Cognitive Impairment In Individuals Research Articles

Proteomic Landscape Associated with Cognitive Impairment in Individuals with Long-term Methamphetamine Dependence

Proteomic Landscape Associated with Cognitive Impairment in Individuals with Long-term Methamphetamine Dependence

Classification of Neurocognition in Japanese Patients with Schizophrenia: A Cluster Analysis

Objectives. Cognitive functions in almost all domains are lower in patients with schizophrenia than those in healthy controls, with the severity of impairment differing between domains. Treatments are being developed to improve cognitive impairment in patients with schizophrenia. However, the pattern of cognitive impairment must be clarified to facilitate treatment. Therefore, this study aimed to classify the patterns of cognitive impairment in individuals and provide treatment suggestions. Methods. Patients with schizophrenia were recruited from two psychiatric hospitals in Japan. Demographic and psychopathological symptoms were assessed using the Positive and Negative Symptoms Scale for Schizophrenia and neurocognitive functions, using the CogHealth battery. The following domains were assessed: processing speed, visual attention, working memory, visual learning, and spatial attention. The scores were standardised and assigned as the same-aged average score. Hierarchical cluster analysis using Ward’s method was performed based on CogHealth scores. Subsequently, one-way analysis of variance (ANOVA) and Tukey’s multiple comparisons were performed to compare the variables in each cluster. Results. In total, 133 participants were classified into four clusters: Cluster 1 (n = 16), with severe cognitive impairment and psychiatric symptoms and the longest stay; Cluster 2 (n = 44), with moderate cognitive impairment and psychiatric symptoms; Cluster 3 (n = 42), with preserved cognitive function, except for spatial perception, and mild psychiatric symptoms; and Cluster 4 (n = 31), with only memory and spatial perception impairment and mild psychiatric symptoms. Implications. The clusters indicate that impairment may occur in all or selective domains. Selective domain impairments may be in spatial perception or in spatial perception and memory. Therefore, it is recommended that treatments for cognitive dysfunction are developed into four subsets considering an individual’s cognitive features.

A Systematic Review of Cognitive Impairment in Individuals With Colorectal Cancer.

Patients with colorectal cancer (CRC) encounter varying degrees of objective and subjective cognitive impairment. The prevalence of objective and subjective cognitive impairment, factors affecting cognitive impairment, and interventions are presented in this review. The CINAHL Plus®, Cochrane Library, Embase®, PsycINFO®, PubMed®, and CNKI databases were systematically searched from the time of the database's establishment to May 2023. Manual searches for the relevant articles in the literature's references were also conducted. The results were independently assessed by two reviewers. 25 studies were included. The prevalence of cognitive impairment in individuals with CRC was measured differently according to study designs. A model of factors contributing to cognitive impairment guided the integration of factors, including cancer treatments, psychosocial factors, and physical and emotional health conditions. Incorporated intervention programs could be integrated between objective and subjective aspects. Interventions relieved cognitive impairment in individuals with CRC. The results of this review supported enhanced assessment and monitoring of cognitive impairment, particularly subjective cognitive impairment.

Reduced Cortical Thickness Correlates of Cognitive Dysfunction in Post-COVID-19 Condition: Insights from a Long-Term Follow-up.

There is a paucity of data on long-term neuroimaging findings from individuals who have developed the post-coronavirus 2019 (COVID-19) condition. Only 2 studies have investigated the correlations between cognitive assessment results and structural MR imaging in this population. This study aimed to elucidate the long-term cognitive outcomes of participants with the post-COVID-19 condition and to correlate these cognitive findings with structural MR imaging data in the post-COVID-19 condition. A cohort of 53 participants with the post-COVID-19 condition underwent 3T brain MR imaging with T1 and FLAIR sequences obtained a median of 1.8 years after Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) infection. A comprehensive neuropsychological battery was used to assess several cognitive domains in the same individuals. Correlations between cognitive domains and whole-brain voxel-based morphometry were performed. Different ROIs from FreeSurfer were used to perform the same correlations with other neuroimaging features. According to the Frascati criteria, more than one-half of the participants had deficits in the attentional (55%, n = 29) and executive (59%, n = 31) domains, while 40% (n = 21) had impairment in the memory domain. Only 1 participant (1.89%) showed problems in the visuospatial and visuoconstructive domains. We observed that reduced cortical thickness in the left parahippocampal region (t(48) = 2.28, P = .03) and the right caudal-middle-frontal region (t(48) = 2.20, P = .03) was positively correlated with the memory domain. Our findings suggest that cognitive impairment in individuals with the post-COVID-19 condition is associated with long-term alterations in the structure of the brain. These macrostructural changes may provide insight into the nature of cognitive symptoms.

