Alzheimer's Disease Neuroimaging Initiative Research Articles

Multi-Modal Diagnosis of Alzheimer's Disease using Interpretable Graph Convolutional Networks.

The interconnection between brain regions in neurological disease encodes vital information for the advancement of biomarkers and diagnostics. Although graph convolutional networks are widely applied for discovering brain connection patterns that point to disease conditions, the potential of connection patterns that arise from multiple imaging modalities has yet to be fully realized. In this paper, we propose a multi-modal sparse interpretable GCN framework (SGCN) for the detection of Alzheimer's disease (AD) and its prodromal stage, known as mild cognitive impairment (MCI). In our experimentation, SGCN learned the sparse regional importance probability to find signature regions of interest (ROIs), and the connective importance probability to reveal disease-specific brain network connections. We evaluated SGCN on the Alzheimer's Disease Neuroimaging Initiative database with multi-modal brain images and demonstrated that the ROI features learned by SGCN were effective for enhancing AD status identification. The identified abnormalities were significantly correlated with AD-related clinical symptoms. We further interpreted the identified brain dysfunctions at the level of large-scale neural systems and sex-related connectivity abnormalities in AD/MCI. The salient ROIs and the prominent brain connectivity abnormalities interpreted by SGCN are considerably important for developing novel biomarkers. These findings contribute to a better understanding of the network-based disorder via multi-modal diagnosis and offer the potential for precision diagnostics. The source code is available at https://github.com/Houliang-Zhou/SGCN.

Hormone therapy is associated with lower Alzheimer’s disease tau biomarkers in post-menopausal females -evidence from two independent cohorts

BackgroundFemales represent approximately 70% of the Alzheimer’s disease (AD) cases and the literature has proposed a connection between the decreased estrogen levels during menopause and an increased AD risk. Previous investigations have predominantly focused on assessing how hormone therapy (HT) affects the likelihood of AD development and cognitive deterioration. However, as the research framework has shifted toward a biomarker-defined AD and alterations in specific biomarkers could take place years before cognitive decline becomes discernible, it is crucial to examine how HT influences AD biomarkers. The main goal of this study was to evaluate the impact of HT on AD biomarker-informed pathophysiology in both cognitively unimpaired (CU) and cognitively impaired (CI) post-menopausal females across the aging and AD spectrum.MethodsThis cross-sectional study included post-menopausal females without HT history (HT-) and with HT (HT+) at the time of PET imaging assessment from two cohorts: the Translational Biomarkers in Aging and Dementia (TRIAD) cohort, and the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Participants underwent magnetic resonance imaging (MRI), positron emission tomography (PET) and biofluid collection. Voxel-based t-tests were performed to assess the differences in amyloid-β (Aβ) and tau neurofibrillary tangles (NFTs) loads between HT- and HT + females. Linear regression models with interaction terms were also conducted to examine the interactive effects of HT and Aβ-PET on regional tau-PET.ResultsHT + females demonstrated significantly lower tau-PET standardized uptake value ratio (SUVR) in Braak I-II ROIs (P < 0.05, Hedges’ g = 0.73), Braak III-IV ROIs (P < 0.0001, Hedges’ g = 0.74) and Braak V-VI ROIs (P < 0.0001, Hedges’ g = 0.69) compared to HT- females. HT + females also showed significantly lower CSF p-tau181 (P < 0.001) and plasma p-tau181 (P < 0.0001) concentrations. Additionally, results from multivariate linear regression models indicated that HT interacts with cortical Aβ and is associated with lower regional NFT load.ConclusionsOverall, findings from this observational study suggest that HT is associated with lower tau neuroimaging and fluid biomarkers in postmenopausal females. Due to the close link between tau and cognition, this study highlights the need for large randomized controlled trials designed to systemically study the influences of HT on AD biomarkers and disease progression.

Axonal injury, sleep disturbances, and memory following traumatic brain injury.

