Progress in brain research, volume 222

Serial Editor Vincent Walsh Institute of Cognitive Neuroscience University College London 17 Queen Square London WC1N 3AR UK Editorial Board Mark Bear, Cambridge, USA Medicine & Translational Neuroscience Hamed Ekhtiari, Tehran, Iran Addiction Hajime Hirase, Wako, Japan Neuronal Microcircuitry Freda Miller, Toronto, Canada Developmental Neurobiology Shane O’Mara, Dublin, Ireland Systems Neuroscience Susan Rossell, Swinburne, Australia Clinical Psychology & Neuropsychiatry Nathalie Rouach, Paris, France Neuroglia Barbara Sahakian, Cambridge, UK Cognition & Neuroethics Bettina Studer, Dusseldorf, Germany Neurorehabilitation Xiao-Jing Wang, New York, USA Computational Neuroscience Elsevier Radarweg 29, PO Box 211, 1000 AE Amsterdam, Netherlands The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, UK 225 Wyman Street, Waltham, MA 02451, USA First edition 2015 Copyright # 2015 Elsevier B.V All rights reserved No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein) Notices Knowledge and best practice in this field are constantly changing As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein ISBN: 978-0-444-63546-4 ISSN: 0079-6123 For information on all Elsevier publications visit our website at http://store.elsevier.com/ Contributors Mohamed Aboseria Department of Biomedical Engineering, The City College of New York, CUNY, New York, NY, USA Devin Adair Department of Biomedical Engineering, The City College of New York, CUNY, New York, NY, USA Steffen Angstmann Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark Til Ole Bergmann Department of Psychology, Christian-Albrechts-University, Kiel, Germany Sven Bestmann Sobell Department of Motor Neuroscience and Movement Disorders, UCL Institute of Neurology, University College London, London, UK Marom Bikson Department of Biomedical Engineering, The City College of New York, CUNY, New York, NY, USA James J Bonaiuto Sobell Department of Motor Neuroscience and Movement Disorders, UCL Institute of Neurology, University College London, London, UK Flavio Fr€ ohlich Department of Psychiatry; Department of Biomedical Engineering; Department of Cell Biology and Physiology; Neuroscience Center and Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Warren M Grill Department of Biomedical Engineering; Department of Electrical and Computer Engineering; Department of Neurobiology, and Department of Surgery, Duke University, Durham, NC, USA Gesa Hartwigsen Department of Psychology, Christian-Albrechts-University, Kiel, Germany Damian Marc Herz Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark Frances Hutchings Interdisciplinary Computing and Complex BioSystems, School of Computing Science, Newcastle University, Newcastle upon Tyne, UK v vi Contributors Marcus Kaiser Interdisciplinary Computing and Complex BioSystems, School of Computing Science, and Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK Anke Karabanov Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark Niranjan Khadka Department of Biomedical Engineering, The City College of New York, CUNY, New York, NY, USA Belen Lafon Department of Biomedical Engineering, The City College of New York, CUNY, New York, NY, USA Simon Little Sobell Department of Motor Neuroscience and Movement Disorders, UCL Institute of Neurology, University College London, London, UK Stefano Mandija Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands Rosalyn Moran Virginia Tech Carilion Research Institute & Bradley Department of Electrical and Computer Engineering, Virginia Tech, and Department of Psychiatry & Behavioral Medicine, Virginia Tech Carilion School of Medicine, Roanoke, VA, USA Antonios P Mourdoukoutas Department of Biomedical Engineering, The City College of New York, CUNY, New York, NY, USA Sebastiaan F.W Neggers Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands Petar I Petrov Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands Estelle Raffin Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark, and Grenoble Institute of Neuroscience, Research Centre U836 Inserm—UJF, Team 11 Brain Function & Neuromodulation, Grenoble, France Contributors Asif Rahman Department of Biomedical Engineering, The City College of New York, CUNY, New York, NY, USA Hartwig Roman Siebner Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, and Department of Neurology, Copenhagen University Hospital Bispebjerg, Copenhagen, Denmark Iris E.C Sommer Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands Axel Thielscher Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark, and Biomedical Engineering Section, Technical University of Denmark, Kongens Lyngby, Denmark Jochen Triesch Frankfurt Institute for Advanced Studies, Goethe University, Frankfurt, Germany Dennis Q Truong Department of Biomedical Engineering, The City College of New York, CUNY, New York, NY, USA Nico A.T van den Berg Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands Yujiang Wang Interdisciplinary Computing and Complex BioSystems, School of Computing Science, Newcastle University, Newcastle upon Tyne, UK Ulf Ziemann Department of Neurology & Stroke, Hertie Institute for Clinical Brain Research, Eberhard-Karls University Tuăbingen, Germany Christoph Zrenner Department of Neurology & Stroke, Hertie Institute for Clinical Brain Research, Eberhard-Karls University Tuăbingen, Germany vii Preface Computational neurostimulation in basic and translational research For a field that started with the application