Progression Of Mild Cognitive Impairment Research Articles

Predicting Alzheimer’s progression in MCI: a DTI-based white matter network model

ObjectiveThis study aimed to identify features of white matter network attributes based on diffusion tensor imaging (DTI) that might lead to progression from mild cognitive impairment (MCI) and construct a comprehensive model based on these features for predicting the population at high risk of progression to Alzheimer’s disease (AD) in MCI patients.MethodsThis study enrolled 121 MCI patients from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Among them, 36 progressed to AD after four years of follow-up. A brain network was constructed for each patient based on white matter fiber tracts, and network attribute features were extracted. White matter network features were downscaled, and white matter markers were constructed using an integrated downscaling approach, followed by forming an integrated model with clinical features and performance evaluation.ResultsAPOE4 and ADAS scores were used as independent predictors and combined with white matter network markers to construct a comprehensive model. The diagnostic efficacy of the comprehensive model was 0.924 and 0.919, sensitivity was 0.864 and 0.900, and specificity was 0.871 and 0.815 in the training and test groups, respectively. The Delong test showed significant differences (P < 0.05) in the diagnostic efficacy of the combined model and APOE4 and ADAS scores, while there was no significant difference (P > 0.05) between the combined model and white matter network biomarkers.ConclusionsA comprehensive model constructed based on white matter network markers can identify MCI patients at high risk of progression to AD and provide an adjunct biomarker helpful in early AD detection.

Directed Functional Connectivity Changes of Triple Networks for Stable and Progressive Mild Cognitive Impairment

Mild cognitive impairment includes two distinct subtypes, namely progressive mild cognitive impairment and stable mild cognitive impairment. While alterations in extensive functional connectivity have been observed in both subtypes, limited attention has been given to directed functional connectivity. A triple network, composed of the central executive network, default mode network, and salience network, is considered to be the core cognitive network. We evaluated the alterations in directed functional connectivity within and between the triple network in progressive and stable mild cognitive impairment groups and investigated its role in predicting disease conversion. Resting-state functional magnetic resonance imaging was used to analyze directed functional connectivity within the triple networks. A correlation analysis was performed to investigate potential associations between altered directed functional connectivity within the triple networks and the neurocognitive performance of the participants. Our study revealed significant differences in directed functional connectivity within and between the triple network in the progressive and stable mild cognitive impairment groups. Altered directed functional connectivity within the triple network was involved in episodic memory and executive function. Thus, the directed functional connectivity of the triple network may be used as an imaging marker of mild cognitive impairment.

[Mechanism of action and clinical trial results of Lecanemab (Leqembi® 200 ‍mg, 500 ‍mg for Intravenous Infusion), a novel treatment for Alzheimer's disease].

Lecanemab is a humanized monoclonal antibody directed against human soluble amyloid-β aggregates. It was developed for the treatment of early Alzheimer's disease (mild cognitive impairment or mild dementia stage of Alzheimer's disease). Among the amyloid-β (Aβ) involved in Alzheimer's disease, Lecanemab selectively binds to the highly neurotoxic Aβ protofibrils, and is thought to reduce Aβ protofibrils and amyloid plaques (Aβ plaques) in the brain. The efficacy and safety of Lecanemab in early Alzheimer's disease were investigated in an international Phase II placebo-controlled study (Study 201) and an international Phase III placebo-controlled study (Study 301). Both studies included Japanese subjects. Lecanemab was given accelerated approval in the United States in January 2023, followed by traditional approval in July 2023. In Japan, it was approved for "control of the progression of mild cognitive impairment or mild dementia stage of Alzheimer's disease" in September 2023, and was added to the NHI drug price list in December 2023.

Sulcal Morphometry Predicts Mild Cognitive Impairment Conversion to Alzheimer's Disease.