Traumatic brain injury (TBI) is associated with sleep deficits, but it is not clear why some report sleep disturbances and others do not. The objective of this study was to assess the associations between axonal injury, sleep, and memory in chronic and acute TBI. Data were acquired from two independent datasets which included 156 older adult veterans (69.8 years) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) with prior moderate-to-severe TBIs and 90 (69.2 years) controls and 374 (39.6 years) from Transforming Research and Clinical Knowledge in TBI (TRACK-TBI) with a recent mild TBI (mTBI) and 87 controls (39.6 years), all who completed an MRI, memory assessment, and sleep questionnaire. Older adults with a prior TBI had a significant association between axonal injury and sleep disturbances [β = 9.52, 95% CI (4.1, 14.9), p = 0.01]. Axonal injury predicted changes in memory over 1-year in TBI [β = -8.72, 95% CI (-18, -2.7), p = 0.03]. We externally validated those findings in TRACK-TBI where axonal injury within 2 weeks after mTBI was significantly associated with higher sleep disturbances in the TBI group at 2 weeks[β = -7.2, 95% CI (-14, -0.50), p = 0.04], 6 months [β = -16, 95% CI (-24, -7.6), p ≤ 0.01], and 12 months post-injury [β = -11, 95% CI (-19, -0.85), p = 0.03]. These associations were not significant in controls. Axonal injury, specifically to the left anterior internal capsule is robustly associated with sleep disturbances in multiple TBI populations. Early assessment of axonal injury following mTBI could identify those at risk for persistent sleep disturbances following injury.

Association of modified dementia risk score with cerebrospinal fluid biomarkers and cognition in adults without dementia

IntroductionThis study aimed to investigate the cognitive profile and prospective cognitive changes in non-demented adults with elevated Modified Dementia Risk Scores (MDRS), while also exploring the potential relationship between these associations and cerebrospinal fluid (CSF) biomarkers of Alzheimer’s disease (AD) pathology and neuroinflammation.MethodsWithin the Chinese Alzheimer’s Biomarker and LifestylE (CABLE) database, 994 participants without dementia were assessed on MDRS, CSF biomarkers and cognition. We examined the associations of the MDRS with CSF biomarkers and cognitive scores using linear regressions. Causal mediation analyses were conducted to analyze the associations among MDRS, brain pathologies, and cognition. The Alzheimer’s Disease Neuroimaging Initiative (ADNI) study was used to validate the mediation effects and to investigate the longitudinal association between MDRS and cognitive decline.ResultsThe results revealed that higher MDRS were linked to poorer cognitive performance (Model 1: PFDR &amp;lt; 0.001; Model 2: PFDR &amp;lt; 0.001) and increases in CSF levels of phosphorylated tau (P-tau, Model 1: PFDR &amp;lt; 0.001; Model 2: PFDR &amp;lt; 0.001), total tau (T-tau, Model 1: PFDR &amp;lt; 0.001; Model 2: PFDR &amp;lt; 0.001), P-tau/Aβ42 ratio (Model 1: PFDR = 0.023; Model 2: PFDR = 0.028), T-tau/Aβ42 ratio (Model 1: PFDR &amp;lt; 0.001; Model 2: PFDR &amp;lt; 0.001) and soluble triggering receptor expressed on myeloid cells 2 (sTrem2, Model 1: PFDR &amp;lt; 0.001; Model 2: PFDR &amp;lt; 0.001) in the CABLE study. The impact of MDRS on cognition was partially mediated by neuroinflammation and tau pathology. These mediation effects were replicated in the ADNI study. Baseline MDRS were significantly associated with future cognitive decline, as indicated by lower scores on the Mini-Mental State Examination (MMSE, Model 1: PFDR = 0.045; Model 2: PFDR &amp;lt; 0.001), ADNI composite memory score (ADNI-MEM, Model 1: PFDR = 0.005; Model 2: PFDR &amp;lt; 0.001), ADNI composite executive function score (ADNI-EF, Model 1: PFDR = 0.045; Model 2: PFDR &amp;lt; 0.001), and higher score on the Alzheimer’s Disease Assessment Scale (ADAS13, Model 1: PFDR = 0.045; Model 2: PFDR &amp;lt; 0.001).DiscussionThe findings of this study revealed significant associations between MDRS and cognitive decline, suggesting a potential role of tau pathology and neuroinflammation in the link between MDRS and poorer cognitive performance in individuals without dementia. Consequently, the MDRS holds promise as a tool for targeted preventive interventions in individuals at high risk of cognitive impairment.

