Citations

AbstractBackgroundLower gray matter connectivity measures are related to faster cognitive decline in prodromal Alzheimer’s Disease (AD). We investigated the biological underpinnings responsible for this loss of brain connections using cerebrospinal fluid (CSF) proteomics.MethodData from ADNI was used to select 82 individuals with MCI and abnormal CSF amyloid (mean age±SD 74±7) and 37 controls with normal amyloid levels (mean age±SD 75±5), for whom repeated MRI‐scans (median of 6 scans over 1.5 follow‐up years) and baseline CSF proteomic data (in total 306 proteins) were available. We used an automated pipeline (Tijms, Series et al. 2012) to construct gray matter networks from 3D‐T1 sequences and calculate connectivity density and gamma – measures previously found to be most strongly related to cognitive decline (Dicks, Vermunt et al. 2020). Linear mixed models were applied to test associations between baseline CSF protein levels and repeated connectivity metrics, controlling for intracranial volume, age and sex. Proteins that demonstrated significant associations (p<0.05) were identified and examined for their biological relevance according to Gene Ontology.ResultConnectivity density declined over time with ‐0.8% per year (pMCI = 0.001; figure 1), which did not differ from controls (pdif = 0.15). Gamma declined faster in MCI than in controls (bMCI = ‐0.016 vs. bCN = ‐0.007, pdif <0.001; figure 1). Higher levels of 99/108 proteins were associated with faster decline in connectivity density (a.o. tau and neuroprentaxins). These proteins were involved in regulation of synapse organization (GO:0050807; pFDR = 7.00E‐07; figure 2). Higher levels of 9/108 proteins associated with less decline and were involved in chemotaxis (GO:0048247; pFDR = 1.40E‐02). Similarly, 25 out 30 proteins (83%) associated with alterations in gamma over time, showed associations of higher levels with less gamma decline and were also involved in chemotaxis (GO:0050926; pFDR = 9.42E‐05; figure 3). There were no protein‐connectivity relationships in controls.ConclusionProteins involved in synapse organization were associated with faster decline in connectivity density over time in patients, while chemotaxis was associated with less decline. This suggests that synaptic degeneration may predict disease progression while immune responses may serve as a resilience mechanism in AD.

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.

AbstractBackgroundThe Standardized Centralized Alzheimer’s & related dementias Neuroimaging (SCAN) project harmonizes processing of prospectively acquired PET and MRI scans from Alzheimer’s disease research centers (ADRCs) across the USA. SCAN data will be integrated with other ADRC data streams within the National Alzheimer’s Coordinating Center (NACC), including the Uniform Data Set which includes rich longitudinal demographic, cognitive, and neuropathological data, resulting in a large, potentially representative cohort. The project has sought to accommodate site‐to‐site variation in image acquisition while permitting harmonization of imaging data, necessitating new analytic approaches for different PET tracers and PET scans without MRIs.MethodPET scans underwent preprocessing for quality control and smoothing to a uniform resolution. MRI‐free processing pipelines and positivity thresholds were developed for each beta‐amyloid (Aβ)‐PET tracer: [11C]PiB, [18F]florbetaben (FBB), [18F]florbetapir (FBP) and [18F]flutafuranol (NAV). A generic Aβ‐PET template was used for spatial normalization and cortical summary SUVRs were calculated in template space. Aβ‐positivity thresholds were determined by examining both MRI‐dependent thresholds transformed into MRI‐free units and a young control mean+2SD. Centiloid (CL) conversions were completed for each Aβ‐PET tracer. Tau‐PET tracers ([18F]flortaucipir (FTP) and [18F]MK‐6240) required tracer‐specific warping templates for best performance relative to gold‐standard MRI‐dependent processing. Demographic and clinical data were obtained from NACC.Result15 ADRCs have contributed data to SCAN as of January 2023. 477 participants had preprocessed, SCAN‐compliant (https://scan.naccdata.org/) Aβ‐PET scans and NACC data; 219 of these participants (46%) also had tau‐PET scans (Table 1). This cohort included 18% of individuals from underrepresented groups. Clinical diagnostic groups ranged from cognitively unimpaired to MCI and dementia (74% of whom had AD dementia). Using pooled Aβ‐PET data across 4 tracers in CLs, there were expected differences in Aβ burden by diagnosis group (Fig. 1) and positive associations with tau (Fig. 2).ConclusionSCAN has successfully harmonized PET data from more than a dozen US ADRCs yielding a large, clinically heterogeneous cohort of deeply phenotyped research participants. SCAN data will be compatible with ADNI data allowing for further pooling of data and greater statistical power to drive critical questions in AD and related dementias research.

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.