Validity of Montreal Cognitive Assessment to Detect Cognitive Impairment in Individuals with Type 2 Diabetes.

Guidelines recommend screening older people (> 60-65years) with type 2 diabetes (T2D) for cognitive impairment, as it has implications in the management of diabetes. The Montreal Cognitive Assessment (MoCA) is a sensitive test for the detection of mild cognitive impairment (MCI) in the general population, but its validity in T2D has not been established. We administered MoCA to patients with T2D (age ≥ 60years) and controls (no T2D), along with a culturally validated neuropsychological battery and functional activity questionnaire. MCI was defined as performance in one or more cognitive domains ≥ 1.0 SD below the control group (on two tests representing a cognitive domain), with preserved functional activities. The discriminant validity of MoCA for the diagnosis of MCI at different cut-offs was ascertained. We enrolled 267 patients with T2D and 120 controls; 39% of the participants with T2D met the diagnostic criteria for MCI on detailed neuropsychological testing. At the recommended cut-off on MoCA (< 26), the sensitivity (94.2%) was high, but the specificity was quite low (29.5%). The cut-off score of < 23 showed an optimal trade-off between sensitivity (69.2%), specificity (71.8%), and diagnostic accuracy (70.8%). The cut-off of < 21 exhibited the highest diagnostic accuracy (74.9%) with an excellent specificity (91.4%), a good positive and negative predictive value (78.5% and 73.7%, respectively). The recommended screening cut-off point on MoCA of < 26 has a suboptimal specificity and may increase the referral burden in memory clinics. A lower cut-off of < 21 on MoCA maximizes the diagnostic accuracy. Interactive Visual Abstract available for this article.

Prevalence of fatigue and cognitive impairment after traumatic brain injury.

Following traumatic brain injury (TBI) some patients develop lingering comorbid symptoms of fatigue and cognitive impairment. The mild cognitive impairment self-reported by patients is often not detected with neurocognitive tests making it difficult to determine how common and severe these symptoms are in individuals with a history of TBI. This study was conducted to determine the relative prevalence of fatigue and cognitive impairment in individuals with a history of TBI. The Fatigue and Altered Cognition Scale (FACs) digital questionnaire was used to assess self-reported fatigue and cognitive impairment. Adults aged 18-70 were digitally recruited for the online anonymous study. Eligible participants provided online consent, demographic data, information about lifetime TBI history, and completed the 20 item FACs questionnaire. A total of 519 qualifying participants completed the online digital study which included 204 participants with a history of TBI of varied cause and severity and 315 with no history of TBI. FACs Total Score was significantly higher in the TBI group (57.7 ± 22.2) compared to non-TBI (39.5 ± 23.9; p<0.0001) indicating more fatigue and cognitive impairment. When stratified by TBI severity, FACs score was significantly higher for all severity including mild (53.9 ± 21.9, p<0.0001), moderate (54.8 ± 24.4, p<0.0001), and severe (59.7 ± 20.9, p<0.0001) TBI. Correlation analysis indicated that more severe TBI was associated with greater symptom severity (p<0.0001, r = 0.3165). Ancillary analysis also suggested that FACs scores may be elevated in participants with prior COVID-19 infection but no history of TBI. Adults with a history of even mild TBI report significantly greater fatigue and cognitive impairment than those with no history of TBI, and symptoms are more profound with greater TBI severity.

