How to Make an Implant that Improves the Brain
The abilities to learn, remember, evaluate, and decide are central to who we are and how we live. Damage to or dysfunction of the brain circuitry that supports these functions can be devastating, leading to Alzheimer’s, schizophrenia, PTSD, or many other disorders. Current treatments, which are drug-based or behavioral, have limited efficacy in treating these problems. There is a pressing need for something more effective.
One promising approach is to build an interactive device to help the brain learn, remember, evaluate, and decide. One might, for example, construct a system that would identify patterns of brain activity tied to particular experiences and then, when called upon, impose those patterns on the brain. Ted Berger, Sam Deadwyler, Robert Hampsom, and colleagues have used this approach (see “Memory Implants”). They are able to identify and then impose, via electrical stimulation, specific patterns of brain activity that improve a rat’s performance in a memory task. They have also shown that in monkeys stimulation can help the animal perform a task where it must remember a particular item.
Their ability to improve performance is impressive. However, there are fundamental limitations to an approach where the desired neural pattern must be known and then imposed. The animals used in their studies were trained to do a single task for weeks or months and the stimulation was customized to produce the right outcome for that task. This is only feasible for a few well-learned experiences in a predictable and constrained environment.
New and complex experiences engage large numbers of neurons scattered across multiple brain regions. These individual neurons are physically adjacent to other neurons that contribute to other memories, so selectively stimulating the right neurons is difficult if not impossible. And to make matters even more challenging, the set of neurons involved in storing a particular memory can evolve as that memory is processed in the brain. As a result, imposing the right patterns for all desired experiences, both past and future, requires technology far beyond what is possible today.
I believe the answer to be an alternative approach based on enhancing flows of information through the brain. The importance of information flow can be appreciated when we consider how the brain makes and uses memories. During learning, information from the outside world drives brain activity and changes in the connections between neurons. This occurs most prominently in the hippocampus, a brain structure critical for laying down memories for the events of daily life. Thus, during learning, external information must flow to the hippocampus if memories are to be stored.
Once information has been stored in the hippocampus, a different flow of information is required to create a long-lasting memory. During periods of rest and sleep, the hippocampus “reactivates” stored memories, driving activity throughout the rest of the brain. Current theories suggest that the hippocampus acts like a teacher, repeatedly sending out what it has learned to the rest of the brain to help engrain memories in more stable and distributed brain networks. This “consolidation” process depends on the flow of internal information from the hippocampus to the rest of the brain.
Finally, when a memory is retrieved a similar pattern of internally driven flow is required. For many memories, the hippocampus is required for memory retrieval, and once again hippocampal activity drives the reinstatement of the memory pattern throughout the brain. This process depends on the same hippocampal reactivation events that contribute to memory consolidation.
Different flows of information can be engaged at different intensities as well. Some memories stay with us and guide our choices for a lifetime, while others fade with time. We and others have shown that new and rewarded experiences drive both profound changes in brain activity, and strong memory reactivation. Familiar and unrewarded experiences drive smaller changes and weaker reactivation. Further, we have recently shown that the intensity of memory reactivation in the hippocampus, measured as the number of neurons active together during each reactivation event, can predict whether an the next decision an animal makes is going to be right or wrong. Our findings suggest that when the animal reactivates effectively, it does a better job of considering possible future options (based on past experiences) and then makes better choices.
These results point to an alternative approach to helping the brain learn, remember and decide more effectively. Instead of imposing a specific pattern for each experience, we could enhance the flow of information to the hippocampus during learning and the intensity of memory reactivation from the hippocampus during memory consolidation and retrieval. We are able to detect signatures of different flows of information associated with learning and remembering. We are also beginning to understand the circuits that control this flow, which include neuromodulatory regions that are often damaged in disease states. Importantly, these modulatory circuits are more localized and easier to manipulate than the distributed populations of neurons in the hippocampus and elsewhere that are activated for each specific experience.
Thus, an effective cognitive neuroprosthetic would detect what the brain is trying to do (learn, consolidate or retrieve) and then amplify activity in the relevant control circuits to enhance the essential flows of information. We know that even in diseases like Alzheimer’s where there is substantial damage to the brain, patients have good days and bad days. On good days the brain smoothly transitions among distinct functions, each associated with a particular flow of information. On bad days these functions may become less distinct and the flows of information muddled. Our goal then, would be to restore the flows of information underlying different mental functions.
A prosthetic device has the potential to adapt to the moment-by-moment changes in information flow necessary for different types of mental processing. By contrast, drugs that seek to treat cognitive dysfunction may effectively amplify one type of processing but cannot adapt to the dynamic requirements of mental function. Thus, constructing a device that makes the brain’s control circuits work more effectively offers a powerful approach to treating disease and maximizing mental capacity.
Loren M. Frank is a professor at the Center for Integrative Neuroscience and the Department of Physiology at the University of California, San Francisco.
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