Citations

AbstractBackgroundNeurite orientation dispersion and density imaging (NODDI) measures from diffusion MRI provide information about tissue microstructure. Our goal was to compare the sensitivity of NODDI to structural MRI (sMRI) in capturing neurodegenerative changes in the gray matter due to aging and dementia.MethodWe included 815 participants [644 cognitively unimpaired (CU), 103 MCI, 68 AD] from the Mayo Clinic Study of Aging (MCSA) and Mayo ADRC with sMRI and multi‐shell diffusion MRI. We assessed gray matter volumes scaled by head size and cortical thicknesses from sMRI and neurite density index (NDI) and isotropic volume fraction (ISOVF) from NODDI. Pairwise Spearman correlations were calculated between imaging measures and Tau‐PET SUVRs in Braak I‐II, III‐IV, and V‐VI regions of interest (ROI) in participants ≥ 60 years. Separate multivariable linear regression models were fit for the prediction of brain age using sMRI and NODDI in 515 CU participants. The predicted age gaps were calculated as chronological age minus predicted age in the remaining participants. We compared age gaps computed by sMRI and NODDI using disease surrogates (APOE4 genotype, AD pathologies via PET, WMH via FLAIR‐MRI, vascular risk, cognition via MMSE).ResultBraak ROIs, sMRI, and NODDI measures were correlated. The correlations between ISOVF and Tau‐PET SUVRs were stronger than correlations between NDI, volume, and thickness and Tau‐PET SUVR (Fig.1). MMSE was more associated with ISOVF than Tau‐PET and other imaging measures. The sMRI regression models had a higher mean absolute error (MAE) of 4.13 years than NODDI models with an MAE of 4.02 years. The NODDI model better predicted age gap separation between CU, MCI, and AD compared to the sMRI model (Fig.2). The NODDI predicted age gap showed stronger correlations with global amyloid‐PET SUVR, meta‐ROI Tau‐PET SUVR, CMC, APOE4, and MMSE (Fig. 3).ConclusionNODDI, especially ISOVF, had stronger correlations with MMSE and Tau‐PET SUVRs across all Braak ROIs. The brain age prediction model trained using NODDI was more sensitive in detecting neurodegeneration changes than sMRI based on associations with disease surrogates. Our work suggests that NODDI in gray matter may be a more sensitive surrogate of neurodegeneration compared to sMRI.

AbstractBackgroundAutomated face recognition can potentially re‐identify de‐identified research brain MRI, PET, and CT, and direct differences of brain measurements from original and de‐faced images are statistically significant but very minor, i.e. below scan‐rescan differences. Their effects on analyses correlating brain imaging biomarkers with clinical variables remain unknown.MethodWe sampled 3 age‐and‐sex‐matched groups (CU, MCI, and clinical AD) from Mayo Clinic aging studies, each with 61 participants with same‐visit 3D T1‐weighted and 3D T2‐FLAIR MRI, PiB amyloid PET, and Flortaucipir tau PET (total n = 183). We automatically measured hippocampal volume, PIB global SUVR, and tau temporal meta‐ROI SUVR using a) our in‐house pipeline with SPM12, MCALT, and ANTs, and b) FreeSurfer 7.3.2 (PETSurfer), and we measured voxels associated with clinical group separation using SPM12. We also measured WMH from FLAIR using a) our in‐house pipeline, b) Lesion Segmentation Tool (LST), and c) FreeSurfer’s Samseg. For each measurement, we calculated pair‐wise group separation AUC values and Spearman’s rho with age and CDR‐SOB. We then de‐faced all PET and MRI using mri_reface, our top‐performing automated de‐facing software that replaces imaged faces with an average face (“re‐facing”), and we repeated all measurements and analyses with de‐faced images. We then compared the magnitudes of clinical correlations with original images and those of de‐faced images.ResultAnalyses of original and de‐faced images had very high agreement. Only 2/55 paired comparisons were significant (Tables 1‐2): FreeSurfer‐measured global PIB SUVR from de‐faced images was more correlated with age (rho 0.003 vs ‐0.227, p = 0.046), and Mayo‐measured WMH volume from original images was more correlated with CDR‐SOB (rho 0.321 vs 0.308, p = 0.020). Another comparison‐pair tested p = 0.041 for an AUC change +0.008, which we consider not practically meaningful. All others had p>0.05. In voxel‐based analyses of CU vs AD participants, all comparisons were qualitatively similar between original and de‐faced variants (Figure 1).ConclusionDe‐facing PET and MRI using mri_reface did not significantly affect 53/55 correlations between imaging and clinical variables; of the two affected, one became stronger with re‐faced images. Stronger correlations with clinical variables may be explained by de‐facing’s standardizing face voxels and artifacts that irrelevantly affect brain segmentations.

