Citations

Abstract White matter microstructure undergoes progressive changes during the lifespan, but the neurobiological underpinnings related to ageing and disease remains unclear. We used an advanced diffusion MRI, Neurite Orientation Dispersion and Density Imaging, to investigate the microstructural alterations due to demographics, common age-related pathological processes (amyloid, tau and white matter hyperintensities) and cognition. We also compared Neurite Orientation Dispersion and Density Imaging findings to the older Diffusion Tensor Imaging model-based findings. Three hundred and twenty-eight participants (264 cognitively unimpaired, 57 mild cognitive impairment 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, fluid attenuated inversion recovery MRI as well as amyloid and tau PET scans were included in this study. White matter tract level diffusion measures were calculated from Diffusion Tensor Imaging and Neurite Orientation Dispersion and Density Imaging. Pearson correlation and multiple linear regression analyses were performed with diffusion measures as the outcome and age, sex, education/occupation, white matter hyperintensities, amyloid and tau as predictors. Analyses were also performed with each diffusion MRI measure as a predictor of cognitive outcomes. Age and white matter hyperintensities were the strongest predictors of all white matter diffusion measures with low associations with amyloid and tau. However, neurite density decrease from Neurite Orientation Dispersion and Density Imaging was observed with amyloidosis specifically in the temporal lobes. White matter integrity (mean diffusivity and free water) in the corpus callosum showed the greatest associations with cognitive measures. All diffusion measures provided information about white matter ageing and white matter changes due to age-related pathological processes and were associated with cognition. Neurite orientation dispersion and density imaging and diffusion tensor imaging are two different diffusion models that provide distinct information about variation in white matter microstructural integrity. Neurite Orientation Dispersion and Density Imaging provides additional information about synaptic density, organization and free water content which may aid in providing mechanistic insights into disease progression.

AbstractBackgroundAlthough semantic dementia (SMD) presents with language decline, patients often develop behavioral changes. Behavioral symptoms in SMD are attributed to both cortical (orbitofrontal cortex atrophy) and subcortical (nucleus accumbens) degeneration in imaging and neuropathological studies. The caudate nucleus has been implicated in early behavioral changes in some phenotypes of frontotemporal dementia. The objective of this study was to determine whether asymmetric ipsilateral caudate nucleus hypometabolism on [18F]‐fluorodeoxyglucose positron emission tomography (FDG‐PET) is associated with early behavioral changes in the absence of frontal hypometabolism in SMD.MethodFifteen patients with a diagnosis of SMD evaluated by the Neurodegenerative Research Group (NRG) at the Mayo Clinic, Rochester from 2006‐2016, who had FDG‐PET, were included in this study. Clinical data was extracted from the medical records including age at presentation, sex, behavioral symptoms and duration of symptoms. Automated average Z‐scores generated from CortexID (GE Healthcare, WI, USA) using 3D stereotactic surface projections were obtained for the following regions bilaterally: temporal lobes, frontal lobes and caudate nucleus. Patients were categorized as left or right hemisphere lateralized based on the higher temporal lobe Z‐scores. Caudate nucleus and frontal lobe Z‐scores were then compared between the two sides for each patient. Hypometabolism was considered present if Z‐scores were greater than 2.ResultOf our cohort, 9/15 (60%) were females with a median age of 68 at presentation and had 3.5 years symptom duration. 13/15 had at least 1 behavioral symptom at presentation. Eleven patients were considered to have left‐lateralized whereas 4 were right‐lateralized in their temporal lobe hypometabolism. Median Z‐scores were significantly different for ipsilateral and contralateral caudate nuclei at 2.5 and 1.6, respectively (p=0.02, Wilcoxon nonparametric test). Frontal lobe median Z‐scores were 1.2 (ipsilateral) and 0.59 (contralateral) with no statistical difference between two sides (p=0.13).ConclusionIn SMD patients who exhibit behavioral symptoms with no frontal hypometabolism on FDG‐PET, asymmetric caudate nuclei hypometabolism should be explored as a possible imaging marker. This implicates abnormal frontal‐striatal circuitry in the development of behavioral symptoms even in the absence of frontal hypometabolism in early behavioral changes in SMD.

