Y Combining tCS and EEG P O C T O N O D Emiliano Santarnecchi E - Berenson-Allen Center for Non-invasive Brain Stimulation, Department of Cognitive Neurology | Beth S Israel Deaconess Medical Center | Harvard Medical School | Boston, MA, USA A E - Center for Complex System study, Engineering and Mathematics Department, University of Siena, Italy L P esantarn@bidmc.harvard.edu Boston, 23 rd February 2017
Outline Y Measuring tCS effects without EEG • P O Measuring effects outside the motor cortex Ø C Measuring focality of tCS interventions Ø T O N Basics of EEG • O EEG signal: features and opportunities Ø D Analysis (ERP, Microstates, Source analysis, ...) Ø E S Examples of EEG-tCS combination Ø A E Beyond EEG • L P TMS-EEG recording Ø
Y P Questions? Comments? Ideas? Feedback? O C T O N O D E • Kirsten Building - KS-450 S A E • esantarn@bidmc.harvard.edu L P • emilianosantarnecchi@gmail.com
Measuring tCS effects without EEG Y P O C T O N O D E S A E L First evidence of tDCS after effect from Nitsche and Paulus, 2000 P Changes in cortical excitability assessed using TMS-EMG
Corticospinal excitability as an index of Brain excitability Y Applied to tCS: limitation for online recording, only after effects P O C T O N O D E S A E L P
tDCS effect on corticospinal excitability: Online and Offline effects Santarnecchi et al., 2014 Y P O C T O N O D E S A E L P
tDCS effect on Subcortical Structures? Y P O C T O N O D E S A E L P Modeling based on tractography, structural MRI, CT scans…. Rossi, Santarnecchi 2016, Philos. Trans A
tDCS Effects on the motor cortex: pre/during/post Y P Anodal and Cathodal O PRE ONLINE POST (30’) tDCS modulate (15’) (15’) C (increase/decrease excitability) right T after the stimulation O respect to Sham. N No significant effects O During the D stimulation. E S Still limited A E to the motor L cortex! P
Are we stimulating the motor cortex? Kuo et al., 2013 Y P O C T O N O D E S A Montage, Timing, Stimulation E site, Duration, Intensity, etc. L suggest a complex scenario P HD-tDCS underlying tCS effects TMS-EMG is not enough
Multifactorial model Y P O C Brain state Behavioural scores (electrophysiological T recording - EEG) O N ? Electrophysiological Individual trait …Brain… O (personality, cognitive responses – EEG/ERPs/etc.. profile) D E Behavioural performance Genetics S Neuroimaging ($$$) (e.g. BDNF) Physiological measuments A (EKG, EDR,..) E Neuroimaging ($$$) EEG/ERPs/??? L fMRI? P BEFORE DURING AFTER
Open questions.. Y • the effect of tCS on Non-Motor regions? P O C • distant effects and changes in the interplay between regions (connectivity) à Network effects? T O N • the Online effects of tCS on brain activity other than “excitability”? O D E S A E Useful information to define tCS parameters L P and increase efficacy of interventions
Electroencephalography Y P O C T O N O Hans Berger D E S A E L P 1934: Fisher and Lowenback first demonstration of epileptiform spikes.
Pros and cons of EEG Y P O C T O N O D E S A E L P
Dominant Oscillations for Different brain regions Beta: movement Y Alpha: automatic movements P Gamma: selective attention O C T O N Ѳ: working /long-term O memory D E S A Alpha: visual perception E L P Θ: spatial orienting
Oscillatory pattern in the brain • Why are oscillatory pattern so important ? Y P 2 . Hierarchical information processing O 1. Pulse processing C T O N Cyclic Excitability Changes O D Rhythmic fluctuations in the local field potential E (LFP), synchronous transmembrane currents in populations of neurons and thus represent cyclic S Multiplexing / Cross- changes in the excitability of local neuronal frequency coupling A populations . • Various aspects of the stimulus are E encoded in different oscillations L simultaneously , but at different P Ongoing oscillatory phase significantly modulates the frequencies. probability of perceiving a near-threshold visual • Efficient coding scheme relying on stimulus. the hierarchical organization of oscillations.
