Search results for DMN (6)

Intrinsic Connectivity Networks: more than just DMN

High resolution fMRI of the Human brain.

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ResearchBlogging.org
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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Intrinsic connectivity Networks: Neurodegenerative link

ResearchBlogging.org
In my last 3 posts, I have talked about ICNs and how they change over developmental time-frame and how many basic ICNs we have in the adult human brain.  This post will talk about neuroegenerative diseases like Alzheimer’s and how the underlying atrophy in neurodegenrative networks closely resembles the underlying ICNs and SCNs.

But first let us brush our knowledge of neurodegenrative dementia- I will be focusing on Alzheimer’s Disease (AD) , Fronto temporal lobe degeneration related dementia ( (bv-FTD) behavioral variant Fronto-temporal dementia , (SD) Sementic dementia and  (PFNA) Progressive non-fluent Aphasia ); the cortico basal syndrome (CBS) and Amyotropic Lateral Sclerosis (ALs/Lou Gherings disease) . What all these diseases have in common is that they are progressively degenerative, related to aging, have both genetic as well as sporadic occurrence, and as we will see affect distinct dissociable brain networks (ICNs/SCNs).

These two studies for eg discuss the 3 distinct  variants of Froto temporal lobe degeneration – the bvFTD, SD and PNFA.  As per the first study:

The clinical hallmark of bv-FTD is a disturbance in the personality and behavior, with changes of mood, motivation, and inhibition, leading to profound social disruption.[1,21,22] As the initial symptoms are neuropsychiatric, without impairment on cognitive screening tests, or overt changes on structural imaging,[23,24] these patients may be inappropriately diagnosed as suffering from a psychiatric disease, usually, depression or personality disorder.[20,25]
Patients may perform normally on standard neuropsychological tests of memory, language, attention, and visual spatial ability, but more recent tests designed to assess emotion processing,[27] social cognition,[28] theory of mind,[29] and complex decision making[30] are more sensitive and may show deficits in early cases, even if standard cognitive battery are normal.[24]

Its interesting to note that bvFTD patients have intact visual-spatial abilities but show socio-emotional deficits (more on this later) .

Semantic dementia
Patients typically present with “loss of memory for words” and show impairment on tests of word comprehension, although the underlying deficit is the amodal store of semantic memory or knowledge about words, objects, people, and sounds.[7] . Patients show a gradual reduction of vocabulary and use high frequency terms (thing, boy), although speech is fluent and well articulated, without phonological or syntactic errors.[8,44,45]
A consistent feature is the impairment of naming objects or anomia. The performance is influenced by the level of familiarity and specificity of items asked. In other words, if the item is extensively encountered by the patient, it is likely to be forgotten later.[45] Likewise, the patient will tend to name objects that are prototypic of their category.[46] For instance, patients are able to name cat, dog, and horse, but not tiger or zebra, and use superordinate or general labels, calling the latter also a cat and horse, and may be just animal.[47]

PNFA
Unlike SD, the presenting features of PNFA are more varied and may reflect breakdown at various stages of speech production, from alterations in lexical retrieval, misarrangements of the words according to grammatical rules, or impaired motor programming of the intended utterance.[11]
Generally speaking, there are problems with the syntactic or motor aspects of speech, causing speech to be halting, slow, and distorted.[54]
Severe agrammatism causes oversimplification of the language production, lack of function words (e.g., prepositions, auxiliary verbs, or articles), or words inflections (i.e., endings of verb or noun according to conjugation or number, respectively).[10] But in the early stages, grammatical errors are subtle and may be difficult to distinguish from common errors or detect in a short interview. Syntactic problems are usually best assessed by testing sentence comprehension.[55]

I’ll now go directly to this study by Seeley et al that shows that there are five distinct ICNs/SCNs that  closely match the underlying atrophy in five distinct such neurodegenerative diseases.  the figure below shows the atrophy maps and the ICNs and SCNs they observed for the 5 diseases they studied, viz AD, bvFTD, SD, PNFA and CBS.

