We thank Yoel Sanchez Araujo and Wendy Georgan for their contribution in the initial design of the web-based platform. We thank An Nguyen and Violet Kozloff for their work on improving the web-based statistical learning tasks, implementing the fMRI tasks, and piloting the tasks in adult participants. We thank Violet Kozloff and Parker Robbins for their contribution in assisting data collection in children. We thank Ibrahim Malik, John Christopher, Trevor Wigal, and Keith Schneider at the Center for Biological and Brain Imaging at the University of Delaware for their assistance in neuroimaging data collection. This work is funded in part by the National Institute on Deafness and other Communication Disorders (PI: Qi; NIH 1R21DC017576) and the National Science Foundation Directorate for Social, Behavioral &#38; Economic Sciences (PI: Schneider, Co-PI: Qi &#38; Golinkoff; NSF 1911462).

The authors have nothing to disclose.

The methods presented in the current protocol provide a multimodal paradigm for understanding the behavioral and neural indices of statistical learning across the course of development. The current design allows for the identification of individual differences in statistical learning ability across modalities and domains, which can be used for future investigation of the relationship between statistical learning and language development. Since an individuals&#8217; statistical learning ability is found to vary across dom...

Statistical learning is an elementary skill supporting the acquisition of rule-governed combinations in language inputs1. Successful statistical learning ability in infants predicts later language learning success2,3. Variability in statistical learning skills in school-aged children has also been associated with vocabulary4 and reading5,6. Difficulty in statistical learning has been proposed as one etiological mechanism underlying language impairment7. Despite the association between statistical learning and language outcomes in both neurotypical and atypical populations, the cognitive and the neural mechanisms underlying statistical learning remain poorly understood. In addition, previous literature has revealed that, within an individual, statistical learning ability is not uniform but independent across domains and modalities6,8,9. The developmental trajectory of statistical learning abilities may further vary across domains and modalities10. These findings emphasize the importance of assessing individual differences in statistical learning across multiple tasks throughout the course of development. However, the field first requires a more systematic investigation of the relationship between statistical learning and first language development. To address these questions, we apply innovative methods including a web-based testing platform11 that reaches a large number of children, and laboratory-based neuroimaging techniques (functional magnetic resonance imaging, or fMRI) that examine the real-time encoding of statistical information.
Standard measures of statistical learning begin with a familiarization phase and are followed by a two-alternative forced choice (2-AFC) task12,13. The familiarization phase introduces a continuous stream of stimuli embedded with statistical regularities, where some stimuli are more likely to co-occur than others. The presentation of these co-occurring stimuli follows a fixed temporal order. Participants are passively exposed to the stream during the familiarization phase, followed by a 2-AFC task that tests whether the participant successfully extracted the patterns. The 2-AFC accuracy task presents two consecutive sequences: one sequence has been presented to the participant during the familiarization phase, while the other is a novel sequence, or contains part of the sequence. Above-chance accuracy on the 2-AFC would indicate successful learning at the group level. Traditional behavioral tasks assessing statistical learning generally rely upon accuracy as the outcome measure of learning. However, accuracy fails to account for the natural learning of information as it unfolds in time. A measure of real-time learning is necessary to tap into the implicit learning process of statistical learning during which children are still encoding the regularities from the inputs14,15,16. Various adaptations across paradigms have been developed in an effort to move away from the 2-AFC measure, towards measures of on-line learning through behavioral responses during the exposure16. Studies utilizing these adaptations which measure reaction time during the exposure phase found they were related to post-learning accuracy17 with better test-retest reliability compared to that of the accuracy in adult learners18.
Neural measures are also foundational to our understanding of how learning unfolds over time, as the implicit process by which language learning occurs likely recruits different neural resources from those used once language is learned19. Neural measures also provide insights into differences in cognitive specializations underlying language ability across special populations20. How the condition contrast is designed in an fMRI study is crucial for how we interpret patterns of neural activation during learning. One common practice is to compare brain responses during the familiarization phase between sequences containing regular patterns versus those containing the same stimuli which are ordered randomly. However, previous research implementing such a random control condition found no evidence for learning in behavior, despite neural differences between structured and random sequences. This might be due to the interference of random sequences on learning of structured sequences, as both were constructed from the same stimuli21,22. Other fMRI studies which utilized backward speech or earlier learning blocks as the control condition confirmed learning took place behaviorally19,23. However, each of these paradigms introduced its own confounding factor, such as the effect of language processing for the former case and the effect of the experimental order for the latter case. Our paradigm uses the random sequence as the control condition but mitigates their interference on participants&#8217; learning of the structured sequences. Our fMRI paradigm also implements a mixed block/event-related design, which allows for the simultaneous modeling of transient trial-related and sustained task-related BOLD signals24. Lastly, and more broadly, neural measures allow for the measurement of learning in populations where eliciting an explicit behavioral response may be difficult (e.g., developmental and special populations)25.
The current protocol adopts a response time measure, in addition to traditional accuracy measures, and examines brain activation during the familiarization phase. The combination of these methods aims to provide a rich dataset for the investigation of real-time learning processes. The web-based platform offers a set of learning measures by including both response time during the exposure phase and accuracy of the 2-AFC task during the test phase. The neuroimaging protocol allows for the investigation of the underlying neural mechanisms supporting statistical learning across domains and modalities. While it is optimal to measure statistical learning within an individual using both the web-based and fMRI protocols, the tasks are designed so that they may be disseminated independently, and therefore, as two independent measures of statistical learning. The fMRI experiments included in the current protocol can help clarify how stimulus encoding, pattern extraction, and other constituent components of statistical learning are represented by particular brain regions and networks.