Being able to differentiate mild cognitive impairment (MCI) patients who would eventually convert (MCIc) to Alzheimer's disease (AD) from those who would not (MCInc) is a key challenge for prognosis. This study aimed to investigate the ability of sulcal morphometry to predict MCI progression to AD, dedicating special attention to an accurate identification of sulci. Twenty-five AD patients, thirty-seven MCI and twenty-five healthy controls (HC) underwent a brain-MR protocol (1.5T scanner) including a high-resolution T1-weighted sequence. MCI patients underwent a neuropsychological assessment at baseline and were clinically re-evaluated after a mean of 2.3 years. At follow-up, 12 MCI were classified as MCInc and 25 as MCIc. Sulcal morphometry was investigated using the BrainVISA framework. Consistency of sulci across subjects was ensured by visual inspection and manual correction of the automatic labelling in each subject. Sulcal surface, depth, length, and width were retrieved from 106 sulci. Features were compared across groups and their classification accuracy in predicting MCI conversion was tested. Potential relationships between sulcal features and cognitive scores were explored using Spearman's correlation. The width of sulci in the temporo-occipital region strongly differentiated between each pair of groups. Comparing MCIc and MCInc, the width of several sulci in the bilateral temporo-occipital and left frontal areas was significantly altered. Higher width of frontal sulci was associated with worse performances in short-term verbal memory and phonemic fluency. Sulcal morphometry emerged as a strong tool for differentiating HC, MCI, and AD, demonstrating its potential prognostic value for the MCI population.

A non-linear relationship between blood pressure and mild cognitive impairment in elderly individuals: A cohort study based on the Chinese longitudinal healthy longevity survey (CLHLS).

Hypertension is an established risk factor for mild cognitive impairment (MCI) in elderly individuals. Nevertheless, the impact of different levels of blood pressure on the progression of MCI remains uncertain. This study aims to investigate the non-linear relationship between blood pressure and MCI in the elderly and detect the critical blood pressure threshold, thus, improving blood pressure management for individuals at high risk of MCI. Data was obtained from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) cohort. We chose normal cognitive elderly individuals who entered the cohort in 2014 for a 5-year follow-up to observe the progression of MCI. Subsequently, we utilized the Cox regression model to identify risk factors for MCI and conducted a Cox-based restricted cubic spline regression (RCS) model to examine the non-linear relationship between systolic blood pressure (SBP) and diastolic blood pressure (DBP) with MCI, determining the critical blood pressure threshold for MCI progression. In the elderly population, female (HR = 1.489, 95% CI: 1.017-2.180), lacking of exercise in the past (HR = 1.714, 95% CI: 1.108-2.653), preferring animal fats (HR = 2.340, 95% CI: 1.348-4.061), increased age (HR = 1.061, 95% CI: 1.038-1.084), increased SBP (HR = 1.036, 95% CI: 1.024-1.048), and increased DBP (HR = 1.056, 95% CI: 1.031-1.081) were associated with MCI progression. After adjusting factors such as gender, exercise, preferred types of fats, and age, both SBP (P non-linear < 0.001) and DBP (P non-linear < 0.001) in elderly individuals exhibited a non-linear association with MCI. The risk of MCI rose when SBP exceeded 135mmHg and DBP was in the range of 80-88mmHg. However, when DBP exceeded 88mmHg, there was a declining trend in MCI progression, although the HR remained above 1. The identified critical blood pressure management threshold for MCI was 135/80mmHg. In this study, we discovered that risk factors affecting the progression of MCI in elderly individuals comprise gender (female), preferring to use animal fat, lack of exercise in the past, increased age, increased SBP, and increased DBP. Additionally, a non-linear relationship between blood pressure levels and MCI progression was confirmed, with the critical blood pressure management threshold for MCI onset falling within the prehypertensive range.

The associations of serum isoleucine with Alzheimer`s disease on assisting diagnosis, predicting conversion and assessing cognition.

Advances in blood biomarker discovery have enabled the improved diagnosis and prognosis of Alzheimer's disease (AD). Most branched-chain amino acids, except isoleucine (Ile), are correlated with both mild cognitive impairment (MCI) and AD. Therefore, this study investigated the association between serum Ile levels and MCI/AD. This study stratified 700 participants from the Alzheimer's Disease Neuroimaging Initiative database into four diagnostic groups: cognitively normal, stable MCI, progressive MCI, and AD. Analysis of covariance and chi-square analyses were used to test the demographic data. Receiver operating curve analyses were used to calculate the diagnostic accuracy of different biomarkers and were compared using MedCalc 20. Additionally, Cox proportional hazards models were used to measure the ability of serum Ile levels to predict disease conversion. Finally, a linear mixed-effects model was used to evaluate the associations between serum Ile levels and cognition, brain structure, and metabolism. Serum Ile concentration was decreased in AD and demonstrated significant diagnostic efficacy. The combination of serum Ile and cerebrospinal fluid (CSF) phosphorylated tau (P-tau) levels improved the diagnostic accuracy in AD compared to T-tau alone. Serum Ile levels significantly predicted the conversion from MCI to AD (cutoff value = 78.3 μM). Finally, the results of this study also revealed a correlation between serum Ile levels and the Alzheimer's Disease Assessment Scale cognitive subscale Q4. Serum Ile level may be a potential biomarker of AD. Ile had independent diagnostic efficacy and significantly improved the diagnostic accuracy of CSF P-tau in AD. Patients with MCI with a lower serum Ile level had a higher risk of progression to AD and a worse cognition assessment.

