Thalamic white matter macrostructure and subnuclei volumes in Parkinson’s disease depression

Depression is a common non-motor feature of Parkinson’s disease (PD) which confers significant morbidity and is challenging to treat. The thalamus is a key component in the basal ganglia-thalamocortical network critical to the pathogenesis of PD and depression but the precise thalamic subnuclei involved in PD depression have not been identified. We performed structural and diffusion-weighted imaging (DWI) on 76 participants with PD to evaluate the relationship between PD depression and grey and white matter thalamic subnuclear changes. We used a thalamic segmentation method to divide the thalamus into its 50 constituent subnuclei (25 each hemisphere). Fixel-based analysis was used to calculate mean fibre cross-section (FC) for white matter tracts connected to each subnucleus. We assessed volume and FC at baseline and 14–20 months follow-up. A generalised linear mixed model was used to evaluate the relationship between depression, subnuclei volume and mean FC for each thalamic subnucleus. We found that depression scores in PD were associated with lower right pulvinar anterior (PuA) subnucleus volume. Antidepressant use was associated with higher right PuA volume suggesting a possible protective effect of treatment. After follow-up, depression scores were associated with reduced white matter tract macrostructure across almost all tracts connected to thalamic subnuclei. In conclusion, our work implicates the right PuA as a relevant neural structure in PD depression and future work should evaluate its potential as a therapeutic target for PD depression.

Early Diagnosis of Alzheimer’s Disease Using Cerebral Catheter Angiogram Neuroimaging: A Novel Model Based on Deep Learning Approaches

Neuroimaging refers to the techniques that provide efficient information about the neural structure of the human brain, which is utilized for diagnosis, treatment, and scientific research. The problem of classifying neuroimages is one of the most important steps that are needed by medical staff to diagnose their patients early by investigating the indicators of different neuroimaging types. Early diagnosis of Alzheimer’s disease is of great importance in preventing the deterioration of the patient’s situation. In this research, a novel approach was devised based on a digital subtracted angiogram scan that provides sufficient features of a new biomarker cerebral blood flow. The used dataset was acquired from the database of K.A.U.H hospital and contains digital subtracted angiograms of participants who were diagnosed with Alzheimer’s disease, besides samples of normal controls. Since each scan included multiple frames for the left and right ICA’s, pre-processing steps were applied to make the dataset prepared for the next stages of feature extraction and classification. The multiple frames of scans transformed from real space into DCT space and averaged to remove noises. Then, the averaged image was transformed back to the real space, and both sides filtered with Meijering and concatenated in a single image. The proposed model extracts the features using different pre-trained models: InceptionV3 and DenseNet201. Then, the PCA method was utilized to select the features with 0.99 explained variance ratio, where the combination of selected features from both pre-trained models is fed into machine learning classifiers. Overall, the obtained experimental results are at least as good as other state-of-the-art approaches in the literature and more efficient according to the recent medical standards with a 99.14% level of accuracy, considering the difference in dataset samples and the used cerebral blood flow biomarker.

Deterministic Tractography Analysis of Rat Brain Using SIGMA Atlas in 9.4T MRI

Preclinical studies using rodents have been the choice for many neuroscience researchers due totheir close reflection of human biology. In particular, research involving rodents has utilized MRI to accurately identify brain regions and characteristics by acquiring high resolution cavity images with different contrasts non-invasively, and this has resulted in high reproducibility and throughput. In addition, tractographic analysis using diffusion tensor imaging to obtain information on the neural structure of white matter has emerged as a major methodology in the field of neuroscience due to its contribution in discovering significant correlations between altered neural connections and various neurological and psychiatric diseases. However, unlike image analysis studies with human subjects where a myriad of human image analysis programs and procedures have been thoroughly developed and validated, methods for analyzing rat image data using MRI in preclinical research settings have seen significantly less developed. Therefore, in this study, we present a deterministic tractographic analysis pipeline using the SIGMA atlas for a detailed structural segmentation and structural connectivity analysis of the rat brain’s structural connectivity. In addition, the structural connectivity analysis pipeline presented in this study was preliminarily tested on normal and stroke rat models for initial observation.

