MRI correlates of neurofibrillary tangle pathology at autopsy: a voxel-based morphometry study

Machine learning on longitudinal multi-modal data enables the understanding and prognosis of Alzheimer's disease progression

Alzheimer's disease (AD) is a complex pathophysiological disease. Allowing for heterogeneity, not only in disease manifestations but also in different progression patterns, is critical for developing effective disease models that can be used in clinical and research settings. We introduce a machine learning model for identifying underlying patterns in Alzheimer's disease (AD) trajectory using longitudinal multi-modal data from the ADNI cohort and the AIBL cohort. Ten biologically and clinically meaningful disease-related states were identified from data, which constitute three non-overlapping stages (i.e., neocortical atrophy [NCA], medial temporal atrophy [MTA], and whole brain atrophy [WBA]) and two distinct disease progression patterns (i.e., NCA → WBA and MTA → WBA). The index of disease-related states provided a remarkable performance in predicting the time to conversion to AD dementia (C-Index: 0.923 ± 0.007). Our model shows potential for promoting the understanding of heterogeneous disease progression and early predicting the conversion time to AD dementia.

AβPP-tau-HAS1 axis trigger HAS1-related nuclear speckles and gene transcription in Alzheimer's disease

As the backbone of the extracellular matrix (ECM) and the perineuronal nets (PNNs), hyaluronic acid (HA) provides binding sites for proteoglycans and other ECM components. Although the pivotal of HA has been recognized in Alzheimer's disease (AD), few studies have addressed the relationship between AD pathology and HA synthases (HASs). Here, HASs in different regions of AD brains were screened in transcriptomic database and validated in AβPP/PS1 mice. We found that HAS1 was distributed along the axon and nucleus. Its transcripts were reduced in AD patients and AβPP/PS1 mice. Phosphorylated tau (p-tau) mediates AβPP-induced cytosolic-nuclear translocation of HAS1, and negatively regulated the stability, monoubiquitination, and oligomerization of HAS1, thus reduced the synthesis and release of HA. Furthermore, non-ubiquitinated HAS1 mutant lost its enzyme activity, and translocated from the cytosol into the nucleus, forming nuclear speckles (NS). Unlike the splicing-related NS, less than 1 % of the non-ubiquitinated HAS1 co-localized with SRRM2, proving the regulatory role of HAS1 in gene transcription, indirectly. Thus, differentially expressed genes (DEGs) related to both non-ubiquitinated HAS1 mutant and AD were screened using transcriptomic datasets. Thirty-nine DEGs were identified, with 64.1 % (25/39) showing consistent results in both datasets. Together, we unearthed an important function of the AβPP-p-tau-HAS1 axis in microenvironment remodeling and gene transcription during AD progression, involving the ubiquitin-proteasome, lysosome, and NS systems.

Association between hypothalamic Alzheimer's disease pathology and body mass index: The Hisayama study

The hypothalamus is the region of the brain that integrates the neuroendocrine system and whole-body metabolism. Patients with Alzheimer's disease (AD) have been reported to exhibit pathological changes in the hypothalamus, such as neurofibrillary tangles (NFTs) and amyloid plaques (APs). However, few studies have investigated whether hypothalamic AD pathology is associated with clinical factors. We investigated the association between AD-related pathological changes in the hypothalamus and clinical pictures using autopsied brain samples obtained from deceased residents of a Japanese community. A total of 85 autopsied brain samples were semi-quantitatively analyzed for AD pathology, including NFTs and APs. Our histopathological studies showed that several hypothalamic nuclei, such as the tuberomammillary nucleus (TBM) and lateral hypothalamic area (LHA), are vulnerable to AD pathologies. NFTs are observed in various neuropathological states, including normal cognitive cases, whereas APs are predominantly observed in AD. Regarding the association between hypothalamic AD pathologies and clinical factors, the degree of APs in the TBM and LHA was associated with a lower body mass index while alive, after adjusting for sex and age at death. However, we found no significant association between hypothalamic AD pathology and the prevalence of hypertension, diabetes, or dyslipidemia. Our study showed that a lower BMI, which is a poor prognostic factor of AD, might be associated with hypothalamic AP pathology and highlighted new insights regarding the disruption of the brain-whole body axis in AD.

Biomarker-based staging of Alzheimer disease: rationale and clinical applications

Disease staging, whereby the spatial extent and load of brain pathology are used to estimate the severity of Alzheimer disease (AD), is pivotal to the gold-standard neuropathological diagnosis of AD. Current in vivo diagnostic frameworks for AD are based on abnormal concentrations of amyloid-β and tau in the cerebrospinal fluid or on PET scans, and breakthroughs in molecular imaging have opened up the possibility of in vivo staging of AD. Focusing on the key principles of disease staging shared across several areas of medicine, this Review highlights the potential for in vivo staging of AD to transform our understanding of preclinical AD, refine enrolment criteria for trials of disease-modifying therapies and aid clinical decision-making in the era of anti-amyloid therapeutics. We provide a state-of-the-art review of recent biomarker-based AD staging systems and highlight their contributions to the understanding of the natural history of AD. Furthermore, we outline hypothetical frameworks to stage AD severity using more accessible fluid biomarkers. In addition, by applying amyloid PET-based staging to recently published anti-amyloid therapeutic trials, we highlight how biomarker-based disease staging frameworks could illustrate the numerous pathological changes that have already taken place in individuals with mildly symptomatic AD. Finally, we discuss challenges related to the validation and standardization of disease staging and provide a forward-looking perspective on potential clinical applications.

Determinants of confrontation naming deficits on the Boston Naming Test associated with transactive response DNA-binding protein 43 pathology

OBJECTIVE

To determine whether poorer performance on the Boston Naming Test (BNT) in individuals with transactive response DNA-binding protein 43 pathology (TDP-43+) is due to greater loss of word knowledge compared to retrieval-based deficits.

METHODS

Retrospective clinical-pathologic study of 282 participants with Alzheimer's disease neuropathologic changes (ADNC) and known TDP-43 status. We evaluated item-level performance on the 60-item BNT for first and last available assessment. We fit cross-sectional negative binomial count models that assessed total number of incorrect items, number correct of responses with phonemic cue (reflecting retrieval difficulties), and number of "I don't know" (IDK) responses (suggestive of loss of word knowledge) at both assessments. Models included TDP-43 status and adjusted for sex, age, education, years from test to death, and ADNC severity. Models that evaluated the last assessment adjusted for number of prior BNT exposures.

RESULTS

43% were TDP-43+. The TDP-43+ group had worse performance on BNT total score at first (p = .01) and last assessments (p = .01). At first assessment, TDP-43+ individuals had an estimated 29% (CI: 7%-56%) higher mean number of incorrect items after adjusting for covariates, and a 51% (CI: 15%-98%) higher number of IDK responses compared to TDP-43-. At last assessment, compared to TDP-43-, the TDP-43+ group on average missed 31% (CI: 6%-62%; p = .01) more items and had 33% more IDK responses (CI: 1% fewer to 78% more; p = .06).

CONCLUSIONS

An important component of poorer performance on the BNT in participants who are TDP-43+ is having loss of word knowledge versus retrieval difficulties.

Associations of sleep disorders with all-cause MCI/dementia and different types of dementia - clinical evidence, potential pathomechanisms and treatment options: A narrative review

Due to worldwide demographic change, the number of older persons in the population is increasing. Aging is accompanied by changes of sleep structure, deposition of beta-amyloid (Aß) and tau proteins and vascular changes and can turn into mild cognitive impairment (MCI) as well as dementia. Sleep disorders are discussed both as a risk factor for and as a consequence of MCI/dementia. Cross-sectional and longitudinal population-based as well as case-control studies revealed sleep disorders, especially sleep-disorderded breathing (SDB) and excessive or insufficient sleep durations, as risk factors for all-cause MCI/dementia. Regarding different dementia types, SDB was especially associated with vascular dementia while insomnia/insufficient sleep was related to an increased risk of Alzheimer's disease (AD). Scarce and still inconsistent evidence suggests that therapy of sleep disorders, especially continuous positive airway pressure (CPAP) in SDB, can improve cognition in patients with sleep disorders with and without comorbid dementia and delay onset of MCI/dementia in patients with sleep disorders without previous cognitive impairment. Regarding potential pathomechanisms via which sleep disorders lead to MCI/dementia, disturbed sleep, chronic sleep deficit and SDB can impair glymphatic clearance of beta-amyloid (Aß) and tau which lead to amyloid deposition and tau aggregation resulting in changes of brain structures responsible for cognition. Orexins are discussed to modulate sleep and Aß pathology. Their diurnal fluctuation is suppressed by sleep fragmentation and the expression suppressed at the point of hippocampal atrophy, contributing to the progression of dementia. Additionally, sleep disorders can lead to an increased vascular risk profile and vascular changes such as inflammation, endothelial dysfunction and atherosclerosis which can foster neurodegenerative pathology. There is ample evidence indicating that changes of sleep structure in aging persons can lead to dementia and also evidence that therapy of sleep disorder can improve cognition. Therefore, sleep disorders should be identified and treated early.

