Inferior Temporal Research Articles

Structural Correlates of Speech Biomarkers in Alzheimer’s Disease and Mild Cognitive Impairment

AbstractBackgroundIn the present study, we aimed to confirm the association between speech characteristics of individuals with Alzheimer’s disease (AD) and amnestic mild cognitive impairment (MCI) and brain regions, and to investigate the neurobiological basis of speech‐based AD detection.MethodIndividuals with MCI (n = 13, age = 78.3), mild‐to‐moderate AD (n = 12, age = 77.8) underwent a structural MRI scan and engaged in three speech tasks: an interview, sentence repetition, and paragraph recall. From the speech samples, we extracted frequency‐related, intensity‐related, and temporal features which were found to be altered in the course of AD and MCI pathology. We selected nine regions of interest (ROI) which were found to be involved in speech production and reduced in MCI and AD pathology; the Inferior Frontal Gyrus (IFG), the Medial Frontal Gyrus (MFG), the Precuneus, the Inferior Parietal Lobe (IPL), Inferior Temporal Gyrus (ITG), Medial Temporal Gyrus (MTG), the Superior Temporal Gyrus (STG), the Fusiform Gyrus (FFG), and the Cerebellum. The linear mixed‐effect analysis was used to identify the associations between speech features and regional brain volumes.ResultThe Left IPL (χ2 (1) = 7.69, corrected P = 0.033), the Right ITG (χ2 (1) = 11.5, corrected P = 0.0128) the Right FFG (χ2 (1) = 9.35, corrected P = 0.0198) reduced the pitch of voice by about 0.887 ± 0.292, 1.368 ± 0.329, and 1.69 ± 0.429 respectively. The associations between the Right FFG (χ2 (1) = 4.58, corrected P = 0.0900), Left FFG (χ2 (1) = 5.87, corrected P = 0.0675) and loudness were marginally significant, with the reduction of volumes in the Right FFG and Left FFG decreasing the loudness by 0.096 ± 0.041, and 0.1165 ± 0.0454. The marginally significant associations were observed between the Right IPL (χ2 (1) = 4.75, corrected P = 0.0900), the Left FFG (χ2 (1) = 0.39, corrected P = 0.53) and the speech rate, and the atrophy of brain regions decreased the speech rate by 0.0824 ± 0.0363, 0.0535 ± 0.0079.ConclusionThe present study identified the associations between speech biomarkers and regional brain volumes providing clinical validity for the speech‐based Alzheimer’s disease detection.

Inter‐network connectivity of early Aβ and tau‐seeded functional networks is associated with longitudinal tau accumulation in at‐risk cognitively normal older adults

AbstractBackgroundFunctional connectivity (FC) between networks may be related to Aβ and tau interactions in preclinical Alzheimer’s disease (AD). Here, we investigated associations between inter‐network FC of early Aβ and tau‐seeded networks, APOEe4, global Aβ, and longitudinal tau accumulation in cognitively healthy older adults (OA).MethodVoxel‐wise PiB‐PET and FTP‐PET OA images were ordered by increasing pathology burden and iteratively compared to a reference set of young adult (YA) PET images. Peak voxels of the earliest significant differences were matched to Brainnetome ROIs and used to construct separate Aβ and tau‐seeded networks within a group average YA FC matrix (Figure 1). Next, the average inter‐network FC between all node pairs between the networks was calculated in OA (n = 60; aged 77.6±5.5). Longitudinal FTP slopes of the entorhinal cortex (EC) and inferior temporal gyrus (ITG) were estimated using linear mixed effects models. Linear regression was used to assess relationships between baseline inter‐network FC, APOEe4 status, baseline global PiB, and both a prior ROI and voxel‐wise FTP slopes.ResultIn all OA, inter‐network FC was not associated with baseline global PIB (t(58) = ‐0.23, p = 0.82), EC FTP slope (t(58) = 0.49, p = 0.63), or ITG FTP slope (t(58) = ‐1.45, p = 0.15). There was a significant interaction between baseline global PiB status and inter‐network FC where FC was negatively associated with ITG FTP slope in PiB+ participants (t(56) = ‐2.01, p = 0.04; Figure 2A). There was also a significant interaction between APOEe4 status and inter‐network FC in predicting ITG FTP slope with a negative relationship only in e4 carriers (t(56) = ‐3.19, p = 0.003; Figure 2B). Voxel‐wise analyses revealed significant negative relationships between inter‐network FC and FTP slope in the left fusiform gyrus and ITG in PiB+ participants and e4 carriers, respectively (Figure 2C‐D).ConclusionLower baseline inter‐network FC of early Aβ and tau‐seeded networks was associated with greater longitudinal tau accumulation outside of the medial temporal lobe (MTL) in cognitively normal OA with higher AD risk (i.e., elevated Aβ or APOEe4). Our results suggest that this network disruption is associated with faster rates of tau accumulation outside of the MTL, possibly reflecting a later stage in the preclinical progression to AD.

