[By: Iara de Almeida Ivo]
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).”
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.