Category Archives: neuroscience

Major conscious and unconcoscious processes in the brain: part 3: Robot minds

This article continues my series on major conscious and unconscious processes in the brain. In my last two posts I have talked about 8 major unconscious processes in the brain viz sensory, motor, learning , affective, cognitive (deliberative), modelling, communications and attentive systems. Today, I will not talk about brain in particular, but will approach the problem from a slightly different problem domain- that of modelling/implementing an artificial brain/ mind.

I am a computer scientist, so am vaguely aware of the varied approaches used to model/implement the brain. Many of these use computers , though not every approach assumes that the brain is a computer.

Before continuing I would briefly like to digress and link to one of my earlier posts regarding the different  traditions of psychological research in personality and how I think they fit an evolutionary stage model . That may serve as a background to the type of sweeping analysis and genralisation that I am going to do. To be fair it is also important to recall an Indian parable of how when asked to describe an elephant by a few blind man each described what he could lay his hands on and thus provided a partial and incorrect picture of the elephant. Some one who grabbed the tail, described it as snake-like and so forth.

With that in mind let us look at the major approaches to modelling/mplementing the brain/intelligence/mind. Also remember that I am most interested in unconscious brain processes till now and sincerely believe that all the unconscious processes can, and will be successfully implemented in machines.   I do not believe machines will become sentient (at least any time soon), but that question is for another day.

So, with due thanks to @wildcat2030, I came across this book today and could immediately see how the different major approaches to artificial robot brains are heavily influenced (and follow) the evolutionary first five stages and the first five unconscious processes in the brain.
The book in question is ‘Robot Brains: Circuits and Systems for Conscious Machines’ by Pentti O. Haikonen and although he is most interested in conscious machines I will restrict myself to intelligent but unconscious machines/robots.

The first chapter of the book (which has made to my reading list) is available at Wiley site in its entirety and I quote extensively from there:

Presently there are five main approaches to the modelling of cognition that could be used for the development of cognitive machines: the computational approach (artificial intelligence, AI), the artificial neural networks approach, the dynamical systems approach, the quantum approach and the cognitive approach. Neurobiological approaches exist, but these may be better suited for the eventual explanation of the workings of the biological brain.

The computational approach (also known as artificial intelligence, AI) towards thinking machines was initially worded by Turing (1950). A machine would be thinking if the results of the computation were indistinguishable from the results of human thinking. Later on Newell and Simon (1976) presented their Physical Symbol System Hypothesis, which maintained that general intelligent action can be achieved by a physical symbol system and that this system has all the necessary and sufficient means for this purpose. A physical symbol system was here the computer that operates with symbols (binary words) and attached rules that stipulate which symbols are to follow others. Newell and Simon believed that the computer would be able to reproduce human-like general intelligence, a feat that still remains to be seen. However, they realized that this hypothesis was only an empirical generalization and not a theorem that could be formally proven. Very little in the way of empirical proof for this hypothesis exists even today and in the 1970s the situation was not better. Therefore Newell and Simon pretended to see other kinds of proof that were in those days readily available. They proposed that the principal body of evidence for the symbol system hypothesis was negative evidence, namely the absence of specific competing hypotheses; how else could intelligent activity be accomplished by man or machine? However, the absence of evidence is by no means any evidence of absence. This kind of ‘proof by ignorance’ is too often available in large quantities, yet it is not a logically valid argument. Nevertheless, this issue has not yet been formally settled in one way or another. Today’s positive evidence is that it is possible to create world-class chess-playing programs and these can be called ‘artificial intelligence’. The negative evidence is that it appears to be next to impossible to create real general intelligence via preprogrammed commands and computations.

The original computational approach can be criticized for the lack of a cognitive foundation. Some recent approaches have tried to remedy this and consider systems that integrate the processes of perception, reaction, deliberation and reasoning (Franklin, 1995, 2003; Sloman, 2000). There is another argument against the computational view of the brain. It is known that the human brain is slow, yet it is possible to learn to play tennis and other activities that require instant responses. Computations take time. Tennis playing and the like would call for the fastest computers in existence. How could the slow brain manage this if it were to execute computations?

The artificial neural networks approach, also known as connectionism, had its beginnings in the early 1940s when McCulloch and Pitts (1943) proposed that the brain cells, neurons, could be modelled by a simple electronic circuit. This circuit would receive a number of signals, multiply their intensities by the so-called synaptic weight values and sum these modified values together. The circuit would give an output signal if the sum value exceeded a given threshold. It was realized that these artificial neurons could learn and execute basic logic operations if their synaptic weight values were adjusted properly. If these artificial neurons were realized as hardware circuits then no programs would be necessary and biologically plausible artificial replicas of the brain might be possible. Also, neural networks operate in parallel, doing many things simultaneously. Thus the overall operational speed could be fast even if the individual neurons were slow. However, problems with artificial neural learning led to complicated statistical learning algorithms, ones that could best be implemented as computer programs. Many of today’s artificial neural networks are statistical pattern recognition and classification circuits. Therefore they are rather removed from their original biologically inspired idea. Cognition is not mere classification and the human brain is hardly a computer that executes complicated synaptic weight-adjusting algorithms.

The human brain has some 10 to the power of 11 neurons and each neuron may have tens of thousands of synaptic inputs and input weights. Many artificial neural networks learn by tweaking the synaptic weight values against each other when thousands of training examples are presented. Where in the brain would reside the computing process that would execute synaptic weight adjusting algorithms? Where would these algorithms have come from? The evolutionary feasibility of these kinds of algorithms can be seriously doubted. Complicated algorithms do not evolve via trial and error either. Moreover, humans are able to learn with a few examples only, instead of having training sessions with thousands or hundreds of thousands of examples. It is obvious that the mainstream neural networks approach is not a very plausible candidate for machine cognition although the human brain is a neural network.

Dynamical systems were proposed as a model for cognition by Ashby (1952) already in the 1950s and have been developed further by contemporary researchers (for example Thelen and Smith, 1994; Gelder, 1998, 1999; Port, 2000; Wallace, 2005). According to this approach the brain is considered as a complex system with dynamical interactions with its environment. Gelder and Port (1995) define a dynamical system as a set of quantitative variables, which change simultaneously and interdependently over quantitative time in accordance with some set of equations. Obviously the brain is indeed a large system of neuron activity variables that change over time. Accordingly the brain can be modelled as a dynamical system if the neuron activity can be quantified and if a suitable set of, say, differential equations can be formulated. The dynamical hypothesis sees the brain as comparable to analog feedback control systems with continuous parameter values. No inner representations are assumed or even accepted. However, the dynamical systems approach seems to have problems in explaining phenomena like ‘inner speech’. A would-be designer of an artificial brain would find it difficult to see what kind of system dynamics would be necessary for a specific linguistically expressed thought. The dynamical systems approach has been criticized, for instance by Eliasmith (1996, 1997), who argues that the low dimensional systems of differential equations, which must rely on collective parameters, do not model cognition easily and the dynamicists have a difficult time keeping arbitrariness from permeating their models. Eliasmith laments that there seems to be no clear ways of justifying parameter settings, choosing equations, interpreting data or creating system boundaries. Furthermore, the collective parameter models make the interpretation of the dynamic system’s behaviour difficult, as it is not easy to see or determine the meaning of any particular parameter in the model. Obviously these issues would translate into engineering problems for a designer of dynamical systems.

The quantum approach maintains that the brain is ultimately governed by quantum processes, which execute nonalgorithmic computations or act as a mediator between the brain and an assumed more-or-less immaterial ‘self’ or even ‘conscious energy field’ (for example Herbert, 1993; Hameroff, 1994; Penrose, 1989; Eccles, 1994). The quantum approach is supposed to solve problems like the apparently nonalgorithmic nature of thought, free will, the coherence of conscious experience, telepathy, telekinesis, the immortality of the soul and others. From an engineering point of view even the most practical propositions of the quantum approach are presently highly impractical in terms of actual implementation. Then there are some proposals that are hardly distinguishable from wishful fabrications of fairy tales. Here the quantum approach is not pursued.

The cognitive approach maintains that conscious machines can be built because one example already exists, namely the human brain. Therefore a cognitive machine should emulate the cognitive processes of the brain and mind, instead of merely trying to reproduce the results of the thinking processes. Accordingly the results of neurosciences and cognitive psychology should be evaluated and implemented in the design if deemed essential. However, this approach does not necessarily involve the simulation or emulation of the biological neuron as such, instead, what is to be produced is the abstracted information processing function of the neuron.

A cognitive machine would be an embodied physical entity that would interact with the environment. Cognitive robots would be obvious applications of machine cognition and there have been some early attempts towards that direction. Holland seeks to provide robots with some kind of consciousness via internal models (Holland and Goodman, 2003; Holland, 2004). Kawamura has been developing a cognitive robot with a sense of self (Kawamura, 2005; Kawamura et al., 2005). There are also others. Grand presents an experimentalist’s approach towards cognitive robots in his book (Grand, 2003).

A cognitive machine would be a complete system with processes like perception, attention, inner speech, imagination, emotions as well as pain and pleasure. Various technical approaches can be envisioned, namely indirect ones with programs, hybrid systems that combine programs and neural networks, and direct ones that are based on dedicated neural cognitive architectures. The operation of these dedicated neural cognitive architectures would combine neural, symbolic and dynamic elements.

However, the neural elements here would not be those of the traditional neural networks; no statistical learning with thousands of examples would be implied, no backpropagation or other weight-adjusting algorithms are used. Instead the networks would be associative in a way that allows the symbolic use of the neural signal arrays (vectors). The ‘symbolic’ here does not refer to the meaning-free symbol manipulation system of AI; instead it refers to the human way of using symbols with meanings. It is assumed that these cognitive machines would eventually be conscious, or at least they would reproduce most of the folk psychology hallmarks of consciousness (Haikonen, 2003a, 2005a). The engineering aspects of the direct cognitive approach are pursued in this book.