Examining Complex Relationships among Cerebrovascular, β‐Amyloid, Tau and Cognitive Measures in a Cognitively Unimpaired Late‐Middle Aged Sample

AbstractBackgroundRecent research linking vascular risk with promotion of tau pathology in the inferior temporal cortex (ITC) suggests a potential mechanism connecting cerebrovascular risk to AD pathology and cognitive decline among cognitively unimpaired (CU) individuals (Yau et al, 2022).We used MRI White Matter Hyperintensity (WMH), a neuroimaging marker of chronic cerebral small‐vessel disease/cerebrovascular disease, to further elucidate longitudinal relationships between cerebrovascular risk, PET AD pathological burden, and cognition.MethodWe examined baseline CU late middle‐aged adults from the University of Wisconsin’s Alzheimer’s disease research cohorts. β‐Amyloid burden was measured with 11C‐Pittsburgh compound B (“PiB”; DVR derived from a 0‐70 min dynamic PET scan; PET A+ threshold∼1.16 DVR/∼CL 17). Cerebrovascular risk was quantified using WMH volumes (T2‐FLAIR images; SPM Lesion Segmentation Tool). Tau burden was measured with florquinitau PET (MK6240 SUVR from 70‐90 min post‐injection). Tau outcomes included bilateral volume‐weighted averages for ITC, Medial temporal lobe (MTL) and Meta‐temporal (MTC) composites. Biomarker values were z‐transformed relative to CU/PET A‐ values. Random intercept models examined interactive effects of baseline WMH and Aβ levels on longitudinal tau (covarying baseline tau age, sex, APOE ε4 count). We modeled time two ways: years since baseline PiB (TimePiB1; replicating Yau, et al models); and, in parallel models, amyloid duration (years) at tau scans was substituted for years since PiB1 in the A+ subset (TimePiB1/PiB+). We also used LME to examine Aβ, WMH, and time interactions in longitudinal cognition (PACC3 and Digit Symbol Substitution).ResultThe mean(SD) tau baseline age was 67.9(6.8) years (sample details in Table1). The most parsimonious models using TimePiB1 included WMH*Aβ*Time for ITC and MTC and WMH*Aβ for MTL. Patterns showed a synergistic effect of WMH and Aβ on tau accumulation that was attenuated using TimePiB1/PiB+ where the most parsimonious models included Aβ_status*TimePiB1/PiB+ (ITC, MTC) or WMH*Aβ_status*TimePiB1/PiB+ (MTL; Figure1). AICc model fits were substantially better using TimePiB1/PiB+. Both Aβ and WMH contributed to cognitive trajectories (Figure2).ConclusionAlthough cerebrovascular risk’s apparent contribution to tau accumulation varied depending on how time was modeled, results suggest that cerebrovascular risk may complement imaging biomarkers in assessing risk of tau accumulation and cognitive decline in preclinical Alzheimer disease.

Sex differences in plasma p‐tau181 accumulation is associated with subsequent tau‐PET signal

AbstractBackgroundPET and post‐mortem studies show that women consistently exhibit higher medial‐temporal and neocortical tau compared to men, independent of age or diagnostic status. What remains unclear is whether such sex differences are also observed in the plasma biomarkers and related to future tau‐PET deposition. We examined baseline and longitudinal phosphorylated‐tau (plasma p‐tau181‐UGOT), which was derived prior to a tau‐PET scan, in order to determine whether changes in p‐tau181 are associated with higher regional tau‐PET in women relative to men.MethodWe selected 313 participants (clinically normal, n = 169; MCI/dementia, n = 144) from ADNI (Female% = 146(46%); AgeMean = 71(±7)years; APOEe4 = 105(34%); Ab+ = 162(52%)) with at least two timepoints of plasma p‐tau181‐UGOT measured in ADNI1/2/GO (time‐pointsMean = 3.7(±1)). Cross‐sectional Ab‐PET and tau‐PET (Flortaucipir) scan were collected in ADNI3 ((time‐difference from baseline plasmaMean = 7.1y(±1.9)). Two a priori tau‐PET partial volume corrected (PVC) regions were selected: entorhinal (EC) and inferior temporal (IT). We used linear regression to examine the influence of baseline p‐tau181 and sex on subsequent tau‐PET, adjusting for time gap between plasma‐PET and tau‐PET. Linear mixed‐effects models probed how retrospective change in plasma tau were associated with regional tau‐PET and sex, covarying for neocortical Ab‐PET, diagnosis, APOEe4 and age. We also examined interactions between sex and variables of interest.ResultBaseline p‐tau181 was strongly associated (b = 0.86 and b = 0.53) with subsequent tau‐PET, although sex did not display main effects in the model, nor did sex moderate the plasma‐PET association. Longitudinal p‐tau181 was not associated with subsequent tau‐PET. Women with accumulating p‐tau181 exhibited higher subsequent tau‐PET burden in EC and IT regions than men (p = 0.04 and p = 0.03, respectively; Figure 1). Higher‐order interactions between sex and tau‐PET with APOEe4, diagnosis or Ab‐PET were not significant, although, women diagnosed with MCI/dementia and elevated Ab seemed to drive the interaction (Figure 2).ConclusionElevated tau‐PET burden in women may be more strongly associated with prior increases in plasma p‐tau181 relative to men. Given the pre‐established association between p‐tau181 and Ab, this could be initial evidence that early events related to the deposition of Ab predict later cortical tau deposition in a sex‐specific manner. Given the novelty of examining longitudinal plasma measures, further investigation is necessitated beyond this preliminary evidence.

Associations Among Depression, Stress, Resilience, and Alzheimer’s Disease Biomarkers in Older Adults Following a Major Societal Stressor

