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Stroke remains a leading cause of adult disability, underscoring why research continues to focus on advancing new treatment methods and neurophysiological indexes. While these studies may be effective, many lack a clear theoretical framework. The current study first determined the optimal combination effects of mirror therapy (MT) with transcranial direct current stimulation (tDCS) on the premotor or primary motor cortex on its short-term and sustained clinical outcomes. We then introduced electroencephalogram (EEG) indexes derived from the gating-by-inhibition model to explore the underlying therapeutic mechanisms. The EEG indexes used in this study focused on the functional involvement for motor generation: alpha power at temporal regions (inhibiting non-motor activity) and central-frontal regions (releasing motor regions from inhibition). Results showed that post-training benefits, measured by Fugl-Meyer Assessment (FMA), were similar across 3 tDCS interventions (premotor, primary motor, sham). EEG seemed more sensitive to the training, with notable responses in the premotor tDCS group. Three months after training, only the premotor tDCS group maintained the gains in FMA, with these improvements correlated with the EEG indexes. Again, this pattern was specific to premotor tDCS. Since the gating-by-inhibition model suggests that EEG index reflects an individual’s psychomotor efficiency, we also found that the baseline EEG index could predict FMA retention. Our findings demonstrate the superiority of combined premotor tDCS with MT and identify functionally oscillatory alpha-band activity in the temporal and central-frontal regions as potentially underlying the therapeutic mechanism. An individual’s spatial pattern of EEG may be effective in predicting upper extremity retention effect.

Combined exercise and cognitive training have been evidenced to be effective in cognitive and physical functions in post-stroke survivors. Recent interest has gradually shifted to technology-aided cognitive rehabilitation. However, clear neural makers or comprehensive behavioral indexes used for evaluating rehabilitation remain unexplored. The study aimed to examine the effects of two types of combined exercise-cognitive training on stroke patients with cognitive dysfunction, focusing on neural and behavioral markers. 39 patients were randomly assigned to sequential exercise-cognitive training, simultaneous exercise-cognitive training or active control groups and underwent 60 minutes/day training, 3 days/week, for 12 weeks. 29 patients ultimately completed the training. The markers/indexes included cognitive function, physical function, instrumental activities of daily living, and caregiver strain. Cognitive function included working memory task performance, neurophysiological markers, and cognitive indexes. The results indicated no d-prime difference between groups after the training. The simultaneous training demonstrated significant improvements in the neurophysiological marker of P300 and theta coherence compared to the other groups. Moreover, the simultaneous training also led to significant enhancements in physical function, as measured by the Rivermead Mobility Index, comparing to the other groups. Further analysis contrasting the two exercise-cognitive trainings revealed that improvements in cognition and multifaceted domains (i.e., instrumental activities of daily living and caregiver strain) were manifested in the simultaneous training. Together with the neural markers identified in the current interventions, the differential impacts of the two interventions indicates the potential of technology-driven and personalized rehabilitation in post-stroke patients.

Abstract Background Cognitive flexibility refers to the capacity to shift between conceptual representations particularly in response to changes in instruction and feedback. It enables individuals to swiftly adapt to changes in their environment and has significant implications for learning. The present study focuses on investigating changes in cognitive flexibility following an intervention programme—Structure Learning training. Methods Participants are pseudo-randomised to either the Training or Control group, while matched on age, sex, intelligence and cognitive flexibility performance. In the Training group, participants undergo around 2 weeks of training (at least 13 sessions) on Structure Learning. In the Control group, participants do not have to undergo any training and are never exposed to the Structure Learning task. The effects of Structure Learning training are investigated at both the behavioural and neural level. We measured covariates that can influence an individual’s training performance before the training phase and outcome measures that can potentially show training benefits after the training phase. At the behavioural level, we investigated outcomes in both cognitive and social aspects with a primary focus on executive functions. At the neural level, we employed a multimodality approach and investigated potential changes to functional connectivity patterns, neurometabolite concentration in the frontal brain regions, and brain microstructure and myelination. Discussion We reported the development of a novel training programme based on Structure Learning that aims to hone a general learning ability to potentially achieve extensive transfer benefits across various cognitive constructs. Potential transfer benefits can be exhibited through better performance in outcome measures between Training and Control participants, and positive associations between training performance and outcomes after the training in Training participants. Moreover, we attempt to substantiate behavioural findings with evidence of neural changes across different imaging modalities by the Structure Learning training. Trial registration National Institutes of Health U.S. National Library of Medicine ClinicalTrials.gov NCT05611788. Registered on 7 November 2022. Protocol version: 11 May 2023.

