Alzheimer's Disease Neuroimaging Initiative Research Articles

Utility of cerebrovascular imaging biomarkers to detect cerebral amyloidosis.

The relationship between cerebrovascular disease (CVD) and amyloid beta (Aβ) in Alzheimer's disease (AD) is understudied. We hypothesized that magnetic resonance imaging (MRI)-based CVD biomarkers-including cerebral microbleeds (CMBs), lacunar infarction, and white matter hyperintensities (WMHs)-would correlate with Aβ positivity on positron emission tomography (Aβ-PET). We cross-sectionally analyzed data from the Alzheimer's Disease Neuroimaging Initiative (ADNI, N=1352). Logistic regression was used to calculate odds ratios (ORs), with Aβ-PET positivity as the standard-of-truth. Following adjustment, WMHs (OR=1.25) and superficial CMBs (OR=1.45) remained positively associated with Aβ-PET positivity (p<0.001). Deep CMBs and lacunes exhibited a varied relationship with Aβ-PET in cognitive subgroups. The combined diagnostic model, which included CVD biomarkers and other accessible measures, significantly predicted Aβ-PET (pseudo-R2=0.41). The study highlights the translational value of CVD biomarkers in diagnosing AD, and underscores the need for more research on their inclusion in diagnostic criteria. gov: ADNI-2 (NCT01231971), ADNI-3 (NCT02854033). Cerebrovascular biomarkers linked to amyloid beta (Aβ) in Alzheimer's disease (AD). White matter hyperintensities and cerebral microbleeds reliably predict Aβ-PET positivity. Relationships with Aβ-PET vary by cognitive stage. Novel accessible model predicts Aβ-PET status. Study supports multimodal diagnostic approaches.

The ADNI4 Digital Study: A novel approach to recruitment, screening, and assessment of participants for AD clinical research.

We evaluated preliminary feasibility of a digital, culturally-informed approach to recruit and screen participants for the Alzheimer's Disease Neuroimaging Initiative (ADNI4). Participants were recruited using digital advertising and completed digital surveys (e.g., demographics, medical exclusion criteria, 12-item Everyday Cognition Scale [ECog-12]), Novoic Storyteller speech-based cognitive test). Completion rates and assessment performance were compared between underrepresented populations (URPs: individuals from ethnoculturally minoritized or low education backgrounds) and non-URPs. Of 3099 participants who provided contact information, 654 enrolled in the cohort, and 595 completed at least one assessment. Two hundred forty-seven participants were from URPs. Of those enrolled, 465 met ADNI4 inclusion criteria and 237 evidenced possible cognitive impairment from ECog-12 or Storyteller performance. URPs had lower ECog and Storyteller completion rates. Scores varied by ethnocultural group and educational level. Preliminary results demonstrate digital recruitment and screening assessment of an older diverse cohort, including those with possible cognitive impairment, are feasible. Improving engagement and achieving educational diversity are key challenges. A total of 654 participants enrolled in a digital cohort to facilitate ADNI4 recruitment. Culturally-informed digital ads aided enrollment of underrepresented populations. From those enrolled, 42% were from underrepresented ethnocultural and educational groups. Digital screening tools indicate>50% of participants likely cognitively impaired. Completion rates and assessment performance vary by ethnocultural group and education.

A hybrid Feature Selection Method Based on Binary PSO Algorithm for Microarray Data Classification

Microarray technology produces data with a large number of dimensions. The abundance of dimensions in the data makes it challenging for machine learning algorithms to extract meaningful information from it. To overcome this limitation, feature selection (FS) techniques can be applied to reduce the dimensionality of the data. FS is a crucial preprocessing step that allows for the handling of high-dimensional data and facilitates more effective and efficient processing. The primary objective of this paper is to develop an efficient FS method that can effectively reduce the dimensionality of microarray data. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is utilized in the first filtering stage to extract informative features. The best feature subset is then determined by using a Particle Swarm Optimization (PSO) algorithm as a wrapper feature selector to identify ideal features. The proposed approach is evaluated using microarray datasets from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and compared with other algorithms. The experimental findings indicate that the proposed approach outperforms the benchmark methods in terms of classification accuracy.

