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“Monorail” could halt spread of brain tumors

Duke’s Ravi Bellamkonda has developed a “Tumor Monorail” which tricks aggressive brain tumors such as glioblastoma into migrating into an external container rather than throughout the brain.  It has been designated “Breakthrough Device” by the U.S. Food and Drug Administration (FDA).

The device mimics the physical properties of the brain’s white matter to entice aggressive tumors to migrate toward the exterior of the brain, where the migrating cells can be collected and removed. It does not to destroy the tumor, but does halt its lethal spread. There are no chemicals or enzymes involved, and there are a wide variety of materials that the device could be made from.

The work is based on rat studies from 2014.  The team hopes to receive FDA approval for human trials by the end of 2019.

Click to view Georgia Tech (whose researchers collaborated with colleagues at Emory and Duke) video

Join ApplySci at the 10th Wearable Tech + Digital Health + Neurotech Silicon Valley conference on February 21-22 at Stanford University — Featuring:  Zhenan Bao – Christof Koch – Vinod Khosla – Walter Greenleaf – Nathan Intrator – John Mattison – David Eagleman – Unity Stoakes – Shahin Farshchi – Emmanuel Mignot – Michael Snyder – Joe Wang – Josh Duyan – Aviad Hai – Anne Andrews – Tan Le – Anima Anandkumar – Pierrick Arnal – Shea Balish – Kareem Ayyad – Mehran Talebinejad – Liam Kaufman – Scott Barclay – Tracy Laabs – George Kouvas

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Artificial skin sensor could help burn victims “feel”

UConn chemists Islam Mosa and Professor James Rusling have developed a sensor that could detect pressure, temperature, and vibration when placed on skin.  

The sensor and silicone tube are wrapped in copper wire and filled with an  iron oxide nanoparticle fluid, which creates an electric current. The copper wire detects the current. When the tube experiences pressure, the nanoparticles move and electric signal changes.

Sound waves also create waves in the fluid, and the signal changes differently than when the tube is bumped.

Magnetic fields were found to alter the signal differently than from pressure or sound waves.  The team could distinguish between the signals caused by walking, running, jumping, and swimming.

The researcher’s goals are to  help burn victims “feel” again, and to provide  early warning for workers exposed to high magnetic fields. The waterproof sensor could also serve as a pool-depth monitoring wearable for children.

Join ApplySci at the 10th Wearable Tech + Digital Health + Neurotech Silicon Valley conference on February 21-22 at Stanford University — Featuring:  Zhenan Bao – Christof Koch – Vinod Khosla – Walter Greenleaf – Nathan Intrator – John Mattison – David Eagleman – Unity Stoakes – Shahin Farshchi – Emmanuel Mignot – Michael Snyder – Joe Wang – Josh Duyan – Aviad Hai – Anne Andrews – Tan Le – Anima Anandkumar – Pierrick Arnal – Shea Balish – Kareem Ayyad – Mehran Talebinejad – Liam Kaufman – Scott Barclay – Tracy Laabs – George Kouvas

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Wireless,biodegradable, flexible arterial-pulse sensor monitors blood flow

Zhenan Bao and colleagues have developed a wireless, battery-free, biodegradable sensor to provide continuous monitoring of blood flow through an artery.  This could provide critical information to doctors after vascular, transplant, reconstructive and cardiac surgery, with out the need for a visit.

Monitoring the success of surgery on blood vessels is difficult, as by the time a problem is detected, additional surgery is usually required.  The goal of the sensor is much earlier intervention.

The sensor wraps  around the healing vessel, where blood pulsing past pushes on its inner surface. As the shape of that surface changes, it alters the sensor’s capacity to store electric charge, which doctors can detect remotely from a device located near the skin but outside the body. That device solicits a reading by pinging the antenna of the sensor, similar to an ID card scanner. In the future, this device could come in the form of a stick-on patch or be integrated into other technology, like a wearable device or smartphone.

Join ApplySci at the 10th Wearable Tech + Digital Health + Neurotech Silicon Valley conference on February 21-22 at Stanford University — Featuring:  Zhenan Bao – Christof Koch – Vinod Khosla – Walter Greenleaf – Nathan Intrator – John Mattison – David Eagleman – Unity Stoakes – Shahin Farshchi – Emmanuel Mignot – Michael Snyder – Joe Wang – Josh Duyan – Aviad Hai – Anne Andrews – Tan Le – Anima Anandkumar – Pierrick Arnal – Shea Balish – Kareem Ayyad – Mehran Talebinejad – Liam Kaufman – Scott Barclay – Tracy Laabs – George Kouvas

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Neural signals translated into speech

Columbia University’s Nima Mesgarani is developing a computer-generated speech method for those who are unable to talk.

How brain signals translate to speech sounds varies from person to person, therefore computer models must be trained individually. The models are most successful when used during open skull surgeries, to remove brain tumors or when electrodes are implanted to pinpoint the origin of seizures before surgery.

