The Power of EEG to Predict Conversion from Mild Cognitive Impairment and Subjective Cognitive Decline to Dementia

Quantitative electroencephalography in cerebral amyloid angiopathy

OBJECTIVE

We investigated whether quantitative electroencephalography (qEEG) correlates with cognition and cortical superficial siderosis (cSS) in cerebral amyloid angiopathy.

METHODS

We included patients with sporadic (sCAA) and hereditary Dutch-type CAA (D-CAA). Spectral measures and the phase lag index (PLI) were analyzed on qEEG. Cognition was assessed with the MoCA and cSS presence was scored on 3T-MRI. Linear regression analyses were performed to investigate these qEEG measures and cognition. Independent samples T-tests were used to analyze the qEEG measure differences between participants with and without cSS.

RESULTS

We included 92 participants (44 D-CAA; 48 sCAA). A lower average peak frequency (β[95 %CI] = 0.986[0.252-1.721]; P = 0.009) and a higher spectral ratio (β[95 %CI] = -0.918[-1.761--0.075]; P = 0.033) on qEEG correlated with a lower MoCA score, irrespective of a history of symptomatic intracerebral hemorrhage (sICH). The PLI showed no correlation to the MoCA. qEEG slowing was not different in those with or without cSS.

CONCLUSIONS

Spectral qEEG (but not PLI) reflects cognitive performance in patients with CAA with and without a history of sICH. We found no association between qEEG slowing and cSS.

SIGNIFICANCE

qEEG could be a valuable biomarker, especially in challenging cognitive testing situations in CAA, and a potential predictive tool in future studies.

Clinically feasible automated MRI volumetry of the brain as a prognostic marker in subjective and mild cognitive impairment

Background/aims

The number of patients suffering from cognitive decline and dementia increases, and new possible treatments are being developed. Thus, the need for time efficient and cost-effective methods to facilitate an early diagnosis and prediction of future cognitive decline in patients with early cognitive symptoms is becoming increasingly important. The aim of this study was to evaluate whether an MRI based software, NeuroQuant® (NQ), producing volumetry of the hippocampus and whole brain volume (WBV) could predict: (1) conversion from subjective cognitive decline (SCD) at baseline to mild cognitive impairment (MCI) or dementia at follow-up, and from MCI at baseline to dementia at follow-up and (2) progression of cognitive and functional decline defined as an annual increase in the Clinical Dementia Rating Scale Sum of Boxes (CDR-SB) score.

Methods

MRI was performed in 156 patients with SCD or MCI from the memory clinic at Oslo University Hospital (OUH) that had been assessed with NQ and had a clinical follow-up examination. Logistic and linear regression analyses were performed with hippocampus volume and WBV as independent variables, and conversion or progression as dependent variables, adjusting for demographic and other relevant covariates including Mini-Mental State Examination-Norwegian Revised Version score (MMSE-NR) and Apolipoprotein E ɛ4 (APOE ɛ4) carrier status.

Results

Hippocampus volume, but not WBV, was associated with conversion to MCI or dementia, but neither were associated with conversion when adjusting for MMSE-NR. Both hippocampus volume and WBV were associated with progression as measured by the annual change in CDR-SB score in both unadjusted and adjusted analyses.

Conclusion

The results indicate that automated regional MRI volumetry of the hippocampus and WBV can be useful in predicting further cognitive decline in patients with early cognitive symptoms.

From Gut Microbiota to Brain Waves: The Potential of the Microbiome and EEG as Biomarkers for Cognitive Impairment

Alzheimer's disease (AD) is a prevalent neurodegenerative disorder and a leading cause of dementia. Aging is a significant risk factor for AD, emphasizing the importance of early detection since symptoms cannot be reversed once the advanced stage is reached. Currently, there is no established method for early AD diagnosis. However, emerging evidence suggests that the microbiome has an impact on cognitive function. The gut microbiome and the brain communicate bidirectionally through the gut-brain axis, with systemic inflammation identified as a key connection that may contribute to AD. Gut dysbiosis is more prevalent in individuals with AD compared to their cognitively healthy counterparts, leading to increased gut permeability and subsequent systemic inflammation, potentially causing neuroinflammation. Detecting brain activity traditionally involves invasive and expensive methods, but electroencephalography (EEG) poses as a non-invasive alternative. EEG measures brain activity and multiple studies indicate distinct patterns in individuals with AD. Furthermore, EEG patterns in individuals with mild cognitive impairment differ from those in the advanced stage of AD, suggesting its potential as a method for early indication of AD. This review aims to consolidate existing knowledge on the microbiome and EEG as potential biomarkers for early-stage AD, highlighting the current state of research and suggesting avenues for further investigation.

