The Bio-Rad PCR song

Just to finish off our introduction to the brave new world of biotechnology advertising, here is the Bio-Rad PCR song. PCR, the polymerase chain reaction, is a laboratory technique used to amplify DNA that uses thermal cycling units made by Bio-Rad and others.

Buy an automatic pipette from a boy band?

I am sitting now in my office in Bock Laboratories at the University of Wisconsin, where I ran a research factory for 30 years, generating Ph.D.s, Post-Docs, and some information on how our eyes turn light into a nerve signal. (My office as a retired professor is what I call a 'view with a room', and is actually upstairs at the top of the building in which my factory occupied half the third floor.) During that period I purchased hundreds of automatic pipettes (for accurately delivering small volumes of liquid) from the Eppendorf company, ordering from a simple dry brochure, and occasionally seeing an add in a scientific magazine.

Here, then, is my latest "Oh my Gawd, how things have changed" experience. Eppendorf using a Boy Band video to advertise its product:

As new kind of science, as data deluge makes the scientific method obsolete...

An article by Chris Anderson in Wired Magazine, pointed out to me by my son Jon, argues that science as we have known it has ended. The argument is that the quest for knowledge that used to begin with grand theories now, in the petabyte age, begins with massive amounts of data. Google has set the new model for science. I show some clips here, and then follow with the contra argument by John Timmers that follows):

Google conquered the advertising world with nothing more than applied mathematics. It didn't pretend to know anything about the culture and conventions of advertising — it just assumed that better data, with better analytical tools, would win the day. And Google was right...Google's founding philosophy is that we don't know why this page is better than that one: If the statistics of incoming links say it is, that's good enough. No semantic or causal analysis is required. That's why Google can translate languages without actually "knowing" them (given equal corpus data, Google can translate Klingon into Farsi as easily as it can translate French into German). And why it can match ads to content without any knowledge or assumptions about the ads or the content.

The hypothesize-model-test model of science is becoming obsolete...The models we were taught in school about "dominant" and "recessive" genes steering a strictly Mendelian process have turned out to be an even greater simplification of reality than Newton's laws. The discovery of gene-protein interactions and other aspects of epigenetics has challenged the view of DNA as destiny and even introduced evidence that environment can influence inheritable traits, something once considered a genetic impossibility...the more we learn about biology, the further we find ourselves from a model that can explain it...There is now a better way. Petabytes allow us to say: "Correlation is enough." We can stop looking for models. We can analyze the data without hypotheses about what it might show. We can throw the numbers into the biggest computing clusters the world has ever seen and let statistical algorithms find patterns where science cannot.

The best practical example of this is the shotgun gene sequencing by J. Craig Venter. Enabled by high-speed sequencers and supercomputers that statistically analyze the data they produce, Venter went from sequencing individual organisms to sequencing entire ecosystems. In 2003, he started sequencing much of the ocean, retracing the voyage of Captain Cook. And in 2005 he started sequencing the air. In the process, he discovered thousands of previously unknown species of bacteria and other life-forms.

Venter can make some guesses about the animals — that they convert sunlight into energy in a particular way, or that they descended from a common ancestor. But besides that, he has no better model of this species than Google has of your MySpace page. It's just data. By analyzing it with Google-quality computing resources, though, Venter has advanced biology more than anyone else of his generation.

This kind of thinking is poised to go mainstream. In February, the National Science Foundation announced the Cluster Exploratory, a program that funds research designed to run on a large-scale distributed computing platform developed by Google and IBM in conjunction with six pilot universities. The cluster will consist of 1,600 processors, several terabytes of memory, and hundreds of terabytes of storage, along with the software, including Google File System, IBM's Tivoli, and an open source version of Google's MapReduce. Early CluE projects will include simulations of the brain and the nervous system and other biological research that lies somewhere between wetware and software.

Here is the immediate rejoinder to this article from John Timmers at Ars Technica.

