Much has been written about the seductive allure of fMRI brain images accompanying research papers and giving them more credence than is deserved; similarly much has been written about the whole enterprise of fMRI based research that tries to find the neural correlates of X,Y,and Z, as if X/Y or Z being human/animal faculties could have a substrate other than neural.
In both of the above cases, while the neuro babble seemingly provides more authority to the underlying argument, it is not clear what value , if any , one gets by just identifying a brain area responsible for X/Y or Z.
Patricia Churchland‘s quest for roots of human/animal morality is similarly besieged by the allure of all things neural- it is to her credit that despite being a philosopher she gets the neuroscience part not just so-so right, but precise and accurate with all caveats included; but what one is left at the end of reading ” Braintrust : what neuroscience tells us about morality” is the feeling that she could have spent more time bolstering her main point that morality arises from sociality rather than talking about oxytoctin or mirror neurons.
While she does treat mirror neuron hyped research with the contempt and dressing that it deserves by trying to explain more than is warranted; her own enthusiasm for Oxytocin as the magical trust molecule or the epitome of moral foundations, deserves similar treatment. Again it is to her credit that she does not shy away form discussing latest studies that have shown oxytocin in not so moral light as in when it is involved in out-group prejudice; but still the discussion of neurotransmitter or vasoprassin or mirror neurons detracts rather than amplifies her thesis that morality evolved from social living.
I am much sympathetic to her main argument that morality may have arrived as the care system became enlarged to cover self, kids, kith and kin, partners and finally strangers. That caring and sharing might be the roots of all goodness in the world was apparent even to miss universe like Sushmita Sen back in 1994, not an unremarkable achievement considering the latest miss America contestants views on evolution. But I digress. The thing is that Patricia should have spent more time on this and bridging the leap from social behavior to moral behavior by maybe using philosophical devices/arguments rather than just peppering her statements with neuroscientific jargon and assuming that that will settle the point.
Along the way she casually dismisses the important work that may support her position like that of Jonathon Haidt- she claims that morality is innate but seems reluctant to grant that it could also have a universal structure.
If you want to know the latest neuro research around sociality – go read this book; you will read all the proper studies with all caveats and without misrepresentations. However, if you were yearning for any philosophical insight into the nature of morality, how ‘is’ and ought’ are not necessarily the same and from where to derive the ‘oughts’ in life you might be in for a disappointment. At least I was.
ps: Disclosure of interest. : I received a free copy of Braintrust for review from Princeton university press.
In my last twoposts I introduced the concept of ICNs and the form they take over developmental time-frame. This post focuses on the most common and consistent ICNs that have been found in the adult humans. To recap, ICNs are found by Independent Component Analysis (ICA) of Resting state functional connectivity MRI (rs-fcMRI) and the number and components of ICNs have been found to vary over the developmental time-frame.
Different studies find different number of components/ICNs and some of the variance is due to different methods used to estimate an delineate the number of components. For eg., in one study multiple methods were used and they led to estimates ranging from 8 to 20 + for the number of components using the same rs-fcMRI scan.
The same study listed the following ICNs out of which 4 are clearly a result of artifact and not true ICN’s.
We sorted the 20 components into two broad classes – functionally relevant components (i.e., ICNs) and scanner/physiological artifactual components – based on visual inspection of each component’s spatial profile (e.g., biological plausibility, comparability to patterns previously reported in ICA-based studies) and timeseries-based power spectrum profile (e.g., whether or not signals < 0.1Hz were prominent). We noted 4 components that appeared to be associated with artifactual sources: cerebrospinal fluid (IC01), white matter (IC03), head motion (IC05), and large vessels (IC16). These four components accounted for 39.4% of the total variance in the resting state fMRI data. Several functionally relevant components consistent with prior reports were also revealed in our results. Two components (IC04 and IC15) are involved in vision. IC09 combines visual and motor regions including the occipital pole, superior parietal cortex and precentral gyrus. IC13 includes brain regions such as the primary motor cortex and primary and association auditory cortices. Several components include regions related to various high-order brain functions: fronto-parietal networks corresponding to cognition and language functions (IC07 and IC19), medial-frontal including anterior cingulate and paracingulate associated with executive control (IC08) and three “default mode” networks (IC10, IC12 and IC14). We found six other components that are rarely reported or investigated systematically corresponding to the cerebellum (IC11 and IC18), a motor-striatal component (IC02), a ventromedial prefrontal component (IC17), a brainstem component (IC06), and a temporal-lobe component (IC20). Of note, we found several components that exhibit anticorrelation relationships between regions (IC04, IC08, IC14 and IC15). In particular, the executive and attentional network (IC08) and the “default mode” network (IC14) demonstrated prominent anti-correlation relationships (Figure S1).