Understanding the complex interplay between tau, amyloid and the network in the spatiotemporal progression of Alzheimer's Disease

It is well known that Aβ and tau proteins are deposited stereotypically in brain regions to cause Alzheimer's disease. The interaction of amyloid and tau in neurodegenerative diseases is a central feature and key to understanding AD pathophysiology. However their mechanisms are controversial, and many aspects do not fit current theories that rely on cell-autonomous factors. While cell culture and animal studies point to various interaction mechanisms between amyloid and tau, their causal direction and mode (local, remote or network-mediated) remain unknown in human subjects. Further, cross-protein interaction is yet to be reconciled with canonical observations that the two species do not co-localize significantly either in space or in time, and do not target the same neuronal populations. To answer these questions quantitatively, in this study we employed a mathematical reaction-diffusion model encoding the biophysical mechanisms underlying self-assembly, trans-neuronal network propagation and enzymtic cross-species coupling of amyloid and tau. We first established that the spatiotemporal evolution of theoretical tau and Aβ correctly predicts empirical patterns of regional Aβ, tau and atrophy. Remarkably, the introduction of a 1-way Aβ→tau interaction was critical to the models' success. In comparison, both the non-interacting and the 2-way interaction models were significantly worse. We also found that network-mediated spread is essential; alternative modes of spread involving proximity or fiber length fare much worse. This mathematical exposition of the "pas de deux" of co-evolving proteins provides crucial quantitative and whole-brain support to the concept of amyloid-facilitated-tauopathy rather than the classic amyloid-cascade or pure-tau hypotheses, and helps explain certain known but poorly understood aspects of AD.

Tau-neurodegeneration mismatch reveals vulnerability and resilience to comorbidities in Alzheimer's continuum

INTRODUCTION

Variability in relationship of tau-based neurofibrillary tangles (T) and neurodegeneration (N) in Alzheimer's disease (AD) arises from non-specific nature of N, modulated by non-AD co-pathologies, age-related changes, and resilience factors.

METHODS

We used regional T-N residual patterns to partition 184 patients within the Alzheimer's continuum into data-driven groups. These were compared with groups from 159 non-AD (amyloid "negative") patients partitioned using cortical thickness, and groups in 98 patients with ante mortem MRI and post mortem tissue for measuring N and T, respectively. We applied the initial T-N residual model to classify 71 patients in an independent cohort into predefined groups.

RESULTS

AD groups displayed spatial T-N mismatch patterns resembling neurodegeneration patterns in non-AD groups, similarly associated with non-AD factors and diverging cognitive outcomes. In the autopsy cohort, limbic T-N mismatch correlated with TDP-43 co-pathology.

DISCUSSION

T-N mismatch may provide a personalized approach for determining non-AD factors associated with resilience/vulnerability in AD.

Synthesizing images of tau pathology from cross-modal neuroimaging using deep learning

Given the prevalence of dementia and the development of pathology-specific disease-modifying therapies, high-value biomarker strategies to inform medical decision-making are critical. In vivo tau-PET is an ideal target as a biomarker for Alzheimer's disease diagnosis and treatment outcome measure. However, tau-PET is not currently widely accessible to patients compared to other neuroimaging methods. In this study, we present a convolutional neural network (CNN) model that imputes tau-PET images from more widely available cross-modality imaging inputs. Participants (n = 1192) with brain T1-weighted MRI (T1w), fluorodeoxyglucose (FDG)-PET, amyloid-PET and tau-PET were included. We found that a CNN model can impute tau-PET images with high accuracy, the highest being for the FDG-based model followed by amyloid-PET and T1w. In testing implications of artificial intelligence-imputed tau-PET, only the FDG-based model showed a significant improvement of performance in classifying tau positivity and diagnostic groups compared to the original input data, suggesting that application of the model could enhance the utility of the metabolic images. The interpretability experiment revealed that the FDG- and T1w-based models utilized the non-local input from physically remote regions of interest to estimate the tau-PET, but this was not the case for the Pittsburgh compound B-based model. This implies that the model can learn the distinct biological relationship between FDG-PET, T1w and tau-PET from the relationship between amyloid-PET and tau-PET. Our study suggests that extending neuroimaging's use with artificial intelligence to predict protein specific pathologies has great potential to inform emerging care models.

A Multimodal Meta-Analytical Evidence of Functional and Structural Brain Abnormalities Across Alzheimer's Disease Spectrum

BACKGROUND

Numerous neuroimaging studies have reported that Alzheimer's disease (AD) spectrum have been linked to alterations in intrinsic functional activity and cortical thickness (CT) of some brain areas. However, the findings have been inconsistent and the correlation with the transcriptional profile and neurotransmitter systems remain largely unknown.

METHODS

We conducted a meta-analysis to identify multimodal differences in the amplitude of low-frequency fluctuation (ALFF)/fractional ALFF (fALFF) and CT in patients with AD and preclinical AD compared to healthy controls (HCs), using the Seed-based d Mapping with Permutation of Subject Images software. Transcriptional data were retrieved from the Allen Human Brain Atlas. The atlas-based nuclear imaging-derived neurotransmitter maps were investigated by JuSpace toolbox.

RESULTS

We included 26 ALFF/fALFF studies comprising 884 patients with AD and 1,020 controls, along with 52 studies comprising 2,046 patients with preclinical AD and 2,336 controls. For CT, we included 11 studies comprising 353 patients with AD and 330 controls. Overall, compared to HCs, patients with AD showed decreased ALFF/fALFF in the bilateral posterior cingulate gyrus (PCC)/precuneus and right angular gyrus, as well as increased ALFF/fALFF in the bilateral parahippocampal gyrus (PHG). Patients with peclinical AD showed decreased ALFF/fALFF in the left precuneus. Additionally, patients with AD displayed decreased CT in the bilateral PHG, left PCC, bilateral orbitofrontal cortex, sensorimotor areas and temporal lobe. Furthermore, gene sets related to brain structural and functional changes in AD and preclincal AD were enriched for G protein-coupled receptor signaling pathway, ion gated channel activity, and components of biological membrane. Functional and structural alterations in AD and preclinical AD were spatially associated with dopaminergic, serotonergic, and GABAergic neurotransmitter systems.

CONCLUSIONS

The multimodal meta-analysis demonstrated that patients with AD exhibited convergent functional and structural alterations in the PCC/precuneus and PHG, as well as cortical thinning in the primary sensory and motor areas. Furthermore, patients with preclinical AD showed reduced functional activity in the precuneus. AD and preclinical AD showed genetic modulations/neurotransmitter deficits of brain functional and structural impairments. These findings may provide new insights into the pathophysiology of the AD spectrum.

Predicting amyloid-PET status in a memory clinic: The role of the novel antero-posterior index and visual rating scales

INTRODUCTION

Visual rating scales are increasingly utilized in clinical practice to assess atrophy in crucial brain regions among patients with cognitive disorders. However, their capacity to predict Alzheimer's disease (AD)-related pathology remains unexplored, particularly within a heterogeneous memory clinic population. This study aims to assess the accuracy of a novel visual rating assessment, the antero-posterior index (API) scale, in predicting amyloid-PET status. Furthermore, the study seeks to determine the optimal cohort-based cutoffs for the medial temporal atrophy (MTA) and parietal atrophy (PA) scales and to integrate the main visual rating scores into a predictive model.

METHODS

We conducted a retrospective analysis of brain MRI and high-resolution TC scans from 153 patients with cognitive disorders who had undergone amyloid-PET assessments due to suspected AD pathology in a real-world memory clinic setting.

RESULTS

The API scale (cutoff ≥1) exhibited the highest accuracy (AUC = 0.721) among the visual rating scales. The combination of the cohort-based MTA and PA threshold with the API yielded favorable accuracy (AUC = 0.787). Analyzing a cohort of MCI/Mild dementia patients below 75 years of age, the API scale and the predictive model improved their accuracy (AUC = 0.741 and 0.813, respectively), achieving excellent results in the early-onset population (AUC = 0.857 and 0.949, respectively).

CONCLUSION

Our study emphasizes the significance of visual rating scales in predicting amyloid-PET positivity within a real-world memory clinic. Implementing the novel API scale, alongside our cohort-based MTA and PA thresholds, has the potential to substantially enhance diagnostic accuracy.

Accurate digital quantification of tau pathology in progressive supranuclear palsy

The development of novel treatments for Progressive Supranuclear Palsy (PSP) is hindered by a knowledge gap of the impact of neurodegenerative neuropathology on brain structure and function. The current standard practice for measuring postmortem tau histology is semi-quantitative assessment, which is prone to inter-rater variability, time-consuming and difficult to scale. We developed and optimized a tau aggregate type-specific quantification pipeline for cortical and subcortical regions, in human brain donors with PSP. We quantified 4 tau objects ('neurofibrillary tangles', 'coiled bodies', 'tufted astrocytes', and 'tau fragments') using a probabilistic random forest machine learning classifier. The tau pipeline achieved high classification performance (F1-score > 0.90), comparable to neuropathologist inter-rater reliability in the held-out test set. Using 240 AT8 slides from 32 postmortem brains, the tau burden was correlated against the PSP pathology staging scheme using Spearman's rank correlation. We assessed whether clinical severity (PSP rating scale, PSPRS) score reflects neuropathological severity inferred from PSP stage and tau burden using Bayesian linear mixed regression. Tufted astrocyte density in cortical regions and coiled body density in subcortical regions showed the highest correlation to PSP stage (r = 0.62 and r = 0.38, respectively). Using traditional manual staging, only PSP patients in stage 6, not earlier stages, had significantly higher clinical severity than stage 2. Cortical tau density and neurofibrillary tangle density in subcortical regions correlated with clinical severity. Overall, our data indicate the potential for highly accurate digital tau aggregate type-specific quantification for neurodegenerative tauopathies; and the importance of studying tau aggregate type-specific burden in different brain regions as opposed to overall tau, to gain insights into the pathogenesis and progression of tauopathies.

