-
Categories
-
Pharmaceutical Intermediates
-
Active Pharmaceutical Ingredients
-
Food Additives
- Industrial Coatings
- Agrochemicals
- Dyes and Pigments
- Surfactant
- Flavors and Fragrances
- Chemical Reagents
- Catalyst and Auxiliary
- Natural Products
- Inorganic Chemistry
-
Organic Chemistry
-
Biochemical Engineering
- Analytical Chemistry
-
Cosmetic Ingredient
- Water Treatment Chemical
-
Pharmaceutical Intermediates
Promotion
ECHEMI Mall
Wholesale
Weekly Price
Exhibition
News
-
Trade Service
2, 2021 /---/ -- Millions of patients with neurological and mental illnesses such as depression, addiction and chronic pain are resistant to treatment.
, about 30 percent of all people with severe depression are completely unresponsive to any medication or psychotherapy.
in short, many traditional forms of treatment for these diseases may have reached their limits.
The study, led by Maryam Shanechi, early career chairman of electrical and computer engineering at the USM Witby School of Engineering, will be published in Nature Biomedical Engineering, paving the way for a promising alternative, "personalized deep brain stimulation."
this work represents an important step towards new treatments for the nervous system and mental illness as a whole.
(Photo Source: www.pixabay.com) So far, the challenge of personalizing deep brain stimulation has been the human brain itself.
disorder may behave differently in each patient's brain.
, brain activity and whether and how each patient responds to stimuli can be very different.
makes it difficult to understand the stimulation effect of a given patient or how to change the stimulation dose (i.e. amplitude or frequency) over time to adapt it to the patient's needs.
Shanechi and her team have found a way to predict how electrical stimulation affects individual brain activity in multiple brain regions by developing new stimulation waveforms and creating new machine learning models.
worked with Bijan Pesaran, a professor of neuroscience at New York University, to demonstrate the model's success in real-world brain stimulation experiments.
, they designed two tools: a novel electrical stimulation wave to map brain activity, and new machine learning techniques that can learn maps from brain data collected during stimulation.
s wave randomly changes its amplitude and frequency over time, allowing us to see and predict the brain's response to various stimulus doses," said Shanechi, a research group.
" just as the apothpity key can open any door, the wave can be applied to anyone's brain and provide a personalized map of its response to stimuli.
to test their hypothesis, the researchers applied their waves to four different regions of the brain.
in each case, they were able to predict the outcome of brain activity in multiple regions for the first time.
means that doctors may soon be able to personalize "doses" of deep brain stimuli in real time by changing the magnitude and frequency of stimuli.
can be seen as a form of brain stimulation that increases or decreases the number of milligrams in the pill.
is huge for people with mental illnesses such as untreated depression or anxiety.
Shanechi and her team have previously developed machine learning techniques to decode symptoms of mental disorders, such as emotions generated by brain activity.
now, with new capabilities that can better predict how stimuli affect brain activity alone, they are looking to combine their findings with personalized therapies for mental disorders.
(Bioon.com) Source: New realm of the naturalm of the nationalityed with the original source of the brain stimulation: Modelling and prediction of the dynamic responses of the large-scale brain networks direct directing stimulation, Nature Biomedical Engineering (2021). DOI: 10.1038/s41551-020-00666-w ,