Slowing of Parameterized Resting-State Electroencephalography After Mild Traumatic Brain Injury

Reported changes in electroencephalography (EEG)-derived spectral power after mild traumatic brain injury (mTBI) remains inconsistent across existing literature. However, this may be a result of previous analyses depending solely on observing spectral power within traditional canonical frequency bands rather than accounting for the aperiodic activity within the collected neural signal. Therefore, the aim of this study was to test for differences in rhythmic and arrhythmic time series across the brain, and in the cognitively relevant frontoparietal (FP) network, and observe whether those differences were associated with cognitive recovery post-mTBI. Resting-state electroencephalography (rs-EEG) was collected from 88 participants (56 mTBI and 32 age- and sex-matched healthy controls) within 14 days of injury for the mTBI participants. A battery of executive function (EF) tests was collected at the first session with follow-up metrics collected approximately 2 and 4 months after the initial visit. After spectral parameterization, a significant between-group difference in aperiodic-adjusted alpha center peak frequency within the FP network was observed, where a slowing of alpha peak frequency was found in the mTBI group in comparison to the healthy controls. This slowing of week 2 (collected within 2 weeks of injury) aperiodic-adjusted alpha center peak frequency within the FP network was associated with increased EF over time (evaluated using executive composite scores) post-mTBI. These findings suggest alpha center peak frequency within the FP network as a candidate prognostic marker of EF recovery and may inform clinical rehabilitative methods post-mTBI.

Correlative factors of poor prognosis and abnormal cellular immune function in patients with Alzheimer's disease

BACKGROUND

Alzheimer's disease (AD) is a serious disease causing human dementia and social problems. The quality of life and prognosis of AD patients have attracted much attention. The role of chronic immune inflammation in the pathogenesis of AD is becoming more and more important.

AIM

To study the relationship among cognitive dysfunction, abnormal cellular immune function, neuroimaging results and poor prognostic factors in patients.

METHODS

A retrospective analysis of 62 hospitalized patients clinical diagnosed with AD who were admitted to our hospital from November 2015 to November 2020. Collect cognitive dysfunction performance characteristics, laboratory test data and neuroimaging data from medical records within 24 h of admission, including Mini Mental State Examination Scale score, drawing clock test, blood T lymphocyte subsets, and neutrophils and lymphocyte ratio (NLR), disturbance of consciousness, extrapyramidal symptoms, electroencephalogram (EEG) and head nucleus magnetic spectroscopy (MRS) and other data. Multivariate logistic regression analysis was used to determine independent prognostic factors. the modified Rankin scale (mRS) was used to determine whether the prognosis was good. The correlation between drug treatment and prognostic mRS score was tested by the rank sum test.

RESULTS

Univariate analysis showed that abnormal cellular immune function, extrapyramidal symptoms, obvious disturbance of consciousness, abnormal EEG, increased NLR, abnormal MRS, and complicated pneumonia were related to the poor prognosis of AD patients. Multivariate logistic regression analysis showed that the decrease in the proportion of T lymphocytes in the blood after abnormal cellular immune function (odd ratio: 2.078, 95% confidence interval: 1.156-3.986, P < 0.05) was an independent risk factor for predicting the poor prognosis of AD. The number of days of donepezil treatment to improve cognitive function was negatively correlated with mRS score (r = 0.578, P < 0.05).

CONCLUSION

The decrease in the proportion of T lymphocytes may have predictive value for the poor prognosis of AD. It is recommended that the proportion of T lymphocytes < 55% is used as the cut-off threshold for predicting the poor prognosis of AD. The early and continuous drug treatment is associated with a good prognosis.

