development | The Mouse Trap

In my last post I introduced the mouse trap readers to ICNs , ICA and the rs-fcMRI (resting state Functional connectivity fMRI) procedure that is used to detect such networks. This post extends that exciting line of work by commenting on 3 papers that list the ICNs found in the developing brain (infant, child , adolescent, adult).

What is important to recognize, and might not be so evident, at the outset, is that these ICNs change over developmental time course both in number and their topology (i.e. their constituent parts) . For eg. in last post I hinted that these ICNs range from 5 in infants to up-to 16 in adults. That figure of 5, was based on this paper by Franssson et al that found that there were 5 ICNs in infants. The accompanying figure shows these networks (I presume ordered by the amount of variance that each component explains) and the textual description (from the abstract) is as follows:

We found five unique resting-states networks in the infant brain that encompassed the primary visual cortex, bilateral sensorimotor areas, bilateral auditory cortex, a network including the precuneus area, lateral parietal cortex, and the cerebellum as well as an anterior network that incorporated the medial and dorsolateral prefrontal cortex.

and

A group level analysis of resting state activity is shown in Fig. 3. Accordingly, Fig. 3 A shows a resting-state network that encompasses primary visual cortex in the occipital lobe, extending into the parietal lobe, whereas the network displayed in Fig. 3 B is predominantly located along the somatosensory and motor cortices bilaterally. The resting-state network shown in Fig. 3 C is primarily located in the superior and posterior parts of the temporal cortex and the inferior parietal cortex, including the auditory area in the superior temporal gyrus. Fig. 3 D shows a resting-state network that encloses the bilateral superior parietal cortex, precuneus as well as the lateral aspects of the cerebellum. Finally, a resting-state network was observed that consisted of the medial as well as the dorsolateral section of the prefrontal cortex (Fig. 3 E).

To me the networks seem to be made-to-order to fit the five/eight stage evo-devo model that I have been championing. The functional networks/stages thus can be labelled as :
1. Sensory (Visual cortex/occipital lobe)

2. Motor (somatosensory/ motor cortex)

3. Memory (temporal cortex/ auditory)

4. Language/Spatial (depending on lateralization and hemisphere) (sup. parietal , cerebellum)

5. Cognition (frontal)

It is apt to pause here and reiterate that these 5 are what are found in infants and come pre-wired; as one grows one makes changes to these functional networks and adds new networks. also these networks do not look the same as that found in adults- the major difference being that in children/infants anatomically near areas also are part of a functional network; while as we grow, presumably based on the fact that other areas are also recruited over developmental time frame, the network gets more distributed and anatomically distant regions also become apart of the intitial local network.

That brings us to our next elegant and beautifully written open-access study that shows how a ‘local’ organization in childhood changes to a distributed organization in adulthood, for the functional networks, while still retaining small-world properties.

For analysis and comparison purposes , the authors chose to focus on 3 well-known and another lesser well-known ICN that is found in adult brain- the 3 well-known being Default Mode Network ( DMN) , 2 task related networks – a fronto-parietal network that to me seems like Executive control network (ECN) and a cingulo-opercular network that to me seems like a Salience network (SAL) and the other lesser well0known ICN centered around Cerebellum (CER) activity.

It is not important which ICNs they chose to study , what is important is the results that they found. They basically found that in children the regions of interest belonging to the ICNs are more closely clustered around anatomical locations like the lobes; but in later adulthood they cluster as per functional network i.e. ICN . This becomes evident if we look at the accompanying figure. The blue shaded region shows all nodes belonging to frontal lobe – they are clustered together in children, but segregate as we move to adulthood (top part of figure A); in contrats the lower part of figure (B) shows the pink shaded cluster that is grouping the DMN regions of interest. We can visually see that in children the DMN areas are not clustered together functionally , but over time they get clustered in a tight network.

Finally, a third study that used grey matter based structural covariance of functional networks came to the same conclusion that some networks grow and develop and change their topology over time.

Network nodes identified from eight widely replicated functional intrinsic connectivity networks served as seed regions to map whole-brain structural covariance patterns in each age group. In general, structural covariance in the youngest age group was limited to seed and contralateral homologous regions. Networks derived using primary sensory and motor cortex seeds were already well-developed in early childhood but expanded in early adolescence before pruning to a more restricted topology resembling adult intrinsic connectivity network patterns. In contrast, language, social–emotional, and other cognitive networks were relatively undeveloped in younger age groups and showed increasingly distributed topology in older children. The so-called default-mode network provided a notable exception, following a developmental trajectory more similar to the primary sensorimotor systems. Relationships between functional maturation and structural covariance networks topology warrant future exploration.

To me the above looks promising.The new technique of rs-fcMRI heralds new insight into the brain structure and function. In the next post we will look more closely on the main ICNs found in adult human brain. Stay tuned.

