What do deactivations in neuroimaging mean?

By now, we’re all familiar with the colourful spots and blobs associated with brain imaging. The assumption is often that areas which ‘light up’ are increasingly activated – and are, as such, implicitly more ‘important’ – in the task under study. But researchers using brain imaging techniques know this to be untrue. Some of the most interesting blobs that have been identified are known to reflect relative decreases in brain activity, often referred to as deactivations. A particularly interesting example, Dr David Nutt and colleagues found that the effects of psilocybin, the psychedelic found in magic mushrooms, are associated with widespread deactivations – suggesting that its effects are counter-intuitively due to less brain activation and/or altered connectivity between regions. For those interested, the controversy surrounding Dr. Nutt – which began around the time he cheekily argued that taking ecstasy was less harmful than horse-riding (see here for copy of the academic paper)  – and his research continue with the funding of a much-needed study looking at the effects of psilocybin on depression.

My colleague Adrianne Huxtable and I briefly weighed in on some of the work related to the interpretation of deactivations in neuroimaging. Because these fMRI studies often use contrasts between two conditions of interest (e.g., brain signals during the experience of pain minus signals during a period of no pain), the identified areas could be reflective of many things of which many ideas abound. For instance, it is unclear how deactivations are related to increases and decreases in neuronal activity and what role specific neurotransmitters and other spatiotemporal dynamics might play.

What this means for the interpretation of neuroimaging signals is still highly debatable. Nonetheless, there are some suggestions which might help make future publications more comparable such as distinguishing between negative BOLD responses and relative deactivations. The take-home message here is that neuroimaging, like all fields of science, is evolving both technically and conceptually – many exciting findings and opportunities will be born from such contentious ideas.

References
Carhart-Harris RL, Erritzoe D, Williams T, Stone JM, Reed LJ, Colasanti A, Tyacke RJ, Leech R, Malizia AL, Murphy K, Hobden P, Evans J, Feilding A, Wise RG, & Nutt DJ (2012). Neural correlates of the psychedelic state as determined by fMRI studies with psilocybin. Proceedings of the National Academy of Sciences of the United States of America, 109 (6), 2138-43 PMID: 22308440

Hayes DJ, & Huxtable AG (2012). Interpreting deactivations in neuroimaging. Frontiers in psychology, 3 PMID: 22347207

Klingner CM, Huonker R, Flemming S, Hasler C, Brodoehl S, Preul C, Burmeister H, Kastrup A, & Witte OW (2011). Functional deactivations: multiple ipsilateral brain areas engaged in the processing of somatosensory information. Human brain mapping, 32 (1), 127-40 PMID: 21157879

The aversive brain

The ability to detect and respond appropriately to aversive things or events in our environment is essential for all organisms, from fruit flies to humans. Although much is known about aversive responding at the psychological level (e.g. displays of fear, disgust) and at the physiological level (e.g. increased heart rate, changes in electrical skin conductance), much less is known at the neuroscientific level.

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The amygdala: an archetype

Some of the best work in this regard has come from Joseph LeDoux’s group regarding the complex anatomical and functional mapping of the amygdala. In his own words:

“Not so long ago it was an obscure region of the brain that attracted relatively little scientific interest. Today it is one of the most heavily studied brain areas, and practically a household word.”

His group’s work was not only largely responsible for putting the amygdala on the popular map, it was pioneering in connecting the dots concerning the amygdala’s role as a psychological-physiological-cognitive integrator of emotional information. It’s role in fear processing, for example, is comparatively well understood, as animals without amygdalae, or altered functioning, show robust changes in fear learning.

But the amygdala is not a single structure. While its subregions are interconnected (directly or via a few synapses), they are each connected with many different regions and networks of the brain – leading some to suggest that the amygdala should not be considered a single anatomical or functional unit at all. For instance, while the central nuclei are well connected to autonomic control regions (e.g. PAG, hypothalamus), and thus help control fearful responses like heart rate or freezing, the basal nuclei are more connected to striatal regions associated with controlling motor responses (like running away), and the lateral nuclei are, for instance, more highly connected to sensory cortices.

Basic emotional/affective processing

While there has been quite a lot of emotion-related research on specific areas such as the amygdala, there is much less known of how regions work together as functional networks. Moreover, many of the studies looking at emotional processing from a network perspective (e.g. typically human neuroimaging) often use complex designs which include highly cognitive tasks.

However, there is good reason to believe in the existence of basic emotional (or ‘affective’, to separate them from higher-level concepts) brain circuits. On this front, Jaak Panksepp was one of the first neuroscientists to suggest that all mammals have basic affective processing and, because of this, the affective lives of non-human mammals should be able to provide us with insight into our own primal affective processing.

For a rough psychological example, one can imagine waking up in an (unknowingly) unpleasant mood, until one’s wife or roommate politely suggests they consider going back to bed. Interestingly, when our sour mood is pointed out (or we’re pointedly asked about it), we do realize we’re unhappy (though it still might not be clear why). Nonetheless, the mood preceded explicit awareness (though not phenomenological awareness).

A cross species network approach

All this suggests the existence of a neural network which regulates the most basic aversive processing. Though many studies have looked at the brain’s responses to passive aversive stimuli (i.e. the subject in the experiment experiences an unpleasant event without having any task to do; for example, getting a shock or hearing an annoying sound), few have tried to put all the pieces together to look at which brain areas are commonly activated for any type of aversive stimulus. In general, human imaging studies have highlighted the involvement of various cortical regions, such as the prefrontal cortex, while animal studies have focused largely on subcortical regions like the periaqueductal gray (PAG) and hypothalamus. However, whether and how these regions form a core neural network of aversion-related processing was unclear.

