Diagnosis Of Mild Cognitive Impairment Research Articles

Clinical validity of the Italian adaptation of the Uniform Data Set Neuropsychological Test Battery (I-UDSNB) in Mild Cognitive Impairment and Alzheimer’s Disease

BackgroundThe identification and staging of Alzheimer’s Disease (AD) represent a challenge, especially in the prodromal stage of Mild Cognitive Impairment (MCI), when cognitive changes can be subtle. Worldwide efforts were dedicated to select and harmonize available neuropsychological instruments. In Italy, the Italian Network of Neuroscience and Neuro-Rehabilitation has promoted the adaptation of the Uniform Data Set Neuropsychological Test Battery (I-UDSNB), collecting normative data from 433 healthy controls (HC).Here, we aimed to explore the ability of I-UDSNB to differentiate between a) MCI and HC, b) AD and HC, c) MCI and AD.MethodsOne hundred thirty-seven patients (65 MCI, 72 AD) diagnosed after clinical-neuropsychological assessment, and 137 HC were included. We compared the I-UDSNB scores between a) MCI and HC, b) AD and HC, c) MCI and AD, with t-tests. To identify the test(s) most capable of differentiating between groups, significant scores were entered in binary logistic and in stepwise regressions, and then in Receiver Operating Characteristic curve analyses.ResultsTwo episodic memory tests (Craft Story and Five Words test) differentiated MCI from HC subjects; Five Words test, Semantic Fluency (vegetables), and TMT-part B differentiated AD from, respectively, HC and MCI.ConclusionsOur findings indicate that the I-UDSNB is a suitable tool for the harmonized and concise assessment of patients with cognitive decline, showing high sensitivity and specificity for the diagnosis of MCI and AD.

Prediction of mild cognitive impairment using EEG signal and BiLSTM network

Mild cognitive impairment (MCI) is a cognitive disease that primarily affects elderly persons. Patients with MCI have impairments in one or more cognitive areas, such as memory, attention, language, and problem-solving. The risk of Alzheimer’s disease development is 10 times higher among individuals who meet the MCI diagnosis than in those who do not have such a diagnosis. Identifying the primary neurophysiological variations between those who are suffering from cognitive impairment and those who are ageing normally may provide helpful techniques to assess the effectiveness of therapies. Event-related Potentials (ERPs) are utilized to investigate the processing of sensory, cognitive, and motor information in the brain. ERPs enable excellent temporal resolution of underlying brain activity. ERP data is complex due to the temporal variation that occurs in the time domain. It is actually a type of electroencephalography (EEG) signal that is time-locked to a specific event or behavior. To remove artifacts from the data, this work utilizes Independent component analysis, finite impulse response filter, and fast Fourier transformation as preprocessing techniques. The bidirectional long short-term memory network is utilized to retain the spatial relationships between the ERP data while learning changes in temporal information for a long time. This network performed well both in modeling and information extraction from the signals. To validate the model performance, the proposed framework is tested on two benchmark datasets. The proposed framework achieved a state-of-the-art accuracy of 96.03% on the SJTU Emotion EEG Dataset dataset and 97.31% on the Chung–Ang University Hospital EEG dataset for the classification tasks.

The cingulate island sign in a mixed memory clinical cohort: Prevalence and diagnostic accuracy

IntroductionVisual rating of the cingulate island sign (CIS) on [18F]fluorodeoxyglucose ([18F]FDG) positron emission tomography (PET) has a high specificity for dementia with Lewy bodies (DLB) in selected cohorts such as DLB versus Alzheimer's disease (AD). In a mixed memory clinical population this study aimed to uncover the prevalence of CIS, the diagnostic accuracy for DLB, and the relationship between CIS and disease severity. MethodsCIS on [18F]FDG-PET was retrospectively assessed with the visual CIS rating scale (CISRs) in 1000 patients with a syndrome diagnosis of mild cognitive impairment (MCI) or dementia with no restrictions in etiological diagnosis. ResultsIn this cohort 24.3 % had a CISRs score ≥1 and 3.5 % had a CISRs score = 4. The prevalence of a CISRs score ≥1 was highest in DLB (74.0 %, n = 57). A CISRs score ≥1 was present in at least 9 % in other diagnostic groups. The prevalence of CIS across disease severities showed no statistically significant difference (p = 0.23). To differentiate DLB from non-DLB the optimal cut-off was a CISRs score ≥1 (balanced accuracy = 77.1 %) in MCI/mild dementia and a CISRs score ≥2 (balanced accuracy = 80.6 %) in moderate/severe dementia. The positive predictive value of a CISRs score = 4 for DLB was 57.7 % in MCI/mild dementia and 33.3 % in moderate/severe dementia. ConclusionThe CISRs is useful in differentiating DLB from other etiologies in a mixed memory clinical population. Balanced accuracy and positive predictive value may vary across disease severities in the population studied.