AbstractBackgroundBetter predicting depression following a stressor like COVID‐19 would help identify vulnerable individuals who might benefit the most from early intervention. We examined change in depressive symptoms relative to the onset of COVID‐19 and longitudinal trajectories of depressive symptoms post‐pandemic‐onset. As a secondary objective, we examined associations of these changes with stress, resilience, and in vivo AD biomarkers.MethodData were analyzed from 101 older adults (cognitively unimpaired at baseline) from the Harvard Aging Brain Study, a longitudinal observational cohort study. Depressive symptoms are assessed annually with the 30‐item Geriatric Depression Scale (GDS), and for these analyses, participants had no/mild self‐reported depression (GDS<11) at the last pre‐COVID examination. GDS‐delta scores were computed using the mean of the post‐COVID‐onset scores subtracted by the last pre‐COVID examination for participants. Levels of resilience (Connor‐Davidson Resilience scale) and stress (Perceived Stress Scale) were assessed within a year after pandemic onset. Cortical amyloid (PiB) and entorhinal (EC) and inferior temporal (IT) tau (FTP) were analyzed using pre‐pandemic acquisition (mean 1.6 years to pandemic onset). We used linear models to predict GDS‐delta and linear mixed effects models to predict GDS trajectory post‐COVID onset from resilience, stress, and the interaction of each with AD biomarkers (PiB and FTP).ResultsGDS‐delta was predicted by resilience (t = ‐4.69, p<0.001) and stress (t = 3.51, p<0.001) such that those with lower resilience and higher stress experienced higher GDS‐delta. EC tau, but not IT tau or cortical amyloid, interacted with stress to predict GDS‐deltas (EC tau*stress: t = 2.04; p = 0.04) indicating that those with higher levels of EC tau and stress experienced higher GDS‐delta. For post‐pandemic‐onset GDS trajectory, no significant longitudinal associations were observed, but there was a marginal interaction between EC‐tau*stress in predicting greater GDS trajectories (t = ‐1.79, p = 0.08).ConclusionResults suggest that change in depressive symptoms in elders following a societal stressor is associated with lower resilience and higher stress; this may be exacerbated by concurrent EC tau pathology. Moreover, elevated depressive symptoms in the years following onset of a societal stressor can be partially predicted by increased EC tau and stress, highlighting a vulnerable population who may benefit from preventive interventions throughout a major stressor.

Does texture analysis classify semantic dementia from Alzheimer’s disease better than volumetric analysis?

AbstractBackgroundTexture analysis may capture subtle changes in the gray matter more sensitively than volumetric analysis. We aimed to investigate the patterns of neurodegeneration in semantic dementia (SD) and Alzheimer’s disease (AD) by comparing the temporal gray matter texture and volume between cognitively normal controls and older adults with SD and AD.MethodWe enrolled all participants from three national university hospitals in Korea. We obtained T1‐weighted magnetic resonance images and compared the gray matter texture and volume of regions of interest (ROIs) between the groups using analysis of variance with Bonferroni posthoc comparisons. We also developed models for classifying SD, AD and control groups using logistic regression analyses, and validated the models using receiver operator characteristics analysis.ResultCompared to the AD group, the SD group showed lower volumes in five ROIs (bilateral temporal poles, and the left inferior, middle, and superior temporal cortices) and higher texture in these five ROIs and two additional ROIs (right inferior temporal and left entorhinal cortices). The performances of both texture‐ and volume‐based models were good and comparable in classifying SD from NC (mean area under the curve [AUC] = 0.914 for texture; mean AUC = 0.894 for volume). However, only the texture‐based model achieved a good level of performance in classifying SD and AD (mean AUC = 0.775 for texture; mean AUC = 0.658 for volume).ConclusionTexture may be a useful neuroimaging marker for early detection of SD in older adults and its differentiation from AD.

Synaptic density is correlated with Aβ plaques and neurofibrillary tau tangles across the clinical and biologic Alzheimer’s disease continuum

AbstractBackgroundThe extent to which amyloid‐β burden and neurofibrillary tau tangle burden are associated with synaptic degeneration in vivo is not well known. PET imaging was used to examine the cross‐sectional relationship between neurofibrillary tau tangle burden and amyloid‐β plaque burden with synaptic density among individuals who spanned the clinical and biologic Alzheimer’s disease continuum.MethodParticipants were recruited from the Wisconsin Alzheimer’s Disease Research Center and the Wisconsin Registry for Alzheimer’s Prevention study. N = 64 participants were included in the analysis based on completed cognitive testing, structural MR and PET imaging using [11C]PiB DVR for amyloid burden, [18F]MK‐6240 SUVR for regional neurofibrillary tau burden, and [11C]UCB‐J DVR for regional synaptic density burden analysis. Amyloid burden was indexed as a global [11C]PiB DVR representing the mean of brain regions that typically accumulate amyloid. UCB‐J DVR and MK‐6240 SUVR regions of interest were selected based on the histopathological staging of tau pathology in Alzheimer’s disease with a focus on regions of tau pathology described by earlier and later Braak stages. The first composite region of interest (ROI1) comprised the hippocampus and entorhinal cortex, and the second (ROI2) comprised the amygdala, fusiform, lingual gyrus, and the inferior temporal gyrus. Linear correlations (Pearson’s r) tested the relationship between UCB‐J DVR and PiB DVR, as well as UCB‐J DVR and MK‐6240 SUVR.ResultGlobal [11C]PiB DVR showed a moderate negative relationship with UCB‐J DVR in ROI1 (r = ‐0.42, p = 0.0006) and a weak relationship with UCB‐J DVR in ROI2 (r = ‐0.33, p = 009). [18F]MK‐6240 SUVR and UCB‐J DVR were negatively correlated in ROI1 (r = ‐0.44, p = 0.0003) and in ROI2 (r = ‐0.42, p = 0.005).ConclusionBoth amyloid burden and tau pathology were associated with lower synaptic density. Tau and synaptic density appeared to show moderate correlations among brain regions involved in both earlier and more advanced tau pathology, whereas amyloid burden appeared to be more strongly related to synaptic density in brain regions where more advanced disease is typically observed.