Prediction of Conversion from Mild Cognitive Impairment to Alzheimer's Disease Using Amyloid PET and Brain MR Imaging Data: A 48-Month Follow-Up Analysis of the Alzheimer's Disease Neuroimaging Initiative Cohort

We developed a novel quantification method named "shape feature" by combining the features of amyloid positron emission tomography (PET) and brain magnetic resonance imaging (MRI) and evaluated its significance in predicting the conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD) in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. From the ADNI database, 334 patients with MCI were included. The brain amyloid smoothing score (AV45_BASS) and brain atrophy index (MR_BAI) were calculated using the surface area and volume of the region of interest in AV45 PET and MRI. During the 48-month follow-up period, 108 (32.3%) patients converted from MCI to AD. Age, Mini-Mental State Examination (MMSE), cognitive subscale of the Alzheimer's Disease Assessment Scale (ADAS-cog), apolipoprotein E (APOE), standardized uptake value ratio (SUVR), AV45_BASS, MR_BAI, and shape feature were significantly different between converters and non-converters. Univariate analysis showed that age, MMSE, ADAS-cog, APOE, SUVR, AV45_BASS, MR_BAI, and shape feature were correlated with the conversion to AD. In multivariate analyses, high shape feature, SUVR, and ADAS-cog values were associated with an increased risk of conversion to AD. In patients with MCI in the ADNI cohort, our quantification method was the strongest prognostic factor for predicting their conversion to AD.

Retrospective Evaluation of Neuropathologic Proxies of the Minimal Atrophy Subtype Compared With Corticolimbic Alzheimer Disease Subtypes

BACKGROUND AND OBJECTIVES

Alzheimer disease (AD) is neuropathologically classified into 3 corticolimbic subtypes based on the neurofibrillary tangle distribution throughout the hippocampus and association cortices: limbic predominant, typical, and hippocampal sparing. In vivo, a fourth subtype, dubbed "minimal atrophy," was identified using structural MRI. The objective of this study was to identify a neuropathologic proxy for the neuroimaging-defined minimal atrophy subtype.

METHODS

We applied 2 strategies in the Florida Autopsied Multi-Ethnic (FLAME) cohort to evaluate a neuropathologic proxy for the minimal atrophy subtype. In the first strategy, we selected AD cases with a Braak tangle stage IV (Braak IV) because of the relative paucity of neocortical tangle involvement compared with Braak >IV. Braak IV cases were compared with the 3 AD subtypes. In the alternative strategy, typical AD was stratified by brain weight and cases having a relatively high brain weight (>75th percentile) were defined as minimal atrophy.

RESULTS

Braak IV cases (n = 37) differed from AD subtypes (limbic predominant [n = 174], typical [n = 986], and hippocampal sparing [n = 187] AD) in having the least years of education (median 12 years, group-wise p < 0.001) and the highest brain weight (median 1,140 g, p = 0.002). Braak IV cases most resembled the limbic predominant cases owing to their high proportion of APOE ε4 carriers (75%, p < 0.001), an amnestic syndrome (100%, p < 0.001), as well as older age of cognitive symptom onset and death (median 79 and 85 years, respectively, p < 0.001). Only 5% of Braak IV cases had amygdala-predominant Lewy bodies (the lowest frequency observed, p = 0.017), whereas 32% had coexisting pathology of Lewy body disease, which was greater than the other subtypes (p = 0.005). Nearly half (47%) of the Braak IV samples had coexisting limbic predominant age-related TAR DNA-binding protein 43 encephalopathy neuropathologic change. Cases with a high brain weight (n = 201) were less likely to have amygdala-predominant Lewy bodies (14%, p = 0.006) and most likely to have Lewy body disease (31%, p = 0.042) compared with those with middle (n = 455) and low (n = 203) brain weight.

DISCUSSION

The frequency of Lewy body disease was increased in both neuropathologic proxies of the minimal atrophy subtype. We hypothesize that Lewy body disease may underlie cognitive decline observed in minimal atrophy cases.

Tau pathology as determinant of changes in atrophy and cerebral blood flow: a multi-modal longitudinal imaging study

PURPOSE

Tau pathology is associated with concurrent atrophy and decreased cerebral blood flow (CBF) in Alzheimer's disease (AD), but less is known about their temporal relationships. Our aim was therefore to investigate the association of concurrent and longitudinal tau PET with longitudinal changes in atrophy and relative CBF.

METHODS

We included 61 individuals from the Amsterdam Dementia Cohort (mean age 65.1 ± 7.5 years, 44% female, 57% amyloid-β positive [Aβ +], 26 cognitively impaired [CI]) who underwent dynamic [18F]flortaucipir PET and structural MRI at baseline and 25 ± 5 months follow-up. In addition, we included 86 individuals (68 CI) who only underwent baseline dynamic [18F]flortaucipir PET and MRI scans to increase power in our statistical models. We obtained [18F]flortaucipir PET binding potential (BPND) and R1 values reflecting tau load and relative CBF, respectively, and computed cortical thickness from the structural MRI scans using FreeSurfer. We assessed the regional associations between i) baseline and ii) annual change in tau PET BPND in Braak I, III/IV, and V/VI regions and cortical thickness or R1 in cortical gray matter regions (spanning the whole brain) over time using linear mixed models with random intercepts adjusted for age, sex, time between baseline and follow-up assessments, and baseline BPND in case of analyses with annual change as determinant. All analyses were performed in Aβ-  cognitively normal (CN) individuals and Aβ+  (CN and CI) individuals separately.

RESULTS

In Aβ+ individuals, greater baseline Braak III/IV and V/VI tau PET binding was associated with faster cortical thinning in primarily frontotemporal regions. Annual changes in tau PET were not associated with cortical thinning over time in either Aβ+ or Aβ-  individuals. Baseline tau PET was not associated with longitudinal changes in relative CBF, but increases in Braak III/IV tau PET over time were associated with increases in parietal relative CBF over time in Aβ + individuals.

CONCLUSION

We showed that higher tau load was related to accelerated cortical thinning, but not to decreases in relative CBF. Moreover, tau PET load at baseline was a stronger predictor of cortical thinning than change of tau PET signal.

TDP-43 pathology effect on volume and flortaucipir uptake in Alzheimer's disease

INTRODUCTION

Alzheimer's disease (AD) patients ≥70 years show smaller medial temporal volumes despite less 18 F-flortaucipir-positron emission tomography (PET) uptake than younger counterparts. We investigated whether TAR DNA-binding protein 43 (TDP-43) was contributing to this volume-uptake mismatch.

METHODS

Seventy-seven participants with flortaucipir-PET and volumetric magnetic resonance imaging underwent postmortem AD and TDP-43 pathology assessments. Bivariate-response linear regression estimated the effect of age and TDP-43 pathology on volume and/or flortaucipir standardized uptake volume ratios of the hippocampus, amygdala, entorhinal, inferior temporal, and midfrontal cortices.

RESULTS

Older participants had lower hippocampal volumes and overall flortaucipir uptake. TDP-43-immunoreactivity correlated with reduced medial temporal volumes but was unrelated to flortaucipir uptake. TDP-43 effect size was consistent across the age spectrum. However, at older ages, the cohort mean volumes moved toward those of TDP-43-positives, reflecting the increasing TDP-43 pathology frequency with age.

DISCUSSION

TDP-43 pathology is a relevant contributor driving the volume-uptake mismatch in older AD participants.

HIGHLIGHTS

TDP-43 pathology affects medial temporal volume loss but not tau radiotracer uptake. Greater TDP-43 pathology effect is seen in old age due to its increasing frequency. TDP-43 pathology is a relevant driver of the volume-uptake mismatch in old AD patients.

Associations of phosphorylated tau pathology with whole-hemisphere ex vivo morphometry in 7 tesla MRI

INTRODUCTION

Neurodegenerative disorders are associated with different pathologies that often co-occur but cannot be measured specifically with in vivo methods.

METHODS

Thirty-three brain hemispheres from donors with an Alzheimer's disease (AD) spectrum diagnosis underwent T2-weighted magnetic resonance imaging (MRI). Gray matter thickness was paired with histopathology from the closest anatomic region in the contralateral hemisphere.

RESULTS

Partial Spearman correlation of phosphorylated tau and cortical thickness with TAR DNA-binding protein 43 (TDP-43) and α-synuclein scores, age, sex, and postmortem interval as covariates showed significant relationships in entorhinal and primary visual cortices, temporal pole, and insular and posterior cingulate gyri. Linear models including Braak stages, TDP-43 and α-synuclein scores, age, sex, and postmortem interval showed significant correlation between Braak stage and thickness in the parahippocampal gyrus, entorhinal cortex, and Broadman area 35.

CONCLUSION

We demonstrated an association of measures of AD pathology with tissue loss in several AD regions despite a limited range of pathology in these cases.

