Here's what Philips expects from this collaboration:

By combining Philips Research’s strengths in microelectronics, signal processing, ultra-low power system design and miniaturization with NeuroNexus Technologies’ expertise in micro-scale electrode design and fabrication, the two companies aim to show the technical feasibility of highly programmable and MRI-safe deep brain stimulation devices. Their initial research will aim to meet the functional requirements of a deep brain stimulation device for the treatment of Parkinson’s disease. This is a degenerative disorder of the central nervous system that impairs people’s motor skills and speech, leading to a progressive loss in quality of life. Recent publications suggest that deep brain stimulation could also be suitable for treating psychiatric disorders such as clinical depression.

Late-stage Parkinson’s disease is increasingly being treated using deep brain stimulation – a technique that involves implantation of a medical device, a “brain pacemaker” that sends electrical impulses to specific parts of the patient’s brain via permanently inserted electrodes. The pacemaker control unit is normally implanted into the patient’s chest or abdomen, with a connecting lead routed under the skin to the brain electrode. While offering an effective therapy that helps many patients, currently available technologies have significant limitations.

“As currently used, deep brain stimulation poses several challenges to both the patient and the physician: The implantation requires a lengthy surgical procedure involving both neurosurgeons and neurologists. Following surgery, setting the right stimulation parameters requires painstaking efforts on the part of the neurologists before the patient can be sent home. In the long term, patients may for example develop spine problems that would require further examination using MRI, but with current implants MRI scans are not possible due to the materials used in the fabrication of DBS electrodes and the stimulators”, explains Prof. Maximilian Mehdorn, Head of Neurosurgery at the Christian-Albrechts University of Kiel, Germany.

The joint research project aims to address these clinical needs, and will leverage Philips’ expertise in medical imaging and surgery planning with the aim of simplifying the implantation process and shortening the surgical procedure. Philips will also contribute to making the entire device MRI compatible so that patients fitted with the implant are not barred from MRI scans. With its world-leading track record in neural micro-electrodes, NeuroNexus Technologies brings in key technology and knowledge for novel brain probes.

Press release: NeuroNexus Technologies and Philips partner to research next-generation deep brain stimulation devices for the treatment of central nervous system disorders...

NeuroNexus Technologies homepage...

Core:Tx® is a software and hardware system that interfaces with a patient, giving him or her real-time feedback on the position and movement of selected joints. At the same time this system provides the clinician with valuable objective data on the patient’s performance and abilities.

Used under the guidance of a therapist or healthcare professional, Core:Tx turns rehabilitation into a wireless, game-like challenge that is entertaining and works for a variety of patients recovering from neuromuscular conditions as well as joint injuries. The Core:Tx system is compatible with and enhances existing rehabilitation, preventative and strengthening protocols.

CoreTx product page...

CoreTx Professional user manual (.pdf)...

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The decision support software that has been developed by Philips Research in collaboration with the University Medical Center Hamburg-Eppendorf (Hamburg, Germany), aims to assist in the interpretation of the images to the point where accurate diagnoses can be made by less experienced physicians. This could make diagnostic services for the differential diagnosis of dementia much more widely available.

The Philips/University Medical Center Hamburg-Eppendorf software works by performing three different steps. The first step is to spatially normalize the patient’s brain-scan image by selecting, rotating and scaling the appropriate image slice in order to align it with a standard template. The second step is to compare this normalized image, voxel-by-voxel, with a library of normal (un-diseased) brain scans in order to identify hypometabolic regions in the patient’s brain. After identifying and color highlighting these hypometabolic regions, the final step is to compare their size, shape and distribution against a set of disease-specific patterns for each type of dementia. The software then quantifies, in the form of a percentage value, the degree to which the patient’s scan matches each disease-specific pattern. Ultimately, the diagnosing physician could take the percentages into account when arriving at his or her diagnosis.

