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UREAP Report
Examining Brain Function and Behaviour in Infants During Naturalistic Play: a fNIRS
Study

By Avory Marnie Zienowicz
Primary Supervisor: Dr. Claudia Gonzalez
Department of Psychology, Thompson Rivers University
Undergraduate Research Experience Award Program

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Summary
My Undergrad Research Experience Award Program (UREAP) has provided me with a
great opportunity to develop research skills throughout the summer. My study focused on
designing a protocol for investigating the development of motor skills in the infant brain,
specifically within the prefrontal (pre-motor), motor, and parietal lobe. The project included a
review of the literature and training on functional near-infrared spectroscopy (fNIRS) to build a
sound protocol for neuroimaging infants while completing motor tasks. With minimal previous
infant motor studies undergoing fNIRS, my study’s protocol was further developed through trial
and error. Specifically, infant protocols are hard to structure and we needed to allow a degree of
flexibility for this population. A large focus of this summer was participant recruitment. For this I
designed posters, leaflets, researched online platforms, and media advertisements. Additionally, I
went in-person to visit local childcare, healthcare, and child activity centres. I then trained on the
Jane App for participant recruitment, a user-friendly practice management software that allows
participants to schedule time slots and get information and forms for our study in a confidential
way. As part of the Baby Brain and Behaviour team, I wrote information about our study, our
purpose, and developed a video introducing the researchers, the lab, and the experiment. During
my UREAP I also learned valuable skills in data processing with software programs used to
collect and process fNIRS and behavioural data. Specifically, I coded video data taken from 5
infants that we tested and identified behavioural markers that would be used to match with the
brain activity collected by the fNIRS. My research activities conducted this summer have aided
in me securing a position in the Psychology Honours program, where I can continue to test more
participants with a sound experimental design and protocol and will also learn to analyze the
data.

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Background
The first two years of life is a critical stage in an infant’s brain and nervous system
development (Bornstein, 2014; Köster et al., 2020; Lloyd-Fox, Blasi, & Elwell, 2010). Studies
on infant development commonly focus on two key periods, around birth and 12 months,
indicating a key interest in the brain at birth and accumulated brain development at the first year
(Azhari et al., 2020). Between 3 to 4 months, infants seem to have separate processes for visual
information proprioceptive information (Corbetta et al., 2014; Corbetta 2021). When the
fundamental skill of reaching for objects emerges at around 3-5 months of age, visual and motor
areas begin to intersect (Corbetta et al., 2014; Corbetta 2021), and through trial and error, infants
increasingly make contact while reaching for objects, forming a process known as reinforcement
learning (Sutton & Barto, 2018). This goal-directed movement is due to functional and structural
alterations within the brain that develops uncontrolled movement into goal-directed behaviours
(Agyeman et al., 2023). However, the developmental changes that occur during this time have
been difficult to measure, particularly, using neuroimaging methods like functional magnetic
resonance imaging. Therefore, attempts at characterizing healthy brain-behaviour development
involves several, some contradicting, theories that are difficult to test (e.g., Corbetta, 2021;
Köster et al., 2020; McGraw, 1943; Nagai et al., 2019).
Developmental Theories
There are many developmental theories that attempt to explain the process of
neurodevelopment in infants as they mature, yet the neural basis to this process is unknown
(Nishiyori et al., 2016; Agyeman, 2023). The dynamic systems approach suggests that motor and

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cognitive development appears as dynamic changes within a complex system, emerging from
interactions between infants and their environment (Gottlieb, Wahlsten, & Lickliter, 1998; Nagai,
2019). Additionally, the maturation of motor skills is not solely based on cortical development,
rather the development and integration of perceptual, cognitive, affective, skeletal, and
neuromuscular systems (Thelen, kelso, & Skala, 1987).
Neural maturationist theorists in the mid 1900’s viewed motor development as a
predetermined blueprint encoded by an individual’s genes (Gesell & Amatruda, 1947). Motor
skill acquisition develops through increased cortical control and receives no contribution from
environmental interactions (Gesell & Amatruda, 1947). McGraw (1935) tested this theory with a
set of infant twins that displayed a similar base level of controlled movements. One infant was
given typical infant care, while the other was subjected to training sessions geared towards
particular motor skills (e.g. climbing). McGraw (1935) found that training one child led to a
higher level of motor skill development compared to the child who received normal care,
suggesting that while a set neural framework exists, environmental learning can further enhance
motor skills.
In contrast, the neuronal group selection theory (NGST) provides a comprehensive
framework that integrates elements of both nature and nurture in explaining motor development.
The NGST posits that motor development is neither exclusively governed by genetically
predetermined neural circuits nor by environmental factors alone (Edelman, 1989; Sporns &
Edelman, 1993). Instead, it is the result of a dynamic interplay between genetic information and
environmental experiences (Edelman, 1989; Sporns & Edelman, 1993). The theory thus suggests
that development begins with groups of interconnected neurons (Edelman, 1989; Sporns &
Edelman, 1993) that are shaped by evolutionary processes and further refined through experience

