The role of visual rating and automated brain volumetry in early detection and differential diagnosis of Alzheimer's disease

BACKGROUND

Medial temporal lobe atrophy (MTA) is a diagnostic marker for mild cognitive impairment (MCI) and Alzheimer's disease (AD), but the accuracy of quantitative MTA (QMTA) in diagnosing early AD is unclear. This study aimed to investigate the accuracy of QMTA and its related components (inferior lateral ventricle [ILV] and hippocampus) with MTA in the early diagnosis of MCI and AD.

METHODS

This study included four groups: normal (NC), MCI stable (MCIs), MCI converted to AD (MCIs), and mild AD (M-AD) groups. Magnetic resonance image analysis software was used to quantify the hippocampus, ILV, and QMTA. MTA was rated by two experienced neurologists. Receiver operating characteristic area under the curve (AUC) analysis was performed to compare their capability in differentiating AD from NC and MCI, and optimal thresholds were determined using the Youden index.

RESULTS

QMTA distinguished M-AD from NC and MCI with higher diagnostic accuracy than MTA, hippocampus, and ILV (AUCNC = 0.976, AUCMCI = 0.836, AUCMCIs = 0.894, AUCMCIc = 0.730). The diagnostic accuracy of QMTA was superior to that of MTA, the hippocampus, and ILV in differentiating MCI from AD. The diagnostic accuracy of QMTA was found to remain the best across age, sex, and pathological subgroups analyzed. The sensitivity (92.45%) and specificity (90.64%) were higher in this study when a cutoff value of 0.635 was chosen for QMTA.

CONCLUSIONS

QMTA may be a better choice than the MTA scale or the associated quantitative components alone in identifying AD patients and MCI individuals with higher progression risk.

Atrophy of hippocampal subfields and amygdala nuclei in subjects with mild cognitive impairment progressing to Alzheimer's disease

The hippocampus and amygdala are the first brain regions to show early signs of Alzheimer's Disease (AD) pathology. AD is preceded by a prodromal stage known as Mild Cognitive Impairment (MCI), a crucial crossroad in the clinical progression of the disease. The topographical development of AD has been the subject of extended investigation. However, it is still largely unknown how the transition from MCI to AD affects specific hippocampal and amygdala subregions. The present study is set to answer that question. We analyzed data from 223 subjects: 75 healthy controls, 52 individuals with MCI, and 96 AD patients obtained from the ADNI. The MCI group was further divided into two subgroups depending on whether individuals in the 48 months following the diagnosis either remained stable (N = 21) or progressed to AD (N = 31). A MANCOVA test evaluated group differences in the volume of distinct amygdala and hippocampal subregions obtained from magnetic resonance images. Subsequently, a stepwise linear discriminant analysis (LDA) determined which combination of magnetic resonance imaging parameters was most effective in predicting the conversion from MCI to AD. The predictive performance was assessed through a Receiver Operating Characteristic analysis. AD patients displayed widespread subregional atrophy. MCI individuals who progressed to AD showed selective atrophy of the hippocampal subiculum and tail compared to stable MCI individuals, who were undistinguishable from healthy controls. Converter MCI showed atrophy of the amygdala's accessory basal, central, and cortical nuclei. The LDA identified the hippocampal subiculum and the amygdala's lateral and accessory basal nuclei as significant predictors of MCI conversion to AD. The analysis returned a sensitivity value of 0.78 and a specificity value of 0.62. These findings highlight the importance of targeted assessments of distinct amygdala and hippocampus subregions to help dissect the clinical and pathophysiological development of the MCI to AD transition.

A novel method for PET connectomics guided by fibre-tracking MRI: Application to Alzheimer's disease

This study introduces a novel brain connectome matrix, track-weighted PET connectivity (twPC) matrix, which combines positron emission tomography (PET) and diffusion magnetic resonance imaging data to compute a PET-weighted connectome at the individual subject level. The new method is applied to characterise connectivity changes in the Alzheimer's disease (AD) continuum. The proposed twPC samples PET tracer uptake guided by the underlying white matter fibre-tracking streamline point-to-point connectivity calculated from diffusion MRI (dMRI). Using tau-PET, dMRI and T1-weighted MRI from the Alzheimer's Disease Neuroimaging Initiative database, structural connectivity (SC) and twPC matrices were computed and analysed using the network-based statistic (NBS) technique to examine topological alterations in early mild cognitive impairment (MCI), late MCI and AD participants. Correlation analysis was also performed to explore the coupling between SC and twPC. The NBS analysis revealed progressive topological alterations in both SC and twPC as cognitive decline progressed along the continuum. Compared to healthy controls, networks with decreased SC were identified in late MCI and AD, and networks with increased twPC were identified in early MCI, late MCI and AD. The altered network topologies were mostly different between twPC and SC, although with several common edges largely involving the bilateral hippocampus, fusiform gyrus and entorhinal cortex. Negative correlations were observed between twPC and SC across all subject groups, although displaying an overall reduction in the strength of anti-correlation with disease progression. twPC provides a new means for analysing subject-specific PET and MRI-derived information within a hybrid connectome using established network analysis methods, providing valuable insights into the relationship between structural connections and molecular distributions. PRACTITIONER POINTS: New method is proposed to compute patient-specific PET connectome guided by MRI fibre-tracking. Track-weighted PET connectivity (twPC) matrix allows to leverage PET and structural connectivity information. twPC was applied to dementia, to characterise the PET nework abnormalities in Alzheimer's disease and mild cognitive impairment.

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.

Genetic characterization of the ALFA study: Uncovering genetic profiles in the Alzheimer's continuum

INTRODUCTION

In 2013, the ALzheimer's and FAmilies (ALFA) project was established to investigate pathophysiological changes in preclinical Alzheimer's disease (AD), and to foster research on early detection and preventive interventions.

METHODS

We conducted a comprehensive genetic characterization of ALFA participants with respect to neurodegenerative/cerebrovascular diseases, AD biomarkers, brain endophenotypes, risk factors and aging biomarkers. We placed particular emphasis on amyloid/tau status and assessed gender differences. Multiple polygenic risk scores were computed to capture different aspects of genetic predisposition. We additionally compared AD risk in ALFA to that across the full disease spectrum from the Alzheimer's Disease Neuroimaging Initiative (ADNI).

RESULTS

Results show that the ALFA project has been successful at establishing a cohort of cognitively unimpaired individuals at high genetic predisposition of AD.

DISCUSSION

It is, therefore, well-suited to study early pathophysiological changes in the preclinical AD continuum. Highlights Prevalence of ε4 carriers in ALzheimer and FAmilies (ALFA) is higher than in the general European population The ALFA study is highly enriched in Alzheimer's disease (AD) genetic risk factors beyond APOE AD genetic profiles in ALFA are similar to clinical groups along the continuum ALFA has succeeded in establishing a cohort of cognitively unimpaired individuals at high genetic AD risk ALFA is well suited to study pathogenic events/early pathophysiological changes in AD.

Individuals with Alzheimer's disease and low tau burden: Characteristics and implications

INTRODUCTION

Abnormal amyloid-beta (Aβ) and tau deposition define Alzheimer's Disease (AD), but non-elevated tau is relatively frequent in patients on the AD pathway.

METHODS

We examined characteristics and regional patterns of 397 Aβ+ unimpaired and impaired individuals with low tau (A+T-) in relation to their higher tau counterparts (A+T+).

RESULTS

Seventy-one percent of Aβ+ unimpaired and 42% of impaired Aβ+ individuals were categorized as A+T- based on global tau. In impaired individuals only, A+T- status was associated with older age, male sex, and greater cardiovascular risk. α-synuclein was linked to poorer cognition, particularly when tau was low. Tau burden was most frequently elevated in a common set of temporal regions regardless of T+/T- status.

DISCUSSION

Low tau is relatively common in patients on the AD pathway and is linked to comorbidities that contribute to impairment. These findings have implications for the selection of individuals for Aβ- and tau-modifying therapies.

Cognitively defined Alzheimer's dementia subgroups have distinct atrophy patterns

INTRODUCTION

We sought to determine structural magnetic resonance imaging (MRI) characteristics across subgroups defined based on relative cognitive domain impairments using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and to compare cognitively defined to imaging-defined subgroups.

METHODS

We used data from 584 people with Alzheimer's disease (AD) (461 amyloid positive, 123 unknown amyloid status) and 118 amyloid-negative controls. We used voxel-based morphometry to compare gray matter volume (GMV) for each group compared to controls and to AD-Memory.

RESULTS

There was pronounced bilateral lower medial temporal lobe atrophy with relative cortical sparing for AD-Memory, lower left hemisphere GMV for AD-Language, anterior lower GMV for AD-Executive, and posterior lower GMV for AD-Visuospatial. Formal asymmetry comparisons showed substantially more asymmetry in the AD-Language group than any other group (p = 1.15 × 10-10 ). For overlap between imaging-defined and cognitively defined subgroups, AD-Memory matched up with an imaging-defined limbic predominant group.

