Mild Cognitive Impairment Subtypes Research Articles

Latent Class Analysis of Sleep in Mild Cognitive Impairment Patients and its Influencing Factors

Background: Individuals with mild cognitive impairment (MCI) frequently experience sleep disorders, which may elevate the risk of developing Alzheimer’s disease. Yet, sleep types in MCI patients and the factors influencing them have not been sufficiently investigated. Objective: The objective of this study was to explore potential sleep typing and its influencing factors in patients with MCI using latent class analysis. Methods: A cross-sectional survey was conducted in Jiangsu Province, China. Cognitive function in older adults was assessed using neuropsychological tests, including the Montreal Cognitive Assessment Scale-Beijing version (MoCA), the Mini-Mental State Examination (MMSE), the Activities of Daily Living Scale (ADL), and the Clinical Dementia Rating Scale (CDR). Sleep quality was evaluated using the Pittsburgh Sleep Quality Index (PSQI). Latent class analysis based on PSQI scores and multinomial logistic regression analyses were employed to explore the influencing factors of sleep typing. Results: The study included a total of 611 patients with MCI. Latent class analysis identified three latent classes to categorize the sleep patterns of MCI patients: the good sleep type (56.6%), the insufficient sleep type (29.6%), and the difficulty falling asleep type (13.7%). Potential sleep typing is influenced by gender, chronic disease, physical exercise, social activity, brain exercise, smoking, frailty, subjective cognitive status, and global cognitive function. Conclusions: The findings of this study underscore the notable heterogeneity in the sleep patterns of patients with MCI. Future research may provide targeted prevention and interventions to address the characteristics and influencing factors of patients with different subtypes of sleep MCI.

Data-driven classification of cognitively normal and mild cognitive impairment subtypes predicts progression in the NACC dataset.

Data-driven neuropsychological methods can identify mild cognitive impairment (MCI) subtypes with stronger associations to dementia risk factors than conventional diagnostic methods. Cluster analysis used neuropsychological data from participants without dementia (mean age=71.6 years) in the National Alzheimer's Coordinating Center (NACC) Uniform Data Set (n=26,255) and the "normal cognition" subsample (n=16,005). Survival analyses examined MCI or dementia progression. Five clusters were identified: "Optimal" cognitively normal (oCN; 13.2%), "Typical" CN (tCN; 28.0%), Amnestic MCI (aMCI; 25.3%), Mixed MCI-Mild (mMCI-Mild; 20.4%), and Mixed MCI-Severe (mMCI-Severe; 13.0%). Progression to dementia differed across clusters (oCN<tCN<aMCI<mMCI-Mild<mMCI-Severe). Cluster analysis identified more MCI cases than consensus diagnosis. In the "normal cognition" subsample, five clusters emerged: High-All Domains (High-All; 16.7%), Low-Attention/Working Memory (Low-WM; 22.1%), Low-Memory (36.3%), Amnestic MCI (16.7%), and Non-amnestic MCI (naMCI; 8.3%), with differing progression rates (High-All<Low-WM=Low-Memory<aMCI<naMCI). Our data-driven methods outperformed consensus diagnosis by providing more precise information about progression risk and revealing heterogeneity in cognition and progression risk within the NACC "normal cognition" group.

Medication management error types: Associations with mild cognitive impairment subtype

Objective: Medication management errors are suspected to be prevalent among older adults with mild cognitive impairment (MCI). This study examined types of simulated medication-taking errors in cognitively normal older adults (CN; n = 131), single domain amnestic MCI (sdMCI, n = 91), and multi-domain MCI (mdMCI, n = 44). Errors were measured using the medication management ability assessment (MMAA). Methods: 266 participants seen for neuropsychological evaluation (94.4% White, 57.9% female, average age = 72, average education = 14 years) completed the MMAA (version 4.1), a performance-based task of medication management. Group differences in MMAA total scores, accuracy, and error types were evaluated using Kruskall-Wallis H tests. This study was the first to explore a newly operationalized error, perseverations, caused by taking a specific dose ≥2 times during the simulation. Results: CN and sdMCI groups had higher MMAA total scores than individuals with mdMCI, indicating better overall performance. The mdMCI group made a higher number of omission errors (missed pills) than other groups, but no differences were found for commission errors (extra pills). The sdMCI group made more perseverative errors compared to the CN group. Conclusions: Individuals with mdMCI made more simulated medication management errors than CN and sdMCI groups, indicating that they may be most vulnerable to difficulties in medication management. In contrast, sdMCI individuals were more likely to make perseverative errors, which may reflect a tendency towards overcompensation of memory loss. Future studies should assess whether MMAA performance is associated with patterns of real-world medication-taking in more diverse samples of older adults.

Shared and Specific Changes of Cortico-Striatal Functional Connectivity in Stable Mild Cognitive Impairment and Progressive Mild Cognitive Impairment.