Towards Evidence-based Care: Promoting Cognitive Well-being of Older People

The article by Mamayson and Lacanaria 1 in Acta Medica Philippina summarized the available evidence on the effects of cognitively stimulating activities (CSA) on the overall cognitive functioning of older people with mild cognitive impairment(MCI). Aside from the new evidence, it also provided insights on how the findings of study will inform the nursing profession in terms of clinical practice, leadership and policy, research, and education.&#x0D; Utilizing a meta-analysis of three randomized controlled trials (RCTs), the authors reported that cognitive training using CSA improves overall cognitive function among older people with MCI. 1 Calligraphy, cognitive, and language trainings werethe cognitively stimulating activities included in the reviewed RCTs which were conducted over the past years in Hong Kong, China, and Greece, respectively. The authors acknowledged that such activities are effective and practical, inexpensive, non- pharmacological approaches to slow down cognitive decline. 1 The strength of the paper is the inclusion of good methodical quality studies and only RCTs which are considered gold standard for evaluating the effectiveness of interventions. However, the authors emphasized that the interpretation of study results requires caution. This is due to the small number of studies reviewed which limits the generalizability of the findings.&#x0D; Older people with mild cognitive impairment are at increased risk of dementia since aging is a primary predictor of MCI progression to dementia. 2 The Global Action Plan on the Public Health Response to Dementia 2017-2025 outlined the growing consensus on protective measures which can reduce the risk of cognitive decline and dementia. These measures include healthy lifestyle (e.g., physical activity, balanced diet, smoking, and alcohol cessation), management of hypertension and diabetes, social engagement, and promotion of cognitively stimulating activities and learning. 3 These are aligned with the recommendations of the Lancet Commission which emphasized that dementia prevention is about policy and individuals. Preventive actions require both public health strategies and individually-tailored interventions. 4&#x0D; In the Philippines and as mandated by the Republic Act No. 9994 or also known as the Expanded Senior Citizens Act, the Department of Health (DOH) in coordination with relevant stakeholders shall institute a national health program andprovide an integrated health service for senior citizens. 5 The DOH developed the Health and Wellness Program for Senior Citizens 6 and several policies on the care of older people 7,8 . Specific policies relevant to cognition include the 2022 Omnibus Health Guidelines for the Elderly. The guidelines contain the conduct of cognitive stimulation therapy by a psychologist or mental health specialist for people with cognitive decline who may benefit from cognitive stimulation. 7 The implementation of this specific guideline is challenging due to the limited number of health workforce. In particular, there are only approximately 1600 psychologists and 500 psychiatrists serving about 110 million Filipinos. 9&#x0D; In the era of and prior to the introduction of evidence-based practice, nurses are one of the major key players in the care of older people. The study of Mamayson and Lacanaria added evidence which will be useful in guiding the current and future professional nursing practice. 1 Nurses can promote cognitive wellness by educating the older patient, family members, and the community on the importance of engaging in appropriate cognitively stimulating activities and the potential benefits of these non-pharmacological approaches. The recommendations on the incorporation of the findings into the gerontology courses, introduction of certification programs for dementia and MCI care specialists, and advocating for policy changes call for action. These recommendations can be put into action in close coordination with the right stakeholders. The involvement of key national government agencies namely Commission on Higher Education, Professional Regulation Commission, Department of Health, National Commission of Senior Citizens, as well as academic institutions, and professional societies such as the Philippine Nurses Association, Gerontology Nurses Association of the Philippines, and the Philippine Society of Geriatrics and Gerontology is crucial.&#x0D; The research recommendations outlined by Mamayson and Lacanaria highlighted the conduct of future studies with larger samples, longer follow ups, less language restrictions, and more rigorous analysis. 1 It is notable that no local study oncognitively stimulating activities that was cited or included in the review. Given this, the country should strengthen local evidence generation to develop age-, gender-, culturally-sensitive, and responsive interventions. The most recent NationalUnified Health Research Agenda of the Philippines included geriatric health and tailoring novel mental health interventions to different life-stages as priority topics. 10 This is an opportunity for professional societies, academe, and research institutions to pursue research, contribute to the local evidence base, promote cognitive well-being, and ultimately, improve the care and quality of life of older Filipinos. 