Detection of Early Stage Alzheimer's Disease in Gradient-Based MR Images Using Deep Learning Methods

Alzheimer's Disease (AD), a form of dementia prevalent in older age, is a neurodegenerative condition impacting brain nerve cells. Early-stage AD symptoms are often subtle, complicating timely diagnosis. Early detection allows for intervention, slowing disease progression and facilitating appropriate treatments. Deep learning methods, particularly gradient-based images, prove promising for early Alzheimer's detection in Magnetic Resonance (MR) imaging. Gradient-based images, highlighting details in low-intensity images and enhancing contrast, play a vital role in determining structures' location, shape, and size, notably in techniques like Magnetic Resonance Imaging (MRI). This study aims to boost model performance in early AD detection by applying the Gradient filter before training deep learning models on diverse-angle and constant-density brain MRI images. The dataset comprises three categories representing early-stage AD, including images of mild cognitive impairment and healthy individuals. Original and gradient-filtered image subsets were inputted into deep learning models. Results indicate superior performance of gradient-based images, with the Densenet201 deep learning model achieving the highest accuracy at 98.63%.

A novel spatiotemporal graph convolutional network framework for functional connectivity biomarkers identification of Alzheimer’s disease

BackgroundFunctional connectivity (FC) biomarkers play a crucial role in the early diagnosis and mechanistic study of Alzheimer’s disease (AD). However, the identification of effective FC biomarkers remains challenging. In this study, we introduce a novel approach, the spatiotemporal graph convolutional network (ST-GCN) combined with the gradient-based class activation mapping (Grad-CAM) model (STGC-GCAM), to effectively identify FC biomarkers for AD.MethodsThis multi-center cross-racial retrospective study involved 2,272 participants, including 1,105 cognitively normal (CN) subjects, 790 mild cognitive impairment (MCI) individuals, and 377 AD patients. All participants underwent functional magnetic resonance imaging (fMRI) and T1-weighted MRI scans. In this study, firstly, we optimized the STGC-GCAM model to enhance classification accuracy. Secondly, we identified novel AD-associated biomarkers using the optimized model. Thirdly, we validated the imaging biomarkers using Kaplan–Meier analysis. Lastly, we performed correlation analysis and causal mediation analysis to confirm the physiological significance of the identified biomarkers.ResultsThe STGC-GCAM model demonstrated great classification performance (The average area under the curve (AUC) values for different categories were: CN vs MCI = 0.98, CN vs AD = 0.95, MCI vs AD = 0.96, stable MCI vs progressive MCI = 0.79). Notably, the model identified specific brain regions, including the sensorimotor network (SMN), visual network (VN), and default mode network (DMN), as key differentiators between patients and CN individuals. These brain regions exhibited significant associations with the severity of cognitive impairment (p < 0.05). Moreover, the topological features of important brain regions demonstrated excellent predictive capability for the conversion from MCI to AD (Hazard ratio = 3.885, p < 0.001). Additionally, our findings revealed that the topological features of these brain regions mediated the impact of amyloid beta (Aβ) deposition (bootstrapped average causal mediation effect: β = -0.01 [-0.025, 0.00], p < 0.001) and brain glucose metabolism (bootstrapped average causal mediation effect: β = -0.02 [-0.04, -0.001], p < 0.001) on cognitive status.ConclusionsThis study presents the STGC-GCAM framework, which identifies FC biomarkers using a large multi-site fMRI dataset.

Cognitive impairment in chronic heart failure. How new therapeutic options for the treatment of chronic heart failure affect cognitive function?