Unveiling the link between glymphatic function and cortical microstructures in post-traumatic stress disorder

PurposeThe discovery of the glymphatic system, crucial for cerebrospinal and interstitial fluid exchange, has enhanced our grasp of brain protein balance and its potential role in neurodegenerative disease prevention and therapy. Detecting early neurodegenerative shifts via noninvasive biomarkers could be key in identifying at-risk individuals for Alzheimer's disease (AD). Our research explores a diffusion tensor imaging (DTI) method that measures cortical mean diffusivity (cMD), potentially a more sensitive indicator of neurodegeneration than traditional macrostructural methods. Materials and methodsWe analyzed 67 post-traumatic stress disorder (PTSD)-diagnosed veterans from the Alzheimer's Disease Neuroimaging Initiative database. Participants underwent structural MRI, DTI, Aβ PET imaging, and cognitive testing. We focused on the DTI-ALPS technique to assess glymphatic function and its relation to cMD, cortical Aβ accumulation, and thickness, accounting for age and APOE ε4 allele variations. ResultsThe cohort, all male with an average age of 68.1 (SD 3.4), showed a strong inverse correlation between DTI-ALPS and cMD in AD-affected regions, especially in the entorhinal, parahippocampal, and fusiform areas. Higher DTI-ALPS readings were consistently linked with greater cortical thickness, independent of Aβ deposits and genetic risk factors. Age and cMD emerged as inversely proportional predictors of DTI-ALPS, indicating a complex interaction with age. ConclusionThe study confirms a meaningful association between glymphatic efficiency and cMD in AD-sensitive zones, accentuating cortical microstructural alterations in PTSD. It positions DTI-ALPS as a viable biomarker for assessing glymphatic function in PTSD, implicating changes in DTI-ALPS as indicative of glymphatic impairment.

A minimalistic approach to classifying Alzheimer’s disease using simple and extremely small convolutional neural networks

Background:There is a broad interest in deploying deep learning-based classification algorithms to identify individuals with Alzheimer’s disease (AD) from healthy controls (HC) based on neuroimaging data, such as T1-weighted Magnetic Resonance Imaging (MRI). The goal of the current study is to investigate whether modern, flexible architectures such as EfficientNet provide any performance boost over more standard architectures. Methods:MRI data was sourced from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and processed with a minimal preprocessing pipeline. Among the various architectures tested, the minimal 3D convolutional neural network SFCN stood out, composed solely of 3x3x3 convolution, batch normalization, ReLU, and max-pooling. We also examined the influence of scale on performance, testing SFCN versions with trainable parameters ranging from 720 up to 2.9 million. Results:SFCN achieves a test ROC AUC of 96.0% while EfficientNet got an ROC AUC of 94.9 %. SFCN retained high performance down to 720 trainable parameters, achieving an ROC AUC of 91.4%. Comparison with existing methods:The SFCN is compared to DenseNet and EfficientNet as well as the results of other publications in the field. Conclusions:The results indicate that using the minimal 3D convolutional neural network SFCN with a minimal preprocessing pipeline can achieve competitive performance in AD classification, challenging the necessity of employing more complex architectures with a larger number of parameters. This finding supports the efficiency of simpler deep learning models for neuroimaging-based AD diagnosis, potentially aiding in better understanding and diagnosing Alzheimer’s disease.

Tau pathology mediated the plasma biomarkers and cognitive function in patients with mild cognitive impairment

