Alzheimer's Disease Neuroimaging Research Articles

Association of hemoglobin with plasma neurofilament light and white matter hyperintensities in Alzheimer's disease continuum

ObjectiveThis study aimed to investigate the association of hemoglobin (Hb) with axonal injury marker plasma neurofilament light (PNFL) and brain structure measurements in the Alzheimer's disease (AD) continuum. MethodsThe data used in this study were collected from the Alzheimer's Disease Neuroimaging Initiative database. Participants with cognitively normal, mild cognitive impairment, and mild dementia were included in the data analyses. All participants had available data on blood tests, PNFL levels, neuropsychological assessments, brain structure measurements (including volumes of white matter hyperintensities [WMH], hippocampus, gray matter, and total brain), and Aβ positron emission tomography standardized uptake value ratio (SUVR) at baseline. Aβ-positive was defined as SUVR threshold value > 1.11. Linear regression, restricted cubic spline, and causal mediation analyses were conducted to investigate the association of Hb concentration with PNFL levels and brain structure measurements. Stratified analyses were also employed to evaluate the association between Hb concentration and PNFL levels across different APOE genotypes and sex. ResultsIn the Aβ-positive group, Hb concentration was associated with PNFL levels (β = −0.022, p = 0.002). Stratified analyses suggested an association between Hb concentration and PNFL in APOE ɛ4 carriers (β = −0.031, p < 0.001) and males (β = −0.030, p < 0.001) but not in non-carriers and females (p > 0.05). Hb concentration was also associated with WMH volume (β = −0.04, p = 0.028), especially in APOE ɛ4 carriers, with mediation analysis revealing that PNFL mediated the association between Hb concentration and WMH volume. The association of Hb concentration with other brain structure measurements was minimal. ConclusionIn the AD continuum, Hb was associated with axonal injury marker PNFL and WMH volume, particularly in APOE ɛ4 carriers.

Ensemble Vision Transformer for Dementia Diagnosis.

In recent years, deep learning has gained momentum in computer-aided Alzheimer's Disease (AD) diagnosis. This study introduces a novel approach, Monte Carlo Ensemble Vision Transformer (MC-ViT), which develops an ensemble approach with Vision transformer (ViT). Instead of using traditional ensemble methods that deploy multiple learners, our approach employs a single vision transformer learner. By harnessing Monte Carlo sampling, this method produces a broad spectrum of classification decisions, enhancing the MC-ViT performance. This novel technique adeptly overcomes the limitation of 3D patch convolutional neural networks that only characterize partial of the whole brain anatomy, paving the way for a neural network adept at discerning 3D inter-feature correlations. Evaluations using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with 7199 scans and Open Access Series of Imaging Studies-3 (OASIS-3) with 1992 scans showcased its performance. With minimal preprocessing, our approach achieved an impressive 90% accuracy in AD classification, surpassing both 2D-slice CNNs and 3D CNNs.

Novel Hippocampus-Centered Methodology for Informative Instance Selection in Alzheimer's Disease Data

The quantity and quality of a dataset play a crucial role in the performance of prediction models. Increasing the amount of data increases the computational requirements and can introduce negligible variations, outliers, and noise. These significantly impact the model performance. Thus, instance selection techniques are crucial for building prediction models with informative data, reducing the dataset size, improving performance, and minimizing computational costs. This study proposed a novel methodology for identifying the most informative two-dimensional slices derived from magnetic resonance imaging (MRI) to study Alzheimer's disease. The efficacy of our methodology was attributable to a hippocampus-centered analysis using data from multiple atlases. The methodology was evaluated by constructing convolutional neural networks to identify Alzheimer's disease, using a consolidated dataset constructed from three standard datasets: Alzheimer's Disease Neuroimaging Initiative, Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing, and Open Access Series of Imaging Studies. The proposed methodology demonstrated a commendable subject-level classification accuracy of approximately (95.00%) when distinguishing between normal cognition and Alzheimer's.