HIGHLIGHTS

Neurodegenerative disorders are associated with co-occurring pathologies that cannot be measured specifically with in vivo methods. Identification of the topographic patterns of these pathologies in structural magnetic resonance imaging (MRI) may provide probabilistic biomarkers. We demonstrated the correlation of the specific patterns of tissue loss from ex vivo brain MRI with underlying pathologies detected in postmortem brain hemispheres in patients with Alzheimer's disease (AD) spectrum disorders. The results provide insight into the interpretation of in vivo structural MRI studies in patients with AD spectrum disorders.

White matter microstructural metrics are sensitively associated with clinical staging in Alzheimer's disease

Introduction

White matter microstructure may be abnormal along the Alzheimer's disease (AD) continuum.

Methods

Diffusion magnetic resonance imaging (dMRI) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI, n = 627), Baltimore Longitudinal Study of Aging (BLSA, n = 684), and Vanderbilt Memory & Aging Project (VMAP, n = 296) cohorts were free-water (FW) corrected and conventional, and FW-corrected microstructural metrics were quantified within 48 white matter tracts. Microstructural values were subsequently harmonized using the Longitudinal ComBat technique and inputted as independent variables to predict diagnosis (cognitively unimpaired [CU], mild cognitive impairment [MCI], AD). Models were adjusted for age, sex, race/ethnicity, education, apolipoprotein E (APOE) ε4 carrier status, and APOE ε2 carrier status.

Results

Conventional dMRI metrics were associated globally with diagnostic status; following FW correction, the FW metric itself exhibited global associations with diagnostic status, but intracellular metric associations were diminished.

Discussion

White matter microstructure is altered along the AD continuum. FW correction may provide further understanding of the white matter neurodegenerative process in AD.

Highlights

Longitudinal ComBat successfully harmonized large-scale diffusion magnetic resonance imaging (dMRI) metrics.Conventional dMRI metrics were globally sensitive to diagnostic status.Free-water (FW) correction mitigated intracellular associations with diagnostic status.The FW metric itself was globally sensitive to diagnostic status. Multivariate conventional and FW-corrected models may provide complementary information.

Voxel-Based and Surface-Based Morphometry Analysis in Patients with Pathologically Confirmed Argyrophilic Grain Disease and Alzheimer’s Disease

Background: Due to clinicoradiological similarities, including amnestic cognitive impairment and limbic atrophy, differentiation of argyrophilic grain disease (AGD) from Alzheimer’s disease (AD) is often challenging. Minimally invasive biomarkers, especially magnetic resonance imaging (MRI), are valuable in routine clinical practice. Although it is necessary to explore radiological clues, morphometry analyses using new automated analytical methods, including whole-brain voxel-based morphometry (VBM) and surface-based morphometry (SBM), have not been sufficiently investigated in patients with pathologically confirmed AGD and AD. Objective: This study aimed to determine the volumetric differences in VBM and SBM analyses between patients with pathologically confirmed AGD and AD. Methods: Eight patients with pathologically confirmed AGD with a lower Braak neurofibrillary tangle stage (<III), 11 patients with pathologically confirmed AD without comorbid AGD, and 10 healthy controls (HC) were investigated. Gray matter volumetric changes in VBM and cortical thickness changes in SBM were compared between the two patient groups (i.e., AGD and AD) and the HC group. Results: In contrast to widespread gray matter volume or cortical thickness loss in the bilateral limbic, temporoparietal, and frontal lobes of the AD group, these were limited, especially in the limbic lobes, in the AGD group, compared with that of the HC group. Although bilateral posterior dominant gray matter volume loss was identified in the AD group compared with the AGD group on VBM, there was no significant cluster between these patient groups on SBM. Conclusion: VBM and SBM analyses both showed a different distribution of atrophic changes between AGD and AD.

Butyrate ameliorates quinolinic acid-induced cognitive decline in obesity models

Obesity is a risk factor for neurodegenerative disease associated with cognitive dysfunction, including Alzheimer's disease. Low-grade inflammation is common in obesity, but the mechanism between inflammation and cognitive impairment in obesity is unclear. Accumulative evidence shows that quinolinic acid (QA), a neuroinflammatory neurotoxin, is involved in the pathogenesis of neurodegenerative processes. We investigated the role of QA in obesity-induced cognitive impairment and the beneficial effect of butyrate in counteracting impairments of cognition, neural morphology, and signaling. We show that in human obesity, there was a negative relationship between serum QA levels and cognitive function and decreased cortical gray matter. Diet-induced obese mice had increased QA levels in the cortex associated with cognitive impairment. At single-cell resolution, we confirmed that QA impaired neurons, altered the dendritic spine's intracellular signal, and reduced brain-derived neurotrophic factor (BDNF) levels. Using Caenorhabditis elegans models, QA induced dopaminergic and glutamatergic neuron lesions. Importantly, the gut microbiota metabolite butyrate was able to counteract those alterations, including cognitive impairment, neuronal spine loss, and BDNF reduction in both in vivo and in vitro studies. Finally, we show that butyrate prevented QA-induced BDNF reductions by epigenetic enhancement of H3K18ac at BDNF promoters. These findings suggest that increased QA is associated with cognitive decline in obesity and that butyrate alleviates neurodegeneration.

Tau-Neurodegeneration mismatch reveals vulnerability and resilience to comorbidities in Alzheimer's continuum

Variability in the relationship of tau-based neurofibrillary tangles (T) and degree of neurodegeneration (N) in Alzheimer's Disease (AD) is likely attributable to the non-specific nature of N, which is also modulated by such factors as other co-pathologies, age-related changes, and developmental differences. We studied this variability by partitioning patients within the Alzheimer's continuum into data-driven groups based on their regional T-N dissociation, which reflects the residuals after the effect of tau pathology is "removed". We found six groups displaying distinct spatial T-N mismatch and thickness patterns despite similar tau burden. Their T-N patterns resembled the neurodegeneration patterns of non-AD groups partitioned on the basis of z-scores of cortical thickness alone and were similarly associated with surrogates of non-AD factors. In an additional sample of individuals with antemortem imaging and autopsy, T-N mismatch was associated with TDP-43 co-pathology. Finally, T-N mismatch training was then applied to a separate cohort to determine the ability to classify individual patients within these groups. These findings suggest that T-N mismatch may provide a personalized approach for determining non-AD factors associated with resilience/vulnerability to Alzheimer's disease.

Diverse limbic comorbidities cause limbic and temporal atrophy in lewy body disease

BACKGROUND

In contrast to Alzheimer's disease (AD)-related pathology, the influence of comorbid limbic-predominant age-related TDP-43 encephalopathy neuropathological change (LATE-NC) or argyrophilic grains (AG) on structural imaging in Lewy body disease (LBD) has seldom been evaluated.

OBJECTIVE

This study aimed to investigate whether non-AD limbic comorbidities, including LATE-NC and AG, cause cortical atrophy in LBD.

METHODS

Seventeen patients with pathologically confirmed LBD with lower Braak neurofibrillary tangle stage (<IV) and 10 healthy controls (HC) were included. Based on the presence of comorbid LATE-NC or AG, LBD patients were subdivided into nine patients with these proteinopathies (mixed LBD [mLBD]) and eight without (pure LBD [pLBD]). In addition to clinical feature evaluation, gray matter atrophy on voxel-based morphometry was compared between the two LBD and HC groups.

RESULTS

The mean age at antemortem magnetic resonance imaging of the mLBD patients was higher than that of the pLBD patients (84.3 ± 3.9 vs. 76.5 ± 10.5; p = .046). Irrespective of the presence or absence of comorbid LATE-NC or AG, all patients were clinically diagnosed with probable dementia with Lewy bodies or Parkinson's disease with dementia, respectively. Compared to the pLBD group, the mLBD group showed more conspicuous cortical atrophy of the bilateral hippocampus, amygdala, and temporal pole.

CONCLUSIONS

Non-AD limbic comorbidities, including LATE-NC and AG, are associated with limbic and temporal atrophy in older patients with LBD. Therefore, the possibility of non-AD limbic comorbidities should be considered in the diagnosis of elderly patients with dementia with clinical symptoms of LBD and medial temporal atrophy.

Identification of causal effects of neuroanatomy on cognitive decline requires modeling unobserved confounders

INTRODUCTION

Carrying out a randomized controlled trial to estimate the causal effects of regional brain atrophy due to Alzheimer's disease (AD) is impossible. Instead, we must estimate causal effects from observational data. However, this generally requires knowing and having recorded all confounders, which is often unrealistic.

METHODS

We provide an approach that leverages the dependencies among multiple neuroanatomical measures to estimate causal effects from observational neuroimaging data without the need to know and record all confounders.

RESULTS

Our analyses of N = 732 $N=732$ subjects from the Alzheimer's Disease Neuroimaging Initiative demonstrate that using our approach results in biologically meaningful conclusions, whereas ignoring unobserved confounding yields results that conflict with established knowledge on cognitive decline due to AD.

DISCUSSION

The findings provide evidence that the impact of unobserved confounding can be substantial. To ensure trustworthy scientific insights, future AD research can account for unobserved confounding via the proposed approach.