Mild cognitive impairment (MCI), the prodromal stage of Alzheimer's disease, has two distinct subtypes: stable MCI (sMCI) and progressive MCI (pMCI). Early identification of the two subtypes has important clinical significance. We aimed to compare the cortico-striatal functional connectivity (FC) differences between the two subtypes of MCI and enhance the accuracy of differential diagnosis between sMCI and pMCI. We collected resting-state fMRI data from 31 pMCI patients, 41 sMCI patients, and 81 healthy controls. We chose six pairs of seed regions, including the ventral striatum inferior, ventral striatum superior, dorsal-caudal putamen, dorsal-rostral putamen, dorsal caudate, and ventral-rostral putamen and analyzed the differences in cortico-striatal FC among the three groups, additionally, the relationship between the altered FC within the MCI subtypes and cognitive function was examined. Compared to sMCI, the pMCI patients exhibited decreased FC between the left dorsal-rostral putamen and right middle temporal gyrus, the right dorsal caudate and right inferior temporal gyrus, and the left dorsal-rostral putamen and left superior frontal gyrus. Additionally, the altered FC between the right inferior temporal gyrus and right putamen was significantly associated with episodic memory and executive function. Our study revealed common and distinct cortico-striatal FC changes in sMCIs and pMCI across different seeds; these changes were associated with cognitive function. These findings can help us understand the underlying pathophysiological mechanisms of MCI and distinguish pMCI and sMCI in the early stage potentially.

Strategic Lacunes Associated With Mild Cognitive Impairment in Rural Chinese Older Adults: A Population-Based Study.

Lacunes are associated with cognitive impairment. We sought to identify strategic lacune locations associated with mild cognitive impairment (MCI) and subtypes of MCI among older adults, and further to examine the role of white matter hyperintensities and perivascular spaces in the association. This population-based cross-sectional study included 1230 dementia-free participants in the brain magnetic resonance imaging substudy (2018-2020) in MIND-China (Multimodal Interventions to Delay Dementia and Disability in Rural China). Lacunes were visually identified in frontal lobe, parieto-occipital lobe, temporal lobe, insula, basal ganglia, thalamus, cerebellum, and brainstem. MCI, amnestic MCI (aMCI), and nonamnestic MCI (naMCI) were defined following the Petersen's criteria. Data were analyzed using logistic regression models. Of the 1230 participants (age, ≥60 years; mean age, 69.40; SD, 4.30 years; 58.5% women), lacunes were detected in 357 people and MCI was defined in 286 individuals, including 243 with aMCI and 43 with naMCI. Lacunes in the supratentorial area, internal capsula, putamen/pallidum, and insula was significantly associated with increased odds ratio of MCI (multivariable-adjusted odds ratio ranged 1.40-3.21; P<0.05) and aMCI (multivariable-adjusted odds ratio ranged 1.46-3.36; P<0.05), whereas lacunes in the infratentorial area and brainstem were significantly associated with naMCI (multivariable-adjusted odds ratio ranged 2.68-3.46; P<0.01). Furthermore, the associations of lacunes in insula and internal capsula with MCI and aMCI, as well as the associations of lacunes in infratentorial area and brainstem with naMCI were present independent of white matter hyperintensities volume and perivascular spaces number. Lacunes in the internal capsula, putamen/pallidum, insula, and brainstem may represent the strategic lacunes that are independently associated with MCI, aMCI, or naMCI in Chinese older adults. URL: https://www.chictr.org.cn; Unique identifier: ChiCTR1800017758.

A radiomics-based brain network in T1 images: construction, attributes, and applications.

T1 image is a widely collected imaging sequence in various neuroimaging datasets, but it is rarely used to construct an individual-level brain network. In this study, a novel individualized radiomics-based structural similarity network was proposed from T1 images. In detail, it used voxel-based morphometry to obtain the preprocessed gray matter images, and radiomic features were then extracted on each region of interest in Brainnetome atlas, and an individualized radiomics-based structural similarity network was finally built using the correlational values of radiomic features between any pair of regions of interest. After that, the network characteristics of individualized radiomics-based structural similarity network were assessed, including graph theory attributes, test-retest reliability, and individual identification ability (fingerprinting). At last, two representative applications for individualized radiomics-based structural similarity network, namely mild cognitive impairment subtype discrimination and fluid intelligence prediction, were exemplified and compared with some other networks on large open-source datasets. The results revealed that the individualized radiomics-based structural similarity network displays remarkable network characteristics and exhibits advantageous performances in mild cognitive impairment subtype discrimination and fluid intelligence prediction. In summary, the individualized radiomics-based structural similarity network provides a distinctive, reliable, and informative individualized structural brain network, which can be combined with other networks such as resting-state functional connectivity for various phenotypic and clinical applications.

