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Journal of the International... Nov 2023Stroke can cause cognitive impairment, which can lead to challenges returning to day-to-day activities. Knowing what factors are associated with cognitive impairment...
OBJECTIVE
Stroke can cause cognitive impairment, which can lead to challenges returning to day-to-day activities. Knowing what factors are associated with cognitive impairment post-stroke can be useful for predicting outcomes and guiding rehabilitation. One such factor is gender: previous studies are inconclusive as to whether gender influences cognitive outcomes post-stroke. Accounting for key variables, we examined whether there are gender differences in cognitive outcomes after stroke.
METHOD
We analyzed data from neuropsychological assessments of 237 individuals tested in the chronic epoch (≥ 3 months) following ischemic stroke. Using ANCOVA and linear mixed modeling, we examined gender as a predictor of cognition as measured by general cognitive ability (g), Full-Scale IQ, and 18 cognitive tests, controlling for age at stroke onset, education, premorbid intelligence, and lesion volume.
RESULTS
There were no significant gender differences in overall cognitive outcomes as measured by g ( = .887) or Full-Scale IQ ( = .801). There were some significant gender differences on specific cognitive tests, with women outperforming men on scores from the Rey Auditory Verbal Learning Test ( < .01) and men outperforming women on the Wechsler Adult Intelligence Scale Arithmetic and Information subtests ( < .01).
CONCLUSIONS
Our findings indicate that men and women have similar overall cognitive outcomes after stroke, when demographic and lesion factors are accounted for. Although men and women differed in their performance on some individual cognitive tests, neither gender performed systematically better or worse. However, for learning, working memory, and verbal knowledge/comprehension, gender may be an important predictor of outcome post-stroke.
Topics: Male; Adult; Humans; Female; Cognition Disorders; Stroke; Neuropsychological Tests; Intelligence Tests; Cognition
PubMed: 36781414
DOI: 10.1017/S1355617723000036 -
Behaviour Research and Therapy Sep 2023The memories for past autobiographical experiences that we share can influence relationship formation and consolidation with important implications for our mental...
The memories for past autobiographical experiences that we share can influence relationship formation and consolidation with important implications for our mental health. However, little is known about how people's responses to our memories can influence subsequent memory sharing. Previous research examined how operant processes (i.e., punishment with aversive sounds) influence the sharing of memories for specific events from our past. Understanding the (social) mechanisms associated with difficulty sharing specific autobiographical memories is important given the association between these difficulties and a range of psychiatric diagnoses. We investigate the effects of verbal and non-verbal social operants on the willingness to share specific autobiographical memories. Participants shared memories with a confederate who coded their memories as specific or non-specific and responded in either an engaged/attentive, dismissive manner or gave no feedback depending on participants' assigned condition. Participants who were reinforced for sharing specific memories and punished for sharing non-specific memories, were more likely to share specific than non-specific memories compared to those who received no feedback. Reinforcement alone was not sufficient for modifying specificity. The ways that we respond to people when they share memories with us can influence their subsequent willingness to share specific events from their past.
Topics: Humans; Memory, Episodic; Conditioning, Operant; Reinforcement, Psychology; Affect; Mental Disorders
PubMed: 37598525
DOI: 10.1016/j.brat.2023.104385 -
Neuropsychologia Aug 2023Disorientation is a frequent consequence of acute brain injury or diffuse disorders, such as confusional states or dementia. Its anatomical correlates are debated.... (Observational Study)
Observational Study
BACKGROUND AND OBJECTIVES
Disorientation is a frequent consequence of acute brain injury or diffuse disorders, such as confusional states or dementia. Its anatomical correlates are debated. Impaired memory as its commonly assumed mechanism predicts that disorientation is associated with medial temporal damage. The alternative is that disorientation reflects defective orbitofrontal reality filtering (ORFi) - a specific failure to identify whether thoughts or memories refer to present reality or not. The latter is a function of the posterior orbitofrontal cortex and connected structures. This study examined the mechanisms and anatomical basis of disorientation in an unselected group of patients with first-ever subacute brain injury.
METHODS
Participants hospitalized for neurorehabilitation were asked to participate in this observational cohort study if they had first-ever organic hemispheric brain dysfunction as evident in a localizable brain lesion or verbal amnesia (often without localizable brain damage). Orientation to time, place, situation and person was tested with a 20-items questionnaire. To identify the mechanisms of disorientation, we determined its correlations with executive tasks, verbal episodic memory, and ORFi in all patients. ORFi was examined with a continuous recognition task, which measures learning and item recognition in the first run, and ORFi as reflected in the increase of false positive responses in the second run (temporal context confusion). Lesions of patients having localizable brain damage were manually delineated and normalized before entering multivariate lesion-symptom-mapping (LSM) to determine anatomical predictors of orientation.