Now to me these computational approaches are all unidimensional-

  1. The computational approach is suited for symbol-manipulation and information-represntation and might give good results when used in systems that have mostly ‘sensory’ features like forming a mental represntation of external world, a chess game etc. Here something (stimuli from world) is represented as something else (an internal symbolic represntation).
  2. The Dynamical Systems approach is guided by interactions with the environment and the principles of feedback control systems and also is prone to ‘arbitrariness’ or ‘randomness’. It is perfectly suited to implement the ‘motor system‘ of brain as one of the common features is apparent unpredictability (volition) despite being deterministic (chaos theory) .
  3. The Neural networks or connectionsim is well suited for implementing the ‘learning system’ of the brain and we can very well see that the best neural network based systems are those that can categorize and classify things just like ‘the learning system’ of the brain does.
  4. The quantum approach to brain, I haven’t studied enough to comment on, but the action-tendencies of ‘affective system’ seem all too similar to the superimposed,simultaneous states that exits in a wave function before it is collapsed. Being in an affective state just means having a set of many possible related and relevant actions simultaneously activated and then perhaps one of that decided upon somehow and actualized. I’m sure that if we could ever model emotion in machine sit would have to use quantum principles of wave functions, entanglemnets etc.
  5. The cognitive approach, again I haven’t go a hang of yet, but it seems that the proposal is to build some design into the machine that is based on actual brain and mind implemntations. Embodiment seems important and so does emulating the information processing functions of neurons. I would stick my neck out and predict that whatever this cognitive approach is it should be best able to model the reasoning and evaluative and decision-making functions of the brain. I am reminded of the computational modelling methods, used to functionally decompose a cognitive process, and are used in cognitive science (whether symbolic or subsymbolic modelling) which again aid in decision making / reasoning (see wikipedia entry)

Overall, I would say there is room for further improvement in the way we build more intelligent machines. They could be made such that they have two models of world – one deterministic , another chaotic and use the two models simulatenously (sixth stage of modelling); then they could communicate with other machines and thus learn language (some simulation methods for language abilities do involve agents communicating with each other using arbitrary tokens and later a language developing) (seventh stage) and then they could be implemented such that they have a spotlight of attention (eighth stage) whereby some coherent systems are amplified and others suppressed. Of course all this is easier said than done, we will need at least three more major approaches to modelling and implementing brain/intelligence before we can model every major unconscious process in the brain. To model consciousness and program sentience is an uphill task from there and would definitely require a leap in our understandings/ capabilities.

Do tell me if you find the above reasonable and do believe that these major approaches to artificial brain implementation are guided and constrained by the major unconscious processes in the brain and that we can learn much about brain from the study of these artificial approaches and vice versa.

Major conscious and unconcoscious processes in the brain

Today I plan to touch upon the topic of consciousness (from which many bloggers shy) and more broadly try to delineate what I believe are the important different conscious and unconscious processes in the brain. I will be heavily using my evolutionary stages model for this.

To clarify myself at the very start , I do not believe in a purely reactive nature of organisms; I believe that apart from reacting to stimuli/world; they also act , on their own, and are thus agents. To elaborate, I believe that neuronal groups and circuits may fire on their own and thus lead to behavior/ action. I do not claim that this firing is under voluntary/ volitional control- it may be random- the important point to note is that there is spontaneous motion.

  1. Sensory system: So to start with I propose that the first function/process the brain needs to develop is to sense its surroundings. This is to avoid predators/ harm in general. this sensory function of brain/sense organs may be unconscious and need not become conscious- as long as an animal can sense danger, even though it may not be aware of the danger, it can take appropriate action – a simple ‘action’ being changing its color to merge with background. 
  2. Motor system:The second function/ process that the brain needs to develop is to have a system that enables motion/movement. This is primarily to explore its environment for food /nutrients. Preys are not going to walk in to your mouth; you have to move around and locate them. Again , this movement need not be volitional/conscious – as long as the animal moves randomly and sporadically to explore new environments, it can ‘see’ new things and eat a few. Again this ‘seeing’ may be as simple as sensing the chemical gradient in a new environmental.
  3. Learning system: The third function/process that the brain needs to develop is to have a system that enables learning. It is not enough to sense the environmental here-and-now. One needs to learn the contingencies in the world and remember that both in space and time. I am inclined to believe that this is primarily pavlovaion conditioning and associative learning, though I don’t rule out operant learning. Again this learning need not be conscious- one need not explicitly refer to a memory to utilize it- unconscious learning and memory of events can suffice and can drive interactions. I also believe that need for this function is primarily driven by the fact that one interacts with similar environments/con specifics/ predators/ preys and it helps to remember which environmental conditions/operant actions lead to what outcomes. This learning could be as simple as stimuli A predict stimuli B and/or that action C predicts reward D .
  4. Affective/ Action tendencies system .The fourth function I propose that the brain needs to develop is a system to control its motor system/ behavior by making it more in sync with its internal state. This I propose is done by a group of neurons monitoring the activity of other neurons/visceral organs and thus becoming aware (in a non-conscious sense)of the global state of the organism and of the probability that a particular neuronal group will fire in future and by thus becoming aware of the global state of the organism , by their outputs they may be able to enable one group to fire while inhibiting other groups from firing. To clarify by way of example, some neuronal groups may be responsible for movement. Another neuronal group may be receiving inputs from these as well as say input from gut that says that no movement has happened for a time and that the organism has also not eaten for a time and thus is in a ‘hungry’ state. This may prompt these neurons to fire in such a way that they send excitatory outputs to the movement related neurons and thus biasing them towards firing and thus increasing the probability that a motion will take place and perhaps the organism by indulging in exploratory behavior may be able to satisfy hunger. Of course they will inhibit other neuronal groups from firing and will themselves stop firing when appropriate motion takes place/ a prey is eaten. Again nothing of this has to be conscious- the state of the organism (like hunger) can be discerned unconsciously and the action-tendencies biasing foraging behavior also activated unconsciously- as long as the organism prefers certain behaviors over others depending on its internal state , everything works perfectly. I propose that (unconscious) affective (emotional) state and systems have emerged to fulfill exactly this need of being able to differentially activate different action-tendencies suited to the needs of the organism. I also stick my neck out and claim that the activation of a particular emotion/affective system biases our sensing also. If the organism is hungry, the food tastes (is unconsciously more vivid) better and vice versa. thus affects not only are action-tendencies , but are also, to an extent, sensing-tendencies.
  5. Decisional/evaluative system: the last function (for now- remember I adhere to eight stage theories- and we have just seen five brain processes in increasing hierarchy) that the brain needs to have is a system to decide / evaluate. Learning lets us predict our world as well as the consequences of our actions. Affective systems provide us some control over our behavior and over our environment- but are automatically activated by the state we are in. Something needs to make these come together such that the competition between actions triggered due to the state we are in (affective action-tendencies) and the actions that may be beneficial given the learning associated with the current stimuli/ state of the world are resolved satisfactorily. One has to balance the action and reaction ratio and the subjective versus objective interpretation/ sensation of environment. The decisional/evaluative system , I propose, does this by associating values with different external event outcomes and different internal state outcomes and by resolving the trade off between the two. This again need not be conscious- given a stimuli predicting a predator in vicinity, and the internal state of the organism as hungry, the organism may have attached more value to ‘avoid being eaten’ than to ‘finding prey’ and thus may not move, but camouflage. On the other hand , if the organisms value system is such that it prefers a hero’s death on battlefield , rather than starvation, it may move (in search of food) – again this could exist in the simplest of unicellular organisms.

Of course all of these brain processes could (and in humans indeed do) have their conscious counterparts like Perception, Volition,episodic Memory, Feelings and Deliberation/thought. That is a different story for a new blog post!

And of course one can also conceive the above in pure reductionist form as a chain below:

sense–>recognize & learn–>evaluate options and decide–>emote and activate action tendencies->execute and move.

and then one can also say that movement leads to new sensation and the above is not a chain , but a part of cycle; all that is valid, but I would sincerely request my readers to consider the possibility of spontaneous and self-driven behavior as separate from reactive motor behavior. 

synaptic plasticity: angelman’s/autism and psychosis

There is a recent article in Nature Neuroscience by Philpot et al regarding how experience-dependent synaptic plasticity is downregulated in Angelmans’ syndrome and perhaps in Autism too, as the Ube3a gene involved is implicated in both disorders.

First a little history about Angelman– it is a disorder caused by deletion/lack of a maternally imprinted UBE3a gene in chromosomal region 15q11-q13 . It is typically contrasted with Prader-Willi syndrome which is caused by a paternally imprinted gene malfunction in the same chromosomal region. Christopher Badcock has used this to contrast Autism (related to Angelman) and Psychosis (more common in PWS) to argue that Autism and Psychosis are due to a genomic imprinting tug of war between fathers and mothers genes.

I have written about Badcock’s and Crespi’s thesis before and how it fits in with my views on Autism and Psychosis; suffice it to say that I am seeing the new study primarily from this prism of Autism and Psychosis dichotomy.

First , let us see what the study tells us:

It uses mouse model that contains silenced maternal Ube3a genes (Ube3a m-p+ mouse), thus trying to make a mouse model of Angelman.

What it found was:

1)    Ube3a expression was markedly reduced in Ube3am-/p+ mice compared with wild-type mice in all three brain regions (visual neocortex, hippocampus,cerebellam). Consistent with previous observations, this attenuation was brain specific, as Ube3a was highly expressed in the liver of both Ube3am+/p- and Ube3am-/p+ mice.

2) To determine the physiological consequences of Ube3a loss on neocortical development, we examined the developmental acquisition of spontaneous excitatory synaptic transmission by recording miniature excitatory postsynaptic currents (mEPSCs) in layer 2/3 pyramidal neurons of visual cortex (see Supplementary Table 1 online for intrinsic membrane properties of recorded neurons). Consistent with previous findings24, 25, mEPSC amplitudes decreased and frequency increased during development in wild-type mice . Just before eye opening (postnatal day 10, P10), mEPSC frequency and amplitude were indistinguishable between wild-type and Ube3am-/p+ mice . Thereafter, mEPSC frequency failed to develop normally in Ube3am-/p+ mice

3)Although dark rearing had no measurable effect on mEPSC amplitude in wild-type mice at P25 , sensory deprivation strongly attenuated the normal developmental increase in mEPSC frequency in wild-type mice . In contrast, dark rearing did not affect mEPSC amplitude or frequency in Ube3am-/p+ mice. Consequently, mEPSC frequency in normally reared Ube3am-/p+ mice was not significantly different from that of dark-reared wild-type mice . These findings demonstrate that, although Ube3a is not necessary for the initial sensory-independent establishment of synaptic connectivity, it is selectively required for experience-dependent maturation of excitatory circuits.