The ICNs linked here to disease represent canonical findings from the ICN literature. Our AD-affected ICN (right ANG seed) corresponds to the ‘‘default mode network’’ that participates in episodic memory (Buckner et al., 2005) and became known for its task-related deactivations across fMRI studies (Fox et al., 2005; Fransson, 2005; Greicius et al., 2003). The ICN targeted in bvFTD (right FI seed) was first identified with ICA (Beckmann et al., 2005) and later linked to emotional salience processing capacities (Seeley et al., 2007) lost in early bvFTD (Seeley, 2008). SD affects an ICN (left Tpole seed) that has escaped previous detection in humans but corresponds to a Tpole-subgenual cingulate-ventral striatum-amygdala network, well-established in nonhuman primates (Mesulam and Mufson, 1982), that shows progressive atrophy in early-stage SD (Brambati et al., 2007). The PNFA-targeted ICN (left IFG seed) includes the frontal operculum, primary and supplementary motor cortices, and inferior parietal lobule bilaterally, linking the language and motor systems that enable speech fluency. This ICN, often divided into left and right hemispheric systems, has been noted in several previous studies (Beckmann et al., 2005; Damoiseaux et al., 2006; De Luca et al., 2006; van den Heuvel et al., 2008). In PNFA, asymmetric degeneration of this system may reflect its accentuated functional and connectional asymmetry in healthy humans (Stark et al., 2008). In CBS, prominent skeletal and ocular motor abnormalities result from disease within a dorsal sensorimotor association network (right PMC seed) detailed in several ICN studies (De Luca et al., 2006; Fox et al., 2005; Vincent et al., 2008) and elegantly mapped in the macaque using convergent ICN, oculomotor task-based fMRI, and axonal tracer methods (Vincent et al., 2007).

They also hypothesize about some diseases they did not study and the possible ICNs associated with them:

ICNs frequently reported (Beckmann et al., 2005; Damoiseaux et al., 2006; De Luca et al., 2006; van den Heuvel et al., 2008) but not studied here include primary and secondary visual networks that may provide substrate for the visual-spatial variant of AD known as the posterior cortical atrophy syndrome (Hof et al., 1997), a primary sensorimotor ICN that may relate to amyotrophic lateral sclerosis (Kassubek et al., 2005), and a lateral frontoparietal executivecontrol network (Seeley et al., 2007; Vincent et al., 2008) that falters in most neurodegenerative diseases as degeneration spreads beyond the sites of initial injury into widely interconnected supervisory neocortical systems.

I think of these arranged on the eight fold evo devo model as follows:

  1. posterior cortical atrophy syndrome: Visual ICN
  2. ALS /CBS : Sensorimotor ICN. (PMC seed)
  3. unknown (to me) neurodegenrative disease (ALS/CBS?) : Auditory cortex ICN
  4. PNFA : syntactic ICN (Inferior Frontal gyrus seed)
  5. Semantic Dementia : Semantic ICN (seed temporal pole)
  6. bvFTD: SALience ICN (seed Froto Insula)
  7. Alzheimer Disease : DMN ICN (seed angular gyrus)
  8. Dementia with Lewy Bodies ?? : Executive Control Network ICN.

Which brings me to the next paper by Seeley et al in which they showed that not only AD and bvFTD are respectively correlated with atrophy of DMN and SAL ICNs; but that as these networks are anti-correlated in normal control humans and as these diseases show opposite clinical profiles , the diseases are also correlated with increased connectivity in the converse network i.e AD leads to increased connectivity in SAL and bvFTD leads to increased connectivity in DMN.

