Stable Mild Cognitive Impairment Research Articles

Combined cognitive assessment and automated MRI volumetry improves the diagnostic accuracy of detecting MCI due to Alzheimer's disease

BackgroundMild cognitive impairment (MCI) confers a high annual risk of 10–15 % of conversion to Alzheimer's disease (AD) dementia. MRI atrophy patterns derived from automated ROI analysis, particularly hippocampal subfield volumes, were reported to be useful in diagnosing early clinical stages of Alzheimer's disease. ObjectiveThe aim of the present study was to combine automated ROI MRI morphometry of hippocampal subfield volumes and cortical thickness estimates using FreeSurfer 6.0 with cognitive measures to predict disease progression and time to conversion from MCI to AD dementia. MethodsBaseline (Neuropsychology, MRI) and clinical follow-up data from 62 MCI patients were analysed retrospectively. Individual cortical thickness and volumetric measures were obtained from T1-weighted MRI. Linear discriminant analysis (LDA) of both, cognitive measures and MRI measures (hippocampal subfields, temporal and parietal lobe volumes), were performed to differentiate MCI converters from stable MCI patients. ResultsOut of 62 MCI patients 21 (34 %) converted to AD dementia within a mean follow-up time of 74.7 ± 36.8 months (mean ± SD, range 12 to 130 months). LDA identified temporal lobe atrophy and hippocampal subfield volumes in combination with cognitive measures of verbal memory, verbal fluency and executive functions to correctly classify 71.4.% of MCI subjects converting to AD dementia and 92.7 % with stable MCI. Lower baseline GM volume of the subiculum and the superior temporal gyrus was associated with faster disease progression of MCI converters. ConclusionCombining cognitive assessment with automated ROI MRI morphometry is superior to using a single test in order to distinguish MCI due to AD from non converting MCI patients.

Lipid Trajectories Improve Risk Models for Alzheimer's Disease and Mild Cognitive Impairment.

To assess the relationship between lipids and cognitive dysfunction, we retrospectively analyzed blood-lipid levels in clinically well-characterized individuals with stable mild cognitive impairment (MCI) or Alzheimer's disease (AD) over the decade prior to first cognitive symptoms. In this case/control cohort study, AD and MCI cases were diagnosed using DSM-IV criteria; MCI cases had not progressed to dementia for ≥5 years; and controls were propensity matched to cases at age of symptom onset (MCI: 116 cases, 435 controls; AD: 215 cases, 483 controls). Participants were grouped based on longitudinal trajectories and quintile of variability independent of the mean (VIM) for total cholesterol, HDL-C, LDL-C, non-HDL-C and ln(triglycerides). Models for the risk of cognitive dysfunction evaluated trajectory and VIM groups, APOE genotype, polygenic risk scores (PRS) for AD and lipid levels, age, comorbidities, and longitudinal correlates of blood-lipid concentrations. Lower HDL-C trajectories (OR = 3.8, 95% CI = 1.3-11.3) and the lowest VIM quintile of non-HDL-C (OR = 2.2, 95% CI = 1.3-3.0) were associated with higher MCI risk. Lower HDL-C trajectories (OR = 3.0, 95% CI = 1.6-5.7) and the lowest VIM quintile of total cholesterol (OR = 2.4, 95% CI = 1.5-3.9) were associated with higher AD risk. The inclusion of lipid-trajectory and VIM groups improved risk-model predictive performance independent of APOE genotype or PRS for AD and lipid levels. These results provide an important real-world perspective on the influence of lipid metabolism and blood-lipid levels on the development of stable MCI and AD.

Baseline serum glutamate: Implications for diagnosis and prediction in mild cognitive impairment and Alzheimer’s disease of the Alzheimer’s Disease Neuroimaging Initiative

