When we decided to become a part of the MOTION Project, it was clear to all of us we would move from our own country. We knew we were leaving there our families, our friends, our favourite food, our MOTHER TONGUE.
From my point of view, the first elements were the ones I was worried about: how much I would miss my mom, my grandma’s pasta, having “aperitivo” with my friends. The idea of living in a country of which I couldn’t understand the official language was almost completely ignored by the naive Valentina I was in the Autumn of 2018.
I thought it was something completely normal, something not that relevant, that could have been overcome without difficulties. Many people I know emigrate to countries where the language spoken doesn’t even share a word with their own mother tongue. How could that possibly have been tough for me, moving to the Netherlands, a country where the language has so many common elements with my beloved Italian? Well, it turned out I was mostly wrong about it.
Duplo or Lego blocks may have been one of your favourite toys when you were younger. A couple of blocks could create a castle, rocket or boat, with just your imagination as limit to your creations. But these fun blocks can also be very useful for research purposes. For example, Duplo construction blocks can provide us with the ideal way to investigate action planning while keeping our cute little participants entertained.
This post relates my experience of collaboration with Joanna – the MOTION PhD student from Radboud University – and what I think is essential to make a collaboration successful.
The choice of the collaborator One of the key to a successful collaboration is the choice of a good work partner. You might have the best research project in the world, if your partner is not competent or if you’re not work-compatible, your project might fail. In the case of the MOTION project, the task wasn’t too hard, as I immediately got along – on a working and personal point of view – with most of the other PhD students. In the end, Joanna and I decided to start a collaboration, as we had the most common interests. And it turned out to be a great choice, as we’re currently carrying out our study together in my university in Milan.
So, how to find the ideal collaborator? First, you have to find someone who has the same scientific interests as you, and who wants to work on the same topic. Then, of course, you also need someone who has good research skills, ideally complementary to yours. But outside of the practical aspects, the personal side is also essential for a successful collaboration. Working with someone you get along with, with whom you can communicate and work efficiently, but also sit back and laugh when needed, is very important. Because you will spent a lot of time together, and because there will always be difficulties to overcome. Doing all of this in a good atmosphere is so much easier!
In my last blog post, I wrote about the difficulties of collecting good brain data from babies. It is a challenge, so you may have been thinking, why bother at all?
Some people say babies are boring – they look like doing nothing other than staring (and crying, sleeping and being fed). For me, that couldn’t be farther from the truth. Babies are always looking, listening and learning. In short, there is always something going on in their brain. We collect brain data to find out exactly what is happening.
In this blog post, I will update you on the last six months. Since the last post, we finally finished data collection of the pre-and-post testing of the longitudinal training study. Immediately after we reached this milestone, we started data collection of the 10-month follow-up. With only 30 more infants to go, we expect all data to be collected by April 2020.
Since October, we have been preprocessing our motion tracking data and trying to understand how to handle this type of data, which turned out to be very frustrating. There is not much known about motion tracking data of three-month-old infants. It was difficult finding the right parameters for filtering, movement unit thresholds, and merging threshold. Another challenge was dealing with the fact that it is difficult to get a three-month-old infant to start reaching from a stationary position. This made finding the exact moment the reach started very challenging. However, as I write this post, I can confidently say that we finally found the right settings and parameters and that we will start data analysis in a few days.
Another big event that happened in the last six months is that we finally submitted our review paper of the sticky mittens paradigm. It feels good to have finally submitted this after a year of reading, writing, and revising.
The physiological reaction to neural activity is modulated by neurometabolic and neurovascular coupling. Increased neural activity triggers an increase in oxygen delivery to the active region a few seconds later.
This reaction can be modulated by measuring the oxygenated haemoglobin and deoxyhaemoglobin expected relative concentration changes in relation to an event in relation to the known canonical model for the haemodynamic response.
Models of this reaction have been created, alike the one in figure (2), from empirical data for adults. Infant studies however, sometimes refer to what came to be known as a IRF or Inverse Response Function, for certain tasks – figure (3).
The rationale behind this effect relates to the maturation of the brain, the hemodynamic response increasingly often takes on a canonical shape. This because the known hemodynamic response relies on a complex interaction between the vascular system, neurons and glial cells, all of which undergo considerable maturation throughout infancy. However, since brain maturation is not homogenous between cortical regions, the hemodynamic response may vary from one brain area to another.
If we decompose each of these systems, in order to take a closer look at known metrics.
Grey matter develops most of its volume until the third year of age and in terms of blood supply, it is labelled that for one year old infants the average CMRO2 (Cerebral metabolic rate of oxygen) was 38.3±17.7 μmol/100g/min and was positively correlated with age (p=0.007, slope 5.2 μmol/100g/min per week), although the highest CMRO2 value in this age range was still less than half of the adult level. (Liu et al. & Solokov et al)
The Cerebral blood volume CBV and Oxygen Saturation Percentage are alto quite variable, not only for infants but also for different cortical structures in same age group infants. (Franceschini et al).