DISCUSSION

MRI findings differ across cognitively defined AD subgroups.

A data-driven approach to complement the A/T/(N) classification system using CSF biomarkers

AIMS

The AT(N) classification system not only improved the biological characterization of Alzheimer's disease (AD) but also raised challenges for its clinical application. Unbiased, data-driven techniques such as clustering may help optimize it, rendering informative categories on biomarkers' values.

METHODS

We compared the diagnostic and prognostic abilities of CSF biomarkers clustering results against their AT(N) classification. We studied clinical (patients from our center) and research (Alzheimer's Disease Neuroimaging Initiative) cohorts. The studied CSF biomarkers included Aβ(1-42), Aβ(1-42)/Aβ(1-40) ratio, tTau, and pTau.

RESULTS

The optimal solution yielded three clusters in both cohorts, significantly different in diagnosis, AT(N) classification, values distribution, and survival. We defined these three CSF groups as (i) non-defined or unrelated to AD, (ii) early stages and/or more delayed risk of conversion to dementia, and (iii) more severe cognitive impairment subjects with faster progression to dementia.

CONCLUSION

We propose this data-driven three-group classification as a meaningful and straightforward approach to evaluating the risk of conversion to dementia, complementary to the AT(N) system classification.

Sex differences in risk factors, burden, and outcomes of cerebrovascular disease in Alzheimer's disease populations

BACKGROUND

White matter hyperintensities (WMHs) are associated with cognitive decline and progression to mild cognitive impairment (MCI) and dementia. It remains unclear if sex differences influence WMH progression or the relationship between WMH and cognition.

METHODS

Linear mixed models examined the relationship between risk factors, WMHs, and cognition in males and females.

RESULTS

Males exhibited increased WMH progression in occipital, but lower progression in frontal, total, and deep than females. For males, history of hypertension was the strongest contributor, while in females, the vascular composite was the strongest contributor to WMH burden. WMH burden was more strongly associated with decreases in global cognition, executive functioning, memory, and functional activities in females than males.

DISCUSSION

Controlling vascular risk factors may reduce WMH in both males and females. For males, targeting hypertension may be most important to reduce WMHs. The results have implications for therapies/interventions targeting cerebrovascular pathology and subsequent cognitive decline.

HIGHLIGHTS

Hypertension is the main vascular risk factor associated with WMH in males A combination of vascular risk factors contributes to WMH burden in females Only small WMH burden differences were observed between sexes Females' cognition was more negatively impacted by WMH burden than males Females with WMHs may have less resilience to future pathology.

The Alzheimer's Disease Neuroimaging Initiative in the era of Alzheimer's disease treatment: A review of ADNI studies from 2021 to 2022

The Alzheimer's Disease Neuroimaging Initiative (ADNI) aims to improve Alzheimer's disease (AD) clinical trials. Since 2006, ADNI has shared clinical, neuroimaging, and cognitive data, and biofluid samples. We used conventional search methods to identify 1459 publications from 2021 to 2022 using ADNI data/samples and reviewed 291 impactful studies. This review details how ADNI studies improved disease progression understanding and clinical trial efficiency. Advances in subject selection, detection of treatment effects, harmonization, and modeling improved clinical trials and plasma biomarkers like phosphorylated tau showed promise for clinical use. Biomarkers of amyloid beta, tau, neurodegeneration, inflammation, and others were prognostic with individualized prediction algorithms available online. Studies supported the amyloid cascade, emphasized the importance of neuroinflammation, and detailed widespread heterogeneity in disease, linked to genetic and vascular risk, co-pathologies, sex, and resilience. Biological subtypes were consistently observed. Generalizability of ADNI results is limited by lack of cohort diversity, an issue ADNI-4 aims to address by enrolling a diverse cohort.

Structural, static, and dynamic functional MRI predictors for conversion from mild cognitive impairment to Alzheimer's disease: Inter-cohort validation of Shanghai Memory Study and ADNI

Mild cognitive impairment (MCI) is a critical prodromal stage of Alzheimer's disease (AD), and the mechanism underlying the conversion is not fully explored. Construction and inter-cohort validation of imaging biomarkers for predicting MCI conversion is of great challenge at present, due to lack of longitudinal cohorts and poor reproducibility of various study-specific imaging indices. We proposed a novel framework for inter-cohort MCI conversion prediction, involving comparison of structural, static, and dynamic functional brain features from structural magnetic resonance imaging (sMRI) and resting-state functional MRI (fMRI) between MCI converters (MCI_C) and non-converters (MCI_NC), and support vector machine for construction of prediction models. A total of 218 MCI patients with 3-year follow-up outcome were selected from two independent cohorts: Shanghai Memory Study cohort for internal cross-validation, and Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort for external validation. In comparison with MCI_NC, MCI_C were mainly characterized by atrophy, regional hyperactivity and inter-network hypo-connectivity, and dynamic alterations characterized by regional and connectional instability, involving medial temporal lobe (MTL), posterior parietal cortex (PPC), and occipital cortex. All imaging-based prediction models achieved an area under the curve (AUC) > 0.7 in both cohorts, with the multi-modality MRI models as the best with excellent performances of AUC > 0.85. Notably, the combination of static and dynamic fMRI resulted in overall better performance as relative to static or dynamic fMRI solely, supporting the contribution of dynamic features. This inter-cohort validation study provides a new insight into the mechanisms of MCI conversion involving brain dynamics, and paves a way for clinical use of structural and functional MRI biomarkers in future.

Longitudinal associations of apathy and regional tau in mild cognitive impairment and dementia: Findings from the Alzheimer's Disease Neuroimaging Initiative

Introduction

It is important to study apathy in Alzheimer's disease (AD) to better understand its underlying neurobiology and develop effective interventions. In the current study, we sought to examine the relationships between longitudinal apathy and regional tau burden in cognitively impaired older adults from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.

Methods

Three hundred and nineteen ADNI participants with mild cognitive impairment (MCI) or AD dementia underwent flortaucipir (FTP) tau positron emission tomography (PET) imaging and clinical assessment with the Neuropsychiatric Inventory (NPI) annually. Longitudinal NPI Apathy (NPI-A) scores were examined in relation to baseline tau PET signal in three a priori selected regions implicated in AD and AD-related apathy (supramarginal gyrus, entorhinal cortex [EC] and rostral anterior cingulate cortex [rACC]). Secondary models were adjusted for global cognition (Mini-Mental State Examination score) and cortical amyloid (florbetapir PET).

Results

Higher baseline supramarginal gyrus and EC tau burden were each significantly associated with greater NPI-A over time, while rACC tau was associated with higher NPI-A but did not predict its trajectory over time. These results were retained for supramarginal and EC tau after adjusting models for global cognition and cortical amyloid.

Discussion

Our findings suggest that baseline in vivo tau burden in parietal and temporal brain regions affected in AD, and less so in a medial frontal region involved in motivational control, is associated with increasing apathy over time in older adults with MCI and AD dementia. Future work studying emergent apathy in relation to not only core AD pathology but also circuit level dysfunction may provide additional insight into the neurobiology of apathy in AD and opportunities for intervention.

Highlights

Tau (Flortaucipir PET) in regions implicated in AD was associated with increasing apathy over timeCortical amyloid was also found to be a robust predictor of the trajectory of apathyEvidence of synergy between regional tau and amyloid in overall higher levels of apathy.

Cortical gray to white matter signal intensity ratio as a sign of neurodegeneration and cognition independent of β-amyloid in dementia

Cortical gray to white matter signal intensity ratio (GWR) measured from T1-weighted magnetic resonance (MR) images was associated with neurodegeneration and dementia. We characterized topological patterns of GWR during AD pathogenesis and investigated its association with cognitive decline. The study included a cross-sectional dataset and a longitudinal dataset. The cross-sectional dataset included 60 cognitively healthy controls, 61 mild cognitive impairment (MCI), and 63 patients with dementia. The longitudinal dataset included 26 participants who progressed from cognitively normal to dementia and 26 controls that remained cognitively normal. GWR was compared across the cross-sectional groups, adjusted for amyloid PET. The correlation between GWR and cognition performance was also evaluated. The longitudinal dataset was used to investigate GWR alteration during the AD pathogenesis. Dementia with β-amyloid deposition group exhibited the largest area of increased GWR, followed by MCI with β-amyloid deposition, MCI without β-amyloid deposition, and controls. The spatial pattern of GWR-increased regions was not influenced by β-amyloid deposits. Correlation between regional GWR alteration and cognitive decline was only detected among individuals with β-amyloid deposition. GWR showed positive correlation with tau PET in the left supramarginal, lateral occipital gyrus, and right middle frontal cortex. The longitudinal study showed that GWR increased around the fusiform, inferior/superior temporal lobe, and entorhinal cortex in MCI and progressed to larger cortical regions after progression to AD. The spatial pattern of GWR-increased regions was independent of β-amyloid deposits but overlapped with tauopathy. The GWR can serve as a promising biomarker of neurodegeneration in AD.

