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Biomedical Image Analysis

The Biomedical Image Analysis group focuses on methods for multimodal image analysis of brain imaging data including structural, functional and metabolic data.

 

With an emphasis on connectivity analyses, our group investigates brain networks of healthy subjects as well as patients with psychiatric diseases, particularly depressed patients.

 

Head of Research Group and Members

Martin Walter

Prof. Dr. Martin Walter (Head)

 

Tel. +49 7071 29-86119 (Secretary: Helga Lennig)

 

 

 
 

helga_lennig_60x60

Helga Lennig
Secretary
Tel. +49 7071 29-86119

 

 

 
 

Luisa Fensky

Luisa Fensky
Scientific coordinator
Ph.D. Candidate
Tel. 07071 29-85753

 

 

 
 

Nils Kroemer

Dr. Nils Kroemer
Junior research group leader
Tel. 07071 29-82021

 

 

 
 

Johan van der Meer

Dr. Johan van der Meer
Research associate

 

 

 
 

Meng Li

Dr. Meng Li
Postdoc

 

 

 
 

Sarah Alizadeh

Sarah Alizadeh
Research associate
Tel. 07071 29-82034

 

 

 
 

Hamidreza Jamalabadi

Hamidreza Jamalabadi
Research associate
Tel. 07071 29-82034

 

 

 
 

Vanessa Teckentrupp

Vanessa Teckentrup
Ph.D. candidate
Tel. 07071 29-82021

 

 

 
 

Tara Chand

Tara Chand
Ph.D. candidate
Tel. 07071 29-82086

 

 

 
 

Louise Martens

Louise Martens
Ph.D. candidate
Tel. 07071 29-82086

 

 

 
 

Marina Krylova

Marina Krylova
Ph.D. candidate
Tel. 07071 29-82086

 

 

 
 
 

Lena Danyeli

Lena Danyeli
Ph.D. candidate
Tel. 07071 29-82086

 

 

 
 
 

Caroline Burrasch

Caroline Burrasch
Ph.D. candidate
Tel. 07071 29-82021

 

 

 
 
 

Galina Surova

Galina Surova
Ph.D. candidate

 

 

 
 

Zumrut Duygusen

Zümrüt Duygu Sen
Research associate
Tel. 07071 29-82086

 

 

 
 

Igor Izyurov

Igor Izyurov
Research associate
Tel. 07071 29-82086

 

 

 
 

Maryam Faramarzi

Maryam Faramarzi
Research associate

 

 

 
 

Kathrin Meyer

Kathrin Meyer
MD-Ph.D. candidate
Tel. 07071 29-85458

 

 

 
 

Greta Amedick

Greta Amedick
MD-Ph.D. candidate
Tel. 07071 29-85753

 

 

 
 

Franziska Mueller

Franziska Müller
MD-Ph.D. candidate
Tel. 07071 29-82021

 

 

 
 

Nataliia Kharlamova

Nataliia Kharlamova
MD-Ph.D. candidate

 

 

 
 

Carolina-Laetitia Fiederer

Carolina-Laetitia Fiederer
MD-Ph.D. candidate

 

 

 
 

Meltem Coemert

Meltem Cömert
MD-Ph.D. candidate

 

 

 
 

Alex Shevtsova

Alex Shevtsova
Master student
Tel. 07071 29-85458

 

 

 
 

Merve Kaptan

Merve Kaptan
Master student
Tel. 07071 29-85458

 

 

 
 

Weijie Zhuo

Weijie Zhuo
Master student

 

 

 
 

Katharina Gotic

Katarina Gotic
Master student

 

 

 
 

Chiara Ritter

Chiara Ritter
Master student

 

 

 
 

Johanna Ade

Johanna Ade
Master student

 

 

 
 

Cindy Boden

Cindy Boden
Master student

 

 

 
 

Monja Neuser

Monja Pascale Neuser
Master student, Student assistant
Tel. 07071 29-82021

 

 

 
 

Dina Lausch

Dina Lausch
Student assistant

 

 

 
 

Rahel Gutbrod

Rahel Gutbrod
Student assistant

 

 

 
 

Vanessa Kasties

Vanessa Kasties
Student assistant

 

 

 

Projects

Molecular alterations manifest in variations of cortical thickness and other measures of atrophy in areas, that subserve crucial functions involved in affective psychiatric diseases. Such areas can be defined by functional magnetic resonance imaging (fMRI), either under certain experimental task conditions or under rest.

