Episodic Memory-Related Imaging Features as Valuable Biomarkers for the Diagnosis of Alzheimer's Disease: A Multicenter Study Based on Machine Learning

Brain network dynamics in patients with single- and multiple-domain amnestic mild cognitive impairment

INTRODUCTION

Brain network dynamics have been extensively explored in patients with amnestic mild cognitive impairment (aMCI); however, differences in single- and multiple-domain aMCI (SD-aMCI and MD-aMCI) remain unclear.

METHODS

Using multicenter datasets, coactivation patterns (CAPs) were constructed and compared among normal control (NC), SD-aMCI, MD-aMCI, and Alzheimer's disease (AD) patients based on individual high-order cognitive network (HOCN) and primary sensory network (PSN) parcellations. Correlations between spatiotemporal characteristics and neuropsychological scores were analyzed.

RESULTS

Compared to NC, SD-aMCI showed temporal alterations in HOCN-dominant CAPs, while MD-aMCI showed alterations in PSN-dominant CAPs. In addition, transitions from SD-aMCI to AD may involve PSN, while MD-aMCI to AD involves both PSN and HOCN. Results were generally consistent across datasets from Chinese and White populations.

DISCUSSION

The HOCN and PSN are distinctively involved in aMCI subtypes and in the transformation between aMCI subtypes and AD, highlighting the necessity of aMCI subtype classification in AD studies.

HIGHLIGHTS

Individual functional network parcellations and coactivation pattern (CAP) analysis were performed to characterize spatiotemporal differences between single- and multiple-domain amnestic mild cognitive impairment (SD-aMCI and MD-aMCI), and between distinct aMCI subtypes and Alzheimer's disease (AD). The analysis of multicenter datasets converged on four pairs of recurrent CAPs, including primary sensory networks (PSN)-dominant CAPs, high-order cognitive networks (HOCN)-dominant CAPs, and PSN-HOCN-interacting CAPs. The HOCN and PSN are distinctively involved in aMCI subtypes and in the transformation between distinct aMCI subtypes and AD.

Episodic memory network characteristics in patients with amnestic mild cognitive impairment accompanied by executive function impairment

OBJECTIVE

To explore the functional connectivity (FC) characteristics of the episodic memory network (EMN) in amnestic mild cognitive impairment (aMCI) patients with different levels of executive function (EF).

METHODS

This study included 76 participants from the Alzheimer's Disease Neuroimaging Initiative database, comprising 23 healthy controls (HCs) and 53 aMCI patients. Based on EF levels, aMCI patients were categorized into aMCI-highEF and aMCI-lowEF groups. Cognitive function scores, pathological markers (cerebrospinal fluid β-amyloid, total tau protein, phosphorylated tau protein, AV45-PET, and FDG-PET), and functional magnetic resonance imaging were collected and compared among the three groups. Seed-based FC analysis was used to examine differences in the EMN among the groups, and partial correlation analysis was employed to investigate the relationship between changes in FC and cognitive function scores as well as pathological markers.

RESULTS

Compared to the aMCI-highEF group, the aMCI-lowEF group exhibited more severe cognitive impairment, decreased cerebral glucose metabolism, and elevated AV45 levels. Significant FC differences in the left superior temporal gyrus (STG) of the EMN were observed among the three groups. Post hoc analysis revealed that the aMCI-lowEF group had increased FC in the left STG compared to the HCs and aMCI-highEF groups, with statistically significant differences. Correlation analysis showed a significant negative correlation between the differences in FC in the left STG of aMCI-highEF and aMCI-lowEF groups and Rey Auditory Verbal Learning Test forgetting scores. Receiver operator characteristic curve analysis indicated an area under the curve of 0.741 for distinguishing between aMCI-highEF and aMCI-lowEF groups based on FC of left STG, with a sensitivity of 0.808 and a specificity of 0.667.

CONCLUSION

aMCI-lowEF exhibits characteristic changes in FC within the EMN, providing theoretical support for the role of EF in mediating EMN alterations and, consequently, impacting episodic memory function.