Astrocyte transcriptomic changes along the spatiotemporal progression of Alzheimer’s disease

AbstractBackgroundAlzheimer’s disease (AD) is defined by widespread accumulation of Ab plaques and phospho‐tau neurofibrillary tangles throughout the brain, with the latter following a stereotypical hierarchical spatiotemporal pattern along neural networks. These AD neuropathological changes are accompanied by a dramatic loss of synapses and neurons as well as prominent morphological and functional changes of astrocytes, collectively termed reactive astrogliosis. Astrocytes also play a critical role in maintaining brain homeostasis. However, their changes along the spatiotemporal progression of AD neuropathology remain largely unknown. To investigate these astrocyte changes, we performed single‐nucleus RNA‐sequencing on 5 brain regions along the stereotypical progression of AD neuropathology from 32 donors representing the entire normal aging‐severe AD spectrum.MethodNuclei were isolated from five brain areas of n = 32 donors with increasing AD neuropathology (total n = 160 samples). The five brain areas were chosen based on their hierarchical accumulation of tau pathology: entorhinal cortex (EC)> inferior temporal gyrus (BA20) > dorsolateral prefrontal cortex (BA46) > secondary visual cortex (V2 or BA18/19) > primary visual cortex (V1 or BA17). To enrich in astrocytic nuclei, NEUN+ and OLIG2+ nuclei were separated via FACS, whereas NEUN‐/OLIG2‐ nuclei (including astrocytes) were subjected to snRNA‐seq resulting in a transcriptomic dataset of 629,755 astrocyte nuclei, which were subsequently examined for common and region‐specific AD related changes.ResultWe discovered temporal gene‐expression‐trajectories with gene sets differentially activated at various disease stages. Surprisingly, a gene set enriched in proteostasis (genes encoding heat shock proteins) and energy metabolism (e.g., ATP synthases) was upregulated at late‐stage but unexpectedly returned to baseline levels at end‐stage, suggesting exhaustion of response in “burnt‐out” astrocytes. The spatial gene‐expression‐trajectories revealed that genes related to tripartite synapses (e.g., GLUL, GRIA2, NRXN1, SLC1A2) are dysregulated in parallel to the stereotypical progression of tangle pathology across regions. In addition, we identified substantial astrocyte heterogeneity across brain regions with a continuum from homeostatic to reactive cells through “intermediate” transitional states.ConclusionOur astrocyte snRNA‐seq dataset encompassing five regions of control and AD brains revealed distinct spatial and temporal gene trajectories, suggesting complex astrocytic dysfunction with AD progression.

Subjective cognitive impairment score associate with regional amyloid burden and brain atrophy in subjective cognitive decline subjects

AbstractBackgroundSubjective cognitive decline (SCD) is now regarded as the first preclinical stage of Alzheimer’s disease (AD) spectrum disorders. It is important to predict which patients would progress to dementia, and screening tools might be helpful. In this study, we aimed to investigate the possible correlations between AD biomarkers‐regional amyloid burden and volume changes‐and Korean Dementia Screening Questionnaire‐Cognition (KDSQ‐C) score in SCD.MethodThis study analyzed SCD data from prospective population cohort study from July 2018 to December 2020. Brain magnetic resonance imaging (MRI) volumetry, visual and standardized uptake value ratio (SUVR) analysis of 18F‐Florbetaben brain amyloid Positron Emission Tomography (PET) were performed. Demographics, physical data, social and physiological functions were also obtained.KDSQ‐C was administered to SCD subjects as part of the clinical evaluation at the 36 month follow‐up visit. KDSQ‐C included 15 questions that assess 3 dimensions: memory impairment (items 1‐5), other cognitive impairments including language impairments (items 6‐10), and the ability to perform complex tasks in daily life (items 11‐15).ResultPrevalence of subjective cognitive impairment (KDSQ‐C ≧6) among 86 subjects was 73.3%.. No significant difference was observed between groups in terms of demographic characteristics including age, sex and APOE Ɛ4 status and neuropsychological test. However, regional SUVR (amygdala, inferior temporal gyrus and precuneus) and global SUVR were significantly higher in subjectively cognitive impaired group(p<0.05). There were no brain volume differences between groups.For volumetry and KDSQ‐C correlation analysis, partial correlation analysis controlled for age, sex and APOE Ɛ4 status showed significant negative correlation between left entorhinal cortex, right hippocampal volume and KDSQ‐C (r = ‐0.230, p = 0.046; r = ‐0.226, p = 0.049). Similar association was also found between KDSQ‐C memory domain score and brain volume.ConclusionKDSQ‐C can be a useful tool for screening SCD patients at a higher risk with higher amyloid deposition and brain atrophy.This study was supported by a grant from the Ministry of Health and Welfare, HI18C0530

Synaptic density is correlated with Aβ plaques and neurofibrillary tau tangles across the clinical and biologic Alzheimer’s disease continuum

