Optimized sequential classification models for mild cognitive impairment screening based on handwriting and speech data.

Decision letter: Stage-dependent differential influence of metabolic and structural networks on memory across Alzheimer’s disease continuum

Background:Gait, speech, and drawing behaviors have been shown to be sensitive to the diagnosis of Alzheimer’s disease (AD) and mild cognitive impairment (MCI). However, previous studies focused on only analyzing individual behavioral modalities, although these studies suggested that each of these modalities may capture different profiles of cognitive impairments associated with AD.Objective:We aimed to investigate if combining behavioral data of gait, speech, and drawing can improve classification performance compared with the use of individual modality and if each of these behavioral data can be associated with different cognitive and clinical measures for the diagnosis of AD and MCI.Methods:Behavioral data of gait, speech, and drawing were acquired from 118 AD, MCI, and cognitively normal (CN) participants.Results:Combining all three behavioral modalities achieved 93.0% accuracy for classifying AD, MCI, and CN, and only 81.9% when using the best individual behavioral modality. Each of these behavioral modalities was statistically significantly associated with different cognitive and clinical measures for diagnosing AD and MCI.Conclusion:Our findings indicate that these behaviors provide different and complementary information about cognitive impairments such that classification of AD and MCI is superior to using either in isolation.

Detecting Mild Cognitive Impairment (MCI) is crucial for mitigating the risk of Alzheimer's disease (AD), a leading global cause of death. However, the current gold standard for AD and MCI detection relies on specialized equipment often limited to large testing centers, particularly in low-resource settings like Thailand. Our previous work aimed to create a cost-effective MCI and AD screening method using fundus images but struggled to differentiate between AD and MCI. Henceforth, we developed the proposed methodology, utilizing DenseNet-121 on polar-transformed and zone-selected fundus images, which significantly enhances AD and MCI classification, achieving 83% accuracy, 90% sensitivity, 77% specificity, 87% precision, and an F-1 score of 88%. Moreover, the model's Grad-Cam++ heatmap highlights vasculature differences, particularly in tortuosity and thickness, between AD and MCI fundus images. Combined with our previous work, we created a fully automated pipeline model for MCI, AD, and Normal aging classification, which is inexpensive, fast, and non-invasive with an overall 3-class accuracy of 88%.

Alzheimer's disease (AD) is a progressive chronic illness that leads to cognitive decline and dementia. Neuroimaging technologies, such as functional magnetic resonance imaging (fMRI), and deep learning approaches offer promising avenues for AD classification. In this study, we investigate the use of fMRI-based functional connectivity (FC) measures, including the Pearson correlation coefficient (PCC), maximal information coefficient (MIC), and extended maximal information coefficient (eMIC), combined with extreme learning machines (ELM) for AD classification. Our findings demonstrate that employing non-linear techniques, such as MIC and eMIC, as features for classification yields accurate results. Specifically, eMIC-based features achieve a high accuracy of 94% for classifying cognitively normal (CN) and mild cognitive impairment (MCI) individuals, outperforming PCC (81%) and MIC (85%). For MCI and AD classification, MIC achieves higher accuracy (81%) compared to PCC (58%) and eMIC (78%). In CN and AD classification, eMIC exhibits the best accuracy of 95% compared to MIC (90%) and PCC (87%). These results underscore the effectiveness of fMRI-based features derived from non-linear techniques in accurately differentiating AD and MCI individuals from CN individuals, emphasizing the potential of neuroimaging and machine learning methods for improving AD diagnosis and classification.