PurposeNumerous studies have highlighted a close link between metabolic imbalances and Alzheimer’s Disease (AD). The advancement of metabolomics has recently enabled the exploration of characteristic metabolic changes associated with AD. Studies indicate that serum glutamate (Glu) levels may correlate with mild cognitive impairment (MCI) and AD. This study aims to further elucidate the characteristics of baseline serum Glu levels in MCI and AD. MethodsThis study included 783 participants from the Alzheimer’s Disease Neuroimaging Initiative-1 (ADNI-1) cohort, categorized into cognitively normal (CN, n = 224), stable MCI (sMCI, n = 181), progressive MCI (pMCI, n = 193), and AD (n = 185). The study aimed to analyze the diagnostic value of baseline serum Glu, to explore its predictive capability for the progression from CN to MCI or AD, and from MCI to AD, and to analyze the relationship between serum Glu and cerebrospinal fluid (CSF) biomarkers and cognitive functions in different diagnostic groups. ResultsCompared to the CN and sMCI groups, the pMCI group showed significantly lower levels of serum Glu, and the AD group also had lower Glu levels compared to the sMCI group. However, serum Glu did not show significant diagnostic value for MCI and AD. Lower levels of serum Glu could predict the progression from MCI to AD. ConclusionSerum Glu levels can predict the progression from MCI to AD, suggesting that it could provide new insights into the pathophysiological mechanisms of AD. However, serum Glu may not be an ideal peripheral biomarker for AD.

Path Integration Detects Prodromal Alzheimer's Disease and Predicts Cognitive Decline.

The entorhinal cortex is the very earliest involvement of Alzheimer's disease (AD). Grid cells in the medial entorhinal cortex form part of the spatial navigation system. We aimed to determine whether path integration performance can be used to detect patients with mild cognitive impairment (MCI) at high risk of developing AD, and whether it can predict cognitive decline. Path integration performance was assessed in 71 patients with early MCI (EMCI) and late MCI (LMCI) using a recently developed 3D virtual reality navigation task. Patients with LMCI were further divided into those displaying characteristic brain imaging features of AD, including medial temporal lobe atrophy on magnetic resonance imaging and posterior hypoperfusion on single-photon emission tomography (LMCI+), and those not displaying such features (LMCI-). Path integration performance was significantly lower in patients with LMCI+than in those with EMCI and LMCI-. A significantly lower performance was observed in patients who showed progression of MCI during 12 months, than in those with stable MCI. Path integration performance distinguished patients with progressive MCI from those with stable MCI, with a high classification accuracy (a sensitivity of 0.88 and a specificity of 0.70). Our results suggest that the 3D virtual reality navigation task detects prodromal AD patients and predicts cognitive decline after 12 months. Our navigation task, which is simple, short (12-15 minutes), noninvasive, and inexpensive, may be a screening tool for therapeutic choice of disease-modifiers in individuals with prodromal AD.

Using eZIS to Predict Progression from MCI to Dementia in Three Years.

(1) Background: Mild cognitive impairment (MCI) due to Alzheimer's disease (AD) progresses to dementia at a higher annual rate, while other MCIs may remain stable or even improve over time. Discriminating progressive from non-progressive cases of MCI is crucial and challenging. (2) Methods: A retrospective study of individuals with MCI was conducted at a university hospital located in southern Taiwan. The researchers collected demographic data, comorbidities, the scores of cognitive tests, three easy Z-score imaging system (eZIS) indicators (severity, extent, and ratio), Fazekas scale scores, mesial temporal atrophy (MTA) scores, clinical outcomes including deterioration of Cognitive Abilities Screening Instrument, Mini-mental State Examination, Clinical Dementia Rating Sum of Box scores, and the conversion from MCI to dementia. Those who converted to dementia in three years and non-converters were compared by the three eZIS indicators to test the predictive utility, and the clinical outcomes were evaluated by regression and ROC curve analysis. (3) Results: The three eZIS indicators were significantly higher in the group of progressive MCI than in stable MCI. eZIS severity is positively correlated with a deterioration in the scores of the Cognitive Abilities Screening Instrument and Clinical Dementia Rating Sum of Box. eZIS severity is also positively correlated with conversion from MCI to dementia. The AUC for severity is 0.719, and the optimal cutoff value of severity for predicting conversion is 1.22. (4) Conclusions: During three years of follow-up, MCI individuals with greater eZIS severity were significantly associated with worse cognitive assessment scores and a higher conversion rate to dementia.

A "glympse" into neurodegeneration: Diffusion MRI and cerebrospinal fluid aquaporin-4 for the assessment of glymphatic system in Alzheimer's disease and other dementias.