Beyond Hypertension: Examining Variable Blood Pressure's Role in Cognition and Brain Structure.

Hypertension or high blood pressure (BP) is one of the twelve modifiable risk factors that contribute to 40% of dementia cases that could be delayed or prevented. Although hypertension is associated with cognitive decline and structural brain changes, less is known about the long-term association between variable BP and cognitive/brain changes. This study examined the relationship between variable BP and longitudinal cognitive, white matter hyperintensity (WMH), gray matter (GM), and white matter (WM) volume change over time and post-mortem neuropathology. A total of 4,606 participants (32,776 follow-ups) from RADC Research Resource Sharing Hub (RUSH) and 2,114 participants (9,827 follow-ups) from Alzheimer's Disease Neuroimaging Initiative (ADNI) were included. Participants were divided into one of three groups: normal, high, or variable BP. Linear mixed models investigated the relationship between BP and cognition, brain structure, and neuropathology. Older adults with variable BP exhibited the highest rate of cognitive decline followed by high and then normal BP. Increased GM volume loss and WMH burden was also observed in variable compared to high and normal BP. In post-mortem neuropathology, both variable and high BP had increased rates compared to normal BP. All the results were consistent across the RUSH and ADNI participants, supporting the generalizability of the findings. Damages potentially associated with variable BP may reduce resilience to future dementia related pathology and increased risk of dementia more than that caused by high BP. Improved treatment and management of variable BP may help reduce cognitive decline in the older adult population.

The Association Between Surgery and Mild Cognitive Impairment: Insight from a Case-Control Study.

Surgery may be associated with postoperative cognitive impairment in elder participants, yet the extent of its association with mild cognitive impairment (MCI) remains undetermined. To determine the relationship between surgery and MCI. The data of participants from the Alzheimer's Disease Neuroimaging Initiative were analyzed, including individuals with MCI or normal cognition. We focused on surgeries conducted after the age of 45, categorized by the number of surgeries, surgical risk, and the age at which surgeries occurred. Multivariable logistic regression was employed to determine the association between surgery and the development of MCI. The study is comprised of 387 individuals with MCI and 578 cognitively normal individuals. The overall surgery exposure (adjusted OR = 1.14, [95% CI 0.83, 1.56], p = 0.43) and the number of surgeries (adjusted OR = 0.92 [0.62, 1.36], p = 0.67 for single exposure, adjusted OR = 1.12 [0.71, 1.78], p = 0.63 for two exposures, adjusted OR = 1.38 [0.95, 2.01], p = 0.09 for three or more exposures compared to no exposure as the reference) were not associated with the development of MCI. However, high-risk surgeries (adjusted OR = 1.79 [1.00, 3.21], p = 0.049) or surgeries occurring after the age of 75 (adjusted OR = 2.01 [1.03, 3.90], p = 0.041) were associated with a greater risk of developing MCI. High risk surgeries occurring at an older age contribute to the development of MCI, indicating a complex of mechanistic insights for the development of postoperative cognitive impairment.

Assessing the Stability of Clusters of Neuropsychiatric Symptoms in Alzheimer's Disease and Mild Cognitive Impairment.

