Citations

AbstractBackgroundDementia with Lewy bodies (DLB) frequently co‐occurs with Alzheimer’s disease (AD) pathologies, exacerbating disease progression. Biophysical models of diffusion imaging data, such as Neurite Orientation Dispersion and Density Imaging (NODDI), may reveal novel insights into the neurobiological substrates of AD on cortical and white matter microstructural injury.MethodA cohort of 57 DLB patients on the DLB spectrum (33 clinically probable DLB and 14 prodromal DLB) and 57 cognitively unimpaired (CU) controls underwent NODDI and PET imaging with [18F]‐Flortaucipir and [11C]‐PiB (Table 1). NODDI parameters were derived from white matter tracts and grey matter regions using the JHU “Eve” and Mayo Clinic Adult Lifespan Template atlases, respectively. Conditional logistic models were used to compare these parameters between the two groups, accounting for the 1:1 matching on age and sex. The associations of tau and amyloid‐β with NODDI parameters across the white matter tracts were examined with linear regressions. A region‐by‐region approach was adopted to evaluate the relationships of tau and amyloid‐β with grey matter NODDI, adjusting for age, APOE, and local grey matter volumes.ResultDLB spectrum patients exhibited extensive white matter abnormalities relative to CU, including lower fractional anisotropy, higher mean diffusivity (MD), and lower tissue‐weighted neurite density (tNDI). In grey matter, these deficits were paralleled with higher MD and lower tNDI and ODI, primarily in the temporoparietal and posterior cortices. Regression models revealed significant associations of elevated [18F]‐Flortaucipir uptake with deficits in temporal and limbic white matter tracts and a temporal‐lobe pattern of higher MD and lower tNDI in the cortex (Figure 1). No associations were observed with [11C]‐PiB in the DLB spectrum.ConclusionThese findings demonstrated an in vivo relationship between tau deposition and neurite deficits that spatially aligned with the canonical AD “cortical signature” of neurodegeneration in both white matter tracts and grey matter regions in DLB. Our study underscores the detrimental impact of AD pathology in DLB and provides a rationale for further investigations into the underlying mechanisms of AD in DLB. Future longitudinal studies tracking the progression of these changes could inform therapeutic strategies for DLB in the presence of AD pathologies

AbstractBackgroundThe TAR DNA‐binding protein of 43 kDa (TDP‐43) is linked to hippocampal volume loss and faster rates of hippocampal atrophy in Alzheimer's disease and primary age‐related tauopathy (PART). Hence, TDP‐43 is becoming an important player in age‐related neurodegeneration. To advance our understanding of TDP‐43 effect on hippocampus, we conducted an imaging‐pathological study to determine which specific hippocampal subfields are affected by TDP‐43 in cases of PART.MethodOne‐hundred fifteen autopsied cases from Mayo Clinic Alzheimer Disease Center, Study of Aging and Neurodegenerative Research Group were analyzed. All had completed antemortem brain MRIs, underwent neuropathological examination per NIA and Alzheimer’s Association (NIA‐AA) criteria and had additional assessment for TDP‐43 deposition. Only cases meeting criteria for PART (Thal phases 0‐2 and Braak NFT stages I‐IV) were included. Hippocampal subfield segmentation was performed using FreeSurfer‐7. Brain volumes were normalized to total intracranial volume (TIV) when comparing TDP‐43 groups. Statistical analyses including logistic regression analyses were utilized to explore associations between TDP‐43 status and hippocampal subfield volumes.ResultForty‐nine of the 115 cases (43%) with PART were female. Thirty‐seven (32%) cases were TDP‐positive. Mean age at last MRI was 84 years. There were no differences in pathological characteristics between both groups except for presence of hippocampal sclerosis which was more frequent in the TDP(+) group (P <0.001). Whole hippocampal volume was significantly smaller in the TDP(+) group (176 ±31 vs. 188 ±27, p=0.049). After stratification of hippocampus into head, body, and tail, TDP‐positive cases had smaller volumes of hippocampal body (p=0.031) and tail (p=0.021) but did not reach significance for hippocampal head (p=0.129). When regions were subdivided into hippocampal subfields, TDP(+) cases showed smaller volumes of molecular layer of the hippocampal body (p=0.046), CA1 body (p=0.031), subiculum body (p=0.023), and presubiculum body (p=0.046) compared to TDP(‐) cases. Logistic regression analysis adjusting for TIV confirmed these associations.ConclusionThis study underscores the association between TDP‐43 and the hippocampus and provides further evidence that TDP‐43 targets multiple regions and subfields of the hippocampus in patients with PART. Intriguingly, regions targeted are those where TDP‐43 deposition is microscopically observed, suggesting a direct “hit” by TDP‐43 deposition.