<table>
	<tbody>
		<tr>
			<td>4 Button Inline Response Device</td>
			<td>Cambridge Research Systems</td>
			<td>SKU: N1348</td>
			<td>An fMRI reponse pad used for measuring in-scanner response time</td>
		</tr>
		<tr>
			<td>Short/Slim Canal Tips</td>
			<td>Comply Foam</td>
			<td>SKU: 40-15028-11</td>
			<td>Short &#38; slim in-ear canal tips are recommended for children to protect hearing and allow for them to hear the stimuli while in the scanner.</td>
		</tr>
		<tr>
			<td>jsPsych</td>
			<td>jsPsych</td>
			<td>https://www.jspsych.org/</td>
			<td>jsPsych is a JavaScript library for running behavioral experiments in a web browser.</td>
		</tr>
		<tr>
			<td>Speech Synthesizer</td>
			<td>Praat</td>
			<td>Version 6.1.14</td>
			<td>This program is an artificial speech synthesizer which was used to create the syllable stimuli.</td>
		</tr>
		<tr>
			<td>Web-based statistical learning tasks</td>
			<td>Zenodo</td>
			<td>http://doi.org/10.5281/zenodo.3820620 (2020).</td>
			<td>All web-based statistical learning tasks are available for free access on Zenodo.</td>
		</tr>
	</tbody>
</table>

measuring statistical learning across modalities and domains in school-aged children via an online platform and neuroimaging techniques

Statistical learning, a fundamental skill to extract regularities in the environment, is often considered a core supporting mechanism of the first language development. While many studies of statistical learning are conducted within a single domain or modality, recent evidence suggests that this skill may differ based on the context in which the stimuli are presented. In addition, few studies investigate learning as it unfolds in real-time, rather focusing on the outcome of learning. In this protocol, we describe an approach for identifying the cognitive and neural basis of statistical learning, within an individual, across domains (linguistic vs. non-linguistic) and sensory modalities (visual and auditory). The tasks are designed to cast as little cognitive demand as possible on participants, making it ideal for young school-aged children and special populations. The web-based nature of the behavioral tasks offers a unique opportunity for us to reach more representative populations nationwide, to estimate effect sizes with greater precision, and to contribute to open and reproducible research. The neural measures provided by the functional magnetic resonance imaging (fMRI) task can inform researchers about the neural mechanisms engaged during statistical learning, and how these may differ across individuals on the basis of domain or modality. Finally, both tasks allow for the measurement of real-time learning, as changes in reaction time to a target stimulus is tracked across the exposure period. The main limitation of using this protocol relates to the hour-long duration of the experiment. Children might need to complete all four statistical learning tasks in multiple sittings. Therefore, the web-based platform is designed with this limitation in mind so that tasks may be disseminated individually. This methodology will allow users to investigate how the process of statistical learning unfolds across and within domains and modalities in children from different developmental backgrounds.