In addition to being evaluated for feasibility and usability in a clinical setting by University Medical Center Hamburg-Eppendorf’s nuclear medicine department, with positive results, the accuracy with which the software can quantify a match between the patient’s brain-scan images and disease-specific patterns has been tested in two retrospective studies.

Both of these studies involved using a library of brain scan images, each image having been previously examined by a clinical expert in order to arrive at a differential diagnosis. During the study, each of these images was analyzed by the software and a diagnostic conclusion drawn from its results, based on the disease-specific match percentages generated. These diagnostic conclusions were then compared to the differential diagnoses made previously by the clinical expert. Both studies employed a so-called ‘leave-one-out cross-validation scheme’, in which each patient’s brain scan (the validation data) was compared to the disease-specific patterns in all the other brain scans (the training data). This is a well-known scheme for minimizing bias in tests where the data set size is limited.

In the first study, based on a University Medical Center Hamburg-Eppendorf library of FDG-PET scans from 83 patients, the software achieved better than 98% correspondence with the clinical expert’s interpretation, when programmed to differentiate between brain scans showing no signs of dementia, those showing characteristics of Alzheimer’s disease and those showing characteristics of Frontotemporal Dementia.

In the second, 48-patient study using FDG-PET images provided by the Austin Hospital (Melbourne, Australia), the software achieved better than 80% correspondence when differentiating between scans that had been expert assessed as being un-diseased, suffering from Alzheimer’s, suffering from Frontotemporal Dementia or suffering from Lewy Body Dementia. This second study, involving the differential diagnosis of four disease classes, was a greater test for the software than the first study, because indications of Alzheimer’s and Lewy Body Dementia occur in similar areas of the brain. Nevertheless, the software was able to differentiate between them.

Philips Research technology backgrounder: Philips develops decision support software to assist in the differential diagnosis of dementia

From the product page:

The PainShield device is a portable, battery powered electronic unit that is connected to a disposable patch through which it delivers localized energy creating therapeutic effect to treat localized pain and induce soft tissue healing. This is made possible due to the company's proprietary technology which allows for the creation of a therapeutic transducer that can be made disposable and incorporated into a patch.

The PainShield product uniquely generates and delivers localized, low frequency, low intensity therapeutic ultrasound by a self adhering patch, place next to the treated area for a period of time.

Product usage has shown wide range of applications including reduction of acute and chronic pain as well as an anti-inflammatory effect. PainShield may be used immediately post-injury and post-op. The product is patch based and does not require for medical personnel to apply it and hold it in place while receiving therapy.

Patient benefits include its ease of application and use, faster recovery time, high compliance, safety, and effectiveness.

Press release: NanoVibronix Receives FDA Clearance for its PainShield™ MD Device

Product page: PainShield

NeuroStar TMS Therapy® is specifically indicated for the treatment of Major Depressive Disorder in adult patients who have failed to achieve satisfactory improvement from one prior antidepressant medication at or above the minimal effective dose and duration in the current episode. In clinical trials with NeuroStar TMS Therapy, these patients had been treated with a median of 4 medication treatment attempts, one of which achieved criteria for adequate dose and duration.

The NeuroStar TMS Therapy system is the first and only TMS Therapy® device cleared by the FDA for the treatment of depression. TMS Therapy is a non-systemic (does not circulate in the bloodstream throughout the body) and non-invasive (does not involve surgery) form of neuromodulation which stimulates nerve cells in an area of the brain that is linked to depression, by delivering highly focused MRI-strength magnetic pulses. Patients being treated by NeuroStar TMS Therapy do not require anesthesia or sedation and remain awake and alert. It is a 40-minute outpatient procedure that is prescribed by a psychiatrist and performed in a psychiatrist’s office. The treatment is typically administered daily for 4-6 weeks.