We detected the classic “default mode” network, although in the form of three components that we interpret as sub-networks. The first is a medial-prefrontal subsystem (IC12), the second is a posterior cingulate/precuneus subsystem (IC10), and the third is a temporal subsystem (IC14). These three subsystems mainly overlap in the posterior cingulate cortex and medial prefrontal cortex (Figure S2). As we discuss below, the existence of three overlapping but differentiable sub-networks may account for some of the variations in the specific spatial distributions or functional specialization of the “default mode” network reported across ICA studies (Buckner et al., 2008; Harrison et al., 2008).
In another famous study by Damoiseaux they found 10 components as follows:
The 10 components showed low-frequency variations in time (mean peak frequency: 0.015 Hz; range 0.005–0.030 Hz) and can be described as follows. Fig. 1 A and A’ shows a pattern that consists predominantly of the peristriate area, and lateral and superior occipital gyrus [Brodmann area (BA) 19], which are areas recognized as part of the visual cortex. Fig. 1 B and B’ shows a cluster consisting of the prefrontal (BA 11), anterior cingulate (BA 32), posterior cingulate (BA 23’31), the inferior temporal gyrus (BA 20’37), and the superior parietal region (BA 7), known as the default-mode network as described by Raichle et al. (18) and Greicius et al. (17). Hippocampal involvement in this component, as described by Greicius et al. (22), is not found. Fig. 1 C, C’, D, and D’ shows components that are predominantly in the left (C and C’) and right (D and D’) hemispheres, the middle frontal and orbital (BA ‘6’9’10), superior parietal (BA 7’40), middle temporal gyrus (BA 21), and the posterior cingulate (BA 23’31; C and C’ only). These are the only components that show strong lateralization and are areas known to be involved in memory function. Fig. 1 E and E’ encompasses part of the striate and parastriate (BA 17’18). The visual cortex is apparent in two separate components. The more lateral visual areas are in Fig. 1 A and A’, and the more medial visual areas are in this figure. Fig. 1 F and F’ shows the pre- and postcentral gyri (BA 1’2’3’4) in one component, representing the motor and sensory network. Fig. 1 G and G’ shows the superior temporal (BA 22) area as the main element of this spatial map. Involvement of the cingulate (BA 23) and superior frontal (BA 9’10) areas is also seen. This cluster of brain regions bears a strong resemblance to the occipitotemporal pathway (ventral stream). Fig. 1 H and H’ involves mainly the superior parietal cortex (BA 7) with additional involvement in the occipitotemporal (BA 37) and precentral (BA 4) areas. Fig. 1 I and I’ involves the superior temporal (BA 22) and insular and postcentral cortex (BA 1’2), which are areas acknowledged to represent the auditory cortex.
To simplify things I propose the following eight ICNs listed in the order of strength/developmental unfolding/ evolutionary precedence, following my proven eight stage evo-devo model. All ICNs referred below are those in study by Zuo et al. unless otherwise stated.
Visual (IC4) fig 1A in Damoeseoux- occipital
Sensorimotorfig 1 F in Damoseousx -pre-post central gyrus
Auditory/memory (IC13) fig 1 I -auditory/temporal cortex
Language/spatial (IC7/IC19) Fig 1C and Fig 1D of damoseoux – fronto-parietal, strongly lateralized in two hemispheres
Some may doubt why I include CERebellum ICN as a basic ICN, but it has been shown that cerebellum not only provides distinct components to existing ICNs , there is an separate Cerebellum ICN also. For eg. Peterson et al used a Cerebellar component in their analysis of how ICNs change over developmental time-frame.