Quantitative electroencephalography (qEEG), apolipoprotein A-I (APOA-I), and apolipoprotein epsilon 4 (APOE ɛ4) alleles for the diagnosis of mild cognitive impairment and Alzheimer's disease

INTRODUCTION

Alzheimer's disease (AD) is the most common type of dementia. Amnestic mild cognitive impairment (aMCI), a pre-dementia stage is an important stage for early diagnosis and intervention. This study aimed to investigate the diagnostic value of qEEG, APOA-I, and APOE ɛ4 allele in aMCI and AD patients and found the correlation between qEEG (Delta + Theta)/(Alpha + Beta) ratio (DTABR) and different cognitive domains.

METHODS

All participants were divided into three groups: normal controls (NCs), aMCI, and AD, and all received quantitative electroencephalography (qEEG), neuropsychological scale assessment, apolipoprotein epsilon 4 (APOE ɛ4) alleles, and various blood lipid indicators. Different statistical methods were used for different data.

RESULTS

The cognitive domains except executive ability were all negatively correlated with DTABR in different brain regions while executive ability was positively correlated with DTABR in several brain regions, although without statistical significance. The consequences confirmed that the DTABR of each brain area were related to MMSE, MoCA, instantaneous memory, and the language ability (p < 0.05), and the DTABR in the occipital area was relevant to all cognitive domains (p < 0.01) except executive function (p = 0.272). Also, occipital DTABR was most correlated with language domain when tested by VFT with a moderate level (r = 0.596, p < 0.001). There were significant differences in T3, T5, and P3 DTABR between both AD and NC and aMCI and NCs. As for aMCI diagnosis, the maximum AUC was achieved when using T3 combined with APOA-I and APOE ε4 (0.855) and the maximum AUC was achieved when using T5 combined with APOA-I and APOE ε4 (0.889) for AD diagnosis.

CONCLUSION

These findings highlight that APOA-I, APOE ɛ4, and qEEG play an important role in aMCI and AD diagnosis. During AD continuum, qEEG DTABR should be taken into consideration for the early detection of AD risk.

Quantitative EEG Spectral and Connectivity Analysis for Cognitive Decline in Amnestic Mild Cognitive Impairment

BACKGROUND

Mild cognitive impairment (MCI) is considered to be the borderline of cognitive changes associated with aging and very early dementia. Cognitive functions in MCI can improve, remain stable or progress to clinically probable AD. Quantitative electroencephalography (qEEG) can become a useful tool for using the analytical techniques to quantify EEG patterns indicating cognitive impairment.

OBJECTIVE

The aim of our study was to assess spectral and connectivity analysis of the EEG resting state activity in amnestic MCI (aMCI) patients in comparison with healthy control group (CogN).

METHODS

30 aMCI patients and 23 CogN group, matched by age and education, underwent equal neuropsychological assessment and EEG recording, according to the same protocol.

RESULTS

qEEG spectral analysis revealed decrease of global relative beta band power and increase of global relative theta and delta power in aMCI patients. Whereas, decreased coherence in centroparietal right area considered to be an early qEEG biomarker of functional disconnection of the brain network in aMCI patients. In conclusion, the demonstrated changes in qEEG, especially, the coherence patterns are specific biomarkers of cognitive impairment in aMCI.

CONCLUSIONS

Therefore, qEEG measurements appears to be a useful tool that complements neuropsychological diagnostics, assessing the risk of progression and provides a basis for possible interventions designed to improve cognitive functions or even inhibit the progression of the disease.

Effectiveness of an enhanced simultaneous cognitive-physical dual-task training based on fairy tales (ESCARF) in older adults with mild cognitive impairment

The aim of this study was to provide a dual-task program that included cognitive and physical training to older adults with mild cognitive impairment (MCI) and evaluate its effects. A single-group pretest-posttest design was performed using 15 older adults with MCI. A 12-week enhanced simultaneous cognitive-physical dual-task training based on fairy tales (ESCARF) program was conducted from September 2019 to December 2019. Participants were assessed using the Korean version of the Montreal Cognitive Assessment, electroencephalography (EEG), muscle strength, flexibility, agility, memory self-efficacy questionnaire, physical self-efficacy scale, and quality of life before and after 6 and 12 weeks of the intervention. The ESCARF program significantly improved cognitive function, physical function, self-efficacy, and quality of life in older adults with MCI. These findings will provide insights into the development and implementation of customized cognitive interventions to prevent or delay the onset of cognitive decline in older adults with MCI.