Clinical Trials Demonstrated Efficacy and Safety of NeuroStar TMS Therapy NeuroStar TMS Therapy was evaluated for efficacy, safety, and tolerability in the acute treatment of major depression in patients who had failed to receive benefit from prior antidepressant medications. A 6-week, randomized, placebo-controlled, double-blind, study1 was conducted to evaluate the safe and effective use of NeuroStar TMS as a monotherapy. An analysis for predictors of response demonstrated that the patients with the best response to NeuroStar TMS Therapy were those who had not benefited from one prior antidepressant medication at an adequate dose and duration in the current episode2. These are the patients for whom NeuroStar TMS Therapy has been cleared by the FDA.

This clinical study population2 was comprised of 164 patients with unipolar, non-psychotic major depressive disorder. Almost all of them (97%) had suffered previous depression episodes. These patients also had an extensive treatment history without a satisfactory improvement. They had received a median of 4 total prior antidepressant treatment attempts in the current episode, one of which achieved treatment adequacy at or above the minimal effective dose and duration. Forty-eight percent were unemployed due to their depression, 35% had a co-morbid anxiety disorder, and all had moderate to severe depressive symptoms.

In the indicated patient population, the following efficacy results were observed in the randomized, controlled study:

The primary efficacy measure, the Montgomery-Asberg Depression Rating Scale (MADRS) symptom score change at 4 weeks, was statistically significantly superior to placebo (p=0.0006), among NeuroStar-treated patients. Similar results were observed with the Hamilton Depression Rating Scale (HAMD) 3.

NeuroStar TMS Therapy-treated patients had statistically significant response3 and remission4 rates, which were approximately twice the rate of placebo-treated patients. The response rate is the percentage of patients who had a >50% improvement in symptoms, and the remission rate is the percentage of patients who achieved virtually complete symptom resolution.

NeuroStar TMS Therapy also produced statistically significant improvements on the HAMD factor scores for core depression symptoms, anxiety symptoms, somatization, and psychomotor retardation.4

Press release: FDA Clears NeuroStar® TMS Therapy for the Treatment of Depression, October 8, 2008 (PDF)

Product page: NeuroStar TMS Therapy System

Flashbacks: FDA to Consider Transcranial Magnetic Stimulation System , Deep TMS Technology by Brainsway, Transforming the Psychiatrist's Office, and many other posts under Medgadget's transcranial magnetic stimulation archive...

Pictured above is Anatomically Correct Testbed Hand, a device that " has three fully actuated fingers that have the same biomechanical structure as the human hand. This hand is used to understand the human hand's biomechanical structure and neural control strategies, and will serve as a prosthetic and surgical tool one day."

NSF explains its push to study the brain:

Interdisciplinary teams will pursue transformative, fundamental research in two areas of great promise: understanding the brain and how its abilities may be used through cognitive optimization and prediction; and developing ways to make complex, interdependent infrastructure systems more resilient and sustainable.

What researchers learn from the brain may open many new paths of discovery, in areas such as computing, robotics, medicine and education. Understanding how the brain moves the hand, for example, could illuminate entirely novel ways to help people who are paralyzed or use prosthetic limbs. Understanding how the brain visually recognizes objects will enable advances in artificial vision systems, robotic intelligence and more.

Full story: New Research to Probe Human Mind and Future Infrastructure Systems...

ENG/EFRI FY 2008 Awards Announcement...

One way to reduce the stress caused by a sleep disorder is to wake up gently. Studies have shown that the best time for an alarm clock to go off is when a person is 'almost awake' in terms of their natural sleep rhythm. At that point the body and brain are ready to wake up, so the change from sleep to wakefulness is least jarring. The researchers focused on this part of the sleep cycle, and developed what they call 'an arousal clock' rather than an alarm clock.

Researchers from Tampere University of Technology and the University of Helsinki, both in Finland, used a simple microphone that is available in most mobile phones to record and analyse movement in 80 subjects over a 6 month period. They found that the technology was adequate to analyse periods of calm and movement in a regular bedroom setting. Dr Tapani Salmi of Tampere University of Technology explained, 'Very soon we noticed that a common microphone is very sensitive to any sounds and voices produced by movements in the bed during night-time. Everyone has heard the typical voices, when a mobile phone has accidentally called you from someone's pocket.'

