Methodological Explorations of Magnetoencephographic TechniquesLaura M. Morett,1Emiliano Santarnecchi2

1University of California, Santa Cruz ✶ 2University of Siena

Neural Basis of MEG

Week 1: Data Acquisition

Currents must be tangential to surface

Deep sources harder to detect

1. Affix sensors to detect head position and artifacts

Data acquisition steps

2. Digitize head position coils for MEG positioning and fMRI coregistration

3. Position participant with head close to sensors; present stimuli

Week 2: Data Preprocessing

Week 3: Source Localization

Week 4: Machine Learning

Overall Conclusions

Post-synaptic currents have long decay and can summate

A changing electric field generates a magnetic field

Need ~ 104-5

Simultaneouslyactivated cells

to be observableat the surface

Currents must be tangential to surface

306 sensors sample at 1000Hz

Deep sources hard to detect

b INb OUT

St (Sensor space)

Further

How important is head positioning?The closer the participant’s head is to the MEG sensors, the more robust the signal.

Closer

Further

Somatosensory stimulation

Theory: SSS and tSSS

SSS: Removes Bout sources of noise (radio communication, power lines, elevators, etc.)

tSSS: Focuses on temporal correlation between Bin and the sensor space St.

Divides signal into two embedded spheres, Bout and Bin, divided by sensor space St.

Raw data

SSS

tSSS

Application: SSS and tSSS

Independent component analysis (ICA)

Raw data tSSS

Averaged signals

Stimuli

Eyeblinks

Before After

Helps remove additional artifacts (e.g., eye blinks)

Applied arbitrary correlation threshold (0.1) betweencomponent time courses and EOG-ECG signals or by manual inspection

True for brain cells as well

Experimental condition Control condition

1 Hz tapping in time to beep(Motor movement)

1 Hz beep only(No motor movement)

300 fT/cm

-300 fT/cm

How do different noise covariance matrices affect signal?

Equivalent Current DipoleDistributed Source

Localization

Isolates a single dipole with a specific direction and confidence volume, based on orientation of magnetic field

Distributed representation of neuronal activity; applies one of several algorithms (MNE, dSPM, etc.) with a noise covariance matrix

Inverse Problem

MEG solutions

Estimate model parameters (the location of brain activity) from measured data (the MEG sensors signals) theoretically infinite possible solutions….

Effect of head position on source localization

Control condition matrix better isolates source of motor activity

Why Machine Learning?

“Cross-validation” Use all subjects/conditions both for train &

test, using a leave n-out approach Compute the average accuracy for all pairs

of categories (%)Test

Training

Brain activity is more multi- than uni-variate. Combined with MEG high temporal resolution, ML allows to highlight a more diffuse “pattern” of brain response with respect to canonical univariate analysis.

Experimental paradigm and classification steps

• Dataset from Sudre et al. 2012 (Neuroimage). 60 pairs of Words classified into 12 semantic categories, randomly presented 20 times.

• Naїve Bayes algorithm applied to classify words from 2 classes (4-folds cross-validation) using averaged MEG signal from different time windows. Repeated for all other semantic category pairs.

Results

- After 200 ms the algorithm can distinguish between word category pairs 58% of the time.

- Overall/single sensors’ signal based classification allows extrapolation of different spatio/temporal features.

Univariate topographical representation of MEG gradiometers, with color coded contributions to classification process.

t

Cla

ssifi

catio

n ac

cura

cy

Time

Multivariate classification accuracy rate using overall MEG sensors’ signal (n=306)

• Temporal resolution: able to study time courses of cognitive processes • Spatial resolution almost as good as fMRI for cortical surface (3mm)• Less invasive than other methods (fMRI, PET)

Special thanks to Erika J.C. Laing, T. J. Amdurs, Leila Wehbe, Seong Gi Kim, & Bill Eddy.

MEG Strengths

• Constrained to cortical surface• Spatial resolution: poor for subcortical structures • Low SNR; requires extensive data preprocessing

MEG Weaknesses

Experimental paradigms

Somatosensory electrical stimulation Finger tapping task

Triggered left arm median/ulnar nerve stimulation at 1Hz

Tapped left index finger in time to beep at 1Hz

Closer

Closer

FurtherGlobal MaximaFurther

Closer

Experimental covariance matrix

Control covariance matrix

500 ft/cm

-100 ms.

-500 ft/cm

500 ms.

300 ft/cm

-300 ft/cm

-400 ms. 100 ms.

Motor activity, -77 ms. Auditory cue, 0 ms.

-300 fT/cm

300 fT/cm300 ft/cm

-300 ft/cm

-400 ms. 100 ms.

Auditory cue, 0 ms.

-400/-350 ms -400/+100 ms Semantic processing

Visual processing