In my last two posts 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.

1. Visual (IC4) fig 1A in Damoeseoux- occipital
2. Sensorimotorfig 1 F in Damoseousx -pre-post central gyrus
3. Auditory/memory (IC13) fig 1 I -auditory/temporal cortex
4. Language/spatial (IC7/IC19) Fig 1C and Fig 1D of damoseoux – fronto-parietal, strongly lateralized in two hemispheres
5. SALience(also Known as SAL) Anterior Insula+ anterior Cingulate
6. Balance and co-ordination (IC 11) – Cerebellum
7. Default Mode Network(IC10, IC12, IC14) , Fig 1 B- Medial frontal, posterior cingulate, Angular gyrus
8. Executive Control Network (IC8)Fig 1J – dorsolateral, prefrontal + sup parietal

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 :

1. Visual (ccalcerine sulcus)
2. Motor (pre-central gyrus)
3. Auditory (Heschel’s gyrus)
4. Syntax (Inferior Frontal Gyrus)
5. Semantics (temporal pole)
6. SALience (Fronto Insula)
7. DMN (Angular Gyrus)
8. ECN (DLPFC)

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

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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

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I came across this study article today by Farb et al, that talks about two distinct neural networks in the brain that are involved in self-reference. To be fair, the networks are somewhat blurred and overlap in naive people, while in people who practice mindfulness meditation, the networks are more distinct and non-overlapping. My interest was piqued as I am a keen follower of default-brain network , which has been implicated in self-referential thinking and this article seems to at one point argue that the narrative self viz ‘me’ is grounded in default brain network, while the experiencer ‘I” has some other nearby related areas as the neural substrates.

But first let us clarify what we mean by ‘me’ and ‘I’. For this I would like to quote form a Gallagher article:

Ever since William James (1890) provided a catalogue of different senses of the self, philosophers and psychologists have been hard at work refining and expanding the possible variations of this concept. Supplementing James’ inventory of physical self, mental self, spiritual self, and the ego, Neisser (1988), for example, suggested important distinctions between ecological, interpersonal, extended, private, and conceptual aspects of self. More recently, reviewing a contentious collection of essays from various disciplines, Strawson (1999) found an overabundance of delineations between cognitive, embodied, fictional, and narrative selves, among others. It would be impossible to review all of these diverse notions of self in this short paper, so I have focused on several recently developed approaches that promise the best exchange between philosophy of mind and the other cognitive sciences. Because these approaches move in divergent theoretical directions they should help to convey the breadth of philosophical analysis on this topic. They can be divided into two groups that are focused, respectively, on two important aspects of self.

A first approach involves various attempts to account for a ‘minimal’ sense of self. If we strip away all of the unessential features of self, the intuition is that there is a basic, immediate, or primitive something that we are still willing to call a self. This approach leaves aside questions about the degree to which the self is extended beyond the short-term or ‘specious’ present to include past thoughts and actions. Although identity over time is a major issue in the philosophical definition of personal identity, the concept of the minimal self is limited to that which is accessible to immediate and present self-consciousness. Non-philosophers have found that certain aspects of the minimal self are relevant to current research in robotics. Furthermore, aspects of the minimal self that involve senses of ownership and agency in the context of both motor action and cognition can be clarified by neurocognitive models (developed to explain pathologies such as schizophrenia) that suggest the involvement of specific brain systems (including prefrontal cortex, SMA, and cerebellum).

A second approach involves conceiving of the self in terms of narrative, a concept imported into the cognitive-science context by Dennett (1991) , but one which may have a more complex significance than indicated in Dennett’s account. The narrative self is extended in time to include memories of the past and intentions toward the future. It is what Neisser refers to as the extended self, and what Dennett calls a ‘nonminimal selfy’ self. Neuropsychological accounts of episodic memory or loss of memory can help to circumscribe the neurological underpinnings of the narrative self.

If you haven’t guessed by now, the minimal self is ‘I’: the doer , experiencer experiencing the immediate present; the narrative self is ‘me’ -an entity stretched in time and living as much in past and future as in the present. The study authors delineate the same as follows (note that they too start with William James reference):(* references removed)

Since William James’ early conceptualization, the ‘self ’ has been characterised as a source of permanence beneath the constantly shifting set of experiences that constitute conscious life. This permanence is often related to the construction of narratives that weave together the threads of temporally disparate experiences into a cohesive fabric. To account for this continuity, William James posited an explanatory ‘me’ to make sense of the ‘I’ acting in the present moment . Recently, progress has been made in characterizing the neural bases of the processes supporting William James’ ‘me’ in the form of ‘narrative’ self-reference , highlighting the role of the medial prefrontal cortices (mPFC) in supporting self awareness by linking subjective experiences across time . The mPFC has been shown to support an array of self-related capacities, including memory for self-traits , traits of similar others , reflected self-knowledge , and aspirations for the future . As such, cortical midline processes may be characterised as supporting narrative self-reference that maintains continuity of identity across time .