of a torpedo fish to the head for the treatment of migraine (Kellaway, 1946; Priori, 2003), neurostimulation has come a long way Where once the humble torpedo fish delivered uncontrolled electricity to the head, neurostimulation devices are now engineered with sophistication and can deliver current to any region of the brain with precision voltage control There is now no denying the contribution that both noninvasive brain stimulation (NIBS) techniques including transcranial direct current (tDCS), alternating current, and transcranial magnetic stimulation (TMS) as well as invasive deep brain stimulation (DBS) have made to improving our understanding of brain function and to helping treat carefully selected patients For example, DBS is now applied routinely for a growing number of neurological and psychiatric disorders, and electrical stimulation therapies are established for use in treating hearing loss (cochlear implants), with visual neurostimulation prosthetics currently under development Several applications of transcranial NIBS techniques have now made the transition into clinical use, while phase and clinical trials for the application of NIBS are proliferating, and increasingly NIBS is also being used to augment healthy brain function, including home use (Bikson et al., 2013) Neurostimulation in basic and translational research therefore remains a dynamic and innovative field However, one can also observe that the success and application of different forms of neurostimulation has galloped ahead of our understanding of the mechanisms through which electrical stimulation of the brain expresses its effects On the one hand, many applications of invasive or noninvasive brain stimulation, such as DBS or TMS, are now used widely for treatment of neurological and psychiatric disorders In these cases, not having a deeper understanding about the underlying mechanism is acceptable if clinical benefits outweigh the possible concerns that arise from any mechanistic ignorance On the other hand, ignorance delays progress and may even lead to intellectual and research investment in dead ends For applications in basic and translational research, the dearth of understanding about key aspects of neurostimulation seems much less acceptable Here, it leads to spurious inference, promotion of simplistic ideas, or plain wrong assumptions/procedures, and poses a hindrance to progressing forward beyond a peak of inflated expectations into a mature field of research, technology, and clinical use (Bestmann et al., 2015) Finally, side effects, even if subtle, may be less acceptable in healthy individuals Using neurostimulation to improve brain function has several challenges (Bestmann et al., 2015; de Berker et al., 2013) A deeper understanding of how xv xvi Preface behavioral changes unfold with brain stimulation would surely help address these issues, spurn further innovation, and quell misuse The question then is: where should such a mechanistic insight come from? This is not trivially answered, not least because there is not one form of neurostimulation Invasive DBS, for example, is focused on a relatively small spatial scale of several millimeters, targets subcortical structures, commonly uses high-frequency ($130 Hz) trains of short biphasic electrical pulses, and is exclusively applied in severe pathology By contrast, most forms of NIBS stimulate several square centimeters of cortical tissue or even entire networks of the brain at once (Bestmann and Feredoes, 2013; Bestmann et al., 2015; de Berker et al., 2013) Pulsed stimulation techniques such as TMS are applied at frequencies rarely exceeding 50 Hz for more than a few pulses (Huang et al., 2005), whereas direct or alternating transcranial current stimulation techniques apply low currents continuously for tens of minutes at a time (Nitsche and Paulus, 2011) This panoply of ways to deliver stimulation complicates comparison of the resulting effects on physiology and behavior The frequent creation of superficial analogies based on concepts used for all types of stimulation, such as changes in excitability, inhibition and excitation, plasticity, or virtual lesions, should thus probably be avoided (Bestmann et al., 2015; de Berker et al., 2013) Another crucial point that is often ignored is that different types of neurostimulation are predominantly investigated at very different levels of observation Drawing parallels between them is often unwarranted or simplistic For example, a lot of knowledge about the impact of DBS rests on direct recordings in animals and novel developments that allow for recording directly from the vicinity of the stimulation electrode in humans These single neuron or local field potential (LFP) recordings starkly contrast with the level of observation for most of the NIBS techniques in humans, where behavioral and neuroimaging measures provide the mainstay of inference on how stimulation expresses its effects As recently argued (Bestmann et al., 2015), even when data from invasive recordings in animals (e.g., Ma´rquez-Ruiz et al., 2012; Rahman et al., 2013) complement current knowledge about the impact of stimulation in humans, the question remains how the effects of neurostimulation at these different levels of observation ought to relate to one another We argue that the field of neurostimulation is now at a stage where quantitative computational models must guide further progress Put simply, there is a striking paucity of quantitative models