This leads to different proportions and in-homogeneity, depending on age and brain region.
However, inter subject differences do not end there, task difficulty depending on brain region is also relevant parameter to consider.
In conclusion, data baseline adapted and specific to paradigm is even more relevant for Infancy studies, given the inter-subject, inter-cortical structure and task subjective variability of responses. We recommend before the beginning of any functional infancy study, taking a look at the review from Issard et al 2018, which covered over 20 years of infancy reported inverse responses, both in fNIRS and BOLD MRI, assessing that:
“The temporal cortex seems to present canonical responses earlier than the occipital and frontal cortices, and follows a more linear developmental trajectory than the occipital cortex. This latter shows a canonical response at birth, but an inverted response later in infancy.
Finally, the frontal cortex shows more variable responses, depending on stimulus complexity and age of participants. Social stimuli, such as speech and faces, elicit canonical responses earlier than non-social stimuli (such as fruits or flashing lights).”
References: Scholkmann, F., Kleiser, S., Metz, A. J., Zimmermann, R., Pavia, J. M., Wolf, U., & Wolf, M. (2014). A review on continuous wave functional near-infrared spectroscopy and imaging instrumentation and methodology. Neuroimage, 85, 6-27.
Issard, C., & Gervain, J. (2018). Variability of the hemodynamic response in infants: Influence of experimental design and stimulus complexity. Developmental cognitive neuroscience, 33, 182-193.
Liu, P., Huang, H., Rollins, N., Chalak, L. F., Jeon, T., Halovanic, C., & Lu, H. (2014). Quantitative assessment of global cerebral metabolic rate of oxygen (CMRO2) in neonates using MRI. NMR in biomedicine, 27(3), 332-340.
Sokoloff, L. (1960). The metabolism of the central nervous system in vivo. Handbook of physiology, section I, neurophysiology, 3, 1843-1864.
Franceschini, M. A., Thaker, S., Themelis, G., Krishnamoorthy, K. K., Bortfeld, H., Diamond, S. G., … & Grant, P. E. (2007). Assessment of infant brain development with frequency-domain near-infrared spectroscopy. Pediatric research, 61(5), 546-551.
Infant-directed song (the academic word for lullaby), is a common way to soothe, calm or put babies to sleep. It is found universally across cultures .
Babies are little Beethoven-to-be, born with remarkable music perception abilities. A study  with neonates revealed that the latter can detect the regularity of beats: recording of their brain activity showed that a particular brain signal was elicited at the time when downbeats were missing.
When visiting our babylab in Lancaster, some people say, “I’m here to do a study with a cap like a jellyfish”. Then I know they are here for my study. Yes, I use this jellyfish cap, called EEG (or electroencephalogram, to be more precise), to monitor baby’s brain activity.
A cap which monitors brain activity sounds cool – but what exactly does it do? When was it invented, by whom, and what can it tell us? In this month’s blog, I’ll talk about three things about EEG, as well as three reasons we should be bothered to measure brain activities despite all the challenges to get good data from cheeky little ones.
For almost a year now I have been working on a study looking at prosociality in infants, specifically the development and their degree of understanding within it. At a young age, 3 months old, infants already display a strong preference for prosocial individuals over antisocial ones (Hamlin et al., 2007; Hamlin & Wynn, 2011). In this study we are investigating the role of individual differences in promoting and shaping understanding of prosocial and antisocial events. We evaluated 5 to 6-month-old infants in their ability to discriminate and prefer prosocial over antisocial individuals using several different methods ERP/EEG, behavioral measures (looking times and manual choice task), and we investigated through questionnaires whether temperament (Rothbart, 1981) and attachment (Condon et al., 2008) styles would affect the emergence of this ability. In total 26 infants were tested and analyzed in the behavioral measures; 7 infants achieved the sufficient number of trails per condition to be included in the ERP analysis.
Our adult, (not always) sophisticated sense of humour might involve laughing at Epic-Cat-Fails videos on the internet or at very bad puns, but do you remember that period when the re-appearance of mum’s face with a “peek-a-boo” was the best joke in the world? Probably not, but it was a fascinating one! Babies start laughing before they start to crawl, walk or talk and, before long, they start producing their very own non-verbal jokes to make people around them laugh. They start smiling at their first month and they laugh for the first time around 4-5 months of age, while they begin a humorous interaction by their 7-8 months. This very early adorable behaviour is proposedly connected to general cognitive development, as well as to the quality of the bond between infants and their caregivers – thus researchers have tried to describe the different ways infants joke and, mainly, what are the things they find amusing.