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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. •We describe distributed fMRI connectivity networks in the mouse brain.•Inter-hemispheric homotopic networks were mapped with ICA and seed regions.•A DMN-like network anti-correlated to parietal cortical regions was identified.•BOLD and cerebral-blood volume weighted contrast produced analogous networks.•Our results pave the way to the use of resting-state fMRI in the mouse.

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. •Network analysis used to map mouse brain functional connectivity hubs at voxel-scale.•Six functional modules were identified, including a default-mode network (DMN).•Highly-connected functional hubs were identified in several sub-regions of the DMN.•Foci of high connection diversity were mapped in associative cortical areas.•The identified hubs exhibit mutual preferential interconnections.

Complex network analyses of functional connectivity have consistently revealed non-random (modular, small-world, scale-free-like) behavior of hard-thresholded networks constructed from the right-tail of the similarity histogram. In the present study we determined network properties resulting from edges constrained to specific ranges across the full correlation histogram, in particular the left (negative-most) tail, and their dependence on the confound signal removal strategy employed. In the absence of global signal correction, left-tail networks comprised predominantly long range connections associated with weak correlations and were characterized by substantially reduced modularity and clustering, negative assortativity and γ<1 Deconvolution of specific confound signals (white matter, CSF and motion) resulted in the most robust within-subject reproducibility of global network parameters (ICCs~0.5). Global signal removal altered the network topology in the left tail, with the clustering coefficient and assortativity converging to zero. Networks constructed from the absolute value of the correlation coefficient were thus compromised following global signal removal since the different right-tail and left-tail topologies were mixed. These findings informed the construction of soft-thresholded networks, replacing the hard thresholding or binarization operation with a continuous mapping of all correlation values to edge weights, suppressing rather than removing weaker connections and avoiding issues related to network fragmentation. A power law adjacency function with β=12 yielded modular networks whose parameters agreed well with corresponding hard-thresholded values, that were reproducible in repeated sessions across many months and evidenced small-world-like and scale-free-like properties. [Display omitted] ►Networks based on the left tail of the correlation histogram have distinct topology. ►Global signal removal substantially alters the properties of left tail networks. ►Soft-thresholding (retaining all edges) retains modular structure of networks. ►Hard- and soft-thresholded network parameters were reproducible over 5–16months.

Cholinergic drugs acting at M1/M4 muscarinic receptors hold promise for the treatment of symptoms associated with brain disorders characterized by cognitive impairment, mood disturbances, or psychosis, such as Alzheimer’s disease or schizophrenia. However, the brain-wide functional substrates engaged by muscarinic agonists remain poorly understood. Here we used a combination of pharmacological fMRI (phMRI), resting-state fMRI (rsfMRI), and resting-state quantitative EEG (qEEG) to investigate the effects of a behaviorally active dose of the M1/M4-preferring muscarinic agonist xanomeline on brain functional activity in the rodent brain. We investigated both the effects of xanomeline per se and its modulatory effects on signals elicited by the NMDA-receptor antagonists phencyclidine (PCP) and ketamine. We found that xanomeline induces robust and widespread BOLD signal phMRI amplitude increases and decreased high-frequency qEEG spectral activity. rsfMRI mapping in the mouse revealed that xanomeline robustly decreased neocortical and striatal connectivity but induces focal increases in functional connectivity within the nucleus accumbens and basal forebrain. Notably, xanomeline pre-administration robustly attenuated both the cortico-limbic phMRI response and the fronto-hippocampal hyper-connectivity induced by PCP, enhanced PCP-modulated functional connectivity locally within the nucleus accumbens and basal forebrain, and reversed the gamma and high-frequency qEEG power increases induced by ketamine. Collectively, these results show that xanomeline robustly induces both cholinergic-like neocortical activation and desynchronization of functional networks in the mammalian brain. These effects could serve as a translatable biomarker for future clinical investigations of muscarinic agents, and bear mechanistic relevance for the putative therapeutic effect of these class of compounds in brain disorders.

Technological developments and greater rigor in the quantitative measurement of biological features in medical images have given rise to an increased interest in using quantitative imaging biomarkers to measure changes in these features. Critical to the performance of a quantitative imaging biomarker in preclinical or clinical settings are three primary metrology areas of interest: measurement linearity and bias, repeatability, and the ability to consistently reproduce equivalent results when conditions change, as would be expected in any clinical trial. Unfortunately, performance studies to date differ greatly in designs, analysis method, and metrics used to assess a quantitative imaging biomarker for clinical use. It is therefore difficult or not possible to integrate results from different studies or to use reported results to design studies. The Radiological Society of North America and the Quantitative Imaging Biomarker Alliance with technical, radiological, and statistical experts developed a set of technical performance analysis methods, metrics, and study designs that provide terminology, metrics, and methods consistent with widely accepted metrological standards. This document provides a consistent framework for the conduct and evaluation of quantitative imaging biomarker performance studies so that results from multiple studies can be compared, contrasted, or combined.