Glial fibrillary acidic protein (GFAP) and neurofilament light (NfL) are putative non-amyloid biomarkers indicative of ongoing inflammatory and neurodegenerative disease processes. Hence, this study aimed to demonstrate the relationship between plasma biomarkers (GFAP and NfL) and 18F-AV-1451 tau PET images, and to explore their effects on cognitive function. Ninety-one participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database and 20 participants from the Shanghai Action of Prevention Dementia for the Elderly (SHAPE) cohort underwent plasma biomarker testing, 18F-AV-1451 tau PET scans and cognitive function assessments. Within the ADNI, there were 42 cognitively normal (CN) individuals and 49 with mild cognitive impairment (MCI). Similarly, in the SHAPE, we had 10 CN and 10 MCI participants. We calculated the standardized uptake value ratios (SUVRs) for the regions of interest (ROIs) in the 18F-AV-1451 PET scans. Using plasma biomarkers and regional SUVRs, we trained machine learning models to differentiate between MCI and CN subjects with ADNI database and validated in SHAPE.Results showed that eight selected variables (including left amygdala SUVR, right amygdala SUVR, left entorhinal cortex SUVR, age, education, plasma NfL, plasma GFAP, plasma GFAP/ NfL) identified by LASSO could differentiate between the MCI and CN individuals, with AUC ranging from 0.783 to 0.926. Additionally, cognitive function was negatively associated with the plasma biomarkers and tau deposition in amygdala and left entorhinal cortex. Increased tau deposition in amygdala and left entorhinal cortex were related to increased plasma biomarkers. Moreover, tau pathology mediated the effect of plasma biomarkers level on the cognitive decline. The present study provides valuable insights into the association among plasma markers (GFAP and NfL), regional tau deposition and cognitive function. This study reports the mediation effect of brain regions tau deposition on the plasma biomarkers level and cognitive function, indicating the significance of tau pathology in the MCI patients.

Joint modeling of varying-disease-state longitudinal ordinal data and time-to-event data with application to alzheimer’s disease

Joint models are routinely used in clinical trials to fit longitudinal and survival data simulta- neously in an integrated fashion. We propose a joint model that incorporates state-specific trajectories for fitting the longitudinal ordinal response and time-to-event data, focusing on its application to Alzheimer’s Disease (AD). The proposed joint model effectively captures the fluctuating cognitive conditions observed before and after the transition between two disease states. By integrating longitudinal data into the survival sub-model through shared trajectories, we can improve the fit of the survival data. A Markov chain Monte Carlo (MCMC) sampling algorithm is developed to carry out Bayesian computation. A variation of the Deviance Information Criterion is developed to assess the fit of each component of the joint model as well as the contribution of the longitudinal data in fitting the survival data. A variation of the concordance (C) index is further derived to assess the discriminatory and predictive performance of the longitudinal sub-model. An in-depth analysis of the real data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database is carried out to demonstrate the applicability of the proposed methodology. Journal of Statistical Research 2024, Vol. 58, No. 1, pp. 49-73.

Dominant hippocampus segmentation with brain atrophy analysis-based AD subtype classification using KLW-RU-Net and T1FL

A progressive neurodegenerative disorder that leads to cognitive decline and memory loss is known as Alzheimer's Disease (AD). Researchers propose an integrated framework centered on brain atrophy analysis and hippocampus segmentation to predict AD and its subtypes. This approach augments the accurate prediction of AD subtype classification, thus addressing the lack of emphasis in prevailing works. Primarily, the T1-weighted brain Magnetic Resonance Imaging (MRI) images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database undergo preprocessing. After that, for spatiotemporal evaluation, region segmentation, tau accumulation analysis, and Feature Extraction (FE), the image is processed. By employing the Fisher-Kolmogorov (FK) model, the essential parameters are extracted in spatio-temporal evaluation. In the meantime, for tissue segmentation, the Knowledge Partitioned Clustering (KPC) is utilized, and for hippocampus segmentation, the Kullback-Leibler Within-layer Regularized UNet (KLW-RU-Net) is employed. Moreover, by binarizing the image, the tau accumulation is analyzed; in addition, Principal Component Analysis (PCA) is utilized for FE. After PCA, the Adaptive Step-sized Gauss Rabbits Optimization (ASGRO) is employed for feature selection. Subsequently, to classify AD, the Dilated Derivative Sigmoid-Weighted DenseNet (DD-SWDN) is employed. Additionally, by employing Type-1 Fuzzy Logic (T1FL), the subtype of AD is classified, thus achieving higher accuracy for AD subtype classification. Therefore, as per these outcomes, the proposed work performs better than the other conventional approaches.

The ADNI PET Core at 20.