Identifying individuals with non-Alzheimer's disease co-pathologies: A precision medicine approach to clinical trials in sporadic Alzheimer's disease

INTRODUCTION

Biomarkers remain mostly unavailable for non-Alzheimer's disease neuropathological changes (non-ADNC) such as transactive response DNA-binding protein 43 (TDP-43) proteinopathy, Lewy body disease (LBD), and cerebral amyloid angiopathy (CAA).

METHODS

A multilabel non-ADNC classifier using magnetic resonance imaging (MRI) signatures was developed for TDP-43, LBD, and CAA in an autopsy-confirmed cohort (N = 214).

RESULTS

A model using demographic, genetic, clinical, MRI, and ADNC variables (amyloid positive [Aβ+] and tau+) in autopsy-confirmed participants showed accuracies of 84% for TDP-43, 81% for LBD, and 81% to 93% for CAA, outperforming reference models without MRI and ADNC biomarkers. In an ADNI cohort (296 cognitively unimpaired, 401 mild cognitive impairment, 188 dementia), Aβ and tau explained 33% to 43% of variance in cognitive decline; imputed non-ADNC explained an additional 16% to 26%. Accounting for non-ADNC decreased the required sample size to detect a 30% effect on cognitive decline by up to 28%.

DISCUSSION

Our results lead to a better understanding of the factors that influence cognitive decline and may lead to improvements in AD clinical trial design.

Morphological connectivity differences in Alzheimer's disease correlate with gene transcription and cell-type

Alzheimer's disease (AD) is one of the most prevalent forms of dementia in older individuals. Convergent evidence suggests structural connectome abnormalities in specific brain regions are linked to AD progression. The biological basis underpinnings of these connectome changes, however, have remained elusive. We utilized an individual regional mean connectivity strength (RMCS) derived from a regional radiomics similarity network to capture altered morphological connectivity in 1654 participants (605 normal controls, 766 mild cognitive impairment [MCI], and 283 AD). Then, we also explored the biological basis behind these morphological changes through gene enrichment analysis and cell-specific analysis. We found that RMCS probes of the hippocampus and medial temporal lobe were significantly altered in AD and MCI, with these differences being spatially related to the expression of AD-risk genes. In addition, gene enrichment analysis revealed that the modulation of chemical synaptic transmission is the most relevant biological process associated with the altered RMCS in AD. Notably, neuronal cells were found to be the most pertinent cells in the altered RMCS. Our findings shed light on understanding the biological basis of structural connectome changes in AD, which may ultimately lead to more effective diagnostic and therapeutic strategies for this devastating disease.

NeuropsychBrainAge: A biomarker for conversion from mild cognitive impairment to Alzheimer's disease

INTRODUCTION

BrainAge models based on neuroimaging data have diagnostic classification power but have replicability issues due to site and patient variability. BrainAge models trained on neuropsychological tests could help distinguish stable mild cognitive impairment (sMCI) from progressive MCI (pMCI) to Alzheimer's disease (AD).

METHODS

A linear regressor BrainAge model was trained on healthy controls using neuropsychological tests and neuroimaging features separately. The BrainAge delta, predicted age minus chronological age, was used to distinguish between sMCI and pMCI.

RESULTS

The cross-validated area under the receiver-operating characteristic (ROC) curve for sMCI versus pMCI was 0.91 for neuropsychological features in contrast to 0.68 for neuroimaging features. The BrainAge delta was correlated with the time to conversion, the time taken for a pMCI subject to convert to AD.

DISCUSSION

The BrainAge delta from neuropsychological tests is a good biomarker to distinguish between sMCI and pMCI. Other neurological and psychiatric disorders could be studied using this strategy.

Highlights

BrainAge models based on neuropsychological tests outperform models based on neuroimaging features when distinguishing between stable mild cognitive impairment (sMCI) from progressive MCI (pMCI) to Alzheimer's disease (AD).The combination of neuropsychological tests with neuroimaging features does not lead to an improvement in sMCI versus pMCI classification compared to using neuropsychological tests on their own.BrainAge delta of both neuroimaging and neuropsychological models was correlated with the time to conversion, the time taken for a pMCI subject to convert to AD.

Exploratory correlation of the human structural connectome with non-MRI variables in Alzheimer's disease

Introduction

Discovery of the associations between brain structural connectivity and clinical and demographic variables can help to better understand the vulnerability and resilience of the brain architecture to neurodegenerative diseases and to discover biomarkers.

Methods

We used four diffusion-MRI databases, three related to Alzheimer's disease (AD), to exploratorily correlate structural connections between 85 brain regions with non-MRI variables, while stringently correcting the significance values for multiple testing and ruling out spurious correlations via careful visual inspection. We repeated the analysis with brain connectivity augmented with multi-synaptic neural pathways.

Results

We found 85 and 101 significant relationships with direct and augmented connectivity, respectively, which were generally stronger for the latter. Age was consistently linked to decreased connectivity, and healthier clinical scores were generally linked to increased connectivity.

Discussion

Our findings help to elucidate which structural brain networks are affected in AD and aging and highlight the importance of including indirect connections.

Style transfer generative adversarial networks to harmonize multisite MRI to a single reference image to avoid overcorrection

Recent work within neuroimaging consortia have aimed to identify reproducible, and often subtle, brain signatures of psychiatric or neurological conditions. To allow for high-powered brain imaging analyses, it is often necessary to pool MR images that were acquired with different protocols across multiple scanners. Current retrospective harmonization techniques have shown promise in removing site-related image variation. However, most statistical approaches may over-correct for technical, scanning-related, variation as they cannot distinguish between confounded image-acquisition based variability and site-related population variability. Such statistical methods often require that datasets contain subjects or patient groups with similar clinical or demographic information to isolate the acquisition-based variability. To overcome this limitation, we consider site-related magnetic resonance (MR) imaging harmonization as a style transfer problem rather than a domain transfer problem. Using a fully unsupervised deep-learning framework based on a generative adversarial network (GAN), we show that MR images can be harmonized by inserting the style information encoded from a single reference image, without knowing their site/scanner labels a priori. We trained our model using data from five large-scale multisite datasets with varied demographics. Results demonstrated that our style-encoding model can harmonize MR images, and match intensity profiles, without relying on traveling subjects. This model also avoids the need to control for clinical, diagnostic, or demographic information. We highlight the effectiveness of our method for clinical research by comparing extracted cortical and subcortical features, brain-age estimates, and case-control effect sizes before and after the harmonization. We showed that our harmonization removed the site-related variances, while preserving the anatomical information and clinical meaningful patterns. We further demonstrated that with a diverse training set, our method successfully harmonized MR images collected from unseen scanners and protocols, suggesting a promising tool for ongoing collaborative studies. Source code is released in USC-IGC/style_transfer_harmonization (github.com).

Identification of an APOE ε4-specific blood-based molecular pathway for Alzheimer's disease risk

INTRODUCTION

The precise apolipoprotein E (APOE) ε4-specific molecular pathway(s) for Alzheimer's disease (AD) risk are unclear.

METHODS

Plasma protein modules/cascades were analyzed using weighted gene co-expression network analysis (WGCNA) in the Alzheimer's Disease Neuroimaging Initiative study. Multivariable regression analyses were used to examine the associations among protein modules, AD diagnoses, cerebrospinal fluid (CSF) phosphorylated tau (p-tau), and brain glucose metabolism, stratified by APOE genotype.

RESULTS

The Green Module was associated with AD diagnosis in APOE ε4 homozygotes. Three proteins from this module, C-reactive protein (CRP), complement C3, and complement factor H (CFH), had dose-dependent associations with CSF p-tau and cognitive impairment only in APOE ε4 homozygotes. The link among these three proteins and glucose hypometabolism was observed in brain regions of the default mode network (DMN) in APOE ε4 homozygotes. A Framingham Heart Study validation study supported the findings for AD.

DISCUSSION

The study identifies the APOE ε4-specific CRP-C3-CFH inflammation pathway for AD, suggesting potential drug targets for the disease.Highlights: Identification of an APOE ε4 specific molecular pathway involving blood CRP, C3, and CFH for the risk of AD.CRP, C3, and CFH had dose-dependent associations with CSF p-Tau and brain glucose hypometabolism as well as with cognitive impairment only in APOE ε4 homozygotes.Targeting CRP, C3, and CFH may be protective and therapeutic for AD onset in APOE ε4 carriers.

