Category Archives: neuroscience

The Algorithm of The Brain

I know that the computer metaphor does not do justice to the brain, but can we conceivably come up with a universal algorithm in how the brain processes stimuli and reacts/responds to them? Further, can we then tie up those algorithmic sub-modules to actual neural subsystems/structures and neurotransmitter systems as substantiated in the physical brain?

That is what I intend to do today, but first let us list our very basic algorithm of how the brain processes stimuli and responds to it. Consider it like a flowchart with each step there being made a decision. At each step that is numbered 1, nothing further happens; at each step numbered 2, further 2 choices are available.

  1. Stimuli comes!
    1. Ignore?
    2. Attend?
      1. Unimportant?
      2. Important?
        1. Default response?
        2. Choose response?
          1. Unfeasible?
          2. Feasible?
            1. Execute response!

Now, let me unpack this a bit. The first step for the purposes of this post is an incoming stimulus. When the stimuli comes we (the brain) can be in different levels of alertness and lookout for incoming stimuli; thus the brain may miss or detect the stimuli. We may be in neuro-vegetative states like sleep and feeding and may be relaxing and miss on both threatening as well rewarding stimuli. Or we can be in a vigilant mode either on lookout for danger or say alert while ready to pounce on prey. A Vigilance system can be reliably conjectured to underlie this and indeed Locus Coerelus- Nor epinephrine (LC-NE) system may just be exactly that system that makes us alert and inhibits neuro vegetative states. Another brain structure relevant here is Amygdala which is popularly known for its role in detecting threatening stimuli, but is involved in pleasant stimuli detection too. Hypersensitiveness of this system can conceivably lead to anxiety at one end (constant lookout for trouble) and addiction (constant lookout for possible gains) at the other. One can also extend this line of reasoning and posit that differential sensitivity of this system may underlie the personality trait of Neuroticism.

Once you have noticed or attended to a stimuli what next? Not every stimuli is salient or important. The next step for the brain is to identify whether the stimuli is indeed important from a functional point of view- whether it is an indicator of, or an actual, reward or punishment. Here comes the incentive salience function of Dopamine. Dopamine neurons in say Nucleus Accumbens (NAcc) area code for whether the incoming stimuli is important or not (see work of Berridge et al) ; if its not important nothing needs to be done; however if it is important and consequential than an appropriate response needs to be executed. Activity has to ensue. Please note that though NAcc is typically thought of as part of a reward circuit, it is equally involved in determining salience of an aversive stimuli. Hypersensitiveness of this incentive salience system can conceivably lead to depression at one end (where all stimuli are important , but negatively toned or aversive) and mania at the other end (where all stimuli are important, but perceived as positively valenced). One can also extend this line of reasoning and associate differential sensitivity in this system to trait of Extraversion.

Once you have determined that the stimuli is important and needs responding, how do you determine the right response? One effortless and ‘hot’ way is to use the default response – if someone threatens you, punch them in the face! The other, more effortful, and ‘cold’ way is to choose a response from the response sets that have been activated or by overriding the default response and selecting something better. This is the selfregulation system. As a brain region, I’m sure ACC has a major role to play here- detecting conflicts between responses and also inhibiting dominant default response. In terms of neurotransmitters I see a role for Serotonin here – regulating the response, especially emotional and instinctual response. Hypersensitiveness of this system may lead to obsessions (rigid thinking) and compulsions (rigid acting) and differential sensitivity in the system may be associated with Conscientiousness.

Now, that you/ your brain has chosen the most appropriate response, one further step needs to be executed before you actually execute the action. Many readers of this blog will be familiar with the Value -Expectancy model of motivation: Value was coded by dopamine neurons using incentive salience, what about expectancy? Basically the V-E model posits that an action will be taken only if you value the outcome and are reasonably sure that you can act in such a way as to achieve the outcome. Neurons in PFC may conceivably code for outcome prediction. PFC is important to predict whether a particular course of action will lead to desired results. It is also conceivable that dopamine neurons may play an important role here. The basic idea is to predict whether you can execute the response and receive the reward or avoid the punishment and only then if the action is feasible, then execute the action. This outcome prediction module I think recruits PFC to a large extent. Hypersensitivity of this system may lead to ADHD and differential sensitivity associated with Openness to experience.

