Mild Cognitive Impairment Individuals Research Articles

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

BackgroundThe Mild Behavioral Impairment Checklist (MBI-C) is a tool for detecting MBI, a neurobehavioral syndrome associated with an increased dementia risk.ObjectiveThis study aimed to evaluate the reliability and validity of the Italian version of the informant-rated MBI-C in an outpatient sample of dementia-free individuals.MethodsA cross-sectional study was conducted on 72 older people without dementia (n = 47, mild cognitive impairment; n = 25, cognitively unimpaired). During the visit, physicians administrated the MBI-C and Neuropsychiatric Inventory Questionnaire (NPI-Q) to the informant. Internal consistency of MBI-C was measured by the Cronbach's coefficient alpha and inter-domain correlation coefficients. Diagnostic performance of MBI-C for clinically identified MBI by ISTAART criteria was assessed through ROC analysis, identifying the optimal cut-off based on the Youden Index. Spearman's correlations were used to evaluate the concurrent validity of MBI-C with the NPI-Q, Mini-Mental State Examination (MMSE), Instrumental Activity of Daily Living (IADL) and 3-item UCLA Loneliness Scale.ResultsMBI-C showed high internal consistency ( = 0.867) and strong inter-domain correlation ( = 0.760 0.859, p < 0.001). The Area Under the Curve (AUC) for detecting clinical MBI was 0.937 (95%CI: 0.865-0.972), with an optimal cut-off of 5.5 (sensitivity = 0.849, specificity = 0.876). The MBI-C total score strongly correlated with the NPI-Q total score ( = 0.820, p < 0.001). Only the MBI-C total score significantly correlated with the 3-item UCLA ( = 0.236, p = 0.046); no significant correlations were found with MMSE and IADL scores.ConclusionsThe Italian version of MBI-C demonstrated strong reliability, validity, and diagnostic performance. Therefore, MBI-C may be a suitable tool for assessing behavioral symptoms in dementia-free individuals.

Subjective Cognitive Decline (SCD) is a heterogeneous condition recognized as the earliest manifestation of Alzheimer's disease (AD). We hypothesized that the heterogeneity of SCD may be synthesized in distinct subtypes. We analyzed data from the AD Neuroimaging Initiative (ADNI) database. For all participants, demographic variables, cognitive measures, APOE genotype, CSF biomarkers, brain MRI, and FDG-PET data were available. Participants underwent follow-up visits every 6 or 12months. 542 cognitively normal (CN), 346 SCD, and 423 early mild cognitive impairment (E-MCI) individuals were included. A data-driven approach based on cognitive measures identified three SCD clusters (k1, k2, k3) that performed differently in verbal memory (k2 outperformed all the groups and k3 showed the poorest performance, p < 0.001) and in executive function (k1 had the lowest scores, p = 0.006). Regarding CSF biomarkers, k2 exhibited lower p-tau (20.6 ± 9.2 vs. 24.2 ± 13.6, p = 0.03) and k3 had higher Aβ42 levels (1131.3 ± 379.8 vs. 942.87 ± 355.3, p = 0.01) compared to the E-MCI group, while there were no differences between k1 and E-MCI. Regarding brain FDG-PET, k1 demonstrated reduced uptake compared to CN, k2, and k3 (p < 0.001). During follow-up, k1 exhibited a higher rate of progression to MCI or dementia compared to k2 and k3 (Log-rank χ2 = 18.18, p = 0.0002) and a steeper decline in general cognition and long-term verbal memory compared to k2. We proposed a three-subgroup system classification for SCD, reflecting different cognitive profiles and longitudinal trajectories. Classifying individuals with SCD may enhance diagnostic pathways and inform personalized interventions.

Metabolomics and lipidomics study on serum metabolite signatures in Alzheimer's disease and mild cognitive impairment.

Mitochondrial methylcytosines as blood-based biomarkers for Alzheimer’s disease dementia prognosis

Background The growing number of older people with Mild Cognitive Impairment (MCI) highlights the need for suitable and effective neuropsychological assessments. The Montreal Cognitive Assessment Basic (MoCA-B) is designed to identify MCI in individuals with lower literacy and education levels. This study seeks to validate the use of MoCA-B in the Persian-speaking population. Methods In this cross-sectional study, the original English version of the MoCA-B test was translated into Persian using the forward-backward method. The study involved 60 cognitively healthy aging individuals, 30 with Alzheimer’s disease, and 30 MCI patients. All participants met the MMSE, MoCA-B, DSM-5, and Albert’s criteria. Results MoCA-B scores in patients with AD were significantly lower than in the patients with MCI and healthy individuals (P < 0.001). They were significantly lower in MCI than individuals without cognitive impairment (P < 0.001). The cutoff score for discriminating between patients with AD/MCI and individuals without cognitive impairment was 20.5 (sensitivity = 95.0%, specificity = 88.3%, AUC = 0.972). Conclusion This study shows that the MoCA-B is a suitable screening tool for distinguishing persons with cognitive impairment (MCI and AD) in the Persian-speaking population.

