Diagnosis Of aMCI Research Articles

Identification of amnestic mild cognitive impairment among Black and White community-dwelling older adults using NIH Toolbox Cognition tablet battery.

Identify which NIH Toolbox Cognition Battery (NIHTB-CB) subtest(s) best differentiate healthy controls (HC) from those with amnestic mild cognitive impairment (aMCI) and compare the discriminant accuracy between a model using a priori "Norm Adjusted" scores versus "Unadjusted" standard scores with age, sex, race/ethnicity, and education controlled for within the model. Racial differences were also examined. Participants were Black/African American (B/AA) and White consensus-confirmed (HC = 96; aMCI = 62) adults 60-85 years old that completed the NIHTB-CB for tablet. Discriminant function analysis (DFA) was used in the Total Sample and separately for B/AA (n = 80) and White participants (n = 78). Picture Sequence Memory (an episodic memory task) was the highest loading coefficient across all DFA models. When stratified by race, differences were noted in the pattern of the highest loading coefficients within the DFAs. However, the overall discriminant accuracy of the DFA models in identifying HCs and those with aMCI did not differ significantly by race (B/AA, White) or model/score type (Norm Adjusted versus Unadjusted). Racial differences were noted despite the use of normalized scores or demographic covariates-highlighting the importance of including underrepresented groups in research. While the models were fairly accurate at identifying consensus-confirmed HCs, the models proved less accurate at identifying White participants with an aMCI diagnosis. In clinical settings, further work is needed to optimize computerized batteries and the use of NIHTB-CB norm adjusted scores is recommended. In research settings, demographically corrected scores or within model correction is suggested.

Do sex and handedness influence general cognition and financial capacity in patients with aMCI and healthy older adults? Emphasis on women's performance.

The effects of sex and handedness on financial capacity performance remain unexplored both in healthy older adults and in patients with amnestic mild cognitive impairment (aMCI). The aim of this study was to study the effect of the above factors (sex, handedness, and health condition), following a factorial experimental design; hence, eight groups (each with ten individuals) with similar demographic characteristics (age and education level) were formed consisting of right/left-handed, women/men and healthy/not healthy (with a diagnosis of aMCI) older adults. Mini-Mental State Examination (MMSE) was administered as a measure of general cognitive ability, and Legal Capacity for Property Law Transactions Assessment Scale (LCPLTAS) was used as an indicator of financial capacity; moreover, GDS-15 was used to assess depressive symptomatology. Self-reports of hand preference were also included. Although as expected healthy men and women regardless of their handedness outperformed aMCI patients on MMSE and LCPLTAS, performance on cash transactions, bank statement management, bill payment, financial decision making, and knowledge of personal assets from LCPLTAS is significantly higher for right-handed aMCI women compared with left-handed aMCI women. Future research should further elucidate the reasons for this left-handed female patient with aMCI profile in larger groups of patients. This is an exploratory study, and the small sample size limits the strength of conclusions; further studies on this topic are needed.

Distress Symptoms of Old Age and Mild Cognitive Impairment are Two Distinct Dimensions in Older Adults Without Major Depression.

Studies in old adults showed bidirectional interconnections between amnestic mild cognitive impairment (aMCI) and affective symptoms and that adverse childhood experiences (ACE) may affect both factors. Nevertheless, these associations may be confined to older adults with clinical depression. To delineate the relationship between clinical symptoms of aMCI and affective symptoms in older adults without major depression (MDD) or dysfunctions in activities of daily living (ADL). This case-control study recruited 61 participants with aMCI (diagnosed using Petersen's criteria) and 59 older adults without aMCI and excluded subjects with MDD and ADL dysfunctions. We uncovered 2 distinct dimensions, namely distress symptoms of old age (DSOA), comprising affective symptoms, perceived stress and neuroticism, and mild cognitive dysfunctions, comprising episodic memory test scores, the total Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) scores. A large part of the variance (37.9%) in DSOA scores was explained by ACE, negative life events (health and financial problems), a subjective feeling of cognitive decline, and education (all positively). ACE and NLE have a highly significant impact on the DSOA score and are not associated with aMCI or its severity. Cluster analysis showed that the diagnosis of aMCI is overinclusive because some subjects with DSOA symptoms may be incorrectly classified as aMCI. The clinical impact is that clinicians should carefully screen older adults for DSOA after excluding MDD. DSOA might be misinterpreted as aMCI.