Hippocampal volume maximally modulates the relationship between subsyndromal symptomatic depression and cognitive impairment in non-demented older adults

BackgroundSubsyndromal symptomatic depression (SSD) is associated with an elevated risk of cognitive impairment in non-demented older adults. Given that hippocampal and middle temporal gyrus atrophy have been shown to cause SSD, our study aimed to investigate the effect of hippocampal volume on the association between SSD and cognitive impairment. Methods338 non-demented older adults from the ADNI (Alzheimer's Disease Neuroimaging Initiative) cohort who underwent cognitive assessments, questionnaires on depressive symptoms and MRI brain were studied. SSD group is defined as a score of 1–5 based on Geriatric Depression Scale scores. We conducted causal mediation analyses to investigate the effect of hippocampal volume on cognitive performance cross-sectionally. ResultsThe SSD group displayed lower left and right hippocampal volume (p<0.01) than the non-SSD group. SSD was linked to poorer cognition and smaller hippocampal volume. We found that hippocampal volume partially mediated the effect of SSD on cognitive performance including the global cognition and the cognitive section of Alzheimer's Disease Assessment Scale, with mediation percentages ranging from 6.45 % to 30.46 %. In addition, we found that the thickness of the left middle temporal, right entorhinal and right fusiform gyrus, brain regions linked to AD, mediate the relationship between SSD and cognition with mediation percentages ranging from 8.67 % to 21.44 %. LimitationsOur article didn't differentiate between mild cognitive impairment and normal population. ConclusionThe associations of SSD and cognitive impairment are linked to alterations in Alzheimer's Disease related brain regions.

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.

Cost-Sensitive Weighted Contrastive Learning Based on Graph Convolutional Networks for Imbalanced Alzheimer's Disease Staging.

Identifying the progression stages of Alzheimer's disease (AD) can be considered as an imbalanced multi-class classification problem in machine learning. It is challenging due to the class imbalance issue and the heterogeneity of the disease. Recently, graph convolutional networks (GCNs) have been successfully applied in AD classification. However, these works did not handle the class imbalance issue in classification. Besides, they ignore the heterogeneity of the disease. To this end, we propose a novel cost-sensitive weighted contrastive learning method based on graph convolutional networks (CSWCL-GCNs) for imbalanced AD staging using resting-state functional magnetic resonance imaging (rs-fMRI). The proposed method is developed on a multi-view graph constructed by the functional connectivity (FC) and high-order functional connectivity (HOFC) features of the subjects. A novel cost-sensitive weighted contrastive learning procedure is proposed to capture discriminative information from the minority classes, encouraging the samples in the minority class to provide adequate supervision. Considering the heterogeneity of the disease, the weights of the negative pairs are introduced into contrastive learning and they are computed based on the distance to class prototypes, which are automatically learned from the training data. Meanwhile, the cost-sensitive mechanism is further introduced into contrastive learning to handle the class imbalance issue. The proposed CSWCL-GCN is evaluated on 720 subjects (including 184 NCs, 40 SMC patients, 208 EMCI patients, 172 LMCI patients and 116 AD patients) from the ADNI (Alzheimer's Disease Neuroimaging Initiative). Experimental results show that the proposed CSWCL-GCN outperforms state-of-the-art methods on the ADNI database.

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.

Neutrophil activation and related disrupted frontolimbic circuit may contribute to cognitive progression of minimal depressive symptoms in non-dementia elderly

BackgroundAs a more common but easily neglected disorder, minimal depressive symptoms (MDS), it is unclear whether and why it increases the risk of cognitive progression in non-dementia elderly. MethodsThe Alzheimer's disease Neuroimaging Initiative (ADNI) database was used to assign 1065 non-dementia elderly into normal control (n = 380) and MDS (n = 685) groups via the Geriatric Depression Scale (GDS). Blood neutrophils, transcriptomics and metabolomics, cerebrospinal fluid (CSF) proteomics, and magnetic resonance imaging (MRI) data were analyzed. ResultsMDS was found to increase the risk of cognitive progression independently of multiple psychological symptoms. Increased levels of blood neutrophils were associated with cognitive progression in MDS, as supported by neutrophil-related pathways by transcriptomic enrichment analysis and multi-omics joint analysis. A disrupted frontolimbic circuit was associated with neutrophil activation in MDS. LimitationsThe heterogeneity of the sample limited the generalizability of results, and the lack of follow-up data limited the research on the mechanism of neutrophil activation influencing cognitive function in MDS. ConclusionsCognitive progression occurs as early as the MDS stage. And this phenomenon may attribute to the neutrophil activation and the related disrupted frontolimbic circuit.

