Oscillatory brain activity is a ubiquitous feature of neuronal dynamics and
the synchronous discharge of neurons is believed to facilitate integration both
within functionally segregated brain areas and between areas engaged by the same
task. There is growing interest in investigating the neural oscillatory networks in
vivo. The aims of this thesis are to (1) develop an advanced method, Dynamic
Causal Modelling for Induced Responses (DCM for IR), for modelling the brain
network functions and (2) apply it to exploit the nonlinear coupling in the motor
system during hand grips and the functional asymmetries during face perception.
DCM for IR models the time-varying power over a range of
frequencies of coupled electromagnetic sources. The model parameters encode
coupling strength among areas and allows the differentiations between linear
(within frequency) and nonlinear (between-frequency) coupling. I applied DCM
for IR to show that, during hand grips, the nonlinear interactions among neuronal
sources in motor system are essential while intrinsic coupling (within source) is
very likely to be linear. Furthermore, the normal aging process alters both the
network architecture and the frequency contents in the motor network.
I then use the bilinear form of DCM for IR to model the experimental
manipulations as the modulatory effects. I use MEG data to demonstrate
functional asymmetries between forward and backward connections during face
perception: Specifically, high (gamma) frequencies in higher cortical areas
suppressed low (alpha) frequencies in lower areas. This finding provides direct
evidence for functional asymmetries that is consistent with anatomical and
physiological evidence from animal studies. Lastly, I generalize the bilinear form of DCM for IR to dissociate the induced responses from evoked ones in terms of
their functional role. The backward modulatory effect is expressed as induced, but
not evoked responses
