Classification of Neuroticism using Psychophysiological Signals During Speaking Task based on Two Different Baseline Measurements

Biosignals from psychophysiological changes can be measured as electroencephalography (EEG), heart rate, skin conductance, and respiration rate, to name a few. They have been used in many research areas including human personality. Neuroticism, one of the five major traits underlie personality, reflects stable tendency towards experiencing negative emotions. An understanding of how neuroticism influences responses to psychological distress may shed a light upon individual differences in emotion self-regulation. To study the causal relationship between neuroticism and psychophysiological signals, a selection of appropriate baseline signals as a reference signal is essential to compare to current experimental signals of interest. Thus, we present classification of neuroticism using psychophysiological signals obtained during a speaking task based on two different baseline measurements (eyes closed and eyes open). Eight healthy male participants consisting of four neurotic and four emotionally stable subjects were recruited based on Eysenck Personality Inventory (EPI) and Big Five Inventory (BFI) scoring system. Four features including mean EEG beta power, heart rate, skin conductance, and respiration rate were used for the classification using a Support Vector Machine (SVM). The results showed higher classification accuracy achieved with eyes open as the baseline (62.5%) as compared to eyes closed as the baseline (37.5%), during speaking task. This indicate the importance of selecting appropriate baseline in analysis involving EEG and physiological signals

A Standards Organization for Open and FAIR Neuroscience: the International Neuroinformatics Coordinating Facility

There is great need for coordination around standards and best practices in neuroscience to support efforts to make neuroscience a data-centric discipline. Major brain initiatives launched around the world are poised to generate huge stores of neuroscience data. At the same time, neuroscience, like many domains in biomedicine, is confronting the issues of transparency, rigor, and reproducibility. Widely used, validated standards and best practices are key to addressing the challenges in both big and small data science, as they are essential for integrating diverse data and for developing a robust, effective, and sustainable infrastructure to support open and reproducible neuroscience. However, developing community standards and gaining their adoption is difficult. The current landscape is characterized both by a lack of robust, validated standards and a plethora of overlapping, underdeveloped, untested and underutilized standards and best practices. The International Neuroinformatics Coordinating Facility (INCF), an independent organization dedicated to promoting data sharing through the coordination of infrastructure and standards, has recently implemented a formal procedure for evaluating and endorsing community standards and best practices in support of the FAIR principles. By formally serving as a standards organization dedicated to open and FAIR neuroscience, INCF helps evaluate, promulgate, and coordinate standards and best practices across neuroscience. Here, we provide an overview of the process and discuss how neuroscience can benefit from having a dedicated standards body

Synthesis of 3D MRI Brain Images With Shape and Texture Generative Adversarial Deep Neural Networks

Generative Adversarial Networks (GAN) are emerging as an exciting training paradigm which promises a step improvement to the impressive feature learning capabilities of deep neural networks. Unlike supervised learning approaches, GAN learns generalizable features without requiring labeled images to achieve new capabilities like distinguishing previously unseen anomalies, creating novel instances of data and factorizing learned features into explainable dimensions in fully unsupervised fashion. The advanced feature learning property of GAN will enable the next generation of computational image understanding tasks. However, GAN models are difficult to train to converge towards good models, especially for high resolution and high dimensional datasets like image volumes. We develop a GAN approach to learn a generative model of T1-contrast 3D MRI image volumes of the healthy human brain by training on 1112 MRI images from the Human Connectome Project. Our method utilizes a first unconditional Super-Resolution GAN, dubbed the shape network, to learn the 3D shape variations in adult brains and a second conditional pix2pix GAN, dubbed the texture network, to upgrade image slices with realistic local contrast patterns. Novel 3D MRI images are synthesized by first applying the 3D voxel-wise deformation map which is generated from the shape network to deform the Montreal Neurological Institute (MNI) brain template and subsequently performing style transfer on axial-wise slices using the texture network. The Maximum Mean Discrepancy (MMD) and Multi-scale Structural Similarity Index Measure (MS-SSIM) scores of MRI image volumes synthesized using our GAN approach are competitive with state-of-art GAN methods. Our work establishes the feasibility of an alternative approach to high-dimensional GAN learning - splitting the type of information content learned among several GANs can be an effective form of regularization and complementary to latent code shaping or super-resolution approaches in state-of-the-art methods

From online resources to collaborative global neuroscience research: where are we heading?