The aim of the study was to investigate the factors that underpin neuropsychiatric symptoms and how they might evolve over time in people with Mild Cognitive Impairment (MCI) and Alzheimer's disease (AD) dementia. Neuropsychiatric symptoms are psychiatric and behavioural manifestations that occur in people with AD. These are highly prevalent along the continuum of the disease, including at the stage of MCI, as well as before cognitive decline. Various small- and large-scale projects have investigated the underlying factors that underpin these symptoms; however, the identification of clear clusters is still a matter of debate; furthermore, no study has investigated how the clusters might change across the development of AD pathology by comparing different time points. Our objective was to investigate the factors that underpin neuropsychiatric symptoms in Alzheimer's disease (AD) and Mild Cognitive Impairment (MCI) and to assess how the loadings might differ based on considerations such as the disease stage of the samples. Data was obtained from the Alzheimer's Disease Neuroimaging Initiative database (adni. loni.usc.edu), using scores from the Neuropsychiatric Inventory, followed up yearly from baseline until month 72. Participant groups included those with MCI or AD dementia, or a mixture of both, with all participants presenting with at least one neuropsychiatric symptom. A series of exploratory Principal Component and Factor (Principal Axis) Analyses were performed using Direct Oblimin rotation. The best-fitting structure was interpreted for each time point. A consistent, unique structure could not be identified, as the factors were unstable over time, both within the MCI and AD groups. However, some symptoms showed a tendency to load on the same factors across most measurements (i.e., agitation with irritability, depression with anxiety, elation with disinhibition, delusions with hallucinations). Although the analyses revealed some degree of co-occurrence of neuropsychiatric symptoms across time points/samples, there was also considerable variation. In the AD group, more discrete syndromes were evident at the early time points, whereas a more complex picture of co-occurring symptoms, with differences likely reflecting disease staging, was seen at later time points. As a clear and distinctive factor structure was not consistently identified across time points/ samples, this highlights the potential importance of sample selection (e.g., disease stage and/or heterogeneity) when studying, for example, the neurobiological underpinnings of neuropsychiatric symptoms.

Detection of Alzheimer’s disease using Otsu thresholding with tunicate swarm algorithm and deep belief network

Introduction: Alzheimer’s Disease (AD) is a degenerative brain disorder characterized by cognitive and memory dysfunctions. The early detection of AD is necessary to reduce the mortality rate through slowing down its progression. The prevention and detection of AD is the emerging research topic for many researchers. The structural Magnetic Resonance Imaging (sMRI) is an extensively used imaging technique in detection of AD, because it efficiently reflects the brain variations.Methods: Machine learning and deep learning models are widely applied on sMRI images for AD detection to accelerate the diagnosis process and to assist clinicians for timely treatment. In this article, an effective automated framework is implemented for early detection of AD. At first, the Region of Interest (RoI) is segmented from the acquired sMRI images by employing Otsu thresholding method with Tunicate Swarm Algorithm (TSA). The TSA finds the optimal segmentation threshold value for Otsu thresholding method. Then, the vectors are extracted from the RoI by applying Local Binary Pattern (LBP) and Local Directional Pattern variance (LDPv) descriptors. At last, the extracted vectors are passed to Deep Belief Networks (DBN) for image classification.Results and Discussion: The proposed framework achieves supreme classification accuracy of 99.80% and 99.92% on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and Australian Imaging, Biomarker and Lifestyle flagship work of ageing (AIBL) datasets, which is higher than the conventional detection models.

Detection of Alzheimer's disease using Otsu thresholding with tunicate swarm algorithm and deep belief network.

Introduction: Alzheimer's Disease (AD) is a degenerative brain disorder characterized by cognitive and memory dysfunctions. The early detection of AD is necessary to reduce the mortality rate through slowing down its progression. The prevention and detection of AD is the emerging research topic for many researchers. The structural Magnetic Resonance Imaging (sMRI) is an extensively used imaging technique in detection of AD, because it efficiently reflects the brain variations. Methods: Machine learning and deep learning models are widely applied on sMRI images for AD detection to accelerate the diagnosis process and to assist clinicians for timely treatment. In this article, an effective automated framework is implemented for early detection of AD. At first, the Region of Interest (RoI) is segmented from the acquired sMRI images by employing Otsu thresholding method with Tunicate Swarm Algorithm (TSA). The TSA finds the optimal segmentation threshold value for Otsu thresholding method. Then, the vectors are extracted from the RoI by applying Local Binary Pattern (LBP) and Local Directional Pattern variance (LDPv) descriptors. At last, the extracted vectors are passed to Deep Belief Networks (DBN) for image classification. Results and Discussion: The proposed framework achieves supreme classification accuracy of 99.80% and 99.92% on the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Australian Imaging, Biomarker and Lifestyle flagship work of ageing (AIBL) datasets, which is higher than the conventional detection models.

Exploring the Genetic Heterogeneity of Alzheimer's Disease: Evidence for Genetic Subtypes.

