white matter regions that can be used in future brain imaging studies to: (1) ascribe DTI or other white matter changes to particular functional brain networks, and (2) compliment resting state fMRI or other functional connectivity analyses.

A Method for Whole Brain Ex Vivo Magnetic Resonance Imaging with Minimal Susceptibility Artifacts

Magnetic resonance imaging (MRI) is a non-destructive technique that is capable of localizing pathologies and assessing other anatomical features (e.g., tissue volume, microstructure, and white matter connectivity) in postmortem, ex vivo human brains. However, when brains are removed from the skull and cerebrospinal fluid (i.e., their normal in vivo magnetic environment), air bubbles and air–tissue interfaces typically cause magnetic susceptibility artifacts that severely degrade the quality of ex vivo MRI data. In this report, we describe a relatively simple and cost-effective experimental setup for acquiring artifact-free ex vivo brain images using a clinical MRI system with standard hardware. In particular, we outline the necessary steps, from collecting an ex vivo human brain to the MRI scanner setup, and have also described changing the formalin (as might be necessary in longitudinal postmortem studies). Finally, we share some representative ex vivo MRI images that have been acquired using the proposed setup in order to demonstrate the efficacy of this approach. We hope that this protocol will provide both clinicians and researchers with a straight-forward and cost-effective solution for acquiring ex vivo MRI data from whole postmortem human brains.

Can T 1w/T 2w ratio be used as a myelin-specific measure in subcortical structures? Comparisons between FSE-based T 1w/T 2w ratios, GRASE-based T 1w/T 2w ratios and multi-echo GRASE-based myelin water fractions

Given the growing popularity of T1‐weighted/T2‐weighted (T1w/T2w) ratio measurements, the objective of the current study was to evaluate the concordance between T1w/T2w ratios obtained using conventional fast spin echo (FSE) versus combined gradient and spin echo (GRASE) sequences for T2w image acquisition, and to compare the resulting T1w/T2w ratios with histologically validated myelin water fraction (MWF) measurements in several subcortical brain structures. In order to compare these measurements across a relatively wide range of myelin concentrations, whole‐brain T1w magnetization prepared rapid acquisition gradient echo (MPRAGE), T2w FSE and three‐dimensional multi‐echo GRASE data were acquired from 10 participants with multiple sclerosis at 3 T. Then, after high‐dimensional, non‐linear warping, region of interest (ROI) analyses were performed to compare T1w/T2w ratios and MWF estimates (across participants and brain regions) in 11 bilateral white matter (WM) and four bilateral subcortical grey matter (SGM) structures extracted from the JHU_MNI_SS ‘Eve’ atlas. Although the GRASE sequence systematically underestimated T1w/T2w values compared to the FSE sequence (revealed by Bland–Altman and mountain plots), linear regressions across participants and ROIs revealed consistently high correlations between the two methods (r2 = 0.62 for all ROIs, r2 = 0.62 for WM structures and r2 = 0.73 for SGM structures). However, correlations between either FSE‐based or GRASE‐based T1w/T2w ratios and MWFs were extremely low in WM structures (FSE‐based, r2 = 0.000020; GRASE‐based, r2 = 0.0014), low across all ROIs (FSE‐based, r2 = 0.053; GRASE‐based, r2 = 0.029) and moderate in SGM structures (FSE‐based, r2 = 0.20; GRASE‐based, r2 = 0.17). Overall, our findings indicated a high degree of correlation (but not equivalence) between FSE‐based and GRASE‐based T1w/T2w ratios, and low correlations between T1w/T2w ratios and MWFs. This suggests that the two T1w/T2w ratio approaches measure similar facets of subcortical tissue microstructure, whereas T1w/T2w ratios and MWFs appear to be sensitized to different microstructural properties. On this basis, we conclude that multi‐echo GRASE sequences can be used in future studies to efficiently elucidate both general (T1w/T2w ratio) and myelin‐specific (MWF) tissue characteristics.

Probabilistic White Matter Atlases of Human Auditory, Basal Ganglia, Language, Precuneus, Sensorimotor, Visual and Visuospatial Networks