This is what they hypothesized:

Behavioural variant frontotemporal dementia (bvFTD) and Alzheimer’s disease, the two most common causes of dementia among patients less than 65 years of age (Ratnavalli et al., 2002), provide a robust conceptual framework for exploring ICN fMRI applications to neurodegenerative disease. Early bvFTD disrupts complex social-emotional functions that rely on anterior peri-allocortical structures, including the anterior cingulate cortex and frontoinsula, as well as the amygdala and striatum (Rosen et al., 2002; Broe et al., 2003; Boccardi et al., 2005; Seeley et al., 2008a). These regions constitute a large-scale ICN in healthy subjects, which we have referred to as the ‘Salience Network’ due to its consistent activation in response to emotionally significant internal and external stimuli (Seeley et al., 2007b). Notably, while this anterior network degenerates, posterior cortical functions survive or even thrive, at times associated with emergent visual creativity (Miller et al., 1998; Seeley et al., 2008b). In contrast, Alzheimer’s disease often preserves social-emotional functioning, damaging instead a posterior hippocampal-cingulo-temporal-parietal network, often referred to as the ‘Default Mode Network’ (DMN) (Raichle et al., 2001; Greicius et al., 2003; Buckner et al., 2005; Seeley et al., 2009). DMN-specific functions continue to stir debate, but elements of this system, especially its posterior cortical nodes, participate in episodic memory (Zysset et al., 2002; Buckner et al., 2005) and visuospatial imagery (Cavanna and Trimble, 2006); functions lost early in Alzheimer’s disease. Just as bvFTD and Alzheimer’s disease show opposing clinical strengths and weaknesses, the Salience Network and DMN show anticorrelated ICN time series (Greicius et al., 2003; Fox et al., 2005; Fransson, 2005; Seeley et al., 2007b), suggesting a reciprocal relationship between these two neural systems. This rich clinical and neuroimaging background led us to hypothesize (as detailed in Seeley et al., 2007a) that bvFTD and Alzheimer’s disease would exert opposing influences on the Salience Network and DMN.

and this is exactly what they found. This is more than enough for today, but I cant leave without posting these two quotes from the paper:

Although patient strengths are rarely cited as important dementia diagnostic clues (Miller et al., 2000), preserved social graces and interpersonal warmth often lead experienced clinicians to suspect Alzheimer’s disease in a patient with mild memory or visuospatial complaints. Remarkably, we found that Alzheimer’s disease produces heightened Salience Network connectivity in anterior cingulate cortex and ventral striatum compared with healthy elderly controls.
Like patients with frontotemporal dementia, children with autism, who feature social-emotional and anatomical deficits akin to bvFTD (Di Martino et al., 2009a), may show superior posterior cortical functions manifesting as extraordinary artistic, arithmetic or mnestic talent (Hou et al., 2000; Treffert, 2009). In the present study, bvFTD showed increased left parietal DMN connectivity that correlated with reduced Salience Network connectivity in right frontoinsular, striatal and cingulate regions.

If you are a regular reader and know my zeal for the ‘opposites on a continuum theory’ you will be right on track as to where I am headed, but for that you have to wait for the next post.

Mathuranath, P., Aswathy, P., & Jairani, P. (2010). Genetics of frontotemporal lobar degeneration Annals of Indian Academy of Neurology, 13 (6) DOI: 10.4103/0972-2327.74246
Hodges, J., & Leyton, C. (2010). Frontotemporal dementias: Recent advances and current controversies Annals of Indian Academy of Neurology, 13 (6) DOI: 10.4103/0972-2327.74249
Zhou, J., Greicius, M., Gennatas, E., Growdon, M., Jang, J., Rabinovici, G., Kramer, J., Weiner, M., Miller, B., & Seeley, W. (2010). Divergent network connectivity changes in behavioural variant frontotemporal dementia and Alzheimer’s disease Brain, 133 (5), 1352-1367 DOI: 10.1093/brain/awq075
Seeley, W., Crawford, R., Zhou, J., Miller, B., & Greicius, M. (2009). Neurodegenerative Diseases Target Large-Scale Human Brain Networks Neuron, 62 (1), 42-52 DOI: 10.1016/j.neuron.2009.03.024

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Intrinsic Connectivity Networks: the adult form

ResearchBlogging.org

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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Intrinsic Connectivity Networks: developmental time course

ResearchBlogging.org
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

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Two views of brain function: Reflexive/reactive or Intrinsic/proactive?

ResearchBlogging.org

A scan of the brain using fMRI
Image via Wikipedia

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.

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M. Raichle (2010). Two Views of Brain function Trends in Cognitive Sciences DOI: 10.1016/j.tics.2010.01.008

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