that span across levels of description and link dose of stimulation through neurophysiology to behavior Computational neurostimulation, as envisaged here, is the use of mechanistic, quantitative models for understanding the physiological and behavioral consequences of neurostimulation Such models must meet several requirements: first, they must be biologically and biophysically grounded in current knowledge This inevitably requires many assumptions with sufficient uncertainty about the specific parameters one should use to incorporate current knowledge into a model Second, they must address the question at hand at an appropriate level of description that is suited to answer that specific question While they may draw upon knowledge (and other models) cast at lower or higher Preface levels of description, the choice of model should be governed by the type of data the model seeks to explain, and that one can obtain experimentally to inform the iterative process between modeling and experimentation Third, models ought to provide “mathematical/computational microscopes” (Moran et al., 2011) in that they can probe unobservable or hidden processes and interactions in observed data Fourth, and related, models should seek to explain what it is that the observed data actually represent in terms of a task or computation that is carried out by a specific system Fifth, and most pertinent to this volume, is the need to explain how the physiological changes produced by stimulation ultimately influence or change cognition and behavior, in both health and disease The last point is unlikely to be achieved without substantial progress on the other requirements, but because neurostimulation is used to alter behavior and cognition, it should remain the ultimate goal Many important issues merit discussion: what levels of description (microscopic–mesoscopic–macroscopic) are most suited to address a specific question at hand; how realistic (i.e., complex) should models be and how should one trade-off biological realism with model complexity and the possibility of overfitting; how generalizable across individuals and behaviors should models be? It is an exciting development that recent work has initiated discussion on these issues and the possible role of different forms of computational models for the field of neurostimulation (Bestmann et al., 2015; Bikson et al., 2015; Bonaiuto and Bestmann, 2015; Frohlich, 2015; Grill, 2015; Hartwigsen et al., 2015; Little and Bestmann, 2015; Moran, 2015; Neggers et al., 2015; Rahman et al., 2015; Triesch et al., 2015; Wang et al., 2015) Of course, substantial advances in the use of models for the field of neurostimulation have already been made Perhaps the most advanced and accepted use of models is in the field of DBS, where neural network models and simulations have made substantial contributions to understanding how different waveforms and stimulation regimes affect local firing (Grill, 2015; Little and Bestmann, 2015) The contributions from this work have started to be applied in designing novel, energy-efficient DBS stimulators In other fields of neurostimulation, particularly the group of NIBS techniques, the use of models is in a much earlier stage of infancy Here, the use of detailed head models and finite element methods to estimate current flow through the brain based on individual MRI scans are most notable, and for tDCS applications (Datta et al., 2013; Kuo et al., 2013) and TMS (Thielscher et al., 2011; Windhoff et al., 2013) are now on the verge of becoming standard procedure Yet, few models presently seek to explain the computations carried out by neural circuits and how these are affected by stimulation, in the sense that they address what it is these circuits and what the information they process reflects A simple example may serve to illustrate this crucial point: if we were to understand a book written in a foreign language, then simulations of current flow are analogous to predicting the distribution of ink on the pages; neural network models then attempt to predict the patterns of letters on each page, and whether these patterns are influenced by stimulation; but crucially, none of these tell us what those letters actually mean If neurostimulation is seen as an attempt to edit the meaning of the letters of a book, then understanding the meaning of the letters first seems crucial xvii xviii Preface The imminent issue that requires addressing is thus to develop quantitative models that span across these level of understanding, and make predictions about how different stimulation procedures culminate in behavioral changes including side effects The reason there is a need for such models is that they force us to formalize our ideas about the physiological basis of brain stimulation, and constrain the possible conclusions we might draw from observed data Such models can be used to simulate data, under specific assumptions about the parameters of the model (e.g., connectivity profiles), which are then compared to observed data Alternatively, generative models incorporate an expected (prior) distribution of parameter values (e.g., baseline firing rates of different types of neurons of a model) based on current knowledge, and a so-called forward model that quantifies the probability that a specific pattern of data (e.g., firing rates in STN neurons, evoked potentials in EEG recordings) results from the parameters of the model In principle, this allows for estimating the (posterior) probability for a specific parameter or set of parameters of the model, given the data one actually observes experimentally Regardless of the specific structure and modeling approach, models explicitly formalize the hypotheses one might have about a mechanism and process, in this case how brain stimulation influences neural circuits Common to all models that will be useful to this debate is that their quantitative nature allows for comparing how the predictions from a model hold up against data observed in vivo This illustrates the iterative loop through which modeling and experimentation inform one another As Arthur C Clarke observed, “Any sufficiently advanced technology is indistinguishable from magic,” and at this stage some of the results emerging from different applications of neurostimulation indeed seem magical It perhaps also seems that some magic is now much needed to develop computational models that will be able to accurately explain how neurostimulation alters neural circuits with sufficient biological realism to accurately predict behavioral outcome and side effects in individuals resulting from these alterations Despite perhaps appearing quixotic at this stage, the field must confront these challenges and should not be deterred from starting the quest for such models The debate is not whether such models are needed, but rather that the field must seek consensus about what the appropriate models and levels of description ought to be in order to help put the field of neurostimulation on a proper mechanistic footing The advances in other fields of neuroscience are testament to how modeling can help to understand complex processes in biology and stimulate novel questions and hypotheses (Moran et al., 2011; Stephan et al., 2015) Computational neurostimulation is in its infancy, but recent work is now initiating a much needed debate and encouraging efforts into the development of appropriate models (Bestmann et al., 2015; Bikson et al., 2015; Bonaiuto and Bestmann, 2015; de Berker et al., 2013; Frohlich, 2015; Grill, 2015; Hartwigsen et al., 2015; Little and Bestmann, 2015; Moran, 2015; Rahman et al., 2015; Triesch et al., 2015; Wang et al., 2015) It is hoped that in the not too distant future, the developments this will spawn will make the current state of the field appear much like how using a fish on the head to treat migraine does to us now The Editor Sven Bestmann Preface REFERENCES Bestmann, S., Feredoes, E., 2013 Combined neurostimulation and neuroimaging in cognitive neuroscience: past, present, and future Ann N.Y Acad Sci 1296, 11–30 Bestmann, S., de Berker, A.O., Bonaiuto, J., 2015 Understanding the behavioural consequences of noninvasive brain stimulation Trends Cogn Sci 19, 13–20 Bikson, M., Bestmann, S., Edwards, D., 2013 Neuroscience: transcranial devices are not playthings Nature 501, 167 Bikson, M., Truong, D.Q., Mourdoukoutas, A.P., Aboseria, M., Khadka, N., Adair, D., Rahman, A., 2015 Modeling sequence and quasi-uniform assumption in computational neurostimulation Prog Brain Res 222, 1–24 Bonaiuto, J., Bestmann, S., 2015 Understanding the nonlinear physiological and behavioral effects of tDCS through computational neurostimulation Prog Brain Res 222, 75–104 Datta, A., Zhou, X., Su, Y., Parra, L.C., Bikson, M., 2013 Validation of finite element model of transcranial electrical stimulation using scalp potentials: implications for clinical dose J Neural Eng 10, 036018 de Berker, A.O., Bikson, M., Bestmann, S., 2013 Predicting the behavioral impact of transcranial direct current stimulation: issues and limitations Front Hum Neurosci 7, 613 Fr€ohlich, 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stimulation on brain tissue: the bridge between brain stimulation and cognition Prog Brain Res 222, 229–260 xix 280 CHAPTER 11 Modeling NTBS effects at multiple levels was combined with TMS to investigate perceptual categorization processes Conditioning Hz offline rTMS was used to decrease activity in the dorsolateral prefrontal cortex (DLPFC) before subjects underwent a speeded perceptual categorization task The authors reported significantly delayed responses and reduced accuracy after DLPFC TMS Drift diffusion modeling revealed that particularly the drift rate (i.e., the rate of information uptake) was decreased after TMS, while nondecision times (i.e., constant processing times in the system) were unaffected Likewise, Soto et al (2012) found a specific modulation of the drift rate when applying online TMS during a working memory task It remains unclear, however, how well behavioral modeling approaches such as sequential sampling models capture TMS-induced modulations of behavioral variables For instance, when combining sequential sampling models with TMS, it might be important to take into account whether accumulation processes in the brain take place locally or centrally (Lo and Wang, 2006; Wong and Wang, 2006) Another aspect is related to the TMS protocol and the induced stimulation effects Hence, a long-lasting offline rTMS protocol will probably result in more global changes, whereas single-pulse online TMS might have to be modeled differently One possibility would be to include them as “shock events” which contaminate a distribution based on a diffusion-like process (Ratcliff and Tuerlinckx, 2002) A more general implication of the findings discussed above is that by accounting for the response distribution and different response strategies, behavioral modeling approaches may