Neural correlates of boredom in music perception

Introduction: Music can elicit powerful emotional responses, the neural correlates of which have not been properly understood. An important aspect about the quality of any musical piece is its ability to elicit a sense of excitement in the listeners. In this study, we investigated the neural correlates of boredom evoked by music in human subjects. Methods: We used EEG recording in nine subjects while they were listening to total number of 10 short-length (83 sec) musical pieces with various boredom indices. Subjects evaluated boringness of musical pieces while their EEG was recording. Results: Using short time Fourier analysis, we found that beta2 rhythm was (16-20 Hz) significantly lower whenever the subjects rated the music as boring in comparison to nonboring. Discussion: The results demonstrate that the music modulates neural activity of various parts of the brain and can be measured using EEG

The Effect of Photobiomodulation Therapy on the Differentiation, Proliferation, and Migration of the Mesenchymal Stem Cell: A Review

Introduction: The purpose of this study is to investigate the effect of a low-power laser on the proliferation, migration, differentiation of different types of mesenchymal stem cells (MSCs) in different studies.Methods: The relevant articles that were published from 2004 to 2019 were collected from the sources of PubMed, Scopus, and only the articles specifically examining the effect of a low-power laser on the proliferation, differentiation, and migration of the MSCs were investigated.Results: After reviewing the literature, only 42 articles were found relevant. Generally, most of the studies demonstrated that different laser parameters increased the proliferation, migration, and differentiation of the MSCs, except the results of two studies which were contradictory. In fact, changing the parameters of a low-power laser would affect the results. On the other hand, the source of the stem cells was reported as a key factor. In addition, the combination of lasers with other therapeutic approaches was found to be more effective.Conclusion: The different parameters of lasers has been found to be effective in the proliferation, differentiation, and migration of the MSCs and in general, a low-power laser has a positive effect on the MSCs, helping to improve different disease models

An Approach toward Artificial Intelligence Alzheimer's Disease Diagnosis Using Brain Signals

Background: Electroencephalography (EEG) signal analysis is a rapid, low-cost, and practical method for diagnosing the early stages of dementia, including mild cognitive impairment (MCI) and Alzheimer’s disease (AD). The extraction of appropriate biomarkers to assess a subject’s cognitive impairment has attracted a lot of attention in recent years. The aberrant progression of AD leads to cortical detachment. Due to the interaction of several brain areas, these disconnections may show up as abnormalities in functional connectivity and complicated behaviors. Methods: This work suggests a novel method for differentiating between AD, MCI, and HC in two-class and three-class classifications based on EEG signals. To solve the class imbalance, we employ EEG data augmentation techniques, such as repeating minority classes using variational autoencoders (VAEs), as well as traditional noise-addition methods and hybrid approaches. The power spectrum density (PSD) and temporal data employed in this study’s feature extraction from EEG signals were combined, and a support vector machine (SVM) classifier was used to distinguish between three categories of problems. Results: Insufficient data and unbalanced datasets are two common problems in AD datasets. This study has shown that it is possible to generate comparable data using noise addition and VAE, train the model using these data, and, to some extent, overcome the aforementioned issues with an increase in classification accuracy of 2 to 7%. Conclusion: In this work, using EEG data, we were able to successfully detect three classes: AD, MCI, and HC. In comparison to the pre-augmentation stage, the accuracy gained in the classification of the three classes increased by 3% when the VAE model added additional data. As a result, it is clear how useful EEG data augmentation methods are for classes with smaller sample numbers

An Approach toward Artificial Intelligence Alzheimer’s Disease Diagnosis Using Brain Signals

Background: Electroencephalography (EEG) signal analysis is a rapid, low-cost, and practical method for diagnosing the early stages of dementia, including mild cognitive impairment (MCI) and Alzheimer’s disease (AD). The extraction of appropriate biomarkers to assess a subject’s cognitive impairment has attracted a lot of attention in recent years. The aberrant progression of AD leads to cortical detachment. Due to the interaction of several brain areas, these disconnections may show up as abnormalities in functional connectivity and complicated behaviors. Methods: This work suggests a novel method for differentiating between AD, MCI, and HC in two-class and three-class classifications based on EEG signals. To solve the class imbalance, we employ EEG data augmentation techniques, such as repeating minority classes using variational autoencoders (VAEs), as well as traditional noise-addition methods and hybrid approaches. The power spectrum density (PSD) and temporal data employed in this study’s feature extraction from EEG signals were combined, and a support vector machine (SVM) classifier was used to distinguish between three categories of problems. Results: Insufficient data and unbalanced datasets are two common problems in AD datasets. This study has shown that it is possible to generate comparable data using noise addition and VAE, train the model using these data, and, to some extent, overcome the aforementioned issues with an increase in classification accuracy of 2 to 7%. Conclusion: In this work, using EEG data, we were able to successfully detect three classes: AD, MCI, and HC. In comparison to the pre-augmentation stage, the accuracy gained in the classification of the three classes increased by 3% when the VAE model added additional data. As a result, it is clear how useful EEG data augmentation methods are for classes with smaller sample numbers