Every so often, someone (generally not a practicing scientist) suggests that it's time to replace science with something better. The desire often seems to be a product of either an exaggerated sense of the potential of new approaches, or a lack of understanding of what's actually going on in the world of science. This week's version, which comes courtesy of Chris Anderson, the Editor-in-Chief of Wired, manages to combine both of these features in suggesting that the advent of a cloud of scientific data may free us from the need to use the standard scientific method.

It's easy to see what has Anderson enthused. Modern scientific data sets are increasingly large, comprehensive, and electronic. Things like genome sequences tell us all there is to know about the DNA present in an organism's cells, while DNA chip experiments can determine every gene that's expressed by that cell. That data's also publicly available—out in the cloud, in the current parlance—and it's being mined successfully. That mining extends beyond traditional biological data, too, as projects like WikiProteins are also drawing on text-mining of the electronic scientific literature to suggest connections among biological activities.

There is a lot to like about these trends, and little reason not to be enthused about them. They hold the potential to suggest new avenues of research that scientists wouldn't have identified based on their own analysis of the data. But Anderson appears to take the position that the new research part of the equation has become superfluous; simply having a good algorithm that recognizes the correlation is enough.

The source of this flight of fancy was apparently a quote by Google's research director, who repurposed a cliché that most scientists are aware of: "All models are wrong, and increasingly you can succeed without them." And Google clearly has. It doesn't need to develop a theory as to why a given pattern of links can serve as an indication of valuable information; all it needs to know is that an algorithm that recognizes specific link patterns satisfies its users. Anderson's argument distills down to the suggestion that science can operate on the same level—mechanisms, models, and theories are all dispensable as long as something can pick the correlations out of masses of data.

Science 2.0 I can't possibly imagine how he comes to that conclusion. Correlations are a way of catching a scientist's attention, but the models and mechanisms that explain them are how we make the predictions that not only advance science, but generate practical applications. One only needs to look at a promising field that lacks a strong theoretical foundation—high-temperature superconductivity springs to mind—to see how badly the lack of a theory can impact progress. Put in more practical terms, would Anderson be willing to help test a drug that was based on a poorly understood correlation pulled out of a datamine? These days, we like our drugs to have known targets and mechanisms of action and, to get there, we need standard science.

Anderson does provide two examples that he feels support his position, but they actually appear to undercut it. He notes that we know quantum mechanics is wrong on some level, but have been unable to craft a replacement theory after decades of work. But he neglects to mention two key things: without the testable predictions made by the theory, we'll never be able to tell how precisely it is wrong and, in those decades where we've failed to find a replacement, the predictions of quantum mechanics have been used to create the modern electronics industry, with the data cloud being a consequence of that.

If anything, his second example is worse. We can now perform large-scale genetic surveys of the life present in remote environments, such as the far reaches of the Pacific. Doing so has informed us that there's a lot of unexplored biodiversity on the bacterial level; fragments of sequence hint at organisms we've never encountered under a microscope. But as Anderson himself notes, the only thing we can do is make a few guesses as to the properties of the organisms based on who their relatives are, an activity that actually requires a working scientific theory, namely evolution. To do more than that, we need to deploy models of metabolism and ecology against the bacteria themselves.

Overall, the foundation of the argument for a replacement for science is correct: the data cloud is changing science, and leaving us in many cases with a Google-level understanding of the connections between things. Where Anderson stumbles is in his conclusions about what this means for science. The fact is that we couldn't have even reached this Google-level understanding without the models and mechanisms that he suggests are doomed to irrelevance. But, more importantly, nobody, including Anderson himself if he had thought about it, should be happy with stopping at this level of understanding of the natural world.

Brain regions active during different economic decisions.

