Our research focuses on neurofeedback, imaging biomarkers of neuromodulatory interventions in mental health disorders and data-driven prediction of treatment response.
Klinik für Psychische Gesundheit
Neurofeedback - Neuromodulation - Mental Health Informatics
Team
Johanna Meeh, PhD candidate
Associate Members:
Linda Orth, PhD candidate
Fabian Zimmer, M.D. candidate
Projects
+ fMRI neurofeedback modification of frontostriatal brain networks in psychiatric disorders
Brain-based therapies such as real time fMRI-neurofeedback can target the key underlying functional deficits in psychiatric disorders and are a promising development. fMRI-Neurofeedback is based on operant conditioning and teaches participants to self-regulate blood-oxygen level-dependent (BOLD) response in target brain regions based on ongoing feedback from their own brain activation. Frontobasal circuitries have a key role in regulating emotional, cognitive and executive control mechanisms and are central to many mental health disorders. We study the feasibility of frontobasal connectivity as a target for neurofeedback-based modulations in psychiatric cohorts such as OCD.
+ Biomarkers of response to ketamine-based neurostimulation
In contrast to the traditional antidepressants, intravenous and nasal Ketamine count as interventions with a rapid onset of action. Rapid antidepressant effect and the possibility of monitoring neurobehavioral effects pre and post intervention facilitate finding biomarkers of response, which can be used to guide additional research on prediction of treatment response in mood disorders. Multimodal examinations consisting of MR imaging and spectroscopy, standardized visits, routine medical data, genetics and blood markers are applied in a longitudinal fashion in our growing clinical cohort.
+ Federated approaches in hospitals to share clinical data
Machine learning models rely on large, diverse datasets. For health research, this can be achieved by multicenter cohorts. We develop data models for mental health research for applications using “Medical Informatics Platform, an EBRAINS based collaborative tool for federation of patient data located in hospitals (https://mip.humanbrainproject.eu/).
+ Text mining of mental health records to identify the relevant and classifiable information
Text mining techniques are considered as a pivotal approach for gaining information from mental health data. Deep learning-based recognition of relevant entities is being applied based on the pre-trained models. Furthermore, methods for normalization of the entities to standard terminologies such as AMDP system are tested.
+ Using the information from the electronic medical records to support decision-making in mental health care.
Clustering methods to identify relevant patient categories and regression models to estimate the relevant factors are used to identify the key features and the relevant combinations that determine the risk for decisions. Electronic medical records count as a reliable source of longitudinal information in investigations of long-term mental health conditions. An information system environment undelies the use of the hospital routine data for data-driven investigations.
Current funding
+DFG (Projektnummer 448334688)
Current selected publications
Sarkheil P, Chechko N, Veselinovic T, Marx G, Neuner I.
Telepsychiatry: the remote care that unifies.
The European Journal of Psychiatry. 2021 Jan-March; 35(1): 64-65. doi.org/10.1016/j.ejpsy.2020.08.004
Sarkheil P, Ibrahim CN, Schneider F, Mathiak K, Klasen M.
Brain Imaging Behav. 2020 Apr;14(2):485-495. doi: 10.1007/s11682-019-00065-z.
Sarkheil P, Odysseos P, Bee I, Zvyagintsev M, Neuner I, Mathiak K.
Functional connectivity of supplementary motor area during finger-tapping in major depression.
Compr Psychiatry. 2020 May;99:152166. doi: 10.1016/j.comppsych.2020.152166.
Zweerings J, Sarkheil P, Keller M, Dyck M, Klasen M, Becker B, Gaebler AJ, Ibrahim CN, Turetsky BI, Zvyagintsev M, Flatten G, Mathiak K.
Neuroimage Clin. 2020;28:102483. doi: 10.1016/j.nicl.2020.102483.
Orth L, Zweerings J, Ibrahim CN, Neuner I, Sarkheil P.
Neuroimage Clin. 2020;27:102324. doi: 10.1016/j.nicl.2020.102324.
Pubmed Link:
https://pubmed.ncbi.nlm.nih.gov/?term=pegah+sarkheil&sort=pubdate
Kontakt
Head:
Priv.-Doz. Dr. rer. nat. Pegah Sarkheil
Tel.: 0251-83-56601
E-Mail: pegah.sarkheil(at)ukmuenster(dot)de
Administrative assistance:
Mrs. K. Ulrich
Mrs. D. Heil
Tel.: +49 (0)251 / 83-56664
Tel.: +49 (0) 251 / 83-58641
Fax: +49 (0)251 / 83-56988
E-Mail: Kerstin.Ulrich(at)ukmuenster(dot)de
E-Mail: Daniela.Heil(at)ukmuenster(dot)de