Systems Neuroengineering: Understanding and Interacting with the Brain

ABSTRACT In this paper, we review the current state-of-the-art techniques used for understanding the inner workings of the brain at a systems level. The neural activity that governs our everyday lives involves an intricate coordination of many processes that can be attributed to a variety of brain regions. On the surface, many of these functions can appear to be controlled by specific anatomical structures; however, in reality, numerous dynamic networks within the brain contribute to its function through an interconnected web of neuronal and synaptic pathways. The brain, in its healthy or pathological state, can therefore be best understood by taking a systems-level approach. While numerous neuroengineering technologies exist, we focus here on three major thrusts in the field of systems neuroengineering: neuroimaging, neural interfacing, and neuromodulation. Neuroimaging enables us to delineate the structural and functional organization of the brain, which is key in understanding how the neural system functions in both normal and disease states. Based on such knowledge, devices can be used either to communicate with the neural system, as in neural interface systems, or to modulate brain activity, as in neuromodulation systems. The consideration of these three fields is key to the development and application of neuro-devices. Feedback-based neuro-devices require the ability to sense neural activity (via a neuroimaging modality) through a neural interface (invasive or noninvasive) and ultimately to select a set of stimulation parameters in order to alter neural function via a neuromodulation modality. Systems neuroengineering refers to the use of engineering tools and technologies to image, decode, and modulate the brain in order to comprehend its functions and to repair its dysfunction. Interactions between these fields will help to shape the future of systems neuroengineering—to develop neurotechniques for enhancing the understanding of whole-brain function and dysfunction, and the management of neurological and mental disorders.

[1]  Andreas Ziehe,et al.  Combining sparsity and rotational invariance in EEG/MEG source reconstruction , 2008, NeuroImage.

[2]  Paul Nuyujukian,et al.  A high performing brain–machine interface driven by low-frequency local field potentials alone and together with spikes , 2015, bioRxiv.

[3]  H. Jasper,et al.  Epilepsy and the functional anatomy of the human brain , 1985 .

[4]  Michael S Okun,et al.  Deep-brain stimulation for Parkinson's disease. , 2012, The New England journal of medicine.

[5]  F. Babiloni,et al.  Estimation of the cortical functional connectivity with the multimodal integration of high-resolution EEG and fMRI data by directed transfer function , 2005, NeuroImage.

[6]  Bin He,et al.  fMRI–EEG integrated cortical source imaging by use of time-variant spatial constraints , 2008, NeuroImage.

[7]  J. Gotman,et al.  Generalized epileptic discharges show thalamocortical activation and suspension of the default state of the brain. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[8]  M. Murray,et al.  EEG source imaging , 2004, Clinical Neurophysiology.

[9]  Lei Ding,et al.  Sparse source imaging in electroencephalography with accurate field modeling , 2008, Human brain mapping.

[10]  V. Caggiano,et al.  Proprioceptive Feedback and Brain Computer Interface (BCI) Based Neuroprostheses , 2012, PloS one.

[11]  Bin He,et al.  Electrophysiological Imaging of Brain Activity and Connectivity—Challenges and Opportunities , 2011, IEEE Transactions on Biomedical Engineering.

[12]  N. V. Thakor,et al.  Translating the Brain-Machine Interface , 2013, Science Translational Medicine.

[13]  Bin He,et al.  The impact of mind-body awareness training on the early learning of a brain-computer interface. , 2014, Technology.

[14]  Bin He,et al.  Magnetoacoustic tomography with magnetic induction (MAT-MI) , 2005, Physics in medicine and biology.

[15]  Julien Cohen-Adad,et al.  Pushing the limits of in vivo diffusion MRI for the Human Connectome Project , 2013, NeuroImage.

[16]  G. Pfurtscheller,et al.  Optimal spatial filtering of single trial EEG during imagined hand movement. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[17]  D. Reato,et al.  Gyri-precise head model of transcranial direct current stimulation: Improved spatial focality using a ring electrode versus conventional rectangular pad , 2009, Brain Stimulation.

[18]  Jianjun Meng,et al.  Combining Motor Imagery With Selective Sensation Toward a Hybrid-Modality BCI , 2014, IEEE Transactions on Biomedical Engineering.

[19]  M. Kringelbach,et al.  Translational principles of deep brain stimulation , 2007, Nature Reviews Neuroscience.