The glymphatic system (GS) is a whole-brain perivascular network, consisting of three compartments: the periarterial and perivenous spaces and the interposed brain parenchyma. GS dysfunction has been implicated in neurodegenerative diseases, particularly Alzheimer's disease (AD). So far, comprehensive research on GS in humans has been limited by the absence of easily accessible biomarkers. Recently, promising non-invasive methods based on magnetic resonance imaging (MRI) along with aquaporin-4 (AQP4) quantification in the cerebrospinal fluid (CSF) were introduced for an indirect assessment of each of the three GS compartments. We recruited 111 consecutive subjects presenting with symptoms suggestive of degenerative cognitive decline, who underwent 3 T MRI scanning including multi-shell diffusion-weighted images. Forty nine out of 111 also underwent CSF examination with quantification of CSF-AQP4. CSF-AQP4 levels and MRI measures-including perivascular spaces (PVS) counts and volume fraction (PVSVF), white matter free water fraction (FW-WM) and mean kurtosis (MK-WM), diffusion tensor imaging analysis along the perivascular spaces (DTI-ALPS) (mean, left and right)-were compared among patients with AD (n = 47) and other neurodegenerative diseases (nAD = 24), patients with stable mild cognitive impairment (MCI = 17) and cognitively unimpaired (CU = 23) elderly people. Two runs of analysis were conducted, the first including all patients; the second after dividing both nAD and AD patients into two subgroups based on gray matter atrophy as a proxy of disease stage. Age, sex, years of education, and scanning time were included as confounding factors in the analyses. Considering the whole cohort, patients with AD showed significantly higher levels of CSF-AQP4 (exp(b) = 2.05, p = .005) and FW-WM FW-WM (exp(b) = 1.06, p = .043) than CU. AQP4 levels were also significantly higher in nAD in respect to CU (exp(b) = 2.98, p < .001). CSF-AQP4 and FW-WM were significantly higher in both less atrophic AD (exp(b) = 2.20, p = .006; exp(b) = 1.08, p = .019, respectively) and nAD patients (exp(b) = 2.66, p = .002; exp(b) = 1.10, p = .019, respectively) compared to CU subjects. Higher total (exp(b) = 1.59, p = .013) and centrum semiovale PVS counts (exp(b) = 1.89, p = .016), total (exp(b) = 1.50, p = .036) and WM PVSVF (exp(b) = 1.89, p = .005) together with lower MK-WM (exp(b) = 0.94, p = .006), mean and left ALPS (exp(b) = 0.91, p = .043; exp(b) = 0.88, p = .010 respectively) were observed in more atrophic AD patients in respect to CU. In addition, more atrophic nAD patients exhibited higher levels of AQP4 (exp(b) = 3.39, p = .002) than CU. Our results indicate significant changes in putative MRI biomarkers of GS and CSF-AQP4 levels in AD and in other neurodegenerative dementias, suggesting a close interaction between glymphatic dysfunction and neurodegeneration, particularly in the case of AD. However, the usefulness of some of these biomarkers as indirect and standalone indices of glymphatic activity may be hindered by their dependence on disease stage and structural brain damage.

Clinical utility of diffusion MRI-derived measures of cortical microstructure in a real-world memory clinic setting.

To investigate cortical microstructural measures from diffusion MRI as "neurodegeneration" markers that could improve prognostic accuracy in mild cognitive impairment (MCI). The prognostic power of Amyloid/Tau/Neurodegeneration (ATN) biomarkers to predict progression from MCI to AD or non-AD dementia was investigated. Ninety patients underwent clinical evaluation (follow-up interval 32 ± 18 months), lumbar puncture, and MRI. Participants were grouped by clinical stage and cerebrospinal fluid Amyloid and Tau status. T1-structural and diffusion MRI scans were analyzed to calculate diffusion metrics related to cortical columnar structure (AngleR, ParlPD, PerpPD+), cortical mean diffusivity, and fractional anisotropy. Statistical tests were corrected for multiple comparisons. Prognostic power was assessed using receiver operating characteristic (ROC) analysis and related indices. A progressive increase of whole-brain cortical diffusion values was observed along the AD continuum, with all A+ groups showing significantly higher AngleR than A-T-. Investigating clinical progression to dementia, the AT biomarkers together showed good positive predictive value (with 90.91% of MCI A+T+ converting to dementia) but poor negative predictive value (with 40% of MCI A-T- progressing to a mix of AD and non-AD dementias). Adding whole-brain AngleR as an N marker, produced good differentiation between stable and converting MCI A-T- patients (0.8 area under ROC curve) and substantially improved negative predictive value (+21.25%). Results support the clinical utility of cortical microstructure to aid prognosis, especially in A-T- patients. Further work will investigate other complexities of the real-world clinical setting, including A-T+ groups. Diffusion MRI measures of neurodegeneration may complement fluid AT markers to support clinical decision-making.