Preliminary Evidence of the Use of Generative AI in Health Care Clinical Services: Systematic Narrative Review

BACKGROUND

Generative artificial intelligence tools and applications (GenAI) are being increasingly used in health care. Physicians, specialists, and other providers have started primarily using GenAI as an aid or tool to gather knowledge, provide information, train, or generate suggestive dialogue between physicians and patients or between physicians and patients' families or friends. However, unless the use of GenAI is oriented to be helpful in clinical service encounters that can improve the accuracy of diagnosis, treatment, and patient outcomes, the expected potential will not be achieved. As adoption continues, it is essential to validate the effectiveness of the infusion of GenAI as an intelligent technology in service encounters to understand the gap in actual clinical service use of GenAI.

OBJECTIVE

This study synthesizes preliminary evidence on how GenAI assists, guides, and automates clinical service rendering and encounters in health care The review scope was limited to articles published in peer-reviewed medical journals.

METHODS

We screened and selected 0.38% (161/42,459) of articles published between January 1, 2020, and May 31, 2023, identified from PubMed. We followed the protocols outlined in the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to select highly relevant studies with at least 1 element on clinical use, evaluation, and validation to provide evidence of GenAI use in clinical services. The articles were classified based on their relevance to clinical service functions or activities using the descriptive and analytical information presented in the articles.

RESULTS

Of 161 articles, 141 (87.6%) reported using GenAI to assist services through knowledge access, collation, and filtering. GenAI was used for disease detection (19/161, 11.8%), diagnosis (14/161, 8.7%), and screening processes (12/161, 7.5%) in the areas of radiology (17/161, 10.6%), cardiology (12/161, 7.5%), gastrointestinal medicine (4/161, 2.5%), and diabetes (6/161, 3.7%). The literature synthesis in this study suggests that GenAI is mainly used for diagnostic processes, improvement of diagnosis accuracy, and screening and diagnostic purposes using knowledge access. Although this solves the problem of knowledge access and may improve diagnostic accuracy, it is oriented toward higher value creation in health care.

CONCLUSIONS

GenAI informs rather than assisting or automating clinical service functions in health care. There is potential in clinical service, but it has yet to be actualized for GenAI. More clinical service-level evidence that GenAI is used to streamline some functions or provides more automated help than only information retrieval is needed. To transform health care as purported, more studies related to GenAI applications must automate and guide human-performed services and keep up with the optimism that forward-thinking health care organizations will take advantage of GenAI.

Alteration and clinical potential in gut microbiota in patients with cerebral small vessel disease

Background

Cerebral small vessel disease (CSVD) is a cluster of microvascular disorders with unclear pathological mechanisms. The microbiota-gut-brain axis is an essential regulatory mechanism between gut microbes and their host. Therefore, the compositional and functional gut microbiota alterations lead to cerebrovascular disease pathogenesis. The current study aims to determine the alteration and clinical value of the gut microbiota in CSVD patients.

Methods

Sixty-four CSVD patients and 18 matched healthy controls (HCs) were included in our study. All the participants underwent neuropsychological tests, and the multi-modal magnetic resonance imaging depicted the changes in brain structure and function. Plasma samples were collected, and the fecal samples were analyzed with 16S rRNA gene sequencing.

Results

Based on the alpha diversity analysis, the CSVD group had significantly decreased Shannon and enhanced Simpson compared to the HC group. At the genus level, there was a significant increase in the relative abundances of Parasutterella, Anaeroglobus, Megasphaera, Akkermansia, Collinsella, and Veillonella in the CSVD group. Moreover, these genera with significant differences in CSVD patients revealed significant correlations with cognitive assessments, plasma levels of the blood-brain barrier-/inflammation-related indexes, and structural/functional magnetic resonance imaging changes. Functional prediction demonstrated that lipoic acid metabolism was significantly higher in CSVD patients than HCs. Additionally, a composite biomarker depending on six gut microbiota at the genus level displayed an area under the curve of 0.834 to distinguish CSVD patients from HCs using the least absolute shrinkage and selection operator (LASSO) algorithm.

Conclusion

The evident changes in gut microbiota composition in CSVD patients were correlated with clinical features and pathological changes of CSVD. Combining these gut microbiota using the LASSO algorithm helped identify CSVD accurately.