Web-based Behavioral Results 
Given the current protocol is designed for easy dissemination with developmental populations, we have included preliminary web-based results based on data from 22 developing school-aged children (Mean (M) age = 9.3 years, Standard Deviation (SD) age = 2.04 years, range = 6.2-12.6 years, 13 girls). In the web-based statistical learning task, children performed significantly better than 0.5 chance-level on all conditions, indicating successful statistical learning at the ...

Watch this Scientific Journal Video about Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques at JoVE.com

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Presented here is a protocol introducing a set of child-friendly statistical learning tasks geared towards examining children&#8217;s learning of temporal statistical patterns across domains and sensory modalities. The developed tasks collect behavioral data using the web-based platform and task-based functional magnetic resonance imaging (fMRI) data for examining neural engagement during statistical learning.

Statistical learning, a fundamental skill to extract regularities in the environment, is often considered a core supporting mechanism of the first ...

measuring-statistical-learning-across-modalities-domains-school-aged

Research

Education

JoVE Core

JoVE Journal

Pharmacokinetics and Pharmacodynamics

Neuroscience

Unit 1

Biostatistics: Introduction

SCIENTISTS IN ACTION

7.4K Views.  University of Delaware. Presented here is a protocol introducing a set of child-friendly statistical learning tasks geared towards examining children’s learning of temporal statistical patterns across domains and sensory modalities. The developed tasks collect behavioral data using the web-based platform and task-based functional magnetic resonance imaging (fMRI) data for examining neural engagement during statistical learning.

Video: Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Functional Neuroimaging Using Ultrasonic Blood-brain Barrier Disruption and Manganese-enhanced MRI

Although mice are the dominant model system for studying the genetic and molecular underpinnings of neuroscience, functional neuroimaging in mice remains technically challenging. One approach, Activation-Induced Manganese-enhanced MRI (AIM MRI), has been used successfully to map neuronal activity in rodents 1-5. In AIM MRI, Mn2+ acts a calcium analog and accumulates in depolarized neurons 6,7. Because Mn2+ shortens the T1 tissue property, regions of elevated neuronal activity will enhance in MRI. Furthermore, Mn2+ clears slowly from the activated regions; therefore, stimulation can be performed outside the magnet prior to imaging, enabling greater experimental flexibility. However, because Mn2+ does not readily cross the blood-brain barrier (BBB), the need to open the BBB has limited the use of AIM MRI, especially in mice.
One tool for opening the BBB is ultrasound. Though potentially damaging, if ultrasound is administered in combination with gas-filled microbubbles (i.e., ultrasound contrast agents), the acoustic pressure required for BBB opening is considerably lower. This combination of ultrasound and microbubbles can be used to reliably open the BBB without causing tissue damage 8-11.
Here, a method is presented for performing AIM MRI by using microbubbles and ultrasound to open the BBB. After an intravenous injection of perflutren microbubbles, an unfocused pulsed ultrasound beam is applied to the shaved mouse head for 3 minutes. For simplicity, we refer to this technique of BBB Opening with Microbubbles and UltraSound as BOMUS 12. Using BOMUS to open the BBB throughout both cerebral hemispheres, manganese is administered to the whole mouse brain. After experimental stimulation of the lightly sedated mice, AIM MRI is used to map the neuronal response.
To demonstrate this approach, herein BOMUS and AIM MRI are used to map unilateral mechanical stimulation of the vibrissae in lightly sedated mice 13. Because BOMUS can open the BBB throughout both hemispheres, the unstimulated side of the brain is used to control for nonspecific background stimulation. The resultant 3D activation map agrees well with published representations of the vibrissae regions of the barrel field cortex 14. The ultrasonic opening of the BBB is fast, noninvasive, and reversible; and thus this approach is suitable for high-throughput and/or longitudinal studies in awake mice.

