Some researchers have begun to map structural brain development with growth charts using magnetic resonance imaging (MRI)³, but there has been much less work on comparable charts of early brain function. As we know, brain function also undergoes rapid change in early life, and this ongoing functional development supports emerging behavioral skills. But there has been very limited progress in identifying whether growth charts can be used to map brain function. Additionally, MRI is limited as a tool for scalable public health implementation due to its prohibitive cost and infrastructure. We sought to develop normative growth curves of infant brain function measured with electroencephalogram (EEG)⁴. EEG is a tool well-suited to this kind of public health developmental context, as it is a scalable, relatively low-cost modality that we can use to measure brain function in humans starting at birth.

We chose to characterize brain function in a key sensory domain: visual neurodevelopment. This domain is important for later visual functioning and may also scaffold later cognitive skills that matter across the lifespan. The visual-evoked potential (VEP) is a robust activity signal that has been measured across species and may reflect features of the neural architecture, such as myelination and underlying cortical circuit function. It can be collected with EEG while an infant watches a visual stimulus, like a flashing checkerboard. Even in infancy, the VEP has a canonical waveform, characterized by an initial negative peak (N1), a later positive peak (P2), and a final negative peak (N2), as shown in the bottom right panel of Figure 1.

Figure 1: Study Overview. 128-channel EEG data were collected in 802 infants (n=1374 EEG observations) between 57 and 579 days old across three continents. These data were a priori harmonized for paradigm, acquisition, and processing parameters across sites. Standard processing was used to extract the visual-evoked potential (VEP) from the EEG data and measure the amplitudes and latencies of the N1, P1, and N2 peaks. We created growth curves for each amplitude and latency.

We collected standardized EEG VEPs from 802 infants (ages 57 to 579 days old) from four longitudinal cohorts across three global sites (i.e., Cape Town, South Africa; São Paulo, Brazil; and Boston, USA), resulting in 1374 observations. These sites represent distinct cultural, linguistic (i.e., primarily speaking Xhosa, Brazilian Portuguese, and English, respectively), geographic, and socioeconomic contexts. For example, the socioeconomic diversity can be seen as more than 60% of the caregivers in the South Africa cohort reported an annual household income of less than $3,303 (60,000 ZAR); whereas 10.3% in the Brazil cohort fell under an HHI of $4,763 ($27,600 Brazilian Real) and only 5.4% in the US cohort reported making $50,000 or less. These differences allowed us to test whether developmental trajectories generalize across markedly different settings.

We developed growth curves for the different EEG signal components of this visual response. After extracting VEP waveforms from the EEG data, they can be characterized by the amplitudes (i.e., heights) and latencies (i.e., times from stimulus onset to peak) of the N1, P1, and N2 peaks. Growth curves for each of these parameters were created. Our modeling approach applied GAMLSS⁵ (generalized additive models of location, scale, and shape), a flexible centile-modeling method that can capture complex nonlinear trajectories and has been used in growth-chart work (see Figure 2). This approach creates flexible centile curves similar to those used in WHO growth standards. However, given the diversity of our three sites, we sought to test whether the developmental patterns we observed were similar across the distinct populations. When we modeled visual neurodevelopment features in two of the sites, we successfully applied the model to the held-out site, suggesting that these developmental trajectories were similar across populations.

Figure 2: Modeling Overview. We independently trained GAMLSS curves for each site and VEP feature (i.e., N1, P1, and N2 amplitudes and latencies). The GAMLSS curves were generalized across sites, indicating that a curve trained on the Boston data was valid for the Cape Town data. Individual centiles were extracted and linked to cognitive scores from a global public health measure (Global Scales of Early Development, Long Form).

Individual centile scores on these growth curves may also associate with broader cognitive development. We found that deviations from these trajectories were associated with Global Scales for Early Development scores, a WHO-developed measure of general development, in the South Africa cohort. Higher centile scores were associated with higher developmental scores at two years of age.

Our research suggests that key VEP developmental patterns are similar across these populations, despite substantial environmental, genetic, and cultural differences. Beyond this cross-site consistency, the VEP is a promising metric of neurodevelopment because of its accessibility. It can be collected in newborns using a simple, portable, and relatively low-cost EEG paradigm. This allows for greater diversity in both the data used to train the models and the settings where this technology could be deployed. Open-source preprocessing software, such as Harvard Automated Preprocessing Pipeline for EEG⁶ (HAPPE; https://github.com/PINE-Lab/HAPPE), can also facilitate standardized processing of EEG data. We have integrated our growth curves from the current dataset into HAPPE, making it easier to extract centile scores from new participants. We hope to continue updating these models as more data are collected and to improve our understanding of how the VEP relates to broader cognitive development. With further replication and prospective validation, VEP growth curves may provide a scalable framework for characterizing atypical developmental trajectories. In the future, they may help identify critical periods and support the assessment of intervention-related change.

