Diagnostic performance of hippocampal volumetry in Alzheimer's disease or mild cognitive impairment: a meta-analysis

Stepping and tapping: combining motor tasks improves cognitive classification

Gait and key-tapping are individually associated with mild cognitive impairment (MCI) and dementia. However, it is unclear if these motor functions are correlated, or whether combining them improves classification of objective (dementia, MCI) and subjective cognitive impairment (SCI). We recruited 73 participants with dementia, 106 MCI, 57 SCI, and 83 cognitively healthy controls (HC). Consensus diagnosis was made after gold-standard interdisciplinary assessment. Fast-paced gait was assessed on an electronic walkway and fast-paced key-tapping on a computer keyboard. Correlations between gait and key-tapping measures (speed, frequency, variability and contact) were tested using Pearson's correlation. Classification accuracy was calculated using area under receiver-operating-characteristic curves (AUC) and compared to the null model comprising age, sex and education. Gait and key-tapping measures correlated moderately. Combined gait and key-tapping speed improved classification accuracy of dementia (.97), and MCI (.91), from HC, but not SCI, compared to gait (dementia: .94, MCI: .87) or the null model (dementia: .89, MCI: .79). Gait and key-tapping measures were associated with Alzheimer's disease and vascular dementia, but the effect size for key-tapping variability was larger in vascular dementia (β: 225.71) compared to Alzheimer's disease (β: 38.30). Gait and key-tapping variability was associated with non-amnestic MCI. Measures of gait were correlated with corresponding key-tapping measures, but their association with cognitive impairment was not the same. Combining gait and key-tapping motor measures improved classification accuracy of MCI and dementia. This suggests gait and key-tapping measures provide information about different aspects of motor-cognitive association worth further investigation.

Artificial Intelligence-Assisted Hippocampal Segmentation and Its Diagnostic Value for Alzheimer's Disease: A Meta-analysis

BACKGROUND

Hippocampal atrophy is a key marker of Alzheimer's disease (AD) and mild cognitive impairment (MCI). Diverse artificial intelligence (AI) architectures for automated hippocampal segmentation have been increasingly reported in neuroimaging research. Different hippocampal automated segmentation methods can be of added value for the AD diagnostic work-up and treatment planning. This study aims to conduct a thorough meta-analysis to evaluate the segmentation accuracy and diagnostic performance of AI-assisted hippocampal segmentation in AD and MCI.

METHODS

We searched PubMed, Embase, Web of Science, and the Cochrane Library up to December 2024. Studies using neuroimaging data to assess AI algorithms for hippocampal segmentation and diagnosis in AD or MCI populations were included. Pooled segmentation accuracy was estimated using the Dice similarity coefficient (DSC) through a random-effects model, while diagnostic performance (sensitivity, specificity, and area under the curve [AUC]) was evaluated using a bivariate mixed-effects model.

RESULTS

A total of 27 studies were included. For segmentation accuracy, pooled DSC values were 0.82 (95% CI: 0.80-0.85) for AD, 0.85 (0.83-0.88) for MCI, and 0.86 (0.84-0.88) for normal controls (NC). Subgroup analyses indicated comparable performance between left and right hippocampi (both DSC: 0.87). Diagnostic meta-analysis demonstrated the highest accuracy for AD vs. NC (sensitivity: 0.87, specificity: 0.91, AUC: 0.95), but lower performance for AD vs. MCI (AUC: 0.80) and MCI vs. NC (AUC: 0.83).

CONCLUSION

AI-assisted hippocampal segmentation achieves good accuracy and demonstrates promising diagnostic capabilities for distinguishing AD from NC, though differentiation between AD and MCI remains challenging. Future high-quality research that applied standardized protocols, external validation, and clinical integration is needed to improve reliability in clinical practice.

Walking and Hippocampal Formation Volume Changes: A Systematic Review

BACKGROUND/OBJECTIVES

Sustaining the human brain's hippocampus from atrophy throughout ageing is critical. Exercise is proven to be effective in promoting adaptive hippocampal plasticity, and the hippocampus has a bidirectional relationship with the physical environment. Therefore, this systematic review explores the effects of walking, a simple physical activity in the environment, on hippocampal formation volume changes for lifelong brain and cognitive health.

