Recognition memory provides the ability to distinguish familiar from novel objects and places, and is important for recording and updating events to guide appropriate behavior. The hippocampus (HPC) and medial prefrontal cortex (mPFC) have both been implicated in recognition memory, but the nature of HPC–mPFC interactions, and its impact on local circuits in mediating this process is not known. Here we show that novelty discrimination is accompanied with higher theta activity (4–10 Hz) and increased c-Fos expression in both these regions. Moreover, theta oscillations were highly coupled between the HPC and mPFC during recognition memory retrieval for novelty discrimination, with the HPC leading the mPFC, but not during initial learning. Principal neurons and interneurons in the mPFC responded more strongly during recognition memory retrieval compared with learning. Optogenetic silencing of HPC input to the mPFC disrupted coupled theta activity between these two structures, as well as the animals' (male Sprague Dawley rats) ability to differentiate novel from familiar objects. These results reveal a key role of monosynaptic connections between the HPC and mPFC in novelty discrimination via theta coupling and identify neural populations that underlie this recognition memory-guided behavior.SIGNIFICANCE STATEMENTMany memory processes are highly dependent on the interregional communication between the HPC and mPFC via neural oscillations. However, how these two brain regions coordinate their oscillatory activity to engage local neural populations to mediate recognition memory for novelty discrimination is poorly understood. This study revealed that the HPC and mPFC theta oscillations and their temporal coupling is correlated with recognition memory-guided behavior. During novel object recognition, the HPC drives mPFC interneurons to effectively reduce the activity of principal neurons. This study provides the first evidence for the requirement of the HPC–mPFC pathway to mediate recognition memory for novelty discrimination and describes a mechanism for how this memory is regulated.

Background: Transcranial alternating current stimulation (tACS) has proven to be an effective treatment for improving cognition, a crucial factor in motor learning. However, current studies are predominantly focused on the motor cortex, and the potential brain mechanisms responsible for the therapeutic effects are still unclear. Given the interconnected nature of motor learning within the brain network, we have proposed a novel approach known as multi-target tACS. This study aims to ascertain whether multi-target tACS is more effective than single-target stimulation in stroke patients and to further explore the potential underlying brain mechanisms by using techniques such as transcranial magnetic stimulation (TMS) and magnetic resonance imaging (MRI). Methods: This study employs a double-blind, sham-controlled, randomized controlled trial design with a 2-week intervention period. Both participants and outcome assessors will remain unaware of treatment allocation throughout the study. Thirty-nine stroke patients will be recruited and randomized into three distinct groups, including the sham tACS group (SS group), the single-target tACS group (ST group), and the multi-target tACS group (MT group), at a 1:1:1 ratio. The primary outcomes are series reaction time tests (SRTTs) combined with electroencephalograms (EEGs). The secondary outcomes include motor evoked potential (MEP), central motor conduction time (CMCT), short interval intracortical inhibition (SICI), intracortical facilitation (ICF), magnetic resonance imaging (MRI), Box and Block Test (BBT), and blood sample RNA sequencing. The tACS interventions for all three groups will be administered over a 2-week period, with outcome assessments conducted at baseline (T0) and 1 day (T1), 7 days (T2), and 14 days (T3) of the intervention phase. Discussion: The study’s findings will determine the potential of 40-Hz tACS to improve motor learning in stroke patients. Additionally, it will compare the effectiveness of multi-target and single-target approaches, shedding light on their respective improvement effects. Through the utilization of techniques such as TMS and MRI, the study aims to uncover the underlying brain mechanisms responsible for the therapeutic impact. Furthermore, the intervention has the potential to facilitate motor learning efficiency, thereby contributing to the advancement of future stroke rehabilitation treatment. Trial registration: Chinese Clinical Trial Registry ChiCTR2300073465. Registered on 11 July 2023.

