In higher-order conditioning paradigms, such as sensory preconditioning or second-order conditioning, discrete (e.g., phasic) or contextual (e.g., static) stimuli can gain the ability to elicit learned responses despite never being directly paired with reinforcement. The purpose of this mini-review is to examine the neuroanatomical basis of high-order conditioning, by selectively reviewing research that has examined the role of the retrosplenial cortex (RSC) in sensory preconditioning and second-order conditioning. For both forms of higher-order conditioning, we first discuss the types of associations that may occur and then review findings from RSC lesion/inactivation experiments. These experiments demonstrate a role for the RSC in sensory preconditioning, suggesting that this cortical region might contribute to higher-order conditioning via the encoding of neutral stimulus-stimulus associations. In addition, we address knowledge gaps, avenues for future research, and consider the contribution of the RSC to higher-order conditioning in relation to related brain structures.Autism Spectrum Disorder (ASD) is a heterogeneous neurodevelopmental disorder with deficient social skills, communication deficits and repetitive behaviors. The prevalence of ASD has increased among children in recent years. Children with ASD experience more sleep problems, and sleep appears to be essential for the survival and integrity of most living organisms, especially for typical synaptic development and brain plasticity. Many methods have been used to assess sleep problems over past decades such as sleep diaries and parent-reported questionnaires, electroencephalography, actigraphy and videosomnography. A substantial number of rodent and non-human primate models of ASD have been generated. Many of these animal models exhibited sleep disorders at an early age. The aim of this review is to examine and discuss sleep disorders in children with ASD. Toward this aim, we evaluated the prevalence, clinical characteristics, phenotypic analyses, and pathophysiological brain mechanisms of ASD. We highlight the current state of animal models for ASD and explore their implications and prospects for investigating sleep disorders associated with ASD.The question of consciousness in other species, not least species very physically different from humans such as insects, is highly challenging for a number of reasons. SEL120 One reason is that we do not have any available empirical method to answer the question. Another reason is that current theories of consciousness disagree about the relation between physical structure and consciousness, i.e., whether consciousness requires specific, say, neural structures or whether consciousness can be realized in different ways. This article sets out to analyze if and how there could be an empirical and/or a theoretical approach to the topic on the basis of current consciousness research in humans.Hyperphosphorylation and the subsequent aggregation of tau protein into neurofibrillary tangles (NFTs) are well-established neuropathological hallmarks of Alzheimer's disease (AD) and associated tauopathies. To further examine the impact and progression of human tau pathology in neurodegenerative contexts, the humanized tau (htau) mouse model was originally created. Despite AD-like tau pathological features recapitulated in the htau mouse model, robustness of behavioral phenotypes has not been fully established. With the ultimate goal of evaluating the htau mouse model as a candidate for testing AD therapeutics, we set out to verify, in-house, the presence of robust, replicable cognitive deficits in the htau mice. The present study shows behavioral data collected from a carefully curated battery of learning and memory tests. Here we report a significant short-term spatial memory deficit in aged htau mice, representing a novel finding in this model. However, we did not find salient impairments in long-term learning and memory previously reported in this mouse model. Here, we attempted to understand the discrepancies in the literature by highlighting the necessity of scrutinizing key procedural differences across studies. Reported cognitive deficits in the htau model may depend on task difficulty and other procedural details. While the htau mouse remains a unique and valuable animal model for replicating late onset AD-like human tau pathology, its cognitive deficits are modest under standard testing conditions. The overarching message is that before using any AD mouse model to evaluate treatment efficacies, it is imperative to first characterize and verify the presence of behavioral deficits in-house.Parkinson's disease (PD) is the second most common neurodegenerative disorder after Alzheimer's disease. It is a chronic and progressive disorder estimated to affect at least 4 million people worldwide. Although the etiology of PD remains unclear, it has been found that the dysfunction of synaptic vesicle endocytosis (SVE) in neural terminal happens before the loss of dopaminergic neurons. Recently, accumulating evidence reveals that the PD-linked synaptic genes, including DNAJC6, SYNJ1, and SH3GL2, significantly contribute to the disruptions of SVE, which is vital for the pathogenesis of PD. In addition, the proteins encoded by other PD-associated genes such as SNCA, LRRK2, PRKN, and DJ-1 also play key roles in the regulation of SVE. Here we present the facts about SVE-related genes and discussed their potential relevance to the pathogenesis of PD.Epilepsy is one of the most common neurological disorders typically characterized by recurrent and uncontrollable seizures, which seriously affects the quality of life of epilepsy patients. The effective tool utilized in the clinical diagnosis of epilepsy is the Electroencephalogram (EEG). The emergence of machine learning promotes the development of automated epilepsy detection techniques. New algorithms are continuously introduced to shorten the detection time and improve classification accuracy. This minireview summarized the latest research of epilepsy detection techniques that focused on acquiring, preprocessing, feature extraction, and classification of epileptic EEG signals. The application of seizure prediction and localization based on EEG signals in the diagnosis of epilepsy was also introduced. And then, the future development trend of epilepsy detection technology has prospected at the end of the article.