RESULTS
Eighty-four patients (61.1 ± 14.4 years, 29 women) were included. Among measures of memory and executive functioning, a step-wise regression retained temporal context confusion (R = -0.71, p < 0.0001), item recognition (R = 0.67, p < 0.0001) and delayed free recall (R = 0.63, p < 0.0001) as significant predictors of orientation. LSM was possible in 67 participants; it revealed an association of disorientation with damage of the right OFC and the bilateral head of the caudate nucleus.
CONCLUSION
Disorientation in non-confused, non-demented patients with first-ever brain damage is associated with impaired orbitofrontal reality filtering and memory dysfunction, but not with executive dysfunction. Its main anatomical determinant is damage to the orbitofrontal cortex and its subcortical relay, the head of the caudate.
Topics: Humans; Female; Confusion; Recognition, Psychology; Brain Injuries; Prefrontal Cortex; Memory, Episodic
PubMed: 37263576
DOI: 10.1016/j.neuropsychologia.2023.108601 -
Brain, Behavior, and Immunity May 2024Recent findings link cognitive impairment and inflammatory-immune dysregulation in schizophrenia (SZ) and bipolar (BD) spectrum disorders. However, heterogeneity and...
Recent findings link cognitive impairment and inflammatory-immune dysregulation in schizophrenia (SZ) and bipolar (BD) spectrum disorders. However, heterogeneity and translation between the periphery and central (blood-to-brain) mechanisms remains a challenge. Starting with a large SZ, BD and healthy control cohort (n = 1235), we aimed to i) identify candidate peripheral markers (n = 25) associated with cognitive domains (n = 9) and elucidate heterogenous immune-cognitive patterns, ii) evaluate the regulation of candidate markers using human induced pluripotent stem cell (iPSC)-derived astrocytes and neural progenitor cells (n = 10), and iii) evaluate candidate marker messenger RNA expression in leukocytes using microarray in available data from a subsample of the main cohort (n = 776), and in available RNA-sequencing deconvolution analysis of postmortem brain samples (n = 474) from the CommonMind Consortium (CMC). We identified transdiagnostic subgroups based on covariance between cognitive domains (measures of speed and verbal learning) and peripheral markers reflecting inflammatory response (CRP, sTNFR1, YKL-40), innate immune activation (MIF) and extracellular matrix remodelling (YKL-40, CatS). Of the candidate markers there was considerable variance in secretion of YKL-40 in iPSC-derived astrocytes and neural progenitor cells in SZ compared to HC. Further, we provide evidence of dysregulated RNA expression of genes encoding YKL-40 and related signalling pathways in a high neuroinflammatory subgroup in the postmortem brain samples. Our findings suggest a relationship between peripheral inflammatory-immune activity and cognitive impairment, and highlight YKL-40 as a potential marker of cognitive functioning in a subgroup of individuals with severe mental illness.
Topics: Humans; Chitinase-3-Like Protein 1; Bipolar Disorder; Neuropsychological Tests; Induced Pluripotent Stem Cells; Brain; Cognition; RNA
PubMed: 38461955
DOI: 10.1016/j.bbi.2024.03.014 -
Journal of Medical Internet Research Dec 2023Dementia has become a major public health concern due to its heavy disease burden. Mild cognitive impairment (MCI) is a transitional stage between healthy aging and...
BACKGROUND
Dementia has become a major public health concern due to its heavy disease burden. Mild cognitive impairment (MCI) is a transitional stage between healthy aging and dementia. Early identification of MCI is an essential step in dementia prevention.
OBJECTIVE
Based on machine learning (ML) methods, this study aimed to develop and validate a stable and scalable panel of cognitive tests for the early detection of MCI and dementia based on the Chinese Neuropsychological Consensus Battery (CNCB) in the Chinese Neuropsychological Normative Project (CN-NORM) cohort.
METHODS
CN-NORM was a nationwide, multicenter study conducted in China with 871 participants, including an MCI group (n=327, 37.5%), a dementia group (n=186, 21.4%), and a cognitively normal (CN) group (n=358, 41.1%). We used the following 4 algorithms to select candidate variables: the F-score according to the SelectKBest method, the area under the curve (AUC) from logistic regression (LR), P values from the logit method, and backward stepwise elimination. Different models were constructed after considering the administration duration and complexity of combinations of various tests. Receiver operating characteristic curve and AUC metrics were used to evaluate the discriminative ability of the models via stratified sampling cross-validation and LR and support vector classification (SVC) algorithms. This model was further validated in the Alzheimer's Disease Neuroimaging Initiative phase 3 (ADNI-3) cohort (N=743), which included 416 (56%) CN subjects, 237 (31.9%) patients with MCI, and 90 (12.1%) patients with dementia.