Leveraging longitudinal diffusion MRI data to quantify differences in white matter microstructural decline in normal and abnormal aging

Introduction

It is unclear how rates of white matter microstructural decline differ between normal aging and abnormal aging.

Methods

Diffusion MRI data from several well-established longitudinal cohorts of aging (Alzheimer's Disease Neuroimaging Initiative [ADNI], Baltimore Longitudinal Study of Aging [BLSA], Vanderbilt Memory & Aging Project [VMAP]) were free-water corrected and harmonized. This dataset included 1723 participants (age at baseline: 72.8 ± 8.87 years, 49.5% male) and 4605 imaging sessions (follow-up time: 2.97 ± 2.09 years, follow-up range: 1-13 years, mean number of visits: 4.42 ± 1.98). Differences in white matter microstructural decline in normal and abnormal agers was assessed.

Results

While we found a global decline in white matter in normal/abnormal aging, we found that several white matter tracts (e.g., cingulum bundle) were vulnerable to abnormal aging.

Conclusions

There is a prevalent role of white matter microstructural decline in aging, and future large-scale studies in this area may further refine our understanding of the underlying neurodegenerative processes.

HIGHLIGHTS

Longitudinal data were free-water corrected and harmonized.Global effects of white matter decline were seen in normal and abnormal aging.The free-water metric was most vulnerable to abnormal aging.Cingulum free-water was the most vulnerable to abnormal aging.

MRI-based Deep Learning Assessment of Amyloid, Tau, and Neurodegeneration Biomarker Status across the Alzheimer Disease Spectrum

Background PET can be used for amyloid-tau-neurodegeneration (ATN) classification in Alzheimer disease, but incurs considerable cost and exposure to ionizing radiation. MRI currently has limited use in characterizing ATN status. Deep learning techniques can detect complex patterns in MRI data and have potential for noninvasive characterization of ATN status. Purpose To use deep learning to predict PET-determined ATN biomarker status using MRI and readily available diagnostic data. Materials and Methods MRI and PET data were retrospectively collected from the Alzheimer's Disease Imaging Initiative. PET scans were paired with MRI scans acquired within 30 days, from August 2005 to September 2020. Pairs were randomly split into subsets as follows: 70% for training, 10% for validation, and 20% for final testing. A bimodal Gaussian mixture model was used to threshold PET scans into positive and negative labels. MRI data were fed into a convolutional neural network to generate imaging features. These features were combined in a logistic regression model with patient demographics, APOE gene status, cognitive scores, hippocampal volumes, and clinical diagnoses to classify each ATN biomarker component as positive or negative. Area under the receiver operating characteristic curve (AUC) analysis was used for model evaluation. Feature importance was derived from model coefficients and gradients. Results There were 2099 amyloid (mean patient age, 75 years ± 10 [SD]; 1110 male), 557 tau (mean patient age, 75 years ± 7; 280 male), and 2768 FDG PET (mean patient age, 75 years ± 7; 1645 male) and MRI pairs. Model AUCs for the test set were as follows: amyloid, 0.79 (95% CI: 0.74, 0.83); tau, 0.73 (95% CI: 0.58, 0.86); and neurodegeneration, 0.86 (95% CI: 0.83, 0.89). Within the networks, high gradients were present in key temporal, parietal, frontal, and occipital cortical regions. Model coefficients for cognitive scores, hippocampal volumes, and APOE status were highest. Conclusion A deep learning algorithm predicted each component of PET-determined ATN status with acceptable to excellent efficacy using MRI and other available diagnostic data. © RSNA, 2023 Supplemental material is available for this article.

Longitudinal hippocampal atrophy in hippocampal sclerosis of aging

Hippocampal sclerosis of aging (HS-A) is a common degenerative neuropathology in older individuals and is associated with dementia. HS-A is characterized by disproportionate hippocampal atrophy at autopsy but cannot be diagnosed during life. Therefore, little is known about the onset and progression of hippocampal atrophy in individuals with HS-A. To better understand the onset and progression of hippocampal atrophy in HS-A, we examined longitudinal hippocampal atrophy using serial MRI in participants with HS-A at autopsy (HS-A+, n = 8) compared to participants with limbic-predominant age-related TDP-43 encephalopathy neuropathological change (LATE-NC) without HS-A (n = 13), Alzheimer's disease neuropathologic change (ADNC) without HS-A or LATE-NC (n = 16), and those without these pathologies (n = 7). We found that participants with HS-A had lower hippocampal volumes compared to the other groups, and this atrophy preceded the onset of dementia. There was also some evidence that rates of hippocampal volume loss were slightly slower in those with HS-A. Together, these results suggest that the disproportionate hippocampal atrophy seen in HS-A may begin early prior to dementia.

The effect of plasma cortisol on hippocampal atrophy and clinical progression in mild cognitive impairment

Introduction

Both elevated cortisol and hippocampal volume have been linked to an increased risk for the development of Alzheimer's disease (AD). This longitudinal study assessed the effects of plasma cortisol on hippocampal atrophy and clinical progression rates in patients with mild cognitive impairment (MCI).

Methods

Patients with amnestic MCI (n = 304) were selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) based on availability of baseline plasma cortisol and hippocampal volume measures, assessed at baseline and during follow-ups. We investigated associations between plasma cortisol, hippocampal volume, and risk of clinical progression to AD over a study period of up to 100 months (mean follow-up time 36.8 months) using linear mixed models, Cox proportional hazards models, and Kaplan-Meier estimators.

Results

Plasma cortisol predicted greater hippocampal atrophy, such that participants with higher cortisol showed faster decline in hippocampal volume over time (interaction: β = -0.15, p = 0.004). Small hippocampal volume predicted a higher risk of clinical progression to AD (haard ratio [HR] = 2.15; confidence in terval [CI], 1.64-2.80; p < 0.001). A similar effect was not found for cortisol (HR = 1.206; CI, 0.82-1.37; p = 0.670) and there was no statistical evidence for an interaction between hippocampal volume and cortisol on clinical progression (HR = 0.81; CI, 0.57-0.17; p = 0.260).

Discussion

Our findings suggest that higher cortisol predicts higher hippocampal atrophy, which in turn is a risk factor for progression to AD. Regulation of the hypothalamic-pituitary-adrenal axis through stress-reducing lifestyle interventions might be a protective factor against hippocampal degeneration at the prodromal stage of AD.

A universal neocortical mask for Centiloid quantification

INTRODUCTION

The Centiloid (CL) project was developed to harmonize the quantification of amyloid beta (Aβ) positron emission tomography (PET) scans to a unified scale. The CL neocortical mask was defined using 11C Pittsburgh compound B (PiB), overlooking potential differences in regional distribution among Aβ tracers. We created a universal mask using an independent dataset of five Aβ tracers, and investigated its impact on inter-tracer agreement, tracer variability, and group separation.

METHODS

Using data from the Alzheimer's Dementia Onset and Progression in International Cohorts (ADOPIC) study (Australian Imaging Biomarkers and Lifestyle + Alzheimer's Disease Neuroimaging Initiative + Open Access Series of Imaging Studies), age-matched pairs of mild Alzheimer's disease (AD) and healthy controls (HC) were selected: 18F-florbetapir (N = 147 pairs), 18F-florbetaben (N = 22), 18F-flutemetamol (N = 10), 18F-NAV (N = 42), 11C-PiB (N = 63). The images were spatially and standardized uptake value ratio normalized. For each tracer, the mean AD-HC difference image was thresholded to maximize the overlap with the standard neocortical mask. The universal mask was defined as the intersection of all five masks. It was evaluated on the Global Alzheimer's Association Interactive Network (GAAIN) head-to-head datasets in terms of inter-tracer agreement and variance in the young controls (YC) and on the ADOPIC dataset comparing separation between HC/AD and HC/mild cognitive impairment (MCI).

RESULTS

In the GAAIN dataset, the universal mask led to a small reduction in the variance of the YC, and a small increase in the inter-tracer agreement. In the ADOPIC dataset, it led to a better separation between HC/AD and HC/MCI at baseline.

DISCUSSION

The universal CL mask led to an increase in inter-tracer agreement and group separation. Those increases were, however, very small, and do not provide sufficient benefits to support departing from the existing standard CL mask, which is suitable for the quantification of all Aβ tracers.

HIGHLIGHTS

This study built an amyloid universal mask using a matched cohort for the five most commonly used amyloid positron emission tomography tracers.There was a high overlap between each tracer-specific mask.Differences in quantification and group separation between the standard and universal mask were small.The existing standard Centiloid mask is suitable for the quantification of all amyloid beta tracers.

