Abstract
Resting state (RS) connectivity has been increasingly studied in healthy and diseased brains in humans and animals. This paper presents a new method to analyze RS data from fMRI that combines multiple seed correlation analysis with graph-theory (MSRA). We characterize and evaluate this new method in relation to two other graph-theoretical methods and ICA. The graph-theoretical methods calculate cross-correlations of regional average time-courses, one using seed regions of the same size (SRCC) and the other using whole brain structure regions (RCCA). We evaluated the reproducibility, power, and capacity of these methods to characterize short-term RS modulation to unilateral physiological whisker stimulation in rats. Graph-theoretical networks found with the MSRA approach were highly reproducible, and their communities showed large overlaps with ICA components. Additionally, MSRA was the only one of all tested methods that had the power to detect significant RS modulations induced by whisker stimulation that are controlled by family-wise error rate (FWE). Compared to the reduced resting state network connectivity during task performance, these modulations implied decreased connectivity strength in the bilateral sensorimotor and entorhinal cortex. Additionally, the contralateral ventromedial thalamus (part of the barrel field related lemniscal pathway) and the hypothalamus showed reduced connectivity. Enhanced connectivity was observed in the amygdala, especially the contralateral basolateral amygdala (involved in emotional learning processes). In conclusion, MSRA is a powerful analytical approach that can reliably detect tiny modulations of RS connectivity. It shows a great promise as a method for studying RS dynamics in healthy and pathological conditions.
Highlights
Since Biswal et al (1995) first described intrinsic functional connectivity in the human brain during rest by functional MRI, the so called resting state (RS) connectivity has been studied intensively and several large scale neural networks have been detected
We evaluated the efficacy of these methods to characterize short-term modulation by comparing RS before and after unilateral whisker stimulation in rats
seed region cross-correlation (SRCC) and regional cross-correlation approach (RCCA) matrices show left shifted distributions with their maxima below z = 0.01, which is below the significance level of the false discovery rate (FDR)
Summary
Since Biswal et al (1995) first described intrinsic functional connectivity in the human brain during rest by functional MRI (fMRI), the so called resting state (RS) connectivity has been studied intensively and several large scale neural networks have been detected (see Raichle, 2011 for review). Despite the influence of individual and instantaneous factors such as mood, physiological and cognitive states, RS networks are remarkably robust and stable over time (Braun et al, 2012; Zuo and Xing, 2014; Du et al, 2015) and species (Lu et al, 2012; Sierakowiak et al, 2015; Gozzi and Schwarz, 2016) They seem to exhibit an evolutionary conserved and fundamental phenomenon of mammalian brain function. Regardless of other interpretations, it has been widely accepted that resting state networks are dynamic in nature (Deco and Corbetta, 2011) They are modulated by prior task activation, which supports the hypothesis that RS functional connectivity plays a role in learning processes and memory consolidation (Albert et al, 2009; Tambini et al, 2010)
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