To help clarify this issue, my colleague and I conducted a translational cross-species investigation on aversion in humans (by performing a meta-analysis of all neuroimaging studies where strictly passively experienced aversive stimuli were used) and other animals (mainly rodents). Our results indicated that many similar brain regions in both animals and humans were recruited during passive aversive processing, such as the anterior cingulate cortex, the anterior insula, and the amygdala as well as other subcortical (e.g. thalamus, midbrain) and cortical (e.g. orbitofrontal) areas. Importantly, involvement of these regions remained independent of sensory modality. Our study provided evidence for a network involved in aversion in both humans and other animals.

What’s the point? Potential for the everyday

Many studies have revealed altered reward-related processing in many psychiatric disorders (e.g. major depressive disorder, schizophrenia), but a few relatively recent studies have also implicated dysfunctional aversive processing (see table below from Hayes & Northoff, 2011, for some examples). Understanding the balance between aversion and reward processing in healthy individuals and those with, for instance, major depressive disorder, may tell us something about why, at the biological level, some stimuli result in unexpected emotional processing for those who are depressed (e.g. rating neutral items as being highly negative).

Looking beyond pathology, a better understanding of aversion from the psychological level down to the complex biology will ultimately translate to improvements on the quality of our lives. Knowing, for instance, precisely how our external and internal environments (e.g. the amount of natural light or what we had for breakfast, respectively) impact our affective states, as well as how our brains process the world in a highly context-dependent manner (and often in counterintuitive ways; beyond the oft touted cognitive-emotional divide), is essential to maximizing our happiness and overall well-being.

Selected references
Hayes, D., & Northoff, G. (2011). Identifying a Network of Brain Regions Involved in Aversion-Related Processing: A Cross-Species Translational Investigation Frontiers in Integrative Neuroscience, 5 DOI: 10.3389/fnint.2011.00049

LeDoux, J. (2007). The amygdala Current Biology, 17 (20) DOI: 10.1016/j.cub.2007.08.005

Panksepp J (2011). The basic emotional circuits of mammalian brains: Do animals have affective lives? Neuroscience and biobehavioral reviews, 35 (9), 1791-804 PMID: 21872619

Pessoa, L. (2008). On the relationship between emotion and cognition Nature Reviews Neuroscience, 9 (2), 148-158 DOI: 10.1038/nrn2317

Physical consciousness?

This guest blog was written by Christian Stevens, a graduate student in the philosophy department at the University of Guelph. His work is in the area of Epistemology and Philosophy of Mind.

We are physical beings in a physical world. This is the thesis of physicalism, the view that reality is made up of one kind of ‘stuff,’ and that stuff is physical. Physicalism, therefore, is what’s typically referred to as an ontological thesis, a thesis about what exists. Historically speaking, perhaps the most popular alternative to monistic views about the nature of reality, of which physicalism is a particular kind, are dualistic views of the nature of reality. Dualists claim that reality is made up of two distinct substances. 17th century philosopher, scientist and mathematician Rene Descartes, famously argued that reality is made up of both mind and matter. Descartes’ substance dualism also included a picture of reality where mind and body (or matter) interact (e.g. my mental desire to lift my arm causes the physical event of my arm going up). This notoriously puzzling view of the relationship between mind and body is often referred to as interactionist dualism or interactionism, for short.

Hempel’s dilemma: what is physicalism?
Now, in Descartes’ time there was, and continued to be for quite awhile after his death, a pretty clear conception of the ‘material’ or the ‘physical.’ At a fundamental level, ordinary objects like tables and chairs were constituted out of minute particles (called corpuscles) that were, in principle, divisible (like entities at a macro-level). The corpuscles were also subject to the law of causality (cause and effect). Part of the reason why modern day materialists prefer to call themselves physicalists is to distance themselves from the above conception of reality, which has since eroded due mainly to striking developments in 20th century physics.

Such developments apparently assure us that causality, as philosopher and mathematician Bertrand Russell once wrote, “…is a relic of a bygone age, surviving, like the monarchy, only because it is erroneously supposed to do no harm”. If the absence of causality at the level of fundamental physics were not enough, we are also no longer at liberty to think of reality as made up of minute particles either; wave-particle duality has shaken the other pillar of the conception of the physical or material. Physicalism, then, might be a far less substantive claim about the nature of reality than initially thought.

Philosopher Carl G. Hempel once attacked how physicalism was defined. He made the point that if physicalism defines itself in relation to current physics, then surely it will end up being false; how many of us seriously believe that physics is not going to change in the next 50 years or less, in which case what was true of the nature of reality at one time will be false at another (an undesirable, if not absurd, result). He also pointed out, however, that if physicalism is defined in terms of some future physics or ideal physics then it is vacuous; who knows what some future or ideal physics will look like? This conundrum is often referred to as Hempel’s dilemma. As far as I’m concerned, we have yet to come up with an entirely satisfying response to the problem.

Do physicality and consciousness meet?
Moreover, in this context (as well as in others, of course,), it is interesting to consider consciousness, in relation to physicalism. Consciousness, or phenomenal consciousness, the ‘felt’ or ‘qualitative’ character of perceptual states, is highly recalcitrant to definition in a non-circular way. Some philosophers, like Kriegel have suggested that we ought to adopt a ‘flexible characterization’ of phenomenal consciousness like ‘the property of mental states that constitutes or generates the mystery surrounding consciousness. This makes sense because consciousness is widely recognized in the philosophical literature as ambiguous (see Block). So talking about a property that generates a mystery surrounding consciousness makes reference to other concepts or aspects of consciousness that are not, typically, considered particularly vexing or mysterious (see Chalmers, for an argument on the separation of ‘easy problems’ from the ‘hard problem’ of consciousness).

