Lateral thinking: Neurodegeneration of the cortical cholinergic system in Alzheimer's disease.

INTRODUCTION: Atrophy of the nucleus basalis of Meynert (NBM) is an early indicator of Alzheimer's disease (AD). However, reduced integrity of the NBM white matter tracts may be more relevant for cognitive impairment and progression to dementia than NBM volume. Research is needed to compare differences in NBM volume and integrity of the lateral and medial NBM tracts across early and later stages of AD progression. METHODS: 187 participants were included in this study who were either healthy controls (HC; n = 50) or had early mild cognitive impairment (EMCI; n = 50), late MCI (LMCI; n = 37), or AD (n = 50). NBM volume was calculated using voxel-based morphometry and mean diffusivity (MD) of the lateral and medial NBM tracts were extracted using probabilistic tractography. Between group differences in NBM volume and tract MD were compared using linear mixed models controlling for age, sex, and either total intracranial volume or MD of a control mask, respectively. Associations between NBM volume and tract MD with executive function, memory, language, and visuospatial function were also analysed. RESULTS: NBM volume was smallest in AD followed by LMCI (p < 0.0001), with no difference between EMCI and HC. AD had highest MD for both tracts compared to all other groups (p < 0.001). Both MCI groups had higher lateral tract MD compared to HC (p < 0.05). Medial tract MD was higher in LMCI (p = 0.008), but not EMCI (p = 0.09) compared to HC. Higher lateral tract MD was associated with executive function (p = 0.001) and language (p = 0.02). DISCUSSION: Integrity of the lateral NBM tract is most sensitive to the earliest stages of AD and should be considered an important therapeutic target for early detection and intervention.

Perspectives on the clinical use of anti-amyloid therapy for the treatment of Alzheimer's disease: Insights from the fields of cancer, rheumatology, and neurology.

Introduction: The advent of disease-modifying therapies for Alzheimer's disease (AD) has raised many questions and debates in the field as to the clinical benefits, risks, and costs of such therapies. The controversies have resulted in the perception that many clinicians are apprehensive about prescribing these medications to their patient populations. There also remains widespread uncertainty as to the economic impact, cost benefit ratio, and safety oversight for use of these medications in standard clinical care settings. Methods: To contextualize such issues, the present study compared anti-amyloid biologic therapy (lecanemab) to four commonly used biologic agents in other fields, including trastuzumab for breast cancer, bevacizumab for lung cancer, etanercept for rheumatoid arthritis, and ocrelizumab for multiple sclerosis. Results: The data presented demonstrate comparable costs, clinical benefits, and risks for these biologic agents in their disparate disease states. Discussion: These results provide context for the costs, clinical benefits, and safety regarding the mainstream use of anti-amyloid biologic agents for the prevention of cognitive loss. While the era of disease-modifying therapies for AD is now in its infancy, there is an expectation that these discoveries will be followed by improved therapies and combination treatments leading to greater efficacy in ameliorating the clinical trajectory of AD. Highlights: Anti-amyloid therapy costs are comparable to other commonly used biologics.Anti-amyloid therapy efficacy is comparable to other commonly used biologics.Anti-amyloid therapy safety is compatible with other commonly used biologics.

Clinical value of plasma pTau181 to predict Alzheimer's disease pathology in a large real-world cohort of a memory clinic.

