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Objective: Although meningioma patients show deficits in objective cognitive functioning (OCF) measured with neuropsychological tests, subjective cognitive functioning (SCF) has received little attention. We investigate SCF from pre- to post-surgery and its associations with OCF, psychological, sociodemographic, and clinical characteristics.Methods: SCF was measured using the Cognitive Failures Questionnaire (CFQ) one day before (T0), and three (T3) and twelve months (T12) after surgery. Patients' scores were compared to normative data and changes over time were assessed. The neuropsychological battery CNS Vital Signs and the Hospital Anxiety and Depression Scale were administered. Correlations of SCF with OCF, psychological, sociodemographic, and clinical characteristics were explored.Results: Patients reported significantly better SCF as compared with controls at T0 (N=54) and T3 (N=242), but not at T12 (N=50). A significant decrease in group level SCF was observed from T0 to T12 (n=24, pConclusions: Meningioma patients experienced better SCF as compared to controls before and three months after surgery, which might be the result of phenomena related to disease and recovery. As the findings suggest that cognitive symptoms might increase later on, future studies should further investigate the course of SCF in meningioma patients. In clinical practice, measurements of SCF should be combined with those of OCF and psychological distress in order to determine whether and which interventions are needed.

Author(s): Tan, Chia Jie; Mah, Jaclyn Jia Jun; Goh, Wei Lin; Poon, Eileen; Harunal Rashid, Mohamad Farid; Chan, Alexandre | Abstract: ObjectiveCancer-related cognitive impairment (CRCI) among adolescent and young adult (AYA) cancer patients with noncentral nervous system (CNS) cancers has not been well studied. In this study, we aimed to describe CRCI-associated trends and characteristics among AYA cancer patients.MethodsIn a longitudinal cohort of AYA cancer patients without CNS disease, CRCI was evaluated over 1 year using the Functional Assessment of Cancer Therapy-Cognitive Function Instrument, a self-reported cognitive outcome measure. CRCI prevalence was quantified using the previously established minimal clinically important difference. CRCI-associated longitudinal trends and factors were evaluated with mixed-effects model analysis.ResultsNinety-one patients (mean age = 28.4 ± 6.7 years) were included. Approximately one-third (34.1%) experienced CRCI at least once during the study follow-up. Female gender (P = .02), Indian ethnicity (P l .01), current smokers (P l .01), anxiety/depressive symptoms (P l .01) and fatigue (P l .01) were found to be associated with poorer cognitive function among AYAs.ConclusionsAlthough AYA cancer patients were relatively young and without CNS disease involvement, a significant proportion of them experienced clinically important decline in cognitive function. With improved understanding of this subject, effective strategies can be formulated to promote awareness of CRCI and mitigate its negative effects among AYA cancer patients.

In MRI, the coherence lifetime T2 is sensitive to the magnetic environment imposed by tissue microstructure and biochemistry in vivo. Here we explore the possibility that the use of T2 relaxometry may provide information complementary to that provided by diffusion tensor imaging (DTI) in ageing of healthy controls (HC), Alzheimer’s disease (AD) and mild cognitive impairment (MCI). T2 and diffusion MRI metrics were quantified in HC and patients with MCI and mild AD using multi-echo MRI and DTI. We used tract-based spatial statistics (TBSS) to evaluate quantitative MRI parameters in white matter (WM). A prolonged T2 in WM was associated with AD, and able to distinguish AD from MCI, and AD from HC. Shorter WM T2 was associated with better cognition and younger age in general. In no case was a reduction in T2 associated with poorer cognition. We also applied principal component analysis, showing that WM volume changes independently of T2, MRI diffusion indices and cognitive performance indices. Our data add to the evidence that age-related and AD-related decline in cognition is in part attributable to WM tissue state, and much less to WM quantity. These observations suggest that WM is involved in AD pathology, and that T2 relaxometry is a potential imaging modality for detecting and characterising WM in cognitive decline and dementia.

Alterations in the relative abundance of synaptic proteins may contribute to hippocampal synaptic dysfunction in Alzheimer’s disease (AD). The extent to which perturbations in synaptic protein expression occur during the earliest stages of cognitive decline remains unclear. We examined protein levels of presynaptic synaptophysin (SYP) and synaptotagmin (SYT), and postsynaptic drebrin (DRB), a marker for dendritic spine plasticity, in the hippocampus of people with an antemortem clinical diagnosis of no cognitive impairment (NCI), mild cognitive impairment (MCI) or mild/moderate AD. Although normalized SYP and SYT levels were preserved, DRB was reduced by approximately 40% in the hippocampus of MCI and AD compared to NCI subjects. This differential alteration of synaptic markers in MCI suggests a selective impairment in hippocampal postsynaptic dendritic plasticity in prodromal AD that likely heralds the onset of memory impairment in symptomatic disease.

Background: Diagnostic criteria of Alzheimer’s disease (AD) emphasize the integration of clinical data and biomarkers. In practice, collection and analysis of patient data vary greatly across different countries and clinics. Objective: The goal was to develop a versatile and objective clinical decision support system that could reduce diagnostic errors and highlight early predictors of AD. Methods: Novel data analysis methods were developed to derive composite disease indicators from heterogeneous patient data. Visualizations that communicate these findings were designed to help the interpretation. The methods were implemented with a software tool that is aimed for daily clinical practice. Results: With the tool, clinicians can analyze available patients as a whole, study them statistically against previously diagnosed cases, and characterize the patients with respect to having AD. The tool is able to work with virtually any patient measurement data, as long as they are stored in electronic format or manually entered into the system. For a subset of patients from the test cohort, the tool was able to predict conversion to AD at an accuracy of 93.6%. Conclusion: The software tool developed in this study provides objective information for early detection and prediction of AD based on interpretable visualizations of patient data.