Analysis of individual alpha frequency in a large cohort from a tertiary memory center.

Precise and timely diagnosis is crucial for the optimal use of emerging disease-modifying treatments for Alzheimer disease (AD). Electroencephalography (EEG), which is noninvasive and cost-effective, can capture neural abnormalities linked to various dementias. This study explores the use of individual alpha frequency (IAF) derived from EEG as a diagnostic and prognostic tool in cognitively impaired patients. This retrospective study included 375 patients from the tertiary Memory Clinic of IRCCS San Raffaele Hospital, Milan, Italy. Participants underwent clinical and neuropsychological assessments, brain imaging, cerebrospinal fluid biomarker analysis, and resting-state EEG. Patients were categorized by amyloid status, the AT(N) classification system, clinical diagnosis, and mild cognitive impairment (MCI) progression to AD dementia. IAF was calculated and compared among study groups. Receiver operating characteristic (ROC) analysis was used to calculate its discriminative performance. IAF was higher in amyloid-negative subjects and varied significantly across AT(N) groups. ROC analysis confirmed IAF's ability to distinguish A-T-N- from the A+T+N+ and A+T-N+ groups. IAF was lower in AD and Lewy body dementia patients compared to MCI and other dementia types, with moderate discriminatory capability. Among A+ MCI patients, IAF was significantly lower in those who converted to AD within 2 years compared to stable MCI patients and predicted time to conversion (p < 0.001, R = 0.38). IAF is a valuable tool for dementia diagnosis and prognosis, correlating with amyloid status and neurodegeneration. It effectively predicts MCI progression to AD, supporting its use in early, targeted interventions in the context of disease-modifying treatments.

Enhancing Feature Selection in MCI Diagnosis using FDG-PET Images: Leveraging Multiple Simple Autoencoder Architectures

Alzheimer’s Disease (AD) is the most common type of neurodegenerative brain disease in elderly people. Early diagnosis of AD is crucial for providing suitable care. Positron Emission Tomography (PET) images and machine learning can be used to support this purpose. In this paper, we present a method for ranking the effectiveness of brain regions of interest (ROIs) to distinguish between stable mild cognitive impairment (sMCI) from progressive mild cognitive impairment (pMCI) in brain PET images based on AutoEncoder (AE). Experiments on the ADNI dataset show that our proposed method significantly improves classifier performance when compared to other popular feature ranking methods such as Fisher score, t-score, and LASSO. Our results suggest that instead of focusing on designing a complex AE structure, we can also use simple-but-multiple AEs for feature ranking. The proposed method could be easily applied to any image dataset where a feature selection is needed.

Choroid plexus volumes and auditory verbal learning scores are associated with conversion from mild cognitive impairment to Alzheimer's disease.

Mild cognitive impairment (MCI) can be the prodromal phase of Alzheimer's disease (AD) where appropriate intervention might prevent or delay conversion to AD. Given this, there has been increasing interest in using magnetic resonance imaging (MRI) and neuropsychological testing to predict conversion from MCI to AD. Recent evidence suggests that the choroid plexus (ChP), neural substrates implicated in brain clearance, undergo volumetric changes in MCI and AD. Whether the ChP is involved in memory changes observed in MCI and can be used to predict conversion from MCI to AD has not been explored. The current study used data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database to investigate whether later progression from MCI to AD (progressive MCI [pMCI], n=115) or stable MCI (sMCI, n=338) was associated with memory scores using the Rey Auditory Verbal Learning Test (RAVLT) and ChP volumes as calculated from MRI. Classification analyses identifying pMCI or sMCI group membership were performed to compare the predictive ability of the RAVLT and ChP volumes. The results indicated a significant difference between pMCI and sMCI groups for right ChP volume, with the pMCI group showing significantly larger right ChP volume (p=.01, 95% confidence interval [-.116, -.015]). A significant linear relationship between the RAVLT scores and right ChP volume was found across all participants, but not for the two groups separately. Classification analyses showed that a combination of left ChP volume and auditory verbal learning scores resulted in the most accurate classification performance, with group membership accurately predicted for 72% of the testing data. These results suggest that volumetric ChP changes appear to occur before the onset of AD and might provide value in predicting conversion from MCI to AD.