Atrophy of Brodmann Area 36 differentiates age‐related subjective memory decline from cognitively normal older adults and is associated with NPTX2

AbstractBackgroundAdvancing age is associated with changes in cognitive functions, particularly memory, although there are significant individual differences between subjects. While some older adults experience minimal cognitive decline, others exhibit more pronounced deterioration that can progress to Alzheimer’s disease dementia (AD). Some older individuals report subjective memory complaints (SMC) despite normal scores on objective memory assessments. Longitudinal studies suggest that SMC may be predictive of AD dementia and precedes the onset of mild cognitive impairment (MCI) by around 15 years. However, given large individual differences and biases towards reporting complaints, objective measures are needed to identify those individuals who are on a trajectory of decline relevant to AD.MethodIn the current study cognitively normal older adults, older adults with SMC, and patients with MCI were recruited from the ADRC and the community to complete neuropsychological assessment, an MRI scan and a lumbar puncture to assess what measures are associated with self‐reported SMC. Structural MRI scans were analyzed using the Automated Segmentation of Hippocampal Subfields (ASHS) pipeline. Cerebrospinal fluid (CSF) was analyzed using standard methods to obtain fluid biomarkers associated with AD.ResultConsistent with previous reports, entorhinal cortex volume significantly differed between cognitively normal older adults and patients with MCI. However, entorhinal cortex volume did not differentiate between controls and SMC subjects. Instead, volume of Brodmann area (BA) 36 was significantly different between control participants and SMC subjects as well as between controls and patients with MCI. Furthermore, volume of the right BA35 and BA36 was moderately correlated with neuronal pentraxin‐2 (NPTX2), a marker of synaptic integrity that is reduced in AD. Volume of the right BA36 was additionally correlated with measures of cognition including the Mini‐Mental Status Exam and objective measures of memory function.ConclusionTogether these findings show that particularly atrophy of BA36 may provide an objective measure differentiating between cognitively normal older adults and those with SMC, and in identifying the early stages of AD prior to a diagnosis of aMCI.

77 Differentiating Amnestic Versus Non-Amnestic Mild Cognitive Impairment Using the NIH Toolbox Cognition Battery

Objective:In research, and particularly clinical trials, it is important to identify persons at high risk for developing Alzheimer’s Disease (AD), such as those with Mild Cognitive Impairment (MCI). However, not all persons with this diagnosis have a high risk of AD as MCI can be broken down further into amnestic MCI (aMCI), who have a high risk specifically for AD, and non-amnestic MCI (naMCI), who are predominantly at risk for other dementias. People with aMCI largely differ from healthy controls and naMCI on memory tasks as it is the hallmark criteria for an amnestic diagnosis. Given the growing use of the NIH Toolbox Cognition battery in research trials, this project investigated which Toolbox Cognition measures best differentiated aMCI from naMCI and in comparison to persons with normal cognition.Participants and Methods:A retrospective data analysis was conducted investigating performance on NIH Toolbox Cognition tasks among 199 participants enrolled in the Michigan Alzheimer’s Disease Research Center. All participants were over age 50 (51-89 years, M=70.64) and had a diagnosis of aMCI (N=74), naMCI (N=24), or Normal Cognition (N=101). Potential demographic differences were investigated using chi-square and ANOVAs. Repeated measure general linear model was used to look at potential group differences in Toolbox Cognition performance, covarying for age which was statistically different in aMCI versus Normal participants. Linear regression was used to determine which cognitive abilities, as measured by the Uniform Data Set-3 (UDS3), might contribute to Toolbox differences noted in naMCI versus aMCI groups.Results:As expected, aMCI had lower Toolbox memory scores compared to naMCI (p=0.007) and Normals (p<0.001). Interestingly, naMCI had lower Oral Reading scores than both aMCI (p=0.008) and Normals (p<0.001). There were no other Toolbox performance differences between the MCI groups. 19.4% of the variance in Oral Reading scores was explained by performance on the following UDS3 measures: Benson delayed recall (inverse relationship) and backward digit span and phonemic fluency (positive relationship).Conclusions:In this study, Toolbox Picture Sequence Memory and Oral Reading scores differentiated aMCI and naMCI groups. While the difference in memory was expected, it was surprising that the naMCI group performed worse than the aMCI and normal groups on the Toolbox Oral Reading task, a task presumed to reflect Crystalized abilities resistive to cognitive decline. Results suggest that Oral Reading is primarily positively associated with working memory and executive tasks from the UDS3, but negatively associated with visual memory. It is possible that the Oral Reading subtest is sensitive to domains of deficit aside from memory that can best distinguish aMCI from naMCI. A better understanding of the underlying features in the Oral Reading task will assist in better characterizing deficit patterns seen in naMCI, making selection of aMCI participants more effective in clinical trials.