4)We therefore compared the properties of neocortical long-term depression (LTD) and LTP at layer 2/3 synapses in visual cortex of wild-type and Ube3am-/p+ mice at both young (P25) and adult (P100) ages. Because layer 2/3 pyramidal neurons receive major inputs from layer 4 pyramidal neurons, layer 2/3 field potentials were evoked by layer 4 stimulation . We began by measuring LTD in young mice using a standard stimulation protocol (1 Hz for 15 min). Although LTD was reliably induced in young wild-type mice, it was absent in young Ube3am-/p+ mice . We also observed deficits in LTP induction. A relatively weak induction protocol (three 1-s trains of 40-Hz stimulation) elicited LTP in young wild-type mice, but failed to reliably induce LTP in young Ube3am-/p+ mice . To test whether the neocortex of Ube3am-/p+ mice was capable of expressing LTP, we also applied a strong LTP stimulation protocol (two 1-s trains of 100-Hz stimulation). This protocol consistently induced LTP in both Ube3am-/p+ and wild-type mice. Thus, as with LTP deficits in hippocampus8, 9, the LTP induction machinery is impaired in the visual cortex of Ube3am-/p+ mice and this deficit in LTP can be overcome with strong stimulation.

5)To determine whether the plasticity deficits in Angelman syndrome mice persisted into adulthood, we tested LTD and LTP in adults (P100). In adult wild-type mice, LTD induced by 1-Hz stimulation was absent, as expected27, whereas LTP could be induced with strong stimulation. In adult Ube3am-/p+ mice, however, neither of these protocols were effective at modifying synaptic strength. These results indicate that wild-type mice show attenuated neocortical plasticity as they mature and that this attenuation of plasticity is more severe in the absence of Ube3a . Furthermore, these data indicate that plasticity defects in Angelman syndrome mice persist into adulthood.

..and so on (go read the full paper)

In a nutshell, what they found was that in presence of visual stimuli, the plasticity (measured by LTP/LTD ) of visual cortex was adversely affected. As sensory stimulus would normally be available while developing, this would adversely affect the plasticity in adolescence/ critical periods and also continue into adulthood.

Thus, Autism/ Angelman are charechterised by less synaptic plasticity in adulthood and during critical development periods. Paradoxically, this loss of synaptic plasticity is concomitant on their it being experience-dependent or having sensory stimuli. If the organism is sensory deprived, it may still retain the normal synaptic plasticity exhibited by similar sensory deprived normal people.

How does this relate to Psychosis? If my thesis is correct that autism and Psychosis are opposites, then I would predict that in either prader-willi or in Psychosis (scheziphrenia etc) there should be excessive experience-dependent plasticity. I was glad to learn that I am not the first one to make that proposition, but someone back in 1995 has argued for Hippocampal synaptic plasticity as an endophenotyoe for Episodic Psychosis. I now quote heavily form that article.

Here is the abstract:

Structural change in the hippocampal formation has become popular as a proposed neurobiological substrate for schizophrenic disorders. It is postulated that behavioral plasticity in the form of long-term potentiation of hippocampal synaptic transmission is an attractive putative mechanism for the mediation of transient psychosis. Moreover, the disturbed hippocampal neuroarchitecture found in schizophrenic brain may be susceptible to potentiation and dysfunctional to the degree that delusions and hallucinations develop. Partial and selective blockade of the receptors mediating potentiation may prove to be an efficient means of preventing psychotic episodes and avoiding further damage to the involved network. Basic research, utilizing experimental models such as intraventricular kainic acid injection, may help to clarify the anatomical and physiological substrate of psychosis.

The Main thesis of the paper is:

1. Anatomical, physiological, pharmacological, and behavioral findings are most consistent with the view that neuropathological changes within the limbic system, specifically within the hippocampal formation, may represent a biological substrate of schizophrenia.

2. The biological mechanism underlying transient psychosis may be long-term potentiation (LTP) of synaptic transmission within the hippocampal formation.

3. The effects of dopamine manipulation on these behaviors may be mediated by direct actions on the compromised limbic system of the psychotic patient.

Further:

Associative plasticity within hippocampus occurs in the form of long-term potentiation (LTP), an experience-dependent increase in synaptic efficacy. Experimentally, LTP is produced by tetanic stimulation of afferent systems (Bliss and Lomo 1973) and has been shown to facilitate simple associative learning (Berger 1984) but disrupt more complex forms of associative plasticity (Robinson et al 1989). Hippocampal LTP has been observed to occur as a consequence of stimulus pairings in classical conditioning (Weisz et al 1984) and appears to be mediated by N-methyl-Daspartate (NMDA) receptors (Harris et al 1984). Pharmacological blockade of NMDA receptors has been shown to disrupt learning and memory in a variety of forms, including simple associations (Stillwell and Robinson 1990), spatial learning (Morris et al 1986; Heale and Harley 1990; Shapiro and Caramanos 1990), conditioned fear (Miserendino et al 1990; Kim et al 1991), olfactory memory (Staubli et al 1989) and gustatory memory (Welzl et al 1990). Some evidence, however, suggests that deficits involve motor impairment as well as disrupted learning (Keith and Rudy 1990)

Hippocampal function is particularly sensitive to neurochemical modulation, and the expression of monoamine receptors in the temporal lobe is altered in schizophrenics (Joyce 1993). Antipsychotics that reduce endogenous dopamine levels (Losonczy et al 1987) exert significant effects on the hippocampus and LTP. Trifluoperazine inhibits induction of LTP in hippocampus (Finn et al 1980), whereas the dopamine antagonist domperidone has been shown to prevent the maintenance of LTP (Frey et al 1990). Long-term effects of antipsychotic drugs include functional supersensitivity of hippocampal pyramidal neurons (Bijak and Smialowski 1989). Thus, individuals with deranged hippocampal neuroarchitecture would be prone to cognitive dysfunction (including, perhaps, perceptual distortion and other schizophrenic symptoms), differentially susceptible to stress, and responsive to amelioration of symptoms via dopamine antagonism. It may be more than coincidence that the time lag between administration of antipsychotic medication (which results in near immediate decrement in dopamine levels) and the attenuation of psychotic symptoms weeks later (Kane 1987) is remarkably consistent with the time parameters of LTP decay (Douglas and Goddard 1975). Also, the selective disruption of “weak” associative responses by antipsychotic drugs (van der Heyden and Bradford 1988) is consistent with interactions between NMDA-receptor blockade and stimulation intensity on induction of LTP (Reed and Robinson 1991).

From the above, at least to me, it is clear that anti-psychotics may work by decreasing LTP/LTD that is enhanced in episodic psychosis. A propensity towards increased experience-dependent enhancement of synaptic palsticty may be at work here and paradoxically the same approach of sensory deprivation, as in Angelman/ Autism may work here too.

Here is the summary:

In summary, potentiation of hippocampal synaptic transmission may be the neurophysiological basis of episodic psychosis. (Post [1993] has proposed a similar process in the amygdala as a useful model in understanding the progression of recurrent affective disorders.) More selective blockade of the NMDA receptor, which mediates LTP, may prove an effective means of attenuating positive symptoms and preventing further accrual of cellular damage in hippocampus.

In my own summation, I am convinced that we would find more synaptic plasticity in Psychotic people and that hyper-plasticity to hypo-plasticity is another dimension on which the autistics and psychotics differ and this again is a result of the genomic imprinting mediated tug-pf-war between the maternal and paternal genomes.

ResearchBlogging.org
PORT, R., & SEYBOLD, K. (1995). Hippocampal synaptic plasticity as a biological substrate underlying episodic psychosis Biological Psychiatry, 37 (5), 318-324 DOI: 10.1016/0006-3223(94)00128-P
Koji Yashiro, Thorfinn T Riday, Kathryn H Condon, Adam C Roberts, Danilo R Bernardo, Rohit Prakash, Richard J Weinberg, Michael D Ehlers & Benjamin D Philpot (2009). Ube3a is required for experience-dependent maturation of the neocortex Nature Neuroscience

The first 30 seconds: Trustworthiness, Dominance and their neural correlates

A lot has already been written in the blogosphre regarding this study that found the brain regions that are involved in first impression formation. I view the study from a slightly different angle , but first let me introduce the study and its main findings.

The study was focused on finding the brain regions that are involved in the impression formation of a new social entity. We all know that we form automatic and consistent first impressions of strangers we meet based on things like their face to the social information that is available about them. The authors theorized that to know which regions of the brain are involved in evaluating a person for the first time, it would be sufficient to know which regions of the brain were engaged more while the evaluation-consistent information was being processed. To understand this logic, consider the brain regions involved in memory and how they are discovered. Typically, a series of words/images to be remembered are presented to the subjects, while simultaneously their brain are imaged. Later a memory recall/recognition test is administered. It is found that some brain regions are consistently more active during encoding of the original stimuli which are later recalled/ recognized correctly. This effect is know as Difference in Memory effect (DM effect). the fact that these areas are differentially engaged during encoding of remembered stimuli as opposed to forgotten stimuli is taken as evidence for the fact that these brain regions are involved in encoding of memory. Similar to this effect, it is found that evaluations that are consistent with the later overall evaluation of the person engage some brain regions more than when the evaluation is inconsistent with the later overall evaluation. This difference in evaluation effect (DE ) can be used to locate the regions that are involved in social evaluation or formation of first impressions.

Previous studies had indicated that dmPFC was engaged in social evaluation; however many cognitive factors other than purely evaluative factors might be in action here.

It has also been indicated that amygdala is involved in both social evaluation and valence based evaluations and might be involved in these first impression formation. So the authors hypothesized that they would find differential activity in amygdala in consistent as opposed to inconsistent evaluations and this is what they actually observed. They also found that PCC was also differentially engaged while forming first impressions and thus was another brain region involved in evaluating others.

Here is the study design:

To test these hypotheses, we developed the difference in evaluation procedure (see Figure), allowing us to sort social information encoding trials by subsequent evaluations. More specifically, we measured blood oxygenation level–dependent (BOLD) signals using whole brain fMRI during exposure to different person profiles. Each profile consisted of 6 person-descriptive sentences implying different personality traits. The sentences varied gradually in their positive to negative valence (or vice versa) but evoked equivalent levels of arousal. A 12-s interval with the face alone separated the positive and the negative segments. Subsequently, an evaluation slide instructed subjects to form their impression on an 8-point scale. On the basis of these evaluations, we determined which of the presented descriptive sentences guided evaluations (evaluation relevant) and which did not (evaluation irrelevant). For example, if a subject’s evaluation was positive, we assigned the positive segment of the profile to the evaluation-relevant category and the negative segment to the evaluation-irrelevant category. We then identified the brain regions dissociating items from each category (that is, difference in evaluation effect). Notably, we correlated subjects’ BOLD signal with their own individual evaluations. This allowed us to identify brain regions that were consistent across subjects in processing evaluation-relevant information regardless of the particular stimuli that they considered. Immediately after the scanning session, subjects underwent a memory-recognition task.