AbstractBackgroundNeuron‐derived extracellular vesicles (NDEVs) are a valuable resource for understanding brain conditions and discovering neurodegenerative diseases biomarkers, notably Alzheimer’s disease (AD). Recent interest focuses on capturing neuron‐specific EVs from patient‐derived samples, characterizing their contents as a pathological reflection of the central nervous system (CNS). Our recent study identified ATPase Na+/K+ Transporting Subunit Alpha 3 (ATP1A3) as a prevalent neuron‐specific EV marker specifically expressed in brains. This study systematically analyzes neuronal EVs to assess the specificity of ATP1A3 to NDEVs and its potential as a target for NDEV pulldown from accessible biofluids for disease monitoring. Additionally, ATP1A3 is compared with other proposed NDEV markers.MethodWe applied immunoelectron microscopy to detect ATP1A3, L1 cell adhesion molecule (L1CAM) and neural cell adhesion molecule 1 (NCAM1) in EVs isolated from iPSC‐derived excitatory neurons, brain tissue, cerebrospinal fluid (CSF) and plasma. Neuronal EV enrichment was achieved through immunoaffinity isolation, utilizing anti‐ATP1A3, L1CAM and NCAM1 antibodies separately. Evaluation involved quantitative mass‐spectrometry, immunoblotting and ELISA. To compare the enrichment of neuronal markers, EVs isolated from CSF and plasma samples were analyzed using ExoView and Nanoimager. Additionally, we investigated the potential of ATP1A3+ plasma EVs as AD biomarker by comparing the value of amyloid‐beta peptide (Aβ) in ATP1A3+ plasma EVs measured by Nanoimager and other plasma AD biomarkers as determined by SIMOA.ResultATP1A3, identified as a neuron‐specific protein, exhibits substantial enrichment in NDEVs isolated from induced human neurons, brain tissue, CSF, and plasma samples, surpassing NCAM1 or L1CAM. Both single‐ and bulk‐EV analysis consistently demonstrate a higher enrichment of ATP1A3 associated EVs in human samples. Immunoprecipitation of ATP1A3+ EVs from human brain tissues reveals superior neuronal cell‐type specificity over NCAM1+ and L1CAM+ EVs by label‐free mass spectrometry. Increased Aβ levels are observed in ATP1A3+ EVs derived from CSF and plasma of AD cases by SIMOA. Moreover, Aβ+ populations in ATP1A3+ EVs from plasma can distinguish AD from mild cognitive impairment and control cases via Nanoimager compared to the conventional quantification of AD markers.ConclusionOur findings demonstrate that ATP1A3 as a promising target for isolating NDEVs from biofluids, offering potential diagnostic advancements in neurological research.

AbstractBackgroundPatterns of regional atrophy and hypometabolism have been observed in dementia with Lewy bodies (DLB). However, determinants of regional vulnerability to structural and functional neurodegeneration remain largely unexplored. First, we investigated the association between regional gene expression and grey matter volumes in probable DLB patients. Since hypometabolism presumably precedes overt brain atrophy, we additionally investigate the association between regional gene expression and hypometabolism in DLB.MethodTo investigate the association between gene expression and regional volumes, 165 DLB patients along with 165 age‐ and sex‐matched healthy controls from three European centres and the Mayo Clinic (USA) were included. Regional volumes were quantified from MRI using SPM12 in 112 cortical, subcortical, and cerebellar brain regions, and compared between groups, using w‐scores. Regional expression data of seven genes involved in the formation and degradation of pathological protein aggregates (APOE, APP, BIN1, GBA, MAPT, SNCA, TMEM175) was extracted from six healthy donors from the Allen Human Brain Atlas and correlated with regional volumetric w‐scores. To assess the predictive value of regional gene expression on regional volumes we used Gaussian stepwise backwards linear regression including all seven genes as predictors. To investigate the association between gene expression and hypometabolism, we are currently applying the same analysis pipeline to FDG‐PET data of 139 DLB patients from 8 European centres.ResultMost brain regions showed lower volumes in DLB patients compared to healthy controls, with differences being most pronounced in occipital and parietal lobes. Regional expression of APOE correlated positively with regional volumes in DLB. Conversely, regional expression of MAPT correlated negatively with regional volumes. Both APOE and MAPT gene expression were significant predictors of regional volumes in the regression analysis. None of the other gene expression values was significantly associated with regional volumes in DLB.ConclusionOur findings show that regional expression of genes associated with the abnormal accumulation of amyloid and tau, common co‐pathologies in DLB, partially account for the brain atrophy pattern observed in DLB patients. Analyses are ongoing for the hypometabolism pattern. Our finding emphasises the relevance of co‐pathologies in predicting atrophy progression and identifying potential targets for future disease‐modifying treatments.

