Y P O C Mechanisms of TES: T O Neurophysiology N Flavio - PowerPoint PPT Presentation

Y P O C Mechanisms of TES: T O Neurophysiology N Flavio Frohlich O D University of North Carolina - Chapel Hill Department of Psychiatry E Department of Cell Biology and Physiology Department of Biomedical Engineering S Department of Neurology Neuroscience Center A North Carolina State University Department of Electrical and Computer Engineering E University of Bern Department of Neurology L www.networkneuroscientist.org P

Y P Conflicts of Interest O C UNC owns IP for my inventions in the field of brain • T stimulation. O UNC has determined a “ COI with administrative • considerations ” for our treatment clinical trials. N I am the founder, chief scientific officer, and • O majority owner of Pulvinar Neuro LLC (paid as consultant). D I speak with many companies and have received • E industry funding from Tal Medical (travel + research + consulting). S I frequently travel and give presentations. I typically A • receive reimbursement and a stipend. E I receive an annual royalty payment for sales of my • L book “Network Neuroscience” from Elsevier. P

Y P O C Standing on the T O N Shoulders of Giants O D E S A E L P

Y P Psychiatry Beyond O C “Chemical Imbalance in the Brain” T O N O D E S A E L P

Y P The Brain is an O Electrical System. C T O N O D E S A E L P

Y P O C T O N O D E S A E L P

Y P O Brain Rhythm C T O N O D E S A E L P Alpha Oscillation

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Y P O Synchronization C T O N O D E S A E L P

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Y P O C T O N O D Attention No Attention E S A E L P Sellers et al (2016)

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Y P O C NEUROTECHNOLOGY T O N O Synergies with other treatments. D Adaptive, individualized therapies. E S Mobile, on-demand diagnosis and treatment. A E L P

Y P O C T O N O D E Brunelin et al. 2012 S A E L P Frohlich et al. 2015

Y P O C T O N O D E S A Sellers et al. 2015 E L P

Y P O Lesson #1 C T O N Do not skip measuring O D brain activity (EEG, fMRI, E S etc.). #BeDifferent A E L P

Y P VERTICAL INTEGRATION O C T Patients Clinical Trials O N Brain Stimulation, COMPLEXITY Human Neurophysiology O D In vivo (Animal) E Electrophysiology S A In vitro (Animal) Electrophysiology TRACTABILITY E L Model Systems P Computer Simulations

Y P O Lesson #2 C T O N Leverage the tools of O D (network) neuroscience. E S #Collaboration A E L P

Y P TRANSCRANIAL CURRENT STIMULATION O STUDY DESIGN C T O Behavioral N Network Target Target Target Engagement O D E S A E L P

Y P O Lesson #3 C T O N Make sure you know your O D target and have a plan how E to engage it. S A E #RationalDesign L P

Y P TARGET ENGAGEMENT O C T How do we best engage a O network target? N O D We need to understand what E the effect of stimulation is on S the brain in terms of A neurophysiology . E L P

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Y P OUTLINE O C 1. Cellular Effects T O N 2. Spatial Targeting O D 3. Targeting Network Dynamics E S A E L P

Y P ELECTRIC FIELDS O C T O N How do electric fields change O electric signaling in neurons? D E S A E L P

Y P O “Anodal” “Cathodal” Depolarized Soma Hyperpolarized Soma C Hyperpolarized Dendrite Depolarized Dendrite T O N O D E S A E L P

Y P CABLE EQUATION O C T O N O D E S A E L P Frohlich and McCormick. 2010

Y P NEURONAL MORPHOLOGY AND STATE O C Change in somatic membrane voltage: T O • Increases with cable length. • Decreases with membrane conductance. N • Increases with cable diameter. O A B D E vs. S A E L P Radmann et al. 2009

Y P O C T O N O D Change in somatic membrane E voltage can be modeled as a sub- S threshold somatic current injection. A E L P Frohlich and McCormick. 2010

Y P O Lesson #4 C T O N tDCS/tACS cause small O D changes in neuronal E membrane voltage. #synergy S A #EndogenousBrainActivity E L P