Longitudinal trajectory effects of different MCI subtypes on general cognitive and daily functions in a population-based cohort of older adults

ObjectivesTo identify different mild cognitive impairment (MCI) phenotypes based on substantial relative impairment in specific cognitive domains and then characterize the complex process of general cognitive and daily functions over time in older adults with these MCI subtypes. MethodsA total of 1020 participants with MCI at baseline from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were recruited. MCI subtypes were obtained based on neuropsychological tests in five cognitive domains: memory (M), visuospatial function (V), language (L), processing speed (P), and executive function (E). General cognitive function and daily function were measured by the Mini-Mental State Examination (MMSE) and the Functional Assessment Questionnaire (FAQ), respectively. Linear mixed models were fitted to curve their trajectories across different MCI subtypes. ResultsConsidering visuospatial function, subtypes were MO (memory impaired only), M&V (memory and visuospatial function impaired) and M&nV (memory impaired and visuospatial function non-impaired). Similar subtypes and naming rules were obtained based on language, executive function, and processing speed. Further, depending on the number of relative impaired cognitive domains M&S and M&M were obtained. Participants with MO had the highest prevalence in the sample (53.4 %), followed by M&nV (31.1 %). Participants with M&V had the highest mean age (74.69 years) at baseline and the greatest dementia conversion rate (53.2 %). The MMSE and FAQ score trajectories changed most slowly in participants with MO while fastest in those with M&V. Obvious different trajectories of both MMSE and FAQ scores were observed across different subtypes based on visuospatial function and executive function. ConclusionCompared to MO, individuals with multi-dimensional cognitive impairment have worse general cognitive and daily functions, especially for those with M&V.

Differential diagnosis of mild cognitive impairment of Alzheimer's disease by Simoa p-tau181 measurements with matching plasma and CSF.

Alzheimer's disease (AD) is characterized by a long preclinical phase. Although late-stage AD/dementia may be robustly differentiated from cognitively normal individuals by means of a clinical evaluation, PET imaging, and established biofluid biomarkers, disease differentiation between cognitively normal and various subtypes of mild cognitive impairment (MCI) remains a challenging task. Differential biomarkers for early-stage AD diagnosis with accessible biofluid samples are urgently needed. Misfolded phosphorylated tau aggregates (p-tau) are present in multiple neurodegenerative diseases known as "tauopathies", with the most common being AD. P-tau181 is a well-established p-tau biomarker to differentiate AD dementia from non-AD pathology. However, it is unclear if p-tau181 is capable of diagnosing MCI, an early AD stage, from cognitively normal subjects, or if it can discriminate MCI subtypes amnestic MCI (aMCI) from non-amnestic MCI (naMCI). Here we evaluated the capability of p-tau181 in diagnosing MCI from cognitively normal subjects and discriminating aMCI from naMCI subtypes. We collected matching plasma and CSF samples of a clinically diagnosed cohort of 35 cognitively normal, 34 aMCI, 17 naMCI, and 31 AD dementia cases (total 117 participants) with supplemental CSF Aβ42 and total tau AD biomarker levels and performed Simoa p-tau181 assays. The diagnostic capabilities of Simoa p-tau181 assays to differentiate these cohorts were evaluated. We found (i) p-tau181 can robustly differentiate MCI or aMCI from cognitively normal cohorts with matching plasma and CSF samples, but such differentiation is weaker in diagnosing naMCI from cognitively normal groups, (ii) p-tau181 is not capable of differentiating aMCI from naMCI cohorts, and (iii) either factor of Aβ or total tau burden markedly improved differentiation power to diagnose aMCI from cognitively normal group. Plasma and CSF p-tau181 levels may serve as a promising biomarker for diagnosing aMCI from normal controls in the preclinical phase. But more robust new biomarkers are needed to differentiate naMCI from cognitively normal cases or to discriminate between MCI subtypes, aMCI from naMCI.

HOPE: Hybrid-Granularity Ordinal Prototype Learning for Progression Prediction of Mild Cognitive Impairment.

Mild cognitive impairment (MCI) is often at high risk of progression to Alzheimer's disease (AD). Existing works to identify the progressive MCI (pMCI) typically require MCI subtype labels, pMCI vs. stable MCI (sMCI), determined by whether or not an MCI patient will progress to AD after a long follow-up. However, prospectively acquiring MCI subtype data is time-consuming and resource-intensive; the resultant small datasets could lead to severe overfitting and difficulty in extracting discriminative information. Inspired by that various longitudinal biomarkers and cognitive measurements present an ordinal pathway on AD progression, we propose a novel Hybrid-granularity Ordinal PrototypE learning (HOPE) method to characterize AD ordinal progression for MCI progression prediction. First, HOPE learns an ordinal metric space that enables progression prediction by prototype comparison. Second, HOPE leverages a novel hybrid-granularity ordinal loss to learn the ordinal nature of AD via effectively integrating instance-to-instance ordinality, instance-to-class compactness, and class-to-class separation. Third, to make the prototype learning more stable, HOPE employs an exponential moving average strategy to learn the global prototypes of NC and AD dynamically. Experimental results on the internal ADNI and the external NACC datasets demonstrate the superiority of the proposed HOPE over existing state-of-the-art methods as well as its interpretability. Source code is made available at https://github.com/thibault-wch/HOPE-for-mild-cognitive-impairment.