Want to learn more about this study? Read our preprint here: https://doi.org/10.1101/2025.03.25.645314

1          Group, W. M. G. R. S. WHO Child Growth Standards based on length/height, weight and age. Acta Paediatr Suppl 450, 76-85 (2006). https://doi.org/10.1111/j.1651-2227.2006.tb02378.x
2          Kuczmarski, R. J. et al. 2000 CDC Growth Charts for the United States: methods and development. Vital Health Stat 11, 1-190 (2002).
3          Bethlehem, R. A. I. et al. Brain charts for the human lifespan. Nature 604, 525-533 (2022). https://doi.org/10.1038/s41586-022-04554-y
4          Margolis, E. T. et al. Growth charts of infant visual neurodevelopment generalize across global contexts. bioRxiv, 2025.2003.2025.645314 (2025). https://doi.org/10.1101/2025.03.25.645314

About the Author

When it comes to babies’ social worlds, one size does not fit all.

Even within the first year of life, infants differ in how their brains respond to social information, such as facial expressions, eye contact and speech. Rather than all babies attending to social cues in the same way, each infant appears to have their own unique pattern of neural engagement. Novel AI-based methods can now be used to personalise brain science and track these individual patterns in real time. This allows us to ask a simple but powerful question: what does each baby’s brain find most engaging in a social exchange?

Studying baby brains, one baby at a time

Traditionally, infant research has focused on group averages. While this approach has taught us a great deal, it can sometimes miss something important: individual differences. To address this, researchers at the Centre for Brain and Cognitive Development at Birkbeck, University of London used a cutting-edge method called Neuroadaptive Bayesian Optimisation (NBO), an artificial-intelligence technique that adapts in real time to each baby’s brain activity^1,2.

In a pre-registered study involving 61 infants aged 5 to 12 months³, babies looked at images of their caregiver’s face, a stranger’s face, or faces that were a mix of both. While babies viewed the faces, we recorded brain activity using electroencephalography (EEG), a safe and non-invasive technique (see Figure 1). We then used a machine learning algorithm that learned from each baby’s brain responses as the experiment unfolded. After each image was shown to the baby, the algorithm estimated which face had sparked the strongest response and used that information to choose what image to show next, allowing us to adjust the experiment moment by moment to each infant’s brain signals. This approach helped us identify which type of face triggered the strongest response for every individual baby.

Figure 1. Baby participants wearing EEG caps that measure their brain activity while they view images of different faces on a screen.

No “average” baby brain

What did we find? There was no single face type that worked best for all babies. Instead, around 85% of infants who completed the study showed strong individual preferences, with different babies responding most strongly to different faces.

An added bonus? This personalised approach also led to lower drop-out rates (about 15%, compared to the 22% typically seen in infant EEG studies). This demonstrates how tailoring experiments to babies by presenting images that elicit the strongest brain response can help keep them engaged.

The BONDS project: personalised approaches to social development

These findings are part of the wider BONDS project (Behaviour and Online Neuroimaging to study the Development of Socialisation), which involved over 120 infants and their families. BONDS combines wearable neuroimaging with artificial intelligence to test what social cues individual babies’ brains attend to.

A recent pre-registered study⁴ extended this work by showing that infants’ brains are sensitive to different combinations of social cues, such as eye gaze, head orientation, and emotional expression, and that the most engaging combinations vary from baby to baby.

Further, BONDS research has shown that personalised methods can be used not only with images on a screen, but also during live social interactions with an experimenter⁵. These studies demonstrate that:

• Infants differ in which social signals (e.g., gaze, infant-directed speech) elicit the strongest neural responses
• Brain engagement during social interaction reflects how motivated and attentive infants are in that moment
• Individual neural preferences can be detected reliably, even in dynamic, real-world social situations

Together, these findings suggest that early social brain development is shaped by individual experience and neural tuning, rather than following a single “typical” pattern.

Follow the baby’s lead

In summary, we showed, with a set of rigorous neuroimaging studies, that babies naturally differ in what captures their attention. Some may be especially drawn to expressive faces or direct eye contact, while others respond more strongly to subtler social cues. Because infants differ in what captures their attention, using personalised neuroimaging approaches to study these early differences in interests could open up new ways to support responsive parenting — that is, noticing what a baby naturally attends to and responding to it. Tuning into these individual preferences may help nurture babies’ attention and emerging social skills.