: The Alzheimer's Disease Neuroimaging Initiative (ADNI) PET Core has evolved over time, beginning with positron emission tomography (PET) imaging of a subsample of participants with [18F]fluorodeoxyglucose (FDG)-PET, adding tracers for measurement of β-amyloid, followed by tau tracers. This review examines the evolution of the ADNI PET Core, the novel aspects of PET imaging in each stage of ADNI, and gives an accounting of PET images available in the ADNI database. The ADNI PET Core has been and continues to be a rich resource that provides quantitative PET data and preprocessed PET images to the scientific community, allowing interrogation of both basic and clinically relevant questions. By standardizing methods across different PET scanners and multiple PET tracers, the Core has demonstrated the feasibility of large-scale, multi-center PET studies. Data managed and disseminated by the PET Core has been critical to defining pathophysiological models of Alzheimer's disease (AD) and helped to drive methods used in modern therapeutic trials. HIGHLIGHTS: The ADNI PET Core began with FDG-PET and now includes three amyloid and three tau PET ligands. The PET Core has standardized acquisition and analysis of multitracer PET images. The ADNI PET Core helped to develop methods that have facilitated clinical trials in AD.

Impaired glymphatic function as a biomarker for subjective cognitive decline: An exploratory dual cohort study.

Subjective cognitive decline (SCD) has been recognized as a potential risk stage for progression to Alzheimer's disease (AD), while glymphatic dysfunction is considered an important characteristic of AD. We hypothesize that glymphatic dysfunction occurs during the SCD stage, aiming to discover potential biomarkers for SCD. Participants from two independent studies, Sino Longitudinal Study on Cognitive Decline (SILCODE, n=654) and the Alzheimer's Disease Neuroimaging Initiative (ADNI, n=650), representing different ethnicities and disease stages, were included to assess glymphatic function using diffusion tensor image analysis along the perivascular space (DTI-ALPS). Abnormal glymphatic function occurs during the SCD stage, with the ALPS index demonstrating excellent classification performance for SCD and normal controls (area under the receiver operating characteristic curve [AUC]SILCODE=0.816, AUCADNI=0.797). Lower ALPS index indicates higher risk of cognitive progression, which is negatively correlated with Subjective Cognitive Decline Questionnaire 9 scores and amyloid positron emission tomography burden. Our study suggests the ALPS index has the potential to serve as a biomarker for SCD. Glymphatic function characterized by the analysis along the perivascular space (ALPS) index becomes abnormal in subjective cognitive decline (SCD), the earliest symptomatic manifestation and preclinical stage of Alzheimer's disease (AD). The ALPS index demonstrates excellent classification performance for SCD and normal controls in the East Asian and Western cohorts. Participants with a lower ALPS index show a higher risk of clinical progression. The ALPS index is closely associated with serval cognitive scales and amyloid beta burden.

A Modified Bio-Inspired Optimizer with Capsule Network for Diagnosis of Alzheimer Disease

Recently, Alzheimer’s disease (AD) is one of the common neurodegenerative disorders, which primarily occurs in old age. Structural magnetic resonance imaging (sMRI) is an effective imaging technique used in clinical practice for determining the period of AD patients. An efficient deep learning framework is proposed in this paper for AD detection, which is inspired from clinical practice. The proposed deep learning framework significantly enhances the performance of AD classification by requiring less processing time. Initially, in the proposed framework, the sMRI images are acquired from a real-time dataset and two online datasets including Australian Imaging, Biomarker and Lifestyle flagship work of ageing (AIBL), and Alzheimer’s Disease Neuroimaging Initiative (ADNI). Next, a fuzzy-based superpixel-clustering algorithm is introduced to segment the region of interest (RoI) in sMRI images. Then, the informative deep features are extracted in segmented RoI images by integrating the probabilistic local ternary pattern (PLTP), ResNet-50, and Visual Geometry Group (VGG)-16. Furthermore, the dimensionality reduction is accomplished by through the modified gorilla troops optimizer (MGTO). This process not only enhances the classification performance but also diminishes the processing time of the capsule network (CapsNet), which is employed to classify the classes of AD. In the MGTO algorithm, a quasi-reflection-based learning (QRBL) process is introduced for generating silverback’s quasi-refraction position for further improving the optimal position’s quality. The proposed fuzzy based superpixel-clustering algorithm and MGTO-CapsNet model obtained a pixel accuracy of 0.96, 0.94, and 0.98 and a classification accuracy of 99.88%, 96.38%, and 99.94% on the ADNI, real-time, and AIBL datasets, respectively.