Free Water MR Imaging of White Matter Microstructural Changes is a Sensitive Marker of Amyloid Positivity in Alzheimer's Disease

BackgroundExtracellular free water (FW) resulting from white matter degeneration limits the sensitivity of diffusion tensor imaging (DTI) in predicting Alzheimer's disease (AD).PurposeTo evaluate the sensitivity of FW‐DTI in detecting white matter microstructural changes in AD. To validate the effectiveness of FW‐DTI indices to predict amyloid‐beta (Aβ) positivity in mild cognitive impairment (MCI) subtypes.Study TypeRetrospective.PopulationThirty‐eight Aβ‐negative cognitively healthy (CH) controls (68.74 ± 8.28 years old, 55% female), 15 Aβ‐negative MCI patients (MCI‐n) (68.87 ± 8.83 years old, 60% female), 29 Aβ‐positive MCI patients (MCI‐p) (73.03 ± 7.05 years old, 52% female), and 29 Aβ‐positive AD patients (72.93 ± 9.11 years old, 55% female).Field Strength/Sequence3.0T; DTI, T1‐weighted, T2‐weighted, T2 star‐weighted angiography, and Aβ PET (18F‐florbetaben or 11C‐PIB).AssessmentFW‐corrected and standard diffusion indices were analyzed using trace‐based spatial statistics. Area under the curve (AUC) in distinguishing MCI subtypes were compared using support vector machine (SVM).Statistical TestsChi‐squared test, one‐way analysis of covariance, general linear regression analyses, nonparametric permutation tests, partial Pearson's correlation, receiver operating characteristic curve analysis, and linear SVM. A P value &lt;0.05 was considered statistically significant.ResultsCompared with CH/MCI‐n/MCI‐p, AD showed significant change in tissue compartment indices of FW‐DTI. No difference was found in the FW index among pair‐wise group comparisons (the minimum FWE‐corrected P = 0.114). There was a significant association between FW‐DTI indices and memory and visuospatial function. The SVM classifier with tissue radial diffusivity as an input feature had the best classification performance of MCI subtypes (AUC = 0.91), and the classifying accuracy of FW‐DTI was all over 89.89%.Data ConclusionFW‐DTI indices prove to be potential biomarkers of AD. The classification of MCI subtypes based on SVM and FW‐DTI indices has good accuracy and could help early diagnosis.Evidence Level4Technical EfficacyStage 2

Early intervention and adding effective doses of EGb761 like Ginkgo extract slow down dementia progression: insights to the neurovascular unit.

Dementia is a progressive neurodegenerative disorder characterized by cognitive decline, memory impairment, and functional deterioration. Pharmacological interventions play a crucial role in managing dementia symptoms and potentially slowing down disease progression. This study aimed to investigate the impact of pharmacological interventions, including acetylcholinesterase inhibitors (AChEIs), memantine, and Gingko extract, on the progression of dementia, with a specific focus on mild cognitive impairment (MCI), Alzheimer's disease (AD), and non-Alzheimer dementias. A total of 547 participants out of 3,547 cases in a specific dataset followed by the same author, including healthy controls, individuals with MCI, AD, and non-Alzheimer dementias, were included in this study. The follow-up duration was up to 211 months, allowing for a minimum 3 visits comprehensive assessment of disease progression. The treatment approaches included AChEIs, memantine, and combination therapy, with variations in the starting time for these treatments based on the dementia type. The use of AChEIs and memantine showed efficacy in improving cognitive function and overall function in individuals with MCI, AD, and non-AD dementias. Combination therapy EGb761 like Gingko extract with AChEIs and/or Memantine demonstrated a slower progression compared to AChEIs alone in individuals with prodromal dementia (MCI) and AD. The starting time for memantine and combination therapy was earlier in non-AD dementia cases compared to AD dementia cases and prodromal dementia. Pharmacological interventions, particularly the use of AChEIs and memantine, can have a positive impact on cognitive function and overall function in individuals with dementia. The combination of AChEIs with EGb761 like Gingko extract may provide additional benefits in slowing down disease progression in AD cases. Early recognition and accurate classification of MCI subtypes are crucial, and the use of EGb761 like Gingko extract is recommended for symptomatic treatment. Future personalized risk predictions based on biomarker constellations may further enhance the multi-target treatment approaches of MCI and different dementia types.