capture subtle TMS-induced changes on the behavioral level that might not be reflected in simple composite measures The application of behavioral models in TMS studies might thus increase the overall proportion of variance explained on the total variance of a model This might ultimately improve the sensitivity and specificity of different behavioral modeling approaches to capture TMSinduced modulations of behavior FUTURE PERSPECTIVES ON COMPUTATIONAL NEUROSTIMULATION IN THE STUDY OF COGNITION Within the past few years, the combination of NTBS with modeling approaches based on neuroimaging, electrophysiological, or behavioral data in studies of cognition has substantially increased our knowledge about the causal role of different brain regions in various aspects of cognition Moreover, these studies provided insights into short-term reorganization and adaptive plasticity on the network level The way forward is to use multimodal approaches that integrate information from different neuroimaging or electrophysiological techniques such as fMRI and EEG, which will substantially increase the validity and reliability of the NTBS-induced effects In this context, the simultaneous application of NTBS and fMRI/EEG would advance the current knowledge on the neurophysiology of the NTBS-induced effects Future studies should also consider the degree of intra- and interhemispheric interaction and compensation of different cognitive networks Here, the use of focal minicoils (or References electrodes) that allow for the simultaneous application of NTBS over multiple brain sites (e.g., Groppa et al., 2012a,b) seems promising These studies should also account for the relatively strong interindividual variation with respect to the direction and intensity of the NTBS-induced modulation using advanced biophysical models Moreover, the inclusion of behavioral parameters in (DCM) models of neuroimaging data could provide additional information with respect to the interaction of key nodes for specific cognitive processes and NTBS-induced changes on the neural network and behavioral level The combination of relatively novel techniques such as TACS or transcranial random noise stimulation with neuroimaging and behavioral modeling approaches might also help to advance current models of cognition Both approaches have been applied to synchronize or desynchronize cortical oscillations during different cognitive tasks and might prove effective for entrainment of cognitive functions In this context, stimulation-induced modulation of behavioral variables and changes in the effective connectivity between network nodes would be important to provide deeper insights into the functional network architecture beyond simple changes in neural activity This might deepen our understanding of the compensatory potential and adaptive plasticity of different task-specific networks Finally, these approaches might help to establish models of cognition that can provide a better understanding of the cognitive consequences of neurological diseases such as stroke or Parkinson’s disease It needs to be borne in mind, however, that aberrant networks (as for instance in 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A.K., Dobel, C., Zavorotnyy, M., Domschke, K., Junghofer, M., 2014 Inhibitory repetitive transcranial magnetic stimulation (rTMS) of the dorsolateral prefrontal cortex modulates early affective processing Neuroimage 101, 193–203 287 Index Note: Page numbers followed by f indicate figures, b indicate boxes and t indicate tables A D Alzheimer’s disease (AD), 127–128 deep brain stimulation (DBS), 136–139, 137b Anodal stimulation, 97–98 Arnold tongue, 46 Axon terminal polarization, 35–36 DCM See Dynamic causal modeling (DCM) DDM See Drift diffusion modeling (DDM) Deep brain stimulation (DBS), 2, 147–148, 192, 202–203 applications Alzheimer’s disease (AD), 136–139, 137b Parkinson’s disease (PD), 139–140, 141f cross-spectral densities (CSDs), 128–129 modeling DCM, 134–136 EEG, 131–134, 132f fMRI, 129–131 therapeutics, 127b Direct current, Direct current stimulation, 26–27, 27–28f Direct electrical stimulation (DES), 249 Direct stimulus-based activation, 276 Dorsolateral prefrontal cortex (DLPFC), 232–233, 278–280 Drift diffusion modeling (DDM), 278–280, 279f D-wave generation, 106–108 Dynamic causal modeling (DCM), 127, 262, 271–275, 272–273f deep brain stimulation (DBS), 134–136 noninvasive transcranial brain stimulation (NTBS), 262, 271–275, 272–273f B Bayesian model inversion, 129–130 Biexponential synaptic conductances, 80 Blood oxygen level dependent (BOLD), 130 Brain stimulation, 42–44, 47f, 49f, 50, 57 C Cable theory formulation, 33–34 Cellular response models, 6–9 Cerebral spinal fluid (CSF), 246, 265–266 Closed-loop, 192–193, 202–203 Computational model, 50, 52, 58–67, 60f, 62f Computational modeling brain activity, 192 brain stimulation, 193 cortical spreading depression (CSD), 193–194, 210–212, 211f Epilepsy, 192, 205–210, 206–207f Parkinson’s disease (PD), 193–194, 201–205, 204f Computational neurostimulation current flow, 3–6 information processing, 9–11 multiscale approaches, 13–15 network changes, 9–11 network to behavior, 11–13 quasi-uniform assumption, 6–9 sequential multistep modeling process, 1–3 Cortex, 42, 47–50 Cortical oscillations, cellular and synaptic properties, 68 Cortical spreading depression (CSD), 193–194, 210–212, 211f Cortical stimulation, Cross-spectral densities (CSDs), 128–129 CSF See Cerebral spinal fluid (CSF) Current–distance