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.
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
In addition it is recognized that temporal correlations across such configurations cannot be arbitrary but they need to meet two conflicting demands while diverse cortical areas should remain functionally segregated from each other they must still perform as a collective i.e. We show that this two-point correlation function extracted from resting-state functional magnetic resonance imaging data exhibits self-similarity in space and time.
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