Archive for the ‘multi-modal imaging’ Category

In a recent review article in Nature Reviews Neuroscience, Antonio Rangel, Colin Camerer and Read Montague suggest a framework for neuroeconomic research. Indeed, the very core of its idea is simple, but not simplistic. After reading the article, I think it will provide a useful reference for future research into neuroeconomics, aka value-based decision making. I’ve made a copy of the model here for you to see:

The caption reads:

Basic computations involved in making a choice. Value-based decision making can be broken down into five basic processes: first, the construction of a representation of the decision problem, which entails identifying internal and external states as well as potential courses of action; second, the valuation of the different actions under consideration; third, the selection of one of the actions on the basis of their valuations; fourth, after implementing the decision the brain needs to measure the desirability of the outcomes that follow; and finally, the outcome evaluation is used to update the other processes to improve the quality of future decisions.

In my own emerging work on this arena, I am trying to combine this with recent advances cognitive neuroscience. First, the advances in imaging genetics, i.e., the knowledge and study of how genetic variance leads to specific changes in neurotransmission, which in turn may affect cognition, emotion and behaviour. Second, the advances in the cognitive neuroscience of ageing, i.e, the relationship between age-related changes in brain structures and functions, and mental alterations.

Briefly put, in a just submitted manuscript, I suggest that the Rangel-Camerer-Montague framework can serve as a model for looking at genotype and age effects. This leads us to three advances: first, it provides a better way to illustrate and understand the minute details of the preference and decision making systems. Second, it serves as a demonstration that individual (and intra-individual) differences must be taken into account. The “economic agent” is not a homogenous subject, but an agent that differs from person to person and with persons over time. Finally, it may also serve as a framework for identifying potential ways to induce alterations in the systems, e.g., through medical intervention. More on this story later, given that the manuscript is accepted 😉 For now, here’s an illustration of how genotype (exemplified through COMT, MAO-A and 5-HT) and age effects may expand the model. Of course, this is only scratching the surface, but I hope you’ll see what I mean.

This is an extended version of the Rangel-Camerer-Montague model. Within each processing node, two dimensions are added, here exemplified with the three primary nodes. The genotype dimension is a categorical variable that divides subjects into two or three classes, while the age dimension is continuous (inset, top left).


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brainconnection.jpgIt’s been a while, and whoah! have we been drowning in work or what? The media here in Denmark have caught on both our stories about teenage brains and stem cells in mother’s brains.

Here is a nice demo of how MRI can be used to study not only the brain per se, but also how mental functions work as different functional and physical networks. In a really neat study Takanashi et al. in NeuroImage combined fMRI and Diffusion Tensor Imaging, a scanning technique that basically makes it possible to calculate the brain fibers in the brain, i.e. their homogeneity, direction and so forth.


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gal507.jpgThe next International Imaging Genetics Conference is opening its doors now for registration. The third year in a row, building on two successful conferences, this third meeting will also house two separate workshops: one on brain imaging for geneticists; and one on genetics for brain imagers. All in the spirit of crossing the bridge between genetics, brain imaging and statistics. As this course was brilliant last year, I’m hoping to attend in January 2007, too.

Here is the announcement:

The First and Second International Imaging Genetics Conferences were held to bring together national and international experts in neuroimaging, genetics, data-mining, visualization and statistics. Targeting physicians and scientific researchers, this annual conference features presentations from investigators world-wide and held in-depth discussions within the emerging field of Imaging Genetics. Given the known importance of both genetics and environment in brain function, and the role of neuroimaging in revealing brain dysfunction, the synergism of integrating genetics with brain imaging will fundamentally change our understanding of human brain function in disease. To fully realize the promise of this synergy, we must develop novel analytic, statistical, and visualization techniques for this new field.

This international symposium was held to initially assess the state of the art in the various established fields of genetics and imaging, and to facilitate the transdisciplinary fusion needed to optimize the development of the emerging field of Imaging Genetics. The Third Annual International Imaging Genetics Conference will be held on January 15th and 16th, 2007 at the Beckman Center of the National Academy of Sciences in Irvine, CA. We look forward to seeing you at this exciting upcoming event.