A Structural Covariance Networks (SCNs) based approach to delineate the devlopemental time course of networks in brain comes closest to the eight stage /networks elaborated above. The study is by Zielenksi et al and use seeds from well known ICNs to grow SCNs in children, adolescents and adults. These are the eight SCNs/ICNs (seeds given in brackets) they studied :
Visual (ccalcerine sulcus)
Motor (pre-central gyrus)
Auditory (Heschel’s gyrus)
Syntax (Inferior Frontal Gyrus)
Semantics (temporal pole)
SALience (Fronto Insula)
DMN (Angular Gyrus)
I am convinced that there are only 8 basic ICNs/SCNs with perhaps the DMN split into 3 sub-networks (as is usual for stage 7) and Speech/syntax split or lateralizaed into 2 distinct ICNs. (as is sometimes the case with stage 4) . If you come across other such basic ICNs do let me know.
Zuo, X., Kelly, C., Adelstein, J., Klein, D., Castellanos, F., & Milham, M. (2010). Reliable intrinsic connectivity networks: Test–retest evaluation using ICA and dual regression approach NeuroImage, 49 (3), 2163-2177 DOI: 10.1016/j.neuroimage.2009.10.080 Damoiseaux, J., Rombouts, S., Barkhof, F., Scheltens, P., Stam, C., Smith, S., & Beckmann, C. (2006). Consistent resting-state networks across healthy subjects Proceedings of the National Academy of Sciences, 103 (37), 13848-13853 DOI: 10.1073/pnas.0601417103 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 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)
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
fMRI has become an important investigation and research tool in trying to locate neural correlates of a function X,Y,Z in the brain. However notwithstanding the allure of seductive neuroscan images, fMRI studies at times leaves us as clueless about the brain and its organization as we were before the studies were conducted.
However , just like plain vanilla structural MRI coupled with BOLD signal analysis had led to fMRI, which was a step forward; the plain vanilla FMRI coupled with resting-state BOLD signal spontaneous fluctuations has led to resting state connectivity fMRI, also called rsc-fMRI, which is another step forward and does not juts enable us to pinpoint a function to a brain, but rather reveals the intrinsic organization of brain by revealing tightly coupled functional neural networks in the brain.
Let us take a step back to look at rsc-fMRI in detail. Basically it has been shown that the brain is never at rest, and at rest too, there are spontaneous fluctuations in the brain (of BOLD signal say in the case of fMRI). It is theorized that those brain areas that how correlated spontaneous fluctuations at rest are part of a functional network and this has been shown to be true by looking at the functional maps so revealed and looking at actual anatomical connectivity, and the circuit involvement in related tasks that the circuit is supposed to be involved in.
While to many people resting state fMRI brings to mind the Default Mode Network ,about which I have blogged before, at rest other brain functional circuits also show correlated spontaneous activity (one theory is that they show spontaneous activity so that important synaptic connections in the network can continue to remain in absence of external input/processing) and looking at such correlate activity one can discern that the regions involved form a functional network.
What is more rsc-fMRI is easy to administer, especially to populations like infants, demented people etc, who may not be able to participate in task-based fMRI studies because of their inability to execute a given task. rs-cfMRI on the other hand requires nothing much excpet lying down quietly in the scanner. The BOLD spontaneous fluctuations, from multiple subjects, are then analyzed using Independent Component Analysis (something like PCA or factor analysis that psychologists use in say personality traits studies) and the number as well as the regions involved in different function neural networks are thus revealed. the function networks thus revealed are called ICNs or Intrinsic Connectivity Networks.
Basic ICNs in humans range form 5 in infants to upto 16 in adults and though that seems like a vast range there seem to be good test-retest reliability and replicablity of basic ICNs found across the studies. Some of the variation seems to be an artifact of developmental maturation of ICNs over time.
I believe these ICNs can be arranged as per the eight stage model and the ACD model and also they have great significances for many psychiatric and neurodegenerative disorders; but for that you have to wait for the subsequent posts.