Heart failure is a serious chronic illness and a global healthcare problem. Patients with heart failure have increased hospital readmission rates and mortality around the world. Heart failure often coexists with comorbidities of which cognitive impairment is particularly important. Сognitive impairment is present in around 30% to 80% in heart failure decreases quality of life and increases the frequency of repeated hospitalizations, increases mortality and worsens the prognosis in such patients. Despite existing research, the underlying pathophysiology of the relationship between heart failure and cognitive impairment remains poorly understood. The article discusses the best available evidence concerning different pathophysiological mechanisms behind cognitive impairment in individuals with heart failure. Heart failure and cognitive impairment have a number of common pathophysiological pathways including reduced cerebral blood flow, inflammation, neurohumoral activations, oxidative stress and changes in serum biomarkers or proteins. Understanding the pathophysiology of diseases will allow the effective evaluation of health status and development of suitable treatments. Prompt detection of cognitive impairment will bring about better clinical outcomes in heart failure patients. This review raises questions about the relationship between heart failure and cognitive dysfunction, provides evidence on the impact of new therapeutic options for treating heart failure on cognitive function, and suggests potential future research directions. For example, implantation of an artificial left ventricle may improve cognitive function; however, there are studies on immediate improvement in cognitive function followed by gradual deterioration over time after implantation of an artificial left ventricle, which requires long-term study In addition, evidence is provided for the effectiveness of using a relaxation scenario in virtual reality technology to reduce anxiety and affective complaints in somatic hospitalized patients, but the ability to improve cognitive function in patients with chronic heart failure remains to be explored.

Development and Validation of a Risk Score for Mild Cognitive Impairment in Individuals with Type 2 Diabetes in China: A Practical Cognitive Prescreening Tool.

Numerous evidence suggests that diabetes increases the risk of cognitive impairment. This study aimed to develop and validate a multivariable risk score model to identify mild cognitive impairment (MCI) in patients with type 2 diabetes mellitus (T2DM). This cross-sectional study included 1256 inpatients (age: 57.5 ± 11.2 years) with T2DM in a tertiary care hospital in China. MCI was diagnosed according to the criteria recommended by the National Institute on Aging-Alzheimer's Association Workgroup, and a MoCA score of 19-25 indicated MCI. Participants were randomly allocated into the derivation and validation sets at 7:3 ratio. Logistic regression models were used to identify predictors for MCI in the derivation set. A scoring system based on the predictors' beta coefficient was developed. Predictive ability of the risk score was tested by discrimination and calibration methods. Totally 880 (285 with MCI, 32.4%) and 376 (167 with MCI, 33.8%) patients were allocated in the derivation and validation set, respectively. Age, education, HbA1c, self-reported history of severe hypoglycemia, and microvascular disease were identified as predictors for MCI and constituted the risk score. The AUCs (95% CI) of the risk score were 0.751 (0.717, 0.784) in derivation set and 0.776 (0.727, 0.824) in validation set. The risk score showed good apparent calibration of observed and predicted MCI probabilities and was capable of stratifying individuals into 3 risk categories by two cut-off points (low risk: ≤ 3, medium risk: 4-13, and high risk ≥ 14). The risk score based on age, education, HbA1c, self-reported history of severe hypoglycemia, and microvascular disease can effectively assess MCI risk in adults with T2DM at different age. It can serve as a practical prescreening tool for early detection of MCI in daily diabetes care.

Clinical variables contributing to the identification of biologically defined subgroups within cognitively unimpaired and mild cognitive impairment individuals.

A lack of consensus exists in linking demographic, behavioral, and cognitive characteristics to biological stages of dementia, defined by the ATN (amyloid, tau, neurodegeneration) classification incorporating amyloid, tau, and neuronal injury biomarkers. Using a random forest classifier we investigated whether 27 demographic, behavioral, and cognitive characteristics allowed distinction between ATN-defined groups with the same cognitive profile. This was done separately for three cognitively unimpaired (CU) (112 A-T-N-; 46 A+T+N+/-; 65 A-T+/-N+/-) and three mild cognitive impairment (MCI) (128 A-T-N-; 223 A+T+N+/-; 94 A-T+/-N+/-) subgroups. Classification-balanced accuracy reached 39% for the CU and 52% for the MCI subgroups. Logical Delayed Recall (explaining 16% of the variance), followed by the Alzheimer's Disease Assessment Scale 13 (14%) and Everyday Cognition Informant (10%), were the most relevant characteristics for classification of the MCI subgroups. Race and ethnicity, marital status, and Everyday Cognition Patient were not relevant (0%). The demographic, behavioral, and cognitive measures used in our model were not informative in differentiating ATN-defined CU profiles. Measures of delayed memory, general cognition, and activities of daily living were the most informative in differentiating ATN-defined MCI profiles; however, these measures alone were not sufficient to reach high classification performance.