A prediction model of dementia conversion for mild cognitive impairment by combining plasma pTau181 and structural imaging features.

The early stages of Alzheimer's disease (AD) are no longer insurmountable. Therefore, identifying at-risk individuals is of great importance for precise treatment. We developed a model to predict cognitive deterioration in patients with mild cognitive impairment (MCI). Based on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, we constructed models in a derivation cohort of 761 participants with MCI (138 of whom developed dementia at the 36th month) and verified them in a validation cohort of 353 cognitively normal controls (54 developed MCI and 19 developed dementia at the 36th month). In addition, 1303 participants with available AD cerebrospinal fluid core biomarkers were selected to clarify the ability of the model to predict AD core features. We assessed 32 parameters as candidate predictors, including clinical information, blood biomarkers, and structural imaging features, and used multivariable logistic regression analysis to develop our prediction model. Six independent variables of MCI deterioration were identified: apolipoprotein E ε4 allele status, lower Mini-Mental State Examination scores, higher levels of plasma pTau181, smaller volumes of the left hippocampus and right amygdala, and a thinner right inferior temporal cortex. We established an easy-to-use risk heat map and risk score based on these risk factors. The area under the curve (AUC) for both internal and external validations was close to 0.850. Furthermore, the AUC was above 0.800 in identifying participants with high brain amyloid-β loads. Calibration plots demonstrated good agreement between the predicted probability and actual observations in the internal and external validations. We developed and validated an accurate prediction model for dementia conversion in patients with MCI. Simultaneously, the model predicts AD-specific pathological changes. We hope that this model will contribute to more precise clinical treatment and better healthcare resource allocation.

Convergence of Accelerated Brain Volume Decline in Normal Aging and Alzheimer's Disease Pathology.

Age represents the largest risk factor for Alzheimer's disease (AD) but is typically treated as a covariate. Still, there are similarities between brain regions affected in AD and those showing accelerated decline in normal aging, suggesting that the distinction between the two might fall on a spectrum. Our goal was to identify regions showing accelerated atrophy across the brain and investigate whether these overlapped with regions involved in AD or where related to amyloid. We used a longitudinal sample of 137 healthy older adults from the Alzheimer's Disease Neuroimaging Initiative (ADNI), who underwent magnetic resonance imaging (MRI). In addition, a total of 79 participants also had longitudinal positron emission tomography (PET) data. We computed linear-mixed effects models for brain regions declining faster than the average to investigate variability in the rate of change. 23 regions displayed a 0.5 standard deviation (SD) above average decline over 2 years. Of these, 52% overlapped with regions showing similar decline in a matched AD sample. Beyond this, the left precuneus, right superior frontal, transverse temporal, and superior temporal sulcus showed accelerated decline. Lastly, atrophy in the precuneus was associated with increased amyloid load. Accelerated decline in normal aging might contribute to the detection of early signs of AD among healthy individuals.

Multimodal Neuroimaging Fusion for Alzheimer's Disease: An Image Colorization Approach With Mobile Vision Transformer