Multi-cohort and longitudinal Bayesian clustering study of stage and subtype in Alzheimer's disease

Understanding Alzheimer's disease (AD) heterogeneity is important for understanding the underlying pathophysiological mechanisms of AD. However, AD atrophy subtypes may reflect different disease stages or biologically distinct subtypes. Here we use longitudinal magnetic resonance imaging data (891 participants with AD dementia, 305 healthy control participants) from four international cohorts, and longitudinal clustering to estimate differential atrophy trajectories from the age of clinical disease onset. Our findings (in amyloid-β positive AD patients) show five distinct longitudinal patterns of atrophy with different demographical and cognitive characteristics. Some previously reported atrophy subtypes may reflect disease stages rather than distinct subtypes. The heterogeneity in atrophy rates and cognitive decline within the five longitudinal atrophy patterns, potentially expresses a complex combination of protective/risk factors and concomitant non-AD pathologies. By alternating between the cross-sectional and longitudinal understanding of AD subtypes these analyses may allow better understanding of disease heterogeneity.

Changes in Measures of Vestibular and Balance Function and Hippocampus Volume in Alzheimer's Disease and Mild Cognitive Impairment

OBJECTIVE

To test the hypotheses that people with Alzheimer's disease and mild cognitive impairment have increased frequency of vestibular impairments and decreased hippocampal volume compared with healthy age-matched controls.

STUDY DESIGN

Retrospective, with some historical controls.

SETTING

Out-patient, tertiary care center.

SUBJECTS

People with mild to moderate dementia diagnosed with Alzheimer's disease and with mild cognitive impairment. Main Outcome Measures: A standard clinical battery of objective tests of the vestibular system, and screening for balance; available clinical diagnostic magnetic resonance imaging (MRIs) were reviewed and postprocessed to quantify the left and right hippocampal volumes utilizing both manual segmentation and computer automated segmentation.

RESULTS

Study subjects (N = 26) had significantly more vestibular impairments, especially on Dix-Hallpike maneuvers and cervical vestibular evoked myogenic potentials (cVEMP), than historical controls. No differences were found between mild and moderate dementia subjects. Independence on instrumental activities of daily living in subjects with age-normal balance approached statistical differences from subjects with age-abnormal balance. MRI data were available for 11 subjects. Subjects with abnormal cVEMP had significantly reduced left hippocampal MRIs using manual segmentation compared with subjects with normal cVEMP.

CONCLUSION

The data from this small sample support and extend previous evidence for vestibular impairments in this population. The small MRI sample set should be considered preliminary evidence, and suggests the need for further research, with a more robust sample and high-resolution MRIs performed for the purpose of hippocampal analysis.

Characterizing Amyloid-Positive Individuals With Normal Tau PET Levels After 5 Years: An ADNI Study

BACKGROUND AND OBJECTIVE

Individuals with biomarker evidence of β-amyloid (Aβ) deposition are increasingly being enrolled in clinical treatment trials but there is a need to identify markers to predict which of these individuals will also develop tau deposition. We aimed to determine whether Aβ-positive individuals can remain tau-negative for at least 5 years and identify characteristics that could distinguish between these individuals and those who develop high tau within this period.

METHODS

Tau PET positivity was defined using a Gaussian mixture model with log-transformed standard uptake value ratio values from 7 temporal and medial parietal regions using all participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI) with flortaucipir PET. Tau PET scans were classified as normal if the posterior probability of elevated tau was less than 1%. Aβ PET positivity was defined based on ADNI cutpoints. We identified all Aβ-positive individuals from ADNI who had normal tau PET more than 5 years after their first abnormal Aβ PET (amyloid with low tau [ALT] group) and all Aβ-positive individuals with abnormal tau PET within 5 years (biomarker AD). In a case-control design, logistic regression was used to model the odds of biomarker AD vs ALT accounting for sex, age, APOE ε4 carriership, Aβ Centiloid, and hippocampal volume.

RESULTS

We identified 45 individuals meeting criteria for ALT and 157 meeting criteria for biomarker AD. The ALT group had a lower proportion of APOE ε4 carriers, lower Aβ Centiloid, larger hippocampal volumes, and more preserved cognition, and were less likely to develop dementia, than the biomarker AD group. APOE ε4, higher Aβ Centiloid, and hippocampal atrophy were independently associated with increased odds of abnormal tau within 5 years. A Centiloid value of 50 effectively discriminated biomarker AD and ALT with 80% sensitivity and specificity. The majority of the ALT participants did not develop dementia throughout the 5-year interval.

DISCUSSION

Aβ-positive individuals can remain tau-negative for at least 5 years. Baseline characteristics can help identify these ALT individuals who are less likely to develop dementia. Conservative Aβ cutpoints should be utilized for clinical trials to better capture individuals with high risk of developing biomarker AD.

WTD-PSD: Presentation of Novel Feature Extraction Method Based on Discrete Wavelet Transformation and Time-Dependent Power Spectrum Descriptors for Diagnosis of Alzheimer's Disease

Alzheimer's disease (AD) is a type of dementia that affects the elderly population. A machine learning (ML) system has been trained to recognize particular patterns to diagnose AD using an algorithm in an ML system. As a result, developing a feature extraction approach is critical for reducing calculation time. The input image in this article is a Two-Dimensional Discrete Wavelet (2D-DWT). The Time-Dependent Power Spectrum Descriptors (TD-PSD) model is used to represent the subbanded wavelet coefficients. The principal property vector is made up of the characteristics of the TD-PSD model. Based on classification algorithms, the collected characteristics are applied independently to present AD classifications. The categorization is used to determine the kind of tumor. The TD-PSD method was used to extract wavelet subbands features from three sets of test samples: moderate cognitive impairment (MCI), AD, and healthy controls (HC). The outcomes of three modes of classic classification methods, including KNN, SVM, Decision Tree, and LDA approaches, are documented, as well as the final feature employed in each. Finally, we show the CNN architecture for AD patient classification. Output assessment is used to show the results. Other techniques are outperformed by the given CNN and DT.

Fractal dimension of the brain in neurodegenerative disease and dementia: A systematic review

Sensitive and specific antemortem biomarkers of neurodegenerative disease and dementia are crucial to the pursuit of effective treatments, required both to reliably identify disease and to track its progression. Atrophy is the structural magnetic resonance imaging (MRI) hallmark of neurodegeneration. However in most cases it likely indicates a relatively advanced stage of disease less susceptible to treatment as some disease processes begin decades prior to clinical onset. Among emerging metrics that characterise brain shape rather than volume, fractal dimension (FD) quantifies shape complexity. FD has been applied in diverse fields of science to measure subtle changes in elaborate structures. We review its application thus far to structural MRI of the brain in neurodegenerative disease and dementia. We identified studies involving subjects who met criteria for mild cognitive impairment, Alzheimer's Disease, Vascular Dementia, Lewy Body Dementia, Frontotemporal Dementia, Amyotrophic Lateral Sclerosis, Parkinson's Disease, Huntington's Disease, Multiple Systems Atrophy, Spinocerebellar Ataxia and Multiple Sclerosis. The early literature suggests that neurodegenerative disease processes are usually associated with a decline in FD of the brain. The literature includes examples of disease-related change in FD occurring independently of atrophy, which if substantiated would represent a valuable advantage over other structural imaging metrics. However, it is likely to be non-specific and to exhibit complex spatial and temporal patterns. A more harmonious methodological approach across a larger number of studies as well as careful attention to technical factors associated with image processing and FD measurement will help to better elucidate the metric's utility.

Expert Consensus on Cognitive Dysfunction in Diabetes

The incidence of diabetes is gradually increasing in China, and diabetes and associated complications, such as cognitive dysfunction have gained much attention in recent time. However, the concepts, clinical treatment, and prevention of cognitive dysfunction in patients with diabetes remain unclear. The Chinese Society of Endocrinology investigated the current national and overseas situation of cognitive dysfunction associated with diabetes. Based on research both in China and other countries worldwide, the Expert Consensus on Cognitive Dysfunction in Diabetes was established to guide physicians in the comprehensive standardized management of cognitive dysfunction in diabetes and to improve clinical outcomes in Chinese patients. This consensus presents an overview, definition and classification, epidemiology and pathogenesis, risk factors, screening, diagnosis, differential diagnosis, treatment, and prevention of cognitive dysfunction in patients with diabetes.

Medial Temporal Lobe Networks in Alzheimer's Disease: Structural and Molecular Vulnerabilities

The medial temporal lobe (MTL) is connected to the rest of the brain through two main networks: the anterior-temporal (AT) and the posterior-medial (PM) systems. Given the crucial role of the MTL and networks in the physiopathology of Alzheimer's disease (AD), the present study aimed at (1) investigating whether MTL atrophy propagates specifically within the AT and PM networks, and (2) evaluating the vulnerability of these networks to AD proteinopathies. To do that, we used neuroimaging data acquired in human male and female in three distinct cohorts: (1) resting-state functional MRI (rs-fMRI) from the aging brain cohort (ABC) to define the AT and PM networks (n = 68); (2) longitudinal structural MRI from Alzheimer's disease neuroimaging initiative (ADNI)GO/2 to highlight structural covariance patterns (n = 349); and (3) positron emission tomography (PET) data from ADNI3 to evaluate the networks' vulnerability to amyloid and tau (n = 186). Our results suggest that the atrophy of distinct MTL subregions propagates within the AT and PM networks in a dissociable manner. Brodmann area (BA)35 structurally covaried within the AT network while the parahippocampal cortex (PHC) covaried within the PM network. In addition, these networks are differentially associated with relative tau and amyloid burden, with higher tau levels in AT than in PM and higher amyloid levels in PM than in AT. Our results also suggest differences in the relative burden of tau species. The current results provide further support for the notion that two distinct MTL networks display differential alterations in the context of AD. These findings have important implications for disease spread and the cognitive manifestations of AD.SIGNIFICANCE STATEMENT The current study provides further support for the notion that two distinct medial temporal lobe (MTL) networks, i.e., anterior-temporal (AT) and the posterior-medial (PM), display differential alterations in the context of Alzheimer's disease (AD). Importantly, neurodegeneration appears to occur within these networks in a dissociable manner marked by their covariance patterns. In addition, the AT and PM networks are also differentially associated with relative tau and amyloid burden, and perhaps differences in the relative burden of tau species [e.g., neurofibriliary tangles (NFTs) vs tau in neuritic plaques]. These findings, in the context of a growing literature consistent with the present results, have important implications for disease spread and the cognitive manifestations of AD in light of the differential cognitive processes ascribed to them.

