This document discusses two studies that aim to maximize the value of MEG/EEG data through improved experimental design and analysis methods. Study 1 uses simulations to examine how signal detectability varies across brain regions and individuals based on factors like regional position and orientation variability. Study 2 analyzes intracranial EEG data to map information flow within the face processing system, finding evidence that areas in the inferior occipital and inferior temporal cortices transfer information to other core and extended face regions via white matter pathways. Overall, the author advocates for more simulation work to optimize MEG/EEG study design and better integration of neuroimaging and connectivity methods to map information flow in the brain.

1. Re-using MEEG data & maximizing its
value: Considerations of statistical
power & white matter connectivity
AINA PUCE
PSYCHOLOGICAL & BRAIN SCIENCES, INDIANA UNIVERSITY, BLOOMINGTON

2. Roadmap
 Pre-amble: post-reproducibility crisis
 Study 1: Detectability of signals at sensor level
 Study 2: Information flow within the face processing system
 Sum up

3. Pre-amble: post-reproducibility crisis
 Need to focus on solid experimental design, data quality,
adequate signal-to-noise & data/code sharing
 best practices rock!
 Need to integrate brain/behavior data from multiple methods
 value added; the sum >> the parts
 Need to put the brain ‘back into the body’
 include continuous measures of behavior

4. Study 1:
Detectability of
signals at sensor
level

5.  Detectability of signals at sensor level – MEG data
Chaumon et al., in revision

6. METHOD
 HCP MEG resting state data [n=89]; preprocessed with HCP pipeline
 Associated with structural MRI, 3D sensor position map, fiducials
 Create single-shell conduction model [FieldTrip] + HCP structural pipeline
segment the cortical mantle [Freesurfer]
 Mesh topology: 64,984 vertices/brain
 Create leadfield matrix in FieldTrip, project dipole sources =10 nA.m
through leadfield to the sensors
 2 different source models: source dipoles either constrained orthogonal to
individual’s cortical mantle [or were free from any anatomical constraint]

7. METHOD
 Monte Carlo simulations; different numbers of trials & subjects; sampling w/o replacement;
‘trial’= 25-ms (50 samples) time segments of the continuous resting state data, at least 2 s
apart from each other
 Split trials randomly in 2 equal sets; add signal in one set; average data across time points &
trials separately for each set
 Paired t-test between 2 sets across subjects at each sensor; note significance (p<0.05, uncorr)
 Spatial cluster-mass based correction for multiple comparisons (Maris & Oostenveld, 2007),
1000 permutations, cluster & significance thresholds both at p<0.05)
 A comparison = significant only when peak sensor (i.e. the sensor at which absolute value of
projection of source signal peaked) was included in a significant cluster
 Procedure repeated 500 times for each trial-by-subject number pair; note number of times
comparison was significant in 500 simulations
 Examine position & orientation variability

8. A simulation of statistical power
across the cortical surface
50 trials, 25 subjects
single dipole placed, in
turn, at every possible
neocortical position

9. Simulations in
some
commonly
activated brain
regions
Fusiform gyrus
relatively low cross-subject
position variability

10. Simulations in
some
commonly
activated brain
regions
Superior occipital gyrus
relatively high cross-subject
position variability

11. Simulations in
some
commonly
activated brain
regions
Insula
relatively low cross-subject
orientation variability

12. Simulations in
some
commonly
activated brain
regions
Superior temporal sulcus
relatively high cross-subject
orientation variability

13. Average
cross-
subject
variability in
the brain
POSITION
cross-subject variability in position = std dev of source position across subjects
FG SOG
ORIENTATION
cross-subject variability in orientation = log of the inverse of average resultant
vector length across individual sources, for each cortical location
Insula STS

14. Interim sum up 1
 Optimizing SNR in MEG studies?
 No single optimal data acquisition protocol e.g. x number of trials, y
number of subjects
 Consider potential activated structures in experiment: design with
adequate SNR for least detectable brain structure!
 Implications analyses of functional & effective connectivity [DCM, graph
analyses]
 How does this apply to EEG studies? MEG/EEG studies?

15. Study 2:
Information flow
within the face
processing system

16. Babo-Rebelo et al., submitted

17. METHOD
 Existing iEEG data set with face processing task [Huijgen et al., 2015]
 Localization of intracranial electrodes [MRI-based]
 ‘Cleaning’ of long-epochs of EEG data [n=18]; reject inter-ictal spikes [n=7]
 Discard incorrect behavioral trials
 Average data by condition in each site in each subject [Face 1, Face 2 etc.]
 Express as bipolar activity; re-check anatomy
 Normalize evoked activity
 Statistical testing:
 1. ID sites with evoked activity Z>1;
 2. cluster-based permutation t-test against zero across time at each site [multiple
comparisons];
 Visualize activity as a function of ROI

18. Paradigm
Huijgen et al, Soc Cogn Affect Neurosci 2015
Face onset
Emotion onset [happy/fearful]
Gaze aversion [or direct gaze]
Checkerboard target
[cong/incong to gaze]

19. Patients

20. Intracranial electrode sites

21. Responsivity

22. Face type
& ROI

23. Effect size across ROIs

24. The STC prefers gaze aversions

25. Patient 17

26. White matter tract endpoints

27. Overlap
analysis

28. IOC sends
information to ITC
[via ILF]
ITC communicates
with STC & FC [via
pARC]

29. Interim sum up 2
 Core & extended structures in face processing pathways
 IOC & ITC important additions
 Information appears to flow from IOC & ITC
 New white matter routes need further testing & verification
 Latencies of evoked iEEG activity have been underutilized for studying
information transfer during face processing tasks

30. Overall sum up: Quo vadis?
 Need for further simulation studies of MEG/scalp EEG & ability
to detect sources at the sensor level
 Share simulation tools
 Improve artifact detection/rejection; machine learning?
 Need for further studies integrating white matter pathways &
MEG/EEG/fMRI data to map out information flow in brain
networks

31. Acknowledgement
 George, Chaumon, Babo-Rebelo: CENIR platform at ICM in Paris; infrastructure funding from
"Investissements d’avenir" ANR-10-IAIHU-06 & ANR-11-INBS-0006
 Dinkerlacker: John Bost Foundation [La Force, France]
 Puce: Indiana University College of the Arts & Sciences [Sabbatical leave]
 2021-2023 CR-CNS grant:
 Puce PI, Pestilli Co-PI NIBIB USA; George, Chaumon Co-PIs INR France