Predicting diagnosis and cognition with 18F-AV-1451 tau PET and structural MRI in Alzheimer's disease

Tau-PET in early cortical Alzheimer brain regions in relation to mild behavioral impairment in older adults with either normal cognition or mild cognitive impairment

Mild Behavioral Impairment (MBI) leverages later-life emergent and persistent neuropsychiatric symptoms (NPS) to identify a high-risk group for incident dementia. Phosphorylated tau (p-tau) is a hallmark biological manifestation of Alzheimer disease (AD). We investigated associations between MBI and tau accumulation in early-stage AD cortical regions. In 442 Alzheimer's Disease Neuroimaging Initiative participants with normal cognition or mild cognitive impairment, MBI status was determined alongside corresponding p-tau and Aβ. Two meta-regions of interest were generated to represent Braak I and III neuropathological stages. Multivariable linear regression modelled the association between MBI as independent variable and tau tracer uptake as dependent variable. Among Aβ positive individuals, MBI was associated with tau uptake in Braak I (β=0.45(0.15), p<.01) and Braak III (β=0.24(0.07), p<.01) regions. In Aβ negative individuals, MBI was not associated with tau in the Braak I region (p=0.11) with a negative association in Braak III (p=.01). These findings suggest MBI may be a sequela of neurodegeneration, and can be implemented as a cost-effective framework to help improve screening efficiency for AD.

Exploration of neuroanatomical characteristics to differentiate prodromal Alzheimer's disease from cognitively unimpaired amyloid-positive individuals

Differentiating clinical stages based solely on positive findings from amyloid PET is challenging. We aimed to investigate the neuroanatomical characteristics at the whole-brain level that differentiate prodromal Alzheimer's disease (AD) from cognitively unimpaired amyloid-positive individuals (CU A+) in relation to amyloid deposition and regional atrophy. We included 45 CU A+ participants and 135 participants with amyloid-positive prodromal AD matched 1:3 by age, sex, and education. All participants underwent 18F-florbetaben positron emission tomography and 3D structural T1-weighted magnetic resonance imaging. We compared the standardized uptake value ratios (SUVRs) and volumes in 80 regions of interest (ROIs) between CU A+ and prodromal AD groups using independent t-tests, and employed the least absolute selection and shrinkage operator (LASSO) logistic regression model to identify ROIs associated with prodromal AD in relation to amyloid deposition, regional atrophy, and their interaction. After applying False Discovery Rate correction at < 0.1, there were no differences in global and regional SUVR between CU A+ and prodromal AD groups. Regional volume differences between the two groups were observed in the amygdala, hippocampus, entorhinal cortex, insula, parahippocampal gyrus, and inferior temporal and parietal cortices. LASSO logistic regression model showed significant associations between prodromal AD and atrophy in the entorhinal cortex, inferior parietal cortex, both amygdalae, and left hippocampus. The mean SUVR in the right superior parietal cortex (beta coefficient = 0.0172) and its interaction with the regional volume (0.0672) were also selected in the LASSO model. The mean SUVR in the right superior parietal cortex was associated with an increased likelihood of prodromal AD (Odds ratio [OR] 1.602, p = 0.014), particularly in participants with lower regional volume (OR 3.389, p < 0.001). Only regional volume differences, not amyloid deposition, were observed between CU A+ and prodromal AD. The reduced volume in the superior parietal cortex may play a significant role in the progression to prodromal AD through its interaction with amyloid deposition in that region.

Comparing a pre-defined versus deep learning approach for extracting brain atrophy patterns to predict cognitive decline due to Alzheimer's disease in patients with mild cognitive symptoms

BACKGROUND

Predicting future Alzheimer's disease (AD)-related cognitive decline among individuals with subjective cognitive decline (SCD) or mild cognitive impairment (MCI) is an important task for healthcare. Structural brain imaging as measured by magnetic resonance imaging (MRI) could potentially contribute when making such predictions. It is unclear if the predictive performance of MRI can be improved using entire brain images in deep learning (DL) models compared to using pre-defined brain regions.

METHODS

A cohort of 332 individuals with SCD/MCI were included from the Swedish BioFINDER-1 study. The goal was to predict longitudinal SCD/MCI-to-AD dementia progression and change in Mini-Mental State Examination (MMSE) over four years. Four models were evaluated using different predictors: (1) clinical data only, including demographics, cognitive tests and APOE ε4 status, (2) clinical data plus hippocampal volume, (3) clinical data plus all regional MRI gray matter volumes (N = 68) extracted using FreeSurfer software, (4) a DL model trained using multi-task learning with MRI images, Jacobian determinant images and baseline cognition as input. A double cross-validation scheme, with five test folds and for each of those ten validation folds, was used. External evaluation was performed on part of the ADNI dataset, including 108 patients. Mann-Whitney U-test was used to determine statistically significant differences in performance, with p-values less than 0.05 considered significant.

RESULTS

In the BioFINDER cohort, 109 patients (33%) progressed to AD dementia. The performance of the clinical data model for prediction of progression to AD dementia was area under the curve (AUC) = 0.85 and four-year cognitive decline was R2 = 0.14. The performance was improved for both outcomes when adding hippocampal volume (AUC = 0.86, R2 = 0.16). Adding FreeSurfer brain regions improved prediction of four-year cognitive decline but not progression to AD (AUC = 0.83, R2 = 0.17), while the DL model worsened the performance for both outcomes (AUC = 0.84, R2 = 0.08). A sensitivity analysis showed that the Jacobian determinant image was more informative than the MRI image, but that performance was maximized when both were included. In the external evaluation cohort from ADNI, 23 patients (21%) progressed to AD dementia. The results for predicted progression to AD dementia were similar to the results for the BioFINDER test data, while the performance for the cognitive decline was deteriorated.

CONCLUSIONS

The DL model did not significantly improve the prediction of clinical disease progression in AD, compared to regression models with a single pre-defined brain region.

Tau-PET in early cortical Alzheimer brain regions in relation to mild behavioral impairment in older adults with either normal cognition or mild cognitive impairment

Mild Behavioral Impairment (MBI) leverages later-life emergent and persistent neuropsychiatric symptoms (NPS) to identify a high-risk group for incident dementia. Phosphorylated tau (p-tau) is a hallmark biological manifestation of Alzheimer disease (AD). We investigated associations between MBI and tau accumulation in early-stage AD cortical regions. In 442 Alzheimer's Disease Neuroimaging Initiative participants with normal cognition or mild cognitive impairment, MBI status was determined alongside corresponding p-tau and Aβ. Two meta-regions of interest were generated to represent Braak I and III neuropathological stages. Multivariable linear regression modelled the association between MBI as independent variable and tau tracer uptake as dependent variable. Among Aβ positive individuals, MBI was associated with tau uptake in Braak I (β =0.45(0.15), p<.01) and Braak III (β =0.24(0.07), p<.01) regions. In Aβ negative individuals, MBI was not associated with tau in the Braak I region (p=.11) with a negative association in Braak III (p=.01). These findings suggest MBI may be a sequela of neurodegeneration, and can be implemented as a cost-effective framework to help improve screening efficiency for AD.

A review of the flortaucipir literature for positron emission tomography imaging of tau neurofibrillary tangles

Alzheimer's disease is defined by the presence of β-amyloid plaques and neurofibrillary tau tangles potentially preceding clinical symptoms by many years. Previously only detectable post-mortem, these pathological hallmarks are now identifiable using biomarkers, permitting an in vivo definitive diagnosis of Alzheimer's disease. 18F-flortaucipir (previously known as 18F-T807; 18F-AV-1451) was the first tau positron emission tomography tracer to be introduced and is the only Food and Drug Administration-approved tau positron emission tomography tracer (Tauvid™). It has been widely adopted and validated in a number of independent research and clinical settings. In this review, we present an overview of the published literature on flortaucipir for positron emission tomography imaging of neurofibrillary tau tangles. We considered all accessible peer-reviewed literature pertaining to flortaucipir through 30 April 2022. We found 474 relevant peer-reviewed publications, which were organized into the following categories based on their primary focus: typical Alzheimer's disease, mild cognitive impairment and pre-symptomatic populations; atypical Alzheimer's disease; non-Alzheimer's disease neurodegenerative conditions; head-to-head comparisons with other Tau positron emission tomography tracers; and technical considerations. The available flortaucipir literature provides substantial evidence for the use of this positron emission tomography tracer in assessing neurofibrillary tau tangles in Alzheimer's disease and limited support for its use in other neurodegenerative disorders. Visual interpretation and quantitation approaches, although heterogeneous, mostly converge and demonstrate the high diagnostic and prognostic value of flortaucipir in Alzheimer's disease.

Comparing a pre-defined versus deep learning approach for extracting brain atrophy patterns to predict cognitive decline due to Alzheimer's disease in patients with mild cognitive symptoms

Background

Predicting future Alzheimer's disease (AD)-related cognitive decline among individuals with subjective cognitive decline (SCD) or mild cognitive impairment (MCI) is an important task for healthcare. Structural brain imaging as measured by magnetic resonance imaging (MRI) could potentially contribute when making such predictions. It is unclear if the predictive performance of MRI can be improved using entire brain images in deep learning (DL) models compared to using pre-defined brain regions.

Methods

A cohort of 332 individuals with SCD/MCI were included from the Swedish BioFINDER-1 study. The goal was to predict longitudinal SCD/MCI-to-AD dementia progression and change in Mini-Mental State Examination (MMSE) over four years. Four models were evaluated using different predictors: 1) clinical data only, including demographics, cognitive tests and APOE e4 status, 2) clinical data plus hippocampal volume, 3) clinical data plus all regional MRI gray matter volumes (N=68) extracted using FreeSurfer software, 4) a DL model trained using multi-task learning with MRI images, Jacobian determinant images and baseline cognition as input. Models were developed on 80% of subjects (N=267) and tested on the remaining 20% (N=65). Mann-Whitney U-test was used to determine statistically significant differences in performance, with p-values less than 0.05 considered significant.

