Deep Learning in Alzheimer's Disease: Diagnostic Classification and Prognostic Prediction Using Neuroimaging Data

Distinct subtypes of spatial brain metabolism patterns in Alzheimer's disease identified by deep learning-based FDG PET clusters

PURPOSE

Alzheimer's disease (AD) is a heterogeneous disease that presents a broad spectrum of clinicopathologic profiles. To date, objective subtyping of AD independent of disease progression using brain imaging has been required. Our study aimed to extract representations of unique brain metabolism patterns different from disease progression to identify objective subtypes of AD.

METHODS

A total of 3620 FDG brain PET images with AD, mild cognitive impairment (MCI), and cognitively normal (CN) were obtained from the ADNI database from 1607 participants at enrollment and follow-up visits. A conditional variational autoencoder model was trained on FDG brain PET images of AD patients with the corresponding condition of AD severity score. The k-means algorithm was applied to generate clusters from the encoded representations. The trained deep learning-based cluster model was also transferred to FDG PET of MCI patients and predicted the prognosis of subtypes for conversion from MCI to AD. Spatial metabolism patterns, clinical and biological characteristics, and conversion rate from MCI to AD were compared across the subtypes.

RESULTS

Four distinct subtypes of spatial metabolism patterns in AD with different brain pathologies and clinical profiles were identified: (i) angular, (ii) occipital, (iii) orbitofrontal, and (iv) minimal hypometabolic patterns. The deep learning model was also successfully transferred for subtyping MCI, and significant differences in frequency (P < 0.001) and risk of conversion (log-rank P < 0.0001) from MCI to AD were observed across the subtypes, highest in S2 (35.7%) followed by S1 (23.4%).

CONCLUSION

We identified distinct subtypes of AD with different clinicopathologic features. The deep learning-based approach to distinguish AD subtypes on FDG PET could have implications for predicting individual outcomes and provide a clue to understanding the heterogeneous pathophysiology of AD.

Mechanisms of multi-omics and network pharmacology to explain traditional chinese medicine for vascular cognitive impairment: A narrative review

BACKGROUND

The term "vascular cognitive impairment" (VCI) describes various cognitive conditions that include vascular elements. It increases the risk of morbidity and mortality in the elderly population and is the most common cognitive impairment associated with cerebrovascular disease. Understanding the etiology of VCI may aid in identifying approaches to target its possible therapy for the condition. Treatment of VCI has focused on vascular risk factors. There are no authorized conventional therapies available right now. The medications used to treat VCI are solely approved for symptomatic relief and are not intended to prevent or slow the development of VCI.

PURPOSE

The function of Chinese medicine in treating VCI has not yet been thoroughly examined. This review evaluates the preclinical and limited clinical evidence to comprehend the "multi-component, multi-target, multi-pathway" mechanism of Traditional Chinese medicine (TCM). It investigates the various multi-omics approaches in the search for the pathological mechanisms of VCI, as well as the new research strategies, in the hopes of supplying supportive evidence for the clinical treatment of VCI.

METHODS

This review used the Preferred Reporting Items for Preferred reporting items for systematic reviews and meta-analyses (PRISMA) statements. Using integrated bioinformatics and network pharmacology approaches, a thorough evaluation and analysis of 25 preclinical studies published up to July 1, 2023, were conducted to shed light on the mechanisms of TCM for vascular cognitive impairment. The studies for the systematic review were located using the following databases: PubMed, Web of Science, Scopus, Cochrane, and ScienceDirect.

RESULTS

We discovered that the multi-omics analysis approach would hasten the discovery of the role of TCM in the treatment of VCI. It will explore components, compounds, targets, and pathways, slowing the progression of VCI from the perspective of inhibiting oxidative stress, stifling neuroinflammation, increasing cerebral blood flow, and inhibiting iron deposition by a variety of molecular mechanisms, which have significant implications for the treatment of VCI.

CONCLUSION

TCM is a valuable tool for developing dementia therapies, and further research is needed to determine how TCM components may affect the operation of the neurovascular unit. There are still some limitations, although several research have offered invaluable resources for searching for possible anti-dementia medicines and treatments. To gain new insights into the molecular mechanisms that precisely modulate the key molecules at different levels during pharmacological interventions-a prerequisite for comprehending the mechanism of action and determining the potential therapeutic value of the drugs-further research should employ more standardized experimental methods as well as more sophisticated science and technology. Given the results of this review, we advocate integrating chemical and biological component analysis approaches in future research on VCI to provide a more full and objective assessment of the standard of TCM. With the help of bioinformatics, a multi-omics analysis approach will hasten the discovery of the role of TCM in the treatment of VCI, which has significant implications for the treatment of VCI.

Neural Computation-Based Methods for the Early Diagnosis and Prognosis of Alzheimer's Disease Not Using Neuroimaging Biomarkers: A Systematic Review

Background

The growing number of older adults in recent decades has led to more prevalent geriatric diseases, such as strokes and dementia. Therefore, Alzheimer's disease (AD), as the most common type of dementia, has become more frequent too.

Background

Objective: The goals of this work are to present state-of-the-art studies focused on the automatic diagnosis and prognosis of AD and its early stages, mainly mild cognitive impairment, and predicting how the research on this topic may change in the future.

Methods

Articles found in the existing literature needed to fulfill several selection criteria. Among others, their classification methods were based on artificial neural networks (ANNs), including deep learning, and data not from brain signals or neuroimaging techniques were used. Considering our selection criteria, 42 articles published in the last decade were finally selected.

Results

The most medically significant results are shown. Similar quantities of articles based on shallow and deep ANNs were found. Recurrent neural networks and transformers were common with speech or in longitudinal studies. Convolutional neural networks (CNNs) were popular with gait or combined with others in modular approaches. Above one third of the cross-sectional studies utilized multimodal data. Non-public datasets were frequently used in cross-sectional studies, whereas the opposite in longitudinal ones. The most popular databases were indicated, which will be helpful for future researchers in this field.

Conclusions

The introduction of CNNs in the last decade and their superb results with neuroimaging data did not negatively affect the usage of other modalities. In fact, new ones emerged.

Verifying and refining early statuses in Alzheimer's disease progression: a possibility from deep feature comparison

Defining the early status of Alzheimer's disease is challenging. Theoretically, the statuses in the Alzheimer's disease continuum are expected to share common features. Here, we explore to verify and refine candidature early statuses of Alzheimer's disease with features learned from deep learning. We train models on brain functional networks to accurately classify between amnestic and non-amnestic mild cognitive impairments and between healthy controls and mild cognitive impairments. The trained models are applied to Alzheimer's disease and subjective cognitive decline groups to suggest feature similarities among the statuses and identify informative subpopulations. The amnestic mild cognitive impairment vs non-amnestic mild cognitive impairments classifier believes that 71.8% of Alzheimer's disease are amnestic mild cognitive impairment. And 73.5% of subjective cognitive declines are labeled as mild cognitive impairments, 88.8% of which are further suggested as "amnestic mild cognitive impairment." Further multimodal analyses suggest that the amnestic mild cognitive impairment-like Alzheimer's disease, mild cognitive impairment-like subjective cognitive decline, and amnestic mild cognitive impairment-like subjective cognitive decline exhibit more Alzheimer's disease -related pathological changes (elaborated β-amyloid depositions, reduced glucose metabolism, and gray matter atrophy) than non-amnestic mild cognitive impairments -like Alzheimer's disease, healthy control-like subjective cognitive decline, and non-amnestic mild cognitive impairments -like subjective cognitive decline. The test-retest reliability of the subpopulation identification is fair to good in general. The study indicates overall similarity among subjective cognitive decline, amnestic mild cognitive impairment, and Alzheimer's disease and implies their progression relationships. The results support "deep feature comparison" as a potential beneficial framework to verify and refine early Alzheimer's disease status.

Retina Oculomics in Neurodegenerative Disease

Ophthalmic biomarkers have long played a critical role in diagnosing and managing ocular diseases. Oculomics has emerged as a field that utilizes ocular imaging biomarkers to provide insights into systemic diseases. Advances in diagnostic and imaging technologies including electroretinography, optical coherence tomography (OCT), confocal scanning laser ophthalmoscopy, fluorescence lifetime imaging ophthalmoscopy, and OCT angiography have revolutionized the ability to understand systemic diseases and even detect them earlier than clinical manifestations for earlier intervention. With the advent of increasingly large ophthalmic imaging datasets, machine learning models can be integrated into these ocular imaging biomarkers to provide further insights and prognostic predictions of neurodegenerative disease. In this manuscript, we review the use of ophthalmic imaging to provide insights into neurodegenerative diseases including Alzheimer Disease, Parkinson Disease, Amyotrophic Lateral Sclerosis, and Huntington Disease. We discuss recent advances in ophthalmic technology including eye-tracking technology and integration of artificial intelligence techniques to further provide insights into these neurodegenerative diseases. Ultimately, oculomics opens the opportunity to detect and monitor systemic diseases at a higher acuity. Thus, earlier detection of systemic diseases may allow for timely intervention for improving the quality of life in patients with neurodegenerative disease.