ABSTRACTMultimodal neuroimaging, combining data from different sources, has shown promise in the classification of the Alzheimer's disease (AD) stage. Existing multimodal neuroimaging fusion methods exhibit certain limitations, which require advancements to enhance their objective performance, sensitivity, and specificity for AD classification. This study uses the use of a Pareto‐optimal cosine color map to enhance classification performance and visual clarity of fused images. A mobile vision transformer (ViT) model, incorporating the swish activation function, is introduced for effective feature extraction and classification. Fused images from the Alzheimer's Disease Neuroimaging Initiative (ADNI), the Whole Brain Atlas (AANLIB), and Open Access Series of Imaging Studies (OASIS) datasets, obtained through optimized transposed convolution, are utilized for model training, while evaluation is achieved using images that have not been fused from the same databases. The proposed model demonstrates high accuracy in AD classification across different datasets, achieving 98.76% accuracy for Early Mild Cognitive Impairment (EMCI) versus LMCI, 98.65% for Late Mild Cognitive Impairment (LMCI) versus AD, 98.60% for EMCI versus AD, and 99.25% for AD versus Cognitive Normal (CN) in the ADNI dataset. Similarly, on OASIS and AANLIB, the precision of the AD versus CN classification is 99.50% and 96.00%, respectively. Evaluation metrics showcase the model's precision, recall, and F1 score for various binary classifications, emphasizing its robust performance.

3-1-3 Weight averaging technique-based performance evaluation of deep neural networks for Alzheimer's disease detection using structural MRI.

Alzheimer's disease (AD) is a progressive neurological disorder. It is identified by the gradual shrinkage of the brain and the loss of brain cells. This leads to cognitive decline and impaired social functioning, making it a major contributor to dementia. While there are no treatments to reverse AD's progression, spotting the disease's onset can have a significant impact in the medical field.Deep learning (DL) has revolutionized medical image classification by automating feature engineering, removing the requirement for human experts in feature extraction. DL-based solutions are highly accurate but demand a lot of training data, which poses a common challenge. Transfer learning (TL) has gained attention for its knack for handling limited data and expediting model training. This study uses TL to classify AD using T1-weighted 3D Magnetic Resonance Imaging (MRI) from the Alzheimer's Disease Neuroimaging (ADNI) database. Four modified pre-trained deep neural networks (DNN), VGG16, MobileNet, DenseNet121, and NASNetMobile, are trained and evaluated on the ADNI dataset. The 3-1-3 weight averaging technique and fine-tuning improve the performance of the classification models. The evaluated accuracies for AD classification are VGG16: 98.75%; MobileNet: 97.5%; DenseNet: 97.5%; and NASNetMobile: 96.25%. The receiver operating characteristic (ROC), precision-recall (PR), and Kolmogorov-Smirnov (KS) statistic plots validate the effectiveness of the modified pre-trained model. Modified VGG16 excels with area under the curve (AUC) values of 0.99 for ROC and 0.998 for PR curves. The proposed approach shows effective AD classification by achieving high accuracy using the 3-1-3 weight averaging technique and fine-tuning.&#xD.

Can volumetric magnetic resonance imaging evaluations be helpful in the follow-up of cognitive functions in cognitively normal Parkinson's disease patients?

In this study, besides the evaluation of gray and white matter changes in cognitively normal Parkinson's disease (PD-CN) patients with volumetric magnetic resonance imaging (MRI) parameters, it was tried to show that some neuropsychological tests may be impaired in PD-CN patients. Twenty-six PD-CN patients and 26 healthy elderly (HC) participants were included in the current study. Global cognitive status was assessed using the mini-mental state examination (MMSE), and the Montreal cognitive assessment scale (MoCA). Attention and executive functions were evaluated using the Wechsler memory scale-revised (WMS-R) digit span test and trail making test (TMT) part A and part B, the Stroop test, semantic and phonemic fluency tests, and clock drawing test. Magnetic resonance imaging (MRI) was acquired according to the Alzheimer's disease neuroimaging initiative (ADNI) protocol. There were no significant differences among groups regarding age, sex, handedness, and years of education. In the comparison of the PD-CN group and the HC group, there was a statistical decrease in the total animal scores, lexical fluency, TMT part A and TMT part B scores in the PD-CN group. Subcortical gray matter volumes (GMV) were significantly lower in PD-CN patients. The PD-CN group had a significantly reduced total volume of right putamen and left angular gyrus compared to that in the HC group. We observed that putamen and angular gyrus volumes were lower in PD-CN patients. On the other hand, TMT part B may be a useful pretest in detecting the conversion of mild cognitive impairment in PD. Significant MRI volumetric measurements and neuropsychological test batteries can be helpful in the clinical follow-up in PD-CN patients.

