Representation of women in neuropsychology research prior to the COVID-19 pandemic.

OBJECTIVE: Prior work has demonstrated that women have been historically underrepresented across various research fields, including neuropsychology. Given these disparities, the goal of this study was to systematically evaluate the inclusion of women as participants in neuropsychology research. The current study builds upon previous research by examining articles from eight peer-reviewed neuropsychology journals published in 2019. METHOD: Empirical articles examining human samples were included in the current review if they were available in English. Eligible articles were examined to glean whether the main topic of the article was related to a gender issue, how gender was categorized, the gender distribution of the sample, whether gender was considered in analyses, whether gender was addressed in the discussion, and what age categories the study examined. RESULTS: There was a relatively even distribution of men (51.76%) and women (48.24%) in neuropsychological research studies reviewed. There were twice as many studies that included only men compared to only women (16 vs. 8 studies), and nearly twice as many studies consisted of ≥ 75% men (16.6%) compared to ≥75% of women (8.5%). Gender-focused research was limited (3%). Furthermore, gender was frequently disregarded in analyses (58%) and often not addressed in the discussion (75%). CONCLUSIONS: The current study highlights the limitations within neuropsychology related to the representation of women in research. Although it is encouraging that neuropsychological research is generally inclusive of women participants, future research should aim to more comprehensively investigate how gender may influence cognitive risk and resilience factors across different clinical presentations. Recommendations to begin addressing this challenge and to move toward more gender-equitable research are provided.

Parental, caregiving, and family leave during clinical neuropsychology postdoctoral training: Recommendations and guidelines from the Women in Neuropsychology (WIN) committee and Education Advisory Committee (EAC) of the Society for Clinical Neuropsychology (SCN; APA division 40).

Objective: Parental and other caregiving leave is important to postdoctoral fellows, yet there is no field-wide recommendation for leave policies among clinical neuropsychology postdoctoral training programs, which is of particular relevance given the two-year requirement for eligibility for board certification. The aims of this manuscript are to (a) discuss general guidelines and recommendations for leave policies, both informed by prior empirical evidence as well as relevant existing policy guidelines from various academic and healthcare organizations, and (b) use vignettes to provide possible solutions for potential leave scenarios. Method: A critical review of literature on family leave from public policy and political science, industrial-organizational psychology, academic medicine, and psychology was conducted and findings were synthesized. Results and Conclusions: Fellowship training programs are encouraged to adopt a competency-based model that permits flexibility in leave during training without necessarily requiring an extended end date. Programs should adopt clear policies and make this information readily available to trainees and think flexibly about training options that best meet the training needs and goals of each individual. We also encourage neuropsychologists at all levels to engage in advocacy for broader systemic supports of trainees seeking equitable family leave.

Inflammatory biomarkers link perceived stress with metabolic dysregulation.

Objective: Perceived stress has been identified as a risk factor for metabolic syndrome. However, the intermediate pathways underlying this relationship are not well understood. Inflammatory responses may be one process by which stress leads to metabolic dysregulation. Prior work has shown that chronic stress is associated with elevated systemic inflammation and that altered inflammatory activity contributes to the pathogenesis of metabolic syndrome. The current analyses tested this hypothesis by examining inflammation as a pathway by which perceived stress affects metabolic health. Methods: Data from the Midlife in the United States Study (MIDUS) (N = 648; Mean age = 52.3) provided measures of perceived stress, inflammatory biomarkers [C-reactive protein (CRP), interleukin-6 (IL-6), E-selectin, fibrinogen, intracellular adhesion molecule-1 (ICAM-1)] and metabolic health markers. Confirmatory factor analysis (CFA) was used to confirm the fit of a hierarchical model of metabolic syndrome in our sample. Structural equation modeling (SEM) was used to test the assumption that inflammation mediates the association between perceived stress and the latent factor representing metabolic syndrome. Results: The CFA of metabolic syndrome demonstrated excellent goodness of fit to our sample [CFI = 0.97, TLI = 0.95, RMSEA = 0.06, SMSR = 0.05]. Mediation analysis with SEM revealed that the indirect pathway linking stress to metabolic dysregulation through inflammation was significant [B = 0.08, SE = 0.01, z = 3.69, p < .001, 95% confidence interval CI (0.04, 0.13)]. Conclusions: These results suggest that inflammatory biomarkers are a viable explanatory pathway for the relationship between perceived stress and metabolic health consequences. Interventions that target psychosocial stress may serve as cost-effective and accessible treatment options for mitigating inflammatory health risks.

Posttraumatic stress symptom severity predicts cognitive decline beyond the effect of Alzheimer's disease biomarkers in Veterans.

