Selective attention to stimulus representations in perception and memory: commonalities and differences

It has been proposed that the deployment of selective attention to perceptual and memory representations might be governed by similar cognitive processes and neural resources. However, evidence for this simple and appealing proposal remains inconclusive, which might be due to a considerable divergence in tasks and cognitive demands when comparing attentional selection in memory versus perception. To examine whether selection in both domains share common attentional processes and only differ in the stimuli they act upon (external vs. internal), we compared behavioral costs or benefits between selection domains. In both domains, participants had to attend a target stimulus from a set of simultaneously presented stimuli or simultaneously active memory representations, respectively, with set, target, or both, being repeated or changed across trials. The results of two experiments delineated principal similarities and differences of selection processes in both domains: While positive priming from stimulus repetition was found in both selection domains, we found no consistent effects of negative priming when shifting the focus of attention to a previously to-be-ignored stimulus. However, priming in the perception task was mainly due to repetitions of the target feature (here: color), whereas for the memory task, repetition of the same set of stimulus representations was most important. We propose that the differences can be attributed to a reduced cognitive effort when the now relevant memory representation had already been pre-activated (even as a distractor) in the previous trial. Additionally, our experiments both underscore the importance of taking stimulus–response associations into account, which may be a hidden factor behind differences between domains. We conclude that any attempt of comparing internal versus external attentional selection has to consider inherent differences in selection dynamics across representational domains

Machine learning‐based classification of Alzheimer's disease and its at‐risk states using personality traits, anxiety, and depression

Background Alzheimer's disease (AD) is often preceded by stages of cognitive impairment, namely subjective cognitive decline (SCD) and mild cognitive impairment (MCI). While cerebrospinal fluid (CSF) biomarkers are established predictors of AD, other non-invasive candidate predictors include personality traits, anxiety, and depression, among others. These predictors offer non-invasive assessment and exhibit changes during AD development and preclinical stages. Methods In a cross-sectional design, we comparatively evaluated the predictive value of personality traits (Big Five), geriatric anxiety and depression scores, resting-state functional magnetic resonance imaging activity of the default mode network, apoliprotein E (ApoE) genotype, and CSF biomarkers (tTau, pTau181, Aβ42/40 ratio) in a multi-class support vector machine classification. Participants included 189 healthy controls (HC), 338 individuals with SCD, 132 with amnestic MCI, and 74 with mild AD from the multicenter DZNE-Longitudinal Cognitive Impairment and Dementia Study (DELCODE). Results Mean predictive accuracy across all participant groups was highest when utilizing a combination of personality, depression, and anxiety scores. HC were best predicted by a feature set comprised of depression and anxiety scores and participants with AD were best predicted by a feature set containing CSF biomarkers. Classification of participants with SCD or aMCI was near chance level for all assessed feature sets. Conclusion Our results demonstrate predictive value of personality trait and state scores for AD. Importantly, CSF biomarkers, personality, depression, anxiety, and ApoE genotype show complementary value for classification of AD and its at-risk stages

Silencing the cardiac potassium channel Kv4.3 by RNA interference in a CHO expression system

RNA interference (RNAi) is a powerful technique for gene silencing, in which the downregulation of mRNA is triggered by short RNAs complementary to a target mRNA sequence, with consequent reduction of the encoded protein. The aim of this study was to test the effects of silencing the expression of the cardiac potassium channel Kv4.3 in a heterologous expression system, in order to investigate the effect of RNAi on channel properties. A Chinese hamster ovary cell line stably expressing Kv4.3 and the accessory beta-subunit KChIP2 was transfected with small-interfering RNAs (siRNAs) targeting Kv4.3. Effects of RNAi were monitored at the mRNA, protein, and functional levels. Real-time PCR and immunofluorescence staining revealed significant reduction of Kv4.3 mRNA and protein expression. These results were confirmed by functional patch-clamp measurements of the transient outward current (I(to)) which was reduced up to 80% by RNAi. We conclude that the use of siRNAs reagents for post-transcriptional gene silencing is a new effective method for the reduction of the expression and function of different ionic channels which may be adapted for studying their role also in native cells