RESULTS
Except for social cognition, all other domains in the CNCB differed between the MCI and CN groups (P<.008). In feature selection results regarding discrimination between the MCI and CN groups, the Hopkins Verbal Learning Test-5 minutes Recall had the best performance, with the highest mean AUC of up to 0.80 (SD 0.02) and an F-score of up to 258.70. The scalability of model 5 (Hopkins Verbal Learning Test-5 minutes Recall and Trail Making Test-B) was the lowest. Model 5 achieved a higher level of discrimination than the Hong Kong Brief Cognitive test score in distinguishing between the MCI and CN groups (P<.05). Model 5 also provided the highest sensitivity of up to 0.82 (range 0.72-0.92) and 0.83 (range 0.75-0.91) according to LR and SVC, respectively. This model yielded a similar robust discriminative performance in the ADNI-3 cohort regarding differentiation between the MCI and CN groups, with a mean AUC of up to 0.81 (SD 0) according to both LR and SVC algorithms.
CONCLUSIONS
We developed a stable and scalable composite neurocognitive test based on ML that could differentiate not only between patients with MCI and controls but also between patients with different stages of cognitive impairment. This composite neurocognitive test is a feasible and practical digital biomarker that can potentially be used in large-scale cognitive screening and intervention studies.
Topics: Humans; Alzheimer Disease; Cognitive Dysfunction; Mental Status and Dementia Tests; Neuropsychological Tests; Machine Learning
PubMed: 38039074
DOI: 10.2196/49147 -
The Lancet. Digital Health Dec 2023Extremely preterm infants (<28 weeks of gestation) are at great risk of long-term neurodevelopmental impairments. Early amplitude-integrated electroencephalogram (aEEG)...
Early qualitative and quantitative amplitude-integrated electroencephalogram and raw electroencephalogram for predicting long-term neurodevelopmental outcomes in extremely preterm infants in the Netherlands: a 10-year cohort study.
BACKGROUND
Extremely preterm infants (<28 weeks of gestation) are at great risk of long-term neurodevelopmental impairments. Early amplitude-integrated electroencephalogram (aEEG) accompanied by raw EEG traces (aEEG-EEG) has potential for predicting subsequent outcomes in preterm infants. We aimed to determine whether and which qualitative and quantitative aEEG-EEG features obtained within the first postnatal days predict neurodevelopmental outcomes in extremely preterm infants.
METHODS
This study retrospectively analysed a cohort of extremely preterm infants (born before 28 weeks and 0 days of gestation) who underwent continuous two-channel aEEG-EEG monitoring during their first 3 postnatal days at Wilhelmina Children's Hospital, Utrecht, the Netherlands, between June 1, 2008, and Sept 30, 2018. Only infants who did not have genetic or metabolic diseases or major congenital malformations were eligible for inclusion. Features were extracted from preprocessed aEEG-EEG signals, comprising qualitative parameters grouped in three types (background pattern, sleep-wake cycling, and seizure activity) and quantitative metrics grouped in four categories (spectral content, amplitude, connectivity, and discontinuity). Machine learning-based regression and classification models were used to evaluate the predictive value of the extracted aEEG-EEG features for 13 outcomes, including cognitive, motor, and behavioural problem outcomes, at 2-3 years and 5-7 years. Potential confounders (gestational age at birth, maternal education, illness severity, morphine cumulative dose, the presence of severe brain injury, and the administration of antiseizure, sedative, or anaesthetic medications) were controlled for in all prediction analyses.
FINDINGS
369 infants were included and an extensive set of 339 aEEG-EEG features was extracted, comprising nine qualitative parameters and 330 quantitative metrics. The machine learning-based regression models showed significant but relatively weak predictive performance (ranging from r=0·13 to r=0·23) for nine of 13 outcomes. However, the machine learning-based classifiers exhibited acceptable performance in identifying infants with intellectual impairments from those with optimal outcomes at age 5-7 years, achieving balanced accuracies of 0·77 (95% CI 0·62-0·90; p=0·0020) for full-scale intelligence quotient score and 0·81 (0·65-0·96; p=0·0010) for verbal intelligence quotient score. Both classifiers maintained identical performance when solely using quantitative features, achieving balanced accuracies of 0·77 (95% CI 0·63-0·91; p=0·0030) for full-scale intelligence quotient score and 0·81 (0·65-0·96; p=0·0010) for verbal intelligence quotient score.