Data-driven classification of cognitively normal and mild cognitive impairment subtypes predicts progression in the NACC dataset.

Data-driven neuropsychological methods can identify mild cognitive impairment (MCI) subtypes with stronger associations to dementia risk factors than conventional diagnostic methods. Cluster analysis used neuropsychological data from participants without dementia (mean age=71.6 years) in the National Alzheimer's Coordinating Center (NACC) Uniform Data Set (n=26,255) and the "normal cognition" subsample (n=16,005). Survival analyses examined MCI or dementia progression. Five clusters were identified: "Optimal" cognitively normal (oCN; 13.2%), "Typical" CN (tCN; 28.0%), Amnestic MCI (aMCI; 25.3%), Mixed MCI-Mild (mMCI-Mild; 20.4%), and Mixed MCI-Severe (mMCI-Severe; 13.0%). Progression to dementia differed across clusters (oCN<tCN<aMCI<mMCI-Mild<mMCI-Severe). Cluster analysis identified more MCI cases than consensus diagnosis. In the "normal cognition" subsample, five clusters emerged: High-All Domains (High-All; 16.7%), Low-Attention/Working Memory (Low-WM; 22.1%), Low-Memory (36.3%), Amnestic MCI (16.7%), and Non-amnestic MCI (naMCI; 8.3%), with differing progression rates (High-All<Low-WM=Low-Memory<aMCI<naMCI). Our data-driven methods outperformed consensus diagnosis by providing more precise information about progression risk and revealing heterogeneity in cognition and progression risk within the NACC "normal cognition" group.

Multiomics analysis to explore blood metabolite biomarkers in an Alzheimer’s Disease Neuroimaging Initiative cohort

Alzheimer's disease (AD) is a neurodegenerative disease that commonly causes dementia. Identifying biomarkers for the early detection of AD is an emerging need, as brain dysfunction begins two decades before the onset of clinical symptoms. To this end, we reanalyzed untargeted metabolomic mass spectrometry data from 905 patients enrolled in the AD Neuroimaging Initiative (ADNI) cohort using MS-DIAL, with 1,304,633 spectra of 39,108 unique biomolecules. Metabolic profiles of 93 hydrophilic metabolites were determined. Additionally, we integrated targeted lipidomic data (4873 samples from 1524 patients) to explore candidate biomarkers for predicting progressive mild cognitive impairment (pMCI) in patients diagnosed with AD within two years using the baseline metabolome. Patients with lower ergothioneine levels had a 12% higher rate of AD progression with the significance of P = 0.012 (Wald test). Furthermore, an increase in ganglioside (GM3) and decrease in plasmalogen lipids, many of which are associated with apolipoprotein E polymorphism, were confirmed in AD patients, and the higher levels of lysophosphatidylcholine (18:1) and GM3 d18:1/20:0 showed 19% and 17% higher rates of AD progression, respectively (Wald test: P = 3.9 × 10–8 and 4.3 × 10–7). Palmitoleamide, oleamide, diacylglycerols, and ether lipids were also identified as significantly altered metabolites at baseline in patients with pMCI. The integrated analysis of metabolites and genomics data showed that combining information on metabolites and genotypes enhances the predictive performance of AD progression, suggesting that metabolomics is essential to complement genomic data. In conclusion, the reanalysis of multiomics data provides new insights to detect early development of AD pathology and to partially understand metabolic changes in age-related onset of AD.

Revealing the heterogeneity of plasma protein and cognitive decline trajectory among Mild Cognitive Impairment patients by clustering of brain atrophy features