AbstractBackgroundDiffusion‐weighted MRI (dMRI) examines tissue microstructure integrity and white matter pathways in vivo. Prior dementia with Lewy bodies (DLB) studies focused on white matter or voxel‐based techniques. Less is known about longitudinal white and grey matter microstructural changes in DLB. We aimed to characterize longitudinal diffusion microstructural changes in individuals with DLB.MethoddMRI scans were collected on individuals with DLB from the Mayo Clinic Longitudinal Imaging Biomarkers of DLB Program. Demographics, clinical evaluations and MRI were collected at baseline, and 12‐ or 24‐months. Free water (FW) and FW‐corrected fractional anisotropy (FAT) were analyzed in grey and white matter using 122 bilateral template regions and tracts of interest from the Mayo Clinic Adult Lifespan Template. Primary outcomes were differences in FW and FAT between baseline and 12‐ or 24‐months.ResultWe identified 23 individuals with DLB or mild cognitive impairment with Lewy bodies (MCI‐LB) from baseline to 12‐months [mean age 69.3 years (SD 9.5); 95.8% male], and 16 individuals from baseline to 24‐months [mean age 67.5 years (SD 9.3); 100% male]. Participants at both time‐points showed worsening in Montreal Cognitive Assessment and Movement Disorders Society Unified Parkinson’s Disease Rating Scale motor scores (p<0.05). We found significant differences in FW, but not FAT, from baseline to 12‐months. Significant increases in FW at both time‐points compared to baseline were observed in the insula, putamen, parahippocampal, and rolandic operculum regions (pfdr<0.05). We found additional more widespread microstructural changes from baseline to 24‐months, with increased FW in the amygdala, entorhinal, mid and posterior cingulum, inferior frontal, hippocampal, pallidum, precuneus and retrosplenial regions (pfdr<0.05). We found decreased FAT from baseline to 24‐months in the cerebellar and superior occipital regions (pfdr<0.01).ConclusionLonger follow‐up at 24‐months identified broader networks of free water changes in individuals with DLB. Results support dMRI as a promising, non‐invasive tool to track disease progression in DLB. More investigation is needed to evaluate the relationship of microstructural changes with clinical progression in DLB.