The results were clear and found that while dmPFC was involved in social evaluations it was not differentially engaged: thus it had a general role to play, perhaps holding the representation of evaluation after it had already been formed; in contrast both amygdala and PCC were differentially recruited and thus underlie the first time evaluations. In the words of the authors:

Understanding the neural substrates of social cognition has been one of the core motivations driving the burgeoning field of social neuroscience. A number of studies have highlighted the dmPFC in the processing of social information. Our results provide further evidence that the dmPFC is recruited to process person-descriptive information during impression formation. However, BOLD responses in this region do not dissociate evaluation-relevant from evaluation-irrelevant information, suggesting that the dmPFC is not essential for the evaluative component of impression formation. In fact, social evaluation recruits brain regions that are not socially specialized but are more generally involved in valuation and emotional processes.

Valuation and emotional processes, as a substantial amount of research has shown, are characteristic of the amygdala. In particular, the amygdala is considered to be a crucial region in learning about motivationally important stimuli. It is also implicated in social inferences that are based on facial and bodily expressions, in inferences of trustworthiness and in the capacity to infer social attributes. Moreover, the involvement of amygdala in social inferences might be independent of awareness or explicit memory. For example, increased amygdala responses were correlated with implicit, but not explicit, measures of the race bias, as well as with presentation of faces previously presented in an emotional, but not neutral, context, regardless of whether subjects could explicitly retrieve this information. Here we provide evidence linking the two domains of affective learning and social processing by showing that the amygdala is engaged in the formation of subjective value assigned to another person in a social encounter.

Although the amygdala is typically implicated in the processing of negative affect and negative stimuli have been shown to modulate it more than positive stimuli, we found that the amygdala processed both positive and negative evaluation-relevant information, suggesting that amygdala activity is driven by factors other than mere valence, such as the motivational importance or salience of the stimuli. This result is consistent with recent findings showing enhanced amygdala responses for both positive and negative stimuli as a function of motivational importance.

Evidence related to the PCC has been more diverse. There have been reports in the social domain, such as involvement in theory of mind and self-referential outward-focused thought33, in memory related processes such as autobiographical memory of family and friends34, and in emotional modulation of memory and attention. More recently, the PCC has been linked with economic decision making, the assignment of subjective value to rewards under risk and uncertainty, and credit assignment in a social exchange. A common denominator of these studies might be that all involved either a social or an outward-directed valuation component. Our task also encompasses these features, extending the role of the PCC to value assignment to social information guiding our first impressions of others.

The amygdala and the PCC are both interconnected with the thalamus as part of a larger circuitry that is implicated in emotion, arousal and learning. Beyond the known role of the amygdala and the PCC in social-information processing and value representation, our results suggest a neural mechanism underlying the online formation of first impressions. When encoding everyday social information during a social encounter, these regions sort information on the basis of its personal and subjective importance and summarize it into an ultimate score, a first impression. Other regions, such as the ventromedial PFC, the striatum and the insula, have also been implicated in valuation processes. However, these regions did not emerge in our difference in evaluation effect analysis. This might suggest a possible dissociation in the valuation network between regions engaged in the formation of value and its subsequent representation and updating. The latter regions would not be engaged during encoding and therefore would not show a difference in evaluation effect but would instead have an effect once the evaluation is formed. The amygdala and the PCC probably participate in both value formation and its representation. The difference in evaluation procedure may provide a useful tool for disentangling the different components of the valuation system and their specific contributions to social versus nonsocial evaluations.

Now I would like to link all this new research with an earlier research on face attributes that found that there were two orthogonal factors that characterize a face- trustworthiness (valence) and dominance. It is important to note that faces are an important mechanism by which we make snap judgments and if it has been found that there are two orthogonal dimensions (found using factor analysis) on which we judge faces and form rifts impressions, there is no reason to suppose that those same two orthogonal factors would not come into play when we form first impressions based on social information and not the face. What I am trying to say is that the non-face social information driven social evaluation would still be structured around the factors of whether the social information pointed to the person as Trustworthy or as Dominant. I would expect that there would be different brain regions specialized for these two functions: We all know too clearly that amygdala is specialized for trustworthiness judgments and that fits in with one of the areas that has been identified for snap judgments. thta leaves us with the PCC, which has normally been implicated in self-referential thinking with an outward and evaluative (as opposed to inward and executive) focus and also a preventive focus. It seems likely that this region would be used to evaluate a social other and judge as to whether he has the ability to execute, harm and dominate oneself. So, what I would like to see is a study that dissociates the scoial information provided to subjects in terms of trustworthiness and dominance factors and sees if there is a dissociation in the evaluative regions of amygdala and PCC; or maybe one can juts factor analyze the results of the original study and see if the same two factors emerge! I am excited,and would love to see these studies being preformed!!
ResearchBlogging.org
Schiller, D., Freeman, J., Mitchell, J., Uleman, J., & Phelps, E. (2009). A neural mechanism of first impressions Nature Neuroscience DOI: 10.1038/nn.2278
Oosterhof, N., & Todorov, A. (2008). The functional basis of face evaluation Proceedings of the National Academy of Sciences, 105 (32), 11087-11092 DOI: 10.1073/pnas.0805664105

SES and the developing brain

I have written about poverty/SES and its effects on brain development/IQ earlier too,and this new review article by Farah and Hackman in TICS is a very good introduction to anyone interested in the issue.

The article reviews the behavioral studies that show that SES is correlated with at least the two brain systems of executive function and language abilities.It also review physiological data that shows that even when behavioral outcomes do not differ ERP can show differential activation in the brains of people with low and middle SES , thus suggesting that differences that may not be detected on behavioral measures may still exist. They also review (f)MRI data that shows no structural differences in the brains of low and middle SES children, but definite functional differences.they also review experimental manipulation of social status in labarotaories, and show how those studies also indicate that SES and executive function are correlated.

They then turn to the million dollar question of the direction of causality and for this infer indirectly based on the SES-IQ causal linkages.

What is the cause of SES differences in brain function? Is it contextual priming? Is it social causation, reflecting the influence of SES on brain development? Alternatively, is it social selection, in which abilities inherited from parents lead to lower SES? Current research on SES and brain development is not designed to answer this question. However, research on SES and IQ is relevant and supports a substantial role of SES and its correlated experience as causal factors.

Slightly less than half of the SES-related IQ variability in adopted children is attributable to the SES of the adoptive family rather than the biological. This might underestimate environmental influences because the effects of prenatal and early postnatal environment are included in the estimates of genetic influence. Additional evidence comes from studies of when poverty was experienced in a child’s life. Early poverty is a better predictor of later cognitive achievement than poverty in middle- or late-childhood, an effect that is difficult to explain by genetics. SES modifies the heritability of IQ, such that in the highest SES families, genes account for most of the variance in IQ because environmental influences are in effect at ceiling in this group, whereas in the lowest SES families, variance in IQ is overwhelmingly dominated by environmental influences because these are in effect the limiting factor in this group. In addition, a growing body of research indicates that cognitive performance is modified by epigenetic mechanisms, indicating that experience has a strong influence on gene expression and resultant phenotypic cognitive traits . Lastly, considerable evidence of brain plasticity in response to experience throughout development indicates that SES influences on brain development are plausible.

Differences in the quality and quantity of schooling is one plausible mechanism that has been proposed. However, many of the SES differences summarized in this article are present in young children with little or no experience of school , so differences in formal education cannot, on their own, account for all of the variance in cognition and brain development attributable to SES. The situation is analogous to that of SES disparities in health, which are only partly explained by differential access to medical services and for which other psychosocial mechanisms are important causal factors .

The last point is really important and can be extended. Access to health services for low SES people may be a reason why , for eg, more schizophrenia incidence is found in low SES neighbourhoods. which brings us to the same chicken-and-egg question of the drift theory of schizophrenia- whether people with schizophrenia drift into low SES or low SES is a risk factor in itself. Exactly this point was brought to my attention when I was interacting with a few budding psychiatrists recently, this Martha Farah theory about the SES leading to lower IQ/ cognitive abilities. It is important to acknowledge that low SES not only leads to left hypo-frontality (another symptom of schizophrenia), schizophrenia is supposed to be due to lessened mylienation and again nutritional factors may have a role to play; also access to health care, exposure to chronic stress and lesser subjective feelings of control may all be mediating afctors that lead low SS to lead to schizophrenia/ psychosis.Also remember that schizophrenia is sort of a devlopmenetal disorder.

Well, I digressed a bit, but the idea is that not only does low SES affect ‘normal’ cognitive abilities, it may even increase the risk for ‘abnormal’ cognitive abilities that may lead to psychosis, and his effect of SES on IQ/cognitive abilities/ risk of mental diseases is mediated by the effect of SES on the developing brain. I have already covered the putative mechanisms by which SES may affect brain development, but just to recap, here I quote from the paper:

Candidate causal pathways from environmental differences to differences in brain development include lead exposure, cognitive stimulation, nutrition, parenting styles and transient or chronic hierarchy effects. One particularly promising area for investigation is the effect of chronic stress. Lower-SES is associated with higher levels of stress in addition to changes in the function of physiological stress response systems in children and adults. Changes in such systems are likely candidates to mediate SES effects as they impact both cognitive performance and brain regions, such as the prefrontal cortex and hippocampus, in which there are SES differences.

We can only hope that the evil of low SES is recognized as soon as possible and if for nothing else, than for advancing science, some intervention studies are done that manipulate the SES variables in the right direction and thus ensure that the full cognitive potential of the children flowers.

ResearchBlogging.org

HACKMAN, D., & FARAH, M. (2009). Socioeconomic status and the developing brain Trends in Cognitive Sciences, 13 (2), 65-73 DOI: 10.1016/j.tics.2008.11.003

Brain Awareness Week: March 16-22, 2009

Dana foundation organises International Brain Awareness Week (BWA) each year and this year I have partenered with them and wish to do something online during that week to raise brain awareness. I would request all my blog friends to do atleast a blog swarm related to brain awareness during that time. Meanwhile I’ll keep it a surprise as to what I plan to do in that week!