Gaussian Process-based prediction of memory performance and biomarker status in ageing and Alzheimer's disease-A systematic model evaluation

Neuroimaging markers based on Magnetic Resonance Imaging (MRI) combined with various other measures (such as genetic covariates, biomarkers, vascular risk factors, neuropsychological tests etc.) might provide useful predictions of clinical outcomes during the progression towards Alzheimer's disease (AD). The use of multiple features in predictive frameworks for clinical outcomes has become increasingly prevalent in AD research. However, many studies do not focus on systematically and accurately evaluating combinations of multiple input features. Hence, the aim of the present work is to explore and assess optimal combinations of various features for MR-based prediction of (1) cognitive status and (2) biomarker positivity with a multi-kernel learning Gaussian process framework. The explored features and parameters included (A) combinations of brain tissues, modulation, smoothing, and image resolution; (B) incorporating demographics & clinical covariates; (C) the impact of the size of the training data set; (D) the influence of dimensionality reduction and the choice of kernel types. The approach was tested in a large German cohort including 959 subjects from the multicentric longitudinal study of cognitive impairment and dementia (DELCODE). Our evaluation suggests the best prediction of memory performance was obtained for a combination of neuroimaging markers, demographics, genetic information (ApoE4) and CSF biomarkers explaining 57% of outcome variance in out-of-sample predictions. The highest performance for Aβ42/40 status classification was achieved for a combination of demographics, ApoE4, and a memory score while usage of structural MRI further improved the classification of individual patient's pTau status.

Deep Cascade-Learning Model via Recurrent Attention for Immunofixation Electrophoresis Image Analysis

Immunofixation Electrophoresis (IFE) analysis has been an indispensable prerequisite for the diagnosis of M-protein, which is an important criterion to recognize diversified plasma cell diseases. Existing intelligent methods of IFE diagnosis commonly employ a single unified classifier to directly classify whether M-protein exists and which isotype of M-protein is. However, this unified classification is not optimal because the two tasks have different characteristics and require different feature extraction techniques. Classifying the M-protein existence depends on the presence or absence of dense bands in IFE data, while classifying the M-protein isotype depends on the location of dense bands. Consequently, a cascading two-classifier framework suitable to the two tasks respectively may achieve better performance. In this paper, we propose a novel deep cascade-learning model, which sequentially integrates a positive-negative classifier based on deep collocative learning and an isotype classifier based on recurrent attention model to address these two tasks respectively. Specifically, the attention mechanism can mimic the visual perception of clinicians, where only the most informative local regions are extracted through sequential partial observations. This not only avoids the interference of redundant regions but also saves computational power. Further, domain knowledge about SP lane and heavy-light-chain lanes is also introduced to assist our attention location. Extensive numerical experiments show that our deep cascade-learning outperforms state-of-the-art methods on recognized evaluation metrics and can effectively capture the co-location of dense bands in different lanes.

Deep Learning-Based Survival Analysis for Receiving a Steatotic Donor Liver Versus Waiting for a Standard Liver

BACKGROUND

An emerging strategy to expand the donor pool is the use of a steatotic donor liver (SDLs; ≥ 30% macrosteatosis on biopsy). With the obesity epidemic and prevalence of nonalcoholic fatty liver disease, SDLs have been reported in 59% of all deceased donors. Many potential candidates need to decide whether to accept an SDL offer or remain on the waitlist for a nonsteatotic donor liver (non-SDL). The objective of this study was to compare the survival of accepting an SDL vs using a non-SDL after waiting various times.

METHODS

Using data from the United States' organ procurement and transplantation network, deep survival learning predictive models were built to compare post-decision survival after accepting an SDL vs waiting for a non-SDL. The comparison subjects contain simulated 20,000 different scenarios of a candidate either accepting an SDL immediately or receiving a non-SDL after waiting various times. The research variables were selected using the LASSO-Cox and Random Survival Forest (RSF) models. The Cox proportional hazards and RSF models were also comparatively included for survival prediction. In addition, personalized survival curves for randomly selected candidates were generated.

RESULT

Deep survival learning outperformed Cox proportional hazards and RSF in predicting the survival of liver transplants. Among the simulations, 25% to 30% of scenarios demonstrated a higher 3-year survival post-decision for candidates accepting an SDL than waiting and receiving a non-SDL. The difference was only 1.43% in 3-year survival post-decision between accepting an SDL and waiting 260 days (mean waitlist time) for a non-SDL. As the number of days on the waitlist increases, the difference in survival between accepting SDLs and waiting for non-SDLs decreases.

CONCLUSIONS

Appropriately used SDLs could expand the donor pool and relieve the candidates' unmet need for donor livers, which presents long-term survival benefits for recipients.

How is Big Data reshaping preclinical aging research?

The exponential scientific and technological progress during the past 30 years has favored the comprehensive characterization of aging processes with their multivariate nature, leading to the advent of Big Data in preclinical aging research. Spanning from molecular omics to organism-level deep phenotyping, Big Data demands large computational resources for storage and analysis, as well as new analytical tools and conceptual frameworks to gain novel insights leading to discovery. Systems biology has emerged as a paradigm that utilizes Big Data to gain insightful information enabling a better understanding of living organisms, visualized as multilayered networks of interacting molecules, cells, tissues and organs at different spatiotemporal scales. In this framework, where aging, health and disease represent emergent states from an evolving dynamic complex system, context given by, for example, strain, sex and feeding times, becomes paramount for defining the biological trajectory of an organism. Using bioinformatics and artificial intelligence, the systems biology approach is leading to remarkable advances in our understanding of the underlying mechanism of aging biology and assisting in creative experimental study designs in animal models. Future in-depth knowledge acquisition will depend on the ability to fully integrate information from different spatiotemporal scales in organisms, which will probably require the adoption of theories and methods from the field of complex systems. Here we review state-of-the-art approaches in preclinical research, with a focus on rodent models, that are leading to conceptual and/or technical advances in leveraging Big Data to understand basic aging biology and its full translational potential.

Detecting conversion from mild cognitive impairment to Alzheimer's disease using FLAIR MRI biomarkers

Mild cognitive impairment (MCI) is the prodromal phase of Alzheimer's disease (AD) and while it presents as an imperative intervention window, it is difficult to detect which subjects convert to AD (cMCI) and which ones remain stable (sMCI). The objective of this work was to investigate fluid-attenuated inversion recovery (FLAIR) MRI biomarkers and their ability to differentiate between sMCI and cMCI subjects in cross-sectional and longitudinal data. Three types of biomarkers were investigated: volume, intensity and texture. Volume biomarkers included total brain volume, cerebrospinal fluid volume (CSF), lateral ventricular volume, white matter lesion volume, subarachnoid CSF, and grey matter (GM) and white matter (WM), all normalized to intracranial volume. The mean intensity, kurtosis, and skewness of the GM and WM made up the intensity features. Texture features quantified homogeneity and microstructural tissue changes of GM and WM regions. Composite indices were also considered, which are biomarkers that represent an aggregate sum (z-score normalization and summation) of all biomarkers. The FLAIR MRI biomarkers successfully identified high-risk subjects as significant differences (p < 0.05) were found between the means of the sMCI and cMCI groups and the rate of change over time for several individual biomarkers as well as the composite indices for both cross-sectional and longitudinal analyses. Classification accuracy and feature importance analysis showed volume biomarkers to be most predictive, however, best performance was obtained when complimenting the volume biomarkers with the intensity and texture features. Using all the biomarkers, accuracy of 86.2 % and 69.2 % was achieved for normal control-AD and sMCI-cMCI classification respectively. Survival analysis demonstrated that the majority of the biomarkers showed a noticeable impact on the AD conversion probability 4 years prior to conversion. Composite indices were the top performers for all analyses including feature importance, classification, and survival analysis. This demonstrated their ability to summarize various dimensions of disease into single-valued metrics. Significant correlation (p < 0.05) with phosphorylated-tau and amyloid-beta CSF biomarkers was found with all the FLAIR biomarkers. The proposed biomarker system is easily attained as FLAIR is routinely acquired, models are not computationally intensive and the results are explainable, thus making this pipeline easily integrated into clinical workflow.

Development and validation of an automatic classification algorithm for the diagnosis of Alzheimer's disease using a high-performance interpretable deep learning network

OBJECTIVES

To develop and validate an automatic classification algorithm for diagnosing Alzheimer's disease (AD) or mild cognitive impairment (MCI).

METHODS AND MATERIALS

This study evaluated a high-performance interpretable network algorithm (TabNet) and compared its performance with that of XGBoost, a widely used classifier. Brain segmentation was performed using a commercially approved software. TabNet and XGBoost were trained on the volumes or radiomics features of 102 segmented regions for classifying subjects into AD, MCI, or cognitively normal (CN) groups. The diagnostic performances of the two algorithms were compared using areas under the curves (AUCs). Additionally, 20 deep learning-based AD signature areas were investigated.