21 Assessment of Semantic Memory Decline in aMCI : Naming and Semantic Knowledge of Unique and Non-Unique Entities

Objective:Semantic memory deficits have been reported in both Alzheimer's disease (AD) and amnestic mild cognitive impairment (aMCI). However, the nature of this decline is still a matter of debate. The aim of this study was to explore the patterns of semantic memory impairment in aMCI by examining performance on naming tasks, and on tests assessing both general and specific semantic knowledge.Participants and Methods:Participants were divided in two groups matched for age and education, one comprising 33 aMCI individuals and the other 39 healthy controls. Three experimental tests assessing naming and semantic knowledge of unique items of famous persons (FACE) and places (PLACE), logos recognition (LOGO: brands and pictograms), and non-unique entities (Boston Naming Test: BNT) were administered, and the performance of the two groups was compared.Results:Lower scores were observed on all naming tests (PLACE, FACE, LOGO and BNT) in the aMCI group compared to controls. On the PLACE test, the general knowledge mean score (M=84.5, SD=12.9) was significantly higher than the specific knowledge mean score (M=54.2, SD=18.5) in aMCI participants (t(31)=11.9, p<.001), but not in controls (general: M=92.2, SD=11.1; specific: M=73.7, SD=15.8), and there was a significant Group X Type of knowledge interaction (F(1,1)=15.13, p <.001, n2 = 18). On the FACE test, in addition to significant group and condition (naming, semantic questions) main effects, a significant interaction was found (F(1,1)=7.19, p = .009, n2 = .09). On the LOGO task, controls were significantly better on brand items (M= 94.4, SD=10.5) than on pictograms (M=83.3, SD=12.2), while no significant difference was noted in aMCI (brands: M=81.5, SD=22.6; pictograms: M=77.5, SD=14.1). Lastly, on the BNT, aMCI participants benefited more from phonemic cues than controls (F(1,1)=16.56, p<.001, n2=19), suggesting a lexical access deficit, in addition to their semantic memory impairment.Conclusions:This study adds to the growing evidence confirming the presence of semantic memory deficits in aMCI. Specific semantic knowledge seems to be more affected than general semantic knowledge, a finding reported in previous studies. Lexical access deficits, in addition to semantic decline, were also observed in the aMCI group. These results allow for a better understanding of the pattern of semantic memory deficits in the prodromal stage of AD and could potentially facilitate diagnosis of aMCI.

Comorbid cerebral amyloid angiopathy in dementia and prodromal stages-Prevalence and effects on cognition.

To determine the contribution of cerebral amyloid angiopathy to cognitive impairment in MCI and dementia. Patients with subjective memory impairment (SMI), amnestic and non-amnestic mild cognitive impairment ((n)aMCI), Alzheimer's disease (AD), mixed and vascular dementia (MD/VD) from our memory clinic were included in this retrospective analysis. Patients underwent neuropsychological testing and cranial magnetic resonance imaging (MRI). Magnetic resonance imaging data sets were analyzed regarding the presence of CAA-related MRI biomarkers to determine CAA prevalence. ANOVAs were used to investigate the contribution of CAA to cognitive impairment within diagnostic groups and to determine whether differences in cognitive test performance between the diagnostic groups are mediated by total CAA burden. 475 patients (222 male, 253 female) with SMI (n=47), naMCI (n=41), aMCI (n=189), early AD (n=9), AD (n=114), MD (n=71) and VD (n=4) were included. Mean age was 73.2 (9.9) years. CAA prevalence was 14.9% in SMI, 14.6% in naMCI, 24.3% in aMCI, 22.2% in early onset AD, 18.4% in late onset AD, 46.5% in MD and 25% in VD. Patients with possible and probable CAA were older than patients without CAA. In particular, diagnosis of aMCI, early onset AD, MD and VD showed high CAA prevalence. In AD but not in aMCI, CAA diagnosis significantly influenced test performance in the CERAD word list recall (F (1,78)=4505; p=0.037; partial eta-square=0.055). Differences in cognitive test performance between the diagnostic groups of naMCI, aMCI, AD and MD were mediated by total CAA burden within AAT simply nouns subtest (F (2,39)=4059; p=0.025; partial eta-square=0.172) and in CERAD verbal fluency test (F (3,129)=3533; p=0.017; partial eta-square=0.076). This retrospective analysis demonstrates high prevalence rates of CAA in cognitive diagnoses. Our data suggest that comorbid CAA independently impacts cognitive test performance in the course of AD with presumably stage-dependent effects. Especially in patients with AD comorbid CAA additionally impairs memory function. Total CAA small vessel disease burden further modulates psychometric differences in cognitive test performance between diagnostic groups regarding word finding and word fluency capabilities.

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.

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. 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. 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. 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.