Alzheimer's disease (AD) is suggested to be a heterogeneous disorder, but limited studies explore the heterogeneity of the Mild Cognitive Impairment (MCI) stage. This study aimed to tackle such problems using the CIMLR (Cancer Integration via Multikernel Learning) algorithm to cluster brain structural features extracted from T1-weighted Magnetic Resonance Images of MCI patients from Alzheimer's Disease Neuroimaging Initiative. The demographic and cognitive results, characteristics of brain structural features, plasma biomarkers, and longitudinal cognitive trajectory were analyzed for each cluster. The CIMLR clustering analysis revealed four distinct clusters. Cluster 1 is the oldest group but has had mild atrophy and moderate progression with elevated Tumor Necrosis Factor Receptor 2 level; and low Brain-Derived Neurotrophic Factor and CD40 Ligand levels. Cluster 2 showed the highest risk for aggressive MCI progression, with abnormal Leptin, Adiponectin, and Creatine kinase-MB values. Cluster 3 exhibited a low level of Monokine Induced by Gamma Interferon and mild atrophy that shared similar patterns with Cluster 1. Cluster 4 represented the healthiest group during longitudinal tracking, with the mildest Parahippocampal atrophy, which was found to be positively correlated with cognitive impairment and amino acid levels. The longitudinal analyses showed the potential of Hepatocyte Growth Factor as a marker for slow cognitive impairment; Cortisol and Neurofilament Light Polypeptide as prognosis markers for aggressive MCI progression. These findings may lay out new suggestions for further research contributing to the accurate diagnosis and precision medicine for dementia and AD.

Shared and Specific Changes of Cortico-Striatal Functional Connectivity in Stable Mild Cognitive Impairment and Progressive Mild Cognitive Impairment.

Mild cognitive impairment (MCI), the prodromal stage of Alzheimer's disease, has two distinct subtypes: stable MCI (sMCI) and progressive MCI (pMCI). Early identification of the two subtypes has important clinical significance. We aimed to compare the cortico-striatal functional connectivity (FC) differences between the two subtypes of MCI and enhance the accuracy of differential diagnosis between sMCI and pMCI. We collected resting-state fMRI data from 31 pMCI patients, 41 sMCI patients, and 81 healthy controls. We chose six pairs of seed regions, including the ventral striatum inferior, ventral striatum superior, dorsal-caudal putamen, dorsal-rostral putamen, dorsal caudate, and ventral-rostral putamen and analyzed the differences in cortico-striatal FC among the three groups, additionally, the relationship between the altered FC within the MCI subtypes and cognitive function was examined. Compared to sMCI, the pMCI patients exhibited decreased FC between the left dorsal-rostral putamen and right middle temporal gyrus, the right dorsal caudate and right inferior temporal gyrus, and the left dorsal-rostral putamen and left superior frontal gyrus. Additionally, the altered FC between the right inferior temporal gyrus and right putamen was significantly associated with episodic memory and executive function. Our study revealed common and distinct cortico-striatal FC changes in sMCIs and pMCI across different seeds; these changes were associated with cognitive function. These findings can help us understand the underlying pathophysiological mechanisms of MCI and distinguish pMCI and sMCI in the early stage potentially.

Associations of Frailty with Neuropsychiatric Symptoms of Alzheimer's Disease: A Longitudinal Study.

Frailty is a vulnerability state increasing the risk of many adverse health outcomes, but little is known about the effects of frailty on neuropsychiatric health. To explore the associations between frailty and the risk of neuropsychiatric symptoms (NPSs) in Alzheimer's disease (AD), especially in its different clinical stages. We included 2,155 individuals assessed using modified frailty index-11 (mFI-11), Neuropsychiatric Inventory (NPI) and Neuropsychiatric Inventory Questionnaire (NPI-Q) in the Alzheimer's Disease Neuroimaging Initiative (ADNI). The relationships between frailty and NPSs were explored with logistic regression models and Cox proportional hazard regression models. Causal mediation analyses were conducted to explore the mediation factors between frailty and NPSs. Among mild cognitive impairment (MCI) participants, frailty was cross-sectionally associated with an increased risk of apathy, and longitudinally associated with increased risk of depression and apathy. Among AD participants, frailty was cross-sectionally associated with increased risk of depression and anxiety, and longitudinally associated with an increased risk of apathy. Among participants with cognitive progression, frailty was associated with increased risk of depression and apathy. In MCI participants, the influence of frailty on NPSs was partially mediated by hippocampus volume, whole brain volume, and monocytes, with mediating proportions ranging from 8.40% to 9.29%. Frailty was associated with NPSs such as depression, anxiety, and apathy among MCI, AD, and cognitive progression participants. Atrophy of the hippocampus and whole brain, as well as peripheral immunity may be involved in the potential mechanisms underlying the above associations.

Curve Walking Reveals More Gait Impairments in Older Adults with Mild Cognitive Impairment than Straight Walking: A Kinect Camera-Based Study.

