I have no doubt that this will eventually be used to target traumatic memories, but I am also quite sure that is not a good thing. We form memories for a reason - the goal should be to reduce the emotional impact of the memories, not erase them.

Manipulating memory with light: Scientists erase specific memories in mice

Date: October 9, 2014
Source: University of California - Davis  

Summary:
Neuroscientists have used light to erase a specific memory in mice, showing how the hippocampus and cortex work together to retrieve memories.

During memory retrieval, cells in the hippocampus connect to cells in the brain cortex.
Credit: Photo illustration by Kazumasa Tanaka and Brian Wiltgen/UC Davis

Just look into the light: not quite, but researchers at the UC Davis Center for Neuroscience and Department of Psychology have used light to erase specific memories in mice, and proved a basic theory of how different parts of the brain work together to retrieve episodic memories.

Optogenetics, pioneered by Karl Diesseroth at Stanford University, is a new technique for manipulating and studying nerve cells using light. The techniques of optogenetics are rapidly becoming the standard method for investigating brain function.

Kazumasa Tanaka, Brian Wiltgen and colleagues at UC Davis applied the technique to test a long-standing idea about memory retrieval. For about 40 years, Wiltgen said, neuroscientists have theorized that retrieving episodic memories -- memories about specific places and events -- involves coordinated activity between the cerebral cortex and the hippocampus, a small structure deep in the brain.

"The theory is that learning involves processing in the cortex, and the hippocampus reproduces this pattern of activity during retrieval, allowing you to re-experience the event," Wiltgen said. If the hippocampus is damaged, patients can lose decades of memories.

But this model has been difficult to test directly, until the arrival of optogenetics.

Wiltgen and Tanaka used mice genetically modified so that when nerve cells are activated, they both fluoresce green and express a protein that allows the cells to be switched off by light. They were therefore able both to follow exactly which nerve cells in the cortex and hippocampus were activated in learning and memory retrieval, and switch them off with light directed through a fiber-optic cable.

They trained the mice by placing them in a cage where they got a mild electric shock. Normally, mice placed in a new environment will nose around and explore. But when placed in a cage where they have previously received a shock, they freeze in place in a "fear response."

Tanaka and Wiltgen first showed that they could label the cells involved in learning and demonstrate that they were reactivated during memory recall. Then they were able to switch off the specific nerve cells in the hippocampus, and show that the mice lost their memories of the unpleasant event. They were also able to show that turning off other cells in the hippocampus did not affect retrieval of that memory, and to follow fibers from the hippocampus to specific cells in the cortex.

"The cortex can't do it alone, it needs input from the hippocampus," Wiltgen said. "This has been a fundamental assumption in our field for a long time and Kazu’s data provides the first direct evidence that it is true."

They could also see how the specific cells in the cortex were connected to the amygdala, a structure in the brain that is involved in emotion and in generating the freezing response.

Co-authors are Aleksandr Pevzner, Anahita B. Hamidi, Yuki Nakazawa and Jalina Graham, all at the Center for Neuroscience. The work was funded by grants from the Whitehall Foundation, McKnight Foundation, Nakajima Foundation and the National Science Foundation.

Story Source:
The above story is based on materials provided by University of California - Davis. Note: Materials may be edited for content and length.

Journal Reference:
Kazumasa Z. Tanaka, Aleksandr Pevzner, Anahita B. Hamidi, Yuki Nakazawa, Jalina Graham, Brian J. Wiltgen. (2014). Cortical Representations Are Reinstated by the Hippocampus during Memory Retrieval. Neuron;  DOI: 10.1016/j.neuron.2014.09.037

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Cortical Representations Are Reinstated by the Hippocampus during Memory Retrieval

Kazumasa Z. Tanaka, Aleksandr Pevzner, Anahita B. Hamidi, Yuki Nakazawa, Jalina Graham, Brian J. Wiltgen

DOI: http://dx.doi.org/10.1016/j.neuron.2014.09.037

Highlights

• Neurons active during context fear learning can be selectively tagged with H2B-GFP
• When tagged CA1 neurons are silenced, memory retrieval is impaired
• CA1 silencing disrupts the activity of tagged neurons in cortex and amygdala
• CA1 reinstates representations in cortex and amygdala during memory retrieval

Summary

The hippocampus is assumed to retrieve memory by reinstating patterns of cortical activity that were observed during learning. To test this idea, we monitored the activity of individual cortical neurons while simultaneously inactivating the hippocampus. Neurons that were active during context fear conditioning were tagged with the long-lasting fluorescent protein H2B-GFP and the light-activated proton pump ArchT. These proteins allowed us to identify encoding neurons several days after learning and silence them with laser stimulation. When tagged CA1 cells were silenced, we found that memory retrieval was impaired and representations in the cortex (entorhinal, retrosplenial, perirhinal) and the amygdala could not be reactivated. Importantly, hippocampal inactivation did not alter the total amount of activity in most brain regions. Instead, it selectively prevented neurons that were active during learning from being reactivated during retrieval. These data provide functional evidence that the hippocampus reactivates specific memory representations during retrieval.