To me the above looks very neat and logical and elegant and I would love your comments regarding the same and also any contradictions you see in literature or any additional thoughts you may have.

Structure of childhood temperaments

An infant

An infant (Photo credit: Wikipedia)

Infants’ and children’s personality structure is studied by studying their ‘temperaments’. To me, personality structure enfolds over time and there are some traits that are more genetic and heritable in nature while the remaining are more self-chosen and under self-control. The former may be named more temperamental in nature,  while the latter may be named more character strengths like.

A model of personality that subsumes but artificially divdes the personality traits into temperaments and character traits is the Cloninger‘s TCI based model of personality. Although popular and theory based, it at times lacks empirical support.

Infant and child psychologists, study personality under the rubric of temperament, as it is assumed that much of the child’s personality is due to genetics and developmental influences are not yet strong/influential enough.

So what are the popular models of childhood temperaments? A synthesis is provided by Zuckerman in his influential book “Psychobiology of personality”. He discusses the models of many influential child theorists and comes to the final list of 6 temperaments that are most relevant/common across schema.

These are (in a developmentally unfolding order (as per me)) :

  1. Negative emotionality – gets upset and cries easily, is easily frightened and/or has a quick temper, and is not easygoing
  2. Approach (sensation seeking) – Approach towards cues of reward or novelty with positive affect 
  3. Activity (energy/vigor)- always on the go from the time of waking, cannot sit still for long, fidgets at meals and similar occasions, prefers active games to quiet ones 
  4. Persistence (perseverance) – The length of time a particular activity is pursued and the continuation in an activity in spite of attempts at interference 
  5. Anger/frustration – Frustration/ anger in response to goal-blocking
  6. Sociability – likes to be with others, makes friends easily, prefers to play with others rather than alone, is not shy.
To this list I would like to add:
  1. Impulsivity (spontaneity)- difficulty in learning self-control and resistance to temptation, gets bored easily, goes from toy to toy quickly.
  2. Sensitivity (sentimentality) – The intensity of stimulation in any sensory modality that is necessary to evoke a response
The reason the above two might not have been noticed by temperamental researchers is because they may unfold/differentiate only at later stage when effort-full control or executive control develops.
This also neatly aligns the temperaments with the eight fold evo-devo theory and the four polarities of Millon.


To boot, the first four temperaments are a dynamics between the polarities of approach(pleasure) – withdrawal (pain) vis-a-vis the polarity of arousal (active) and inhibition (passive).

Similarly, the last four temperaments can be conceived of as the dynamics between self/other and being broadly or narrowly focused and engaged.

To elaborate, the first group of temperaments can be associated with avoidance motivation and the last group with approach motivation. In the former, a sensitivity to feel threatening stimuli painfully leads to negative emotionality or Fear; while when derives pleasure from the same one feels Thrill/excitement/surprise and has sensation seeking or approach temperament. Similarly, a sensitivity to approach the desirable stimuli actively by showing Activity or passively by showing interest (from a distance) leads to the other two dimensions.

Similar dynamic exists for e.g. for anger/frustration and sociability – when one is governed by social concerns and is focused on others (con-specifics) , at times of conflicts/stress one may fight/show aggression or utilize the strategy of tend/befriend. The inclination towards former results in aggressive/conduct disorder/anti-social temperaments; while a propensity for latter results in agreeable/sociable temperaments.

Similarly, one can hypothesize that when one is self-focused and in pursuit of solitary activities, one either is very internally driven, impulsive and spontaneous; or one is more externally sensitive to context and is still socially conformant.

Finally, here are the mappings between childhood temperaments and adult personality traits as per me:

  1. -ve emotionality: Neuroticism
  2. Sensation seeking/approach:  Extraversion
  3. Activity : Extraversion
  4. Persistence: Conscientiousness   
  5. Anger/frustration: Non-conformity
  6. Sociability: Agreeableness
  7. Implusivity: Extraversion
  8. Sensitivity: Neurotincism
The above assumes a five factor model of adult personality with non-conformity replacing Openness to experience as the fifth factor in the FFM/OCEAN model. In the next post I’ll address the latest/most reasonable structure of adult personality. 
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Dopamine: prediction-error vs. incentive salience

Delay and Trace conditioning. CS = conditioned...

