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Project 10739: The Movement Diagnostic System



People involved:


Dr. Fleur van Rootselaar
Project function: Project Leader
Organisation: Academisch Medisch Centrum

Winfred Mugge
Project function: Post Doc
Organisation: Technische Universiteit Delft

Sarvi Sharifi
Project function: PhD candidate
Organisation: Academisch Medisch Centrum

Frauke Luft
Project function: PhD candidate
Organisation: Universiteit Twente

Prof.dr. Frans van der Helm
Project function: Co-applicant, Program Leader
Organisation: Technische Universiteit Delft

Dr.ir. Alfred Schouten
Project function: Co-applicant
Organisation: Technische Universiteit Delft

Dr. Marina de Koning-Tijssen
Project function: Co-applicant
Organisation: University Medical Center Groningen

Dr.ir. Lo Bour
Project function: Co-applicant
Organisation: Academisch Medisch Centrum

Dr.ir. Ciska Heida
Project function: Co-applicant
Organisation: Universiteit Twente


Onderzoek naar tremor in AMC

 De afdeling neurologie van het Academisch Medisch Centrum verricht momenteel wetenschappelijk onderzoek naar tremor. Het onderzoek is bedoeld om meer te weten te komen over de hersennetwerken die betrokken zijn bij het ontstaan van tremor en deze informatie te gebruiken om de diagnostiek te verbeteren.

In het kader van wetenschappelijk onderzoek zijn we op zoek naar patiënten met essentiële tremor, ziekte van Parkinson, schrijfkramp en gezonde vrijwilligers.
Zie: www.tremoronderzoek.nl

Newsletter (2) November 2012

The project Movement Diagnostic System designs a novel system to differentiate movement disorders and to investigate pathological brain networks in movement disorders with the help of EEG and fMRI combined with EMG, accelerometry and a wrist manipulator. The project is a collaboration of the Academic Medical Center (Sarvi Sharifi, Lo Bour, Fleur van Rootselaar), Delft University of Technology (Winfred Mugge, Alfred Schouten), the University of Twente (Frauke Luft, Ciska Heida) and industrial partners including Moog Inc., Noldus and TMSi.

After two years of tackling the technological challenges of developing MR compatible equipment, we have currently started with the first recordings in patients outside the MR scanner and are making preparations for the recordings inside the MR scanner. We aim to complete 100 patient recordings by the end of 2013. Here we will give you an update on our project so far.



Background

Diagnosis of movement disorders is currently mainly based on clinical observation and pattern recognition. Underlying pathological brain networks have not been used in the diagnostic assessment, even though this could aid accurate and early diagnosis. By using our novel approach to accurately locate specific pathological brain networks in movement disorders (starting with Parkinson's Disease, Essential Tremor and Writer's Cramp), we aim to enable early diagnosis and designated treatment.

Development of experimental protocol. Our experimental protocol targets parts of the human motor system that are likely to be involved in movement disorders. Few examples of the tests are: entrainment of hand tremor and dystonia by means of the wrist manipulator to influence(faulty) neuronal feedback loops, auditory cueing during hand tapping to determine its influence on brain activations and motor performance, and mapping the default mode of the neuronal networks of each patient group with the help of movement measures. The protocol is divided into two sessions:
1) outside the MR-environment using EMG-EEG and a conventional robot system, and
2) inside the MR-scanner using EMG-fMRI and a newly developed MR-compatible setup.

Several studies have already been performed, including 3 successful student projects on the design of the MR-compatible manipulator, the type of perturbations and on motor tasks that selectively activate brain areas. The development of the MR-compatible experimental setup is in its final stage and the experimental setup outside the MR-environment is fully operational. Currently, the first patient measurements are being performed using the setup outside the MR-environment. These patients will be followed up by a measurement session inside the MR-scanner early 2013.

TMSi continues to work on the electromagnetic shielding for the TMSi amplifier and the electric cables with ferrite that were specifically designed for the project to reduce induction of the scanner on EMG. MOOG is currently pursuing two fundamentally different actuation systems for the MR-compatible robot, one using hydraulics and one using push rods. The sensors use fiber optics to prevent interference with the imaging. We developed a program in LabVIEW to acquire the data, which is synchronized with the video system using software from Noldus. The involved companies will continue the development of the MR-equipment during the patient recordings. This way the equipment can be fine-tuned and possibly commercialized.
 

Introduction

A diagnostic system will be developed to capture abnormal brain activations in movement disorders patients, combining functional MRI, measures of muscle activity, video tracking devices and a wrist manipulator to perturb the human motor system. Two PhD students with a background in Biomedical/Electrical Engineering, Physics or Neurosciences and demonstrated interest in neurotechnology have been recruited, one for a position at the AMC, Amsterdam (Sarvi Sharifi) and one for a position at the UT, Enschede (Frauke Luft); a postdoc on robotic manipulators works at the TUD (Winfred Mugge).