Exact and fast automatic analyses of electrocardiogram (ECG) signal is critical for the computer based diagnosis of cardiac diseases, which has attracted many researches' attentions in recent years. In the present study, a novel QRS complex detection approach based on differential threshold method was proposed, which was designed as three analysis steps of an improved differential processor, a detector of the R wave, and a detector of the Q and S waves. The QRS complex detection performance of the proposed algorithm was further evaluated with clinical ECG records in patients coming from the widely recognized Massachusetts Institute of Technology Database (MIT-BIH DB). As a result, 67,167 beats were correctly determined with the proposed approach from a total of 67,197 QRS beats (30 ECG records in variant rhythms, 30 minutes for each), while the numbers of false positives and false negatives were 251 and 208, respectively. The summarized sensitivity and positive predictivity of the proposed method were obtained as 99.69% and 99.63%, respectively. Meanwhile, the computing time of the proposed algorithm for a ECG record of 30 min is only 1.40 seconds, which shows a distinct advantage on computation requirement compared with previous works. In conclusion, the proposed straightforward algorithm for QRS complex detection would potentially be implemented easily for portable monitoring applications with both a better performance and a lower computation requirement.

Background: Non-invasive brain stimulation techniques have been widely used in patients with mild cognitive impairment (MCI) to accelerate the recovery of their cognitive functions. However, the clinical efficacy of single non-invasive stimulation techniques in treating MCI still requires further improvement. The combination of two non-invasive neuromodulation techniques can enhance the synergistic effects of the treatment. Repetitive transcranial magnetic stimulation (rTMS) regulates the cortical-subcortical network in a “top-down” manner, while transcutaneous auricular vagus nerve stimulation (taVNS) modulates the brainstem-limbic system-cortical pathway in a “bottom-up” fashion. We will combine rTMS and taVNS, anticipating synergistic regulation through dual pathways to achieve multi-level neural remodeling effects and improve MCI.Methods: This study will investigate the effectiveness of combined rTMS and taVNS therapy in improving the cognitive function of MCI patients. We will enroll 88 participants and randomly assign them to single-stimulation groups and combined-stimulation groups. The single-stimulation groups will be further randomized in a 1:1 ratio into a rTMS + sham taVNS stimulation group and a taVNS + sham rTMS stimulation group; the combined-stimulation groups will be randomized in a 1:1 ratio into an rTMS + taVNS group and an rTMS sham stimulation + taVNS sham stimulation group. All patients will receive treatment for 4 weeks. Assessments will be conducted before treatment (T0), 4-week treatment (T1), and 4-week post-treatment follow-up (T2). The primary outcome measure will be the Chinese version of the Montreal Cognitive Assessment Basic (MoCA-B), while secondary outcome measures will include the Rivermead Behaviour Memory Test (RBMT), the modified Barthel Index (MBI) for activities of daily living, and the latency and amplitude of event-related potential (ERP) P300.Discussion: This study is a clinical randomized controlled trial, which innovatively combines two non-invasive modulation techniques to improve cognitive function in patients with MCI. This study can validate the clinical efficacy of the combined TMS + taVNS stimulation, providing a theoretical basis for the application of this technology in clinical settings.

Objective : Age has been shown to be a crucial factor for the EEG and fMRI small-world networks during sleep. However, the characteristics of the age-related network based on the sleep ECG signal and how the network changes during different sleep stages are poorly understood. This study focuses on exploring the age-related scale-free and small-world network properties of the ECG signal from male subjects during distinct sleep stages, including the wakeful (W), light sleep (LS), deep sleep (DS) and rapid eye movement (REM) stages. Approach : The subjects are divided into two age groups: a younger (age ⩽ 40, n = 11) group and an older group (age > 40, n = 25). Main results : For the scale-free network analysis, our results reveal a distinctive pattern of the scale free network topologies between the two age groups, including the mean degree ( ), the clustering coefficient ( ), and the path length ( ) features, such as the slope distribution of in the younger group increased from 1.99 during W to above 2.05 during DS. In addition, the results indicate that the small-world properties can be found across all sleep stages in both age groups. However, the small-world index in the LS and REM stages significantly decreased with age ( p = 0.0006 and p = 0.05, respectively). Significance : The comparison analysis result indicates that the network topology variations in the sleep ECG signals are prone to show age-relevant differences that could be used for sleep stage classification and sleep disorder diagnosis.