Category Archives: brain

On Staying In Touch

from reference 1

In the world of neurotechnology, the prospect of exploiting the brain’s inherently electrical quality by interfacing it with our own devices has become fairly commonplace. However, the main problem with such techniques is that the methods we have for making connections with the brain are inherently short term (on the order of weeks). This makes the dream of using implanted electronic interfaces for applications like controlling robot prosthetics one relegated to the future.

However, a study recently published in Nature details an advance in this field. The authors of this study were able to use brain signals from the motor cortex of a monkey to control his own limb (they’d anesthetized the normal neural pathways to make sure the endogenous connections were inoperable during the test). The unique quality of this feat lay, however, in the device used to read the signals from the monkey’s brain. The implanted electrode had small piezoelectric motors which allowed it to move around in the monkeys brain in small steps (1 micrometer at a time), so that it was able to move towards strong signals, and back off neurons when it got to close, to keep from damaging them.

The connections are still only maintainable for about a month, but this type of technology and thinking is exactly what is needed to turn long-term electrical interfacing with the human brain into a reality.

1. Moritz CT, Perlmutter SI, Fetz EE. Direct control of paralysed muscles by cortical neurons. Nature, doi:10.1038/nature07418

On the nth Sense

from reference 1

It is widely documented that pheromones effect the behavior of insects, fish and mammals. However, locating both the pheromone molecules themselves and the anatomy for detecting them has proven challenging. A recent study, however, has identified the cells responsible for detecting so-called ‘alarm pheromones’ in mice1.

The image above shows the implicated structures at various levels of detail. In the upper left, you can see a section of a mouse head, with a box around the GG: the Gruenberg ganglion, named for Hans Gruenberg, who first identified the set of cells in 1973.

There is every reason to assume that human beings are susceptible to the effects of pheromones, especially since the ganglion identified in the study mentioned above is present in humans. The prospect of having our behavior (or physiology in general) manipulated by artificial use of these molecules is both exciting and scary.


1. Brechbühl J, Klaey M, Broillet M-C. Grueneberg ganglion cells mediate alarm pheromone detection in mice. Science 321: 1092-1095, 2008.

On the Importance of Single Spikes

from reference 1

As mentioned numerous times before in this forum, neurons in the brain communicate by action potentials: pulses of voltage that usually propagate from the cell body (soma) down a specialized outcropping of membrane called the axon which synapses onto other neurons. Usually, these synapses link pre-synaptic axons to post-synaptic dendrites, cellular structures specialized for recieving input.

Until recently, it was thought impossible for a single action potential, initiated in soma, to cause a second, post-synaptic neuron to fire an action potential; rather, as has been extensively documented, single neurons require many simultaneous dendritic inputs which are summed together to cause an action potential to be initiated in the soma. Recent research, however, has identified a cell type found exclusively in the cerebral cortex of human beings which seems to contradict this generalization. These neurons, termed “chandelier cells” are able to cause a chain of post-synaptic events (action potentials in several cells) lasting up to, on average, 37 milliseconds, ten times longer than had been previously assumed possible1.

The article reporting these findings, published in the estimable journal PLoS Biology, describes one feature that the authors feel is of paramount importance to this phenomenon. Apparently chandelier cells are much more likely to make axo-axonic connections. That is, they send their pulses of activity not to dentrites, but to other axons. The reason for this somewhat exotic type of connectivity is that chandelier cells normally turn off the output of other neurons by sending inhibitory signals that cancel action potentials being sent down axons of the chandelier’s targets. It seems then, that single chandelier cell action potentials inhibit other cells which are themselves inhibitory, indirectly exciting the targets of these secondary inhibitory cells.

The relevance of these findings to human cognition or consciousness is unclear, but this represents a significant advancement for our understanding of the functional connectivity of the human brain.

1. Molnár G, Oláh S, Komlósi G, Füle M, Szabadics J, et al. (2008) Complex Events Initiated by Individual Spikes in the Human Cerebral Cortex. PLoS Biol 6(9): e222 doi:10.1371/journal.pbio.0060222

On Rodent Parkinsons

The cover of the journal Brain

Therapies based on stem cells rely heavily on our ability to coax these blank-cellular-slates into taking on specific forms. Stem cells are exciting as possible sources of medicinal therapy because they have the potential to become any type of cell in the body, but in order for their utility to be realized, we must be able to reliably effect their fates. The process of turning a stem cell into a specific cell type is called, logically, differentiation. With the exception of the immune system, the brain has more cell-types than any other organ, not to mention some of the most differentiated (exotic or distinct) types. Thus, many scientists are busily engaged in the activity of deducing molecular algorithms for deterministic control of their cellular end-state.

One disease where there seems to be a clear connection between cell-type-specific disfunction and pathology is Parkinson’s Disease. In this debilitating condition, the afflicted progressively loose motor function due to a lack of stimulation of their motor corticies (the area responsible for directing movement in the human brain) by dopaminergic neurons found in the amygdala (another brain region associated with emotion and reward). Further, it appears to be the case that the reason for this lack of stimulation is simply a lack of production of dopamine by these dopaminergic amygdalar neurons. The cell-type specificity of the disease makes it an an excellent candidate for treatment by replacing the existing hypoactive neurons with newly differentiated stem cell versions of their kind, which should have normal dopamine production abilities.