BACKGROUND: The identification of patients with an elevated risk of developing Alzheimer's disease (AD) dementia and eligible for the disease-modifying treatments (DMTs) in the earliest stages is one of the greatest challenges in the clinical practice. Plasma biomarkers has the potential to predict these issues, but further research is still needed to translate them to clinical practice. Here we evaluated the clinical applicability of plasma pTau181 as a predictive marker of AD pathology in a large real-world cohort of a memory clinic. METHODS: Three independent cohorts (modelling [n = 991, 59.7% female], testing [n = 642, 56.2% female] and validation [n = 441, 55.1% female]) of real-world patients with subjective cognitive decline (SCD), mild cognitive impairment (MCI), AD dementia, and other dementias were included. Paired cerebrospinal fluid (CSF) and plasma samples were used to measure AT(N) CSF biomarkers and plasma pTau181. FINDINGS: CSF and plasma pTau181 showed correlation in all phenotypes except in SCD and other dementias. Age significantly influenced the biomarker's performance. The general Aß(+) vs Aß(-) ROC curve showed an AUC = 0.77 [0.74-0.80], whereas the specific ROC curve of MCI due to AD vs non-AD MCI showed an AUC = 0.89 [0.85-0.93]. A cut-off value of 1.30 pg/ml of plasma pTau181 exhibited a sensitivity of 93.57% [88.72-96.52], specificity of 72.38% [62.51-79.01], VPP of 77.85% [70.61-83.54], and 8.30% false negatives in the subjects with MCI of the testing cohort. The HR of cox regression showed that patients with MCI up to this cut-off value exhibited a HR = 1.84 [1.05-3.22] higher risk to convert to AD dementia than patients with MCI below the cut-off value. INTERPRETATION: Plasma pTau181 has the potential to be used in the memory clinics as a screening biomarker of AD pathology in subjects with MCI, presenting a valuable prognostic utility in predicting the MCI conversion to AD dementia. In the context of a real-world population, a confirmatory test employing gold-standard procedures is still advisable. FUNDING: This study has been mainly funded by Ace Alzheimer Center Barcelona, Instituto de Salud Carlos III (ISCIII), Biomedical Research Networking Centre in Neurodegenerative Diseases (CIBERNED), Spanish Ministry of Science and Innovation, Fundación ADEY, Fundación Echevarne and Grífols S.A.

Increased reliance on heuristic thinking in mild cognitive impairment.

Reasoning can be fast, automatic, and intuitive or slow, deliberate, and analytical. Use of one cognitive reasoning style over the other has broad implications for beliefs, but differences in cognitive style have not previously been reported in those with mild cognitive impairment (MCI). Here, the cognitive reflection test is used to measure cognitive style in healthy older adults and those with MCI. Those with MCI performed worse than cognitively healthy older adults, indicating they are more likely to engage in intuitive thinking than age-matched adults. This association is reliable after controlling for additional cognitive, self-report, and demographic factors. Across all measures, subjective cognitive decline was the best predictor of cognitive status. A difference in cognitive style represents a novel behavioral marker of MCI, and future work should explore whether this explains a broader pattern of reasoning errors in those with MCI, such as susceptibility to scams or impaired financial reasoning.

Effects of a food supplement containing phosphatidylserine on cognitive function in Chinese older adults with mild cognitive impairment: A randomized double-blind, placebo-controlled trial.

BACKGROUND: Phosphatidylserine (PS) and α-Linolenic acid (ALA), are positively associated with cognitive function, but their combination effects and possible mechanisms remain unclear. We aimed to explore the effects on cognition and potential mechanism of the supplements. METHODS: This randomized, double-blind, placebo-controlled trial recruited 190 MCI patients in Tianjin, China, and randomly assigned in intervention group and placebo group. Each group consumed two capsules every day for 12 months. Each capsule for intervention group contains 144 mg ALA, 31.5 mg PS and 3.6 mg Ginkgo total flavonoids as main functional components, with 0.48 mg Vitamin B1 (as thiamine hydrochloride), 0.48 mg Vitamin B6 (as pyridoxine hydrochloride) and 90 µg folic acid as supplement. Capsules for placebo group were identical but contain no active ingredients. Cognitive function, serum n-3 polyunsaturated fatty acids (PUFAs) and neurotransmitters were assessed at baseline and 12 months. Linear mixed effects model and causal mediation analysis were conducted to explore the effects and potential mechanism of the intervention. RESULTS: A total of 190 participants (mean [SD] age, 67.95 [5.62] years; 70 (36.8 %) male and 120 (63.2 %) female) were randomized to the placebo group (n = 95) and intervention group (n = 95). Compared with placebo group, the intervention group had statistically significant improvements in arithmetic testing (ß, 0.688; 95 % CI, 0.103-1.274), the similarity test (ß, 1.070; 95 % CI, 0.472-1.667) and short-term memory (ß, 0.600; 95 % CI, 0.399-0.800). Besides, the intervention group had statistically significant increases in serum ALA (ß, 1.620; 95 % CI, 0.967-2.265), DHA (ß, 2.797; 95 % CI, 1.075-4.532), EPA (ß, 1.472; 95 % CI, 0.296-2.643), acetylcholine (ß, 0.441; 95 % CI, 0.415-0.468), GABA (ß, 0.009; 95 % CI, 0.001-0.016) and 5-HT (ß, 0.160; 95 % CI, 0.081-0.238) compared to the placebo group. And the intervention may improve short-term memory by increasing serum ALA levels (average causal mediation effect = 0.132, 95 % CI, 0.053-0.225) with 19.7 % mediation proportion. CONCLUSIONS: This food supplement containing phosphatidylserine could improve different cognitive functions of MCI patients, especially short-term memory, and increase serum n-3 PUFAs and neurotransmitters levels. Serum ALA level might play a mediation role.

