Signal Processing - May 2016 - 60

Frequency Histogram

8,000

Phase Histogram
120

7,000
6,000

90 300

Occurrence Rate (24.5%)
6.2

60

150

3.7

30

5,000

1.2

4,000

180

0

-1.2

3,000
-3.7

2,000

210

330
-6.2

1,000
0

240
0.01

0.07 0.13 0.19 0.25

270

300

FIGuRe 7. The multiband state with 25% occurrence rate showing the most power in the 0.07 and 0.13 frequency bands. Phase histogram and color
indicate the phase of the dynamics.

to separate five different frequency bands with magnitude
and phase. This particular state has most of its power within
0.07-0.13 Hz, has some strong 0° and 180° phase patterns,
and captures some very interesting patterns. The results from
a large resting-state fMRI data set (N = 400) identified two
states, with similar correlation patterns but distinct frequency
profiles, one of which was highly predictive of males versus
females [31]. This provides additional evidence that ignoring
the dynamic information obscures important knowledge.

Validation
Quite a few studies have been published that provide important information validating the presence of chronnectomic
information in fMRI data. For example, one study of a large
subject (N = 400) data set performed a split-half replication and also varied a number of parameters, including the
number of estimated states and the window size [30], [57].
Other studies have shown that dynamic connectivity tracks
closely with sleep state [28] and psychedelic experience [58],
is reflected in both humans and macaques [51], and is associated with daydreaming [59]. Cross-validated classification
also appears to be more powerful when applied to dynamic
connectivity measures [4], [60]. Comparison of dynamic
connectivity measures in the presence of tasks that activate
known brain regions also provides powerful evidence to support the presence of connectivity states [18], [61].

Concurrent EEg/fMrI experiments
Concurrent EEG provides a useful way to validate these
dynamic changes by providing convergent evidence for them.
Although EEG alone cannot provide a ground-truth measure
because EEG and fMRI are generated by and sensitive to very
different sources, we do expect that fMRI changes in connectivity over time that reflect neuronal changes will also be
detectable with EEG.
60

An illustrative example examining differences in dynamics associated with the eyes-open (EO) versus eyes-closed
(EC) state is presented in Figure 8. Concurrent EEG/fMRI
data were collected using a Brain Products EEG system
(Gilching, Germany) that had been previously used to collect
data comparing a variety of frequencies in EEG with fMRI
data in the resting state for EO and EC [62], [63]. The results
from a preliminary analysis of these data using a group ICA
approach to evaluate temporal dynamics are shown in Figure 8
[64]. Figure 8(a)-(f) shows two dynamic states estimated
from the fMRI data. Both of these states showed a significant difference between EO versus EC, with state 1 occurring
dominantly for EO and state 5 showing significantly more
occurrence during the EC stage and demonstrating more
EEG alpha power. The anticorrelation with brain regions
associated with inner reflection (regions in the widely studied default-mode network [65]) was also stronger in the EO
data. As the states were associated with more EEG drowsiness measures, these anticorrelations diminished and then
subsided [64].
This is only a relatively simple approach to relating EEG
and fMRI data. More advanced methods that take advantage
of the joint information during the estimation process would
likely be even more fruitful in demonstrating the benefits of
dynamic connectivity [63], [66]-[69].

Incorporating dynamics improves contrast to noise
The data shown in Figure 9 were evaluated from a normative resting-state fMRI data set (N = 200 HCs) using a
simple model that incorporates an explicit static subspace
(while modeling the dynamic information in a nuisance subspace). In this case, the model that incorporates the dynamic
information [Figure 9(a)] shows a higher contrast-to-noise
ratio than when the dynamic information is completely
ignored [Figure 9(b)]. This result also provides strong support

IEEE Signal Processing Magazine

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May 2016

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Table of Contents for the Digital Edition of Signal Processing - May 2016

Signal Processing - May 2016 - Cover1
Signal Processing - May 2016 - Cover2
Signal Processing - May 2016 - 1
Signal Processing - May 2016 - 2
Signal Processing - May 2016 - 3
Signal Processing - May 2016 - 4
Signal Processing - May 2016 - 5
Signal Processing - May 2016 - 6
Signal Processing - May 2016 - 7
Signal Processing - May 2016 - 8
Signal Processing - May 2016 - 9
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Signal Processing - May 2016 - Cover3
Signal Processing - May 2016 - Cover4
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