At any rate, my point in bringing all this up here is that given the vagueness of both physicalism and consciousness, perhaps we should consider the possibility of a rapprochement between consciousness and the physical, as neutral monism suggests. Neutral monism is the view that reality consists of one neutral kind of stuff (which is neither mental nor physical). But, while we might have trouble understanding what is meant by ‘physical’ at some deep level, we seem to have less difficulty coming up with candidates for the constitution of consciousness in terms of some fairly well-behaved phenomenon (e.g., certain patterns of neural activity in the pre-frontal cortex). Though, I would suggest, this is not necessarily to say that we understand how consciousness could be physical in this sense. It also leaves open the question as to why we should count neural activity in the brain as physical, or how understanding neural activity as physical is best understood.

The explanatory gap: How far can science take us?
Part of a commitment to physicalism for many, also involves a commitment to the explanatory power of the sciences. The sciences appear to be able to provide us with a deep understanding of the nature of the cosmos, life and the like. Now, here’s where things get tricky: if there are reasons for thinking that the sciences can never adequately explain a certain phenomenon, is that evidence for thinking that said phenomenon is, therefore, non-physical? There are, as far as most philosophers of mind are concerned, powerful considerations that can be adduced in favor of an explanatory gap between consciousness and the physical (see Levine). Is this, on the face of it, evidence that physicalism is false? Some in the philosophical community say yes, others no. I think that if we don’t have a clear grasp on what calling something physical means, then we are committed to thinking that the sciences, and especially, physics, do not admit of explanatory gaps.

The physicalist would like to have the best of both worlds: a robust conception of the physical without any gaps whatsoever. But, a commitment to physicalism, at a minimum, requires one to defend either the explanatory power of the sciences, or some fairly robust conception of the physical. So, if there is some phenomenon that, in principle, cannot be understood (explained) by us, and we have no clear conception of what the physical is, or, more precisely, what calling that particular phenomena physical would amount to, then physicalism is in terrible shape. So, in the case of consciousness, we need either a fairly robust understanding of what it would be/mean to call it a physical phenomenon (as a first step anyway), or we need to be able to explain consciousness in a way that’s relevantly similar to explanations of other canonically physical stuff/processes. Since we do, presumably, have a fairly robust conception of what kind of stuff could be constitutive of consciousness, then even if we don’t understand exactly how that very stuff could be constitutive of consciousness, physicalism may, nevertheless, emerge unscathed.

My preferred route for defending physicalism, however, (like many philosophers) is to try and bridge the explanatory gap. This will, I think, require questioning models philosophers have in mind for how explanation tends to work in the sciences (for an example of such a model see Chalmers and Jackson). If we can refute such models then we will have a better chance at showing how the sciences have the resources to explain consciousness in a way that’s consonant with explanations figuring in physics or biology etc.… without, perhaps, having to take a stand on either the relationship between the sciences (do all the so-called special sciences reduce to physics?), or say what exactly is meant by the physical, both of which, I think, must ultimately be done if we decide to accept the explanatory gap consciousness is said to produce. This, alas, is liable to be no small feat!

Selected references
Block, N.J., O. Flanagan, and G. Guzeldere (eds.) 1997. The Nature of Consciousness: Philosophical Debates. Cambridge, MA: MIT Press.

Chalmers, D. 1995. Facing up to the problem of consciousness. Journal of Consciousness Studies, 2: 200-19.

Chalmers, D., & Jackson, F. (2001). Conceptual Analysis and Reductive Explanation The Philosophical Review, 110 (3) DOI: 10.2307/2693648

Hempel, C.G. 1969.Reduction: Ontological and Linguistic Facets. Philosophy, Science, and Method: 179–199.

Levine, J. 1983. Materialism and qualia: the explanatory gap. Pacific Philosophical Quarterly, 64: 354-361.

Kriegel, U. 2009. Subjective Consciousness: A Self-Representational Theory. NY: Oxford University Press.

Russell, B. 1917. On the Notion of Cause, ch.9 in Mysticism and Logic and Other Essays. London: Unwin.

Is it time for a conceptual revolution in neuroscience?

A recent article by Russell Poldrack and colleagues begins with an apt quote from Rutherford D. Rogers, the former Yale University Librarian:

“We’re drowning in information and starving for knowledge”

They chose this quote to reflect what some brain researchers (myself included) might call a major roadblock in contemporary neuroscience – a huge amount of data covering a conceptual landscape riddled with ambiguities. Although there has been great technological advancement in neuroscience – for instance, the relatively rapid development of functional magnetic resonance imaging and optogenetics – the conceptual foundation and integration (or what the authors call the ‘semantic infrastructure’) necessary for bridging the brain-psychology chasm is not as strong.

There are too many examples of this to note. I would point, for instance, to concepts like reward or creativity. The authors point out ‘working memory’ (Is it holding information in one’s awareness? Manipulating information in awareness? Does it involve both of these ideas or even others?). They then discuss the longstanding tendency, particularly within cognitive neuroscience, to equate the type of task performed in the laboratory with the mental construct. For instance, they point out, the Sternberg Item Recognition Paradigm is typically used to investigate aspects of working memory (and inferences to working memory ability or impairment are justified in most cases), but not all of the task conditions within the paradigm can be equally associated with working memory (e.g. there are cognitive and motor aspects that would likely not be associated at all with working memory in terms of brain functioning). And so, calling it the Sternberg working memory task, as many do, conflates the task with theoretical assumptions about which many experts might not agree.