Prognostic relevance of gait-related cognitive functions for dementia conversion in amnestic mild cognitive impairment

BackgroundIncreasing research suggests that gait abnormalities can be a risk factor for Alzheimer’s Disease (AD). Notably, there is growing evidence highlighting this risk factor in individuals with amnestic Mild Cognitive Impairment (aMCI), however further studies are needed. The aim of this study is to analyze cognitive tests results and brain-related measures over time in aMCI and examine how the presence of gait abnormalities (neurological or orthopedic) or normal gait affects these trends. Additionally, we sought to assess the significance of gait and gait-related measures as prognostic indicators for the progression from aMCI to AD dementia, comparing those who converted to AD with those who remained with a stable aMCI diagnosis during the follow-up.MethodsFour hundred two individuals with aMCI from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database were included. Robust linear mixed-effects models were used to study the impact of gait abnormalities on a comprehensive neuropsychological battery over 36 months while controlling for relevant medical variables at baseline. The impact of gait on brain measures was also investigated. Lastly, the Cox proportional-hazards model was used to explore the prognostic relevance of abnormal gait and neuropsychological associated tests.ResultsWhile controlling for relevant covariates, we found that gait abnormalities led to a greater decline over time in attention (DSST) and global cognition (MMSE). Intriguingly, psychomotor speed (TMT-A) and divided attention (TMT-B) declined uniquely in the abnormal gait group. Conversely, specific AD global cognition tests (ADAS-13) and auditory-verbal memory (RAVLT immediate recall) declined over time independently of gait profile. All the other cognitive tests were not significantly affected by time or by gait profile. In addition, we found that ventricles size increased faster in the abnormal gait group compared to the normal gait group. In terms of prognosis, abnormal gait (HR = 1.7), MMSE (HR = 1.09), and DSST (HR = 1.03) covariates showed a higher impact on AD dementia conversion.ConclusionsThe importance of the link between gait and related cognitive functions in terms of diagnosis, prognosis, and rehabilitation in aMCI is critical. We showed that in aMCI gait abnormalities lead to executive functions/attention deterioration and conversion to AD dementia.

Method of differential diagnosis of mild cognitive impairment of various origins: cross sectional study

INTRODUCTION:The differential diagnosis of mild cognitive impairment (MCI), due to the high prevalence in the population and the rapid increase in incidence, is an urgent task. The most common causes leading to the development of cognitive impairment are Alzheimer’s disease (AD), cerebrovascular pathology, and their combination. AD usually manifests as an amnestic type of mild cognitive impairment (aMCI) at the pre-dementia stage. Subcortical vascular mild cognitive impairment (svMCI) is considered as the prodromal stage of subcortical vascular dementia. According to the results of pathomorphological studies, it was found that subfields of the hippocampal formation are selective vulnerability to AD, ischemia/hypoxia, and aging.Currently, using the FreeSurfer 6.0 software, it is possible to obtain quantitative indicators of the hippocampal formation subfieldsin vivo.The current trend in medicine is the development and implementation of new diagnostic solutions based on artificial intelligence and machine learning. One of the machine learning algorithms is binary logistic regression, which we used in the course of the study for the differential diagnosis of MCI of various origins.OBJECTIVE:To develop a method for the differential diagnosis of mil cognitive impairment of various origins.MATERIALS AND METHODS:The study included patients with the syndrome of mild cognitive impairment who were examined in the department of geriatric psychiatry of the V.M.Bekhterev National Medical Research Center for Psychiatry and Neurology, from which two groups were formed: group 1 — patients with aMCI, group 2 — patients with svMCI. Conditionally healthy volunteers, comparable in age, sex and level of education, made up the 3rdgroup (control). MRI examination was performed using a Excelart Vantage Atlas XGV magnetic resonance imaging system (Toshiba, Japan) with a magnetic field strength of 1.5 T, followed by MR morphometry of the subfields of the hippocampal formation.Statistics:Statistical analysis was carried out using data that was converted from a database in Microsoft Excel to the statistical package IBM SPSS 21. To develop a differential diagnosis method, based on the data obtained, the binary regression method and ROC analysis were used.RESULTS:Based on the obtained MR-morphometry data, a method was developed using the binary logistic regression equation. The value of p≥0.5 makes it possible to classify the patient to the aMCI group, and the value of p&lt;0.5 — to the svMCI. The sensitivity of the method is 90%, the specificity is 80%, and the accuracy is 85%.DISCUSSION:Using binary logistic regression, the selection of variants of sets of variables (quantitative indicators) was carried out, for which ROC curves were constructed. The selection criterion was the area under the ROC curve — the AUC criterion (Area Under the Curve). The largest area under the curve (AUC=0.824) in the differential diagnosis of aMCI from svMCI was determined for the combination of the volume of the left subiculum and the thickness of the right entorhinal cortex.Since patients in the aMCI group have a significantly lower number of vascular foci than in the svMCI group (p&lt;0.05), at the next stage, one more variable, the volume fraction, was added to the selected combination of two variables (volume of the left subiculum and thickness of the right entorhinal cortex) hypointense foci. When conducting an ROC analysis with a combination of three variables, an increase in AUC to 0.892 was noted. Further, using a combination of three variables and a binary logistic regression equation, a method for differential diagnosis of aMCI from svMCI was developed.CONCLUSION:The method of differential diagnosis based on binary logistic regression using MR morphometry data allows to distinguish patients with aMCI from patients with svMCI with high sensitivity and specificity.