The Default Brain Network: implications for Autism and Schizophrenia

This blog post has been triggered by a recent news article that found that the default network in schizophrenics was both hyperactive and hyperconnected during rest, and it remained so as they performed demanding cognitive tasks. To quote:

The researchers were especially interested in the default system, a network of brain regions whose activity is suppressed when people perform demanding mental tasks. This network includes the medial prefrontal cortex and the posterior cingulate cortex, regions that are associated with self-reflection and autobiographical memories and which become connected into a synchronously active network when the mind is allowed to wander.

Whitfield-Gabrieli found that in the schizophrenia patients, the default system was both hyperactive and hyperconnected during rest, and it remained so as they performed the memory tasks. In other words, the patients were less able than healthy control subjects to suppress the activity of this network during the task. Interestingly, the less the suppression and the greater the connectivity, the worse they performed on the hard memory task, and the more severe their clinical symptoms.

“We think this may reflect an inability of people with schizophrenia to direct mental resources away from internal thoughts and feelings and toward the external world in order to perform difficult tasks,” Whitfield-Gabrieli explained.

The hyperactive default system could also help to explain hallucinations and paranoia by making neutral external stimuli seem inappropriately self-relevant. For instance, if brain regions whose activity normally signifies self-focus are active while listening to a voice on television, the person may perceive that the voice is speaking directly to them.

The default system is also overactive, though to a lesser extent, in first-degree relatives of schizophrenia patients who did not themselves have the disease. This suggests that overactivation of the default system may be linked to the genetic cause of the disease rather than its consequences.

The study on which this report is based , is supposedly published in advanced online PNAS edition of 19 jan, but I am unable to locate it. However, my readers know my obsession with Autism and Schizophrenia as diametrically opposed disorders theory and so I was seen reading all the other relevant studies related to default Network and especially how it may be differentially and oppositely activated in Autism and Schizophrenia.

First I would like to refer you to an extremely good overview of Default Network by Buckner, Schacter et al which is freely available. I’ll now present some quotes from the paper that are relevant to my thesis. I start with the abstract:

Thirty years of brain imaging research has converged to define the brain’s default network—a novel and only recently appreciated brain system that participates in internal modes of cognition. Here we synthesize past observations to provide strong evidence that the default network is a specific, anatomically defined brain system preferentially active when individuals are not focused on the external environment. Analysis of connectional anatomy in the monkey supports the presence of an interconnected brain system. Providing insight into function, the default network is active when individuals are engaged in internally focused tasks including autobiographical memory retrieval, envisioning the future, and conceiving the perspectives of others. Probing the functional anatomy of the network in detail reveals that it is best understood as multiple interacting subsystems. The medial temporal lobe subsystem provides information from prior experiences in the form of memories and associations that are the building blocks of mental simulation. The medial prefrontal subsystem facilitates the flexible use of this information during the construction of self-relevant mental simulations. These two subsystems converge on important nodes of integration including the posterior cingulate cortex. The implications of these functional and anatomical observations are discussed in relation to possible adaptive roles of the default network for using past experiences to plan for the future, navigate social interactions, and maximize the utility of moments when we are not otherwise engaged by the external world. We conclude by discussing the relevance of the default network for understanding mental disorders including autism, schizophrenia, and Alzheimer’s disease.

Some snippets from the introduction:

A common observation in brain imaging research is that a specific set of brain regions—referred to as the default network—is engaged when individuals are left to think to themselves undisturbed . Probing this phenomenon further reveals that other kinds of situations, beyond freethinking, engage the default network. For example, remembering the past, envisioning future events, and considering the thoughts and perspectives of other people all activate multiple regions within the default network . These observations prompt one to ask such questions as: What do these tasks and spontaneous cognition share in common? and what is the significance of this network to adaptive function? The default network is also disrupted in autism, schizophrenia, and Alzheimer’s disease, further encouraging one to consider how the functions of the default network might be important to understanding diseases of the mind. (emphasis mine)

Then they review some history including how default brain activity was recognized when it was found that metabolic demands and blood glucose consumption of brain as a whole remained the same even when the brain was at ‘rest’ viv-a-vis involved in an active task. They also review how when baseline PET/fMRI rest activity was compared to many disparate tasks related fMRI/ PET activity , then while some task-relevant areas showed activations related to baseline, many correlated areas of brain, the default network, showed deactivation in the task-related conditions as compared to baseline. The modern interpretation is that the default network is active at rest and places metabolic demands on the brain. They then reference the seminal work of Rachile et al and how that made the default network as a study area in itself.

They further elaborate on how the default network may be identified as an interconnected and functional brain system and list various approaches like spontaneous correlations at rest, seeding from a RoI and determining the areas correlated to activity in seed region etc, to determine the components of the default network. While dMPFC and PCC are implicated in all analysis, the case for vMPFC, IPL, HF+ and LTC is also strong.

I’ll skip most of this stuff , including comparative analysis. Suffice it to note here that the default brain regions are up to 30% more metabolically demanding then the rest of the brain and are recently evolved/ selected for. this becomes significant in view of recent studies showing that schizophrenia may be a result of selection for metabolism related genes.

The interesting part begins when trying to determine the behavioral/cognitive correlates of this default brain activity. The consensus seems to be that it is used for daydreaming, reconstructing the past, simulating the future, taking other peoples perspective, self-referential processes and in general stimulus independent thought.

A shared human experience is our active internal mental life. Left without an immediate task that demands full attention, our minds wander jumping from one passing thought to next—what William James (1890) called the “stream of consciousness.” We muse about past happenings, envision possible future events, and lapse into ideations about worlds that are far from our immediate surroundings. In lay terms, these are the mental processes that make up fantasy, imagination, daydreams, and thought. A central issue for our present purposes is to understand to what degree, if any, the default network mediates these forms of spontaneous cognition. The observation that the default network is most active during passive cognitive states, when thought is directed toward internal channels, encourages serious consideration of the possibility that the default network is the core brain system associated with spontaneous cognition, and further that people have a strong tendency to engage the default network during moments when they are not otherwise occupied by external tasks.

Support for the same is then provided. The next task the authors undertake is that of determining the function, usefulness and evolutionary rationale for this default brain activity. Two ,in my opinion not mutually exclusive, theories are offered. One is simulation of something that is not tied to current reality (whether it be past memories, future expectations and scenarios or other peoples intentions, beliefs, perspectives). The other theory is that the default mode is a diffused attentional/ exploration state and is suppressed by foveal attention/task focus. The over activity of default network in Schizophrenia can be related to both theories equally well.

In this section, we explore two possible functions of the network, while recognizing that it is too soon to rule out various alternatives. One possibility is that the default network directly supports internal mentation that is largely detached from the external world. Within this possibility, the default network plays a role in constructing dynamic mental simulations based on personal past experiences such as used during remembering, thinking about the future, and generally when imagining alternative perspectives and scenarios to the present. This possibility is consistent with a growing number of studies that activate components of the default network during diverse forms of self-relevant mentalizing as well as with the anatomic observation that the default network is coupled to memory systems and not sensory systems. Another possibility is that the default network functions to support exploratory monitoring of the external environment when focused attention is relaxed. This alternative possibility is consistent with more traditional ideas of posterior parietal function but does not explain other aspects of the data such as the default network’s association with memory structures. It is important to recognize that the correlational nature of available data makes it difficult to differentiate between possibilities, especially because focus on internal channels of thought is almost always correlated with a change in external attention . We also explore in this section an intriguing functional property of the default network: the default network operates in opposition to other brain systems that are used for focused external attention and sensory processing. When the default network is most active, the external attention system is attenuated and vice versa.

To me both the Sentinel and the Internal Mentation hypothesis appear to be somewhat valid and relevant to Schizophrenia. One can attribute Psychosis to both increased ‘watchfulness’ and and increased internal mentation or mentalizing and I have written about the second hypothesis in detail previously.

The most relevant part of the paper is their discussion of Autism, Schizophrenia and Alzheimer’s. I reproduce the entire autism and Schizophrenia section , highlighting a few points:

Autism Spectrum Disorders

The autism spectrum disorders (ASD) are developmental disorders characterized by impaired social interactions and communication. Symptoms emerge by early childhood and include stereotyped (repetitive) behaviors. Baron-Cohen and colleagues (1985) proposed that a core deficit in many children with ASD is the failure to represent the mental states of others, as needed to solve theory-of-mind tasks. Based on an extensive review of the functional anatomy that supports theory-of-mind and social interaction skills, Mundy (2003) proposed that the MPFC may be central for understanding the disturbances in ASD. Given the convergent evidence presented here that suggests the default network contributes to such functions, it is natural to explore whether the default network is disrupted in ASD.

Developmental disruption of the default network, in particular disruption linked to the MPFC, might result in a mind that is environmentally focused and absent a conception of other people’s thoughts. The inability to interact with others in social contexts would be an expected behavioral consequence. It is important to also note that such disruptions, if identified, may not be linked to the originating developmental events that cause ASD but rather reflect a developmental endpoint. That is, dysfunction of the default network and associated symptoms may emerge as an indirect consequence of early developmental events that begin outside the network.

Many studies have explored whether ASD is associated with morphological differences in brain structure. The general conclusion from this literature is that the brain changes are complex, reflecting differences in growth rates and attenuation of growth (see Brambilla et al. 2003 for review). At certain developmental stages these differences are manifest as overgrowth and at later stages as undergrowth. Early observations have implicated the cerebellum. A further consistent observation has been that the amygdala is increased in volume in children with ASD (e.g., Abell et al. 1999, Schumann et al. 2004), perhaps as a reflection of abnormal regulation of brain growth (Courchesne et al. 2001). While not discussed earlier because of our focus on cortical regions, the amygdala is known to contribute to social cognition (Brothers 1990, Adolphs 2001, Phelps 2006) and interacts with regions within the default network. The amygdala has extensive projections to orbital frontal cortex (OFC) and vMPFC (Carmichael & Price 1995).

Of perhaps more direct relevance to the default network, dMPFC has shown volume reduction in several studies of ASD that used survey methods to explore regional differences in brain volume (Abell et al. 1999, McAlonan et al. 2005). The effects are subtle and will require further exploration, but it is noteworthy that, of those studies that have looked, several have noted dMPFC volume reductions in ASD. Of interest, a study using voxel-based morphometry to investigate grey matter differences in male adolescents with ASD noted that several regions within the default network exhibited a relative increase in grey matter volume compared to the control population (Waiter et al. 2004). Because this observation has generally not been replicated in adult ASD groups, future studies should investigate whether complex patterns of overgrowth and undergrowth of the regions within the default network exist in ASD and, if so, whether they track behavioral improvement on tests of social function (see also Carper & Courchesne 2005).