Classification of Alzheimer’s disease by using tau PET images and deep convolutional neural networks

Neurodegenerative disorders such as Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) significantly impact brain function and cognition. Advanced neuroimaging techniques, particularly Magnetic Resonance Imaging (MRI), play a crucial role in diagnosing these conditions by detecting structural abnormalities. This study leverages the ADNI and OASIS datasets, renowned for their extensive MRI data, to develop effective models for detecting AD and MCI. The research conducted three sets of tests, comparing multiple groups: multi-class classification (AD vs. Cognitively Normal (CN) vs. MCI), binary classification (AD vs. CN, and MCI vs. CN), to evaluate the performance of models trained on ADNI and OASIS datasets. Key preprocessing techniques such as Gaussian filtering, contrast enhancement, and resizing were applied to both datasets. Additionally, skull stripping using U-Net was utilized to extract features by removing the skull. Several prominent deep learning architectures including DenseNet-201, EfficientNet-B0, ResNet-50, ResNet-101, and ResNet-152 were investigated to identify subtle patterns associated with AD and MCI. Transfer learning techniques were employed to enhance model performance, leveraging pre-trained datasets for improved Alzheimer's MCI detection. ResNet-101 exhibited superior performance compared to other models, achieving 98.21% accuracy on the ADNI dataset and 97.45% accuracy on the OASIS dataset in multi-class classification tasks encompassing AD, CN, and MCI. It also performed well in binary classification tasks distinguishing AD from CN. ResNet-152 excelled particularly in binary classification between MCI and CN on the OASIS dataset. These findings underscore the utility of deep learning models in accurately identifying and distinguishing neurodegenerative diseases, showcasing their potential for enhancing clinical diagnosis and treatment monitoring.

Early diagnosis of Alzheimer's disease (AD) based on neuroimaging and fluid biomarker data has attracted a lot of interest in medical image analysis. Most existing studies have been focusing on two-class classification problems, e.g., distinguishing AD patients from cognitive normal (CN) elderly or distinguishing mild cognitive impairment (MCI) individuals from CN elderly. However, to achieve the goal of early diagnosis of AD, we need to identify individuals with AD and MCI, especially MCI individuals who will convert to AD, in a single setting, which essentially is a multi-class classification problem. In this paper, we propose an ordinal ranking based classification method for distinguishing CN, MCI non-converter (MCI-NC), MCI converter (MCI-C), and AD at an individual level, taking into account the inherent ordinal severity of brain damage caused by normal aging, MCI, and AD, rather than formulating the classification as a multi-class classification problem. Experiment results indicate that the proposed method can achieve a better performance than traditional multi-class classification techniques based on multimodal neuroimaging and CSF biomarker data of the ADNI.

Quantitative perfusion assessment with arterial spin labeling magnetic resonance imaging for predicting cognitive decline in Alzheimer's disease: A systematic review and meta-analysis.

Amyloid β‐specific T cell response is enhanced in individuals with mild cognitive impairment

The delineation of biomarkers and neuropsychiatric symptoms across normal cognition, mild cognitive impairment (MCI), and dementia stages holds significant promise for early diagnosis and intervention strategies. This research investigates the association of neuropsychiatric symptoms, evaluated via the Neuropsychiatric Inventory (NPI), with cerebrospinal fluid (CSF) biomarkers (Amyloid-β42, P-tau, T-tau) across a spectrum of cognitive states to enhance diagnostic accuracy and treatment approaches. Drawing from the National Alzheimer's Coordinating Center's Uniform Data Set Version 3, comprising 977 individuals with normal cognition, 270 with MCI, and 649 with dementia. To assess neuropsychiatric symptoms, we employed the NPI to understand the behavioral and psychological symptoms associated with each cognitive category. For the analysis of CSF biomarkers, we measured levels of Amyloid-β42, P-tau, and T-tau using the enzyme-linked immunosorbent assay (ELISA) and Luminex multiplex xMAP assay protocols. These biomarkers are critical in understanding the pathophysiological underpinnings of Alzheimer's disease and its progression, with specific patterns indicative of disease stage and severity. This study cohort consists of 1896 participants, which is composed of 977 individuals with normal cognition, 270 with MCI, and 649 with dementia. Dementia is characterized by significantly higher NPI scores, which are largely reflective of mood-related symptoms (p < 0.001). In terms of biomarkers, normal cognition shows median Amyloid-β at 656.0 pg/mL, MCI at 300.6 pg/mL, and dementia at 298.8 pg/mL (p < 0.001). Median P-tau levels are 36.00 pg/mL in normal cognition, 49.12 pg/mL in MCI, and 58.29 pg/mL in dementia (p < 0.001). Median T-tau levels are 241.0 pg/mL in normal cognition, 140.6 pg/mL in MCI, and 298.3 pg/mL in dementia (p < 0.001). Furthermore, the T-tau/Aβ-42 ratio increases progressively from 0.058 in the normal cognition group to 0.144 in the MCI group, and to 0.209 in the dementia group (p < 0.001). Similarly, the P-tau/Aβ-42 ratio also escalates from 0.305 in individuals with normal cognition to 0.560 in MCI, and to 0.941 in dementia (p < 0.001). The notable disparities in NPI and CSF biomarkers among normal, MCI and Alzheimer's patients underscore their diagnostic potential. Their combined assessment could greatly improve early detection and precise diagnosis of MCI and dementia, facilitating more effective and timely treatment strategies.