Oxytocin Attenuates the Stress-Induced Reinstatement of Alcohol-Seeking in Male Rats: Role of the Central Amygdala

Factors such as stress and anxiety often contribute to alcohol-dependent behavior and can trigger a relapse of alcohol addiction and use. Therefore, it is important to investigate potential pharmacological interventions that may alleviate the influence of stress on addiction-related behaviors. Previous studies have demonstrated that the neuropeptide oxytocin has promising anxiolytic potential in mammals and may offer a pharmacological target to diminish the emotional impact on reinstatement of alcohol-seeking. The purpose of the present study was to investigate the effect of oxytocin on stress-induced alcohol relapse and identify a neural structure mediating this effect through the use of an ethanol self-administration and yohimbine-induced reinstatement paradigm. While yohimbine administration resulted in the reinstatement of ethanol-seeking behavior, the concurrent administration of yohimbine and oxytocin attenuated this effect, suggesting that oxytocin may disrupt stress-induced ethanol-seeking behavior. The central amygdala (CeA) is a structure that drives emotional responses and robustly expresses oxytocin receptors. Intra-CeA oxytocin similarly attenuated the yohimbine-induced reinstatement of ethanol-seeking behavior. These results demonstrate that oxytocin has the potential to attenuate stress-induced relapse into ethanol-seeking behavior, and that this mechanism occurs specifically within the central amygdala.

Sex Differences in Substance Use Disorders: A Neurobiological Perspective

Clinical studies provide fundamental knowledge of substance use behaviors (substance of abuse, patterns of use, relapse rates). The combination of neuroimaging approaches reveal correlation between substance use disorder (SUD) and changes in neural structure, function, and neurotransmission. Here, we review these advances, placing special emphasis on sex specific findings from structural neuroimaging studies of those dependent on alcohol, nicotine, cannabis, psychostimulants, or opioids. Recent clinical studies in SUD analyzing sex differences reveal neurobiological changes that are differentially impacted in common reward processing regions such as the striatum, hippocampus, amygdala, insula, and corpus collosum. We reflect on the contribution of sex hormones, period of drug use and abstinence, and the potential impact of these factors on the interpretation of the reported findings. With the overall recognition that SUD impacts the brains of females and males differentially, it is of fundamental importance that future research is designed with sex as a variable of study in this field. Improved understanding of neurobiological changes in males and females in SUD will advance knowledge underlying sex-specific susceptibility and the neurobiological impact in these disorders. Together these findings will inform future treatments that are tailor designed for improved efficacy in females and males with SUD.

Mirror neuron: a beautiful unnecessary concept

Mirror neurons theory, which had been put forward in the eighties based on the results of cognitive research experiments on the macaque monkeys, has prima facie been further validated by the extensive cognitive neurosciences investigations of primates and humans, over the past three decades. The concept was initially prompted by the fact that the brain activity patterns of the subjects were nearly similar, whether the activity was performed or observed by them. And presently, learning of various natures and empathy, and perhaps some aspects of survival, are ascribed to the operations of this class of neurons. Obviously the added complexity on the already complex field of neurosciences cannot be underestimated; and of course there are opponents of the theory, and some profound questions have been raised. Present work, though also in opposition, is based on completely different ground: the fact that the ingenious and grand efforts of the proponents of the theory can be explicated in the realm of the established neural structure of the brain and its computational operations. This possibility is based on the recent discovery of the tactile nature of the vision sensation. Ironically all the results, which form the basis of the mirror neuron concept, also serve to provide the conceptual proof of the new vision theory, which preempts any need for the introduction of the new class of neurons. The vision theory, partially validated through the efforts of the development of the tactile vision substitution systems (TVSS) and ironically also by some to the point mirror neuron experimental works, are sufficient to explain the processes behind empathy, learning and perhaps other mental phenomena; and as such, the need for presumption of additional class of neurons is dispelled. The mental phenomena, which rendered the claim of the mirror neurons, are simply the consequence of subjects beings variably touched by the state of the living environment, through the coherent tactile operation of all senses (four already known as having tactile nature); eyes having the most prominent role: It is the brain’s response (the computations outputs) as motor cortex activity,-- subsequent to the discernment of the streaming massive tactile input data, to appropriately coordinate the observer’s perceived (tactile) engagement, conditioned by the her mental intentional stance sourced in the brain’s protocols (acquired neural patterns)--which is misinterpreted as the evidence for the conceptualization of the mirror neuron.

Applications of Scanning Electron Microscopy Using Secondary and Backscattered Electron Signals in Neural Structure