The issue of fuzzy concepts and inconsistent usage of terminology is, of course, not new to science as a whole. Nor is it new to neuroscience – from Bennett and Hacker’s ‘Philosophical Foundations of Neuroscience’ to other recent articles on the subject, such as Bilder’s ‘Neuropsychology 3.0: Evidence-based science and practice’. Every branch of science (and perhaps every rigorously studied topic, including those not using the scientific method directly) has had to come to terms with its terms. Poldrack and colleagues point out the relatively well-known example of Gene Ontology – which provided explicit descriptions of various gene-related concepts (e.g. regarding biological components or processes). What is new, is the creation by the authors of an informatics resource which aims to identify particular usages of all terms and associated sub-terms, effectively allowing anyone to perform an ‘intelligent’ search of such concepts.

Their Cognitive Atlas is an open collaborative project (inspired by projects like Wikipedia). It will avoid imposing a single ontology, which would cause considerable consternation across subfields, and will embrace the current messy state of affairs which represent the agreements and dissenting opinions of experts.

Overall, this seems to me to be a great approach to the problem of fuzzy concepts. I wonder though, should this be a ‘cognitive’ atlas? I’ve often found the term ‘cognitive’ to be too fuzzy in and of itself and wonder how (and if) this will impact this project. If the authors are looking to help bridge the divide between brain function and psychology in general, I suspect it would be best to include all brain-related concepts (including those used in all of neuroscience, psychology, psychiatry and also those in non-human animal studies). It just might turn out, for example, that the difference between the ‘reward’ concept across disciplines, and between studies involving humans and other animals, might not be all that large. Or better yet, the conceptual differences in one area might reveal a limitation or unexplored terrain in another.

When we finally escape the conceptual quagmires within neuroscience, the next great challenge will be to align the neuroscientific concepts with everything else.

 

Selected references
Bilder RM (2011). Neuropsychology 3.0: evidence-based science and practice. Journal of the International Neuropsychological Society : JINS, 17 (1), 7-13 PMID: 21092355

Cromwell HC, & Panksepp J (2011). Rethinking the cognitive revolution from a neural perspective: How overuse/misuse of the term ‘cognition’ and the neglect of affective controls in behavioral neuroscience could be delaying progress in understanding the BrainMind. Neuroscience and biobehavioral reviews PMID: 21345347

Marshall, P. (2009). Relating Psychology and Neuroscience: Taking Up the Challenges Perspectives on Psychological Science, 4 (2), 113-125 DOI: 10.1111/j.1745-6924.2009.01111.x

Poldrack RA, Kittur A, Kalar D, Miller E, Parker S, Sabb F and Bilder RM. (2011) The cognitive atlas: toward a knowledge foundation for cognitive neuroscience. September 2011|Volume5|Article17| 1

Neuro self-help?

Does knowledge of brain function alter brain function? The development of psychology is based on this premise, either implicitly or explicitly. Where psychologists focus on behaviour as the main measure of interest, linking behaviour directly to brain function has been one of the popular mysteries and goals of neuroscience throughout the last few decades. This may seem to be a relatively simple task at first glance. For instance, great strides have been made in motor and visual neuroscience, where numerous studies have connected the activity of neurons directly to different aspects of motor commands such as planning and muscle engagement. However, there are still many unanswered questions within these subfields such as whether the so-called primary ‘motor’ or ‘visual’ cortices are really just processing basic motor- or visual-related inputs (for some interesting studies on the visual system, see here and here).

Although virtually every brain researcher assumes that behaviour relies^* on brain function, a growing number are now focused on how our own brain function can alter itself (mostly through behavioural decisions and actions). From work on the value of meditation and mindfulness to the burgeoning field of neuroeconomics (which combines economic theories and reward/aversion- and decision-related neuroscience to understand why we generally value certain things over others and ultimately make the choices we do), every additional study helps us better outline both our potential limitations as well as our tremendous capacity for change. These ideas have become part of the global consciousness, in part due to books about brain plasticity and in how to use a basic knowledge of neuroscience in your everyday life, as David Rock argues in his well-written and practical book “Your Brain at Work”.

But how does mere knowledge really alter brain function? In a study published this year by Elizabeth Phelps and colleagues at New York University, they found that giving subjects explicit knowledge about the probability of receiving a reward following a cue lead to both better performance and clear changes in brain activity when compared to trial-and-error feedback. Probabilistic knowledge of reward outcomes resulted in less activation of the nucleus accumbens, ventromedial prefrontal cortex and hippocampal area (regions involved in the processing of reward learning and valuation) compared to the no-knowledge, trial-and-error, condition. What’s more, the dorsolateral prefrontal cortex appeared more active for win conditions (when the person guessed correctly based on the cue) when people were given information about the probability of getting a reward. As this region had negative functional connectivity to those noted above, the author’s suggested that the dorsolateral prefrontal cortex may be involved in the on-line dampening of responses in reward-related regions.

So, while contemporary neuroscience is helping us connect behaviour to brain-based activity, we are still left with the gap in how, exactly, we can use this information to alter our future brain function and behaviour – even if there is an exponential growth in the number of companies (some good, most not) that want to convince you, for a fee, that the answer to controlling your own brain’s plasticity lies with them.