AbstractBackgroundProgressive supranuclear palsy (PSP) can present with different clinical variants which show distinct, but partially overlapping, patterns of neurodegeneration and tau deposition in a PSP network of regions, including cerebellar dentate, superior cerebellar peduncle, midbrain, thalamus, basal ganglia, and frontal lobe. We sought to determine whether disruptions in functional connectivity within this PSP network measured using resting‐state functional MRI (rs‐fMRI) differed between PSP‐Richardson’s syndrome and the cortical and subcortical variants of PSP.MethodStructural MRI and rs‐fMRI scans were collected for 40 PSP‐RS, 24 PSP‐cortical (12 speech and language; 10 corticobasal syndrome; 2 frontal) and 36 PSP‐subcortical (18 parkinsonism; 11 progressive gait freezing; 6 postural instability; 1 oculomotor) participants who met the Movement Disorder Society PSP clinical criteria (Table 1). Ninety‐six participants underwent flortaucipir‐PET scans. MRIs were processed using CONN Toolbox. Functional connectivity between seeds placed throughout the PSP network was compared between each PSP group and 83 healthy controls, and between the PSP groups, covarying for age and sex.ResultsConnectivity was reduced throughout the network in PSP‐RS compared to controls (Figure 1A), involving cerebellar dentate, midbrain nuclei, subthalamic nuclei, basal ganglia, thalamus, and frontal regions. Frontal regions showed reduced connectivity to other regions in the network in PSP‐cortical, including substantia nigra and superior cerebellar peduncle. Disruptions in connectivity in PSP‐subcortical were less pronounced, with the strongest disruption between the pallidum and putamen. When PSP groups were compared to each other (Figure 1B), PSP‐RS had lower connectivity from the thalamus and cerebellar dentate to the cortex compared to PSP‐subcortical and from the thalamus to substantia nigra than PSP‐cortical. PSP‐subcortical had lower connectivity from the subthalamic nucleus to caudate than PSP‐RS and from the subthalamic nucleus to substantia nigra than PSP‐cortical and higher connectivity from the thalamus to the prefrontal cortex than both the other variants. Voxel‐based analyses replicated these patterns. There was moderate evidence that tau uptake in the PSP network regions influenced connectivity between these regions in PSP.ConclusionsPatterns of disrupted functional connectivity revealed both variant‐specific and shared disease pathways among PSP variants, providing insight into the disease heterogeneity beyond patterns of atrophy.

AbstractBackgroundInexpensive, non‐invasive tests may improve the identification of persons at increased risk for cognitive decline and dementia. We compared impairment in odor identification and global cognition with neuro‐imaging biomarkers to predict cognitive decline and dementia in the population‐based Mayo Clinic Study of Aging (MCSA).MethodAt the 2008 assessment, 647 participants who were ≥ 55 years old with at least one follow‐up had the following procedures: modified Blessed Information‐Memory‐Concentration Test (BIMCT), 12‐item Brief Smell Identification Test (BSIT), brain magnetic resonance imaging (MRI), and Positron Emission Tomography (PET) amyloid imaging with 11C‐Pittsburgh compound B (11C‐PiB). A subsample (n=556) also had 18F‐fluorodeoxyglucose (18F‐FDG) PET scans. The utility of these baseline measures to predict cognitive decline (cognitively unimpaired to mild cognitive impairment [MCI] or dementia, or MCI to dementia) was examined during follow‐up (mean 8.1 SD 3.4 years). Transition to dementia was a secondary outcome.ResultAmong 647 participants without dementia (mean age 74.0 SD 8.1 years; 54.4% male) at baseline, 102 participants manifested cognitive decline and 34 participants transitioned to dementia. In Cox regression survival analyses for cognitive decline, PiB PET showed the strongest predictive utility (hazard ratio HR 5.05, 95% CI = 3.32‐7.69, p<.001). Impaired BSIT (HR 3.46, 95% CI = 2.34‐5.11, p<.001), impaired BIMCT (HR 3.63, 95% CI 2.46‐5.36, p<.001), MRI and FDG measures were also significant predictors. After adjusting for age, sex and education, these predictors remained significant with attenuated effects (PiB HR 3.89, 95% CI 2.54‐5.95, p<.001; BSIT HR 2.77, 95% CI 1.85‐4.14, p<.001; BIMCT HR 3.3, 95% CI 2.21‐4.95, p<.001). After adding these demographic variables in the model, the combination of BSIT and BIMCT showed strong predictive utility (C‐index=0.85), similar to PiB PET (C‐index=0.84). Combining all predictors showed a C‐index of 0.89. These measures showed similar but stronger results for dementia transition.ConclusionTests of global cognition and odor identification provide a brief, non‐invasive, inexpensive method comparable to PET amyloid imaging for identifying community‐dwelling persons likely to decline cognitively or transition to dementia. Other potential uses for this combination include identification of persons likely to benefit from disease‐modifying medications and determination of eligibility for prevention trials.