Chronic stress is a risk factor for dementia but whether it explains unique variance in cognitive decline in older adults above Alzheimer's disease (AD) biomarkers is unknown. In a preclinical cohort of Vietnam Veterans, we examined the relationship between posttraumatic stress disorder (PTSD) symptom severity, AD biomarkers of beta-amyloid (Aß) and tau, and change in cognitive performance on two widely-used screeners, the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA). Analyses indicated that PTSD symptom severity was associated with a greater decline on the MMSE (p < 0.04) and MoCA (p < 0.024) after adjusting for biomarkers of AD, notably on the attention scale of the MoCA and the memory index of the MMSE. These analyses survived multiple comparison corrections. Taken together, PTSD symptom severity is associated with accelerated cognitive decline. Treating PTSD should be considered instrumental to maintaining cognitive function as adults age.

Posttraumatic stress disorder symptom severity is associated with reduced Montreal Cognitive Assessment scores in a sample of Vietnam War Veterans.

The goal of the present study was to examine associations between posttraumatic stress disorder (PTSD) symptom severity, the number of stressors experienced, and cognitive outcomes in a sample of U.S. Vietnam War Veterans (N = 274). Adults between 60 and 85 years of age completed a Vietnam Veterans Alzheimer's Disease Neuroimaging Initiative Project visit. A modified version of the Life Stressor Checklist-Revised (LSC-R) was used to assess the number of stressful experiences participants experienced, current PTSD severity scores were measured via the Clinician-Administered PTSD Scale for DSM-IV (CAPS-IV), and cognition was assessed using the Montreal Cognitive Assessment (MoCA). Linear regressions were conducted to examine the effect of CAPS-IV and LSC-R scores on cognitive performance. Higher CAPS-IV scores were associated with worse cognitive outcomes on the MoCA, ΔF(1, 264) = 12.686, p < .001, R2 = .142. In contrast, the number of reported stressful experiences was not associated with cognitive outcomes. After accounting for multiple comparisons, findings indicated that CAPS-IV severity scores were significantly associated with the MoCA memory index. In a sample of older Veterans, PTSD symptom severity, but not the number of reported stressors, was associated with poorer performance on a well-established cognitive function screening tool. Analyses of specific MoCA domains indicated that memory may be driving this association. These findings suggest that highly arousing stressors characteristic of PTSD, rather than stressful experiences more broadly, contribute to this association. Future work can use these findings to explore whether treating PTSD symptoms may help maintain cognitive function during the aging process.

Characterizing Beliefs About Breast Cancer and Comorbid Diabetes After Cancer Treatment.

PURPOSE: To investigate how breast cancer survivors with comorbid diabetes mellitus (diabetes) conceptualize their illnesses. PARTICIPANTS & SETTING: 19 community-dwelling, English- or Spanish-speaking women with diabetes in New York, New York, who received chemotherapy or hormone therapy for stage I-IIIA breast cancer in the past five years. METHODOLOGIC APPROACH: Semistructured interviews were administered by trained research staff, and were audio recorded and transcribed. Three coders reviewed transcripts through an iterative coding process. An interpretive descriptive approach was used to identify themes. FINDINGS: Major themes included an inverse relationship between illness control and concern, variation in perceived illness permanence, and differences in illness consequences. Women with a greater perceived control over breast cancer viewed their diabetes as a larger concern; others felt that their breast cancer could not be controlled but diabetes could. IMPLICATIONS FOR NURSING: Understanding how breast cancer survivors view diabetes and cancer may explain the variation in survivors' self-management behaviors, and how it may influence their attitudes and behaviors in the context of cancer treatment.

Machine learning identifies novel markers predicting functional decline in older adults.

The ability to carry out instrumental activities of daily living, such as paying bills, remembering appointments and shopping alone decreases with age, yet there are remarkable individual differences in the rate of decline among older adults. Understanding variables associated with a decline in instrumental activities of daily living is critical to providing appropriate intervention to prolong independence. Prior research suggests that cognitive measures, neuroimaging and fluid-based biomarkers predict functional decline. However, a priori selection of variables can lead to the over-valuation of certain variables and exclusion of others that may be predictive. In this study, we used machine learning techniques to select a wide range of baseline variables that best predicted functional decline in two years in individuals from the Alzheimer's Disease Neuroimaging Initiative dataset. The sample included 398 individuals characterized as cognitively normal or mild cognitive impairment. Support vector machine classification algorithms were used to identify the most predictive modality from five different data modality types (demographics, structural MRI, fluorodeoxyglucose-PET, neurocognitive and genetic/fluid-based biomarkers). In addition, variable selection identified individual variables across all modalities that best predicted functional decline in a testing sample. Of the five modalities examined, neurocognitive measures demonstrated the best accuracy in predicting functional decline (accuracy = 74.2%; area under the curve = 0.77), followed by fluorodeoxyglucose-PET (accuracy = 70.8%; area under the curve = 0.66). The individual variables with the greatest discriminatory ability for predicting functional decline included partner report of language in the Everyday Cognition questionnaire, the ADAS13, and activity of the left angular gyrus using fluorodeoxyglucose-PET. These three variables collectively explained 32% of the total variance in functional decline. Taken together, the machine learning model identified novel biomarkers that may be involved in the processing, retrieval, and conceptual integration of semantic information and which predict functional decline two years after assessment. These findings may be used to explore the clinical utility of the Everyday Cognition as a non-invasive, cost and time effective tool to predict future functional decline.