[20]  M. Roulston Estimating the errors on measured entropy and mutual information , 1999 .

[21]  A. Benabid,et al.  Long-term suppression of tremor by chronic stimulation of the ventral intermediate thalamic nucleus , 1991, The Lancet.

[22]  Katarzyna J. Blinowska,et al.  A new method of the description of the information flow in the brain structures , 1991, Biological Cybernetics.

[23]  Jens Haueisen,et al.  Time-frequency mixed-norm estimates: Sparse M/EEG imaging with non-stationary source activations , 2013, NeuroImage.

[24]  E. N. Harvey,et al.  THE EFFECT OF HIGH FREQUENCY SOUND WAVES ON HEART MUSCLE AND OTHER IRRITABLE TISSUES , 1929 .

[25]  A. Schwartz,et al.  High-performance neuroprosthetic control by an individual with tetraplegia , 2013, The Lancet.

[26]  J. Fujimoto,et al.  In vivo endoscopic optical biopsy with optical coherence tomography. , 1997, Science.

[27]  Miguel A. L. Nicolelis,et al.  A Brain-Machine Interface Instructed by Direct Intracortical Microstimulation , 2009, Front. Integr. Neurosci..

[28]  Kojiro Matsushita,et al.  Patient-Specific Cortical Electrodes for Sulcal and Gyral Implantation , 2015, IEEE Transactions on Biomedical Engineering.

[29]  A. Dale,et al.  Improved Localizadon of Cortical Activity by Combining EEG and MEG with MRI Cortical Surface Reconstruction: A Linear Approach , 1993, Journal of Cognitive Neuroscience.

[30]  J. Farquhar,et al.  Comparing tactile and visual gaze-independent brain–computer interfaces in patients with amyotrophic lateral sclerosis and healthy users , 2014, Clinical Neurophysiology.

[31]  Bin He,et al.  Neuromodulation for Brain Disorders: Challenges and Opportunities , 2013, IEEE Transactions on Biomedical Engineering.

[32]  M L Boninger,et al.  Ten-dimensional anthropomorphic arm control in a human brain−machine interface: difficulties, solutions, and limitations , 2015, Journal of neural engineering.

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

[34]  G. Pfurtscheller,et al.  Self-Paced Operation of an SSVEP-Based Orthosis With and Without an Imagery-Based “Brain Switch:” A Feasibility Study Towards a Hybrid BCI , 2010, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[35]  Ramin Pashaie,et al.  Single Optical Fiber Probe for Fluorescence Detection and Optogenetic Stimulation , 2013, IEEE Transactions on Biomedical Engineering.

[36]  C. Granger Investigating Causal Relations by Econometric Models and Cross-Spectral Methods , 1969 .

[37]  V. Ntziachristos,et al.  FMT-XCT: in vivo animal studies with hybrid fluorescence molecular tomography–X-ray computed tomography , 2012, Nature Methods.

[38]  F. Gonzalez-Lima,et al.  Structural equation modeling and its application to network analysis in functional brain imaging , 1994 .

[39]  Essa Yacoub,et al.  High-field fMRI unveils orientation columns in humans , 2008, Proceedings of the National Academy of Sciences.

[40]  J. Shimony,et al.  Resting-State fMRI: A Review of Methods and Clinical Applications , 2013, American Journal of Neuroradiology.

[41]  F. L. D. Silva,et al.  Event-related EEG/MEG synchronization and desynchronization: basic principles , 1999, Clinical Neurophysiology.

[42]  Nicolas Y. Masse,et al.  Reach and grasp by people with tetraplegia using a neurally controlled robotic arm , 2012, Nature.

[43]  David M. Santucci,et al.  Learning to Control a Brain–Machine Interface for Reaching and Grasping by Primates , 2003, PLoS biology.

[44]  Giulio Tononi,et al.  Estimation of Cortical Connectivity From EEG Using State-Space Models , 2010, IEEE Transactions on Biomedical Engineering.

[45]  Gary H. Glover,et al.  Grand Challenges in Mapping the Human Brain: NSF Workshop Report , 2013, IEEE Transactions on Biomedical Engineering.

[46]  Todd D. Krieg,et al.  Transcranial Magnetic Stimulation , 2013 .

[47]  Karl J. Friston Modalities, Modes, and Models in Functional Neuroimaging , 2009, Science.