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and behaviour, with synaptic connections being strengthened or weakened based on sensory
input and activity (Sporns & Edelman, 1993).
Modern day theorists suggest that motor skills are acquired through action, perception,
intrinsic motivation, and interactions with the environment (Corbetta, 2021; Köster et al., 2020;
Nagai et al., 2019; Nishiyori et al., 2015). When looking at the development of motor skills in
reaching and stepping, Nishiyori et al (2015) based their theory on the developmental systems
approach (Gottlieb, Wahlsten, & Lickliter, 1998) and TNGS (Edelman, 1989; Sporns &
Edelman, 1993). The result of this study contributed to their predictions that as infants explore
initial movements, they begin to acquire goal-directed movements through repetition and trial
and error (Nishiyori et al., 2015). During this process, specific brain regions and neural networks
display a large range of activation (Nishiyori et al., 2015). However, after practice and further
development of motor skills, the regions and networks begin to show a decrease in disbursed
activation and increase in focal areas of activity (Nishiyori et al., 2015).
Köster et al (2020) theorizes that neural maturation during the development of motor
skills are learned through a process of predictive processing. The framework of predictive
processing posits that effective interactions with the surrounding environment is dependent on
one’s capacity to refine predictions about how its actions will influence proprioceptive
experiences (Helmholtz, 1867; Köster et al., 2020). Additionally, this process can also be based
on predictions of social and physical behaviours of the external world (Schubotz, 2015).
However, Köster et al. (2020) argue that intrinsic motivation and curiosity play a significant role
in driving predictive processing during motor skill development. The natural curious and
explorative nature of infants drives infants to interact with their environment and each failed
attempt gives information of their surroundings (Köster et al., 2020). Through these novel

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experiences, infants can adjust their predictive models resulting in an increase in refined
behaviours (Köster et al., 2020).
Like Köster and colleagues (2020) theory, Nagai et al (2019) propose that predictive
learning of sensory information is a key feature in cognitive maturation. By using an internal
model of the world, humans can predict the behaviour of their surroundings and intentionally
manage bodily movements (Nagai et al., 2019). However, the ability to update a predictor based
on previous sensory signals and executing an action based on a prediction are the two
mechanisms that leads the development of motor skills.
Numerous theories attempt to explain the development of refined motor skills in infants,
often offering conflicting perspectives (e.g., Köster et al., 2020; McGraw, 1935; Nagai et al.,
2019). This inconsistency highlights the need for further research in this area. Despite modern
theories emphasizing the role of perception, action, interaction, and predictive learning in motor
skill acquisition (Corbetta, 2021; Köster et al., 2020; Nagai et al., 2019), the neural mechanisms
underlying these processes remain largely unknown. Therefore, further research is needed to
explore the learning process behind goal-directed movements and the neural correlates that
contribute to motor skill maturation.
Regions of interest
Given previous findings, my study will focus on certain regions of interest (ROI)
consisting of the prefrontal (pre-motor) cortex, motor cortex, and parietal cortex, each of our
ROIs play a distinct yet interconnected role in the process of reaching and grasping. The premotor cortex (pMC) is particularly involved in the planning and preparation of motor actions.
For example, this region is responsible for the anticipatory adjustments and mediations necessary