AbstractBackgroundThe extent to which amyloid‐ß burden and neurofibrillary tau tangle burden are associated with synaptic degeneration in vivo is not well known. PET imaging was used to examine the cross‐sectional relationship between neurofibrillary tau tangle burden and amyloid‐ß plaque burden with synaptic density among individuals who spanned the clinical and biologic Alzheimer’s disease continuum.MethodParticipants were recruited from the Wisconsin Alzheimer’s Disease Research Center and the Wisconsin Registry for Alzheimer’s Prevention study. N = 64 participants were included in the analysis based on completed cognitive testing, structural MR and PET imaging using [11C]PiB DVR for amyloid burden, [18F]MK‐6240 SUVR for regional neurofibrillary tau burden, and [11C]UCB‐J DVR for regional synaptic density burden analysis. Amyloid burden was indexed as a global [11C]PiB DVR representing the mean of brain regions that typically accumulate amyloid. UCB‐J DVR and MK‐6240 SUVR regions of interest were selected based on the histopathological staging of tau pathology in Alzheimer’s disease with a focus on regions of tau pathology described by earlier and later Braak stages. The first composite region of interest (ROI1) comprised the hippocampus and entorhinal cortex, and the second (ROI2) comprised the amygdala, fusiform, lingual gyrus, and the inferior temporal gyrus. Linear correlations (Pearson’s r) tested the relationship between UCB‐J DVR and PiB DVR, as well as UCB‐J DVR and MK‐6240 SUVR.ResultGlobal [11C]PiB DVR showed a moderate negative relationship with UCB‐J DVR in ROI1 (r = ‐0.42, p = 0.0006) and a weak relationship with UCB‐J DVR in ROI2 (r = ‐0.33, p = 009). [18F]MK‐6240 SUVR and UCB‐J DVR were negatively correlated in ROI1 (r = ‐0.44, p = 0.0003) and in ROI2 (r = ‐0.42, p = 0.005).ConclusionBoth amyloid burden and tau pathology were associated with lower synaptic density. Tau and synaptic density appeared to show moderate correlations among brain regions involved in both earlier and more advanced tau pathology, whereas amyloid burden appeared to be more strongly related to synaptic density in brain regions where more advanced disease is typically observed.

Mapping changes in synaptic vesicle protein 2A (SV2A) in neurodegenerative diseases

AbstractBackgroundSynaptic loss is an early feature of neurodegenerative diseases and the best correlate of clinical decline. Synaptophysin is a pre‐synaptic protein used as a marker of synaptic integrity. Although no biomarker is available to measure synaptic integrity in the clinical setting, various potential PET‐scan tracers are under development. Tracers to detect synaptic vesicle protein 2A (SV2A), an integral glycoprotein in the membrane of synaptic vesicles, are among the most promising among those in development. However, the topographical distribution of SV2A in the human brain and the degree of regional changes in different neurodegenerative conditions are unclear, making it challenging to establish the utility of SV2A tracers in clinical practice. To close this gap, we mapped the density of SV2A in five areas vulnerable to age‐related neuropathology in postmortem brain tissue of subjects with four different types of neurodegenerative disease and healthy controls. Furthermore, we compared SV2A maps to synaptophysin maps obtained from adjacent slides.MethodTable 1 details the subjects' features. The regions of interest include middle and inferior frontal gyri, inferior temporal gyrus, angular gyrus, and CA1 sector/entorhinal cortex. 8 µm thick FFPE adjacent slides underwent immunohistochemistry for SV2A (1:150, Atlas Antibodies, rabbit polyclonal) or synaptophysin (1:75, Atlas Antibodies, rabbit polyclonal). All slides were scanned using a calibrated Zeiss Axioscan and quantified using the Zen software. Density was measured in 40 cortical ROIs (20 white & 20 gray matter) per area/case, randomly distributed. Values were normalized for white matter intensity.ResultSVA2 loss was present in all disease conditions. The pattern of change was region and disease‐specific (Fig 1). Interestingly, the correlation between SV2A and synaptophysin density was poor to moderate (Fig 2).ConclusionReduced SV2A expression/level in all diseases indicates that SV2A is a potential marker to detect neurodegeneration. However, the lack of correlation between synaptophysin and SV2A level changes suggests that these markers signal different pathways. Additional research is needed to investigate the relationship between regional loss of SV2A and its potential significance as a marker for neurodegenerative disorders. We are performing Western blotting and autoradiography in the same samples to confirm the immunohistochemistry findings.

Predicting cognitive dysfunction and regional hubs using Braak staging amyloid-beta biomarkers and machine learning

Mild cognitive impairment (MCI) is a transitional stage between normal aging and early Alzheimer’s disease (AD). The presence of extracellular amyloid-beta (Aβ) in Braak regions suggests a connection with cognitive dysfunction in MCI/AD. Investigating the multivariate predictive relationships between regional Aβ biomarkers and cognitive function can aid in the early detection and prevention of AD. We introduced machine learning approaches to estimate cognitive dysfunction from regional Aβ biomarkers and identify the Aβ-related dominant brain regions involved with cognitive impairment. We employed Aβ biomarkers and cognitive measurements from the same individuals to train support vector regression (SVR) and artificial neural network (ANN) models and predict cognitive performance solely based on Aβ biomarkers on the test set. To identify Aβ-related dominant brain regions involved in cognitive prediction, we built the local interpretable model-agnostic explanations (LIME) model. We found elevated Aβ in MCI compared to controls and a stronger correlation between Aβ and cognition, particularly in Braak stages III–IV and V–VII (p < 0.05) biomarkers. Both SVR and ANN, especially ANN, showed strong predictive relationships between regional Aβ biomarkers and cognitive impairment (p < 0.05). LIME integrated with ANN showed that the parahippocampal gyrus, inferior temporal gyrus, and hippocampus were the most decisive Braak regions for predicting cognitive decline. Consistent with previous findings, this new approach suggests relationships between Aβ biomarkers and cognitive impairment. The proposed analytical framework can estimate cognitive impairment from Braak staging Aβ biomarkers and delineate the dominant brain regions collectively involved in AD pathophysiology.