Results

In the test set, 21 patients (32.3%) progressed to AD dementia. The performance of the clinical data model for prediction of progression to AD dementia was area under the curve (AUC)=0.87 and four-year cognitive decline was R2=0.17. The performance was significantly improved for both outcomes when adding hippocampal volume (AUC=0.91, R2=0.26, p-values <0.05) or FreeSurfer brain regions (AUC=0.90, R2=0.27, p-values <0.05). Conversely, the DL model did not show any significant difference from the clinical data model (AUC=0.86, R2=0.13). A sensitivity analysis showed that the Jacobian determinant image was more informative than the MRI image, but that performance was maximized when both were included.

Conclusions

The DL model did not significantly improve the prediction of clinical disease progression in AD, compared to regression models with a single pre-defined brain region.

Diagnosis of Alzheimer's disease by joining dual attention CNN and MLP based on structural MRIs, clinical and genetic data

Alzheimer's disease (AD) is an irreversible central nervous degenerative disease, while mild cognitive impairment (MCI) is a precursor state of AD. Accurate early diagnosis of AD is conducive to the prevention and early intervention treatment of AD. Although some computational methods have been developed for AD diagnosis, most employ only neuroimaging, ignoring other data (e.g., genetic, clinical) that may have potential disease information. In addition, the results of some methods lack interpretability. In this work, we proposed a novel method (called DANMLP) of joining dual attention convolutional neural network (CNN) and multilayer perceptron (MLP) for computer-aided AD diagnosis by integrating multi-modality data of the structural magnetic resonance imaging (sMRI), clinical data (i.e., demographics, neuropsychology), and APOE genetic data. Our DANMLP consists of four primary components: (1) the Patch-CNN for extracting the image characteristics from each local patch, (2) the position self-attention block for capturing the dependencies between features within a patch, (3) the channel self-attention block for capturing dependencies of inter-patch features, (4) two MLP networks for extracting the clinical features and outputting the AD classification results, respectively. Compared with other state-of-the-art methods in the 5CV test, DANMLP achieves 93% and 82.4% classification accuracy for the AD vs. MCI and MCI vs. NC tasks on the ADNI database, which is 0.2%∼15.2% and 3.4%∼26.8% higher than that of other five methods, respectively. The individualized visualization of focal areas can also help clinicians in the early diagnosis of AD. These results indicate that DANMLP can be effectively used for diagnosing AD and MCI patients.

Neuroimaging biomarkers of glial activation for predicting the annual cognitive function decline in patients with Alzheimer's disease

BACKGROUND

Glial activation is central to the pathogenesis of Alzheimer's disease (AD). However, researchers have not demonstrated its relationship to longitudinal cognitive deterioration. We aimed to compare the prognostic effects of baseline positron emission tomography (PET) imaging of glial activation and amyloid/tau pathology on the successive annual cognitive decline in patients with AD.

METHODS

We selected 17 patients diagnosed with mild cognitive impairment or AD. We assessed the annual changes in global cognition and memory. Furthermore, we assessed the predictive effects of baseline amyloid and tau pathology indicated by cerebrospinal fluid (CSF) concentrations and PET imaging of glial activation (11C-DPA-713-binding potential in the area of Braak 1-3 [11C-DPA-713-BPND]) on global cognition and memory using a stepwise regression analysis.

RESULTS

The final multiple regression model of annual changes in global cognition and memory scores included 11C-DPA-713-BPND as the predictor. The CSF Aβ42/40 ratios and p-tau concentrations were removed from the final model. In stepwise Bayesian regression analysis, the Bayes factor-based model comparison suggested that the best model included 11C-DPA-713-BPND as the predictor of decline in global cognition and memory.

CONCLUSIONS

Translocator protein-PET imaging of glial activation is a stronger predictor of AD clinical progression than the amount of amyloid/tau pathology measured using CSF concentrations. Glial activation is the primary cause of tau-induced neuronal toxicity and cognitive deterioration, thereby highlighting the potential of blocking maladaptive microglial responses as a therapeutic strategy for AD treatment.

Tau: a biomarker of Huntington's disease

Developing effective treatments for patients with Huntington's disease (HD)-a neurodegenerative disorder characterized by severe cognitive, motor and psychiatric impairments-is proving extremely challenging. While the monogenic nature of this condition enables to identify individuals at risk, robust biomarkers would still be extremely valuable to help diagnose disease onset and progression, and especially to confirm treatment efficacy. If measurements of cerebrospinal fluid neurofilament levels, for example, have demonstrated use in recent clinical trials, other proteins may prove equal, if not greater, relevance as biomarkers. In fact, proteins such as tau could specifically be used to detect/predict cognitive affectations. We have herein reviewed the literature pertaining to the association between tau levels and cognitive states, zooming in on Alzheimer's disease, Parkinson's disease and traumatic brain injury in which imaging, cerebrospinal fluid, and blood samples have been interrogated or used to unveil a strong association between tau and cognition. Collectively, these areas of research have accrued compelling evidence to suggest tau-related measurements as both diagnostic and prognostic tools for clinical practice. The abundance of information retrieved in this niche of study has laid the groundwork for further understanding whether tau-related biomarkers may be applied to HD and guide future investigations to better understand and treat this disease.

Distinct resting-state functional connectivity patterns of Anterior Insula affected by smoking in mild cognitive impairment

Smoking is a modifiable risk factor for Alzheimer's disease (AD). The insula plays a vital role in both smoking and cognition. However, the smoking effects on insula-related networks in cognitively normal controls (CN) and mild cognitive impairment (MCI) patients remain unknown. We identified 129 CN (85 non-smokers and 44 smokers) and 83 MCI (54 non-smokers and 29 smokers). Each underwent neuropsychological assessment and MRI (structural and resting-state functional). Seed-based functional analyses in the anterior and posterior insula were performed to calculate the functional connectivity (FC) with voxels in the whole brain. Mixed-effect analyses were performed to explore the interactive effects on smoking and cognitive status. Associations between FC and neuropsychological scales were assessed. Mixed-effect analyses revealed the FC differences between the right anterior insula (RAI) with the left middle temporal gyrus (LMTG) and that with the right inferior parietal lobule (RIPL) (p < 0.01, cluster level < 0.05, two-tailed, gaussian random field correction). The FC of RAI in both LMTG and RIPL sees a significant decrease in MCI smokers (p < 0.01). Smoking affects insula FC differently between MCI and CN, and could decrease the insula FC in MCI patients. Our study provides evidence of neural mechanisms between smoking and AD.

Head-to-head comparison of plasma and PET imaging ATN markers in subjects with cognitive complaints

BACKGROUND

Gaining more information about the reciprocal associations between different biomarkers within the ATN (Amyloid/Tau/Neurodegeneration) framework across the Alzheimer's disease (AD) spectrum is clinically relevant. We aimed to conduct a comprehensive head-to-head comparison of plasma and positron emission tomography (PET) ATN biomarkers in subjects with cognitive complaints.

METHODS

A hospital-based cohort of subjects with cognitive complaints with a concurrent blood draw and ATN PET imaging (18F-florbetapir for A, 18F-Florzolotau for T, and 18F-fluorodeoxyglucose [18F-FDG] for N) was enrolled (n = 137). The β-amyloid (Aβ) status (positive versus negative) and the severity of cognitive impairment served as the main outcome measures for assessing biomarker performances.

RESULTS

Plasma phosphorylated tau 181 (p-tau181) level was found to be associated with PET imaging of ATN biomarkers in the entire cohort. Plasma p-tau181 level and PET standardized uptake value ratios of AT biomarkers showed a similarly excellent diagnostic performance for distinguishing between Aβ+ and Aβ- subjects. An increased tau burden and glucose hypometabolism were significantly associated with the severity of cognitive impairment in Aβ+ subjects. Additionally, glucose hypometabolism - along with elevated plasma neurofilament light chain level - was related to more severe cognitive impairment in Aβ- subjects.

CONCLUSION

Plasma p-tau181, as well as 18F-florbetapir and 18F-Florzolotau PET imaging can be considered as interchangeable biomarkers in the assessment of Aβ status in symptomatic stages of AD. 18F-Florzolotau and 18F-FDG PET imaging could serve as biomarkers for the severity of cognitive impairment. Our findings have implications for establishing a roadmap to identifying the most suitable ATN biomarkers for clinical use.

Tau-PET is superior to phospho-tau when predicting cognitive decline in symptomatic AD patients

INTRODUCTION

Biomarkers for the prediction of cognitive decline in patients with amnestic mild cognitive impairment (MCI) and amnestic mild dementia are needed for both clinical practice and clinical trials.

METHODS

We evaluated the ability of tau-PET (positron emission tomography), cortical atrophy on magnetic resonance imaging (MRI), baseline cognition, apolipoprotein E gene (APOE) status, plasma and cerebrospinal fluid (CSF) levels of phosphorylated tau-217, neurofilament light (NfL), and amyloid beta (Aβ)42/40 ratio (individually and in combination) to predict cognitive decline over 2 years in BioFINDER-2 and Alzheimer's Disease Neuroimaging Initiative (ADNI).

RESULTS

Baseline tau-PET and a composite baseline cognitive score were the strongest independent predictors of cognitive decline. Cortical thickness and NfL provided some additional information. Using a predictive algorithm to enrich patient selection in a theoretical clinical trial led to a significantly lower required sample size.

DISCUSSION

Models including baseline tau-PET and cognition consistently provided the best prediction of change in cognitive function over 2 years in patients with amnestic MCI or mild dementia.