RESULTS

Between December 2014 and March 2017, 161 AD, 153 MCI, and 306 CN cases were enrolled. Another 120 AD, 90 MCI, and 141 CN cases were included for the internal validation. Public datasets were used for external validation. TabNet with volume features had an AUC of 0.951 (95% confidence interval [CI], 0.947-0.955) for AD vs CN, which was similar to that of XGBoost (0.953 [95% CI, 0.951-0.955], p = 0.41). External validation revealed the similar performances of two classifiers using volume features (0.871 vs. 0.871, p = 0.86). Likewise, two algorithms showed similar performances with one another in classifying MCI. The addition of radiomics data did not improve the performance of TabNet. TabNet and XGBoost focused on the same 13/20 regions of interest, including the hippocampus, inferior lateral ventricle, and entorhinal cortex.

CONCLUSIONS

TabNet shows high performance in AD classification and detailed interpretation of the selected regions.

CLINICAL RELEVANCE STATEMENT

Using a high-performance interpretable deep learning network, the automatic classification algorithm assisted in accurate Alzheimer's disease detection using 3D T1-weighted brain MRI and detailed interpretation of the selected regions.

KEY POINTS

• MR volumetry data revealed that TabNet had a high diagnostic performance in differentiating Alzheimer's disease (AD) from cognitive normal cases, which was comparable with that of XGBoost. • The addition of radiomics data to the volume data did not improve the diagnostic performance of TabNet. • Both TabNet and XGBoost selected the clinically meaningful regions of interest in AD, including the hippocampus, inferior lateral ventricle, and entorhinal cortex.

Algorithmic Fairness of Machine Learning Models for Alzheimer Disease Progression

Importance

Predictive models using machine learning techniques have potential to improve early detection and management of Alzheimer disease (AD). However, these models potentially have biases and may perpetuate or exacerbate existing disparities.

Objective

To characterize the algorithmic fairness of longitudinal prediction models for AD progression.

Design, Setting, and Participants

This prognostic study investigated the algorithmic fairness of logistic regression, support vector machines, and recurrent neural networks for predicting progression to mild cognitive impairment (MCI) and AD using data from participants in the Alzheimer Disease Neuroimaging Initiative evaluated at 57 sites in the US and Canada. Participants aged 54 to 91 years who contributed data on at least 2 visits between September 2005 and May 2017 were included. Data were analyzed in October 2022.

Exposures

Fairness was quantified across sex, ethnicity, and race groups. Neuropsychological test scores, anatomical features from T1 magnetic resonance imaging, measures extracted from positron emission tomography, and cerebrospinal fluid biomarkers were included as predictors.

Main Outcomes and Measures

Outcome measures quantified fairness of prediction models (logistic regression [LR], support vector machine [SVM], and recurrent neural network [RNN] models), including equal opportunity, equalized odds, and demographic parity. Specifically, if the model exhibited equal sensitivity for all groups, it aligned with the principle of equal opportunity, indicating fairness in predictive performance.

Results

A total of 1730 participants in the cohort (mean [SD] age, 73.81 [6.92] years; 776 females [44.9%]; 69 Hispanic [4.0%] and 1661 non-Hispanic [96.0%]; 29 Asian [1.7%], 77 Black [4.5%], 1599 White [92.4%], and 25 other race [1.4%]) were included. Sensitivity for predicting progression to MCI and AD was lower for Hispanic participants compared with non-Hispanic participants; the difference (SD) in true positive rate ranged from 20.9% (5.5%) for the RNN model to 27.8% (9.8%) for the SVM model in MCI and 24.1% (5.4%) for the RNN model to 48.2% (17.3%) for the LR model in AD. Sensitivity was similarly lower for Black and Asian participants compared with non-Hispanic White participants; for example, the difference (SD) in AD true positive rate was 14.5% (51.6%) in the LR model, 12.3% (35.1%) in the SVM model, and 28.4% (16.8%) in the RNN model for Black vs White participants, and the difference (SD) in MCI true positive rate was 25.6% (13.1%) in the LR model, 24.3% (13.1%) in the SVM model, and 6.8% (18.7%) in the RNN model for Asian vs White participants. Models generally satisfied metrics of fairness with respect to sex, with no significant differences by group, except for cognitively normal (CN)-MCI and MCI-AD transitions (eg, an absolute increase [SD] in the true positive rate of CN-MCI transitions of 10.3% [27.8%] for the LR model).

Conclusions and Relevance

In this study, models were accurate in aggregate but failed to satisfy fairness metrics. These findings suggest that fairness should be considered in the development and use of machine learning models for AD progression.

Circular-SWAT for deep learning based diagnostic classification of Alzheimer's disease: application to metabolome data

BACKGROUND

Deep learning has shown potential in various scientific domains but faces challenges when applied to complex, high-dimensional multi-omics data. Alzheimer's Disease (AD) is a neurodegenerative disorder that lacks targeted therapeutic options. This study introduces the Circular-Sliding Window Association Test (c-SWAT) to improve the classification accuracy in predicting AD using serum-based metabolomics data, specifically lipidomics.

METHODS

The c-SWAT methodology builds upon the existing Sliding Window Association Test (SWAT) and utilizes a three-step approach: feature correlation analysis, feature selection, and classification. Data from 997 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) served as the basis for model training and validation. Feature correlations were analyzed using Weighted Gene Co-expression Network Analysis (WGCNA), and Convolutional Neural Networks (CNN) were employed for feature selection. Random Forest was used for the final classification.

FINDINGS

The application of c-SWAT resulted in a classification accuracy of up to 80.8% and an AUC of 0.808 for distinguishing AD from cognitively normal older adults. This marks a 9.4% improvement in accuracy and a 0.169 increase in AUC compared to methods without c-SWAT. These results were statistically significant, with a p-value of 1.04 × 10ˆ-4. The approach also identified key lipids associated with AD, such as Cer(d16:1/22:0) and PI(37:6).

INTERPRETATION

Our results indicate that c-SWAT is effective in improving classification accuracy and in identifying potential lipid biomarkers for AD. These identified lipids offer new avenues for understanding AD and warrant further investigation.

FUNDING

The specific funding of this article is provided in the acknowledgements section.

ArcheD, a residual neural network for prediction of cerebrospinal fluid amyloid-beta from amyloid PET images

Detection and measurement of amyloid-beta (Aβ) aggregation in the brain is a key factor for early identification and diagnosis of Alzheimer's disease (AD). We aimed to develop a deep learning model to predict Aβ cerebrospinal fluid (CSF) concentration directly from amyloid PET images, independent of tracers, brain reference regions or preselected regions of interest. We used 1870 Aβ PET images and CSF measurements to train and validate a convolutional neural network ("ArcheD"). We evaluated the ArcheD performance in relation to episodic memory and the standardized uptake value ratio (SUVR) of cortical Aβ. We also compared the brain region's relevance for the model's CSF prediction within clinical-based and biological-based classifications. ArcheD-predicted Aβ CSF values correlated strongly with measured Aβ CSF values ( r =0.81; p <0.001) and showed correlations with SUVR and episodic memory measures in all participants except in those with AD. For both clinical and biological classifications, cerebral white matter significantly contributed to CSF prediction ( q <0.01), specifically in non-symptomatic and early stages of AD. However, in late-stage disease, brain stem, subcortical areas, cortical lobes, limbic lobe, and basal forebrain made more significant contributions (q<0.01). Considering cortical gray matter separately, the parietal lobe was the strongest predictor of CSF amyloid levels in those with prodromal or early AD, while the temporal lobe played a more crucial role for those with AD. In summary, ArcheD reliably predicted Aβ CSF concentration from Aβ PET scans, offering potential clinical utility for Aβ level determination and early AD detection.

Deep learning combining FDG-PET and neurocognitive data accurately predicts MCI conversion to Alzheimer's dementia 3-year post MCI diagnosis

INTRODUCTION

This study reports a novel deep learning approach to predict mild cognitive impairment (MCI) conversion to Alzheimer's dementia (AD) within three years using whole-brain fluorodeoxyglucose (FDG) positron emission tomography (PET) and cognitive scores (CS).

METHODS

This analysis consisted of 150 normal controls (CN), 257 MCI, and 205  AD subjects from ADNI. FDG-PET and CS were obtained at MCI diagnosis to predict AD conversion within three years of MCI diagnosis using convolutional neural networks.

RESULTS

Neurocognitive scores predicted better than FDG-PET per se, but the best model was a combination of FDG-PET, age, and neurocognitive data, yielding an AUC of 0.785 ± 0.096 and a balanced accuracy of 0.733 ± 0.098. Saliency maps highlighted putamen, thalamus, inferior frontal gyrus, parietal operculum, precuneus cortices, calcarine cortices, temporal gyrus, and planum temporale to be important for prediction.

DISCUSSION

Deep learning accurately predicts MCI conversion to AD and provides neural correlates of brain regions associated with AD conversion.

Machine learning-based classification of Alzheimer's disease and its at-risk states using personality traits, anxiety, and depression

BACKGROUND

Alzheimer's disease (AD) is often preceded by stages of cognitive impairment, namely subjective cognitive decline (SCD) and mild cognitive impairment (MCI). While cerebrospinal fluid (CSF) biomarkers are established predictors of AD, other non-invasive candidate predictors include personality traits, anxiety, and depression, among others. These predictors offer non-invasive assessment and exhibit changes during AD development and preclinical stages.