Essential Tremor (ET), Parkinsonian tremor (PD) and limb dystonia are ubiquitous and disabling. Currently, diagnosis fails due to limited diagnostic tools. ET, PD and dystonia are diseases of the central nervous system (CNS). This knowledge is hardly used in diagnosis, probably because neuronal networks are complex and situated deep in the brain. Our goal is to develop a Movement Diagnostic System to accurately locate specific CNS pathological changes by differentiating between normal and abnormal brain activations related to (in)voluntary movements. The system will use a 'closed-loop approach', combining functional MRI (fMRI), measures of muscle activity, video tracking devices and a wrist manipulator to perturb the human motor system. The project includes the development of MR-compatible equipment. Firth of all, a high-end MR compatible robotic manipulator is being developed within this project (Winfred Mugge) in close collaboration with MOOG inc. With the manipulator we can apply external motor and sensory perturbations by wrist movements within the MR environment. Furthermore, MR compatible kinematic sensors are developed together with TMSi. Movement parameters that measure movement and tremor help us localize pathologic brain networks related to tremor. The kinematics must be tested thoroughly in the MR-environment and the data require post-processing due to enormous MR artifacts (Frauke Luft and Sarvi Sharifi). In order to synchronize video with measurement data we will develop a video system together with Noldus.

Our experimental setup will subsequently be evaluated in healthy controls and in ET, PD and dystonia patients. The measurements take place in the AMC and are performed by all three researchers. Our experimental protocol targets parts of the human motor system that are likely to be involved in movement disorders.

Currently, the first patient measurements are being performed using the setup outside the MR-environment. These patients will be followed up by a measurement session inside the MR-scanner early 2013.
The MDS project is supervised by Ciska Heida (UT), Alfred Schouten (TU Delft), Lo Bour (AMC) and Fleur van Rootselaar (AMC).

 


Newsletter (1) April 2012

Project The Movement Diagnostic System designs a novel system to differentiate movement disorders and investigates pathological brain networks in movement disorders with help of EMG-fMRI, accelerometery and a wrist manipulator. The project is a collaboration of the Academic Medical Center, the Delft University of Technology and the University of Twente.

Diagnosis of movement disorders is currently mainly based on clinical observation and pattern recognition. Underlying pathological brain networks have not been used in the diagnostic assessment even though this could aid accurate and early specific diagnosis. By using our novel approach to accurately locate specific pathological brain networks in movement disorders (starting with Parkinson's Disease, Essential Tremor and Writer's Cramp), we aim to enable early diagnosis and designated treatment.

Currently, we designed an experimental protocol to target parts of the human motor system that are likely related to the movement disorders. Few examples of the tests are: entrainment of hand tremor and dystonia by means of the wrist manipulator to influence(faulty) neuronal feedback loops, the influence of auditory cueing during hand tapping on brain activations and motor performance, and mapping the default mode networks with the help of movement measures for each patient group.

The development of the MR-compatible experimental set-up is at an advanced stage and several pilot studies have been performed to validate the electromagnetic shielding for the TMSi amplifier and the electric cables with ferrite that were specifically designed for the project to reduce induction of the scanner on EMG. With the help of software from Noldus, our set-up is synchronized and a video system is integrated to enable the visualization of video images corresponding to events in our measurements.The MOOG MR-compatible robot is hydraulically actuated and uses fiber optics as sensors, effectively enabling wrist manipulation in the scanner without the risk of affecting the imaging. Using the MR-compatible load cell, a pilot study on 5 healthy subjects showed that specific pairs of motor tasks allow for targeted activation ofthe brain areas of interest and paves the way for more extensive research and eventually improve movement disorder diagnostics.

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Introduction
 

A diagnostic system will be developed to capture abnormal brain activations in movement disorders patients, combining functional MRI, measures of muscle activity, video tracking devices and a wrist pertubator to manipulate the motor control systems. Two PhD students with a background in Biomedical/Electrical Engineering, Physics or Neurosciences and demonstrated interest in neurotechnology have been recruited, one for a position at the AMC, Amsterdam (contact a.f.vanrootselaar@amc.uva.nl) and one for a position at the UT, Enschede (contact t.heida@ewi.utwente.nl); a third PhD student works at the TUD (Delft).

Essential Tremor (ET), Parkinsonian tremor (PD) and limb dystonia are ubiquitous and disabling. Currently, diagnosis fails due to limited diagnostic tools. ET, PD and dystonia are diseases of the central nervous system (CNS). This knowledge is hardly used in diagnosis, probably because neuronal networks are complex and situated deep in the brain. Our goal is to develop a Movement Diagnostic System to accurately locate specific CNS pathological changes by differentiating between normal and abnormal brain activations related to (in)voluntary movements. The system will use a 'closed-loop approach', combining functional MRI (fMRI), measures of muscle activity, video tracking devises and a wrist pertubator to manipulate motor control systems. Therefore, MR compatible equipment, including a video motion capture system, MR compatible kinematic sensors and a high-end MR compatible robotic manipulator to apply external motor and sensory perturbations by wrist movements within the MR environment will be developed within the project. This will subsequently be evaluated in healthy controls and in ET, PD and dystonia patients. Finally the potential of the Movement Diagnostic System has to be demonstrated in a patient study.

Partners

The project is a collaboration between the Academic Medical Centre (University of Amsterdam), the Technical University Delft (www.bmeche.tudelft.nl), the University of Twente (http://bss.ewi.utwente.nl/),
and industrial partners including Moog Inc. (http://www.moog.com), Noldus (http://www.noldus.com/), CenS (Micro)Electronics BV (http://www.CenSelect.nl) and Twente Medical Systems International BV (TMSi, http://www.TMSi.com/).