[48]  G. Pfurtscheller,et al.  Brain-Computer Interfaces for Communication and Control. , 2011, Communications of the ACM.

[49]  G Calhoun,et al.  Brain-computer interfaces based on the steady-state visual-evoked response. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[50]  G. Feng,et al.  Next-Generation Optical Technologies for Illuminating Genetically Targeted Brain Circuits , 2006, The Journal of Neuroscience.

[51]  R HASSLER,et al.  Physiological observations in stereotaxic operations in extrapyramidal motor disturbances. , 1960, Brain : a journal of neurology.

[52]  Warren M Grill,et al.  Implanted neural interfaces: biochallenges and engineered solutions. , 2009, Annual review of biomedical engineering.

[53]  K. Deisseroth,et al.  Millisecond-timescale, genetically targeted optical control of neural activity , 2005, Nature Neuroscience.

[54]  Lei Ding,et al.  Motor imagery classification by means of source analysis for brain–computer interface applications , 2004, Journal of neural engineering.

[55]  Klaus-Robert Müller,et al.  Neurophysiological predictor of SMR-based BCI performance , 2010, NeuroImage.

[56]  R. S. Hinks,et al.  Time course EPI of human brain function during task activation , 1992, Magnetic resonance in medicine.

[57]  Christoph M. Michel,et al.  EEG mapping and source imaging , 2012 .

[58]  Andrew S. Whitford,et al.  Cortical control of a prosthetic arm for self-feeding , 2008, Nature.

[59]  Bin He,et al.  EEG Source Imaging Enhances the Decoding of Complex Right-Hand Motor Imagery Tasks , 2016, IEEE Transactions on Biomedical Engineering.

[60]  Nitish V. Thakor,et al.  Demonstration of a Semi-Autonomous Hybrid Brain–Machine Interface Using Human Intracranial EEG, Eye Tracking, and Computer Vision to Control a Robotic Upper Limb Prosthetic , 2014, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[61]  Hongyang Lu,et al.  Pulsed Transcranial Ultrasound Stimulation Immediately After The Ischemic Brain Injury is Neuroprotective , 2015, IEEE Transactions on Biomedical Engineering.

[62]  E. Hoffman,et al.  A positron-emission transaxial tomograph for nuclear imaging (PETT). , 1975, Radiology.

[63]  D J McFarland,et al.  An EEG-based brain-computer interface for cursor control. , 1991, Electroencephalography and clinical neurophysiology.

[64]  J. Briers,et al.  Laser speckle contrast imaging for measuring blood flow , 2007 .

[65]  N Accornero,et al.  Polarization of the human motor cortex through the scalp , 1998, Neuroreport.

[66]  Marco Ferrari,et al.  A brief review on the history of human functional near-infrared spectroscopy (fNIRS) development and fields of application , 2012, NeuroImage.

[67]  J. Lefaucheur Neurophysiology of cortical stimulation. , 2012, International review of neurobiology.

[68]  Karl J. Friston,et al.  Dynamic causal modelling , 2003, NeuroImage.

[69]  J. Wolpaw,et al.  A practical, intuitive brain–computer interface for communicating ‘yes’ or ‘no’ by listening , 2014, Journal of neural engineering.

[70]  Gerwin Schalk,et al.  A brain–computer interface using electrocorticographic signals in humans , 2004, Journal of neural engineering.

[71]  R. Andersen,et al.  Decoding motor imagery from the posterior parietal cortex of a tetraplegic human , 2015, Science.

[72]  Á. Pascual-Leone,et al.  Noninvasive human brain stimulation. , 2007, Annual review of biomedical engineering.

[73]  José del R. Millán,et al.  Towards Independence: A BCI Telepresence Robot for People With Severe Motor Disabilities , 2015, Proceedings of the IEEE.

[74]  Bin He,et al.  High-Definition Transcranial Direct Current Stimulation Induces Both Acute and Persistent Changes in Broadband Cortical Synchronization: A Simultaneous tDCS–EEG Study , 2014, IEEE Transactions on Biomedical Engineering.

[75]  Joshua E. Motelow,et al.  Cortical and subcortical networks in human secondarily generalized tonic-clonic seizures. , 2009, Brain : a journal of neurology.