The editor's choice section of science magazine spotlights an interesting paper in J. Neurosci:

When we make economic decisions, for example the purchase of a good or a service, our brain has to perform at least three computations. First, it has to assess the goal value of the good: in economic terms, our maximal willingness to pay. Second, it has to assess the decision value of the good: the goal value minus the unavoidable costs. Third, there is a prediction error, which indicates the deviation from one's expectations of reward; the prediction error is positive when something better than expected happens and negative when the opposite occurs. Unfortunately, these three related quantities are intermingled and are often highly correlated, making it challenging to isolate the neural regions performing these computations.

Hare et al. have attempted to measure goal value, decision value, and prediction error in a single neuroimaging task so that they could dissociate these parameters. They found that ventral striatum activation reflected prediction error and not goal or decision value. However, activity in the medial orbitofrontal cortex and the central orbitofrontal cortex correlated with goal value and decision value, respectively.

Here is a summary figure from the paper:

Figure - Combined activation maps for goal values (GVs), decision values (DVs), and prediction errors (PEs). Activity correlated with GVs in the mOFC is shown in red, activity correlated with DVs in the cOFC is shown in yellow, and activity correlated with PEs in the ventral striatum is shown in green.

ScienceHack - monkey brain moving robotic arm

I stumbled across this site with interesting science videos from a number of areas (biology, psychology, robotics, etc.). They are mainly at a superficial 'gee whiz' level, but quite engaging. Here is the Monkey moving a robotic arm. This is work from the Pittsburgh group, described in Nature, which promises to lead to effective therapy for human patients paralyzed by strokes, spinal-cord injuries and degenerative neuromuscular disease.

Can 'emergence' put spirituality back into nature?

The anti-reductionist view of emergence undergoes cycles of popularity as a philosophical topic. Valerie Hardcastle gives a rather critical review (in Jour. Consciousness Studies, Vol. 14, No. 11, pp.119-122) of a recent collection "The Re-Emergence of Emergence - The Emergentist Hypothesis from Science to Religion" edited by Clayton and Davies (Oxford Univ. Press, 2006). This emergentism is 'feel good' stuff. I think most of us get a bit frightened and a bit dried and shriveled up at the implications of strong reductionism in which all the explanatory arrows point down. Reversing the reductionist’s causal arrow with a comprehensive theory of emergence and self-organization that breaks no laws of physics and yet cannot be explained by them is a laudable project, but as Hardcastle wryly notes, one that continues to fail the "where's the beef" test.

Michael Shermer offers a very appealing gloss in his "Skeptic" column in the Scientific American, with the title: Sacred Science - Can emergence break the spell of reductionism and put spirituality back into nature? He specifically reviews a new book by Stuart Kauffman, Reinventing the Sacred (Basic Books, 2008). Denis Noble also reviews Kauffman's book in Science Magazine. Here are some clips from Shermer's column:
Kaufman:

...reverses the reductionist’s causal arrow with a comprehensive theory of emergence and self-organization that Kaufman says “breaks no laws of physics” and yet cannot be explained by them. God “is our chosen name for the ceaseless creativity in the natural universe, biosphere and human cultures.” In Kauffman’s emergent universe, reductionism is not wrong so much as incomplete. It has done much of the heavy lifting in the history of science, but reductionism cannot explain a host of as yet unsolved mysteries, such as the origin of life, the biosphere, consciousness, evolution, ethics and economics... How would a reductionist explain the biosphere, for example? “One approach would be, following Newton, to write down the equations for the evolution of the biosphere and solve them. This cannot be done,” Kauffman avers. “We cannot say ahead of time what novel functionalities will arise in the biosphere. Thus we do not know what variables—lungs, wings, etc.—to put into our equations. The Newtonian scientific framework where we can prestate the variables, the laws among the variables, and the initial and boundary conditions, and then compute the forward behavior of the system, cannot help us predict future states of the biosphere.”... This problem is not merely an epistemological matter of computing power, Kauffman cautions; it is an ontological problem of different causes at different levels. Something wholly new emerges at these higher levels of complexity.