Theory of mind in mild cognitive impairment and Parkinson’s disease: The role of memory impairment

BackgroundSocial cognition is impaired in Parkinson’s disease (PD). Whether social cognitive impairment (iSC) is a by-product of the underlying cognitive deficits in PD or a process independent of cognitive status is unknown. To this end, the present study was designed to investigate the weight of specific cognitive deficits in social cognition, considering different mild cognitive impairment subtypes of PD (PD-MCI).MethodsFifty-eight PD patients underwent a neuropsychological battery assessing executive functions, memory, language, and visuospatial domains, together with social cognitive tests focused on theory of mind (ToM). Patients were divided into subgroups according to their clinical cognitive status: amnestic PD-MCI (PD-aMCI, n = 18), non-amnestic PD-MCI (PD-naMCI, n = 16), and cognitively unimpaired (PD-CU, n = 24). Composite scores for cognitive and social domains were computed to perform mediation analyses.ResultsMemory and language impairments mediated the effect of executive functioning in social cognitive deficits in PD patients. Dividing by MCI subgroups, iSC occurred more frequently in PD-aMCI (77.8%) than in PD-naMCI (18.8%) and PD-CU (8.3%). Moreover, PD-aMCI performed worse than PD-CU in all social cognitive measures, whereas PD-naMCI performed worse than PD-CU in only one subtype of the affective and cognitive ToM tests.ConclusionsOur findings suggest that ToM impairment in PD can be explained by memory dysfunction that mediates executive control. ToM downsides in the amnesic forms of PD-MCI may suggest that subtle changes in social cognition could partly explain future transitions into dementia. Hence, the evaluation of social cognition in PD is critical to characterize a possible behavioral marker of cognitive decline.

Multimorbidity, cognitive phenotypes, and Alzheimer's disease plasma biomarkers in older adults: A population-based study.

To examine the burden and clusters of multimorbidity in association with mild cognitive impairment (MCI), dementia, and Alzheimer's disease (AD)-related plasma biomarkers among older adults. This population-based study included 5432 participants (age ≥60 years); of these, plasma amyloid beta (Aβ), total tau, and neurofilament light chain (NfL) were measured in a subsample (n=1412). We used hierarchical clustering to generate five multimorbidity clusters from 23 chronic diseases. We diagnosed dementia and MCI following international criteria. Data were analyzed using logistic and linear regression models. The number of chronic diseases was associated with dementia (multivariable-adjusted odds ratio= 1.22; 95% confidence interval [CI]= 1.11 to 1.33), AD (1.13; 1.01 to 1.26), vascular dementia (VaD) (1.44; 1.25 to 1.64), and non-amnestic MCI (1.25; 1.13 to 1.37). Metabolic cluster was associated with VaD and non-amnestic MCI, whereas degenerative ocular cluster was associated with AD (p<0.05). The number of chronic diseases was associated with increased plasma Aβ and NfL (p<0.05). Multimorbidity burden and clusters are differentially associated with subtypes of dementia and MCI and AD-related plasma biomarkers in older adults. We used hierarchical clustering to generate five clusters of multimorbidity. The presence and load of multimorbidity were associated with dementia and mild cognitive impairment. Multimorbidity clusters were differentially associated with subtypes of dementia and Alzheimer's disease plasma biomarkers.

Abnormal functional activation during response inhibition in mild cognitive impairment: an fMRI study

AbstractBackgroundDeclines in response inhibition are observed both typical and atypical aging, i.e., mild cognitive impairment (MCI). Increased functional activation in frontoparietal regions during response inhibition has been interpreted to represent the emergence of a compensatory mechanism in older adulthood. While electrophysiology studies suggest the decline in response inhibition in MCI reflects a similar but amplified process as seen in normal aging this has not been examined thoroughly using functional MRI (fMRI).Method31 cognitively normal older adults (M age = 73.6, 9 male) and 22 adult patients with MCI (M age = 67.2, 14 male) performed a Go/NoGo task during an fMRI scan. Group differences in accuracy and reaction time were examined for Go and NoGo trials. Individual BOLD signal change during task fMRI was examined, then voxel‐wise group comparison was conducted for MCI vs. HC groups, using age, gender, education, and gray matter density as covariates. Permutation‐estimated cluster size at alpha &lt;.05 was defined to correct for errors of multiple comparisons.ResultBehaviorally, the MCI group made significantly more errors during NoGo and Go trials (p&lt;.001, p = .032, respectively), however, average reaction time did not differ significantly between groups. During NoGo trials, greater activation in the anterior default mode network (DMN), right dorsolateral prefrontal cortex (DLPFC), and motor areas was observed for MCI vs. HC, while during go trials, there was greater activation in DMN and right insula (corrected p&lt;0.05). However, when Go vs. NoGo trials were examined, MCI showed more activation in right insula and DLPFC, but less activation in the motor area at trend level (p&lt;0.05, uncorrected). While, there were no significant task performance differences between MCI subtypes (amnestic vs. non‐amnestic), amnestic MCI (aMCI) showed more activation in posterior DMN and dorsal attention area during NoGo trials (corrected p&lt;0.05).ConclusionMCI demonstrated significantly greater activation during both Go and NoGo trials and made significantly more errors in performance during both trial types as well. While the MCI subtypes did not exhibit differences in performance on the task, the aMCI exhibited significantly greater activation in multiple regions. Our findings suggested inefficient DMN suppression during task performance in the MCI cohort, especially in the aMCI subgroup.

