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Post-synaptic currents have long decay and can summate. Methodological Explorations of Magnetoencephographic Techniques. Laura M. Morett, 1 Emiliano Santarnecchi 2. 1 University of California, Santa Cruz ✶ 2 University of Siena. Neural Basis of MEG. Week 2: Data Preprocessing. - PowerPoint PPT Presentation
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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