Kennedy and colleagues (2006) recently used fMRI to directly explore the functional integrity of the default network in ASD. In their study, young adults with ASD and age-matched individuals without ASD were imaged during passive tasks and demanding active tasks that elicit strong activity differences in the default network. While the control participants showed the typical pattern of activity in the default network during the passive tasks, such activity was absent in the individuals with ASD. Direct comparison between the groups revealed differences in vMPFC and PCC. Moreover, in an exploratory analysis of individual differences within the ASD group, those individuals with the greatest social impairment (measured using a standardized diagnostic inventory) were those with the most atypical vMPFC activity levels (Fig. 16). An intriguing possibility suggested by the authors of the study and extended by Iacoboni (2006) is that the failure to modulate the default network in ASD is driven by differential cognitive mentation during rest, specifically a lack of self-referential processing.

Another recent study using analysis of intrinsic functional correlations showed that the default network correlations were weaker in ASD (Cherkassky et al. 2006).Of note, the individuals with ASD showed differences in a fronto-parietal network that has been recently hypothesized to control interactions between the default network and brain systems linked to external attention (Vincent et al. 2007b). These data in ASD suggest an interesting possibility: the default network may be largely intact in ASD but under utilized perhaps because of a dysfunction in control systems that regulate its use.

Schizophrenia

Schizophrenia is a mental illness characterized by altered perceptions of reality. Auditory hallucinations, paranoid and bizarre delusions, and disorganized speech are common positive clinical symptoms (Liddle 1987). Cognitive tests also reveal negative symptoms, including impaired memory and attention (Kuperberg & Heckers 2000). These symptoms lead to questions about their relationship to the default network for a few reasons. The first reason surrounds the association of the default network with internal mentation. Many symptoms of schizophrenia stem from misattributions of thought and therefore raise the question of an association with the default network because of its functional connection with mental simulation. A second related reason has to do with the broader context of control of the default network. While still poorly understood, there appears to be dynamic competition between the default network and brain systems supporting focused external attention (Fransson 2005, Fox et al. 2005, Golland et al. 2007, Tian et al. 2007, see also Williamson 2007). Frontal-parietal systems are candidates for controlling these interactions (Vincent et al. 2007b). The complex symptoms of schizophrenia could arise from a disruption in this control system resulting in an overactive (or inappropriately active) default network. The normally strongly defined boundary between perceptions arising from imagined scenarios and those from the external world might become blurry, including the boundary between self and other (similar to that proposed by Frith 1996).

Three studies have provided preliminary data supporting the possibility that the default network is functionally overactive. Garrity and colleagues (2007) recently reported an analysis of correlations among default network regions in patients with schizophrenia. Studying a sizable data sample (21 patients and 22 controls), they explored task-associated activity modulations within the default network and identified largely similar correlations among default network regions in patients and controls. Differences were noted in specific subregions, as were differences in the dynamics of activity as measured from the timecourses of the fMRI signal. Of particular interest, they noted that within the patient group, the positive symptoms of the disease (e.g., hallucinations, delusions, and thought confusions) were correlated with increased default network activity during the passive epochs, including MPFC and PCC/Rsp. In a related analysis, Harrison et al. (2007) noted accentuated default network activity during passive task epochs in patients with schizophrenia as contrasted to controls, again suggesting an overactive default network. Moreover, within the patient group, poor performance was again correlated with MPFC activation during the passive as compared to the active tasks. Finally, Zhou and colleagues (2007) found that regions constituting the default network were functionally correlated with each other to a significantly higher degree in patients than in control participants. Thus, while the data are limited, these studies converge to suggest that patients with schizophrenia have an overactive default network, as would be expected if the boundary between imagination and reality were disrupted. Overactivity within the network correlates with task performance (Harrison et al. 2007) and clinical symptoms (Garrity et al. 2007).

I now link to two abstracts form Autism and default network research by Kennedy et al:

Several regions of the brain (including medial prefrontal cortex, rostral anterior cingulate, posterior cingulate, and precuneus) are known to have high metabolic activity during rest, which is suppressed during cognitively demanding tasks. With functional magnetic resonance imaging (fMRI), this suppression of activity is observed as “deactivations,” which are thought to be indicative of an interruption of the mental activity that persists during rest. Thus, measuring deactivation provides a means by which rest-associated functional activity can be quantitatively examined. Applying this approach to autism, we found that the autism group failed to demonstrate this deactivation effect. Furthermore, there was a strong correlation between a clinical measure of social impairment and functional activity within the ventral medial prefrontal cortex. We speculate that the lack of deactivation in the autism group is indicative of abnormal internally directed processes at rest, which may be an important contribution to the social and emotional deficits of autism.

In their discussion they make explicit the fact that in Autism, the default Netwrok may be under active.

There are two possible reasons why the ASD group failed to show the typical deactivation effect. One possibility is that midline resting network activity during both rest and task performance is high, and, thus, a subtraction between these conditions would reveal no difference in activity levels. We believe, however, that it is unlikely that high midline network activity was maintained during the cognitively demanding number task in autism for several reasons. First, as mentioned previously, behavioral performance was similar between control and ASD groups. This result, however, would be unexpected if the ASD group were carrying out additional mental processing that control subjects inhibit during cognitively demanding conditions. Second, positron-emission tomography studies of autism, which provide an absolute measure of brain metabolism, have found reduced, as opposed to increased, glucose metabolism in rACC and PCC (36) during task performance, as compared with controls. Furthermore, one positron-emission tomography study found that lower blood flow in MPFC and rACC at rest was correlated with more severe social and communicative impairments in subjects with autism (37), a finding similar to our correlational results. Third, reduced anatomical volumes and neurochemical deficiencies have consistently been observed in MPFC?rACC in adults with autism (reviewed in ref. 26), likely indicative of a reduced functioning of these regions. Therefore, an alternative explanation, the one to which we attribute the lack of deactivation, is that midline activity is low during rest. We suggest, then, that the absence of deactivation in this network indicates that the mental processes that normally occur at rest are absent or abnormal in autism.

What are these mental processes that dominate during rest? Evidence in the literature to date seems to suggest that tasks that induce certain types of internal processing activate this resting network. Examples of such tasks are self- and other-person judgments (4, 6, 7, 19–22, 38–45), person familiarity judgments (24, 25), emotion processing (15–17, 46), perspective-taking (22, 47), passive observation of social interactions vs. nonsocial interactions (18), relaxation based on interoceptive biofeedback (48, 49), conceptual judgments (based on internal knowledge stores) vs. perceptual judgments (50), and episodic memory tasks (51), among others [moral decision making (52), joint attention experience (23), and pleasantness judgments (53)]. Therefore, the activity in these regions at rest might simply reflect the extent to which these types of internally directed thoughts are engaged at rest. In fact, a particularly intriguing behavioral study found that individuals with ASD report very different internal thoughts than control subjects (54, 55), lending support to our interpretation that an absence of this resting activity in autism may be directly related to abnormal internal thought. Admittedly, this is a speculative hypothesis but one that can be explicitly tested.

Another of their recent papers comes to the same conclusion.

Recent studies of autism have identified functional abnormalities of the default network during a passive resting state. Since the default network is also typically engaged during social, emotional and introspective processing, dysfunction of this network may underlie some of the difficulties individuals with autism exhibit in these broad domains. In the present experiment, we attempted to further delineate the nature of default network abnormality in autism using experimentally constrained social and introspective tasks. Thirteen autism and 12 control participants were scanned while making true/false judgments for various statements about themselves (SELF condition) or a close other person (OTHER), and pertaining to either psychological personality traits (INTERNAL) or observable characteristics and behaviors (EXTERNAL). In the ventral medial prefrontal cortex/ventral anterior cingulate cortex, activity was reduced in the autism group across all judgment conditions and also during a resting condition, suggestive of task-independent dysfunction of this region. In other default network regions, overall levels of activity were not different between groups. Furthermore, in several of these regions, we found group by condition interactions only for INTERNAL/EXTERNAL judgments, and not SELF/OTHER judgments, suggestive of task-specific dysfunction. Overall, these results provide a more detailed view of default network functionality and abnormality in autism.

If you want to read more about Schizophrenia – default network linkage , read here. If you want to read about Default Network in general , read here ( a very good blog I have recently discovered).

I think the case is settled that at least in the case of Default Network activations, Schizophrenia and Autism are on opposite poles. One has too much default brain activity, the other too little. Also, the function of default network suggests that it is primarily the focus on self and the ability to imagine that is disrupted in autism and heightend to dramatic effects in Schizophrenics.
ResearchBlogging.org

R. L. BUCKNER, J. R. ANDREWS-HANNA, D. L. SCHACTER (2008). The Brain’s Default Network: Anatomy, Function, and Relevance to Disease Annals of the New York Academy of Sciences, 1124 (1), 1-38 DOI: 10.1196/annals.1440.011
D. P. Kennedy, E. Courchesne (2008). Functional abnormalities of the default network during self- and other-reflection in autism Social Cognitive and Affective Neuroscience, 3 (2), 177-190 DOI: 10.1093/scan/nsn011
D. P. Kennedy (2006). Failing to deactivate: Resting functional abnormalities in autism Proceedings of the National Academy of Sciences, 103 (21), 8275-8280 DOI: 10.1073/pnas.0600674103

Neurological correlates of Poverty

While people generally do not squirm on reading a headline claiming neural correlates of religion, god, trust, consciousness, political/ sexual orientation etc, I am sure the title neural correlates of Poverty would have lead to some uneasy shuffling around. How can poverty that is clearly a result of economic opportunities/ capabilities be reduced to brain? Are we claiming that low inherent IQ and the neural correlates thereof define and lead to poverty? Or is the claim instead that poverty leads to definite changes in the brain, which may lead to manifestation of low IQ and the sustenance of the vicious circle of poverty? The regular readers of the blog will know which side of the fence I am sitting on!

The blogosphere is normally abuzz with controversial topics like atheism, meaninglessness of evolution and race and gender differences(for eg.  in IQ) and people defend these sacred dictum doggedly, claiming that ‘is’ and ‘ought’ need not be confused, especially in a cold, logical science which deals with all facts and should not be guided by values. Yet, the same blogosphere generally silently ignores, or does not take a stand , when the ‘is’ and ‘ought’ are in sync and something morally significant is also found to be scientifically valid. Rather the apology for such facts is made very cautiously, with the spirit of not offending the people who have a different, and in my view, an inferior moral system.