Associations between air pollution exposure and empirically derived subtypes of mild cognitive impairment in older women

AbstractBackgroundExposure to air pollution in older adulthood is a novel risk factor for mild cognitive impairment (MCI). MCI is a heterogenous construct with different subtypes likely reflecting different disease etiologies. Understanding if long‐term exposure to air pollution is differentially associated with MCI subtypes may provide insight into the neurotoxic effects of air pollution exposure on the aging brain.MethodParticipants included women from the Women’s Health Initiative Memory Study. Data from 651 women without dementia who screened positive for cognitive impairment on the modified mini‐mental state examination (3MS) and a randomly selected comparison sample of 2,560 women who were cognitively intact based on the 3MS were utilized. Participants were administered the 3MS annually (1996‐2008). Those who screened positive for cognitive impairment on the 3MS completed the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) cognitive battery and the Trailmaking Test parts A and B. Average annual levels of particulate matter of aerodynamic diameter&lt;2.5 (PM2.5) and nitrogen dioxide (NO2) were estimated at the participant’s residence using regionalized national universal kriging models and averaged over the 3‐year period before the cognitive assessment. Latent class structural equation models (SEM) were constructed to identify MCI subtypes. We then examined associations between air pollution exposure and latent class membership using multinomial regression while adjusting for demographic, lifestyle, and clinical confounders.ResultFour significant MCI subtypes were identified (Figure 1): multidomain MCI (n = 147; 23%), amnestic MCI (n = 141; 22%), dysnomic/dysexecutive MCI (n = 200; 31%), and subtle MCI (n = 163; 25%). Women living in locations with higher levels of PM2.5 (per interquartile range [IQR] = 3.93 µg/m3; OR = 1.28; p = .04) or NO2 (per IQR = 9.25 ppb; OR = 1.36; p = .02) were more likely to be classified as having subtle MCI compared to normal comparisons. One IQR increase in NO2 was also associated with increased likelihood of being classified with dysnomic/dysexecutive MCI (OR = 1.38; p = .003) compared to normal comparisons.ConclusionOur study illustrates that long‐term exposure to PM2.5 and NO2 may be associated with specific subtypes of MCI. Our findings provide further evidence that air pollution may exert neurotoxic effects on brain areas underlying executive function and language ability in older women.

Abnormal functional activation during response inhibition in mild cognitive impairment: an fMRI study

AbstractBackgroundDeclines in response inhibition are observed both typical and atypical aging, i.e., mild cognitive impairment (MCI). Increased functional activation in frontoparietal regions during response inhibition has been interpreted to represent the emergence of a compensatory mechanism in older adulthood. While electrophysiology studies suggest the decline in response inhibition in MCI reflects a similar but amplified process as seen in normal aging this has not been examined thoroughly using functional MRI (fMRI).Methods31 cognitively normal older adults (M age = 73.6, 9 male) and 22 adult patients with MCI (M age = 67.2, 14 male) performed a Go/NoGo task during an fMRI scan. Group differences in accuracy and reaction time were examined for Go and NoGo trials. Individual BOLD signal change during task fMRI was examined, then voxel‐wise group comparison was conducted for MCI vs. HC groups, using age, gender, education, and gray matter density as covariates. Permutation‐estimated cluster size at alpha &lt;.05 was defined to correct for errors of multiple comparisons.ResultsBehaviorally, the MCI group made significantly more errors during NoGo and Go trials (p&lt;.001, p = .032, respectively), however, average reaction time did not differ significantly between groups. During NoGo trials, greater activation in the anterior default mode network (DMN), right dorsolateral prefrontal cortex (DLPFC), and motor areas was observed for MCI vs. HC, while during go trials, there was greater activation in DMN and right insula (corrected p&lt;0.05). However, when Go vs. NoGo trials were examined, MCI showed more activation in right insula and DLPFC, but less activation in the motor area at trend level (p&lt;0.05, uncorrected). While, there were no significant task performance differences between MCI subtypes (amnestic vs. non‐amnestic), amnestic MCI (aMCI) showed more activation in posterior DMN and dorsal attention area during NoGo trials (corrected p&lt;0.05).ConclusionMCI demonstrated significantly greater activation during both Go and NoGo trials and made significantly more errors in performance during both trial types as well. While the MCI subtypes did not exhibit differences in performance on the task, the aMCI exhibited significantly greater activation in multiple regions. Our findings suggested inefficient DMN suppression during task performance in the MCI cohort, especially in the aMCI subgroup.