Association between Intake of Edible Mushrooms and Algae and the Risk of Cognitive Impairment in Chinese Older Adults.

Previous studies have investigated the association between diet and cognitive impairment, yet there is limited investigation into the link between edible mushrooms and algae intake and cognitive decline. This study aims to explore the association between edible mushrooms and algae intake and the risk of cognitive impairment in individuals aged 65 years and above in China. Cross-sectional data from the 2018 Chinese Longitudinal Healthy Longevity Survey (CLHLS) formed the basis of this study. Edible mushrooms and algae intake was evaluated using a simplified food frequency questionnaire (FFQ) and cognitive function was assessed using the Mini-Mental State Examination (MMSE). A binary logistic regression model was used to evaluate odds ratios (ORs) and 95% confidence intervals (CIs), with subgroup analysis conducted. Among 14,150 older adults, the average age was (85.33 ± 11.55), with a cognitive impairment prevalence of 22.7; multi-model adjustments showed a 25.3% lower probability of cognitive impairment for those occasionally consuming edible mushrooms and algae (OR: 0.747, 95% CI: 0.675~0.826). Furthermore, a 29% lower risk was observed in those with daily intake (OR: 0.710, 95% CI: 0.511~0.987). Subgroup analysis demonstrated significant risk reduction in women (OR: 0.589, 95% CI: 0.375~0.925, p = 0.022), individuals with disability in activities of daily living (OR: 0.568, 95% CI: 0.367~0.878, p = 0.011), and those with low social activity levels (OR: 0.671, 95% CI: 0.473~0.950, p = 0.025). This study concludes that edible mushrooms and algae intake significantly impacts the risk of cognitive impairment in older adults. These results provide insights and impetus for further research into this area. Additional cohort studies or intervention trials are necessary to confirm the potential benefits of edible mushrooms and algae in promoting cognitive health.

Dementia risk and thalamic nuclei volumetry in healthy midlife adults: the PREVENT Dementia study.

A reduction in the volume of the thalamus and its nuclei has been reported in Alzheimer's disease, mild cognitive impairment and asymptomatic individuals with risk factors for early-onset Alzheimer's disease. Some studies have reported thalamic atrophy to occur prior to hippocampal atrophy, suggesting thalamic pathology may be an early sign of cognitive decline. We aimed to investigate volumetric differences in thalamic nuclei in middle-aged, cognitively unimpaired people with respect to dementia family history and apolipoprotein ε4 allele carriership and the relationship with cognition. Seven hundred participants aged 40-59 years were recruited into the PREVENT Dementia study. Individuals were stratified according to dementia risk (approximately half with and without parental dementia history). The subnuclei of the thalamus of 645 participants were segmented on T1-weighted 3 T MRI scans using FreeSurfer 7.1.0. Thalamic nuclei were grouped into six regions: (i) anterior, (ii) lateral, (iii) ventral, (iv) intralaminar, (v) medial and (vi) posterior. Cognitive performance was evaluated using the computerized assessment of the information-processing battery. Robust linear regression was used to analyse differences in thalamic nuclei volumes and their association with cognitive performance, with age, sex, total intracranial volume and years of education as covariates and false discovery rate correction for multiple comparisons. We did not find significant volumetric differences in the thalamus or its subregions, which survived false discovery rate correction, with respect to first-degree family history of dementia or apolipoprotein ε4 allele status. Greater age was associated with smaller volumes of thalamic subregions, except for the medial thalamus, but only in those without a dementia family history. A larger volume of the mediodorsal medial nucleus (Pfalse discovery rate = 0.019) was associated with a faster processing speed in those without a dementia family history. Larger volumes of the thalamus (P = 0.016) and posterior thalamus (Pfalse discovery rate = 0.022) were associated with significantly worse performance in the immediate recall test in apolipoprotein ε4 allele carriers. We did not find significant volumetric differences in thalamic subregions in relation to dementia risk but did identify an interaction between dementia family history and age. Larger medial thalamic nuclei may exert a protective effect on cognitive performance in individuals without a dementia family history but have little effect on those with a dementia family history. Larger volumes of posterior thalamic nuclei were associated with worse recall in apolipoprotein ε4 carriers. Our results could represent initial dysregulation in the disease process; further study is needed with functional imaging and longitudinal analysis.