I believe whenever people discuss poverty/SES, they have either of the two moral systems: first, the world is unfair and poor people are poor because of some external factors/ circumstances; addressing them may solve/ eliminate the problem of poverty;  and second: the world is fair (like an idealized free market) and if someone is poor they are due to either inherent internal flaws (bad genes) or maybe bad choices (they want to be poor/ are lazy and unindustrious etc); so the problem of poverty cannot/ should not be solved.  I subscribe to the first moral system and believe in interventions to solve the problem of poverty. I am glad to have scientific facts to my side and have been addressing these issues in a series of posts .

The latest impetus to write on the topic comes form reading Lehrer’s post titled Poverty and the brain at the Frontal cortex and I am glad to have found a fellow blogger who doesn’t mind speaking on such controversial topics and take a stand for ‘is’ that is in sync with ‘ought’. It is an excellent post regarding how early interventions can help alleviate poverty and how a poor person suffers from the viscous circle of poverty by the mediating influence of brain and IQ.

Lehrer also mentions the work of Martha Farah (of Visual Agnosia fame whose earlier work was on vision) on the same and I recommend reading at least this article by Martha and colleagues, although many other invaluable gems are present on her site.

The article begins with an anecdotal reference to how Martha first became aware of the gravity of the issue, when she saw her babysitters / maids steeped in poverty and the low IQ and SES viscous circle. this resonates with me and I can easily relate to this as my child enjoys a lot of toys while our maid’s children are faced with lack.

I would now quote extensively from the aforementioned article:

It seemed to me that children’s experience of the world is very different in low and middle SES environments. Most middle SES children have abundant opportunities to explore the world, literally, in terms of people met and places seen, and figuratively, in terms of the world of ideas. In contrast, low SES children generally have fewer interactions with the wider world and much of what they do experience is stressful. Basic research with animals has established the powerful effects of both environmental impoverishment and stress on the developing brain.

She then goes on to make out the case for NCC of poverty:

For the sake of exploring the cognitive neuroscience perspective on transgenerational poverty, and discovering what, if anything, it can contribute to correcting socioeconomic inequality, the first order of business is to ask whether socioeconomic status bears any straightforward relation to brain development. On the face of things it might seem unlikely that characteristics such as income, education and job status, which are typically used to estimate SES, would bear any systematic relationship to physiological processes such as those involved in brain development. It is, however, well established that SES affects physical health through a number of different causal pathways (Adler et al. 1994), many of which could play a role in brain development. It is also clear that poverty is associated with differences in brain function on the basis of the differences in standardized test performance cited earlier, as cognitive tests reflect the function of the brain. However, for a cognitive neuroscience approach to be helpful, the relations between socioeconomic status and the brain must be relatively straightforward and generalizable. The first question that my collaborators and I addressed was therefore: Can we generalize about the neurocognitive correlates of socioeconomic status? Once we have established the neurocognitive profile of childhood poverty, we can begin to test more specific hypotheses about causal mechanisms.

I will now digress a little from the main topic and introduce the five neurocognitive systems that Martha and colleagues have identified and how they tested some children from low and middle SES for finding their capabilities in these systems.

The children were tested on a battery of tasks adapted from the cognitive neuroscience literature, designed to assess the functioning of five key neurocognitive systems. These systems are described briefly here.

• The Prefrontal/Executive system enables flexible responding in situations where the appropriate response may not be the most routine or attractive one, or where it requires maintenance or updating of information concerning recent events. It is dependent on prefrontal cortex, a late-maturing brain region that is disproportionately developed in humans.

• The Left perisylvian/Language system is a complex, distributed system encompassing semantic, syntactic and phonological aspects of language and dependent predominantly on the temporal and frontal areas of the left hemisphere that surround the Sylvian fissure.

• The Medial temporal/Memory system is responsible for one-trial learning, the ability to retain a representation of a stimulus after a single exposure to it (which contrasts with the ability to gradually strengthen a representation through conditioning-like mechanisms), and is dependent on the hippocampus and related structures of the medial temporal lobe.

• The Parietal/Spatial cognition system underlies our ability to mentally represent and manipulate the spatial relations among objects, and is primarily dependent upon posterior parietal cortex.

• The Occipitotemporal/Visual cognition system is responsible for pattern recognition and visual mental imagery, translating image format visual representations into more abstract representations of object shape and identity, and reciprocally translating visual memory knowledge into image format representations (mental images).

Not surprisingly, in view of the literature on SES and standardized cognitive tests, the middle SES children performed better than the low SES children on the battery of tasks as a whole. For some systems, most notably the Left perisylvian/Language system and the Prefrontal/Executive system, the disparity between low and middle SES kindergarteners was both large and statistically significant.

Thus, they found, in a small group of children , that Language and Executive systems’ performance differed in low and middle SES children and they were able to replicate this finding with a larger group of children too. This time they broke executive function further into components and found a finer granularity of how SES affects the brain:

As before, the language system showed a highly significant relationship to SES, as did executive functions including Lateral prefrontal/Working memory and Anterior cingulate/Cognitive control components and the Parietal/Spatial cognition system. With a more demanding delay between exposure and test in the memory tasks, we also found a difference in the Medial temporal/Memory system. Performance on the Parietal/spatial system tests also differed as a function of SES.

They also did some studies with older children and to summarize the results of all these studies in their own words:

In sum, although the outcome of each study was different, there were also commonalities among them despite different tasks and different children tested at different ages. The most robust neurocognitive correlates of SES appear to involve the Left perisylvian/Language system, the Medial temporal/Memory system (insofar as SES effects were found in both studies that tested memory with an adequate delay) and the Prefrontal/Executive system, in particular its Lateral prefrontal/Working memory and Anterior cingulate/Cognitive control components. Children growing up in low SES environments perform less well on tests that tax the functioning of these specific systems.

Next they look at the causal versus correlational nature of findings and if causal, then the directions of causality. It is this paragraph , that amazed me, for they seem to be apologetic for the fact that their findings are also ethically good ones.

Do these associations reflect the effects of SES on brain development, or the opposite direction of causality? Perhaps families with higher innate language, executive and memory abilities tend to acquire and maintain a higher SES. Such a mechanism seems likely, a priori, as it would be surprising if genetic influences on cognitive ability did not, in the aggregate, contribute to individual and family SES. However, it seems also seems likely that causality operates in the opposite direction as well, with SES influencing cognitive ability through childhood environment. Note that the direction of causality is an empirical issue, not an ethical one. The issue of whether and to what extent SES differences cause neurocognitive differences or visa versa should not be confused with the issue of whether we have an obligation to help children of any background become educated, productive citizens.

Then, quite important from this blog’s point of view, they review the literature that supports SES to IQ direction of causality.

Cross-fostering studies of within- and between -SES adoption suggest that roughly half the IQ disparity in children is experiential (Capron & Duyme, 1989; Schiff & Lewontin, 1986). If anything, these studies are likely to err in the direction of underestimating the influence of environment because the effects of prenatal and early postnatal environment are included in the estimates of genetic influences in adoption studies. A recent twin study by Turkheimer and colleagues (2003) showed that, within low SES families, IQ variation is far less genetic than environmental in origin. Additional evidence comes from studies of when, in a child’s life, poverty was experienced. Within a given family that experiences a period of poverty, the effects are greater on siblings who were young during that period (Duncan et al. 1994), an effect that cannot be explained by genetics. In sum, multiple sources of evidence indicate that SES does indeed have an effect on cognitive development, although its role in the specific types of neurocognitive system development investigated here is not yet known.

Next they tried to tease out what specific SES related factors can affect the different neurocognitive systems. They list both physical and psychological factors that have been hypothesized and researched on in relation to SES and IQ.

Potential causes, physical and psychological

What aspects of the environment might be responsible for the differences in neurocognitive development between low and middle SES children? A large set of possibilities exist, some affecting brain development by their direct effects on the body and some by less direct psychological mechanisms. Three somatic factors have been identified as significant risk factors for low cognitive achievement by the Center for Children and Poverty (1997): inadequate nutrition, substance abuse (particularly prenatal exposure), and lead exposure.

As with potential physical causes, the set of potential psychological causes for the SES gap in cognitive achievement is large, and the causes are likely to exert their effects synergistically. Here we will review research on differences in cognitive stimulation and stress.

They then discuss the psychological factors, which they then investigated, in more detail.

One difference between low and middle SES families that seems predictable, even in the absence of any other information, is that low SES children are likely to receive less cognitive stimulation than middle SES children. Their economic status alone predicts that they will have fewer toys and books and less exposure to zoos, museums and other cultural institutions because of the expense of such items and activities. This is indeed the case (Bradley et al. 2001a) and has been identified as a mediator between SES and measures of cognitive achievement (Bradley and Corwyn 1999; Brooks-Gunn and Duncan 1997; McLoyd 1998). Such a mediating role is consistent with the results of neuroscience research with animals. Starting many decades ago (e.g., Volkmar & Greenough, 1972) researchers began to observe the powerful effects of environmental stimulation on brain development. Animals reared in barren laboratory cages showed less well developed brains by a number of different anatomical and physiological measures, compared with those reared in more complex environments with opportunities to climb, burrow and socialize (see van Praag et al 2000 for a review).

The lives of low SES individuals tend to be more stressful for a variety of reasons, some of which are obvious: concern about providing for basic family needs, dangerous neighborhoods, and little control over one’s work life. Again, research bears out this intuition: Turner and Avison (2003) confirmed that lower SES is associated with more stressful life events by a number of different measures. The same appears to be true for children as well as adults, and is apparent in salivary levels of the stress hormone cortisol (Lupien et al. 2001).

Why is stress an important consideration for neurocognitive development? Psychological stress causes the secretion of cortisol and other stress hormones, which affect the brain in numerous ways (McEwen 2000). The immature brain is particularly sensitive to these effects. In basic research studies of rat brain development, rat pups are subjected to the severe stress of prolonged separation from the mother and stress hormone levels predictably climb. The later anatomy and function of the brain is altered by this early neuroendocrine phenomenon. The brain area most affected is the medial temporal area needed for memory, although prefrontal systems involved in the regulation of the stress response are also impacted (Meaney et al. 1996).

They then go on to discuss how this information can be used to formulate mechanisms that mediate the effect of low SES on diffrent neurocognitive systems.