A transformer-based unified multimodal framework for Alzheimer's disease assessment

In Alzheimer's disease (AD) assessment, traditional deep learning approaches have often employed separate methodologies to handle the diverse modalities of input data. Recognizing the critical need for a cohesive and interconnected analytical framework, we propose the AD-Transformer, a novel transformer-based unified deep learning model. This innovative framework seamlessly integrates structural magnetic resonance imaging (sMRI), clinical, and genetic data from the extensive Alzheimer's Disease Neuroimaging Initiative (ADNI) database, encompassing 1651 subjects. By employing a Patch-CNN block, the AD-Transformer efficiently transforms image data into image tokens, while a linear projection layer adeptly converts non-image data into corresponding tokens. As the core, a transformer block learns comprehensive representations of the input data, capturing the intricate interplay between modalities. The AD-Transformer sets a new benchmark in AD diagnosis and Mild Cognitive Impairment (MCI) conversion prediction, achieving remarkable average area under curve (AUC) values of 0.993 and 0.845, respectively, surpassing those of traditional image-only models and non-unified multimodal models. Our experimental results confirmed the potential of the AD-Transformer as a potent tool in AD diagnosis and MCI conversion prediction. By providing a unified framework that jointly learns holistic representations of both image and non-image data, the AD-Transformer paves the way for more effective and precise clinical assessments, offering a clinically adaptable strategy for leveraging diverse data modalities in the battle against AD.

Hybrid-feature based spherical quasi-conformal registration for AD-induced hippocampal surface morphological changes

Background and objectiveEstablishing accurate one-to-one morphological correspondence between different hippocampal surfaces is a solid foundation for the analysis of AD-induced hippocampal morphological changes. However, owing to the large variations between hippocampal surfaces, exiting registration work either fails to obtain the accurate matching of local and overall morphological features or does not preserve the bijectivity during parametric mapping. For this reason, this study proposes a hybrid-feature based spherical quasi-conformal registration (HSQR) method that can effectively maintain the diffeomorphic property while meeting the hybrid-feature matching constraints in the spherical parameter domain. MethodsThe HSQR algorithm is primarily achieved through hippocampal surface hybrid feature extraction and spherical quasi-conformal registration. First, hybrid features for a comprehensive morphological description of the hippocampal surface were established, which included essential anatomical features (landmarks) and mean curvature (intensity) features to ensure the accuracy of surface morphology alignment. Second, spherical parameterization was applied to genus-0 closed surfaces, such as the hippocampus, which maximized the preservation of the original local surface morphology through area-preserving properties. Third, a novel spherical quasi-conformal registration algorithm that can handle large deformations is established. It transforms a 3D spherical parameter domain into a 2D plane parameter domain using iterative local stereo projection to improve the efficiency of the registration algorithm. Subsequently, by controlling the Beltramin coefficient, the hybrid morphological features could be aligned while ensuring bijection before and after registration. ResultsUsing a cohort including 161 patients with amyloid-β (Aβ) positive Alzheimer disease (AD), 234 Aβ positive mild cognitive impairment (MCI) and 266 Aβ negative cognitively unimpaired (CU) individuals from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, we set up the experiment which indicated that the HSQR-based whole bilateral hippocampal atrophy features demonstrated the stronger statistical power for group morphological differences of CU vs. MCI with q-value: 0.0453 for left hippocampus and 0.0401 for right hippocampus and group morphological differences of AD vs. MCI with q-value: 0.0282 for left hippocampus and 0.0421 for right hippocampus. ConclusionsOur registration algorithm may provide a solid foundation for the accurate quantification of hippocampal surface morphological changes for the differential diagnosis and tracking of AD.

Explore the Role of Frailty as a Contributor to the Association Between AT(N) Profiles and Cognition in Alzheimer's Disease.