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to shape the hand according to the properties of an object (Tunik, Houk, & Grafton., 2009). The
pMC integrates sensory information with motor commands, facilitating the selection and
initiation of appropriate motor actions (Buch et al. 2006; Wenderoth et al., 2004). Moreover, this
area is active during the observation of actions, which suggests its involvement in action
understanding and motor learning processes (Cattaneo et al., 2009; Rizzolatti et al., 1996).
The motor cortex is primarily responsible for the execution of voluntary movements,
including those required for reaching and grasping (Cattaneo et al., 2009; Nishiyori et al., 2016).
This region is crucial for controlling fine motor skills and coordinating muscle activity
(Nishiyori et al., 2016). Neurons in the motor cortex have been found to encode many aspects of
movements such as direction, velocity, length, movement adjustments (Georgopoulos et al.,
1982; Morgan & Schwartz, 1999), and muscle synergy (Holdefer & Miller, 2002). In addition,
the motor cortex receives sensory input from the thalamus, which plays an important role in
voluntary and skilled movements (Asanuma & Mackel, 1989).
The parietal lobe is an integral part of processing spatial and sensory information
(Wenderoth et al., 2004), allowing for guided movements during reaching and grasping. The
posterior parietal cortex (PPC) is particularly involved in transforming sensory inputs into motor
commands, enabling accurate hand movements towards an object (Andersen & Cui, 2009).
Additionally, this region integrates visual and proprioceptive information, which is crucial for
maintaining an accurate perception of the spatial relationship between the hand and the object
(Andersen & Cui, 2009). An individual area of the PPC called Area 5 has a major role in spatial
information for the limbs and arm reaching (Kalaska et al., 1997). The parietal lobe also has been
found to be active during movement observations, specifically within the inferior parietal lobule
(IPL) (Fogassi et al., 2005). Neurons within the IPL encode distinct movements, and they release

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while observing other movements (Fogassi et al., 2005). Each region involved in movement has
many networks and subnetworks to communicate and coordinate efforts to achieve movement
goals (Wenderoth et al., 2004).
fNIRS
Over the past decade, there has been an increase in the number of studies using fNIRS
(Azhari et al., 2020; Ichikawa et al., 2019; Nishiyori et al., 2016). fNIRS is a non-invasive
neuroimaging technique that measures oxygenated (HbO2) and deoxygenated (HbR) hemoglobin
chromophore changes in brain tissue following neuronal activity (Li et al., 2024; Lloyd-Fox,
Blasi, & Elwell, 2010; Pinti et al., 2020). Though fMRI and fNIRS measure the same
hemodynamic responses, fNIRS has the advantage of higher temporal resolution, portability, and
low sensitivity to body movements, enabling infants to have a degree of natural movement
(Lloyd-Fox, Blasi, & Elwell, 2010; Nishiyori et al., 2016; Pinti et al., 2020). fNIRS uses
transmitters that produce near-infrared light at various wavelengths (ranging from 700-900). To
reach and measure cortical brain tissue, the infrared light penetrates through the layers of the
head (hair, skin, skull, cerebrospinal fluid). The light is then absorbed, attenuated, or scattered
and is detected with receiving optodes. With the advantage of fNIRS allowing infants the
freedom of movement during tasks, it is then a cost effective, convenient, and sensitive technique
to measure and better characterize the functional brain-behaviour changes in infant development.
Further research is needed to make up for the limited time-on-task from infants which
impacts the quantity of data and thus the reliability and validity of findings (Crista et al, 2013;
Nishiyori et al, 2016). Notably, multiple studies have highlighted the need to directly correlate
fNIRS activity with performance (Lloyd-Fox et al., 2015; Nishiyori, 2016; Nishiyori et al, 2016;
de Oliveira et al., 2018). The main objective of our study is to address the knowledge gap by

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examining and correlating brain-behaviour in typically developing infants. Once an optimal
protocol design is identified, prefrontal, motor, and parietal brain activity will be measured using
fNIRS whilst babies engage in naturalistic movements and reaching and grasping. This study
consists of a longitudinal design to examine the changes in brain maturation as infants develop.
Given recent findings, we predict focused brain activity and increased connectivity with more
maturation in each ROI. My secondary aim will be to share event (e.g., reach) fNIRS data in
open access repositories to help aid future study’s direction and protocol.

