Electric source localization adds evidence for task-specific CNVs

This study was an attempt to replicate recent magnetoencephalographic (MEG) findings on human task-specific CNV sources (Basile et al., Electroencephalography and Clinical Neurophysiology 90, 1994, 157-165) by means of a spatio-temporal electric source localization method (Scherg and von Cramon, Electroencephalography and Clinical Neurophysiology 62, 1985, 32-44; Scherg and von Cramon, Electroencephalography and Clinical Neurophysiology 65, 1986, 344-360; Scherg and Berg, Brain Electric Source Analysis Handbook, Version 2). The previous MEG results showed CNV sources in the prefrontal cortex of the two hemispheres for two tasks used, namely visual pattern recognition and visual spatial recognition tasks. In the right hemisphere, the sources were more anterior and inferior for the spatial recognition task than for the pattern recognition task. In the present study we obtained CNVs in five subjects during two tasks identical to the MEG study. The elicited electric potentials were modeled with four spatio-temporal dipoles for each task, three of which accounted for the visual evoked response and one that accounted for the CNV. For all subjects the dipole explaining the CNV was always localized in the frontal region of the head, however, the dipole obtained during the visual spatial recognition task was more anterior than the one obtained during the pattern recognition task. Thus, task-specific CNV sources were again observed, although the stable model consisted of only one dipole located close to the midline instead of one dipole in each hemisphere. This was a major difference in the CNV sources between the previous MEG and the present electric source analysis results. We discuss the possible basis for the difference between the two methods used to study slow brain activity that is believed to originate from extended cortical patches

Minor and Unsystematic Cortical Topographic Changes of Attention Correlates between Modalities

In this study we analyzed the topography of induced cortical oscillations in 20 healthy individuals performing simple attention tasks. We were interested in qualitatively replicating our recent findings on the localization of attention-induced beta bands during a visual task [1], and verifying whether significant topographic changes would follow the change of attention to the auditory modality. We computed corrected latency averaging of each induced frequency bands, and modeled their generators by current density reconstruction with Lp-norm minimization. We quantified topographic similarity between conditions by an analysis of correlations, whereas the inter-modality significant differences in attention correlates were illustrated in each individual case. We replicated the qualitative result of highly idiosyncratic topography of attention-related activity to individuals, manifested both in the beta bands, and previously studied slow potential distributions [2]. Visual inspection of both scalp potentials and distribution of cortical currents showed minor changes in attention-related bands with respect to modality, as compared to the theta and delta bands, known to be major contributors to the sensory-related potentials. Quantitative results agreed with visual inspection, supporting to the conclusion that attention-related activity does not change much between modalities, and whatever individual changes do occur, they are not systematic in cortical localization across subjects. We discuss our results, combined with results from other studies that present individual data, with respect to the function of cortical association areas

Deep Learning of Resting-state Electroencephalogram Signals for 3-class Classification of Alzheimer’s Disease, Mild Cognitive Impairment and Healthy Ageing

Objective. This study aimed to produce a novel deep learning (DL) model for the classification of subjects with Alzheimer's disease (AD), mild cognitive impairment (MCI) subjects and healthy ageing (HA) subjects using resting-state scalp electroencephalogram (EEG) signals. Approach. The raw EEG data were pre-processed to remove unwanted artefacts and sources of noise. The data were then processed with the continuous wavelet transform, using the Morse mother wavelet, to create time-frequency graphs with a wavelet coefficient scale range of 0-600. The graphs were combined into tiled topographical maps governed by the 10-20 system orientation for scalp electrodes. The application of this processing pipeline was used on a data set of resting-state EEG samples from age-matched groups of 52 AD subjects (82.3 ± 4.7 years of age), 37 MCI subjects (78.4 ± 5.1 years of age) and 52 HA subjects (79.6 ± 6.0 years of age). This resulted in the formation of a data set of 16197 topographical images. This image data set was then split into training, validation and test images and used as input to an AlexNet DL model. This model was comprised of five hidden convolutional layers and optimised for various parameters such as learning rate, learning rate schedule, optimiser, and batch size. Main results. The performance was assessed by a tenfold cross-validation strategy, which produced an average accuracy result of 98.9 ± 0.4% for the three-class classification of AD vs MCI vs HA. The results showed minimal overfitting and bias between classes, further indicating the strength of the model produced. Significance. These results provide significant improvement for this classification task compared to previous studies in this field and suggest that DL could contribute to the diagnosis of AD from EEG recordings

Functional coupling of sensorimotor and associative areas during a catching ball task: a qEEG coherence study

