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From one-size-fits-all psychiatry to stratified psychiatry: Brain markers and heart-brain-coupling
Martijn Arns, PhDDone
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The Berger’s discovery revisited: How and why the brain’s dominant rhythm relates to cognition
Tzvetan Popov, PhDDone
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Phase-amplitude coupling in EEG as a Parkinsonian biomarker
Prof. Thomas R. KnöscheDone
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Welcome Address
Martijn SchreuderDone
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Principles and challenges of fMRI-based ‘brain reading’
Prof. John-Dylan HaynesDone
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Towards personalised neuromodulation in mental health: A non-invasive avenue of network research into dynamic brain circuits and their dysfunction
Prof. Alexander SackDone
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Multi-center validation of dry vs. gel-based EEG cap performance
Prof. Patrique FiedlerDone
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Accelerated Intermittent Theta Burst Stimulation: Antidepressant and anti-suicidal effects
Roberto Goya-Maldonado, MDDone
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The potential of brain rhythms to gauge the vulnerability of an individual to developing chronic pain
Prof. Ali MazaheriDone
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High-fidelity continuous monitoring of physiology anywhere with RDS
Louis Mayaud, PhDDone
David Haslacher studied computer science in Munich, artificial intelligence in Utrecht, and computational neuroscience in Tübingen. Since then, he has been developing the combination of transcranial alternating current stimulation with electro- and magnetoencephalography at the Clinical Neurotechnology Laboratory of the Charité – Universitätsmedizin Berlin. He is now finishing his PhD on closed-loop neuromodulation under the guidance of Surjo Soekadar, and is interested in developing more precise and effective treatments for psychiatric and neurological disorders.
Neuromodulation techniques such as transcranial alternating current stimulation (tACS) are a promising treatment approach for several neurological and psychiatric disorders, but suffer from variable effects due in part to their brain-state dependency. In this talk, I will show how electroencephalography (EEG) has become a useful tool to understand the immediate effects of tACS, and to implement closed-loop systems where tACS is adapted to ongoing brain oscillations in real-time. Finally, some potential clinical applications of such closed-loop approaches are discussed.