AbstractBackgroundTwo main risk factors of Alzheimer’s disease (AD) are aging and APOE‐ε4. However, some individuals remain cognitively normal despite having these risk factors. They are considered “cognitively resilient”. This study aimed to identify molecular factors that confer cognitive resilience in APOE‐ε4 carriers ≥ 80 years of age and may serve as biomarkers.MethodWe applied weighted gene co‐expression network analysis (WGCNA) to generate consensus co‐expression networks from blood of participants in two antemortem cohorts, the Mayo Clinic Study of Aging (MCSA, n=105), and the Alzheimer’s Disease Neuroimaging Initiative (ADNI, n=91), using RNA‐sequencing and microarray data, respectively. We associated these networks with resilience (resilient vs non‐resilient), cognitive endophenotypes and hippocampal volume. Preservation between consensus networks from blood and those derived from postmortem brain tissues of AD and control donors from AMP‐AD (n=1174) was evaluated. We validated the human findings in four AD mouse models. Finally, machine learning models were utilized to discriminate cases (AD+mild cognitive impairment (MCI)) from controls in MCSA, ADNI and ANMerge antemortem cohorts.ResultFour consensus networks were significantly correlated with a memory phenotype (logical memory delayed recall=LMDR) and hippocampal volume in both MCSA and ADNI. Among these, blood expression module M3 was most preserved with the brain transcriptome. M3 was enriched with NDUF hub genes that are involved in the mitochondrial respiratory chain. Expression levels of M3 and many blood NDUFs had significant associations with better LMDR and hippocampal volume. In brain, NDUFs were upregulated in controls compared to AD, and their expression levels were associated with better global cognition and decreased AD neuropathology. Many NDUFs were significantly downregulated in the hippocampus or cortex of AD mice compared to wild‐types. Lastly, models that included blood NDUFs improved diagnostic accuracy of AD+MCI compared to models that only included demographic and risk variables (age, sex, APOE‐ε4) in MCSA, ADNI and ANMerge. In MCSA and ADNI, adding NDUFs’ expression to models that included established blood biomarkers (Aβ42/40, ptau181, NFL) further improved diagnostic accuracy.ConclusionOur results suggest that mitochondrial NDUFs are centrally‐linked peripheral molecular signatures that may be resilience factors against AD and serve as both therapeutic targets and novel diagnostic biomarkers.

This commentary examines Topological Data Analysis (TDA) in radiology imaging, highlighting its revolutionary potential in medical image interpretation. TDA, which is grounded in mathematical topology, provides novel insights into complex, high-dimensional radiological data through persistent homology and topological features. We explore TDA’s applications across medical imaging domains, including tumor characterization, cardiovascular imaging, and COVID-19 detection, where it demonstrates 15–20% improvements over traditional methods. The synergy between TDA and artificial intelligence presents promising opportunities for enhanced diagnostic accuracy. While implementation challenges exist, TDA’s ability to uncover hidden patterns positions it as a transformative tool in modern radiology.