The Importance of Subjective Cognitive Decline Recognition and the Potential of Molecular and Neurophysiological Biomarkers-A Systematic Review

Subjective cognitive decline (SCD) and mild cognitive impairment (MCI) are early stages of Alzheimer's disease (AD). Neurophysiological markers such as electroencephalography (EEG) and event-related potential (ERP) are emerging as alternatives to traditional molecular and imaging markers. This paper aimed to review the literature on EEG and ERP markers in individuals with SCD. We analysed 30 studies that met our criteria, with 17 focusing on resting-state or cognitive task EEG, 11 on ERPs, and two on both EEG and ERP parameters. Typical spectral changes were indicative of EEG rhythm slowing and were associated with faster clinical progression, lower education levels, and abnormal cerebrospinal fluid biomarkers profiles. Some studies found no difference in ERP components between SCD subjects, controls, or MCI, while others reported lower amplitudes in the SCD group compared to controls. Further research is needed to explore the prognostic value of EEG and ERP in relation to molecular markers in individuals with SCD.

Electroencephalography and mild cognitive impairment research: A scoping review and bibliometric analysis (ScoRBA)

Mild cognitive impairment (MCI) is often considered a precursor to Alzheimer's disease (AD) and early diagnosis may help improve treatment effectiveness. To identify accurate MCI biomarkers, researchers have utilized various neuroscience techniques, with electroencephalography (EEG) being a popular choice due to its low cost and better temporal resolution. In this scoping review, we analyzed 2310 peer-reviewed articles on EEG and MCI between 2012 and 2022 to track the research progress in this field. Our data analysis involved co-occurrence analysis using VOSviewer and a Patterns, Advances, Gaps, Evidence of Practice, and Research Recommendations (PAGER) framework. We found that event-related potentials (ERP), EEG, epilepsy, quantitative EEG (QEEG), and EEG-based machine learning were the primary research themes. The study showed that ERP/EEG, QEEG, and EEG-based machine learning frameworks provide high-accuracy detection of seizure and MCI. These findings identify the main research themes in EEG and MCI and suggest promising avenues for future research in this field.

Degradation of EEG microstates patterns in subjective cognitive decline and mild cognitive impairment: Early biomarkers along the Alzheimer's Disease continuum?

Alzheimer's disease (AD) pathological changes may begin up to decades earlier than the appearance of the first symptoms of cognitive decline. Subjective cognitive decline (SCD) could be the first pre-clinical sign of possible AD, which might be followed by mild cognitive impairment (MCI), the initial stage of clinical cognitive decline. However, the neural correlates of these prodromic stages are not completely clear yet. Recent studies suggest that EEG analysis tools characterizing the cortical activity as a whole, such as microstates and cortical regions connectivity, might support a characterization of SCD and MCI conditions. Here we test this approach by performing a broad set of analyses to identify the prominent EEG markers differentiating SCD (n = 57), MCI (n = 46) and healthy control subjects (HC, n = 19). We found that the salient differences were in the temporal structure of the microstates patterns, with MCI being associated with less complex sequences due to the altered transition probability, frequency and duration of canonic microstate C. Spectral content of EEG, network connectivity, and spatial arrangement of microstates were instead largely similar in the three groups. Interestingly, comparing properties of EEG microstates in different cerebrospinal fluid (CSF) biomarkers profiles, we found that canonic microstate C displayed significant differences in topography in AD-like profile. These results show that the progression of dementia might be associated with a degradation of the cortical organization captured by microstates analysis, and that this leads to altered transitions between cortical states. Overall, our approach paves the way for the use of non-invasive EEG recordings in the identification of possible biomarkers of progression to AD from its prodromal states.

Normative Scores on the Norwegian Version of the Mini-Mental State Examination

BACKGROUND

The Mini-Mental State Examination (MMSE), a simple test for measuring global cognitive function, is frequently used to evaluate cognition in older adults. To decide whether a score on the test indicates a significant deviation from the mean score, normative scores should be defined. Moreover, because the test may vary depending on its translation and cultural differences, normative scores should be established for national versions of the MMSE.

OBJECTIVE

We aimed to examine normative scores for the third Norwegian version of the MMSE.

