Summary: Purpose:We assessed rates of symptoms of anxiety and depression among pediatric patients with epilepsy.Methods: We administered the Revised Child Manifest Anxiety Scale (RCMAS), and Child Depression Inventory (CDI) to 44 epilepsy patients aged 7-18 years (mean age 12.4 years). Demographic, socioeconomic, and epilepsy-related information was examined in relation to depression and anxiety scores.Results: No patients had been previously identified to have depression or anxiety. However, 26% had significantly increased depression scores and 16% met critieria for significant anxiety symptomatology. Conclusions: Symptoms of depression and anxiety are common among pediatric patients with epilepsy and appear to be overlooked by care providafs. Key Words: AnxietyDepression-Epilepsy-Seizures-Pediatric.Many controversial studies suggest that patients with epilepsy are at high risk for psychiatric disturbances (1-3) including depression (43) and anxiety (6-8). Most such studies are based on adults; there are far fewer studies of psychiatric symptoms in children and adolescents with seizures. Although depression in childhood has been reported to occur with administration of barbiturates (9), very little is known about overall rates and determinants of depression and anxiety in pediatric patients with epilepsy.Rutter et al. (10) reported psychiatric disturbances in as many as 33% of children with epilepsy but did not specifically delineate affective disorders. Hoare (1 1) noted higher rates of behavioral difficulties in children with epilepsy than in children with diabetes mellitus, but did not determine rates of anxiety and depression. The present study was therefore designed to (a) determine the degree to which the affective disorders (depression and anxiety) had been detected and treated in previous clinical care, (b) determine the frequency of depressive and anxiety-related symptoms among children and adolescents with epilepsy at present, and (c) examine the relationship between self-reported anxiety and depression symptoms with demographic and seizure-related factors. METHODSInclusion and exclusion criteria were as follows: Study entry was offered consecutively to outpatients (aged 7-18 years) with epilepsy (defined as recurrent unprovoked seizures) attending the Pediatric Neurology Department at the University at Stony Brook. Patients with mental retardation were excluded. Patients and their parents completed several self-report measures that examined the following variables:1. Demograptiic variables. Patient ages and sex were recorded. Ages were divided into groups aged 7-12 and 13-18 years. For each child, 1 parent completed the Hollingshead Index, a measure of socioeconomic status (SES) which contains questions about family income, marital and occupational status, and education (12). Scores from the Hollingshead Index were divided into scores of ~2 9 , 2 9 4 8 , and >48 to define lower, middle, and upper SES groups.

BackgroundAccurate identification of individuals at high risk of dementia influences clinical care, inclusion criteria for clinical trials and development of preventative strategies. Numerous models have been developed for predicting dementia. To evaluate these models we undertook a systematic review in 2010 and updated this in 2014 due to the increase in research published in this area. Here we include a critique of the variables selected for inclusion and an assessment of model prognostic performance.MethodsOur previous systematic review was updated with a search from January 2009 to March 2014 in electronic databases (MEDLINE, Embase, Scopus, Web of Science). Articles examining risk of dementia in non-demented individuals and including measures of sensitivity, specificity or the area under the curve (AUC) or c-statistic were included.FindingsIn total, 1,234 articles were identified from the search; 21 articles met inclusion criteria. New developments in dementia risk prediction include the testing of non-APOE genes, use of non-traditional dementia risk factors, incorporation of diet, physical function and ethnicity, and model development in specific subgroups of the population including individuals with diabetes and those with different educational levels. Four models have been externally validated. Three studies considered time or cost implications of computing the model.InterpretationThere is no one model that is recommended for dementia risk prediction in population-based settings. Further, it is unlikely that one model will fit all. Consideration of the optimal features of new models should focus on methodology (setting/sample, model development and testing in a replication cohort) and the acceptability and cost of attaining the risk variables included in the prediction score. Further work is required to validate existing models or develop new ones in different populations as well as determine the ethical implications of dementia risk prediction, before applying the particular models in population or clinical settings.

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IMPORTANCE Cerebral amyloid-β aggregation is an early event in Alzheimer disease (AD). Understanding the association between amyloid aggregation and cognitive manifestation in persons without dementia is important for a better understanding of the course of AD and for the design of prevention trials. OBJECTIVE To investigate whether amyloid-β aggregation is associated with cognitive functioning in persons without dementia. DESIGN, SETTING, AND PARTICIPANTS This cross-sectional study included 2908 participants with normal cognition and 4133 with mild cognitive impairment (MCI) from 53 studies in the multicenter Amyloid Biomarker Study. Normal cognition was defined as having no cognitive concerns for which medical help was sought and scores within the normal range on cognitive tests. Mild cognitive impairment was diagnosed according to published criteria. Study inclusion began in 2013 and is ongoing. Data analysis was performed in January 2017. MAIN OUTCOMES AND MEASURES Global cognitive performance as assessed by the Mini-Mental State Examination (MMSE) and episodic memory performance as assessed by a verbal word learning test. Amyloid aggregation was measured with positron emission tomography or cerebrospinal fluid biomarkers and dichotomized as negative (normal) or positive (abnormal) according to study-specific cutoffs. Generalized estimating equations were used to examine the association between amyloid aggregation and low cognitive scores (MMSE score ≤27 or memory z score≤−1.28) and to assess whether this association was moderated by age, sex, educational level, or apolipoprotein E genotype. RESULTS Among 2908 persons with normal cognition (mean [SD] age, 67.4 [12.8] years), amyloid positivity was associated with low memory scores after age 70 years (mean difference in amyloid positive vs negative, 4% [95% CI, 0%–7%] at 72 years and 21% [95% CI, 10%–33%] at 90 years) but was not associated with low MMSE scores (mean difference, 3% [95% CI, −1% to 6%], P = .16). Among 4133 patients with MCI (mean [SD] age, 70.2 [8.5] years), amyloid positivity was associated with low memory (mean difference, 16% [95% CI, 12%–20%], P < .001) and low MMSE (mean difference, 14% [95% CI, 12%–17%], P < .001) scores, and this association decreased with age. Low cognitive scores had limited utility for screening of amyloid positivity in persons with normal cognition and those with MCI. In persons with normal cognition, the age-related increase in low memory score paralleled the age-related increase in amyloid positivity with an intervening period of 10 to 15 years. CONCLUSIONS AND RELEVANCE Although low memory scores are an early marker of amyloid positivity, their value as a screening measure for early AD among persons without dementia is limited.