The relationship between Alzheimer's disease (AD)-related pathology and cognition was not exactly consistent. To explore whether the association between AD pathology and cognition can be moderated by frailty. We included 1711 participants from the Alzheimer's Disease Neuroimaging Initiative database. Levels of cerebrospinal fluid amyloid-β, p-tau, and t-tau were identified for AD-related pathology based on the amyloid-β/tau/neurodegeneration (AT[N]) framework. Frailty was measured using a modified Frailty Index-11 (mFI-11). Regression and interaction models were utilized to assess the relationship among frailty, AT(N) profiles, and cognition. Moderation models analyzed the correlation between AT(N) profiles and cognition across three frailty levels. All analyses were corrected for age, sex, education, and APOEɛ4 status. In this study, frailty (odds ratio [OR] = 1.71, p < 0.001) and AT(N) profiles (OR = 2.00, p < 0.001) were independently associated with cognitive status. The model fit was improved when frailty was added to the model examining the relationship between AT(N) profiles and cognition (p < 0.001). There was a significant interaction between frailty and AT(N) profiles in relation to cognitive status (OR = 1.12, pinteraction = 0.028). Comparable results were obtained when Mini-Mental State Examination scores were utilized as the measure of cognitive performance. The association between AT(N) profiles and cognition was stronger with the levels of frailty. Frailty may diminish patients' resilience to AD pathology and accelerate cognitive decline resulting from abnormal AD-related pathology. In summary, frailty contributes to elucidating the relationship between AD-related pathology and cognitive impairment.

Data sharing as the foundation of discovery: ADNI and breakthroughs in Alzheimer's disease.

The COVID pandemic has shown that when the research community comes together, we can conquer the most complex biomedical challenges. Collaboration and teamwork among federal agencies, private organizations, and researchers have been crucial in the development of vaccines and therapeutics against COVID. Possibly the first example of such cross-functional collaboration is the Alzheimer's Disease Neuroimaging Initiative (ADNI), the largest and longest continually monitored Alzheimer's study. ADNI was designed and operated as a public-private partnership, managed by the Foundation for the National Institutes of Health. This article shows how recent successes in the Alzheimer's field are directly a result of ADNI's open and transparent sharing of knowledge, expertise, and resources, which have allowed researchers to advance their understanding of Alzheimer's and tackle challenges in a relatively short period of time. ADNI's approach to open-source innovation also served as a model for addressing other complex diseases and led to numerous collaborative research initiatives. HIGHLIGHTS: The Alzheimer's Disease Neuroimaging Initiative (ADNI) was designed, structured, and operated as a public-private partnership, managed by the Foundation for the National Institutes of Health. The recent successes in the Alzheimer's field are directly a result of ADNI's efforts. Open and transparent sharing of knowledge, expertise, and resources allowed researchers to advance their understanding of Alzheimer's and tackle challenges in a relatively short period of time.

Linking the neural signature of response time variability to Alzheimer’s disease pathology and cognitive functioning

Abstract Promising evidence has suggested potential links between mind-wandering and Alzheimer’s disease (AD). Yet, older adults with diagnosable neurocognitive disorders show reduced meta-awareness, thus questioning the validity of probe-assessed mind-wandering in older adults. In prior work, we employed response time variability as an objective, albeit indirect, marker of mind-wandering to identify patterns of functional connectivity that predicted mind-wandering. In the current study, we evaluated the association of this connectome-based, mind-wandering model with cerebral spinal fluid (CSF) p-tau/Aβ42 ratio in 289 older adults from the Alzheimer’s Disease NeuroImaging Initiative (ADNI). Moreover, we examined if this model was similarly associated with individual differences in composite measures of global cognition, episodic memory, and executive functioning. Edges from the high response time variability model were significantly associated with CSF p-tau/Aβ ratio. Furthermore, connectivity strength within edges associated with high response time variability was negatively associated with global cognition and episodic memory functioning. This study provides the first empirical support for a link between an objective neuromarker of mind-wandering and AD pathophysiology. Given the observed association between mind-wandering and cognitive functioning in older adults, interventions targeted at reducing mind-wandering, particularly before the onset of AD pathogenesis, may make a significant contribution to the prevention of AD-related cognitive decline.

Osteopontin: A novel marker of pre-symptomatic sporadic Alzheimer's disease.