Background: Despite the popularity of functional connectivity analyses and the well-known topology of several intrinsic cortical networks, relatively little is known about the white matter regions (i.e., structural connectivity) underlying these networks. In the current study, we have therefore performed fMRI-guided diffusion tensor imaging (DTI) tractography to create probabilistic white matter atlases for eight previously identified functional brain networks, including the Auditory, Basal Ganglia, Language, Precuneus, Sensorimotor, Primary Visual, Higher Visual and Visuospatial Networks. Methods: Whole-brain diffusion imaging data were acquired from a cohort of 32 healthy volunteers, and were warped to the ICBM template using a two-stage, high-dimensional, non-linear spatial normalization procedure. Deterministic tractography, with fractional anisotropy (FA) ≥0.15 and deviation angle <50°, was then performed using the Fiber Association by Continuous Tracking (FACT) algorithm, and a multi-ROI approach to identify tracts of interest. Regions-of-interest (ROIs) for each of the eight networks were taken from a pre-existing atlas of functionally defined regions to explore all ROI-to-ROI connections within each network, and all resulting streamlines were saved as binary masks to create probabilistic atlases (across participants) for tracts between each ROI-to-ROI pair. Results: The resulting functionally-defined white matter atlases (i.e., for each tract and each network as a whole) were saved as NIFTI images in stereotaxic ICBM coordinates, and have been added to the UManitoba-JHU Functionally-Defined Human White Matter Atlas (http://www.nitrc.org/projects/uofm_jhu_atlas/). Conclusion: To the best of our knowledge, this work represents the first attempt to comprehensively identify and map white matter connectomes for the Auditory, Basal Ganglia, Language, Precuneus, Sensorimotor, Primary Visual, Higher Visual and Visuospatial Networks. Therefore, the resulting probabilistic atlases represent a unique tool for future neuroimaging studies wishing to ascribe voxel-wise or ROI-based changes (i.e., in DTI or other quantitative white matter imaging signals) to these functional brain networks.

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.

Spatial Correlation of Pathology and Perfusion Changes within the Cortex and White Matter in Multiple Sclerosis

BACKGROUND AND PURPOSE: The spatial correlation between WM and cortical GM disease in multiple sclerosis is controversial and has not been previously assessed with perfusion MR imaging. We sought to determine the nature of association between lobar WM, cortical GM, volume and perfusion. MATERIALS AND METHODS: Nineteen individuals with secondary-progressive multiple sclerosis, 19 with relapsing-remitting multiple sclerosis, and 19 age-matched healthy controls were recruited. Quantitative MR perfusion imaging was used to derive CBF, CBV, and MTT within cortical GM, WM, and T2-hyperintense lesions. A 2-step multivariate linear regression (corrected for age, disease duration, and Expanded Disability Status Scale) was used to assess correlations between perfusion and volume measures in global and lobar normal-appearing WM, cortical GM, and T2-hyperintense lesions. The Bonferroni adjustment was applied as appropriate. RESULTS: Global cortical GM and WM volume was significantly reduced for each group comparison, except cortical GM volume of those with relapsing-remitting multiple sclerosis versus controls. Global and lobar cortical GM CBF and CBV were reduced in secondary-progressive multiple sclerosis compared with other groups but not for relapsing-remitting multiple sclerosis versus controls. Global and lobar WM CBF and CBV were not significantly different across groups. The distribution of lobar cortical GM and WM volume reduction was disparate, except for the occipital lobes in patients with secondary-progressive multiple sclerosis versus those with relapsing-remitting multiple sclerosis. Moderate associations were identified between lobar cortical GM and lobar normal-appearing WM volume in controls and in the left temporal lobe in relapsing-remitting multiple sclerosis. No significant associations occurred between cortical GM and WM perfusion or volume. Strong correlations were observed between cortical-GM perfusion, normal appearing WM and lesional perfusion, with respect to each global and lobar region within HC, and RRMS and SPMS patients (R2 ≤ 0.96, P < .006 and R2 ≤ 0.738, P < .006). CONCLUSIONS: The weak correlation between lobar WM and cortical GM volume loss and perfusion reduction suggests the independent pathophysiology of WM and cortical GM disease.

The relationship between white matter fiber damage and gray matter perfusion in large-scale functionally defined networks in multiple sclerosis

Background: Recent studies utilizing perfusion as a surrogate of cortical integrity show promise for overall cognition, but the association between white matter (WM) damage and gray matter (GM) integrity in specific functional networks is not previously studied. Objective: To investigate the relationship between WM fiber integrity and GM node perfusion within six functional networks of relapsing-remitting multiple sclerosis (RRMS) and secondary progressive multiple sclerosis (SPMS) patients. Methods: Magnetic resonance imaging (MRI) and neurocognitive testing were performed on 19 healthy controls (HC), 39 RRMS, and 45 SPMS patients. WM damage extent and severity were quantified with T2-hyper/T1-hypointense (T2h/T1h) lesion volume and degree of perfusion reduction in lesional and normal-appearing white matter (NAWM), respectively. A two-step linear regression corrected for confounders was employed. Results: Cognitive impairment was present in 20/39 (51%) RRMS and 25/45 (53%) SPMS patients. GM node perfusion was associated with WM fiber damage severity (WM hypoperfusion) within each network—including both NAWM (R2 = 0.67–0.89, p < 0.0001) and T2h (R2 = 0.39–0.62, p < 0.0001) WM regions—but was not significantly associated (p > 0.01) with WM fiber damage extent (i.e. T2h/T1h lesion volumes). Conclusion: Overall, GM node perfusion was associated with severity rather than extent of WM network damage, supporting a primary etiology of GM hypoperfusion.