Cortical Thickness and Complexity in aMCI Patients: Altered Pattern Analysis and Early Diagnosis.

BACKGROUND: Amnestic Mild Cognitive Impairment (aMCI) is a prodromal phase of Alzheimer's disease. Although recent studies have focused on cortical thickness as a key indicator, cortical complexity has not been exhaustively investigated. OBJECTIVES: To investigate the altered patterns of cortical features in aMCI patients and their correlation with memory function for early identification. METHODS: 25 aMCI patients and 54 normal controls underwent neuropsychological assessments and 3D-T1 MRI scans. Cortical thickness and complexity measures were calculated using CAT12 software. Differences between groups were analyzed using two-sample t-tests, and multiple linear regression was employed to identify features associated with memory function. A support vector machine (SVM) model was constructed using multidimensional structural indicators to evaluate diagnostic performance. RESULTS: aMCI patients exhibited extensive reductions in cortical thickness (pFDR-corrected <0.05), with complexity reduction predominantly in the left parahippocampal, entorhinal, rostral anterior cingulate, fusiform, and orbitofrontal (pFWE-corrected<0.05). Cortical indicators exhibited robust correlations with auditory verbal learning test (AVLT) scores. Specifically, the fractal dimension of the left medial orbitofrontal region was independently and positively associated with AVLT-short delayed score (r=0.348, p=0.002), while the gyrification index of the left rostral anterior cingulate region showed independent positive correlations with AVLT-long delayed and recognition scores (r=0.408, p=0.000; r=0.332, p=0.003). Finally, the SVM model integrating these cortical features achieved an AUC of 0.91, with 82.28% accuracy, 76% sensitivity, and 85.19% specificity. CONCLUSION: Cortical morphological indicators provide important neuroimaging evidence for the early diagnosis of aMCI. Integrating multiple structural indicators significantly improves diagnostic accuracy.

Efficacy and safety of choline alphoscerate for amnestic mild cognitive impairment: a randomized double-blind placebo-controlled trial.

BACKGROUND: Effective interventions for overall healthy subjects with mild cognitive impairment are currently limited. Choline alphoscerate (alpha glyceryl phosphorylcholine, αGPC) is a choline-containing phospholipid used to treat cognitive function impairments in specific neurological conditions. This study aimed to investigate the efficacy and safety of αGPC in individuals diagnosed with mild cognitive impairment. METHODS: In this multicenter, randomized, placebo-controlled trial, 100 study subjects with mild cognitive impairment underwent a double-blind SHCog™ soft capsule (600 mg αGPC) or placebo treatment for 12 weeks. The primary efficacy outcome included changes from baseline on the Alzheimer's Disease Assessment Scale-cognitive subscale (ADAS-cog). Safety assessments included regular monitoring of adverse events, and clinical laboratory tests were conducted at baseline and the end of the trial. RESULTS: After 12 weeks of αGPC treatment, the ADAS-cog score decreased by 2.34 points, which was significantly greater than the change observed in the placebo group. No serious AEs were reported, and no study subjects discontinued the intervention because of AEs. There was no significant difference in incidence rate of AEs between the αGPC group and the placebo group. CONCLUSION: This study suggests that αGPC is a safe and effective intervention for improving cognitive function in study subjects with mild cognitive impairment. TRIAL REGISTRATION: Clinical Research Information Service; Osong (Chungcheongbuk-do): Korea Centers for Disease Control and Prevention, Ministry of Health and Welfare (Republic of Korea); KCT0008797; A 12-week, multicenter, randomized, double-blind, placebo-controlled human application study to evaluate the efficacy and safety of SH_CAPK08 on cognitive function improvement in mild cognitive decline.

