Methodological Explorations of Magnetoencephographic Techniques

1. Post-synaptic currents have long decay and can summate Methodological Explorations of Magnetoencephographic Techniques Laura M. Morett,1Emiliano Santarnecchi2 1University of California, Santa Cruz ✶ 2University of Siena Neural Basis of MEG Week 2: Data Preprocessing Week 3: Source Localization Week 4: Machine Learning Theory: SSS and tSSS Inverse Problem WhyMachineLearning? Estimate model parameters (the location of brain activity) from measured data (the MEG sensors signals)  theoretically infinite possible solutions…. Brainactivityis more multi- than uni-variate. Combinedwith MEG high temporal resolution, MLallowstohighlight a more diffuse “pattern” ofbrainresponsewith respecttocanonicalunivariateanalysis. Divides signal into two embedded spheres, Bout and Bin, divided by sensor space St. A changing electric field generates a magnetic field MEG solutions SSS: Removes Bout sources of noise (radio communication, power lines, elevators, etc.) b OUT True for brain cells as well tSSS: Focuses on temporal correlation between Bin and the sensor space St. b IN “Cross-validation” Useallsubjects/conditionsbothfortrain & test, using aleaven-out approach Currents must be tangential to surface Currents must be tangential to surface St (Sensorspace) Distributed Source Localization Compute the average accuracy for all pairs of categories (%) EquivalentCurrentDipole Deep sources harder to detect Distributedrepresentationofneuronalactivity; appliesoneofseveralalgorithms (MNE,dSPM, etc.) with a noisecovariancematrix Deep sources hard to detect Training Isolates a single dipolewith a specific direction and confidencevolume, based on orientationofmagneticfield Application: SSS and tSSS Test Experimentalparadigm and classificationsteps • DatasetfromSudreet al. 2012 (Neuroimage). 60 pairsofWordsclassifiedinto 12 semanticcategories, randomlypresented 20 times. • Naїve Bayesalgorithmappliedtoclassifywordsfrom 2 classes (4-folds cross-validation) usingaveraged MEG signalfromdifferenttime windows. Repeatedforallothersemanticcategorypairs. Effectof head position on source localization Further • Results • After 200 ms the algorithm can distinguishbetween word categorypairs 58% of the time. • - Overall/single sensors’ signalbasedclassificationallowsextrapolationofdifferentspatio/temporalfeatures. Classificationaccuracy Multivariate classificationaccuracy rate usingoverall MEG sensors’ signal (n=306) Further Global Maxima Further Closer Week 1: Data Acquisition t Data acquisition steps Raw data Closer Closer How do differentnoisecovariancematricesaffectsignal? SSS Averaged signals 300 ft/cm 300 ft/cm 300 fT/cm 300 fT/cm Visual processing tSSS 1. Affix sensors to detect head position and artifacts Univariatetopographicalrepresentationof MEG gradiometers, with color codedcontributionstoclassificationprocess. 2. Digitize head position coils for MEG positioning and fMRIcoregistration 3. Position participant with head close to sensors; present stimuli -400/-350 ms Semantic processing -400/+100 ms Time -400ms. 100ms. -400ms. 100ms. Raw data tSSS Experimentalparadigms -300 ft/cm -300 fT/cm -300 fT/cm -300 ft/cm Need ~ 104-5 Simultaneously activated cells to be observable at the surface Motor activity, -77 ms. Auditory cue, 0 ms. Auditory cue, 0 ms. Somatosensoryelectricalstimulation Finger tapping task Independent component analysis (ICA) Experimentalcondition Controlcondition Triggered left arm median/ulnar nerve stimulation at 1Hz Tapped left index finger in time to beep at 1Hz Overall Conclusions 1 Hz tapping in time to beep (Motor movement) 1 Hz beeponly (No motor movement) Helps remove additional artifacts (e.g., eye blinks) Eyeblinks Applied arbitrary correlation threshold (0.1) betweencomponent time courses and EOG-ECG signals or bymanualinspection MEG Strengths How important is head positioning? • 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) The closer the participant’s head is to the MEG sensors, the more robust the signal. 306 sensors sample at 1000Hz Stimuli MEG Weaknesses 500 ft/cm Closer • Constrained to cortical surface • Spatial resolution: poor for subcortical structures • Low SNR; requires extensive data preprocessing Further Controlcovariancematrix Experimentalcovariancematrix -100 ms. 500 ms. Specialthanksto Erika J.C.Laing, T. J. Amdurs, Leila Wehbe, Seong Gi Kim, & Bill Eddy. Controlconditionmatrixbetterisolates source of motor activity Somatosensory stimulation Before After -500 ft/cm