Looking ahead

By using personalised neuroimaging approaches, researchers are gaining a richer understanding of how babies engage with others from the very start of life. This work highlights the importance of recognising and respecting individual differences in early development — both in science and in everyday parenting.

The BONDS project is now expanding beyond infancy to study neurotypical and autistic toddlers, focusing on how children interact with their parents during real social exchanges. By studying parent–child interaction at this stage, the project aims to capture how diverse developmental pathways unfold, and how personalised approaches can help us understand and support children with different strengths and needs.

To learn more about this research and future findings, visit the BONDS project website: https://sites.google.com/view/bonds-project/

References:

1. Lorenz, R., Hampshire, A., & Leech, R. (2017). Neuroadaptive Bayesian optimization and hypothesis testing. Trends in cognitive sciences, 21(3), 155-167.
2. Gui, A., Throm, E. V., da Costa, P. F., Haartsen, R., Leech, R., & Jones, E. J. (2022). Proving and improving the reliability of infant research with neuroadaptive Bayesian optimization. Infant and Child Development, 31(5), e2323.
3. Throm, E., Gui, A., Haartsen, R., da Costa, P. F., Leech, R., Mason, L., & Jones, E. J. (2025). Combining Real‐Time Neuroimaging With Machine Learning to Study Attention to Familiar Faces During Infancy: A Proof of Principle Study. Developmental Science, 28(1), e13592.
4. Gui, A., Throm, E., da Costa, P. F., Penza, F., Mayans, M. A., Jordan-Barros, A., Haartsen, R., Leech, R., & Jones, E. J. H. (2024). Neuroadaptive Bayesian optimisation to study individual differences in infants’ engagement with social cues. Developmental Cognitive Neuroscience, 68, 101401.
5. Throm, E., Gui, A., Haartsen, R., da Costa, P. F., Leech, R., & Jones, E. J. (2023). Real-time monitoring of infant theta power during naturalistic social experiences. Developmental Cognitive Neuroscience, 63, 101300.

About the Author

As we launch into 2026—despite painfully freezing temperatures in NYC and a calendar with overly optimistic back-to-back-to-back meetings—I can’t help but feel an immense sense of warm gratitude for the opportunity to mentor outstanding undergraduates, graduate students, research staff, and postdoctoral scientists on our ever-favorite topic of infant development. I am likewise grateful for the opportunity to continue to learn from and contribute to members in the International Congress of Infant Studies. As I enter my 35th year as Professor at NYU, preceded by 4 years of PhD mentorship under Marc Bornstein (yup, I am very old!), I attended ~20 ICIS conferences, more than any other conference. That’s because ICIS is my favorite society by far. I admire the exquisite balance it offers between cutting-edge scholarship and simply having a good time with a vibrant, energetic, visionary, and absolutely welcoming international community of researchers at all stages of their careers. I can’t wait to add another meeting to my list this July in Panama.

Looking back to the end of 2025, November and December were all ICIS. The two weeks before ‘submission deadline’ our lab was frenzied to say the least (I would venture a guess that this was the case for many of you). It was filled with meetings and collaborations on abstracts around infant object interactions, communication, language, social interactions, temporal features of behavior, the home environment, culture, and even motor development (thanks to my sister lab, led by Karen Adolph). Somehow, we managed to submit who knows how many posters, talks, and symposia (I lost count).

So, what is the point of my writing this blog? Why do I feel compelled to express the deep fulfillment I feel about the work we do, the sharing of our research with others, and learning about the pioneering work of our colleagues (many of whom are also good friends)? Why do I feel compelled to applaud the open sharing that allows everyone in our field to learn and grow from each other in ways that make the whole much more than the sum of its parts? Why do I feel compelled to celebrate the sometimes-half-full glass of infancy research even at times when the half-empty glass appears to be front and center? Because I cannot think of anything more important than seeking to understand how infants learn and develop and the factors that propel their learning. The questions we ask as a field, and our excitement around scientific discovery and the testing of hypotheses, must always be louder than any obstacles or concerns. In truth, our glass is overflowing—with new ideas, new methods of data collection, new technologies, and new analytic approaches that allow us to collectively push science forward.

Of course, I am quite aware of the reality that many mentees and staff in my lab, department, university (and surely universities around the globe) are concerned about the future—the job market, academia, grant funding. Students are further concerned about countless other pressures and hurdles they must overcome—their comprehensive exams, R scripts that won’t run, how much they need to publish to land a postdoc or faculty position, and how to handle the comments of ‘Reviewer 2’ (if you are Reviewer #2, I prefer you don’t let us know).