Neuroscience has emerged as a richly transdisciplinary field, poised to leverage potential synergies with information technology. To investigate the complex nervous system in its normal function and the disease state, researchers in the field are increasingly reliant on generating, sharing and analyzing diverse data from multiple experimental paradigms at multiple spatial and temporal scales. There is growing recognition that brain function must be investigated from a systems perspective. This requires an integrated analysis of genomic, proteomic, anatomical, functional, topological and behavioural information to arrive at accurate scientific conclusions. The integrative neuroinformatics approaches for exploring complex structure-function relationships in the nervous system have been extensively reviewed. To support neuroscience research, the neuroscientific community also generates and maintains web-accessible databases of experimental and computational data and innovative software tools. Neuroinformatics is an emerging sub-field of neuroscience which focuses on addressing the unique technological and computational challenges to integrate and analyze the increasingly high-volume, multi-dimensional, and fine-grain data generated from neuroscience experiments. The most visible contributions from neuroinformatics include the myriad reference atlases of brain anatomy (human and other mammals such as rodents, primates and pig), gene and protein sequences and the bioinformatics software tools for alignment, matching and identification. Other neuroinformatics initiatives include the various open-source preprocessing and processing software and workflows for data analysis as well as the specifications for data format and software interoperability that allow seamless exchange of data between labs, software tools and modalities

Output channel design for collecting closely-spaced particle streams from spiral inertial separation devices

Recent advances in inertial microfluidics designs have enabled high throughput, label-free separation of cells for a variety of bioanalytical applications. Various device configurations have been proposed for binary separation with a focus on enhancing the separation distance between particle streams to improve the efficiency of separate particle collection. These configurations have not demonstrated scaling beyond 3 particle streams either because the channel width is a constraint at the collection outlets or particle streams would be too closely spaced to be collected separately. We propose a method to design collection outlets for inertial focusing and separation devices which can collect closely-spaced particle streams and easily scale to an arbitrary number of collection channels without constraining the outlet channel width, which is the usual cause of clogging or cell damage. According to our approach, collection outlets are a series of side-branching channels perpendicular to the main channel of egress. The width and length of the outlets can be chosen subject to constraints from the position of the particle streams and fluidic resistance ratio computed from fluid dynamics simulations. We show the efficacy of this approach by demonstrating a successful collection of upto 3 particle streams of 7μm, 10μm and 15μm fluorescent beads which have been focused and separated by a spiral inertial device with a separation distance of only 10μm -15μm. With a throughput of 1.8mL/min, we achieved collection efficiency exceeding 90% for each particle at the respective collection outlet. The flexibility to use wide collection channels also enabled us to fabricate the microfluidic device with an epoxy mold that was created using xurography, a low cost, and imprecise fabrication technique

Towards Multiplex Molecular Diagnosis—A Review of Microfluidic Genomics Technologies

Highly sensitive and specific pathogen diagnosis is essential for correct and timely treatment of infectious diseases, especially virulent strains, in people. Point-of-care pathogen diagnosis can be a tremendous help in managing disease outbreaks as well as in routine healthcare settings. Infectious pathogens can be identified with high specificity using molecular methods. A plethora of microfluidic innovations in recent years have now made it increasingly feasible to develop portable, robust, accurate, and sensitive genomic diagnostic devices for deployment at the point of care. However, improving processing time, multiplexed detection, sensitivity and limit of detection, specificity, and ease of deployment in resource-limited settings are ongoing challenges. This review outlines recent techniques in microfluidic genomic diagnosis and devices with a focus on integrating them into a lab on a chip that will lead towards the development of multiplexed point-of-care devices of high sensitivity and specificity

Study on the Optimum Cutting Parameters of an Aluminum Mold for Effective Bonding Strength of a PDMS Microfluidic Device

Master mold fabricated using micro milling is an easy way to develop the polydimethylsiloxane (PDMS) based microfluidic device. Achieving high-quality micro-milled surface is important for excellent bonding strength between PDMS and glass slide. The aim of our experiment is to study the optimal cutting parameters for micro milling an aluminum mold insert for the production of a fine resolution microstructure with the minimum surface roughness using conventional computer numerical control (CNC) machine systems; we also aim to measure the bonding strength of PDMS with different surface roughnesses. Response surface methodology was employed to optimize the cutting parameters in order to obtain high surface smoothness. The cutting parameters were demonstrated with the following combinations: 20,000 rpm spindle speed, 50 mm/min feed rate, depth of cut 5 µm with tool size 200 µm or less; this gives a fine resolution microstructure with the minimum surface roughness and strong bonding strength between PDMS–PDMS and PDMS–glass