METHODS

In a cross-sectional design, we comparatively evaluated the predictive value of personality traits (Big Five), geriatric anxiety and depression scores, resting-state functional magnetic resonance imaging activity of the default mode network, apoliprotein E (ApoE) genotype, and CSF biomarkers (tTau, pTau181, Aβ42/40 ratio) in a multi-class support vector machine classification. Participants included 189 healthy controls (HC), 338 individuals with SCD, 132 with amnestic MCI, and 74 with mild AD from the multicenter DZNE-Longitudinal Cognitive Impairment and Dementia Study (DELCODE).

RESULTS

Mean predictive accuracy across all participant groups was highest when utilizing a combination of personality, depression, and anxiety scores. HC were best predicted by a feature set comprised of depression and anxiety scores and participants with AD were best predicted by a feature set containing CSF biomarkers. Classification of participants with SCD or aMCI was near chance level for all assessed feature sets.

CONCLUSION

Our results demonstrate predictive value of personality trait and state scores for AD. Importantly, CSF biomarkers, personality, depression, anxiety, and ApoE genotype show complementary value for classification of AD and its at-risk stages.

An artificial intelligence-based prognostic prediction model for hemorrhagic stroke

PURPOSE

The prognosis following a hemorrhagic stroke is usually extremely poor. Rating scales have been developed to predict the outcomes of patients with intracerebral hemorrhage (ICH). To date, however, the prognostic prediction models have not included the full range of relevant imaging features. We constructed a clinic-imaging fusion model based on convolutional neural networks (CNN) to predict the short-term prognosis of ICH patients.

MATERIALS AND METHODS

This was a multi-center retrospective study, which included 1990 patients with ICH. Two CNN-based deep learning models were constructed to predict the neurofunctional outcomes at discharge; these were validated using a nested 5-fold cross-validation approach. The models' predictive efficiency was compared with the original ICH scale and the ICH grading scale. Poor neurological outcome was defined as a Glasgow Outcome Scale (GOS) score of 1-3.

RESULTS

The training and test sets included 1599 and 391 patients, respectively. For the test set, the clinic-imaging fusion model had the highest area under the curve (AUC = 0.903), followed by the imaging-based model (AUC = 0.886), the ICH scale (AUC = 0.777), and finally the ICH grading scale (AUC = 0.747).

CONCLUSION

The CNN prognostic prediction model based on neuroimaging features was more effective than the ICH scales in predicting the neurological outcomes of ICH patients at discharge. The CNN model's predictive efficiency slightly improved when clinical data were included.

Mechanism of breast cancer immune microenvironment in prognosis of heart failure

The treatment of breast cancer can potentially impose a burden on the heart, leading to an increased risk of heart failure. Studies have shown that more than half of breast cancer patients die from non-tumor-related causes, with cardiovascular disease (CVD) being the leading cause of death. However, the underlying mechanism linking breast cancer prognosis and heart failure remains unclear. To investigate this, we conducted an analysis where we compared the differentially expressed genes (DEGs) in early and advanced breast cancer with genes associated with heart failure. This analysis revealed 18 genes that overlapped between the two conditions, with 15 of them being related to immune function. This suggests that immune pathways may play a role in the prognosis of breast cancer patients with heart failure. Using gene expression data from 1260 breast cancer patients, we further examined the impact of these 15 genes on survival time. Additionally, through enrichment analysis, we explored the functions and pathways associated with these genes in relation to breast cancer and heart failure. By constructing a transformer model, we discovered that the expression patterns of these 15 genes can accurately predict the occurrence of heart failure. The model achieved an AUC of 0.86 and an AUPR of 0.91. Moreover, through analysis of single-cell sequencing data from breast cancer patients undergoing PD-1 treatment and experiencing heart failure, we identified a significant number of cell-type-specific genes that were shared between both diseases. This suggests that changes in gene expression in immune cells following breast cancer treatment may be associated with the development of heart failure.

New era of artificial intelligence and machine learning-based detection, diagnosis, and therapeutics in Parkinson's disease

Parkinson's disease (PD) is characterized by the loss of neuronal cells, which leads to synaptic dysfunction and cognitive defects. Despite the advancements in treatment strategies, the management of PD is still a challenging event. Early prediction and diagnosis of PD are of utmost importance for effective management of PD. In addition, the classification of patients with PD as compared to normal healthy individuals also imposes drawbacks in the early diagnosis of PD. To address these challenges, artificial intelligence (AI) and machine learning (ML) models have been implicated in the diagnosis, prediction, and treatment of PD. Recent times have also demonstrated the implication of AI and ML models in the classification of PD based on neuroimaging methods, speech recording, gait abnormalities, and others. Herein, we have briefly discussed the role of AI and ML in the diagnosis, treatment, and identification of novel biomarkers in the progression of PD. We have also highlighted the role of AI and ML in PD management through altered lipidomics and gut-brain axis. We briefly explain the role of early PD detection through AI and ML algorithms based on speech recordings, handwriting patterns, gait abnormalities, and neuroimaging techniques. Further, the review discuss the potential role of the metaverse, the Internet of Things, and electronic health records in the effective management of PD to improve the quality of life. Lastly, we also focused on the implementation of AI and ML-algorithms in neurosurgical process and drug discovery.

An Approach to Binary Classification of Alzheimer's Disease Using LSTM

In this study, we use LSTM (Long-Short-Term-Memory) networks to evaluate Magnetic Resonance Imaging (MRI) data to overcome the shortcomings of conventional Alzheimer's disease (AD) detection techniques. Our method offers greater reliability and accuracy in predicting the possibility of AD, in contrast to cognitive testing and brain structure analyses. We used an MRI dataset that we downloaded from the Kaggle source to train our LSTM network. Utilizing the temporal memory characteristics of LSTMs, the network was created to efficiently capture and evaluate the sequential patterns inherent in MRI scans. Our model scored a remarkable AUC of 0.97 and an accuracy of 98.62%. During the training process, we used Stratified Shuffle-Split Cross Validation to make sure that our findings were reliable and generalizable. Our study adds significantly to the body of knowledge by demonstrating the potential of LSTM networks in the specific field of AD prediction and extending the variety of methods investigated for image classification in AD research. We have also designed a user-friendly Web-based application to help with the accessibility of our developed model, bridging the gap between research and actual deployment.

Multi-modal cross-attention network for Alzheimer's disease diagnosis with multi-modality data

Alzheimer's disease (AD) is a neurodegenerative disorder, the most common cause of dementia, so the accurate diagnosis of AD and its prodromal stage mild cognitive impairment (MCI) is significant. Recent studies have demonstrated that multiple neuroimaging and biological measures contain complementary information for diagnosis. Many existing multi-modal models based on deep learning simply concatenate each modality's features despite substantial differences in representation spaces. In this paper, we propose a novel multi-modal cross-attention AD diagnosis (MCAD) framework to learn the interaction between modalities for better playing their complementary roles for AD diagnosis with multi-modal data including structural magnetic resonance imaging (sMRI), fluorodeoxyglucose-positron emission tomography (FDG-PET) and cerebrospinal fluid (CSF) biomarkers. Specifically, the imaging and non-imaging representations are learned by the image encoder based on cascaded dilated convolutions and CSF encoder, respectively. Then, a multi-modal interaction module is introduced, which takes advantage of cross-modal attention to integrate imaging and non-imaging information and reinforce relationships between these modalities. Moreover, an extensive objective function is designed to reduce the discrepancy between modalities for effectively fusing the features of multi-modal data, which could further improve the diagnosis performance. We evaluate the effectiveness of our proposed method on the ADNI dataset, and the extensive experiments demonstrate that our MCAD achieves superior performance for multiple AD-related classification tasks, compared to several competing methods. Also, we investigate the importance of cross-attention and the contribution of each modality to the diagnostics performance. The experimental results demonstrate that combining multi-modality data via cross-attention is helpful for accurate AD diagnosis.

A systematic review on machine learning and deep learning techniques in the effective diagnosis of Alzheimer's disease

Alzheimer's disease (AD) is a brain-related disease in which the condition of the patient gets worse with time. AD is not a curable disease by any medication. It is impossible to halt the death of brain cells, but with the help of medication, the effects of AD can be delayed. As not all MCI patients will suffer from AD, it is required to accurately diagnose whether a mild cognitive impaired (MCI) patient will convert to AD (namely MCI converter MCI-C) or not (namely MCI non-converter MCI-NC), during early diagnosis. There are two modalities, positron emission tomography (PET) and magnetic resonance image (MRI), used by a physician for the diagnosis of Alzheimer's disease. Machine learning and deep learning perform exceptionally well in the field of computer vision where there is a requirement to extract information from high-dimensional data. Researchers use deep learning models in the field of medicine for diagnosis, prognosis, and even to predict the future health of the patient under medication. This study is a systematic review of publications using machine learning and deep learning methods for early classification of normal cognitive (NC) and Alzheimer's disease (AD).This study is an effort to provide the details of the two most commonly used modalities PET and MRI for the identification of AD, and to evaluate the performance of both modalities while working with different classifiers.