But then I remind everyone in the lab to take a pause and reflect on how totally cool and exciting their work is. I tell my students and staff that their only job right now, their only concern, should be to grow and be passionate about their research and ultimately to do good (hopefully great) work that advances the science of infancy. Then, when we get together for our weekly lab meeting, the excitement of their work takes on a new life. We get to watch videos—hundreds of hours of videos—of babies and toddlers yelling “NO!” to their moms or dads, crawling and walking from room to room, flitting from object to object as they explore their surroundings and generate feedback from the people around them. We watch videos of cultural practices around the globe, from cradling to cuddling, talking to walking. We observe and quantify the unique physical characteristics of apartments in NYC, Hong Kong, and Seoul South Korea, which contain hundreds of child-designed toys, and we contrast those environments with homes in Tajikistan, which are spread over courtyards and where infants play with boulders and twigs, their cribs, plastic water bottles, and the pots and pans their mothers use to cook. We talk about the next study we will run, the grant that just got funded (yay!), the next grant we are currently writing, and how to design our protocols. We start the process of translating and adapting methods and measures to different languages, thankfully made possible by the multilingual community of committed and eager researchers who populate our lab. We upload our videos, coding manuals, spreadsheets and so on to Databrary.org so that authorized investigators can address new questions that we don’t have the bandwidth or expertise to tackle, thereby capitalizing on the investments of federal agencies, foundations, and families who generously give their time to us. Then, we stop to write our abstracts, about what we saw and what we learned, and we submit our work to ICIS.

We may have been frenzied toward the end of 2025, but what could be more fulfilling than clicking submit to share our science? And now, in the blustery cold of winter, we will sit back to await the news, celebrate the overflowing cup of infancy research, and plan our trip to Panama to attend the greatest international conference in developmental science out there. See you all soon!

About the Author

Across four preregistered experiments, we had 1.5- to 8-year-olds play a card-matching game on a tablet or video screen (see Figure 1). The game consisted of three face-down cards that “fly” in. There were two cards on the bottom and one on the top. Each of the two bottom cards flipped over to reveal an image. Then, they flipped face down. Finally, the top card flipped over to reveal a match with one of the two bottom cards. It is there that we looked to see whether children looked at (1.5- to 2.5-year-olds) or tapped (3- to 8-year-olds) the correct matching card. There are four trials of this card-matching game where the images on the cards are all unique, and four trials where the images on the cards are repeated from trial to trial. In this repeated condition, the first trial may have an orange on the left card and a banana on the right card. The second trial may then have the orange on the right and the banana on the left. If toddlers and young children are affected by proactive interference, then on later trials, they may have more trouble picking out which side the matching card was on in the repeated condition compared to the version where they see all new cards in each trial.

Figure 1. The sequence of events in a test trial for the card-matching game.

So how do children do? Our first study² using this card-matching game across three preregistered experiments (N = 245, 111 boys) found that 2.5- to 8-year-old children will, like you, walk to yesterday’s parking spot instead of today’s. Our fourth preregistered experiment^3, tested 1.5- to 2.5-year-olds (N = 34) and found that toddlers also make more memory mistakes when old information (“the orange was on the left last time…”) can interfere with new information (“was the orange on the left or the right this time?”). This may not sound too surprising, as we all know that children make much bigger (and funnier, at times) memory mistakes than getting mixed up in a card-matching game. We indeed expected that children, especially young toddlers, would be more susceptible to something like proactive interference than older children or adults. What’s important about quantifying this is that most measures of memory performance in children and toddlers don’t take proactive interference into account, and thus may be reporting children’s memory as worse than it really is.

Plenty of laboratory and clinical tasks measuring memory performance in toddlers and children involve repeated trials where children have to remember the same or similar information over and over again^4,5,6. Although this is an intuitive way to get an average memory “score”, if children’s memory is disproportionately affected by proactive interference, then their performance will tend to decline across trials and thus lead to an underestimation of their actual memory abilities. For example, in our card-matching game, children must remember (1) the image on the bottom-left card, (2) the image on the bottom-right card, and after they see the top card and need to choose the match, they need to remember (3) where (right or left) the matching card was. Previous work has shown that by 9 months of age, infants can keep track of “what” went “where” for two objects at a time⁷. Yet when we induce proactive interference in our experiment, 1.5- to 2.5-year-olds’ performance drops to chance level at this “what” went “where” task by the 4th trial.