The performance of connected speech is correlated with cognitive tests in very early Alzheimer disease

AbstractBackgroundLanguage impairment is one of the most important clinical features of Alzheimer disease. However, it is very difficult to detect at a very early stage. In this study, we try to analyze the language performance via a set of language tests. At the same time, various biomarkers were also checked and compared.MethodFrom 2020 to 2021, we recruited 11 aMCI and 24 normal controls from the memory clinic of Cardinal Tian Hospital in Taiwan. The clinical diagnosis of aMCI is based on clinical history (with short‐term memory impairment over 6 months), brain MRI, amyloid PET, and various neuropsychological tests. All the participants received the Picture Description Test (PDT), Taiwanese Boston naming test (T‐BNT, 30 items) and semantic fluency test. In PDT, the participants were requested to describe three Taiwanese‐culture‐based pictures to evaluate their performance of connected speech. The responses were recorded, transcribed, and subsequently analyzed by the Chinese tokenize system. Linguistic variables such as the number of total words, content words, grammatically intact sentences, long‐pauses (more than 2s), and fillers were recorded and analyzed.ResultIn our cohort, their baseline MMSE scores are lower (24.1 ± 3.1 V.S. 28.9 ± 1.6, p&lt;0.001). The amyloid positive rate is significantly higher in the aMCI group (54.8% V.S. 29.1%, p = 0.05). The scores of T‐BNT (19.9 ± 4.7 V.S. 24.9 ± 2.3, p&lt;0.001) and semantic fluency test (9.7 ± 3.8 V.S. 17.3 ± 4.0, p&lt;0.001) were significantly lower in the aMCI group. The multiple regression model showed that the variable of non‐repeated words and numbers of long‐pauses were significant in the final model and explained of 32% the total variance of the MMSE (R2 = 0.32; p&lt;0.01).ConclusionEven in the MCI stage, the language‐based examinations can be very different from the normal controls. More non‐repeated words and less long‐pauses in connected speech predict better cognitive performance. Combining with biomarkers, these linguistic variables may detect AD at the earlier stage.

0906 Feasibility of measures of sleep, circadian timing and lipid profiles for identifying mild cognitive impairment

Abstract Introduction Sleep disruption and circadian misalignment has been evident in patients with mild cognitive impairment (MCI). Together, sleep and circadian rhythm dysregulation can contribute to dyslipidemia, which has been proposed to play crucial roles in neurodegenerative process according to its known neurotoxicity. We aimed to compare parameters regarding lipid, nocturnal sleep and circadian timing between amnestic MCI (aMCI) and normal controls (NC), and to determine whether those parameters can be associated with the presence of aMCI in the elderly. Methods Participants above 50 years old were recruited from the Dementia Clinic at Kangwon National University Hospital, and two Public Care Centers for dementia patients. Neurocognitive tests were administered to each participant. The diagnosis of aMCI was made according to Petersen’s criteria. Eighteen aMCI patients (76.6 ±6.1 years) and 21 NC (70.4±6.7years) were finally selected. Actigraphy monitoring (Actiwatch 2; Respironics, USA) was conducted for 5 days. The dim light melatonin onset (DLMO) as an indicator of circadian timing was determined from five hourly saliva samples obtained before sleep onset. Fasting plasma total cholesterol, triglycerides, high density lipoprotein cholesterol, and low density lipoprotein cholesterol were assayed. The sleep parameters, DLMO and lipid profiles were compared between the two groups. For these measures, receiver operating characteristic (ROC) analysis was performed to assess the usefulness of discriminating between the aMCI and NC. Results There were no significant differences in sleep parameters, DLMO and lipid profiles between the aMCI and NC groups. The bedtime and sleep onset were derived as indicators for discriminating aMCI (p=0.003 and p=0.054, respectively). The area under the ROC for bedtime was 0.861(cut-off scores ≥ 22:02, sensitivity= 0.76, specificity=0.75). Conclusion We did not reveal the usefulness of the DLMO and lipid profiles for identifying aMCI. However, we found that the bedtime had good discrimination power between aMCI and cognitively normal elderly, suggesting later sleep habits would be associated with the presence of aMCI in the elderly. Support (if any) Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science and ICT (2020R1F1A1050416); Research Funds from the Institute of Medical Science, Chosun University, Republic of Korea, 2022.

Brain Volumes and Metacognitive Deficits in Knowledge of Self, Task and Strategies in Mathematics: A Preliminary Pilot One-Year Longitudinal Study in aMCI Patients Compared to Healthy Controls.

