Investigation of possible associations of the BDNF, SNAP-25 and SYN III genes with the neurocognitive measures: BDNF and SNAP-25 genes might be involved in attention domain, SYN III gene in executive function

OBJECTIVES

Attention-deficit/hyperactivity disorder (ADHD) is a heterogeneous disorder and Sluggish Cognitive Tempo (SCT) might be a second inattention disorder that might be even affected by different attention pathways. SCT is characterized by daydreaming, mental confusion, staring blankly and hypoactivity. In the present study, we evaluated 5 common variants (rs6265, rs3746544, rs1051312, rs133946 and rs133945) located in 3 candidate genes (BDNF, SNAP25 and SYN III) that are known to take part in synaptic plasticity and neurotransmitter transmission.

METHODS

We tested the effects of these variants on neuropsychological findings assessed by a computer-based neuropsychological test battery in children with inattention symptoms (SCT and/or ADHD).

RESULTS

BDNF (rs6265), SNAP25 (rs3746544 and rs1051312) and SYN III (rs133946 and rs133945) polymorphisms were associated with variable cognitive measures. BDNF gene (rs6265) polymorphism Met allele carriers and SNAP25 gene (rs3746544) T allele carriers had an association with the attention domain. SNAP25 gene (rs1051312) C allele carriers were only associated with reaction time scores. Cognitive flexibility, which is one of the key components of executive function evaluation and shifting attention test scores were associated with BDNF (rs6265) Met allele and SYN III (rs133946) gene G allele. SYN III (rs133945) gene C allele carriers had an association with verbal memory correct hit scores.

CONCLUSIONS

As a conclusion, BDNF, SNAP25 and SYN III genes were associated with specific neurocognitive outcomes in children with inattention symptoms. It is important to note that exploring genotyping effects on neurocognitive functions instead of a heterogeneous psychiatric diagnosis can improve our understanding of psychopathologies.

Repetitive transcranial magnetic stimulation for cognitive function in adults with bipolar disorder: A pilot study

BACKGROUND

Cognitive deficits are prevalent in bipolar disorder and are a significant contributor to negative patient-reported outcomes. Herein we conducted a pilot study of repetitive transcranial magnetic stimulation (rTMS) to improve cognitive function in adults with bipolar disorder.

METHODS

The study was a triple-blinded, randomized, placebo-control trial. Participants (aged 18 to 60) with a diagnosis of DSM-5-defined bipolar disorder (I or II) were recruited and randomized (N=36) to receive either a sham treatment (n=20) or an active rTMS treatment (n=16). Patients completed the Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) Consensus Cognitive Battery (MCCB) at baseline and 1-2 weeks after the rTMS intervention.

RESULTS

A significant group by time interaction was observed in the Hopkins Verbal Learning Test-Revised (HVLT-R), (F (1, 34) = 17.0, p < 0.001, partial η² = 0.33). Post-hoc analysis revealed that although both groups did not significantly differ at baseline (p = 0.58), patients in the active rTMS group significantly improved following neurostimulation (p = 0.02) for HVLT-R. Moreover, within-subject analysis indicated that the active rTMS group significantly improved in score from pre-treatment to post-treatment (p < 0.001), while the sham group did not improve (p = 0.94) for HVLT-R. No significant differences were seen in the other cognitive measures.

LIMITATIONS

The study was conducted in a small sample .

CONCLUSION

This pilot study, which was intended to establish feasibility, suggests that rTMS may offer benefit in select domains of cognitive functioning in bipolar disorder. None of the measures across subdomains revealed a dyscognitive effect.

Cognitive Impairment After Intracerebral Hemorrhage: A Systematic Review and Meta-Analysis

OBJECTIVE

The present systematic review and meta-analysis analyzes the available clinical literature on post-intracerebral hemorrhage (ICH) cognitive impairment.

METHODS

We conducted a systematic review with meta-analysis following PRISMA guidelines. A search of bibliographic databases up to July 31, 2020 yielded 2155 studies. Twenty articles were included in our final qualitative systematic review and 18 articles in quantitative meta-analysis.

RESULTS

Based on analysis of data from 18 studies (3270 patients), we found prevalence of post-ICH cognitive impairment to be 46% (confidence interval, 35.9-55.9), with a follow-up duration ranging from 8 days to 4 years. The estimated pooled prevalence of cognitive decline decreased over longitudinal follow-up, from 55% (range, 37.7%-71.15%) within 6 months of ICH to 35% (range, 27%-42.7%) with >6 months to 4 years follow-up after ICH. The modalities used to evaluate cognitive performance after ICH in studies varied widely, ranging from global cognitive measures to domain-specific testing. The cognitive domain most commonly affected included nonverbal IQ, information processing speed, executive function, memory, language, and visuoconstructive abilities. Prognostic factors for poor cognitive performance included severity of cortical atrophy, age, lobar ICH location, and higher number of hemorrhages at baseline.

CONCLUSIONS

The prevalence of post-ICH cognitive impairment is high. Despite the heterogeneity among studies, the present study identified cognitive domains most commonly affected and predictors of cognitive impairment after ICH. In future, prospective cohort studies with larger sample sizes and standardized cognitive domains testing could more accurately determine prevalence and prognostic factors of post-ICH cognitive decline.

Artificial intelligence and neuropsychological measures: The case of Alzheimer's disease

One of the current challenges in the field of Alzheimer's disease (AD) is to identify patients with mild cognitive impairment (MCI) that will convert to AD. Artificial intelligence, in particular machine learning (ML), has established as one of more powerful approach to extract reliable predictors and to automatically classify different AD phenotypes. It is time to accelerate the translation of this knowledge in clinical practice, mainly by using low-cost features originating from the neuropsychological assessment. We performed a meta-analysis to assess the contribution of ML and neuropsychological measures for the automated classification of MCI patients and the prediction of their conversion to AD. The pooled sensitivity and specificity of patients' classifications was obtained by means of a quantitative bivariate random-effect meta-analytic approach. Although a high heterogeneity was observed, the results of meta-analysis show that ML applied to neuropsychological measures can lead to a successful automatic classification, being more specific as screening rather than prognosis tool. Relevant categories of neuropsychological tests can be extracted by ML that maximize the classification accuracy.