INTERPRETATION
These findings highlight the potential benefits of using early postnatal aEEG-EEG features to automatically recognise extremely preterm infants with poor outcomes, facilitating the development of an interpretable prognostic tool that aids in decision making and therapy planning.
FUNDING
European Commission Horizon 2020.
Topics: Infant; Child; Humans; Infant, Newborn; Child, Preschool; Infant, Extremely Premature; Cohort Studies; Retrospective Studies; Netherlands; Electroencephalography
PubMed: 37940489
DOI: 10.1016/S2589-7500(23)00198-X -
Obesity (Silver Spring, Md.) Jul 2023This study explored the association of BMI and insulin sensitivity with cognitive performance in type 2 diabetes.
OBJECTIVE
This study explored the association of BMI and insulin sensitivity with cognitive performance in type 2 diabetes.
METHODS
A cross-sectional analysis of data from the baseline assessment of the Glycemia Reduction Approaches in Diabetes: a Comparative Effectiveness Study (GRADE) was conducted. BMI was used as a surrogate of adiposity and the Matsuda index as the measure of insulin sensitivity. Cognitive tests included the Spanish English Verbal Learning Test, the Digit Symbol Substitution Test, and the letter and animal fluency tests.
RESULTS
Cognitive assessments were completed by 5018 (99.4%) of 5047 participants aged 56.7 ± 10.0 years, of whom 36.4% were female. Higher BMI and lower insulin sensitivity were related to better performance on memory and verbal fluency tests. In models including BMI and insulin sensitivity simultaneously, only higher BMI was related to better cognitive performance.
CONCLUSIONS
In this study, higher BMI and lower insulin sensitivity in type 2 diabetes were cross-sectionally associated with better cognitive performance. However, only higher BMI was related to cognitive performance when both BMI and insulin sensitivity were considered simultaneously. The causality and mechanisms for this association need to be determined in future studies.
Topics: Female; Humans; Male; Body Mass Index; Cognition; Cross-Sectional Studies; Diabetes Mellitus, Type 2; Insulin Resistance; Middle Aged; Aged
PubMed: 37368512
DOI: 10.1002/oby.23785 -
The Lancet. Healthy Longevity Aug 2023Cognitive abilities, particularly memory, normally decline with age. However, some individuals, often designated as superagers, can reach late life with the memory...
BACKGROUND
Cognitive abilities, particularly memory, normally decline with age. However, some individuals, often designated as superagers, can reach late life with the memory function of individuals 30 years younger. We aimed to characterise the brain structure of superagers and identify demographic, lifestyle, and clinical factors associated with this phenotype.
METHODS
We selected cognitively healthy participants from the Vallecas Project longitudinal cohort recruited between Oct 10, 2011, and Jan 14, 2014, aged 79·5 years or older, on the basis of their delayed verbal episodic memory score. Participants were assessed with the Free and Cued Selective Reminding Test and with three non-memory tests (the 15-item version of the Boston Naming Test, the Digit Symbol Substitution Test, and the Animal Fluency Test). Participants were classified as superagers if they scored at or above the mean values for a 50-56-year-old in the Free and Cued Selective Reminding Test and within one standard deviation of the mean or above for their age and education level in the three non-memory tests, or as typical older adults if they scored within one standard deviation of the mean for their age and education level in the Free and Cued Selective Reminding Test. Data acquired as per protocol from up to six yearly follow-ups were used for longitudinal analyses.
FINDINGS
We included 64 superagers (mean age 81·9 years; 38 [59%] women and 26 [41%] men) and 55 typical older adults (82·4 years; 35 [64%] women and 20 [36%] men). The median number of follow-up visits was 5·0 (IQR 5·0-6·0) for superagers and 5·0 (4·5-6·0) for typical older adults. Superagers exhibited higher grey matter volume cross-sectionally in the medial temporal lobe, cholinergic forebrain, and motor thalamus. Longitudinally, superagers also showed slower total grey matter atrophy, particularly within the medial temporal lobe, than did typical older adults. A machine learning classification including 89 demographic, lifestyle, and clinical predictors showed that faster movement speed (despite no group differences in exercise frequency) and better mental health were the most differentiating factors for superagers. Similar concentrations of dementia blood biomarkers in superager and typical older adult groups suggest that group differences reflect inherent superager resistance to typical age-related memory loss.
INTERPRETATION
Factors associated with dementia prevention are also relevant for resistance to age-related memory decline and brain atrophy, and the association between superageing and movement speed could provide potential novel insights into how to preserve memory function into the ninth decade.
