How do we know how the brain works?—Analyzing whole brain activities with classic mathematical and machine learning methods

[1]  James G. King,et al.  Reconstruction and Simulation of Neocortical Microcircuitry , 2015, Cell.

[2]  T. Matsushima,et al.  Striatal and Tegmental Neurons Code Critical Signals for Temporal-Difference Learning of State Value in Domestic Chicks , 2016, Front. Neurosci..

[3]  K. Kimura,et al.  Neuronal, mathematical, and molecular bases of perceptual decision-making in C. elegans , 2019, Neuroscience Research.

[4]  M. Ohkura,et al.  A high signal-to-noise Ca2+ probe composed of a single green fluorescent protein , 2001, Nature Biotechnology.

[5]  Edward F. Chang,et al.  Speech synthesis from neural decoding of spoken sentences , 2019, Nature.

[6]  N. Logothetis.  What we can do and what we cannot do with fMRI , 2008, Nature.

[7]  R Cameron Craddock,et al.  A whole brain fMRI atlas generated via spatially constrained spectral clustering , 2012, Human brain mapping.

[8]  Gary H. Glover,et al.  A quantitative comparison of NIRS and fMRI across multiple cognitive tasks , 2011, NeuroImage.

[9]  A. Abi-Dargham,et al.  The search for imaging biomarkers in psychiatric disorders , 2016, Nature Medicine.

[10]  Y. Niv.  Reinforcement learning in the brain , 2009 .

[11]  Takeo Watanabe,et al.  A small number of abnormal brain connections predicts adult autism spectrum disorder , 2016, Nature Communications.

[12]  Y Kamitani,et al.  Neural Decoding of Visual Imagery During Sleep , 2013, Science.

[13]  S. Brenner,et al.  The structure of the nervous system of the nematode Caenorhabditis elegans. , 1986, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[14]  Rainer Goebel,et al.  Investigating directed cortical interactions in time-resolved fMRI data using vector autoregressive modeling and Granger causality mapping. , 2003, Magnetic resonance imaging.

[15]  D. Hubel,et al.  Receptive fields of single neurones in the cat's striate cortex , 1959, The Journal of physiology.

[16]  Toru Yanagawa,et al.  Untangling Brain-Wide Dynamics in Consciousness by Cross-Embedding , 2015, PLoS Comput. Biol..

[17]  M. Menken,et al.  The wall between neurology and psychiatry , 2002, BMJ : British Medical Journal.

[18]  Gordon Pipa,et al.  Transfer entropy—a model-free measure of effective connectivity for the neurosciences , 2010, Journal of Computational Neuroscience.

[19]  Nicholas A. Steinmetz,et al.  Distributed coding of choice, action, and engagement across the mouse brain , 2019, Nature.

[20]  Ha Hong,et al.  Performance-optimized hierarchical models predict neural responses in higher visual cortex , 2014, Proceedings of the National Academy of Sciences.

[21]  Manuel Zimmer,et al.  Nested Neuronal Dynamics Orchestrate a Behavioral Hierarchy across Timescales , 2019, Neuron.

[22]  I. Mori,et al.  Behavioral plasticity, learning, and memory in C. elegans , 2013, Current Opinion in Neurobiology.

[23]  L .Paninski,et al.  Neural data science: accelerating the experiment-analysis-theory cycle in large-scale neuroscience , 2017, Current Opinion in Neurobiology.

[24]  G. Buzsáki,et al.  Organic electronics for high-resolution electrocorticography of the human brain , 2016, Science Advances.

[25]  H. Adeli,et al.  Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis , 2015, Seizure.

[26]  R. Portugues,et al.  Evidence accumulation during a sensorimotor decision task revealed by whole-brain imaging , 2019, Nature Neuroscience.

[27]  Connor Brennan,et al.  A quantitative model of conserved macroscopic dynamics predicts future motor commands , 2019, eLife.

[28]  Andrew T. Drysdale,et al.  Resting-state connectivity biomarkers define neurophysiological subtypes of depression , 2016, Nature Medicine.

[29]  CM Lewis,et al.  Recording of brain activity across spatial scales , 2015, Current Opinion in Neurobiology.

[30]  Violeta G. Lopez-Huerta,et al.  Population imaging of neural activity in awake behaving mice , 2019, Nature.

[31]  Meryem A Yücel,et al.  Functional Near Infrared Spectroscopy: Enabling Routine Functional Brain Imaging. , 2017, Current opinion in biomedical engineering.

[32]  S. Herculano‐Houzel,et al.  The search for true numbers of neurons and glial cells in the human brain: A review of 150 years of cell counting , 2016, The Journal of comparative neurology.

[33]  Niall Holmes,et al.  Moving magnetoencephalography towards real-world applications with a wearable system , 2018, Nature.

[34]  J. Gallant,et al.  Reconstructing Visual Experiences from Brain Activity Evoked by Natural Movies , 2011, Current Biology.

[35]  Lester Melie-García,et al.  Estimating brain functional connectivity with sparse multivariate autoregression , 2005, Philosophical Transactions of the Royal Society B: Biological Sciences.

