1. Comodulation and Coherence in Normal and Clinical Populations
AAPB :50-4:10 Oral Paper Session 2
Chagall
Birthday
David A Kaiser, Ph.D.
Rochester Institute of Technology

2. Defining my terms Raw EEGs are voltages across time
In time domain, we estimate
amplitude (positive and negative values)
at a sample rate (only positive)
In frequency domain, we estimate
magnitude (only positive)
phase (positive and negative)
at a frequency
Spectral power is magnitude squared

3. How similar are two signals?
Cross-correlation reveals similarities in time between signals. (e.g., Barlow, 1951; Brazier & Casby, 1951)
Cross-spectral analysis reveals similarities in frequency… [next slide]

4. How similar are two signals?
Cross-spectral analysis reveals similarities in frequency.
Signals may be similar in phase, magnitude, or both
phase analysis: coherence (Goodman, 1957; Walter, 1961)
magnitude analysis: comodulation (Pearson, 1896; Kaiser, 1994)
For example, does cortical activity become more or less similar after treatment?

5. Cross-spectral analysis
Coherence estimates phase consistency
Comodulation estimates magnitude consistency …between signals at each specified frequency across time
Coh = average normalized cross-spectrum amplitude2
Comod = average normalized cross-product amplitude
Coh range from to 1.0
Comod range from -1.0 to 1.0
Confusing point: Tukey called “coherency”
the square root of coherence

6. Comodulation was invented to examine low spatial resolution concerns of EEG topography (e.g., volume conduction, Nunez, 1990)
Does surface EEG reflect cortical potentials well?
- if not, all neighbors will be equally correlated with each other
- if so, correlations will be stronger within functionally-related areas

7. Signals are … …comodulated if their magnitude relationship is stable
…coherent if their phase relationship is stable

8. Coherence analysis provides
Coherence (Coh)
Phase delay (+/-180o)
Comodulation analysis provides
Comodulation (Comod)
Proportion: Site 1/Site 2

9. Functional model for dominant frequency
...suggests common response
Multiple networks (related but dissimilar) organize neural activity
...suggests common generator
Single network organizes neural activity

10. Comodulated but not coherent
Pacemaker network partly segregated by cortical feedback
Complex recruitment
Coordination
Coherent but not comodulated
Pacemaker network unified
Primitive recruitment
Synchronization

11. Feedback system Fastforward system
Modulators
Corticothalamic projections
Slow, diffuse, weak
Sustained consciousness (i.e., self-)
Fastforward system
Drivers
Thalamocortical projections
Fast, focal, strong
(Momentary) Consciousness

12. Why comodulation analysis is performed on magnitude (mV) and not on power (mV2)
Brief history of power spectral analysis in EEG
Dietsch (1932) analyzed 7 EEG signals using Fourier (1831).
Cooley & Tukey (1965) invented FFT algorithm, reducing computer workload, allowing practical spectral applications
Dumermuth & Fluhler (1967) applied FFT to EEG
BUT ...

13. Why assume brain rhythms and mental activity are related by a power function? Are changes in brain behavior actually associated with larger changes in mental behavior (i.e. reason for using the power spectrum)?
Might brain and mind activity be more linearly related at this level of investigation (i.e., reason for using the magnitude spectrum)?

14. Comodulation versus Coherence Data sets
Group N=
Females
Right handed
Age
mean
Age range
ADHD 7 1
All
9.0
6-11
Asperger 11 3
10.6
7-16
Child 10 5
10.4
5-13
Adult 20
28.2
23-39
THANKS to Jolene Ross & Jim Caunt for ADHD, some of the AS data; Coralee Thompson for normal children

15. EEG Comodulation and Coherence values are often very similar!
Eyes Closed Replications
Within subject, n=20 EC1 v 2:
r = .91 Coh
r = .84 Comod
Being more reliable also can mean less sensitive to state differences
Dark bars = Comod Red/green bars = Coherence

16. How to read our Comod & Coh maps
Rho DATA Z-SCORE from norms

17. Typical Atypical (25 college students) (1 college student)
If you build it (adult pattern of frontal lobe myelination), it still takes time for them to come…
Typical Atypical (25 college students) (1 college student)

18. MTBI patient Rage Disorder

19. Autobiography dysfunction associated with reduced right anterior temporal pole connectivity (Asperger’s, Schizophrenia)

20. Hypermodulations after stroke, 74yF (seen during math task only!)

21. Autism as severe global disconnectivity

22. Asperger v Child Norms 8-12 Hz (in Std Error)

23. ADHD v Child Norms 8-12 Hz (in Std Error)

24. Global Comodulation by age 19-site mean of 18 comparisons each site
Life is about making connections...
Global Comodulation by age 19-site mean of 18 comparisons each site

25. …but not too fast!...
Beta hypercoherence between occipital and medial frontal cortex, esp. right-sided, during rest for Asperger children
(resembles adult pattern)

26. …in fact, slowing down the rate of connections at some times in your life may even do you some good! One form of Intelligence (neotenous) resists the natural neural integration trajectory
Neural & “behavioral” (Ph.D) indices of neoteny