A mobile application using automatic speech analysis for classifying Alzheimer's disease and mild cognitive impairment

Cerebellar functional connectivity changes have been reported in Alzheimer's disease (AD), but a comprehensive framework integrating these findings is lacking. This retrospective study investigates the cerebello-thalamo-cortical (CTC) circuit in AD, using functional gradient analysis to elucidate deficits and potential biomarkers. We analyzed data from 246 participants enrolled in the Alzheimer's Disease Neuroimaging Initiative (ADNI-3; NCT02854033), including 58 with AD, 103 with mild cognitive impairment (MCI), and 85 cognitively normal (CN) controls, matched for age and sex. All individuals underwent comprehensive neuropsychological assessments (MMSE, MoCA, ADAS-Cog) and MRI scans. We extracted mean time series for 270 brain regions (an extended Power atlas) and computed pairwise functional connectivity, focusing on CTC circuitry. Thalamic and cerebellar connectivity gradients were derived using voxel-wise correlation matrices and the BrainSpace toolbox, defining thalamic and cerebellar masks from the Melbourne subcortical atlas and AAL atlas, respectively. ANCOVA with post hoc analyses, controlling for age and sex, was conducted to assess abnormal CTC connectivity across AD, MCI, and CN groups. LASSO regression identified edges within the CTC circuitry that significantly differed between AD and CN, MCI and CN, AD and MCI, as well as was used to construct Logistic classification model. Pearson correlations were performed to examine relationships between mean CTC connectivity, individual edges, and cognitive scores (MMSE, MoCA, ADAS-Cog). To explore the hierarchical organization of the thalamus and cerebellum, global gradient distributions were compared across groups using two-sample Kolmogorov-Smirnov tests. Additionally, ANCOVA was applied to compare subfield- and functional-level gradients of the thalamus and cerebellum among AD, MCI, and CN. False discovery rate (FDR) corrections were used, setting the statistical significance threshold was set at P < 0.05. AD and MCI individuals exhibited increased CTC connectivity compared to CN (all P < 0.05). Average CTC connectivity did not correlate with cognitive scores (P > 0.05), but specific CTC edges were correlated. LASSO regression identified 20 discriminative edges, achieving high accuracy in AD-CN classification (AUC = 0.92 training, AUC = 0.80 test). Thalamic and cerebellar gradient distributions differed significantly across groups (all P < 0.05), with specific regions showing distinct gradient scores. Five cerebellar functional networks exhibited decreased gradient scores. Significant CTC hyperconnectivity in AD and MCI compared with CN suggests early thalamic and cerebellar dysregulation. Classification analyses effectively distinguished AD vs. CN but were moderate for MCI vs. CN and limited for MCI vs. AD. Gradient analyses revealed global- and subfield-level disruptions in AD, emphasizing the role of thalamic and cerebellar interactions in cognitive decline and offering potential diagnostic markers and therapeutic targets.

