Intranasal vasopressin modulates resting state brain activity across multiple neural systems: Evidence from a brain imaging machine learning study

Vasopressin as Possible Treatment Option in Autism Spectrum Disorder

Autism spectrum disorder (ASD) is rather common, presenting with prevalent early problems in social communication and accompanied by repetitive behavior. As vasopressin was implicated not only in salt-water homeostasis and stress-axis regulation, but also in social behavior, its role in the development of ASD might be suggested. In this review, we summarized a wide range of problems associated with ASD to which vasopressin might contribute, from social skills to communication, motor function problems, autonomous nervous system alterations as well as sleep disturbances, and altered sensory information processing. Beside functional connections between vasopressin and ASD, we draw attention to the anatomical background, highlighting several brain areas, including the paraventricular nucleus of the hypothalamus, medial preoptic area, lateral septum, bed nucleus of stria terminalis, amygdala, hippocampus, olfactory bulb and even the cerebellum, either producing vasopressin or containing vasopressinergic receptors (presumably V1a). Sex differences in the vasopressinergic system might underline the male prevalence of ASD. Moreover, vasopressin might contribute to the effectiveness of available off-label therapies as well as serve as a possible target for intervention. In this sense, vasopressin, but paradoxically also V1a receptor antagonist, were found to be effective in some clinical trials. We concluded that although vasopressin might be an effective candidate for ASD treatment, we might assume that only a subgroup (e.g., with stress-axis disturbances), a certain sex (most probably males) and a certain brain area (targeting by means of virus vectors) would benefit from this therapy.

Arginine Vasopressin, Synaptic Plasticity, and Brain Networks

The arginine vasopressin (AVP), a neurohypophysial hormone, is synthesized within specific sites of the central nervous system and axonally transported to multiple areas, acting as a neurotransmitter/ neuromodulator. In this context, AVP acts primarily through vasopressin receptors A and B and is involved in regulating complex social and cognition behaviors and basic autonomic function. Many earlier studies have shown that AVP as a neuromodulator affects synaptic plasticity. This review updates our current understanding of the underlying molecular mechanisms by which AVP affects synaptic plasticity. Moreover, we discuss AVP modulatory effects on event-related potentials and blood oxygen level-dependent responses in specific brain structures, and AVP effects on the network level oscillatory activity. We aimed at providing an overview of the AVP effects on the brain from the synaptic to the network level.

Intrinsic brain activity patterns across large-scale networks predict reciprocity propensity

Reciprocity is prevalent across human societies, but individuals are heterogeneous regarding their reciprocity propensity. Although a large body of task-based brain imaging measures has shed light on the neural underpinnings of reciprocity at group level, the neural basis underlying the individual differences in reciprocity propensity remains largely unclear. Here, we combined brain imaging and machine learning techniques to individually predict reciprocity propensity from resting-state brain activity measured by fractional amplitude of low-frequency fluctuation. The brain regions contributing to the prediction were then analyzed for functional connectivity and decoding analyses, allowing for a data-driven quantitative inference on psychophysiological functions. Our results indicated that patterns of resting-state brain activity across multiple brain systems were capable of predicting individual reciprocity propensity, with the contributing regions distributed across the salience (e.g., ventrolateral prefrontal cortex), fronto-parietal (e.g., dorsolateral prefrontal cortex), default mode (e.g., ventromedial prefrontal cortex), and sensorimotor (e.g., supplementary motor area) networks. Those contributing brain networks are implicated in emotion and cognitive control, mentalizing, and motor-based processes, respectively. Collectively, these findings provide novel evidence on the neural signatures underlying the individual differences in reciprocity, and lend support the assertion that reciprocity emerges from interactions among regions embodied in multiple large-scale brain networks.

Detecting and Extracting Brain Hemorrhages from CT Images Using Generative Convolutional Imaging Scheme

Purpose

The need for computerized medical assistance for accurate detection of brain hemorrhage from Computer Tomography (CT) images is more mandatory than conventional clinical tests. Recent technologies and advanced computerized algorithms follow Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) techniques to improve medical diagnosis platforms. This technology is making the diagnosis practice of brain issues easier for medical practitioners to analyze and identify diseases with an assured degree of precision and performance.

Methods

As the existing CT image analysis models use standard procedures to detect hemorrhages, the need for DL-based data analysis is essential to provide more accurate results. Generally, the existing techniques are limited with image training efficiency, image filtering procedures, and runtime system tuning modules. On the scope, this work develops a DL-based automated analysis of CT scan slices to find various levels of brain hemorrhages. Notably, this proposed system integrates Convolutional Neural Network (CNN) and Generative Adversarial Network (GAN) architectures as Integrated Generative Adversarial-Convolutional Imaging Model (IGACM) for extracting the CT image features for detecting brain hemorrhages.

Results

This system produces good results and takes lesser training time than existing techniques. This proposed system effectively works over CT images and classifies the abnormalities with more accuracy than current techniques. The experiments and results deliver the optimal detection of hemorrhages with better accuracy. It shows that the proposed system works with 5% to 10% of the better performance compared to other diagnostic techniques.

Conclusion

The complex nature of CT images leads to noncorrelated feature complexities in diagnosis models. Considering the issue, the proposed system used GAN-based effective sampling techniques for enriching complex image samples into CNN training phases. This concludes the effective contribution of the proposed IGACM technique for detecting brain hemorrhages than the existing diagnosis models.