Narrative self-reference stands in stark contrast to the immediate, agentic ‘I’ supporting the notion of momentary experience as an expression of selfhood. Most examinations of self-reference ignore mechanisms of momentary consciousness, which may represent core aspects of self-experience achieved earlier in development and may have evolved in earlier animal species. Indeed, little is known about whether the neural substrates underlying momentary self-reference are one and the same, or distinct from, cortical midline structures supporting narrative experience. One hypothesis suggests that awareness of momentary self-reference is neurally distinct from narrative self-reference and is derived from neural markers of transient body states, in particular, right lateralised exteroceptive somatic and interoceptive insular cortices. In the present study, we examined this thesis.

In short using fMRI, they tried to find the different hypothesized neural networks underlying the two senses of self and did find evidence for clear segregation in those practicing mindfulness meditation. Their methodology however, is not fool proof and this they themselves note in their conclusions. Here are their findings:

Consistent with a theory of self-reference as mentalising, linguistically mediated and of higher order executive origin , participants engaged midline prefrontal cortices and a left lateralised linguistic-semantic network (inferior lateral PFC, middle temporal and angular gyri) during NF (narrative focus: ‘me’ condition). Demonstrating a default bias towards NF as previously revealed in ‘resting’ mind wandering states , relatively restricted reductions in the cortical midline network were found when attention was explicitly directed towards a moment-to-moment EF (experiential focus: ‘I’ condition) in novice participants with little training in this form of self-reflection. These individuals revealed increased left lateralised prefrontal-parietal activations during EF likely reflecting greater task-related linguistic processing that has been shown to be associated with decreased medial prefrontal recruitment .

So what they found was that a part of default network was engaged in ‘me’ condition; while task-related areas were recruited in “I” condition and appropriate task-related suppression of some part of default network observed. This effect was with naive subjects, but with those trained in mindfulness meditation, they observed a sort of double dissociation:

Following an intensive 8 week course in mindfulness meditation, during which individuals learn to develop the capacity to monitor moment-to-moment experience, EF resulted in a pronounced shift away from midline cortices towards a right lateralised network comprised of the ventral and dorsolateral PFC, as well as right insula, SII and inferior parietal lobule. Consistent with a dual-mode hypothesis of self-awareness, these results suggest a fundamental neural dissociation in modes of self-representation that support distinct, but habitually integrated, aspects of self-reference: (i) higher order self-reference characterised by neural processes supporting awareness of a self that extends across time and (ii) more basic momentary self-reference characterised by neural changes supporting awareness of the psychological present. The latter, represented by evolutionary older neural regions, may represent a return to the neural origins of identity, in which self-awareness in each moment arises from the integration of basic interoceptive and exteroceptive bodily sensory processes. In contrast, the narrative mode of self-reference may represent an overlearned mode of information processing that has become automatic through practice, consistent with established findings on training-induced automaticity.

To me this sounds interesting: If I had to stretch my neck and relate this to autism and schizophrenia , I would say that based on earlier coverage on this blog: Schizophrenics have a higher default brain activity and perhaps try to spin too much of a narrative. Perhaps they are the ones that would best benefit with mindfulness meditation trainings to calm their default ‘me’ and activate the ‘I’ also at relevant times. On the opposite side, one is all too aware of the here-and-now feeling of self that many autistics have- a direct and immediate perceptual relation with world. Perhaps, they too can benefit from some for of mindfulness meditation by learning to use the default brain network too at times – letting teh mind wander and spinning a tale (however fictional) about themselves.

Farb, N., Segal, Z., Mayberg, H., Bean, J., McKeon, D., Fatima, Z., & Anderson, A. (2007). Attending to the present: mindfulness meditation reveals distinct neural modes of self-reference Social Cognitive and Affective Neuroscience, 2 (4), 313-322 DOI: 10.1093/scan/nsm030
Gallagher, S. (2000). Philosophical conceptions of the self: implications for cognitive science Trends in Cognitive Sciences, 4 (1), 14-21 DOI: 10.1016/S1364-6613(99)01417-5

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The Society for Neuroscience(SfN) was formed 40 years ago and to commemorate the occasion, the journal of Neuroscience has made some review articles open-access. They are written by leading luminaries in their filed and are somewhat scholarly- though I found some of them pretty accessible too.

Two articles relate to reviewing memory research in the past 40 years and both are a pretty good read. The first is written by Larry Squire and gives you a broad overview of memory research. The second by Eric Kandel focuses more on the molecular aspects of memory formation- but is an excellent article and ends with 11 still unresolved questions for the next 40 years in the memory research.

There is another article by Marcus Raichle that I found pretty interesting, partly because of my continuing fascination with the default brain network and the intrinsic activity of the brain. this again is a very accessible article that brings one up to speed on the 40 yrs of imaging with special focus on the default brain network.

There are other retrospectives there including one on neurotransmitters so go to the source and enjoy the ride.

Hat Tip: Mind Hacks
Squire, L. (2009). Memory and Brain Systems: 1969-2009 Journal of Neuroscience, 29 (41), 12711-12716 DOI: 10.1523/JNEUROSCI.3575-09.2009
Kandel, E. (2009). The Biology of Memory: A Forty-Year Perspective Journal of Neuroscience, 29 (41), 12748-12756 DOI: 10.1523/JNEUROSCI.3958-09.2009
Raichle, M. (2009). A Paradigm Shift in Functional Brain Imaging Journal of Neuroscience, 29 (41), 12729-12734 DOI: 10.1523/JNEUROSCI.4366-09.2009

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