[76]  Giacomo Koch,et al.  A common polymorphism in the brain‐derived neurotrophic factor gene (BDNF) modulates human cortical plasticity and the response to rTMS , 2008, The Journal of physiology.

[77]  Morten L. Kringelbach,et al.  The autonomic effects of deep brain stimulation—a therapeutic opportunity , 2012, Nature Reviews Neurology.

[78]  Bin He,et al.  Noninvasive Brain-Computer Interfaces Based on Sensorimotor Rhythms , 2015, Proceedings of the IEEE.

[79]  Jose M. Carmena,et al.  Closed-Loop Decoder Adaptation Shapes Neural Plasticity for Skillful Neuroprosthetic Control , 2014, Neuron.

[80]  Nicholas G. Hatsopoulos,et al.  Brain-machine interface: Instant neural control of a movement signal , 2002, Nature.

[81]  Jon A. Mukand,et al.  Neuronal ensemble control of prosthetic devices by a human with tetraplegia , 2006, Nature.

[82]  Dae-Shik Kim,et al.  Global and local fMRI signals driven by neurons defined optogenetically by type and wiring , 2010, Nature.

[83]  D. Lehmann,et al.  Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain. , 1994, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[84]  Steen Moeller,et al.  Multiband multislice GE‐EPI at 7 tesla, with 16‐fold acceleration using partial parallel imaging with application to high spatial and temporal whole‐brain fMRI , 2010, Magnetic resonance in medicine.

[85]  E M Sevick-Muraca,et al.  Translation of near-infrared fluorescence imaging technologies: emerging clinical applications. , 2012, Annual review of medicine.

[86]  Á. Pascual-Leone,et al.  Technology Insight: noninvasive brain stimulation in neurology—perspectives on the therapeutic potential of rTMS and tDCS , 2007, Nature Clinical Practice Neurology.

[87]  J. Wolpaw,et al.  P300-based brain-computer interface (BCI) event-related potentials (ERPs): People with amyotrophic lateral sclerosis (ALS) vs. age-matched controls , 2015, Clinical Neurophysiology.

[88]  G Becker,et al.  Transcranial color-coded real-time sonography in adults. , 1990, Stroke.

[89]  Gabor T. Herman,et al.  Fundamentals of Computerized Tomography: Image Reconstruction from Projections , 2009, Advances in Pattern Recognition.

[90]  K. Lafleur,et al.  Quadcopter control in three-dimensional space using a noninvasive motor imagery-based brain–computer interface , 2013, Journal of neural engineering.

[91]  Nitish V. Thakor,et al.  Anisotropic Processing of Laser Speckle Images Improves Spatiotemporal Resolution , 2012, IEEE Transactions on Biomedical Engineering.

[92]  Barry Horwitz,et al.  The elusive concept of brain connectivity , 2003, NeuroImage.

[93]  R. Romo,et al.  Somatosensory discrimination based on cortical microstimulation , 1998, Nature.

[94]  Hansjörg Scherberger,et al.  Decoding a Wide Range of Hand Configurations from Macaque Motor, Premotor, and Parietal Cortices , 2015, The Journal of Neuroscience.

[95]  Luiz A. Baccalá,et al.  Partial directed coherence: a new concept in neural structure determination , 2001, Biological Cybernetics.

[96]  R. Turner,et al.  Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. , 1992, Proceedings of the National Academy of Sciences of the United States of America.

[97]  A. Williams,et al.  Transcranial focused ultrasound modulates the activity of primary somatosensory cortex in humans , 2014, Nature Neuroscience.

[98]  Klaas E. Stephan,et al.  Dynamic causal modelling: A critical review of the biophysical and statistical foundations , 2011, NeuroImage.

[99]  Ivan Volosyak,et al.  SSVEP-based Bremen–BCI interface—boosting information transfer rates , 2011, Journal of neural engineering.

[100]  Achim Schweikard,et al.  H-coil: Induced electric field properties and input/output curves on healthy volunteers, comparison with a standard figure-of-eight coil , 2009, Clinical Neurophysiology.

[101]  M. Nitsche,et al.  Excitability changes induced in the human motor cortex by weak transcranial direct current stimulation , 2000, The Journal of physiology.

[102]  Gregory A. Worrell,et al.  Ictal source analysis: Localization and imaging of causal interactions in humans , 2007, NeuroImage.