Duration of Untreated Prodromal Psychosis and Cognitive Impairments

The possible association between the duration of untreated prodromal symptoms (DUPrS) and cognitive functioning in individuals at clinical high risk (CHR) for psychosis remains underexplored. To investigate the intricate interplay between DUPrS, cognitive performance, and conversion outcomes, shedding light on the potential role of DUPrS in shaping cognitive trajectories and psychosis risk in individuals at CHR for psychosis. This cohort study of individuals at CHR for psychosis was conducted at the Shanghai Mental Health Center in China from January 10, 2016, to December 29, 2021. Participants at CHR for psychosis typically exhibit attenuated positive symptoms; they were identified according to the Structured Interview for Prodromal Syndromes, underwent baseline neuropsychological assessments, and were evaluated at a 3-year clinical follow-up. Data were analyzed from August 25, 2021, to May 10, 2023. Duration of untreated prodromal symptoms and cognitive impairments in individuals at CHR for psychosis. The primary study outcome was conversion to psychosis. The DUPrS was categorized into 3 groups based on percentiles (33rd percentile for short [≤3 months], 34th-66th percentile for median [4-9 months], and 67th-100th percentile for long [≥10 months]). The DUPrS, cognitive variables, and the risk of conversion to psychosis were explored through quantile regression and Cox proportional hazards regression analyses. This study included 506 individuals (median age, 19 [IQR, 16-21] years; 53.6% [n = 271] women). The mean (SD) DUPrS was 7.8 (6.857) months, and the median (IQR) was 6 (3-11) months. The short and median DUPrS groups displayed poorer cognitive performance than the long DUPrS group in the Brief Visuospatial Memory Test-Revised (BVMT-R) (Kruskal-Wallis χ2 = 8.801; P = .01) and Category Fluency Test (CFT) (Kruskal-Wallis χ2 = 6.670; P = .04). Quantile regression analysis revealed positive correlations between DUPrS rank and BVMT-R scores (<90th percentile of DUPrS rank) and CFT scores (within the 20th-70th percentile range of DUPrS rank). Among the 506 participants, 20.8% (95% CI, 17.4%-24.5%) converted to psychosis within 3 years. Cox proportional hazards regression analysis identified lower educational attainment (hazard ratio [HR], 0.912; 95% CI, 0.834-0.998), pronounced negative symptoms (HR, 1.044; 95% CI, 1.005-1.084), and impaired performance on the Neuropsychological Assessment Battery: Mazes (HR, 0.961; 95% CI, 0.924-0.999) and BVMT-R (HR, 0.949; 95% CI, 0.916-0.984) tests as factors associated with conversion. The finding of this cohort study suggest the intricate interplay between DUPrS, cognitive performance, and conversion risk in individuals at CHR for psychosis. The findings emphasize the importance of considering both DUPrS and cognitive functioning in assessing the trajectory of these individuals.

