UNIverse - Public Research Portal
MALEavatar

Prof. Dr. med. Peter Fuhr

Department of Clinical Research

Projects & Collaborations

17 found
Show per page
Project cover

Prediction of Patient-Specific Deep Brain Stimulation Parameters

Research Project  | 1 Project Members

Deep brain stimulation (DBS) has become one of the most important neurostimulation therapies for movement disorders as Parkinson’s disease (PD) and essential tremor (ET). Despite the success of this therapy the underlying mechanism(s) of action and optimal implantation locations are still incompletely known, and the post-operative stimulation parameter programming procedure remains time consuming, especially when using segmented stimulation leads. Our multidisciplinary, international consortium collaborating partially for more than 15 years, has a long-lasting experience in DBS research concerning brain imaging, patient-specific electric field (EF) simulation, tractography, intraoperative physiological measurements and atlas generation. The project relies on these established methods and is directly linked to an ongoing cross disciplinary international DBS research.

The primary objective of the present exploratory project is to set-up and evaluate a workflow for an inverse analysis algorithm to predict chronic DBS parameters in patients suffering from ET and implanted in Zona incerta (ZI) or the nucleus ventrointermedius of the thalamus (Vim).

To reach this goal, four main Work Packages (WPs) are planned within the proposed project.

WP1: Patient-specific improvement and adverse effect maps will be set-up for new data from the clinical partners, based on patient-specific EF simulations for intraoperative stimulation tests before DBS lead implantation and chronic stimulation with the corresponding clinical effects.

WP2: Mono- and multi-centric ZI/Vim ET DBS atlases with therapeutic (sweet spots) and adverse effect areas will be generated from these individual maps and possibly as well a PD atlas for the subthalamic nucleus.

WP3: The atlases will be projected to new patients. An algorithm for stimulation parameter prediction will be developed based on the mono-centric atlas to determine position and necessary stimulation parameters: the corresponding EFs should cover therapeutically effective and avoid adverse effect regions from the atlas.

WP4: The proof of concept will be performed with patient cohorts from two further clinical centers and through a comparison between predicted and finally chosen chronic stimulation parameters and stimulated volumes.

The stimulation atlases developed with larger patient cohorts are expected to enable new knowledge about the optimal areas to stimulate and those to avoid and thus about the optimal target position in different movement disorders. The inverse analysis of the stimulation atlases should support clinicians in choosing the stimulation parameters. A successful outcome will result in the replacement of the trial-and-error principal of the programming sessions. This will reduce the number of medical visits and programming time, and in consequence increase the patient comfort. In the long term, this approach could be applied to the surgical planning procedure to propose the optimal implant position. In summary, the approach proposed will take the next step in patient management in going from “mental imagination” by the medical staff to “intuitive visualization” to further improve DBS therapy.

Project cover

Entropy and Synchrony Markers for Modeling Cognitive Decline in Patients with Parkinsons Disease

Research Project  | 2 Project Members

Parkinson's disease dementia (PDD) is a complication in the course of Parkinson's disease (PD). The pathophysiological process, however, is not completely understood, and it is of high practical importance to develop new methods for detecting the cognitive decline in PD in a very early state. Recent studies have shown that quantitative EEG (QEEG) measurements are among the most promising methods to predict and monitor cognitive decline. While QEEG is not affected by repetitive examination artifacts, limitations include that the conventional analysis by power spectra doesn't reflect sufficiently the complexity of the underlying neurophysiological process. Therefore, we aim to establish an analytical AI-based tool operating on entropy and synchrony measures to capture more of the complex mechanisms underlying cognitive decline in some patients with PD.