Brain structure and allelic associations in Alzheimer's disease

BACKGROUND

Alzheimer's disease (AD), the most prevalent form of dementia, affects 6.5 million Americans and over 50 million people globally. Clinical, genetic, and phenotypic studies of dementia provide some insights of the observed progressive neurodegenerative processes, however, the mechanisms underlying AD onset remain enigmatic.

AIMS

This paper examines late-onset dementia-related cognitive impairment utilizing neuroimaging-genetics biomarker associations.

MATERIALS AND METHODS

The participants, ages 65-85, included 266 healthy controls (HC), 572 volunteers with mild cognitive impairment (MCI), and 188 Alzheimer's disease (AD) patients. Genotype dosage data for AD-associated single nucleotide polymorphisms (SNPs) were extracted from the imputed ADNI genetics archive using sample-major additive coding. Such 29 SNPs were selected, representing a subset of independent SNPs reported to be highly associated with AD in a recent AD meta-GWAS study by Jansen and colleagues.

RESULTS

We identified the significant correlations between the 29 genomic markers (GMs) and the 200 neuroimaging markers (NIMs). The odds ratios and relative risks for AD and MCI (relative to HC) were predicted using multinomial linear models.

DISCUSSION

In the HC and MCI cohorts, mainly cortical thickness measures were associated with GMs, whereas the AD cohort exhibited different GM-NIM relations. Network patterns within the HC and AD groups were distinct in cortical thickness, volume, and proportion of White to Gray Matter (pct), but not in the MCI cohort. Multinomial linear models of clinical diagnosis showed precisely the specific NIMs and GMs that were most impactful in discriminating between AD and HC, and between MCI and HC.

CONCLUSION

This study suggests that advanced analytics provide mechanisms for exploring the interrelations between morphometric indicators and GMs. The findings may facilitate further clinical investigations of phenotypic associations that support deep systematic understanding of AD pathogenesis.

Operationalizing the centiloid scale for [18F]florbetapir PET studies on PET/MRI

INTRODUCTION

The Centiloid scale aims to harmonize amyloid beta (Aβ) positron emission tomography (PET) measures across different analysis methods. As Centiloids were created using PET/computerized tomography (CT) data and are influenced by scanner differences, we investigated the Centiloid transformation with data from Insight 46 acquired with PET/magnetic resonanceimaging (MRI).

METHODS

We transformed standardized uptake value ratios (SUVRs) from 432 florbetapir PET/MRI scans processed using whole cerebellum (WC) and white matter (WM) references, with and without partial volume correction. Gaussian-mixture-modelling-derived cutpoints for Aβ PET positivity were converted.

RESULTS

The Centiloid cutpoint was 14.2 for WC SUVRs. The relationship between WM and WC uptake differed between the calibration and testing datasets, producing implausibly low WM-based Centiloids. Linear adjustment produced a WM-based cutpoint of 18.1.

DISCUSSION

Transformation of PET/MRI florbetapir data to Centiloids is valid. However, further understanding of the effects of acquisition or biological factors on the transformation using a WM reference is needed.

HIGHLIGHTS

Centiloid conversion of amyloid beta positron emission tomography (PET) data aims to standardize results.Centiloid values can be influenced by differences in acquisition.We converted florbetapir PET/magnetic resonance imaging data from a large birth cohort.Whole cerebellum referenced values could be reliably transformed to Centiloids.White matter referenced values may be less generalizable between datasets.

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.

Fast three-dimensional image generation for healthy brain aging using diffeomorphic registration

Predicting brain aging can help in the early detection and prognosis of neurodegenerative diseases. Longitudinal cohorts of healthy subjects scanned through magnetic resonance imaging (MRI) have been essential to understand the structural brain changes due to aging. However, these cohorts suffer from missing data due to logistic issues in the recruitment of subjects. This paper proposes a methodology for filling up missing data in longitudinal cohorts with anatomically plausible images that capture the subject-specific aging process. The proposed methodology is developed within the framework of diffeomorphic registration. First, two novel modules are introduced within Synthmorph, a fast, state-of-the-art deep learning-based diffeomorphic registration method, to simulate the aging process between the first and last available MRI scan for each subject in three-dimensional (3D). The use of image registration also makes the generated images plausible by construction. Second, we used six image similarity measurements to rearrange the generated images to the specific age range. Finally, we estimated the age of every generated image by using the assumption of linear brain decay in healthy subjects. The methodology was evaluated on 2662 T1-weighted MRI scans from 796 healthy participants from 3 different longitudinal cohorts: Alzheimer's Disease Neuroimaging Initiative, Open Access Series of Imaging Studies-3, and Group of Neuropsychological Studies of the Canary Islands (GENIC). In total, we generated 7548 images to simulate the access of a scan per subject every 6 months in these cohorts. We evaluated the quality of the synthetic images using six quantitative measurements and a qualitative assessment by an experienced neuroradiologist with state-of-the-art results. The assumption of linear brain decay was accurate in these cohorts (R2  ∈ [.924, .940]). The experimental results show that the proposed methodology can produce anatomically plausible aging predictions that can be used to enhance longitudinal datasets. Compared to deep learning-based generative methods, diffeomorphic registration is more likely to preserve the anatomy of the different structures of the brain, which makes it more appropriate for its use in clinical applications. The proposed methodology is able to efficiently simulate anatomically plausible 3D MRI scans of brain aging of healthy subjects from two images scanned at two different time points.

Hippocampus shape characterization with 3D Zernike transformation in clinical Alzheimer's disease progression

Alzheimer's disease (AD) is a neurodegenerative disease and the most common cause of dementia among older adults. Mild cognitive impairment (MCI) is considered a transitional phase between healthy cognitive aging and dementia. Progressive brain volume reduction/atrophy, particularly of the hippocampus, is associated with the transition from normal to MCI, and then to AD. We aimed to develop methods to characterize the shape of hippocampus and explore its potential as an imaging marker to monitor clinical AD progression. We implemented a 3D Zernike transformation to characterize the shape changes of hippocampus in 428 older subjects with high-quality T1 -weighted volumetric brain scans from the Alzheimer's Disease Neuroimaging Initiative data set (151 normal, 258 MCI, and 19 AD). Over 2 years, 15 cognitively normal subjects converted to MCI, and 42 subjects with MCI converted to AD. We found a significant correlation between hippocampal volume changes and Zernike shape metrics. Before a clinical diagnosis of AD, the shapes of the left and right hippocampi changed slowly. After AD diagnosis, both volume and shape changed rapidly but were uncorrelated to each other. During the transition from a clinical diagnosis of MCI to AD, the shape of the left and right hippocampi changed in a correlated manner but became uncorrelated after AD diagnosis. Finally, the pace of hippocampus shape change was associated with its shape and the subject's age and disease condition. In conclusion, the hippocampus shape features characterized with 3D Zernike transformation, in complement to volume measures, may serve as a novel imaging marker to monitor clinical AD progression.

State, trait, and accumulated features of the Alzheimer's Disease Assessment Scale Cognitive Subscale (ADAS-Cog) in mild Alzheimer's disease

Background

The Alzheimer's Disease Assessment Scale Cognitive Subscale (ADAS-Cog) is used to assess decline in memory, language, and praxis in Alzheimer's disease (AD).

Methods

A latent state-trait model with autoregressive effects was used to determine how much of the ADAS-Cog item measurement was reliable, and of that, how much of the information was occasion specific (state) versus consistent (trait or accumulated from one visit to the next).

Results

Participants with mild AD (n = 341) were assessed four times over 24 months. Praxis items were generally unreliable as were some memory items. Language items were generally the most reliable, and this increased over time. Only two ADAS-Cog items showed reliability >0.70 at all four assessments, word recall (memory) and naming (language). Of the reliable information, language items exhibited greater consistency (63.4% to 88.2%) than occasion specificity, and of the consistent information, language items tended to reflect effects of AD progression that accumulated from one visit to the next (35.5% to 45.3%). In contrast, reliable information from praxis items tended to come from trait information. The reliable information in the memory items reflected more consistent than occasion-specific information, but they varied between items in the relative amounts of trait versus accumulated effects.

Conclusions

Although the ADAS-Cog was designed to track cognitive decline, most items were unreliable, and each item captured different amounts of information related to occasion-specific, trait, and accumulated effects of AD over time. These latent properties complicate the interpretation of trends seen in ordinary statistical analyses of trials and other clinical studies with repeated ADAS-Cog item measures.