Early diagnosis of mild cognitive impairment and Alzheimer's disease using multimodal feature-based deep learning models in a Chinese elderly population.

A challenge in developing informative neuroimaging biomarkers for early diagnosis of Alzheimer's disease is the need to identify biomarkers that are evident before the onset of clinical symptoms, and which have sufficient sensitivity and specificity on an individual patient basis. Recent literature suggests that spatial patterns of brain atrophy discriminate amongst Alzheimer's disease, mild cognitive impairment (MCI) and cognitively normal (CN) older adults with high accuracy on an individual basis, thereby offering promise that subtle brain changes can be detected during prodromal Alzheimer's disease stages. Here, we investigate whether these spatial patterns of brain atrophy can be detected in CN and MCI individuals and whether they are associated with cognitive decline. Images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were used to construct a pattern classifier that recognizes spatial patterns of brain atrophy which best distinguish Alzheimer's disease patients from CN on an individual person basis. This classifier was subsequently applied to longitudinal magnetic resonance imaging scans of CN and MCI participants in the Baltimore Longitudinal Study of Aging (BLSA) neuroimaging study. The degree to which Alzheimer's disease-like patterns were present in CN and MCI subjects was evaluated longitudinally in relation to cognitive performance. The oldest BLSA CN individuals showed progressively increasing Alzheimer's disease-like patterns of atrophy, and individuals with these patterns had reduced cognitive performance. MCI was associated with steeper longitudinal increases of Alzheimer's disease-like patterns of atrophy, which separated them from CN (receiver operating characteristic area under the curve equal to 0.89). Our results suggest that imaging-based spatial patterns of brain atrophy of Alzheimer's disease, evaluated with sophisticated pattern analysis and recognition methods, may be useful in discriminating among CN individuals who are likely to be stable versus those who will show cognitive decline. Future prospective studies will elucidate the temporal dynamics of spatial atrophy patterns and the emergence of clinical symptoms.

Alzheimer's disease (AD) presents typically gray matter atrophy and white matter abnormalities in neuroimaging, suggesting that the gray-white matter boundary could be altered in individuals with AD. The purpose of this study was to explore differences of gray-white matter boundary Z-score (gwBZ) and its tissue volume (gwBTV) between patients with AD, amnestic mild cognitive impairment (MCI), and cognitively normal (CN) elderly participants. Three-dimensional T1-weight images of a total of 227 participants were prospectively obtained from our institute from 2006 to 2022 to map gwBZ and gwBTV on images. Statistical analyses of gwBZ and gwBTV were performed to compare the three groups (AD, MCI, CN), to assess their correlations with age and Korean version of the Mini-Mental State Examination (K-MMSE), and to evaluate their effects on AD classification in the hippocampus. This study included 62 CN participants (71.8 ± 4.8 years, 20 males, 42 females), 72 MCI participants (72.6 ± 5.1 years, 23 males, 49 females), and 93 AD participants (73.6 ± 7.7 years, 22 males, 71 females). The AD group had lower gwBZ and gwBTV than CN and MCI groups. K-MMSE showed positive correlations with gwBZ and gwBTV whereas age showed negative correlations with gwBZ and gwBTV. The combination of gwBZ or gwBTV with K-MMSE had a high accuracy in classifying AD from CN in the hippocampus with an area under curve (AUC) value of 0.972 for both. gwBZ and gwBTV were reduced in AD. They were correlated with cognitive function and age. Moreover, gwBZ or gwBTV combined with K-MMSE had a high accuracy in differentiating AD from CN in the hippocampus. These findings suggest that evaluating gwBZ and gwBTV in AD brain could be a useful tool for monitoring AD progression and diagnosis.