Effects of regional white matter hyperintensities and β-amyloid on domain-specific cognition and progression to dementia.

Mild cognitive impairment (MCI) is a transitional stage before Alzheimer disease and related dementias (ADRD). The link between AD and increased health care resource utilization (HCRU) and costs is well established but not the economic burden of MCI. To estimate the incremental economic burden of individuals with MCI in the United States. This was a retrospective cohort study that derived data from the MarketScan Commercial and Medicare Supplemental Databases. The observation period was from January 1, 2014, through December 31, 2019. Included individuals were (1) aged at least 50years, (2) had at least 2years of pre-index (ie, date of their first MCI diagnosis) continuous health plan enrollment, and (3) had at least 1year of post-index continuous health plan enrollment. Individuals were excluded if they had (1) at least 1 claim with a diagnosis of Parkinson disease at any time during the study period, (2) at least 1 claim with a diagnosis of ADRD at any time before the index date, or (3) at least 1 pharmacy claim for an ADRD medication (donepezil, memantine, memantine/donepezil, galantamine, or rivastigmine) at any time before the index date. Outcomes included all-cause HCRU and health care costs for incident MCI individuals (MCI cohort) and matched individuals without MCI or dementia (control cohort) during the 12-month follow-up period. Controls were matched at a 3:1 ratio by age, sex, region, and index year. In total, 5,185 individuals met the criteria for the MCI cohort and 15,555 for the control cohort. Mean age at baseline was 67years and 57.7% were female in both cohorts. The MCI cohort had a higher comorbidity burden compared with the control cohort (1.5 vs 1.0 and 2.6 vs 1.8, respectively; P < 0.0001) All comorbidities assessed at baseline were more prevalent in the MCI cohort than in the control. Adjusted all-cause HCRU for all points of service and adjusted all-cause mean costs in total ($32,318 vs $13,894; mean ratio [MR] = 2.33, 95% CI = 2.23-2.43), for emergency department ($4,460 vs $3,849; MR = 1.16, 95% CI = 1.08-1.25), outpatient ($16,054 vs $7,265; MR = 2.21, 95% CI = 2.12-2.30), and pharmacy ($5,503 vs $2,933; MR = 1.88, 95% CI = 1.78-1.97) (all P < 0.0001) were significantly higher for the MCI cohort. The economic burden of MCI was more than double that for similar individuals without MCI or dementia. Timely diagnosis and intervention are key to delaying progression to AD and reducing associated costs.

Mild cognitive impairment (MCI) represents a transitional neurocognitive state vulnerable to environmental modulation, yet the exposomic underpinnings remain poorly characterized. In this study, we performed integrated urinary exposome and metabolome profiling in 30 MCI patients and 30 matched controls using a broad-spectrum targeted LC-MS/MS platform encompassing 239 xenobiotics and 688 endogenous metabolites. To characterize systemic environmental-metabolic interactions, exposome-metabolome (E × M) correlation networks were constructed through bootstrap-resampled Spearman analysis. Although total xenobiotic burdens were comparable between groups, MCI individuals exhibited significantly elevated chemical richness and E × M network hyperconnectivity, suggesting heightened metabolic reactivity to environmental stimuli. A core differential E × M network was delineated, identifying 1-hydroxypyrene, perfluorooctanoic acid, and NEtFOSAA as central environmental hubs, and acetylcholine, guanine, and l-methionine as key metabolic nodes associated with MCI status and AD8 cognitive scores. These molecules converge on oxidative stress, neuroinflammation, cholinergic dysregulation, and epigenetic perturbation pathways. Our findings underscore the pathophysiological relevance of chemical-metabolic crosstalk in early cognitive decline and advocate for exposome-informed precision neurology frameworks.