AbstractBackgroundPresymptomatic frontotemporal lobar degeneration (FTLD) mutation carriers as young as their thirties display gray matter (GM) deficits and alterations in the specific neural networks targeted during the symptomatic phase (Lee et al. NeuroImage Clin 2017; Bertrand et al. JAMA Neurol 2018). These studies raise the question of a potential neurodevelopmental role of pathogenic mutations in FTLD, yet sufficient research in young adults is lacking.MethodLeveraging 3T MRI brain scans from UCSF and the ALLFTD Consortia, we studied structural and functional connectivity alterations in 31 presymptomatic mutation carriers [PreSxAll; 12 C9orf72,13 MAPT, 6 GRN, mean age: 25.2±3.9, range 18‐30 years] and 28 demographically‐matched healthy controls [HC; mean age: 26.5±3.1, range 18‐30 years]. We performed voxel‐based morphometry (VBM) to compare GM volume between HC and PreSxAll, and HC and each mutation carrier subgroup using ANCOVA (SPM12). VBM statistical results were thresholded at a height of p<0.001 uncorrected or p<0.05 familywise error corrected. We used seed‐based task‐free functional MRI (tf‐fMRI) analysis to examine salience network (SN) and default mode network (DMN) connectivity. Single‐subject connectivity maps were analyzed using ANCOVA in SPM for group comparisons and masked to the relevant network. Tf‐fMRI results were thresholded at a height of p<0.05 and extent of p<0.05. For all analyses, nuisance covariates included age, sex, education, handedness, scanner and TIV (GM only).ResultWe found no significant GM differences between groups. PreSxAll showed regions of SN hyperconnectivity compared with HC, specifically in the bilateral insula, right superior frontal gyrus, bilateral midcingulate cortex, and the left cerebellum. Compared to controls, each mutation carrier subgroup featured SN hyperconnectivity within key network hubs, such as the insula and anterior cingulate cortex. The DMN showed no significant alterations for PreSxAll vs. HC. Scattered small clusters of DMN alterations were noted in subcortical regions and the cerebellum in presymptomatic C9orf72 and GRN.ConclusionDespite the absence of GM deficits in young adult FTLD mutation carriers, tf‐fMRI detected SN alterations in these carriers compared with healthy controls. Future studies focused on other neuroimaging measures will further explore differences in young adult FTLD mutation carriers.

AbstractBackgroundA hallmark of Alzheimer’s disease is amyloid‐β (Aβ) deposition in the brain parenchyma as senile plaques which Aß frequently accompanies in the cerebral vasculature as cerebral amyloid angiopathy (CAA). Previous studies demonstrated that the cerebrovascular system is essential for Aβ clearance and interactions exist between cerebrovascular disease and AD. Neuroimaging data, including measures on small vessel disease, amyloid deposits, and microhemorrhages (MCH), can reflect both cerebrovascular disease and AD pathology, thereby serving as intermediate phenotypes of both conditions. We aim to identify genes and co‐expression networks in blood associated with these neuroimaging endophenotypes and the biological pathways enriched in these perturbed transcriptional networks.MethodWe conducted differential gene expression and Weighted Gene Co‐Expression Network analyses (WGCNA) on blood RNA sequencing data of 407 participants from the longitudinally followed Mayo Clinic Study of Aging cohort. The neuroimaging phenotypes include MCH, infarction, and amyloid deposits measured by Pittsburgh compound B positron emission tomography (PiB PET).ResultWGCNA analysis showed a nominally significant association (p<0.05) between several module eigengenes (MEs) and MCH and PiB PET. One module, M42, remained significantly associated with PiB PET after Bonferroni correction for all 43 modules tested. Pathway enrichment analysis of M42 identified two significant pathways (FDR‐adjusted p<0.05) including phosphodiesterase I activity and IgE binding. These pathways contain genes such as MS4A2 encoding the high‐affinity IgE receptor involved in the immune response. Similarly, ENPP2 in the phosphodiesterase I activity pathway represents an additional candidate gene previously implicated in Alzheimer’s disease.ConclusionWe generated co‐expression networks and identified one network module with a statistically significant association between brain amyloid deposits and blood transcriptome data. Further investigations of this network and its enriched pathways are necessary to understand their roles in amyloidosis, CAA, and neurodegeneration. Future studies will include seeking replication of these results in other datasets. Such studies can help elucidate the biological mechanisms of pathogenesis of cerebrovascular damage and amyloid deposits. In addition, in a clinical setting, blood levels of transcripts discovered in this study may serve as predictive or diagnostic biomarkers with mechanistic implications.