It comes as no surprise that toddlers’ working memory would be sensitive to interference effects. Our research has the following takeaway: through critically assessing old assumptions, we can come to new conclusions that impact how future research is conducted. This is the beauty of scientific progress, and it wouldn’t be possible without the R15 grant from the National Institutes of Health (NIH) we received to help pay for equipment and reimbursing the hundreds of families who volunteered their time to participate in our research. Federal grants not only provide financial stability to research projects, but they also buy time. An awarded NIH grant allows researchers to spend time training and mentoring the future generation of scientists. For example, the current project is co-led by an undergraduate research assistant, Zane Mourad, who is not only a co-author on this blog post, but they are also being fully immersed in research thanks to the time that grants allow faculty and graduate students to train mentees. Therefore, only through this kind of major funding could we tackle our next questions: are some kids more susceptible to proactive interference than others? If so, do cognitive assessments appropriately take this into account? We hope to continue our research into children’s memory and forgetting.

1. Hamilton, M., Ross, A., Blaser, E., & Kaldy, Z. (2022). Proactive interference and the development of working memory. Wiley Interdisciplinary Reviews – Cognitive Science, 13(3), e1593.
2. Hamilton, M., Roper, T., Blaser, E., & Kaldy, Z. (2024). Can’t get it out of my head: Proactive interference in the visual working memory of 3- to 8-year-old children. Developmental Psychology, 60(3), 582–594.
3. Koolhaas, C., Hamilton, M., Roper, T., Blaser, E., Kaldy, Z. Proactive interference disrupts 2-year-olds’ working memory. Poster presentation at the Biennial Meeting of the Society for Research in Child Development, May 1 – May 3, 2025, Minneapolis, MN.
4. Fitzpatrick, C., & Pagani, L. S. (2012). Toddler working memory skills predict kindergarten school readiness. Intelligence, 40(2), 205-212.
5. Morra, S., Gandolfi, E., Panesi, S., & Prandelli, L. (2021). A working memory span task for toddlers. Infant Behavior and Development, 63, 101550.
6. Willoughby, M. T., Blair, C. B., Wirth, R. J., & Greenberg, M. (2010). The measurement of executive function at age 3 years: psychometric properties and criterion validity of a new battery of tasks. Psychological Assessment, 22(2), 306.
7. Kaldy, Z., & Leslie, A. M. (2003). Identification of objects in 9‐month‐old infants: integrating ‘and ‘information. Developmental Science, 6(3), 360-373.

About the Author

September 10, 1943—October 23, 2025

Judy S. DeLoache was born on September 10, 1943 in the small town of Holyoke, Colorado where she grew up on a wheat farm. There were only 38 people in her graduating class. But being from a small town did not keep her from having a big life. She received her BA and MA degrees at Georgia State College where she worked with Professor James Pate, an experimentalist and statistics instructor.  At the time, her research subjects were rats, and her first publication was “Hippocampal lesions and spontaneous alternation behavior in the rat,” which appeared in Physiology and Behavior. Jim saw her potential and encouraged her to pursue a PhD, so in 1969, she left Georgia and moved to the University of Illinois in Champaign-Urbana, where she began her PhD studying infant memory with Dr. Les Cohen.  After receiving her degree in 1973, she took a position as an Assistant Professor at Florida Atlantic University.  But after only a year, she returned to Illinois, after meeting her husband, Jerry Clore. They were married in 1977, and their son Ben was born in 1978.  She spent several years there on soft money, receiving financial and moral support from Professors Ann Brown and Joe Campione, which enabled her to apply for and receive her first NIH research grant called “Representational Functioning in Young Children.” This grant was continuously funded for the entirety of her career.

In 1981, she became an Assistant Professor in the small Department of Human Development and Family Ecology at the University of Illinois, where she came up through the ranks and eventually served as department chair.  She was then hired by the Psychology Department in 1991, where she taught until 2000, after being awarded an Alumni Distinguished Professorship in 1999. In 2001, she moved to the University of Virginia, where she was the Keenan Professor of Psychology until she retired in 2014.

Judy’s successful career as a developmental scientist can be at least partially attributed to the fact that she was a brilliant observer of human behavior. It was in her early memory work that she used a scale model of a larger room to hide an object to see if children could remember where it was hidden in an identical full-sized room. One thing that struck her was that 2.5-year-olds unexpectedly failed this seemingly easy task. Where some researchers would be distraught and call this a failure, Judy observed something interesting. She went on to use this “scale model task” to explore the developmental shift in children’s ability to use a miniature model as a representation of a larger room. Her initial findings using this task led to her seminal paper in Science entitled “Rapid change in the symbolic functioning of very young children,” (1987), and launched a decades long career on young children’s understanding of symbols.