Cellular Senescence: From Mechanisms to Current Biomarkers and Senotherapies

An increase in life expectancy in developed countries has led to a surge of chronic aging-related diseases. In the last few decades, several studies have provided evidence of the prominent role of cellular senescence in many of these pathologies. Key traits of senescent cells include cell cycle arrest, apoptosis resistance, and secretome shift to senescence-associated secretory phenotype resulting in increased secretion of various intermediate bioactive factors important for senescence pathophysiology. However, cellular senescence is a highly phenotypically heterogeneous process, hindering the discovery of totally specific and accurate biomarkers. Also, strategies to prevent the pathologic effect of senescent cell accumulation during aging by impairing senescence onset or promoting senescent cell clearance have shown great potential during in vivo studies, and some are already in early stages of clinical translation. The adaptability of these senotherapeutic approaches to human application has been questioned due to the lack of proper senescence targeting and senescence involvement in important physiologic functions. In this review, we explore the heterogeneous phenotype of senescent cells and its influence on the expression of biomarkers currently used for senescence detection. We also discuss the current evidence regarding the efficacy, reliability, development stage, and potential for human applicability of the main existing senotherapeutic strategies. SIGNIFICANCE STATEMENT: This paper is an extensive review of what is currently known about the complex process of cellular senescence and explores its most defining features. The main body of the discussion focuses on how the multifeature fluctuation of the senescence phenotype and the physiological role of cellular senescence have both caused a limitation in the search for truly reliable senescence biomarkers and the progression in the development of senotherapies.

Accurate Prediction of Alzheimer's Disease Progression Trajectory via a Novel Encoder-Decoder LSTM Architecture

With an increase in life expectancy, there has been an increase in the aged population globally, and around 10% of this population suffers from Alzheimer's disease. Alzheimer's hugely impacts the quality of life and well-being of older adults and their caregivers. Thus, it is an emerging challenge to improve the early diagnosis and prognosis of the disease. Detecting hidden patterns in complex multidimensional datasets using recent advancements in machine learning provides a tremendous opportunity to meet this crucial need. In this study, using multimodal features and an individual's clinical status on one or more time points, we aimed to predict the individual's cognitive test scores, changes in Magnetic Resonance Imaging features, and the individual's diagnostic status for the next three years. We presented a novel Encoder-Decoder Long Short-Term Memory deep-learning model architecture for implementing our prediction. We applied it to data from the Alzheimer's Disease Neuroimaging Initiative, comprising longitudinal data of 1737 participants and 12,741 instances. The proposed model was found to be competent, with a validation accuracy of 0.941, a multi-class area under the curve of 0.960, and a test accuracy of 0.88 in identifying the various stages of Alzheimer's disease progression in patients with an initially cognitively normal or mild cognitive impairment which is a significant improvement over previous methods.Clinical relevance- The proposed approach can help improve diagnostic understanding of Alzheimer's Disease progression and assist in the early detection of various stages of Alzheimer's Disease based on clinical heterogeneity.

Gray matter reserve determines glymphatic system function in young-onset Alzheimer's disease: Evidenced by DTI-ALPS and compared with age-matched controls

BACKGROUND

The diffusion tensor imaging analysis along the perivascular space (ALPS)-index can be used to model the glymphatic system in vivo.

AIM

This study explores putative mechanisms between prediction of ALPS-index and cognitive outcomes in young-onset Alzheimer's disease (YOAD) and age-matched controls (CTLs) and analyzes whether the link was mediated by the integrity of ALPS-index-anchored cerebral gray matter (GM).

METHODS

We enrolled 130 patients with YOAD and 137 CTLs. All participants underwent three-dimensional T1 -weighted MRI, diffusion tensor imaging and cognitive tests. We constructed GM regions correlated with the ALPS-index in the YOAD and CTL groups. For the GM regions significantly correlated with the ALPS-index and cognitive measures, we extracted a 4-mm radius sphere. In the YOAD and CTL groups, we used mediator analysis to explore the ALPS-index as predictor, GM partitions as mediators, and significant cognitive test scores as outcomes.

RESULTS

Patient group had significantly lower ALPS-index. The ALPS-index was associated with GM volume in the cerebellar gray, dorsolateral prefrontal, thalamus, superior frontal, amygdala and hippocampus, and these coherent regions coincided with those showing GM atrophy in the YOAD group. Mediation analysis of the YOAD group suggested that the relationships between the ALPS-index and cognitive performance were fully mediated by the integrity of ALPS-index coherent GM areas.

DISCUSSION

Reserved GM mediates the link between the glymphatic system and cognition. Our findings suggest that GM integrity rather than the glymphatic system could serve as a direct cognitive test scores predictor in patients with YOAD.

The neuropathological landscape of Hispanic and non-Hispanic White decedents with Alzheimer disease

Despite the increasing demographic diversity of the United States' aging population, there remain significant gaps in post-mortem research investigating the ethnoracial heterogeneity in the neuropathological landscape of Alzheimer Disease (AD). Most autopsy-based studies have focused on cohorts of non-Hispanic White decedents (NHWD), with few studies including Hispanic decedents (HD). We aimed to characterize the neuropathologic landscape of AD in NHWD (n = 185) and HD (n = 92) evaluated in research programs across three institutions: University of California San Diego, University of California Davis, and Columbia University. Only persons with a neuropathologic diagnosis of intermediate/high AD determined by NIA Reagan and/or NIA-AA criteria were included. A frequency-balanced random sample without replacement was drawn from the NHWD group using a 2:1 age and sex matching scheme with HD. Four brain areas were evaluated: posterior hippocampus, frontal, temporal, and parietal cortices. Sections were stained with antibodies against Aβ (4G8) and phosphorylated tau (AT8). We compared the distribution and semi-quantitative densities for neurofibrillary tangles (NFTs), neuropil threads, core, diffuse, and neuritic plaques. All evaluations were conducted by an expert blinded to demographics and group status. Wilcoxon's two-sample test revealed higher levels of neuritic plaques in the frontal cortex (p = 0.02) and neuropil threads (p = 0.02) in HD, and higher levels of cored plaques in the temporal cortex in NHWD (p = 0.02). Results from ordinal logistic regression controlling for age, sex, and site of origin were similar. In other evaluated brain regions, semi-quantitative scores of plaques, tangles, and threads did not differ statistically between groups. Our results demonstrate HD may be disproportionately burdened by AD-related pathologies in select anatomic regions, particularly tau deposits. Further research is warranted to understand the contributions of demographic, genetic, and environmental factors to heterogeneous pathological presentations.

[Research on mild cognitive impairment diagnosis based on Bayesian optimized long-short-term neural network model]

The recurrent neural network architecture improves the processing ability of time-series data. However, issues such as exploding gradients and poor feature extraction limit its application in the automatic diagnosis of mild cognitive impairment (MCI). This paper proposed a research approach for building an MCI diagnostic model using a Bayesian-optimized bidirectional long short-term memory network (BO-BiLSTM) to address this problem. The diagnostic model was based on a Bayesian algorithm and combined prior distribution and posterior probability results to optimize the BO-BiLSTM network hyperparameters. It also used multiple feature quantities that fully reflected the cognitive state of the MCI brain, such as power spectral density, fuzzy entropy, and multifractal spectrum, as the input of the diagnostic model to achieve automatic MCI diagnosis. The results showed that the feature-fused Bayesian-optimized BiLSTM network model achieved an MCI diagnostic accuracy of 98.64% and effectively completed the diagnostic assessment of MCI. In conclusion, based on this optimization, the long short-term neural network model has achieved automatic diagnostic assessment of MCI, providing a new diagnostic model for intelligent diagnosis of MCI.

A review of visual sustained attention: neural mechanisms and computational models

Sustained attention is one of the basic abilities of humans to maintain concentration on relevant information while ignoring irrelevant information over extended periods. The purpose of the review is to provide insight into how to integrate neural mechanisms of sustained attention with computational models to facilitate research and application. Although many studies have assessed attention, the evaluation of humans' sustained attention is not sufficiently comprehensive. Hence, this study provides a current review on both neural mechanisms and computational models of visual sustained attention. We first review models, measurements, and neural mechanisms of sustained attention and propose plausible neural pathways for visual sustained attention. Next, we analyze and compare the different computational models of sustained attention that the previous reviews have not systematically summarized. We then provide computational models for automatically detecting vigilance states and evaluation of sustained attention. Finally, we outline possible future trends in the research field of sustained attention.

Interpretable machine learning for dementia: A systematic review

INTRODUCTION

Machine learning research into automated dementia diagnosis is becoming increasingly popular but so far has had limited clinical impact. A key challenge is building robust and generalizable models that generate decisions that can be reliably explained. Some models are designed to be inherently "interpretable," whereas post hoc "explainability" methods can be used for other models.

METHODS

Here we sought to summarize the state-of-the-art of interpretable machine learning for dementia.

RESULTS

We identified 92 studies using PubMed, Web of Science, and Scopus. Studies demonstrate promising classification performance but vary in their validation procedures and reporting standards and rely heavily on popular data sets.