Prediction of Alzheimer's disease progression within 6 years using speech: A novel approach leveraging language models.

Identification of individuals with mild cognitive impairment (MCI) who are at risk of developing Alzheimer's disease (AD) is crucial for early intervention and selection of clinical trials. We applied natural language processing techniques along with machine learning methods to develop a method for automated prediction of progression to AD within 6 years using speech. The study design was evaluated on the neuropsychological test interviews of n=166 participants from the Framingham Heart Study, comprising 90 progressive MCI and 76 stable MCI cases. Our best models, which used features generated from speech data, as well as age, sex, and education level, achieved an accuracy of 78.5% and a sensitivity of 81.1% to predict MCI-to-AD progression within 6 years. The proposed method offers a fully automated procedure, providing an opportunity to develop an inexpensive, broadly accessible, and easy-to-administer screening tool for MCI-to-AD progression prediction, facilitating development of remote assessment. Voice recordings from neuropsychological exams coupled with basic demographics can lead to strong predictive models of progression to dementia from mild cognitive impairment. The study leveraged AI methods for speech recognition and processed the resulting text using language models. The developed AI-powered pipeline can lead to fully automated assessment that could enable remote and cost-effective screening and prognosis for Alzehimer's disease.

Predicting the Conversion from Mild Cognitive Impairment to Alzheimer's Disease Using Graph Frequency Bands and Functional Connectivity-Based Features.

Accurate prediction of the progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) is crucial for disease management. Machine learning techniques have demonstrated success in classifying AD and MCI cases, particularly with the use of resting-state functional magnetic resonance imaging (rs-fMRI) data.This study utilized three years of rs-fMRI data from the ADNI, involving 142 patients with stable MCI (sMCI) and 136 with progressive MCI (pMCI). Graph signal processing was applied to filter rs-fMRI data into low, middle, and high frequency bands. Connectivity-based features were derived from both filtered and unfiltered data, resulting in a comprehensive set of 100 features, including global graph metrics, minimum spanning tree (MST) metrics, triadic interaction metrics, hub tendency metrics, and the number of links. Feature selection was enhanced using particle swarm optimization (PSO) and simulated annealing (SA). A support vector machine (SVM) with a radial basis function (RBF) kernel and a 10-fold cross-validation setup were employed for classification. The proposed approach demonstrated superior performance, achieving optimal accuracy with minimal feature utilization. When PSO selected five features, SVM exhibited accuracy, specificity, and sensitivity rates of 77%, 70%, and 83%, respectively. The identified features were as follows: (Mean of clustering coefficient, Mean of strength)/Radius/(Mean Eccentricity, and Modularity) from low/middle/high frequency bands of graph. The study highlights the efficacy of the proposed framework in identifying individuals at risk of AD development using a parsimonious feature set. This approach holds promise for advancing the precision of MCI to AD progression prediction, aiding in early diagnosis and intervention strategies.

Alteration in amygdala subfield volumes and their association with cognition in mild cognitive impairment.

The amygdala has an important role in cognitive and affective functions. The involvement of amygdala and related limbic structures is implicated in many aspects of memory and emotion in mild cognitive impairment (MCI). In the present study, we aimed to compare the volumetric measurements of amygdala and its subfields as well as their association with cognitive functions in stable MCI (sMCI). We performed Addenbrooke's cognitive examination III (ACE-III) test, as well as high-resolution T1-weighted images from 31 participants with sMCI and 31 age-matched healthy controls. The amygdala subfield volumes were extracted using Freesurfer software, and group differences were assessed using general linear model (GLM) with age, gender, education and estimated intracranial volume (ICV) as covariates. Partial correlation was also calculated between cognitive scores and volumes of amygdala subfields in healthy controls and sMCI participants controlling for estimated ICV. sMCI participants exhibited significantly reduced volumes in most of the right amygdala subfields, including basal nucleus, accessory basal nucleus, central nucleus, medial nucleus, corticoamygdaloid transition area, and whole amygdala, as well as significantly reduced right amygdala/hippocampus ratio compared to healthy controls. In addition, our results revealed statistically significant positive correlations between ACE memory scores and the volumes of right central nucleus, right medial nucleus, right cortical nucleus, and the right whole amygdala, in sMCI. Our findings revealed volumetric reductions in most of the right amygdala subfields along with its association with the memory functions in sMCI. These findings provide valuable insights into the underlying anatomical factors contributing to neurocognitive symptoms in MCI.