Tau PET positivity predicts clinically relevant cognitive decline driven by Alzheimer's disease compared to comorbid cases; proof of concept in the ADNI study.

β-amyloid (Aβ) pathology is not always coupled with Alzheimer's disease (AD) relevant cognitive decline. We assessed the accuracy of tau PET to identify Aβ(+) individuals who show prospective disease progression. 396 cognitively unimpaired and impaired individuals with baseline Aβ and tau PET and a follow-up of ≥ 2 years were selected from the Alzheimer's Disease Neuroimaging Initiative dataset. The participants were dichotomously grouped based on either clinical conversion (i.e., change of diagnosis) or cognitive deterioration (fast (FDs) vs. slow decliners (SDs)) using data-driven clustering of the individual annual rates of cognitive decline. To assess cognitive decline in individuals with isolated Aβ(+) or absence of both Aβ and tau (T) pathologies, we investigated the prevalence of non-AD comorbidities and FDG PET hypometabolism patterns suggestive of AD. Baseline tau PET uptake was higher in Aβ(+)FDs than in Aβ(-)FD/SDs and Aβ(+)SDs, independently of baseline cognitive status. Baseline tau PET uptake identified MCI Aβ(+) Converters and Aβ(+)FDs with an area under the curve of 0.85 and 0.87 (composite temporal region of interest) respectively, and was linearly related to the annual rate of cognitive decline in Aβ(+) individuals. The T(+) individuals constituted largely a subgroup of those being Aβ(+) and those clustered as FDs. The most common biomarker profiles in FDs (n = 70) were Aβ(+)T(+) (n = 34, 49%) and Aβ(+)T(-) (n = 19, 27%). Baseline Aβ load was higher in Aβ(+)T(+)FDs (M = 83.03 ± 31.42CL) than in Aβ(+)T(-)FDs (M = 63.67 ± 26.75CL) (p-value = 0.038). Depression diagnosis was more prevalent in Aβ(+)T(-)FDs compared to Aβ(+)T(+)FDs (47% vs. 15%, p-value = 0.021), as were FDG PET hypometabolism pattern not suggestive of AD (86% vs. 50%, p-value = 0.039). Our findings suggest that high tau PET uptake is coupled with both Aβ pathology and accelerated cognitive decline. In cases of isolated Aβ(+), cognitive decline may be associated with changes within the AD spectrum in a multi-morbidity context, i.e., mixed AD.

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.

An estimate of the longitudinal pace of aging from a single brain scan predicts dementia conversion, morbidity, and mortality.

To understand how aging affects functional decline and increases disease risk, it is necessary to develop accurate and reliable measures of how fast a person is aging. Epigenetic clocks measure aging but require DNA methylation data, which many studies lack. Using data from the Dunedin Study, we introduce an accurate and reliable measure for the rate of longitudinal aging derived from cross-sectional brain MRI: the Dunedin Pace of Aging Calculated from NeuroImaging or DunedinPACNI. Exporting this measure to the Alzheimer's Disease Neuroimaging Initiative and UK Biobank neuroimaging datasets revealed that faster DunedinPACNI predicted participants' cognitive impairment, accelerated brain atrophy, and conversion to diagnosed dementia. Underscoring close links between longitudinal aging of the body and brain, faster DunedinPACNI also predicted physical frailty, poor health, future chronic diseases, and mortality in older adults. Furthermore, DunedinPACNI followed the expected socioeconomic health gradient. When compared to brain age gap, an existing MRI aging biomarker, DunedinPACNI was similarly or more strongly related to clinical outcomes. DunedinPACNI is a "next generation" MRI measure that will be made publicly available to the research community to help accelerate aging research and evaluate the effectiveness of dementia prevention and anti-aging strategies.