Mild cognitive impairment (MCI) can be an early sign of Alzheimer's disease and other types of dementia detectable through gait analysis. Curve walking, which demands greater cognitive and motor skills, may be more sensitive in MCI detection than straight walking. However, few studies have compared gait performance in older adults with and without MCI in these conditions. To compare the capability of curve and straight walking tests for the detection of MCI among older adults. We employed a Kinect v.2 camera to record the gait of 55 older adults (30 healthy controls, 25 with MCI) during single-task straight and curve walking tests. We examined 50 gait markers and conducted statistical analyses to compare groups and conditions. The trail was approved with protocol No. IR.SEMUMS.REC.1398.237 by the ethics committee of Semnan University of Medical Sciences in Iran. Older adults with MCI exhibited more compromised gait performance, particularly during curve walking. Curve walking outperformed straight walking in MCI detection, with several gait markers showing significant differences between healthy controls and MCI patients. These markers encompass average velocity, cadence, temporal markers (e.g., gait cycle subphase durations), spatial markers (e.g., foot position changes during gait subphases), and spatiotemporal markers (e.g., step and stride velocities). Our study suggests curve walking as a more informative and challenging test for MCI detection among older adults, facilitating early diagnosis using non-invasive, cost-effective tools like the Kinect v.2 camera, complementing cognitive assessments in early diagnosis, and tracking MCI progression to dementia.

A novel spatiotemporal graph convolutional network framework for functional connectivity biomarkers identification of Alzheimer’s disease

BackgroundFunctional connectivity (FC) biomarkers play a crucial role in the early diagnosis and mechanistic study of Alzheimer’s disease (AD). However, the identification of effective FC biomarkers remains challenging. In this study, we introduce a novel approach, the spatiotemporal graph convolutional network (ST-GCN) combined with the gradient-based class activation mapping (Grad-CAM) model (STGC-GCAM), to effectively identify FC biomarkers for AD.MethodsThis multi-center cross-racial retrospective study involved 2,272 participants, including 1,105 cognitively normal (CN) subjects, 790 mild cognitive impairment (MCI) individuals, and 377 AD patients. All participants underwent functional magnetic resonance imaging (fMRI) and T1-weighted MRI scans. In this study, firstly, we optimized the STGC-GCAM model to enhance classification accuracy. Secondly, we identified novel AD-associated biomarkers using the optimized model. Thirdly, we validated the imaging biomarkers using Kaplan–Meier analysis. Lastly, we performed correlation analysis and causal mediation analysis to confirm the physiological significance of the identified biomarkers.ResultsThe STGC-GCAM model demonstrated great classification performance (The average area under the curve (AUC) values for different categories were: CN vs MCI = 0.98, CN vs AD = 0.95, MCI vs AD = 0.96, stable MCI vs progressive MCI = 0.79). Notably, the model identified specific brain regions, including the sensorimotor network (SMN), visual network (VN), and default mode network (DMN), as key differentiators between patients and CN individuals. These brain regions exhibited significant associations with the severity of cognitive impairment (p < 0.05). Moreover, the topological features of important brain regions demonstrated excellent predictive capability for the conversion from MCI to AD (Hazard ratio = 3.885, p < 0.001). Additionally, our findings revealed that the topological features of these brain regions mediated the impact of amyloid beta (Aβ) deposition (bootstrapped average causal mediation effect: β = -0.01 [-0.025, 0.00], p < 0.001) and brain glucose metabolism (bootstrapped average causal mediation effect: β = -0.02 [-0.04, -0.001], p < 0.001) on cognitive status.ConclusionsThis study presents the STGC-GCAM framework, which identifies FC biomarkers using a large multi-site fMRI dataset.

Prediction Of Mild Cognitive Impairment to Alzheimer’s Disease Conversion Via Machine Learning

Alzheimer’s Disease (AD) is a progressive neurodegenerative condition characterized by deterioration of cognitive functions. Although there does not exist a cure for AD, early diagnosis and intervention during stages of Mild Cognitive Impairment (MCI) can be incredibly beneficial in slowing its development. However, predicting the conversion from MCI to AD remains a challenging task. This paper aims to provide an overview of the current research on predicting MCI to AD conversion, with a focus on machine learning methodologies to aid feature extraction and classification. Through a literature review, we aim to offer insights into the latest state-of-the-art techniques to predict MCI to AD conversion as well as prevailing trends in this field including deep learning, transfer learning, contrastive learning, and graph neural networks. We conclude from our study that utilizing AD/HC classes for feature extraction is a promising approach for generating discriminative features for stable MCI and progressive MCI classification. In addition, employing multi-modality models is instrumental in attaining a robust validation framework for the early diagnosis of AD.

