Department Neuroscience, Carney Institute for Brain Sciences, Brown University, Providence, United States; Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, United States
Dylan S Daniels
Department Neuroscience, Carney Institute for Brain Sciences, Brown University, Providence, United States
Department Neuroscience, Yale University, New Haven, United States; Department of Biostatistics, Yale University, New Haven, United States
Nicholas T Carnevale
Department Neuroscience, Yale University, New Haven, United States
Mainak Jas
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, United States; Harvard Medical School, Boston, United States
Department Neuroscience, Carney Institute for Brain Sciences, Brown University, Providence, United States
Michael L Hines
Department Neuroscience, Yale University, New Haven, United States
Matti Hämäläinen
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, United States; Harvard Medical School, Boston, United States
Department Neuroscience, Carney Institute for Brain Sciences, Brown University, Providence, United States; Center for Neurorestoration and Neurotechnology, Providence VAMC, Providence, United States
Magneto- and electro-encephalography (MEG/EEG) non-invasively record human brain activity with millisecond resolution providing reliable markers of healthy and disease states. Relating these macroscopic signals to underlying cellular- and circuit-level generators is a limitation that constrains using MEG/EEG to reveal novel principles of information processing or to translate findings into new therapies for neuropathology. To address this problem, we built Human Neocortical Neurosolver (HNN, https://hnn.brown.edu) software. HNN has a graphical user interface designed to help researchers and clinicians interpret the neural origins of MEG/EEG. HNN’s core is a neocortical circuit model that accounts for biophysical origins of electrical currents generating MEG/EEG. Data can be directly compared to simulated signals and parameters easily manipulated to develop/test hypotheses on a signal’s origin. Tutorials teach users to simulate commonly measured signals, including event related potentials and brain rhythms. HNN’s ability to associate signals across scales makes it a unique tool for translational neuroscience research.