Automated classification of Alzheimer's disease, mild cognitive impairment, and cognitively normal patients using 3D convolutional neural network and radiomic features from T1-weighted brain MRI: A comparative study on detection accuracy.

OBJECTIVES: Alzheimer's disease (AD) is a common neurodegenerative disorder that primarily affects older individuals. Due to its high incidence, an accurate and efficient stratification system could greatly aid in the clinical diagnosis and prognosis of AD patients. Convolutional neural networks (CNN) approaches have demonstrated exceptional performance in the automated stratification of AD, mild cognitive impairment (MCI) and cognitively normal (CN) participants using MRI, owing to their high predictive accuracy and reliability. Therefore, we aimed to develop an algorithm based on CNN and radiomic features derived from ROIs of bilateral hippocampus and amygdala in brain MRI for stratification between AD, MCI and CN. METHODS: In this study, we proposed a CNN and radiomic features-based algorithm using the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. T1-weighted images were used. We utilized three datasets, including AD (199 cases, 602 images), MCI (200 cases, 948 images), and CN (200 cases, 853 images), to perform binary classification (AD vs. CN, AD vs. MCI, and MCI vs. CN). Finally, we obtained the accuracy (ACC) and the area under the curve of the receiver operating characteristic curve (AUC) to evaluate the performance of the algorithm. RESULTS: Our proposed algorithm achieved acceptable overall discrimination accuracy. In the term of AD vs CN, radiomic-based algorithm alone obtained ACC of 82.6 % and AUC of 88.8, CNN-based algorithm obtained ACC of 80 % and AUC of 87.2 and their fusion showed ACC of 84.4 % and AUC of 90. In the term of MCI vs CN, radiomic-based algorithm alone obtained ACC of 71.6 % and AUC of 77.8, CNN-based algorithm obtained ACC of 69 % and AUC of 75 and their fusion showed ACC of 72.7 % and AUC of 80. In the term of AD vs MCI, radiomic-based algorithm alone obtained ACC of 57 % and AUC of 57.5, CNN-based algorithm obtained ACC of 56.6 % and AUC of 57.7 and their fusion showed ACC of 58 % and AUC of 59.5. CONCLUSION: In conclusion, it has been determined that hippocampus and amygdala-based stratification using CNN features and radiomic features-based algorithm is a promising method for the classification of AD, MCI, and CN participants. ADVANCES IN KNOWLEDGE: This study proposed an automated procedures based on MRI-derived radiomic features and CNN for classification between AD, MCI and CN.

Comparative efficacy of non-invasive brain stimulation on cognition function in patients with mild cognitive impairment: a systematic review and network meta-analysis.

BACKGROUND: Mild cognitive impairment (MCI) is a critical time window for implementing prevention strategies to attenuate or delay cognitive decline. Non-invasive brain stimulation (NIBS) techniques are promising non-pharmacological therapies for improving the cognitive function of MCI, but it is unclear which type of NIBS protocol is most effective. This study aimed to compare and rank the beneficial effect of different NIBS methods/protocols on cognitive function and examine the acceptability of NIBS in patients with MCI. METHODS: Electronic search of PubMed, Cochrane Library, EMBASE, China National Knowledge Infrastructure, Wanfang Database, and Chongqing VIP Database up to November 2023. Patients with diagnosis of MCI were included. The primary outcomes were acceptability and pre-post treatment changes in global cognitive function, and the secondary outcomes were specific cognitive domains (language and executive function). All network meta­analysis procedures were performed under the frequentist model. A protocol for this systematic review was registered in PROSPERO (Registration number: CRD42023441448). RESULTS: A network meta-analysis was conducted on 19 eligible RCTs consisting of 599 subjects. Compared with the sham stimulation, Repetitive Transcranial Magnetic Stimulation over the Bilateral dorsolateral prefrontal cortex (rTMS-F3F4) showed the strongest improvement in global cognitive function in MCI patients (SMD =1.52[95%CIs =0.49 to 2.56]), followed by rTMS over the left dorsolateral prefrontal cortex (rTMS-F3) (SMD =1.25[95%CIs =0.57 to 1.93]); Moreover, rTMS-F3F4 showed more significant efficacy in language function (SMD =0.96[95%CIs = 0.20 to 1.72]); No statistically significant differences were found among the other cognitive domains. Compared with the rTMS-F4, rTMS-F3F4 showed a stronger improvement in global cognitive function in MCI patients (SMD =1.80[95%CIs =0.02 to 3.59]). Similar results were obtained in subgroup analyses of cognitive function. All the methods were well-tolerated with an acceptable safety profile. CONCLUSION: The present findings provide evidence of the benefits of NIBS, especially TMS stimulating the bilateral dorsolateral prefrontal cortex, for the beneficial effect on cognitive and language function in patients with MCI. However, because few studies were available for inclusion, additional well-designed, large-scale RCTs are warranted to support exploring longer-term dynamic effects.