Trajectories of Cognitive Impairment in Adults Bearing Vascular Risk Factors, with or without Diagnosis of Mild Cognitive Impairment: Findings from a Longitudinal Study Assessing Executive Functions, Memory, and Social Cognition

With the aging of the population, a key concern of both societies and health services is to keep the population cognitively healthy until the maximum age limit. It is a well-known fact that vascular aging has a negative effect on the cognitive skills of adults, putting them at greater risk of developing dementia. The present longitudinal study aimed to evaluate the main dimensions of cognition in two pathological groups with different health profiles: a group of adults with vascular risk factors (VRF) (n = 35) and a group of adults with vascular risk factors and mild cognitive impairment (VRF + MCI) (n = 35). The two groups were matched in age, education, and gender. They were assessed with extensive neuropsychological testing at three different times with a distance of about 8 months between them; the assessment regarded executive functions, memory capacity, and Theory of Mind abilities. The analyses carried out were (a) mixed-measures ANOVA, (b) repeated measures ANOVA, and (c) ANOVA. The findings showed that global cognitive status and short-term memory are the main cognitive abilities that decline in community dwelling people bearing VRF. Hence, this group of adults should be examined at least every 2 years for this decline. As regards people with both VRF and MCI, it seems that the assessment of Theory of Mind abilities can better capture their further impairment. Global cognitive status, task/rule switching function, and long-term memory (delayed verbal recall) were revealed as the abilities that clearly and steadily differentiate VRF people with and without MCI.

Potential of brain age in identifying early cognitive impairment in subcortical small-vessel disease patients

Background

Reliable and individualized biomarkers are crucial for identifying early cognitive impairment in subcortical small-vessel disease (SSVD) patients. Personalized brain age prediction can effectively reflect cognitive impairment. Thus, the present study aimed to investigate the association of brain age with cognitive function in SSVD patients and assess the potential value of brain age in clinical assessment of SSVD.

Materials and methods

A prediction model for brain age using the relevance vector regression algorithm was developed using 35 healthy controls. Subsequently, the prediction model was tested using 51 SSVD patients [24 subjective cognitive impairment (SCI) patients and 27 mild cognitive impairment (MCI) patients] to identify brain age-related imaging features. A support vector machine (SVM)-based classification model was constructed to differentiate MCI from SCI patients. The neurobiological basis of brain age-related imaging features was also investigated based on cognitive assessments and oxidative stress biomarkers.

Results

The gray matter volume (GMV) imaging features accurately predicted brain age in individual patients with SSVD (R² = 0.535, p < 0.001). The GMV features were primarily distributed across the subcortical system (e.g., thalamus) and dorsal attention network. SSVD patients with age acceleration showed significantly poorer Mini-Mental State Examination and Montreal Cognitive Assessment (MoCA) scores. The classification model based on GMV features could accurately distinguish MCI patients from SCI patients (area under the curve = 0.883). The classification outputs of the classification model exhibited significant associations with MoCA scores, Trail Making Tests A and B scores, Stroop Color and Word Test C scores, information processing speed total scores, and plasma levels of total antioxidant capacity in SSVD patients.

Conclusion

Brain age can be accurately quantified using GMV imaging data and shows potential clinical value for identifying early cognitive impairment in SSVD patients.

Convergent functional changes of the episodic memory impairment in mild cognitive impairment: An ALE meta-analysis

Background

Mild cognitive impairment (MCI) is considered to be an intermediate stage between normal aging and Alzheimer's disease (AD). The earliest and most common symptom of MCI is impaired episodic memory. When episodic memory is impaired in MCI patients, specific functional changes occur in related brain areas. However, there is currently a lack of a unified conclusion on this change. Therefore, the purpose of this meta-analysis is to find MRI-specific functional changes in episodic memory in MCI patients.

Methods

Based on three commonly used indicators of brain function: functional connectivity (FC), the amplitude of low-frequency fluctuation /fractional amplitude of low-frequency fluctuation (ALFF/fALFF), and regional homogeneity (ReHo), we systematically searched PubMed, Web of Science and Ovid related literature and conducted the strict screening. Then we use the activation likelihood estimation (ALE) algorithm to perform the coordinate-based meta-analysis.

Results

Through strict screening, this meta-analysis finally included 21 related functional neuroimaging research articles. The final result displays that functional changes of episodic memory in MCI patients are mainly located in the parahippocampal gyrus, precuneus, posterior cingulate gyrus, cuneus, middle temporal gyrus, middle frontal gyrus, lingual gyrus, and thalamus.

Conclusions

There are specific functional changes in episodic memory brain regions in MCI patients, and the brain functional network can regulate episodic memory through these brain regions. And these specific changes can assist in the early diagnosis of MCI, providing new ideas and directions for early identification and intervention in the process of MCI.