Brain gray matter volume mediated the relationship between plasma p‐tau and cognition of early Alzheimer’s disease

AbstractBackgroundCognitive deterioration and atrophy of the brain gray matter volume (GMV) are the main symptoms of Alzheimer’s disease (AD). Accumulated pieces of evidence have shown that plasma concentrations of phosphorylated‐tau (p‐tau) performed better in early diagnosing, differentiating, and monitoring the progression of AD. However, the relationship between plasma p‐tau concentrations, the GMV of brain regions, and cognitive function remains poorly understood. Previous studies indicate that brain GMV may mediate the relationship between biomarkers and cognitive function, and in this study, we aimed to investigate whether GMV plays a mediating role in the association between plasma p‐tau concentrations and cognition.Method99 participants in total, including 47 patients with a diagnosis of early AD and 52 cognitively unimpaired (CU) individuals, were enrolled in this study. All participants completed laboratory tests, magnetic resonance imaging scans, and neuropsychological assessments. Plasma p‐tau217 and p‐tau181 concentrations were measured using an enzyme‐linked immunosorbent assay kit. Voxel‐based morphometry was performed to assess brain GMV. Partial correlations and mediation analyses were performed in the AD group.ResultConcentrations of both plasma p‐tau217 and p‐tau181 were significantly higher in the AD group than in the CU group. Patients with AD had significant brain GMV atrophy in the right hippocampus, bilateral middle temporal gyrus, and right inferior temporal gyrus. In the AD group, there were significant correlations between plasma p‐tau217 concentrations, GMV, and Mini‐Mental State Examination (MMSE) scores. Brain GMV of the right hippocampus mediated the association between plasma p‐tau217 concentrations and MMSE scores. We did not find a significant correlation between plasma p‐tau181 concentrations and MMSE scores.ConclusionThe findings indicate that p‐tau217 was a promising biomarker for central processes affecting brain GMV and cognitive function. It may provide potential targets for future intervention and treatment of tau‐targeting therapies at the early stage of AD.

Neuronal populations in the inferior temporal gyrus with truncated tau inclusions occur as early as those with hyperphosphorylated tau inclusions in Alzheimer’s disease, however they do not completely overlap

AbstractBackgroundAlzheimer’s disease (AD) features stereotypical spread of hyperphosphorylated tau (p‐tau) and beta‐amyloid. Although other pathological tau posttranslational modifications (PTMs) have been described in AD, a prevalent disease model preconizes that other tau PTMs always overlap with p‐tau, making the latter an excellent marker of tau pathology burden. We recently showed in experimental studies that truncated tau (tr‐tau), a pathological tau PTM generated via cleavage by active caspases, is as common as p‐tau in neurons at late AD stages; however, only about 40% of tr‐tau positive neurons also show p‐tau positivity. This makes tr‐tau a potential marker of AD previously invisible at neuropathological investigation or a potential target of diagnostic tool development. We sought to determine how early in AD tr‐tau is detected compared to p‐tau and to what degree tr‐tau and p‐tau neuronal populations overlap at early AD stages.MethodsWe used multiplex immunofluorescence to probe tr‐tau (D13, D402, D418, TauC3) and p‐tau (PHF1) species in the same tissue slides of postmortem human brain tissue of healthy controls and subjects at progressive AD stages. We quantified neuronal tau pathology and colocalization in digital images of inferior temporal gyrus (ITG) as representative of an area involved in p‐tau pathology in intermediate stages.ResultsOur preliminary analysis included 14 cases (4 controls, 10 AD) from all Braak stages (1:1:2:3:3:2:2 for Braak 0 through 6). Of the cases used in the study, 71% were male and the mean (SD) age of death was 82.3 (10.9). As expected, p‐tau was detected from Braak 4; however, we detected tr‐tau species from Braak 3 (Fig. 1). Both p‐tau and tr‐tau burden increased with Braak stage (Fig. 2). The overlap between tr‐tau and p‐tau positive neuron populations was 30.8% at Braak 4 (Fig. 3A) and increased to 54.6% at Braak 6 (Fig. 3B).ConclusionNeuronal tr‐tau deposits develop in AD as early as p‐tau pathology. Even in earlier stages, the % of overlapping is moderate. This corroborates tr‐tau as a potential biomarker of tau pathology in AD not covered by p‐tau screening. We are currently finalizing analysis of a much larger cohort including other brain regions.

Healthy Dietary Intake Reduces Longitudinal Brain Iron Accumulation in Older Adults

AbstractBackgroundBrain iron, which tends to accumulate with normal aging, negatively affects cognitive performance. However, the impact of brain iron on cognition appears modifiable. In a recent study from our group [1], we demonstrated that those older adults with high dietary intake of antioxidants, iron chelating nutrients and polyunsaturated fatty acids (Factor 2 in [1]), tended to have lower brain iron and better working memory performance than expected for their age. Here, we extend our previous work by evaluating associations between baseline consumption of Factor 2 nutrients and longitudinal brain iron accumulation, evaluated three years after baseline.MethodsForty‐three, cognitively normal, older adults (26 women, age range 63‐88 years) underwent MRI‐based structural brain imaging and quantitative susceptibility mapping for assessment of brain iron concentration at two time points: baseline (T1) and three years after baseline (T2). Additionally, at T1, nutritional‐intake was assessed via a validated questionnaire. Nutrients were grouped into nutrition factors based on previous literature and factor analysis. Factor 2, comprised vitamin E, lysine, DHA omega‐3 and LA omega‐6, representing antioxidants, iron chelating nutrients and polyunsaturated fatty acids.ResultsVoxelwise, whole‐brain, linear mixed‐effects models (qFDR &lt; 0.05), controlling for longitudinal gray matter volume alterations, age, gender and education, revealed brain iron accumulation within several subcortical and cortical brain regions. Importantly, a significant time point by nutrition interaction was also observed. Specifically, those participants who consumed higher amounts of Factor 2 nutrients at baseline exhibited lower longitudinal brain iron accumulation within the left middle frontal gyrus, left superior parietal lobule, left insular cortex, right inferior temporal cortex, and bilateral hippocampus. Time point by nutrition interactions were not observed for other nutrition factors discussed in [1], such as those comprised of nutrients found in fruits and vegetables, or in green tea.ConclusionOur results indicate that dietary patterns rich in oxidative stress‐reducing antioxidants, iron chelating nutrients and polyunsaturated fatty acids may slow down age‐related, brain iron accumulation in older adults.