Alzheimer's disease (AD) exhibits considerable phenotypic heterogeneity, suggesting the potential existence of subtypes. AD is under substantial genetic influence, thus identifying systematic variation in genetic risk may provide insights into disease origins. We investigated genetic heterogeneity in AD risk through a multi-step analysis. We performed principal component analysis (PCA) on AD-associated variants in the UK Biobank (AD cases = 2,739, controls = 5,478) to assess structured genetic heterogeneity. Subsequently, a biclustering algorithm searched for distinct disease-specific genetic signatures among subsets of cases. Replication tests were conducted using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset (AD cases = 500, controls = 470). We categorized a separate set of ADNI individuals with mild cognitive impairment (MCI; n = 399) into genetic subtypes and examined cognitive, amyloid, and tau trajectories. PCA revealed three distinct clusters ("constellations") driven primarily by different correlation patterns in a region of strong LD surrounding the MAPT locus. Constellations contained a mixture of cases and controls, reflecting disease-relevant but not disease-specific structure. We found two disease-specific biclusters among AD cases. Pathway analysis linked bicluster-associated variants to neuron morphogenesis and outgrowth. Disease-relevant and disease-specific structure replicated in ADNI, and bicluster 2 exhibited increased cerebrospinal fluid p-tau and cognitive decline over time. This study unveils a hierarchical structure of AD genetic risk. Disease-relevant constellations may represent haplotype structure that does not increase risk directly but may alter the relative importance of other genetic risk factors. Biclusters may represent distinct AD genetic subtypes. This structure is replicable and relates to differential pathological accumulation and cognitive decline over time.

Stress testing the Centiloid: Precision and variability of PET quantification of amyloid pathology.

Assessing the potential sources of bias and variability of the Centiloid (CL) scale is fundamental for its appropriate clinical application. We included 533 participants from AMYloid imaging to Prevent Alzheimer's Disease (AMYPAD DPMS) and Alzheimer's Disease Neuroimaging Initiative (ADNI) cohorts. Thirty-two CL pipelines were created using different combinations of reference region (RR), RR and target types, and quantification spaces. Generalized estimating equations stratified by amyloid positivity were used to assess the impact of the quantification pipeline, radiotracer, age, brain atrophy, and harmonization status on CL. RR selection and RR type impact CL the most, particularly in amyloid-negative individuals. The standard CL pipeline with the whole cerebellum as RR is robust against brain atrophy and differences in image resolution, with 95% confidence intervals below±3.95 CL for amyloid beta positivity cutoffs (CL<24). The standard CL pipeline is recommended for most scenarios. Confidence intervals should be considered when operationalizing CL cutoffs in clinical and research settings. We developed a framework for evaluating Centiloid (CL) variability to different factors. Reference region selection and delineation had the highest impact on CL values. Whole cerebellum (WCB) and whole cerebellum plus brainstem (WCB+BSTM) as reference regions yielded consistent results across tracers. The standard CL pipeline is robust against atrophy and image resolution variation. Estimated within- and between-pipeline variability (95% confidence interval) in absolute CL units.

The role of the Alzheimer's Disease Neuroimaging Initiative in establishing the Dominantly Inherited Alzheimer Network.

The Dominantly Inherited Alzheimer Network (DIAN) initially was funded by the National Institute on Aging (NIA) in 2008 and thus was able to adopt and incorporate the protocols developed by the Alzheimer's Disease Neuroimaging Initiative (ADNI) that had been established by the NIA in 2004. The use of ADNI protocols for DIAN neuroimaging studies and assays of biological fluids for Alzheimer disease (AD) biomarkers permitted examination of the hypothesis that autosomal dominant AD (ADAD), studied by DIAN, and "sporadic" late-onset AD (LOAD), studied by ADNI, shared the same pathobiological construct. In a collaborative effort, the longitudinal DIAN and ADNI databases were compared and the findings supported the conclusion that ADAD and LOAD share a similar pathophysiology. The importance of the DIAN study thus is amplified by its relevance to LOAD, as characterized by the "parent" ADNI program.