National and sub-national trend of prevalence and burden of dementia in Iran, from 1990 to 2013; Study protocol

Background: Dementia is a disabling syndrome, which generally affects aged population more than any other age groups. This syndrome has a growing prevalence and incidence worldwide. The prevalence and burden of this group of diseases in Iran have not been estimated in a community-based study yet. This paper aims to explain the systematic approach, data sources, research methodology, and statistical analysis that will be used to quantify the prevalence and burden of dementia at national and sub-national levels. Methods: This is the protocol of a secondary data study that explains the design and method of conducting the study. We will use several sources of data that will include a systematic review of articles and gray literature which have reported the prevalence or incidence of dementia and its uncertainty at national and sub-national levels in Iran, in addition to data about dementia-specific drug sales per each year at provincial levels, as well as data extracted from 23 million health insurance prescriptions over 8 years and some data from medical documents of Iranian Alzheimer's Association members. The technical groups of National and Sub-national Burden of Disease will collect some covariate data, such as age and sex structure of population, urbanization status, mean years of schooling, plasma cholesterol, fasting plasma glucose, and systolic and diastolic blood pressure at provincial levels which will be used in our models. Two statistical models, namely spatio-temporal and hierarchical autoregressive models, will be used for interpolation and extrapolation of missing data. Conclusion: It seems that the study of national and subnational burden of dementia could provide more accurate estimation of prevalence and burden of dementia in Iran with an acceptable level of uncertainty than the previous studies

Developing a New Generation of Integrated Micro-Spec Far Infrared Spectrometers for the EXperiment for Cryogenic Large-Aperture Intensity Mapping (EXCLAIM)

The current state of far-infrared astronomy drives the need to develop compact, sensitive spectrometers for future space and ground-based instruments. Here we present details of the $\rm \mu$-Spec spectrometers currently in development for the far-infrared balloon mission EXCLAIM. The spectrometers are designed to cover the $\rm 555 - 714\ \mu$m range with a resolution of $\rm R\ =\ \lambda / \Delta\lambda\ =\ 512$at the$\rm 638\ \mu$m band center. The spectrometer design incorporates a Rowland grating spectrometer implemented in a parallel plate waveguide on a low-loss single-crystal Si chip, employing Nb microstrip planar transmission lines and thin-film Al kinetic inductance detectors (KIDs). The EXCLAIM$\rm \mu$-Spec design is an advancement upon a successful$\rm R = 64\ \mu$-Spec prototype, and can be considered a sub-mm superconducting photonic integrated circuit (PIC) that combines spectral dispersion and detection. The design operates in a single$M{=}2$grating order, allowing one spectrometer to cover the full EXCLAIM band without requiring a multi-order focal plane. The EXCLAIM instrument will fly six spectrometers, which are fabricated on a single 150 mm diameter Si wafer. Fabrication involves a flip-wafer-bonding process with patterning of the superconducting layers on both sides of the Si dielectric. The spectrometers are designed to operate at 100 mK, and will include 355 Al KID detectors targeting a goal of NEP${\sim}8\times10^{-19}$$\rm W/\sqrt{Hz}$. We summarize the design, fabrication, and ongoing development of these$\rm \mu$-Spec spectrometers for EXCLAIM.Comment: 9 pages, 5 figures, to appear in the Proceedings of the SPIE Astronomical Telescopes + Instrumentation (2022

Tackling dementia globally: the Global Dementia Prevention Program (GloDePP) collaboration

Daniel Reidpath - ORCID: 0000-0002-8796-0420 https://orcid.org/0000-0002-8796-0420http://dx.doi.org/10.7189/jogh.09.0201039pubpub

Tackling dementia globally: the Global Dementia Prevention Program (GloDePP) collaboration

Daniel Reidpath - ORCID: 0000-0002-8796-0420 https://orcid.org/0000-0002-8796-0420http://dx.doi.org/10.7189/jogh.09.0201039pubpub

Experiment for cryogenic large-aperture intensity mapping: instrument design

The experiment for cryogenic large-aperture intensity mapping (EXCLAIM) is a balloon-borne telescope designed to survey star formation in windows from the present to z  =  3.5. During this time, the rate of star formation dropped dramatically, while dark matter continued to cluster. EXCLAIM maps the redshifted emission of singly ionized carbon lines and carbon monoxide using intensity mapping, which permits a blind and complete survey of emitting gas through statistics of cumulative brightness fluctuations. EXCLAIM achieves high sensitivity using a cryogenic telescope coupled to six integrated spectrometers employing kinetic inductance detectors covering 420 to 540 GHz with spectral resolving power R  =  512 and angular resolution ≈4  arc min. The spectral resolving power and cryogenic telescope allow the survey to access dark windows in the spectrum of emission from the upper atmosphere. EXCLAIM will survey 305  deg2 in the Sloan Digital Sky Survey Stripe 82 field from a conventional balloon flight in 2023. EXCLAIM will also map several galactic fields to study carbon monoxide and neutral carbon emission as tracers of molecular gas. We summarize the design phase of the mission