Systemic vascular risk, white matter injury, and relative cerebral blood flow independently contribute to cognitive decline beyond amyloid and tau burden

AbstractBackgroundAge, systemic vascular risk factors, white matter injury, cerebral blood flow, amyloid, and tau burden are well‐established contributors to late‐life cognitive decline. Given the strong relationship among these measures, we proposed a model to extract latent factors and isolate domain‐specific measures that underlie cognitive decline in preclinical AD.MethodData were available for 166 cognitively unimpaired older adults from Harvard Aging Brain Study (Table1). We used the following measures: 1) cortical amyloid burden from PiB‐PET; 2) inferior temporal cortex FTP‐PET; 3) relative cerebral blood flow (R1) from dynamically‐acquired PiB‐PET; 4) white matter hyperintensities (WMH) from FLAIR‐MRI; 5) Peak width of skeletonized mean diffusivity (PSMD) from DTI‐MRI; 6) gray matter volume (GMV) from MPRAGE‐MRI; 7) Framingham Heart Study cardiovascular disease risk (CVD); 8) Preclinical Alzheimer Cognitive Composite‐5 (PACC). We extracted slopes using linear mixed effect models for longitudinal PACC decline and GMV atrophy (follow‐up = 7.4±2.8 years). The remaining data were obtained from baseline. We used exploratory factor analysis (varimax rotations) to extract orthogonal factors. These factors were used in a regression model to explain PACC decline. We next examined whether GMV atrophy acts as a mediator of factors and PACC decline. Subsequently, we investigated whether factors interact to further explain PACC decline.ResultsOriginal variables were highly interrelated (Figure1A) and correlated with PACC decline (Figure1B). Scree test showed 6 factors were sufficient (cumulative variance:96%, Figure2A). All factors except Factor5 (age) independently explained PACC decline (Figure2B, r2 = 0.44). Including GMV atrophy removed effect of Factor1 (white matter injury, Figure2B, r2 = 0.51). Factor3 (amyloid) interacted with Factor1 and Factor4 (tau) in explaining PACC decline (Figure2C, r2 = 0.58).ConclusionLatent factors of systemic vascular risk, white matter injury, and relative cerebral blood flow (but not age) independently explain cognitive decline beyond amyloid and tau burden in this preclinical AD cohort. GMV atrophy mediates these associations with the strongest effect on white matter injury suggesting a strong link between white matter injury and neurodegeneration. These results suggest that current markers of cerebrovascular injury contribute to cognitive decline beyond systemic vascular risk (CVD), amyloid, and tau burden. The mechanism by which systemic vascular risk impacts cognition requires further explication.

Longitudinal cortical thinning is associated with cognitive decline in MCI and Alzheimer’s disease in the Korean population

AbstractUndAlzheimer’s disease (AD) is characterized by cognitive dysfunction likely secondary to progressive neurodegeneration in the brain. However, to what extent regional cortical thickness (CT) changes are related to cognitive decline in the Asian population remains unclear.MethodWe measured CT using Freesurfer v6 from T1‐weighted magnetic resonance imaging (MRI) data in 603 Korean participants, including 71 young and 288 old cognitively unimpaired (CU young and CU old), 149 with mild cognitive impairment (MCI), and 92 AD from the Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer’s disease (KBASE). Cognitive function of each participant was quantified by the MMSE. MRI and cognitive data were acquired at baseline (n = 603) and a 2‐year follow‐up visit (n = 380). Linear mixed‐effect models were used to estimate diagnosis group, time, and group‐by‐time interaction effects on CT measurements, with age, sex, and education as covariates. Pearson’s correlations between CT measurements and MMSE total score were computed, corrected for multiple comparisons by false discovery rate (FDR).ResultAt baseline, CU old participants had lower CT values than CU young participants in the inferior parietal cortex and frontal and parietal regions. In comparison with CU old participants, MCI and AD showed widespread lower CT values, more pronounced in the medial temporal lobe (i.e., the entorhinal cortex and parahippocampal gyrus), inferior temporal cortex, medial prefrontal cortex, and medial parietal cortex (i.e., the posterior cingulate cortex and precuneus), with AD lower than MCI in these regions. These regions also showed the most longitudinal atrophy in both AD and MCI over 2 years. AD patients showed faster and more widespread atrophy patterns than MCI and CU old participants. We identified significant (PFDR&lt;0.05) positive linear correlations between cognitive decline and regional CT measurements, particularly in regions showing significant cortical thinning across time.ConclusionIn this study, we replicated the typical cross‐sectional and longitudinal brain atrophy patterns, and relationships with cognitive decline in an Asian cohort. Our findings support the generalizability of atrophy patterns in MCI and AD, suggesting common neurobiological mechanisms underlying AD symptoms across different ethnoracial populations.