[103]  Bin He,et al.  Brain–Computer Interfaces Using Sensorimotor Rhythms: Current State and Future Perspectives , 2014, IEEE Transactions on Biomedical Engineering.

[104]  N.V. Thakor,et al.  Spatiotemporal characteristics of low-frequency functional activation measured by laser speckle imaging , 2005, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[105]  Marc H Schieber,et al.  State-based decoding of hand and finger kinematics using neuronal ensemble and LFP activity during dexterous reach-to-grasp movements. , 2013, Journal of neurophysiology.

[106]  G. Pell,et al.  Motor cortex activation by H-coil and figure-8 coil at different depths. Combined motor threshold and electric field distribution study , 2014, Clinical Neurophysiology.

[107]  Hannes Bleuler,et al.  Active tactile exploration enabled by a brain-machine-brain interface , 2011, Nature.

[108]  Cuntai Guan,et al.  Brain–Computer Interface for Neurorehabilitation of Upper Limb After Stroke , 2015, Proceedings of the IEEE.

[109]  Bin He,et al.  Mapping the bilateral visual integration by EEG and fMRI , 2009, NeuroImage.

[110]  Nitish V. Thakor,et al.  Simultaneous Neural Control of Simple Reaching and Grasping With the Modular Prosthetic Limb Using Intracranial EEG , 2014, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[111]  L. Miller,et al.  Restoration of grasp following paralysis through brain-controlled stimulation of muscles , 2012, Nature.

[112]  L. Cohen,et al.  Brain–machine interface in chronic stroke rehabilitation: A controlled study , 2013, Annals of neurology.

[113]  Lihong V. Wang,et al.  Photoacoustic Tomography: In Vivo Imaging from Organelles to Organs , 2012, Science.

[114]  Karl J. Friston,et al.  Neural modeling and functional brain imaging: an overview , 2000, Neural Networks.

[115]  A. Barker,et al.  NON-INVASIVE MAGNETIC STIMULATION OF HUMAN MOTOR CORTEX , 1985, The Lancet.

[116]  E. Donchin,et al.  Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. , 1988, Electroencephalography and clinical neurophysiology.

[117]  A. Doud,et al.  Continuous Three-Dimensional Control of a Virtual Helicopter Using a Motor Imagery Based Brain-Computer Interface , 2011, PloS one.

[118]  Wei Wang,et al.  Collaborative Approach in the Development of High‐Performance Brain–Computer Interfaces for a Neuroprosthetic Arm: Translation from Animal Models to Human Control , 2014, Clinical and translational science.

[119]  Bin He,et al.  Dynamic imaging of ictal oscillations using non-invasive high-resolution EEG , 2011, NeuroImage.

[120]  M. Morrell Responsive cortical stimulation for the treatment of medically intractable partial epilepsy , 2011, Neurology.

[121]  Bin He,et al.  Graph analysis of epileptogenic networks in human partial epilepsy , 2011, Epilepsia.

[122]  Krishna V. Shenoy,et al.  Combining Decoder Design and Neural Adaptation in Brain-Machine Interfaces , 2014, Neuron.

[123]  J. Peters,et al.  Closing the sensorimotor loop: haptic feedback facilitates decoding of motor imagery , 2011, Journal of neural engineering.

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

[125]  Bernhard Strasser,et al.  A novel coil array for combined TMS/fMRI experiments at 3 T , 2014, Magnetic resonance in medicine.

[126]  Nan Li,et al.  Laser Speckle Contrast Imaging: Theory, Instrumentation and Applications , 2013, IEEE Reviews in Biomedical Engineering.

[127]  Ravi S. Menon,et al.  Intrinsic signal changes accompanying sensory stimulation: functional brain mapping with magnetic resonance imaging. , 1992, Proceedings of the National Academy of Sciences of the United States of America.

[128]  Jessica A. Cardin,et al.  Noninvasive optical inhibition with a red-shifted microbial rhodopsin , 2014, Nature Neuroscience.

[129]  Afraim Salek-Haddadi,et al.  Event-Related fMRI with Simultaneous and Continuous EEG: Description of the Method and Initial Case Report , 2001, NeuroImage.

[130]  K. Harada,et al.  Localized stimulation of neural tissues in the brain by means of a paired configuration of time-varying magnetic fields , 1988 .