Structure focused neurodegeneration convolutional neural network for modelling and classification of Alzheimer’s disease

Alzheimer’s disease (AD), the predominant form of dementia, is a growing global challenge, emphasizing the urgent need for accurate and early diagnosis. Current clinical diagnoses rely on radiologist expert interpretation, which is prone to human error. Deep learning has thus far shown promise for early AD diagnosis. However, existing methods often overlook focal structural atrophy critical for enhanced understanding of the cerebral cortex neurodegeneration. This paper proposes a deep learning framework that includes a novel structure-focused neurodegeneration CNN architecture named SNeurodCNN and an image brightness enhancement preprocessor using gamma correction. The SNeurodCNN architecture takes as input the focal structural atrophy features resulting from segmentation of brain structures captured through magnetic resonance imaging (MRI). As a result, the architecture considers only necessary CNN components, which comprises of two downsampling convolutional blocks and two fully connected layers, for achieving the desired classification task, and utilises regularisation techniques to regularise learnable parameters. Leveraging mid-sagittal and para-sagittal brain image viewpoints from the Alzheimer’s disease neuroimaging initiative (ADNI) dataset, our framework demonstrated exceptional performance. The para-sagittal viewpoint achieved 97.8% accuracy, 97.0% specificity, and 98.5% sensitivity, while the mid-sagittal viewpoint offered deeper insights with 98.1% accuracy, 97.2% specificity, and 99.0% sensitivity. Model analysis revealed the ability of SNeurodCNN to capture the structural dynamics of mild cognitive impairment (MCI) and AD in the frontal lobe, occipital lobe, cerebellum, temporal, and parietal lobe, suggesting its potential as a brain structural change digi-biomarker for early AD diagnosis. This work can be reproduced using code we made available on GitHub.

Diffusion model enables quantitative CBF analysis of Alzheimer's Disease.

Cerebral blood flow (CBF) measured by arterial spin labeling (ASL) is a promising biomarker for Alzheimer's Disease (AD). ASL data from multiple vendors were included in the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. However, the M0 images were missing in Siemens ASL data, prohibiting CBF quantification. Here, we utilized a generative diffusion model to impute the missing M0 and validated generated CBF data with acquired data from GE. A conditional latent diffusion model was trained to generate the M0 image and validate it on an in-house dataset ( N =55) based on image similarity metrics, accuracy of CBF quantification, and consistency with the physical model. This model was then applied to the ADNI dataset (Siemens: N =211) to impute the missing M0 for CBF calculation. We further compared the imputed data (Siemens) and acquired data (GE) regarding regional CBF differences by AD stages, their classification accuracy for AD prediction, and CBF trajectory slopes estimated by a mixed effect model. The trained diffusion model generated the M0 image with high fidelity (Structural similarity index, SSIM=0.924±0.019; peak signal-to-noise ratio, PSNR=33.348±1.831) and caused minimal bias in CBF values (mean difference in whole brain is 1.07±2.12ml/100g/min). Both generated and acquired CBF data showed similar differentiation patterns by AD stages, similar classification performance, and decreasing slopes with AD progression in specific AD-related regions. Generated CBF data also improved accuracy in classifying AD stages compared to qualitative perfusion data. This study shows the potential of diffusion models for imputing missing modalities for large-scale studies of CBF variation with AD.

Head-to-head comparison of leading blood tests for Alzheimer's disease pathology.

Blood tests have the potential to improve the accuracy of Alzheimer disease (AD) clinical diagnosis, which will enable greater access to AD-specific treatments. This study compared leading commercial blood tests for amyloid pathology and other AD-related outcomes. Plasma samples from the Alzheimers Disease Neuroimaging Initiative were assayed with AD blood tests from C2N Diagnostics, Fujirebio Diagnostics, ALZPath, Janssen, Roche Diagnostics, and Quanterix. Outcomes measures were amyloid positron emission tomography (PET), tau PET, cortical thickness, and dementia severity. Logistic regression models assessed the classification accuracies of individual or combined plasma biomarkers for binarized outcomes, and Spearman correlations evaluated continuous relationships between individual plasma biomarkers and continuous outcomes. Measures of plasma p-tau217, either individually or in combination with other plasma biomarkers, had the strongest relationships with all AD outcomes. This study identified the plasma biomarker analytes and assays that most accurately classified amyloid pathology and other AD-related outcomes.

Pattern of heterogeneity in normal brain ageing: screening for mild cognitive impairment and its risk of progression with a radiomics model.