Zuo, X., Kelly, C., Adelstein, J., Klein, D., Castellanos, F., & Milham, M. (2010). Reliable intrinsic connectivity networks: Test–retest evaluation using ICA and dual regression approach NeuroImage, 49 (3), 2163-2177 DOI: 10.1016/j.neuroimage.2009.10.080 Zhang, H., Duan, L., Zhang, Y., Lu, C., Liu, H., & Zhu, C. (2011). Test–retest assessment of independent component analysis-derived resting-state functional connectivity based on functional near-infrared spectroscopy NeuroImage, 55 (2), 607-615 DOI: 10.1016/j.neuroimage.2010.12.007 AUER, D. (2008). Spontaneous low-frequency blood oxygenation level-dependent fluctuations and functional connectivity analysis of the ‘resting’ brain Magnetic Resonance Imaging, 26 (7), 1055-1064 DOI: 10.1016/j.mri.2008.05.008 Damoiseaux, J., Rombouts, S., Barkhof, F., Scheltens, P., Stam, C., Smith, S., & Beckmann, C. (2006). Consistent resting-state networks across healthy subjects Proceedings of the National Academy of Sciences, 103 (37), 13848-13853 DOI: 10.1073/pnas.0601417103
Marcus Raichle, who had initially discovered the default brain network, has a new review article in TICS, that argues that brain activity should be understood as primarily an intrinsic and proactive process rather than a reflexive or reactive process.
He bases this argument on the fact that resting brain consumes 20 % of body’s energy requirements , but this is the consumption of default mode network within the brain. If the brain is engaged in some task than energy consumption does not increase by more than 5 %. thus the meat of the brain energy is consumed for DMN and is used for intrinsic functions , not directly related to task demands.
In an experimental setting, it is very easy to be lured by the task related fMRI activity or ERP’s as they reflect responses of brain to carefully controlled variables in lab settings; however one should not lose sight of the fact that the apparent noise and random intrinsic activity is there for a purpose and may sound mysterious as it may be correlated more to spontaneous activity rather than directly mappable to measurable environmental variables.
Another argument Raichle gives relates to the visual system. Here the input from Retina is highly degraded (from 10 to the power of 10 bits per sec to 100 bits per sec that is supposed to be bandwidth for conscious processing) and Marcus argues that the anatomy of cortex with may feedback connection from higher cortical areas suggests that much of the activity is predictive in nature trying to fit the reduced environmental input to a rich internal representation. Of course Jeff Hawkins and others have been similarly arguing for a more predictive role of the entire cortex, rather than a reactive and information processing role as is traditionally assumed.
He then goes on to argue that the coherence observed in the spontaneous fluctuations in fMRI BOLD signals , especially in the DMN are reflective of more than daydreaming or mind wandering and this intrinsic activity needs investigation as it is present even in non-conscious states. He also posits that these spontaneous fluctuations representing intrinsic activity lead to behavioral variability and I am somehow reminded of Bjorn Brembs (@brembs) paper on flies and how they showed spontaneous behavioral fluctuations…if only one could identify the DMN in flies and monitor its fluctuations using an fMRI machine:-) , one would have hard data to prove Rachelle’s claim.
He does relate spontaneous fluctuations in BOLD signal to slow cortical potentials (SCP) and the SCP’s to cortical excitability and stakes a claim that thus spontaneous fluctuations measure the degree to which cortical neurons are ready to be fired. this provides a handle by which we can investigate how and what function the intrinsic activity serves (answer: the phase of SCPs codes and predicts the most probable signal timing from the external world). the brain is in a constant flux between trying to predict and looking for novel stuff.
That brings me to Linas and his book I of the Vortex. I have only read it partially but was fairly impressed by its argument for the primacy of motoricity. I encourage readers to read that book for an alternative view from the sensory and perception driven psychology and neuroscience mainstream approach. I firmly believe that we re primarily motor creatures rather than sensory ones and more clues could be found about us suing the motor approach to cognition/ consciousness/ whatever.