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Resting-state functional Magnetic Resonance Imaging (rsfMRI) of the human brain has revealed multiple large-scale neural networks within a hierarchical and complex structure of coordinated functional activity. These distributed neuroanatomical systems provide a sensitive window on brain function and its disruption in a variety of neuropathological conditions. The study of macroscale intrinsic connectivity networks in preclinical species, where genetic and environmental conditions can be controlled and manipulated with high specificity, offers the opportunity to elucidate the biological determinants of these alterations. While rsfMRI methods are now widely used in human connectivity research, these approaches have only relatively recently been back-translated into laboratory animals. Here we review recent progress in the study of functional connectivity in rodent species, emphasising the ability of this approach to resolve large-scale brain networks that recapitulate neuroanatomical features of known functional systems in the human brain. These include, but are not limited to, a distributed set of regions identified in rats and mice that may represent a putative evolutionary precursor of the human default mode network (DMN). The impact and control of potential experimental and methodological confounds are also critically discussed. Finally, we highlight the enormous potential and some initial application of connectivity mapping in transgenic models as a tool to investigate the neuropathological underpinnings of the large-scale connectional alterations associated with human neuropsychiatric and neurological conditions. We conclude by discussing the translational potential of these methods in basic and applied neuroscience. [ABSTRACT FROM AUTHOR]

Abstract: Laboratory mouse models represent a powerful tool to elucidate the biological foundations of disease, but translation to and from human studies rely upon valid cross-species measures. Resting-state functional connectivity (rsFC) represents a promising translational probe of brain function; however, no convincing demonstration of the presence of distributed, bilateral rsFC networks in the mouse brain has yet been reported. Here we used blood oxygen level dependent (BOLD) and cerebral blood volume (CBV) weighted fMRI to demonstrate the presence of robust and reproducible resting-state networks in the mouse brain. Independent-component analysis (ICA) revealed inter-hemispheric homotopic rsFC networks encompassing several established neuro-anatomical systems of the mouse brain, including limbic, motor and parietal cortex, striatum, thalamus and hippocampus. BOLD and CBV contrast produced consistent networks, with the latter exhibiting a superior anatomical preservation of brain regions close to air-tissue interfaces (e.g. ventral hippocampus). Seed-based analysis confirmed the inter-hemispheric specificity of the correlations observed with ICA and highlighted the presence of distributed antero-posterior networks anatomically homologous to the human salience network (SN) and default-mode network (DMN). Consistent with rsFC investigations in humans, BOLD and CBV-weighted fMRI signals in the DMN-like network exhibited spontaneous anti-correlation with neighbouring fronto-parietal areas. These findings demonstrate the presence of robust distributed intrinsic functional connectivity networks in the mouse brain, and pave the way for the application of rsFC readouts in transgenic models to investigate the biological underpinnings of spontaneous BOLD fMRI fluctuations and their derangement in pathological states. [Copyright &y& Elsevier]

Abstract: Even more than in cognitive research applications, moving fMRI to the clinic and the drug development process requires the generation of stable and reliable signal changes. The performance characteristics of the fMRI paradigm constrain experimental power and may require different study designs (e.g., crossover vs. parallel groups), yet fMRI reliability characteristics can be strongly dependent on the nature of the fMRI task. The present study investigated both within-subject and group-level reliability of a combined three-task fMRI battery targeting three systems of wide applicability in clinical and cognitive neuroscience: an emotional (face matching), a motivational (monetary reward anticipation) and a cognitive (n-back working memory) task. A group of 25 young, healthy volunteers were scanned twice on a 3T MRI scanner with a mean test–retest interval of 14.6days. FMRI reliability was quantified using the intraclass correlation coefficient (ICC) applied at three different levels ranging from a global to a localized and fine spatial scale: (1) reliability of group-level activation maps over the whole brain and within targeted regions of interest (ROIs); (2) within-subject reliability of ROI-mean amplitudes and (3) within-subject reliability of individual voxels in the target ROIs. Results showed robust evoked activation of all three tasks in their respective target regions (emotional task=amygdala; motivational task=ventral striatum; cognitive task=right dorsolateral prefrontal cortex and parietal cortices) with high effect sizes (ES) of ROI-mean summary values (ES=1.11–1.44 for the faces task, 0.96–1.43 for the reward task, 0.83–2.58 for the n-back task). Reliability of group level activation was excellent for all three tasks with ICCs of 0.89–0.98 at the whole brain level and 0.66–0.97 within target ROIs. Within-subject reliability of ROI-mean amplitudes across sessions was fair to good for the reward task (ICCs=0.56–0.62) and, dependent on the particular ROI, also fair-to-good for the n-back task (ICCs=0.44–0.57) but lower for the faces task (ICC=−0.02–0.16). In conclusion, all three tasks are well suited to between-subject designs, including imaging genetics. When specific recommendations are followed, the n-back and reward task are also suited for within-subject designs, including pharmaco-fMRI. The present study provides task-specific fMRI reliability performance measures that will inform the optimal use, powering and design of fMRI studies using comparable tasks. [Copyright &y& Elsevier]