Similar ontological differences exist in the self-organized emergence of consciousness, morality and the economy...economics and evolution are complex adaptive systems that learn and grow as they evolve from simple to complex...they are autocatalytic, containing self-driving feedback loops...such phenomena “cannot be deduced from physics, have causal powers of their own, and therefore are emergent real entities in the universe.” This creative process of emergence, Kauffman contends, “is so stunning, so overwhelming, so worthy of awe, gratitude and respect, that it is God enough for many of us. God, a fully natural God, is the very creativity in the universe.”

Shermer ends noting that Kaufman's:

God 2.0 is a deity worthy of worship. But I am skeptical that it will displace God 1.0, Yahweh, whose Bronze Age program has been running for 6,000 years on the software of our brains and culture.

Strengths and Limits of fMRI studies on the brain

Nikos Logothetis offers a long and detailed discussion of what we can and cannot learn from brain imaging approaches. I'm giving a few clips from his discussion:

fMRI is not and will never be a mind reader, as some of the proponents of decoding-based methods suggest, nor is it a worthless and non-informative 'neophrenology' that is condemned to fail, as has been occasionally argued.

The principal advantages of fMRI lie in its noninvasive nature, ever-increasing availability, relatively high spatiotemporal resolution, and its capacity to demonstrate the entire network of brain areas engaged when subjects undertake particular tasks. One disadvantage is that, like all haemodynamic-based modalities, it measures a surrogate signal whose spatial specificity and temporal response are subject to both physical and biological constraints. A more important shortcoming is that this surrogate signal reflects neuronal mass activity.


Figure - Two slices of GE-EPI demonstrating the high functional signal-to-noise ratio (SNR) of the images, but also the strong contribution of macrovessels. The yellow areas (indicated with the green arrows) are pia vessels, an example of which is shown in the inset scanning electron microscopy image (total width of inset, 2 mm). For the functional images red indicates low and yellow indicates high.

MRI may soon provide us with images of a fraction of a millimetre (for example, 300 x 300 m² with a couple of millimetres slice thickness or 500 x 500 x 500 m³ isotropic), which amount to voxel volumes of about two–three orders of magnitude smaller than those currently used in human imaging. With an increasing number of acquisition channels such resolution may ultimately be attained in whole-head imaging protocols, yielding unparalleled maps of distributed brain activity in great regional detail and with reasonable—a couple of seconds—temporal resolution. Would that be enough for using fMRI to understand brain function?

The answer obviously depends on the scientific question and the spatial scale at which this question could be addressed—"it makes no sense to read a newspaper with a microscope", as neuroanatomist Valentino Braitenberg once pointed out. To understand the functioning of the microcircuits in cortical columns or of the cell assemblies in the striosomes of basal ganglia, one must know a great deal about synapses, neurons and their interconnections. To understand the functioning of a distributed large-scale system, such as that underlying our memory or linguistic capacities, one must first know the architectural units that organize neural populations of similar properties, and the interconnections of such units. With 10¹⁰ neurons and 10¹⁴ connections in the cortex alone, attempting to study dynamic interactions between subsystems at the level of single neurons would probably make little sense, even if it were technically feasible. It is probably much more important to understand better the differential activity of functional subunits—whether subcortical nuclei, or cortical columns, blobs and laminae—and the instances of their joint or conditional activation. If so, whole-head imaging with a spatial resolution, say, of 0.7 0.7 mm² in slices of 1-mm thickness, and a sampling time of a couple of seconds, might prove optimal for the vast majority of questions in basic and clinical research. More so, because of the great sensitivity of the fMRI signal to neuromodulation. Neuromodulatory effects, such as those effected by arousal, attention, memory, and so on, are slow and have reduced spatiotemporal resolution and specificity.

Logothesis offers a concluding perspective.