Structural and microstructural predictors of cognitive decline in deep brain stimulation of subthalamic nucleus in Parkinson’s disease

Background and objectivesThe intricate relationship between deep brain stimulation (DBS) in Parkinson’s disease (PD) and cognitive impairment has lately garnered substantial attention. The presented study evaluated pre-DBS structural and microstructural cerebral patterns as possible predictors of future cognitive decline in PD DBS patients. MethodsPre-DBS MRI data in 72 PD patients were combined with neuropsychological examinations and follow-up for an average of 2.3 years after DBS implantation procedure using a screening cognitive test validated for diagnosis of mild cognitive impairment in PD in a Czech population – Dementia Rating Scale 2. ResultsPD patients who would exhibit post-DBS cognitive decline were found to have, already at the pre-DBS stage, significantly lower cortical thickness and lower microstructural complexity than cognitively stable PD patients. Differences in the regions directly related to cognition as bilateral parietal, insular and cingulate cortices, but also occipital and sensorimotor cortex were detected. Furthermore, hippocampi, putamina, cerebellum and upper brainstem were implicated as well, all despite the absence of pre-DBS differences in cognitive performance and in the position of DBS leads or stimulation parameters between the two groups. ConclusionsOur findings indicate that the cognitive decline in the presented PD cohort was not attributable primarily to DBS of the subthalamic nucleus but was associated with a clinically silent structural and microstructural predisposition to future cognitive deterioration present already before the DBS system implantation.

Evaluating Cognitive Impairment in a Large Health Care System: The Cognition in Primary Care Program

Background: The prevalence of Alzheimer’s disease and related disorders (ADRD) is rising. Primary care providers (PCPs) will increasingly be required to play a role in its detection but lack the training to do so. Objective: To develop a model for cognitive evaluation which is feasible in primary care and evaluate its implementation in a large health system. Methods: The Cognition in Primary Care Program consists of web-based training together with integrated tools built into the electronic record. We implemented the program among PCPs at 14 clinics in a large health system. We (1) surveyed PCPs to assess the impact of training on their confidence to evaluate cognition, (2) measured the number of cognitive assessments they performed, and (3) tracked the number of patients diagnosed with mild cognitive impairment (MCI). Results: Thirty-nine PCPs completed the training which covered how to evaluate cognition. Survey response rate from those PCPs was 74%. Six months after the end of the training, they reported confidence in assessing cognition (mean 4.6 on 5-point scale). Cognitive assessments documented in the health record increased from 0.8 per month before the training to 2.5 in the six months after the training. Patients who were newly diagnosed with MCI increased from 4.2 per month before the training to 6.0 per month in the six months after the training. Conclusions: This model for cognitive evaluation in a large health system was shown to increase cognitive testing and increase diagnoses of MCI. Such improvements are essential for the timely detection of ADRD.

The Effects of Psychosocial and Traumatic Stressors on MCI Diagnosis

Background and Objective: Toxic stress exposure can have effects across the lifespan. Studies of civilians and veterans suggest a connection between psychosocial and traumatic stressor exposure in adulthood and a diagnosis of dementia later in life. The objective of this study was to investigate the impact of psychosocial and traumatic stressors on rates of MCI (Mild Cognitive Impairment) diagnosis in Vietnam Era Twin Study of Aging participants. Methods: 1,237 twin participants from the VETSA study were aged 61.72 ± 2.44 years at the time of data collection. Traumatic stress was measured by clinical interviewing, with psychosocial stressors quantified by self-report measures. Neuropsychological assessment determined MCI diagnosis. Previously conducted genotyping determined ApoE genotype. Mixed model analysis was used to determine effects on MCI diagnosis. Results: Our results from the mixed model analysis did not find a significant relationship between psychosocial and traumatic stress exposure and MCI diagnosis. PTSD diagnosis, measured by the DIS-III-R, collected for the Harvard Drug Study in 1996 (F = 0.249, p = 0.618) does not have a significant effect on MCI diagnosis. Life stress exposure, measured by the Holmes and Rahe (1967), (F = 0.249, p = 0.618) does not have a significant effect on MCI diagnosis. Significant associations were determined using the Type III fixed effects. Associations were considered statistically significant at p < 0.05, two-tailed. Implications: Few subjects in Wave 2 of VETSA had MCI (n = 147), due in part to the age of the participants at the time (Mean 61.72 ± (2.44 years)). This led to a lack of power in our analysis. Future studies should examine all available VETSA data.

