Complex brain functions are not merely implemented within individual brain areas but in distributed networks of structurally and functionally interconnected brain regions. For example there is evidence that a mainly fronto-parietal network subserves cognitive control. Such networks' functional architecture and integrity can be studied using functional magnetic resonance imaging (fMRI) while the subject is in a resting state. As they are less complex compared to conventional task-based fMRI-protocols resting-state acquisitions seem to be convenient for clinical applications.There is an increasing use of fMRI data analysis methods considering complex activation or connectivity patterns at the same time. These methods are often termed multi-voxel or multivariate pattern analysis (MVPA). Until today MVPA has been predominantly used within subjects: Machine-learning algorithms were applied in order to identify patterns in the data that discriminate e.g. two different functional states of the brain (in terms of different task conditions like finger-tapping vs. rest).Yet only few studies have used MVPA methods for classification across subjects, e.g. in order to assign single subjects to different groups instead of discriminating cognitive states. MVPA across subjects raises several specific issues like dealing with interindividual differences of brain morphology, which can not be solved with methods identical to those applied in conventional univariate fMRI analyses.

Aim of this project is to evaluate MVPA analysis strategies across participants and underlying circumstances with a focus on examining the integrity of functional brain networks. Especially, our medium-term goal is to contribute to the imaginable diagnostic application of cross-subject MVPA of resting-state fMRI data in the context of systemic brain disease and mental disorders. Our recent efforts have focused on subjects with major depressive disorder and chronic pain.Group members
Prof. Dr. rer. nat. Dr. med. Bettina Pfleiderer
Dr. med. Benedikt Sundermann
Stephan Christoph FederCooperations
Forschungsverbund BiDirect-StudiePublicationsSundermann B, Pfleiderer B.
Functional connectivity profile of the human inferior frontal junction: involvement in a cognitive control network.
BMC Neurosci 2012, 13:119 (DOI) Sundermann B, Herr D, Schwindt W, Pfleiderer B.
Multivariate Classification of Blood Oxygen Level–Dependent fMRI Data with Diagnostic Intention: A Clinical Perspective
Am J Neuroradiol., 2013, (epub ahead of print)Sundermann B, Burgmer M, Pogatzki-Zahn E, Gaubitz M, Stüber C, Wessolleck E, Heuft G, Pfleiderer B
Diagnostic classification based on functional connectivity in chronic pain: model optimization in fibromyalgia and rheumatoid arthritis
Acad Radiol, 2014, 21(3):369-77 (DOI)