Diabetes and anxiety adversely affect cognition in multiple sclerosis

OBJECTIVEnTo determine whether comorbid diabetes and hypertension are associated with cognition in multiple sclerosis (MS) after accounting for psychiatric comorbidities.nnnMETHODSnParticipants completed a structured psychiatric interview, the Hospital Anxiety and Depression Scale (HADS), a comorbidity questionnaire, and cognitive testing including the Symbol Digit Modalities Test (SDMT), California Verbal Learning Test (CVLT-II), Brief Visuospatial Memory Test-Revised (BVMT-R), and verbal fluency. Test scores were converted to age-, sex- and education-adjusted z-scores. We evaluated associations between diabetes and hypertension and the four cognitive z-scores using a multivariate linear model, adjusting for comorbid depression and anxiety disorders, psychotropic medications, disease-modifying therapies, smoking status and body mass index.nnnRESULTSnOf 111 participants, most were women (82.9%) with relapsing remitting MS (83.5%), of mean (SD) age 49.6 (12.7) years. Comorbidity was common; 22.7% participants had hypertension, 10.8% had diabetes, 9.9% had current major depression, and 9.9% had current anxiety disorders. Mean (SD) z-scores were: SDMT -0.66 (1.15), CVLT-II -0.43 (1.32), BVMT-R -0.49 (1.07) and fluency -0.59 (0.86). Diabetes (pu202f=u202f0.02) and anxiety disorder (pu202f=u202f0.02) were associated with cognitive function overall. Diabetes was associated with lower BVMT-R (βu202f=u202f-1.18, pu202f=u202f0.0015) and fluency (βu202f=u202f-0.63, pu202f=u202f0.037) z-scores. Anxiety was associated with lower SDMT (βu202f=u202f-1.07, pu202f=u202f0.0074) z-scores. Elevated anxiety symptoms (HADS-Au202f≥u202f11) were associated with lower z-scores on the SDMT and CVLT-II.nnnCONCLUSIONnComorbidities, including diabetes and anxiety, are associated with cognitive dysfunction in MS. Their presence may contribute to the heterogeneous pattern of impairments seen across individuals and they may represent targets for improved management of cognitive symptoms.

Effect of echo time and T 2 -weighting on GRASE-based T 1 w/T 2 w ratio measurements at 3T

Tissue contrast can be enhanced by dividing T1-weighted (T1w) images by T2-weighted (T2w) images to map the so-called T1w/T2w ratio, which has become an increasingly popular technique for quantifying brain tissue changes associated with neurodevelopment, aging, and a variety of neurodegenerative diseases. However, although it is self-evident that T1w/T2w ratios increase with the amount of T2-weighting in the T2w image - which is determined by the echo time (TE), all else being equal - longer TEs also reduce the signal-to-noise ratio (SNR) of the T2w images, and it is not clear how these SNR characteristics affect the reliability of T1w/T2w measurements. Therefore, the current study systematically investigated how different amounts of T2-weighting affected T1w/T2w measurements in order to determine whether there is an optimal amount of T2-weighting. T1w images were acquired from 10 neurologically healthy adults using a 3D turbo field echo (TFE) sequence, and a series of T2-weighted images were extracted from a multi-echo 3D combined gradient- and spin-echo (GRASE) sequence. Analyses of 12 anatomically defined brain regions revealed that both the mean and standard deviation of the T1w/T2w measurements increased exponentially with TE of the T2w images, and that T2w images with TEu202f≈u202f120-160u202fms yielded the most consistent/reproducible contrast between white matter ROIs and the whole-brain T1w/T2w signal. Furthermore, comparisons between T1w/T2w measurements and multi-component T2-relaxation myelin water fractions (MWFs) in the same brain regions revealed that T2w images with TEu202f≥u202f160u202fms drastically reduced the degree of correlation between T1w/T2w measurements and MWF. Overall, these findings suggest that: 1) there is a substantial trade-off between increased T1w/T2w contrast (based on longer TEs for the T2w images) and the reliability of quantitative T1w/T2w signals; and 2) the optimal TE for T2w GRASE scans is between 120u202fms and 160u202fms for calculating T1w/T2w ratios.