ObjectivesTo determine the effect of computerised cognitive training (CCT) on improving cognitive function for older adults with mild cognitive impairment (MCI).DesignSystematic review and meta-analysis.Data sourcesPubMed, Embase, Web of Science and the Cochrane Library were searched through January 2018.Eligibility criteriaRandomised controlled trials comparing CCT with control conditions in those with MCI aged 55+ were included.Data extraction and synthesisTwo independent reviewers extracted data and assessed the risk of bias. Effect sizes (Hedges’ g and 95% CIs) were calculated and random-effects meta-analyses were performed where three or more studies investigated a comparable intervention and outcome. Heterogeneity was quantified using the I2 statistic.Results18 studies met the inclusion criteria and were included in the analyses, involving 690 participants. Meta-analysis revealed small to moderate positive treatment effects compared with control interventions in four domains as follows: global cognitive function (g=0.23, 95% CI 0.03 to 0.44), memory (g=0.30, 95% CI 0.11 to 0.50), working memory (g=0.39, 95% CI 0.12 to 0.66) and executive function (g=0.20, 95% CI −0.03 to 0.43). Statistical significance was reached in all domains apart from executive function.ConclusionsThis meta-analysis provides evidence that CCT improves cognitive function in older people with MCI. However, the long-term transfer of these improvements and the potential to reduce dementia prevalence remains unknown. Various methodological issues such as heterogeneity in outcome measures, interventions and MCI symptoms and lack of intention-to-treat analyses limit the quality of the literature and represent areas for future research.

ObjectivesThis review provides a broad overview of the effectiveness of interventions for subjective cognitive decline (SCD) in improving psychological well-being, metacognition and objective cognitive performance.MethodsDatabases including PubMed, Web of Science and Cochrane Systematic Reviews were searched up to August 2017 to identify randomised controlled trials evaluating interventions for SCD. Interventions were categorised as psychological, cognitive, lifestyle or pharmacological. Outcomes of interest included psychological well-being, metacognitive ability and objective cognitive performance. To assess the risk of bias, three authors independently rated study validity using criteria based on the Critical Appraisal Skills Programme. Random-effects meta-analyses were undertaken where three or more studies investigated similar interventions and reported comparable outcomes.ResultsTwenty studies met inclusion criteria and 16 had sufficient data for inclusion in the meta-analyses. Of these, only seven were rated as being high quality. Group psychological interventions significantly improved psychological well-being (g=0.40, 95% CI 0.03 to 0.76; p=0.03) but the improvement they conferred on metacognitive ability was not statistically significant (g=0.26, 95% CI −0.22 to 0.73; p=0.28). Overall, cognitive training interventions led to a small, statistically significant improvement in objective cognitive performance (g=0.13, 95% CI 0.01 to 0.25; p=0.03). However, the pooled effect sizes of studies using active control groups (g=0.02, 95% CI −0.19 to 0.22; p=0.85) or reporting global cognitive measures (g=0.06, 95% CI –0.19 to 0.31; p=0.66) were non-significant.ConclusionsThere is a lack of high-quality research in this field. Group psychological interventions improve psychological well-being and may also improve metacognition. A large, high-quality study is indicated to investigate this further. There is no evidence to suggest that cognitive interventions improve global cognitive performance and the clinical utility of small improvements in specific cognitive domains is questionable. There is a lack of research considering lifestyle interventions and poor quality evidence for pharmacological interventions.PROSPERO registration numberCRD42017079391.

An increasing proportion of cognitive difficulties are recognized to have a functional cause, the chief clinical indicator of which is internal inconsistency. When these symptoms are impairing or distressing, and not better explained by other disorders, this can be conceptualized as a cognitive variant of functional neurological disorder, termed functional cognitive disorder (FCD). FCD is likely very common in clinical practice but may be under-diagnosed. Clinicians in many settings make liberal use of the descriptive term mild cognitive impairment (MCI) for those with cognitive difficulties not impairing enough to qualify as dementia. However, MCI is an aetiology-neutral description, which therefore includes patients with a wide range of underlying causes. Consequently, a proportion of MCI cases are due to non-neurodegenerative processes, including FCD. Indeed, significant numbers of patients diagnosed with MCI do not ‘convert’ to dementia. The lack of diagnostic specificity for MCI ‘non-progressors’ is a weakness inherent in framing MCI primarily within a deterministic neurodegenerative pathway. It is recognized that depression, anxiety and behavioural changes can represent a prodrome to neurodegeneration; empirical data are required to explore whether the same might hold for subsets of individuals with FCD. Clinicians and researchers can improve study efficacy and patient outcomes by viewing MCI as a descriptive term with a wide differential diagnosis, including potentially reversible components such as FCD. We present a preliminary definition of functional neurological disorder–cognitive subtype, explain its position in relation to other cognitive diagnoses and emerging biomarkers, highlight clinical features that can lead to positive diagnosis (as opposed to a diagnosis of exclusion), and red flags that should prompt consideration of alternative diagnoses. In the research setting, positive identifiers of FCD will enhance our recognition of individuals who are not in a neurodegenerative prodrome, while greater use of this diagnosis in clinical practice will facilitate personalised interventions.

Alcohol-dependence is a common, complex and debilitating disorder with genetic and environmental influences. Here we show that alcohol consumption increases following mutations to the γ-aminobutyric acidA receptor (GABAAR) β1 subunit gene (Gabrb1). Using N-ethyl-N-nitrosourea mutagenesis on an alcohol-averse background (F1 BALB/cAnN × C3H/HeH), we develop a mouse model exhibiting strong heritable preference for ethanol resulting from a dominant mutation (L285R) in Gabrb1. The mutation causes spontaneous GABA ion channel opening and increases GABA sensitivity of recombinant GABAARs, coupled to increased tonic currents in the nucleus accumbens, a region long-associated with alcohol reward. Mutant mice work harder to obtain ethanol, and are more sensitive to alcohol intoxication. Another spontaneous mutation (P228H) in Gabrb1 also causes high ethanol consumption accompanied by spontaneous GABA ion channel opening and increased accumbal tonic current. Our results provide a new and important link between GABAAR function and increased alcohol consumption that could underlie some forms of alcohol abuse.

Awareness of one's own cognitive processes (metacognition) or of one's own illness or deficits ( anosognosia ) can be impaired in people with Alzheimer's disease (AD). The neural correlates of anosognosia within AD remain inconclusive. Understanding anosognosia is of importance because of its impact on carer burden and increased institutionalization. A systematic review of structural and functional neuroimaging studies was conducted to identify specific brain regions associated with anosognosia within AD. Thirty-two studies were included in the systematic review. Reduced gray matter density, cerebral blood flow, and hypometabolism in 8 key regions were significantly associated with increased anosognosia scores in people with AD. The most frequently associated regions were the inferior frontal gyrus, anterior cingulate cortex, and medial temporal lobe. Other key regions include the superior frontal gyrus, medial frontal gyrus, orbitofrontal cortex, posterior cingulate cortex, and the insula. Identifying brain regions associated with anosognosia can aid understanding and identification of anosognosia in people with AD and potentially facilitate improvements in care.