Tau positron emission tomography in preclinical Alzheimer's disease

Rates of tau accumulation in cognitively unimpaired older adults are subtle, with magnitude and spatial patterns varying in recent reports. Regional accumulation also likely varies in the degree to which accumulation is amyloid-β-dependent. Thus, there is a need to evaluate the pattern and consistency of tau accumulation across multiple cognitively unimpaired cohorts and how these patterns relate to amyloid burden, in order to design optimal tau end points for clinical trials. Using three large cohorts of cognitively unimpaired older adults, the Anti-Amyloid Treatment in Asymptomatic Alzheimer's and companion study, Longitudinal Evaluation of Amyloid Risk and Neurodegeneration (n = 447), the Alzheimer's Disease Neuroimaging Initiative (n = 420) and the Harvard Aging Brain Study (n = 190), we attempted to identify regions with high rates of tau accumulation and estimate how these rates evolve over a continuous spectrum of baseline amyloid deposition. Optimal combinations of regions, tailored to multiple ranges of baseline amyloid burden as hypothetical clinical trial inclusion criteria, were tested and validated. The inferior temporal cortex, fusiform gyrus and middle temporal cortex had the largest effect sizes of accumulation in both longitudinal cohorts when considered individually. When tau regions of interest were combined to find composite weights to maximize the effect size of tau change over time, both longitudinal studies exhibited a similar pattern-inferior temporal cortex, almost exclusively, was optimal for participants with mildly elevated amyloid β levels. For participants with highly elevated baseline amyloid β levels, combined optimal composite weights were 53% inferior temporal cortex, 31% amygdala and 16% fusiform. At mildly elevated levels of baseline amyloid β, a sample size of 200/group required a treatment effect of 0.40-0.45 (40-45% slowing of tau accumulation) to power an 18-month trial using the optimized composite. Neither a temporal lobe composite nor a global composite reached 80% power with 200/group with an effect size under 0.5. The focus of early tau accumulation on the medial temporal lobe has resulted from the observation that the entorhinal cortex is the initial site to show abnormal levels of tau with age. However, these abnormal levels do not appear to be the result of a high rate of accumulation in the short term, but possibly a more moderate rate occurring early with respect to age. While the entorhinal cortex plays a central role in the early appearance of tau, it may be the inferior temporal cortex that is the critical region for rapid tau accumulation in preclinical Alzheimer's disease.

The Sensitivity of Tau Tracers for the Discrimination of Alzheimer's Disease Patients and Healthy Controls by PET

Hyperphosphorylated tau aggregates, also known as neurofibrillary tangles, are a hallmark neuropathological feature of Alzheimer's disease (AD). Molecular imaging of tau by positron emission tomography (PET) began with the development of [18F]FDDNP, an amyloid β tracer with off-target binding to tau, which obtained regional specificity through the differing distributions of amyloid β and tau in AD brains. A concerted search for more selective and affine tau PET tracers yielded compounds belonging to at least eight structural categories; 18F-flortaucipir, known variously as [18F]-T807, AV-1451, and Tauvid®, emerged as the first tau tracer approved by the American Food and Drug Administration. The various tau tracers differ concerning their selectivity over amyloid β, off-target binding at sites such as monoamine oxidase and neuromelanin, and degree of uptake in white matter. While there have been many reviews of molecular imaging of tau in AD and other conditions, there has been no systematic comparison of the fitness of the various tracers for discriminating between AD patient and healthy control (HC) groups. In this narrative review, we endeavored to compare the binding properties of the various tau tracers in vitro and the effect size (Cohen's d) for the contrast by PET between AD patients and age-matched HC groups. The available tracers all gave good discrimination, with Cohen's d generally in the range of two-three in culprit brain regions. Overall, Cohen's d was higher for AD patient groups with more severe illness. Second-generation tracers, while superior concerning off-target binding, do not have conspicuously higher sensitivity for the discrimination of AD and HC groups. We suppose that available pharmacophores may have converged on a maximal affinity for tau fibrils, which may limit the specific signal imparted in PET studies.

Rates of regional tau accumulation in ageing and across the Alzheimer's disease continuum: an AIBL 18F-MK6240 PET study

BACKGROUND

Tau positron emission tomography (PET) imaging enables longitudinal observation of tau accumulation in Alzheimer's disease (AD). 18F-MK6240 is a high affinity tracer for the paired helical filaments of tau in AD, widely used in clinical trials, despite sparse longitudinal natural history data. We aimed to evaluate the natural history of tau accumulation, and the impact of disease stage and reference region on the magnitude and effect size of regional change.

METHODS

One hundred and eighty-four participants: 89 cognitively unimpaired (CU) beta-amyloid negative (Aβ-), 44 CU Aβ+, 51 cognitively impaired Aβ+ (26 with mild cognitive impairment [MCI] and 25 with dementia) had follow-up 18F-MK6240 PET for one to four years (median 1.48). Regional standardised uptake value ratios (SUVR) were generated. Two reference regions were examined: cerebellar cortex and eroded subcortical white matter.

FINDINGS

CU Aβ- participants had very low rates of tau accumulation in the mesial temporal lobe (MTL). In CU Aβ+, significantly higher rate of accumulation was seen in the MTL (particularly the amygdala), extending into the inferior temporal lobes. In MCI Aβ+, the rate of accumulation was greatest in the lateral temporal, parietal and lateral occipital cortex, and plateaued in the MTL. Accumulation was global in AD Aβ+, except for a plateau in the MTL. The eroded subcortical white matter reference region showed no significant advantage over the cerebellar cortex and appeared prone to spill-over in AD participants. Data fitting suggested approximately 15-20 years to accumulate tau to typical AD levels.

INTERPRETATION

Tau accumulation occurs slowly. Rates vary according to brain region, disease stage and tend to plateau at high levels. Rates of tau accumulation are best measured in the MTL and inferior temporal cortex in preclinical AD and in large neocortical areas, in MCI and AD.

FUNDING

NHMRC; Cerveau Technologies.

Episodic Memory-Related Imaging Features as Valuable Biomarkers for the Diagnosis of Alzheimer's Disease: A Multicenter Study Based on Machine Learning

BACKGROUND

Individualized and reliable biomarkers are crucial for diagnosing Alzheimer's disease (AD). However, lack of accessibility and neurobiological correlation are the main obstacles to their clinical application. Machine learning algorithms can effectively identify personalized biomarkers based on the prominent symptoms of AD.

METHODS

Episodic memory-related magnetic resonance imaging (MRI) features of 143 patients with amnesic mild cognitive impairment (MCI) were identified using a multivariate relevance vector regression algorithm. The support vector machine classification model was constructed using these MRI features and verified in 2 independent datasets (N = 994). The neurobiological basis was also investigated based on cognitive assessments, neuropathologic biomarkers of cerebrospinal fluid, and positron emission tomography images of amyloid-β plaques.

RESULTS

The combination of gray matter volume and amplitude of low-frequency fluctuation MRI features accurately predicted episodic memory impairment in individual patients with amnesic MCI (r = 0.638) when measured using an episodic memory assessment panel. The MRI features that contributed to episodic memory prediction were primarily distributed across the default mode network and limbic network. The classification model based on these features distinguished patients with AD from normal control subjects with more than 86% accuracy. Furthermore, most identified episodic memory-related regions showed significantly different amyloid-β positron emission tomography measurements among the AD, MCI, and normal control groups. Moreover, the classification outputs significantly correlated with cognitive assessment scores and cerebrospinal fluid pathological biomarkers' levels in the MCI and AD groups.

CONCLUSIONS

Neuroimaging features can reflect individual episodic memory function and serve as potential diagnostic biomarkers of AD.

Tau as a biomarker of cognitive impairment and neuropsychiatric symptom in Alzheimer's disease

The A/T/N research framework has been proposed for the diagnosis and prognosis of Alzheimer's disease (AD). However, the spatial distribution of ATN biomarkers and their relationship with cognitive impairment and neuropsychiatric symptoms (NPS) need further clarification in patients with AD. We scanned 83 AD patients and 38 cognitively normal controls who independently completed the mini-mental state examination and Neuropsychiatric Inventory scales. Tau, Aβ, and hypometabolism spatial patterns were characterized using Statistical Parametric Mapping together with [18F]flortaucipir, [18F]florbetapir, and [18F]FDG positron emission tomography. Piecewise linear regression, two-sample t-tests, and support vector machine algorithms were used to explore the relationship between tau, Aβ, and hypometabolism and cognition, NPS, and AD diagnosis. The results showed that regions with tau deposition are region-specific and mainly occurred in inferior temporal lobes in AD, which extensively overlaps with the hypometabolic regions. While the deposition regions of Aβ were unique and the regions affected by hypometabolism were widely distributed. Unlike Aβ, tau and hypometabolism build up monotonically with increasing cognitive impairment in the late stages of AD. In addition, NPS in AD were associated with tau deposition closely, followed by hypometabolism, but not with Aβ. Finally, hypometabolism and tau had higher accuracy in differentiating the AD patients from controls (accuracy = 0.88, accuracy = 0.85) than Aβ (accuracy = 0.81), and the combined three were the highest (accuracy = 0.95). These findings suggest tau pathology is superior over Aβ and glucose metabolism to identify cognitive impairment and NPS. Its results support tau accumulation can be used as a biomarker of clinical impairment in AD.