Image via Wikipedia

The exact role that dopamine plays in learning remains controversial; some think it acts as a prediction error signal, while Berrdige et al believe that dopamine codes for incentive salience.


A recent paper throws some light on the issue. It uses a  simple Pavlovian conditioning paradigm. To recap, US and CS are paired and after some time CS starts predicting the reward ; however the twist to usual Pavlovian conditioning is that when CS is presented before US; some rats become ‘sign trackers’ i.e. as soon as the CS comes start engaging with it; while other are ‘goal trackers’ i.e. as soon as CS comes start engaging with where the US would ultimately appear.

To elaborate,  both types of rats are able to learn that CS predicts US , but only sign tracker s attach importance to CS in itself. Also if they are given an option to indulge in instrumental behavior to bring forth the CS (in absence of US) , it seems only the sign trackers are more willing to do work to get the CS and are thus motivated enough by Cs in itself. In other words, while both goal trackers and sign trackers endow CS with predictive capabilities; only sign trackers also endow it with incentive salience.


If all this seems confusing , consider the fact that we are all conditioned to like food/sex; but a secondary reinforcer like money which may predict that food will follow, might become a reward in itself and motivate some of us. while for some money may be as good as it is an indicator of food/sex to come; for other money may acquire an importance/ motivational value in itself.


After that crude analogy, lets return to our sign trackers; these rats are found in wild populations also, but a selectively bred rat breed that has been bred for Novelty preference (bHR)  also displays these behaviors  prominently. On the other hand those selectively bred not to show novelty preference are goal trackers by large. (bLR)

What the authors of this study showed was a dissociation between the necessity of dopamine for learning and performance in Pavlovian conditioning. they showed that while dopamine is not required for learning the prediction part (i.e. even in absence of dopamine both goal trackers and sign trackers could learn that CS predicts US) , it is indeed required for performance (i.e. in absence of dopamine neither goal trackers or sign trackers would perform the task whereby their learning of CS/US association resulted in overt behavior. ) . Further , it was found that for sign tracker dopamine was required for the sign tracking behavior.

To me, and to the authors too,  the results seem to indicate that some individuals are more prone to associate incentive salience to CS and their primary mode of learning is via incentive salience mechanism of dopamine; these are also the one more susceptible to maladaptive behavior. However the learning that results in association of CS with US does not need dopamine; the association can happen without dopamine; but no behavior results if either CS/ US is not able to trigger dopamine release or able to tell the brain that this incentive/stimuli is salient.

To me this bodes victory for the Berridge et al camp of incentive salience theory of dopamine function, to whom I have always been more sympathetic ! do you agree?

Flagel, S., Clark, J., Robinson, T., Mayo, L., Czuj, A., Willuhn, I., Akers, C., Clinton, S., Phillips, P., & Akil, H. (2010). A selective role for dopamine in stimulus–reward learning Nature, 469 (7328), 53-57 DOI: 10.1038/nature09588

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Dichotomies; or Psychology in a nutshell

"Two buckets" view of heritability.

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The field of psychology abounds with dichotomies– some of which are patently false/outdated, as per the grapevine. The familiar ones include Nature-nurture and mind-brain; in the former it is assumed that now everything is a mixture of both nature and nurture while in the latter both mind and brain have been conflated to be the same. However as separate disciplines of neurology and psychology attest , and the naive disorder classification system scientists themselves use, which squarely puts one disorder as psychological while other as more neurological attests, there is some merit in considering things at different level of explanation- at the brain or neuronal level of explanation and at the mind or self/ organism level of explanation,


In this article I argue that not only there is merit in these dichotomies, but that these dichotomies grasp fundamental aspects of being and all provide a glimpse of the proverbial elephant to the blind men that we are.