Simple Quantification of Surface Uptake in F-18 Florapronol PET/CT Imaging for the Validation of Alzheimer’s Disease

We developed a novel quantification method named shape feature using F-18 florapronol positron emission tomography–computed tomography (PET/CT) and evaluated its sensitivity and specificity for discriminating between patients with Alzheimer’s disease (AD) and patients with mild cognitive impairment or other precursors dementia (non-AD). We calculated the cerebral amyloid smoothing score (CASS) and brain atrophy index (BAI) using the surface area and volume of the region of interest in PET images. We calculated gray and white matter from trained CT data, prepared using U-net. Shape feature was calculated by multiplying CASS with BAI scores. We measured region-based standard uptake values (SUVr) and performed receiver operating characteristic (ROC) analysis to compare SUVr, shape feature, CASS, and BAI score. We investigated the relationship between shape feature and neuropsychological tests. Fifty subjects (23 with AD and 27 with non-AD) were evaluated. SUVr, shape feature, CASS, and BAI score were significantly higher in patients with AD than in those with non-AD. There was no statistically significant difference between shape feature and SUVr in ROC analysis. Shape feature correlated well with mini-mental state examination scores. Shape feature can effectively quantify beta-amyloid deposition and atrophic changes in the brain. These results suggest that shape feature is useful in the diagnosis of AD.

OUP accepted manuscript

Alzheimer’s disease-related atrophy in the posterior cingulate cortex, a key node of the default mode network, is present in the early stages of disease progression across clinical phenotypic variants of the disease. In the typical amnestic variant, posterior cingulate cortex neuropathology has been linked with disrupted connectivity of the posterior default mode network, but it remains unclear if this relationship is observed across atypical variants of Alzheimer’s disease. In the present study, we first sought to determine if tau pathology is consistently present in the posterior cingulate cortex and other posterior nodes of the default mode network across the atypical Alzheimer’s disease syndromic spectrum. Second, we examined functional connectivity disruptions within the default mode network and sought to determine if tau pathology is related to functional disconnection within this network. We studied a sample of 25 amyloid-positive atypical Alzheimer’s disease participants examined with high-resolution MRI, tau (¹⁸F-AV-1451) PET, and resting-state functional MRI. In these patients, high levels of tau pathology in the posteromedial cortex and hypoconnectivity between temporal and parietal nodes of the default mode network were observed relative to healthy older controls. Furthermore, higher tau signal and reduced grey matter density in the posterior cingulate cortex and angular gyrus were associated with reduced parietal functional connectivity across individual patients, related to poorer cognitive scores. Our findings converge with what has been reported in amnestic Alzheimer’s disease, and together these observations offer a unifying mechanistic feature that relates posterior cingulate cortex tau deposition to aberrant default mode network connectivity across heterogeneous clinical phenotypes of Alzheimer’s disease.

Emotion Processing Dysfunction in Alzheimer's Disease: An Overview of Behavioral Findings, Systems Neural Correlates, and Underlying Neural Biology

We described behavioral studies to highlight emotional processing deficits in Alzheimer's disease (AD). The findings suggest prominent deficit in recognizing negative emotions, pronounced effect of positive emotion on enhancing memory, and a critical role of cognitive deficits in manifesting emotional processing dysfunction in AD. We reviewed imaging studies to highlight morphometric and functional markers of hippocampal circuit dysfunction in emotional processing deficits. Despite amygdala reactivity to emotional stimuli, hippocampal dysfunction conduces to deficits in emotional memory. Finally, the reviewed studies implicating major neurotransmitter systems in anxiety and depression in AD supported altered cholinergic and noradrenergic signaling in AD emotional disorders. Overall, the studies showed altered emotions early in the course of illness and suggest the need of multimodal imaging for further investigations. Particularly, longitudinal studies with multiple behavioral paradigms translatable between preclinical and clinical models would provide data to elucidate the time course and underlying neurobiology of emotion processing dysfunction in AD.

Agreement Between Automated and Manual MRI Volumetry in Alzheimer's Disease: A Systematic Review and Meta-Analysis

BACKGROUND

Automated magnetic resonance imaging (MRI) volumetry is a promising tool to evaluate regional brain volumes in dementia and especially Alzheimer's disease (AD).

PURPOSE

To compare automated methods and the gold standard manual segmentation in measuring regional brain volumes on MRI across healthy controls, patients with mild cognitive impairment, and patients with dementia due to AD.

STUDY TYPE

Systematic review and meta-analysis.

DATA SOURCES

MEDLINE, Embase, and PsycINFO were searched through October 2021.

FIELD STRENGTH

1.0 T, 1.5 T, or 3.0 T.

ASSESSMENT

Two review authors independently identified studies for inclusion and extracted data. Methodological quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2).

STATISTICAL TESTS

Standardized mean differences (SMD; Hedges' g) were pooled using random-effects meta-analysis with robust variance estimation. Subgroup analyses were undertaken to explore potential sources of heterogeneity. Sensitivity analyses were conducted to examine the impact of the within-study correlation between effect estimates on the meta-analysis results.

RESULTS

Seventeen studies provided sufficient data to evaluate the hippocampus, lateral ventricles, and parahippocampal gyrus. The pooled SMD for the hippocampus, lateral ventricles, and parahippocampal gyrus were 0.22 (95% CI -0.50 to 0.93), 0.12 (95% CI -0.13 to 0.37), and -0.48 (95% CI -1.37 to 0.41), respectively. For the hippocampal data, subgroup analyses suggested that the pooled SMD was invariant across clinical diagnosis and field strength. Subgroup analyses could not be conducted on the lateral ventricles data and the parahippocampal gyrus data due to insufficient data. The results were robust to the selected within-study correlation value.

DATA CONCLUSION

While automated methods are generally comparable to manual segmentation for measuring hippocampal, lateral ventricle, and parahippocampal gyrus volumes, wide 95% CIs and large heterogeneity suggest that there is substantial uncontrolled variance. Thus, automated methods may be used to measure these regions in patients with AD but should be used with caution.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 3.

Corpus Callosum Atrophy in Detection of Mild and Moderate Alzheimer's Disease Using Brain Magnetic Resonance Image Processing and Machine Learning Techniques

Background:

The total number of people with dementia is projected to reach 82 million in 2030 and 152 in 2050. Early and accurate identification of the underlying causes of dementia, such as Alzheimer’s disease (AD) is of utmost importance. A large body of research has shown that imaging techniques are most promising technologies to improve subclinical and early diagnosis of dementia. Morphological changes, especially atrophy in various structures like cingulate gyri, caudate nucleus, hippocampus, frontotemporal lobe, etc., have been established as markers for AD. Being the largest white matter structure with a high demand of blood supply from several main arterial systems, anatomical alterations of the corpus callosum (CC) may serve as potential indication neurodegenerative disease.

Objective:

To detect mild and moderate AD using brain magnetic resonance image (MRI) processing and machine learning techniques.

Methods:

We have performed automatic detection and segmentation of the CC and calculated its morphological features to feed into a multivariate pattern analysis using support vector machine (SVM) learning techniques.

Results:

Our results using large patients’ cohort show CC atrophy-based features are capable of distinguishing healthy and mild/moderate AD patients. Our classifiers obtain more than 90%sensitivity and specificity in differentiating demented patients from healthy cohorts and importantly, achieved more than 90%sensitivity and > 80%specificity in detecting mild AD patients.

Conclusion:

Results from this analysis are encouraging and advocate development of an image analysis software package to detect dementia from brain MRI using morphological alterations of the CC.

Anatomically Standardized Detection of MRI Atrophy Patterns in Early-Stage Alzheimer's Disease

MRI studies have consistently identified atrophy patterns in Alzheimer’s disease (AD) through a whole-brain voxel-based analysis, but efforts to investigate morphometric profiles using anatomically standardized and automated whole-brain ROI analyses, performed at the individual subject space, are still lacking. In this study we aimed (i) to utilize atlas-derived measurements of cortical thickness and subcortical volumes, including of the hippocampal subfields, to identify atrophy patterns in early-stage AD, and (ii) to compare cognitive profiles at baseline and during a one-year follow-up of those previously identified morphometric AD subtypes to predict disease progression. Through a prospectively recruited multi-center study, conducted at four Austrian sites, 120 patients were included with probable AD, a disease onset beyond 60 years and a clinical dementia rating of ≤1. Morphometric measures of T1-weighted images were obtained using FreeSurfer. A principal component and subsequent cluster analysis identified four morphometric subtypes, including (i) hippocampal predominant (30.8%), (ii) hippocampal-temporo-parietal (29.2%), (iii) parieto-temporal (hippocampal sparing, 20.8%) and (iv) hippocampal-temporal (19.2%) atrophy patterns that were associated with phenotypes differing predominately in the presentation and progression of verbal memory and visuospatial impairments. These morphologically distinct subtypes are based on standardized brain regions, which are anatomically defined and freely accessible so as to validate its diagnostic accuracy and enhance the prediction of disease progression.