FUNDING
Queen Sofia Foundation, CIEN Foundation, Spanish Ministry of Science and Innovation, Alzheimer's Association, European Research Council, MAPFRE Foundation, Carl Zeiss Foundation, and the EU Comission for Horizon 2020.
TRANSLATION
For the Spanish translation of the abstract see Supplementary Materials section.
Topics: Female; Male; Humans; Brain; Cognition; Dementia; Phenotype; Atrophy
PubMed: 37454673
DOI: 10.1016/S2666-7568(23)00079-X -
Alzheimer's Research & Therapy Aug 2023Subjective cognitive decline (SCD) is a risk factor for Alzheimer's disease (AD); however, the rates of cognitive decline are variable according to underlying... (Observational Study)
Observational Study
BACKGROUND
Subjective cognitive decline (SCD) is a risk factor for Alzheimer's disease (AD); however, the rates of cognitive decline are variable according to underlying pathologies and biomarker status. We conducted an observational study and aimed to investigate baseline characteristics and biomarkers related with cognitive declines in SCD. Our study also assessed whether SCD participants showed different cognitive and biomarker trajectories according to baseline amyloid deposition.
METHODS
This study is a part of a longitudinal cohort study conducted in multi-centers in South Korea between 2018 and 2021. Individuals (≥ 60 years old) with persistent cognitive complaint despite of normal cognitive functions were eligible for the study. All participants underwent neuropsychological tests, florbetaben PET scans, plasma amyloid markers, and brain MRI scans. Annual follow-up evaluations included neuropsychological tests and assessments for clinical progressions. Regional brain volumetry and amyloid burden represented by PET-based standardized uptake value ratio (SUVR) were measured. We compared cognitive and brain atrophic changes over 24 months between amyloid positive-SCD (Aβ + SCD) and amyloid negative-SCD (Aβ-SCD) groups. Baseline factors associated with cognitive outcomes were investigated.
RESULTS
A total of 120 participants with SCD were enrolled and 107 completed follow-up evaluations. Aβ + SCD participants (n = 20, 18.5%) were older and more frequently APOE4 carriers compared with Aβ-SCD participants (n = 87). Baseline cognitive scores were not different between the two groups, except the Seoul Verbal Learning Test (SVLT) scores showing lower scores in the Aβ + SCD group. After 24 months, plasma amyloid markers were higher, and regional volumes (entorhinal, hippocampal, and pallidum) were smaller in the Aβ + SCD participants compared with Aβ-SCD participants adjusted by age, sex, and baseline volumes. SVLT delayed recall and controlled oral word association test (COWAT) scores indicated more declines in Aβ + SCD participants. Baseline left entorhinal volumes were related to verbal memory decline, while baseline frontal volumes and global SUVR values were related to frontal functional decline.
CONCLUSION
Aβ + SCD participants showed more cognitive decline and medial temporal atrophic changes during 24 months. Baseline neurodegeneration and amyloid burden were related with future cognitive trajectories in SCD.
TRIAL REGISTRATION
This study was registered at CRIS (KCT0003397).
Topics: Humans; Middle Aged; Amyloid beta-Peptides; Longitudinal Studies; Prospective Studies; Alzheimer Disease; Cognitive Dysfunction; Positron-Emission Tomography; Cognition; Cohort Studies; Biomarkers
PubMed: 37550761
DOI: 10.1186/s13195-023-01273-y -
Progress in Neurobiology Nov 2023Neural networks are successfully used to imitate and model cognitive processes. However, to provide clues about the neurobiological mechanisms enabling human cognition,... (Review)
Review
Neural networks are successfully used to imitate and model cognitive processes. However, to provide clues about the neurobiological mechanisms enabling human cognition, these models need to mimic the structure and function of real brains. Brain-constrained networks differ from classic neural networks by implementing brain similarities at different scales, ranging from the micro- and mesoscopic levels of neuronal function, local neuronal links and circuit interaction to large-scale anatomical structure and between-area connectivity. This review shows how brain-constrained neural networks can be applied to study in silico the formation of mechanisms for symbol and concept processing and to work towards neurobiological explanations of specifically human cognitive abilities. These include verbal working memory and learning of large vocabularies of symbols, semantic binding carried by specific areas of cortex, attention focusing and modulation driven by symbol type, and the acquisition of concrete and abstract concepts partly influenced by symbols. Neuronal assembly activity in the networks is analyzed to deliver putative mechanistic correlates of higher cognitive processes and to develop candidate explanations founded in established neurobiological principles.
Topics: Humans; Brain; Language; Learning; Neural Networks, Computer; Memory, Short-Term
PubMed: 37482195
DOI: 10.1016/j.pneurobio.2023.102511