Oscillatory pattern and periodicity in behaviour • Why are oscillatory patterns so important? Canolty, Science 2005 Y P 3. “Communication-through-coherence” Theory O C T O • Communication being facilitated when two oscillatory populations are aligned to their high N excitability phases. O D • Effective communication relies on spikes from the sending population reaching the receiving population E at a phase of high excitability. S A • Changes in synchronization between distant brain E areas (possibly reflecting communication) are L systematically related to task performance . P
Y P O C T O N EEG recording and analysis O D E S A E L P
EEG recording Y • International 10-20 system High-Density EEG P • Left side: odd numbers O (64-256 Channels) C • Right side: even numbers T • Numbers increase from the hemispheric line towards the edges.. O Letter indicates brain regions (lobes). N O D – Fp prefrontal E – F frontal S – C central A E – T temporal L – P parietal P – O Occipital
EEG recording Y P 1. SPONTENEOUS O C • Meaningful data with ~5’ of recording • Eyes open/closed T O 2. EVOCKED N O D E S A E L P Well known Evoked Response Potential (ERP )(P300, N100, ..) TMS-EEG
EEG features Y P O C T O N O D E S A E L P fMRI
Time vs Frequency Analysis Y P O C T O N O D E S A E L P
Event-Related Potentials (ERPs) Y P O C T O N O D E S EEG response to visual stimuli A E L P Example of auditory evoked potentials
Event-Related Potentials and Source Analysis Y Attempt to localize cortical/subcortical Sources responsible for P the EEG topography of interest. O C T O N O D E S A E L P Algorithm-threshold-model dependent results….
EEG Connectivity analysis Zhavoronkova et al., 2013 Y P Traumatic Brain Injury O C T O N O D E S A E Extract signal for all the electrodes L P Correlation / coherence / etc
EEG Microstates Khanna et al. 2014 Y P O C T O N O D E S A E L P Sequence of spatially defined Topographies
EEG Microstates Y P O C T O N O D E S A E L P Four major Microstates (explain ~75% variance)
EEG Microstates Y P O C T O N O D EEG Microstates and fMRI Resting-state networks E S A E Significant differences in L 1 2 3 4 Alzheimer, Schizophrenia, ADHD P Synthax analysis
EEG Microstates and Cognition Santarnecchi et al., under revision Y P O C T O N O D E S A E L P Microstate Topography changes with Cognitive Training Microstate Frequency correlates with Abstract Reasoning
Advantages of tCS + EEG Y P • Understanding the role of brain oscillations in both motor and non- O motor regions , in both the healthy and pathological brain . C T • Measure both local and distant effects. O N • Guide tCS intervention on the basis of and online/offline monitoring O of brain states. D E S A How can tCS + EEG be implemented? E L P
tCS + EEG approaches Y P Resting or Resting or tCS O OFFLINE Event Event (no EEG C related EEG related EEG recording) T O N ? Resting or EEG Resting or O ONLINE Event recording Event D related EEG during tCS related EEG E S A ? E EEG-Guided, tCS guided Resting or Resting or L closed-loop Event related Event related by EEG P system EEG EEG recording
tCS and EEG: variables Y P O C T O N O D E S A E L P
EEG-Guided tCS: Stimulation Parameters (Frequency, phase,etc.) Zahele et al., 2012 Y Frequency P O Individual Alpha frequency C T O N O D E S A E • tACS on the occipital cortex at individual alpha frequency L P • Resting EEG à increase in alpha in parieto-central electrodes, no effects on surrounding frequencies
EEG-Guided tCS: Stimulation Parameters (Frequency, phase,etc.) Vossen et al., 2015 Y Frequency P O Individual Alpha frequency C T O N O D E S A E L P
EEG-Guided tCS: Stimulation Parameters (Frequency, phase,etc.) Neuling et al., 2012 Y Phase 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
State dependency: Eyes Open vs. Eyes Closed Neuling et al., 2013 Y P O Significant increase in alpha-power C after individual-alpha frequency T tACS when applied with Eyes open, O but not with Eyes closed. N O D E S A E L P
Y P O C T O N O D E S A E L P
Closed-Loop Studies in Animal Berenyi et al., 2012 Y P O C • Rodent model of generalized T epilepsy. O • Detection of interictal spikes N triggers tCS at 1Hz O D E Aborts the spike-wave S discharge burst A E L P Tremor suppression using EEG features from the motor cortex?
Y P O C T O N O D E S A E L P
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