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Cognitive Prostheses[edit]

Cognitive prostheses seek to restore cognitive function to individuals with brain tissue loss due to injury, disease, or stroke by performing the function of the damaged tissue with integrated circuits.[1] The theory of localization states that brain functions are localized to a specific portion of the brain. [2] However, recent studies on brain plasticity suggest that the brain is capable of rewiring itself so that an area of the brain traditionally associated with a particular function (i.e. Auditory Cortex) can perform functions associated with another portion of the brain. (i.e. Auditory Cortex processing visual information).[3] This plasticity suggests that the brain is hard-wired to be connected to computer circuits. [4] Implants could take advantage of brain plasticity to restore cognitive function even if the native tissue has been destroyed.

Applications[edit]

Alzheimer's Disease[edit]

Alzheimer's Disease is projected to affect more than 107 million people worldwide by the year 2050. [5] Due to increased life spans, more and more people are being affected by Alzheimer's disease. In the United States this fact has important repercussions. With many baby boomers reaching retirement age the strain on the medicare and medicaid systems may become too great. Alzheimer's disease renders individuals incapable of supporting themselves. Unfortunately many of the more severe cases of Alzheimer's patients end up in nursing homes. Even a small measure of success by cognitive implants would help keep Alzheimer's patients out of nursing homes longer and lessen the load on medicare and medicaid.

Hippocampal Deficits[edit]

Dr. Theodore Berger at the University of Southern California is developing a prosthetic for treatments of hippocampal detriments including Alzheimer's[1]. Degenerative hippocampal neurons are the root cause of the memory disorders that accompany Alzheimer’s disease. Also, hippocampal pyramidal cells are extremely sensitive to even brief periods of anoxia, like those that occur during stroke. Loss of hippocampal neurons in the dentate gyrus, an area associated with new memory formation has been attributed to blunt head trauma.[6] Hippocampal dysfunction has also been linked to epileptic activity.[1] This demonstrates the wide scope of neural damage and neurodegenerative disease conditions for which a hippocampal prosthesis would be clinically relevant.

Traumatic Brain Injury[edit]

More than 1.4 million people in the United States suffer traumatic brain injury.[7] Orthosis for TBI patients to control limb movement via devices that read neurons in brain, calculate limb trajectory, and stimulate needed motor pools to make movement. (Anderson Paper, Cole at NIH - specifically "Computer software as an orthosis for Brain Injury",

Parkinson's Disease[edit]

Nearly 1 million people in the United States are affected by Parkinson's Disease.[8] Deep Brain Stimulation relieves symptoms of Parkinson's Disease for numerous patients. [9] Parkinson's Disease patients could benefit from a cortical device that mimics the natural signals needed to promote dopamine production. Another possible avenue for mitigation of PD is a device that supplements dopamine when given specific neuronal inputs which would let the body regulate dopamine levels with it's intrinsic sensors.

Speech Deficits[edit]

Approximately 7.5 million people in the United States have trouble speaking.[10] Many of these can be attributed to aphasias. The success of cochlear implants suggest that cortical implants to the speech areas of the brain can be developed to improve speech in such patients.

Paralysis[edit]

According to the Christopher and Dana Reeve Foundation's Paralysis Resource Center, approximately 6 million people are living with paralysis in the United States. Paralysis results from many sources, stroke, traumatic brain injurty, neurodegenerative diseases like multiple sclerosis and Lou Gehrig's Disease, and congenital sources. Many patients would benefit from a prosthetic device that controls limb movement via devices that read neurons in brain, calculate limb trajectory, and stimulate the needed motor pools to make movement. This technology is being developed at the Andersen Lab, located at the California Institute of Technology. The goal is to develop a device to enable locked in patients, those without the ability to move or speak, to communicate with other persons.

Societal Impact/Market Information[edit]

Nearly 1 million people in the United States are affected by Parkinson's Disease.[8]

Alzheimer's Disease is projected to affect more than 107 million people worldwide by the year 2050. [5]

Just these two diseases indicate that there is already a large market for cognitive neural prosthetics, with more potential markestspace revealed in traumatic brain injury and speech problems (particularly damage to Broca's or Wernicke's areas).

More than 1.4 million people in the United States suffer traumatic brain injury.[7]

Approximately 7.5 million people in the United States have trouble speaking.[10] Many of these can be attributed to aphasias.