[36]  Daniel Rivero,et al.  Automatic feature extraction using genetic programming: An application to epileptic EEG classification , 2011, Expert Syst. Appl..

[37]  T. Insel,et al.  Brain disorders? Precisely , 2015, Science.

[38]  Thomas L. Griffiths,et al.  Supplementary Information for Natural Speech Reveals the Semantic Maps That Tile Human Cerebral Cortex , 2022 .

[39]  M. D’Esposito,et al.  The Variability of Human, BOLD Hemodynamic Responses , 1998, NeuroImage.

[40]  K. Kapur,et al.  Longitudinal EEG power in the first postnatal year differentiates autism outcomes , 2019, Nature Communications.

[41]  Tomoyasu Horikawa,et al.  Generic decoding of seen and imagined objects using hierarchical visual features , 2015, Nature Communications.

[42]  Konrad Paul Kording,et al.  Could a Neuroscientist Understand a Microprocessor? , 2016, bioRxiv.

[43]  David A. Leopold,et al.  Dynamic functional connectivity: Promise, issues, and interpretations , 2013, NeuroImage.

[44]  Nicholas V. Annetta,et al.  Restoring cortical control of functional movement in a human with quadriplegia , 2016, Nature.

[45]  Karl J. Friston,et al.  Large-scale DCMs for resting-state fMRI , 2017, Network Neuroscience.

[46]  Daniel W. Davies,et al.  Machine learning for molecular and materials science , 2018, Nature.

[47]  A. P. Georgopoulos,et al.  Neuronal population coding of movement direction. , 1986, Science.

[48]  Jasmin Kevric,et al.  Performance evaluation of empirical mode decomposition, discrete wavelet transform, and wavelet packed decomposition for automated epileptic seizure detection and prediction , 2018, Biomed. Signal Process. Control..

[49]  S. Bressler,et al.  Large-scale brain networks in cognition: emerging methods and principles , 2010, Trends in Cognitive Sciences.

[50]  R. Prevedel,et al.  Brain-wide 3D imaging of neuronal activity in Caenorhabditis elegans with sculpted light , 2013, Nature Methods.

[51]  S. Rombouts,et al.  Consistent resting-state networks across healthy subjects , 2006, Proceedings of the National Academy of Sciences.

[52]  Leonie Koban,et al.  Representation, Pattern Information, and Brain Signatures: From Neurons to Neuroimaging , 2018, Neuron.

[53]  Pieter R. Roelfsema,et al.  Mind Reading and Writing: The Future of Neurotechnology , 2018, Trends in Cognitive Sciences.

[54]  Alexander Bertrand,et al.  A generic EEG artifact removal algorithm based on the multi-channel Wiener filter , 2018, Journal of neural engineering.

[55]  Guohua Shen,et al.  Deep image reconstruction from human brain activity , 2017, bioRxiv.

[56]  Rainer Goebel,et al.  "Who" Is Saying "What"? Brain-Based Decoding of Human Voice and Speech , 2008, Science.

[57]  Michele Piana,et al.  Inverse Modeling for MEG/EEG Data , 2017, bioRxiv.

[58]  K. Hashimoto,et al.  In actio optophysiological analyses reveal functional diversification of dopaminergic neurons in the nematode C. elegans , 2016, Scientific Reports.

[59]  Sergey L. Gratiy,et al.  Fully integrated silicon probes for high-density recording of neural activity , 2017, Nature.

[60]  Danielle Smith Bassett,et al.  Small-World Brain Networks , 2006, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.

[61]  Stephanie C. Seeman,et al.  Bright and photostable chemigenetic indicators for extended in vivo voltage imaging , 2018, Science.

[62]  Konrad P. Körding,et al.  The roles of supervised machine learning in systems neuroscience , 2018, Progress in Neurobiology.

[63]  R. Tsien,et al.  Fluorescent indicators for Ca2+based on green fluorescent proteins and calmodulin , 1997, Nature.

[64]  Theodore H. Lindsay,et al.  Global Brain Dynamics Embed the Motor Command Sequence of Caenorhabditis elegans , 2015, Cell.

[65]  Mert R. Sabuncu,et al.  Machine learning in resting-state fMRI analysis , 2018, Magnetic resonance imaging.

[66]  Feng Zhang,et al.  Multimodal fast optical interrogation of neural circuitry , 2007, Nature.

[67]  Dinggang Shen,et al.  State-space model with deep learning for functional dynamics estimation in resting-state fMRI , 2016, NeuroImage.

[68]  Philipp J. Keller,et al.  Whole-brain functional imaging at cellular resolution using light-sheet microscopy , 2013, Nature Methods.

[69]  Martin M. Monti,et al.  Human Neuroscience , 2022 .

[70]  Sandeep Purao,et al.  Data-Driven Meets Theory-Driven Research in the Era of Big Data: Opportunities and Challenges for Information Systems Research , 2018, J. Assoc. Inf. Syst..

[71]  Nicholas A. Steinmetz,et al.  Spontaneous behaviors drive multidimensional, brainwide activity , 2019, Science.