Associations of gut microbiota alterations with clinical, metabolic, and immune-inflammatory characteristics of chronic schizophrenia

The present study had the following aims: 1) to compare gut microbiota composition in patients with schizophrenia and controls and 2) to investigate the association of differentially abundant bacterial taxa with markers of inflammation, intestinal permeability, lipid metabolism, and glucose homeostasis as well as clinical manifestation. A total of 115 patients with schizophrenia during remission of positive and disorganization symptoms, and 119 controls were enrolled. Altogether, 32 peripheral blood markers were assessed. A higher abundance of Eisenbergiella, Family XIII AD3011 group, Eggerthella, Hungatella, Lactobacillus, Olsenella, Coprobacillus, Methanobrevibacter, Ligilactobacillus, Eubacterium fissicatena group, and Clostridium innocuum group in patients with schizophrenia was found. The abundance of Paraprevotella and Bacteroides was decreased in patients with schizophrenia. Differentially abundant genera were associated with altered levels of immune-inflammatory markers, zonulin, lipid profile components, and insulin resistance. Moreover, several correlations of differentially abundant genera with cognitive impairment, higher severity of negative symptoms, and worse social functioning were observed. The association of Methanobrevibacter abundance with the level of negative symptoms, cognition, and social functioning appeared to be mediated by the levels of interleukin-6 and RANTES. In turn, the association of Hungatella with the performance of attention was mediated by the levels of zonulin. The findings indicate that compositional alterations of gut microbiota observed in patients with schizophrenia correspond with clinical manifestation, intestinal permeability, subclinical inflammation, lipid profile alterations, and impaired glucose homeostasis. Subclinical inflammation and impaired gut permeability might mediate the association of gut microbiota alterations with psychopathological symptoms and cognitive impairment.

EGCG-like non-competitive inhibitor of DYRK1A rescues cognitive defect in a down syndrome model

Overexpression of the chromosome 21 DYRK1A gene induces morphological defects and cognitive impairments in individuals with Down syndrome (DS) and in DS mice models. Aging neurons of specific brain regions of patients with Alzheimer's disease, DS and Pick's disease have increased DYRK1A immunoreactivity suggesting a possible association of DYRK1A with neurofibrillary tangle pathology. Epigallocatechin-3-gallate (EGCG) displays appreciable inhibition of DYRK1A activity and, contrary to all other published inhibitors, EGCG is a non-competitive inhibitor of DYRK1A. Prenatal exposure to green tea polyphenols containing EGCG protects from brain defects induced by overexpression of DYRK1A.In order to produce more robust and possibly more active analogues of the natural compound EGCG, here we synthetized new EGCG-like molecules with several structural modifications to the EGCG skeleton. We replaced the ester boun of EGCG with a more resistant amide bond. We also replaced the oxygen ring by a methylene group. And finally, we positioned a nitrogen atom within this ring. The selected compound was shown to maintain the non-competitive property of EGCG and to correct biochemical and behavioral defects present in a DS mouse model. In addition it showed high stability and specificity.

Utility of the Brief Assessment of Cognitive Health (BACH) computerized screening tool in identifying MS-related cognitive impairment

BackgroundCurrent guidelines recommend that individuals with MS are screened annually for processing speed deficits, often using the Symbol Digit Modalities Test (SDMT). However, given the heterogeneity of cognitive deficits in individuals with MS, other screening measures that assess a range of cognitive domains are necessary. The current cross-sectional study aimed to examine the ability of the computerized, self-administered Brief Assessment of Cognitive Health (BACH) screening measure to detect the presence of cognitive impairment in adults with MS as determined by performance on a standard neuropsychological test battery. MethodsSeventy-two individuals with MS completed the BACH and a comprehensive neuropsychological test battery. Receiver operating characteristic (ROC) analyses were conducted to investigate the ability of the BACH to identify cognitively impaired and cognitively intact individuals. ROC analyses were also conducted to compare the ability of the SDMT to discriminate between cognitively intact and cognitively impaired groups as a comparison with the BACH. ResultsCognitive impairment was observed in 56 % of the sample. The BACH showed acceptable ability to discriminate between cognitively intact and cognitively impaired groups (AUC = 0.78). Additionally, the BACH was able to adequately predict cognitive impairment in domains other than processing speed (AUC = 0.71). The SDMT also demonstrated adequate utility in identifying individuals with cognitive impairment (AUC = 0.73); however, the SDMT was not able to adequately predict cognitive impairment in domains other than processing speed (AUC = 0.56). ConclusionThe BACH showed adequate ability to detect cognitive impairment in individuals with MS. The BACH was able to identify impairments across various assessed cognitive domains, including individuals with and without processing speed deficits.