Background: Mild Cognitive Impairment (MCI) represents an intermediate stage between normal cognitive aging and Alzheimer's Disease (AD). Early and accurate identification of MCI is crucial for implementing interventions that may delay or prevent further cognitive decline. This study aims to develop a machine learning-based model for differentiating between Cognitively Normal (CN) individuals and MCI patients using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Methods: An ensemble classification approach was designed by integrating Extra Trees, Random Forest, and Light Gradient Boosting Machine (LightGBM) algorithms. Feature selection emphasized clinically relevant biomarkers, including Amyloid-β 42, phosphorylated tau, diastolic blood pressure, age, and gender. The dataset was split into training and held-out test sets. A probability thresholding strategy was employed to flag uncertain predictions for potential deferral, enhancing model reliability in borderline cases. Results: The final ensemble model achieved an accuracy of 83.2%, a recall of 80.2%, and a precision of 86.3% on the independent test set. The probability thresholding mechanism flagged 23.3% of cases as uncertain, allowing the system to abstain from low-confidence predictions. This strategy improved clinical interpretability and minimized the risk of misclassification in ambiguous cases. Conclusions: The proposed AI-driven ensemble model demonstrates strong performance in classifying MCI versus CN individuals using multimodal ADNI data. Incorporating a deferral mechanism through uncertainty estimation further enhances the model's clinical utility. These findings support the integration of machine learning tools into early screening workflows for cognitive impairment.

BackgroundHandwriting and speech are served as reliable signatures for detecting cognitive decline, playing a pivotal role in the early diagnosing Alzheimer's disease (AD) and mild cognitive impairment (MCI). However, current unimodal approaches for diagnosing AD and MCI have demonstrated constraints in classification accuracy, potentially overlooking the synergistic value of combining handwriting and speech data.ObjectivePresenting an innovative multi-modal screening classification model, that harnesses handwriting and speech analysis to enhance MCI detection, aiming to overcome the constraints of single-modality approaches by integrating data from both modalities, thereby improving diagnostic accuracy.MethodsProposing a multimodal classification model based on gated recurrent unit (GRU) and attention mechanism, treating handwriting and speech data as sequence inputs. The model was constructed and tested on a dataset of 41 participants, including 20 MCI patients and 21 cognitively normal (CN) individuals. To mitigate the risk of overfitting due to the small sample size, we employed a 10-fold cross-validation strategy to ensure the robustness of the results.ResultsOur multimodal classification model achieved an accuracy of 95.2% for MCI versus CN individuals, which shows a significant improvement compared to the results of single-modality. This result indicates the effectiveness of the cross-fusion model in enhancing classification performance, offering a promising approach for the early diagnosis of neurodegenerative diseases.ConclusionsThe proposed GRU_CA effectively improves early MCI detection by fusing handwriting and speech data, outperforming a single modality. It shows strong potential for deployment in primary healthcare settings and establishes a foundation for future research on more complex diagnostic tasks, including CN, MCI, and AD classification, as well as longitudinal studies.

Individuals with mild cognitive impairment (MCI) and an abnormal amyloid biomarker (A+) are at considerable increased risk to develop dementia. Still, these individuals vary greatly in rates of cognitive decline, and the mechanisms underlying this heterogeneity remain largely unclear. One factor related to increased risk of progression to dementia is having an abnormal tau status (T+), but this still explains only part of the variance. Furthermore, previous work has indicated that MCI A+ individuals with T- or T+ are characterized by distinct molecular processes as reflected by distinct cerebrospinal fluid (CSF) proteomic profiles. As such, it could be hypothesized that differences in rates of cognitive decline in A+ MCI with abnormal or normal tau status may be explained by distinct underlying mechanisms. We studied this question using an untargeted CSF proteomic approach in individuals with MCI and abnormal amyloid. We measured untargeted TMT mass spectrometry proteomics in CSF of 80 A+ MCI individuals from the Amsterdam Dementia Cohort (age 66±7.9 years, 52 [65%] T+). For each protein we tested if CSF levels were related to time to progression to dementia using Cox survival models; and with decline on the MMSE with linear mixed models, correcting for age, sex, and education. We validated our results in the independent Alzheimer's Disease Neuroimaging Initiative (ADNI) that employed the orthogonal CSF Soma logic protein measures in 245 CSF A+ MCI individuals (age 73±7.2 years, 135 [55%] T+). In total, we found 664 (29%) proteins to be related to cognitive decline in A+T+ and 718 (31%) proteins in A+T-. In A+T+ higher levels of 393 proteins that were associated with synaptic plasticity processes, and lower levels of 271 proteins associated with the immune function processes predicted steeper decline on the MMSE and faster progression to dementia. In A+T-, higher levels of 306 proteins that were related to blood-brain barrier impairment and lower levels of 412 proteins associated with synaptic plasticity processes predicted steeper decline. 67% of pathways associated with decline in A+T+ and 58% in A+T- were replicated in ADNI. In conclusion, cognitive decline in A+ MCI individuals with and without tau may involve distinct underlying pathophysiology. These findings suggest that treatments aiming to delay cognitive decline may need tailoring according to the underlying mechanism of these patient groups, and that amyloid and tau levels could aid in stratification of selecting patients.