The goal of stroke rehabilitation is to establish a robust protocol for patients to live independently in community. Firstly, we examined the impact of 3 hybridized transcranial direct current stimulation (tDCS)-mirror therapy interventions on activities of daily life (ADL) in stroke patients. Secondly, we explored the underlying therapeutic mechanisms with theory-driven electroencephalography (EEG) indexes in the alpha band. This was achieved by identifying the unique contributions of alpha power in motor production to ADL in relation to the premotor cortex (PMC), primary cortex (M1), and Sham tDCS with mirror therapy. The results showed that, although post-intervention ADL improvement was comparable among the three tDCS groups, one of the EEG indexes differentiated the interventions. Neural-behavioral correlation analyses revealed that different types of ADL improvements consistently corresponded with alpha power in the temporal lobe exclusively in the PMC tDCS group (all rs > 0.39). By contrast, alterations in alpha power in the central-frontal region were found to vary, with ADL primarily in the M1 tDCS group (r = −0.6 or 0.7), with the benefit depending on the complexity of the ADL. In conclusion, this research suggested two potential therapeutic mechanisms and demonstrated the additive benefits of introducing theory-driven neural indexes in explaining ADL.

To better capture the polygenic architecture of Alzheimer’s disease (AD), we developed a joint genetic score, MetaGRS. We incorporated genetic variants for AD and 24 other traits from two independent cohorts, NACC (n = 3,174, training set) and UPitt (n = 2,053, validation set). One standard deviation increase in the MetaGRS is associated with about 57% increase in the AD risk [hazard ratio (HR) = 1.577, p = 7.17 E-56], showing little difference from the HR for AD GRS alone (HR = 1.579, p = 1.20E-56), suggesting similar utility of both models. We also conducted APOE-stratified analyses to assess the role of the e4 allele on risk prediction. Similar to that of the combined model, our stratified results did not show a considerable improvement of the MetaGRS. Our study showed that the prediction power of the MetaGRS significantly outperformed that of the reference model without any genetic information, but was effectively equivalent to the prediction power of the AD GRS.

Abstract While awaiting the COVID-19 vaccines, researchers have been actively exploring the effectiveness of existing vaccines against the new virus, among which the BCG vaccine (Bacillus Calmette-Guérin) receives the most attention. While many reports suggest a potential role for BCG immunization in ameliorating SARS-CoV-2 infection, these findings remain controversial. With country-level COVID-19 outbreak data from Johns Hopkins University Coronavirus Resource Center, and BCG program data from World Atlas of BCG Policies and Practices and WHO/UNICE, we estimated a dynamic model to investigate the effect of BCG vaccination across time during the pandemic. Our results reconcile these varying reports regarding protection by BCG against COVID-19 in a variety of clinical scenarios and model specifications. We observe a notable protective effect of the BCG vaccine during the early stage of the pandemic. However, we do not see any strong evidence for protection during the later stages. We also see that a higher proportion of vaccinated young population may confer some level of communal protection against the virus in the early pandemic period, even when the proportion of vaccination in the older population is low. Our results highlight that while BCG may offer some protection against COVID-19, we should be cautious in interpreting the estimated effectiveness as it may vary over time and depend on the age structure of the vaccinated population.

