Category: Parkinson’s

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Brain Epilepsy Parkinson's

Transparent brain implant could improve neuromodulation

University of Wisconsin professor Justin Williams and colleagues have developed a graphene based, transparent sensor implant to help researchers better view the brain.  Unlike existing devices, the sensor’s micro electrode arrays work in tandem with imaging technologies.  This could improve neuromodulation therapies used to control symptoms, restore function, and relieve pain in patients with hypertension, epilepsy, or Parkinson’s.  Researchers have been limited in their ability to directly observe how the body generates electrical signals, or how it reacts to externally generated electrical signals.  According to Williams, “Clear electrodes in combination with recent technological advances in optogenetics and optical voltage probes will enable researchers to isolate those biological mechanisms. This fundamental knowledge could be catalytic in dramatically improving existing neuromodulation therapies and identifying new therapies.”

 

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Parkinson's Sensors Wearables

Wearable and app for Parkinson’s tracking

Intel and the Michael J. Fox Foundation have combined smartwatches with analytics software to gauge the impact of Parkinson’s medications.  (Intel press release here.)

25 clinical trial participants wore (originally crowdfunded) Pebble watches to track tremors, gait, sleep patterns and other indicators for four days.  300 data points per second per patient were relayed to the cloud every day. Machine learning tools analyzed the data to understand medication and treatment effectiveness and disease progression.

Intel and the foundation will launch an app for Parkinson’s patients to report their medication intake and how they are feeling. They aim to study the effects of medication on motor symptoms, based on changes detected in patient data collected from the smartwatches.

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Brain Ears Parkinson's

Cochlear implant pulses deliver DNA for gene therapy

UNSW Professor Gary Housley used electrical pulses from a cochlear implant to deliver gene therapy, successfully regrowing auditory nerves.  Until now, the “bionic ear” has been largely constrained by the neural interface.

In the study, Professor Housley and colleagues used the cochlear implant electrode array for novel “close-field” electroporation to transduce mesenchymal cells lining the cochlear perilymphatic canals with a naked complementary DNA gene construct driving expression of brain-derived neurotrophic factor and a green fluorescent protein reporter. The focusing of electric fields by particular cochlear implant electrode configurations led to surprisingly efficient gene delivery to adjacent mesenchymal cells. The resulting BDNF expression stimulated regeneration of spiral ganglion neurites, which had atrophied 2 weeks after ototoxic treatment, in a bilateral sensorineural deafness model..

Integration of this technology into other “bionic” devices, such as electrode arrays used in deep brain stimulation, could create opportunities for safe, directed gene therapy of complex neurological disorders.

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Monitoring Parkinson's Sensors

Wearable sensors monitor Parkinson’s symptoms

Kinesia’s HomeView and ProView technologies provide standardized platforms to quantify Parkinson’s disease symptoms in the clinic and at home.

Physicians have the tools to quantify tremor, assess dyskinesia and measure bradykinesia remotely. A patient uses a take home kit, programmed to specific symptoms and treatments,to complete motor tests several times a day. The patient can also enter touch screen diary information about how they are feeling and when medications were taken. Physicians can view web-based reports including automated severity scoring and videos that show symptom changes during the day in response to treatments such as medication or deep brain stimulation.

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Parkinson's Sensors

Gait sensor for Parkinson’s patients could prevent falls

http://www.scientificamerican.com/article.cfm?id=could-a-simple-ankle-sensor-help-with-parkinsons-symptoms

University of Alabama professor Emil Jovanov is developing a sensory cue device to detect freezing of gait episodes that lead to falls and serious injuries.  It uses sensors embedded in a shoe or attached to the ankle. As soon as the system senses a gait freeze, it transmits an auditory cue (such as the word “walk”) to an earpiece, prompting the patient to keep moving.

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Autism Brain Eyes Machine Learning Parkinson's

Eye tracking data helps diagnose autism, ADHD, Parkinson’s

http://www.scientificamerican.com/article.cfm?id=eye-tracking-software-may-reveal-autism-and-other-brain-disorders

USC’s Laurent Itti and researchers from Queen’s University in Ontario have created a data heavy, low cost method of identifying brain disorders through eye tracking.  Subjects watch a video for 15 minutes while their eye movements are recorded. An enormous amount of data is generated as the average person makes three to five saccadic eye movements per second.  Itti’s team uses advanced machine learning algorithms to enable a computer to recognize patterns without explicit human instruction.

The proof of concept study found that the algorithm could classify mental disorders through eye movement patterns.  Parkinson’s patients were identified with nearly 90 percent accuracy.  Children with ADHD or fetal alcohol spectrum disorder were identified with 77 percent accuracy.  “This is very different from what people have done before. We’re trying to have completely automated interpretation of the eye movement data,” said Itti.

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Parkinson's

Brain imaging technique for early movement disorder diagnosis

http://news.ufl.edu/2013/06/13/brain-imaging/

Professor David Vaillancourt of the University of Florida believes that a diffusion tensor imaging technique could allow clinicians to assess movement disorders earlier, leading to improved treatment interventions and therapies.

Movement disorders such as Parkinson’s disease, essential tremor, multiple system atrophy and progressive supranuclear palsy exhibit similar symptoms in the early stages, which can make it challenging to assign a specific diagnosis. Often, the original diagnosis changes as the disease progresses.

Diffusion tensor imaging, known as DTI, is a non-invasive method that examines the diffusion of water molecules within the brain and can identify key areas that have been affected as a result of damage to gray matter and white matter in the brain. Vaillancourt and his team measured areas of the basal ganglia and cerebellum in individuals, and used a statistical approach to predict group classification. By asking different questions within the data and comparing different groups to one another, they were able to show distinct separation among disorders.

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Machine Learning Parkinson's

Machine learning algorithms analyze mobile phone data for Parkinson’s research

http://mobihealthnews.com/22076/michael-j-fox-foundation-takes-first-step-toward-crowdsourced-research/

The Michael J. Fox Foundation is exploring how data sourced from mobile phones and analyzed with machine learning algorithms can improve Parkinson’s research.  The research was crowdsourced via a public competition.

The initial study included 16 individuals — nine patients, seven control.  For 8 weeks, 4-5 hours per day, each carried a smartphone with seven sensors collecting data. Inputs of a built-in accelerometer, data about the user’s tone of voice, how much the phone was turned on and used, data from the built-in compass and GPS, and an ambient light sensor were analyzed. A machine learning algorithm was developed to use the data to identify the Parkinson’s patients from the control group and identify what stage of the disease users were in.