Exploiting task relationships for Alzheimer's disease cognitive score prediction via multi-task learning

Alzheimer's disease (AD) is highly prevalent and a significant cause of dementia and death in elderly individuals. Motivated by breakthroughs of multi-task learning (MTL), efforts have been made to extend MTL to improve the Alzheimer's disease cognitive score prediction by exploiting structure correlation. Though important and well-studied, three key aspects are yet to be fully handled in an unified framework: (i) appropriately modeling the inherent task relationship; (ii) fully exploiting the task relatedness by considering the underlying feature structure. (iii) automatically determining the weight of each task. To this end, we present the Bi-Graph guided self-Paced Multi-Task Feature Learning (BGP-MTFL) framework for exploring the relationship among multiple tasks to improve overall learning performance of cognitive score prediction. The framework consists of the two correlation regularization for features and tasks, ℓ_2,1 regularization and self-paced learning scheme. Moreover, we design an efficient optimization method to solve the non-smooth objective function of our approach based on the Alternating Direction Method of Multipliers (ADMM) combined with accelerated proximal gradient (APG). The proposed model is comprehensively evaluated on the Alzheimer's disease neuroimaging initiative (ADNI) datasets. Overall, the proposed algorithm achieves an nMSE (normalized Mean Squared Error) of 3.923 and an wR (weighted R-value) of 0.416 for predicting eighteen cognitive scores, respectively. The empirical study demonstrates that the proposed BGP-MTFL model outperforms the state-of-the-art AD prediction approaches and enables identifying more stable biomarkers.

Fully Connected Multi-Kernel Convolutional Neural Network Based on Alzheimer's Disease Diagnosis

BACKGROUND

There is a shortage of clinicians with sufficient expertise in the diagnosis of Alzheimer's disease (AD), and cerebrospinal fluid biometric collection and positron emission tomography diagnosis are invasive. Therefore, it is of potential significance to obtain high-precision automatic diagnosis results from diffusion tensor imaging (DTI) through deep learning, and simultaneously output feature probability maps to provide clinical auxiliary diagnosis.

OBJECTIVE

We proposed a factorization machine combined neural network (FMCNN) model combining a multi-function convolutional neural network (MCNN) with a fully convolutional network (FCN), while accurately diagnosing AD and mild cognitive impairment (MCI); corresponding fiber bundle visualization results are generated to describe their status.

METHODS

First, the DTI data is preprocessed to eliminate the influence of external factors. The fiber bundles of the corpus callosum (CC), cingulum (CG), uncinate fasciculus (UNC), and white matter (WM) were then tracked based on deterministic fiber tracking. Then the streamlines are input into CNN, MCNN, and FMCNN as one-dimensional features for classification, and the models are evaluated by performance evaluation indicators. Finally, the fiber risk probability map is output through FMCNN.

RESULTS

After comparing the model performance indicators of CNN, MCNN, and FMCNN, it was found that FMCNN showed the best performance in the indicators of accuracy, specificity, sensitivity, and area under the curve. By inputting the fiber bundles of the 10 regions of interest (UNC_L, UNC_R, UNC, CC, CG, CG+UNC, CG+CC, CC+UNC, CG+CC+UNC, and WM into CNN, MCNN, and FMCNN, respectively), WM shows the highest accuracy in CNN, MCNN, and FMCNN, which are 88.41%, 92.07%, and 96.95%, respectively.

CONCLUSION

The FMCNN proposed here can accurately diagnose AD and MCI, and the generated fiber probability map can represent the risk status of AD and MCI.

A Machine Learning-Based Approach to Discrimination of Tauopathies Using [18 F]PM-PBB3 PET Images

BACKGROUND

We recently developed a positron emission tomography (PET) probe, [¹⁸ F]PM-PBB3, to detect tau lesions in diverse tauopathies, including mixed three-repeat and four-repeat (3R + 4R) tau fibrils in Alzheimer's disease (AD) and 4R tau aggregates in progressive supranuclear palsy (PSP). For wider availability of this technology for clinical settings, bias-free quantitative evaluation of tau images without a priori disease information is needed.

OBJECTIVE

We aimed to establish tau PET pathology indices to characterize PSP and AD using a machine learning approach and test their validity and tracer capabilities.

METHODS

Data were obtained from 50 healthy control subjects, 46 patients with PSP Richardson syndrome, and 37 patients on the AD continuum. Tau PET data from 114 regions of interest were subjected to Elastic Net cross-validation linear classification analysis with a one-versus-the-rest multiclass strategy to obtain a linear function that discriminates diseases by maximizing the area under the receiver operating characteristic curve. We defined PSP- and AD-tau scores for each participant as values of the functions optimized for differentiating PSP (4R) and AD (3R + 4R), respectively, from others.

RESULTS

The discriminatory ability of PSP- and AD-tau scores assessed as the area under the receiver operating characteristic curve was 0.98 and 1.00, respectively. PSP-tau scores correlated with the PSP rating scale in patients with PSP, and AD-tau scores correlated with Mini-Mental State Examination scores in healthy control-AD continuum patients. The globus pallidus and amygdala were highlighted as regions with high weight coefficients for determining PSP- and AD-tau scores, respectively.

CONCLUSIONS

These findings highlight our technology's unbiased capability to identify topologies of 3R + 4R versus 4R tau deposits. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Intraneuronal sortilin aggregation relative to granulovacuolar degeneration, tau pathogenesis and sorfra plaque formation in human hippocampal formation

Extracellular β-amyloid (Aβ) deposition and intraneuronal phosphorylated-tau (pTau) accumulation are the hallmark lesions of Alzheimer’s disease (AD). Recently, “sorfra” plaques, named for the extracellular deposition of sortilin c-terminal fragments, are reported as a new AD-related proteopathy, which develop in the human cerebrum resembling the spatiotemporal trajectory of tauopathy. Here, we identified intraneuronal sortilin aggregation as a change related to the development of granulovacuolar degeneration (GVD), tauopathy, and sorfra plaques in the human hippocampal formation. Intraneuronal sortilin aggregation occurred as cytoplasmic inclusions among the pyramidal neurons, co-labeled by antibodies to the extracellular domain and intracellular C-terminal of sortilin. They existed infrequently in the brains of adults, while their density as quantified in the subiculum/CA1 areas increased in the brains from elderly lacking Aβ/pTau, with pTau (i.e., primary age-related tauopathy, PART cases), and with Aβ/pTau (probably/definitive AD, pAD/AD cases) pathologies. In PART and pAD/AD cases, the intraneuronal sortilin aggregates colocalized partially with various GVD markers including casein kinase 1 delta (Ck1δ) and charged multivesicular body protein 2B (CHMP2B). Single-cell densitometry established an inverse correlation between sortilin immunoreactivity and that of Ck1δ, CHMP2B, p62, and pTau among pyramidal neurons. In pAD/AD cases, the sortilin aggregates were reduced in density as moving from the subiculum to CA subregions, wherein sorfra plaques became fewer and absent. Taken together, we consider intraneuronal sortilin aggregation an aging/stress-related change implicating protein sorting deficit, which can activate protein clearance responses including via enhanced phosphorylation and hydrolysis, thereby promoting GVD, sorfra, and Tau pathogenesis, and ultimately, neuronal destruction and death.

Multimodal deep learning for Alzheimer's disease dementia assessment

Worldwide, there are nearly 10 million new cases of dementia annually, of which Alzheimer's disease (AD) is the most common. New measures are needed to improve the diagnosis of individuals with cognitive impairment due to various etiologies. Here, we report a deep learning framework that accomplishes multiple diagnostic steps in successive fashion to identify persons with normal cognition (NC), mild cognitive impairment (MCI), AD, and non-AD dementias (nADD). We demonstrate a range of models capable of accepting flexible combinations of routinely collected clinical information, including demographics, medical history, neuropsychological testing, neuroimaging, and functional assessments. We then show that these frameworks compare favorably with the diagnostic accuracy of practicing neurologists and neuroradiologists. Lastly, we apply interpretability methods in computer vision to show that disease-specific patterns detected by our models track distinct patterns of degenerative changes throughout the brain and correspond closely with the presence of neuropathological lesions on autopsy. Our work demonstrates methodologies for validating computational predictions with established standards of medical diagnosis.

Multiscale structural mapping of Alzheimer's disease neurodegeneration

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A multiscale structural mapping (MSSM) procedure is proposed for the quantification of neurodegeneration in Alzheimer's disease using a single structural brain image.

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The MSSM procedure captures both macrostructural properties and indirect index of tissue microstructure throughout the cerebral cortex.

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The MSSM procedure provides enhanced ability for the detection of degeneration in Alzheimer’s disease and mild cognitive impairment compared to traditional measures such as cortical thickness and hippocampal volume and therefore may provide a sensitive measure of Alzheimer’s disease neurodegeneration.

The recently described biological framework of Alzheimer’s disease (AD) emphasizes three types of pathology to characterize this disorder, referred to as the ‘amyloid/tau/neurodegeneration’ (A-T-N) status. The ‘neurodegenerative’ component is typically defined by atrophy measures derived from structural magnetic resonance imaging (MRI) such as hippocampal volume. Neurodegeneration measures from imaging are associated with disease symptoms and prognosis. Thus, sensitive image-based quantification of neurodegeneration in AD has an important role in a range of clinical and research operations. Although hippocampal volume is a sensitive metric of neurodegeneration, this measure is impacted by several clinical conditions other than AD and therefore lacks specificity. In contrast, selective regional cortical atrophy, known as the ‘cortical signature of AD’ provides greater specificity to AD pathology. Although atrophy is apparent even in the preclinical stages of the disease, it is possible that increased sensitivity to degeneration could be achieved by including tissue microstructural properties in the neurodegeneration measure. However, to facilitate clinical feasibility, such information should be obtainable from a single, short, noninvasive imaging protocol. We propose a multiscale MRI procedure that advances prior work through the quantification of features at both macrostructural (morphometry) and microstructural (tissue properties obtained from multiple layers of cortex and subcortical white matter) scales from a single structural brain image (referred to as ‘multi-scale structural mapping’; MSSM). Vertex-wise partial least squares (PLS) regression was used to compress these multi-scale structural features. When contrasting patients with AD to cognitively intact matched older adults, the MSSM procedure showed substantially broader regional group differences including areas that were not statistically significant when using cortical thickness alone. Further, with multiple machine learning algorithms and ensemble procedures, we found that MSSM provides accurate detection of individuals with AD dementia (AUROC = 0.962, AUPRC = 0.976) and individuals with mild cognitive impairment (MCI) that subsequently progressed to AD dementia (AUROC = 0.908, AUPRC = 0.910). The findings demonstrate the critical advancement of neurodegeneration quantification provided through multiscale mapping. Future work will determine the sensitivity of this technique for accurately detecting individuals with earlier impairment and biomarker positivity in the absence of impairment.

