Reid, Robert I.

AbstractBackgroundNeurite Orientation Dispersion and Density Imaging (NODDI) is becoming increasingly common as multiband acceleration becomes available for clinical scanning, but fitting the model can be extremely slow. Since the release of the original MATLAB implementation in 2012, three alternatives have become popular: Accelerated Microstructure Imaging via Convex Optimization (AMICO), the Microstructure Diffusion Toolbox (MDT), and the CUDA Diffusion Modelling Toolbox (cuDIMOT). They accelerate fitting through reformulation and/or using GPUs, but the implementation differences raise the question of which approach is best. To our knowledge all previous comparisons lacked knowledge of the true values and used the original implementation as the nominally correct one, with in vivo data. We remedy that using simulations with known NODDI values, in addition to in vivo scans.MethodWe created a software phantom with Neurite Density Index (NDI) values linearly increasing from 0.1 to 1.0 in x, Orientation Dispersion (ODI) from 0 to 1 in y, and Isotropic water Volume Fraction (ISOVF) from 0 to 0.9 in z. We simulated five scans using the ADNI3 advanced diffusion MRI protocol. The phantom had 20 voxels in each direction, and the fiber bundle principal direction randomly varied between the simulations. The simulations were fit by the original, AMICO, MDT, and cuDIMOT implementations, and root mean square errors were calculated as shown in Table 1. A discrepancy measure was also calculated using the brain voxels of three in vivo human scans acquired using the same protocol on a Siemens Prisma, substituting the original implementation’s values for the truth since the latter is unknown for in vivo scans.ResultcuDIMOT had the least error (for ODI < 0.8) and was fastest. The original implementation was a close second in accuracy but much slower. cuDIMOT’s ODI underestimation can be corrected with a transfer function in the noise‐free case, but that approach fails with actual scans.ConclusionThe original or cuDIMOT implementations should be used, with cuDIMOT the favorite when at least one CUDA‐capable GPU is available and the number of scans are large. Correcting cuDIMOT’s ODI underestimation using a different initialization or form of Dawson’s integral should be investigated.

AbstractBackgroundDiffusion MRI (dMRI) based quantification of microstructural changes in white matter (WM) are increasingly proposed to measure cerebrovascular disease (CVD) related changes. Our objective was to compare the proposed dMRI markers using postmortem neuropathologic CVD data and then evaluate the predictors of longitudinal dMRI markers using serial scans.MethodWe identified n=51 participants (mean age: 83.8 years, 63% males) with ante‐mortem dMRI and postmortem CVD evaluation and n=718 participants (mean age: 71.1 years, 56% males) with at least two dMRI scans from the population‐based sample of Mayo Clinic Study of Aging. We computed dMRI measures proposed for measuring CVD: Free Water (FW), Fractional Anisotropy (FA) adjusted for FW (FAadj), Peak width Skeletonized Mean Diffusivity (PSMD), and FA of the genu of corpus callosum (Genu‐FA). We used the FW and PSMD (PSMD release 1.8.1) kits from the MarkVCID consortium.Using weighted linear regression models with adjustments for MRI scan time to death, we evaluated associations between the baseline dMRI and two pathology CVD scores: Strozyk (represents the presence and number of macroscopic lesions) and Kalaria (represents the summary score of vessel wall modifications) scales. We ran linear mixed effect models with all available serial dMRI measures as outcomes and vascular risk (measured by the number of cardiovascular and metabolic conditions‐CMC), baseline imaging measures (amyloid and white matter hyperintensities (WMH)) and their interaction with time as key predictors.ResultMost dMRI markers were predictors of postmortem CVD pathology (Table 1). In longitudinal dMRI models, terms associated with amyloid explained little variability in dMRI (<1%) (Table 2). WMH was a significant predictor of baseline and decline in dMRI measures with WMH explaining a considerable percentage of variability in FW (34%) and PSMD (28%). Vascular risk measures were significant predictors of three out of the four dMRI measures.ConclusionThe diffusion measures proposed as surrogate markers of CVD map reasonably well to CVD pathological scales and do not change substantially as a function of baseline amyloidosis. Our findings shed light on variability in the proposed diffusion markers using postmortem data and longitudinal imaging data. Further work is needed to evaluate their clinical utility.