[131]  Andrea A. Kühn,et al.  High-Frequency Stimulation of the Subthalamic Nucleus Suppresses Oscillatory β Activity in Patients with Parkinson's Disease in Parallel with Improvement in Motor Performance , 2008, The Journal of Neuroscience.

[132]  Steen Moeller,et al.  Pushing spatial and temporal resolution for functional and diffusion MRI in the Human Connectome Project , 2013, NeuroImage.

[133]  W. A. Sarnacki,et al.  Electroencephalographic (EEG) control of three-dimensional movement , 2010, Journal of neural engineering.

[134]  Andrew K. Dunn,et al.  Laser Speckle Contrast Imaging of Cerebral Blood Flow , 2011, Annals of Biomedical Engineering.

[135]  Mark Hallett,et al.  A Coil Design for Transcranial Magnetic Stimulation of Deep Brain Regions , 2002, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[136]  K. Deisseroth,et al.  Optogenetic investigation of neural circuits underlying brain disease in animal models , 2012, Nature Reviews Neuroscience.

[137]  David A. Boas,et al.  Twenty years of functional near-infrared spectroscopy: introduction for the special issue , 2014, NeuroImage.

[138]  William W. McDonald,et al.  Efficacy and Safety of Transcranial Magnetic Stimulation in the Acute Treatment of Major Depression: A Multisite Randomized Controlled Trial , 2007, Biological Psychiatry.

[139]  G. A. Miller,et al.  Comparison of different cortical connectivity estimators for high‐resolution EEG recordings , 2007, Human brain mapping.

[140]  P. Basser,et al.  MR diffusion tensor spectroscopy and imaging. , 1994, Biophysical journal.

[141]  M. Molinari,et al.  Brain–computer interface boosts motor imagery practice during stroke recovery , 2015, Annals of neurology.

[142]  S. Lisanby,et al.  Electric field depth–focality tradeoff in transcranial magnetic stimulation: Simulation comparison of 50 coil designs , 2013, Brain Stimulation.

[143]  Toshimitsu Musha,et al.  Electric Dipole Tracing in the Brain by Means of the Boundary Element Method and Its Accuracy , 1987, IEEE Transactions on Biomedical Engineering.

[144]  Matthew D. Johnson,et al.  Spatial steering of deep brain stimulation volumes using a novel lead design , 2011, Clinical Neurophysiology.

[145]  Brendan Z. Allison,et al.  The Hybrid BCI , 2010, Frontiers in Neuroscience.

[146]  E. Fetz,et al.  Direct control of paralyzed muscles by cortical neurons , 2008, Nature.

[147]  S. Tong,et al.  Real-time high resolution laser speckle imaging of cerebral vascular changes in a rodent photothrombosis model. , 2014, Biomedical optics express.

[148]  E. Halgren,et al.  Dynamic Statistical Parametric Mapping Combining fMRI and MEG for High-Resolution Imaging of Cortical Activity , 2000, Neuron.

[149]  José del R. Millán,et al.  Brain-Controlled Wheelchairs: A Robotic Architecture , 2013, IEEE Robotics & Automation Magazine.

[150]  John S. Thornton,et al.  Functional MRI with active, fully implanted, deep brain stimulation systems: Safety and experimental confounds , 2007, NeuroImage.

[151]  Karl J. Friston,et al.  Evaluation of different measures of functional connectivity using a neural mass model , 2004, NeuroImage.

[152]  Bin He,et al.  Gradient‐based electrical properties tomography (gEPT): A robust method for mapping electrical properties of biological tissues in vivo using magnetic resonance imaging , 2015, Magnetic resonance in medicine.

[153]  Jong-Hwan Lee,et al.  Focused ultrasound modulates region-specific brain activity , 2011, NeuroImage.

[154]  S. Tillery,et al.  Transcranial Pulsed Ultrasound Stimulates Intact Brain Circuits , 2010, Neuron.

[155]  C. Stam,et al.  Synchronization likelihood: an unbiased measure of generalized synchronization in multivariate data sets , 2002 .

[156]  Robert D Flint,et al.  Long term, stable brain machine interface performance using local field potentials and multiunit spikes , 2013, Journal of neural engineering.

[157]  Ciprian Catana,et al.  Simultaneous PET-MRI: a new approach for functional and morphological imaging , 2008, Nature Medicine.