AbstractBackgroundDisproportionately enlarged subarachnoid‐space hydrocephalus (DESH), characterized by tight high convexity CSF spaces, ventriculomegaly, and enlarged Sylvian fissures, is thought to be an indirect marker of a CSF dynamics disorder. Although this imaging phenotype was originally described in normal pressure hydrocephalus (NPH), it has since been more widely recognized in the aging population with a prevalence of 6‐7% in the Mayo Clinic Study of Aging (MCSA) 1. The clinical significance of DESH with regards to cognitive decline outside of NPH is not yet well‐defined. The goal of this work is to determine if DESH is associated with risk of cognitive impairment.MethodParticipants in the population‐based MCSA who met the following criteria were included: age ≥ 65 years, 3T MRI, cognitively unimpaired at enrollment, and at least one follow‐up visit. A support vector machine based method to detect the DESH imaging features on T1‐weighted MRI was used to calculate “computational DESH” or CDESH scores2 (Figure 1). Cox proportional hazard models were fit with events defined as progression from cognitively unimpaired to mild cognitive impairment or dementia. Time was defined as years from MCSA enrollment to the first visit with cognitive impairment with censoring at last follow‐up. CDESH was modelled as a spline to allow for non‐linear effects. Covariates included age, sex, education, APOE status, cortical thickness, white matter hyperintensity (WMH) volume, and total intracranial volume.ResultParticipant demographics are summarized in Table 1. The model including the above co‐variates showed a significant CDESH effect (p=0.02) with an approximately 20% greater hazard of progression for a CDESH score of +1 versus −1 (HR 1.2, 95% CI 1.0‐1.4) (Figure 2).ConclusionImaging features of disordered CSF dynamics are an independent predictor of subsequent cognitive decline in the MCSA, among other well‐known factors including age, cortical thickness, and APOE status. Therefore, since DESH contributes to cognitive decline and is present in the general population, identifying individuals with DESH features may be important clinically and also used as a selection stratification variable for clinical trials. References: (1) Graff‐Radford et al doi:10.1212/WNL.0000000000008616; (2) N. Gunter et al doi:10.1016/j.nicl.2018.11.015.

AbstractBackgroundRecent evidence has implicated brain networks in the pathophysiology of Alzheimer’s disease (AD). However, the phenotypic variability in AD, inter‐individual differences in tau PET uptake patterns, and dynamic nature of functional connectivity have been relatively neglected. In this talk, I will discuss our recent work linking measures of dynamic brain organization to spatially independent tau PET patterns defined in a phenotypically diverse cohort.MethodsWe identified all Mayo Clinic participants with concurrent amyloid PET (PiB), tau PET (AV1451) and task‐free fMRI (impaired participants were all Aβ+). We used independent component analysis (ICA) to identify spatially independent patterns of tau PET uptake. For the fMRI data, we used a Hidden‐Markov Model (HMM) design to identify distinct patterns of activity/connectivity (‘states’). The ICA and HMM parameters were used to project/segment an independent cohort (ADNI) and subsequent longitudinal timepoints for the Mayo cohort. Multilevel Bayesian regression models were fit in Stan to explore the association between phenotype, component load and brain state.ResultsThe optimal HMM model identified 10 brain states (Figure 1). The optimal ICA decomposition identified 42 tau PET components, of which 13 were potentially related to AD pathobiology (Figure 1). Component loads and brain state dwell time differed between diagnostic groups in both Mayo and ADNI cohorts. One state was much more likely to occur in AD in both cohorts (State 8). Cross‐sectionally, State 8 dwell time was associated with higher tau loads, while higher baseline dwell‐time was predictive of ∆tau in participants with at least 2 timepoints (Figure 2).ConclusionWe found links between dynamics of functional connectivity, tau deposition, and cognitive impairment across the AD spectrum using two different cohorts and longitudinal data. Specifically: 1) our method for characterizing tau PET uptake was able to capture the magnitude and spatial pattern of binding across diverse phenotypes; 2) Our data supports a role for dynamic brain connectivity in cognitive impairment due to Alzheimer’s disease; 3) Dynamic connectivity appears to be directly related to AD pathobiology as measured by tau PET. Incorporating the time‐varying nature of connectivity will be important in future studies of connectome‐based disease progression in AD.

AbstractBackgroundWhile β‐amyloid and tau PET imaging are widely studied in cognitive aging and Alzheimer’s disease (AD), little is known about findings in aging multiple sclerosis (MS) patients. As aging is an essential factor in developing neurodegeneration in MS, β‐amyloid and tau biomarkers may be utilized to understand the influence of AD pathophysiology on cognitive aging in MS.MethodWe studied patients with MS (n=16) and controls (n=80) matched for age, sex and APOE ε4 status from the population‐based Mayo Clinic Study of Aging who underwent Pittsburgh compound‐B (PiB) PET imaging. Twelve of the 16 patients with MS and 60 matching controls also underwent flortaucipir (AV1451) tau PET. Cortical PiB and AV1451 standard uptake value ratios (SUVr) from previously determined AD signature regions and from the entire cortex were calculated for group comparisons and testing for associations with age.ResultAD signature PiB SUVr (p=0.044), total cortical PiB SUVr (p=0.048) and the frequency of abnormal PiB SUVrs (p=0.040) were lower in patients with MS than controls. Although AD signature and total cortical AV1451 SUVrs were not different between the groups, the frequency of abnormal AV1451 SUVrs was higher (p=0.041) in patients with MS than controls. The association of AD signature PiB SUVr with age was steeper in the controls compared to patients with MS (p=0.002). Similarly, the association of total cortical PiB SUVr with age was steeper in the controls compared to patients with MS (p=0.002). There was no difference in the association of AV1451 SUVr findings with age between the MS patients and controls (Figure 1). The AD signature PiB SUVr increased more with increasing AD signature AV1451 SUVr in controls than in patients with MS (p=0.03) (Figure 2).ConclusionIn patients with MS, cortical β‐amyloid deposition was lower than age‐matched controls, while cortical tau deposition was not different between the groups. This suggests that some aspect of MS pathobiology retards the accumulation of β‐amyloid, but not the accumulation of tau.