Her largest contribution to developmental science was her theory of dual representation, which explains how children come to understand that a symbolic object—like a picture, map, or scale model—can be both a real object and a representation of something else at the same time. Her work has made a huge impact on how developmental psychologists have studied how children come to understand that pictures, models, books, maps, digital media, and other symbolic objects stand for real-world things. Her work shed a new light on how representational insight develops and how it supports long-term learning, and has influenced not only theories of cognitive development, but also practical approaches to education and media design for young children. Her seminal “shrinking room study,” which advanced her dual representation theory, was selected for the Society for Research in Child Development (SRCD)’s list of “Twenty Studies that Fascinated Child Psychology,” in 2003, properly putting her name next to giants in the field like Harry Harlow, Eleanor Gibson, and Jean Piaget.

Judy was also the recipient of numerous national and international awards including the APA William James Fellow Award, Fellow of the American Association for the Advancement of Science (AAAS), Fellow of the American Academy of Arts and Sciences, Member of the Society of Experimental Psychologists, SRCD Senior Distinguished Contributions Award, and the Distinguished Contribution Award from the International Congress on Infant Studies (ICIS).

Despite being a brilliant and world-renown scientist, Judy was down to earth, kind, and generous with her time. And she was fun. She loved life, she loved people, and she loved her students. She especially loved her husband Jerry, her son Ben and daughter-in-law Laura, and her grandchildren Waverly and Wilder. Of all of her many accomplishments, her family was the one she was most proud of. To me, Judy was a teacher, a mentor, and a friend. She was always the life of the party—the person everyone in the room wanted to talk to. She was one of my favorite people. And if you knew her, I’m certain she was one of yours too.

Vanessa LoBue

The big picture

Thanks to modern neonatal care, more preterm babies are surviving than ever before. Yet more than thirteen million babies worldwide are still born early each year, and many spend their first days or weeks in neonatal intensive-care units (NICUs) ([1]; [2]). Being born early can place children at higher risk for long-term challenges with movement, attention, and behavior ([3]; [4]; [5]).

Alongside medicines when they are needed (e.g., [6]), researchers have been testing gentle, non-drug approaches that can soothe and support development – like carefully structured sounds (white noise, heartbeat recordings, and other environmental sounds) that help stabilize sleep and reduce stress ([10]; [11]). Music – whether passively listened to or used in a therapeutic way ([12]-[15]) – has also been linked to better physiological regulation and emotional well-being in infants, with longer-term benefits for mental health and social inclusion ([16]; [17]).

Because premature infants are extra sensitive to noise, sound in the NICU has to be handled with care: average levels outside the incubator should stay around 45–50 dB, with brief peaks below 65 dB ([18]; [19]). And none of this replaces the power of everyday bonding. Early, sensitive parent–infant interactions set the stage for healthy brain development – but those interactions can be harder to achieve after a stressful or traumatic birth ([20]; [21]). With that context in mind, our team asked a simple question with big stakes: Do a caregiver’s relationship to the baby (kinship) and the pitch of their voice (which tends to be lower in men and higher in women) change how a preterm infant’s brain responds to singing? To find out, we created a precision-music medicine protocol designed specifically for fragile newborns [22].

What we did

We ran a small, carefully controlled study in the NICU. 30 very-preterm infants (born at or before 32 weeks gestation) took part. In the experimental group (15 infants), over four consecutive days, each baby experienced the same short, 11.5-minute “listening session”, but the singer changed from day to day: father, mother, a male music therapist, and a female music therapist – in randomized order. That let us tease apart two things at once: kinship (parent vs. non-parent) and voice type (typically lower-pitched male voices vs. higher-pitched female voices). Each session included quiet periods for comparison and two kinds of sounds: a single musical note and a lullaby. While the babies listened, we recorded their brain activity using an infant-friendly electroencephalography (EEG) set up. We focused on the “delta” brainwaves range – slow brain waves linked to cortical maturation in premature infants – because it is a useful window into how the brain is organizing itself at this stage.

What we found

In our preliminary report published in Plos One journal [22], showed that mothers’ singing tended to evoke strong slow-wave responses under most conditions. But during the lullaby – a richer, more complex sound – father’ singing produced the strongest average response, followed by the male music therapist, then the mother, and then the female music therapist.

When we analyzed the full cohort of all 15 infants in the experimental group, the overall pattern held. The lullaby prompted especially robust slow-wave activity, and paternal singing again came out on top. To zero in on whether this had more to do with voice type than the specific people, we grouped male voices (father + male therapist) and female voices (mother + female therapist). Male voices elicited significantly higher slow-wave (delta) activity during the lullaby condition even in this case. In everyday terms: a lower-pitched lullaby seemed to sync more strongly with these babies’ developing brain rhythms.

Of course, this is a small study, so we should be cautious. But the results suggest that both the complexity of the sound (like a full lullaby) and the voice’s pitch can shape how a very-preterm infant’s brain responds.