A recent paper appearing in the journal Brain reports the results of a study in which the researchers have achieved just such a therapeutic cell-type replacement in rats with a “model” of human Parkinson’s disease (ref. 1). They report that motor function was restored by this approach, and further that the longevity of the differentiated cells was related to their restorative efficacy. Further examples of work like this promise to revolutionize the treatment of a host of diseases.

1. Sanchez-Pernaute R, Lee H, Patterson M, Reske-Nielsen C, Yoshizaki T, Sonntag KC, Studer L, Isacson O. (2008) Parthenogenetic dopamine neurons from primate embryonic stem cells restore function in experimental Parkinson’s disease. Brain.

On the Positive Effects of Variability

from reference 1

An emerging idea in neuroscience is that noise is a good thing, in moderation. Neural activity is very noisy, there is a large degree of variability in the temporal and frequency domains of the spiking of brain cells. It is thought that this variability actually contributes to the robustness of the system. One concrete example is stochastic resonance. In that phenomenon, randomly perturbing neural activity can push it over some threshold such that a sensory event is detected, or an ambiguous decision is made, one way or the other. It may be difficult to see this as beneficial, but especially as we are fantastic learning machines, simply making a decision, or having a perceptual event occur (even when there has been none) contributes to the system’s learning abilities far more than indecision or non-detection.

In a more macroscopic example, a recent paper analyzing variability in brain activity across several age groups has found it to be quite positive. “Behaviorally, children showed slower, more variable response times (RT), and less accurate recognition than adults. However, brain signal variability increased with age, and showed strong negative correlations with intrasubject RT variability and positive correlations with accuracy. Thus, maturation appears to lead to a brain with greater functional variability, which is indicative of enhanced neural complexity. This variability may reflect a broader repertoire of metastable brain states and more fluid transitions among them that enable optimum responses. Our results suggest that the moment-to-moment variability in brain activity may be a critical index of the cognitive capacity of the brain.1

1. McIntosh AR, Kovacevic N, Itier RJ. (2008) Increased brain signal variability accompanies lower behavioral variability in development. PLoS Comput Biol. 4(7):e1000106.

On Language Influencing Non-verbal Thought

from reference 1

Does the language we speak influence our non-verbal thoughts? This question is a stratifying one: some think that language is synonymous with thought, while others consider it a component of our mental abilities or a type of output, no more representative of underlying cogitation than the way we walk or move our arms.

A paper published in the Proceedings of the National Academy of Science weighs in on this matter with experimental results indicating that individuals who speak very different languages (English, Turkish, Chinese, & Spanish) seem to non-verbally represent events in similar ways.

Specifically, in one task, the researchers asked their subjects to communicate an event such as “boy tilts glass” (read in each individual’s native language) with gestures. In a second task, they were asked to reconstruct an event using pictures. In order to quantify performance in these tasks, the researchers examined the ordering that gestures or pictures were used. They reasoned that because grammatical structures dictate that words be used in potentially divergent ways depending on language, that this structure might extend to the order in which gestures or pictures are used as well. Interestingly, they found that there was no quantitative difference in performance between speakers of different languages, suggesting that there is a common underlying mode of event representation which is minimally influenced by spoken language.

1. Goldin-Meadow S, So WC, Ozyürek A, Mylander C. (2008) The natural order of events: how speakers of different languages represent events nonverbally. Proc Natl Acad Sci USA. 105(27):9163-8.

On The Wiring Diagram

From reference 1

The human brain has roughly 100 Billion neurons and each neuron has between 1000 and 10000 synapses (connections), thus approximately 500 Trillion synapses. This makes the problem of determining the connectivity, or the wiring diagram of the brain absurdly complex. This is one of the most fundamental problems confronting neuroscientists today because the solution to many problems of how the brain works would be made much easier if we simply knew the structure that it is built on.

A recent piece of computational research (published a wonderful PLOS journal) suggests a novel statistical method to identify which synapses of a given neuron are active at a given time. The author of this study simulated the output of many single neurons when a particular subset of it’s synapses were active. This characterization was based on the number of action potentials the neuron fired in response to the activity of these many specific synapses. Next, the author examined the changes in the output when a single additional synapse was activated along with the baseline subset. He found that if he simulated the addition of one synapse ~80 times, he could measure significant changes in the output of the simulated neuron such that it was possible in subsequent tests to reliably predict when this synapse was active.

The authors suggestion is that taking this technique out of the computer and into the world of real brains (or small slices of brain, as is commonly employed), would facilitate the task of elucidating the numerous connections in the brain. While this is true, it must be said that this method is good for asking the following question: Which neurons are connected to one neuron that I know very well? In other words, somebody interested in applying this work would have to have one neuron of interest and then stimulate every other neuron that might be connected to it in order to determine the connectivity. In this sense, the approach is a far cry from revealing the wiring of the brain, but it certainly does help.

1. Bhalla US. (2008) How to record a million synaptic weights in a hippocampal slice. PLoS Comput Biol. 4(6):e1000098.

On The Brain In Your Face

Figure 1 (from reference 1) showing the employed stimuli (a) and a schematic of the model they used (b).

Commonly held wisdom says that processing of visual features such as lines, forms, and motion is limited to higher cortical areas (for example, the medial temporal lobe, or area MT). Recent research shows, however, that the retina itself can extract motion signals, underscoring the subtle computational prowess of the bit of your brain that lives in your eye.

1. Baccus, S. A., Olveczky, B. P., Manu, M. & Meister, M. (2008) A Retinal Circuit That Computes Object Motion. The Journal of Neuroscience, 28(27):6807-6817