* For the philosophically inclined, I realize that the term ‘relies’ could be, strictly speaking, argued against or replaced entirely with other terms. My use of the word here is very general and aims to avoid the discussion altogether – for now.

Selected references
Li J, Delgado MR, & Phelps EA (2011). How instructed knowledge modulates the neural systems of reward learning. Proceedings of the National Academy of Sciences of the United States of America, 108 (1), 55-60 PMID: 21173266

Moradi F, Buračas GT, & Buxton RB (2011). Attention strongly increases oxygen metabolic response to stimulus in primary visual cortex. NeuroImage PMID: 21839179

Watkins S, Shams L, Josephs O, & Rees G (2007). Activity in human V1 follows multisensory perception. NeuroImage, 37 (2), 572-8 PMID: 17604652

Brain-based evidence for multiple intelligences?

Is there any brain-based evidence for the theory of multiple intelligences? From my viewpoint, the answer seems clear: Yes….and no. (Germans have a nice colloquialism for this in ‘jein’, pronounced yine.)

The theory of multiple intelligences was originally proposed by the psychologist Howard Gardner to account for the fact that there appear to be many cognitive abilities that are not subsumed by the concept of general intelligence.

(Tangentially, it’s fascinating that the IQ test is still largely synonymous with intelligence in popular consciousness. Originally developed by Alfred Binet as a tool to identify specific learning deficiencies in children – arguably still it’s best use in contemporary form today – the IQ test has been mistaken time and time again as a single metric of intelligence. Historically misused, for example, in both World Wars to assess and assign conscripts to intelligence-relevant positions, to screen new immigrants to the States, to predetermine academic/career paths for young children, and for general hiring practices in many sectors including emergency responders, the IQ test still often misused and misinterpreted in contemporary societies. The internet is littered with emotionally-driven, and often painfully amusing, related discussions. Nonetheless, as some psychologists today point out, the evolution of the IQ test has resulted in more reliable tests, with some interesting predictive power, which focus on multiple cognitive attributes instead of a single general metric of ‘intelligence’.)

It is typically accepted that the eight multiple intelligences are: Spatial, linguistic, logical-mathematical, bodily-kinesthetic, musical, interpersonal, intrapersonal, and naturalistic. Though the theory has been attacked from many angles (often pointing out the ambiguities or potential circularities inherent in the use of such broad, fuzzy, concepts), many believe it to be of high practical value when used appropriately – especially within the education sector. But the question remains, is there any brain-based evidence for multiple intelligences (MI) theory?

Now, for the no part…

The broad conclusion from the argument between localizationalists (those believing that the brain was mostly subdivided into areas which performed separate functions) and equipotentialists (those believing that psychological processes were spread out roughly evenly across the brain) is that both were correct to some degree. Although the details are still largely debated, no respectable brain expert lies strictly in one camp. For example, though some regions of the brain are more involved in processing semantic (e.g. hippocampus) vs. motor (e.g. cerebellum) memories, all types of memory processing are distributed across intricate brain networks. However, this applies to a fundamental principle of brain function like learning or memory and cannot necessarily be mapped one-to-one onto broader psychological concepts (which can be confusing because we often use terms like memory as a catch-all for every process related to the basic principles).

So the idea, for instance, that musical intelligence is contained within a brain network is tantamount to a category error. There is no evidence that the disruption of processing in any brain region (due, for instance, to some sort of brain damage) will cause a direct and predictable change/loss in ‘musical intelligence’. Of course, there are case studies which describe the loss of musical abilities or characteristics following distinct brain lesions – such as the loss of emotional connection/enjoyment associated with listening to music following a stroke affecting a region of the right parietal cortex. However, this should not be confused with implicating the right parietal lobe as being a key component of the ‘musical intelligence’ network (an example of such an approach can be seen here.

Now, for the yes part…

Nonetheless, along this same line of thought, the idea that damage to the right parietal lobe can impair musical ability in some specific way is consistent with the idea that a global measure of musical ability (or musical intelligence) will also be affected. Research into the neural basis of music-related emotions is still in its infancy, but we’re beginning to learn more about which circuits might be involved in regulating associated aspects such as emotional contagion, expectancy, and memories by exploring this relationship.

So, in both a metaphorical sense as well as in an attempt to measure global musical ability (as well as the other purported intelligences), the theory of MI can be useful. In may be particularly useful at the early stages of development, for instance, where Early Childhood Educators can use MI as a rough guide for how to engage children in all aspects of learning through their particular interests / learning styles. Nonetheless, because there is no direct relationship between MI theory and brain functioning, the best overall future approach is likely to move away from the metaphorical and toward looking for indicators of abilities/traits which most closely reflect the principles of brain function (e.g. exceptional/impaired ability to acquire motor memories). In this way, each of us throughout our personal and professional lives may be able to best regulate and support our own brain and behaviour.

Selected references

Collins JW (2007). The neuroscience of learning. The Journal of neuroscience nursing : journal of the American Association of Neuroscience Nurses, 39 (5), 305-10 PMID: 17966298

Koelsch S (2010). Towards a neural basis of music-evoked emotions. Trends in cognitive sciences, 14 (3), 131-7 PMID: 20153242

Miller L (1986). ‘Narrow localizationism’ in psychiatric neuropsychology. Psychological medicine, 16 (4), 729-34 PMID: 3823292

Satoh M, Nakase T, Nagata K, & Tomimoto H (2011). Musical anhedonia: Selective loss of emotional experience in listening to music. Neurocase PMID: 21714738

 

Human Brain Mapping (HBM) 2011

Human Brain Mapping in Quebec City was an excellent, if not slightly overwhelming, conference which brought together the often discrepant worlds of neuroscience, psychology, psychiatry, physics, engineering, mathematics, and computer science. (Did I miss anyone?) For a first-timer – and someone relatively new to the world of neuroimaging – it was a whirlwind opportunity to try to absorb as much wisdom as possible about how best to peer through the frosted looking glass and make the most of what we see.