Monday January 15th:

  • Nicholas Schork, UCSD “Multivariate Analysis of Combined Imaging and Genomic Data”
  • Eleazer Eskin, UCSD “Analysis of Complex Traits Through Intermediate Phenotypes.”
  • Tom Nichols, University of Michigan “Statistical Challenges & Opportunities in Imaging Genetics”
  • Fabio Macciardi, University of Toronto “Integrating Imaging Genetics Methods in Schizophrenia.”
  • David Goldman, NIAAA “Genes and Neurobiologies in the Addictions”
  • David Goldstein, Duke Institute for Genome Sciences and Policy “Neuropsychiatric pharmacogenetics”
  • Daniel Weinberger, NIMH/NIH: TBA

Tuesday January 16th:

  • Joseph Callicott, NIMH “Does risk for schizophrenia arise from multiple genes in vulnerable pathways? Evidence from DISC1 and FEZ1”
  • Lisa Eyler, UCSD “Genetics of Brain and Cognition: A Twin Study of Aging”
  • Fei Wang, Peking University “Neuregulin 1 Genetic Variation and anterior cingulum integrity in schizophrenia and in health.”
  • Andreas Meyer-Lindenberg, NIMH/NIH “Genetic characterization of prefrontal-subcortical interactions in humans.”

**New for 2007** Sunday January 14th:

*** Half-day Workshop tutorials will be offered the day before the conference at the Beckman Center- see website for details***

Workshop 1: What Geneticists need to know about Brain Imaging
Workshop 2: What Brain Imagers need to know about Genetics

Registration and conference information can be found at the conference website


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brainimagingbehavior.jpgIt’s not every day that we see a new journal emerging. However, Springer now launches a new journal called Brain Imaging and Behavior. According to the mission statement, the goal of the journal is to

publish innovative, clinically-relevant research using neuroimaging approaches to enhance the understanding of neural mechanisms underlying disorders of cognition, affect and motivation, and their treatment or prevention.

In this sense, the journal seems to have the ultimate goal of disease understanding and treatment. However, as they write, research on individual differences in representation of normal functions is important as well. What I find particularly interesting is that “brain imaging” is taken to imply a whole range of imaging methods in the study of the brain. It involves everything from the higher cognitive functions to molecular imaging methods. This implies the different approaches that involves genetics, behaviour and neuroimaging, AKA imaging genetics.

Brain Imaging and Behavior sounds like a very interesting initiative. I had problems finding the first online articles (this link). And would it not be better if the journal, just as any science journal, was free? It would be good if they followed the example of PLoS.


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Gene-hyped as we are here at BrainEthics, I'm mentioning a few articles that are highlighting the relationship between genes, brain and mind. As neuroscience deals with the wet matter of the mind – the fatty, information-processing and massively energy consuming body part we call the brain – we must also realize that the basic building block of the brain is the genome.

This is not as simple as it may sound. Genes simply do not merely encode how a cell is to look like or function. Genes only react to the environment in which they are situated. The development of, say, a hippocampal cell is not encoded in the genome per se; it stems from the influence of the local environment of that part of the brain, and how the brain cell (at the developmental stage actually more like a stem cell) migrates and connects to the network that will develop into the hippocampus. Neuroscience is certainly sorting out the nitty-gritty details on this, with the fantastic work by people such as Pasco Rakic. IMHO, even neuroimaging cannot escape this turn: we must move from the "blobology" of fMRI, PET and all other neuroimaging methods (this even applies to the clever diffusion fMRI, as described recently), and towards a better understanding of what goes on within these blobs. Just as I briefly mentioned in my post about Nikos Logothetis.

So here's a few additions in the story about imaging genetics, the study where neuroimaging is informed by genetic variations:

What is the hidden structure of the genome? The human genome is much more diverse and dynamic than what one could get the impression of through normal genotyping studies. Variations abound, and in a number beyond what we normally think. These are not just curiosities; they play important roles in the way that the body (and brain) develops and functions. In a news article in Nature, Andrew Sharp briefly presents two ways to study the genome, and how the new insight about variability influences current research. From the article: "The genetics community is only just beginning to appreciate the extent of structural variation present in the human genome and its role in human disease. Although we now have a finished human genome sequence in hand, geneticists have begun to appreciate that this is in fact a highly dynamic structure. The realization that the long-awaited reference sequence represents only one version of the human genome, which has significant large-scale variation between any two individuals, means that techniques such as these for investigating genome structure will be in high demand."

What is a gene? In another Nature news article, Helen Pearson discusses how RNA is becoming the new field of studying how genetic information is implemented in the organism. From the article: "In classical genetics, a gene was an abstract concept – a unit of inheritance that ferried a characteristic from parent to child. As biochemistry came into its own, those characteristics were associated with enzymes or proteins, one for each gene. And with the advent of molecular biology, genes became real, physical things – sequences of DNA which when converted into strands of so-called messenger RNA could be used as the basis for building their associated protein piece by piece. The great coiled DNA molecules of the chromosomes were seen as long strings on which gene sequences sat like discrete beads. This picture is still the working model for many scientists. But those at the forefront of genetic research see it as increasingly old-fashioned – a crude approximation that, at best, hides fascinating new complexities and, at worst, blinds its users to useful new paths of enquiry."