Validation of regression-based change formulae for mild cognitive impairment and Alzheimer's disease.

INTRODUCTION: Identification of cognitive decline is critical in older adults at risk for dementia. In a 2020 study reported in Archives of Clinical Neuropsychology, Kiselica and colleagues developed standardized regression-based (SRB) change formulae for the Uniform Data Set 3.0 Neuropsychological Battery in cognitively unimpaired older adults. However, validation of their applicability in impaired individuals is needed. METHODS: Using longitudinal data on 5974 participants (cognitively unimpaired, mild cognitive impairment, dementia) from the National Alzheimer's Coordinating Center, SRB change scores were calculated for each individual and compared across groups. RESULTS: Across 6 to 24 months, minimal cognitive change was observed in cognitively unimpaired participants. Modest declines were seen in those with mild cognitive impairment and substantial declines in those with dementia. Change scores were negatively correlated with the Clinical Dementia Rating scale. In impaired individuals, SRB scores indicated more decline in those with positive amyloid scans. DISCUSSION: Validation of SRB scores affords greater confidence in employing them in clinical and research settings. Highlights: Validation of regression-based cognitive change scores in impaired samples.Clear differences on change scores across three groups (intact, MCI, dementia).Largely stable scores in intact participants, but notable decline in MCI and dementia.Moderate to strong relationship between change scores and the Clinical Dementia Rating scale sum of boxes.

Dementia-focused programs in older adult centers and health care use among individuals with dementia.

There is growing attention to community-based services for preventing adverse health care outcomes among people aging with dementia. We explored whether the availability of dementia-centered programming within older adult centers (ie, senior centers)-specifically, adult day services (ADS), social adult day centers (SADCs), memory cafes, and caregiver support-is associated with reduced hospitalization, emergency room use, and total Medicare costs for community-dwelling individuals ages 75 and older with Alzheimer's disease and related dementias (ADRD), and whether associations differ by the relative size of the local jurisdiction. We used a novel dataset that links Medicare claims data with data from an organizational census of municipally based Massachusetts older adult centers. Living in a community with an older adult center that facilitates access to ADS and/or SADCs was associated with reduced hospital utilization and costs among residents in smaller jurisdictions. We found no evidence for associations concerning memory cafes or support groups. These findings underscore the potential of older adult centers in curbing health care costs and acute care usage among individuals with ADRD, particularly in smaller communities with centers that provide access to ADS.

Investigating patient eligibility for anti-amyloid monoclonal antibody treatment of Alzheimer's disease: real-world data from an Austrian psychiatric memory clinic population.

BACKGROUND: Pharmacological treatment options for patients with dementia owing to Alzheimer's disease are limited to symptomatic therapy. Recently, the US Food and Drug Administration approved the monoclonal antibody lecanemab for the treatment of amyloid-positive patients with mild cognitive impairment (MCI) and early Alzheimer´s dementia. European approval is expected in 2024. Data on the applicability and eligibility for treatment with anti-amyloid monoclonal antibodies outside of a study population are lacking. AIMS: This study examined eligibility criteria for lecanemab in a real-world memory clinic population between 1 January 2022 and 31 July 2023. METHOD: We conducted a retrospective, single-centre study applying the clinical trial eligibility criteria for lecanemab to out-patients of a specialised psychiatric memory clinic. Eligibility for anti-amyloid treatment was assessed following the phase 3 inclusion and exclusion criteria and the published recommendations for lecanemab. RESULTS: The study population consisted of 587 out-patients. Two-thirds were diagnosed with Alzheimer's disease (probable or possible Alzheimer's disease dementia in 43.6% of cases, n = 256) or MCI (23%, n = 135), and 33.4% (n = 196) were diagnosed with dementia or neurocognitive disorder owing to another aetiology. Applying all lecanemab eligibility criteria, 11 (4.3%) patients with dementia and two (1.5%) patients with MCI would have been eligible for treatment with this compound, whereas 13 dementia (5.1%) and 14 (10.4%) MCI patients met clinical inclusion criteria, but had no available amyloid status. CONCLUSIONS: Even in a memory clinic with a good infrastructure and sufficient facilities for dementia diagnostics, most patients do not meet the eligibility criteria for treatment with lecanemab.