It was remarkable to hear about the advancements and contentious issues of brain imaging from many of the people responsible for developing the techniques and analytical approaches first-hand. Personally having a better idea about the best ways forward, while being careful to respect the limitations of each approach, it is clear that this was the kind of transdisciplinary conference that will continue to have relevance as the young science slowly ages. In particular, Stephen Smith, Christian Beckman, Victor Solo and Martin Lindquist, all warned the ~2300 attendees to be wary of strong claims that some approaches reveal ‘causal’ connections in the brain (e.g. Granger causality, DCM, ICA). Despite these caveats, however, they reminded us that with careful consideration, these approaches can help clarify brain structure and function. Some of this interesting recent work can be found here and here.

A related theme throughout the conference was the analysis of neural networks and the myriad ways in which this can be done (correctly or otherwise). For instance, Ed Bullmore focused on the benefits of using brain graphs – a series of interconnected (measured by connecting lines or ‘edges’) brain areas (or ‘nodes’) – to map neural networks. He described the vast usefulness of this approach both visually as well as semi-quantitatively (e.g. once mapped, it becomes possible to directly compare one neural network to another, and also to non-neural networks such as LinkedIn), and pointed out that this approach highlights the apparent trade-offs between physical connection costs and topological efficiency (e.g. connections within the brain are not maximally energy efficient, and this may be due to the increased gains in processing seen with focused, wide-spread, network activity. Incidentally, there is some evidence that people with schizophrenia, for instance, show greater disorganized network activity and reduced efficiency – though interpretations of these findings are still somewhat contentious). Together, these studies and many others contribute to a better understanding of the human connectome.

One of the highlights of the conference was the Talairach Lecture by Karl Deisseroth on one of the most promising neuroscience techniques to emerge in the last decade – optogenetics. Though it is a newer brain imaging technique and has obvious direct connections to magnetic resonance imaging, I have to confess I was positively surprised to find a researcher focused on non-human animal work have such a prominent role at this conference. While the gist of the lecture and the opening of the conference (with pictures) was summed up nicely and briefly by a fellow HBMer, it’s worth noting that Dr Deisseroth emphasized that his team’s ground-breaking work would have been absolutely impossible without many prior advances in science. More importantly, he underscored the fact that much of that research (such as advancements in microbial opsins and a better understanding of how photoreceptors can help control neural activity) was initially not motivated by any clear hypotheses related to neuroscience nor, in fact, was there any clear practical ‘use’ to such research.

All in all, I found this conference to be highly informative. Though it was very heavy on brain imaging methods, and much less focused on underlying neurobiology and functional activity, there was still more information than could possibly be absorbed across many domains. Importantly, while this is an essential meeting for MRI methodologists, neurobiology-leaning neuroscientists like myself had an excellent opportunity to broaden their methodological armament – if ever so slightly.

Selected references
Bullmore ET, & Bassett DS (2011). Brain graphs: graphical models of the human brain connectome. Annual review of clinical psychology, 7, 113-40 PMID: 21128784

Fenno L, Yizhar O, & Deisseroth K (2011). The development and application of optogenetics. Annual review of neuroscience, 34, 389-412 PMID: 21692661

Lindquist MA, & Sobel ME (2011). Graphical models, potential outcomes and causal inference: Comment on Ramsey, Spirtes and Glymour. NeuroImage, 57 (2), 334-6 PMID: 20970507

Lynall ME, Bassett DS, Kerwin R, McKenna PJ, Kitzbichler M, Muller U, & Bullmore E (2010). Functional connectivity and brain networks in schizophrenia. The Journal of neuroscience : the official journal of the Society for Neuroscience, 30 (28), 9477-87 PMID: 20631176

Smith SM, Miller KL, Salimi-Khorshidi G, Webster M, Beckmann CF, Nichols TE, Ramsey JD, & Woolrich MW (2011). Network modelling methods for FMRI. NeuroImage, 54 (2), 875-91 PMID: 20817103

Canadian College of Neuropsychopharmacology conference 2011

The Canadian College of Neuropsychopharmacology (CCNP) is the premier college in Canada for expert knowledge on the pharmacology of brain function. This year’s conference was held in la belle ville de Montréal. Though recent years had a much greater attendance – as the CCNP combined with other groups, such as the Canadian Association for Neuroscience and the Scandinavian College of Neuropsychopharmacology – the quality of talks was still exceptional.

The three day conference began with the Presidential Symposium, which focused on current perspectives on dopamine-glutamate interactions in mood and substance abuse disorders. Susan Sesack talked about anatomical perspectives, as reflected in some of her recent work on midbrain nuclei and the habenula (e.g. here), which was an extension and update of previous work. Marina Wolf and Paul Vezina talked about what we currently know about glutamate and dopamine receptors in the nucleus accumbens/ventral striatum with special reference to their effects on stimulant drugs (e.g. cocaine, amphetamine). The first symposium was closed with a talk by Gregor Hasler who focused on recent neuroimaging data underscoring the role of catecholamines (dopamine and noradrenalin) and glutamate in major depressive disorders in humans.