Prevalence of and Annual Conversion Rates to Mild Cognitive Impairment and Dementia: Prospective, Longitudinal Study of an Essential Tremor Cohort.

Despite recent attention to cognitive impairment in essential tremor, few studies examine rates of conversion to diagnoses of mild cognitive impairment and dementia. Development of dementia in essential tremor is associated with loss of functional ability and a doubling of mortality rate. This prospective, longitudinal study comprehensively reports the prevalence and incidence of, and the annual rates of conversion to, mild cognitive impairment and dementia in an essential tremor cohort. Patients underwent detailed cognitive assessments and were assigned diagnoses of normal cognition, mild cognitive impairment, or dementia. There were 222 patients at baseline (mean age = 79.3 ± 9.7 years), and 177 patients participated in follow-up evaluations at 18, 36, 54, and 72 months (mean years of observation = 5.1 ± 1.7). Data were compared to those of historical controls and Parkinson disease patients. The cumulative prevalence of dementia and average annual conversion rate of mild cognitive impairment to dementia were 18.5% and 12.2%, nearly three times higher than rates in the general population, and approximately one half the magnitude of those reported for Parkinson disease patients. The cumulative prevalence of mild cognitive impairment (26.6%) was almost double that of the general population, but less than that in Parkinson disease populations. We present the most complete exposition of the longitudinal trajectory of cognitive impairment in an essential tremor cohort yet presented. The prevalence of and conversion rates to dementia in essential tremor fall between those associated with the natural course of aging and the more pronounced rates observed in Parkinson disease. ANN NEUROL 2024.

Establishment of a mild cognitive impairment risk model in middle-aged and older adults: a longitudinal study.

Early identification individuals at high risk of mild cognitive impairment (MCI) is essential for prevention and intervention strategies of dementia, such as Alzheimer's disease. MCI prediction considering the interdependence of predictors in longitudinal data needs to be further explored. We aimed to employ machine learning (ML) to develop and verify a prediction model of MCI. In a longitudinal population-based cohort of China Health and Retirement Longitudinal Study (CHARLS), 8390 non-MCI participants were enrolled. The diagnosis of MCI was based on the aging-associated cognitive decline (AACD), and 13 factors (gender, education, marital status, residence, diabetes, hypertension, depression, hearing impairment, social isolation, physical activity, drinking status, body mass index and expenditure) were finally selected as predictors. We implemented a long short-term memory (LSTM) to predict the MCI risks in middle-aged and older adults within 7years. The Receiver Operating Characteristic curve (ROC) and calibration curve were used to evaluate the performance of the model. Through 7years of follow-up, 1925 participants developed MCI. The model for all incident MCI achieved an AUC of 0.774, and its deployment to the participants followed 2, 4, and 7years achieved results of 0.739, 0.747, and 0.750, respectively. The model was well-calibrated with predicted probabilities plotted against the observed proportions of cognitive impairment. Education level, gender, marital status, and depression contributed most to the prediction of MCI. This model could be widely applied to medical institutions, even in the community, to identify middle-aged and older adults at high risk of MCI.

Selective vulnerability of hippocampal sub-regions in patients with subcortical vascular mild cognitive impairment.