The limitations of fMRI are not related to physics or poor engineering, and are unlikely to be resolved by increasing the sophistication and power of the scanners; they are instead due to the circuitry and functional organization of the brain, as well as to inappropriate experimental protocols that ignore this organization. The fMRI signal cannot easily differentiate between function-specific processing and neuromodulation, between bottom-up and top-down signals, and it may potentially confuse excitation and inhibition. The magnitude of the fMRI signal cannot be quantified to reflect accurately differences between brain regions, or between tasks within the same region. The origin of the latter problem is not due to our current inability to estimate accurately cerebral metabolic rate of oxygen (CMRO2) from the BOLD signal, but to the fact that haemodynamic responses are sensitive to the size of the activated population, which may change as the sparsity of neural representations varies spatially and temporally. In cortical regions in which stimulus- or task-related perceptual or cognitive capacities are sparsely represented (for example, instantiated in the activity of a very small number of neurons), volume transmission (see Supplementary Information)—which probably underlies the altered states of motivation, attention, learning and memory—may dominate haemodynamic responses and make it impossible to deduce the exact role of the area in the task at hand. Neuromodulation is also likely to affect the ultimate spatiotemporal resolution of the signal.

This having been said, and despite its shortcomings, fMRI is currently the best tool we have for gaining insights into brain function and formulating interesting and eventually testable hypotheses, even though the plausibility of these hypotheses critically depends on used magnetic resonance technology, experimental protocol, statistical analysis and insightful modelling. Theories on the brain's functional organization (not just modelling of data) will probably be the best strategy for optimizing all of the above. Hypotheses formulated on the basis of fMRI experiments are unlikely to be analytically tested with fMRI itself in terms of neural mechanisms, and this is unlikely to change any time in the near future.

Dan Dennett: Ants, terrorism, and the awesome power of memes

My son Jonathan sent me this link to an engaging talk by Dan Dennett given some time ago. I heard it back then, and think it is worth passing on...

The futurist: machines as smart as ourselves

John Tierney does a nice write up of the debate over the ideas of futurist Ray Kurzweil. (I've always thought that Kurzweil was simple proof of the proposition that if you propose any 10 crazy things, one of them will turn out to be right. People remember the correct prophesy, and forget the mistakes.) Still.... the guy has been right on a number of times. Here is part of the discussion of our cognitive/emotional repertoire being bested by machines ( (possibly piggybacked onto our biological hardware). This event is referred to as "the singularity." Kurzweil proposes that:

..by the 2020s we’ll be adding computers to our brains and building machines as smart as ourselves...This serene confidence is not shared by neuroscientists like Vilayanur S. Ramachandran, who discussed future brains with Dr. Kurzweil at the festival. It might be possible to create a thinking, empathetic machine, Dr. Ramachandran said, but it might prove too difficult to reverse-engineer the brain’s circuitry because it evolved so haphazardly...“My colleague Francis Crick used to say that God is a hacker, not an engineer,” Dr. Ramachandran said. “You can do reverse engineering, but you can’t do reverse hacking.”...Dr. Kurzweil’s predictions come under intense scrutiny in the engineering magazine IEEE Spectrum, which devotes its current issue to the Singularity. Some of the experts writing in the issue endorse Dr. Kurzweil’s belief that conscious, intelligent beings can be created, but most think it will take more than a few decades....He is accustomed to this sort of pessimism and readily acknowledges how complicated the brain is. But if experts in neurology and artificial intelligence (or solar energy or medicine) don’t buy his optimistic predictions, he says, that’s because exponential upward curves are so deceptively gradual at first.

“Scientists imagine they’ll keep working at the present pace,” he told me after his speech. “They make linear extrapolations from the past. When it took years to sequence the first 1 percent of the human genome, they worried they’d never finish, but they were right on schedule for an exponential curve. If you reach 1 percent and keep doubling your growth every year, you’ll hit 100 percent in just seven years.”

Dr. Kurzweil is so confident in these curves that he has made a $10,000 bet with Mitch Kapor, the creator of Lotus software. By 2029, Dr. Kurzweil wagers, a computer will pass the Turing Test by carrying on a conversation that is indistinguishable from a human’s.