relationship, 149–150 E EEG See Electroencephalography (EEG) Effective connectivity, 269–270 Electrical activation, Electrical stimulation, 1–3, 147–148, 149f, 158 Electric field calculations, 265–268 Electroencephalography (EEG), 111–113, 127–128, 264 deep brain stimulation (DBS), 131–134, 132f Electromyography (EMG), 231, 249 Energy efficiency, 150 Energy-optimal pulse duration, 152–154, 153f Energy-optimal waveform shape, 154–155 Entrainment, 44, 47f Epilepsy, 192, 205–210, 206–207f deep brain stimulation (DBS), 129–131 289 290 Index F N Faraday’s electromagnetic induction principle, 230 Finite element models (FEMs), 196–197, 197f, 235–237, 247–248, 265–266 Food and Drug Administration (FDA), 232–233 Functional connectivity, 269–270 Functional magnetic resonance imaging (fMRI), 129–131, 264, 269–272, 275, 280–281 deep brain stimulation (DBS), 129–131 Network activity, 10 Neural mass models, 133 Neural network model implementation and analyses accuracy thresholds, 84–87, 85f, 86t, 87f decision speed and prestimulus bias, 96–97, 96f decision time, prestimulus bias, 93 decision times, 87–89, 88f neural dynamics, 89–91, 90f prestimulus bias and input contrast, 93–95, 94–95f selection accuracy, prestimulus bias, 91–93, 92f intensity-dependent changes, 83 model architecture, 78–80, 79f simulating tDCS, 81–83, 82t synapse and neuron model, 80–81 Neural stimulation efficiency of, 150–151 energy-efficient, 151–152 energy-optimal pulse duration, 152–154, 153f energy-optimal waveform, 154–155 optimized pulse shapes, 157–158 stimulation waveform, 148–150, 149f, 155–156 Neurodegenerative disease, 127–129 Alzheimer’s disease (AD), 127–128 Parkinson’s disease (PD), 128 Neuromodulation, 1–3, 6, NEURON software, 106 Noninvasive brain stimulation (NIBS), 76 cellular effects of axon terminal polarization, 35–36 cable theory, 33–34 direct current stimulation, 26–27, 27–28f Hodgkin–Huxley-based neurons, 34–35 modeling electrical stimulation, 27–30, 29f numerical methods, 37 quantifying membrane polarization, 30–32, 31f quantitative framework, 36–37 uniform electric field, 32–33 computational models, 58–67 cortical oscillations transcranial alternating current stimulation, 50–53 transcranial magnetic stimulation, 47–50 dynamic systems theory, 44–47, 45f, 47f oscillations in vitro studies, 54–57 in vivo studies, 57–58, 60f synthesis and outlook, 67–70 Noninvasive electric stimulation, 196–198, 197f Noninvasive magnetic stimulation, 198–199 G GABAergic (GABAa) receptor dynamics, 133 Globus pallidus interna (GPi), 202 Gray matter (GM), 235 H Hodgkin–Huxley (HH) model, 34–35, 157 I In silico computational paradigm, 128 neural polarization, 78 noninvasive brain stimulation (NIBS), 76 Parkinson’s disease, 201–205 patient-specific closed-loop stimulation protocols, 193–194, 214f therapeutic brain stimulation, 213 Intracellular voltage, 35 Invasive electrical stimulation, 200 I-waves, 105–106, 106–108 See also TMS-induced I-waves L L2/3 cells, 110–111 Leaky integrate-and-fire (LIF) neural model, 80–81 Long-term depression (LTD)-like effects, 116 M Magnetoencephalography (MEG), 264 Mechanistic modeling, 196–197, 199 Migraine, 198–199, 210–212 Modeling electrical stimulation, 27–30, 29f Motor cortex (M1), 105–108 Motor–evoked potentials (MEPs), 111–113, 231, 249 Motor threshold (MT), 231–232 MR magnetic field mapping (MR-MFM), 250 Multifocal TMS, 263 Index Noninvasive transcranial brain stimulation (NTBS) behavioral effects of, 276–280, 277f cognition, computational neurostimulation, 280–281 effective connectivity, 269–275 psychophysiological interaction, 270–271 electrical fields, 265–269, 267f offline transcranial stimulation, 264 online transcranial stimulation, 263 paradoxical TMS effects, 264–265 Numerical methods, noninvasive brain stimulation, 37 O Obsessive–compulsive disorder (OCD), 232–233 Offline transcranial stimulation, 264 Online transcranial stimulation, 263 Optimization, stimulation parameters, 147–148 Optogenetics, 200–201 P Paradoxical TMS effects, 264–265 Parkinson’s disease (PD), 193–194, 201–205, 204f deep brain stimulation (DBS), 139–140, 141f Patient-specific closed-loop stimulation, 192–194, 202–203, 214f Patient-specific data, 193 Plasticity, 264, 269–270 Population-level description, 194–196, 195f Psychophysiological interaction method, 270–271 Psychophysiological interactions (PPIs), 269–270 Q Quantifying membrane polarization, 30–32, 31f Quasi-uniform assumption, 6–9 R Repetitive transcranial magnetic stimulation (rTMS), 42, 263 S Seizures, 205 Selectivity, stimulation parameters, 148 Sequential multistep modeling process, 1–3 Sliding scale concept, 12–13 Stimulating Peripheral Activity to Relieve Conditions (SPARC), 126 Stimulation See also Neural stimulation Stimulation frequency, mapping of, 67 Stimulation intensity level, 83, 97 Stimulation modalities, 194–196, 195f Subgenual cingulate (SCG) region, 127 Subthalamic nucleus (STN), 202, 274 Synchronization, 44, 45f T TCS See Transcranial current stimulation (TCS) TDCS See transcranial direct current stimulation (tDCS) TES See Transcranial electrical stimulation (TES) Therapeutic brain stimulation in silico, 213 Theta-burst stimulation (TBS), 117f TMS See Transcranial magnetic stimulation (TMS) TMS-evoked cortical potentials (TEPs), 266–268 TMS-induced I-waves coil geometry, 113–115 individual brain anatomy, 113–115 modeling plasticity induction, 115–116, 117f ongoing brain activity, 110–113, 112–113f pulse waveform and direction, 113–115, 115f