White matter structure and derived network properties are used to predict the progression from mild cognitive impairment of older adults to Alzheimer’s disease

ObjectiveTo identify white matter fiber injury and network changes that may lead to mild cognitive impairment (MCI) progression, then a joint model was constructed based on neuropsychological scales to predict high-risk individuals for Alzheimer's disease (AD) progression among older adults with MCI.MethodsA total of 173 MCI patients were included from the Alzheimer's Disease Neuroimaging Initiative(ADNI) database and randomly divided into training and testing cohorts. Forty-five progressed to AD during a 4-year follow-up period. Diffusion tensor imaging (DTI) techniques extracted relevant DTI quantitative features for each patient. In addition, brain networks were constructed based on white matter fiber bundles to extract network property features. Ensemble dimensionality reduction was applied to reduce both DTI quantitative features and network features from the training cohort, and machine learning algorithms were added to construct white matter signature. In addition, 52 patients from the National Alzheimer's Coordinating Center (NACC) database were used for external validation of white matter signature. A joint model was subsequently generated by combining with scale scores, and its performance was evaluated using data from the testing cohort.ResultsBased on multivariate logistic regression, clinical dementia rating and Alzheimer’s disease assessment scales (CDRS and ADAS, respectively) were selected as independent predictive factors. A joint model was constructed in combination with the white matter signature. The AUC, sensitivity, and specificity in the training cohort were 0.938, 0.937, and 0.91, respectively, and the AUC, sensitivity, and specificity in the test cohort were 0.905, 0.923, and 0.872, respectively. The Delong test showed a statistically significant difference between the joint model and CDRS or ADAS scores (P < 0.05), yet no significant difference between the joint model and the white matter signature (P = 0.341).ConclusionThe present results demonstrate that a joint model combining neuropsychological scales can be constructed by using machine learning and DTI technology to identify MCI patients who are at high-risk of progressing to AD.

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.

Contributions of the ADNI Biostatistics Core.

The goal of the Biostatistics Core of the Alzheimer's Disease Neuroimaging Initiative (ADNI) has been to ensure that sound study designs and statistical methods are used to meet the overall goals of ADNI. We have supported the creation of a well-validated and well-curated longitudinal database of clinical and biomarker information on ADNI participants and helped to make this accessible and usable for researchers. We have developed a statistical methodology for characterizing the trajectories of clinical and biomarker change for ADNI participants across the spectrum from cognitively normal to dementia, including multivariate patterns and evidence for heterogeneity in cognitive aging. We have applied these methods and adapted them to improve clinical trial design. ADNI-4 will offer us a chance to help extend these efforts to a more diverse cohort with an even richer panel of biomarker data to support better knowledge of and treatment for Alzheimer's disease and related dementias. HIGHLIGHTS: The Alzheimer's Disease Neuroimaging Initiative (ADNI) Biostatistics Core provides study design and analytic support to ADNI investigators. Core members develop and apply novel statistical methodology to work with ADNI data and support clinical trial design. The Core contributes to the standardization, validation, and harmonization of biomarker data. The Core serves as a resource to the wider research community to address questions related to the data and study as a whole.

The informatics of ADNI.

The Alzheimer's Disease Neuroimaging Initiative (ADNI) has revolutionized the landscape of Alzheimer's research through its Informatics Core, which has facilitated unprecedented data standardization and sharing. Over 20 years, ADNIestablished a robust informatics framework, enabling the validation of biomarkers and supporting global research efforts. The Informatics Core, centered at the Laboratory of Neuro Imaging (LONI), provides a comprehensive data hub that ensures data quality, accessibility, and security, fostering over 5600 publications and significant scientific advancements. By embracing open data sharing principles, ADNIset a gold standard in data transparency, allowing over 26,000 investigators from 169 countries to access and download a wealth of multimodal data. This collaborative approachnot only accelerated biomarker discovery and drug development and advanced our understanding of Alzheimer's disease but also has served as a model for other research initiatives, demonstrating the transformative potential of carefully designed informatics models and shared data in driving global scientific progress. HIGHLIGHTS: Accelerating biomarker discovery and drug development for Alzheimer's disease. Alzheimer's Disease Neuroimaging Initiative's (ADNI's) open data sharing drives scientific progress. Data exploration and coupled analytics to data archives.