You should also check out John Horgan's caustic comments on the whole singularity bit in a special IEEE spectrum feature, which ends with:

Let's face it. The singularity is a religious rather than a scientific vision. The science-fiction writer Ken MacLeod has dubbed it “the rapture for nerds,” an allusion to the end-time, when Jesus whisks the faithful to heaven and leaves us sinners behind.

Such yearning for transcendence, whether spiritual or technological, is all too understandable. Both as individuals and as a species, we face deadly serious problems, including terrorism, nuclear proliferation, overpopulation, poverty, famine, environmental degradation, climate change, resource depletion, and AIDS. Engineers and scientists should be helping us face the world's problems and find solutions to them, rather than indulging in escapist, pseudoscientific fantasies like the singularity.

Blogging as self-medication

Maybe I've found one of the reasons I do this blog (other than to keep me off the streets): An article by Jessica Wapner in the June issue of Scientific American discusses studies on the therapeutic value of blogging. Blogging is claimed to provide physiological benefits similar to those that have been shown for expressive writing (serving as a stress-coping mechanism, improving memory and sleep, and boosting immune cell activity.) Blogging may act as a "placebo for getting satisfied." The blogosphere offers an antidote to social isolation. (Checking out my 'mdbownds' YouTube video postings reveals that the Debussy Reverie video has been viewed 98,739 times and 157 comments made; this mindblog gets 500-600 visitors each day. While this is social connection, I totally don't know any of you people, except for a handful of friends.) I find fleeting virtual world contacts a pallid substitute for real life huggable friends, and sometimes fret that my time spent hunkering over a keyboard provides too convenient an excuse for the harder work of being a robust member of real (versus virtual) social groups.

If you haven't OD'ed on the internet already....

Have a look at this site, which points to "20 websites that can change your life." (with 2 more added by feedback from viewers). Engaging a number of them (especially twitter) would appear to destroy any remnants of time or privacy that your life might contain.

Mind Reading with fMRI

From the Nature Editor's summary:

Recent functional magnetic resonance imaging (fMRI) studies have shown that, based on patterns of activity evoked by different categories of visual images, it is possible to deduce simple features in the visual scene, or to which category it belongs. Kay et al. take this approach a tantalizing step further. Their newly developed decoding method, based on quantitative receptive field models that characterize the relationship between visual stimuli and fMRI activity in early visual areas, can identify with high accuracy which specific natural image an observer saw, even for an image chosen at random from 1,000 distinct images. This prompts the thought that it may soon be possible to decode subjective perceptual experiences such as visual imagery and dreams, an idea previously restricted to the realm of science fiction.

The abstract from Kay et al., followed by one figure:

A challenging goal in neuroscience is to be able to read out, or decode, mental content from brain activity. Recent functional magnetic resonance imaging (fMRI) studies have decoded orientation, position, and object category from activity in visual cortex. However, these studies typically used relatively simple stimuli (for example, gratings) or images drawn from fixed categories (for example, faces, houses), and decoding was based on previous measurements of brain activity evoked by those same stimuli or categories. To overcome these limitations, here we develop a decoding method based on quantitative receptive-field models that characterize the relationship between visual stimuli and fMRI activity in early visual areas. These models describe the tuning of individual voxels for space, orientation and spatial frequency, and are estimated directly from responses evoked by natural images. We show that these receptive-field models make it possible to identify, from a large set of completely novel natural images, which specific image was seen by an observer. Identification is not a mere consequence of the retinotopic organization of visual areas; simpler receptive-field models that describe only spatial tuning yield much poorer identification performance. Our results suggest that it may soon be possible to reconstruct a picture of a person's visual experience from measurements of brain activity alone.