Early diagnosis of subcortical vascular mild cognitive impairment (svMCI) is clinically essential because it is the most reversible subtype of all cognitive impairments. Since structural alterations of hippocampal sub-regions have been well studied in neurodegenerative diseases with pathophysiological cognitive impairments, we were eager to determine whether there is a selective vulnerability of hippocampal sub-fields in patients with svMCI. Our study included 34 svMCI patients and 34 normal controls (NCs), with analysis of T1 images and Montreal Cognitive Assessment (MoCA) scores. Gray matter volume (GMV) of hippocampal sub-regions was quantified and compared between the groups, adjusting for age, sex, and education. Additionally, we explored correlations between altered GMV in hippocampal sub-fields and MoCA scores in svMCI patients. Patients with svMCI exhibited selectively reduced GMV in several left hippocampal sub-regions, such as the hippocampal tail, hippocampal fissure, CA1 head, ML-HP head, CA4 head, and CA3 head, as well as decreased GMV in the right hippocampal tail. Specifically, GMV in the left CA3 head was inversely correlated with MoCA scores in svMCI patients. Our findings indicate that the atrophy pattern of patients with svMCI was predominantly located in the left hippocampal sub-regions. The left CA3 might be a crucial area underlying the distinct pathophysiological mechanisms of cognitive impairments with subcortical vascular origins.

Neuropsychiatric symptoms and white matter hyperintensities in older adults without dementia.

We aimed to examine associations between neuropsychiatric symptoms (NPS) and white matter hyperintensities (WMH) status in older adults without dementia under the hypothesis that WMH increased the odds of having NPS. Longitudinal analysis of data acquired from the National Alzheimer's Coordinating Center Uniform Data Set. Data were derived from 46 National Institute on Aging - funded Alzheimer's Disease Research Centers. NACC participants aged ≥50 years with available data on WMH severity with a diagnosis of mild cognitive impairment (MCI) or who were cognitively unimpaired (CU) were studied. Among 4617 CU participants, 376 had moderate and 54 extensive WMH. Among 3170 participants with MCI, 471 had moderate and 88 had extensive WMH. Using Cardiovascular Health Study (CHS) scores, WMH were coded as no to mild (CHS score: 0-4), moderate (score: 5-6) or extensive (score: 7-8). NPS were quantified on the Neuropsychiatric Inventory Questionnaire. Binary logistic regression models estimated the odds of reporting each of 12 NPS by WMH status separately for individuals with MCI or who were CU. Compared to CU individuals with no to mild WMH, the odds of having elation [9.87, (2.63-37.10)], disinhibition [4.42, (1.28-15.32)], agitation [3.51, (1.29-9.54)] or anxiety [2.74, (1.28-5.88)] were higher for the extensive WMH group, whereas the odds of having disinhibition were higher for the moderate WMH group [1.94, (1.05-3.61)]. In the MCI group, he odds of NPS did not vary by WMH status. Extensive WMH were associated with higher odds of NPS in CU older adults but not in those with MCI.

Development of Improved Weighed Quantum Lion Optimization with Smooth Support Vector Machine for Alzheimer’s Disease

Accurate diagnosis of Alzheimer's disease (AD) and Mild Cognitive Impairment (MCI) was identified on an early stage is essential in the healthcare industry to stop degeneration. The Smooth Support Vector Machine (SSVM) model, Principal Component Analysis (PCA), feature extraction, and Magnetic Resonance Imaging (MRI) image prepossessing are the components for the diagnosis of AD is proposed in this research at early stage. To assist in the classifier's training, we proposed a novel Improved Weighed Quantum Lion Optimization (IWQLO). The SSVM parameters are specifically proposed to be optimized using a new Switching delayed Lion Optimization (SLO) algorithm. The IWQLO-SSVM approach was effectively used to classify AD and MCI utilizing MRI scans of the Alzheimer's disease Neuroimaging Initiative (ADNI) database and Outcome and Assessment Information Set (OASIS) database. For six example scenarios, the classification accuracy of our proposed method is acceptable. Testing show that the proposed approach improves the performance measures such as accuracy, precision, specificity, sensitivity and recall for detecting the early stage AD diagnosis.

Development of a smartphone screening test for preclinical Alzheimer’s disease and validation across the dementia continuum