AbstractBackgroundMulti‐compartment modelling of white matter (WM) microstructure using Neurite Orientation Dispersion and Density Imaging (NODDI) can provide information on WM health through the neurite (axons and dendrites) density index and free water measures. We hypothesized that cerebrovascular disease (CVD), Alzheimer’s disease (AD), and an FDG pattern suggestive of TDP‐43 proteinopathy would be associated with distinct NODDI readouts of WM damage which would be informative for identifying the substrate for cognitive impairment.MethodWe identified two independent cohorts with multi‐shell diffusion MRI, amyloid and tau PET, and cognitive assessments. One, were participants from the Mayo Clinic Study of Aging, a population‐based cohort of 347 elderly randomly sampled from the Olmsted county, Minnesota. The second were 61 amyloid positive AD participants from the Mayo AD Research Center.ResultWe observed an increase in free water and decrease in neurite density in the genu of the corpus callosum associated with vascular risk factors, which we refer to as vascular WM component (Figure 1). Tau PET signal reflective of 3R/4R tau deposition was associated with worsening neurite density index in the temporal WM where we measured parahippocampal cingulum and inferior temporal WM bundles. Worsening temporal neurite density was associated with (antemortem confirmed) FDG TDP‐43 signature. Post‐mortem neuropathologic data on a small subset (n=9) of this sample lend support to our findings. In the population‐based cohort where vascular disease was more prevalent, the vascular WM component explained variability in global cognition (partial R2 of free water and neurite density = 8.3%) and MMSE performance (8.2%) which was comparable to amyloid PET (7.4% for global cognition and 6.6% for memory) (Table 1). In the AD dementia clinic‐based cohort, tau deposition was the greatest contributor to cognitive performance (9.6%), but there was also a non‐trivial contribution of the temporal WM component (8.5%) to cognitive performance. The differences observed between the two cohorts were reflective of their distinct clinical composition.ConclusionWhite matter microstructural damage assessed using advanced diffusion models may add significant value for distinguishing the underlying substrate (whether CVD versus neurodegenerative disease caused by tau deposition or TDP‐43 pathology) for cognitive impairment in older individuals.

AbstractBackgroundNeurite Orientation Dispersion and Density Imaging (NODDI) is becoming increasingly common as multiband acceleration becomes available for clinical scanning, but fitting the model can be extremely slow. Since the release of the original MATLAB implementation in 2012, three alternatives have become popular: Accelerated Microstructure Imaging via Convex Optimization (AMICO), the Microstructure Diffusion Toolbox (MDT), and the CUDA Diffusion Modelling Toolbox (cuDIMOT). They accelerate fitting through reformulation and/or using GPUs, but the implementation differences raise the question of which approach is best. To our knowledge all previous comparisons lacked knowledge of the true values and used the original implementation as the nominally correct one, with in vivo data. We remedy that using simulations with known NODDI values, in addition to in vivo scans.MethodWe created a software phantom with Neurite Density Index (NDI) values linearly increasing from 0.1 to 1.0 in x, Orientation Dispersion (ODI) from 0 to 1 in y, and Isotropic water Volume Fraction (ISOVF) from 0 to 0.9 in z. We simulated five scans using the ADNI3 advanced diffusion MRI protocol. The phantom had 20 voxels in each direction, and the fiber bundle principal direction randomly varied between the simulations. The simulations were fit by the original, AMICO, MDT, and cuDIMOT implementations, and root mean square errors were calculated as shown in Table 1. A discrepancy measure was also calculated using the brain voxels of three in vivo human scans acquired using the same protocol on a Siemens Prisma, substituting the original implementation’s values for the truth since the latter is unknown for in vivo scans.ResultcuDIMOT had the least error (for ODI < 0.8) and was fastest. The original implementation was a close second in accuracy but much slower. cuDIMOT’s ODI underestimation can be corrected with a transfer function in the noise‐free case, but that approach fails with actual scans.ConclusionThe original or cuDIMOT implementations should be used, with cuDIMOT the favorite when at least one CUDA‐capable GPU is available and the number of scans are large. Correcting cuDIMOT’s ODI underestimation using a different initialization or form of Dawson’s integral should be investigated.

