How and when education improves cognitive capacity is an issue of profound societal importance. Education and later-life education-related factors, such as occupational complexity and engagement in cognitive-intellectual activities, are frequently considered indices of cognitive reserve, but whether their effects are truly causal remains unclear. In this study, after accounting for general cognitive ability (GCA) at an average age of 20 y, additional education, occupational complexity, or engagement in cognitive-intellectual activities accounted for little variance in late midlife cognitive functioning in men age 56–66 (n= 1009). Age 20 GCA accounted for 40% of variance in the same measure in late midlife and approximately 10% of variance in each of seven cognitive domains. The other factors each accounted for <1% of the variance in cognitive outcomes. The impact of these other factors likely reflects reverse causation—namely, downstream effects of early adult GCA. Supporting that idea, age 20 GCA, but not education, was associated with late midlife cortical surface area (n= 367). In our view, the most parsimonious explanation of our results, a meta-analysis of the impact of education, and epidemiologic studies of the Flynn effect is that intellectual capacity gains due to education plateau in late adolescence/early adulthood. Longitudinal studies with multiple cognitive assessments before completion of education would be needed to confirm this speculation. If cognitive gains reach an asymptote by early adulthood, then strengthening cognitive reserve and reducing later-life cognitive decline and dementia risk may really begin with improving educational quality and access in childhood and adolescence.

ObjectiveTo better concurrently address emotional and neuropsychological symptoms common in veterans with comorbid post-traumatic stress disorder (PTSD) and history of traumatic brain injury (TBI), we integrated components of compensatory cognitive training from the Cognitive Symptom Management and Rehabilitation Therapy (CogSMART) programme into cognitive processing therapy (CPT) for PTSD to create a hybrid treatment, SMART-CPT (CogSMART+CPT). This study compared the efficacy of standard CPT with SMART-CPT for treatment of veterans with comorbid PTSD and history of TBI reporting cognitive symptoms.MethodsOne hundred veterans with PTSD, a history of mild to moderate TBI and current cognitive complaints were randomised and received individually delivered CPT or SMART-CPT for 12 weeks. Participants underwent psychological, neurobehavioural and neuropsychological assessments at baseline, on completion of treatment and 3 months after treatment.ResultsBoth CPT and SMART-CPT resulted in clinically significant reductions in PTSD and postconcussive symptomatology and improvements in quality of life. SMART-CPT resulted in additional improvements in the neuropsychological domains of attention/working memory, verbal learning/memory and novel problem solving.ConclusionSMART-CPT, a mental health intervention for PTSD, combined with compensatory cognitive training strategies, reduces PTSD and neurobehavioural symptoms and also provides added value by improving cognitive functioning.

Objective Research demonstrates heterogeneous neuropsychological profiles among individuals with mild cognitive impairment (MCI). However, few studies have included visuoconstructional ability or used latent mixture modeling to statistically identify MCI subtypes. We therefore examined whether unique neuropsychological MCI profiles could be ascertained using latent profile analysis (LPA), and subsequently investigated cerebrospinal fluid (CSF) biomarkers, genotype, and longitudinal clinical outcomes between the empirically-derived classes. Methods 806 participants diagnosed via the Alzheimer’s Disease Neuroimaging Initiative (ADNI) MCI criteria received a comprehensive neuropsychological battery assessing visuoconstructional ability, language, attention/executive function, and episodic memory. Test scores were adjusted for demographic characteristics using standardized regression coefficients based on “robust” normal control performance (n=260). Calculated z-scores were subsequently used in the LPA, and CSF-derived biomarkers, genotype, and longitudinal clinical outcome were evaluated between the LPA-derived MCI classes. Results Statistical fit indices suggested a 3-class model was the optimal LPA solution. The 3-class LPA consisted of a mixed impairment MCI class (n=106), an amnestic MCI class (n=455), and an LPA-derived normal class (n=245). Additionally, the amnestic and mixed classes were more likely to be APOE e4+ and have worse AD CSF biomarkers than LPA-derived normal subjects. Conclusions Our study supports significant heterogeneity in MCI neuropsychological profiles using LPA and extends prior work (Edmonds et al., 2015) by demonstrating a lower rate of progression in the approximately one-third of ADNI MCI individuals who may represent “false-positive” diagnoses. Our results underscore the importance of using sensitive, actuarial methods for diagnosing MCI, as current diagnostic methods may be over-inclusive.