- A new deep brain
stimulator that sends electric signals in patterns was designed by a
research team from Duke University.
- The new device
aided in lowering energy requirement by 75% and proved effective in
treating symptoms of Parkinson's disease as standard devices in
- If found
effective, the device might not require battery replacement for the entire
life of a patient with Parkinson's disease.
method of using supercomputers that 'evolve' better patterns of electric shock
which, when delivered deep into the brain, can be used to treat symptoms of
. Disease, finds a study from the Duke University.
newly designed electric patterns can be customized for particular diseases and
may also do away with battery replacement procedure that are currently required
by many patients.
‘Efficient deep brain stimulation that produces electric signals in patterns can improve energy efficiency and provide relief from symptoms of Parkinsonís disease.’
technique of deep brain stimulation was initially introduced during the year
1987 and was designed to send electrical signals into the brain of people
suffering from neurological symptoms. It is used to treat symptoms such as
stiffness, walking problems, tremor as well as slow movement. Deep brain
stimulation does not affect the healthy tissue of the brain by destroying
neurons but it aids in blocking electrical signals from certain targeted areas
of the brain.
this procedure is used for patients for whom medications do not adequately
control the symptoms. In deep brain simulation, a neurostimulator, which is a
device similar to that of a pacemaker, is used to deliver specific signals to
areas that are associated with controlling movement and block signals that lead
to tremor or other Parkinson's disease symptoms.
exact region of the brain from which the electric nerve signals that trigger
symptoms of Parkinson's disease is identified using magnetic resonance imaging
(MRI) or by using computer
tomography(CT). The neurostimulator is generally implanted into the basal
ganglia and many patients have improved motor function.
research team from Duke University discovered that there were no specific time
lapse between electrical signals and that random pattern produced better
results. This showed that random passage of electric signals was more
significant than non-stop passage of signals. The researchers discovered many
different patterns that were effective.
researchers have developed specific algorithms that can purposefully send
electric signals in specific patterns. This aided in reducing the energy of the
stimulator by 75% while there was no loss of treatment benefits. The algorithms
can also be tailored for individual needs. The study was published in the Journal Translational Medicine
to Dr. Warren Grill who is an Edmund T. Pratt Jr. School Professor of Biomedical
Engineering, reducing the energy use meant that the primary cells used in the
device will last longer. Earlier the batteries had to be replaced with a risk
of infection every time they were replaced. The batteries generally lasted only
3 to 5 years and for a patient who received an implant at 50, it would mean
several battery replacement procedures over the period of his life.
Grill and his colleagues devised patterns in timing in which each second was
split into 5 segments, which were further divided into 200 segments. One
repetition pattern constituted every segment with a millisecond long slice
receiving a pulse or a blank. These patterns resulted in one hundred
quindecillion possible patterns. The research team utilized computational
evolution that was used to identify promising patterns.
method that was used by Dr. Grill and colleagues were very similar to evolution
but it was conducted within the computer. The patterns that were used became
better as time passed to cater to the needs of the patient, rather than a fixed
set of signals.
the evolutionary algorithm creates 10 patterns of deep brain stimulation and
tests these patterns in a computer model of Parkinson's
disease. When a pattern performs well it will lead to other
patterns. Small variations are included into the patterns to create a highly
efficient pattern. The patterns were measured by the algorithm on two
counts, effectiveness and efficiency.
The computer selected for patterns
that utilized minimum energy but produced results just like standard and
number of pulses that each pattern consisted of was 45 pulses per second, which
is a drastic reduction from 130-180 that are currently in use. This would save
energy by 60 to 75%, doubling or tripling the lifetime of the implanted
battery. The results were encouraging when tested on mice and the research team
is looking forward to testing on humans.
current deep brain stimulators cannot deliver the patterns that Dr. Grill and
his team designed. Therefore, there were other measures that were included. The
team designed their own test devices and in collaboration with neurosurgeons
from Duke Health as well as Emory Healthcare in Atlanta, Parkinson's
disease patients who visited the hospital for battery replacement
were studied. The battery replacement procedure required only local anesthesia,
therefore the nerve impulses were still active. When the neurotransmitter
device was removed to replace the battery, the devise that was designed by the
researchers were tested temporarily.
device that was designed by the researchers performed as well as the standard
device that has been in use for many years. However, the new device required
substantially less amount of energy when compared to the currently used device.
exact mechanism of action of wrong neurological signals triggering symptoms of Parkinson's
disease is unknown but the design of this new device would aid in improving the
quality of life of patients. Repeated trips to the doctor to change batteries
may not be required and further studies about patterns that are effective may
provide a better insight into how these signals work.
- Facts on Deep Brain Stimulation - (http://www.parkinson.org/understanding-parkinsons/treatment/surgery-treatment-options/Deep-Brain-Stimulation)