Abstract: In the study of functional connectivity, fMRI data can be represented mathematically as a network of nodes and links, where image voxels represent the nodes and the connections between them reflect a degree of correlation or similarity in their response. Here we show that, within this framework, functional imaging data can be partitioned into ‘communities’ of tightly interconnected voxels corresponding to maximum modularity within the overall network. We evaluated this approach systematically in application to networks constructed from pharmacological MRI (phMRI) of the rat brain in response to acute challenge with three different compounds with distinct mechanisms of action (d-amphetamine, fluoxetine, and nicotine) as well as vehicle (physiological saline). This approach resulted in bilaterally symmetric sub-networks corresponding to meaningful anatomical and functional connectivity pathways consistent with the purported mechanism of action of each drug. Interestingly, common features across all three networks revealed two groups of tightly coupled brain structures that responded as functional units independent of the specific neurotransmitter systems stimulated by the drug challenge, including a network involving the prefrontal cortex and sub-cortical regions extending from the striatum to the amygdala. This finding suggests that each of these networks includes general underlying features of the functional organization of the rat brain. [Copyright &y& Elsevier]

Abstract: Functional connectivity patterns derived from neuroimaging data may be represented as graphs or networks, with individual image voxels or anatomically-defined structures representing the nodes, and a measure of correlation between the responses in each pair of nodes determining the edges. This explicit network representation allows network-analysis approaches to be applied to the characterization of functional connections within the brain. Much recent research in complex networks has focused on methods to identify community structure, i.e. cohesive clusters of strongly interconnected nodes. One class of such algorithms determines a partition of a network into ‘sub-networks'' based on the optimization of a modularity parameter, thus also providing a measure of the degree of segregation versus integration in the full network. Here, we demonstrate that a community structure algorithm based on the maximization of modularity, applied to a functional connectivity network calculated from the responses to acute fluoxetine challenge in the rat, can identify communities whose distributions correspond to anatomically meaningful structures and include compelling functional subdivisions in the brain. We also discuss the biological interpretation of the modularity parameter in terms of segregation and integration of brain function. [Copyright &y& Elsevier]

Abstract: The effects of cocaine on cerebral blood flow and tissue oxygen levels in the rat brain were investigated with concurrent laser Doppler flowmetry and fluorescence quenching spectroscopy. Responses elicited by mild hypercapnia were used as calibration to assess the effects of cocaine on oxidative metabolism. Intravenous cocaine challenge of 0.5 mg/kg induced significant increases in tissular oxygenation and perfusion in all regions investigated (primary motor cortex, medial prefrontal cortex and dorsal striatum). Mild hypercapnia, a challenge that affects haemodynamics but not metabolism, elicited comparable changes in blood flow but substantially larger changes in tissue oxygen levels. These differences in tissue oxygen build-up suggest that increased oxidative metabolism is a significant component of the cerebral metabolic response to acute cocaine challenge. The implications for the interpretation of pharmacological MRI data are discussed. [Copyright &y& Elsevier]

Abstract: We describe a stereotaxic rat brain MRI template set with a co-registered digital anatomical atlas and illustrate its application to the analysis of a pharmacological MRI (phMRI) study of apomorphine. The template set includes anatomical images and tissue class probability maps for brain parenchyma and cerebrospinal fluid (CSF). These facilitate the use of standard fMRI software for spatial normalisation and tissue segmentation of rat brain data. A volumetric reconstruction of the Paxinos and Watson rat brain atlas is also co-localised with the template, enabling the atlas structure and stereotaxic coordinates corresponding to a feature within a statistical map to be interactively reported, facilitating the localisation of functional effects. Moreover, voxels falling within selected brain structures can be combined to define anatomically based 3D volumes of interest (VOIs), free of operator bias. As many atlas structures are small relative to the typical resolution of phMRI studies, a mechanism for defining composite structures as agglomerations of individual atlas structures is also described. This provides a simple and robust means of interrogating structures that are otherwise difficult to delineate and an objective framework for comparing and classifying compounds based on an anatomical profile of their activity. These developments allow a closer alignment of pre-clinical and clinical analysis techniques. [Copyright &y& Elsevier]

Abstract: MRI time series experiments produce a wealth of information contained in two or three spatial dimensions that evolve over time. Such experiments can, for example, localize brain response to pharmacological stimuli, but frequently the spatiotemporal characteristics of the cerebral response are unknown a priori and variable, and thus difficult to evaluate using hypothesis-based methods alone. Here we used features in the temporal dimension to group voxels with similar time courses based on a nonparametric discrete wavelet transform (DWT) representation of each time course. Applying the DWT to each voxel decomposes its temporal information into coefficients associated with both time and scale. Discarding scales in the DWT that are associated with high-frequency oscillations (noise) provided a straight-forward data reduction step and decreased the computational burden. Optimization-based clustering was then applied to the remaining wavelet coefficients in order to produce a finite number of voxel clusters. This wavelet-based cluster analysis (WCA) was evaluated using two representative classes of MRI neuroimaging experiments. In perfusion-weighted MRI, following occlusion of the middle cerebral artery (MCAO), WCA differentiated healthy tissue and different regions within the ischemic hemisphere. Following an acute cocaine challenge, WCA localized subtle differences in the pharmacokinetic profile of the cerebral response. We conclude that WCA provides a robust method for blind analysis of time series image data. [Copyright &y& Elsevier]