A technique is described for broadly opening the blood-brain barrier in the mouse using microbubbles and ultrasound. Using this technique, manganese can be administered to the mouse brain. Because manganese is an MRI contrast agent that accumulates in depolarized neurons, this approach enables imaging of neuronal activity.

Although mice are the dominant model system for studying the genetic and molecular underpinnings of neuroscience, functional neuroimaging in mice remains ...

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Magneto- and electroencephalography (MEG/EEG) are neuroimaging techniques that provide a high temporal resolution particularly suitable to investigate the cortical networks involved in dynamical perceptual and cognitive tasks, such as attending to different sounds in a cocktail party. Many past studies have employed data recorded at the sensor level only, i.e., the magnetic fields or the electric potentials recorded outside and on the scalp, and have usually focused on activity that is time-locked to the stimulus presentation. This type of event-related field / potential analysis is particularly useful when there are only a small number of distinct dipolar patterns that can be isolated and identified in space and time. Alternatively, by utilizing anatomical information, these distinct field patterns can be localized as current sources on the cortex. However, for a more sustained response that may not be time-locked to a specific stimulus (e.g., in preparation for listening to one of the two simultaneously presented spoken digits based on the cued auditory feature) or may be distributed across multiple spatial locations unknown a priori, the recruitment of a distributed cortical network may not be adequately captured by using a limited number of focal sources.
Here, we describe a procedure that employs individual anatomical MRI data to establish a relationship between the sensor information and the dipole activation on the cortex through the use of minimum-norm estimates (MNE). This inverse imaging approach provides us a tool for distributed source analysis. For illustrative purposes, we will describe all procedures using FreeSurfer and MNE software, both freely available. We will summarize the MRI sequences and analysis steps required to produce a forward model that enables us to relate the expected field pattern caused by the dipoles distributed on the cortex onto the M/EEG sensors. Next, we will step through the necessary processes that facilitate us in denoising the sensor data from environmental and physiological contaminants. We will then outline the procedure for combining and mapping MEG/EEG sensor data onto the cortical space, thereby producing a family of time-series of cortical dipole activation on the brain surface (or "brain movies") related to each experimental condition. Finally, we will highlight a few statistical techniques that enable us to make scientific inference across a subject population (i.e., perform group-level analysis) based on a common cortical coordinate space.

We use magneto- and electroencephalography (MEG/EEG), combined with anatomical information captured by magnetic resonance imaging (MRI), to map the dynamics of the cortical network associated with auditory attention.

Magneto- and electroencephalography (MEG/EEG) are neuroimaging techniques that provide a high temporal resolution particularly suitable to investigate the ...

Recording Single Neurons' Action Potentials from Freely Moving Pigeons Across Three Stages of Learning

While the subject of learning has attracted immense interest from both behavioral and neural scientists, only relatively few investigators have observed single-neuron activity while animals are acquiring an operantly conditioned response, or when that response is extinguished. But even in these cases, observation periods usually encompass only a single stage of learning, i.e. acquisition or extinction, but not both (exceptions include protocols employing reversal learning; see Bingman&#160;et al.1 for an example). However, acquisition and extinction entail different learning mechanisms and are therefore expected to be accompanied by different types and/or loci of neural plasticity.
Accordingly, we developed a behavioral paradigm which institutes three stages of learning in a single behavioral session and which is well suited for the simultaneous recording of single neurons&#39; action potentials. Animals are trained on a single-interval forced choice task which requires mapping each of two possible choice responses to the presentation of different novel visual stimuli (acquisition). After having reached a predefined performance criterion, one of the two choice responses is no longer reinforced (extinction). Following a certain decrement in performance level, correct responses are reinforced again (reacquisition). By using a new set of stimuli in every session, animals can undergo the acquisition-extinction-reacquisition process repeatedly. Because all three stages of learning occur in a single behavioral session, the paradigm is ideal for the simultaneous observation of the spiking output of multiple single neurons. We use pigeons as model systems, but the task can easily be adapted to any other species capable of conditioned discrimination learning.