Why this matters for families

These findings push back on the idea that only a mother’s voice “counts” in early interventions. Certainly, mothers remain essential – their voices reliably supported strong brain responses under simpler sound conditions, but fathers’ singing – especially a real lullaby – may offer a unique boost.

What could this look like in practice? We need to start inviting fathers in NICUs more often and more systematically, where bonding can be interrupted by medical needs. In these terms, structured paternal singing may help spark healthy brain rhythms and strengthen father–infant bonding and later attachment right from the start. Furthermore, when a mother cannot sing on a given day – because of medical recovery, logistics, or simply exhaustion – a father’s lullaby can serve as a powerful stand-in, helping maintain consistent, meaningful sensory input for the baby. For mothers who are singing most often, it can also help to experiment with a slightly lower pitch range or a slower tempo to see whether it better matches the baby’s calm, slow rhythms, especially during lullabies. Remember: sound levels in the NICU must stay gentle and safe, and nothing replaces nurturing contact and responsive caregiving. Singing is one supportive tool among many.

Why public funding also matters

This work was possible because of hospital/state support – the kind of public investment that lets teams build safe, family-centered protocols and bring advanced tools to the bedside. Findings like ours, therefore, do not just refine how we think about early bonding. They point to practical changes that national and international health policies can encourage, such as expanding paternal leave and making room for father-infant co-regulation in the earliest days.

We should also mention that studies like this are careful and resource intensive. We used infant-grade EEG, a data-cleaning process tailored to fragile signals, and a study design where each baby experienced every experimental condition so we could spot real differences with a small group. That approach is efficient – it squeezes more clarity out of fewer participants – but it only works when public funding supports skilled clinician-scientist teams, specialized equipment, and the time needed to do the work right (ideally including follow-up as children grow).

Without that broader public infrastructure, questions at the intersection of sound, relationships, and early brain development would remain unanswered, and precision, family-centered care underpowered. Therefore, our research and findings, we believe, make a strong case for continued public stewardship: even modest investments in precision, family-focused neonatal care can generate knowledge and insights that improve care now and reduce costs later – by lowering the burden of neurodevelopmental difficulties across childhood. In short, public investment pays off twice – first by protecting fragile beginnings and again by strengthening the society those children will help build.

References

[1] Bradley, E., Blencowe, H., Moller, A. B., et al. (2025). Born too soon: Global epidemiology of preterm birth and drivers for change. Reproductive Health, 22(Suppl. 2), 105. https://doi.org/10.1186/s12978-025-02033-x

[2] Cheong, J. L., Spittle, A. J., Burnett, A. C., Anderson, P. J., & Doyle, L. W. (2020). Have outcomes following extremely preterm birth improved over time? Seminars in Fetal & Neonatal Medicine, 25(3), 101114. https://doi.org/10.1016/j.siny.2020.101114

[3] Camerota, M., & Lester, B. M. (2025). Neurobehavioral outcomes of preterm infants: Toward a holistic approach. Pediatric Research, 97(5), 1475–1480. https://doi.org/10.1038/s41390-024-03505-9

[4] Song, I. G. (2023). Neurodevelopmental outcomes of preterm infants. Clinical and Experimental Pediatrics, 66(7), 281–287. https://doi.org/10.3345/cep.2022.00822

[5] Twilhaar, E. S., Wade, R. M., de Kieviet, J. F., van Goudoever, J. B., van Elburg, R. M., & Oosterlaan, J. (2018). Cognitive outcomes of children born extremely or very preterm since the 1990s and associated risk factors: A meta‐analysis and meta‐regression. JAMA Pediatrics, 172(4), 361–367. https://doi.org/10.1001/jamapediatrics.2017.5323

[6] Juul, S. E., Comstock, B. A., Wadhawan, R., Mayock, D. E., Courtney, S. E., Robinson, T., … Heagerty, P. J. (2020). A randomized trial of erythropoietin for neuroprotection in preterm infants. New England Journal of Medicine, 382(3), 233–243.