AbstractBackgroundPresymptomatic C9orf72 expansion carriers show regions of low local gyrification index (lGI) as early as their thirties (Caverzasi et al. JNNP 2019) and regions of gyrification decline have been reported (van Veenhuijzen et al. Ann Neurol 2022). These findings raise the question as to whether abnormal gyrification represents a neurodevelopmental difference or early neurodegeneration in frontotemporal lobar degeneration (FTLD) mutation carriers. Yet, previous studies focusing on young adults is lacking and no study has explored gyrification in the two other common FTLD mutations, GRN and MAPT. Here, we analyzed lGI in young adult presymptomatic FTLD mutation carriers.MethodLGI quantifies buried cortex within the sulcal folds compared to the visible cortex on the outer surface, with larger values indicating more folding (Schaer et al. IEEE trans med imaging 2008). Mean lGI values, as measured by a parcel‐wise analysis implemented in FreeSurfer v.6.0, were compared between 31 presymptomatic (preSx) carriers (12 C9orf72, 13 MAPT, 6 GRN, mean age: 25.2 ± 3.91 years) and 28 demographically‐matched healthy controls (HC: mean age: 25.6 ± 3.8 years). We used ANCOVA to compare all presymptomatic carriers combined (preSxAll) and HC to identify regions with significant lGI differences regressing age, sex, education, handedness, total intracranial volume and scanner type as nuisance covariates. Significant group differences were analyzed by Dunnett’s post‐hoc tests between each mutation subgroup and HC (p<0.05).ResultCompared to HC, preSxAll had higher mean gyrification in the right caudal (F = 5.07, p = 0.03) and rostral anterior cingulate (F = 6.22, p = 0.17), and lower lGI in the left middle temporal gyrus (F = 4.22, p = 0.046). Within these regions, post‐hoc analyses showed that preSx GRN had greater gyrification in the right caudal anterior cingulate (t = 2.57, p = 0.039, 95% CI = [0.005, 0.248]) and preSx C9orf72 had lower lGI in the left middle temporal gyrus (t = ‐3.14, p = 0.009, 95% CI = [‐0.37, ‐0.04]) vs. HC.ConclusionYoung adult FTLD mutation carriers displayed higher gyrification in the right anterior cingulate and lower gyrification in the left middle temporal gyrus compared to HC. Future studies in children will confirm the extent to which these gyrification abnormalities represent neurodevelopmental differences.

AbstractBackgroundCo‐morbid Alzheimer’s disease (AD) pathology in the form of amyloid‐β plaques and tau neurofibrillary tangles are frequently observed in people with probable dementia with Lewy bodies (DLB). Neurite Orientation Dispersion and Density Imaging (NODDI) is an emerging technique to characterize white matter (WM) tissue microstructure, although NODDI alterations and associations with AD biomarkers remain undefined in DLB. The objectives of this study are to compare NODDI and DTI parameters between DLB and controls and (ii) evaluate the pattern of DTI and NODDI changes associated with amyloid and tau deposition.MethodConsecutive patients within the DLB spectrum (n = 45), including probable DLB (n = 32) and probable Mild Cognitive Impairment with Lewy bodies (MCI‐LB) (n = 13) from the Mayo Clinic ADRC underwent multi‐shell diffusion MRI, [18F]‐Flortaucipir and [11C]‐PiB PET. A comparison group of 45 age‐ and gender‐matched clinically unimpaired controls (CU) was included. Voxelwise non‐parametric permutation models were used to determine pair‐wise group differences and assess the associations of AD biomarkers with fractional anisotropy (FA), mean diffusivity (MD), tissue‐weighted neurite density index (tNDI; NDI x (1 – Free water fraction)) and orientation dispersion index (ODI), controlling for age. Complementary analyses were performed using regions‐of‐interest (ROI) obtained from the JHU atlas.ResultCompared to CU, the DLB spectrum group exhibited widespread decreases in FA and tNDI and increases in MD and ODI, with the most pronounced effects in the cingulum bundles, uncinate, inferior and superior longitudinal fasciculi (Fig. 1). Greater [18F]‐Flortaucipir uptake was also associated with lower FA and tNDI, and higher MD and ODI in the DLB spectrum compared to CU, independently of age and [11C]‐PiB uptake (Fig. 2). In contrast, no associations were found between [11C]‐PiB uptake and any of the DTI or NODDI parameters. Complementary ROI analyses corroborated these findings.ConclusionWidespread loss of WM integrity was observed on DTI and NODDI in probable DLB and prodromal DLB. These findings were associated with greater cortical tau, but not with amyloid‐β deposition. Although alpha‐synuclein pathology underlies the loss of WM integrity in DLB, amyloid‐independent tau pathology may also be an important influence on white matter integrity within the DLB spectrum.