To evaluate the prevalence, subtypes and risk associations of mild cognitive impairment in elderly Indian individuals with type 2 diabetes

AbstractBackgroundDiabetes is an important modifiable risk factor for dementia. The prevalence of mild cognitive impairment (MCI) in type 2 diabetes (T2D) is high (You et al, 2021). This data is largely derived using screening tools (MMSE or MoCA), with little information on MCI subtypes. Evidence from the South Asian region is particularly scarce, where prevalence of diabetes is high, and is seen a decade earlier compared to Caucasians.MethodWe have established a cohort of elders (³ 60 years of age), with and without diabetes, visiting a tertiary care hospital in North India (from 2019, ongoing). The participants underwent an interview to collect information on their demographic profile, risk associations, vascular health, and other comorbidities. Culturally validated neuropsychological battery was used for cognitive evaluation. MoCA was done in a subset of patients.ResultWe recruited 840 participants in the cohort, 683 with T2DM and 157 without T2DM (controls). The mean age of the cohort was 65.3±4.5 years, 65.2% were males. Risk profile is described in Table 1. The cognitive z scores of participants with T2DM ranged from ‐0.25 to +0.44. The worst affected domain was attention, working memory and executive functions (Table 2). Using an actuarial definition of MCI (Bondi MW et al, 2014), the prevalence in participants with T2D was 29.7%. 17.3% had amnestic impairment, 14.8% dysexecutive, 14.2% language, 2.4% visuo‐perceptual and 9.1% multidomain impairment. The cognitive raw scores (by dementia risk stratification) are provided in Table 3. On logistic regression, lower education 3.45(2.09,5.68), depression 2.04(1.26,3.29), poor sleep quality 1.77(1.15,2.71), family income 1.70(1.22,2.38), followed by age 1.05(1.00,1.10) had a significant association with global low cognition.ConclusionRoughly one‐third of elders with T2D visiting tertiary care services in North India have mild cognitive impairment. Amnestic subtype is the most frequent. Attention, working memory and executive functions are worst affected. Enhancing the cognitive reserve, mood and sleep quality may be important targets in prevention of cognitive decline in the elderly population with T2D.

Classifying Memory Clinic Patients Using the Digital Clock Drawing TestTM, Mini‐Mental State Examination, and Philadelphia (repeatable) Verbal Learning Test

AbstractBackgroundDigital cognitive assessments (DCA) enable quick, sensitive, and reliable assessment of neurocognitive operations that underlie dementia and mild cognitive impairment (MCI). We examined how well the DCTclockTM DCA, Mini‐Mental State Examination (MMSE), and Philadelphia (repeatable) Veral Learning Test (PrVLT) can classify memory clinic patients by cognitive impairment profile.Method106 serial referrals to an outpatient memory clinic were assessed with the DCTclock, MMSE, and P(r)VLT. Using statistical criteria for subtle cognitive impairment (SCI, Edmonds et al., 2015) and MCI (Jak et al., 2008), patients were classified into cognitively normal (CN, n = 21), SCI (n = 33), amnestic MCI (aMCI, n = 22), and dysexecutive/mixed MCI (d/mMCI, n = 30) groups. A series of stepwise logistic regression analyses were conducted with CN participants as the criterion group.ResultIn the first analysis (Table 1), P(r)VLT‐recognition (X2 = 24.39, p&lt;0.001) entered the model first followed by the DCTclock Score (X2 = 16.68, p&lt;0.001). P(r)VLT‐recognition classified aMCI patients (Wald = 11.21, p&lt;0.001). DCTclock (Wald = 8.97, p&lt;0.001) and P(r)VLT‐recognition (Wald = 4.06, p&lt;0.001) classified d/mMCI patients. In the second analysis the four command DCTclock composites were analyzed, excluding the MMSE and P(r)VLT‐recognition scores, Spatial Reasoning classified patients into their respective groups (SCI, Wald = 3.68, p&lt;0.055; aMCI, Wald = 8.84, p&lt;0.003; d/mMCI, Wald = 19.12 p&lt;0.001). Third, analyzing DCTclock command Spatial Reasoning, MMSE, and P(r)VLT‐recognition, the P(r)VLT score, again, entered first (X2 = 24.39, p&lt;0.001) followed by DCTclock Spatial Reasoning (X2 = 18.95, p&lt;0.001). DCTclock Spatial Reasoning classified SCI patients (Wald = 4.66, p&lt;0.031), P(r)VLT‐recognition classified aMCI patients (Wald = 9.11, p&lt;0.003), and both DCTclock command Spatial Reasoning (Wald = 10.56, p&lt;0.001) and P(r)VLT‐recognition (Wald = 3.32, p&lt;.068) classified d/mMCI patients.ConclusionUnlike the MMSE that takes 10‐15 minutes to administer and is scored subjectively, all DCTclock data are digitally obtained and automatically scored, requiring no examiner interface for administration and interpretation, thus obviating clinical judgment, ensuring inter‐rater reliability, and enabling more efficient cognitive assessments. In this series of logistic regression analyses, a combination of DCTclock and P(r)VLT clearly outperformed the MMSE in classifying non‐demented memory clinic patients into cognitively normal, subtle cognitive impairment, and MCI subtypes. These results support the efficacy of DCAs in clinical trials and to screen for emergent cognitive impairment in primary care and specialty medical practices.