Highlights

Studies have described unfavorable psychometric properties of the Alzheimer's Disease Assessment Scale Cognitive Subscale (ADAS-Cog), bringing into question its ability to track changes in cognition uniformly over time. There remains a need to estimate how much of the ADAS-Cog measurement is reliable, of that how much is occasion specific versus consistent, and of the consistent information, how much represents enduring traits versus autoregressive effects (i.e., effects of Alzheimer's disease [AD] progression carried over from one assessment to the next).A latent state-trait model with autoregressive effects in mild AD found most items to be unreliable, and each item to capture different amounts of occasion-specific, trait, and autoregressive information. Language items, specifically, naming and the memory item word recall, were the most reliable.Psychometric idiosyncrasies of individual items complicate the interpretation of their summed score, biasing ordinary statistical analyses of repeated measures in mild AD. Future studies should consider item trajectories individually.

Associations of erythrocyte omega-3 fatty acids with cognition, brain imaging and biomarkers in the Alzheimer's disease neuroimaging initiative: cross-sectional and longitudinal retrospective analyses

BACKGROUND

The association between omega-3 (ω-3) PUFAs and cognition, brain imaging and biomarkers is still not fully established.

OBJECTIVES

The aim was to analyze the cross-sectional and retrospective longitudinal associations between erythrocyte ω-3 index and cognition, brain imaging, and biomarkers among older adults.

METHODS

A total of 832 Alzheimer's Disease Neuroimaging Initiative 3 (ADNI-3) participants, with a mean (SD) age of 74.0 (7.9) y, 50.8% female, 55.9% cognitively normal, 32.7% with mild cognitive impairment, and 11.4% with Alzheimer disease (AD) were included. A low ω-3 index (%EPA + %DHA) was defined as the lowest quartile (≤3.70%). Cognitive tests [composite score, AD Assessment Scale Cognitive (ADAS-Cog), Wechsler Memory Scale (WMS), Trail Making Test, Category Fluency, Mini-Mental State Examination, Montreal Cognitive Assessment] and brain variables [hippocampal volume, white matter hyperintensities (WMHs), positron emission tomography (PET) amyloid-β (Aβ) and tau] were considered as outcomes in regression models.

RESULTS

Low ω-3 index was not associated with cognition, hippocampal, and WMH volume or brain Aβ and tau after adjustment for demographics, ApoEε4, cardiovascular disease, BMI, and total intracranial volume in the cross-sectional analysis. In the retrospective analysis, low ω-3 index was associated with greater Aβ accumulation (adjusted β = 0.02; 95% CI: 0.01, 0.03; P = 0.003). The composite cognitive score did not differ between groups; however, low ω-3 index was significantly associated with greater WMS-delayed recall cognitive decline (adjusted β = -1.18; 95% CI: -2.16, -0.19; P = 0.019), but unexpectedly lower total ADAS-Cog cognitive decline. Low ω-3 index was cross-sectionally associated with lower WMS performance (adjusted β = -1.81, SE = 0.73, P = 0.014) and higher tau accumulation among ApoE ε4 carriers.

CONCLUSIONS

Longitudinally, low ω-3 index was associated with greater Aβ accumulation and WMS cognitive decline but unexpectedly with lower total ADAS-Cog cognitive decline. Although no associations were cross-sectionally found in the whole population, low ω-3 index was associated with lower WMS cognition and higher tau accumulation among ApoE ε4 carriers. The Alzheimer's Disease Neuroimaging Initiative (ADNI) is registered at clinicaltrials.gov as NCT00106899.

Statistical power of transcriptome-wide association studies

Transcriptome-Wide Association Studies (TWASs) have become increasingly popular in identifying genes (or other endophenotypes or exposures) associated with complex traits. In TWAS, one first builds a predictive model for gene expressions using an expression quantitative trait loci (eQTL) data set in stage 1, then tests the association between the predicted gene expression and a trait based on a large, independent genome-wide association study (GWAS) data set in stage 2. However, since the sample size of the eQTL data set is usually small and the coefficient of multiple determination (i.e.,R 2 ${R}^{2}$ ) of the model for many genes is also small, a question of interest is to what extent these factors affect the statistical power of TWAS. In addition, in contrast to a standard (univariate) TWAS (UV-TWAS) considering only a single gene at a time, multivariate TWAS (MV-TWAS) methods have recently emerged to account for the effects of multiple genes, or a gene's nonlinear effects, simultaneously. With the absence of the power analysis for these MV-TWAS methods, it would be of interest to investigate whether one can gain or lose power by using the newly proposed MV-TWAS instead of UV-TWAS. In this paper, we first outline a general method for sample size/power calculations for two-sample TWAS, then use real data-the Alzheimer's Disease Neuroimaging Initiative (ADNI) expression quantitative trait loci (eQTL) data and the Genotype-Tissue Expression (GTEx) eQTL data for stage 1, the International Genomics of Alzheimer's Project Alzheimer's disease (AD) GWAS summary data and UK Biobank (UKB) individual-level data for stage 2-to empirically address these questions. Our most important conclusions are the following. First, a sample size of a few thousands (~8000) would suffice in stage 1, where the power of TWAS would be more determined by cis-heritability of gene expression. Second, as in the general case of simple regression versus multiple regression, the power of MV-TWAS may be higher or lower than that of UV-TWAS, depending on the specific relationships among the GWAS trait and multiple genes (or linear and nonlinear terms of the same gene's expression levels), such as their correlations and effect sizes. Interestingly, several top genes with large power gains in MV-TWAS (over that in UV-TWAS) were known to be (and in our data more significantly) associated with AD. We also reached similar conclusions in an application to the GTEx whole blood gene expression data and UKB GWAS data of high-density lipoprotein cholesterol. The proposed method and the conclusions are expected to be useful in planning and designing future TWAS and other related studies (e.g., Proteome- or Metabolome-Wide Association Studies) when determining the sample sizes for the two stages.

Turning and multitask gait unmask gait disturbance in mild-to-moderate multiple sclerosis: Underlying specific cortical thinning and connecting fibers damage

Multiple sclerosis (MS) causes gait and cognitive impairments that are partially normalized by compensatory mechanisms. We aimed to identify the gait tasks that unmask gait disturbance and the underlying neural correlates in MS. We included 25 patients with MS (Expanded Disability Status Scale score: median 2.0, interquartile range 1.0-2.5) and 19 healthy controls. Fast-paced gait examinations with inertial measurement units were conducted, including straight or circular walking with or without cognitive/motor tasks, and the timed up and go test (TUG). Receiver operating characteristic curve analysis was performed to distinguish both groups by the gait parameters. The correlation between gait parameters and cortical thickness or fractional anisotropy values was examined by using three-dimensional T1-weighted imaging and diffusion tensor imaging, respectively (corrected p < .05). Total TUG duration (>6.0 s, sensitivity 88.0%, specificity 84.2%) and stride velocity during cognitive dual-task circular walking (<1.12 m/s, 84.0%, 84.2%) had the highest discriminative power of the two groups. Deterioration of these gait parameters was correlated with thinner cortical thickness in regional areas, including the left precuneus and left temporoparietal junction, overlapped with parts of the default mode network, ventral attention network, and frontoparietal network. Total TUG duration was negatively correlated with fractional anisotropy values in the deep cerebral white matter areas. Turning and multitask gait may be optimal to unveil partially compensated gait disturbance in patients with mild-to-moderate MS through dynamic balance control and multitask processing, based on the structural damage in functional networks.

Increased functional connectivity between nucleus basalis of Meynert and amygdala in cognitively intact elderly along the Alzheimer's continuum

BACKGROUND

A growing body of research reported the degeneration of the basal forebrain (BF) cholinergic system in the early course of Alzheimer's disease (AD). However, functional changes of the BF in asymptomatic individuals along the Alzheimer's continuum remain unclear.

METHODS

A total of 229 cognitively intact participants were included from the Alzheimer's Disease Neuroimaging Initiative dataset and further divided into four groups based on the "A/T" profile using amyloid and tau positron emission tomography (PET). All A-T+ subjects were excluded. One hundred and seventy-three subjects along the Alzheimer's continuum (A-T-, A+ T-, A+ T+) were used for further study. The seed-based functional connectivity (FC) maps of the BF subregions (Ch1-3 and Ch4 [nucleus basalis of Meynert, NBM]) with whole-brain voxels were constructed. Analyses of covariance to detect the between-group differences and to further investigated the relations between FC values and AD biomarkers or cognition.

RESULTS

We found increased FC between right Ch4 and bilateral amygdala among three groups, and the FC value could well distinguish between the A-T- group and the Alzheimer's continuum groups. Furthermore, increased FC between the Ch4 and amygdala was associated with higher pathological burden reflected by amyloid and tau PET in the entire population as well as better logistic memory function in A + T+ group.

CONCLUSION

Our study demonstrated the NBM functional connectivity increased in cognitively normal elderly along the Alzheimer's continuum, which indicated a potential compensatory mechanism to counteract pathological changes in AD and maintain intact cognitive function.