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Y P O C The cadaver research “should make the crowd nervous that T favors tDCS and tACS,” says David Poeppel, a neuroscientist and psychologist at NYU. O N Marom Bikson, a biomedical engineer at The City College of New York in New York City who uses computer models and O slices of rat brain to study the mechanisms of tDCS and tACS, says that many in the field already accepted that the 1 or 2 D milliamps the methods use don’t directly trigger firing. E The tDCS field is “a sea of bullshit and bad science—and I S say that as someone who has contributed some of the papers that have put gas in the tDCS tank,” says neuroscientist A Vincent Walsh of University College London. “It really needs E to be put under scrutiny like this.” L P

Y P High-Density EEG with Digitizer O C TMS-tDCS-EEG study T O N O D TMS (left precentral gyrus) 2mA tDCS (M1-SO montage) using Neuronavigation Anode Cathode Sham E S A E L P

Y P Replication (Motor-Evoked Potential) O C T O N O D E S A E L P Ahn et al., in preparation.

Y P Grand-averaged TMS-evoked potential (TEP) O C T O N O D E S A E L P Ahn et al., in preparation.

Y P SPATIAL TARGETING O C Resistivity T Tissue [Ohm cm] O Copper 2e-6 N CSF 64 O Cortex 350 D White Matter 650 Bone 8,000-16,000 E S A E L P

Y P IMPLEMENTATION O C T O • MR Scan N • Tissue segmentation • Numerical solution (e.g. finite elements). O D 1. Develop you own code E 2. Collaborate 3. Buy tool / use free tool S A E L P

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Y P O C T O N O D E S A E L P Modeling performed by Angel Peterchev Sellers et al 2015

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Y P O Lesson #5 C T O N MR scan + Segmentation + O D EF modeling = Spatial E Targeting S A #KnowYour3D E L #HowGoodisHD P

Y P O STRUCTURE DYNAMICS C T O N O D E S A E L P BEHAVIOR

Y P MODELING DYNAMICS O C T O N O D E S A E L P Frohlich 2014

Y P OSCILLATIONS O C T O N O D E S A E Caution: Most tACS literature refers to the L peak-to-peak amplitude as amplitude . P

Y P NETWORK DYNAMICS O C Raw Trace Spectrum T O 1. Raw trace. N 2. Spectrum: Power as a function of frequency. O 3. Spectrogram: Spectrum as D a function of time. 4. Coherence: Interaction E between two sites as a S function of frequency. A E L P

Y P O C 1. Raw trace. T 2. Spectrum: Power as a function of frequency. O 3. Spectrogram: Spectrum as a function of time. N O D Raw Trace E Spectrogram S A E L P

Y P 1. Raw trace. O 2. Spectrum: Power as a function of frequency. C 3. Spectrogram: Spectrum as a function of time. 4. Coherence: Interaction between two sites as a function T of frequency. O N O D E S A E L P

Y P O Lesson #6 C T O N Brain rhythms effectively O D targeted by rhythmic brain E stimulation S A #MiddleSchoolMath E L P

Y P TARGETING BRAIN NETWORK DYNAMICS O C T Berger 1929 O N O Neuroconn Write / Input Read / Output D tACS EEG E S A E L P Transcranial Alternating Current Stimulation (tACS)

Y P NATURALISTIC ELECTRIC FIELDS O C T O N O D E S A E L P Frohlich and McCormick. 2010

Y P ARNOLD TONGUE O C T O N O D E S A E L P Frohlich 2014

Y P SPIKING NEURAL MODEL (NETWORK) O C T O N O D E S A E L P Ali et al. 2013

Y P SPATIO-TEMPORAL DYNAMICS O C T O N O D E S A E L P Ali et al. 2013

Y P O C T O N O D E S A E L P Ali et al. 2013

Y P STIMULATION PHASE O C T O N O D E S A E L P Ali et al. 2013

Y P HOTSPOTS O C T O N O D E S A E L P Ali et al. 2013

Y P NETWORK-LEVEL MECHANISM O C T O N O D E S A E L P Ali et al. 2013

Y P CELLULAR-LEVEL MECHANISM O C T O N O D E S A E L P Ali et al. 2013

Y P O TARGETING A C SUBPOPULATION T O N O D E S A E L P Ali et al. 2013

Y P NETWORK RESONANCE O C T O N O D E S A E L P Ali et al. 2013

Y P PHASE SLIPPING O C T O N O D E S A E L P Ali et al. 2013

Y P O C T O N O D E S A E Frequency (Stimulation-Endogenous) [Hz] L Negahbani et al. (2019, BioAxiv) P

Y P INTERACTING NETWORKS O C T O N O D E S A E L P Kutchko and Frohlich 2013