DISCUSSION

Future work should incorporate clinicians to validate explanation methods and make conclusive inferences about dementia-related disease pathology. Critically analyzing model explanations also requires an understanding of the interpretability methods itself. Patient-specific explanations are also required to demonstrate the benefit of interpretable machine learning in clinical practice.

A Novel Approach Utilizing Machine Learning for the Early Diagnosis of Alzheimer's Disease

Alzheimer's disease (AD) is one of the leading causes of dementia among older people. In addition, a considerable portion of the world's population suffers from metabolic problems, such as Alzheimer's disease and diabetes. Alzheimer's disease affects the brain in a degenerative manner. As the elderly population grows, this illness can cause more people to become inactive by impairing their memory and physical functionality. This might impact their family members and the financial, economic, and social spheres. Researchers have recently investigated different machine learning and deep learning approaches to detect such diseases at an earlier stage. Early diagnosis and treatment of AD help patients to recover from it successfully and with the least harm. This paper proposes a machine learning model that comprises GaussianNB, Decision Tree, Random Forest, XGBoost, Voting Classifier, and GradientBoost to predict Alzheimer's disease. The model is trained using the open access series of imaging studies (OASIS) dataset to evaluate the performance in terms of accuracy, precision, recall, and F1 score. Our findings showed that the voting classifier attained the highest validation accuracy of 96% for the AD dataset. Therefore, ML algorithms have the potential to drastically lower Alzheimer's disease annual mortality rates through accurate detection.

Three-round learning strategy based on 3D deep convolutional GANs for Alzheimer's disease staging

Accurately diagnosing of Alzheimer's disease (AD) and its early stages is critical for prompt treatment or potential intervention to delay the the disease's progression. Convolutional neural networks (CNNs) models have shown promising results in structural MRI (sMRI)-based diagnosis, but their performance, particularly for 3D models, is constrained by the lack of labeled training samples. To address the overfitting problem brought on by the insufficient training sample size, we propose a three-round learning strategy that combines transfer learning with generative adversarial learning. In the first round, a 3D Deep Convolutional Generative Adversarial Networks (DCGAN) model was trained with all available sMRI data to learn the common feature of sMRI through unsupervised generative adversarial learning. The second round involved transferring and fine-tuning, and the pre-trained discriminator (D) of the DCGAN learned more specific features for the classification task between AD and cognitively normal (CN). In the final round, the weights learned in the AD versus CN classification task were transferred to the MCI diagnosis. By highlighting brain regions with high prediction weights using 3D Grad-CAM, we further enhanced the model's interpretability. The proposed model achieved accuracies of 92.8%, 78.1%, and 76.4% in the classifications of AD versus CN, AD versus MCI, and MCI versus CN, respectively. The experimental results show that our proposed model avoids overfitting brought on by a paucity of sMRI data and enables the early detection of AD.

Visual deep learning of unprocessed neuroimaging characterises dementia subtypes and generalises across non-stereotypic samples

BACKGROUND

Dementia's diagnostic protocols are mostly based on standardised neuroimaging data collected in the Global North from homogeneous samples. In other non-stereotypical samples (participants with diverse admixture, genetics, demographics, MRI signals, or cultural origins), classifications of disease are difficult due to demographic and region-specific sample heterogeneities, lower quality scanners, and non-harmonised pipelines.

METHODS

We implemented a fully automatic computer-vision classifier using deep learning neural networks. A DenseNet was applied on raw (unpreprocessed) data from 3000 participants (behavioural variant frontotemporal dementia-bvFTD, Alzheimer's disease-AD, and healthy controls; both male and female as self-reported by participants). We tested our results in demographically matched and unmatched samples to discard possible biases and performed multiple out-of-sample validations.

FINDINGS

Robust classification results across all groups were achieved from standardised 3T neuroimaging data from the Global North, which also generalised to standardised 3T neuroimaging data from Latin America. Moreover, DenseNet also generalised to non-standardised, routine 1.5T clinical images from Latin America. These generalisations were robust in samples with heterogenous MRI recordings and were not confounded by demographics (i.e., were robust in both matched and unmatched samples, and when incorporating demographic variables in a multifeatured model). Model interpretability analysis using occlusion sensitivity evidenced core pathophysiological regions for each disease (mainly the hippocampus in AD, and the insula in bvFTD) demonstrating biological specificity and plausibility.

INTERPRETATION

The generalisable approach outlined here could be used in the future to aid clinician decision-making in diverse samples.

FUNDING

The specific funding of this article is provided in the acknowledgements section.

Revealing the correlations between brain cortical characteristics and susceptibility genes for Alzheimer disease: a cross-sectional study

BACKGROUND

Alzheimer disease (AD) is a progressive neurodegenerative disease closely related to genes and characterized by the atrophy of the cerebral cortex. Correlations between imaging phenotypes and the susceptibility genes for AD, as demonstrated in the findings of genome-wide association studies (GWASs), still need to be addressed due to the complicated structure of the human cortex.

METHODS

In our study, an improved GWAS method, whole cortex characteristics GWAS (WCC-GWAS), was proposed. The WCC-GWAS uses multiple cortex characteristics of gray-matter volume (GMV), cortical thickness (CT), cortical surface area (CSA), and local gyrification index (LGI). A cohort of 496 participants was enrolled and divided into 4 groups: normal control (NC; n=122), early mild cognitive impairment (EMCI; n=196), late mild cognitive impairment (LMCI; n=62), and AD (n=116). Based on the Desikan-Killiany atlas, the brain was parcellated into 68 brain regions, and the WCC of each brain region was individually calculated. Four cortex characteristics of GMV, CT, CSA, and LGI across the 4 groups optimized with multiple comparisons and the ReliefF algorithm were taken as magnetic resonance imaging (MRI) brain phenotypes. Under the model of multiple linear additive genetic regression, the correlations between the MRI brain phenotypes and single-nucleotide polymorphisms (SNPs) were deduced.

RESULTS

The findings identified 2 prominent correlations. First, rs7309929 of neuron navigator 3 (NAV3) located on chromosome 12 correlated with the decreased GMV for the left middle temporal gyrus (P=0.0074). Second, rs11250992 of long intergenic non-protein-coding RNA 700 (LINC00700) located on chromosome 10 correlated with the decreased CT for the left supramarginal gyrus (P=0.0019).

CONCLUSIONS

The findings suggested that the correlations between phenotypes and genotypes could be effectively evaluated. The strategy of extracting MRI phenotypes as endophenotypes provided valuable indications in AD GWAS.

Exploiting machine learning models to identify novel Alzheimer’s disease biomarkers and potential targets

We still do not have an effective treatment for Alzheimer's disease (AD) despite it being the most common cause of dementia and impaired cognitive function. Thus, research endeavors are directed toward identifying AD biomarkers and targets. In this regard, we designed a computational method that exploits multiple hub gene ranking methods and feature selection methods with machine learning and deep learning to identify biomarkers and targets. First, we used three AD gene expression datasets to identify 1/ hub genes based on six ranking algorithms (Degree, Maximum Neighborhood Component (MNC), Maximal Clique Centrality (MCC), Betweenness Centrality (BC), Closeness Centrality, and Stress Centrality), 2/ gene subsets based on two feature selection methods (LASSO and Ridge). Then, we developed machine learning and deep learning models to determine the gene subset that best distinguishes AD samples from the healthy controls. This work shows that feature selection methods achieve better prediction performances than the hub gene sets. Beyond this, the five genes identified by both feature selection methods (LASSO and Ridge algorithms) achieved an AUC = 0.979. We further show that 70% of the upregulated hub genes (among the 28 overlapping hub genes) are AD targets based on a literature review and six miRNA (hsa-mir-16-5p, hsa-mir-34a-5p, hsa-mir-1-3p, hsa-mir-26a-5p, hsa-mir-93-5p, hsa-mir-155-5p) and one transcription factor, JUN, are associated with the upregulated hub genes. Furthermore, since 2020, four of the six microRNA were also shown to be potential AD targets. To our knowledge, this is the first work showing that such a small number of genes can distinguish AD samples from healthy controls with high accuracy and that overlapping upregulated hub genes can narrow the search space for potential novel targets.

Performance of machine learning algorithms for dementia assessment: impacts of language tasks, recording media, and modalities

OBJECTIVES

Automatic speech and language assessment methods (SLAMs) can help clinicians assess speech and language impairments associated with dementia in older adults. The basis of any automatic SLAMs is a machine learning (ML) classifier that is trained on participants' speech and language. However, language tasks, recording media, and modalities impact the performance of ML classifiers. Thus, this research has focused on evaluating the effects of the above-mentioned factors on the performance of ML classifiers that can be used for dementia assessment.

METHODOLOGY

Our methodology includes the following steps: (1) Collecting speech and language datasets from patients and healthy controls; (2) Using feature engineering methods which include feature extraction methods to extract linguistic and acoustic features and feature selection methods to select most informative features; (3) Training different ML classifiers; and (4) Evaluating the performance of ML classifiers to investigate the impacts of language tasks, recording media, and modalities on dementia assessment.

RESULTS

Our results show that (1) the ML classifiers trained with the picture description language task perform better than the classifiers trained with the story recall language task; (2) the data obtained from phone-based recordings improves the performance of ML classifiers compared to data obtained from web-based recordings; and (3) the ML classifiers trained with acoustic features perform better than the classifiers trained with linguistic features.