This study aimed to develop a normal brain ageing model based on magnetic resonance imaging and radiomics, therefore identifying radscore, an imaging indicator representing white matter heterogeneity and exploring the significance of radscore in detecting people's cognitive changes. Three hundred sixty cognitively normal (CN) subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database and 105 CN subjects from the Parkinson's Progression Markers Initiative database were used to develop the model. In ADNI, 230 mild cognitive impairment (MCI) subjects were matched with 230 CN old-aged subjects to evaluate their heterogeneity difference. One hundred four MCI subjects with 48months of follow-up were divided into low and high heterogeneity groups. Kaplan-Meier survival curve analysis was used to observe the importance of heterogeneity results for predicting MCI progression. The area under the receiver operating characteristic curve of the model in the training, internal test and external test sets was 0.7503, 0.7512 and 0.7514, respectively. There was a significantly positive correlation between age and radscore of CN subjects (r = 0.501; P < .001). The radscore of MCI subjects was significantly higher than that of matched CN subjects (P < .001). The median radscore ratios of MCI to CN from four age groups (66-70y, 71-75y, 76-80y and 81-85y) were 1.611, 1.760, 1.340 and 1.266, respectively. The probability to progression of low and high heterogeneity groups had a significant difference (P = .002). When radscore is significantly higher than that of normal ageing, it is necessary to alert the possibility of cognitive impairment and deterioration.

Comparison of deep learning architectures for predicting amyloid positivity in Alzheimer's disease, mild cognitive impairment, and healthy aging, from T1-weighted brain structural MRI.

Abnormal β-amyloid (Aβ) accumulation in the brain is an early indicator of Alzheimer's disease (AD) and is typically assessed through invasive procedures such as PET (positron emission tomography) or CSF (cerebrospinal fluid) assays. As new anti-Alzheimer's treatments can now successfully target amyloid pathology, there is a growing interest in predicting Aβ positivity (Aβ+) from less invasive, more widely available types of brain scans, such as T1-weighted (T1w) MRI. Here we compare multiple approaches to infer Aβ + from standard anatomical MRI: (1) classical machine learning algorithms, including logistic regression, XGBoost, and shallow artificial neural networks, (2) deep learning models based on 2D and 3D convolutional neural networks (CNNs), (3) a hybrid ANN-CNN, combining the strengths of shallow and deep neural networks, (4) transfer learning models based on CNNs, and (5) 3D Vision Transformers. All models were trained on paired MRI/PET data from 1,847 elderly participants (mean age: 75.1 yrs. ± 7.6SD; 863 females/984 males; 661 healthy controls, 889 with mild cognitive impairment (MCI), and 297 with Dementia), scanned as part of the Alzheimer's Disease Neuroimaging Initiative. We evaluated each model's balanced accuracy and F1 scores. While further tests on more diverse data are warranted, deep learning models trained on standard MRI showed promise for estimating Aβ + status, at least in people with MCI. This may offer a potential screening option before resorting to more invasive procedures.

Regional-based static and dynamic alterations in Alzheimer disease: a longitudinal study.

Alzheimer disease (AD) leads to cognitive decline and alters functional connectivity (FC) in key brain regions. Resting-state functional magnetic resonance imaging (rs-fMRI) assesses these changes using static-FC for overall correlation and dynamic-FC for temporal variability. In AD, there is altered FC compared to normal conditions. The present study investigates possible region-specific functional abnormalities occurring longitudinally over 1 year. Our aim is to evaluate the potential usefulness of the static and dynamic approaches in identifying biomarkers of AD progression. The study involved 15 AD and 20 healthy participants from the Alzheimer's Disease Neuroimaging Initiative 2 (ADNI2) database, tracked over 2 visits within 1 year. Using constrained-independent component analysis, we assessed FC changes across 80-regions of interest in AD over the year, examining both static and dynamic conditions. The average regional FC decreased in AD compared to healthy subjects at baseline and after 1 year. The dynamic condition identifies similarities with a few additional changes in the FC compared to the static condition. In both analyses, the baseline assessment revealed reduced connectivity between the following regions: right-middle-occipital and left-superior-occipital, left-hippocampus and right-postcentral, left-lingual and left-fusiform, and precuneus and left-thalamus. Additionally, increased connectivity was found between the left-superior-occipital and precuneus regions. In the 1-year AD assessment, increased connectivity was noted between the right-superior-temporal-pole and right-insular, right-hippocampus and left-caudate, right-middle-occipital and right-superior-temporal-pole, and posterior-cingulate-cortex and middle-temporal-pole regions. Significant changes were observed at baseline in the frontal, occipital, and core basal-ganglia regions, progressing towards the temporal lobe and subcortical regions in the following year. After 1 year, we observed the aforementioned region-specific neurological differences in AD, significantly aiding diagnosis and disease tracking.

