As long as our brain is a mystery, the universe, the...

As long as our brain is a mystery,

the universe, the reflection of the

structure [function] of the brain will

also be a mystery.

—Ramon y Cajal

Using EEG to Solvethe Mystery… Plus Some Other Tidbits

Alexander J. Shackman

Postle Lab Meeting

September 2009

The Big Picture

Ψ = f ( )

Use brain activity to characterize the mechanisms underlying psychological constructs

e.g., working memory, anxiety, etc.

Electroencephalography (EEG)Scalp-Recorded Brain Electrical Activity

Acquisition Pipeline

- Anti-aliasing Filters (e.g., 0.1 – 200Hz)- Amplification (e.g., 10, 000)- Analog/Digital conversion- Sampling (e.g., 500 Hz)- Calibration (μV)

Luck, 2005

128-chan ‘net’

raw EEG

Transforming EEG into a Useful Metric

Ψ = f ( )EEG Metrics

raw EEG

3 Kinds of Analysis

[1] Event-Related Potentials (ERPs)- time-domain- event-locked average voltage, μV- Metric = component amp, latency

raw EEG

3 Kinds of Analysis[1] Event-Related Potentials (ERPs)- time-domain- event-locked average voltage, μV-Metric = Amp, latency of “components,”

e.g., P300

Tidbit 1: Teaser for My Next Talk

Shackman et al., in prep. A; Maxwell, Shackman et al., in prep.

raw EEG SafeThreat of Shock

Impact of Threat-Evoked Anxiety on WM

raw EEG

TB2: If You’re Clever with ERPs—Nature!

Vogel & Machizawa, 2004; Vogel et al., 2005

Tidbit 2, cont’d: A Preliminary Pitch…

Micah

Tidbit 2, cont’d: A Preliminary Pitch…

+ +

WM-RelatedERP (CDA)

SS fMRI Stereo-TMS

= Nature!

Happy Brad

Ok, enough tidbits … we now continue with our regularly scheduled program

raw EEG

[2] Spectral Analyses (FFTs)- frequency-domain- use Fast Fourier Transform to

decompose complex signal (EEG)into constituent frequencies (sine waves) of different phases and amplitudes

- Metric = power density (μV2/Hz)for “bands,” eg, alpha (8-13Hz)

3 Kinds of Analysis

Aside: FFT

Gauss, c. 1805; Cooley & Tukey, 1965

Aside: Hardcore FFT

Gauss, c. 1805; Cooley & Tukey, 1965

3 Kinds of AnalysisTypical Power Spectra: Gray Bar Indicates Alpha Band (8-13Hz)

Shackman et al., under review A

3 Kinds of AnalysisTypical Power Spectra: Topography of Factor Analytically Derived Bands

Shackman et al., under review A

Alpha Bands, α

raw EEG

[3] Event-Related Spectral Perturbations- time + frequency domain- event-locked spectral power, μV2/Hz/ms

3 Kinds of Analysis

3 Kinds of Analysis

Shackman et al., Brain Topog., 2009

ERSP Time-Locked to Face Presentation

Ψ = f ( )Spectral Activity

A Slight Hiccup in Solving the Mystery?

Residual Ocular Artifactin the Delta band?


In Alpha-Low?


Residual Muscle(EMG) Artifact

in Beta/Gamma?


In Alpha-High?

Ψ = f ( )EEG + Artifact

Separating neural signals from biological artifacts is a ubiquitous problem for any neurophysiological investigation (cf. motion correction).

Biological artifacts can compromise sensitivity or masquerade as genuine effects, particularly when they are confounded with statistical contrasts.

Inferential issues — Did activation go up or down? How much? Where?

When such artifacts are markedly smaller than neural signals, they can be safely ignored. When they are rare, they may be rejected with trivial consequences.

Shackman et al., Brain Topog., 2009

Inferential Impact of Artifacts

Electromyogenic (EMG) Artifacts

McMenamin, Shackman et al, under review, Psychophys, 2009; Shackman et al., BT, 2009

Scalp Musculature EMG, Time Domain

- Widespread on the scalp

- Big: potentially 1-2 orders of magnitude larger than EEG effects …So even modest residual contamination can distort inference

- Widespread in the frequencydomain … detectable into the thetaband (~4-7 Hz)

-Worst of all, it’s confounded withcognitive load, emotion, stimulus processing


Shackman et al, Brain Topog, 2009

Realistic example: Residual EMG at right “dlPFC” sensor (F4) after art. rejection


Shackman et al, Brain Topog, 2009

RelaxedTense

Realistic example: Residual EMG at right “dlPFC” sensor (F4) after art. rejection

So Now What— Is Correction Possible?