To begin with , the most fundamental dichotomy I consider is that of BRAIN-MIND or DETERMINISM-FREE WILL. To me the proponents of BRAIN fall on  the DETERMINISM side of the table, while those of MIND fall into the FREE WILL camp. Let me elaborate. On the one side is reductive materialism that believes everything can be reduced to and explained in terms of neural firings and that all behavior is predetermined( from say the time of the Big Bang); on the other hand are people who tout the  HARD problems and propose that qualia exists, subjectivity exists, agency exists, even if the basis for that be found only in quantum effects, or rather the basis for which will never be found in classical brain based accounts but will always be non-computable/ non -comprehensible but intuitively grasped by phenomenological experiences alone.  To me there is merit in both arguments and my personal belief is that we are both determined and free, both brain and mind and that one is not the same as other but entails a different sort of world view. If I can go out on a limb, the first view of BRAIN is mechanistic/autistic in nature; while the second view that of MIND is mentalistic/psychotic in nature.  But we are moving ahead of ourselves.

The first belief system, that based around BRAIN/ DETERMINISM, is not without its own challenges/dichotomies. Consider that the BRAIN is sculpted and so everything is pre-DETERMINED. Who sculpted the BRAIN? NATURE or NURTURE? Both GENES and ENVIRONMENT can be equally strongly deterministic and capable of shaping our brain and predisposing us to act in a particular way.  No matter whether you believe in the all-empowering GENES or in the power of SITUATION to elicit behavior, or in the childhood influences that still govern adult REACTIONS, or believe in middle ground of developmental unfolding and epigenetic mechanisms, the predominant theme is that of doom and gloom and predestination. So NATURE-NURTURE is the dichotomy relevant here.

What about the FREE WILL/MIND camp? They too have to answer some tough questions as to what causes agency- is it REASON or PASSION? Does the freedom come from a lifetime of UNCONSCIOUS HABIT that gets engrained as character/PASSION or do we make a CONSCIOUS and REASONED  DECISION every time we ACT ? Is it FREE because it is an inbuilt IMPULSE; or is it  WILL because it can veto and CONTROL? The focus is squarely on ACTIONS- but Actions driven by PASSIONS or Actions driven by REASON? Note that in the NATURE-NURTURE theme the focus was on REACTIONS- what hidden force (genes/environment) causes us to react so and so; here the focus is on actions and what drives them ;  here the focus is on the perennial battle between romanticism and enlightenment /rationalism.   We grant that someone acts- but what is the basis of that action- is it PASSION or is it REASON? is it hidden, unconscious and spontaneous or is it deliberate, conscious and planned? the basic dichotomy here is between PASSION and REASON as the drivers of human action.


What about finer levels of dichotomies. Here again , on further analysis one can see that NATURE /GENES has a dichotomy in terms of Paternal genes or Maternal genes (working at cross-purposes at times as per genomic imprinting theory) ? NURTURE/ENVIRONMENT has a dichotomy in terms of SHARED (or PASSIVE) ENV. influences versus nons0hared or ACTIVE (niche constructing) ENV. influences.  PASSION has tensions between SPONTANEITY/random/Life force/EROS versus HABIT/ingrained/Death instinct/THANTOS; while  REASON has to balance between IRRATIONAL (mythos/chaotic) reasons versus RATIONAL (logos/orderly) reasons .

GENES are historical past facing; ENVIRONMENT is organism past + present facing; PASSION is Present + organism future  facing; while REASON is totally future facing.

So where am I getting from here.  It is to my ABCD model of psychology. Affect, Behavior , Desire/Motivation and Cognition.

To me, BRAIN-MIND/ DETERMINISM-FREE WILL debate is a manifestation of debates between primacy of Affect/behavior over motivation/cognition. Motivation /cognition are not directly observable/ measurable while in some sense affect and behavior are . Further, in BRAIN side there is tension between Affect (mostly inbuilt or genetic)  and  Behavior (mostly learned and a result of environmental influences) ; in a similar view, on the MIND side there is tension between Motivation (FREE/ PASSION) and Cognition (WILL/REASON).


Of course, there are finer levels of dichotomies embedded in ABCD as per the eight stage model where each of ABCD splits in two components based around- that of pleasure-pain, active-passive, self-other and broad-narrow. To me these dichotomies make perfect sense now.