More than 6.5 million people in the United States have suffered stroke.[11]

Obstacles[edit]

Mathematical Modeling[edit]

Accurate characterization of the nonlinear input/output (I/O) parameters of the normally functioning tissue to be replaced is paramount to designing a prosthetic that mimics normal biologic synaptic signals.[12] [13] Mathematical modeling of these signals is a complex task "because of the nonlinear dynamics inherent in the cellular/molecular mechanisms comprising neurons and their synaptic connections." [14][15][16] The output of nearly all brain neurons are dependent on which post-synaptic inputs are active and in what order the inputs are received. (spatial and temporal properties, respectively).[1]

Once the I/O parameters are modeled mathematically, integrated circuits are designed to mimic the normal biologic signals. For the prosthetic to perform like normal tissue, it must process the input signals, a process known as transformation, in the same way as normal tissue.

Size[edit]

Implantable devices must by very small to be implanted directly in the brain, roughly the size of a quarter.

Wireless Controlling Devices can be mounted outside of the skull and should be smaller than a pager.

Power Consumption[edit]

Power consumption drives battery size. Optimization of the implanted circuits reduces power needs. Implanted devices currently need on-board power sources. Once the battery runs out, surgery is needed to replace the unit. Longer battery life correlates to fewer surgeries needed to replace batteries. One option that could be used in the medical field to recharge implant batteries without surgery or wires is being used in powered toothbrushes. These devices make of inductive coupling to recharge batteries. Another strategy is to convert electromagnetic energy into electrical energy, as in radio frequency identification tags.

Bio Compatibility[edit]

Cognitive prostheses are implanted directly in the brain, so biocompatibility is very important obstacle to overcome. Materials used in the housing of the device, the electrode material, and electrode insulation must be chosen for long term implantation. Subject to Standards: ISO 14708-3 2008-11-15, Implants for Surgery - Active implantable medical devices Part 3: Implantable neurostimulators.


Crossing the Blood Brain Barrier can introduce pathogens or other materials that may cause an immune response. The brain has its own immune system that acts differently than the immune system of the rest of the body.

Questions to answer:How does this affect material choice? Does the brain have unique phages that act differently and may affect materials thought to be bio compatible in other areas of the body?

Data Transmission[edit]

Wireless Transmission is being developed to allow continuous recording of neuronal signals of individuals in their daily life. This allows physicians and clinicians to capture more data, ensuring that short term events like epileptic seizures can be recorded, allowing better treatment and characterization of neural disease.

A small, light weight device has been developed that allows constant recording of primate brain neurons at Stanford University. [17] This technology also enables neuroscientists to study the brain outside of the controlled environment of a lab.

Methods of data transmission must be robust and secure. Neurosecurity is a new issue. Makers of cognitive implants must prevent unwanted downloading of information or thoughts from and uploading of detrimental data to the device that may interrupt function. Though it currently poses no threat, mind-hacking must be prevented.

Correct Implantation[edit]

Implantation of the device presents many problems. First, the correct presynaptic inputs must be wired to the correct postsynaptic inputs on the device. Secondly, the outputs from the device must be targeted correctly on the desired tissue. Thirdly, the brain must learn how to use the implant. Various studies in brain plasticity (int link) suggest that this may be possible through exercises designed with proper motivation.

Current Developments[edit]

Andersen Lab[edit]

The Andersen Lab builds on research done previously by Musallam and show that high-level cognitive signals in the post parietal cortex, or PPC, can be used to decode the target position of reaching motions.[18] Signals like these could be used to directly control a prosthetic device. Functionally speaking, the PPC is situated between sensory and motor areas in the brain. It is involved in converting sensory inputs into plans for action, a phenomenon known as sensory – motor integration.

Within the PPC is an area known as the post parietal reach region, or PRR for short. This area has been shown to be most active when an individual is planning and executing a movement. The PRR receives direct visual information, indicating that vision may be the primary sensory input. The PRR encodes the targets for reaching in visual coordinates relative to the current direction of gaze AKA retinal coordinates.[19] Because it is coding the goal of the movement and not all the different variables required for the limb to contact the target, the planning signals of the PRR are considered cognitive in nature. Decoding these signals is important to help paralyzed patients, especially those with damage to areas of the brain that calculate limb movement variables, or relay this information to motor neurons. Perhaps the most astonishing possibility is utilizing these signals to provide ‘locked in’ individuals, those without the ability to move or speak, an avenue of communication.

First, Andersen and collegues placed electrode arrays onto the dorsal premotor cortex, the PRR, and medial interparietal area (MIP) of monkeys to record signals made by these regions while the monkeys looked at a computer screen. After the monkeys touched a central cue spot on the screen and looked at a central fixation point (red), another cue (green) popped up briefly then disappeared. The monkeys were given a juice reward if they reached to where the newly vanished target was at the end of a short memory period, about 1.5 seconds. The recordings were made when the monkeys were planning movement, but sitting motionless in the dark absent of eye movements, ensuring that motor and sensory information were not influencing the planning activity.