[7] Rohmah, I., Mukminin, M. A., Hasan, F., Romadlon, D. S., Chang, K. M., Chen, K. H., & Chiu, H. Y. (2025). Comparative effects of nonpharmacological interventions on sleep-wake states among preterm infants in neonatal intensive care units: A systematic review and network meta-analysis. Intensive and Critical Care Nursing, 91, 104168. https://doi.org/10.1016/j.iccn.2025.104168

[8] Shen, Q., Huang, Z., Leng, H., Luo, X., & Zheng, X. (2022). Efficacy and safety of non-pharmacological interventions for neonatal pain: An overview of systematic reviews. BMJ Open, 12(9), e062296. https://doi.org/10.1136/bmjopen-2022-062296

[9] Sofologi, M., Pliogou, V., Bonti, E., Efstratopoulou, M., Kougioumtzis, G. A., Papatzikis, E., … & Papantoniou, G. (2022). An investigation of working memory profile and fluid intelligence in children with neurodevelopmental difficulties. Frontiers in psychology, 12, 773732. https://doi.org/10.3389/fpsyg.2021.773732

[10] Zhang, Q., Huo, Q., Chen, P., Yao, W., & Ni, Z. (2024). Effects of white noise on preterm infants in the neonatal intensive care unit: A meta-analysis of randomised controlled trials. Nursing Open, 11(1), e2094. https://doi.org/10.1002/nop2.2094

[11] Zhang, S., & He, C. (2023). Effect of the sound of the mother’s heartbeat combined with white noise on heart rate, weight, and sleep in premature infants: A retrospective comparative cohort study. Annals of Palliative Medicine, 12(1), 11120–11120.

[12] Jaschke, A. C., Papatzikis, E., & Haslbeck, F. B. (2025). Medical Neurohumanities: Sharing Insights from Medicine, Neuroscience, and Music in Paediatric Care. Frontiers in Neuroscience, 19, 1648030. https://doi.org/10.3389/fnins.2025.1648030

[13] Papatzikis, E., Agapaki, M., Selvan, R. N., Hanson-Abromeit, D., Gold, C., Epstein, S., … Pandey, V. (2024). Music medicine and music therapy in neonatal care: A scoping review of passive music listening research applications and findings on infant development and medical practice. BMC Pediatrics, 24(1), 829.

[14] Yakobson, D., Gold, C., Beck, B. D., Elefant, C., Bauer-Rusek, S., & Arnon, S. (2021). Effects of live music therapy on autonomic stability in preterm infants: A cluster-randomized controlled trial. Children, 8(11), 1077. https://doi.org/10.3390/children8111077

[15] Haslbeck, F. B., Jakab, A., Held, U., Bassler, D., Bucher, H. U., & Hagmann, C. (2020). Creative music therapy to promote brain function and brain structure in preterm infants: A randomized controlled pilot study. NeuroImage: Clinical, 25, 102171. https://doi.org/10.1016/j.nicl.2020.102171

[16] Agapaki, M., Pinkerton, E. A., & Papatzikis, E. (2022). Music and neuroscience research for mental health, cognition, and development: Ways forward. Frontiers in Psychology, 13, 976883. https://doi.org/10.3389/fpsyg.2022.976883

[17] Papatzikis, E., & Rishony, H. (2022). What is music for neuroplasticity?: Combined value on infant development and inclusion. In Rethinking inclusion and transformation in special education (pp. 160-177). IGI Global Scientific Publishing. https://doi.org/10.4018/978-1-6684-4680-5.ch010

[18] Almadhoob, A., & Ohlsson, A. (2020). Sound reduction management in the neonatal intensive care unit for preterm or very low birth weight infants. The Cochrane Database of Systematic Reviews, 1(1), CD010333. https://doi.org/10.1002/14651858.CD010333.pub3

[19] Smith, S. W., Ortmann, A. J., & Clark, W. W. (2018). Noise in the neonatal intensive care unit: A new approach to examining acoustic events. Noise & Health, 20(95), 121–130. https://doi.org/10.4103/nah.NAH_53_17

[20] Shaw, R. J., Givrad, S., Poe, C., Loi, E. C., Hoge, M. K., & Scala, M. (2023). Neurodevelopmental, mental health, and parenting issues in preterm infants. Children, 10(9), 1565. https://doi.org/10.3390/children10091565

[21] Boissel, L., Pinchaux, E., Guilé, M., Corde, P., Crovetto, C., Diouf, M., Mariana, C., Meynier, J., Picard, C., Scoury, D., Cohen, D., Benarous, X., Viaux-Savelon, S., & Guilé, J. M. (2022). Development and reliability of the coding system evaluating maternal sensitivity to social interactions with 34- to 36-week postmenstrual age preterm infants. Frontiers in Psychiatry, 13, 938482. https://doi.org/10.3389/fpsyt.2022.938482

[22] Papatzikis E., Dimitropoulos K., Tataropoulou K., Kyrtsoudi M., Pasoudi E., O’Toole JM., et al. (2025) The father’s singing voice may impact premature infants’ brain more than their mother’s: A NICU single-arm exploratory study protocol and preliminary data on a singing and EEG framework based on the fundamental frequency of voice and kinship. PLoS One 20(8): e0328211. https://doi.org/10.1371/journal.pone.0328211

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