Potential association of bone mineral density loss with cognitive impairment and central and peripheral amyloid-β changes: a cross-sectional study

BACKGROUND

There is some evidence in the literature that older adults with cognitive impairments have a higher risk for falls and osteoporotic hip fractures. Currently, the associations between bone health and cognitive health have not been extensively studied. Thus, the present cross-sectional study aims to investigate the relationship between markers of bone loss and cognitive performance in older adults with and without osteopenia as well as older adults with cognitive impairments (i.e., Alzheimer's disease [AD]).

METHODS

Sixty-two non-osteopenia participants and one hundred three osteopenia participants as the cohort 1 and 33 cognitively normal non-AD participants and 39 AD participants as the cohort 2 were recruited. To assess cognitive and bone health, hip bone mineral density (BMD) and cognitive performance (via Minimal Mental State Examination [MMSE] and/or Auditory Verbal Learning Test-delayed recall [AVLT-DR]) were assessed. Furthermore, in cohort 1, plasma amyloid-β (Aβ) levels, and in cohort 2, cerebrospinal fluid (CSF) Aβ levels were determined.

RESULTS

We observed that (1) compared with non-osteopenia participants, BMD values (t = - 22.806; 95%CI: - 1.801, - 1.484; p < 0.001), MMSE scores (t = - 5.392; 95%CI: - 3.260, - 1.698; p < 0.001), and AVLT-DR scores (t = - 4.142; 95%CI: - 2.181, - 0.804; p < 0.001), plasma Aβ42 levels (t = - 2.821; 95%CI: - 1.737, - 0.305; p = 0.01), and Aβ42/40 ratio (t = - 2.020; 95%CI: - 0.009, - 0.001; p = 0.04) were significantly lower in osteopenia participants; (2) plasma Aβ42/40 ratio showed a mediate effect for the association between BMD values and the performance of cognitive function in osteopenia participants by mediation analysis, adjusting age, sex, years of education, and body mass index (BMI); (3) BMD values (95%CI: - 1.085, 0.478; p < 0.001) were significantly reduced in AD participants as compared with cognitively normal non-AD participants; (4) in AD participants, the interactive effects of BMD and CSF Aβ42/40 ratio on MMSE scores was found by regression analysis, controlling age, sex, years of education, and BMI; (5) BMD can distinguish AD participants from cognitively normal non-AD participants with AUC of 0.816 and distinguish participants with the cognitive impairment from cognitively normal participants with AUC of 0.794.

CONCLUSION

Our findings suggest a relationship between bone health and cognitive health. Given the correlations between BMD and important markers of cognitive health (e.g., central and peripheral pathological change of Aβ), BMD might serve as a promising and easy-accessible biomarker. However, more research is needed to further substantiate our findings.

Reduced Inter-Voxel Whiter Matter Integrity in Subjective Cognitive Decline: Diffusion Tensor Imaging With Tract-Based Spatial Statistics Analysis

Subjective cognitive decline (SCD), a self-reported worsening in cognition concurrent with normal performance on standardized neuropsychological tests, has gained much attention due to its high risks in the development of mild cognitive impairments or Alzheimer’s disease. The existing cross-sectional diffusion tensor imaging (DTI) studies in SCD have shown extremely controversial findings. Furthermore, all of these studies investigated diffusion properties within the voxel, such as fractional anisotropy, mean diffusivity, or axial diffusivity (DA). However, it remains unclear whether individuals with SCD demonstrate alterations of diffusion profile between voxels and their neighbors, as indexed by local diffusion homogeneity (LDH). We selected 30 healthy controls (HCs) and 23 SCD subjects to acquire their whole-brain DTI. Diffusion images were compared using the tract-based spatial statistics method. Diffusion indices with significant between-group tract clusters were extracted from each individual for further region-of-interest (ROI)-based comparisons. Our results showed that subjects with SCD demonstrated reduced LDH in the left superior frontal gyrus (SFG) and DA in the right anterior cingulate cortex compared with the HC group. In contrast, the SCD group showed higher LDH values in the left lingual gyrus (LG) compared with the HC group. Notably, LDH in the left SFG was significantly and negatively correlated with LDH in the left LG. In conclusion, white matter (WM) integrity in the left SFG, right ACC, and left LG is altered in SCD, suggesting that individuals with SCD exhibit detectable changes in WM tracts before they demonstrate objective cognitive deficits.