AbstractBackgroundNeurodegeneration of the substantia nigra in dementia with Lewy bodies is associated with iron deposition, which increases the magnetic susceptibility of the substantia nigra on MRI. The objective of this study was to measure the magnetic susceptibility of the substantia nigra in patients with dementia with Lewy bodies (DLB) and patients with mild cognitive impairment with at least one core feature of DLB (MCI‐DLB) compared to cognitively unimpaired controls (CU) by quantitative susceptibility mapping (QSM).MethodWe studied clinically diagnosed patients with DLB (n=36), patients with MCI‐DLB (n=16), and age‐, gender‐matched CU (n=104) in a case‐control study. MR images used for QSM were performed on 3T MRI with a multi‐echo (5 echoes) 3D gradient recalled echo sequence with a bipolar‐readout. Three‐dimensional regions of interest (ROIs) of bilateral substantia nigra, based on the DISTAL atlas, were propagated from the Mayo Clinic Adult Lifespan Template (MCALT) to each subject’s T1 image using ANTs nonlinear registration. The mean susceptibility values of the substantia nigra were extracted by aligning each subjects QSM image to their T1 image and corresponding ROIs with a 6 DOF rigid registration.ResultThe mean susceptibility values of the substantia nigra bilaterally was 0.110 ± 0.026 for DLB, 0.099 ± 0.028 for MCI‐DLB and 0.091 ± 0.025 for controls (Figure 1). No difference was found across the mean susceptibility from left, right and combined bilateral substantia nigra. Compared to controls, patients with DLB had higher susceptibility in the substantia nigra (p<0.001). The susceptibility of substantia nigra showed an increasing trend from controls to MCI‐DLB and to DLB (p<0.001). There was no correlation between the Unified Parkinson's Disease Rating Scale (UPDRS) and the mean susceptibility in the substantia nigra in patients with MCI‐DLB and DLB (r=0.03; p=0.82).ConclusionQuantitative susceptibility mapping is sensitive to the increased magnetic susceptibility due to higher iron content in the substantia nigra in DLB. The trend of increasing susceptibility from CU to MCI‐DLB and to DLB suggests that iron deposition in the substantia nigra starts to increase as early as the prodromal stage in DLB and continues to increase as the disease progresses independent of the severity of Parkinsonism.

AbstractBackgroundData shared for research use must be de‐identified to protect participant confidentiality. A recent study has shown that face imagery from brain imaging studies could be used to re‐identify anonymous participants via automatic face recognition, linking them with all study‐collected protected health information. Here we measure how well existing software for removing MRI face imagery protects against this privacy threat.MethodWe recruited 153 participants with existing 3D FLAIR MRI and photographed their face from 5 varying angles. We used surf_ice to reconstruct each participant’s face from their MRI and tested whether free, publicly‐available face recognition software (Microsoft Azure) could match their photos to their correct MRI. We tested two existing programs for removing face voxels in MRI: mri_deface (FreeSurfer) and pydeface (python package), and one in‐house implementation. We observed that existing methods often left participants’ eyes intact while removing most of their nose/mouth/chin. This prevented Azure from detecting the reconstructions as faces, therefore preventing its attempt of face recognition. We hypothesized that the retained eye imagery alone could be sufficient for face recognition, if a face were detected. Therefore, we also reconstructed each de‐faced MRI after applying novel pre‐processing that replaced any removed/missing face voxels with those from our population‐average template, e.g. replacing missing noses/mouths/chins with population‐average noses/mouths/chins, before testing face recognition.ResultsWithout any MRI de‐facing, Microsoft Azure correctly matched 78% of participants’ photos to their MRI. After mri_deface and pydeface, 8% and 6% were still identified, respectively. After preprocessing the de‐faced MRI to replace missing areas with those from a population‐average template prior to face reconstruction, the face imagery remaining in the de‐faced images was enough to allow re‐identification of 23% and 20% of participants, respectively. With an in‐house face removal implementation that removes the whole face from every image, 0% of faces were re‐identified from the standard reconstructions, and 5% were identified from the “missing‐parts‐replacement” reconstructions.ConclusionsEven after applying popular MRI de‐facing software, Microsoft Azure face recognition automatically re‐identified up to 23% of participants. Better de‐facing software is needed to remove more of the face more reliably, without affecting brain measurements from de‐faced images.