METHOD

PubMed, Scopus, and Web of Science were searched for studies on humans published up to November 2022 examining hippocampal volume changes and walking. Twelve studies met the inclusion criteria. Study quality was assessed using the PEDro scale and ROBINS-I tool. A narrative synthesis explored walking factors associated with total, subregional, and hemisphere-specific hippocampal volume changes.

RESULTS

Overall, walking had positive effects on hippocampal volumes. Several studies found benefits of higher-intensity and greater amounts of walking for total hippocampal volume. The subiculum increased after low-intensity walking and nature exposure, while the parahippocampal gyrus benefited from vigorous intensity. The right hippocampus increased with spatial navigation during walking. No studies examined the effect of walking on the dentate gyrus.

CONCLUSIONS

This systematic review highlights walking as a multifaceted variable that can lead to manifold adaptive hippocampal volume changes. These findings support the promotion of walking as a simple, effective strategy to enhance brain health and prevent cognitive decline, suggesting the design of physical environments with natural and biophilic characteristics and layouts with greater walkability and cognitive stimulation. Future research is encouraged to explore the hippocampal subregional changes instead of focusing on total hippocampal volume, since the hippocampal formation is multicompartmental and subfields respond differently to different walking-related variables.

The current status and challenges of olfactory dysfunction study in Alzheimer's Disease

Olfactory functioning involves multiple cognitive processes and the coordinated actions of various neural systems. Any disruption at any stage of this process may result in olfactory dysfunction, which is consequently widely used to predict the onset and progression of diseases, such as Alzheimer's Disease (AD). Although the underlying mechanisms have not yet been fully unraveled, apparent changes were observed in olfactory brain areas form patients who suffer from AD by means of medical imaging and electroencephalography (EEG). Olfactory dysfunction holds significant promise in detecting AD during the preclinical stage preceding mild cognitive impairment (MCI). Owing to the strong specificity, olfactory tests are prevalently applied for screening in community cohorts. And combining olfactory tests with other biomarkers may further establish an optimal model for AD prediction in studies of specific olfactory dysfunctions and improve the sensitivity and specificity of early AD diagnosis.

Matrix Remodeling Enzymes as Potential Fluid Biomarkers of Neurodegeneration in Alzheimer's Disease

This study investigated the diagnostic accuracy of plasma biomarkers-specifically, matrix metalloproteinase (MMP-9), tissue inhibitor of metalloproteinase (TIMP-1), CD147, and the MMP-/TIMP-1 ratio in patients with Alzheimer's disease (AD) dementia. The research cohort comprised patients diagnosed with probable AD dementia and a control group of cognitively unimpaired (CU) individuals. Neuroradiological assessments included brain magnetic resonance imaging (MRI) following dementia protocols, with subsequent volumetric analysis. Additionally, cerebrospinal fluid (CSF) AD biomarkers were classified using the A/T/N system, and apolipoprotein E (APOE) ε4 carrier status was determined. Findings revealed elevated plasma levels of MMP-9 and TIMP-1 in AD dementia patients compared to CU individuals. Receiver operating characteristic (ROC) curve analysis demonstrated significant differences in the areas under the curve (AUC) for MMP-9 (p < 0.001) and TIMP-1 (p < 0.001). Notably, plasma TIMP-1 levels were significantly lower in APOE ε4+ patients than in APOE ε4- patients (p = 0.041). Furthermore, APOE ε4+ patients exhibited reduced hippocampal volume, particularly in total, right, and left hippocampal measurements. TIMP-1 levels exhibited a positive correlation, while the MMP-9/TIMP-1 ratio showed a negative correlation with hippocampal volume parameters. This study sheds light on the potential use of TIMP-1 as a diagnostic marker and its association with hippocampal changes in AD.