Medial Temporal Lobe Subregional Atrophy in Aging and Alzheimer's Disease: A Longitudinal Study

Medial temporal lobe (MTL) atrophy is a key feature of Alzheimer's disease (AD), however, it also occurs in typical aging. To enhance the clinical utility of this biomarker, we need to better understand the differential effects of age and AD by encompassing the full AD-continuum from cognitively unimpaired (CU) to dementia, including all MTL subregions with up-to-date approaches and using longitudinal designs to assess atrophy more sensitively. Age-related trajectories were estimated using the best-fitted polynomials in 209 CU adults (aged 19–85). Changes related to AD were investigated among amyloid-negative (Aβ−) (n = 46) and amyloid-positive (Aβ+) (n = 14) CU, Aβ+ patients with mild cognitive impairment (MCI) (n = 33) and AD (n = 31). Nineteen MCI-to-AD converters were also compared with 34 non-converters. Relationships with cognitive functioning were evaluated in 63 Aβ+ MCI and AD patients. All participants were followed up to 47 months. MTL subregions, namely, the anterior and posterior hippocampus (aHPC/pHPC), entorhinal cortex (ERC), Brodmann areas (BA) 35 and 36 [as perirhinal cortex (PRC) substructures], and parahippocampal cortex (PHC), were segmented from a T1-weighted MRI using a new longitudinal pipeline (LASHiS). Statistical analyses were performed using mixed models. Adult lifespan models highlighted both linear (PRC, BA35, BA36, PHC) and nonlinear (HPC, aHPC, pHPC, ERC) trajectories. Group comparisons showed reduced baseline volumes and steeper volume declines over time for most of the MTL subregions in Aβ+ MCI and AD patients compared to Aβ− CU, but no differences between Aβ− and Aβ+ CU or between Aβ+ MCI and AD patients (except in ERC). Over time, MCI-to-AD converters exhibited a greater volume decline than non-converters in HPC, aHPC, and pHPC. Most of the MTL subregions were related to episodic memory performances but not to executive functioning or speed processing. Overall, these results emphasize the benefits of studying MTL subregions to distinguish age-related changes from AD. Interestingly, MTL subregions are unequally vulnerable to aging, and those displaying non-linear age-trajectories, while not damaged in preclinical AD (Aβ+ CU), were particularly affected from the prodromal stage (Aβ+ MCI). This volume decline in hippocampal substructures might also provide information regarding the conversion from MCI to AD-dementia. All together, these findings provide new insights into MTL alterations, which are crucial for AD-biomarkers definition.

Ex vivo MRI atlas of the human medial temporal lobe: characterizing neurodegeneration due to tau pathology

Tau neurofibrillary tangle (NFT) pathology in the medial temporal lobe (MTL) is closely linked to neurodegeneration, and is the early pathological change associated with Alzheimer's disease (AD). To elucidate patterns of structural change in the MTL specifically associated with tau pathology, we compared high-resolution ex vivo MRI scans of human postmortem MTL specimens with histology-based pathological assessments of the MTL. MTL specimens were obtained from twenty-nine brain donors, including patients with AD, other dementias, and individuals with no known history of neurological disease. Ex vivo MRI scans were combined using a customized groupwise diffeomorphic registration approach to construct a 3D probabilistic atlas that captures the anatomical variability of the MTL. Using serial histology imaging in eleven specimens, we labelled the MTL subregions in the atlas based on cytoarchitecture. Leveraging the atlas and neuropathological ratings of tau and TAR DNA-binding protein 43 (TDP-43) pathology severity, morphometric analysis was performed to correlate regional MTL thickness with the severity of tau pathology, after correcting for age and TDP-43 pathology. We found significant correlations between tau pathology and thickness in the entorhinal cortex (ERC) and stratum radiatum lacunosum moleculare (SRLM). When focusing on cases with low levels of TDP-43 pathology, we found strong associations between tau pathology and thickness in the ERC, SRLM and the subiculum/cornu ammonis 1 (CA1) subfields of the hippocampus, consistent with early Braak stages.

Volumetry of Olfactory Structures in Mild Cognitive Impairment and Alzheimer's Disease: A Systematic Review and a Meta-Analysis

Olfactory decline is an early symptom of Alzheimer's disease (AD) and is a predictor of conversion from mild cognitive impairment (MCI) to AD. Olfactory decline could reflect AD-related atrophy of structures related to the sense of smell. The aim of this study was to verify whether the presence of a clinical diagnosis of AD or MCI is associated with a volumetric decrease in the olfactory bulbs (OB) and the primary olfactory cortex (POC). We conducted two systematic reviews, one for each region and a meta-analysis. We collected articles from PsychNet, PubMed, Ebsco, and ProQuest databases. Results showed large and heterogeneous effects indicating smaller OB volumes in patients with AD (k = 6, g = -1.21, 95% CI [-2.19, -0.44]) and in patients with MCI compared to controls. There is also a trend for smaller POC in patients with AD or MCI compared to controls. Neuroanatomical structures involved in olfactory processing are smaller in AD and these volumetric reductions could be measured as early as the MCI stage.

Combined Model of Aggregation and Network Diffusion Recapitulates Alzheimer's Regional Tau-Positron Emission Tomography

Background: Alzheimer's disease involves widespread and progressive deposition of misfolded protein tau (τ), first appearing in the entorhinal cortex, coagulating in longer polymers and insoluble fibrils. There is mounting evidence for "prion-like" trans-neuronal transmission, whereby misfolded proteins cascade along neuronal pathways, giving rise to networked spread. However, the cause-effect mechanisms by which various oligomeric τ species are produced, aggregate, and disseminate are unknown. The question of how protein aggregation and subsequent spread lead to stereotyped progression in the Alzheimer brain remains unresolved. Materials and Methods: We address these questions by using mathematically precise parsimonious modeling of these pathophysiological processes, extrapolated to the whole brain. We model three key processes: τ monomer production; aggregation into oligomers and then into tangles; and the spatiotemporal progression of misfolded τ as it ramifies into neural circuits via the brain connectome. We model monomer seeding and production at the entorhinal cortex, aggregation using Smoluchowski equations; and networked spread using our prior Network-Diffusion model. Results: This combined aggregation-network-diffusion model exhibits all hallmarks of τ progression seen in human patients. Unlike previous theoretical studies of protein aggregation, we present here an empirical validation on in vivo imaging and fluid τ measurements from large datasets. The model accurately captures not just the spatial distribution of empirical regional τ and atrophy but also patients' cerebrospinal fluid phosphorylated τ profiles as a function of disease progression. Conclusion: This unified quantitative and testable model has the potential to explain observed phenomena and serve as a test-bed for future hypothesis generation and testing in silico.

Differential trajectories of hypometabolism across cognitively-defined Alzheimer's disease subgroups

•

Cognitive-subgroups can be identified among individuals with AD dementia.

•

Subgroup-specific patterns and longitudinal trajectories of hypometabolism observed.

•

Regional hypometabolism matched respective cognitive profiles of subgroups.

•

Cognitive-classification yields biologically distinct subgroups.

Disentangling biologically distinct subgroups of Alzheimer’s disease (AD) may facilitate a deeper understanding of the neurobiology underlying clinical heterogeneity. We employed longitudinal [¹⁸F]FDG-PET standardized uptake value ratios (SUVRs) to map hypometabolism across cognitively-defined AD subgroups. Participants were 384 amyloid-positive individuals with an AD dementia diagnosis from ADNI who had a total of 1028 FDG-scans (mean time between first and last scan: 1.6 ± 1.8 years). These participants were categorized into subgroups on the basis of substantial impairment at time of dementia diagnosis in a specific cognitive domain relative to the average across domains. This approach resulted in groups of AD-Memory (n = 135), AD-Executive (n = 8), AD-Language (n = 22), AD-Visuospatial (n = 44), AD-Multiple Domains (n = 15) and AD-No Domains (for whom no domain showed substantial relative impairment; n = 160). Voxelwise contrasts against controls revealed that all AD-subgroups showed progressive hypometabolism compared to controls across temporoparietal regions at time of AD diagnosis. Voxelwise and regions-of-interest (ROI)-based linear mixed model analyses revealed there were also subgroup-specific hypometabolism patterns and trajectories. The AD-Memory group had more pronounced hypometabolism compared to all other groups in the medial temporal lobe and posterior cingulate, and faster decline in metabolism in the medial temporal lobe compared to AD-Visuospatial. The AD-Language group had pronounced lateral temporal hypometabolism compared to all other groups, and the pattern of metabolism was also more asymmetrical (left < right) than all other groups. The AD-Visuospatial group had faster decline in metabolism in parietal regions compared to all other groups, as well as faster decline in the precuneus compared to AD-Memory and AD-No Domains. Taken together, in addition to a common pattern, cognitively-defined subgroups of people with AD dementia show subgroup-specific hypometabolism patterns, as well as differences in trajectories of metabolism over time. These findings provide support to the notion that cognitively-defined subgroups are biologically distinct.