Spatially resolved neural slowing predicts impairment and amyloid burden in Alzheimer's disease

An extensive electrophysiological literature has proposed a pathological ‘slowing’ of neuronal activity in patients on the Alzheimer’s disease spectrum. Supported by numerous studies reporting increases in low-frequency and decreases in high-frequency neural oscillations, this pattern has been suggested as a stable biomarker with potential clinical utility. However, no spatially resolved metric of such slowing exists, stymieing efforts to understand its relation to proteinopathy and clinical outcomes. Further, the assumption that this slowing is occurring in spatially overlapping populations of neurons has not been empirically validated.

In the current study, we collected cross-sectional resting state measures of neuronal activity using magnetoencephalography from 38 biomarker-confirmed patients on the Alzheimer’s disease spectrum and 20 cognitively normal biomarker-negative older adults. From these data, we compute and validate a new metric of spatially resolved oscillatory deviations from healthy ageing for each patient on the Alzheimer’s disease spectrum.

Using this Pathological Oscillatory Slowing Index, we show that patients on the Alzheimer’s disease spectrum exhibit robust neuronal slowing across a network of temporal, parietal, cerebellar and prefrontal cortices. This slowing effect is shown to be directly relevant to clinical outcomes, as oscillatory slowing in temporal and parietal cortices significantly predicted both general (i.e. Montreal Cognitive Assessment scores) and domain-specific (i.e. attention, language and processing speed) cognitive function. Further, regional amyloid-β accumulation, as measured by quantitative ¹⁸F florbetapir PET, robustly predicted the magnitude of this pathological neural slowing effect, and the strength of this relationship between amyloid-β burden and neural slowing also predicted attentional impairments across patients.

These findings provide empirical support for a spatially overlapping effect of oscillatory neural slowing in biomarker-confirmed patients on the Alzheimer’s disease spectrum, and link this effect to both regional proteinopathy and cognitive outcomes in a spatially resolved manner. The Pathological Oscillatory Slowing Index also represents a novel metric that is of potentially high utility across a number of clinical neuroimaging applications, as oscillatory slowing has also been extensively documented in other patient populations, most notably Parkinson’s disease, with divergent spectral and spatial features.

A whole-process interpretable and multi-modal deep reinforcement learning for diagnosis and analysis of Alzheimer's disease∗

Objective. Alzheimer's disease (AD), a common disease of the elderly with unknown etiology, has been adversely affecting many people, especially with the aging of the population and the younger trend of this disease. Current artificial intelligence (AI) methods based on individual information or magnetic resonance imaging (MRI) can solve the problem of diagnostic sensitivity and specificity, but still face the challenges of interpretability and clinical feasibility. In this study, we propose an interpretable multimodal deep reinforcement learning model for inferring pathological features and the diagnosis of AD.Approach. First, for better clinical feasibility, the compressed-sensing MRI image is reconstructed using an interpretable deep reinforcement learning model. Then, the reconstructed MRI is input into the full convolution neural network to generate a pixel-level disease probability risk map (DPM) of the whole brain for AD. The DPM of important brain regions and individual information are then input into the attention-based fully deep neural network to obtain the diagnosis results and analyze the biomarkers. We used 1349 multi-center samples to construct and test the model.Main results.Finally, the model obtained 99.6% ± 0.2%, 97.9% ± 0.2%, and 96.1% ± 0.3% area under curve in ADNI, AIBL and NACC, respectively. The model also provides an effective analysis of multimodal pathology, predicts the imaging biomarkers in MRI and the weight of each individual item of information. In this study, a deep reinforcement learning model was designed, which can not only accurately diagnose AD, but analyze potential biomarkers.Significance. In this study, a deep reinforcement learning model was designed. The model builds a bridge between clinical practice and AI diagnosis and provides a viewpoint for the interpretability of AI technology.

Assessment of Tau Pathology as Measured by 18F-THK5317 and 18F-Flortaucipir PET and Their Relation to Brain Atrophy and Cognition in Alzheimer's Disease

BACKGROUND

In Alzheimer's disease (AD), the abnormal aggregation of hyperphosphorylated tau leads to synaptic dysfunction and neurodegeneration. Recently developed tau PET imaging tracers are candidate biomarkers for diagnosis and staging of AD.

OBJECTIVE

We aimed to investigate the discriminative ability of 18F-THK5317 and 18F-flortaucipir tracers and brain atrophy at different stages of AD, and their respective associations with cognition.

METHODS

Two cohorts, each including 29 participants (healthy controls [HC], prodromal AD, and AD dementia patients), underwent 18F-THK5317 or 18F-flortaucipir PET, T1-weighted MRI, and neuropsychological assessment. For each subject, we quantified regional 18F-THK5317 and 18F-flortaucipir uptake within six bilateral and two composite regions of interest. We assessed global brain atrophy for each individual by quantifying the brain volume index, a measure of brain volume-to-cerebrospinal fluid ratio. We then quantified the discriminative ability of regional 18F-THK5317, 18F-flortaucipir, and brain volume index between diagnostic groups, and their associations with cognition in patients.

RESULTS

Both 18F-THK5317 and 18F-flortaucipir outperformed global brain atrophy in discriminating between HC and both prodromal AD and AD dementia groups. 18F-THK5317 provided the highest discriminative ability between HC and prodromal AD groups. 18F-flortaucipir performed best at discriminating between prodromal and dementia stages of AD. Across all patients, both tau tracers were predictive of RAVL learning, but only 18F-flortaucipir predicted MMSE.

CONCLUSION

Our results warrant further in vivo head-to-head and antemortem-postmortem evaluations. These validation studies are needed to select tracers with high clinical validity as biomarkers for early diagnosis, prognosis, and disease staging, which will facilitate their incorporation in clinical practice and therapeutic trials.

The human connectome in Alzheimer disease - relationship to biomarkers and genetics

The pathology of Alzheimer disease (AD) damages structural and functional brain networks, resulting in cognitive impairment. The results of recent connectomics studies have now linked changes in structural and functional network organization in AD to the patterns of amyloid-β and tau accumulation and spread, providing insights into the neurobiological mechanisms of the disease. In addition, the detection of gene-related connectome changes might aid in the early diagnosis of AD and facilitate the development of personalized therapeutic strategies that are effective at earlier stages of the disease spectrum. In this article, we review studies of the associations between connectome changes and amyloid-β and tau pathologies as well as molecular genetics in different subtypes and stages of AD. We also highlight the utility of connectome-derived computational models for replicating empirical findings and for tracking and predicting the progression of biomarker-indicated AD pathophysiology.

Soluble P-tau217 reflects amyloid and tau pathology and mediates the association of amyloid with tau

Alzheimer's disease is characterized by β-amyloid plaques and tau tangles. Plasma levels of phospho-tau217 (P-tau217) accurately differentiate Alzheimer's disease dementia from other dementias, but it is unclear to what degree this reflects β-amyloid plaque accumulation, tau tangle accumulation, or both. In a cohort with post-mortem neuropathological data (N = 88), both plaque and tangle density contributed independently to higher P-tau217, but P-tau217 was not elevated in patients with non-Alzheimer's disease tauopathies (N = 9). Several findings were replicated in a cohort with PET imaging ("BioFINDER-2", N = 426), where β-amyloid and tau PET were independently associated with P-tau217. P-tau217 concentrations correlated with β-amyloid PET (but not tau PET) in early disease stages and with both β-amyloid and (more strongly) tau PET in late disease stages. Finally, P-tau217 mediated the association between β-amyloid and tau in both cohorts, especially for tau outside of the medial temporal lobe. These findings support the hypothesis that plasma P-tau217 concentration is increased by both β-amyloid plaques and tau tangles and is congruent with the hypothesis that P-tau is involved in β-amyloid-dependent formation of neocortical tau tangles.

Spatio-spectral relationships between pathological neural dynamics and cognitive impairment along the Alzheimer's disease spectrum

INTRODUCTION

Numerous studies have described aberrant patterns of rhythmic neural activity in patients along the Alzheimer's disease (AD) spectrum, yet the relationships between these pathological features and cognitive decline are uncertain.

METHODS

We acquired magnetoencephalography (MEG) data from 38 amyloid-PET biomarker-confirmed patients on the AD spectrum and a comparison group of biomarker-negative cognitively normal (CN) healthy adults, alongside an extensive neuropsychological battery.

RESULTS

By modeling whole-brain rhythmic neural activity with an extensive neuropsychological profile in patients on the AD spectrum, we show that the spectral and spatial features of deviations from healthy adults in neural population-level activity inform their relevance to domain-specific neurocognitive declines.

DISCUSSION

Regional oscillatory activity represents a sensitive metric of neuronal pathology in patients on the AD spectrum. By considering not only the spatial, but also the spectral, definitions of cortical neuronal activity, we show that domain-specific cognitive declines can be better modeled in these individuals.

Towards clinical application of tau PET tracers for diagnosing dementia due to Alzheimer's disease

The recent development of several tau positron emission tomography (PET) tracers represents a major milestone for the Alzheimer's disease (AD) field. These tau PET tracers bind tau neurofibrillary tangles, a key neuropathological characteristic of AD that is tightly linked to synaptic loss, brain atrophy, and cognitive decline. It is notable that these tau PET tracers show low uptake in most non-AD tauopathies and other neurodegenerative disorders, resulting in a diagnostic specificity that is superior to that of amyloid beta (Aβ) PET and biofluid markers, especially at an older age when incidental Aβ pathology is common. Furthermore, tau PET tracers diagnostically outperform widely used MRI markers. Given its excellent diagnostic performance due to the combination of high sensitivity and specificity for detecting tau pathology in AD dementia, we hypothesize that tau PET can become an important diagnostic tool in specialized clinics for the differential diagnosis of dementia syndromes where AD is among the major possible underlying diseases.