Figure Legend - The experiment consisted of two stages. In the first stage, model estimation, fMRI data were recorded while each subject viewed a large collection of natural images. These data were used to estimate a quantitative receptive-field model for each voxel. In the second stage, image identification, fMRI data were recorded while each subject viewed a collection of novel natural images. For each measurement of brain activity, we attempted to identify which specific image had been seen. This was accomplished by using the estimated receptive-field models to predict brain activity for a set of potential images and then selecting the image whose predicted activity most closely matches the measured activity.

Depressing news: antidepressants don't work?

In the April issue of Nature Reviews Neuroscience, Claudia Wiedemann reviews reactions to a meta analysis by Kirsch et al. of data on antidepressant drugs submitted to the Food and Drug Administration that resulted in the licensing of four of the most commonly prescribed antidepressants: the selective serotonin reuptake inhibitors (SSRIs) Prozac, Seroxat, Effexor and Serzone. For anything but the most severe depression, there was no difference between the drugs and placebos. Kirsch suggests that there is little reason to prescribe anti-depressant medication to any but the most severely depressed patients. The conclusion of the study:

Drug–placebo differences in antidepressant efficacy increase as a function of baseline severity, but are relatively small even for severely depressed patients. The relationship between initial severity and antidepressant efficacy is attributable to decreased responsiveness to placebo among very severely depressed patients, rather than to increased responsiveness to medication.

Amazing Images...

From the Wellcome Image Awards Gallery:

Reality getting to you?

Perhaps try escaping into one of these devices? (I don't think I'll go there just yet....)

Watching waves of activity sweep across the brain

This is sensory physiology in the age of YouTube. Peterson and colleagues have used a voltage sensitive dye technique to watch the wave of first sensory area and then motor area excitation that is caused by a tiny deflection of a face whisker of a mouse:

Single brief whisker deflections evoked highly distributed depolarizing cortical sensory responses, which began in the primary somatosensory barrel cortex and subsequently excited the whisker motor cortex. The spread of sensory information to motor cortex was dynamically regulated by behavior and correlated with the generation of sensory-evoked whisker movement. Sensory processing in motor cortex may therefore contribute significantly to active tactile sensory perception.

The video shows the response when a mouses whisker touches an edge:

The movement of the C2 whisker was filmed with a high-speed camera at 500 Hz in an awake behaving mouse during an active touch sequence. Sensorimotor cortex was simultaneously imaged with VSD. At the time indicated by the vertical dotted line, the whisker contacts the object evoking a spreading sensorimotor response, first in S1 and subsequently in M1. The single trial imaging of cortical activity and the behavioral filming are matched frame-by-frame, synchronized through TTL pulses.

A non-invasive brain-machine interface?

Waldert et al. show that hand movement direction can be decoded from magneto- (MEG) and electro-(EEG) encephalography recordings. They suggest that this might make it possible to design a non-invasive brain machine interface. Here is their abstract:

Brain activity can be used as a control signal for brain–machine interfaces (BMIs). A powerful and widely acknowledged BMI approach, so far only applied in invasive recording techniques, uses neuronal signals related to limb movements for equivalent, multidimensional control of an external effector. Here, we investigated whether this approach is also applicable for noninvasive recording techniques. To this end, we recorded whole-head MEG during center-out movements with the hand and found significant power modulation of MEG activity between rest and movement in three frequency bands: an increase for ≤7 Hz (low-frequency band) and 62–87 Hz (high-{gamma} band) and a decrease for 10–30 Hz (β band) during movement. Movement directions could be inferred on a single-trial basis from the low-pass filtered MEG activity as well as from power modulations in the low-frequency band, but not from the β and high-{gamma} bands. Using sensors above the motor area, we obtained a surprisingly high decoding accuracy of 67% on average across subjects. Decoding accuracy started to rise significantly above chance level before movement onset. Based on simultaneous MEG and EEG recordings, we show that the inference of movement direction works equally well for both recording techniques. In summary, our results show that neuronal activity associated with different movements of the same effector can be distinguished by means of noninvasive recordings and might, thus, be used to drive a noninvasive BMI.