Next, the researchers conducted brain-control trials using neural activity data recorded from 2 tenths of a second to 1 second of the memory period to decode the intended reach destination. A brain-machine interface used the decoded data to move a cursor to the spot on the screen where the monkeys planned to move, without using their limbs. Monkeys were rewarded with juice if the correct target was decoded and the cue was flashed again, providing visual reinforcement. After a month or two of training, the monkeys were much better at hitting the target. This learning is a testament to the brain’s natural plasticity, and creates an opportunity for patients to improve how they operate the prosthesis with training. Each time the patient uses the prosthetic system, the brain could automatically make subtle adjustments to the input signal recorded by the system.

Finally, the researchers used reach trials to decode intentions in healthy monkeys. However, paralyzed patients cannot perform reach trials for the scientists to record reach intention data. Adaptive databases overcome this scenario. Each time a reach decoding is successful, it is added to the database. If the number of database entries is kept constant, one trial, (a less successful one) must be deleted. Eventually the database will contain only successful decodes, making the system work better each time the patient uses it. This suggests a FIFO, or first-in, first-out, setup. The oldest data drops out first. Initially filling the database will be difficult, but with rigorous training and many trials, the system will be able to accurately discern the user’s intentions. This process, along with the brain’s plasticity, should enable people to control a myriad of prostheses, and perhaps even motorized wheel chairs. Furthermore, in the future precision devices such as surgical tools could be controlled directly by the brain instead of controls manipulated by the motor system.



Hippocampal Prosthetic[edit]

Dr. Theodore Berger’s research lab at the University of California seeks to develop models of mammalian neural systems, currently the hippocampus, essential for learning and memory. The goal is to make an implantable device that replicates the way living hippocampal neurons behave and exchange electrical signals. If successful, it would be a large step towards a biomedical solution for Alzheimer's symptoms. Complications from brain injury to motor areas of the brain like reduced coordination could be improved. Speech and language problems caused by stroke could be reversed. To accomplish this, the device will listen for neuronal signals going to the hippocampus with implanted electrode arrays, calculate what the outgoing response of normal hippocampus neurons would be, and then to stimulate neurons in other parts of the brain, hopefully just like the tissue did before damage or degeneration.

Technologies Involved[edit]

Local Field Potentials[edit]

Local field potentials (LFPs) are electrophysiological signals that are related to the sum of all dendritic synaptic activity within a volume of tissue. Recent studies suggest goals and expected value are high-level cognitive functions that can be used for neural cognitive prostheses. [20]

  • explain how they are used
  • how they are better than other methods

Automated Movable Electrical Probes[edit]

One hurdle to overcome is the long term implantation of electrodes. If the electrodes are moved by physical shock or the brain moves in relation to electrode position, the electrodes could be recording different nerves. Adjustment to electrodes is necessary to maintain an optimal signal. Individually adjusting multi electrode arrays is a very tedious and time consuming process. Development of automatically adjusting electrodes would mitigate this problem. Anderson’s group is currently collaborating with Yu-Chong Tai’s lab and the Burdick lab (all at Cal Tech) to make such a system that uses electrolysis-based actuators to independently adjust electrodes in a chronically implanted array of electrodes.[21]

MRI[edit]

Used for imaging to determine correct positioning

Imaged Guided Surgical Techniques[edit]

Image-Guided Surgery is used to precisely position brain implants. [20]

Future Directions[edit]

Self-charging implants that use bioenergy to reacharge would eliminate the need for costly and risky surgeries to change implant batteries.

Memory/Brain off-loading and subsequent uploading to learn new information quickly. Researchers at the Georgia Institute of Technology are researching mammalian memory cells to determine exactly how we learn. The techniques used in the Potter Lab can be used to study and enhance the activities of neural prosthetics devices.

Controlling complex machinery with thoughts instead of converting motor movements into commands for machines would allow greater accuracy and enable users to distance themselves from hazardous environments.

Other future directions include devices to maintain focus, to stabilize/induce mood, to help patients with damaged cortices feel and express emotions, and to enable true telepathic communication, not simply picking up visual/auditory cues and guessing emotional state or subject of thought from context.