AbstractBackgroundRepetitive head impacts (RHI) from American football can lead to tau and non‐tau pathologies that might present as white matter hyperintensities (WMH) on FLAIR MRI. In 2022, we published a study in Alzheimer’s & Dementia that examined WMH and their association with risk factors and clinical function in former elite football players. That study will be presented during this symposium along with new unpublished data that used imaging and fluid biomarkers to examine etiological correlates (tau, amyloid, neurodegeneration, axonal injury, neuroinflammation) of WMH in the same cohort.MethodsThe multi‐site DIAGNOSE CTE Research Project recruited 180 football players and 60 asymptomatic males without RHI (“controls”), all 45‐74 years (Table 1). Participants completed MRI, lumbar puncture, and neuropsychological testing. Lesion Segmentation Toolbox estimated FLAIR WMH, FreeSurfer derived total cortical thickness, and a diffusion pipeline derived average global FA. CSF was analyzed for p‐tau181, Aβ1‐42, NfL, and sTREM2. Tobit regressions compared football players (n = 149) and controls (n = 53) on total and regional log‐WMH and estimated the effects of years of football and age of first exposure (AFE) to football on log‐WMH. Linear regressions evaluated log‐WMH and clinical associations in football players. Structural equation modeling (SEM) examined effects between log‐WMH and cortical thickness, FA, and the CSF biomarkers. Analyses accounted for age, race, revised Framingham Stroke Risk Profile (rFSRP), body mass index, APOE ε4, and evaluation site.ResultsAlosco et al. (2022) found older (i.e., 60+) but not younger football players had greater total, frontal, temporal and parietal log‐WMH compared to controls (FDR‐adjusted p‐values<0.05; Table 2). Among older football players, younger AFE was associated with greater log‐WMH (beta = ‐0.13, 95% CI = ‐0.23,‐0.02). Greater log‐WMH corresponded to worse Trails A‐B (beta = ‐4.32, 95% CI = ‐8.59,‐0.05) and List Learning Long Delay scores (beta = ‐0.56, 95% CI = ‐0.93,‐0.19). Subsequent unpublished SEM analyses in football players (Figure 1) showed the following direct effects on log‐WMH: higher rFSRP, higher CSF p‐tau181, lower FA, and reduced cortical thickness. There were no indirect effects. Compared to controls, SEM associations were stronger in football players exception for cortical thickness.ConclusionsFLAIR WMH might have unique imaging characteristics, risk factors, and pathological underpinnings in people exposed to RHI.

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.

AbstractBackgroundThere is a longstanding ambiguity regarding the clinical diagnosis of dementia syndromes predominantly targeting executive functions versus behavior/personality. This is due to a lack of knowledge about the macro‐scale anatomy underlying these symptomatologies, a partial overlap in clinical features, and the fact that a single underlying pathology can give rise to both phenotypes and vice‐versa.MethodWe included data from patients with genetic, biomarker, and/or autopsy evidence for fronto‐temporal lobar degeneration (FTLD) diagnosed with behavioral variant fronto‐temporal dementia (bvFTD, n = 30) and patients with biomarker and/or autopsy evidence for Alzheimer’s disease (AD) pathology diagnosed with either an initial and predominant dysexecutive (dAD, n = 52), behavioral (bvAD, n = 7), or amnestic (aAD, n = 28) syndrome. We assessed group‐wise differences in clinical/cognitive features and 18Fluorodeoxyglucose‐positron emission tomography (FDG‐PET) patterns. This was followed by a spectral covariance decomposition between FDG‐PET images to yield latent patterns of relative metabolism (“eigenbrains”). These eigenbrains were subsequently linked to clinical/cognitive data and meta‐analytic topics reflecting a wide range of mental abilities. We used a linear discriminant analysis (LDA) to perform eigenbrain‐based diagnostic predictions.ResultdAD and bvFTD patients were the youngest at symptom onset, followed by bvAD, then aAD. dAD patients had worse cognitive performance on nearly all cognitive domains compared to other groups. Hypometabolism was observed across associative cortices in dAD, temporo‐parietal areas in aAD, and fronto‐temporal areas in bvFTD and bvAD (Fig. 1). The spectral covariance decomposition yielded nine eigenbrains which explained 61% of the variance in patterns of FDG‐PET, and only the first three are presented (Fig. 2). These eigenbrains revealed that relative hypometabolism in association cortices, notably lateral parietal areas, associated with dysexecutive symptomatology and a lower likelihood of behavior/personality problems, whereas relative hypometabolism restricted to frontal and temporopolar areas showed opposite associations. The LDA yielded an accuracy of 82.1% in predicting diagnostic category (Fig. 3).ConclusionThis study suggests distinct macro‐scale underpinnings underlying predominant dysexecutive versus behavioral symptomatology in degenerative dementia phenotypes. This has implication for the implementation of clinical and research criteria for these dementia syndromes and highlights the importance of data‐driven techniques to inform the classification of degenerative diseases.