Monday, February 1, 2016

Highlights and new discoveries in Neuroscience (January 2016)

In the first issue of this monthly digest series you can find out how researchers can read your mind to predict what you're seeing in real time, what the brain has in common with the World Wide Web, why the brain might require the same amount of energy when at rest as compared to when it is doing something massively complicated, and much more.

Decoding brain waves to predict what someone is seeing in real time

For the first time, scientists have been able to predict in real-time what category of objects people are viewing simply by decoding brain waves. By looking at the combined information of event-related potentials (voltages from hundreds of thousands of neurons activated by an image) and broadband spectral changes (processing of power measurements across a wide range of frequencies), researchers from Rajesh Rao's lab at the University of Washington were able to classify randomly shown grayscale images of faces and houses using Fisher linear discriminant analysis (LDA). Their analyses revealed that the two recorded signals carry different physiological information, and, when used together, allow for unprecedented accuracy and precision in decoding human perception.

The subjects, watching a computer monitor, were shown a random sequence of pictures: brief (400 millisecond) flashes of images of human faces and houses, interspersed with blank gray screens. Their task was to watch for an image of an upside-down house and verbally report this target, which appeared once during each of 3 runs (3 of 300 stimuli). Patients identified the target with less than 3 percent errors across all 21 experimental runs.

Averaged broadband power at two multi-electrode locations (1 and 4) following presentation of different images; note that responses to people are stronger than to houses. (credit: K.J. Miller et al./PLoS Comput Biol)

The computational software sampled and digitized the brain signals 1,000 times per second to extract their characteristics. The software also analyzed the data to determine which combination of electrode locations and signal types correlated best with what each subject actually saw. Using cross-validation, the software was able to predict with 96% accuracy whether and when (within 20 ms) subjects were seeing a house, a face, or a gray screen.

The research, published 28 January in PLOS Computational Biology, may lead to an effective way to help locked-in patients (who were paralyzed or have had a stroke) communicate, the scientists suggest.

Sources: KurzweilAI, PLOS Computational Biology.

Montreal Neurological Institute (MNI) joins open-science movement to accelerate science

Guy Rouleau, the director of McGill University's Montreal Neurological Institute (MNI) and Hospital in Canada, has announced that starting this year, any work done at the renowned institute will conform to the principles of the open-science movement. This means that any and all results and data will be made freely available at the time of publication. Because of this, the institute will probably not be able to pursue any patents related to any of its discoveries.

MNI will be the first scientific institute to do this. "It's an experiment; no one has ever done this before," Rouleau says. The intent is that neuroscience research will become more efficient if duplication is reduced and data are shared more widely and earlier. Opening access to the tissue samples in MNI's biobank and to its extensive databank of brain scans and other data will have a major impact, Rouleau hopes. "We think that it is a way to accelerate discovery and the application of neuroscience."

Let's hope that other institutions will follow suit.

Source: Science

Grand Loop Theory of the brain

IBM Research have published a theory of how the brain works based on information theory, which could help explain the high metabolic cost of resting state dynamics; or why the brain requires similar amounts of energy when at rest as compared to when it is doing something massively complicated. Their Grand Loop theory posits that the brain is in essence an information-based exchange network, constantly looping signals across the major sensory, limbic, and motor categories of Brodmann areas to improve the efficiency of information processing.

(A) Granularity of different neorcortical areas, adapted from von Economo (1929). Colors at bottom correspond to the map in (B). (B) von Economo's neocortical tiling based on the granularity of large regions of neocortex spanning multiple Brodmann areas. The location of three Brodmann areas per stage are waypoints along a feed forward Grand Loop (arrows). (C) These Brodmann areas are connected based on projection data. Evidence that feed forward connections progress from granular to agranular areas provides directionality. The reciprocal feedback loop is not shown. (credit: James Kozloski/Front Neuroanat)

This is done even at rest, where consequent loops of information supposedly increase the signal entropy. In an evolutionary context, this activity might be viewed as pre-adapating the brain to selecting novel behaviors in novel contexts, before engaing with the environment. Once the repeated loop traversals have pre-adapted the brain, the environment can be explored to seek reward.

Two distinct functional signaling pathways are hypothesized: feed forward for driving a supragranular entropy maximizing network, and feedback for traversal of a infragranular behavior generation network. When these functional pathways operate out of their expected regime, the result is neurodegenerative diseases.

Source: Frontiers in Neuroanatomy.


Using optogenetics and other technology, researchers have for the first time precisely manipulated the bursting activity of the thalamus, demonstrating that coordinated bursts of activity serve to focus the brain's attention on issues requiring immediate attention. (Cell Reports)

New data by researchers at the Salk Institute reveal that the brain's memory capacity might be in the petabyte range, ten times higher than previously thought, in the same ballpark as the World Wide Web. They found that each synapse might store 4.7 bits, which is equivalent to a minimum of 26 distinguishable synaptic strengths. (eLife)

A Dartmouth College study sheds light on how the brain fills in the gaps of how we visually perceive the world around us. They found that intermediate object features, which aren't in the retinal signals but are inferred during kinetic transformation, are reconstructed in neural responses at early stages of cortical processing, presumably via feedback from high-level brain areas. (PNAS)

Researchers at UC San Francisco have found that boys and girls with sensory processing disorder (SPD) have altered pathways for brain connectivity when compared to typically developing children, and the difference predicts challenges with auditory and tactile processing. (Frontiers in Neuroanatomy)

New evidence was found for the expansion of the precuneus in human evolution. The precuneus is a major hub of brain organization, a central node of the default-mode network, and plays an essential role in visuospatial integration. Together, the comparative neuroanatomical and paleontological evidence suggest that precuneus expansion is a neurological specialization of Homo sapiens that evolved in the last 150,000 years that may be associated with recent human cognitive specializations. (Brain Structure and Function)

Researchers at Penn Medicine grew neural networks in their lab that can be safely delivered with minimal disruption to brain tissue, in order to replace lost axonal tracks in the brains of patients with severe head injuries, strokes, or neurodegenerative diseases. (Journal of Neural Engineering)

Next month

Things to look out for next month:

Anything I missed? Sound away in the comment section! Have something of interest or want your discovery to be featured in next month's issue? Let me know via mbeyeler (at) uci (dot) edu.

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