Project cover

Computer aided Methods for Diagnosis and Early Risk Assessment for Parkinson`s Disease Dementia

Research Project  | 4 Project Members

Neurodegenerative disorders begin insidiously in midlife and are relentlessly progressive. Currently, there exists no established curative or protective treatment, and they constitute a major and increasing health problem and, in consequence, an economic burden in aging populations globally. Parkinson's disease (PD), following Alzheimer's disease (AD), is the second most common neurodegenerative disorder worldwide, estimated to occur in approximately 1% of population above 60 and at least in 3% in individuals above 80 years of age. In Switzerland, about 15'000 persons are diagnosed with PD. In addition to motor signs, which due to recent medical progress can be treated satisfactorily in most cases, non-motor symptoms and signs severely affect the well-being of patients. They include mood disorders, psychosis, cognitive decline, disorders of circadian rhythms, as well as vegetative and cardiovascular dysregulation. Neurodegeneration in PD progresses for years before clinical diagnosis is possible, at which time e.g. 80% of dopaminergic neurons in the Substantia nigra are lost already. Therefore, any clinical targeting disease modification, prognosis and personalized treatment including guiding the indication for deep brain stimulation (DBS) requires reliable and valid biomarkers. The main goal of this research project is the identification of a pertinent set of genetic and neurophysiological markers for diagnosis and early risk assessment of PD-dementia. Our approach has a distinct interdisciplinary basis, in that it fosters close collaborations between physicians, neuroscientists, psychiatrists, psychologists, computer scientists and statisticians. Based on current research findings we postulate that a combination of (1) quantitative electroencephalographic measures (QEEG, e.g. frequency power and connectivity patterns and network analysis), (2) genetic biomarkers (e.g. MAPT, COMT, GBA, APOE) and (3) neuropsychological assessment improves early recognition and monitoring of cognitive decline in PD. To test this hypothesis, this project proposes an interdisciplinary long-term study of patients diagnosed with PD without signs of dementia, among them a subgroup of patients undergoing DBS. The workup of the proposed study includes collection of clinical, neuropsychological, neurophysiological and genotyping data at the baseline, as well as at 3, 4 and 5 years follow-ups. Sophisticated statistical models that can deal with noisy measurements, missing values and heterogeneous data types will be used to extract the best combination of biomarkers and neuropsychological variables for diagnosis and prediction of prognosis of PD-dementia. Besides this clinical perspective, this project further aims at deciphering the unknown disease mechanisms in PD both on a genetic and neurophysiological level, with particular emphasis of the interplay of genetic markers and temporal changes in the functional connectivity of the brain over time.

Project cover

Improved prediction and montoring of CNS disorders with advanced neurophysiological and genetic assessment

Research Project  | 10 Project Members

Objective: To establish a numerical model for better characterization and prediction of the course of the two most prevalent chronic neurological disorders with high impact on quality of life in young and elderly human beings, respectively: Multiple Sclerosis (MS) and Alzheimer's disease (AD). The numerical model will contain clinical, neuropsychological, genetic, imaging and neurophysiological data. Background: Although diagnosis of MS has greatly improved over the last decade, reliable prediction of the disease course (prognosis) is still not satisfying. In AD and other dementia types diagnosis is more difficult early in the disorder and depends in part on the course of symptoms. In both disease groups, clinical examination is still the main tool to assess the course of disease and the grade of impairment. Neurophysiological measurements like electroencephalography (EEG) at rest and during visual and sensory stimulation (evoked potentials, EP) represent parameters of impulse propagation in the central nervous system. These measures are likely to be abnormal early in the course of MS and AD. Therefore, they may add important information on the prognosis in MS and AD, and on the differential diagnosis of dementias. Recent technical developments allow the recording of EEG and EP with high resolution (256 channels) resulting in precise identification and localization of pathological changes. Genetic testing is likely to further improve the prediction of the disease course. Methods: In the MS subproject one hundred patients and fifty age-matched healthy controls will be examined three times at yearly intervals. Clinical and neuropsychological examination will be complemented by high-resolution EEG and EP, genetic testing and brain imaging by magnetic resonance tomography. In the AD subproject, forty patients with dementia will be compared to forty age matched healthy controls in regard to their cognitive performance, genetic profile and results of high resolution EEG and EP. All results of the different tests will be analyzed with a statistical model, which summarizes all data of an individual to a score to predict the clinical course in MS and AD. Significance: Reliable markers of disease progression and prognosis would allow to conduct clinical trials with a smaller number of patients or in less time, thus reaching clinically meaningful results more efficiently. This is especially important in MS and AD, where innovative treatment options are entering the phase of clinical testing in coming years. Moreover, improved prediction of the course of MS and AD may be useful even in individual patients for counselling and treatment decisions.