Characteristics of subjective cognitive decline associated with amyloid positivity

INTRODUCTION

The evidence for characteristics of persons with subjective cognitive decline (SCD) associated with amyloid positivity is limited.

METHODS

In 1640 persons with SCD from 20 Amyloid Biomarker Study cohort, we investigated the associations of SCD-specific characteristics (informant confirmation, domain-specific complaints, concerns, feelings of worse performance) demographics, setting, apolipoprotein E gene (APOE) ε4 carriership, and neuropsychiatric symptoms with amyloid positivity.

RESULTS

Between cohorts, amyloid positivity in 70-year-olds varied from 10% to 76%. Only older age, clinical setting, and APOE ε4 carriership showed univariate associations with increased amyloid positivity. After adjusting for these, lower education was also associated with increased amyloid positivity. Only within a research setting, informant-confirmed complaints, memory complaints, attention/concentration complaints, and no depressive symptoms were associated with increased amyloid positivity. Feelings of worse performance were associated with less amyloid positivity at younger ages and more at older ages.

DISCUSSION

Next to age, setting, and APOE ε4 carriership, SCD-specific characteristics may facilitate the identification of amyloid-positive individuals.

Identifying the regional substrates predictive of Alzheimer's disease progression through a convolutional neural network model and occlusion

Progressive brain atrophy is a key neuropathological hallmark of Alzheimer's disease (AD) dementia. However, atrophy patterns along the progression of AD dementia are diffuse and variable and are often missed by univariate methods. Consequently, identifying the major regional atrophy patterns underlying AD dementia progression is challenging. In the current study, we propose a method that evaluates the degree to which specific regional atrophy patterns are predictive of AD dementia progression, while holding all other atrophy changes constant using a total sample of 334 subjects. We first trained a dense convolutional neural network model to differentiate individuals with mild cognitive impairment (MCI) who progress to AD dementia versus those with a stable MCI diagnosis. Then, we retested the model multiple times, each time occluding different regions of interest (ROIs) from the model's testing set's input. We also validated this approach by occluding ROIs based on Braak's staging scheme. We found that the hippocampus, fusiform, and inferior temporal gyri were the strongest predictors of AD dementia progression, in agreement with established staging models. We also found that occlusion of limbic ROIs defined according to Braak stage III had the largest impact on the performance of the model. Our predictive model reveals the major regional patterns of atrophy predictive of AD dementia progression. These results highlight the potential for early diagnosis and stratification of individuals with prodromal AD dementia based on patterns of cortical atrophy, prior to interventional clinical trials.

Measurement of neurodegeneration using a multivariate early frame amyloid PET classifier

Introduction

Amyloid measurement provides important confirmation of pathology for Alzheimer's disease (AD) clinical trials. However, many amyloid positive (Am+) early-stage subjects do not worsen clinically during a clinical trial, and a neurodegenerative measure predictive of decline could provide critical information. Studies have shown correspondence between perfusion measured by early amyloid frames post-tracer injection and fluorodeoxyglucose (FDG) positron emission tomography (PET), but with limitations in sensitivity. Multivariate machine learning approaches may offer a more sensitive means for detection of disease related changes as we have demonstrated with FDG.

Methods

Using summed dynamic florbetapir image frames acquired during the first 6 minutes post-injection for 107 Alzheimer's Disease Neuroimaging Initiative subjects, we applied optimized machine learning to develop and test image classifiers aimed at measuring AD progression. Early frame amyloid (EFA) classification was compared to that of an independently developed FDG PET AD progression classifier by scoring the FDG scans of the same subjects at the same time point. Score distributions and correlation with clinical endpoints were compared to those obtained from FDG. Region of interest measures were compared between EFA and FDG to further understand discrimination performance.

Results

The EFA classifier produced a primary pattern similar to that of the FDG classifier whose expression correlated highly with the FDG pattern (R-squared 0.71), discriminated cognitively normal (NL) amyloid negative (Am-) subjects from all Am+ groups, and that correlated in Am+ subjects with Mini-Mental State Examination, Clinical Dementia Rating Sum of Boxes, and Alzheimer's Disease Assessment Scale-13-item Cognitive subscale (R = 0.59, 0.63, 0.73) and with subsequent 24-month changes in these measures (R = 0.67, 0.73, 0.50).

Discussion

Our results support the ability to use EFA with a multivariate machine learning-derived classifier to obtain a sensitive measure of AD-related loss in neuronal function that correlates with FDG PET in preclinical and early prodromal stages as well as in late mild cognitive impairment and dementia.

Highlights

The summed initial post-injection minutes of florbetapir positron emission tomography  correlate with fluorodeoxyglucose.A machine learning classifier enabled sensitive detection of early prodromal Alzheimer's disease.Early frame amyloid (EFA) classifier scores correlate with subsequent change in Mini-Mental State Examination, Clinical Dementia Rating Sum of Boxes, and Alzheimer's Disease Assessment Scale-13-item Cognitive subscale.EFA classifier effect sizes and clinical prediction outperformed region of interest standardized uptake value ratio.EFA classification may aid in stratifying patients to assess treatment effect.

Contribution of Alzheimer's biomarkers and risk factors to cognitive impairment and decline across the Alzheimer's disease continuum

INTRODUCTION

Amyloid beta (Aβ), tau, and neurodegeneration jointly with the Alzheimer's disease (AD) risk factors affect the severity of clinical symptoms and disease progression.

METHODS

Within 248 Aβ-positive elderly with and without cognitive impairment and dementia, partial least squares structural equation pathway modeling was used to assess the direct and indirect effects of imaging biomarkers (global Aβ-positron emission tomography [PET] uptake, regional tau-PET uptake, and regional magnetic resonance imaging-based atrophy) and risk-factors (age, sex, education, apolipoprotein E [APOE], and white-matter lesions) on cross-sectional cognitive impairment and longitudinal cognitive decline.

RESULTS

Sixteen percent of variance in cross-sectional cognitive impairment was accounted for by Aβ, 46% to 47% by tau, and 25% to 29% by atrophy, although 53% to 58% of total variance in cognitive impairment was explained by incorporating mediated and direct effects of AD risk factors. The Aβ-tau-atrophy pathway accounted for 50% to 56% of variance in longitudinal cognitive decline while Aβ, tau, and atrophy independently explained 16%, 46% to 47%, and 25% to 29% of the variance, respectively.

DISCUSSION

These findings emphasize that treatments that remove Aβ and completely stop downstream effects on tau and neurodegeneration would only be partially effective in slowing of cognitive decline or reversing cognitive impairment.

Brain simulation augments machine-learning-based classification of dementia

Introduction

Computational brain network modeling using The Virtual Brain (TVB) simulation platform acts synergistically with machine learning (ML) and multi-modal neuroimaging to reveal mechanisms and improve diagnostics in Alzheimer's disease (AD).

Methods

We enhance large-scale whole-brain simulation in TVB with a cause-and-effect model linking local amyloid beta (Aβ) positron emission tomography (PET) with altered excitability. We use PET and magnetic resonance imaging (MRI) data from 33 participants of the Alzheimer's Disease Neuroimaging Initiative (ADNI3) combined with frequency compositions of TVB-simulated local field potentials (LFP) for ML classification.

Results

The combination of empirical neuroimaging features and simulated LFPs significantly outperformed the classification accuracy of empirical data alone by about 10% (weighted F1-score empirical 64.34% vs. combined 74.28%). Informative features showed high biological plausibility regarding the AD-typical spatial distribution.

Discussion

The cause-and-effect implementation of local hyperexcitation caused by Aβ can improve the ML-driven classification of AD and demonstrates TVB's ability to decode information in empirical data using connectivity-based brain simulation.

Association of the top 20 Alzheimer's disease risk genes with [18F]flortaucipir PET

Introduction

We previously reported genetic associations of the top Alzheimer's disease (AD) risk alleles with amyloid deposition and neurodegeneration. Here, we report the association of these variants with [18F]flortaucipir standardized uptake value ratio (SUVR).

Methods

We analyzed the [18F]flortaucipir scans of 352 cognitively normal (CN), 160 mild cognitive impairment (MCI), and 54 dementia (DEM) participants from Alzheimer's Disease Neuroimaging Initiative (ADNI)2 and 3. We ran step-wise regression with log-transformed [18F]flortaucipir meta-region of interest SUVR as the outcome measure and genetic variants, age, sex, and apolipoprotein E (APOE) ε4 as predictors. The results were visualized using parametric mapping at familywise error cluster-level-corrected P < .05.

Results

APOE ε4 showed significant (P < .05) associations with tau deposition across all disease stages. Other significantly associated genes include variants in ABCA7 in CN, CR1 in MCI, BIN1 and CASS4 in MCI and dementia participants.

Discussion

We found significant associations to tau deposition for ABCA7, BIN1, CASS4, and CR1, in addition to APOE ε4. These four variants have been previously associated with tau metabolism through model systems.