Neuropsychiatric symptoms: Risk factor or disease marker? A study of structural imaging biomarkers of Alzheimer's disease and incident cognitive decline.

Neuropsychiatric symptoms (NPS) are risk factors for Alzheimer's disease (AD) but can also manifest secondary to AD pathology. Mild behavioral impairment (MBI) refers to later-life emergent and persistent NPS that may mark early-stage AD. To distinguish MBI from NPS that are transient or which represent psychiatric conditions (non-MBI NPS), we investigated the effect of applying MBI criteria on NPS associations with AD structural imaging biomarkers and incident cognitive decline. Data for participants (n = 1273) with normal cognition (NC) or mild cognitive impairment (MCI) in the National Alzheimer's Coordinating Center Uniform Data Set were analyzed. NPS status (MBI, non-MBI NPS) was derived from the Neuropsychiatric Inventory Questionnaire and psychiatric history. Normalized measures of bilateral hippocampal (HPC) and entorhinal cortex (EC) volume, and AD meta-region of interest (ROI) mean cortical thickness were acquired from T1-weighted magnetic resonance imaging scans. Multivariable linear and Cox regressions examined NPS associations with imaging biomarkers and incident cognitive decline, respectively. MBI was associated with lower volume and cortical thickness in all ROIs in both NC and MCI, except for EC volume in NC. Non-MBI NPS were only associated with lower HPC volume in NC. Although both of the NPS groups showed higher hazards for MCI/dementia than No NPS, MBI participants showed more rapid decline. Although both types of NPS were linked to HPC atrophy, only NPS that emerged and persisted in later-life, consistent with MBI criteria, were related to AD neurodegenerative patterns beyond the HPC. Moreover, MBI predicted faster progression to dementia than non-MBI NPS.

Five-year effects of cognitive training in individuals with mild cognitive impairment.

INTRODUCTION: In a 5-year follow-up study, we investigated the enduring effects of cognitive training on older adults with mild cognitive impairment (MCI). METHODS: A randomized controlled single-blind trial involved 145 older adults with MCI, assigned to cognitive training (MEMO+), an active control psychosocial intervention, or a no-contact condition. Five-year effects were measured on immediate and delayed memory recall, the Montreal Cognitive Assessment screening test (MoCA), self-reported strategy use, and daily living difficulties. RESULTS: At follow-up, participants who received cognitive training showed a smaller decline in delayed memory and maintained MoCA scores, contrasting with greater declines in the control groups. Cognitive training participants outperformed controls in both delayed memory and MoCA scores at the 5-year time point. No significant group differences were observed in self-reported strategy use or difficulties in daily living. DISCUSSION: Cognitive training provides long-term benefits by mitigating memory decline and slowing clinical symptom progression in older adults with MCI. Highlights: Cognitive training reduced the 5-year memory decline of persons with MCI.Cognitive training also reduced decline on the Montreal Cognitive Assessment (MoCA).No intervention effect was found on strategy use or activities of daily living.

Assessing the Potential of EEG in Early Detection of Alzheimer's Disease: A Systematic Comprehensive Review (2000-2023).

Background: As the prevalence of Alzheimer's disease (AD) grows with an aging population, the need for early diagnosis has led to increased focus on electroencephalography (EEG) as a non-invasive diagnostic tool. Objective: This review assesses advancements in EEG analysis, including the application of machine learning, for detecting AD from 2000 to 2023. Methods: Following PRISMA guidelines, a search across major databases resulted in 25 studies that met the inclusion criteria, focusing on EEG's application in AD diagnosis and the use of novel signal processing and machine learning techniques. Results: Progress in EEG analysis has shown promise for early AD identification, with techniques like Hjorth parameters and signal compressibility enhancing detection capabilities. Machine learning has improved the precision of differential diagnosis between AD and mild cognitive impairment. However, challenges in standardizing EEG methodologies and data privacy remain. Conclusions: EEG stands out as a valuable tool for early AD detection, with the potential to integrate into multimodal diagnostic approaches. Future research should aim to standardize EEG procedures and explore collaborative, privacy-preserving research methods.