Metacognitive knowledge has been little investigated in aMCI patients. The aim of this study is to examine whether there are specific deficits in knowledge of self, task and strategies in mathematical cognition, due its importance for everyday functioning, mainly due to its importance for financial capacity in old age. A total of 24 patients with a diagnosis of aMCI and one-to-one 24 matched individuals (similar age, education and gender) were examined at three time points in a year with a number of neuropsychological tests and a slightly modified version of the Metacognitive Knowledge in Mathematics Questionnaire (MKMQ). We analyzed longitudinal MRI data regarding various brain areas for the aMCI patients. Results indicated that the aMCI group differed in all MKMQ subscale scores at the three time points compared to healthy controls. Correlations were found only for metacognitive avoidance strategies and left and right amygdala volumes at baseline, while after twelve months correlations were found for avoidance and right and left parahippocampal volumes. These preliminary results highlight the role of specific brain regions that could be used as indices in clinical practice for the detection of metacognitive knowledge deficits that are found in aMCI.

Diagnostic Performance of a Tablet Computer-Based Cognitive Screening Test for Identification of Amnestic Mild Cognitive Impairment.

Early and appropriate diagnosis of amnestic mild cognitive impairment (aMCI) is clinically important because aMCI is considered the prodromal stage of dementia caused by Alzheimer's disease (AD). aMCI is assessed using the comprehensive neuropsychological (NP) battery, but it is rater-dependent and does not provide quick results. Thus, we investigated the performance of the computerized cognitive screening test (Inbrain Cognitive Screening Test; Inbrain CST) in the diagnosis of aMCI and compared its performance to that of the Korean version of the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) test (CERAD-K), a comprehensive and pencil-and-paper NP test. A total of 166 participants were included in this cross-sectional study. The participants were recruited as part of a prospective, community-based cohort study for MCI (PREcision medicine platform for mild cognitive impairment on multi-omics, imaging, evidence-based R&amp;BD; PREMIER). All participants were assessed using the CERAD-K and the Inbrain CST. The Inbrain CST comprised seven subtests that assessed the following five cognitive domains: attention, language, visuospatial, memory, and executive functions. Seventy-six participants underwent brain magnetic resonance imaging and [18F]-flutemetamol positron emission tomography (PET). We evaluated the diagnostic performance of the Inbrain CST for the identification of aMCI by comparing the findings with those of CERAD-K. We also determined the characteristics of aMCI patients as defined by the CERAD-K and Inbrain CST. Of the 166 participants, 93 were diagnosed with aMCI, while 73 were cognitively unimpaired. The sensitivity of the Inbrain CST for aMCI diagnosis was 81.7%, and its specificity was 84.9%. Positive and negative predictive values were 87.4% and 78.5%, respectively. The diagnostic accuracy was 83.1%, and the error rate was 16.9%. Demographic and clinical characteristics between individuals with aMCI defined by the Inbrain CST and CERAD-K were not significantly different. The frequency of positive amyloid PET scan, the hippocampal/parahippocampal volumes, and AD signature cortical thickness did not differ between the patients with aMCI defined by CERAD-K and those with aMCI defined by the Inbrain CST. The Inbrain CST showed sufficient sensitivity, specificity, and positive and negative predictive values for diagnosing objective memory impairment in aMCI. In addition, aMCI patients identified by CERAD-K and the Inbrain CST showed comparable clinical and neuroimaging characteristics. Therefore, the Inbrain CST can be considered an alternative test to supplement the limitations of existing pencil-and-paper NP tests.

Diagnostic accuracy of the Stricker Learning Span and Mayo Test Drive Composite for amnestic Mild Cognitive Impairment.

AbstractBackgroundRemote assessment tools offer significant promise for aiding early detection of cognitive impairment. Mayo Test Drive (MTD): Test Development through Rapid Iteration, Validation and Expansion, is a web‐based platform for remote self‐administered assessment that includes a computer adaptive word list memory test (Stricker Learning Span; SLS) and a measure of processing speed (Symbols Test). We examined the diagnostic accuracy of the SLS and a MTD composite (SLS max span, SLS trials 1–5 total correct, SLS delay correct, [Symbols correct item response time*‐1]) for amnestic mild cognitive impairment (aMCI). We also explored diagnostic accuracy for a broader group that included individuals with possible MCI (pMCI; see Figure 1).MethodsParticipants were recruited from the Mayo Clinic Study of Aging for this ancillary remote study. 226 were cognitively unimpaired (CU; concordant CU diagnosis by 3 independent raters). Fifty‐six participants had possible MCI (at least 1 of 3 raters indicated MCI) and 16 had a consensus diagnosis of aMCI. Primary outcome variables were SLS sum of trials, AVLT sum of trials, Symbols correct items response time, and MTD composite. Mean difference analyses used linear model ANOVAs (alpha = .05). Receiver operating characteristic (ROC) curves were applied; we derived optimal cutoff scores based on the Youden index method.ResultsBoth aMCI and possible MCI groups showed significantly lower performance than the CU group on SLS (Hedge’s g aMCI = ‐1.72, pMCI = ‐1.04), Symbols (Hedge’s g aMCI = 1.38, pMCI = 0.64), and MTD composite (Hedge’s g aMCI = ‐1.89, pMCI = ‐1.09); see Tables 1 and 2 (all p‘s&lt;.01 even when additionally covarying age, education, sex). Total area under the curve was high for differentiating of CU vs. aMCI (MTD Composite = 0.91, SLS = 0.91) and acceptable for CU vs. pMCI (MTD Composite = 0.77, SLS = 0.77); see Table 2. Consistent with our flexible platform, a variety of device types were used to complete remote testing (32% smartphone, 12% tablet, 56% PC).ConclusionsMTD and the SLS show high diagnostic accuracy for aMCI. MTD is a flexible, brief, and easy‐to‐use remote cognitive assessment tool with great potential for scalable use in future studies seeking to maximize inclusion of individuals with aMCI in clinical trials or for cognitive screening in clinical settings.

