Section:
New Results
Connectivity-informed fMRI Activation Detection
A growing interest has emerged in studying the correlation structure
of spontaneous and task-induced brain activity to elucidate the
functional architecture of the brain. In particular, functional
networks estimated from resting state (RS) data were shown to exhibit
high resemblance to those evoked by stimuli. Motivated by these
findings, we propose a novel generative model that integrates
RS-connectivity and stimulus-evoked responses under a unified
analytical framework. Our model permits exact closed-form solutions
for both the posterior activation effect estimates and the model
evidence. To learn RS networks, graphical LASSO and the oracle
approximating shrinkage technique are deployed. On a cohort of 65
subjects, we demonstrate increased sensitivity in fMRI activation
detection using our connectivity-informed model over the standard
univariate approach. Our results thus provide further evidence for
the presence of an intrinsic relationship between brain activity
during rest and task, the exploitation of which enables higher
detection power in task-driven studies.
See also [23] and Fig 8 .
Figure
8. Real data results. (a) rate of parcels with significant
activation differences averaged across contrasts vs. p-value
thresholds. (b) Parcels detected by contrasting computation
against sentence processing task, and (c) auditory against visual
task. Red = detected by only OAS- CM. Purple = detected by both
OAS-CM and GL-CM. Blue = detected by all methods. |