Hormonal abnormalities in alexithymia

Alexithymia is a personality trait characterized by difficulties in emotion recognition and regulation that is associated with deficits in social cognition. High alexithymia levels are considered a transdiagnostic risk factor for a range of psychiatric and medical conditions, including depression, anxiety, and autism. Hormones are known to affect social-emotional cognition and behavior in humans, including the neuropeptides oxytocin and vasopressin, the steroid hormones testosterone and estradiol, the stress hormone cortisol as well as thyroid hormones. However, few studies have investigated hormonal effects on alexithymia and on alexithymia-related impairments in emotion regulation and reactivity, stress response, and social cognition. Here, we provide a brief overview of the evidence linking alexithymia to abnormalities in hormone levels, particularly with regard to cortisol and oxytocin, for which most evidence exists, and to thyroid hormones. We address the current lack of research on the influence of sex hormones on alexithymia and alexithymia-related deficits, and lastly provide future directions for research on associations between hormonal abnormalities and deficits in emotion regulation and social cognition associated with alexithymia.

Convolutions in the rendition of nose to brain therapeutics from bench to bedside: Feats & fallacies

Brain, a subtle organ of multifarious nature presents plethora of physiological, metabolic and bio-chemical convolutions that impede the delivery of biomolecules and thereby resulting in truncated therapeutic outcome in pathological conditions of central nervous system (CNS). The absolute bottleneck in the therapeutic management of such devastating CNS ailments is the BBB. Another pitfall is the lack of efficient technological platforms (due to high cost and low approval rates) as well as limited clinical trials (due to failures of neuro‑leads in late-stage pipelines) for CNS disorders which has become a literal brain drain with poorest success rates compared to other therapeutic areas, owing to time consuming processes, tremendous convolutions and conceivable adverse effects. With the advent of intranasal delivery (via direct N2B or indirect nose to blood to brain), several novel drug delivery carriers viz. unmodified or surface modified nanoparticle based carriers, lipid based colloidal nanocarriers and drysolid/liquid/semisolid nanoformulations or delivery platforms have been designed as a means to deliver therapeutic agents (small and large molecules, peptides and proteins, genes) to brain, bypassing BBB for disorders such as Alzheimer's disease (AD), Parkinson's disease (PD), epilepsy, schizophrenia and CNS malignancies primarily glioblastomas. Intranasal application offers drug delivery through both direct and indirect pathways for the peripherally administered psychopharmacological agents to CNS. This route could also be exploited for the repurposing of conventional drugs for new therapeutic uses. The limited clinical translation of intranasal formulations has been primarily due to existence of barriers of mucociliary clearance in the nasal cavity, enzyme degradation and low permeability of the nasal epithelium. The present review literature aims to decipher the new paradigms of nano therapeutic systems employed for specific N2B drug delivery of CNS drugs through in silico complexation studies using rationally chosen mucoadhesive polymers (exhibiting unique physicochemical properties of nanocarrier's i.e. surface modification, prolonging retention time in the nasal cavity, improving penetration ability, and promoting brain specific delivery with biorecognitive ligands) via molecular docking simulations. Further, the review intends to delineate the feats and fallacies associated with N2B delivery approaches by understanding the physiological/anatomical considerations via decoding the intranasal drug delivery pathways or critical factors such as rationale and mechanism of excipients, affecting the permeability of CNS drugs through nasal mucosa as well as better efficacy in terms of brain targeting, brain bioavailability and time to reach the brain. Additionally, extensive emphasis has also been laid on the innovative formulations under preclinical investigation along with their assessment by means of in vitro /ex vivo/in vivo N2B models and current characterization techniques predisposing an efficient intranasal delivery of therapeutics. A critical appraisal of novel technologies, intranasal products or medical devices available commercially has also been presented. Finally, it could be warranted that more reminiscent pharmacokinetic/pharmacodynamic relationships or validated computational models are mandated to obtain effective screening of molecular architecture of drug-polymer-mucin complexes for clinical translation of N2B therapeutic systems from bench to bedside.

Multivariate morphological brain signatures enable individualized prediction of dispositional need for closure

Need for closure (NFC) reflects stable individual differences in the desire for a quick, definite, and stable answer to a question. A large body of research has documented the association between NFC and various cognitive, emotional and social processes. Despite considerable interest in psychology, little effort has been made to uncover the neural substrates of individual variations in NFC. Herein, we took a data-driven approach to predict NFC trait combining machine learning framework and the whole-brain grey matter volume (GMV) features, which represent a reliable brain imaging measure and have been commonly employed to explore neural basis underlying individual differences of cognition and behaviors. Brain regions contributing to the prediction were then subjected to functional connectivity and decoding analyses for a quantitative inference on their psychophysiological functions. Our results indicated that multivariate patterns of GMV derived from multiple regions across distributed brain systems predicted NFC at individual level. The contributing regions are distributed across the emotional processing network (e.g., striatum), cognitive control network (e.g., lateral prefrontal cortex), social cognition network (e.g., temporoparietal junction) and perceptual processing network (e.g., occipital cortex). The current study provided the first evidence that dispositional NFC is embodied in multiple large-scale brain networks, helping to delineate a more complete picture about the neuropsychological processes that support individual differences in NFC. Beyond these findings, the current interdisciplinary approach to constructing and interpreting neuroimaging-based prediction model of personality traits would be informative to a wide range of future studies on personality.