Electrophysiological Methods in Developmental Research

Melissa Pangelinan, Ph.D. 27 August 2015

Overview

•  Pineda (2008) –  Mirror system vs. Motor system neuroanatomy –  Mu rhythms

•  Taylor & Baldeweg (2002) –  EEG (frequency domain), –  ERP components (time-domain)

•  Relationship with intracranial EEG

–  Evidence of change across development

Evidence for Early Mirroring

Melzoff & Moore (1977) Science

The “problems”

•  Development: How does the mirror system arise? •  Correspondence: How does the observer know what

the observed agent’s resonance activation patterns are?

•  Control: How do you efficiently control a mirroring

system? Why don’t we ALWAYS mirror?

Answers…

•  Sensorimotor transformations necessary for computing muscle activations and kinematic goals from observed actions

… but they also serve answer the development,

correspondence and control.

The “Core” MNS •  A functional spectrum: Mimicry -> simulation (action

understanding)

The full system

Quiz: Identify the “motor system”

•  SMG: •  IP/IPL: •  SI/S2: •  M1: •  SMA: •  PMd/PMv: •  BA 46: •  IFG:

Quiz: Identify the “motor system”

•  SMG: spatial orientation/semantic

•  IP/IPL: Limb/eye mvt •  SI/S2: kinematic, integration

across body parts •  M1: elicits motor commands •  SMA: planning/coordination •  PMd/PMv: encodes multiple

movements •  BA 46: attention/working

memory •  IFG: action obs/imitation

MNS <-> M1

•  Premotor -> hand and face representations in M1/S1 –  Reciprocal connections –  Dum & Strick (2005)

•  Extrinsic vs. intrinsic coding in PM vs. M1

MNS<->S1

•  IPL integrates information from multiple modalities (i.e., S1, V1, A1)

•  S1->PMv

•  S1->IFG during somatosensory stimulation –  Working memory of tactile stimulation

MNS Changes with Experience and Learning

•  Hebbian or auto-associative learning –  Activity of MNS changes with experience –  Ballet vs. Capoeira

•  Calvo-Merino et al., (2005)

•  Utility in predictive coding/simulation

Mu Rhythms

•  Alpha (8 – 13 Hz) & Beta (14-25 Hz) •  Movement suppresses (desynchrony) mu

–  Inhibition enhances (synchrony) mu

•  Sensorimotor cortex source of mu –  Beta more anterior than alpha –  Somatotopic?

•  Temporal dynamics different during action, observation, and listening

•  Lateral inhibition of local networks –  Hand inhibits foot and tongue

Analytic Aside

How to we actually measure oscillations?

Filtering…

•  What is a filter?

•  Why do we use filters? What types of noise exist?

•  Basic application of a Fourier transform!

Signal Decomposition

Signal Decomposition Using Fourier Transforms

•  Assumption: EEG signal is a linear combination of sine and cosine functions

•  Definitions: –  Power spectral density (PSD): measured as power per unit

frequency (voltage2/Hz) •  Area = total power •  Welch, Thomson Multitaper, Yule-Walker Autoregressive

–  Mean-square (power) spectrum: measures power at a specific frequency (voltage2)

Harmonics/Aliasing

•  Signals are periodic in the frequency domain •  Period = sampling frequency •  Harmonics show up as “spectral copies” •  If the sampling rate is too low, special copies

overlap in real frequency range…

Parameters & MATLAB commands •  y = sampled data •  n = length(y) = number of samples •  Fs = samples/unit time (sampling rate) •  dt = 1/Fs = time increment •  t = (0:n-1)/Fs = time range •  Y = fft(y) = discrete Fourier transform (DFT) •  abs(Y) = amplitude of DFT •  abs(Y).^2/n = power of DFT •  Fs/n = frequency increment •  F = (0:n-1)*(Fs/n) = frequency range •  Fs/2 = Nyquist frequency

Theta Alpha Beta

EEG as complementary to ERP

•  Similar age-related changes in EEG and ERP components

•  Extract the time course of EEG

–  Event-related synchrony and desynchrony

•  Compare the sources of the EEG or ERP components

Methodological Considerations

•  Data collection: –  When to record? –  Timing of trials –  Recording sub-threshold movements (EMG!) –  Recording bandwidth

•  Analytics: –  Window length of FFT –  Data length –  Filter/FFT type –  Baseline