AbstractBackgroundWe aimed to assess axonal integrity in MAPT mutation carriers and early white matter (WM) neurodegeneration in asymptomatic carriers using traditional diffusion tensor imaging (DTI) measurements, i.e., fractional anisotropy (FA) and mean diffusivity (MD), and neurite orientation dispersion and density imaging (NODDI).MethodWe included available multi‐shell diffusion MRI data from 21 MAPT mutation carriers (16 asymptomatic; 5 symptomatic) and 31 non‐carrier family members as controls from the ALLFTD consortium. We used linear mixed‐effect models to assess WM abnormalities measured with FA and MD, neurite density index (NDI) and orientation dispersion index (ODI), combined across hemispheres. Separate models were conducted in all MAPT mutation carriers relative to controls while accounting for relatedness and age, then in symptomatic and asymptomatic carriers relative to controls. ComBat was used to harmonize data across sites.ResultIn models contrasting all MAPT carriers versus controls, WM alterations involved the cingulum bundle (FA, ODI), fronto‐temporal, entorhinal (FA, MD, NDI), and the inferior temporal WM (FA) after correcting for multiple comparisons (Fig. 1). In models contrasting symptomatic and asymptomatic MAPT carriers versus controls, symptomatic carriers had widespread involvement of the fronto‐temporal WM including the cingulum (FA, ODI), entorhinal (MD, FA, NDI), amygdala (MD, NDI) and inferior temporal (FA) areas after correcting for multiple comparisons. Asymptomatic carriers showed higher entorhinal MD and lower inferior temporal and cingulum FA relative to controls, and a more widespread pattern of higher ODI involved the medial temporal, superior temporal and occipital WM as well as long association and projection fibers including the internal capsule, cingulate bundle and fronto‐occipital fasciculi, although they were not statistically significant after correction for multiple comparisons.ConclusionTraditional DTI and novel NODDI measurements revealed some extent of overlap as well as divergences in patterns of WM abnormalities, indicating axonal loss is a feature of neurodegeneration in MAPT mutation carriers. Our results suggest that traditional DTI and novel NODDI measurements may have different sensitivities to WM alterations in MAPT mutation carriers, and that axon‐based metrics, particularly ODI measurements, may be more sensitive to the early WM involvement during the asymptomatic stage of the disease.