Neural Correlates of Cognitive Performance in Alzheimer's Disease- and Lewy Bodies-Related Cognitive Impairment

BACKGROUND

Clinicopathological studies have demonstrated that the neuropsychological profiles and outcomes are different between two dementia subtypes, namely Alzheimer's disease (AD) and Lewy bodies-related disease.

OBJECTIVE

We investigated the neural correlates of cognitive dysfunction in patients with AD-related cognitive impairment (ADCI) and those with Lewy bodies-related cognitive impairment (LBCI).

METHODS

We enrolled 216 ADCI patients, 183 LBCI patients, and 30 controls. Cortical thickness and diffusion tensor imaging analyses were performed to correlate gray matter and white matter (WM) abnormalities to cognitive composite scores for memory, visuospatial, and attention/executive domains in the ADCI spectrum (ADCI patients and controls) and the LBCI spectrum (LBCI patients and controls) separately.

RESULTS

Memory dysfunction correlated with cortical thinning and increased mean diffusivity in the AD-prone regions, particularly the medial temporal region, in ADCI. Meanwhile, it only correlated with increased mean diffusivity in the WM adjacent to the anteromedial temporal, insula, and basal frontal cortices in LBCI. Visuospatial dysfunction correlated with cortical thinning in posterior brain regions in ADCI, while it correlated with decreased fractional anisotropy in the corpus callosum and widespread WM regions in LBCI. Attention/executive dysfunction correlated with cortical thinning and WM abnormalities in widespread brain regions in both disease spectra; however, ADCI had more prominent correlation with cortical thickness and LBCI did with fractional anisotropy values.

CONCLUSIONS

Our study demonstrated that ADCI and LBCI have different neural correlates with respect to cognitive dysfunction. Cortical thinning had greater effects on cognitive dysfunction in the ADCI, while WM disruption did in the LBCI.

Differential patterns of gray matter volumes and associated gene expression profiles in cognitively-defined Alzheimer's disease subgroups

The clinical presentation of Alzheimer's disease (AD) varies widely across individuals but the neurobiological mechanisms underlying this heterogeneity are largely unknown. Here, we compared regional gray matter (GM) volumes and associated gene expression profiles between cognitively-defined subgroups of amyloid-β positive individuals clinically diagnosed with AD dementia (age: 66 ± 7, 47% male, MMSE: 21 ± 5). All participants underwent neuropsychological assessment with tests covering memory, executive-functioning, language and visuospatial-functioning domains. Subgroup classification was achieved using a psychometric framework that assesses which cognitive domain shows substantial relative impairment compared to the intra-individual average across domains, which yielded the following subgroups in our sample; AD-Memory (n = 41), AD-Executive (n = 117), AD-Language (n = 33), AD-Visuospatial (n = 171). We performed voxel-wise contrasts of GM volumes derived from 3Tesla structural MRI between subgroups and controls (n = 127, age 58 ± 9, 42% male, MMSE 29 ± 1), and observed that differences in regional GM volumes compared to controls closely matched the respective cognitive profiles. Specifically, we detected lower medial temporal lobe GM volumes in AD-Memory, lower fronto-parietal GM volumes in AD-Executive, asymmetric GM volumes in the temporal lobe (left < right) in AD-Language, and lower GM volumes in posterior areas in AD-Visuospatial. In order to examine possible biological drivers of these differences in regional GM volumes, we correlated subgroup-specific regional GM volumes to brain-wide gene expression profiles based on a stereotactic characterization of the transcriptional architecture of the human brain as provided by the Allen human brain atlas. Gene-set enrichment analyses revealed that variations in regional expression of genes involved in processes like mitochondrial respiration and metabolism of proteins were associated with patterns of regional GM volume across multiple subgroups. Other gene expression vs GM volume-associations were only detected in particular subgroups, e.g., genes involved in the cell cycle for AD-Memory, specific sets of genes related to protein metabolism in AD-Language, and genes associated with modification of gene expression in AD-Visuospatial. We conclude that cognitively-defined AD subgroups show neurobiological differences, and distinct biological pathways may be involved in the emergence of these differences.

Bridging Scales in Alzheimer's Disease: Biological Framework for Brain Simulation With The Virtual Brain

Despite the acceleration of knowledge and data accumulation in neuroscience over the last years, the highly prevalent neurodegenerative disease of AD remains a growing problem. Alzheimer's Disease (AD) is the most common cause of dementia and represents the most prevalent neurodegenerative disease. For AD, disease-modifying treatments are presently lacking, and the understanding of disease mechanisms continues to be incomplete. In the present review, we discuss candidate contributing factors leading to AD, and evaluate novel computational brain simulation methods to further disentangle their potential roles. We first present an overview of existing computational models for AD that aim to provide a mechanistic understanding of the disease. Next, we outline the potential to link molecular aspects of neurodegeneration in AD with large-scale brain network modeling using The Virtual Brain (www.thevirtualbrain.org), an open-source, multiscale, whole-brain simulation neuroinformatics platform. Finally, we discuss how this methodological approach may contribute to the understanding, improved diagnostics, and treatment optimization of AD.

Protein contributions to brain atrophy acceleration in Alzheimer's disease and primary age-related tauopathy

Alzheimer's disease is characterized by the presence of amyloid-β and tau deposition in the brain, hippocampal atrophy and increased rates of hippocampal atrophy over time. Another protein, TAR DNA binding protein 43 (TDP-43) has been identified in up to 75% of cases of Alzheimer's disease. TDP-43, tau and amyloid-β have all been linked to hippocampal atrophy. TDP-43 and tau have also been linked to hippocampal atrophy in cases of primary age-related tauopathy, a pathological entity with features that strongly overlap with those of Alzheimer's disease. At present, it is unclear whether and how TDP-43 and tau are associated with early or late hippocampal atrophy in Alzheimer's disease and primary age-related tauopathy, whether either protein is also associated with faster rates of atrophy of other brain regions and whether there is evidence for protein-associated acceleration/deceleration of atrophy rates. We therefore aimed to model how these proteins, particularly TDP-43, influence non-linear trajectories of hippocampal and neocortical atrophy in Alzheimer's disease and primary age-related tauopathy. In this longitudinal retrospective study, 557 autopsied cases with Alzheimer's disease neuropathological changes with 1638 ante-mortem volumetric head MRI scans spanning 1.0-16.8 years of disease duration prior to death were analysed. TDP-43 and Braak neurofibrillary tangle pathological staging schemes were constructed, and hippocampal and neocortical (inferior temporal and middle frontal) brain volumes determined using longitudinal FreeSurfer. Bayesian bivariate-outcome hierarchical models were utilized to estimate associations between proteins and volume, early rate of atrophy and acceleration in atrophy rates across brain regions. High TDP-43 stage was associated with smaller cross-sectional brain volumes, faster rates of brain atrophy and acceleration of atrophy rates, more than a decade prior to death, with deceleration occurring closer to death. Stronger associations were observed with hippocampus compared to temporal and frontal neocortex. Conversely, low TDP-43 stage was associated with slower early rates but later acceleration. This later acceleration was associated with high Braak neurofibrillary tangle stage. Somewhat similar, but less striking, findings were observed between TDP-43 and neocortical rates. Braak stage appeared to have stronger associations with neocortex compared to TDP-43. The association between TDP-43 and brain atrophy occurred slightly later in time (∼3 years) in cases of primary age-related tauopathy compared to Alzheimer's disease. The results suggest that TDP-43 and tau have different contributions to acceleration and deceleration of brain atrophy rates over time in both Alzheimer's disease and primary age-related tauopathy.

Investigating the Spatial Associations Between Amyloid-β Deposition, Grey Matter Volume, and Neuroinflammation in Alzheimer's Disease

Background:

It has been proposed that amyloid-β (Aβ) plays a causal role in Alzheimer’s disease (AD) by triggering a series of pathologic events—possibly including neuroinflammation—which culminate in progressive brain atrophy. However, the interplay between the two pathological molecular events and how both are associated with neurodegeneration is still unclear.

Objective:

We aimed to estimate the spatial inter-relationship between neurodegeneration, neuroinflammation and Aβ deposition in a cohort of 20 mild AD patients and 17 healthy controls (HC).

Methods:

We resorted to magnetic resonance imaging to measure cortical atrophy, using the radiotracer ¹¹C-PK11195 PET to measure neuroinflammation levels and ¹¹C-PiB PET to assess Aβ levels. Between-group comparisons were computed to explore AD-related changes in the three types of markers. To examine the effects of each one of the molecular pathologic mechanisms on neurodegeneration we computed: 1) ANCOVAs with the anatomic data, controlling for radiotracer uptake differences between groups and 2) voxel-based multiple regression analysis between-modalities. In addition, associations in anatomically defined regions of interests were also investigated.

Results:

We found significant differences between AD and controls in the levels of atrophy, neuroinflammation, and Aβ deposition. Associations between Aβ aggregation and brain atrophy were detected in AD in a widely distributed pattern, whereas associations between microglia activation and structural measures of neurodegeneration were restricted to few anatomically regions.

Conclusion:

In summary, Aβ deposition, as opposed to neuroinflammation, was more associated with cortical atrophy, suggesting a prominent role of Aβ in neurodegeneration at a mild stage of the AD.