How heritable is Alzheimer's Disease? According to a study by Margaret Gatz and colleagues, the influence of genetics is very high, and higher than often thought. For a best-fit model estimation, genes are though to be responsible for 79%. Environmental risk factors do indeed play a role, but not as high as we might think from previous reports.

How to read the genome and its functional implications? Simon Fisher and Clyde Francks have a nice review in Trends in Cognitive Science on the genetic influence of dyslexia. Dyslexia is being used here as a model to explain how to use genotyping in a meaningful way to study the genetic influence on cognitive function and dysfunction.

These are just to mention a few important articles from this burgeoning scientific field. More are bound to follow, and we'll cover them the best we can here at BrainEthics.

(BTW, the image used here is from the Rakic Lab, illustrating the migration of brain cells during development. At the Human Brain Mapping conference in Toronto last year, Rakic held a superb talk showing movies of migrating cells. It was a true eye-opener to those of us who had not realized how much actually goes on during development. I can see that they are setting up a page for a media gallery, hopefully that will cover some of these excellent movies. I'll be watching that page closely.)


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In a just published paper in PNAS by Le Bihan and colleagues, a technique called diffusion MRI is used to measure the activation of the brain. This is rather unusual. Diffusion MRI is normally used to measure the diffusion, or movement, of water in the brain. Grey matter is relatively disorganized and water is less restricted than in white matter, where myelinated fibres constrain the direction of molecular movements. This is illustrated by a figure that I have made for an upcoming Elsevier textbook (full size image opens in new window).

Using this method it is possible to map out the major fibre tracts in the brain. Even better, it is possible to do tractography: following the white matter fibre bundles from a seed point and calculate the physical connections from this (see figure below)

Le Bihan and colleagues takes diffusion MRI one step further by applying it to study the brain's activation! The background assumption is: as neurons get (relatively more) activated, a lot of physical movement occurs in and around the neurons. The neuron consumes more oxygen and nutrients; internally in the cell, a lot of movement of these substances, and the movement of signal substances occur, inaddition to energy distribution throughout the cell; and the whole cascade of neurotransmitter release and effects is due to movement across the synaptic cleft. So neural firing causes movement both inside and outside the cell.

Now, what did diffusion MRI do again? It measures the movement (!) of water molecules in the brain. As a brain region gets activated, molecules move relatively more than at rest. As a reult, it should be possible to measure brain activity by looking at where in the brain that molecule movement increases. This is exactly what Le Bihan and colleagues have done. And they provide neat results that it actually works! And, as they argue, diffusion fMRI is a more direct measure of neural activation than the more used BOLD fMRI, which is an index of a complex and delayed mechanism of relative blood oxygenation in regions of the brain.

What remains to find out is what the signal really represents. While it is thought that neuroimaning tools such as BOLD fMRI and EEG measures the activation and energy consumption in the dendrites, we know little about the underlying neural mechanism in diffusion fMRI. Could it be mostly due to movement across the synaptic cleft, and hence be a measure of action potentials; could it be due to movement within the cells; or due to transport of molecules (oxygen + nutrients) across the cell membrane; or all at once? Today, this is an open question. But the mere idea of having yet another MRI tool for measuring the brain's activation, and with good spatial resolution plus the promise of better temporal resolution, is really worth noticing.

We'll be tracking the development of this tool closely!


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Several studies today are looking at different changes in the brain structures in healthy vs. non-healthy development, and other brain diseases. In a study just published in the Journal of Neuroscience, David van Essen and colleagues studied the brains of people suffering from Williams syndrome, a rare genetic developmental order that is characterised by "a distinctive, "elfin" facial appearance, an unusually cheerful demeanor, ease with strangers, mental retardation coupled with an unusual facility with language, a love for music, cardiovascular problems such as supravalvular aortic stenosis, and transient hypercalcemia."

In this study, van Essen found that the brains of these people were different in 33 areas, and that there was a surprising bilateral symmetry to the differences from the control group. 16 changes in the left hemisphere was mirrored by 16 changes in the right hemisphere. In addition, hemispheric asymmetry of the temporal lobe was generally smaller in this group compared to controls.