Abstract Primary four-repeat tauopathies are characterized by depositions of the four-repeat isoform of the microtubule binding protein, tau. The two most common sporadic four-repeat tauopathies are progressive supranuclear palsy and corticobasal degeneration. Because tau PET tracers exhibit poor binding affinity to four-repeat pathology, determining how well in vivo MRI findings relate to underlying pathology is critical to evaluating their utility as surrogate markers to aid in diagnosis and as outcome measures for clinical trials. We studied the relationship of cross-sectional imaging findings, such as MRI volume loss and diffusion tensor imaging white matter tract abnormalities, to tau histopathology in four-repeat tauopathies. Forty-seven patients with antemortem 3 T MRI volumetric and diffusion tensor imaging scans plus post-mortem pathological diagnosis of a four-repeat tauopathy (28 progressive supranuclear palsy; 19 corticobasal degeneration) were included in the study. Tau lesion types (pretangles/neurofibrillary tangles, neuropil threads, coiled bodies, astrocytic lesions) were semiquantitatively graded in disease-specific cortical, subcortical and brainstem regions. Antemortem regional volumes, fractional anisotropy and mean diffusivity were modelled using linear regression with post-mortem tau lesion scores considered separately, based on cellular type (neuronal versus glial), or summed (total tau). Results showed that greater total tau burden was associated with volume loss in the subthalamic nucleus (P = 0.001), midbrain (P &lt; 0.001), substantia nigra (P = 0.03) and red nucleus (P = 0.004), with glial lesions substantially driving the associations. Decreased fractional anisotropy and increased mean diffusivity in the superior cerebellar peduncle correlated with glial tau in the cerebellar dentate (P = 0.04 and P = 0.02, respectively) and red nucleus (P &lt; 0.001 for both). Total tau and glial pathology also correlated with increased mean diffusivity in the midbrain (P = 0.02 and P &lt; 0.001, respectively). Finally, increased subcortical white matter mean diffusivity was associated with total tau in superior frontal and precentral cortices (each, P = 0.02). Overall, results showed clear relationships between antemortem MRI changes and pathology in four-repeat tauopathies. Our findings show that brain volume could be a useful surrogate marker of tau pathology in subcortical and brainstem regions, whereas white matter integrity could be a useful marker of tau pathology in cortical regions. Our findings also suggested an important role of glial tau lesions in the pathogenesis of neurodegeneration in four-repeat tauopathies. Thus, development of tau PET tracers selectively binding to glial tau lesions could potentially uncover mechanisms of disease progression.

AbstractBackgroundSummarizing the multiplicity and heterogeneity of cerebrovascular disease (CVD) features into a single measure has been difficult in both pathology and imaging studies. The objective of this work was to evaluate the association between neuroimaging surrogates of CVD and two available neuropathologic CVD scales in those with both antemortem imaging CVD measures and post‐mortem CVD evaluation. We used (1) Strozyk scale (Strozyk et. al. NBA 2010) which is based on presence and number of three macroscopic lesions (large infarctions, lacunar infarctions, and leukoencephalopathy) and (2) Kalaria scale (Deramecourt et. al. Neurology 2012) which summarizes detailed evaluation of vessel wall modifications (arteriolosclerosis, amyloid angiopathy), perivascular spaces, myelin loss, microinfarctions, and large infarctions.MethodWe identified n=51 individuals in Mayo Clinic Study of Aging (mean age of 83.8 years, 37% female) with MRI scans within 5 years of death. We computed CVD measures from DTI (fractional anisotropy of the genu of the corpus callosum [FAGCC] and fractional anisotropy of cingulum adjoining the hippocampus [FACGH]), FLAIR (white matter hyperintensities [WMH] and total number of infarctions), and T2* GRE (total number of microbleeds). We also computed our recently developed imaging CVD composite score which summarizes the variability in all individual CVD imaging measures described above. We computed the Strozyk and Kalaria scales on postmortem tissue. We used weighted linear regression models with adjustments for MRI scan time to death to evaluate the associations between the antemortem neuroimaging measures and pathology scales in those who had scans within 3 years of death (N=31) and 5 years of death (N=51) (tables 1 and 2).ResultOf all the neuroimaging measures, DTI measures were the best predictors of the Strozyk and Kalaria Scales (p<0.05). In sensitivity analyses of data in Table 1, FAGCC explained 44% and FACGH predicted only 5.5% variability in white matter sub‐component of the Strozyk scale. The CVD composite score was a poor surrogate of the composite pathological scales.ConclusionMicrostructural white matter injury measurement using DTI may be a good surrogate of pathological CVD scales and possibly captures diffuse (and early) changes to white matter and secondary neurodegeneration due to lesions.