Recording Single Neurons&#039; Action Potentials from Freely Moving Pigeons Across Three Stages of Learning

Learning new stimulus-response associations engages a wide range of neural processes which are ultimately reflected in changing spike output of individual neurons. Here we describe a behavioral protocol allowing for the continuous registration of single-neuron activity while animals acquire, extinguish, and reacquire a conditioned response within a single experimental session.

While the subject of learning has attracted immense interest from both behavioral and neural scientists, only relatively few investigators have observed ...

Electrophysiological Method for Recording Intracellular Voltage Responses of Drosophila Photoreceptors and Interneurons to Light Stimuli In Vivo

Voltage responses of insect photoreceptors and visual interneurons can be accurately recorded with conventional sharp microelectrodes. The method described here enables the investigator to measure long-lasting (from minutes to hours) high-quality intracellular responses from single Drosophila R1-R6 photoreceptors and Large Monopolar Cells (LMCs) to light stimuli. Because the recording system has low noise, it can be used to study variability among individual cells in the fly eye, and how their outputs reflect the physical properties of the visual environment. We outline all key steps in performing this technique. The basic steps in constructing an appropriate electrophysiology set-up for recording, such as design and selection of the experimental equipment are described. We also explain how to prepare for recording by making appropriate (sharp) recording and (blunt) reference electrodes. Details are given on how to fix an intact fly in a bespoke fly-holder, prepare a small window in its eye and insert a recording electrode through this hole with minimal damage. We explain how to localize the center of a cell's receptive field, dark- or light-adapt the studied cell, and to record its voltage responses to dynamic light stimuli. Finally, we describe the criteria for stable normal recordings, show characteristic high-quality voltage responses of individual cells to different light stimuli, and briefly define how to quantify their signaling performance. Many aspects of the method are technically challenging and require practice and patience to master. But once learned and optimized for the investigator's experimental objectives, it grants outstanding in vivo neurophysiological data.

Electrophysiological Method for Recording Intracellular Voltage Responses of Drosophila Photoreceptors and Interneurons to Light Stimuli In Vivo

Sharp microelectrodes enable accurate electrophysiological characterization of photoreceptor and visual interneuron output in living Drosophila. Here we show how to use this method to record high-quality voltage responses of individual cells to controlled light stimulation. This method is ideal for studying neural information processing in insect compound eyes.

Voltage responses of insect photoreceptors and visual interneurons can be accurately recorded with conventional sharp microelectrodes. The method ...

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Microstate and omega complexity are two reference-free electroencephalography (EEG) measures that can represent the temporal and spatial complexities of EEG data and have been widely used to investigate the neural mechanisms in some brain disorders. The goal of this article is to describe the protocol underlying EEG microstate and omega complexity analyses step by step. The main advantage of these two measures is that they could eliminate the reference-dependent problem inherent to traditional spectrum analysis. In addition, microstate analysis makes good use of high time resolution of resting-state EEG, and the four obtained microstate classes could match the corresponding resting-state networks respectively. The omega complexity characterizes the spatial complexity of the whole brain or specific brain regions, which has obvious advantage compared with traditional complexity measures focusing on the signal complexity in a single channel. These two EEG measures could complement each other to investigate the brain complexity from the temporal and spatial domain respectively.

This article describes the protocol underlying electroencephalography (EEG) microstate analysis and omega complexity analysis, which are two reference-free EEG measures and highly valuable to explore the neural mechanisms of brain disorders.

Microstate and omega complexity are two reference-free electroencephalography (EEG) measures that can represent the temporal and spatial complexities of ...

