Y P O C Mechanisms of T Transcranial Current Stimulation O N - PowerPoint PPT Presentation

Y P O C Mechanisms of T Transcranial Current Stimulation O N O D Flavio Frohlich University of North Carolina - Chapel Hill E Department of Psychiatry S Department of Cell Biology and Physiology Department of Biomedical Engineering A Department of Neurology Neuroscience Center E www.networkneuroscientist.org L P www.facebook.com/FrohlichLabUNC

Y Conflicts of Interest P O C UNC owns IP related with FF as the lead inventor. • T UNC has determined the absence of a conflict of • O interest (COI) for the majority of work presented N here and has determined a “ COI with administrative considerations ” for the clinical O trials in the Frohlich Lab. D FF is the founder, chief scientific officer, and • majority owner of Pulvinar Neuro LLC. E Received industry funding from Tal Medical (travel + • S research) A I frequently travel and give presentations. I typically • E receive reimbursement and a stipend. L My preferred brain stimulation modality is doppio • P espresso .

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

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

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 S A E L P

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

Y P O C T O N O D E S A E Sellers et al. 2015 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 VERTICAL INTEGRATION P O C Patients T Clinical Trials O N Brain Stimulation, COMPLEXITY Human Neurophysiology O D In vivo (Animal) E Electrophysiology S A In vitro (Animal) E Electrophysiology TRACTABILITY 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 TRANSCRANIAL CURRENT STIMULATION P O STUDY DESIGN C T O Behavioral Network Target N 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 TARGET ENGAGEMENT P O C How do we best engage a T 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

Y OUTLINE P 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 electric signaling in neurons? O D E S A E L P

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

Y CABLE EQUATION P 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 • Increases with cable length. O • 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

Y SPATIAL TARGETING P O C Resistivity Tissue T [Ohm cm] O Copper 2e-6 N CSF 64 O Cortex 350 D White Matter 650 E Bone 8,000-16,000 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 S 3. Buy tool / use free tool A E L P

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

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

Y 1. Raw trace. P 2. Spectrum: Power as a function of frequency. O 3. Spectrogram: Spectrum as a function of time. C 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 TARGETING BRAIN NETWORK DYNAMICS P 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 NATURALISTIC ELECTRIC FIELDS P O C T O N O D E S A E L P Frohlich and McCormick. 2010

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

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

Y PHASE SLIPPING P 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 Stitt, Negahbani, et al. (in prep)

Y P O C T O N O D E S A E L P Stitt, Negahbani, et al. (in prep)

Y P O C T O N O D E S A E L P Stitt, Negahbani, et al. (in prep)

Y P O C T O N O D E S A E L P Stitt, Negahbani, et al. (in prep)

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

Y MULTISTABILITY P O C T “Rapid Fire” “Slow Propagating” “Spiral Waves” O N O D E S A E L P Kutchko and Frohlich 2013

Y STATE SWITCHING BY tACS P O C T O N O D E S A E Kutchko and Frohlich 2013 L P

Y P O Lesson #7 C T O N Complexity of brain dynamics O requires computer simulations D to understand target E S engagement. A E L P #MultiStability #NerdForPresident

Y TARGET: ALPHA OSCILLATIONS P O C T O N O D E “Offline” state, long-range • S functional connectivity, gating. A E Neurofeedback, rTMS (10 Hz), tACS, L • P others…

Y COGNITIVE ENHANCEMENT P O C T “increased alpha power during creative ideation is among the most consistent O findings in neuroscientific research on N creativity” (Fink and Benedek, 2010) O High Creative Ideation Low Creative Ideation D E S A E L P Lustenberger et al. (2015)

Y ENHANCING CREATIVITY P O C T O N O D E S A E Blinding was successful (p > 0.2). • L 10 Hz tACS significantly enhances creativity as measured by the Torrance • P Test of Creative Thinking (7.45 % ± 3.11 % S.E.M.; F 1,16 = 5.14, p = 0.036). No enhancement with 40Hz-tACS.. • Lustenberger et al. (2015)

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

Y P FEEDBACK tACS TO MODULATE SLEEP SPINDLES O C T O N O D E S A E L P Lustenberger et al. (2015)

IMPROVING MEMORY Y P O CONSOLIDATION C T O N O D E S A E L P Lustenberger et al. (2015)

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

Y P O Lesson #8 C T O N Individualize with feedback O stimulation to enhance target D engagement. E S #OMGWasThatASpindle A E L P