Acute pancreatitis (AP) is a serious inflammatory condition of the pancreas that can be associated with chylomicronemia syndrome (CS). Currently, no study has explored the differences between non-CS-associated AP and CS-associated AP in terms of gene expression. Transcriptomic profiles of blood samples from patients with AP were retrieved from GSE194331 (non-CS-associated) and GSE149607 (CS-associated). GSE31568 was used to examine the linkage between non-CS-associated AP and the expression of micro RNAs (miRNAs). Differentially expressed genes (DEGs) were identified, a gene regulatory network was constructed, and hub genes were defined. Subsequently, single-sample gene set enrichment analysis (ssGSEA) scores of hub genes were calculated to represent their regulatory-level activity. A total of 1851 shared DEGs were identified between non-CS-associated and CS-associated AP. Neutrophils were significantly enriched in both conditions. In non-CS-associated AP, miRNAs including hsa-miR-21, hsa-miR-146a, and hsa-miR-106a demonstrated a lower expression level as compared with the healthy control. Furthermore, the expression patterns and regulatory activities were largely opposite between non-CS-associated and CS-associated AP, with significantly lower estimated neutrophils in the latter case. In summary, we found that the regulation of neutrophils was altered in AP. There was a different gene expression pattern and lower estimated neutrophil infiltration in CS-associated AP. Whether these findings are clinically significant requires further investigation.

ADP ribosylation factor (Arf) GTPases are key regulators of membrane traffic at the Golgi complex. In yeast, Arf guanine nucleotide-exchange factor (GEF) Syt1p activates Arf-like protein Arl1p, which was accompanied by accumulation of golgin Imh1p at late Golgi, but whether and how this function of Syt1p is regulated remains unclear. Here, we report that the inositol-requiring kinase 1 (Ire1p)-mediated unfolded protein response (UPR) modulated Arl1p activation at late Golgi. Arl1p activation was dependent on both kinase and endo-RNase activities of Ire1p. Moreover, constitutively active transcription factor Hac1p restored the Golgi localization of Arl1p and Imh1p in IRE1-deleted cells. Elucidating the mechanism of Ire1p-Hac1p axis actions, we found that it regulated phosphorylation of Syt1p, which enhances Arl1p activation, recruitment of Imh1p to the Golgi, and Syt1p interaction with Arl1p. Consistent with these findings, the induction of UPR by tunicamycin treatment increases phosphorylation of Syt1p, resulting in Arl1p activation. Thus, these findings clarify how the UPR influences the roles of Syt1p, Arl1p, and Imh1p in Golgi transport. [ABSTRACT FROM AUTHOR]

Near- and far-space coding in the human brain is a dynamic process. Areas in dorsal, as well as ventral visual association cortex, including right posterior parietal cortex (rPPC), right frontal eye field (rFEF), and right ventral occipital cortex (rVO), have been shown to be important in visuospatial processing, but the involvement of these areas when the information is in near or far space remains unclear. There is a need for investigations of these representations to help explain the pathophysiology of hemispatial neglect, and the role of near and far space is crucial to this. We used a conjunction visual search task using an elliptical array to investigate the effects of transcranial magnetic stimulation delivered over rFEF, rPPC, and rVO on the processing of targets in near and far space and at a range of horizontal eccentricities. As in previous studies, we found that rVO was involved in far-space search, and rFEF was involved regardless of the distance to the array. It was found that rPPC was involved in search only in far space, with a neglect-like effect when the target was located in the most eccentric locations. No effects were seen for any site for a feature search task. As the search arrays had higher predictability with respect to target location than is often the case, these data may form a basis for clarifying both the role of PPC in visual search and its contribution to neglect, as well as the importance of near and far space in these. [ABSTRACT FROM AUTHOR]

The dorsal attentional network is known for its role in directing top-down visual attention toward task-relevant stimuli. This goal-directed nature of the dorsal network makes it a suitable candidate for processing and extracting predictive information from the visual environment. In this review we briefly summarize some of the findings that delineate the neural substrates that contribute to predictive learning at both levels within the dorsal attentional system: including the frontal eye field (FEF) and posterior parietal cortex (PPC). We also discuss the similarities and differences between these two regions when it comes to learning predictive information. The current findings from the literature suggest that the FEFs may be more involved in top-down spatial attention, whereas the parietal cortex is involved in processing task-relevant attentional influences driven by stimulus salience, both contribute to the processing of predictive cues at different time points. [ABSTRACT FROM AUTHOR]