Recent Advances in Imaging of Preclinical, Sporadic, and Autosomal Dominant Alzheimer's Disease

Observing Alzheimer's disease (AD) pathological changes in vivo with neuroimaging provides invaluable opportunities to understand and predict the course of disease. Neuroimaging AD biomarkers also allow for real-time tracking of disease-modifying treatment in clinical trials. With recent neuroimaging advances, along with the burgeoning availability of longitudinal neuroimaging data and big-data harmonization approaches, a more comprehensive evaluation of the disease has shed light on the topographical staging and temporal sequencing of the disease. Multimodal imaging approaches have also promoted the development of data-driven models of AD-associated pathological propagation of tau proteinopathies. Studies of autosomal dominant, early sporadic, and late sporadic courses of the disease have shed unique insights into the AD pathological cascade, particularly with regard to genetic vulnerabilities and the identification of potential drug targets. Further, neuroimaging markers of b-amyloid, tau, and neurodegeneration have provided a powerful tool for validation of novel fluid cerebrospinal and plasma markers. This review highlights some of the latest advances in the field of human neuroimaging in AD across these topics, particularly with respect to positron emission tomography and structural and functional magnetic resonance imaging.

Clinical validity of increased cortical uptake of [18F]flortaucipir on PET as a biomarker for Alzheimer's disease in the context of a structured 5-phase biomarker development framework

PURPOSE

In 2017, the Geneva Alzheimer's disease (AD) Biomarker Roadmap initiative adapted the framework of the systematic validation of oncological diagnostic biomarkers to AD biomarkers, with the aim to accelerate their development and implementation in clinical practice. With this work, we assess the maturity of [¹⁸F]flortaucipir PET and define its research priorities.

METHODS

The level of maturity of [¹⁸F]flortaucipir was assessed based on the AD Biomarker Roadmap. The framework assesses analytical validity (phases 1-2), clinical validity (phases 3-4), and clinical utility (phase 5).

RESULTS

The main aims of phases 1 (rationale for use) and 2 (discriminative ability) have been achieved. [¹⁸F]Flortaucipir binds with high affinity to paired helical filaments of tau and has favorable kinetic properties and excellent discriminative accuracy for AD. The majority of secondary aims of phase 2 were fully achieved. Multiple studies showed high correlations between ante-mortem [¹⁸F]flortaucipir PET and post-mortem tau (as assessed by histopathology), and also the effects of covariates on tracer binding are well studied. The aims of phase 3 (early detection ability) were only partially or preliminarily achieved, and the aims of phases 4 and 5 were not achieved.

CONCLUSION

Current literature provides partial evidence for clinical utility of [¹⁸F]flortaucipir PET. The aims for phases 1 and 2 were mostly achieved. Phase 3 studies are currently ongoing. Future studies including representative MCI populations and a focus on healthcare outcomes are required to establish full maturity of phases 4 and 5.

Mild behavioral impairment and its relation to tau pathology in preclinical Alzheimer's disease

Mild behavioral impairment (MBI) is suggested as risk marker for neurodegenerative diseases, such as Alzheimer's disease (AD). Recently, pathologic tau deposition in the brain has been shown closely related to clinical manifestations, such as cognitive deficits. Yet, associations between tau pathology and MBI have rarely been investigated. It is further debated if MBI precedes cognitive deficits in AD. Here, we explored potential mechanisms by which MBI is related to AD, this by studying associations between MBI and tau in preclinical AD. In all, 50 amyloid-β-positive cognitively unimpaired subjects (part of the BioFINDER-2 study) underwent MBI-checklist (MBI-C) to assess MBI, and the Alzheimer's Disease Assessment Scale - Cognitive subscale (ADAS-Cog) delayed word recall (ADAS-DR) to assess episodic memory. Early tau pathology was determined using tau-PET ([¹⁸F]RO948 retention in entorhinal cortex/hippocampus) and cerebrospinal fluid (CSF) P-tau₁₈₁. Regression models were used to test for associations. We found that higher tau-PET signal in the entorhinal cortex/hippocampus and CSF P-tau₁₈₁ levels were associated with higher MBI-C scores (β = 0.010, SE = 0.003, p = 0.003 and β = 1.263, SE = 0.446, p = 0.007, respectively). When MBI-C and ADAS-DR were entered together in the regression models, tau-PET (β = 0.009, p = 0.009) and CSF P-tau₁₈₁ (β = 0.408, p = 0.006) were predicted by MBI-C, but not ADAS-DR. We conclude that in preclinical AD, MBI is associated with tau independently from memory deficits. This denotes MBI as an important early clinical manifestation related to tau pathology in AD.

Spatial Relationships between Molecular Pathology and Neurodegeneration in the Alzheimer's Disease Continuum

A deeper understanding of the spatial relationships of β-amyloid (Aβ), tau, and neurodegeneration in Alzheimer's disease (AD) could provide insight into pathogenesis and clinical trial design. We included 81 amyloid-positive patients (age 64.4 ± 9.5) diagnosed with AD dementia or mild cognitive impairment due to AD and available 11C-PiB (PIB), 18F-Flortaucipir (FTP),18F-FDG-PET, and 3T-MRI, and 31 amyloid-positive, cognitively normal participants (age 77.3 ± 6.5, no FDG-PET). W-score voxel-wise deviation maps were created and binarized for each imaging-modality (W > 1.64, P < 0.05) adjusting for age, sex, and total intracranial volume (sMRI-only) using amyloid-negative cognitively normal adults. For symptomatic patients, FDG-PET and atrophy W-maps were combined into neurodegeneration maps (ND). Aβ-pathology showed the greatest proportion of cortical gray matter suprathreshold voxels (spatial extent) for both symptomatic and asymptomatic participants (median 94-55%, respectively), followed by tau (79-11%) and neurodegeneration (41-3%). Amyloid > tau > neurodegeneration was the most frequent hierarchy for both groups (79-77%, respectively), followed by tau > amyloid > neurodegeneration (13-10%) and amyloid > neurodegeneration > tau (6-13%). For symptomatic participants, most abnormal voxels were PIB+/FTP+/ND- (median 35%), and the great majority of ND+ voxels (91%) colocalized with molecular pathology. Amyloid spatially exceeded tau and neurodegeneration, with individual heterogeneities. Molecular pathology and neurodegeneration showed a progressive overlap along AD course, indicating shared vulnerabilities or synergistic toxic mechanisms.

Imaging of Tau Pathology in Neurodegenerative Diseases: An Update

Pathological accumulated misfolded tau underlies various neurodegenerative diseases and associated clinical syndromes. To diagnose those diseases reliable before death or even at early stages, many different tau-specific radiotracers have been developed in the last decade to be used with positron-emission-tomography. In contrast to amyloid-β imaging, different isoforms of tau exist further complicating radiotracer development. First-generation radiotracers like [¹¹C]PBB3, [¹⁸F]AV1451 and [¹⁸F]THK5351 have been extensively investigated in vitro and in vivo. In Alzheimer's disease (AD), high specific binding could be detected, and evidence of clinical applicability recently led to clinical approval of [¹⁸F]flortaucipir ([¹⁸F]AV1451) by the FDA. Nevertheless, absent or minor binding to non-AD tau isoforms and high off-target binding to non-tau brain structures limit the diagnostic applicability especially in non-AD tauopathies demanding further tracer development. In vitro assays and autoradiography results of next-generation radiotracers [¹⁸F]MK-6240, [¹⁸F]RO-948, [¹⁸F]PM-PBB3, [¹⁸F]GTP-1 and [¹⁸F]PI-2620 clearly indicate less off-target binding and high specific binding to tau neurofibrils. First in human studies have been conducted with promising results for all tracers in AD patients, and also some positive experience in non-AD tauopathies. Overall, larger scaled autoradiography and human studies are needed to further evaluate the most promising candidates and support future clinical approval.

Anisotropy of Anomalous Diffusion Improves the Accuracy of Differentiating and Grading Alzheimer's Disease Using Novel Fractional Motion Model

Background and Purpose: Recent evidence shows that the fractional motion (FM) model may be a more appropriate model for describing the complex diffusion process of water in brain tissue and has shown to be beneficial in clinical applications of Alzheimer's disease (AD). However, the FM model averaged the anomalous diffusion parameter values, which omitted the impacts of anisotropy. This study aimed to investigate the potential feasibility of anisotropy of anomalous diffusion using the FM model for distinguishing and grading AD patients. Methods: Twenty-four patients with AD and 11 matched healthy controls were recruited, diffusion MRI was obtained from all participants and analyzed using the FM model. Generalized fractional anisotropy (gFA), an anisotropy metric, was introduced and the gFA values of FM-related parameters, Noah exponent (α) and the Hurst exponent (H), were calculated and compared between the healthy group and AD group and between the mild AD group and moderate AD group. The receiver-operating characteristic (ROC) analysis and the multivariate logistic regression analysis were used to assess the diagnostic performances of the anisotropy values and the directionally averaged values. Results: The gFA(α) and gFA(H) values of the moderate AD group were higher than those of the mild AD group in left hippocampus. The gFA(α) value of the moderate AD group was significantly higher than that of the healthy control group in both the left and right hippocampus. The gFA(ADC) values of the moderate AD group were significantly lower than those of the mild AD group and healthy control group in the right hippocampus. Compared with the gFA(α), gFA(H), α, and H, the ROC analysis showed larger areas under the curves for combination of α + gFA(α) and the combination of H + gFA(H) in differentiating the mild AD and moderate AD groups, and larger area under the curves for combination of α + gFA(α) in differentiating the healthy controls and AD groups. Conclusion: The anisotropy of anomalous diffusion could significantly differentiate and grade patients with AD, and the diagnostic performance was improved when the anisotropy metric was combined with commonly used directionally averaged values. The utility of anisotropic anomalous diffusion may provide novel insights to profoundly understand the neuropathology of AD.