AbstractBackgroundLarge‐scale studies comparing sporadic early‐onset AD (EOAD, age<65) and late‐onset AD (LOAD, age≥65) are lacking. We compared amyloid‐PET outcomes (positivity rate and amyloid burden) between patients clinically diagnosed with sporadic EOAD vs LOAD, leveraging data from the Longitudinal Early‐Onset AD Study (LEADS) and the Alzheimer’s Disease Neuroimaging Initiative 3 (ADNI3).Method731 patients meeting the 2011 NIA‐AA criteria for AD dementia or MCI were included (505 early‐onset from LEADS, 226 late‐onset from ADNI3, Table 1). All participants underwent amyloid‐PET with [18F]Florbetaben or [18F]Florbetapir. Amyloid positivity was centrally determined by a process involving a visual read by a trained expert and PET‐only quantification; in case of a discrepancy, a read from an independent physician acted as a tiebreaker. Logistic regressions in each cohort examined relations between amyloid positivity and age, sex, MMSE and APOE4 genotype. Amyloid burden was independently quantified in Centiloids using an MRI‐based pipeline. Mean Centiloids in LEADS and ADNI were compared with two‐way ANOVA, for visually positive and visually negative scans.ResultAmyloid positivity rate was higher in LEADS (76%) than ADNI (64%, p<0.001, Figure 1A). Lower MMSE and APOE4 genotype increased odds of amyloid positivity in both cohorts, although the APOE4 effect was stronger in ADNI than LEADS (OR=10.1 versus 2.4, p=0.007, Table 2). Amyloid positivity was more common in females across cohorts, but this effect was only statistically significant in LEADS (Table 2). Centiloids were bimodally distributed in both cohorts, although the separation between positive and negative scans was more prominent in LEADS (Figure 1B). Visually positive scans had significantly higher Centiloids in LEADS than in ADNI, whereas no cohort difference was observed for visually negative scans (Figure 1C). Sensitivity analyses showed that this effect was driven by patients with MCI (CDR≤0.5; Figure 1D‐E).ConclusionThe lower amyloid positivity rate in ADNI might be due to AD‐mimicking pathologies being more common at an older age. The higher amyloid burden in early‐onset, amyloid‐positive patients could reflect younger patients being diagnosed later in the disease course compared to typical, late‐onset patients. Alternatively, younger patients might tolerate higher neuropathology burden due to higher brain reserve or fewer co‐pathologies.

AbstractBackgroundIn vivo studies using the tau PET tracers have shown high performance for the diagnosis of Alzheimer’s disease dementia and patterns of tracer uptake that resemble those observed in post‐mortem studies. However, tau tracers present distinct patterns of binding that might influence their performance in detecting AD pathology. In a head‐to‐head study, we investigated the performance of [18F]MK6240 and [18F]Flortaucipir for the diagnosis of AD.MethodWe assessed 132 individuals from the HEAD study (58 CU Aβ‐, 15 CU Aβ+, 14 MCI Aβ‐, 32 MCI Aβ+ and 13 AD dementia) with Aβ‐PET, [18F]MK6240 and [18F]Flortaucipir. Voxel‐wise receiver operating characteristic curves (ROC) of the two tau tracers were used to contrast groups provided the area under the curve (AUC) for disease diagnosis or biomarkers positivity.ResultThe brain maps showed numerically higher and more spread AUC for [18F]Flortaucipir to discriminate CU Aβ‐ from CU Aβ+ (Figure 1A). The difference between tracers’ AUC was greater in Braaks IV and V (Figure 2A), both regions that are not expected to have tau accumulation in CU Aβ+ individuals, reflecting a potential off‐target binding for [18F]Flortaucipir. To differentiate CU Aβ‐ from MCI Aβ+ individuals, [18F]MK6240 presented a numerically higher AUC than [18F]Flortaucipir in Braak I and II and similar AUC in other Braak regions (Figure 1B, 2B). We observed a high performance of [18F]MK6240 and [18F]Flortaucipir in differentiating AD dementia from CU Aβ‐ individuals. However, [18F]MK6240 exhibits a higher AUC than [18F]Flortaucipir in all Braak regions, especially Braak V‐VI (Figure 1C, 2C). Finally, [18F]MK6240 presented higher AUC in all Braak regions to discriminate MCI Aβ‐ from MCI Aβ+ individuals (Figure 1D, 2D).ConclusionOur results indicate that [18F]MK6240 and [18F]Flortaucipir present high accuracy to discriminate AD from CU Aβ‐ individuals. However, [18F]MK6240 presents higher AUC to discriminate AD and MCI Aβ+ from CU Aβ‐ individuals than [18F]Flortaucipir. Together, our head‐to‐head study sheds light on the distinct patterns of binding for Tau‐PET tracers.