Since Williams syndrome is caused by a deletion of material in region q11.2 of chromosome 7, we can speculate that these functions normally subserve both the development and function of these brain regions, in addition to an effect of strength of hemispheric specialization and lateralization. One interesting point is that the genes affected here produce diverse changes in the brain, not strictly following traditional cognitive-functional borders. Williams syndrome is not a brain-only disease, also affecting other somatic functions such as the cardiac system. One can only speculate at this point how many regions of chromosome 7 affects the brain, how many the cardiac system, and to what extent there is an overlap between gene-brain-body mapping.

Here is the abstract:

Van Essen D, Dierker D, Snyder A, Raichle ME, Reiss A, Korenberg J. Symmetry of cortical folding abnormalities in Williams syndrome revealed by surface-based analyses. The Journal of Neuroscience, May 17, 2006.

We analyzed folding abnormalities in the cerebral cortex of subjects with Williams syndrome (WS), a genetically based developmental disorder, using surface-based analyses applied to structural magnetic resonance imaging data. Surfaces generated from each individual hemisphere were registered to a common atlas target (the PALS-B12 atlas). Maps of sulcal depth (distance from the cerebral hull) were combined across individuals to generate maps of average sulcal depth for WS and control subjects, along with depth-difference maps and t-statistic maps that accounted for within-group variability. Significant structural abnormalities were identified in 33 locations, arranged as 16 bilaterally symmetric pairs plus a lateral temporal region in the right hemisphere. Discrete WS folding abnormalities extended across a broad swath from dorsoposterior to ventroanterior regions of each hemisphere, in cortical areas associated with multiple sensory modalities as well as regions implicated in cognitive and emotional behavior. Hemispheric asymmetry in the temporal cortex is reduced in WS compared with control subjects. These findings provide insights regarding possible developmental mechanisms that give rise to folding abnormalities and to the spectrum of behavioral characteristics associated with WS.

While discussing Williams syndrome, it is important to mention the work of Andreas Meyer-Lindenberg, who is doing an impressive amount of work on this rare disease, and reporting dazzling findings on the relationship between genes, brain and (social) behaviour.


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Nikos LogothetisYesterday, Nikos Logothetis gave a great talk at the annual keynote lecture for the Copenhagen University Research Priority Area "Body and Mind". In the lecture, Logothetis touched upon several issues on the workings of the brain – from his perspective. But at the later Master-class where it was possible to have a one-to-one discussion with him, there was little doubt about Logothetis' view about how to understand the workings of the brain and mind. And especially how to study it with neuroimaging techniques.

BOLD fMRI, he claims, can tell us something about where in the brain something is happening. But other than that, it can tell us very little about what happens in that region. In other words, Logothetis is not fascinated by the current "blobology" (AKA neo-phrenology) that is seen in much of today's neuroimaging research papers. Logothetis argues that in order to say anything intelligible about brain function, we need to go beyond the current focus on where in the brain something is happening. We need to move towards integrating multiple imaging modalities in order to get a better picture of neural processes. Logothetis himself suggested and talked mostly about EEG and deep electrodes, and MEG, in combination with BOLD fMRI. But at the master class Logothetis also discussed the use of multiple MRI modalities such as the combination of perfusion MRI (Arterial Spin Labeing) and BOLD fMRI. But BOLD alone? No way!Multimodal approach to imaging ageing

To the right you can see the key slide from my talk at the Master class, positing the problem of combining measurements of perfusion and atrophy to BOLD fMRI measures, as co-variates. In ageing studies, we can see BOLD fMRI changes, but there is a question whether the well-known changes in brain perfusion and atrophy plays a role in chaning the BOLD signal. That was my question to Logothetis. Click on the image to see the full details.

Oh, and did I mention that Logothetis gives little for the current neo-phrenological thoughts about a 1:1 match between a cognitive function and a brain area? The brain doesn't work that way…


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The Center for Integrated Molecular Brain Imaging (CIMBI) is now officially opened. The overall idea behind this massive project is to study cognitive, psychological and biological phenomena with a multi-modal approach, combining data from genotyping, PET scanning and MRI scanning. The main project of CIMBI is to study “the neural bases of personality dimensions that predispose individuals to affective and substance use disorders, with special emphasis on the serotonergic neurotransmitter system”. In other words: to study the biological mechanisms behind personality formation. They are currently recruiting (and looking for) the best-qualified personnel for the new available positions.