100% accuracy in automatic face detection.

A problem with the automatic face recognition systems being tested in some airport security screening systems is that none can cope with the kind of image variability encountered in the real world. Jenkins and Burton have used a simple averaging technique to increase the accuracy of an industry standard face-recognition algorithm from 54% to 100%. They averaged the images from 20 different photographs for each of 25 male celebrities who were also in a large public online database of 31,077 photographs of famous faces, comprising an average of nine different photos for each of 3628 celebrities - these images were highly variable in their quality and covered a wide range of lighting conditions, facial expressions, poses, and age. Using the FaceVACS (Cognitec Systems GmbH, Dresden, Germany)industry standard face-recognition system that has been widely adopted, they fed this database their averaged images for each of 25 male celebrities who were also in the online database (excluding photos that were both in their sample and in the database). With the averaged images, the database returned the correct identification 100% of the time. When individual photographs were presented to the database the correct identification was returned only ~50% of the time. From their text:

We demonstrated this improvement with a commercially available algorithm and an online face database over which we had no control. We suggest that image averaging enhances performance by stabilizing the face image. With standard photographs, the match tends to be dominated by aspects of the image that are not diagnostic of identity (e.g., lighting and pose). Averaging together multiple photographs of the same person dilutes these transients while preserving aspects of the image that are consistent across photos. The resulting images capture the visual essence of an individual's face and elevate machine performance to the standard of familiar face recognition in humans. It would be technically straightforward to incorporate an average image into identification documents. Doing so would greatly reduce the incidence of face-recognition errors and raise the prospect of a viable automatic face-recognition infrastructure.


Example photographs of Bill Clinton and their average (right). [Image 1, photo by Marc Nozell (www.flickr.com/photos/marcn/534512066); image 2, photo by Roger Goun (www.flickr.com/photos/sskennel/829574139); image 20, photo by Nelson Pavlosky (www.flickr.com/photos/skyfaller/26752190). All photos were used under a Creative Commons license.] Different pictures of a single face can vary enormously, making automatic recognition difficult. Averaging together multiple photos of the same face stabilizes the image, improving performance dramatically.

Sharing posts, bookmarking, related content, etc.

My friend Kelly Doering who does internet consulting and website design, has just helped me dink around a bit with the mechanics of MindBlog. You will now notice a single icon after each post that lets you bookmark or share a post using more than 30 of the major services like StumbleUpon, Reddit, Del.icio.us, Reddit, etc. If you haven't already, you should try the sphere:related content link that may appear just below the bookmark icon with a post that interests you. It sometimes does a very nice job of bringing up related material on other websites.

Poetry in your genome?

Now that we are able to synthesize complete genomes for organisms, we can also write what we want in its individual genes. Andrew Pollack describes several such literary efforts:

You were expecting poetry, perhaps? The secret messages hidden in J. Craig Venter’s synthetic bacterial genome have now been revealed. They are Dr. Venter’s name, and that of his research institute and co-workers....Dr. Venter announced last week in the journal Science that his team had become the first to synthesize the complete DNA of a bacterium. He revealed that the genome had five “watermarks,” sequences of genetic code that would spell words using the letters for the amino acids that would be produced by the DNA...Wired Science reported Monday that it had ferreted out the messages, with help from government scientists. One watermark said “VenterInstitvte,” using the unusual spelling because there is no amino acid represented by the letter “u.”...The other messages were CraigVenter, HamSmith, GlassandClyde and CindiandClyde for his co-authors Hamilton O. Smith, Clyde A. Hutchison III, John I. Glass and Cynthia Andrews-Pfannkoch. A Venter spokeswoman confirmed them...In 2003, scientists from Icon Genetics, a German biotechnology company, engineered the plant Arabidopsis thaliana to contain a line from Virgil’s “Georgics,” with the meaning “Neither can every soil bear every fruit.”