• Need to assess validity

• Factorial design– Independently vary presence/absence of EEG vs. EMG– Instruct participants to …

• Close/Open Eyes (“Berger maneuver”) Modulates activity in the alpha band: Close > Open

• Tense/Relax MusclesModulates myogenic activity: Tense > Relaxed

– Assess sensitivity and specificity in the EEG (alpha) band• Sensitivity: Correction should render EMG-corrupted data

statistically identical to EMG-free data

• Specificity: Correction should not change EMG-free data

Validation: No Correction

Shackman et al, Brain Topog, 2009; McMenamin, Shackman et al., Psychophys, 2009

Validating Correction: Regression-Based Correction

Shackman et al, Brain Topog, 2009; McMenamin, Shackman et al., Psychophys, 2009

EEG Source Modeling—Can Also Assess Validity in the Source Space

McMenamin, Shackman et al., Psychophys, 2009, under review; see also Shackman et al., Psych Sci, in press

128-Channel EEG Low Resolution Electromagnetic Tomography LORETA

Application of ICA to EMG Correction

• Independent Component Analysis (ICA)– Means of blindly separating statistically

unrelated temporal components (time-series)

– “Fancy” version of PCA or Factor Analysis

Langers, Neuroimage, 2009

Application of ICA to EMG Correction

• Independent Component Analysis (ICA)– Rapidly become one of the most popular

techniques for correcting biological artifacts in EEG

– To date, evidence that ICA is a sensitive and specific tool for correcting EMG specifically is semi-quantitative (e.g., before/after pix for ‘representative’ subject, dB reduction)

McMenamin, Shackman et al., under review; Shackman et al., BT, 2009

ICA Pipeline

McMenamin, Shackman et al., under review

• Raw data

• Principal components analysis to reduce dimensionality (64 PCs)

• ICA 64 ICs / participant

• Manual classification – Big Time Investment!– 64 ICs per subject @ 2 min** per IC + initial inter-rater reliability training

• Remove “bad” ICs and reconstruct the “corrected” time-series

• Assess sensitivity and specificity on the scalp and in the intracerebral(LORETA) source space using ROIs (peak EEG/EMG)

The Conundrum: What to Drop?• 4 Kinds of ICs

– Neurogenic ICs – Keep!

– Myogenic ICs – Drop!

Neurogenic/EEG Myogenic/EMG

The Conundrum: What to Drop?


• 4 Kinds of ICs– Neurogenic ICs – Keep!


– Other Biological/Physical Artifacts – Drop!

The Conundrum: Other Artifacts


60 Hz AC Artifact



Blinks



EKG



Saccades

The Conundrum: What to Drop?


• 4 Kinds of ICs– Neurogenic ICs – Keep!


– Other Biological/Physical Artifacts – Drop!

– Everything Else - ????

The Conundrum: Everything Else?


Mixed EMG + EEG Components – Should We Throw the Baby w/ the Bathwater?



What About Unclassifiable “Noise” Components? Or Components that account for trivial amounts of variance?

Noise



This ain’t just a couple of ICs…



…or a percentage or two of variance

Try All Combinations


EMG


“Pure” EMG ICs…

…and EMG-dominant mixed ICs

…and EEG-dominant mixed ICs

Everything Else


Gross/Ocular

Noise Low Variance

Huge Impact on Variance Retained


Scalp ROIs for Sensitivity/Specificity


Scalp Sensitivity / Specificity


Max EMG / Max ‘NNNM’ shows good (but not great) performance on the scalp

++ Excellent; + Adequate; ? Questionable; — Poor

But …


• None of the protocols proved adequate in the source space … Max/Max overcorrected the data

• Replicating what we saw using regression-based correction (McMenamin et al., 2009)

• Why the discrepancy?

But …


1. Life on the OutsideROIs don’t capture the quality of correction at channels lying outside … but those channels areused to compute the LORETA solution

1. + vs. ++None of the protocols showed consistently excellent sensitivity/specificity within the ROIs… oftentimes, one or more “worst case” channels showed evidence of less than perfect validity

For Instance: Myogenic Contrast


Inadequate Sensitivity

More Fundamentally—ICA Doesn’t Perfectly Separate

EEG/EMG


Mixed ICs—Early sign of trouble?

2 Plausible Explanations

1. Model order- 64 ICs is too many, leading to ‘splitting’

of ‘pure’ ICs into mixed ICs- Ss vary in the ‘right’ number?

2. EMG violates assumptions of ICA- too Gaussian?- too temporally correlated with EEG?

Avoid source modeling for EMG-contaminated

spectral (FFT) analyses

Use Max/Max ICA protocol for scalp analyses

Try alternative blind source separation

algorithms (AMUSE, JADE)

Estimate model order for each subject

Use ERPs!

Where Do We GoFrom Here?

Happy Brad

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