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Principles and challenges of fMRI-based ‘brain reading’
Prof. John-Dylan HaynesDone
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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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High-fidelity continuous monitoring of physiology anywhere with RDS
Louis Mayaud, 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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Language mapping on patients with parenchymatous tumor in language eloquent areas
Jimmy Landry Zepa YotedjeDone
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Real world AI in neurosciences for the benefit of doctors and patients
Stephane Doyen, PhDDone
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Mapping and targeting with TMS
Prof. Thomas KnöscheDone
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Own data, not hardware
Cecilia Mazzetti, PhDDone
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Do I want to know? Artificial intelligence as a predictive tool in the diagnosis and treatment of cognitive impairment. Development of EEG-based functional network analyses
Prof. Ira Haraldsen, MDDone
After completing her undergraduate degree at the University of Toronto, Wolff continued her education at the University
of Regensburg (Germany), studying for a master’s degree in experimental and clinical neuroscience. She completed her thesis work
at the Royal College of Surgeons of Ireland on postsynaptic protein characterization (PSD-95) in schizophrenia. After its completion,
she began her PhD in Neuroscience in the Faculty of Medicine at the University of Ottawa (Canada). Changing directions, her work
there centered on interindividual variability of complex cognitive tasks in healthy human electrophysiology (EEG). Employing methods
of time-frequency analysis, neural dynamics and complexity, Wolff’s resulting work was awarded the Governor General’s Gold Medal
for academic excellence. Currently, she is finishing her postdoctoral research fellowship at the Institute for Mental Health Research
(Ottawa) where her work focuses on neural variability and dynamics in psychiatric disorders.
Currently, one quarter of all medical disorders are mental health disorders. Until now, medical science has lacked sufficient
understanding about the physical brain mechanisms and how they relate to the mind. And, more specifically, how the physical brain
mechanisms relate to the symptoms of disorders of the mind. If scientific brain biomarkers could be identified, they could be used for
a more precise diagnosis of mental disorders and more effective, individualized treatments. Using schizophrenia (SCZ) as an example,
recent EEG-derived research findings from NMHD scientists have identified biomarkers that relate to the clinical features of this disorder.
Linking the dynamics of spatial and temporal patterns in the brain, as measured using EEG, with symptom severity of SCZ - measured
with the PANSS subscales - the resulting stepwise linear regression models could be used to monitor treatment progress and/or
assess treatment efficacy. In sum, as shown in the case of SCZ, NMHD has used EEG research on neural dynamics to link the clinical
symptoms of this disorder to brain-based neural markers which could be used to evaluate treatment response or efficacy in individual
patients.