We investigate the role of osteopontin (OPN) in participants with Pre-symptomatic Alzheimer's disease (AD), mild cognitive impairment (MCI), and in AD brains. Cerebrospinal fluid (CSF) OPN, AD, and synaptic biomarker levels were measured in 109 cognitively unimpaired (CU), parental-history positive Pre-symptomatic Evaluation of Experimental or Novel Treatments for Alzheimer's Disease (PREVENT-AD) participants, and in 167 CU and 399 participants with MCI from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. OPN levels were examined as a function of amyloid beta (Aβ) and tau positivity. Survival analyses investigated the link between OPN and rate of conversion to AD. In PREVENT-AD, CSF OPN was positively correlated with synaptic biomarkers. In PREVENT-AD and ADNI, OPN was elevated in CSF Aβ42/40(+)/total tau(+) and CSF Aβ42/40(+)/phosphorylated tau181(+) individuals. In ADNI, OPN was increased in Aβ(+) positron emission tomography (PET) and tau(+) PET individuals, and associated with an accelerated rate of conversion to AD. OPN was elevated in autopsy-confirmed AD brains. Strong associations between CSF OPN and key markers of AD pathophysiology suggest a significant role for OPN in tau neurobiology, particularly in the early stages of the disease. In the Pre-symptomatic Evaluation of Experimental or Novel Treatments for Alzheimer's Disease cohort, we discovered that cerebrospinal fluid (CSF) osteopontin (OPN) levels can indicate early synaptic dysfunction, tau deposition, and neuronal loss in cognitively unimpaired elderly with a parental history. CSF OPN is elevated in amyloid beta(+) positron emission tomography (PET) and tau(+) PET individuals. Elevated CSF OPN is associated with an accelerated rate of conversion to Alzheimer's disease (AD). Elevated CSF OPN is associated with an accelerated rate of cognitive decline on the Alzheimer's Disease Assessment Scale-Cognitive subscale 13, Montreal Cognitive Assessment, Mini-Mental State Examination, and Clinical Dementia Rating Scale Sum of Boxes. OPN mRNA and protein levels are significantly upregulated in the frontal cortex of autopsy-confirmed AD brains.

MRI Signature of α-Synuclein Pathology in Asymptomatic Stages and a Memory Clinic Population

The lack of an in vivo measure for α-synuclein (α-syn) pathology until recently has limited thorough characterization of its brain atrophy pattern, especially during early disease stages. To assess the association of state-of-the-art cerebrospinal fluid (CSF) seed amplification assays (SAA) α-syn positivity (SAA α-syn+) with magnetic resonance imaging (MRI) structural measures, across the continuum from clinically unimpaired (CU) to cognitively impaired (CI) individuals, in 3 independent cohorts, and separately in CU and CI individuals, the latter reflecting a memory clinic population. Cross-sectional data were used from the Swedish BioFINDER-2 study (inclusion, 2017-2023) as the discovery cohort and the Swedish BioFINDER-1 study (inclusion, 2007-2015) and Alzheimer's Disease Neuroimaging Initiative (ADNI; inclusion 2005-2022) as replication cohorts. All cohorts are from multicenter studies, but the BioFINDER cohorts used 1 MRI scanner. CU and CI individuals fulfilling inclusion criteria and without missing data points in relevant metrics were included in the study. All analyses were performed from 2023 to 2024. Presence of α-syn pathology, estimated by baseline CSF SAA α-syn. The primary outcomes were cross-sectional structural MRI measures either through voxel-based morphometry (VBM) or regions of interest (ROI) including an automated pipeline for cholinergic basal forebrain nuclei CH4/4p (nucleus basalis of Meynert [NBM]) and CH1/2/3. Secondary outcomes were domain-specific cross-sectional cognitive measures. Analyses were adjusted for CSF biomarkers of Alzheimer pathology. A total of 2961 participants were included in this study: 1388 (mean [SD] age, 71 [10] years; 702 female [51%]) from the BioFINDER-2 study, 752 (mean [SD] age, 72 [6] years; 406 female [54%]) from the BioFINDER-1 study, and 821 (mean [SD] age, 75 [8] years; 449 male [55%]) from ADNI. In the BioFINDER-2 study, VBM analyses in the whole cohort revealed a specific association between SAA α-syn+ and the cholinergic NBM, even when adjusting for Alzheimer copathology. ROI-based analyses in the BioFINDER-2 study focused on regions involved in the cholinergic system and confirmed that SAA α-syn+ was indeed independently associated with smaller NBM (β = -0.271; 95% CI, -0.399 to -0.142; P <.001) and CH1/2/3 volumes (β = -0.227; 95% CI, -0.377 to -0.076; P =.02). SAA α-syn+ was also independently associated with smaller NBM volumes in the separate CU (β = -0.360; 95% CI, -0.603 to -0.117; P =.03) and CI (β = -0.251; 95% CI, -0.408 to -0.095; P =.02) groups. Overall, the association between SAA α-syn+ and NBM volume was replicated in the BioFINDER-1 study and ADNI cohort. In CI individuals, NBM volumes partially mediated the association of SAA α-syn+ with attention/executive impairments in all cohorts (BioFINDER-2, β = -0.017; proportion-mediated effect, 7%; P =.04; BioFINDER-1, β = -0.096; proportion-mediated effect, 19%; P =.04; ADNI, β = -0.061; proportion-mediated effect, 20%; P =.007). In this cohort study, SAA α-syn+ was consistently associated with NBM atrophy already during asymptomatic stages. Further, in memory clinic CI populations, SAA α-syn+ was associated with NBM atrophy, which partially mediated α-syn-induced attention/executive impairment.