BackgroundDementia prevalence is predicted to triple to 152 million globally by 2050. Alzheimer’s disease (AD) constitutes 70% of cases. There is an urgent need to identify individuals with preclinical AD, a 10–20-year period of progressive brain pathology without noticeable cognitive symptoms, for targeted risk reduction. Current tests of AD pathology are either too invasive, specialised or expensive for population-level assessments. Cognitive tests are normal in preclinical AD. Emerging evidence demonstrates that movement analysis is sensitive to AD across the disease continuum, including preclinical AD. Our new smartphone test, TapTalk, combines analysis of hand and speech-like movements to detect AD risk. This study aims to [1] determine which combinations of hand-speech movement data most accurately predict preclinical AD [2], determine usability, reliability, and validity of TapTalk in cognitively asymptomatic older adults and [3], prospectively validate TapTalk in older adults who have cognitive symptoms against cognitive tests and clinical diagnoses of Mild Cognitive Impairment and AD dementia.MethodsAim 1 will be addressed in a cross-sectional study of at least 500 cognitively asymptomatic older adults who will complete computerised tests comprising measures of hand motor control (finger tapping) and oro-motor control (syllabic diadochokinesis). So far, 1382 adults, mean (SD) age 66.20 (7.65) years, range 50–92 (72.07% female) have been recruited. Motor measures will be compared to a blood-based AD biomarker, phosphorylated tau 181 to develop an algorithm that classifies preclinical AD risk. Aim 2 comprises three sub-studies in cognitively asymptomatic adults: (i) a cross-sectional study of 30–40 adults to determine the validity of data collection from different types of smartphones, (ii) a prospective cohort study of 50–100 adults ≥ 50 years old to determine usability and test-retest reliability, and (iii) a prospective cohort study of ~1,000 adults ≥ 50 years old to validate against cognitive measures. Aim 3 will be addressed in a cross-sectional study of ~200 participants with cognitive symptoms to validate TapTalk against Montreal Cognitive Assessment and interdisciplinary consensus diagnosis.DiscussionThis study will establish the precision of TapTalk to identify preclinical AD and estimate risk of cognitive decline. If accurate, this innovative smartphone app will enable low-cost, accessible screening of individuals for AD risk. This will have wide applications in public health initiatives and clinical trials.Trial registrationClinicalTrials.gov identifier: NCT06114914, 29 October 2023. Retrospectively registered.

Distance-based novelty detection model for identifying individuals at risk of developing Alzheimer's disease.

Novelty detection (ND, also known as one-class classification) is a machine learning technique used to identify patterns that are typical of the majority class and can discriminate deviations as novelties. In the context of Alzheimer's disease (AD), ND could be employed to detect abnormal or atypical behavior that may indicate early signs of cognitive decline or the presence of the disease. To date, few research studies have used ND to discriminate the risk of developing AD and mild cognitive impairment (MCI) from healthy controls (HC). In this work, two distinct cohorts with highly heterogeneous data, derived from the Australian Imaging Biomarkers and Lifestyle (AIBL) Flagship Study of Ageing project and the Fujian Medical University Union Hospital (FMUUH) China, were employed. An innovative framework with built-in easily interpretable ND models constructed solely on HC data was introduced along with proposing a strategy of distance to boundary (DtB) to detect MCI and AD. Subsequently, a web-based graphical user interface (GUI) that incorporates the proposed framework was developed for non-technical stakeholders. Our experimental results indicate that the best overall performance of detecting AD individuals in AIBL and FMUUH datasets was obtained by using the Mixture of Gaussian-based ND algorithm applied to single modality, with an AUC of 0.8757 and 0.9443, a sensitivity of 96.79% and 89.09%, and a specificity of 89.63% and 90.92%, respectively. The GUI offers an interactive platform to aid stakeholders in making diagnoses of MCI and AD, enabling streamlined decision-making processes. More importantly, the proposed DtB strategy could visually and quantitatively identify individuals at risk of developing AD.

Differential expression of N-glycopeptides derived from serum glycoproteins in mild cognitive impairment (MCI) patients.

Mild cognitive impairment (MCI) is an early stage of memory loss that affects cognitive abilities with the aging of individuals, such as language or visual/spatial comprehension. MCI is considered a prodromal phase of more complicated neurodegenerative diseases such as Alzheimer's. Therefore, accurate diagnosis and better understanding of the disease prognosis will facilitate prevention of neurodegeneration. However, the existing diagnostic methods fail to provide precise and well-timed diagnoses, and the pathophysiology of MCI is not fully understood. Alterations of the serum N-glycoproteome expression could represent an essential contributor to the overall pathophysiology of neurodegenerative diseases and be used as a potential marker to assess MCI diagnosis using less invasive procedures. In this approach, we identified N-glycopeptides with different expressions between healthy and MCI patients from serum glycoproteins. Seven of the N-glycopeptides showed outstanding AUC values, among them the antithrombin-III Asn224 + 4-5-0-2 with an AUC value of 1.00 and a p value of 0.0004. According to proteomics and ingenuity pathway analysis (IPA), our data is in line with recent publications, and the glycoproteins carrying the identified N-sites play an important role in neurodegeneration.