Rusu et al (2014) model description, 106, 107f key findings, 106–110 Transcranial alternating current stimulation (tACS), 43, 50–53 Transcranial current stimulation (TCS), 196, 198–199 Transcranial direct current stimulation (tDCS), 2, 25–26, 43, 76–77, 196, 230–231, 262 intensity-dependent impact accuracy thresholds, 84–87, 85f, 86t, 87f on decision times, 87–89, 88f on neural dynamics, 89–91, 90f prestimulus bias on decision time, 93, 94f input contrast on decision speed, 96–97, 96f input contrast on selection, 93–95, 95f on selection accuracy, 91–93, 92f Transcranial electrical stimulation (TES), 196 Transcranial magnetic stimulation (TMS), 198–199, 262, 264–265, 275–276 cognition action potentials, 238–242, 240f current patterns and neuronal computations, 235–238, 236f computing local currents BEM, 247–248 empirical validation, 248–251, 250f FEM, 247–248 291 292 Index Transcranial magnetic stimulation (TMS) (Continued) head model, 246–247 TMS coil, 243–246, 244f, 252–253 dorsolateral prefrontal cortex (DLPFC), 232–233 electromyogram (EMG), 231 Faraday’s electromagnetic induction principle, 230 motor-evoked potentials (MEPs), 231 motor threshold (MT), 231–232 transcranial direct current stimulation (tDCS), 230–231 Two-way ANOVA, 84, 86t V Virtual lesion, 263–265 Voltage-controlled stimulation, W White matter (WM), 235 Other volumes in PROGRESS IN BRAIN RESEARCH Volume 167: Stress Hormones and Post Traumatic Stress Disorder: Basic Studies and Clinical Perspectives, by E.R de Kloet, M.S Oitzl and E Vermetten (Eds.) – 2008, ISBN 978-0-444-53140-7 Volume 168: Models of Brain and Mind: Physical, Computational and Psychological Approaches, by R Banerjee and B.K Chakrabarti (Eds.) – 2008, ISBN 978-0-444-53050-9 Volume 169: Essence of Memory, by W.S Sossin, J.-C Lacaille, V.F Castellucci and S Belleville (Eds.) – 2008, ISBN 978-0-444-53164-3 Volume 170: Advances in Vasopressin and Oxytocin – From Genes to Behaviour to Disease, by I.D Neumann and R Landgraf (Eds.) – 2008, ISBN 978-0-444-53201-5 Volume 171: Using Eye Movements as an Experimental Probe of Brain Function—A Symposium in Honor of Jean Buăttner-Ennever, by Christopher Kennard and R John Leigh (Eds.) – 2008, ISBN 978-0-444-53163-6 Volume 172: Serotonin–Dopamine Interaction: Experimental Evidence and Therapeutic Relevance, by Giuseppe Di Giovanni, Vincenzo Di Matteo and Ennio Esposito (Eds.) – 2008, ISBN 978-0-444-53235-0 Volume 173: Glaucoma: An Open Window to Neurodegeneration and Neuroprotection, by Carlo Nucci, Neville N Osborne, Giacinto Bagetta and Luciano Cerulli (Eds.) – 2008, ISBN 978-0-444-53256-5 Volume 174: Mind and Motion: The Bidirectional Link Between Thought and Action, by Markus Raab, Joseph G Johnson and Hauke R Heekeren (Eds.) – 2009, 978-0-444-53356-2 Volume 175: Neurotherapy: Progress in Restorative Neuroscience and Neurology — Proceedings of the 25th International Summer School of Brain Research, held at the Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands, August 25–28, 2008, by J Verhaagen, E.M Hol, I Huitinga, J Wijnholds, A.A Bergen, G.J Boer and D.F Swaab (Eds.) –2009, ISBN 978-0-12-374511-8 Volume 176: Attention, by Narayanan Srinivasan (Ed.) – 2009, ISBN 978-0-444-53426-2 Volume 177: Coma Science: Clinical and Ethical Implications, by Steven Laureys, Nicholas D Schiff and Adrian M Owen (Eds.) – 2009, 978-0-444-53432-3 Volume 178: Cultural Neuroscience: Cultural Influences On Brain Function, by Joan Y Chiao (Ed.) – 2009, 978-0-444-53361-6 Volume 179: Genetic models of schizophrenia, by Akira Sawa (Ed.) – 2009, 978-0-444-53430-9 Volume 180: Nanoneuroscience and Nanoneuropharmacology, by Hari Shanker Sharma (Ed.) – 2009, 978-0-444-53431-6 Volume 181: Neuroendocrinology: The Normal Neuroendocrine System, by Luciano Martini, George P Chrousos, Fernand Labrie, Karel Pacak and Donald W Pfaff (Eds.) – 2010, 978-0-444-53617-4 Volume 182: Neuroendocrinology: Pathological Situations and Diseases, by Luciano Martini, George P Chrousos, Fernand Labrie, Karel Pacak and Donald W Pfaff (Eds.) – 2010, 978-0-444-53616-7 Volume 183: Recent Advances in Parkinson’s Disease: Basic Research, by Anders Bj€orklund and M Angela Cenci (Eds.) – 2010, 978-0-444-53614-3 Volume 184: Recent Advances in Parkinson’s Disease: Translational and Clinical Research, by Anders Bj€orklund and M Angela Cenci (Eds.) – 2010, 978-0-444-53750-8 Volume 185: Human Sleep and Cognition Part I: Basic Research, by Gerard A Kerkhof and Hans P.A Van Dongen (Eds.) – 2010, 978-0-444-53702-7 Volume 186: Sex Differences in the Human Brain, their Underpinnings and Implications, by Ivanka Savic (Ed.) – 2010, 978-0-444-53630-3 Volume 187: Breathe, Walk and Chew: The Neural Challenge: Part I, by Jean-Pierre Gossard, Re´jean Dubuc and Arlette Kolta (Eds.) – 2010, 978-0-444-53613-6 Volume 188: Breathe, Walk and Chew; The Neural Challenge: Part II, by Jean-Pierre Gossard, Re´jean Dubuc and Arlette Kolta (Eds.) – 2011, 978-0-444-53825-3 Volume 189: Gene Expression to Neurobiology and Behaviour: Human Brain Development and Developmental Disorders, by Oliver Braddick, Janette Atkinson and Giorgio M Innocenti (Eds.) – 2011, 978-0-444-53884-0 293 294 Other volumes in PROGRESS IN BRAIN RESEARCH Volume 190: Human Sleep and Cognition Part II: Clinical and Applied Research, by Hans P.A Van Dongen and Gerard A Kerkhof (Eds.) – 2011, 978-0-444-53817-8 Volume 191: Enhancing Performance for Action and perception: Multisensory Integration, Neuroplasticity and Neuroprosthetics: Part I, by Andrea