Scanning electron microscopy (SEM) has contributed to elucidating the ultrastructure of bio-specimens in three dimensions. SEM imagery detects several kinds of signals, of which secondary electrons (SEs) and backscattered electrons (BSEs) are the main electrons used in biological and biomedical research. SE and BSE signals provide a three-dimensional (3D) surface topography and information on the composition of specimens, respectively. Among the various sample preparation techniques for SE-mode SEM, the osmium maceration method is the only approach for examining the subcellular structure that does not require any reconstruction processes. The 3D ultrastructure of organelles, such as the Golgi apparatus, mitochondria, and endoplasmic reticulum has been uncovered using high-resolution SEM of osmium-macerated tissues. Recent instrumental advances in scanning electron microscopes have broadened the applications of SEM for examining bio-specimens and enabled imaging of resin-embedded tissue blocks and sections using BSE-mode SEM under low-accelerating voltages; such techniques are fundamental to the 3D-SEM methods that are now known as focused ion-beam SEM, serial block-face SEM, and array tomography (i.e., serial section SEM). This technical breakthrough has allowed us to establish an innovative BSE imaging technique called section-face imaging to acquire ultrathin information from resin-embedded tissue sections. In contrast, serial section SEM is a modern 3D imaging technique for creating 3D surface rendering models of cells and organelles from tomographic BSE images of consecutive ultrathin sections embedded in resin. In this article, we introduce our related SEM techniques that use SE and BSE signals, such as the osmium maceration method, semithin section SEM (section-face imaging of resin-embedded semithin sections), section-face imaging for correlative light and SEM, and serial section SEM, to summarize their applications to neural structure and discuss the future possibilities and directions for these methods.

Brain is modulated by neuronal plasticity during postnatal development

Neuroplasticity is referred to the ability of the nervous system to change its structure or functions as a result of former stimuli. It is a plausible mechanism underlying a dynamic brain through adaptation processes of neural structure and activity patterns. Nevertheless, it is still unclear how the plastic neural systems achieve and maintain their equilibrium. Additionally, the alterations of balanced brain dynamics under different plasticity rules have not been explored either. Therefore, the present article primarily aims to review recent research studies regarding homosynaptic and heterosynaptic neuroplasticity characterized by the manipulation of excitatory and inhibitory synaptic inputs. Moreover, it attempts to understand different mechanisms related to the main forms of synaptic plasticity at the excitatory and inhibitory synapses during the brain development processes. Hence, this study comprised surveying those articles published since 1988 and available through PubMed, Google Scholar and science direct databases on a keyword-based search paradigm. All in all, the study results presented extensive and corroborative pieces of evidence for the main types of plasticity, including the long-term potentiation (LTP) and long-term depression (LTD) of the excitatory and inhibitory postsynaptic potentials (EPSPs and IPSPs).

“The Accessory Ethmoidal Canal Does Not Necessarily Contain an Arterial Structure”

Introduction: The aim of our study is to describe the prevalence of the accessory ethmoidal artery in endonasal endoscopic cadaver dissections and to identify its intraorbital origin. Material and Methods: From 2018 to 2020, thirty-four nasal dissections were performed in seventeen adult cadaveric heads. We performed a complete ethmoidectomy to identify the ethmoidal canals. Then, we removed the bony canal and the lamina papiracea to verify the injected vessel and to confirm the vascular structure inside the canal. Results: We found the anterior ethmoidal canal (AEC) and the posterior ethmoidal canal (PEC) in 100% of nasal cavities (34/34). We identified 4 accessory ethmoidal canals (AcEC) in the 34 nasal fossae dissected (12%). All AEC contained an arterial vessel. The AcEC contained an arterial vascular structure in 2 cases, a neural structure in other specimen, and in the fourth case no structure could be verified. In 32 of 34 nasal cavities, the PEC contained an artery and only in 2 cases the PEC did not contain any vascular structure. In these specimens, we observed that the AcEC with an arterial vessel inside (6%) was closer to the posterior canal than the anterior canal. Conclusion: According to our findings, we can suggest that the presence of a canal does not necessarily imply the presence of an arterial vessel, and that presence of the accessory ethmoidal artery could be associated with the absence of posterior ethmoidal artery.

An improved data-free surrogate model for solving partial differential equations using deep neural networks

Partial differential equations (PDEs) are ubiquitous in natural science and engineering problems. Traditional discrete methods for solving PDEs are usually time-consuming and labor-intensive due to the need for tedious mesh generation and numerical iterations. Recently, deep neural networks have shown new promise in cost-effective surrogate modeling because of their universal function approximation abilities. In this paper, we borrow the idea from physics-informed neural networks (PINNs) and propose an improved data-free surrogate model, DFS-Net. Specifically, we devise an attention-based neural structure containing a weighting mechanism to alleviate the problem of unstable or inaccurate predictions by PINNs. The proposed DFS-Net takes expanded spatial and temporal coordinates as the input and directly outputs the observables (quantities of interest). It approximates the PDE solution by minimizing the weighted residuals of the governing equations and data-fit terms, where no simulation or measured data are needed. The experimental results demonstrate that DFS-Net offers a good trade-off between accuracy and efficiency. It outperforms the widely used surrogate models in terms of prediction performance on different numerical benchmarks, including the Helmholtz, Klein–Gordon, and Navier–Stokes equations.