Methods

Participants
So far, a total 5 Infants were recruited between the ages of 6 to 13 m/o. Recruitment
involved leaflets and posters in community areas, posters on different social media platforms
(e.g., Facebook groups), and radio advertisements. Infants were also recruited by visiting local
hospitals, daycares, early childhood education programs, and children’s therapy centres. Parents
filled a brief medical history for their child, but there are no restrictions on eligibility based on
health. Each child completed at least one session but were invited to participate across 3
sessions, at 6 months, 9 months and 12 months. For each session completed, parents/guardians
received a $40 gift card to Starbucks or Indigo. Prior to the study, parents/guardians are
debriefed through written and oral information on the purpose and all procedures of the
experiment. All participant’s parent/guardian will give written consent prior to any experimental
procedure. This study has been approved by the Thompson Rivers University’s ethics committee.
Session Overview

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Prior to beginning the experiment, it can be helpful to provide the child time to get
acclimated to the environment and researchers present. During this period, we will give the child
toys to begin interacting with while we describe to the parent the study’s design, answer any
questions, and ensure the informed consent sheets have been signed. It is important when going
over the procedure to ensure the parent is comfortable with the process, as they can influence
their child’s contentment (Raschle et al., 2012). Once complete we will make two measurements
on the infant’s head, one between the inion and nasion and the other between the left and right
pre-auricular points (Nishiyori et al., 2016). From those two measurements we can locate the Cz
point of the head (Jasper 1958). With those measurements we gently apply the fNIRS cap into
position, we secure and adjust the hair as needed. The order of our study’s tasks is subjective to
each participant. For example, infants can come in hungry and want to reach for snacks right
away, but some may be playful. A very important aspect of this study and working with infants is
that we can not be demanding, working with what is available and keeping the child happy is the
main priority throughout the study. The child will then be placed into a special fit infant chair
(seat 26 cm from the ground, backrest approximately reclined 15° from a vertical position) with a
strap going around the waist and between the legs for stability. During the experiment, parents
are asked to sit next to their child and help engage them in the tasks at hand. Each full session
will last between 45 to 75 minutes, depending on the willingness and mood of the child.
Tasks
Reaching and grasping
I will be investigating motor skills during two specific behaviours, naturalistic play and
reaching and grasping. For the reaching and grasping tasks, there are three conditions: baseline,
reach and grasp, and observation. During the baseline condition, participants will view a

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children’s sensory video (Hey Bear Sensory, 2019) for a minimum of a 10s period between the
reach and observation conditions. This baseline condition will serve as a baseline for the
hemodynamic responses of the following tasks. During the reach and grasp condition, children
will be encouraged to reach for small snacks (cheerios and yogurt dots) for a minimum of one
minute. The observation condition consists of the participant observing videos of other infants
reaching and grasping for a minimum of one minute.
Table 1.
The experimental design will be in the following order, participants will complete three rounds of
tasks
Reach & Grasp Experiment
Observation 1

Baseline

Reach & grasp

Baseline

Observation

~10s

~1min

~30s

~1min

Observation 2
Observation 3

Free Play
The free play task consists of three conditions: Baseline, free play, and observation. The baseline
will remain the same as the prior tasks. During the free play condition, children will be
encouraged to play freely with small toys, stuffed animals, and balls for a minimum of one
minute. All toys used are around 5” (L) x 5” (W) x 5” (H). For the observation condition
participants will view a video of an infant engaging in free play for a minimum period of one
minute.
Table 2.

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Infants will engage in three rounds of free play tasks
Free Play Experiment
Baseline

Free Play

Baseline

Observation

~10s

~1min

~30s

~1min

Observation 1
Observation 2
Observation 3

fNIRS Data Acquisition
Infants will be seated in an infant chair and fitted with a specialized Brite ergonomic cap with
integrated wireless fNIRS optodes (Artinis, Medical Systems, The Netherlands). The light
emitting optodes will cover bilateral pre-motor, motor, and parietal areas of the brain (22
channels) (see figure 3). The fNIRS system operates at two wavelengths, 760 and 850 nm,
sampled at a rate of 25 Hz. The Optodes were placed 3cm apart, however one short separation
channel (SSCH) was placed on the left hemisphere over the motor cortex. These SSCH are 1.5
cm apart and help reduce “noise,” movement artifacts and physiological noise. Afterwards we
will ensure that the infant can tolerate the cap and adjust the optodes as needed. Once complete I
will begin with a cue that will mark the start of fNIRS and behavioural data collection. The cue is
set by E-prime 3.0 (Psychology Software Tools, PA, USA), which is presented on a laptop (Dell
Latitude 3410, 14” HD, 1920 x 1200 resolution) and sets off an auditory cue and inserts a time
sync within the fNIRS data. During the experiment, the infant will have close contact with the
parent/caregiver to help induce a high level of comfort. Between the reaching and free play tasks,
the fNIRS system is recalibrated (Autobrite) and each optode signal is checked.
Figure 1.

