The study explored using electroencephalography (EEG) recordings in infants to predict neurodevelopmental outcomes. Specifically, the study measured the variance of relative power across EEG recordings in infants at 1 month of age. Higher variance of relative power at 1 month was found to predict those infants considered at risk for developmental delays. Additionally, variance of relative power at 1 month contributed to predicting scores on motor, cognitive, and language assessments at both 1 month and 3 months of age. However, the analysis and reporting of the study's methods and results lacked clarity and rigor.

2. 1. Early detection of atypical neurological development increases the potential for successful
interventions (Novack et al. 2017; te Velde et al. 2019).
Source: te Velde, A., Morgan, C., Novak, I.,
Tantsis, E., & Badawi, N. (2019). Early
Diagnosis and Classification of Cerebral
Palsy: An Historical Perspective and
Barriers to an Early Diagnosis. Journal of
clinical medicine, 8(10), 1599.

3. 2. It is necessary suitable measures of fetal and infant brain function and development, in order to guide
and monitor interventions seeking to promote healthy brain development in the early years prior to
functional impairment emerging (Holt et al. 2011).
Source: Holt, R. L., & Mikati, M. A.
(2011). Care for child development:
basic science rationale and effects of
interventions. Pediatric
neurology, 44(4), 239-253.

4. Nunez P, Srinivasan R. Electrical fields of the brain.
2nd ed. New York, NY: Oxford University Press;
2005.
3. Electroencephalography (EEG) offers one non-invasive tool with the potential to identify and quantify
atypical brain development (Cilio MR, 2015; El-Dib et al. 2011, Hayashi-Kurahashi et al. 2012, Périvier et
al. 2016, Shalak et al. 2003, Xiao et al. 2017).

5. “EEG power represents amount of activity in certain frequency
bands of the signal”
EEG Neurophysiological Basis of EEG
• Single neuron activity is too small to
be picked up by EEG;
• EEG reflects the summation of the
synchronous activity of many
neurons with similar spatial
orientations;
• Cortical pyramidal neurons produce
most of the EEG signal;
• Deep sources (subcortical areas) are
much more difficult to detect than
currents near the skull.
Nunez P, Srinivasan R. Electrical fields of the brain. 2nd ed. New York,
NY: Oxford University Press; 2005.

6. Electrode Placement
Standard placements of electrodes on the human scalp (10-20 SYSTEM): A, auricle; C,
central; F, frontal; Fp, frontal pole; O, occipital; P, parietal; T, temporal.

7. Pros
• Good time resolution, ms compared to s
with fMRI
• Portable and affordable (MEG is not)
• More tolerant to subject movement than
fMRI
• EEG is silent and so useful for studying
auditory processing
• Can be combined with fMRI or TMS
Against
• Low spatial resolution
• Artifacts / Noise

8. Magnetoencephalography (MEG) techniques.
A. CTF MEG (MEG International Services)
(photograph courtesy of the Down Syndrome
Research Foundation and MEG International
Services Limited).
B. CTF MEG fMEG (fetal MEG) (MEG
International Services) (photograph courtesy of
University Hospital Tübingen).
C. Artemis 123 BABYSQUID (Superconducting
Quantum Interference Devices) (Tristan
Technologies) (photograph courtesy of Tristan
Technologies). D. SARA CTF fetal MEG (CTF
Systems) (photograph courtesy of University of
Arkansas for Medical Sciences).
Source: Cheung, T. P., Grunau, R. E., Synnes, A.,
Eswaran, H., & Doesburg, S. M. (2015).
Magnetoencephalography: Neurophysiologic
Imaging for Perinatal Brain
Development. NeoReviews, 16(9), e544-e550.

9. Introduce a new measure of EEG – Variance of relative power of
resting state to be included as an optimally accurate and efficient
prediction of neurodevelopmental outcomes.

10. PARTICIPANTS
• Sample of convenience, data were collected between 17 February 2015 and 18 June 2016.
• A total of 22 infants with typical development (TD) participated, between 38 and 203 days of age. There were 2
infants measured once, the other 20 were measured once per month for 3 to 6 visits.
• A total of 11 infants broadly at risk (AR) for developmental disability participated (6 high-risk preterm, 4 low-risk
pre-term, 1high-risk full-term), aged between 40 and 225 days (adjusted for prematurity). Infants AR were
assessed once per month for 3 to 5 visits.
• Assessments started as close as possible of 1 month of age, and continued until the infant sucessfully reached
and grasped a toy with high skill.
• Inclusion criteria (TD): infants were from singleton, full-term births (over 38 weeks). Exclusion criteria (TD): infants
experiencing complications during birth, or with any known visual, orthopedic or neurologic impairment at the time
of assessment,or with a score at or below the 5th percentile for their age on the Bayley III. Inclusion criteria (AR):
infants were born before 36 weeks of gestation (low risk) or defined as at high risk for developmental delay per
the definition of the state of California.