METHODS

We used data from two sources: the Norwegian Registry of Persons Assessed for Cognitive Symptoms (NorCog) and the Trøndelag Health Study (HUNT). After persons with dementia, mild cognitive impairment, and disorders that may cause cognitive impairment were excluded, the sample contained 1,050 cognitively healthy persons, 860 from NorCog, and 190 from HUNT, whose data we subjected to regression analyses.

RESULTS

The normative MMSE score varied from 25 to 29, depending on years of education and age. More years of education and younger age were associated with higher MMSE scores, and years of education was the strongest predictor.

CONCLUSION

Mean normative MMSE scores depend on test takers' years of education and age, with level of education being the strongest predictor.

An attention-based deep learning approach for the classification of subjective cognitive decline and mild cognitive impairment using resting-state EEG

Objective. This study aims to design and implement the first deep learning (DL) model to classify subjects in the prodromic states of Alzheimer's disease (AD) based on resting-state electroencephalographic (EEG) signals.Approach. EEG recordings of 17 healthy controls (HCs), 56 subjective cognitive decline (SCD) and 45 mild cognitive impairment (MCI) subjects were acquired at resting state. After preprocessing, we selected sections corresponding to eyes-closed condition. Five different datasets were created by extracting delta, theta, alpha, beta and delta-to-theta frequency bands using bandpass filters. To classify SCDvsMCI and HCvsSCDvsMCI, we propose a framework based on the transformer architecture, which uses multi-head attention to focus on the most relevant parts of the input signals. We trained and validated the model on each dataset with a leave-one-subject-out cross-validation approach, splitting the signals into 10 s epochs. Subjects were assigned to the same class as the majority of their epochs. Classification performances of the transformer were assessed for both epochs and subjects and compared with other DL models.Main results. Results showed that the delta dataset allowed our model to achieve the best performances for the discrimination of SCD and MCI, reaching an Area Under the ROC Curve (AUC) of 0.807, while the highest results for the HCvsSCDvsMCI classification were obtained on alpha and theta with a micro-AUC higher than 0.74.Significance. We demonstrated that DL approaches can support the adoption of non-invasive and economic techniques as EEG to stratify patients in the clinical population at risk for AD. This result was achieved since the attention mechanism was able to learn temporal dependencies of the signal, focusing on the most discriminative patterns, achieving state-of-the-art results by using a deep model of reduced complexity. Our results were consistent with clinical evidence that changes in brain activity are progressive when considering early stages of AD.

The CERAD Word List Memory Test: Normative Data Based on a Norwegian Population-Based Sample of Healthy Older Adults 70 Years and Above. The HUNT Study

BACKGROUND

The CERAD Word List Memory Test (WLMT) is widely used in the assessment of older adults with suspected dementia. Although normative data of the WLMT exist in many different regions of the world, normative data based on large population-based cohorts from the Scandinavian countries are lacking.

OBJECTIVE

To develop normative data for the WLMT based on a large population-based Norwegian sample of healthy older adults aged 70 years and above, stratified by age, gender, and education.

METHODS

A total of 6,356 older adults from two population-based studies in Norway, HUNT4 70 + and HUNT4 Trondheim 70+, were administered the WLMT. Only persons with normal cognitive function were included. We excluded persons with a diagnosis of mild cognitive impairment (MCI) and dementia, and persons with a history of stroke and/or depression. This resulted in 3,951 persons aged between 70 and 90 years, of whom 56.2% were females. Regression-based normative data were developed for this sample.

RESULTS

Age, gender, and education were significant predictors of performance on the WLMT list-learning subtests and the delayed recall subtest, i.e., participants of younger age, female sex, and higher education level attained higher scores compared to participants of older age, male sex, and lower level of education.

CONCLUSION

Regression-based normative data from the WMLT, stratified by age, gender, and education from a large population-based Norwegian sample of cognitively healthy older adults aged 70 to 90 years are presented. An online norm calculator is available to facilitate scoring of the subtests (in percentiles and z-scores).

Quantitative EEG for early differential diagnosis of dementia with Lewy bodies

Introduction

Differentiating between the two most common forms of dementia, Alzheimer's dementia and dementia with Lewy bodies (DLB) remains difficult and requires the use of invasive, expensive, and resource-intensive techniques. We aimed to investigate the sensitivity and specificity of electroencephalography quantified using the statistical pattern recognition method (qEEG-SPR) for identifying dementia and DLB.