AbstractBackgroundGenetic mutations causative of familial fronto‐temporal lobar degeneration (f‐FTLD) are highly predictive of a specific proteinopathy, but there exists substantial inter‐individual variability in their system‐level pathophysiology, which can be assessed with 18Fluorodeoxyglucose‐positron emission tomography (FDG‐PET). Determining data‐driven patterns of network degeneration suggestive of underlying specific FTLD‐related genetic mutations is paramount to guide complex clinical decision making supported by the quantification of FDG‐PET.MethodWe collected clinical and FDG‐PET data from 39 patients with f‐FTLD, including 11 carrying the C9orf72 hexanucleotide expansion (1 asymptomatic, 9 with a predominant behavioral phenotype, 1 with a predominant language phenotype), 12 carrying a GRN mutation (1 asymptomatic, 7 behavioral, 4 language), and 16 carrying a MAPT mutation (6 asymptomatic, 10 behavioral). We performed a spectral covariance decomposition analysis between FDG‐PET images to yield latent patterns reflective of a distribution of metabolism opposing poles of relative hyper‐ and hypo‐metabolism across the entire brain (“eigenbrains” or EBs). We then conducted linear discriminant analyses (LDAs) to perform EB‐based multiclass predictions of genetic mutation and clinical phenotype.ResultThe spectral decomposition analysis yielded five eigenbrains, which explained 58.79% of the covariance between FDG‐PET images (Fig. 1). Gradients indicative of relative hypometabolism in left fronto‐temporo‐parietal areas distinguished GRN mutation carriers from other genetic mutations (EB1, EB2, EB4) and were associated with clinical phenotypes predominantly involving language (EB1, EB2, EB3). Conversely, those indicative of relative hypometabolism in prefrontal areas (EB3) and temporopolar areas (EB2) with a right hemispheric predominance were mostly associated with clinical phenotypes predominantly involving behavior/personality, and EB2 distinguished MAPT mutation carriers from other genetic mutations. The LDAs yielded accuracies of 80% and 75% in predicting genetic status and predominant clinical phenotype, respectively (Fig. 2).ConclusionFive EBs explained a high proportion of covariance in patterns of network degeneration across FTLD‐related genetic mutations. These EBs contained information relevant to the system‐level physiology associated with FTLD‐related pathogenic mechanisms and that allowed the prediction of clinical phenotype with relatively high accuracy. This supports the utility of FDG‐PET to guide the development of tools supporting clinical decision making and ongoing efforts to develop network‐based biomarkers to track disease progression and risk of phenoconversion in f‐FTLD.

AbstractBackgroundQuantitative rapid eye movement (REM) sleep without atonia (RSWA) recorded during polysomnography (PSG) represents loss of normal REM sleep atonia. RSWA is the neurophysiologic signature of REM sleep behavior disorder (RBD). Visually scored RSWA is a candidate biomarker for synucleinopathies. However, visual RSWA scoring requires expert scoring, limiting widespread application. We comparatively analyzed RSWA in DLB and iRBD using the automated Ferri REM Atonia Index (RAI).MethodWe quantitatively analyzed the RAI in 12 DLB and 20 iRBD patients, compared to 20 controls using Hypnolab Software for Sleep Analysis (SWS Soft, Inc, Colognola ai Colli, Italy). PSG data were converted to European Data Format (.EDF), and the submentalis surface electromyogram (EMG) signal was notch‐filtered at 60 Hertz and rectified. Automatically calculated RAI scores were comparatively analyzed across groups (DLB, iRBD, controls) using Kruskal Wallis H tests for group comparisons, and regression analyses determined relationships between diagnosis, age, and sex. Logistic regression ROC determined optimal RAI for distinguishing iRBD from DLB. When interpreting RAI, lower values represent greater amounts of RSWA, and higher values represent more normal REM atonia.ResultMean age was older in DLB than in iRBD or controls (72.6 vs. 68.7 vs. 66.5 years). All 12 DLB participants were men, the iRBD group had 18 men and 2 women, and controls were comprised of 16 men and 4 women. Mean RAI was lowest in DLB (0.46), intermediate in iRBD (0.71) and highest in controls (0.96), indicating a gradient in RSWA amounts from DLB patients (highest) to controls (lowest) (Figure). Regression demonstrated diagnosis independently predicted RAI, controlling for age and sex (adjusted R2 = 0.49, p = 0.0001). The RAI that optimally distinguished iRBD from DLB was 0.46 (specificity 85%, sensitivity 58%, AUC = 0.75).ConclusionThe RAI demonstrated a clear gradient throughout the Lewy body spectrum; highest (indicating lowest RSWA amounts) in controls, intermediate in iRBD, and lowest (indicating greatest RSWA) in DLB. The RAI is a promising, time efficient and broadly applicable candidate biomarker for DLB and iRBD. Automated quantitative RSWA could be a valuable tool for deep phenotyping throughout the Lewy body disease spectrum.