The latest phase of our research is an attempt to make use of the description of the SES disparities in neurocognitive development in testing hypotheses about the causal pathways. Drawing on our previous research that identified three neurocognitive systems as having the most robust differences as a function of SES (Perisylvian/Language, Medial temporal/Memory, and Prefrontal/Executive), we are now testing hypotheses concerning the determinants of individual differences in the development of these systems in children of low SES. Specifically, we are investigating the role of childhood cognitive stimulation and social/emotional nurturance (Farah et al. 2005; Childhood experience and neurocognitive development: Dissociation of cognitive and emotional influences).

They then describe an observational study of interaction between children and parents and how they assess the cognitive simulation an social/emotional nurturance using HOME assessment battery. What they found follows:

Children’s performance on the tests of Left perisylvian/Language was predicted by average cognitive stimulation. This was the sole factor identified as predicting language ability by forward stepwise regression, and one of three factors identified by backwards stepwise regression, along with the child’s gender and the mother’s IQ. In contrast, performance on tests of Medial temporal/Memory ability was predicted by average social/emotional nurturance. This was the sole factor identified as predicting memory ability by forward stepwise regression and one of three factors identified by backwards stepwise regression, along with the child’s age and cognitive stimulation. The relation between memory and early emotional experience is consistent with the animal research cited earlier, showing a deleterious effect of stress hormones on hippocampal development. Our analyses did not reveal any systematic relation of the predictor variables considered here to Lateral prefrontal/Working memory or Anterior cingulate/Cognitive control function. In conclusion, different aspects of early experience affect different systems of the developing brain. Cognitive stimulation influences the development of language, whereas social/emotional nurturance affects the development of memory but not language.

Here is what they conclude:

What are the implications for society of a more mechanistic understanding of the effects of childhood poverty on brain development? To different degrees, and in different ways, we regard children as the responsibility of both parents and society. Parents’ responsibility begins before birth and encompasses virtually every aspect of the child’s life. Society’s responsibility is more circumscribed. In the United States, for example, society’s contribution to the cognitive development of children begins at age 5 or 6, depending on whether public kindergarten is offered. The physical health and safety of all infants and children is a social imperative, however, well before school age. Laws requiring lead abatement in homes occupied by children exemplify our societal commitment to protect them from the neurological damage caused by this neurotoxin. Research on the effects of early life stress and limited cognitive stimulation has begun to show that these concomitants of poverty have negative effects on neurological development too, by mechanisms no less concrete and real. Thus, neuroscience may recast the disadvantages of childhood poverty as a bioethical issue rather than merely one of economic opportunity.

In my view the societal implications are far reaching, if low SES leads to lowered cognitive functioning, it becomes our duty to provide more cognitive stimulation and ensure that all children get sufficient social/ emotional nurturance so that their IQ can flower to its full potential.

I would have liked to end on this note, but cant help pointing that the five neurocognitive systems Martha has identified, to me seems to follow in stages, with the later systems maturing later :

1) Occipital/ visual : describe/ perceive the world/ self
2) Parietal/ spatial:explain the world/self (may be involved in consciousness)
3) Temporal/ Memory: predict the world/self
4) Frontal/ executive: control the world/ self
5) Sylvian/ Language: improve the world/ self

We all know that language abilities develop the oldest and vision is more or less developed at birth; also the fact that SES should affect the latter stages of neurocognitive systems also gels in. the fact that cognitive stimulation affects language and emotional/social nurturance affects memory to me also fits in.

Anyway whatever the implication sof this research for stage theories, they have far reaching and imprortanat implications for social policy and education.
ResearchBlogging.org
Farah, M.J.,Noble, K.G. and Hurt, H. (2005). Poverty, privilege and brain development: Emprical findings and ethical implications. In J. Illes (Ed.) Neuroethics in the 21st Century. New York: Oxford University Press.

Autism and Schizophrenia: a minicolumnar deficit?

It has been my long standing thesis that Autism and Schizophrenia are opposite poles on a continuum; and the most recent evidence I would like to allude to is the mini columnar structure and abnormalities associetd with it in both the diseases. 

Let me at the outset, say that I am not an expert on mini-columns and hardly understand them, so would be glad if somebody corrected me or pointed out errors in the analysis.
First let me report on Schizophrenia from an abstract of a paper titled Mean cell spacing abnormalities in the neocortex of patients with schizophrenia by M Casanova et al

It has been postulated that the prefrontal cortices of schizophrenic patients have significant alterations in their interneuronal (neuropil) space. The present study re-examines this finding based on measurements of mean cell spacing within the cell minicolumn. The population studied consisted of 13 male schizophrenic patients (DSM-IV criteria) and 13 age-matched controls. Photomicrographs of Brodmann’s areas 9, 4 (M1), 3b (S1), and 17 (V1) were analyzed with computerized image analysis to measure parameters of minicolumnar morphometry, i.e., columnarity index (CI), minicolumnar width (CW), dispersion of minicolumnar width (VCW), and mean interneuronal distance (MCS). The results indicate alterations in the mean cell spacing of schizophrenic patients according to both the lamina and cortical area examined. The lack of variation in the columnarity index argues in favor of a defect postdating the formation of the cell minicolumn.

To simplify the terms, I assume that CI relates to number of minicolumns in the neocortical broadmann area under consideration; CW is generally refered to as the width of the minicolumns i.e how big a particular minicolumn is , VCW I presume is related to how widely are the minicolumns themselves spaced from each other ie. the distance between two mini-columns and the last MCS is related to how densely neurons are packed within a mini-column.
Now for Schizophrenia, what I could find on the net, seems to suggest that they have reduced MCS as compared to controls i.e the neurons of schizophrenics are more densely packed within a mini-column as compared to controls .  Also, it was found that the density was greatest in core region and lesser so in the outer neuropil region of the mini-column.
An  opposite pattern is observed in Autism. Here is the abstract of article titled: Disruption in the inhibitory architecture of the cell minicolumn: implications for Autisim. by M Casanova et al again: 

The modular arrangement of the neocortex is based on the cell minicolumn: a self-contained ecosystem of neurons and their afferent, efferent, and interneuronal connections. The authors’ preliminary studies indicate that minicolumns in the brains of autistic patients are narrower, with an altered internal organization. More specifically, their minicolumns reveal less peripheral neuropil space and increased spacing among their constituent cells. The peripheral neuropil space of the minicolumn is the conduit, among other things, for inhibitory local circuit projections. A defect in these GABAergic fibers may correlate with the increased prevalence of seizures among autistic patients. This article expands on our initial findings by arguing for the specificity of GABAergic inhibition in the neocortex as being focused around its mini- and macrocolumnar organization. The authors conclude that GABAergic interneurons are vital to proper minicolumnar differentiation and signal processing (e.g., filtering capacity of the neocortex), thus providing a putative correlate to autistic symptomatology.

Now the above clearly shows that the Autistics have an increased interneuronal space in mini-columns as opposed to normals and thus have lesser density of neurons within a minicolumn. However, they have more number of minicolumns to make up for this so that overall the number of neurons in the Broadmann area remains the same or the overall neuronal density does not differ. 
We can extend the results in other directions and hypothesize that 
1) Autistics will have increased no. of mini-columns, narrower mini-columns, narrowly spaced minicolumns and decreased neuronal density within a mini0-column as compared to controls.
2) Scchizophrenics will have lesser no. of minicolumns, wider mini-columns, widely spaced minicolumns and increased neuronal density within a mini-column as compared to controls.
Some of the above hypothesis is already supported and the rest may be in press/ under lab trials. 
What this means for in cognitive terms and how this translates to autistic and schizophrenic behaviour is another issue that I may address later (once I understand more of this minicolumn stuff!!)

ResearchBlogging.org
D Buxhoeveden (2000). Reduced interneuronal space in schizophrenia Biological Psychiatry, 47 (7), 681-682 DOI: 10.1016/S0006-3223(99)00275-9

M CASANOVA, L DEZEEUW, A SWITALA, P KRECZMANSKI, H KORR, N ULFIG, H HEINSEN, H STEINBUSCH, C SCHMITZ (2005). Mean cell spacing abnormalities in the neocortex of patients with schizophrenia Psychiatry Research, 133 (1), 1-12 DOI: 10.1016/j.psychres.2004.11.004

Manuel F. Casanova, Daniel Buxhoeveden, Juan Gomez (2003). Disruption in the Inhibitory Architecture of the Cell Minicolumn: Implications for Autisim The Neuroscientist, 9 (6), 496-507 DOI: 10.1177/1073858403253552

Obesity and the dopamine connection

I had blogged previously regarding the obesity and dopamine connection and how obese people have been shown to have lesser dopamine receptors in the brain. The studies till now were correlational and there was a possibility that eating more food and the resultant obesity may be a cause and may play a part that decreases the dopamine receptors just like in addicted individuals(the addicted people also have less dopamine receptors).

However a new study by Stice and colleague in the latest Science edition has shown that the less number of dopamine receptors is a cause rather than a consequence of obesity. This they did by studying a genetic variation, A1 allele of the TaqIA restriction fragment length polymorphism , that leads to lesser number of dopamine receptors. They found that having this allele increased the risk for obesity and using FMRI they were also able to show that this was mediated by reduced dopamine signaling in the striatum.

Here is the abstract of the study:

The dorsal striatum plays a role in consummatory food reward, and striatal dopamine receptors are reduced in obese individuals, relative to lean individuals, which suggests that the striatum and dopaminergic signaling in the striatum may contribute to the development of obesity. Thus, we tested whether striatal activation in response to food intake is related to current and future increases in body mass and whether these relations are moderated by the presence of the A1 allele of the TaqIA restriction fragment length polymorphism, which is associated with dopamine D2 receptor (DRD2) gene binding in the striatum and compromised striatal dopamine signaling. Cross-sectional and prospective data from two functional magnetic resonance imaging studies support these hypotheses, which implies that individuals may overeat to compensate for a hypofunctioning dorsal striatum, particularly those with genetic polymorphisms thought to attenuate dopamine signaling in this region.

Now this should not be news to readers of this blog, because this was exactly what I had proposed in my earlier blog pots. Low number of dopamine receptors leading to lower dopamine rush, leading to overeating and obesity. It is heartening to see the same being confirmed; though we will need more evidence to settle the direction of causality.

ResearchBlogging.org
E. Stice, S. Spoor, C. Bohon, D. M. Small (2008). Relation Between Obesity and Blunted Striatal Response to Food Is Moderated by TaqIA A1 Allele Science, 322 (5900), 449-452 DOI: 10.1126/science.1161550