A bow tie representation of the network of connections between cortical areas in the brain. (Credit: University of Notre Dame)

The image above represents the authors' conception of how the brain organizes itself for the most efficient processing of information. Here are a couple of passages from the research summary, explaining the architecture and how it functions (with comparisons to the internet and other complex information processing systems):

Using brain-wide and consistent tracer data, the researchers describe the cortex as a network of connections with a "bow tie" structure characterized by a high-efficiency, dense core connecting with "wings" of feed-forward and feedback pathways to the rest of the cortex (periphery). The local circuits, reaching to within 2.5 millimeters and taking up more than 70 percent of all the connections in the macaque cortex, are integrated across areas with different functional modalities (somatosensory, motor, cognitive) with medium- to long-range projections.
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This bow tie arrangement is a typical feature of self-organizing information processing systems. The paper notes that the cortex has some analogies with information-processing networks such as the World Wide Web, as well as metabolism, the immune system and cell signaling. The core-periphery bow tie structure, they say, is "an evolutionarily favored structure for a wide variety of complex networks" because "these systems are not in thermodynamic equilibrium and are required to maintain energy and matter flow through the system." The brain, however, also shows important differences from such systems. For example, destination addresses are encoded in information packets sent along the Internet, apparently unlike in the brain, and location and timing of activity are critical factors of information processing in the brain, unlike in the Internet.

This is another piece of info in the growing awareness of how the brain works as a collection of networks.

Full Citation:
N. T. Markov, M. Ercsey-Ravasz, D. C. Van Essen, K. Knoblauch, Z. Toroczkai, H. Kennedy. (2013, Nov 1). Cortical High-Density Counterstream Architectures. Science, 342 (6158): 1238406 DOI: 10.1126/science.1238406

New Insights Into Brain Neuronal Networks

Nov. 4, 2013 — A paper published in a special edition of the journal Science proposes a novel understanding of brain architecture using a network representation of connections within the primate cortex. Zoltán Toroczkai, professor of physics at the University of Notre Dame and co-director of the Interdisciplinary Center for Network Science and Applications, is a co-author of the paper "Cortical High-Density Counterstream Architectures."

Using brain-wide and consistent tracer data, the researchers describe the cortex as a network of connections with a "bow tie" structure characterized by a high-efficiency, dense core connecting with "wings" of feed-forward and feedback pathways to the rest of the cortex (periphery). The local circuits, reaching to within 2.5 millimeters and taking up more than 70 percent of all the connections in the macaque cortex, are integrated across areas with different functional modalities (somatosensory, motor, cognitive) with medium- to long-range projections.

The authors also report on a simple network model that incorporates the physical principle of entropic cost to long wiring and the spatial positioning of the functional areas in the cortex. They show that this model reproduces the properties of the connectivity data in the experiments, including the structure of the bow tie. The wings of the bow tie emerge from the counterstream organization of the feed-forward and feedback nature of the pathways. They also demonstrate that, contrary to previous beliefs, such high-density cortical graphs can achieve simultaneously strong connectivity (almost direct between any two areas), communication efficiency, and economy of connections (shown via optimizing total wire cost) via weight-distance correlations that are also consequences of this simple network model.

This bow tie arrangement is a typical feature of self-organizing information processing systems. The paper notes that the cortex has some analogies with information-processing networks such as the World Wide Web, as well as metabolism, the immune system and cell signaling. The core-periphery bow tie structure, they say, is "an evolutionarily favored structure for a wide variety of complex networks" because "these systems are not in thermodynamic equilibrium and are required to maintain energy and matter flow through the system." The brain, however, also shows important differences from such systems. For example, destination addresses are encoded in information packets sent along the Internet, apparently unlike in the brain, and location and timing of activity are critical factors of information processing in the brain, unlike in the Internet.

"Biological data is extremely complex and diverse," Toroczkai said. "However, as a physicist, I am interested in what is common or invariant in the data, because it may reveal a fundamental organizational principle behind a complex system. A minimal theory that incorporates such principle should reproduce the observations, if not in great detail, but in extent. I believe that with additional consistent data, as those obtained by the Kennedy team, the fundamental principles of massive information processing in brain neuronal networks are within reach."

Here is the full abstract from Science (the full article is behind a paywall, but I will have a paper copy in the next few days, thanks to a PT client with a subscription):

Background

The cerebral cortex is divisible into many individual areas, each exhibiting distinct connectivity profiles, architecture, and physiological characteristics. Interactions among cortical areas underlie higher sensory, motor, and cognitive functions. Graph theory provides an important framework for understanding network properties of the interareal weighted and directed connectivity matrix reported in recent studies. 

Density and topology of the cortical graph. (Left) The 66% density of the cortical matrix (black triangle) is considerably greater than in previous reports (colored points) and is inconsistent with a small-world network. (Right) A bow-tie representation of the high-density cortical matrix. The high-efficiency cortical core has defined relations with the cortical periphery in the two fans.

Advances

We derive an exponential distance rule that predicts many binary and weighted features of the cortical network, including efficiency of information transfer, the high specificity of long-distance compared to short-distance connections, wire length minimization, and the existence of a highly interconnected cortical core. We propose a bow-tie representation of the cortex, which combines these features with hierarchical processing.

Outlook

The exponential distance rule has important implications for understanding scaling properties of the cortex and developing future large-scale dynamic models of the cortex.