 

Resting state fMRI acquisition and analysis is performed and allows due to its high potential as a standard clinical diagnostic means promising evidence for example regarding discrimination of both healthy subjects and patients as well as subtypes of diseases by application of pattern recognition algorithms. Conclusions about network activity of the brain can be either drawn from seed-based approaches like functional connectivity (FC), amplitudes of low-frequency fluctuations (Alff), regional homogeneity (ReHo) and graph theory, or data-driven approaches like Independent Component Analysis (ICA).

 

On the anatomical level, structural connectivity between relevant brain regions is further investigated using diffusion tensor imaging (DTI).

 

To get a better understanding of potentially dysbalanced metabolites or transmitters in the brain,
we use magnetic resonance spectroscopy (MRS) to assess regions that exhibit either anatomical or functional deficits. This allows not only to develop future biomarkers predicting subsequent disease progress on the basis of subtle biochemical changes, but it may also lead towards therapeutical interventions that can be attuned to a specific molecular deficit under certain psychopathological conditions. Furthermore, we investigate changes in behaviour and emotionality that originate from affective disorders, such as empathy, attachment, arousal, and salience processing using task-based fMRI paradigms.

 

 

1) Resting-state Dynamics and their changes in depressed patients

 

It has been shown that functional connectivity, revealed by temporal correlation of spontaneous low frequency signal at rest, is not stable, even across a short time window of 5-10 TR.

 

proj6a
proj6b
 

 

To understand the functional meaning of resting state dynamics of connectivity, we use novel analysis methods to manipulate connectivity brain states, and explore their interrelation to other state markers of the autonomous nervous system. Secondly we explore these markers for relevant disease related information which could not be extracted by current observations of mean resting state connectivity.

 

 

2) Influence of structural network variability on affect regulation in healthy subjects and depression

 

proj10
Transient functional connectivities, which change during contextual demands or as a function of ongoing state changes, are thought to follow a backbone of structural connectivity. In the same line, the extent of functional activations is thought to rely heavily on the structural integrity of a region.
 
 

 

While these relationships are ideal for a complex understanding of neurobiological origins of abnormal network properties in disease, covariation in structure across regions or fiber tracks across regions can also be utilized to contribute to understanding the functional neuroanatomy in humans.

 

We therefore use DTI and cortical thickness measurements, including longitudinal observations at different field strengths, to generate a link of abnormal structural, histological, functional and metabolic evidence in depression.

 

 

3) Metabolism-BOLD-interaction during task- and resting state fMRI

 

proj7
Metabolism-BOLD-interaction during task- and resting state fMRI
Multimodal investigations allow us to go further when interpreting the neurobiological meaning of observed effects in BOLD Signal changes or functional connectivity. We use combinations of Magnetic Resonance Spectroscopy (MRS) and fMRI from 3T to 7T which allow us to compare the inter-individual variation in classical fMRI markers with changes in concentrations of GABA, Glutamate or Glutamine.
 

 

proj7b

In healthy controls this helps to define physiological mechanisms of inter-regional integration while in patients, we can get closer to potential cellular origins of abnormal fMRI findings.
 

 

 

4) Non-invasive interventions and Neurofeedback

 

proj12
Next to psychotherapy and neuropsychopharmacology, a whole set of alternative treatment options has been developed to address deviant brain function in depression. Alteration of brain states by specific manipulation of subnetworks is thus a major field of current research in our group.
 

 

While some networks could be affected by training of focussed attention or mindfulness, more specific targeting could be reached via direct neurofeedback of regional signals or EEG network configuration. We use parallel EEG-fMRI and novel features of brain function during real-time fMRI to determine the additional potential of such methods, given that neurofeedback already has seen a history across several modalities. Other mechanisms include self monitoring such as during meditation and inclusion of EMG signatures of mimic expressions.

 

 

5) Abnormal brain network behavior in major depression

 

proj8
The network disorder, underlying pathological processing of internal and external stimuli in affective disorders can also be investigated on a whole network level.
 

 

proj8b
Advantages include that also regions, for which currently no strong relationship with clinical or psychological markers had been established, may be revealed, if functionally altered. Also understanding the brain as a functional network, state of the art graph network approaches can help to define, which specific aspect of network dysfunction prevails at the level of a single region or of a functional module or at whole brain level.
 

 






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