Classifying Memory Clinic Patients Using the Digital Clock Drawing TestTM, Mini‐Mental State Examination, and Philadelphia (repeatable) Verbal Learning Test

AbstractBackgroundDigital cognitive assessments (DCA) enable quick, sensitive, and reliable assessment of neurocognitive operations that underlie dementia and mild cognitive impairment (MCI). We examined how well the DCTclockTM DCA, Mini‐Mental State Examination (MMSE), and Philadelphia (repeatable) Veral Learning Test (PrVLT) can classify memory clinic patients by cognitive impairment profile.Method106 serial referrals to an outpatient memory clinic were assessed with the DCTclock, MMSE, and P(r)VLT. Using statistical criteria for subtle cognitive impairment (SCI, Edmonds et al., 2015) and MCI (Jak et al., 2008), patients were classified into cognitively normal (CN, n = 21), SCI (n = 33), amnestic MCI (aMCI, n = 22), and dysexecutive/mixed MCI (d/mMCI, n = 30) groups. A series of stepwise logistic regression analyses were conducted with CN participants as the criterion group.ResultIn the first analysis (Table 1), P(r)VLT‐recognition (X2= 24.39, p&lt;0.001) entered the model first followed by the DCTclock Score (X2= 16.68, p&lt;0.001). P(r)VLT‐recognition classified aMCI patients (Wald = 11.21, p&lt;0.001). DCTclock (Wald = 8.97, p&lt;0.001) and P(r)VLT‐recognition (Wald = 4.06, p&lt;0.001) classified d/mMCI patients. In the second analysis the four command DCTclock composites were analyzed, excluding the MMSE and P(r)VLT‐recognition scores, Spatial Reasoning classified patients into their respective groups (SCI, Wald = 3.68, p&lt;0.055; aMCI, Wald = 8.84, p&lt;0.003; d/mMCI, Wald = 19.12 p&lt;0.001). Third, analyzing DCTclock command Spatial Reasoning, MMSE, and P(r)VLT‐recognition, the P(r)VLT score, again, entered first (X2= 24.39, p&lt;0.001) followed by DCTclock Spatial Reasoning (X2= 18.95, p&lt;0.001). DCTclock Spatial Reasoning classified SCI patients (Wald = 4.66, p&lt;0.031), P(r)VLT‐recognition classified aMCI patients (Wald = 9.11, p&lt;0.003), and both DCTclock command Spatial Reasoning (Wald = 10.56, p&lt;0.001) and P(r)VLT‐recognition (Wald = 3.32, p&lt;.068) classified d/mMCI patients.ConclusionUnlike the MMSE that takes 10‐15 minutes to administer and is scored subjectively, all DCTclock data are digitally obtained and automatically scored, requiring no examiner interface for administration and interpretation, thus obviating clinical judgment, ensuring inter‐rater reliability, and enabling more efficient cognitive assessments. In this series of logistic regression analyses, a combination of DCTclock and P(r)VLT clearly outperformed the MMSE in classifying non‐demented memory clinic patients into cognitively normal, subtle cognitive impairment, and MCI subtypes. These results support the efficacy of DCAs in clinical trials and to screen for emergent cognitive impairment in primary care and specialty medical practices.

Item‐based scoring and serial position of words generated during the Category Fluency Test: differences between controls and MCI subtypes

AbstractBackgroundThe order in which words are recalled during the 1‐minute Category Fluency Test (CFT) can be informative about the integrity of the underlying semantic memory (SM) system. Typically, the first entries tend to be easier exemplars and, as the category is explored in more depth during the course of the minute, words generated tend to become more difficult. We hypothesised that an item‐based scoring method that quantifies this increase in difficulty will help identify the semantic feature(s) most sensitive to the loss of SM integrity observed in people with a form of mild cognitive impairment (MCI).MethodTwo‐hundred participants (age: 58.68 ± 12.08 years) were sub‐divided into four clinical diagnostic groups: 50 healthy controls, 50 individuals with amnestic MCI, 50 individuals with non‐amnestic MCI and 50 individuals with subjective cognitive complaints but no objective deficits. No differences in age, years of education or gender existed across the four groups. All participants were administered a 1‐minute CFT (two categories: animals and fruits). Individual performance was scored word‐by‐word according to nine semantic features: age of acquisition, frequency, prevalence, recognition time, valence, arousal, dominance, sensorimotor interaction and concreteness. Correlation coefficients (Spearman’s rho) were calculated between each feature and the serial position of each word within the list. Correlations were then converted to z‐scores and analysed across the four groups with Kruskal‐Wallis one‐way analyses of variance.ResultThe z‐converted correlation between serial position and dominance was the only one differing across groups (at a p = 0.009). Bonferroni‐corrected post‐hoc tests showed a difference between healthy controls and individuals with amnestic MCI (p = 0.008). The decline in word dominance was significantly steeper in the group of amnestic MCI (z = ‐0.322) than in controls (z = ‐0.199).ConclusionThe study of SM is relevant to characterising the clinical mechanisms of normal and abnormal ageing. A procedure that focuses on CFT words difficulty and order of recall can complement classic methods that are typically used to detect loss of SM integrity. This pilot study indicates that ‘dominance’ is a semantic feature that might help characterise loss of SM integrity in individuals with amnestic MCI.