Estimating Dementia Onset: AT(N) Profiles and Predictive Modeling in Mild Cognitive Impairment Patients.

Mild Cognitive Impairment (MCI) usually precedes the symptomatic phase of dementia and constitutes a window of opportunities for preventive therapies. The objective of this study was to predict the time an MCI patient has left to reach dementia and obtain the most likely natural history in the progression of MCI towards dementia. This study was conducted on 633 MCI patients and 145 subjects with dementia through 4726 visits over 15 years from Alzheimer Disease Neuroimaging Initiative (ADNI) cohort. A combination of data from AT(N) profiles at baseline and longitudinal predictive modeling was applied. A data-driven approach was proposed for categorical diagnosis prediction and timeline estimation of cognitive decline progression, which combined supervised and unsupervised learning techniques. A reduced vector of only neuropsychological measures was selected for training the models. At baseline, this approach had high performance in detecting subjects at high risk of converting from MCI to dementia in the coming years. Furthermore, a Disease Progression Model (DPM) was built and also verified using three metrics. As a result of the DPM focused on the studied population, it was inferred that amyloid pathology (A+) appears about 7 years before dementia, and tau pathology (T+) and neurodegeneration (N+) occur almost simultaneously, between 3 and 4 years before dementia. In addition, MCI-A+ subjects were shown to progress more rapidly to dementia compared to MCI-A- subjects. Based on proposed natural histories and cross-sectional and longitudinal analysis of AD markers, the results indicated that only a single cerebrospinal fluid sample is necessary during the prodromal phase of AD. Prediction from MCI into dementia and its timeline can be achieved exclusively through neuropsychological measures.

Predicting the rapid progression of mild cognitive impairment by intestinal flora and blood indicators through machine learning method.

The aim of the work was to establish a prediction model of mild cognitive impairment (MCI) progression based on intestinal flora by machine learning method. A total of 1013 patients were recruited, in which 87 patients with MCI finished a two-year follow-up. To establish a prediction model, 61 patients were randomly divided into a training set and 26 patients were divided into a testing set. A total of 121 features including demographic characteristics, hematological indicators, and intestinal flora abundance were analyzed. Of the 87 patients who finished a two-year follow-up, 44 presented rapid progression. Model 1 was established based on 121 features with the accuracy 85%, sensitivity 85%, and specificity 83%. Model 2 was based on the first fifteen features of Model 1, (triglyceride, uric acid, alanine transaminase, F-Clostridiaceae, G-Megamonas, S-Megamonas, G-Shigella, G-Shigella, S-Shigella, average hemoglobin concentration, G-Alistipes, S-Collinsella, median cell count, average hemoglobin volume, Low-density lipoprotein), with the accuracy 97%, sensitivity 92%, and specificity 100%. Model 3 was based on the first ten features of Model 1, with the accuracy 97%, sensitivity 86%, and specificity 100%. Other models based on the demographic characteristics, hematological indicators, or intestinal flora abundance features presented lower sensitivity and specificity. The 15 features (including intestinal flora abundance) could establish an effective model for predicting rapid MCI progression.

A deep feature fusion network with global context and cross-dimensional dependencies for classification of mild cognitive impairment from brain MRI

Background and objectives:The accurate identification of people with Mild Cognitive Impairment (MCI) who may develop Alzheimer's disease (AD) holds significant importance in facilitating timely intervention and treatment. However, current classification methods have yet to be effective due to the subtle nature of the features involved. This research aims to improve the performance of the MCI classification by enhancing the feature representation in brain MRI. MethodsWe propose an integrated model that combines Group Shuffle Depth-wise Convolution (GSDW), Global Context Network (GCN), Hybrid Multi-Focus Attention Block (HMAB), and EfficientNet-B0 architecture for MCI classification. This model extracts fine-grained features, contextual information, and long-range dependencies and aggregates low-level details with high-level context information to learn discriminative features. ResultsOur comprehensive evaluation demonstrates significant improvements in the classification performance for both progressive MCI (pMCI) and stable MCI (sMCI), surpassing existing approaches. The proposed model achieved a notable accuracy of 77.2%. ConclusionsThis study introduces a novel feature fusion technique that combines global contextual representations and cross-dimensional dependencies to enhance classification results. These findings highlight the potential of the proposed framework for early identification and intervention in individuals at risk of cognitive decline.