Integrating Biomarkers From Virtual Reality and Magnetic Resonance Imaging for the Early Detection of Mild Cognitive Impairment Using a Multimodal Learning Approach: Validation Study

BACKGROUND

Early detection of mild cognitive impairment (MCI), a transitional stage between normal aging and Alzheimer disease, is crucial for preventing the progression of dementia. Virtual reality (VR) biomarkers have proven to be effective in capturing behaviors associated with subtle deficits in instrumental activities of daily living, such as challenges in using a food-ordering kiosk, for early detection of MCI. On the other hand, magnetic resonance imaging (MRI) biomarkers have demonstrated their efficacy in quantifying observable structural brain changes that can aid in early MCI detection. Nevertheless, the relationship between VR-derived and MRI biomarkers remains an open question. In this context, we explored the integration of VR-derived and MRI biomarkers to enhance early MCI detection through a multimodal learning approach.

OBJECTIVE

We aimed to evaluate and compare the efficacy of VR-derived and MRI biomarkers in the classification of MCI while also examining the strengths and weaknesses of each approach. Furthermore, we focused on improving early MCI detection by leveraging multimodal learning to integrate VR-derived and MRI biomarkers.

METHODS

The study encompassed a total of 54 participants, comprising 22 (41%) healthy controls and 32 (59%) patients with MCI. Participants completed a virtual kiosk test to collect 4 VR-derived biomarkers (hand movement speed, scanpath length, time to completion, and the number of errors), and T1-weighted MRI scans were performed to collect 22 MRI biomarkers from both hemispheres. Analyses of covariance were used to compare these biomarkers between healthy controls and patients with MCI, with age considered as a covariate. Subsequently, the biomarkers that exhibited significant differences between the 2 groups were used to train and validate a multimodal learning model aimed at early screening for patients with MCI among healthy controls.

RESULTS

The support vector machine (SVM) using only VR-derived biomarkers achieved a sensitivity of 87.5% and specificity of 90%, whereas the MRI biomarkers showed a sensitivity of 90.9% and specificity of 71.4%. Moreover, a correlation analysis revealed a significant association between MRI-observed brain atrophy and impaired performance in instrumental activities of daily living in the VR environment. Notably, the integration of both VR-derived and MRI biomarkers into a multimodal SVM model yielded superior results compared to unimodal SVM models, achieving higher accuracy (94.4%), sensitivity (100%), specificity (90.9%), precision (87.5%), and F1-score (93.3%).

CONCLUSIONS

The results indicate that VR-derived biomarkers, characterized by their high specificity, can be valuable as a robust, early screening tool for MCI in a broader older adult population. On the other hand, MRI biomarkers, known for their high sensitivity, excel at confirming the presence of MCI. Moreover, the multimodal learning approach introduced in our study provides valuable insights into the improvement of early MCI detection by integrating a diverse set of biomarkers.

Automatic detection of mild cognitive impairment based on deep learning and radiomics of MR imaging

PURPOSE

Early and rapid diagnosis of mild cognitive impairment (MCI) has important clinical value in improving the prognosis of Alzheimer's disease (AD). The hippocampus and parahippocampal gyrus play crucial roles in the occurrence of cognitive function decline. In this study, deep learning and radiomics techniques were used to automatically detect MCI from healthy controls (HCs).

METHOD

This study included 115 MCI patients and 133 normal individuals with 3D-T1 weighted MR structural images from the ADNI database. The identification and segmentation of the hippocampus and parahippocampal gyrus were automatically performed with a VB-net, and radiomics features were extracted. Relief, Minimum Redundancy Maximum Correlation, Recursive Feature Elimination and the minimum absolute shrinkage and selection operator (LASSO) were used to reduce the dimensionality and select the optimal features. Five independent machine learning classifiers including Support Vector Machine (SVM), Random forest (RF), Logistic Regression (LR), Bagging Decision Tree (BDT), and Gaussian Process (GP) were trained on the training set, and validated on the testing set to detect the MCI. The Delong test was used to assess the performance of different models.

RESULT

Our VB-net could automatically identify and segment the bilateral hippocampus and parahippocampal gyrus. After four steps of feature dimensionality reduction, the GP models based on combined features (11 features from the hippocampus, and 4 features from the parahippocampal gyrus) showed the best performance for the MCI and normal control subject discrimination. The AUC of the training set and test set were 0.954 (95% CI: 0.929-0.979) and 0.866 (95% CI: 0.757-0.976), respectively. Decision curve analysis showed that the clinical benefit of the line graph model was high.