AbstractBackgroundElevated systolic blood pressure (SBP) and diastolic blood pressure (DBP) are risk factors for white matter (WM) injury associated with cognitive decline and dementia. White matter hyperintensities (WMH) on MRI, markers of WM injury, increase with aging and are more common in women than in men after the age of 60. We examined SBP and DBP in relation to WMH volume and diffusion MRI measures of WM integrity in postmenopausal women.MethodWomen (n=144) with median age of 67 (range 58‐72) and of good cardiovascular health who were previously enrolled in the multi‐site randomized clinical trial, Kronos Early Estrogen Prevention Study (KEEPS), participated in the present observational KEEPS Continuation Study. SBP, DBP, and MRI data were collected approximately 9 years after the end of KEEPS menopausal hormones versus placebo 4‐year interventions. WM integrity was assessed with WMH volume measurements (log transformed) on structural MRI and with measures of WM microstructure on diffusion MRI. Multiple linear regression analyses, adjusted for age, were used to test for associations of SBP and DBP with MRI measures.ResultThe median SBP was 128 (range 86‐180) and DBP was 77 (range 47‐106). Higher SBP (r=0.332;p=0.015) and DBP (r=0.346;p=0.007) were associated with higher WMH volumes. Among the WM tracts, higher SBP was associated with lower fractional anisotropy (FA) in lateral orbitofrontal gyrus WM (r=0.198;p=0.039) and sagittal stratum (r=0.226;p=0.043), and higher DBP was associated with lower FA in the superior occipital gyrus WM (r=0.370;p=0.045). Higher SBP and DBP were associated with higher mean diffusivity (MD) in the external capsule [(r=0.311;p=0.007);(r=375;p<0.001)] and the fronto‐occipital fasciculus [(r=0.286;p=0.016); (r=0.303;p=0.008)]. Furthermore, higher DBP was associated with higher MD in the anterior corona radiata (r=0.233;p=0.039), internal capsule (r=0.303;p=0.042), and the middle (r=0.275; p=0.03) and superior frontal gyrus (r=0.269;p=0.03) WM.ConclusionElevated SBP and DBP measurements are associated with greater WM injury in postmenopausal women. In particular, the frontal lobe WM and its connecting tracts may be more vulnerable to elevation in SBP and DBP. Whether BP control reduces the risk for WM injury, a risk factor for dementia, in post‐menopausal women warrants study.

AbstractBackgroundWhite matter microstructure undergoes progressive changes during the life‐span but the neurobiological underpinnings related to aging and disease remains unclear. We used an advanced diffusion MRI, Neurite Orientation Dispersion and Density Imaging (NODDI), to investigate the microstructural alterations due to demographics, common age‐related pathological processes (amyloid, tau, and white matter hyperintensities (WMH)), and cognition. We also compared NODDI findings to the older Diffusion Tensor Imaging (DTI) model based findings.Method328 participants (264 cognitively unimpaired, 57 mild cognitive impairment (MCI), and 7 dementia with a mean age of 68.3 ±13.1 years) from the Mayo Clinic Study of Aging with multi‐shell diffusion imaging, FLAIR‐MRI as well as amyloid and tau PET scans were included in this study. White matter (WM) tract level diffusion measures (Figure 1) were calculated from DTI and NODDI. Pearson correlation and multiple linear regression analyses were performed with diffusion measures as the outcome and age, sex, education/occupation, WMH, amyloid, and tau as predictors. Analyses were also performed with each dMRI measure as a predictor of cognitive outcomes.ResultAge and WMH were the strongest predictors of all WM diffusion measures with low associations with amyloid and tau (Figure 1). However, neurite density decrease from NODDI was observed with amyloidosis specifically in the temporal lobes (Figure 2). WM integrity (mean diffusivity and free water) in the corpus callosum showed the greatest associations with cognitive measures (Table 1). All diffusion measures provided information about WM aging and WM disease processes and were associated with cognition.ConclusionNODDI and DTI are two different biophysical models that provide distinct information about variation in WM microstructural integrity. NODDI provides additional information about synaptic density, organization, and free water content which may aid in providing mechanistic insights into disease progression.

Abstract Introduction The aim of this study was to examine white matter hyperintensities (WMH) and fractional anisotropy (FA) in empirically derived incident mild cognitive impairment (MCI) subtypes. Methods We evaluated 188 participants with incident MCI in the Mayo Clinic Study of Aging (MCSA) identified as having one of four cluster‐derived subtypes: subtle cognitive impairment, amnestic, dysnomic, and dysexecutive. We used linear regression models to evaluate whole brain and regional WMH volumes. We examined fractional anisotropy (FA) on a subset of 63 participants with diffusion tensor imaging. Results Amnestic and dysexecutive subtypes had higher WMH volumes in differing patterns than cognitively unimpaired; the dysexecutive subtype had higher WMH than subtle cognitive impairment. There was widespread WM degeneration in long association and commissural fibers in the amnestic, dysnomic, and dysexecutive subtypes, and corpus callosum FA accounted for significant variability in global cognition. Discussion White matter changes likely contribute to cognitive symptoms in incident MCI.