The Association Between Brain Metabolic Biomarkers Using 18F-FDG and Cognition and Vascular Risk Factors, as well as Its Usefulness in the Diagnosis and Staging of Alzheimer's Disease.

Background: 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) is valuable in Alzheimer's disease (AD) workup. Objective: To explore the effectiveness of 18F-FDG PET in differentiating and staging AD and associations between brain glucose metabolism and cognitive functions and vascular risk factors. Methods: 107 participates including 19 mild cognitive impairment (MCI), 38 mild AD, 24 moderate AD, 15 moderate-severe AD, and 11 frontotemporal dementia (FTD) were enrolled. Visual and voxel-based analysis procedures were utilized. Cognitive conditions, including 6 cognitive function scores and 7 single-domain cognitive performances, and vascular risk factors linked to hypertension, hyperlipidemia, diabetes, and obesity were correlated with glucose metabolism in AD dementia using age as a covariate. Results: 18F-FDG PET effectively differentiated AD from FTD and also differentiated MCI from AD subtypes with significantly different hypometabolism (except for mild AD) (height threshold p < 0.001, all puncorr < 0.05, the same below). The cognitive function scores, notably Mini-Mental State Examination and Montreal Cognitive Assessment, correlated significantly with regional glucose metabolism in AD participants (all p < 0.05), whereas the single-domain cognitive performance and vascular risk factors were significantly associated with regional glucose metabolism in MCI patients (all p < 0.05). Conclusions: This study underlines the vital role of 18F-FDG PET in identifying and staging AD. Brain glucose metabolism is associated with cognitive status in AD dementia and vascular risk factors in MCI, indicating that 18F-FDG PET might be promising for predicting cognitive decline and serve as a visual framework for investigating underlying mechanism of vascular risk factors influencing the conversion from MCI to AD.

[Classification of mild cognitive impairment and normal cognition using an automated voice-based testing application].

AIM: An easy-to-use tool that can detect cognitive decline in mild cognitive impairment (MCI) is required. In this study, we aimed to construct a machine learning model that discriminates between MCI and cognitively normal (CN) individuals using spoken answers to questions and speech features. METHODS: Participants of ≥50 years of age were recruited from the Silver Human Resource Center. The Japanese Version of the Mini-Mental State Examination (MMSE-J) and Clinical Dementia Rating (CDR) were used to obtain clinical information. We developed a research application that presented neuropsychological tasks via automated voice guidance and collected the participants' spoken answers. The neuropsychological tasks included time orientation, sentence memory tasks (immediate and delayed recall), and digit span memory-updating tasks. Scores and speech features were obtained from spoken answers. Subsequently, a machine learning model was constructed to classify MCI and CN using various classifiers, combining the participants' age, gender, scores, and speech features. RESULTS: We obtained a model using Gaussian Naive Bayes, which classified typical MCI (CDR 0.5, MMSE ≤26) and typical CN (CDR 0 and MMSE ≥29) with an area under the curve (AUC) of 0.866 (accuracy 0.75, sensitivity 0.857, specificity 0.712). CONCLUSIONS: We built a machine learning model that can classify MCI and CN using spoken answers to neuropsychological questions. Easy-to-use MCI detection tools could be developed by incorporating this model into smartphone applications and telephone services.

Association between blood-based protein biomarkers and brain MRI in the Alzheimer's disease continuum: a systematic review.