Plasma ADAM10 Levels and their Association with Alzheimer's Disease Diagnosis in Older Adults with Fewer Years of Formal Education.

Low educational attainment is a potential risk factor for Alzheimer's disease (AD) development. Alpha-secretase ADAM10 plays a central role in AD pathology, attenuating the formation of beta-amyloid peptides and, therefore, their aggregation into senile plaques. This study seeks to investigate ADAM10 as a blood-based biomarker in mild cognitive impairment (MCI) and AD in a diverse group of community-dwelling older adults, focusing on those with limited educational attainment. Participants were recruited from public health services. Cognition was evaluated using MMSE and ACE-R batteries. Blood samples were collected to analyze plasma ADAM10 levels. A logistic regression was conducted to verify the influence of plasma ADAM10 on the AD diagnosis. Significant differences in age, years of education, prescribed medications, and cognitive test scores were found between the MCI and AD groups. Regarding cognitive performance, both ACE-R and MMSE scores displayed significant differences between groups, with post-hoc analyses highlighting these distinctions, particularly between AD and cognitively healthy individuals. Elevated plasma ADAM10 levels were associated with a 4.5-fold increase in the likelihood of a diagnosis of MCI and a 5.9-fold increase in the likelihood of a diagnosis of AD. These findings suggest that ADAM10 levels in plasma serve as a valuable biomarker for assessing cognitive status in older individuals with low education attainment. This study underscores the potential utility of plasma ADAM10 levels as a blood-based biomarker for cognitive status, especially in individuals with low educational backgrounds, shedding light on their relevance in AD development and diagnosis.

Unearthing Subtle Cognitive Variations: A Digital Screening Tool for Detecting and Monitoring Mild Cognitive Impairment

Early diagnosis of mild cognitive impairment (MCI) is pivotal in mitigating the risk of cognitive impairment and the onset of dementia. However, prevailing clinical cognitive screening tests and biomarker assessment approaches often suffer from drawbacks such as high cost, invasiveness, time consumption, or subjectivity. In China, existing digital cognitive assessment tools face multiple challenges due to their distinctive cultural, language, and healthcare landscape. Therefore, we embarked on a study to develop a digital cognitive assessment tool, evaluate its efficacy in distinguishing between healthy individuals and MCI patients, and examine its acceptability among Chinese older adults. Through a series of symposiums, programming, and interviews with stakeholders, we iteratively designed the "BrainNursing" mobile application. The system consists of eleven single tasks and three dual tasks, each taking only 1–3 minutes. Subsequently, we conducted statistical comparisons of movement kinetics and physiological signals recorded during cognitive testing in 181 older adults to investigate which parameters could serve as effective digital biomarkers for MCI screening. Leveraging machine learning classifiers and a majority voting principle, we evaluated the classification performance of the BrainNursing system in detecting MCI, yielding an accuracy rate of 90.3%. Furthermore, our analysis of movement kinetics revealed that time, score, and sequence features are crucial in cognitive assessment. Contrasting with their healthy counterparts, MCI patients exhibited decreased heart rate variability, increased sympathetic nervous system activity, and weakened autonomic nervous system regulation in response to stimuli during cognitive testing. Finally, user experience feedback indicated that participants universally perceived BrainNursing as a convenient and user-friendly cognitive screening tool and provided valuable insights for further improvement.

Mild Cognitive Impairment (MCI): Evaluating a new treatment pathway for service users and carers

The prevalence of MCI is increasing in older adults; living with MCI not only negatively affects older people’s daily living and quality of life, but also increases the likelihood of developing dementia in the future. However, not all individuals will progress to dementia, which highlights the importance of developing preventative strategies and appropriate interventions to delay the onset of dementia. Given the current emphasis across the healthcare system on the need for psychosocial interventions that reduce the risk of dementia, the Specialist Dementia and Frailty Service (SDFS) in West Essex has launched a new MCI pathway, which runs as a pilot for one year to offer support for individuals with MCI and their caregivers. The current service evaluation aims to evaluate older people’s and their carers’ experiences of receiving a diagnosis in conjunction with exploring ideas about what they would find helpful in terms of post diagnostic support. Semi-structured interviews were conducted with service users and their carers over the telephone. Service users had received an MCI diagnosis and were either awaiting an intervention from the pathway or had received some form of post diagnostic support. Data was collected through an interview schedule and analysed using thematic analysis (TA). Themes highlighted the varying emotional experiences of receiving the diagnosis, the confusing and ambiguous process of MCI, the helpful and less helpful aspects of the support received, and ideas for future support. Findings from the service evaluation fit with the wider MCI literature and research. Some implications for the memory service are discussed, including individualised support, peer support, additional regular check ins and follow ups from the service, and support for carers.