Online Transcranial Magnetic Stimulation Protocol for Measuring Cortical Physiology Associated with Response Inhibition

We describe the development of a reproducible, child-friendly motor response inhibition task suitable for online Transcranial Magnetic Stimulation (TMS) characterization of primary motor cortex (M1) excitability and inhibition. Motor response inhibition prevents unwanted actions and is abnormal in several neuropsychiatric conditions. TMS is a non-invasive technology that can quantify M1 excitability and inhibition using single- and paired-pulse protocols and can be precisely timed to study cortical physiology with high temporal resolution. We modified the original Slater-Hammel (S-H) stop signal task to create a &#34;racecar&#34; version with TMS pulses time-locked to intra-trial events. This task is self-paced, with each trial initiating after a button push to move the racecar towards the 800 ms target. GO trials require a finger-lift to stop the racecar just before this target. Interspersed randomly are STOP trials (25%) during which the dynamically adjusted stop signal prompts subjects to prevent finger-lift. For GO trials, TMS pulses were delivered at 650 ms after trial onset; whereas, for STOP trials, the TMS pulses occurred 150 ms after the stop signal. The timings of the TMS pulses were decided based on electroencephalography (EEG) studies showing event-related changes in these time ranges during stop signal tasks. This task was studied in 3 blocks at two study sites (n=38) and we recorded behavioral performance and event-related motor-evoked potentials (MEP). Regression modelling was used to analyze MEP amplitudes using age as a covariate with multiple independent variables (sex, study site, block, TMS pulse condition [single- vs. paired-pulse], trial condition [GO, successful STOP, failed STOP]). The analysis showed that TMS pulse condition (p&#60;0.0001) and its interaction with trial condition (p=0.009) were significant. Future applications for this online S-H/TMS paradigm include the addition of simultaneous EEG acquisition to measure TMS-evoked EEG potentials. A potential limitation is that in children, the TMS pulse sound could&#160;affect behavioral task performance.

We describe an experimental procedure to quantify excitability and inhibition of primary motor cortex during a motor response inhibition task by using Transcranial Magnetic Stimulation throughout the course of a Stop Signal Task.

We describe the development of a reproducible, child-friendly motor response inhibition task suitable for online Transcranial Magnetic Stimulation (TMS) ...

SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware

Many scientifically and agriculturally important insects use antennae to detect the presence of volatile chemical compounds and extend their proboscis during feeding. The ability to rapidly obtain high-resolution measurements of natural antenna and proboscis movements and assess how they change in response to chemical, developmental, and genetic manipulations can aid the understanding of insect behavior. By extending our previous work on assessing aggregate insect swarm or animal group movements from natural and laboratory videos using the video analysis software SwarmSight, we developed a novel, free, and open-source software module, SwarmSight Appendage Tracking (SwarmSight.org) for frame-by-frame tracking of insect antenna and proboscis positions from conventional web camera videos using conventional computers. The software processes frames about 120 times faster than humans, performs at better than human accuracy, and, using 30 frames per second (fps) videos, can capture antennal dynamics up to 15 Hz. The software was used to track the antennal response of honey bees to two odors and found significant mean antennal retractions away from the odor source about 1 s after odor presentation. We observed antenna position density heat map cluster formation and cluster and mean angle dependence on odor concentration.

This protocol describes steps for using the novel software, SwarmSight, for frame-by-frame tracking of insect antenna and proboscis positions from conventional web camera videos using conventional computers. The free, open-source software processes frames about 120 times faster than humans and performs at better than human accuracy.

Many scientifically and agriculturally important insects use antennae to detect the presence of volatile chemical compounds and extend their proboscis ...

An Objective and Reproducible Test of Olfactory Learning and Discrimination in Mice

Olfaction is the predominant sensory modality in mice and influences many important behaviors, including foraging, predator detection, mating, and parenting. Importantly, mice can be trained to associate novel odors with specific behavioral responses to provide insight into olfactory circuit function. This protocol details the procedure for training mice on a Go/No-Go operant learning task. In this approach, mice are trained on hundreds of automated trials daily for 2&#8211;4 weeks and can then be tested on novel Go/No-Go odor pairs to assess olfactory discrimination, or be used for studies on how odor learning alters the structure or function of the olfactory circuit. Additionally, the mouse olfactory bulb (OB) features ongoing integration of adult-born neurons. Interestingly, olfactory learning increases both the survival and synaptic connections of these adult-born neurons. Therefore, this protocol can be combined with other biochemical, electrophysiological, and imaging techniques to study learning and activity-dependent factors that mediate neuronal survival and plasticity.

