Proper circadian photoentrainment is crucial for the survival of many organisms. In mammals, intrinsically photosensitive retinal ganglion cells (ipRGCs) can use the photopigment melanopsin to sense light independently from rod and cone photoreceptors and send this information to many brain nuclei such as the suprachiasmatic nucleus (SCN), the site of the central circadian pacemaker. Here, we measure ionic currents and develop mathematical models of the electrical activity of two types of ipRGCs M1, which projects to the SCN, and M4, which does not. We illustrate how their ionic properties differ, mainly how ionic currents generate lower spike rates and depolarization block in M1 ipRGCs. Both M1 and M4 cells have large geometries and project to higher visual centers of the brain via the optic nerve. Using a partial differential equation model, we show how axons of M1 and M4 cells faithfully convey information from the soma to the synapse even when the signal at the soma is attenuated due to depolarization block. Finally, we consider an ionic model of circadian photoentrainment from ipRGCs synapsing on SCN neurons and show how the properties of M1 ipRGCs are tuned to create accurate transmission of visual signals from the retina to the central pacemaker, whereas M4 ipRGCs would not evoke nearly as efficient a postsynaptic response. This work shows how ipRGCs and SCN neurons' electrical activities are tuned to allow for accurate circadian photoentrainment.Behavioral and psychological symptoms of dementia (BPSD) ubiquitously disturb all patients with dementia at some point in the disease course. Although a plethora of non-pharmacological and pharmacological methods targeting the relief BPSD have been developed, the therapeutic effect is still far from ideal. Here, a rat BPSD model combining the physiological changes with mental insults was successfully established. Meanwhile, our results indicated that TMP attenuated anxious behavior using an elevated plus maze (EPM) test, ameliorated recognitive ability and sociability through a novel object recognition test (NORT) and social interaction test (SIT), and improved learning and memory impairments via a Barnes maze in rats with bilateral common carotid arteries occlusion (BCCAO) plus chronic restraint stress (CRS). https://www.selleckchem.com/products/4-Methylumbelliferone(4-MU).html Given that hippocampus chronic cerebral hypoperfusion (CCH) always causes damage to the hippocampus, and the majority of cognitive impairments, behaviors, and stress responses are associated with pathology in the hippocampus including anxiety and depression, we paid attention to investigate the role of the hippocampus in BPSD. Our results indicated that Tetramethylpyrazine (TMP) attenuated anxiety and ameliorated recognitive ability, sociability, learning, and memory impairments due to alleviating dendritic and spine deficits, and upregulating the expression of synapse-related proteins (including PSD95, SYN, GAP43, SYP) in the hippocampus. We also found that the underlying mechanism was that TMP could activate the TrkB/ERK/CREB signaling pathway to promote synaptic remodeling in vivo and in vitro. Mechanically, the present study enlarges the therapeutic scope of TMP in neurodegenerative disorders and provides basic knowledge and feasible candidates for treating BPSD, particularly for vascular dementia.Visual attention is the cognitive process that mediates the selection of important information from the environment. This selection is usually controlled by bottom-up and top-down attentional biasing. Since for most humans vision is the dominant sense, visual attention is critically important for higher-order cognitive functions and related deficits are a core symptom of many neuropsychiatric and neurological disorders. Here, we summarize the importance and relative contributions of different neuromodulators and neurotransmitters to the neural mechanisms of top-down and bottom-up attentional control. We will not only review the roles of widely accepted neuromodulators, such as acetylcholine, dopamine and noradrenaline, but also the contributions of other modulatory substances. In doing so, we hope to shed some light on the current understanding of the role of neurochemistry in shaping neuron properties contributing to the allocation of attention in the visual field.Tissue pathology in multiple sclerosis (MS) is highly complex, requiring multi-dimensional analysis. In this study, our goal was to test the feasibility of obtaining high angular resolution diffusion imaging (HARDI) metrics through single-shell modeling of diffusion tensor imaging (DTI) data, and investigate how advanced measures from single-shell HARDI and DTI tractography perform relative to classical DTI metrics in assessing MS pathology. We examined 52 relapsing-remitting MS patients who had 3T anatomical brain MRI and DTI. Single-shell HARDI modeling yielded 5 sub-voxel-based metrics, totalling 11 diffusion measures including 4 DTI and 2 tractography metrics. Based on machine learning of 3-dimensional regions of interest, we evaluated the importance of the measures through several tissue classification tasks. These included two within-subject comparisons lesion versus normal appearing white matter (NAWM); and lesion core versus shell. Further, by stratifying patients as having high (above 75% ile ) and lritical for improved understanding of MS pathology.Connectivity within the human connectome occurs between multiple neuronal systems-at small to very large spatial scales. Independent component analysis (ICA) is potentially a powerful tool to facilitate multi-scale analyses. However, ICA has yet to be fully evaluated at very low (10 or fewer) and ultra-high dimensionalities (200 or greater). The current investigation used data from the Human Connectome Project (HCP) to determine the following (1) if larger networks, or meta-networks, are present at low dimensionality, (2) if nuisance sources increase with dimensionality, and (3) if ICA is prone to overfitting. Using bootstrap ICA, results suggested that, at very low dimensionality, ICA spatial maps consisted of Visual/Attention and Default/Control meta-networks. At fewer than 10 components, well-known networks such as the Somatomotor Network were absent from results. At high dimensionality, nuisance sources were present even in denoised high-quality data but were identifiable by correlation with tissue probability maps.