Blood-based biomarkers (BBM) are becoming easily detectable tools to reveal pathological changes in Alzheimer's disease (AD). A comprehensive and up-to-date overview of the association between BBM and brain MRI parameters is not available. This systematic review aimed to summarize the literature on the associations between the main BBM and MRI markers across the clinical AD continuum. A systematic literature search was carried out on PubMed and Web of Science and a total of 33 articles were included. Hippocampal volume was positively correlated with Aß42 and Aß42/Aß40 and negatively with Aß40 plasma levels. P-tau181 and p-tau217 concentrations were negatively correlated with temporal grey matter volume and cortical thickness. NfL levels were negatively correlated with white matter microstructural integrity, whereas GFAP levels were positively correlated with myo-inositol values in the posterior cingulate cortex/precuneus. These findings highlight consistent associations between various BBM and brain MRI markers even in the pre-clinical and prodromal stages of AD. This suggests a possible advantage in combining multiple AD-related markers to improve accuracy of early diagnosis, prognosis, progression monitoring and treatment response.

Machine learning algorithms to predict mild cognitive impairment in older adults in China: A cross-sectional study.

OBJECTIVE: This study aimed to explore the predictive value of machine learning (ML) in mild cognitive impairment (MCI) among older adults in China and to identify important factors causing MCI. METHODS: In this study, 6434 older adults were selected based on the data of the China Health and Elderly Care Longitudinal Survey (CHARLS) in 2020, and the dataset was subsequently divided into the training set and the test set, with a ratio of 6:4. To construct a prediction model for MCI in older adults, six ML algorithms were used, including logistic regression, KNN, SVM, decision tree (DT), LightGBM, and random forest (RF). The Delong test was used to compare the differences of ROC curves of different models, while decision curve analysis (DCA) was used to evaluate the model performance. The important contributions of the prediction results were then used to explain the model by the SHAP value.The Matthews correlation coefficient (MCC) was calculated to evaluate the performance of the models on imbalanced datasets. Additionally, causal analysis and counterfactual analysis were conducted to understand the feature importance and variable effects. RESULTS: The area under the ROC curve of each model range from 0.71 to 0.77, indicating significant difference (P < 0.01). The DCA results show that the net benefits of LightGBM is the largest within various probability thresholds. Among all the models, the LightGBM model demonstrated the highest performance and stability. The five most important characteristics for predicting MCI were educational level, social events, gender, relationship with children, and age. Causal analysis revealed that these variables had a significant impact on MCI, with an average treatment effect of -0.144. Counterfactual analysis further validated these findings by simulating different scenarios, such as improving educational level, increasing age, and increasing social events. CONCLUSION: The ML algorithm can effectively predict the MCI of older adults in China and identify the important factors causing MCI.

Unlocking the potential of exercise: harnessing myokines to delay musculoskeletal aging and improve cognitive health.

Objectives: This review aims to summarize the common physiological mechanisms associated with both mild cognitive impairment (MCI) and musculoskeletal aging while also examining the relevant literature on how exercise regulation influences the levels of shared myokines in these conditions. Methods: The literature search was conducted via databases such as PubMed (including MEDLINE), EMBASE, and the Cochrane Library of Systematic Reviews. The searches were limited to full-text articles published in English, with the most recent search conducted on 16 July 2024. The inclusion criteria for this review focused on the role of exercise and myokines in delaying musculoskeletal aging and enhancing cognitive health. The Newcastle‒Ottawa Scale (NOS) was utilized to assess the quality of nonrandomized studies, and only those studies with moderate to high quality scores, as per these criteria, were included in the final analysis. Data analysis was performed through narrative synthesis. Results: The primary outcome of this study was the evaluation of myokine expression, which included IL-6, IGF-1, BDNF, CTSB, irisin, and LIF. A total of 16 studies involving 633 older adults met the inclusion criteria. The current exercise modalities utilized in these studies primarily consisted of resistance training and moderate-to high-intensity cardiovascular exercise. The types of interventions included treadmill training, elastic band training, aquatic training, and Nordic walking training. The results indicated that both cardiovascular exercise and resistance exercise could delay musculoskeletal aging and enhance the cognitive functions of the brain. Additionally, different types and intensities of exercise exhibited varying effects on myokine expression. Conclusion: Current evidence suggests that exercise mediates the secretion of specific myokines, including IL-6, IGF-1, BDNF, CTSB, irisin, and LIF, which establish self-regulatory circuits between the brain and muscle. This interaction enhances cognitive function in the brain and improves skeletal muscle function. Future research should focus on elucidating the exact mechanisms that govern the release of myokines, the correlation between the intensity of exercise and the secretion of these myokines, and the distinct processes by which myokines influence the interaction between muscle and the brain.