Fransson, P., Skiold, B., Horsch, S., Nordell, A., Blennow, M., Lagercrantz, H., & Aden, U. (2007). Resting-state networks in the infant brain Proceedings of the National Academy of Sciences, 104 (39), 15531-15536 DOI: 10.1073/pnas.0704380104
Fair, D., Cohen, A., Power, J., Dosenbach, N., Church, J., Miezin, F., Schlaggar, B., & Petersen, S. (2009). Functional Brain Networks Develop from a “Local to Distributed” Organization PLoS Computational Biology, 5 (5) DOI: 10.1371/journal.pcbi.1000381
Zielinski, B., Gennatas, E., Zhou, J., & Seeley, W. (2010). Network-level structural covariance in the developing brain Proceedings of the National Academy of Sciences, 107 (42), 18191-18196 DOI: 10.1073/pnas.1003109107

: Image via Wikipedia

I recently came across this article by Rosengren and Hickling about how children explain seemingly impossible or extraordinary transformations in terms of magic or trickery or natural/physical explanations based on their ages and developmental level.

To summarize the study , I’m presenting the abstract:

Children’s magical explanations and beliefs were investigated in 2 studies. In Study 1, we first asked 4- and 5-year-old children to judge the possibility of certain object transformations and to suggest mechanisms that might accomplish them. We then presented several commonplace transformations (e.g., cutting a string) and impossible events (magic tricks). Prior to viewing these transformations, children suggested predominantly physical mechanisms for the events and judged the magical ones to be impossible. After seeing the impossible events, many 4-year-olds explained them as “magic,” whereas 5- year-olds explained them as “tricks.” In Study 2, we replaced the magic tricks with “extraordinary” events brought about by physical or chemical reactions (e.g., heat causing paint on a toy car to change color). Prior to viewing the “extraordinary” transformations, children judged them to be impossible. After viewing these events, 4-yearolds gave more magical and fewer physical explanations than did 5-year-olds. Follow-up interviews revealed that most 4-year-olds viewed magic as possible under the control of an agent (magician) with special powers, whereas most 5-year-olds viewed magic as tricks that anyone can learn. In a third study, we surveyed parents to assess their perceptions and conceptions of children’s beliefs in magic and fantasy flgures. Parents perceived their children as believing in a number of magic and fantasy flgures and reported encouraging such beliefs to some degree. Taken together, these findings suggest that many 4-year-olds view magic as a plausible mechanism, yet reserve magical explanations for certain real world events which violate their causal
expectations.

In effect, the children were shown some impossible transformations like making color appear on the pages of a blank coloring book; at the same time they were also shown some commonplace transformations like a piece of Play-Doh changing shape. They were asked to provide causal reasons for these transformations both a priori and after the transformations were demonstrated. Important form my point of view was the finding that all children showed this effect that for impossible transformations though before the vent they provided physical/natural explanations, after seeing the event, they changed their stance and labeled them as ‘magic’ or ‘trick’ as per their development level. To quote:

Children of both ages gave more physical/natural explanations prior to seeing the transformations than after seeing the
events, F(l, 46) = 36, p < .001, but gave more trick and magic responses after seeing the transformations than before seeing them, Fs(l, 46) > 50, p < .001.
Very few magic explanations were provided for the commonplace events before or after the viewing of the events; however, both groups of children provided significantly more magic explanations following the magic events than prior to these events. There was no difference between the two age groups in the number of magic explanations given prior to seeing the magic events; however, after viewing the magic events the 4-ye£ir-oIds gave significantly more magic responses (M = 2.96) than the 5-year-oIds did (M = 1.09). Similar to the results for the magic explanations, few trick responses were provided for the commonplace events before or after the viewing of the commonplace events.

To me this is a significant result, that after seeing something impossible we classify it as either magic or trickery, but prior to that we believe we could have provided a natural and causal explanation. To take an example, we all know statistics and would agree that there is a statistical probability that we are thinking of someone and the person phones at the same time. However, when we do think of someone and he calls at the same time and this happens say once or twice in a row, we will not tend to resort to statistical reasoning; we’ll either think in magical terms (magical thinking– my intention to remember/talk to them caused them to phone me; or psychic ability– that there is a deep connection between us) or we will try to think this a as a trickery played on us (perhaps they or someone is secretly watching me and my intentions and as soon as I reach to make a phone call, they call me instead juts to make fun/play a silly trick). Both types of thinking are fertile ground for psychosis and delusions.

It is now known that many people prone to psychosis suffer from an unusual amounts of anomalous experiences and also have magical ideation. To those of us who do not have those unusual experiences, it is very easy to dismiss what the effects having such anomalous experiences would have on our causal thinking abilities. We in our blue-pill Matrix where things are ordered and in their place following known causal relations, believe everything is fine with the world. to someone who has taken the red pill and is having anomalous experiences, it is difficult to believe that there isn’t a world apart from the matrix where magical rules may apply! (OK, the matrix analogy is not good, but it does make a point that it is difficult to comprehend the reality that someone delusional may be living in).

To return to my example of thinking of calling someone and picking the phone and at the same time receibving a callfor that perosn, such coincidences may be marked as causal by psychosis prone minds beacue again they have been hypothesized to have high and sensitive coincidence detectors and a ‘jump to conclusions’ bias. Given these facts they may be more prone to attribute magical causality instead of normal causality and get freaked out. Magical thinking and delusions may follow naturally from these. Anomalous experience may not just be important to explain hallucinations, but may be important for explaining delusions too.

Rosengren, K., & Hickling, A. (1994). Seeing Is Believing: Children’s Explanations of Commonplace, Magical, and Extraordinary Transformations Child Development, 65 (6) DOI: 10.1111/j.1467-8624.1994.tb00838.x