Texas panhandle beef production tour, a high-impact compressed course in animal science.

Many animal science students have little exposure to working livestock production systems prior to college. As such, they can lack insight into day-to-day challenges and rationale behind decision making in these systems, opening the door for the adoption of misconceptions frequently promoted in the popular press. In addition, students identify a lack of first-hand knowledge and experience in the industry as a challenge to their educational success. Field trips stimulate interest and motivation, provide context for learning, and influence long-term career goals, but are underutilized in higher education. The potential impact of such experiences prompted the creation of the Texas Panhandle Beef Production Tour, a 2-credit hour compressed course. Students on this tour visited beef production sites in the Texas Panhandle ranging from cow-calf operations, to feedlots and packing plants. To cement learning through reflection, students responded to a series of questions before, during, and after visiting these sites to probe preconceptions, observations, and outcomes of the experience. We performed a retroactive qualitative evaluation of these reflections (n = 22) to determine cogent themes. Emergent themes included surprise at the intensive systems of data collection and management and the level of technology used at each site. Cattle were calmer and more comfortable than expected at the feedlots and packing plants. Students expressed new appreciation and understanding of course material and a desire to share their insights with others after completing the tour. Finally, participants gained a broader view of industry opportunities and returned with renewed motivation to pursue additional hands-on opportunities. Participation in this course provided valuable insight into the livestock production industry and motivated students to explore new career options and address their own preconceptions of the industry through independent inquiry. The creation of similar courses may be useful to address misconceptions, create personal connections with course material, and broaden career interests in animal science students.

DEEP: A dual EEG pipeline for developmental hyperscanning studies.

Cutting-edge hyperscanning methods led to a paradigm shift in social neuroscience. It allowed researchers to measure dynamic mutual alignment of neural processes between two or more individuals in naturalistic contexts. The ever-growing interest in hyperscanning research calls for the development of transparent and validated data analysis methods to further advance the field. We have developed and tested a dual electroencephalography (EEG) analysis pipeline, namely DEEP. Following the preprocessing of the data, DEEP allows users to calculate Phase Locking Values (PLVs) and cross-frequency PLVs as indices of inter-brain phase alignment of dyads as well as time-frequency responses and EEG power for each participant. The pipeline also includes scripts to control for spurious correlations. Our goal is to contribute to open and reproducible science practices by making DEEP publicly available together with an example mother-infant EEG hyperscanning dataset.

Electrical Source Imaging in Freely Moving Rats: Evaluation of a 12-Electrode Cortical Electroencephalography System.

This work presents and evaluates a 12-electrode intracranial electroencephalography system developed at the National Institute of Mental Health (Klecany, Czech Republic) in terms of an electrical source imaging (ESI) technique in rats. The electrode system was originally designed for translational research purposes. This study demonstrates that it is also possible to use this well-established system for ESI, and estimates its precision, accuracy, and limitations. Furthermore, this paper sets a methodological basis for future implants. Source localization quality is evaluated using three approaches based on surrogate data, physical phantom measurements, and in vivo experiments. The forward model for source localization is obtained from the FieldTrip-SimBio pipeline using the finite-element method. Rat brain tissue extracted from a magnetic resonance imaging template is approximated by a single-compartment homogeneous tetrahedral head model. Four inverse solvers were tested: standardized low-resolution brain electromagnetic tomography, exact low-resolution brain electromagnetic tomography (eLORETA), linear constrained minimum variance (LCMV), and dynamic imaging of coherent sources. Based on surrogate data, this paper evaluates the accuracy and precision of all solvers within the brain volume using error distance and reliability maps. The mean error distance over the whole brain was found to be the lowest in the eLORETA solution through signal to noise ratios (SNRs) (0.2 mm for 25 dB SNR). The LCMV outperformed eLORETA under higher SNR conditions, and exhibiting higher spatial precision. Both of these inverse solvers provided accurate results in a phantom experiment (1.6 mm mean error distance across shallow and 2.6 mm across subcortical testing dipoles). Utilizing the developed technique in freely moving rats, an auditory steady-state response experiment provided results in line with previously reported findings. The obtained results support the idea of utilizing a 12-electrode system for ESI and using it as a solid basis for the development of future ESI dedicated implants.

Disrupted network connectivity in pediatric brain tumor survivors is a signature of injury.

