Human Neocortical Neurosolver

人类新皮质神经解算器

• 批准号: 9535315
• 负责人: MATTI HAMALAINEN
• 金额: $ 50.97万
• 依托单位: BROWN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-30 至 2020-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9535315
• 关键词: Anatomy; Animal Model; Animals; Architecture; Area; Autistic Disorder; Biophysics; Brain; Collaborations; Computer software; Coupling; Custom; Data; Development; Disease; Documentation; Electrocorticogram; Electroencephalography; Foundations; Frequencies; Genetic; Goals; Growth; Human; Image; Intuition; Magnetoencephalography; Measures; Mental disorders; Methods; Modality; Modeling; Neurosciences; Output; Pathologic; Pathology; Pattern; Property; Pythons; Research; Research Personnel; Resources; Schizophrenia; Sensory; Signal Transduction; Software Tools; Source; Specificity; Structure; Techniques; Testing; Time; Translations; Work; design; dipole moment; experience; human imaging; information processing; insight; mathematical model; millisecond; neocortical; nervous system disorder; neural model; neurophysiology; novel; optogenetics; relating to nervous system; response; sensor; sensory cortex; software development; source localization; success; temporal measurement; tool; two-photon; user-friendly

Abstract The field of neuroscience is experiencing unprecedented growth in the ability to record from and manipulate brain circuits in humans and in animal models. MEG/EEG are the leading methods to non-invasively record human neural dynamics with millisecond temporal resolution. However, it is still extremely difficult to interpret the underlying cellular and circuit level generators of these `macro-scale' signals without simultaneous invasive recordings. This difficulty limits the translation of MEG/EEG finding into novel principles of information processing, or into new treatment modalities for neural pathologies. As such, there is a pressing need, and a unique opportunity, to bridge the `macro-scale' single with the underlying `meso-scale” cellular and circuit level generators. This problem is ideal for neural modeling where we can have specificity at both scales. We propose to build a user-friendly GUI driven neural modeling software tool, “Human Neocortical Neurosolver (HNN)” that enables researchers without mathematical or neural modeling experience to test and develop hypotheses on the cellular and circuit level origin of their source localized MEG/EEG or ECoG data. Our software will work from a foundation of detailed anatomical and biophysical constraints to generate hypotheses as to the neural origin of observed neocortical brain signals. We will work with identified test-case users with existing MEG/EEG data to develop our model into a tool they can use to test and develop specific hypotheses on the neural origin of activity from one or multiple brain areas. We will also integrate the model with the source localization software MNE, so researchers can compute MEG/EEG source estimates and test hypotheses on the neural origin of their data in one integrated software package. We will build resources for freely using and expanding the software through the Neuroscience Gateway Portal, and online documentation and a user forum for interaction between users and developers.

摘要