
Anat Dahan
Ort Braude College of Engineering, Department of Software Engineering
Dr. Anat Dahan is a faculty member at the Department of Software Engineering, at Ort Braude College of Engineering. She received her PhD at the Technion Institute of Technology. Her research interests include methods for analysis of biological markers for understanding of neurodevelopmental disorders as ADHD and ASD. Such markers include movement recordings and neural signals of EEG and fNIRS recordings. She studies methods for integrating advances in machine learning for understanding and classifying neural signals and neural connectivity.

Yafeng Pan
Karolinska Institutet, Department of Clinical Neuroscience
Dr. Yafeng Pan is a postdoctoral researcher at Department of Clinical Neuroscience, Karolinska Institutet. His research interests are in the areas of human social interactions. More specifically, his research focuses on how brain-to-brain coupling across individuals tracks (and underlies) interpersonal cooperation, decision-making, learning, and other high-level social processes. He uses behavioral, electrophysiological (EEG), neuroimaging (fNIRS), and neuromodulation (tACS) methods, combined with the hyper-scanning (the measurement of brain activity from multiple individuals simultaneously) technique, to investigate the neural mechanisms that mediate human social behaviors.

Hila Z. Gvirts-Prbolovski
Ariel University, Department of Behavioral Sciences and Psychology
Dr. Hila Z. Gvirts-Provolvski served as a faculty member at the Psychology Department Ariel University. Her main research interest centers on the neural activity that drives human behavior. Her work seeks to develop a comprehensive understanding of social alignment and its linkage to other social process by combining behavioral research together with neural and clinical research. To address these issues, she also focuses on study social alignment in different disorders associated with social deficits, such as autism, and ADHD.
Previous studies have characterized the brain connectome to explain a wide range of behaviors and conditions in several population groups. Functional connectivity (FC) is defined as the temporal coincidence of neurophysiological events of anatomically distinct brain regions. Several methods were developed to evaluate FC in EEG and fMRI to assess the similarity of signals in different regions.
Functional near-infrared spectroscopy (fNIRS) is increasingly used for research and clinical applications because of its advantages: applicability, ecological validity, tolerance to movements, and low cost. Evaluating functional connectivity with fNIRS requires specific methods to be tailored for assessing signal similarity in order to evaluate both intra and inter neural synchrony. Importantly, assessing similarity does not always answer questions regarding directionality. Directionality can be studied by looking for effective connectivity describing networks of directional effects of one neural element over another. Additional methods must be developed to answer questions regarding directionality. Functional and effective connectivity are of special interest in the emerging field of hyper-scanning—a technique for simultaneously recording brain activity of different subjects as they interact with each other. This field studies inter-brain correlations between the interacting subjects.
In this collection, we present a variety of methods for assessing functional and effective inter and intra brain connectivity with fNIRS.
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2021
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2021
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