The new alarm clock is built in to a mobile phone. The subject sets the desired alarm time as normal and places the phone nearby (usually beneath the pillow). The phone analyses the subject's 'sleep movement sounds'. Twenty minutes before the alarm is set to go off, the phone determines when the subject is making 'almost awake' sounds, and gives off a soft alarm signal.

The 'arousal clock' is less stressful than a conventional alarm clock, and sleep-diary analysis indicated that subjects benefited after using the new device for a week. Dr Salmi reported that using the clock continuously 'helps the internal clock in your brain learn the proper sleep rhythms'. No alarm signal is given before the set alarm time if the subject is sleeping calmly, as subjects who experience very deep sleep at alarm time do not appreciate any alarm, no matter how gentle.

Movement analysis is commonly used in combination with other sleep diagnostics to screen for somnipathies; the Finnish study's innovation was in using a wireless technology. Because the device is not physically attached to the patient, the act of analysing movement did not interfere with the subjects during sleep.

The new technology is capable of performing several all-night recordings, so it can be used to analyse a subject's sleep patterns over the course of a few days. Importantly, it can be used to diagnose sleep disorders in regions that do not have sleep clinics. It is convenient and cost efficient compared to the conventional battery of tests used in sleep clinics.

HappyWakeUp download page...

Supported mobile phones...

CORDIS press release: Mobile phone technology makes waking up easier

From MIT Tech Review:

Electrodes are placed in a water-based solution of carbon nanotubes; when a small voltage is applied to sites on the electrodes, carbon nanotubes localize there and can be fixed. Joseph Pancrazio, a neuroscientist at the National Institute of Neurological Disorders and Stroke, says that Keefer's electrode modification "is something that can be done readily." This means that other labs experimenting with neural prosthetics are likely to adopt the technique. By contrast, Pancrazio says, other methods for interfacing carbon nanotubes with neurons have required the use of special substrates and must be done at very high temperatures.

Pancrazio says that the nanotube coating might enable researchers to make smaller electrodes that cause fewer side effects. Using conventional electrodes for deep-brain stimulation, Pancrazio says, "you end up stimulating not only the area of interest but also other regions, leading to speech dysfunction and other problems." The ideal electrode would be small enough to interact with only a single neuron. But when electrodes are miniaturized, their impedance increases and their performance decreases. Electrodes coated in carbon nanotubes might be more amenable to miniaturization.

More at MIT Technology Review...

Image: In these scanning electron microscope images, electrodes coated with carbon nanotubes, like the one on the right, are more conductive and better at interfacing with nervous tissue. The electrode on the left is bare. Credit: Edward Keefer

The robot's biological brain is made up of cultured neurons which are placed onto a multi electrode array (MEA). The MEA is a dish with approximately 60 electrodes which pick up the electrical signals generated by the cells. This is then used to drive the movement of the robot. Every time the robot nears an object, signals are directed to stimulate the brain by means of the electrodes. In response, the brain's output is used to drive the wheels of the robot, left and right, so that it moves around in an attempt to avoid hitting objects. The robot has no additional control from a human or a computer, its sole means of control is from its own brain.

The researchers are now working towards getting the robot to learn by applying different signals as it moves into predefined positions. It is hoped that as the learning progresses, it will be possible to witness how memories manifest themselves in the brain when the robot revisits familiar territory.

Professor Kevin Warwick from the School of Systems Engineering, said: "This new research is tremendously exciting as firstly the biological brain controls its own moving robot body, and secondly it will enable us to investigate how the brain learns and memorises its experiences. This research will move our understanding forward of how brains work, and could have a profound effect on many areas of science and medicine."

Video from the New Scientist:

University of Reading press release: Robot with a Biological Brain: new research provides insights into how the brain works...

More at the New Scientist...

(hat tip: Drudge Report)