Resting State Functional Connectivity of the Rat Claustrum

Since its conception three decades ago, the idea that the striatum consists of a dorsal sensorimotor part and a ventral portion processing limbic information has sparked a quest for functional correlates and anatomical characteristics of the striatal divisions. But this classic dorsal-ventral distinction might not offer the best view of striatal function. Anatomy and neurophysiology show that the two striatal areas have the same basic structure and that sharp boundaries are absent. Behaviorally, a distinction between dorsolateral and ventromedial seems most valid, in accordance with a mediolateral functional zonation imposed on the striatum by its excitatory cortical, thalamic and amygdaloid inputs. Therefore, this review presents a synthesis between the dorsal-ventral distinction and the more mediolateral-oriented functional striatal gradient.

The claustrum is a thin, irregular, sheet-like neuronal structure hidden beneath the inner surface of the neocortex in the general region of the insula. Its function is enigmatic. Its anatomy is quite remarkable in that it receives input from almost all regions of cortex and projects back to almost all regions of cortex. We here briefly summarize what is known about the claustrum, speculate on its possible relationship to the processes that give rise to integrated conscious percepts, propose mechanisms that enable information to travel widely within the claustrum and discuss experiments to address these questions.

Recent studies have illustrated that motion-related artifacts remain in resting-state fMRI (rs-fMRI) data even after common corrective processing procedures have been applied, but the extent to which head motion distorts the data may be modulated by the corrective approach taken. We compare two different methods for estimating nuisance signals from tissues not expected to exhibit BOLD fMRI signals of neuronal origin: 1) the more commonly used mean signal method and 2) the principal components analysis approach (aCompCor: Behzadi et al., 2007). Further, we investigate the added benefit of "scrubbing" (Power et al., 2012) following both methods. We demonstrate that the use of aCompCor removes motion artifacts more effectively than tissue-mean signal regression. In addition, inclusion of more components from anatomically defined regions of no interest better mitigates motion-related artifacts and improves the specificity of functional connectivity estimates. While scrubbing further attenuates motion-related artifacts when mean signals are used, scrubbing provides no additional benefit in terms of motion artifact reduction or connectivity specificity when using aCompCor. Copyright © 2014 Elsevier Inc. All rights reserved.