Event-related potential and EEG oscillatory predictors of verbal memory in mild cognitive impairment

Reliable biomarkers of memory decline are critical for the early detection of Alzheimer's disease. Previous work has found three EEG measures, namely the event-related brain potential P600, suppression of oscillatory activity in the alpha frequency range (∼10 Hz) and cross-frequency coupling between low theta/high delta and alpha/beta activity, each of which correlates strongly with verbal learning and memory abilities in healthy elderly and patients with mild cognitive impairment or prodromal Alzheimer's disease. In the present study, we address the question of whether event-related or oscillatory measures, or a combination thereof, best predict the decline of verbal memory in mild cognitive impairment and Alzheimer's disease. Single-trial correlation analyses show that despite a similarity in their time courses and sensitivities to word repetition, the P600 and the alpha suppression components are minimally correlated with each other on a trial-by-trial basis (generally |r _s| < 0.10). This suggests that they are unlikely to stem from the same neural mechanism. Furthermore, event-related brain potentials constructed from bandpass filtered (delta, theta, alpha, beta or gamma bands) single-trial data indicate that only delta band activity (1-4 Hz) is strongly correlated (r = 0.94, P < 0.001) with the canonical P600 repetition effect; event-related potentials in higher frequency bands are not. Importantly, stepwise multiple regression analyses reveal that the three event-related brain potential/oscillatory measures are complementary in predicting California Verbal Learning Test scores (overall R ^{2 '} s in 0.45-0.63 range). The present study highlights the importance of combining EEG event-related potential and oscillatory measures to better characterize the multiple mechanisms of memory failure in individuals with mild cognitive impairment or prodromal Alzheimer's disease.

Novel tau biomarkers phosphorylated at T181, T217 or T231 rise in the initial stages of the preclinical Alzheimer's continuum when only subtle changes in Aβ pathology are detected

In Alzheimer's disease (AD), tau phosphorylation in the brain and its subsequent release into cerebrospinal fluid (CSF) and blood is a dynamic process that changes during disease evolution. The main aim of our study was to characterize the pattern of changes in phosphorylated tau (p-tau) in the preclinical stage of the Alzheimer's continuum. We measured three novel CSF p-tau biomarkers, phosphorylated at threonine-181 and threonine-217 with an N-terminal partner antibody and at threonine-231 with a mid-region partner antibody. These were compared with an automated mid-region p-tau181 assay (Elecsys) as the gold standard p-tau measure. We demonstrate that these novel p-tau biomarkers increase more prominently in preclinical Alzheimer, when only subtle changes of amyloid-β (Aβ) pathology are detected, and can accurately differentiate Aβ-positive from Aβ-negative cognitively unimpaired individuals. Moreover, we show that the novel plasma N-terminal p-tau181 biomarker is mildly but significantly increased in the preclinical stage. Our results support the idea that early changes in neuronal tau metabolism in preclinical Alzheimer, likely in response to Aβ exposure, can be detected with these novel p-tau assays.

Resting-state functional connectivity and amyloid burden influence longitudinal cortical thinning in the default mode network in preclinical Alzheimer's disease

Proteinopathies are key elements in the pathogenesis of age-related neurodegenerative diseases, particularly Alzheimer's disease (AD), with the nature and location of the proteinopathy characterizing much of the disease phenotype. Susceptibility of brain regions to pathology may partly be determined by intrinsic network structure and connectivity. It remains unknown, however, how these networks inform the disease cascade in the context of AD biomarkers, such as beta-amyloid (Aβ), in clinically-normal older adults.The default-mode network (DMN), a prominent intrinsic network, is heavily implicated in AD due to its spatial overlap with AD atrophy patterns and tau deposition. We investigated the influence of baseline Aβ positron emission tomography (PET) signal and intrinsic DMN connectivity on DMN-specific cortical thinning in 120 clinically-normal older adults from the Harvard Aging Brain Study (73 ± 6 years, 58% Female, CDR = 0). Participants underwent11C Pittsburgh Compound-B (PiB) PET, 18F flortaucipir (FTP) PET, and resting-state MRI scans at baselineand longitudinal MRI (3.6 ± 0.96 scans; 5.04 ± 0.8 years). Linear mixed models tested relationships between baseline PiB and DMN connectivity on cortical thinning in a composite of DMN regions. Lower DMN connectivity was associated with faster cortical thinning, but only in those with elevated baseline PiB-PET signal. This relationship was network specific, in that the frontoparietal control network did not account for the observed association. Additionally, the relationship was independent of inferior temporal lobe FTP-PET signal. Our findings provide evidence that compromised DMN connectivity, in the context of preclinical AD, foreshadows neurodegeneration in DMN regions.

Correlation of Alzheimer Disease Neuropathologic Staging with Amyloid and Tau Scintigraphic Imaging Biomarkers

PET neuroimaging of amyloid-β (Aβ) provides an in vivo biomarker for pathologic changes associated with Alzheimer disease (AD). Aβ-targeted agents have been approved by the Food and Drug Administration, with additional agents, most notably targeting tau, currently under clinical investigation and one approved in May 2020. These agents, along with nonscintigraphic biomarkers from blood and cerebrospinal fluid, have provided an opportunity to investigate the pathogenesis, prodromal changes, and time course of the disease in living individuals. The current understanding is that the neuropathologic changes of the AD continuum begin up to 25 y before the onset of clinical symptomatology. The opportunities afforded by in vivo biomarkers of AD, whether by serum, cerebrospinal fluid examination or PET, have transformed the design of AD therapeutic trials by shifting focus to the preclinical stages of disease. Future disease-modifying therapies, should they be forthcoming, will rely heavily on the use of approved biomarkers or biomarkers currently under investigation to confirm the presence of target pathology. Understanding the progressive neuropathologic changes that occur in AD-and how scintigraphic findings relate to these changes-will help the interpreting physician to fully appreciate the implications of the scintigraphic findings and provide a basis to interpret the examinations. The recently adopted National Institute on Aging-Alzheimer Association guidelines define postmortem AD neuropathologic changes as a composite score based on 3 elements. These elements are the extent of involvement (spread) by cerebral Aβ based on the progression model defined by the Thal Aβ phases, the extent of involvement (spread) by neurofibrillary tangles (composed of hyperphosphorylated tau proteins) based on the progression model defined by Braak, and the Consortium to Establish a Registry for Alzheimer's Disease score, which describes the density of neuritic plaques based on certain key locations in the neocortex. This paper will review the 3 elements that define the National Institute on Aging-Alzheimer's Association scoring system and discusses current evidence on how these elements relate to findings based on Aβ and tau PET scintigraphy.

Cohort discovery and risk stratification for Alzheimer's disease: an electronic health record-based approach

BACKGROUND

We sought to leverage data routinely collected in electronic health records (EHRs), with the goal of developing patient risk stratification tools for predicting risk of developing Alzheimer's disease (AD).

METHOD

Using EHR data from the University of Michigan (UM) hospitals and consensus-based diagnoses from the Michigan Alzheimer's Disease Research Center, we developed and validated a cohort discovery tool for identifying patients with AD. Applied to all UM patients, these labels were used to train an EHR-based machine learning model for predicting AD onset within 10 years.

RESULTS

Applied to a test cohort of 1697 UM patients, the model achieved an area under the receiver operating characteristics curve of 0.70 (95% confidence interval = 0.63-0.77). Important predictive factors included cardiovascular factors and laboratory blood testing.

CONCLUSION

Routinely collected EHR data can be used to predict AD onset with modest accuracy. Mining routinely collected data could shed light on early indicators of AD appearance and progression.

Development and validation of an interpretable deep learning framework for Alzheimer's disease classification

Alzheimer's disease is the primary cause of dementia worldwide, with an increasing morbidity burden that may outstrip diagnosis and management capacity as the population ages. Current methods integrate patient history, neuropsychological testing and MRI to identify likely cases, yet effective practices remain variably applied and lacking in sensitivity and specificity. Here we report an interpretable deep learning strategy that delineates unique Alzheimer's disease signatures from multimodal inputs of MRI, age, gender, and Mini-Mental State Examination score. Our framework linked a fully convolutional network, which constructs high resolution maps of disease probability from local brain structure to a multilayer perceptron and generates precise, intuitive visualization of individual Alzheimer's disease risk en route to accurate diagnosis. The model was trained using clinically diagnosed Alzheimer's disease and cognitively normal subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset (n = 417) and validated on three independent cohorts: the Australian Imaging, Biomarker and Lifestyle Flagship Study of Ageing (AIBL) (n = 382), the Framingham Heart Study (n = 102), and the National Alzheimer's Coordinating Center (NACC) (n = 582). Performance of the model that used the multimodal inputs was consistent across datasets, with mean area under curve values of 0.996, 0.974, 0.876 and 0.954 for the ADNI study, AIBL, Framingham Heart Study and NACC datasets, respectively. Moreover, our approach exceeded the diagnostic performance of a multi-institutional team of practicing neurologists (n = 11), and high-risk cerebral regions predicted by the model closely tracked post-mortem histopathological findings. This framework provides a clinically adaptable strategy for using routinely available imaging techniques such as MRI to generate nuanced neuroimaging signatures for Alzheimer's disease diagnosis, as well as a generalizable approach for linking deep learning to pathophysiological processes in human disease.