Data is fed into neural networks, which process patterns by passing information through layers of computational nodes. The networks learn by adjusting connections between nodes. In the study, networks were exposed to recordings of speech that a person produced or heard and data on simultaneous brain activity.

Mesgarani’s team used data from five epilepsy patients. The network analyzed recordings from the auditory cortex as participants heard recordings of stories and people naming digits from zero to nine. The computer then reconstructed spoken numbers from neural data alone.

Click to view Science magazine’s sound file of the computer reconstruction of brain activity.

Join ApplySci at the 10th Wearable Tech + Digital Health + Neurotech Silicon Valley conference on February 21-22 at Stanford University — Featuring:  Zhenan Bao – Christof Koch – Vinod Khosla – Walter Greenleaf – Nathan Intrator – John Mattison – David Eagleman – Unity Stoakes – Shahin Farshchi – Emmanuel Mignot – Michael Snyder – Joe Wang – Josh Duyan – Aviad Hai – Anne Andrews – Tan Le – Anima Anandkumar – Pierrick Arnal – Shea Balish – Kareem Ayyad – Mehran Talebinejad – Liam Kaufman – Scott Barclay

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Alzheimer’s detected by AI 6 years before diagnosis

In a recent study, Jae Ho Sohn and UCSD colleagues used an AI to analyze glucose-monitoring PET scans to detect early-stage Alzheimer’s disease six years before  diagnosis.

The algorithm was trained on PET scans from patients who were eventually diagnosed with  Alzheimer’s disease, MCI, or no disorder. It was able to  identify 92% of patients who developed Alzheimer’s disease in the first test set and 98% in the second test set, 75.8 months before diagnosis on average.v

Join ApplySci at the 10th Wearable Tech + Digital Health + Neurotech Silicon Valley conference on February 21-22 at Stanford University — Featuring:  Zhenan Bao – Christof Koch – Vinod Khosla – Walter Greenleaf – Nathan Intrator – John Mattison – David Eagleman – Unity Stoakes – Shahin Farshchi – Emmanuel Mignot – Michael Snyder – Joe Wang – Josh Duyan – Aviad Hai – Anne Andrews – Tan Le – Anima Anandkumar – Hugo Mercier – Shea Balish – Kareem Ayyad – Mehran Talebinejad – Liam Kaufman – Scott Barclay

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Fingertip wearable measures disease-associated grip strength

IBM researchers are studying grip strength, which is associated with the effectiveness of Parkinson’s drugs, cognitive function in schizophrenics, cardiovascular health, and elderly mortality.

To better understand these markers, Steve Heisig, Gaddi Blumrosen and colleagues have developed a prototype wearable that continuously measures how a fingernail bends and moves.

The project began as an attempt to capture the medication state Parkinson’s patients, but was soon expanded to measure the tactile sensing of pressure, temperature, surface textures and other indicators of various diseases. Nail bending was measured throughout the day, and AI was used to analyze the data for disease association.

The system consists of strain gauges attached to the fingernail and a small computer that samples strain values, collects accelerometer data and communicates with a smart watch. The watch runs machine learning models to rate bradykinesia, tremor, and dyskinesia.

The work is also being used in the development of a fingertip-structure modeled device that could  help quadriplegics communicate.

Click to view IBM video

Join ApplySci at the 10th Wearable Tech + Digital Health + Neurotech Silicon Valley conference on February 21-22 at Stanford University — Featuring:  Zhenan Bao – Christof Koch – Vinod Khosla – Walter Greenleaf – Nathan Intrator – John Mattison – David Eagleman – Unity Stoakes – Shahin Farshchi – Emmanuel Mignot – Michael Snyder – Joe Wang – Josh Duyan – Aviad Hai – Anne Andrews – Tan Le – Anima Anandkumar – Hugo Mercier – Shea Balish – Kareem Ayyad – Mehran Talebinejad – Liam Kaufman – Scott Barclay

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Wearable haptic feedback/stimulation band to address Parkinson’s symptoms

Microsoft has submitted a patent application for a wearable band that uses haptic feedback for stimulation when wrapped around limbs or joints.  It is meant to alleviate Parkinson’s symptoms, including tremors and muscle stiffness.

Haptic actuators are distributed across a band that is adjusted to a  “duty cycle” which responds to data derived from wearable sensors, including accelerometers, gyroscopes, heart-rate sensors, and electromyography sensors, as well as tablets or phones.

Examples include stylus sensors communicating with a wrist-worn device to detect involuntary motion while writing. The actuators would then be used to reduce the involuntary motion.  The wearable itself could also detect the motion of the actuators.

The patent describes stimulation “provided through the vibration of two or more actuators within the wearable device. In various examples, the wearable device may additionally comprise a second channel for the provision of therapeutic stimulation, such as an audio channel (e.g. the wearable device may additionally comprise a speaker or buzzer),”

The sensors could be integrated into a patch on a shoulder or other joint, or into clothing.