AbstractBackgroundThe critical‐period theory of menopausal hormone therapy (mHT) suggests that if menopausal hormone therapy begins shortly after menopause, it may have beneficial effects on brain structure and cognitive health. We investigated white matter integrity with diffusion tensor MRI (DTI) 3 years after the end of mHT trial in recently menopausal women who were randomized shortly after menopause.MethodParticipants of this study were enrolled in the Kronos Early Estrogen Prevention Study (aged 42–56 years; 5–36 months past menopause). Participants received the following treatments: 0.45 mg/d oral conjugated equine estrogen (oCEE, n=19); 50 μg/d transdermal 17β‐estradiol (tE2, n=22); or placebo (n=33) for 4 years. Oral progesterone (200 mg/d) was given to hormone therapy groups for 12 days each month. DTI was performed 3 years after the end of 4 years of hormone therapy (n=75), which corresponded to 7 years after randomization. Voxel‐based analysis was performed on DTI to evaluate fractional anisotropy (FA) and mean diffusivity (MD) differences between treatment groups (uncorrected p<0.001).ResultVoxel based analysis showed higher FA, a measure of white matter integrity, in the superior corona radiata, anterior limb of the internal capsule and splenium of the corpus callosum of participants treated with oCEE compared to placebo (p <0.001, Figure 1A). Furthermore, higher FA was observed in the superior corona radiata, anterior limb of internal capsule and thalamus in participants treated with tE2 compared to placebo (p <0.001, Figure 1B). There were no differences in FA findings between oCEE and tE2 groups, and no MD differences between treatments groups.ConclusionA relatively higher FA in a variety of white matter regions in the oCEE and tE2 groups compared to placebo suggest that mHT may have long‐term protective effects on white matter integrity. Previously, we had shown that women in the tE2 group preserved their dorsolateral prefrontal cortex volumes better than the placebo group over 7 years. Our findings suggest that the tracts that connect the dorsolateral prefrontal cortex and the thalamus such as the superior corona radiata and the anterior limb of internal capsule may particularly benefit from mHT.

AbstractBackgroundDementia with Lewy bodies (DLB) is neuropathologically defined by the presence of α‐synuclein aggregations, but many DLB cases show significant Alzheimer’s disease (AD) pathology in the form of β‐amyloid and tau. Our first objective was to investigate the effect of AD co‐pathology on functional network failure in DLB. Secondly, we studied how the distribution of tau relates to functional connectivity in DLB.MethodTwenty‐three DLB, 23 age‐ and sex‐matched AD, and 90 cognitively unimpaired (CU) participants underwent AV1451 tau‐PET, Pittsburgh compound‐B (PiB) PET, and resting state (rs)‐fMRI scans. The rs‐fMRI data were used to calculate the Network Failure Quotient (NFQ), a marker of functional network failure within the default mode network. The NFQ was then correlated with overall cortical tau standardized uptake value ratio (SUVr). The strength of inter‐regional functional connectivity was assessed using the Schaefer100 atlas. Tau‐PET covariance was measured as the correlation in AV1451 SUVr between any two regions across participants. The association between region‐to‐region functional connectivity and tau‐PET covariance was assessed using linear regression. Additionally, we identified the region with highest and the region with lowest tau SUVrs (tau hot‐ and coldspot) and tested whether tau SUVr in all other brain regions was associated with the strength of functional connectivity to these tau hot‐ and coldspots.ResultOverall higher cortical tau‐PET uptake correlated with higher NFQ (i.e. greater functional network failure) in DLB who were PiB+ (N=14, ρ=0.60, p=0.02), but not in DLB who were PiB‐ (N=9, ρ=‐0.06, p=0.87). Higher functional connectivity between regions was associated with higher tau covariance in CU, AD, and DLB, independent of PiB status. Higher functional connectivity of a target region to the tau hotspot (i.e. inferior/medial temporal cortex) was related to higher AV1451 SUVRs in the target region whereas higher functional connectivity to the tau coldspot (i.e. sensory‐motor cortex) was related to lower AV1451 SUVr in the target region.ConclusionGreater tau burden in DLB patients with β‐amyloid co‐pathology is associated with an AD‐like functional network failure. As previously demonstrated within the AD spectrum, functional connectivity may contribute to the spreading of tau across the brain also in DLB.