Extracellular Sortilin Proteopathy Relative to β-Amyloid and Tau in Aged and Alzheimer's Disease Human Brains

Amyloid plaques and neurofibrillary tangles (NFTs) are hallmark lesions of Alzheimer's disease (AD) related to β-amyloid (Aβ) deposition and intraneuronal phosphorylated tau (pTau) accumulation. Sortilin C-terminal fragments (shortened as "sorfra") can deposit as senile plaque-like lesions within AD brains. The course and pattern of sorfra plaque formation relative to Aβ and pTau pathogenesis remain unknown. In the present study, cerebral and subcortical sections in postmortem human brains (n = 46) from aged and AD subjects were stained using multiple markers (6E10, β-secretase 1, pTau, and sortilin antibodies, as well as Bielschowsky silver stain). The course and pattern of sorfra plaque formation relative to Thal Aβ and Braak NFT pathogenic stages were determined. Sorfra plaques occurred in the temporal, inferior frontal and occipital neocortices in cases with Thal 1 and Braak III stages. They were also found additionally in the hippocampal formation, amygdala, and associative neocortex in cases with Thal 2-4 and Braak IV-V. Lastly, they were also found in the primary motor, somatosensory, and visual cortices in cases with Thal 4-5 and Braak VI. Unlike Aβ and pTau pathologies, sorfra plaques did not occur in subcortical structures in cases with Aβ/pTau lesions in Thal 3-5/Braak IV-VI stages. We establish here that sorfra plaques are essentially a cerebral proteopathy. We believe that the development of sorfra plaques in both cortical and hippocampal regions proceeds in a typical spatiotemporal pattern, and the stages of cerebral sorfra plaque formation partially overlap with that of Aβ and pTau pathologies.

Cerebral Microhemorrhage at MRI in Mild Cognitive Impairment and Early Alzheimer Disease: Association with Tau and Amyloid β at PET Imaging

Background Growing evidence indicates an association between cerebral microhemorrhages (MHs) and amyloid β accumulation in Alzheimer disease (AD), but to the knowledge of the authors the association with tau burden is unknown. Purpose To investigate the association between cerebral MH load and tau pathologic structure measured in healthy older individuals and individuals along the AD spectrum, stratified by using the A (amyloid β)/T (tau)/N (neurodegeneration) biomarker classification system. Materials and methods In this prospective cohort study, participants from the AD Neuroimaging Initiative were included (healthy control participants, participants with mild cognitive impairment, and participants with AD dementia; data from October 2005 to January 2019). T2*-weighted gradient-echo MRI was performed to quantify MH, fluorine 18 (¹⁸F) flortaucipir (AV-1451) PET was performed to quantify tau, and ¹⁸F-florbetaben/¹⁸F- florbetapir (AV45) PET was performed to quantify amyloid β to study associations of MH with regional and global tau and amyloid β load. Associations with cerebrospinal fluid (CSF) biomarkers (amyloid β1-42, total tau, phosphorylated tau 181) were also assessed. Analysis of covariance and Spearman rank correlation test for cross-sectional analysis and Wilcoxon signed rank test for longitudinal analyses were used, controlling for multiple comparisons (Bonferroni significance threshold, P < .008). Results Evaluated were 343 participants (mean age, 75 years ± 7; 186 women), including 205 participants who were A-TN- (mean age, 73 years ± 7; 115 women), 80 participants who were A+TN- (mean age, 76 years ± 7; 38 women), and 58 participants who were A+TN+ (mean age, 77 ± 8; 34 women). MH count was associated with global (Spearman ρ = 0.27; P = .004) and frontal (ρ = 0.27; P = .005) amyloid β load and global tau load (ρ = 0.31; P = .001). In a longitudinal analysis, MH count increased significantly over approximately 5 years in the entire cohort (T-1, 81 [range, 0-6 participants]; T0, 214 [range, 0-58 participants]; P < .001), in A+TN+ (T-1, 20 [range, 0-5 participants]; T0, 119 [range, 1-58 participants]; P < .001), A+TN- (T-1, 31 [range, 0-6 participants]; T0, 43 [range, 0-8 participants]; P = .03), and A-TN- (T-1, 30 [range, 0-4 participants]; T0, 52 [range, 0-6 participants]; P = .007). A higher MH count was associated with higher future global (ρ = 0.29; P = .008) and parietal (ρ = 0.31; P = .005) amyloid β and parietal tau load (ρ = 0.31; P = .005). Conclusion Cerebral microhemorrhage load is associated spatially with tau accumulation, both cross-sectionally and longitudinally. © RSNA, 2020 Online supplemental material is available for this article.

Microglial activation and tau burden predict cognitive decline in Alzheimer's disease

Tau pathology, neuroinflammation, and neurodegeneration are key aspects of Alzheimer's disease. Understanding whether these features predict cognitive decline, alone or in combination, is crucial to develop new prognostic measures and enhanced stratification for clinical trials. Here, we studied how baseline assessments of in vivo tau pathology (measured by 18F-AV-1451 PET), neuroinflammation (measured by 11C-PK11195 PET) and brain atrophy (derived from structural MRI) predicted longitudinal cognitive changes in patients with Alzheimer's disease pathology. Twenty-six patients (n = 12 with clinically probable Alzheimer's dementia and n = 14 with amyloid-positive mild cognitive impairment) and 29 healthy control subjects underwent baseline assessment with 18F-AV-1451 PET, 11C-PK11195 PET, and structural MRI. Cognition was examined annually over the subsequent 3 years using the revised Addenbrooke's Cognitive Examination. Regional grey matter volumes, and regional binding of 18F-AV-1451 and 11C-PK11195 were derived from 15 temporo-parietal regions characteristically affected by Alzheimer's disease pathology. A principal component analysis was used on each imaging modality separately, to identify the main spatial distributions of pathology. A latent growth curve model was applied across the whole sample on longitudinal cognitive scores to estimate the rate of annual decline in each participant. We regressed the individuals' estimated rate of cognitive decline on the neuroimaging components and examined univariable predictive models with single-modality predictors, and a multi-modality predictive model, to identify the independent and combined prognostic value of the different neuroimaging markers. Principal component analysis identified a single component for the grey matter atrophy, while two components were found for each PET ligand: one weighted to the anterior temporal lobe, and another weighted to posterior temporo-parietal regions. Across the whole-sample, the single-modality models indicated significant correlations between the rate of cognitive decline and the first component of each imaging modality. In patients, both stepwise backward elimination and Bayesian model selection revealed an optimal predictive model that included both components of 18F-AV-1451 and the first (i.e. anterior temporal) component for 11C-PK11195. However, the MRI-derived atrophy component and demographic variables were excluded from the optimal predictive model of cognitive decline. We conclude that temporo-parietal tau pathology and anterior temporal neuroinflammation predict cognitive decline in patients with symptomatic Alzheimer's disease pathology. This indicates the added value of PET biomarkers in predicting cognitive decline in Alzheimer's disease, over and above MRI measures of brain atrophy and demographic data. Our findings also support the strategy for targeting tau and neuroinflammation in disease-modifying therapy against Alzheimer's disease.

Impaired Parahippocampal Gyrus-Orbitofrontal Cortex Circuit Associated with Visuospatial Memory Deficit as a Potential Biomarker and Interventional Approach for Alzheimer Disease

The parahippocampal gyrus-orbitofrontal cortex (PHG-OFC) circuit in humans is homologous to the postrhinal cortex (POR)-ventral lateral orbitofrontal cortex (vlOFC) circuit in rodents. Both are associated with visuospatial malfunctions in Alzheimer's disease (AD). However, the underlying mechanisms remain to be elucidated. In this study, we explored the relationship between an impaired POR-vlOFC circuit and visuospatial memory deficits through retrograde tracing and in vivo local field potential recordings in 5XFAD mice, and investigated alterations of the PHG-OFC circuit by multi-domain magnetic resonance imaging (MRI) in patients on the AD spectrum. We demonstrated that an impaired glutamatergic POR-vlOFC circuit resulted in deficient visuospatial memory in 5XFAD mice. Moreover, MRI measurements of the PHG-OFC circuit had an accuracy of 77.33% for the classification of amnestic mild cognitive impairment converters versus non-converters. Thus, the PHG-OFC circuit explains the neuroanatomical basis of visuospatial memory deficits in AD, thereby providing a potential predictor for AD progression and a promising interventional approach for AD.

The A4 study: β-amyloid and cognition in 4432 cognitively unimpaired adults

Objective

To clarify the preclinical stage of Alzheimer’s disease by estimating when β‐amyloid accumulation first becomes associated with changes in cognition.

Methods

Here we studied a large group (N = 4432) of cognitively unimpaired individuals who were screened for inclusion in the A4 trial (age 65–85) to assess the effect of subthreshold levels of β‐amyloid on cognition and to identify which cognitive domains first become affected.

Results

β‐amyloid accumulation was linked to significant cognitive dysfunction in cognitively unimpaired participants with subthreshold levels of β‐amyloid in multiple measures of memory (Logical Memory Delayed Recall, P = 0.03; Free and Cued Selective Reminding Test, P < 0.001), the Preclinical Alzheimer’s Cognitive Composite (P = 0.01), and was marginally associated with decreased executive function (Digit Symbol Substitution, P = 0.07). Significantly, decreased cognitive scores were associated with suprathreshold levels of β‐amyloid, across all measures (P < 0.05). The Free and Cued Selective Reminding Test, a list recall memory test, appeared most sensitive to β‐amyloid ‐related decreases in average cognitive scores, outperforming all other cognitive domains, including the narrative recall memory test, Logical Memory.

Interpretation

Clinical trials for cognitively unimpaired β‐amyloid‐positive individuals will include a large number of individuals where mechanisms downstream from β‐amyloid pathology are already activated. These findings have implications for primary and secondary prevention of Alzheimer’s disease.