Diagnostic performance of molecular imaging methods in predicting the progression from mild cognitive impairment to dementia: an updated systematic review.

Epidemiological and logistical reasons are slowing the clinical validation of the molecular imaging biomarkers in the initial stages of neurocognitive disorders. We provide an updated systematic review of the recent advances (2017-2022), highlighting methodological shortcomings. Studies reporting the diagnostic accuracy values of the molecular imaging techniques (i.e., amyloid-, tau-, [18F]FDG-PETs, DaT-SPECT, and cardiac [123I]-MIBG scintigraphy) in predicting progression from mild cognitive impairment (MCI) to dementia were selected according to the Preferred Reporting Items for a Systematic Review and Meta-Analysis (PRISMA) method and evaluated with the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Main eligibility criteria were as follows: (1) ≥ 50 subjects with MCI, (2) follow-up ≥ 3years, (3) gold standard: progression to dementia or diagnosis on pathology, and (4) measures of prospective accuracy. Sensitivity (SE) and specificity (SP) in predicting progression to dementia, mainly to Alzheimer's dementia were 43-100% and 63-94% for [18F]FDG-PET and 64-94% and 48-93% for amyloid-PET. Longitudinal studies were lacking for less common disorders (Dementia with Lewy bodies-DLB and Frontotemporal lobe degeneration-FTLD) and for tau-PET, DaT-SPECT, and [123I]-MIBG scintigraphy. Therefore, the accuracy values from cross-sectional studies in a smaller sample of subjects (n > 20, also including mild dementia stage) were chosen as surrogate outcomes. DaT-SPECT showed 47-100% SE and 71-100% SP in differentiating Lewy body disease (LBD) from non-LBD conditions; tau-PET: 88% SE and 100% SP in differentiating DLB from Posterior Cortical Atrophy. [123I]-MIBG scintigraphy differentiated LBD from non-LBD conditions with 47-100% SE and 71-100% SP. Molecular imaging has a moderate-to-good accuracy in predicting the progression of MCI to Alzheimer's dementia. Longitudinal studies are sparse in non-AD conditions, requiring additional efforts in these settings.

Metabolites of intestinal fora can be used as diagnostic and progressive markers for mild cognitive impairment.

The aim of the work was to analyze the metabolites of the intestinal microbiota from the patients with mild cognitive impairment (MCI) and progressive MCI due to Alzheimer's disease (AD). Two cohorts were established. The first one included 87 subjects with 30 healthy controls (NC), 22 patients with MCI due to AD, and 35 patients with AD. The second cohort included 87 patients with MCI due to AD, who were followed up for 2 years and finally were divided into progressive MCI due to AD group (P-G) and unprogressive MCI due to AD group (U-G) according their cognitive levels. Fecal samples were collected to all patients at the baseline time point. Differential metabolites were subjected to pathway analysis by MetaboAnalyst. In the first cohort, we found 21 different metabolites among the three groups (AD, MCI, and NC). In the second cohort, we identified 19 differential metabolites between the P-G and U-G groups. By machine learning analysis, we found that seven characteristic metabolites [Erythrodiol, alpha-Curcumene, Synephrine, o-Hydroxylaminobenzoate, 3-Amino-4-hydroxybenzoic acid, 2-Deoxystreptamine, and 9(S] were of characteristic significance for the diagnosis of MCI due to AD, and six metabolites (Indolelactate, Indole-3-acetaldehyde, L-Proline, Perillyl, Mesaconate, and Sphingosine) were the characteristic metabolites of early warning for the progression of MCI due to AD. D-Glucuronic acid was negatively correlated with Apolipoprotein E4 (APOE4). Perillyl alcohol was negatively correlated with all of the five biomarkers [P-tau181, Neurofilament light chain (NF-light), Aβ1-42, Aβ1-40, and glial fibrillary acidic protein (GFAP)], but Indoleacetaldehyde was positively correlated with three biomarkers (P-tau181, Aβ1-42, and GFAP). Three characteristic metabolites (3-Amino-4-hydroxybenzoate, 2-Deoxystreptamine, and p-Synephrine) were positively correlated with Aβ1-42. 2-Deoxystreptamine, 9(S)-HPOT, and Indoleacetaldehyde were positively correlated with GFAP. L-Proline and Indoleacetaldehyde were positively correlated with NF-light. Specific metabolites of intestinal fora can be used as diagnostic and progressive markers for MCI.