During wakefulness and in absence of performing tasks or sensory processing, the default-mode network (DMN), an intrinsic central nervous system (CNS) network, is in an active state. Non-human primate and human CNS imaging studies have identified the DMN in these two species. Clinical imaging studies have shown that the pattern of activity within the DMN is often modulated in various disease states (e.g., Alzheimer's, schizophrenia or chronic pain). However, whether the DMN exists in awake rodents has not been characterized. The current data provides evidence that awake rodents also possess ‘DMN-like’ functional connectivity, but only subsequent to habituation to what is initially a novel magnetic resonance imaging (MRI) environment as well as physical restraint. Specifically, the habituation process spanned across four separate scanning sessions (Day 2, 4, 6 and 8). At Day 8, significant (p<0.05) functional connectivity was observed amongst structures such as the anterior cingulate (seed region), retrosplenial, parietal, and hippocampal cortices. Prior to habituation (Day 2), functional connectivity was only detected (p<0.05) amongst CNS structures known to mediate anxiety (i.e., anterior cingulate (seed region), posterior hypothalamic area, amygdala and parabracial nucleus). In relating functional connectivity between cingulate-default-mode and cingulate-anxiety structures across Days 2-8, a significant inverse relationship (r = −0.65, p = 0.0004) was observed between these two functional interactions such that increased cingulate-DMN connectivity corresponded to decreased cingulate anxiety network connectivity. This investigation demonstrates that the cingulate is an important component of both the rodent DMN-like and anxiety networks.

Matrix metalloproteinase‐9 (MMP‐9) and NADPH oxidase contribute to blood–brain barrier (BBB) disruption after ischemic stroke. We have previously shown that normobaric hyperoxia (NBO) treatment reduces MMP‐9 and oxygen free radical generation in ischemic brain. In this study, we tested the hypothesis that NBO protects the BBB through inhibiting NADPH oxidase‐mediated MMP‐9 induction in transient focal cerebral ischemia. Male Sprague–Dawley rats (n = 69) were given NBO (95% O₂) or normoxia (21% O₂) during 90‐min filament occlusion of the middle cerebral artery. Cerebral microvessels were isolated for analyzing MMP‐9 and NADPH oxidase. BBB damage was non‐invasively quantified with magnetic resonance imaging. In normoxic rats, both NADPH oxidase catalytic subunit gp91^phox and MMP‐9 expression were up‐regulated in ischemic hemispheric microvessels after 90‐min middle cerebral artery occlusion with 22.5 h reperfusion. Inhibition of NADPH oxidase with apocynin reduced the MMP‐9 increase, indicating a causal link between NADPH oxidase‐derived superoxide and MMP‐9 induction. NBO treatment inhibited gp91^phox expression, NADPH oxidase activity, and MMP‐9 induction, which led to significantly less BBB damage and brain edema in the ischemic brain. These results suggest that gp91^phox containing NADPH oxidase plays an important role in MMP‐9 induction in ischemic BBB microvasculature, and that NBO treatment may attenuate MMP‐9 induction and brain edema through inhibiting NADPH oxidase after transient cerebral ischemia.

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Neuroimaging studies have yielded significant advances in the understanding of neural processes relevant to the development and persistence of addiction. However, these advances have not explored extensively for diagnostic accuracy in human subjects. The aim of this study was to develop a statistical approach, using a machine learning framework, to correctly classify brain images of cocaine-dependent participants and healthy controls. In this study, a framework suitable for educing potential brain regions that differed between the two groups was developed and implemented. Single Photon Emission Computerized Tomography (SPECT) images obtained during rest or a saline infusion in three cohorts of 2–4 week abstinent cocaine-dependent participants (n = 93) and healthy controls (n = 69) were used to develop a classification model. An information theoretic-based feature selection algorithm was first conducted to reduce the number of voxels. A density-based clustering algorithm was then used to form spatially connected voxel clouds in three-dimensional space. A statistical classifier, Support Vectors Machine (SVM), was then used for participant classification. Statistically insignificant voxels of spatially connected brain regions were removed iteratively and classification accuracy was reported through the iterations. The voxel-based analysis identified 1,500 spatially connected voxels in 30 distinct clusters after a grid search in SVM parameters. Participants were successfully classified with 0.88 and 0.89 F-measure accuracies in 10-fold cross validation (10xCV) and leave-one-out (LOO) approaches, respectively. Sensitivity and specificity were 0.90 and 0.89 for LOO; 0.83 and 0.83 for 10xCV. Many of the 30 selected clusters are highly relevant to the addictive process, including regions relevant to cognitive control, default mode network related self-referential thought, behavioral inhibition, and contextual memories. Relative hyperactivity and hypoactivity of regional cerebral blood flow in brain regions in cocaine-dependent participants are presented with corresponding level of significance. The SVM-based approach successfully classified cocaine-dependent and healthy control participants using voxels selected with information theoretic-based and statistical methods from participants’ SPECT data. The regions found in this study align with brain regions reported in the literature. These findings support the future use of brain imaging and SVM-based classifier in the diagnosis of substance use disorders and furthering an understanding of their underlying pathology.