Here, we train mice on an associative learning task to test odor discrimination. This protocol also allows for studies on learning-induced structural changes in the brain.

Olfaction is the predominant sensory modality in mice and influences many important behaviors, including foraging, predator detection, mating, and ...

Neuroimaging Field Methods Using Functional Near Infrared Spectroscopy (NIRS) Neuroimaging to Study Global Child Development: Rural Sub-Saharan Africa

Portable neuroimaging approaches provide new advances to the study of brain function and brain development with previously inaccessible populations and in remote locations. This paper shows the development of field functional Near Infrared Spectroscopy (fNIRS) imaging to the study of child language, reading, and cognitive development in a rural village setting of C&#244;te d&#39;Ivoire. Innovation in methods and the development of culturally appropriate neuroimaging protocols allow&#160;a first-time look into the brain&#39;s development and children&#39;s learning outcomes in understudied environments. This paper demonstrates protocols for transporting and setting up a mobile laboratory, discusses considerations for field versus laboratory neuroimaging, and presents a guide for developing neuroimaging consent procedures and building meaningful long-term collaborations with local government and science partners. Portable neuroimaging methods can be used to study complex child development contexts, including the impact of significant poverty and adversity on brain development. The protocol presented here has been developed for use in C&#244;te d&#39;Ivoire, the world&#39;s primary source of cocoa, and where reports of child labor in the cocoa sector are common. Yet, little is known about the impact of child labor on brain development and learning. Field neuroimaging methods have the potential to yield new insights into such urgent issues, and the development of children globally.

Neuroimaging Field Methods Using Functional Near Infrared Spectroscopy NIRS Neuroimaging to Study Global Child Development: Rural Sub-Saharan Africa

Portable neuroimaging approaches (functional Near Infrared Spectroscopy) provide advances to the study of the brain in previously inaccessible regions; here, rural C&#244;te d&#39;Ivoire. Innovation in methods and development of culturally-appropriate neuroimaging protocols permits novel study of the brain&#39;s development and children&#39;s learning outcomes in environments with significant poverty and adversity.

Portable neuroimaging approaches provide new advances to the study of brain function and brain development with previously inaccessible populations and in ...

Studying Brain Function in Children Using Magnetoencephalography

Magnetoencephalography (MEG) is a non-invasive neuroimaging technique which directly measures magnetic fields produced by the electrical activity of the human brain. MEG is quiet and less likely to induce claustrophobia compared with magnetic resonance imaging (MRI). It is therefore a promising tool for investigating brain function in young children. However, analysis of MEG data from pediatric populations is often complicated by head movement artefacts which arise as a consequence of the requirement for a spatially-fixed sensor array that is not affixed to the child's head. Minimizing head movements during MEG sessions can be particularly challenging as young children are often unable to remain still during experimental tasks. The protocol presented here aims to reduce head movement artefacts during pediatric MEG scanning. Prior to visiting the MEG laboratory, families are provided with resources that explain the MEG system and the experimental procedures in simple, accessible language. An MEG familiarization session is conducted during which children are acquainted with both the researchers and the MEG procedures. They are then trained to keep their head still whilst lying inside an MEG simulator. To help children feel at ease in the novel MEG environment, all of the procedures are explained through the narrative of a space mission. To minimize head movement due to restlessness, children are trained and assessed using fun and engaging experimental paradigms. In addition, children's residual head movement artefacts are compensated for during the data acquisition session using a real-time head movement tracking system. Implementing these child-friendly procedures is important for improving data quality, minimizing participant attrition rates in longitudinal studies, and ensuring that families have a positive research experience.

This article introduces a child-friendly research protocol designed to improve data quality by reducing head movement during pediatric magnetoencephalography (MEG). We familiarize families with the MEG environment, train children to remain still using an MEG simulator, and correct for residual head movement artefacts using a real-time head movement detection system.

Magnetoencephalography (MEG) is a non-invasive neuroimaging technique which directly measures magnetic fields produced by the electrical activity of the ...