Join ApplySci at the 10th Wearable Tech + Digital Health + Neurotech Silicon Valley conference on February 21-22 at Stanford University — Featuring:  Zhenan Bao – Christof Koch – Vinod Khosla – Walter Greenleaf – Nathan Intrator – John Mattison – David Eagleman – Unity Stoakes – Shahin Farshchi – Emmanuel Mignot – Michael Snyder – Joe Wang – Josh Duyan – Aviad Hai – Anne Andrews – Tan Le – Anima Anandkumar – Hugo Mercier

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3D-printed, bluetooth-controlled ingestible capsule delivers drugs, senses environment

MIT’s Bob Langer and Giovanni Traverso have developed a 3D-printed, wirelessly-controlled, ingestible capsule that can  deliver drugs, sense environmental conditions, or both.  It can reside in the stomach for a month.  Data is sent to a user’s phone, and instructions from the phone are sent to the device.  The sensor could also communicate with other wearable and implantable devices, and send the combined data to a doctor.

The technology could improve drug delivery in conditions where drugs must be taken over a long period.  It can also sense infections, allergic reactions, or other events, and then release a drug accordingly.

Join ApplySci at the 10th Wearable Tech + Digital Health + Neurotech Silicon Valley conference on February 21-22 at Stanford University — Featuring:  Zhenan Bao – Christof Koch – Vinod Khosla – Walter Greenleaf – Nathan Intrator – John Mattison – David Eagleman – Unity Stoakes – Shahin Farshchi – Emmanuel Mignot – Michael Snyder – Joe Wang – Josh Duyan – Aviad Hai – Anne Andrews – Tan Le – Anima Anandkumar – Hugo Mercier

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DARPA-developed closed loop therapies for neuropsychiatric illness

Led by Justin Sanchez, DARPA’s SUBNETS program develops responsive, adaptable, personalized closed-loop therapies for neuropsychiatric illness that incorporate recording and analysis of brain activity with near-real-time neural stimulation to correct or mitigate brain dysfunction.

The technology detects ongoing dynamic changes in brain activity associated with fluctuations in mood, and uses the data to deliver precisely timed therapeutic stimulation.

The premise is that brain function and dysfunction — rather than being relegated to distinct anatomical regions of the brain — play out across distributed neural systems. By understanding what healthy brain activity looks like across these sub-networks, compared to unhealthy brain activity, and identifying predictive biomarkers that indicate changing state, DARPA is developing interventions that maintain a healthy brain state within a normal range of emotions. 

Three recent papers show that decoding technology can predict changes in mood from recorded neural signals; a brain sub-network appears to contribute to depression, especially in those with anxiety; and moderate to severe depression symptoms can be alleviated using open-loop neural stimulation delivered to the orbitofrontal cortex to modulate a sub-network that contributes to depression. 

This work is inspired by Sanchez’s commitment to finding better treatments for the millions of veterans who suffer from neuropsychiatric illness, which have been limited by a lack of a mechanistic understanding of how these illnesses manifest in the brain.

These findings encompass key discoveries and technologies to enable the SUBNETS goal of a closed-loop system that can detect ongoing dynamic changes in brain activity associated with fluctuations in mood, and that can use this information to deliver precisely timed therapeutic stimulation to improve brain function in individuals living with neuropsychiatric illnesses.

Join ApplySci at the 10th Wearable Tech + Digital Health + Neurotech Silicon Valley conference on February 21-22 at Stanford University — Featuring:  Zhenan Bao – Christof Koch – Vinod Khosla – Walter Greenleaf – Nathan Intrator – John Mattison – David Eagleman – Unity Stoakes – Shahin Farshchi – Emmanuel Mignot – Michael Snyder – Joe Wang – Josh Duyan – Aviad Hai – Anne Andrews – Tan Le – Anima Anandkumar – Hugo Mercier

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EEG identifies cognitive motor dissociation

Nicholas Schiff and Weill Cornell colleagues have developed an EEG-based method for measuring the delay in brain processing of continuous natural speech in patients with severe brain injury. Study results correlated with fMRI obtained evidence, commonly used to identify the capacity to perform cognitively demanding tasks. EEG can be used for long periods, and is cheaper and more accessible than fMRI.

This type of monitoring can identify patients with severe brain injury who have preserved high-level cognition despite showing limited or no consciousness.

According to Schiff: “This approach may be a more effective and efficient method for initially identifying patients with severe brain injuries who are very aware but are otherwise unable to respond, a condition called cognitive motor dissociation.”

Join ApplySci at the 10th Wearable Tech + Digital Health + Neurotech Silicon Valley conference on February 21-22 at Stanford University — Featuring:  Zhenan Bao – Christof Koch – Vinod Khosla – Walter Greenleaf – Nathan Intrator – John Mattison – David Eagleman – Unity Stoakes – Shahin Farshchi – Emmanuel Mignot – Michael Snyder – Joe Wang – Josh Duyan – Aviad Hai – Anne Andrews – Tan Le – Anima Anandkumar – Hugo Mercier