CONCLUSION

This research demonstrates that we can improve the performance of automatic SLAMs as dementia assessment methods if we: (1) Use the picture description task to obtain participants' speech; (2) Collect participants' voices via phone-based recordings; and (3) Train ML classifiers using only acoustic features. Our proposed methodology will help future researchers to investigate the impacts of different factors on the performance of ML classifiers for assessing dementia.

Classification of Alzheimer's disease using robust TabNet neural networks on genetic data

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases and its onset is significantly associated with genetic factors. Being the capabilities of high specificity and accuracy, genetic testing has been considered as an important technique for AD diagnosis. In this paper, we presented an improved deep learning (DL) algorithm, namely differential genes screening TabNet (DGS-TabNet) for AD binary and multi-class classifications. For performance evaluation, our proposed approach was compared with three novel DLs of multi-layer perceptron (MLP), neural oblivious decision ensembles (NODE), TabNet as well as five classical machine learnings (MLs) including decision tree (DT), random forests (RF), gradient boosting decision tree (GBDT), light gradient boosting machine (LGBM) and support vector machine (SVM) on the public data set of gene expression omnibus (GEO). Moreover, the biological interpretability of global important genetic features implemented for AD classification was revealed by the Kyoto encyclopedia of genes and genomes (KEGG) and gene ontology (GO). The results demonstrated that our proposed DGS-TabNet achieved the best performance with an accuracy of 93.80% for binary classification, and with an accuracy of 88.27% for multi-class classification. Meanwhile, the gene pathway analyses demonstrated that there existed two most important global genetic features of AVIL and NDUFS4 and those obtained 22 feature genes were partially correlated with AD pathogenesis. It was concluded that the proposed DGS-TabNet could be used to detect AD-susceptible genes and the biological interpretability of susceptible genes also revealed the potential possibility of being AD biomarkers.

The foundation and architecture of precision medicine in neurology and psychiatry

Neurological and psychiatric diseases have high degrees of genetic and pathophysiological heterogeneity, irrespective of clinical manifestations. Traditional medical paradigms have focused on late-stage syndromic aspects of these diseases, with little consideration of the underlying biology. Advances in disease modeling and methodological design have paved the way for the development of precision medicine (PM), an established concept in oncology with growing attention from other medical specialties. We propose a PM architecture for central nervous system diseases built on four converging pillars: multimodal biomarkers, systems medicine, digital health technologies, and data science. We discuss Alzheimer's disease (AD), an area of significant unmet medical need, as a case-in-point for the proposed framework. AD can be seen as one of the most advanced PM-oriented disease models and as a compelling catalyzer towards PM-oriented neuroscience drug development and advanced healthcare practice.

Towards better interpretable and generalizable AD detection using collective artificial intelligence

Alzheimer's Disease is the most common cause of dementia. Accurate diagnosis and prognosis of this disease are essential to design an appropriate treatment plan, increasing the life expectancy of the patient. Intense research has been conducted on the use of machine learning to identify Alzheimer's Disease from neuroimaging data, such as structural magnetic resonance imaging. In recent years, advances of deep learning in computer vision suggest a new research direction for this problem. Current deep learning-based approaches in this field, however, have a number of drawbacks, including the interpretability of model decisions, a lack of generalizability information and a lower performance compared to traditional machine learning techniques. In this paper, we design a two-stage framework to overcome these limitations. In the first stage, an ensemble of 125 U-Nets is used to grade the input image, producing a 3D map that reflects the disease severity at voxel-level. This map can help to localize abnormal brain areas caused by the disease. In the second stage, we model a graph per individual using the generated grading map and other information about the subject. We propose to use a graph convolutional neural network classifier for the final classification. As a result, our framework demonstrates comparative performance to the state-of-the-art methods in different datasets for both diagnosis and prognosis. We also demonstrate that the use of a large ensemble of U-Nets offers a better generalization capacity for our framework.

Attri-VAE: Attribute-based interpretable representations of medical images with variational autoencoders

Deep learning (DL) methods where interpretability is intrinsically considered as part of the model are required to better understand the relationship of clinical and imaging-based attributes with DL outcomes, thus facilitating their use in the reasoning behind the medical decisions. Latent space representations built with variational autoencoders (VAE) do not ensure individual control of data attributes. Attribute-based methods enforcing attribute disentanglement have been proposed in the literature for classical computer vision tasks in benchmark data. In this paper, we propose a VAE approach, the Attri-VAE, that includes an attribute regularization term to associate clinical and medical imaging attributes with different regularized dimensions in the generated latent space, enabling a better-disentangled interpretation of the attributes. Furthermore, the generated attention maps explained the attribute encoding in the regularized latent space dimensions. Using the Attri-VAE approach we analyzed healthy and myocardial infarction patients with clinical, cardiac morphology, and radiomics attributes. The proposed model provided an excellent trade-off between reconstruction fidelity, disentanglement, and interpretability, outperforming state-of-the-art VAE approaches according to several quantitative metrics. The resulting latent space allowed the generation of realistic synthetic data in the trajectory between two distinct input samples or along a specific attribute dimension to better interpret changes between different cardiac conditions.

Hybrid Whale and Gray Wolf Deep Learning Optimization Algorithm for Prediction of Alzheimer’s Disease

In recent years, finding the optimal solution for image segmentation has become more important in many applications. The whale optimization algorithm (WOA) is a metaheuristic optimization technique that has the advantage of achieving the global optimal solution while also being simple to implement and solving many real-time problems. If the complexity of the problem increases, the WOA may stick to local optima rather than global optima. This could be an issue in obtaining a better optimal solution. For this reason, this paper recommends a hybrid algorithm that is based on a mixture of the WOA and gray wolf optimization (GWO) for segmenting the brain sub regions, such as the gray matter (GM), white matter (WM), ventricle, corpus callosum (CC), and hippocampus (HC). This hybrid mixture consists of two steps, i.e., the WOA and GWO. The proposed method helps in diagnosing Alzheimer’s disease (AD) by segmenting the brain sub regions (SRs) by using a hybrid of the WOA and GWO (H-WOA-GWO, which is represented as HWGO). The segmented region was validated with different measures, and it shows better accuracy results of 92%. Following segmentation, the deep learning classifier was utilized to categorize normal and AD images. The combination of WOA and GWO yields an accuracy of 90%. As a result, it was discovered that the suggested method is a highly successful technique for identifying the ideal solution, and it is paired with a deep learning algorithm for classification.

Biomarkers of diagnosis, prognosis, pathogenesis, response to therapy: Convergence or divergence? Lessons from Alzheimer's disease and synucleinopathies

Alzheimer's disease (AD) is the most common disorder associated with cognitive impairment. Recent observations emphasize the pathogenic role of multiple factors inside and outside the central nervous system, supporting the notion that AD is a syndrome of many etiologies rather than a "heterogeneous" but ultimately unifying disease entity. Moreover, the defining pathology of amyloid and tau coexists with many others, such as α-synuclein, TDP-43, and others, as a rule, not an exception. Thus, an effort to shift our AD paradigm as an amyloidopathy must be reconsidered. Along with amyloid accumulation in its insoluble state, β-amyloid is becoming depleted in its soluble, normal states, as a result of biological, toxic, and infectious triggers, requiring a shift from convergence to divergence in our approach to neurodegeneration. These aspects are reflected-in vivo-by biomarkers, which have become increasingly strategic in dementia. Similarly, synucleinopathies are primarily characterized by abnormal deposition of misfolded α-synuclein in neurons and glial cells and, in the process, depleting the levels of the normal, soluble α-synuclein that the brain needs for many physiological functions. The soluble to insoluble conversion also affects other normal brain proteins, such as TDP-43 and tau, accumulating in their insoluble states in both AD and dementia with Lewy bodies (DLB). The two diseases have been distinguished by the differential burden and distribution of insoluble proteins, with neocortical phosphorylated tau deposition more typical of AD and neocortical α-synuclein deposition peculiar to DLB. We propose a reappraisal of the diagnostic approach to cognitive impairment from convergence (based on clinicopathologic criteria) to divergence (based on what differs across individuals affected) as a necessary step for the launch of precision medicine.

The status of digital pathology and associated infrastructure within Alzheimer's Disease Centers

Digital pathology (DP) has transformative potential, especially for Alzheimer disease and related disorders. However, infrastructure barriers may limit adoption. To provide benchmarks and insights into implementation barriers, a survey was conducted in 2019 within National Institutes of Health's Alzheimer's Disease Centers (ADCs). Questions covered infrastructure, funding sources, and data management related to digital pathology. Of the 35 ADCs to which the survey was sent, 33 responded. Most respondents (81%) stated that their ADC had digital slide scanner access, with the most frequent brand being Aperio/Leica (62.9%). Approximately a third of respondents stated there were fees to utilize the scanner. For DP and machine learning (ML) resources, 41% of respondents stated none was supported by their ADC. For scanner purchasing and operations, 50% of respondents stated they received institutional support. Some were unsure of the file size of scanned digital images (37%) and total amount of storage space files occupied (50%). Most (76%) were aware of other departments at their institution working with ML; a similar (76%) percentage were unaware of multiuniversity or industry partnerships. These results demonstrate many ADCs have access to a digital slide scanner; additional investigations are needed to further understand hurdles to implement DP and ML workflows.