Methods

Thirty-two outpatients and 16 controls underwent clinical assessment (by two blinded neurologists), EEG recording, and a 6-month follow-up clinical assessment. EEG data were processed using a qEEG-SPR protocol to derive a Dementia Index (positive or negative) and DLB index (positive or negative) for each participant which was compared against the diagnosis given at clinical assessment. Confusion matrices were used to calculate sensitivity, specificity, and predictive values for identifying dementia and DLB specifically.

Results

Clinical assessment identified 30 cases of dementia, 2 of which were diagnosed clinically with possible DLB, 14 with probable DLB and DLB was excluded in 14 patients. qEEG-SPR confirmed the dementia diagnosis in 26 out of the 32 patients and led to 6.3% of false positives (FP) and 9.4% of false negatives (FN). qEEG-SPR was used to provide a DLB diagnosis among patients who received a positive or inconclusive result of Dementia index and led to 13.6% of FP and 13.6% of FN. Confusion matrices indicated a sensitivity of 80%, a specificity of 89%, a positive predictive value of 92%, a negative predictive value of 72%, and an accuracy of 83% to diagnose dementia. The DLB index showed a sensitivity of 60%, a specificity of 90%, a positive predictive value of 75%, a negative predictive value of 81%, and an accuracy of 75%. Neuropsychological scores did not differ significantly between DLB and non- DLB patients. Head trauma or story of stroke were identified as possible causes of FP results for DLB diagnosis.

Conclusion

qEEG-SPR is a sensitive and specific tool for diagnosing dementia and differentiating DLB from other forms of dementia in the initial state. This non-invasive, low-cost, and environmentally friendly method is a promising diagnostic tool for dementia diagnosis which could be implemented in local care settings.

Autonomic function predicts cognitive decline in mild cognitive impairment: Evidence from power spectral analysis of heart rate variability in a longitudinal study

Background

Despite the emerging clinical relevance of heart rate variability (HRV) as a potential biomarker of cognitive decline and as a candidate target for intervention, there is a dearth of research on the prospective relationship between HRV and cognitive change. In particular, no study has addressed this issue in subjects with a diagnosis of cognitive status including cognitive impairment.

Objective

To investigate HRV as a predictor of cognitive decline in subjects with normal cognition (NC) or Mild Cognitive Impairment (MCI). Specifically, we tested the literature-based hypothesis that the HRV response to different physical challenges would predict decline in different cognitive domains.

Methods

This longitudinal study represents the approximately 3-year follow-up of a previous cross-sectional study enrolling 80 older outpatients (aged ≥ 65). At baseline, power spectral analysis of HRV was performed on five-minute electrocardiographic recordings at rest and during a sympathetic (active standing) and a parasympathetic (paced breathing) challenge. We focused on normalized HRV measures [normalized low frequency power (LFn) and the low frequency to high frequency power ratio (LF/HF)] and on their dynamic response from rest to challenge (Δ HRV). Extensive neuropsychological testing was used to diagnose cognitive status at baseline and to evaluate cognitive change over the follow-up via annualized changes in cognitive Z-scores. The association between Δ HRV and cognitive change was explored by means of linear regression, unadjusted and adjusted for potential confounders.

Results

In subjects diagnosed with MCI at baseline a greater response to a sympathetic challenge predicted a greater decline in episodic memory [adjusted model: Δ LFn, standardized regression coefficient (β) = −0.528, p = 0.019; Δ LF/HF, β = −0.643, p = 0.001] whereas a greater response to a parasympathetic challenge predicted a lesser decline in executive functioning (adjusted model: Δ LFn, β = −0.716, p < 0.001; Δ LF/HF, β = −0.935, p < 0.001).

Conclusion

Our findings provide novel insight into the link between HRV and cognition in MCI. They contribute to a better understanding of the heart-brain connection, but will require replication in larger cohorts.