Body Mass Index and Polygenic Risk for Alzheimer's Disease Predict Conversion to Alzheimer's Disease.

Body mass index (BMI) is a risk factor for Alzheimer's disease (AD) although the relationship is complex. Obesity in midlife is associated with increased risk for AD, whereas evidence supports both higher and lower BMI increasing risk for AD in late life. This study examined the influence of individual differences in genetic risk for AD to further clarify the relationship between late-life BMI and conversion to AD. Participants included 52 individuals diagnosed as having mild cognitive impairment (MCI) at baseline who converted to AD within 24 months and 52 matched MCI participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. BMI was measured at baseline. Genetic risk for AD was assessed via genome-wide polygenic risk scores. Conditional logistic regression models were run to determine if BMI and polygenic risk predicted conversion to AD. Results showed an interaction between BMI and genetic risk, such that individuals with lower BMI and higher polygenic risk were more likely to convert to AD relative to individuals with higher BMI. These results remained significant after adjusting for cerebrospinal fluid biomarkers of AD. Exploratory sex-stratified analyses revealed this relationship only remained significant in males. These results show that higher genetic risk in the context of lower BMI predicts conversion to AD in the next 24 months, particularly among males. These findings suggest that genetic risk for AD in the context of lower BMI may serve as a prodromal risk factor for future conversion to AD.

Genetic Risk for Alzheimer's Disease Moderates the Association Between Medial Temporal Lobe Volume and Episodic Memory Performance Among Older Adults.

BACKGROUND: A complex set of interactions between biological, genetic, and environmental factors likely underlies the development of Alzheimer's disease (AD). Identifying which of these factors is most associated with AD is important for early diagnosis and treatment. OBJECTIVE: We sought to examine genetic risk and structural brain volume on episodic memory in a sample of older adults ranging from cognitively normal to those diagnosed with AD. METHODS: 686 adults (55-91 years old) completed a 3T MRI scan, baseline cognitive assessments, and biospecimen collection through the Alzheimer's Disease Neuroimaging Initiative. Hierarchical linear regression analyses examined main and interaction effects of medial temporal lobe (MTL) volume and polygenic hazard score (PHS), indicating genetic risk for AD, on a validated episodic memory composite score. RESULTS: Genetic risk moderated the relationship between MTL volume and memory, such that individuals with high PHS and lower hippocampal and entorhinal volume had lower memory composite scores [ΔF (1,677) = 4.057, p = 0.044, ΔR2 = 0.002]. Further analyses showed this effect was driven by the left hippocampus [ΔF(1,677) = 5.256, p = 0.022, ΔR2 = 0.003] and right entorhinal cortex [ΔF (1,677) = 6.078, p = 0.014, ΔR2 = 0.003]. CONCLUSIONS: Among those with high genetic risk for AD, lower volume was associated with poorer memory. Results suggest that the interaction between AD genetic risk and MTL volume increases the likelihood for memory impairment among older adults. Results from this study suggest that genetic risk and brain volume should be considered key factors in tracking cognitive decline.

Multimethod assessment of baseline depression and relationship to hepatitis C treatment discontinuation.

Objective The primary study objective is to determine which measures of depression are associated with early discontinuation of hepatitis C virus infection treatment and to determine which measure best characterizes the depression that develops during treatment. Methods Seventy-eight treatment-naïve subjects who initiated pegylated interferon/ribavirin treatment for hepatitis C virus infection were included. Baseline depression was assessed with the Structured Interview for the Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM-IV), the Hamilton Depression Rating Scale, and the Beck Depression Inventory-II. The latter two measures were repeated at treatment weeks 12 and 24. Results Depression scores, as measured by the three instruments, lacked adequate consistency. Baseline depression as measured by the Beck Depression Inventory-II, but not by the other scales, was associated with early treatment discontinuation at weeks 12 and 24. Changes in depression during treatment were restricted to somatic symptoms. Of those who completed treatment, those who were not depressed at baseline tended to demonstrate significant depression increases during treatment. Conclusion The Beck Depression Inventory-II is recommended to assess depression prior to hepatitis C virus infection treatment. Somatic symptoms of depression should be monitored during treatment. Baseline depression as measured by the Beck Depression Inventory-II was associated with early treatment discontinuation. The Beck Depression Inventory-II, Structured Interview for DSM-IV, and Hamilton Depression Rating Scale yielded results that were not consistent with each other in this sample. Future research should focus on standardizing depression assessment in medically ill populations to identify measures that predict treatment discontinuation.