To extend to one particular domain of personality psychology: you have deterministic personality theories emphasizing traits or behaviorism and you see a conflict/debate in personality theory in terms of Person (genes/traits) vs. situation(environment) variables.   On the other hand are free-will theories of personality centered around Psychoanalytic theories and Phenomenological/existential theories where the fundamental conflicts is between conscious/ and unconscious; between past and future orientation, between passion/libido and reason/actualization.


To extend to another domain – that of psychopathology- Motivation defects in EROS and THANTOS lead to Mania and Depression respectively and remain in conflict with each other; Cognitive deficits in REASONs, that is, in MYTHOS based chaotic/dreamy/irrational reasoning versus LOGOS based orderly/reality-oriented and logical reasoning lead to the opposed and yet conflated phenotypes of Autism spectrum disorders and Psychotic spectrum disorders.

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Intrinsic Connectivity Networks: the adult form

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: more than just DMN

High resolution fMRI of the Human brain.

Image via Wikipedia
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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The Quest for the Holy Glial

Astrocyte - Astrocytes can be visualized in cu...

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If you plan to read only one book on brain this year, make it a point to read ‘The Other Brain’ by Douglas Fields. While most of us in the psycho-neuro field have been focusing on the neurons, there is a silent revolution that is taking place about our understanding of Glial cells and how they may be crucial to our understanding of many Brain related complications ranging from  infections like the prion disease to neuro-degenerative disorder to the brain cancers. Glias of various sizes and shapes have much to do with these and by narrowly focusing on the neurons we may be missing half the picture (literally).

But I would have not so heartily recommended this book to all psychology and neuroscience lovers, had it been another dry discourse meant to showcase one’s expertise or managed to just compile the most recent findings; – while filled with cutting edge scientific facts,  it is the ability of the author to weave that into a narrative , to get us not just deeply interested in this Glial quest but to feel ourselves as a part of this quest- this monumental understanding of that other 85% of our brain cells – that makes reading this book a riveting and fulfilling experience.

While I did know that Glias constitute the major part of our brain tissue, and that they are important in myelination and thus speedy conduction of Acton potentials , my knowledge of Glia was limited to this superficial account ; the many new things I personally learned included the fact below:

  • Brain cancers are due to Glia (as adult neurogenises is rare and strictly controlled)- seems a no-brainer when you think of it;
  • The different types of Glial cells ; how they look like (beautiful pictures)  and how microglia are actually the immune system of the brain .
  • Schwann Glia are only present in peripheral nervous system and Oligodendrocytes only in CNS– and while Schwann cells can help the growth post injury in PNS( they form a pathway that guides new axons) , Oligodendrocytes actually hamper the process and thus brain/spinal cord injury leads to irreversible damage.
  • Along the way I read a beautiful description of how an electron microscope actually looks like and how it feels to operate one to view the sections and the sectioning procedure.
  • The science as well as the politics behind the Prion infection mechanism discovery. How Gajdusek defied all odds to work with Papua New Guinea people.
  • Glias also do computations by using Ca2+ signalling and listen to  (and possibly modify) neuron conversations. How that is imaged using new technologies including optogenetics.

There are many more such golden nuggets spread all over the book and the writing style is pure joy to read. I’m still only half-way through the book, but thought of sharing my joy at finding such a good book with the mouse trap readers. the book does great timely service by highlighting the important glail research and making it mainstream. I hope more scientists study and understand Glias and that Glias become as much a part of our discourse as  neurons are and find their place in psychology and neuroscience textbooks.

Full disclosure: I received a free copy of the book for review by Simon and Schuster.

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neural developmental stages for dummies

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I’m no expert when it comes to complex questions like that related to neural development, but to my naive mind the major stages involved in neural development seem to follow the eight stages as outlined in in the eight stage evo-devo model.

The first stage is normally involved with the coming in being of a particular new form, in this case the birth and differentiation of a neuronal cell form its precursor stem cells.

The second stage typically involves motion and in this case refers to the migration of immature neurons from their birthplaces in the embryo to their final positions.

The third stage typically refers to connections and branching and in this case refers to the outgrowth of axons and dendrites from neurons.

The fourth stage is typically a social/ dormant sort of state and in this case refers to the guidance of the motile growth cone through the embryo towards postsynaptic partners.

The fifth stage typically is about achieving closure/integration/individuality and in this case refers to the generation of synapses between these axons and their postsynaptic partner.