Recent advances in functional connectivity methods have made it possible to identify brain hubs — a set of highly connected regions serving as integrators of distributed neuronal activity. The integrative role of hub nodes makes these areas points of high vulnerability to dysfunction in brain disorders, and abnormal hub connectivity profiles have been described for several neuropsychiatric disorders. The identification of analogous functional connectivity hubs in preclinical species like the mouse may provide critical insight into the elusive biological underpinnings of these connectional alterations. To spatially locate functional connectivity hubs in the mouse brain, here we applied a fully-weighted network analysis to map whole-brain intrinsic functional connectivity (i.e., the functional connectome) at a high-resolution voxel-scale. Analysis of a large resting-state functional magnetic resonance imaging (rsfMRI) dataset revealed the presence of six distinct functional modules related to known large-scale functional partitions of the brain, including a default-mode network (DMN). Consistent with human studies, highly-connected functional hubs were identified in several sub-regions of the DMN, including the anterior and posterior cingulate and prefrontal cortices, in the thalamus, and in small foci within well-known integrative cortical structures such as the insular and temporal association cortices. According to their integrative role, the identified hubs exhibited mutual preferential interconnections. These findings highlight the presence of evolutionarily-conserved, mutually-interconnected functional hubs in the mouse brain, and may guide future investigations of the biological foundations of aberrant rsfMRI hub connectivity associated with brain pathological states. [ABSTRACT FROM AUTHOR]

Abstract: Functional connectivity analyses of fMRI data can provide a wealth of information on the brain functional organization and have been widely applied to the study of the human brain. More recently, these methods have been extended to preclinical species, thus providing a powerful translational tool. Here, we review methods and findings of functional connectivity studies in the rat. More specifically, we focus on correlation analysis of pharmacological MRI (phMRI) responses, an approach that has enabled mapping the patterns of connectivity underlying major neurotransmitter systems in vivo. We also review the use of novel statistical approaches based on a network representation of the functional connectivity and their application to the study of the rat brain functional architecture. [Copyright &y& Elsevier]

Amygdala function is of high interest for cognitive, social and psychiatric neuroscience, emphasizing the need for reliable assessments in humans. Previous work has indicated unsatisfactorily low within-subject reliability of amygdala activation fMRI measures. Based on basic science evidence for strong habituation of amygdala response to repeated stimuli, we investigated whether a quantification of habituation provides additional information beyond the usual estimate of the overall mean activity. We assessed the within-subject reliability of amygdala habituation measures during a facial emotion matching paradigm in 25 healthy subjects. We extracted the amygdala signal decrement across the course of the fMRI run for the two test–retest measurement sessions and compared reliability estimates with previous findings based on mean response amplitude. Retest-reliability of the session-wise amygdala habituation was significantly higher than the evoked amygdala mean amplitude (intraclass correlation coefficients (ICC) = 0.53 vs. 0.16). To test the task-specificity of this finding, we compared the retest-reliability of amygdala habituation across two different tasks. Significant amygdala response decrement was also seen in a cognitive task (n-back working memory) that did not per se activate the amygdala, but was totally unreliable in that context (ICC ~ 0.0), arguing for task-specificity. Together the results show that emotion-dependent amygdala habituation is a robust and considerably more reliable index than the mean amplitude, and provides a robust potential endpoint for within-subject designs including pharmaco-fMRI studies. [ABSTRACT FROM AUTHOR]

Abstract: Pain and somatosensory processing involves an interaction of multiple neuronal networks. One result of these complex interactions is the presence of differential responses across brain regions that may be incompletely modeled by a straightforward application of standard general linear model (GLM) approaches based solely on the applied stimulus. We examined temporal blood oxygenation-level dependent (BOLD) signatures elicited by two stimulation paradigms (brush and heat) providing innocuous and noxious stimuli. Data were acquired from 32 healthy male subjects (2 independent cohorts). Regional time courses and model-free analyses of the first cohort revealed distinct temporal features of the BOLD responses elicited during noxious versus innocuous stimulation. Specifically, a biphasic (dual peak) BOLD signal was observed in response to heat but much less so in response to brush stimuli. This signal was characterized by a stimulus-locked response along with a second peak delayed by ∼12.5 s. A cross-validation error analysis determined a modified design matrix comprising two explanatory variables (EVs) as a parsimonious means to model the biphasic responses within a GLM framework. One EV was directly derived from the stimulation paradigm (EV1), while the second EV (EV2) was EV1 shifted by 12.5 s. The 2EV GLM analysis enabled a more detailed characterization of the elicited BOLD responses, particularly during pain processing. This was confirmed by application of the model to a second, independent cohort[AU1]. Furthermore, the delayed component of the biphasic response was strongly associated with the noxious heat stimuli, suggesting that this may represent a sensitive fMRI link of pain processing. [Copyright &y& Elsevier]