Cognition is compromised in pediatric brain tumor survivors but the neurophysiological basis of this compromise remains unclear. We hypothesized that reduced neural synchronization across brain networks is involved. To test this, we evaluated group differences using a retrospective cohort comparison design between 24 pediatric brain tumor survivors [11.81 ± 3.27)] and 24 age matched healthy children [12.04 ± 3.28)] in functional connectivity within a cerebellar network to examine local effects of the tumor, a whole brain network to examine diffuse effects of treatment (i.e., chemotherapy and radiation), and across multiple intrinsic connectivity networks. Neural activity was recorded during magnetoencephalography scanning while participants were at rest and functional connectivity within networks was measured using the phase lag index. We corroborated our findings using a computational model representing the local tumor effects on neural synchrony. Compared to healthy children, pediatric brain tumor survivors show increased functional connectivity for theta and beta frequency bands within the cerebellar network and increased functional connectivity for the theta band within the whole brain network that again localized to the cerebellum. Computational modeling showed that increased synchrony in the theta bad is observed following local clustering as well as sparse interarea brain connectivity. We also observed increased functional connectivity for the alpha frequency band in the ventral attention network and decreased functional connectivity within the gamma frequency band in the motor network within paedatric brain tumor survivors versus healthy children. Notably, increased gamma functional connectivity within the motor network predicted decreased reaction time on behavioral tasks in pediatric brain tumor survivors. Disrupted network synchrony may be a signature of neurological injury and disease.

FieldTrip Made Easy: An Analysis Protocol for Group Analysis of the Auditory Steady State Brain Response in Time, Frequency, and Space.

The auditory steady state evoked response (ASSR) is a robust and frequently utilized phenomenon in psychophysiological research. It reflects the auditory cortical response to an amplitude-modulated constant carrier frequency signal. The present report provides a concrete example of a group analysis of the EEG data from 29 healthy human participants, recorded during an ASSR paradigm, using the FieldTrip toolbox. First, we demonstrate sensor-level analysis in the time domain, allowing for a description of the event-related potentials (ERPs), as well as their statistical evaluation. Second, frequency analysis is applied to describe the spectral characteristics of the ASSR, followed by group level statistical analysis in the frequency domain. Third, we show how time- and frequency-domain analysis approaches can be combined in order to describe the temporal and spectral development of the ASSR. Finally, we demonstrate source reconstruction techniques to characterize the primary neural generators of the ASSR. Throughout, we pay special attention to explaining the design of the analysis pipeline for single subjects and for the group level analysis. The pipeline presented here can be adjusted to accommodate other experimental paradigms and may serve as a template for similar analyses.

Group Analysis in FieldTrip of Time-Frequency Responses: A Pipeline for Reproducibility at Every Step of Processing, Going From Individual Sensor Space Representations to an Across-Group Source Space Representation.

An important aim of an analysis pipeline for magnetoencephalographic (MEG) data is that it allows for the researcher spending maximal effort on making the statistical comparisons that will answer his or her questions. The example question being answered here is whether the so-called beta rebound differs between novel and repeated stimulations. Two analyses are presented: going from individual sensor space representations to, respectively, an across-group sensor space representation and an across-group source space representation. The data analyzed are neural responses to tactile stimulations of the right index finger in a group of 20 healthy participants acquired from an Elekta Neuromag System. The processing steps covered for the first analysis are MaxFiltering the raw data, defining, preprocessing and epoching the data, cleaning the data, finding and removing independent components related to eye blinks, eye movements and heart beats, calculating participants' individual evoked responses by averaging over epoched data and subsequently removing the average response from single epochs, calculating a time-frequency representation and baselining it with non-stimulation trials and finally calculating a grand average, an across-group sensor space representation. The second analysis starts from the grand average sensor space representation and after identification of the beta rebound the neural origin is imaged using beamformer source reconstruction. This analysis covers reading in co-registered magnetic resonance images, segmenting the data, creating a volume conductor, creating a forward model, cutting out MEG data of interest in the time and frequency domains, getting Fourier transforms and estimating source activity with a beamformer model where power is expressed relative to MEG data measured during periods of non-stimulation. Finally, morphing the source estimates onto a common template and performing group-level statistics on the data are covered. Functions for saving relevant figures in an automated and structured manner are also included. The protocol presented here can be applied to any research protocol where the emphasis is on source reconstruction of induced responses where the underlying sources are not coherent.