Diagnosis of Early Mild Cognitive Impairment Based on Associated High-Order Functional Connection Network Generated by Multimodal MRI

Diagnosis of Early Mild Cognitive Impairment Based on Associated High-Order Functional Connection Network Generated by Multimodal MRI

Predictive models for Alzheimer's disease diagnosis and MCI identification: The use of cognitive scores and artificial intelligence algorithms

The paper presents a comprehensive study on predictive models for Alzheimer's disease (AD) and Mild cognitive impairment (MCI) diagnosis, implementing a combination of cognitive scores and artificial intelligence algorithms. The research includes detailed analyses of clinical and demographic variables such as age, education, and various cognitive and functional scores, investigating their distributions and correlations with AD and MCI. The study utilizes several machine-learning classifiers, comparing their performance through metrics like accuracy, precision, and area under the ROC curve (AUC). Key findings include the influence of gender on AD prevalence, the potential protective effect of education, and the significance of functional decline and cognitive performance scores in the models. The results demonstrate the effectiveness of ensemble methods and the robustness of the models across different data subsets, highlighting the potential of artificial intelligence in enhancing diagnostic accuracy for Alzheimer's disease and Mild cognitive impairment.

Difference of Cerebrospinal Fluid Biomarkers and Neuropsychiatric Symptoms Profiles among Normal Cognition, Mild Cognitive Impairment, and Dementia Patient.

The delineation of biomarkers and neuropsychiatric symptoms across normal cognition, mild cognitive impairment (MCI), and dementia stages holds significant promise for early diagnosis and intervention strategies. This research investigates the association of neuropsychiatric symptoms, evaluated via the Neuropsychiatric Inventory (NPI), with cerebrospinal fluid (CSF) biomarkers (Amyloid-β42, P-tau, T-tau) across a spectrum of cognitive states to enhance diagnostic accuracy and treatment approaches. Drawing from the National Alzheimer's Coordinating Center's Uniform Data Set Version 3, comprising 977 individuals with normal cognition, 270 with MCI, and 649 with dementia. To assess neuropsychiatric symptoms, we employed the NPI to understand the behavioral and psychological symptoms associated with each cognitive category. For the analysis of CSF biomarkers, we measured levels of Amyloid-β42, P-tau, and T-tau using the enzyme-linked immunosorbent assay (ELISA) and Luminex multiplex xMAP assay protocols. These biomarkers are critical in understanding the pathophysiological underpinnings of Alzheimer's disease and its progression, with specific patterns indicative of disease stage and severity. This study cohort consists of 1896 participants, which is composed of 977 individuals with normal cognition, 270 with MCI, and 649 with dementia. Dementia is characterized by significantly higher NPI scores, which are largely reflective of mood-related symptoms (p < 0.001). In terms of biomarkers, normal cognition shows median Amyloid-β at 656.0 pg/mL, MCI at 300.6 pg/mL, and dementia at 298.8 pg/mL (p < 0.001). Median P-tau levels are 36.00 pg/mL in normal cognition, 49.12 pg/mL in MCI, and 58.29 pg/mL in dementia (p < 0.001). Median T-tau levels are 241.0 pg/mL in normal cognition, 140.6 pg/mL in MCI, and 298.3 pg/mL in dementia (p < 0.001). Furthermore, the T-tau/Aβ-42 ratio increases progressively from 0.058 in the normal cognition group to 0.144 in the MCI group, and to 0.209 in the dementia group (p < 0.001). Similarly, the P-tau/Aβ-42 ratio also escalates from 0.305 in individuals with normal cognition to 0.560 in MCI, and to 0.941 in dementia (p < 0.001). The notable disparities in NPI and CSF biomarkers among normal, MCI and Alzheimer's patients underscore their diagnostic potential. Their combined assessment could greatly improve early detection and precise diagnosis of MCI and dementia, facilitating more effective and timely treatment strategies.