External Links[edit]

References[edit]

  1. ^ a b c d Berger, T. W., Ahuja, A., Courellis, S. H., Deadwyler, S. A., Erinjippurath, G., Gerhardt, G. A., et al. (2005). Restoring lost cognitive function. Ieee Engineering in Medicine and Biology Magazine, 24(5), 30-44.
  2. ^ Zolamorgan, S. (1995). LOCALIZATION OF BRAIN-FUNCTION - THE LEGACY OF GALL,FRANZ,JOSEPH (1758-1828). [Review]. Annual Review of Neuroscience, 18, 359-383.
  3. ^ Allman, B. L., Keniston, L. P., & Meredith, M. A. (2009). Adult deafness induces somatosensory conversion of ferret auditory cortex. Proceedings of the National Academy of Sciences of the United States of America, 106(14), 5925-5930.
  4. ^ Doidge, N. (2007). The Brain That Changes Itself: Stories of Personal Triumph from the Frontiers of Brain Science. New York, NY:James H. Silberman Books
  5. ^ a b Brookmeyer, R; Johnson, E; Ziegler-Graham, K; Arrighi, HM (July 2007). "Forecasting the global burden of Alzheimer’s disease". Alzheimer's and Dementia 3 (3): 186–91.
  6. ^ Helen Scharfman, ed (2007). The Dentate Gyrus: A comprehensive guide to structure, function, and clinical imiplications. 163. 1-840.
  7. ^ a b Center for Disease Control and Prevention. http://www.cdc.gov/NCIPC/tbi/FactSheets/Facts_About_TBI.pdfTraumatic Brain Injury. Accessed 11/14/2009. Updated 07/2006.
  8. ^ a b Parkinson's Disease Foundation
  9. ^ Li, S., Arbuthnott, G. W., Jutras, M. J., Goldberg, J. A., & Jaeger, D. (2007). Resonant antidromic cortical circuit activation as a consequence of high-frequency subthalamic deep-brain stimulation. [Article]. Journal of Neurophysiology, 98(6), 3525-3537.
  10. ^ a b National Institute on Deafness and Other Communication Disorders, National Institutes of Health. http://www.nidcd.nih.gov/health/statistics/vsl.asp Accessed 11/21/2009. Updated 6/18/2009.
  11. ^ National Center for Health Statistics, Hyattsville, MD. Center for Disease Control and Prevention Accessed 11/28/2009. Updated 05/15/2009.
  12. ^ Bertaccini, D., & Fanelli, S. (2009). Computational and conditioning issues of a discrete model for cochlear sensorineural hypoacusia. [Article]. Applied Numerical Mathematics, 59(8), 1989-2001.
  13. ^ Marmarelis, V. Z. (1993). IDENTIFICATION OF NONLINEAR BIOLOGICAL-SYSTEMS USING LAGUERRE EXPANSIONS OF KERNELS. [Article]. Annals of Biomedical Engineering, 21(6), 573-589.
  14. ^ T.W. Berger, T.P. Harty, X. Xie, G. Barrionuevo, and R.J. Sclabassi, “Modeling of neuronal networks through experimental decomposition,” in Proc. IEEE 34th Mid Symp. Cir. Sys., Monterey, CA, 1991, vol. 1, pp. 91–97.
  15. ^ T.W. Berger, G. Chauvet, and R.J. Sclabassi, “A biologically based model of functional properties of the hippocampus,” Neural Netw., vol. 7, no. 6–7, pp. 1031–1064, 1994.
  16. ^ S.S. Dalal, V.Z. Marmarelis, and T.W. Berger, “A nonlinear positive feedback model of glutamatergic synaptic transmission in dentate gyrus,” in Proc. 4th Joint Symp. Neural Computation, California, 1997, vol. 7, pp. 68–75.
  17. ^ HermesC: Low-Power Wireless Neural Recording System for Freely Moving Primates Chestek, C.A.; Gilja, V.; Nuyujukian, P.; Kier, R.J.; Solzbacher, F.; Ryu, S.I.; Harrison, R.R.; Shenoy, K.V.; Neural Systems and Rehabilitation Engineering, IEEE Transactions on Volume 17, Issue 4, Aug. 2009 Page(s):330 - 338.
  18. ^ Anderson, R.A. et al. (2008) Decoding Trajectories from Posterior Parietal Cortex. The Journal of Neuroscience 28(48):12913–12926.
  19. ^ Batista, A.P. et al. (1999) Reach plans in eye-centered coordinates. Science 285, 257–260.
  20. ^ a b Andersen, R. A., Burdick, J. W., Musallam, S., Pesaran, B., & Cham, J. G. (2004). Cognitive neural prosthetics. Trends in Cognitive Sciences, 8(11), 486-493.
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