<p>Abstract</p> <p>Background</p> <p>Catching an object is a complex movement that involves not only programming but also effective motor coordination. Such behavior is related to the activation and recruitment of cortical regions that participates in the sensorimotor integration process. This study aimed to elucidate the cortical mechanisms involved in anticipatory actions when performing a task of catching an object in free fall.</p> <p>Methods</p> <p>Quantitative electroencephalography (qEEG) was recorded using a 20-channel EEG system in 20 healthy right-handed participants performed the catching ball task. We used the EEG coherence analysis to investigate subdivisions of alpha (8-12 Hz) and beta (12-30 Hz) bands, which are related to cognitive processing and sensory-motor integration.</p> <p>Results</p> <p>Notwithstanding, we found the main effects for the factor block; for alpha-1, coherence decreased from the first to sixth block, and the opposite effect occurred for alpha-2 and beta-2, with coherence increasing along the blocks.</p> <p>Conclusion</p> <p>It was concluded that to perform successfully our task, which involved anticipatory processes (i.e. feedback mechanisms), subjects exhibited a great involvement of sensory-motor and associative areas, possibly due to organization of information to process visuospatial parameters and further catch the falling object.</p

Professional Training in Beekeeping: A Cross-Country Survey to Identify Learning Opportunities

first_pagesettingsOrder Article Reprints Open AccessArticle Professional Training in Beekeeping: A Cross-Country Survey to Identify Learning Opportunities by Raquel P. F. Guiné 1ORCID,Jorge Oliveira 1ORCID,Catarina Coelho 1,2,*ORCID,Daniela Teixeira Costa 1,Paula Correia 1ORCID,Helena Esteves Correia 1ORCID,Bjørn Dahle 3,Melissa Oddie 3,Risto Raimets 4,Reet Karise 4ORCID,Luis Tourino 5,Salvatore Basile 6,Emilio Buonomo 6,Ivan Stefanic 7 andCristina A. Costa 1ORCID 1 CERNAS Research Centre, Polytechnic Institute of Viseu, 3504-510 Viseu, Portugal 2 CECAV, Animal and Veterinary Research Center, University of Trás-os-Montes e Alto Douro, Quinta de Prados, Apartado 1013, 5000-801 Vila Real, Portugal 3 Norwegian Beekeepers Association, 2040 Kløfta, Norway 4 Institute of Agricultural and Environmental Sciences, Estonian University of Life Sciences, 51014 Tartu, Estonia 5 Eosa Estrategia y Organización SA, 36202 Vigo, Spain 6 Bio-Distretto Cilento, 84052 Salerno, Italy 7 Tera Tehnopolis, 31000 Osijek, Croatia * Author to whom correspondence should be addressed. Sustainability 2023, 15(11), 8953; https://doi.org/10.3390/su15118953 Received: 21 April 2023 / Revised: 24 May 2023 / Accepted: 31 May 2023 / Published: 1 June 2023 (This article belongs to the Special Issue Prospects Challenges and Sustainability of the Agri-Food Supply Chain in the New Global Economy II) Download Browse Figures Review Reports Versions Notes Abstract Habitat loss, climate change, and other environmental degradations pose severe challenges to beekeepers. Therefore, this sector needs to rely on updated information so that the intervening actors can deal with the problems. In this context, and assuming that professional training can greatly help those acting in the beekeeping sector, this work intended to investigate the gaps in the updated knowledge of beekeepers and how these can be filled through lifelong learning. The research was conducted in seven European countries (Croatia, Estonia, Finland, Italy, Norway, Portugal, and Spain). The data were collected through a questionnaire survey translated into the native languages of all participating countries. The results revealed that the topics of highest interest are apiary health and pest control and the management of the colonies throughout the year. The beekeepers update their knowledge through family, complemented by professional training, with participants preferring in-person courses as well as, in the workplace or in internships. The learning methodologies they consider most useful are project-based learning and learning through gamification. The videos and paper books or manuals are particularly valued as learning materials, and practical exercises are considered the most helpful assessment format. Finally, considering the effect of sociodemographic variables on the learning experiences and preferences of beekeeping actors, it was observed that the country was the most influential of the variables under study. In conclusion, this work revealed valuable information that should be used to design professional training actions to help the professionals in the beekeeping sector enhance their competencies and be better prepared to manage their activities successfully.info:eu-repo/semantics/publishedVersio

Effects Of Modafinil And Bromazepam On Decision-Making: A P300 Analysis

Drug influence on the decision making process has been scarcely studied. Researchers have driven the hypothesis that drugs might cause interference on cortical circuits. The aim of the present study is to evaluate the electrophysiological and behavioral changes occurring in the P300 after ingestion of modafinil (200mg), bromazepam (6mg) and placebo in healthy subjects exposed to a sensorimotor task based on the oddball paradigm. The sample for this study consisted of 10 subjects of both sexes, with ages ranging between 20 and 45, who were submitted to a quantitative electroencephalography. The experimental procedure was carried out in three visits, before and after drug ingestion. The results demonstrated a significant increase in the P300 latency and amplitude for the target condition, when compared to the non-target condition, for all analyzed electrodes. No significant difference was found for group or moment. A statistically significant difference was found for the group variable in the behavioral analysis. Such results suggest that the P300 is a measure, which is not sensitive to drug ingestion. On the other hand, the measure presented certain level of sensitivity when the subjects faced two different conditions in the decision making process orientation