Polygenic Score for Conscientiousness Is a Protective Factor for Reversion from Mild Cognitive Impairment to Normal Cognition.

Spontaneous reversion from mild cognitive impairment (MCI) to normal cognition (NC) is little known. Based on the data of the Genetics of Personality Consortium and MCI participants from Alzheimer's Disease Neuroimaging Initiative, the authors investigate the effect of polygenic scores (PGS) for personality traits on the reversion of MCI to NC and its underlying neurobiology. PGS analysis reveals that PGS for conscientiousness (PGS-C) is a protective factor that supports the reversion from MCI to NC. Gene ontology enrichment analysis and tissue-specific enrichment analysis indicate that the protective effect of PGS-C may be attributed to affecting the glutamatergic synapses of subcortical structures, such as hippocampus, amygdala, nucleus accumbens, and caudate nucleus. The structural covariance network (SCN) analysis suggests that the left whole hippocampus and its subfields, and the left whole amygdala and its subnuclei show significantly stronger covariance with several high-cognition relevant brain regions in the MCI reverters compared to the stable MCI participants, which may help illustrate the underlying neural mechanism of the protective effect of PGS-C.

Associations of Serum Isoleucine with Mild Cognitive Impairment and Alzheimer's Disease.

Advances in blood biomarker discovery have enabled the improved diagnosis and prognosis of Alzheimer&apos;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&apos;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 by 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) improved the diagnostic accuracy in AD compared to total tau (T-tau) alone. Serum Ile levels significantly predicted the conversion from MCI to AD (cutoff value of 78.3 μM). Finally, the results of this study also revealed a correlation between serum Ile levels and the Alzheimer&apos;s Disease Assessment Scale cognitive subscale Q4. Serum Ile may be a potential biomarker of AD. Ile had independent diagnostic efficacy and significantly improved the diagnostic accuracy of CSF P-tau in AD. MCI patients with a lower serum Ile level had a higher risk of progression to AD and a worse cognition assessment.

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.

Economic Impact of Progression from Mild Cognitive Impairment to Alzheimer Disease in the United States

BackgroundLimited evidence exists on the economic burden of individuals who progress from mild cognitive impairment (MCI) to Alzheimer disease and related dementia disorders (ADRD).ObjectivesTo assess the all-cause health care resource utilization and costs for individuals who develop ADRD following an MCI diagnosis compared to those with stable MCI.DesignThis was a retrospective cohort study from January 01, 2014, to December 31, 2019.SettingThe Merative MarketScan Commercial and Medicare Databases were used.ParticipantsIndividuals were included if they: (1) were aged 50 years or older; (2) had ≥1 claim with an MCI diagnosis based on the International Classification of Diseases, Ninth Revision (ICD-9) code of 331.83 or the Tenth Revision (ICD-10) code of G31.84; and had continuous enrollment. Individuals were excluded if they had a diagnosis of Parkinson’s disease or ADRD or prescription of ADRD medication.MeasurementsOutcomes included all-cause utilization and costs per patient per year in the first 12 months following MCI diagnosis, in total and by care setting: inpatient admissions, emergency department (ED) visits, outpatient visits, and pharmacy claims.ResultsOut of the total of 5185 included individuals, 1962 (37.8%) progressed to ADRD (MCI-to-ADRD subgroup) and 3223 (62.2%) did not (Stable MCI subgroup). Adjusted all-cause utilization was higher for all care settings in the MCI-to-ADRD subgroup compared with the Stable MCI subgroup. Adjusted all-cause mean total costs ($34599 vs $24541; mean ratio [MR], 1.41 [95% CI, 1.31–1.51]; P<.001), inpatient costs ($47463 vs $38004; MR, 1.25 [95% CI, 1.08–1.44]; P=.002), ED costs ($4875 vs $3863; MR, 1.26 [95% CI, 1.11–1.43]; P<.001), and outpatient costs ($16652 vs $13015; MR, 1.28 [95% CI, 1.20–1.37]; P<.001) were all significantly higher for the MCI-to-ADRD subgroup compared with the Stable MCI subgroup.ConclusionsIndividuals who progressed from MCI to ADRD had significantly higher health care costs than individuals with stable MCI. Early identification of MCI and delaying its progression is important to improve patient and economic outcomes.

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.

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.

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.