Brain Microstructural Changes in Patients with Amnestic mild Cognitive Impairment : Detected by Neurite Orientation Dispersion and Density Imaging (NODDI) Combined with Machine Learning.

This study investigated brain microstructural changes in patients with amnestic mild cognitive impairment (aMCI) by retrospectively analyzing neurite orientation dispersion and density imaging (NODDI) data with machine learning algorithms. A total of 26 aMCI patients and 24 healthy controls (HC) underwent NODDI magnetic resonance imaging (MRI) examinations. The NODDI parameters including neurite density index (NDI), orientation dispersion index (ODI), and volume fraction of isotropic water molecules (Viso) were estimated. Machine learning algorithms such as K‑nearest neighbor (KNN), logistic regression (LR), random forest (RF), and support vector machine (SVM) were used to evaluate the diagnostic efficacy of NODDI parameters in predicting aMCI. The differences in the NODDI parameter values between the aMCI and HC groups were analyzed using the independent sample t‑test, False discovery rate (FDR) correction was used for multiple testing. After adjusting for age, sex, and educational years, partial correlation analysis was used to evaluate the relationship between NODDI parameters and clinical cognitive status of aMCI patients. The NDI, ODI, and Viso values of white matter (WM) and gray matter (GM) structure templates combined with the KNN, LR, RF and SVM machine learning algorithms accomplished the discrimination between aMCI and HC groups. The NDI and ODI values decreased (pvalue range, < 0.001-0.042) and Viso values increased (pvalue range, < 0.001-0.043) in the aMCI group compared to the HCs. The NDI, ODI, and Viso values of the WM and GM structure templates with significant differences were significantly correlated with mini-mental state examination (MMSE) and Montreal cognitive assessment (MoCA) scores. NODDI combined with machine learning algorithms is apromising strategy for early diagnosis of aMCI. Moreover, NODDI parameters correlated with the clinical cognitive status of aMCI patients.

Developing a Danish version of the LASSI-L test – reliability and predictive value in patients with mild cognitive impairment, mild dementia due to AD and subjective cognitive decline

ABSTRACT Tests measuring proactive semantic interference as The Loewenstein-Acevedo Scales for Semantic Interference and Learning (LASSI-L), has shown promising diagnostic properties for the diagnosis of Mild Cognitive Impairment (MCI) and dementia. LASSI-L may also be efficient in predicting cognitive decline in at-risk individuals. There is an unmet need to examine the diagnostic properties of the LASSI-L in a Danish context where traditional neuropsychological tests are typically applied when diagnosing possible dementia disorders. To investigate the reliability, convergent validity, and predictive value of the new Danish LASSI-L version in aMCI and mild dementia due to Alzheimer’s disease (AD). From a memory clinic we included 17 aMCI patients, 15 patients with mild dementia (AD), 17 patients with Subjective Cognitive Decline (SCD), and 30 healthy controls. Neuropsychological assessment was applied in all patients, and biomarker analyses were performed for patients with aMCI and mild AD. Cronbach’s alpha was 0.94. Patients with aMCI and mild dementia differed significantly from healthy controls on all LASSI-L measures. ROC analyses showed a very high AUC value for both patients with aMCI [0.85–0.97] and mild dementia [0.93–0.99]. SCD patients generally did not differ from controls, except for significantly lower scores on one item (Cued Recall A1) LASSI-L had high reliability and promising predictive value in the diagnosis of aMCI and mild AD due to AD. SCD patients diagnosed in a memory clinic did not differ significantly from healthy on the LASSI-L.

Machine Learning Model to Predict Diagnosis of Mild Cognitive Impairment by Using Radiomic and Amyloid Brain PET.