A prospective multicenter validation study of a machine learning algorithm classifier on quantitative electroencephalogram for differentiating between dementia with Lewy bodies and Alzheimer’s dementia

Background and purpose

An early and accurate diagnosis of Dementia with Lewy bodies (DLB) is critical because treatments and prognosis of DLB are different from Alzheimer’s disease (AD). This study was carried out in Japan to validate an Electroencephalography (EEG)-derived machine learning algorithm for discriminating DLB from AD which developed based on a database of EEG records from two different European countries.

Methods

In a prospective multicenter study, patients with probable DLB or with probable AD were enrolled in a 1:1 ratio. A continuous EEG segment of 150 seconds was recorded, and the EEG data was processed using MC-004, the EEG-based machine learning algorithm, with all clinical information blinded except for age and gender.

Results

Eighteen patients with probable DLB and 21 patients with probable AD were the included for the analysis. The performance of MC-004 differentiating probable DLB from probable AD was 72.2% (95% CI 46.5–90.3%) for sensitivity, 85.7% (63.7–97.0%) for specificity, and 79.5% (63.5–90.7%) for accuracy. When limiting to subjects taking ≤5 mg donepezil, the sensitivity was 83.3% (95% CI 51.6–97.9), the specificity 89.5% (66.9–98.7), and the accuracy 87.1% (70.2–96.4).

Conclusions

MC-004, the EEG-based machine learning algorithm, was able to discriminate between DLB and AD with fairly high accuracy. MC-004 is a promising biomarker for DLB, and has the potential to improve the detection of DLB in a diagnostic process.

Machine learning, artificial intelligence and the prediction of dementia

PURPOSE OF REVIEW

Artificial intelligence and its division machine learning are emerging technologies that are increasingly applied in medicine. Artificial intelligence facilitates automatization of analytical modelling and contributes to prediction, diagnostics and treatment of diseases. This article presents an overview of the application of artificial intelligence in dementia research.

RECENT FINDINGS

Machine learning and its branch Deep Learning are widely used in research to support in diagnosis and prediction of dementia. Deep Learning models in certain tasks often result in better accuracy of detection and prediction of dementia than traditional machine learning methods, but they are more costly in terms of run times and hardware requirements. Both machine learning and Deep Learning models have their own strengths and limitations. Currently, there are few datasets with limited data available to train machine learning models. There are very few commercial applications of machine learning in medical practice to date, mostly represented by mobile applications, which include questionnaires and psychometric assessments with limited machine learning data processing.

SUMMARY

Application of machine learning technologies in detection and prediction of dementia may provide an advantage to psychiatry and neurology by promoting a better understanding of the nature of the disease and more accurate evidence-based processes that are reproducible and standardized.

The Montreal Cognitive Assessment: Normative Data from a Large, Population-Based Sample of Cognitive Healthy Older Adults in Norway—The HUNT Study

Background: Several studies have found that normative scores on the Montreal Cognitive Assessment Scale (MoCA) vary depending on the person’s education and age. The evidence for different normative scores between sexes is poor. Objective: The main aim of the study was to determine normative scores on the MoCA for Norwegian older adults stratified by educational level, age, and sex. In addition, we aimed to explore sex differences in greater detail. Methods: From two population-based studies in Norway, we included 4,780 people age 70 years and older. People with a diagnosis of dementia or mild cognitive impairment, a history of stroke, and depression were excluded. Trained health personnel tested the participants with the MoCA. Results: The mean MoCA score varied between 22 and 27 and was highest among women 70–74 years with education >13 years and lowest among men age 85 and older with education ≤10 years. Education, age, and sex were significant predictors of MoCA scores. Conclusion: In the present study of cognitively healthy Norwegian adults 70 years and older, we found that the normative score on the MoCA varied between 22 and 27 depending on a person’s education, age, and sex. We suggest that normative scores should be determined taking these three variables into consideration.

Resting EEG spectral slopes are associated with age-related differences in information processing speed

BACKGROUND

Previous research has shown the slope of the EEG power spectrum differentiates between older and younger adults in various experimental cognitive tasks. We extend that work, assessing the relation between the EEG power spectrum and performance on the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS).

METHODS

Twenty-one younger and twenty-three older adults completed the RBANS with EEG data collected at rest. Using spectral parameterization, we tested the mediating effect of the spectral slope on differences in subsequent cognitive task performance.