Small vessel disease burden and functional brain connectivity in mild cognitive impairment

BackgroundThe role of small vessel disease in the development of dementia is not yet completely understood. Functional brain connectivity has been shown to differ between individuals with and without cerebral small vessel disease. However, a comprehensive measure of small vessel disease quantifying the overall damage on the brain is not consistently used and studies using such measure in mild cognitive impairment individuals are missing. MethodFunctional brain connectivity differences were analyzed between mild cognitive impairment individuals with absent or low (n = 34) and high (n = 34) small vessel disease burden using data from the Parelsnoer Institute, a Dutch multicenter study. Small vessel disease was characterized using an ordinal scale considering: lacunes, microbleeds, perivascular spaces in the basal ganglia, and white matter hyperintensities. Resting state functional MRI data using 3 Tesla scanners was analyzed with group-independent component analysis using the CONN toolbox. ResultsFunctional connectivity between areas of the cerebellum and between the cerebellum and the thalamus and caudate nucleus was higher in the absent or low small vessel disease group compared to the high small vessel disease group. ConclusionThese findings might suggest that functional connectivity of mild cognitive impairment individuals with low or absent small vessel disease burden is more intact than in mild cognitive impairment individuals with high small vessel disease. These brain areas are mainly responsible for motor, attentional and executive functions, domains which in previous studies were found to be mostly associated with small vessel disease markers. Our results support findings on the involvement of the cerebellum in cognitive functioning.

Development of a blood‐based biomarker signature for detection of cognitive impairment through a targeted metabolomics approach deciphering the association with depression

AbstractBackgroundA series of pathological episodes set off the development of cognitive impairment, which precede years before the onset of clinical symptoms. Disrupted glucose and fatty acid metabolism notably contributes to the development of cognitive impairment and further advancement to clinical dementia. Disrupted metabolism leads to the secretion of various inflammatory eicosanoids leading to neuroinflammation. Alteration of some specific metabolites in plasma are noted during these metabolic changes. Depression is a key risk factor of cognitive impairment, and similar changes in plasma metabolites have observed in the pathophysiology of depression. This aim of this project is to establish a blood‐based biomarker signature for early detection of cognitive impairment in individuals with depression through quantifying plasma levels of metabolites associated with glucose and fatty acid metabolism in plasma samples from the participants of the Sydney Memory and Ageing Study (MAS).MethodSamples of 275 participants were randomly collected from baseline and 251 from an 8 year follow up study. At baseline there were 130 healthy participants, 93 participants with MCI, 36 participants with depression and 16 participants had both MCI and depression. Plasma samples were analysed to quantify the levels of fatty acids and carnitines using chemical derivatization – UPLC‐MRM/MS.ResultSignificant correlations were observed between the biomarkers like FA040 butyric acid (P: 0.034), FA224 (P: 0.032), acotinic acid (P:0.027) with MMSE. Biomarkers like C70carnitine (P:0.010), C90carnitine (P: 0.033), C150carnitine (P: 0.016), C160 carnitines (P: 0.004) significantly correlated with GDS scales. Linear regression showed significance between C90 carnitine (P: 0.021; β: 0.139) ad GDS scale. Whereas linear regression showed significant relationship between FA224 (P: 040, β: ‐.122), acotinic acid (P: 0.042, β: ‐.122), FA040 butyric acid (P: 0.036, β: ‐.193) with MMSE indicative of cognitive impairment.ConclusionThe study will help to identify candidate metabolites of perturbed fatty acid metabolism and establish a plasma biomarker signature of early cognitive impairment, by deciphering the association with depression as a risk factor. An association will be established between central AD pathology and peripheral metabolic changes, and potential treatment options targeting the right biochemical pathways will be identified.

Assessment of cognitive impairment in individuals with mental disorders who are subject to criminal liability: A neuropsychological approach

Assessment of cognitive impairment in individuals with mental disorders who are subject to criminal liability: A neuropsychological approach