Other notable symposia included “Rodent ultrasonic vocalizations as an emerging tool in neuropsychopharmacology”, in which John Yeomans and colleagues underscored the increasing need, usefulness, and current limitations to understanding the utterances of rats, and “Serotonin and social behaviour during development and adulthood” in which the young researchers Linda Booij and Marije aan het Rot chaired excellent presentations. Of particular note, Susannah Murphy discussed her recent work supporting the notion that one key action of antidepressant treatments may be to alter emotional biases. For instance, it is well known that patients with major depression are more likely to report neutral stimuli as being negative and that aversive stimuli are deemed highly aversive compared to healthy control subjects.

Finally, plenary lectures by Simon Young and Barry Everitt covered a wide range of the work in their respective fields and raised many interesting questions along the way. Dr Young’s talk on “The neurobiology of social interactions” focused on work related to how increases in serotonin (via tryptophan loading) generally decrease quarrelsome behaviours while increasing one’s agreeableness and the perception of agreeableness in others. He questioned to what degree the results from other studies investigating the effects of acute tryptophan depletion (which results in acute decreases in brain serotonin production) in social cooperation may actually be the result of changes in agreeableness/quarrelsomeness as opposed to the complex social explanations (e.g. alterations in cooperativity) that have been suggested.

Dr Everitt’s talk “From impulsive actions to compulsive habits in drug addiction” reviewed a small portion of his immense body of work focused on drug reward/seeking. His recent work really underscores the need for future research to move beyond simple lesion or local brain explorations and towards the complexities of neurotransmitter interactions and brain circuits across regions. For instance, only by simultaneous lesions of the nucleus accumbens core and injections of dopamine antagonists into the dorsolateral striatum on the other side of the brain was his group able to identify the importance of the ‘spiraling’ striatal-nigro-striatal circuitry on drug (e.g. cocaine) seeking behaviour. Also interesting was the notion that decreased dopamine D2/3 receptors in the nucleus accumbens shell is related to increased impulsivity and predictions of cocaine seeking behaviour, although these factors appear to be independent of dopamine release as a whole or the animal’s natural tendency to respond to novelty.

Overall, with speakers from all over the world, this year’s CCNP conference reflected the immense international advances in neuropsychopharmacology over the past few years. It also underscored the idea that knowledge about neurochemical interactions will be necessary for a full understanding of brain function – well beyond drug addiction alone.

Selected references
aan het Rot M, Moskowitz DS, Pinard G, & Young SN (2006). Social behaviour and mood in everyday life: the effects of tryptophan in quarrelsome individuals. Journal of psychiatry & neuroscience : JPN, 31 (4), 253-62 PMID: 16862243

Dalley JW, Fryer TD, Brichard L, Robinson ES, Theobald DE, Lääne K, Peña Y, Murphy ER, Shah Y, Probst K, Abakumova I, Aigbirhio FI, Richards HK, Hong Y, Baron JC, Everitt BJ, & Robbins TW (2007). Nucleus accumbens D2/3 receptors predict trait impulsivity and cocaine reinforcement. Science (New York, N.Y.), 315 (5816), 1267-70 PMID: 17332411

Murphy SE (2010). Using functional neuroimaging to investigate the mechanisms of action of selective serotonin reuptake inhibitors (SSRIs). Current pharmaceutical design, 16 (18), 1990-7 PMID: 20370666

Sesack SR, Carr DB, Omelchenko N, & Pinto A (2003). Anatomical substrates for glutamate-dopamine interactions: evidence for specificity of connections and extrasynaptic actions. Annals of the New York Academy of Sciences, 1003, 36-52 PMID: 14684434

Serotonin & reward

In an article entitled “5-HT receptors and reward-related behaviour: A review”, Andrew Greenshaw and I discuss the involvement of the many different serotonin receptors in reward. While dopamine has largely become synonymous with reward, in both the professional literature as well as in pop culture, the role of other neurotransmitters is slowly gaining focus. It is becoming clearer that no single chemical is responsible for reward- or aversion-related processing, and that the orchestration of many is necessary.

What’s more, we suggest that it may be misleading to think of 5-HT as having a single role in this regard. Instead of being a chemical involved in ‘reward’ or ‘aversion’, it likely has many complex actions on emotion through its effects on at least 14 distinct receptor subtypes. These receptors have structural and functional differences that translate ultimately to different effects on the target cell. As there can be many receptor subtypes on any one given cell, the release of 5-HT onto this cell can actually result in opposing effects.

It is for these reasons that we suggested that the content (e.g. receptor function, localization in the synapse and in the brain) and context (e.g. type of behavioural paradigm, type of rewarding drug under consideration), surrounding the involvement of 5-HT in reward-related processing in the brain, must be considered.

The concepts of reward and aversion, even at their most basic level, are remarkably complex. The ultimate goal is to help understand how these processes affect every aspect of our daily lives – answering questions such as “why do you and I value different things?”, “why do we make such different decisions?”, “how can I maximize my own personal experiences of reward from day-to-day and in the long run?”, and “given that so many psychiatric and neurologic disorders involve alterations in emotional processing, is a basic understanding of reward and aversion necessary for prevention or more effective treatments?”

To read the entire article, go here.

References
Hayes DJ, & Greenshaw AJ (2011). 5-HT receptors and reward-related behaviour: A review. Neuroscience and biobehavioral reviews, 35 (6), 1419-49 PMID: 21402098

Mentorship: Open Letter to an Undergraduate

The value of mentorship is apparent across almost every field, scientific or otherwise. While neuroscientists have yet to approach the social complexities associated with the mentor-mentee relationship, the value of mentorship isn’t lost on them. From the Society for Neuroscience mentorship program to local university programs, professionals and students alike seem to understand the importance of strong mentorship.