Feasibility of 18F‐florzolotau Quantification in Patients with Alzheimer’s Disease Based on an MRI‐free Tau PET Template

AbstractBackgroundQuantification of tau accumulation using positron emission tomography (PET) is critical for the diagnosis of Alzheimer’s disease (AD). This study aimed to evaluate the feasibility of 18F‐florzolotau quantification in patients with AD using a magnetic resonance imaging (MRI)‐free tau PET template, since individual high‐resolution MRI is costly and not always available in practice.Method 18F‐florzolotau PET and MRI scans were obtained in a discovery cohort including 1) patients within the AD continuum (n = 87), 2) cognitively impaired patients with non‐AD (n = 32), and 3) cognitively unimpaired subjects (n = 24). The validation cohort comprised 24 patients with AD. Following MRI‐dependent spatial normalization (standard approach) in randomly selected subjects (n = 40) to cover the entire spectrum of cognitive function, selected PET images were averaged to create the 18F‐florzolotau‐specific template. Standardized uptake value ratios (SUVRs) were calculated in five predefined regions of interest (ROIs) including entorhinal cortex, inferior temporal cortex, temporal meta‐ROI, temporoparietal cortex, and neocortical areas. MRI‐free and MRI‐dependent methods were compared in terms of continuous and dichotomous agreement, diagnostic performances, and associations with specific cognitive domains.ResultMRI‐free SUVRs had high continuous and dichotomous agreement with MRI‐dependent measures for all ROIs (intraclass correlation coefficient ≥ 0.980; agreement ≥ 94.5%). Similar findings were observed for AD‐related effect sizes, diagnostic performances with respect to categorization across the cognitive spectrum, and associations with cognitive domains. The robustness of the MRI‐free approach was confirmed in the validation cohort.ConclusionThe use of an 18F‐florzolotau‐specific template is a valid alternative to MRI‐dependent spatial normalization, improving the clinical generalizability of this second‐generation tau tracer.

Regional correlation analysis between neuronal metabolism and synaptic density using [18F]FDG and [11C]UCB‐J

AbstractBackgroundNeuronal hypometabolism measured with [18F]FDG PET scans can be used to identify neurodegeneration as part of the AT(N) (amyloid, tau, neurodegeneration) framework in AD. PET imaging of synaptic vesicle glycoprotein SV2A using [11C]UCB‐J provides an index of synaptic density, which has been shown to decrease during neurodegeneration. The purpose of this study was to examine the regional relationship between neuronal metabolism and synaptic density in participants across the continuum of AD‐related pathology.MethodParticipants were recruited from the Wisconsin ADRC and the Wisconsin Registry for Alzheimer’s Prevention (WRAP) study. A subset of 13 participants (Table 1) were selected who had completed cognitive testing, structural MR imaging, and PET imaging with the following: [11C]PiB centiloid (CL) for amyloid +/‐ status, [18F]MK‐6240 SUVR for tau +/‐ status, [11C]UCB‐J DVR for regional synaptic density burden, and [18F]FDG SUVR for regional glucose metabolism. UCB‐J and FDG analysis used a whole cerebellum reference region; PiB and MK‐6240 analysis used cerebellar GM reference regions. Inter‐tracer (UCB‐J/FDG) correlations were performed in Freesurfer‐defined bilateral Braak‐associated ROIs (Medial Temporal Lobe [MTL]: entorhinal cortex, hippocampus, and amygdala; Ventral Temporal Lobe [VTL]: fusiform, lingual, and inferior temporal gyri), signature regions of hypometabolism in AD (posterior cingulate, parietal, and frontal cortices) and a region with only marginal hypometabolism in AD (occipital cortex). Associations were determined using Pearson correlations (r).ResultAll ROIs demonstrate a positive association between FDG SUVR and UCB‐J DVR, with r &gt; 0.7 and p &lt; 0.01 (Figure 1). The occipital, frontal, and posterior cingulate cortices demonstrate the strongest correlations (r = 0.84, 0.83, and 0.81, respectively).ConclusionIn cognitively impaired and unimpaired participants showing varying degrees of ß‐amyloid and neurofibrillary tau burden, strong positive correlations are observed in regions associated with AD tau pathology and hypometabolism. Correlations are also observed in regions not typically associated with hypometabolism. This suggests that a relationship may exist between synaptic density and neuronal metabolism in the presence of AD neuropathology.

Trajectory of MRI and FDG‐PET biomarkers in Alzheimer’s disease

AbstractBackgroundAlzheimer’s disease (AD) has a long preclinical phase, which is hypothesised to start with the accumulation of β‐amyloid (Aβ) plaques, followed by tau neurofibrillary tangles and neurodegeneration. Despite presenting quite different types of information, MRI‐derived volumetric measures and FDG‐PET‐derived metabolism are both commonly used as proxy of neurodegeneration in AD. The aim of this work is to assess the trajectory of both of these biomarkers in AD, relative to time to amyloid‐positivity.Method3289 MRI scans (682 participants) and 2472 FDG‐PET scans (1296 participants) were included from the ADNIGO, 1,2 and 3 cohorts. For MRI scans, total volume was calculated for a meta‐ROI consisting of entorhinal, inferior, middle temporal and fusiform gyri. For FDG‐PET, standardized uptake value ratios (SUVR) were calculated from 30‐60 min post injection for a meta‐ROI consisting of the angular, posterior cingulate and inferior temporal gyri, normalised to the pons. All metrices were transformed into z‐scores using the references values of the Aβ‐negative subjects. Age of amyloid‐positivity was determined by fitting a nonlinear mixed effects model to 3607 Centloid values corresponding to 1690 participants. Next, time to amyloid‐positivity was calculated for each scan. The relationship between time to amyloid‐positivity and (z‐scored) SUVR or cortical volume values was modelled using a non‐linear mixed effects model with spline function and trajectories from both modalities were compared.ResultsBaseline demographics can be found in Table 1. Figure 1 shows the trajectory of amyloid deposition that was used to determine age of amyloid‐positivity. Cortical volume and SUVR showed similar trajectories over time, with steeper decline in both measures after the event of amyloid‐positivity (Figure 2). The overlay of both biomarker trajectories shows that for the meta‐ROI, there is no difference in when these biomarkers become abnormal.ConclusionsThe present results suggest that measures of neurodegeneration from MRI and FDG‐PET scans corresponding to a meta‐ROI, follow very similar trajectories. Future work will investigate if this also holds true for smaller regions of interest.