The potential utility of arterial spin labeling in predicting brain amyloidosis.

We aimed to calculate population attributable fractions (PAFs) for incident dementia and examine sex differences in individuals with no cognitive impairment (NCI) or mild cognitive impairment (MCI). Longitudinal data from the Rush University Memory and Aging Project (MAP) were analyzed. Cox proportional-hazards models were used to estimate covariate-adjusted hazard ratios for incident dementia and calculate weighted PAFs within each cognitive status/sex subgroup. The analytical sample comprised 1481 NCI (76.7% female) and 515 MCI (69.7% female) participants. Overall PAFs were similar for NCI (18.2%) and MCI (18.6%) subgroups, however, sex differences were evident. Males had higher PAFs than females in both NCI (42.5%vs. 25.1%) and MCI (51.5%vs. 12.4%), with differing risk factor profiles. These findings support the notion that dementia risk is modifiable after the onset of MCI and that the number of potentially preventable dementia cases may be higher in males than in females. The proportion of potentially preventable dementia cases was similar for no cognitive impairment (NCI) and mild cognitive impairment (MCI) individuals. For both cognitive states, a higher proportion of potentially preventable dementia cases was observed in males compared to females. The profiles of modifiable risk factors contributing to dementia differed between males and females. Lifestyle related risk factors were more prominent contributors to preventable dementia in males. Psychosocial risk factors, such as depression and social isolation, were important contributors in females.

Temporal cortical microstructural changes precede cortical atrophy during memory decline. We used neurite orientation dispersion and density imaging (NODDI) to assess such early microstructural change. Cognitively unimpaired (CU, n=725) and mildly cognitively impaired (MCI, n=111) participants from the Mayo Clinic Study of Aging underwent 3T magnetic resonance imaging (MRI), including NODDI and neuropsychological evaluation for calculation of memory z scores. Linear mixed effects modelling assessed the relationship between temporal cortical Neurite Density Index (NDI), Orientation Dispersion Index (ODI), and both baseline and mean annual change in memory z scores. NDI was significantly associated with both baseline memory z scores and mean annual change of memory z scores, in the hippocampi and amygdalae. Similar significant associations with ODI were seen in hippocampi, parahippocampal, and fusiform gyri. Temporal cortical NDI and ODI are early imaging biomarkers of cortical microstructural integrity that may predict memory decline in CU and MCI individuals. We imaged 836 participants in the Mayo Clinic Study of Aging, who were either cognitively unimpaired (CU) or suffered from mild cognitive impairment (MCI). Neurite orientation dispersion and density imaging (NODDI) is an advanced diffusion magnetic resonance imaging (MRI) technique We found significant associations between new NODDI imaging biomarkers of microstructural integrity and memory function in CU and MCI individuals These relationships were both cross-sectional in nature and associated with future memory decline Future application of NODDI imaging biomarkers in the setting of anti-amyloid monoclonal antibody therapy may provide greater insight into memory decline than current cortical atrophy measures.

Supplementation of medium-chain triglycerides combined with docosahexaenoic acid improves cognitive function in Chinese older adults with mild cognitive impairment: A randomized double-blind, placebo-controlled trial.

Awareness of baseline functioning and sensitivity to improvement in older people with and without mild cognitive impairment receiving a computerized functional skills training program.