11. Table 1. Infant characteristics

12. Table 1. Infant characteristics

13. ASSESSMENT
• Infants were measured at home, in the morning. Three families preferred to come to the laboratory for data
collection. Each visit lasted for arround 1h.
• Clinical assessments were Bayley III (Motor, Cognitive and Language ) and anthropometric measurements.
EEG assessment
• During each visit, EEG data were acquired using a Biosemi system with 32-electrode infant headcaps
(standard 10/20 system) at sampling rate of 512 Hz. Infants sat on the lap of a caregiver. First, 2 trials of 20-
second resting-state EEG data were recorded. During resting state recording, a lighted, spinning globe toy was
presented out of participants’ reach to attract their visual attention and minimize head and body movement.

14. EEG ANALYSIS
• Only resting state EEG data were analyzed here, ranging from 14–82 seconds. Resting-state EEG variables
explored here are individual power, relative power, and variance of relative power. Briefly, EEG data from all
electrodes were re-referenced to the average of T7 and T8. Next, a bandpass infinite impulse response filter
(0.3–30 Hz) was applied to the re-referenced data.
• Resting EEG segments with noisy segments were rejected. After rejection, remaining EEG data from 11 infants
AR and 22 infants with TD were: AR visit 1 = 11, AR visit 3 = 9, TD visit 1 = 21, TD visit 3 = 13.
• Power spectral density (PSD) was estimated using the “pwelch” function in MATLAB. PSDs were transformed
into relative powers so that spectral activities from all individual sessions were directly comparable.
• The relative powers were calculated between 0 and 30 Hz. For each frequency bin within this range and each
electrode, relative power was computed by dividing PSD by the sum PSD from all bins. Variance of relative
power was calculated as the standard deviation of the 32 relative power measurements for each infant,
calculated by taking the standard deviation of peak power across each channel.

16. Statistical analysis
• Multivariate linear regression was conducted to predict current (visit 1) and future (visit 3) Bayley scores using
resting-state EEG data. Statistical analyses were performed using R, version 3.5.1. Bayley score models
were compared using analysis of variance.
• PREDICTION OF AR STATUS FIRST VISIT: A baseline statistical model (a model that only included age in
days and at-risk status) was compared to a nested model of the baseline model features plus variance of
relative power to determine significant predictive effects of variance of relative power. Analysis of variance
(ANOVA) was used to determine significant predictive effects of variance of relative power across Bayley
scores.
• PREDICTION OF AR STATUS THIRD VISIT: On average, visit 3 took place 60 days after visit 1. The 3-
regressor model using age, at-risk status, and variance of relative power was compared against a 2-
regressor model using age and at-risk status only.

18. 1 month 8 months

19. AR
TD

20. Age +AR status Age +AR status+variance rel. PSD

21. • Higher variance of relative power predicted AR status. Authors proposed that high
variance may represent less organized cortical activity associated with na atypical
trajectory of brain development and compare with data that distinguish infants with TD
from infants at high risk for autismo disorders.
• Variance of relative power provides a significant contribution to 1st visit score prediction
of Bayley raw fine motor, Bayley raw cognitive, Bayley total raw score, Bayley motor
composite score. Further, we found that variance of relative power from visit 1 contributes
to predictionof Bayley raw fine motor score at visit 3.

22.  The study explore new measurements of EEG recording in infants to explain
neurodevelopmental outcomes.
 Collect EEG data in early age - 1 month.
 Data collection was at home
 Provide raw data

23.  The writing was confused and with some grammatical mistakes;
 To much transformation of data – relative power, when and how they did? Raw data does
not match with values on the figure.
 Variance of relative power was measured across the 32 electrodes. Is it not expected or
desirable to have variance across electrodes? In my opinnion, this measure does not
reflex variance or “repertoire variance” of the brain. Maybe, variance should be measured
from coherence or synchronization and desynchronization of rythms.
 At the DN laboratory we believe that variance is good and stereotyped behaviors are bad,
which is in agreement with NGS theory of development.
 Discussion was very weak.