Regional-based static and dynamic alterations in Alzheimer disease: a longitudinal study.

Alzheimer disease (AD) leads to cognitive decline and alters functional connectivity (FC) in key brain regions. Resting-state functional magnetic resonance imaging (rs-fMRI) assesses these changes using static-FC for overall correlation and dynamic-FC for temporal variability. In AD, there is altered FC compared to normal conditions. The present study investigates possible region-specific functional abnormalities occurring longitudinally over 1 year. Our aim is to evaluate the potential usefulness of the static and dynamic approaches in identifying biomarkers of AD progression. The study involved 15 AD and 20 healthy participants from the Alzheimer's Disease Neuroimaging Initiative 2 (ADNI2) database, tracked over 2 visits within 1 year. Using constrained-independent component analysis, we assessed FC changes across 80-regions of interest in AD over the year, examining both static and dynamic conditions. The average regional FC decreased in AD compared to healthy subjects at baseline and after 1 year. The dynamic condition identifies similarities with a few additional changes in the FC compared to the static condition. In both analyses, the baseline assessment revealed reduced connectivity between the following regions: right-middle-occipital and left-superior-occipital, left-hippocampus and right-postcentral, left-lingual and left-fusiform, and precuneus and left-thalamus. Additionally, increased connectivity was found between the left-superior-occipital and precuneus regions. In the 1-year AD assessment, increased connectivity was noted between the right-superior-temporal-pole and right-insular, right-hippocampus and left-caudate, right-middle-occipital and right-superior-temporal-pole, and posterior-cingulate-cortex and middle-temporal-pole regions. Significant changes were observed at baseline in the frontal, occipital, and core basal-ganglia regions, progressing towards the temporal lobe and subcortical regions in the following year. After 1 year, we observed the aforementioned region-specific neurological differences in AD, significantly aiding diagnosis and disease tracking.

Amyloid-β but not tau accumulation is strongly associated with longitudinal cognitive decline.

Alzheimer's disease (AD) pathology is featured by the extracellular accumulation of amyloid-β (Aβ) plaques and intracellular tau neurofibrillary tangles in the brain. We studied whether Aβ and tau accumulation are independently associated with future cognitive decline in the AD continuum. Data were acquired from the Alzheimer's Disease Neuroimaging Initiative (ADNI) public database. A total of 1272 participants were selected based on the availability of Aβ-PET and CSF tau at baseline and of those 777 participants with follow-up visits. We found that Aβ-PET and CSF tau pathology were related to cognitive decline across the AD clinical spectrum, both as potential predictors for dementia progression. Among them, Aβ-PET (A + T- subjects) is an independent reliable predictor of longitudinal cognitive decline in terms of ADAS-13, ADNI-MEM, and MMSE scores rather than tau pathology (A - T+ subjects), indicating tau accumulation is not closely correlated with future cognitive impairment without being driven by Aβ deposition. Of note, a high percentage of APOE ε4 carriers with Aβ pathology (A+) develop poor memory and learning capacity. Interestingly, this condition is not recurrence in terms of the ADNI-MEM domain when adding APOE ε4 status. Finally, the levels of Aβ-PET SUVR related to glucose hypometabolism more strongly in subjects with A + T- than A - T+ both happen at baseline and longitudinal changes. In conclusion, Aβ-PET alone without tau pathology (A + T-) measure is an independent reliable predictor of longitudinal cognitive decline but may nonetheless forecast different status of dementia progression. However, tau accumulation alone without Aβ pathology background (A - T+) was not enough to be an independent predictor of cognitive worsening.