M Green, C Elaine Chapman, John F Kalaska and Franco Lepore (Eds.) – 2011, 978-0-444-53752-2 Volume 192: Enhancing Performance for Action and Perception: Multisensory Integration, Neuroplasticity and Neuroprosthetics: Part II, by Andrea M Green, C Elaine Chapman, John F Kalaska and Franco Lepore (Eds.) – 2011, 978-0-444-53355-5 Volume 193: Slow Brain Oscillations of Sleep, Resting State and Vigilance, by Eus J.W Van Someren, Ysbrand D Van Der Werf, Pieter R Roelfsema, Huibert D Mansvelder and Fernando H Lopes da Silva (Eds.) – 2011, 978-0-444-53839-0 Volume 194: Brain Machine Interfaces: Implications For Science, Clinical Practice And Society, by Jens Schouenborg, Martin Garwicz and Nils Danielsen (Eds.) – 2011, 978-0-444-53815-4 Volume 195: Evolution of the Primate Brain: From Neuron to Behavior, by Michel A Hofman and Dean Falk (Eds.) – 2012, 978-0-444-53860-4 Volume 196: Optogenetics: Tools for Controlling and Monitoring Neuronal Activity, by Thomas Kn€opfel and Edward S Boyden (Eds.) – 2012, 978-0-444-59426-6 Volume 197: Down Syndrome: From Understanding the Neurobiology to Therapy, by Mara Dierssen and Rafael De La Torre (Eds.) – 2012, 978-0-444-54299-1 Volume 198: Orexin/Hypocretin System, by Anantha Shekhar (Ed.) – 2012, 978-0-444-59489-1 Volume 199: The Neurobiology of Circadian Timing, by Andries Kalsbeek, Martha Merrow, Till Roenneberg and Russell G Foster (Eds.) – 2012, 978-0-444-59427-3 Volume 200: Functional Neural Transplantation III: Primary and stem cell therapies for brain repair, Part I, by Stephen B Dunnett and Anders Bj€orklund (Eds.) – 2012, 978-0-444-59575-1 Volume 201: Functional Neural Transplantation III: Primary and stem cell therapies for brain repair, Part II, by Stephen B Dunnett and Anders Bj€orklund (Eds.) – 2012, 978-0-444-59544-7 Volume 202: Decision Making: Neural and Behavioural Approaches, by V.S Chandrasekhar Pammi and Narayanan Srinivasan (Eds.) – 2013, 978-0-444-62604-2 Volume 203: The Fine Arts, Neurology, and Neuroscience: Neuro-Historical Dimensions, by Stanley Finger, Dahlia W Zaidel, Franc¸ois Boller and Julien Bogousslavsky (Eds.) – 2013, 978-0-444-62730-8 Volume 204: The Fine Arts, Neurology, and Neuroscience: New Discoveries and Changing Landscapes, by Stanley Finger, Dahlia W Zaidel, Franc¸ois Boller and Julien Bogousslavsky (Eds.) – 2013, 978-0-444-63287-6 Volume 205: Literature, Neurology, and Neuroscience: Historical and Literary Connections, by Anne Stiles, Stanley Finger and Franc¸ois Boller (Eds.) – 2013, 978-0-444-63273-9 Volume 206: Literature, Neurology, and Neuroscience: Neurological and Psychiatric Disorders, by Stanley Finger, Franc¸ois Boller and Anne Stiles (Eds.) – 2013, 978-0-444-63364-4 Volume 207: Changing Brains: Applying Brain Plasticity to Advance and Recover Human Ability, by Michael M Merzenich, Mor Nahum and Thomas M Van Vleet (Eds.) – 2013, 978-0-444-63327-9 Volume 208: Odor Memory and Perception, by Edi Barkai and Donald A Wilson (Eds.) – 2014, 978-0-444-63350-7 Volume 209: The Central Nervous System Control of Respiration, by Gert Holstege, Caroline M Beers and Hari H Subramanian (Eds.) – 2014, 978-0-444-63274-6 Volume 210: Cerebellar Learning, Narender Ramnani (Ed.) – 2014, 978-0-444-63356-9 Volume 211: Dopamine, by Marco Diana, Gaetano Di Chiara and Pierfranco Spano (Eds.) – 2014, 978-0-444-63425-2 Volume 212: Breathing, Emotion and Evolution, by Gert Holstege, Caroline M Beers and Hari H Subramanian (Eds.) – 2014, 978-0-444-63488-7 Volume 213: Genetics of Epilepsy, by Ortrud K Steinlein (Ed.) – 2014, 978-0-444-63326-2 Volume 214: Brain Extracellular Matrix in Health and Disease, by Asla Pitkaănen, Alexander Dityatev and Bernhard Wehrle-Haller (Eds.) – 2014, 978-0-444-63486-3 Other volumes in PROGRESS IN BRAIN RESEARCH Volume 215: The History of the Gamma Knife, by Jeremy C Ganz (Ed.) – 2014, 978-0-444-63520-4 Volume 216: Music, Neurology, and Neuroscience: Historical Connections and Perspectives, by Francáois Boller, Eckart Altenmuăller, and Stanley Finger (Eds.) – 2015, 978-0-444-63399-6 Volume 217: Music, Neurology, and Neuroscience: Evolution, the Musical Brain, Medical Conditions, and Therapies, by Eckart Altenmuăller, Stanley Finger, and Franc¸ois Boller (Eds.) – 2015, 978-0-444-63551-8 Volume 218: Sensorimotor Rehabilitation: At the Crossroads of Basic and Clinical Sciences, by Numa Dancause, Sylvie Nadeau, and Serge Rossignol (Eds.) – 2015, 978-0-444-63565-5 Volume 219: The Connected Hippocampus, by Shane O’Mara and Marian Tsanov (Eds.) – 2015, 978-0-444-63549-5 Volume 220: New Trends in Basic and Clinical Research of Glaucoma: A Neurodegenerative Disease of the Visual System, by Giacinto Bagetta and Carlo Nucci (Eds.) – 2015, 978-0-444-63566-2 Volume 221: New Trends in Basic and Clinical Research of Glaucoma: A Neurodegenerative Disease of the Visual System, by Giacinto Bagetta and Carlo Nucci (Eds.) – 2015, 978-0-12-804608-1 295 ... TMS-induced I-waves in human motor cortex Prog Brain Res 222, 105–124 Wang, Y., Hutchings, F., Kaiser, M., 2015 Computational modeling of neurostimulation in brain diseases Prog Brain Res 222, ... transition into clinical use, while phase and clinical trials for the application of NIBS are proliferating, and increasingly NIBS is also being used to augment healthy brain function, including home... improvement in symptoms) without stimulation-generated side effects Specificity can be enhanced by guiding current to specific brain regions (Step 1) but since no brain region is involved in one brain