Convolutional neural networks (CNNs) have been the standard for computer vision tasks and are frequently applied in medical conditions, such as in Alzheimer’s disease (AD). Recently, Vision Transformers (ViTs) have been introduced, which provide a strong alternative to CNNs by discarding the convolution approach in favor of the attention mechanism. This allows modeling global and distant relationships within distinct parts of an image without relying on the strong inductive biases present in CNNs. A common precursor stage of AD is a syndrome called mild cognitive impairment (MCI). However, not all individuals diagnosed with MCI progress to AD. The establishment of reliable classification models that predict converters versus non-converters would be a valuable tool to support clinical decision-making, such as enabling early treatment. Hence, in this investigation a transfer learning approach was used by applying a pretrained ViT model, fine-tuned on the ADNI dataset comprising 575 subjects with MCI. We included baseline T1-weighted structural MRI data from 299 stable MCI and 276 progressive MCI individuals, who developed Alzheimer’s disease within 36 months. Inputs to the model were three normalized axial slices covering areas of the hippocampal region, consisting of the combined gray and white matter segmentations. The final model was evaluated over multiple runs to obtain stable performance estimates, yielding an average area under the receiver operating characteristic curve (AUC-ROC) on the test set of 0.74 ± 0.02 (mean ± SD), an accuracy of 0.69 ± 0.03, a sensitivity of 0.65 ± 0.07, a specificity of 0.72 ± 0.06, and a F1-score for the pMCI class of 0.67 ± 0.04. By specifically focusing on axial slices covering the hippocampal region, we aimed to target the brain structure often reported as being the first affected by the disease, while our results indicate that a ViT approach achieves reasonable classification accuracy for predicting the conversion from MCI to AD.

Abstract Introduction We discovered a novel signal termed “Headpulse Sleep Bursts” (HPSB) whereby the “headpulse” (HP, a phenomenon measurable in humans using highly sensitive accelerometers in contact with the head) transiently and periodically increases in amplitude for a few seconds during sleep attempts, independent of sleep stage. HPSB have been observed in all subjects tested we have measured. Human neurodegenerative disorders are hypothesized to be caused by lack of brain protein clearance during sleep. We report here on the frequency of sleep bursts in a cohort of subjects with Mild Cognitive Impairment (MCI) to see if HPSB are less frequent compared to controls. Methods 4 MCI individuals (1 female; median age 56 years (IQR 51-62)) did concomitant HP and SP recordings. HP was recorded using a UCSF-designed headband with force transducers on the right temporal scalp (analyzed through MATLAB). Sleep Profiler (Advanced Brain Monitoring Inc) was used to assess sleep in 30s epochs. The SP/HP data were aligned and analyzed in register. Results Five nights of concomitant recordings of at least 4hrs of sleep were recorded for a total of 39.2 hours. Like controls (n=22) all MCI patients (n=4) exhibited HPSB; however, the frequency and pattern during various sleep stages in MCI patients differed compared to controls. Compared to controls, HPSB in MCI were 26.5% (P = 0.036) less frequent compared to the awake state prior to sleep onset, and this difference was dominated by a 45.8% (p = 0.044) reduction in stage N2 sleep and 41.4% (P = 0.063) in stages N1-N3. Conclusion This is the first report of HPSB phenomenon in humans with a form of neurodegenerative disease. HPBS were observed in MCI patients and controls. However, MCI patients had fewer HPSB, and when they occurred were particularly suppressed in non-REM sleep. If HPSB are produced by an active glymphatic drainage system, the observation that fewer HPSB are found in MCI patients supports the hypothesis that neurodegeneration is associated with lesser glymphatic activity. We are continuing to record from a larger cohort of subjects with neurodegenerative diseases. Support (if any) The John Madden Family, Rainwater charitable foundation, NIH 01AG060477, R01AG064314.

Mild cognitive impairment (MCI) represents an early stage of Alzheimer's disease (AD), characterized by subtle clinical symptoms that pose challenges for accurate diagnosis. The quest for the identification of MCI individuals has highlighted the importance of comprehending the underlying mechanisms of disease causation. Integrated analysis of brain imaging and genomics offers a promising avenue for predicting MCI risk before clinical symptom onset. However, most existing methods face challenges in: 1) mining the brain network-specific topological structure and addressing the single nucleotide polymorphisms (SNPs)-related noise contamination and 2) extracting the discriminative properties of brain imaging genomics, resulting in limited accuracy for MCI diagnosis. To this end, a modality-aware discriminative fusion network (MA-DFN) is proposed to integrate the complementary information from brain imaging genomics to diagnose MCI. Specifically, we first design two modality-specific feature extraction modules: the graph convolutional network with edge-augmented self-attention module (GCN-EASA) and the deep adversarial denoising autoencoder module (DAD-AE), to capture the topological structure of brain networks and the intrinsic distribution of SNPs. Subsequently, a discriminative-enhanced fusion network with correlation regularization module (DFN-CorrReg) is employed to enhance inter-modal consistency and between-class discrimination in brain imaging and genomics. Compared to other state-of-the-art approaches, MA-DFN not only exhibits superior performance in stratifying cognitive normal (CN) and MCI individuals but also identifies disease-related brain regions and risk SNPs locus, which hold potential as putative biomarkers for MCI diagnosis.