CONCLUSION

The GP classifier based on 15 radiomics features of bilateral hippocampal and parahippocampal gyrus could detect MCI from normal controls with high accuracy based on conventional MR images. Our fully automatic model could rapidly process the MRI data and give results in 1 minute, which provided important clinical value in assisted diagnosis.

A comprehensive characterization of hippocampal feature ensemble serves as individualized brain signature for Alzheimer's disease: deep learning analysis in 3238 participants worldwide

OBJECTIVES

Hippocampal characterization is one of the most significant hallmarks of Alzheimer's disease (AD); rather, the single-level feature is insufficient. A comprehensive hippocampal characterization is pivotal for developing a well-performing biomarker for AD. To verify whether a comprehensive characterization of hippocampal features of gray matter volume, segmentation probability, and radiomics features could better distinguish AD from normal control (NC), and to investigate whether the classification decision score could serve as a robust and individualized brain signature.

METHODS

A total of 3238 participants' structural MRI from four independent databases were employed to conduct a 3D residual attention network (3DRA-Net) to classify NC, mild cognitive impairment (MCI), and AD. The generalization was validated under inter-database cross-validation. The neurobiological basis of the classification decision score as a neuroimaging biomarker was systematically investigated by association with clinical profiles, as well as longitudinal trajectory analysis to reveal AD progression. All image analyses were performed only upon the single modality of T1-weighted MRI.

RESULTS

Our study exhibited an outstanding performance (ACC = 91.6%, AUC = 0.95) of the comprehensive characterization of hippocampal features in distinguishing AD (n = 282) from NC (n = 603) in Alzheimer's Disease Neuroimaging Initiative cohort, and ACC = 89.2% and AUC = 0.93 under external validation. More importantly, the constructed score was significantly correlated with clinical profiles (p < 0.05), and dynamically altered over the AD longitudinal progression, provided compelling evidence of a solid neurobiological basis.

CONCLUSIONS

This systemic study highlights the potential of the comprehensive characterization of hippocampal features to provide an individualized, generalizable, and biologically plausible neuroimaging biomarker for early detection of AD.

KEY POINTS

• The comprehensive characterization of hippocampal features exhibited ACC = 91.6% (AUC = 0.95) in classifying AD from NC under intra-database cross-validation, and ACC = 89.2% (AUC = 0.93) in external validation. • The constructed classification score was significantly associated with clinical profiles, and dynamically altered over the AD longitudinal progression, which highlighted its potential of being an individualized, generalizable, and biologically plausible neuroimaging biomarker for early detection of AD.

A novel cascade machine learning pipeline for Alzheimer's disease identification and prediction

Introduction

Alzheimer's disease (AD) is a progressive and irreversible brain degenerative disorder early. Among all diagnostic strategies, hippocampal atrophy is considered a promising diagnostic method. In order to proactively detect patients with early Alzheimer's disease, we built an Alzheimer's segmentation and classification (AL-SCF) pipeline based on machine learning.

Methods

In our study, we collected coronal T1 weighted images that include 187 patients with AD and 230 normal controls (NCs). Our pipeline began with the segmentation of the hippocampus by using a modified U2-net. Subsequently, we extracted 851 radiomics features and selected 37 features most relevant to AD by the Hierarchical clustering method and Least Absolute Shrinkage and Selection Operator (LASSO) algorithm. At last, four classifiers were implemented to distinguish AD from NCs, and the performance of the models was evaluated by accuracy, specificity, sensitivity, and area under the curve.

Results

Our proposed pipeline showed excellent discriminative performance of classification with AD vs NC in the training set (AUC=0.97, 95% CI: (0.96-0.98)). The model was also verified in the validation set with Dice=0.93 for segmentation and accuracy=0.95 for classification.

Discussion

The AL-SCF pipeline can automate the process from segmentation to classification, which may assist doctors with AD diagnosis and develop individualized medical plans for AD in clinical practice.