Numerous academic papers have been written on the topic. Some of their main findings include the notion that strong partnerships should be formed at the earliest points in education, as early as kindergarten. Others point to the need for good mentor-mentee matching, focusing on value-based compatibility and the need for the mentee to be as proactive as the mentor. Interestingly, a 2007 survey of NIH-funded scientists found that the probability of ‘problematic research behaviour’ (e.g. questionable use of funds or peer review process, but not outright misconduct like fabrication or plagiarism) declined in researchers that received ethical, research-related, or personal mentorship, but increased in those who received mentorship on financial or ‘professional-survival’ issues. Unsurprisingly, these results underscore the need for more positive, and contextually appropriate, mentor-mentee relationships.

Recently, a University of Toronto colleague and friend from the undergrad days put a general call out to Alum (and to me in particular) asking if we “could provide some words of wisdom for a current student?” As a graduate of ’03, I struggled with what to offer that would be different from what others (particularly the older, wiser, Alum) might say. While there may be no overt revelations here, and no nirvana-inspiring tips, I do have a few personal observations for the scientist and non-scientist alike.

In no particular order:

1. Finding out what it is you love in life (and subsequently, the kind of person you want to be) is hard work and on-going. (I’ve never met a person that this came naturally for – although some might challenge this, it is my experience that a few probing questions reveals otherwise.) The best way to go about this is to – almost literally – throw yourself into things, even though they may initially lead to discomfort. Crossing ‘dislikes’ off your list is every bit as important as finding out what you love to do, and ultimately brings you clarity to what qualities/things are most valuable to you personally. Studies done years ago found that undergrads with goals were more likely to be successful and satisfied years later – regardless of whether they ended up doing exactly what they said they’d be doing.
2. Despite how bright you are (or think you might be), it’s always easier to psychologize someone else. Seeing your own strengths and faults is far more difficult. This, among many other good reasons, is why it is essential to find good mentors. I have been very lucky in this regard, but I have also been proactive. Like in #1, if you aren’t ‘throwing yourself’ into new situations and environments, you are far less likely to meet people with the potential to change the course of your life.
3. Don’t be afraid to fail. I have to admit, I am afraid to fail – and allowing for the possibility of failure is a constant struggle. Nonetheless, it is my overwhelming experience that taking calculated, thoughtful, risks (that could possibly lead to failure and/or humiliation) leads to rewards far more often than not. In fact, with a nice balance of risk and potential for reward, even failures can ultimately lead to success (and are often prerequisites for the biggest accomplishments).
4. Maintain your integrity. This, of course, isn’t a license for pushing your moral beliefs on others or taking a righteous stance. Instead, approaching all people and situations with a genuine intention of honesty, transparency, and empathy seems to me to be the most effective path to a continued healthy and happy life – for yourself as well as those around you. (I recommend reading Sam Harris’ “The Moral Landscape” for an interesting tie of morality and neuroscience.) Though we often hear of the most unscrupulous among us stealing the prize from those more deserving, it is my contention (conveniently supported by much research) that this is the much hyped exception rather than the rule.
5. Finally, don’t sell yourself short. More importantly, sell yourself! There is no one more knowledgeable about your skills, goals, and desires than you. If you’re following the points above – which ultimately help us to maintain a realistic and accurate self-image – there is less chance that you’re over-selling yourself. If you don’t let people know what you are interested in and capable of, you cannot have a reasonable expectation of getting to where you want to go.
6. Ok, one more – a 5b if you like. Doing all of these things will help you build a community of people that know you, trust you, can rely on you, and are ultimately as interested in your success and happiness as you are in theirs. Research from the brain sciences shows us repeatedly that helping to build, and be an active member of, a network of people is a key aspect of happiness.

So, good luck! And if you’re interested in going into/knowing more about science or neuroscience, feel free to reach out.

References
Anderson, M., Horn, A., Risbey, K., Ronning, E., De Vries, R., & Martinson, B. (2007). What Do Mentoring and Training in the Responsible Conduct of Research Have To Do with Scientists??? Misbehavior? Findings from a National Survey of NIH-Funded Scientists Academic Medicine, 82 (9), 853-860 DOI: 10.1097/ACM.0b013e31812f764c

Hamos JE (2006). Framing K-12 partnerships in order to make a difference. Academic medicine : journal of the Association of American Medical Colleges, 81 (6 Suppl) PMID: 16723826

Frei, E., Stamm, M., & Buddeberg-Fischer, B. (2010). Mentoring programs for medical students – a review of the PubMed literature 2000 – 2008 BMC Medical Education, 10 (1) DOI: 10.1186/1472-6920-10-32

Locke, E., & Latham, G. (2002). Building a practically useful theory of goal setting and task motivation: A 35-year odyssey. American Psychologist, 57 (9), 705-717 DOI: 10.1037//0003-066X.57.9.705

Morisano, D., Hirsh, J., Peterson, J., Pihl, R., & Shore, B. (2010). Setting, elaborating, and reflecting on personal goals improves academic performance. Journal of Applied Psychology, 95 (2), 255-264 DOI: 10.1037/a0018478

Zerzan, J., Hess, R., Schur, E., Phillips, R., & Rigotti, N. (2009). Making the Most of Mentors: A Guide for Mentees Academic Medicine, 84 (1), 140-144 DOI: 10.1097/ACM.0b013e3181906e8f