A pragmatic dementia risk score for patients with mild cognitive impairment in a memory clinic population: Development and validation of a dementia risk score using routinely collected data

Introduction

This study aimed to develop and validate a 3-year dementia risk score in individuals with mild cognitive impairment (MCI) based on variables collected in routine clinical care.

Methods

The prediction score was trained and developed using data from the National Alzheimer's Coordinating Center (NACC). Selection criteria included aged 55 years and older with MCI. Cox models were validated externally using two independent cohorts from the Prospective Registry of Persons with Memory Symptoms (PROMPT) registry and the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.

Results

Our Mild Cognitive Impairment to Dementia Risk (CIDER) score predicted dementia risk with c-indices of 0.69 (95% confidence interval [CI] 0.66-0.72), 0.61 (95% CI 0.59-0.63), and 0.72 (95% CI 0.69-0.75), for the internally validated and the external validation PROMPT, and ADNI cohorts, respectively.

Discussion

The CIDER score could be used to inform clinicians and patients about the relative probabilities of developing dementia in patients with MCI.

Effects of age, amyloid, sex, and APOE ε4 on the CSF proteome in normal cognition

Introduction

It is important to understand which biological processes change with aging, and how such changes are associated with increased Alzheimer's disease (AD) risk. We studied how cerebrospinal fluid (CSF) proteomics changed with age and tested if associations depended on amyloid status, sex, and apolipoprotein E Ɛ4 genotype.

Methods

We included 277 cognitively intact individuals aged 46 to 89 years from Alzheimer's Disease Neuroimaging Initiative, European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery, and Metabolic Syndrome in Men. In total, 1149 proteins were measured with liquid chromatography mass spectrometry with multiple reaction monitoring/Rules-Based Medicine, tandem mass tag mass spectrometry, and SOMAscan. We tested associations between age and protein levels in linear models and tested enrichment for Reactome pathways.

Results

Levels of 252 proteins increased with age independently of amyloid status. These proteins were associated with immune and signaling processes. Levels of 21 proteins decreased with older age exclusively in amyloid abnormal participants and these were enriched for extracellular matrix organization.

Discussion

We found amyloid-independent and -dependent CSF proteome changes with older age, perhaps representing physiological aging and early AD pathology.

Network-wise concordance of multimodal neuroimaging features across the Alzheimer's disease continuum

Background

Concordance between cortical atrophy and cortical glucose hypometabolism within distributed brain networks was evaluated among cerebrospinal fluid (CSF) biomarker-defined amyloid/tau/neurodegeneration (A/T/N) groups.

Method

We computed correlations between cortical thickness and fluorodeoxyglucose metabolism within 12 functional brain networks. Differences among A/T/N groups (biomarker normal [BN], Alzheimer's disease [AD] continuum, suspected non-AD pathologic change [SNAP]) in network concordance and relationships to longitudinal change in cognition were assessed.

Results

Network-wise markers of concordance distinguish SNAP subjects from BN subjects within the posterior multimodal and language networks. AD-continuum subjects showed increased concordance in 9/12 networks assessed compared to BN subjects, as well as widespread atrophy and hypometabolism. Baseline network concordance was associated with longitudinal change in a composite memory variable in both SNAP and AD-continuum subjects.

Conclusions

Our novel study investigates the interrelationships between atrophy and hypometabolism across brain networks in A/T/N groups, helping disentangle the structure-function relationships that contribute to both clinical outcomes and diagnostic uncertainty in AD.

Regional Radiomics Similarity Networks Reveal Distinct Subtypes and Abnormality Patterns in Mild Cognitive Impairment

Individuals with mild cognitive impairment (MCI) of different subtypes show distinct alterations in network patterns. The first aim of this study is to identify the subtypes of MCI by employing a regional radiomics similarity network (R2SN). The second aim is to characterize the abnormality patterns associated with the clinical manifestations of each subtype. An individual-level R2SN is constructed for N = 605 normal controls (NCs), N = 766 MCI patients, and N = 283 Alzheimer's disease (AD) patients. MCI patients' R2SN profiles are clustered into two subtypes using nonnegative matrix factorization. The patterns of brain alterations, gene expression, and the risk of cognitive decline in each subtype are evaluated. MCI patients are clustered into "similar to the pattern of NCs" (N-CI, N = 252) and "similar to the pattern of AD" (A-CI, N = 514) subgroups. Significant differences are observed between the subtypes with respect to the following: 1) clinical measures; 2) multimodal neuroimaging; 3) the proportion of progression to dementia (61.54% for A-CI and 21.77% for N-CI) within three years; 4) enriched genes for potassium-ion transport and synaptic transmission. Stratification into the two subtypes provides new insight for risk assessment and precise early intervention for MCI patients.

The neuroinflammatory marker sTNFR2 relates to worse cognition and tau in women across the Alzheimer's disease spectrum

Introduction

Despite women showing greater Alzheimer's disease (AD) prevalence, tau burden, and immune/neuroinflammatory response, whether neuroinflammation impacts cognition differently in women versus men and the biological basis of this impact remain unknown. We examined sex differences in how cerebrospinal fluid (CSF) neuroinflammation relates to cognition across the aging-mild cognitive impairment (MCI)-AD continuum and the mediating role of phosphorylated tau (p-tau) versus other AD biomarkers.

Methods

Participants included 284 individuals from the Alzheimer's Disease Neuroimaging Initiative study. CSF neuroinflammatory markers included interleukin-6, tumor necrosis factor α, soluble tumor necrosis factor receptor 2 (sTNFR2), and chitinase-3-like protein 1. AD biomarkers were CSF p-tau181 and amyloid beta1-42 levels and magnetic resonance imaging measures of hippocampal and white matter hyperintensity volumes.

Results

We found a sex-by-sTNFR2 interaction on Mini-Mental State Examination and Clinical Dementia Rating-Sum of Boxes. Higher levels of sTNFR2 related to poorer cognition in women only. Among biomarkers, only p-tau181 eliminated the female-specific relationships between neuroinflammation and cognition.

Discussion

Women may be more susceptible than men to the adverse effects of sTNFR2 on cognition with a potential etiological link with tau to these effects.

Examining the role of repeated test exposure over 12 months across ADNI protocols

Objective: Changes to study protocols during longitudinal research may alter cognitive testing schedules over time. Unlike in prior Alzheimer's Disease Neuroimaging Initiative (ADNI) protocols, where testing occurred twice annually, participants enrolled in the ADNI-3 are no longer exposed to cognitive materials at 6 months. This may affect their 12-month performance relative to earlier ADNI cohorts, and potentially confounds data harmonization attempts between earlier and later ADNI protocols. Method: Using data from participants enrolled across multiple ADNI protocols, this study investigated whether test exposure during 6-month cognitive evaluation influenced scores on subsequent 12-month evaluation. Results: No interaction effects were observed between test exposure group and time at 12 months on cognitive performance. No improvements, and limited declines, were seen between baseline and 12-month follow-up scores on most measures. Conclusions: The 6-month testing session had minimal impact on 12-month performance in ADNI. Collapsing longitudinal data across ADNI protocols in future research appears appropriate.

Features of the superficial white matter as biomarkers for the detection of Alzheimer's disease and mild cognitive impairment: A diffusion tensor imaging study

BACKGROUND

With the development of medical imaging and processing tools, accurate diagnosis of diseases has been made possible by intelligent systems. Owing to the remarkable ability of support vector machines (SVMs) for diseases diagnosis, extensive research has been conducted using the SVM algorithm for the classification of Alzheimer's disease (AD) and mild cognitive impairment (MCI).

OBJECTIVES

In this study, we applied an automated method to classify patients with AD and MCI and healthy control (HC) subjects based on the diffusion tensor imaging (DTI) features in the superficial white matter (SWM).

PARTICIPANTS

For this purpose, DTI data were downloaded from the Alzheimer's Disease Neuroimaging Initiative (ADNI). This method employed DTI data from 72 subjects: 24 subjects as HC, 24 subjects with MCI, and 24 subjects with AD.

MEASURE

ments: DTI processing was performed using DSI Studio software and all machine learning analyses were performed using MATLAB software.

RESULTS

The linear kernel of SVM was the best classifier, with an accuracy of 95.8% between the AD and HC groups, followed by the quadratic kernel of SVM with an accuracy of 83.3% between the MCI and HC groups and the Gaussian kernel of SVM with an accuracy of 83.3% between the AD and MCI groups.

CONCLUSIONS

Given the importance of diagnosing AD and MCI as well as the role of superficial white matter in the diagnosis of neurodegenerative diseases, in this study, the features of different DTI methods of the SWM are discussed, which could be a useful tool to assist in the diagnosis of AD and MCI.