Predicting Brain Amyloid Positivity from T1 weighted brain MRI and MRI-derived Gray Matter, White Matter and CSF maps using Transfer Learning on 3D CNNs

Abnormal β-amyloid (Aβ) accumulation in the brain is an early indicator of Alzheimer's disease and practical tests could help identify patients who could respond to treatment, now that promising anti-amyloid drugs are available. Even so, Aβ positivity (Aβ+) is assessed using PET or CSF assays, both highly invasive procedures. Here, we investigate how well Aβ+ can be predicted from T1 weighted brain MRI and gray matter, white matter and cerebrospinal fluid segmentations from T1-weighted brain MRI (T1w), a less invasive alternative. We used 3D convolutional neural networks to predict Aβ+ based on 3D brain MRI data, from 762 elderly subjects (mean age: 75.1 yrs. ± 7.6SD; 394F/368M; 459 healthy controls, 67 with MCI and 236 with dementia) scanned as part of the Alzheimer's Disease Neuroimaging Initiative. We also tested whether the accuracy increases when using transfer learning from the larger UK Biobank dataset. Overall, the 3D CNN predicted Aβ+ with 76% balanced accuracy from T1w scans. The closest performance to this was using white matter maps alone when the model was pre-trained on an age prediction in the UK Biobank. The performance of individual tissue maps was less than the T1w, but transfer learning helped increase the accuracy. Although tests on more diverse data are warranted, deep learned models from standard MRI show initial promise for Aβ+ estimation, before considering more invasive procedures.

Deep belief network-based approach for detecting Alzheimer's disease using the multi-omics data

Alzheimer's disease (AD) is the most uncertain form of Dementia in terms of finding out the mechanism. AD does not have a vital genetic factor to relate to. There were no reliable techniques and methods to identify the genetic risk factors associated with AD in the past. Most of the data available were from the brain images. However, recently, there have been drastic advancements in the high-throughput techniques in bioinformatics. It has led to focused researches in discovering the AD causing genetic risk factors. Recent analysis has resulted in considerable prefrontal cortex data with which classification and prediction models can be developed for AD. We have developed a Deep Belief Network-based prediction model using the DNA Methylation and Gene Expression Microarray Data, with High Dimension Low Sample Size (HDLSS) issues. To overcome the HDLSS challenge, we performed a two-layer feature selection considering the biological aspects of the features as well. In the two-layered feature selection approach, first the differentially expressed genes and differentially methylated positions are identified, then both the datasets are combined using Jaccard similarity measure. As the second step, an ensemble-based feature selection approach is implemented to further narrow down the gene selection. The results show that the proposed feature selection technique outperforms the existing commonly used feature selection techniques, such as Support Vector Machine Recursive Feature Elimination (SVM-RFE), and Correlation-based Feature Selection (CBS). Furthermore, the Deep Belief Network-based prediction model performs better than the widely used Machine Learning models. Also, the multi-omics dataset shows promising results compared to the single omics.

Rise of Deep Learning Clinical Applications and Challenges in Omics Data: A Systematic Review

This research aims to review and evaluate the most relevant scientific studies about deep learning (DL) models in the omics field. It also aims to realize the potential of DL techniques in omics data analysis fully by demonstrating this potential and identifying the key challenges that must be addressed. Numerous elements are essential for comprehending numerous studies by surveying the existing literature. For example, the clinical applications and datasets from the literature are essential elements. The published literature highlights the difficulties encountered by other researchers. In addition to looking for other studies, such as guidelines, comparative studies, and review papers, a systematic approach is used to search all relevant publications on omics and DL using different keyword variants. From 2018 to 2022, the search procedure was conducted on four Internet search engines: IEEE Xplore, Web of Science, ScienceDirect, and PubMed. These indexes were chosen because they offer enough coverage and linkages to numerous papers in the biological field. A total of 65 articles were added to the final list. The inclusion and exclusion criteria were specified. Of the 65 publications, 42 are clinical applications of DL in omics data. Furthermore, 16 out of 65 articles comprised the review publications based on single- and multi-omics data from the proposed taxonomy. Finally, only a small number of articles (7/65) were included in papers focusing on comparative analysis and guidelines. The use of DL in studying omics data presented several obstacles related to DL itself, preprocessing procedures, datasets, model validation, and testbed applications. Numerous relevant investigations were performed to address these issues. Unlike other review papers, our study distinctly reflects different observations on omics with DL model areas. We believe that the result of this study can be a useful guideline for practitioners who look for a comprehensive view of the role of DL in omics data analysis.

OViTAD: Optimized Vision Transformer to Predict Various Stages of Alzheimer's Disease Using Resting-State fMRI and Structural MRI Data

Advances in applied machine learning techniques for neuroimaging have encouraged scientists to implement models to diagnose brain disorders such as Alzheimer's disease at early stages. Predicting the exact stage of Alzheimer's disease is challenging; however, complex deep learning techniques can precisely manage this. While successful, these complex architectures are difficult to interrogate and computationally expensive. Therefore, using novel, simpler architectures with more efficient pattern extraction capabilities, such as transformers, is of interest to neuroscientists. This study introduced an optimized vision transformer architecture to predict the group membership by separating healthy adults, mild cognitive impairment, and Alzheimer's brains within the same age group (>75 years) using resting-state functional (rs-fMRI) and structural magnetic resonance imaging (sMRI) data aggressively preprocessed by our pipeline. Our optimized architecture, known as OViTAD is currently the sole vision transformer-based end-to-end pipeline and outperformed the existing transformer models and most state-of-the-art solutions. Our model achieved F1-scores of 97%±0.0 and 99.55%±0.39 from the testing sets for the rs-fMRI and sMRI modalities in the triple-class prediction experiments. Furthermore, our model reached these performances using 30% fewer parameters than a vanilla transformer. Furthermore, the model was robust and repeatable, producing similar estimates across three runs with random data splits (we reported the averaged evaluation metrics). Finally, to challenge the model, we observed how it handled increasing noise levels by inserting varying numbers of healthy brains into the two dementia groups. Our findings suggest that optimized vision transformers are a promising and exciting new approach for neuroimaging applications, especially for Alzheimer's disease prediction.

Effects of Patchwise Sampling Strategy to Three-Dimensional Convolutional Neural Network-Based Alzheimer's Disease Classification

In recent years, the rapid development of artificial intelligence has promoted the widespread application of convolutional neural networks (CNNs) in neuroimaging analysis. Although three-dimensional (3D) CNNs can utilize the spatial information in 3D volumes, there are still some challenges related to high-dimensional features and potential overfitting issues. To overcome these problems, patch-based CNNs have been used, which are beneficial for model generalization. However, it is unclear how the choice of a patchwise sampling strategy affects the performance of the Alzheimer's Disease (AD) classification. To this end, the present work investigates the impact of a patchwise sampling strategy for 3D CNN based AD classification. A 3D framework cascaded by two-stage subnetworks was used for AD classification. The patch-level subnetworks learned feature representations from local image patches, and the subject-level subnetwork combined discriminative feature representations from all patch-level subnetworks to generate a classification score at the subject level. Experiments were conducted to determine the effect of patch partitioning methods, the effect of patch size, and interactions between patch size and training set size for AD classification. With the same data size and identical network structure, the 3D CNN model trained with 48 × 48 × 48 cubic image patches showed the best performance in AD classification (ACC = 89.6%). The model trained with hippocampus-centered, region of interest (ROI)-based image patches showed suboptimal performance. If the pathological features are concentrated only in some regions affected by the disease, the empirically predefined ROI patches might be the right choice. The better performance of cubic image patches compared with cuboidal image patches is likely related to the pathological distribution of AD. The image patch size and training sample size together have a complex influence on the performance of the classification. The size of the image patches should be determined based on the size of the training sample to compensate for noisy labels and the problem of the curse of dimensionality. The conclusions of the present study can serve as a reference for the researchers who wish to develop a superior 3D patch-based CNN model with an appropriate patch sampling strategy.

A transfer learning approach for multiclass classification of Alzheimer's disease using MRI images

Alzheimer's is an acute degenerative disease affecting the elderly population all over the world. The detection of disease at an early stage in the absence of a large-scale annotated dataset is crucial to the clinical treatment for the prevention and early detection of Alzheimer's disease (AD). In this study, we propose a transfer learning base approach to classify various stages of AD. The proposed model can distinguish between normal control (NC), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and AD. In this regard, we apply tissue segmentation to extract the gray matter from the MRI scans obtained from the Alzheimer's Disease National Initiative (ADNI) database. We utilize this gray matter to tune the pre-trained VGG architecture while freezing the features of the ImageNet database. It is achieved through the addition of a layer with step-wise freezing of the existing blocks in the network. It not only assists transfer learning but also contributes to learning new features efficiently. Extensive experiments are conducted and results demonstrate the superiority of the proposed approach.