RESULTS

Older adults performed reliably worse on the RBANS overall, and on the Attention and Delayed Memory domains specifically. However, evidence of mediation was only found for the Coding subtest.

CONCLUSIONS

The slope of the resting EEG power spectrum mediated age-related differences in cognition, but only in a task requiring speeded processing. Mediation was not statistically significant for delayed memory, even though age-related differences were present.

Sex Differences on Montreal Cognitive Assessment and Mini-Mental State Examination Scores and the Value of Self-Report of Memory Problems among Community Dwelling People 70 Years and above: The HUNT Study

INTRODUCTION

The aims were to examine if the total and item scores on the Montreal Cognitive Assessment (MoCA) and the Mini-Mental State Examination (MMSE) and self-reported memory problems differed between older women and men, and if self-reported memory problems were associated with scores on the 2 tests.

METHODS

We included 309 home-dwelling people aged 70 years and older, 155 women, mean age 75.6 (SD 4.1) years, and 154 men, mean age 76.0 (SD 4.6) years. They were examined with MoCA and MMSE, and they answered 2 questions: "have you experienced any memory problems" and "have you experienced significant memory problems the last 5 years?"

RESULTS

The participants scored significantly higher on the MMSE (women 28.0 [1.8], men 28.4 [1.4]) than on MoCA (women 24.6 [3.3], men 24.3 [3.1]). Spearman's rho was 0.36 between the tests. Women scored significantly higher than men on delayed recall of MoCA (3.0 [1.6] vs. 2.4 [1.6]), whereas men scored significantly higher on visuoconstruction (3.8 [1.2] vs. 3.5 [1.0]) and serial subtraction on MoCA (2.7 [0.6] vs. 2.5 [0.8]) and serial sevens on MMSE (4.5 [0.8] vs. 4.1 [1.1]). Multivariate linear regression analyses revealed that female sex, younger age, and higher education were associated with a higher score on MoCA, whereas age and education were associated with a higher score on MMSE. About half of the participants (no sex difference) had experienced significant memory problems the last 5 years, and they had significantly lower scores on both tests.

CONCLUSIONS

The MoCA score was associated with sex, age, and education, whereas sex did not influence the MMSE score. The question "have you experienced significant memory problems the last 5 years?" may be useful to evaluate older people's cognition.

Simultaneous cognitive-physical dual task training based on fairy tales in older adults with mild cognitive impairment: A pilot study

In this study, we aimed to develop a simultaneous cognitive-physical dual-task training program based on familiar cultural backgrounds using fairy tales and to explore its feasibility and preliminary effects, including effects on neurophysiological, cognitive, and physical functions. A single-group pretest-posttest design (n = 9) was employed to evaluate the effects of the cognitive-physical intervention performed for 60-90 min once a week for 12 weeks. The findings showed that perceived memory and physical self-efficacy, muscle strength, and cognitive function were significantly increased after the intervention. Although the relative beta band power measured using electroencephalography showed a tendency to increase in eight brain domains after the 12-week intervention, the changes were not significant. Findings suggested that the intervention was feasible and provided beneficial effects on cognitive and physical functions in older adults with mild cognitive impairment. Future research on larger sample sizes using randomized controlled trials is needed to determine the effectiveness of such interventions on neurophysiological functions.

Artificial Intelligence for Alzheimer's Disease: Promise or Challenge?

Decades of experimental and clinical research have contributed to unraveling many mechanisms in the pathogenesis of Alzheimer's disease (AD), but the puzzle is still incomplete. Although we can suppose that there is no complete set of puzzle pieces, the recent growth of open data-sharing initiatives collecting lifestyle, clinical, and biological data from AD patients has provided a potentially unlimited amount of information about the disease, far exceeding the human ability to make sense of it. Moreover, integrating Big Data from multi-omics studies provides the potential to explore the pathophysiological mechanisms of the entire biological continuum of AD. In this context, Artificial Intelligence (AI) offers a wide variety of methods to analyze large and complex data in order to improve knowledge in the AD field. In this review, we focus on recent findings and future challenges for AI in AD research. In particular, we discuss the use of Computer-Aided Diagnosis tools for AD diagnosis and the use of AI to potentially support clinical practices for the prediction of individual risk of AD conversion as well as patient stratification in order to finally develop effective and personalized therapies.