Advancing Tau PET Quantification in Alzheimer Disease with Machine Learning: Introducing THETA, a Novel Tau Summary Measure.

Alzheimer disease (AD) exhibits spatially heterogeneous 3- or 4-repeat tau deposition across participants. Our overall goal was to develop an automated method to quantify the heterogeneous burden of tau deposition into a single number that would be clinically useful. Methods: We used tau PET scans from 3 independent cohorts: the Mayo Clinic Study of Aging and Alzheimer's Disease Research Center (Mayo, n = 1,290), the Alzheimer's Disease Neuroimaging Initiative (ADNI, n = 831), and the Open Access Series of Imaging Studies (OASIS-3, n = 430). A machine learning binary classification model was trained on Mayo data and validated on ADNI and OASIS-3 with the goal of predicting visual tau positivity (as determined by 3 raters following Food and Drug Administration criteria for 18F-flortaucipir). The machine learning model used region-specific SUV ratios scaled to cerebellar crus uptake. We estimated feature contributions based on an artificial intelligence-explainable method (Shapley additive explanations) and formulated a global tau summary measure, Tau Heterogeneity Evaluation in Alzheimer's Disease (THETA) score, using SUV ratios and Shapley additive explanations for each participant. We compared the performance of THETA with that of commonly used meta-regions of interest (ROIs) using the Mini-Mental State Examination, the Clinical Dementia Rating-Sum of Boxes, clinical diagnosis, and histopathologic staging. Results: The model achieved a balanced accuracy of 95% on the Mayo test set and at least 87% on the validation sets. It classified tau-positive and -negative participants with an AUC of 1.00, 0.96, and 0.94 on the Mayo, ADNI, and OASIS-3 cohorts, respectively. Across all cohorts, THETA showed a better correlation with the Mini-Mental State Examination and the Clinical Dementia Rating-Sum of Boxes (ρ ≥ 0.45, P < 0.05) than did meta-ROIs (ρ < 0.44, P < 0.05) and discriminated between participants who were cognitively unimpaired and those who had mild cognitive impairment with an effect size of 10.09, compared with an effect size of 3.08 for meta-ROIs. Conclusion: Our proposed approach identifies positive tau PET scans and provides a quantitative summary measure, THETA, that effectively captures heterogeneous tau deposition observed in AD. The application of THETA for quantifying tau PET in AD exhibits great potential.

Analyzing heterogeneity in biomarker discriminative performance through partial time-dependent receiver operating characteristic curve modeling.

This study investigates the heterogeneity of a biomarker's discriminative performance for predicting subsequent time-to-event outcomes across different patient subgroups. While the area under the curve (AUC) for the time-dependent receiver operating characteristic curve is commonly used to assess biomarker performance, the partial time-dependent AUC (PAUC) provides insights that are often more pertinent for population screening and diagnostic testing. To achieve this objective, we propose a regression model tailored for PAUC and develop two distinct estimation procedures for discrete and continuous covariates, employing a pseudo-partial likelihood method. Simulation studies are conducted to assess the performance of these procedures across various scenarios. We apply our model and inference procedure to the Alzheimer's Disease Neuroimaging Initiative data set to evaluate potential heterogeneities in the discriminative performance of biomarkers for early Alzheimer's disease diagnosis based on patients' characteristics.