The study aimed to develop a deep learning model for predicting amnestic mild cognitive impairment (aMCI) diagnosis using radiomic features and amyloid brain PET. Subjects (n = 328) from the Alzheimer's Disease Neuroimaging Initiative database and the EudraCT 2015-001184-39 trial (159 males, 169 females), with a mean age of 72 ± 7.4 years, underwent PET/CT with 18 F-florbetaben. The study cohort consisted of normal controls (n = 149) and subjects with aMCI (n = 179). Thirteen gray-level run-length matrix radiomic features and amyloid loads were extracted from 27 cortical brain areas. The least absolute shrinkage and selection operator regression was used to select features with the highest predictive value. A feed-forward neural multilayer network was trained, validated, and tested on 70%, 15%, and 15% of the sample, respectively. Accuracy, precision, F1-score, and area under the curve were used to assess model performance. SUV performance in predicting the diagnosis of aMCI was also assessed and compared with that obtained from the machine learning model. The machine learning model achieved an area under the receiver operating characteristic curve of 90% (95% confidence interval, 89.4-90.4) on the test set, with 80% and 78% for accuracy and F1-score, respectively. The deep learning model outperformed SUV performance (area under the curve, 71%; 95% confidence interval, 69.7-71.4; 57% accuracy, 48% F1-score). Using radiomic and amyloid PET load, the machine learning model identified MCI subjects with 84% specificity at 81% sensitivity. These findings show that a deep learning algorithm based on radiomic data and amyloid load obtained from brain PET images improves the prediction of MCI diagnosis compared with SUV alone.

Diminishing accelerated long-term forgetting in mild cognitive impairment: Study protocol for a prospective, double-blind, placebo-controlled, randomized controlled trial

BackgroundHarnessing the lifelong potential of the human brain for neuroplasticity may serve to maintain the viability of neural structures and postpone the onset of cognitive decline. The absence of effective pharmacological interventions to counter memory decline has encouraged scientists to test the possibility that noninvasive electrical stimulation may serve as an additional tool to improve memory abilities.Previous research showed that electrical stimulation of the greater occipital nerve enhances memory recall performance in young and older healthy subjects. This study aims to extend these findings to determine the effect of transcutaneous electrical stimulation of the greater occipital nerve on the improvement of episodic memory in individuals with amnestic Mild Cognitive Impairment (aMCI). Methods/designThis study is a prospective, double-blind, placebo-controlled, randomized parallel-group study. A total of 100 individuals with a diagnosis of aMCI according to NIA/AA will be recruited. Participants will be randomly assigned to one of four groups. One group will receive active non-invasive transcutaneous electrical stimulation of greater occipital nerve (NITESGON), while three groups will serve as controls (i.e., sham NITESGON, active NITESGON with local anesthesia, and active NITESGON on the C5/C6 nerve). The primary outcome, i.e., memory recall, will be determined by a word association task, and will be recorded at baseline, 7 days after NITESGON, and 28 days after NITESGON. The secondary outcome is neurophysiological changes determined by resting state EEG and will be assessed immediately before and after NITESGON. DiscussionThe results will add new insights into improving episodic memory in individuals with aMCI. Trial registration#NCT05289804 (clinicaltrial.gov) Protocol approval id#SPREC102021-23 (Ethics Committee at Trinity College Dublin, School of Psychology)

Comparison of dynamic susceptibility contrast enhanced MR and FDG-PET brain studies in patients with Alzheimer’s disease and amnestic mild cognitive impairment

BackgroundThe aim of this study was to compare Dynamic Susceptibility Contrast Enhanced MRI (DSC-MRI) and PET with [18F]flurodeoxyglucose (FDG-PET) in the diagnosis of Alzheimer’s Disease (AD) and amnestic Mild Cognitive Impairment (aMCI).MethodsTwenty-seven age-and sex-matched patients with AD, 39 with aMCI and 16 controls underwent brain DSC-MRI followed by FDG-PET. Values of relative Cerebral Blood Volume (rCBV) and rCBV z-scores from frontal, temporal, parietal and PCG cortices were correlated with the rate of glucose metabolism from PET. Sensitivity, specificity and accuracy of DSC-MRI and FDG-PET in the diagnosis of AD and aMCI were assessed and compared.ResultsIn AD, hypoperfusion was found within all the examined locations, while in aMCI in both parietal and temporal cortices and left PCG. FDG-PET showed the greatest hypometabolism in parietal, temporal and left PCG regions in both AD and aMCI. FDG-PET was more accurate in distinguishing aMCI from the controls than DSC-MRI. In the AD and combined group (AD + aMCI) there were numerous correlations between DSC-MRI and FDG-PET results.ConclusionsIn AD the patterns of hypoperfusion and glucose hypometabolism are similar, thus DSC-MRI may be a competitive method to FDG-PET. FDG-PET is a more accurate method in the diagnosis of aMCI.