One part of the CIMBI project involves looking at how genes coding for seretonin affect the seretonin transport function, and furthermore how the function of seretonergic areas of the brain operate depending on the genetic makeup of a subject. In this latter part, I am involved in doing the MRI study, including three fMRI protocols:

  1. Processing of facial affect – how genes affect the processing of facial expression, especially the difference between aversive and neutral faces.
  2. Memory processing in the medial temporal lobe (MTL) – how different parts of the MTL make different contribution to specific phases in memory processing: preparation, encoding, rehearsal and retrieval.
  3. Categorization task – the difference between choosing between high-specificity options (within-category choices, e.g. “donkey or zebra”) or low-specificity options (between-category choices, e.g. “living or non-living”)

Data will be combined between fMRI (BOLD and perfusion), genotype and seretonine function as measured with PET. In addition we are looking at the relative contribution of changes in volume and form of MTL areas to the overall signal differences found in other modalities.

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It has taken me some time to digest the impressions from the 1½ day International Imaging Genetics conference held in Irvine a couple of weeks ago. This is probably because it was hard to sort the different issues out initially. The conference had speakers from genetics, statistics and neuroimaging.

Correspondingly, there were three major Imaging Genetics (IG) themes one can sort this conference into: genetics methods, statistical approaches and visualisations, and neuroimaging related issues.

IG Statistics
Doing statistics is a humbling experience, and the IG conference was a wonderful reminder of this. In the burgeoning field of IG, many studies that have been published in top rated journals would probably not even make it past the editor today. On the other hand, it’s only been a few years since one could get an article published in Nature or Science because you found some signal in the brain during a cognitive task. Anyway, the statistics were no exception to all the demonstrations associated with doing statistics, although it came with a twist. Since IG is a combination of at least two approaches – genetics and neuroimaging – each study must seek to accommodate to the pitfalls and premises of both approaches. This is not a simple task: neuroimaging contains a multitude of different statistical approaches, in addition to an overwhelming number of issues and pitfalls when it comes to the design, collection and preparation (i.e. preprocessing) stages in a study. I can only guess that the same goes for genetics.

During lunch, I heard a geneticist asking “What is a voxel? Is it like a pixel?”. So IG has a long way to go in order to reach a full, common understanding and sharing of ideas, concepts and methods. I’m certainly asking just the same kind of beginner’s questions about genetics. So when Bernie Devlin said about the speaker before him, Tom Nichols, “I am glad that Tom says he does not know much about genetics. I can assure you – he doesn’t!” he was making this very point.

But the IG statistics also had some very interesting and directly useful aspects. Nik Schork talked about different ways to visualise and analyse IG data, and demonstrated a most impressive toolbox of different methods for doing so. Unfortunately, I can’t find any illustrations online (nor in any article) to show this. I’ll get back with as soon as something comes up. See also this video of one of his talks.

This part was probably the hardest, since so much relied on one’s knowledge about genetics. Haplotype, SNPs, alleles and so on, just to mention a few. If you have not heard about this before, you’re not alone. But even knowing about these keywords and concepts, bringing them together with neuroimaging really poses a test of your working memory ability… I’ll expose my lack of understanding of these issues here,yet still mention the hapmap project and its tremendous usefulness in assessing the distribution of haplotypes in different populations. Not directly viable when doing neuroimaging studies, but it can influence the likelihood that you choose to study one haplotype rather than others.

This is by far the easiest part of the conference, at least to me, and I guess geneticists had the harder time in this part of the conference. However, one can also see that the neuroimaging studies that were presented here really demonstrated the end results of the tedious work that had been presented in the foregoing talks. Since neither the basics of neuroimaging signals, stats or pitfalls were presented as such, researchers from other – non-neuroimaging – approaches probably had an easier time than us genetics-nogoods had previously…

Basically, one could say that IG brings a new tool to look at what drives your neuroimaging data, even in healthy individuals. Studies by researchers as Ahmad Hariri, Dan Wainberger and Andreas Meyer-Lindenberg illustrates this point clearly. Their studies have now demonstrated that a natural variation in specific alleles produce different responses in not only the brains of the different subjects, but even how behaviour is affected. This includes a study of how long and short versions of a seretonin transporter gene affects brain regions affected in depression. It can also demonstrate how genes affect the brain to produce a higher risk of developing schizophrenia, or how a gene influences brain size. It can also be used to enhance our understanding of different cognitive functions, such as attentional networks, in the brain.

For last year’s conference you can now download video recordings and the slideshows of the talks from the Irvine IG conference homepage. I suspect that the talks for this year will be available soon, too.

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