After this the sixth ,seventh and eight stage refer to qualitatively different sort of development – that at synapse. We have synapse formation followed by changes in synaptic strength due to learning and memory; and finally the pruning of synapses to yield the best possible redundant neuronal system.

To me everything looks to follow the eight stage pattern; but does these major stages of neural development make sense to you too and seem to follow distinctive stage patterns?

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

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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

Perceived age as a bio-marker of ageing
Do you look younger than your age? If so you have reasons to cheer! According to a new study as per Kaare et al, the perceived age is directly related to the actual ageing and inversely related to your telomere length.


It is well established that telomere length is a good indicator of ageing and also plays a crucial role in diseases like cancer, and when it becomes too small hastens cell apostasies.   in this study, what Kaare et al found that of the twins, the one who had more perceived age also had a shorter telomere length on average and thus was more aged.

They also found a long -term effect of perceived age on mortality and thus more corroborating proof about this association. They used a cohort study of twins to reach their conclusions. I’ll quote from their abstract and discussion:

Results: For all three groups of assessors, perceived age was significantly associated with survival, even after adjustment for chronological age, sex, and rearing environment. Perceived age was still significantly associated with survival after further adjustment for physical and cognitive functioning. The likelihood that the older looking twin of the pair died first increased with increasing discordance in perceived age within the twin pair—that is, the bigger the difference in perceived age within the pair, the more likely that the older looking twin died first. Twin analyses suggested that common genetic factors influence both perceived age and survival. Perceived age, controlled for chronological age and sex, also correlated significantly with physical and cognitive functioning as well as with leucocyte telomere length.

Conclusion: Perceived age—which is widely used by clinicians as a general indication of a patient’s health—is a robust biomarker of ageing that predicts survival among those aged 70 and correlates with important functional and molecular ageing phenotypes.

Perceived age predicts survival among people aged 70, even after adjustment for chronological age, sex, and other readily measurable biomarkers of ageing. Perceived age also correlates with age related phenotypes such as physical and cognitive functioning and leucocyte telomere length. Clinicians use perceived age as part of their assessment of patients, but research on the validity of the approach has been sparse.1 13 14 We have shown that perceived age based on facial photographs is a robust biomarker of ageing that does not depend on the sex, age, and professional background of the assessors.
In our analysis, the comparison within pairs of dizygotic twins controlled for rearing environment and, on average, half the genetic factor variants present in a population, while the comparison within pairs of monozygotic twins controlled for all genetic factors and rearing environment. We found indication of common genetic factors influencing both perceived age and survival because controlling for genetic factors (the comparison within monozygotic pairs) removed the association between perceived age and survival (fig 3). This was in contrast with the results for the overall twin sample and for the dizygotic twins, where comparison within pairs showed a clear “dose response” association between perceived age and survival (fig 2). Hence, the comparison within pairs suggests that there are genetic factors influencing both survival and perceived age (for example, genetic factors that influence the condition of cardiovascular tissue could affect the risk of myocardial infarction as well as the appearance of skin). Full details of this study design can be found elsewhere.

it is important to note that they found the association only in dizygotic twins and not in monozygotic twins, so apparently genetic factors determine both perceived age and actual mortality/ageing. If the effect had been also found in monozygotic twins perhaps epigenetic /non-shared environmental factors would be the deciding factor, but in their absence it is wise to conclude that genes are the third factor that has led to perceived age and ageing correlation and neither is causative of the other. Alternately , underlying tissue ageing might directly affect perceived age and might be evolutionary coded for m, especially in females, so that males could determine the youth and fecundity accurately. In that way the direction would be causal but in the other direction.

What it means is that if you have young looks as per your age, there is reason to rejoice; if not you can not do much by looking young even if you indulge all your money in face lifts etc. Of course there are other benefits of looking young artificially, but increased actual age might not be one of them.

Christensen, K., Thinggaard, M., McGue, M., Rexbye, H., Hjelmborg, J., Aviv, A., Gunn, D., van der Ouderaa, F., & Vaupel, J. (2009). Perceived age as clinically useful biomarker of ageing: cohort study BMJ, 339 (dec11 2) DOI: 10.1136/bmj.b5262

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