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Right and left hemisphere optode array where white represents transmitting optodes and black
represents transmitting optodes. The source-detector pairings make up 22 channels across the
pre-motor, motor, and parietal lobe. A short separation channel in the left hemisphere has been
placed on channel 17 (T7 & R7)

Video Data Acquisition
For behavioural data we use 2 digital cameras (Panasonic Lumix Gh6, Lens: Panasonic
Leica DG Vario-Elmarit 12-60mm) sampled at 21 frames per second. The first camera will be
positioned 3 ft in front of the infant and mounted to a stand approximately 75 cm tall, and the
second camera set around 5 ft to the left of the infant and mounted to a stand around 68 cm tall.
The cue from E-prime will be used to connect the behavioural data with fNIRS data.

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Data Processing
Video data was analyzed through BORIS (Friard, 2024), a behavioural software program
that is used for logging live observations. I have created an ethogram (see figure 5) to be able to
code live events and rate the quality of movement, observation, and rest conditions. Each event
was rated on the level of engagement of the participants, with good signifying high engagement
and poor low engagement. Moderate was given when participants had momentary distractions.
Figure 2.
Inserting live observations in BORIS

The ethogram consists of five behaviours/events: time sync (t), baseline (b), reaching and
grasping (r), free play, (f), and observation (o).
Figure 3.
Ethogram used to code live events in BORIS

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Once complete, events are exported to excel where the event times are converted to
match the fNIRS data. Then we can combine our behavioural observations with our fNIRS data
within Oxysoft 4.0 (Artinis, Medical Systems, The Netherlands), which allows for me to insert
the events manually. The fNIRS and behavioural data is then fully combined in Oxysoft and
exported to the AnalyZIR software, which converts the data into the form of a snirf file for
further analysis in MATLAB (MATLAB, 2021).
Data Analysis
A neural connectivity analysis will also be implemented to extract movement artifacts
and physiological noise (Huppert, 2016). Then we will implement a general linear model (GLM)
that will be used to find significant brain activity within specific channels for naturalistic play
and reach conditions.
Future Directions
My UREAP has provided me with valuable research experience, this summer I got the
opportunity to engage in the process of developing and designing my experiment. It can be rare
for students to be so closely involved in the design of a study, so I consider myself quite lucky to
get such a detailed learning experience that will help me with future research endeavors. As we
continue to test participants and begin to analyze the data, we will be checking to ensure that our
design is well thought out and note what changes we could improve upon in the future. I was

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also able to learn how to use data collection software and begin coding my first few participant’s
fNIRS and behavioural data. Working with the NIRS system has been an amazing experience,
throughout the summer I was able to learn how to properly set up the NIRS cap, check for good
signals of each transmitter and receiver, and to navigate Oxysoft for data collection.
Since this project is a longitudinal design, I plan to continue this research into my
Honours Thesis, where I can continue recruitment, test more participants, and learn to analyze
the data. Since beginning my studies, I quickly learned that my area of interest is brain and
behaviour, and conducting research is my primary goal in the future. Once completing my
bachelor’s degree, I hope to continue into a master’s degree in cognitive science. Working with a
special population such as babies and carers is quite a unique opportunity that will help me in my
future career. Learning complex techniques like fNIRS and behavioural coding will also be
helpful given these techniques may be implemented in future research settings.
This research can provide further understanding of the brain-behaviour changes that
occur in infant development. Our results will provide insight into how the brain re-organizes as a
child builds motor skills by examining brain activity using fNIRS and correlating such activity
with movements. The infants will produce repetitive movements such as reaching, touching,
grasping, and kicking as with previous studies. However, unlike previous studies, our study will
be able to identify brain activity over multiple areas of the brain as the infants produce and
rehearse these movements. It is important to note that this study will also serve as time one (cross
sectional) and continue as part of a longitudinal design, which are largely lacking in
developmental neuroscience. Future research will benefit from the characterization of brain
activity synced with behaviour for the identification of and to facilitate early interventions for
atypical developmental trajectories.

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