Feng Han1 and Xiao Liu1,2
1Department of Biomedical Engineering, Pennsylvania State University, State College, PA, United States, 2Institute for Cyber Science, Pennsylvania State University, State College, PA, United States
Synopsis
It
has been shown that the maximal correlation between rsfMRI connectivity and
behavioral measures occurs along a positive-negative mode direction
characterizing the change of the overall goodness of behavior. Here, we had a
thorough examination of rsfMRI/MEG connectivity along this positive-negative
mode direction. We found that behavioral changes are associated with significant
connectivity modulations that are however distinct at the lower-order
sensory/motor areas and higher-order cognitive regions. Moreover, this
hierarchy-dependent connectivity modulation is similar for rsfMRI and middle-frequency
MEG signals, but reversed for gamma-band MEG signals. The findings may provide
novel insight into the neural basis of inter-subject behavioral variability.
INTRODUCTION
A recent data-driven canonical correlation analysis
(CCA) on the Human Connectome Project (HCP) data from a large cohort of
subjects has identified a mode direction maximally linking individuals’ resting-state
fMRI (rsfMRI) connectivity and their demographical/behavioral measures1. Interestingly, this “positive-negative” mode
direction is characterized by a gradual change of the overall goodness of
behavioral traits. However, it has been shown that the rsfMRI-based
connectivity measures are susceptible to various non-neuronal noise2 and also incapable of separating
frequency-specific neuronal processes3–5. In this study, we used magnetoencephalography
(MEG) signals to assess frequency-specific functional connectivity and then
examined their modulation, as well as the correspondence with rsfMRI
connectivity, across individuals along the “positive-negative” mode direction.METHODS
We used the behavioral and imaging data from 87 HCP
subjects whose MEG data are available6. For each subject, functional connectivity matrices
of 200 brain parcels7 were derived by correlating rsfMRI signals or bandlimited
MEG powers of eight frequency bands: delta, 1.5-4 Hz; theta, 4-8 Hz; alpha,
8-15 Hz; beta low, 15-26 Hz; beta high, 26-35 Hz; gamma low, 35-50Hz; gamma
mid, 50-76 Hz; gamma high, 76-120 Hz. The source level bandlimited powers from resting-state
MEG was estimated with Beamformer filters inverse algorithms (“bfblpenv”)8,9. We sorted the rsfMRI and MEG connectivity
matrices based on the hierarchical level of the 200 parcels, which was
estimated according to the principal gradient (PG) of rsfMRI connectivity obtained
previously10. The PG presents a direction going from the
low-order sensory/motor areas to high-order cognitive regions. On the other
hand, we replicated the previous study1 and derived the positive-negative CCA mode of rsfMRI
connectivity and behavior, and then obtained a CCA score for each subject that
represents his/her position along this mode direction. Then, we correlated,
across subjects, the CCA score with the strength (i.e., the absolute value of
correlation) of single rsfMRI or MEG connection to see how each connection is
modulated with behavioral changes along the “positive-negative” mode. RESULTS
Inspection
of individual data suggests systematic changes in rsfMRI connectivity and
behavioral measures along the CCA mode direction. Consistent with the previous
finding1, subjects with higher
CCA scores appear to have more positive traits (Fig. 1A), e.g., the performance
in picture vocabulary test, intelligence test and money related
self-regulation/impulsivity, and less negative traits, e.g., skipped items in cognition
test and self-report about the somatic anxiety. Their rsfMRI connectivity also showed
significant modulations, particularly at relatively lower-order areas (the
top-left of the sorted connectivity matrices) (Fig. 1A). Correlating the CCA
score and rsfMRI connectivity strength revealed a clear gradient across the
cortical hierarchy: much stronger negative correlations were seen within the lower-hierarchy
regions (Fig. 1B). This is evident by a large contrast between two blocks
covering the lower-order (cyan) and higher-order (purple) respectively (Fig.
1C). We repeated the same analysis for MEG connectivity of different
bandlimited powers. The correlations between the CCA score and MEG connectivity
strength also showed cross-hierarchy contrasts for almost all the frequency
bands (Fig. 2A). However, this cross-hierarchy contrast shows opposite signs
for the low and middle (<26 Hz) frequency bands and high-frequency gamma
bands (>26 Hz) (Fig. 2B). The modulation of the middle-frequency MEG
connectivity with the CCA score resembles that of the rsfMRI connectivity,
i.e., a larger reduction of connectivity strength at the sensory/motor regions,
whereas the gamma-band MEG connectivity is more significantly modulated in the
high-order cognitive regions. DISCUSSION
By inspecting the rsfMRI/MEG connectivity change along
a positive-negative mode direction characterizing the overall goodness of
behavior, we found a hierarchy-dependent connectivity modulation. Subjects with
more positive traits tend to have weaker rsfMRI connectivity and
middle-frequency (<26 Hz) MEG connectivity at the low-order sensory/motor
areas, as well as reduced high-frequency (>26 Hz) MEG connectivity in the
high-order cognitive regions. The similar changes in rsfMRI connectivity and
middle-frequency MEG connectivity are consistent with previous studies showing
a close relationship between the two11,12. The opposite changes of the middle-frequency
and high-frequency MEG connectivity suggest that the brain activity within
these two frequency ranges may be linked to distinct brain processes. For
example, the middle-frequency brain activity has been hypothesized to represent
an ongoing inhibitory process13,14 whereas the broadband gamma activity has been
linked to the general excitability of cortical regions15. Overall, the rsfMRI and MEG results
consistently suggest that the brain connectivity modulations related to the
overall goodness of behavior are distinct across cortical regions of different
hierarchy, and are dependent on the frequency of neuronal oscillations.CONCLUSION
Changes
in the overall goodness of behavior across individuals are associated with
distinct connectivity changes at brain regions of different hierarchies. These
behavior-related connectivity changes also are also frequency dependent and
show opposite patterns for the middle- and high-frequency brain activity. Acknowledgements
This
study is supported by the NIH Pathway to Independence Award (K99/R00).References
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