Background Sepsis-associated acute kidney injury (SA-AKI) is associated with high morbidity, with no current therapies available beyond continuous renal replacement therapy (CRRT). Systemic inflammation and endothelial dysfunction are key drivers of SA-AKI. We sought to measure differences between endothelial dysfunction markers among children with and without SA-AKI, test whether this association varied across inflammatory biomarker-based risk strata, and develop prediction models to identify those at highest risk of SA-AKI. Methods Secondary analyses of prospective observational cohort of pediatric septic shock. Primary outcome of interest was the presence of ≥ Stage II KDIGO SA-AKI on day 3 based on serum creatinine (D3 SA-AKI SCr). Biomarkers including those prospectively validated to predict pediatric sepsis mortality (PERSEVERE-II) were measured in Day 1 (D1) serum. Multivariable regression was used to test the independent association between endothelial markers and D3 SA-AKI SCr. We conducted risk-stratified analyses and developed prediction models using Classification and Regression Tree (CART), to estimate risk of D3 SA-AKI among prespecified subgroups based on PERSEVERE-II risk. Results A total of 414 patients were included in the derivation cohort. Patients with D3 SA-AKI SCr had worse clinical outcomes including 28-day mortality and need for CRRT. Serum soluble thrombomodulin (sTM), Angiopoietin-2 (Angpt-2), and Tie-2 were independently associated with D3 SA-AKI SCr. Further, Tie-2 and Angpt-2/Tie-2 ratios were influenced by the interaction between D3 SA-AKI SCr and risk strata. Logistic regression demonstrated models predictive of D3 SA-AKI risk performed optimally among patients with high- or intermediate-PERSEVERE-II risk strata. A 6 terminal node CART model restricted to this subgroup of patients had an area under the receiver operating characteristic curve (AUROC) 0.90 and 0.77 upon tenfold cross-validation in the derivation cohort to distinguish those with and without D3 SA-AKI SCr and high specificity. The newly derived model performed modestly in a unique set of patients ( n  = 224), 84 of whom were deemed high- or intermediate-PERSEVERE-II risk, to distinguish those patients with high versus low risk of D3 SA-AKI SCr. Conclusions Endothelial dysfunction biomarkers are independently associated with risk of severe SA-AKI. Pending validation, incorporation of endothelial biomarkers may facilitate prognostic and predictive enrichment for selection of therapeutics in future clinical trials among critically ill children. Graphical abstract

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. •Large-scale connectivity networks can be mapped with fMRI in mice and rats.•Both rats and mice have a putative default mode network (DMN) homologue.•We describe the topological and functional organisation of the rodent DMN.•Low anaesthesia levels preserve network topology.•Examples of fMRI connectivity mapping in transgenic mice are illustrated.

Background Multiple organ dysfunction syndrome (MODS) is a critical driver of sepsis morbidity and mortality in children. Early identification of those at risk of death and persistent organ dysfunctions is necessary to enrich patients for future trials of sepsis therapeutics. Here, we sought to integrate endothelial and PERSEVERE biomarkers to estimate the composite risk of death or organ dysfunctions on day 7 of septic shock. Methods We measured endothelial dysfunction markers from day 1 serum among those with existing PERSEVERE data. TreeNet® classification model was derived incorporating 22 clinical and biological variables to estimate risk. Based on relative variable importance, a simplified 6-biomarker model was developed thereafter. Results Among 502 patients, 49 patients died before day 7 and 124 patients had persistence of MODS on day 7 of septic shock. Area under the receiver operator characteristic curve (AUROC) for the newly derived PERSEVEREnce model to predict death or day 7 MODS was 0.93 (0.91–0.95) with a summary AUROC of 0.80 (0.76–0.84) upon tenfold cross-validation. The simplified model, based on IL-8, HSP70, ICAM-1, Angpt2/Tie2, Angpt2/Angpt1, and Thrombomodulin, performed similarly. Interaction between variables—ICAM-1 with IL-8 and Thrombomodulin with Angpt2/Angpt1—contributed to the models’ predictive capabilities. Model performance varied when estimating risk of individual organ dysfunctions with AUROCS ranging from 0.91 to 0.97 and 0.68 to 0.89 in training and test sets, respectively. Conclusions The newly derived PERSEVEREnce biomarker model reliably estimates risk of death or persistent organ dysfunctions on day 7 of septic shock. If validated, this tool can be used for prognostic enrichment in future pediatric trials of sepsis therapeutics. Graphical abstract

High-resolution anatomical image data in preclinical brain PET and SPECT studies is often not available, and inter-modality spatial normalization to an MRI brain template is frequently performed. However, this procedure can be challenging for tracers where substantial anatomical structures present limited tracer uptake. Therefore, we constructed and validated strain- and tracer-specific rat brain templates in Paxinos space to allow intra-modal registration. PET [18F]FDG, [11C]flumazenil, [11C]MeDAS, [11C]PK11195 and [11C]raclopride, and SPECT [99mTc]HMPAO brain scans were acquired from healthy male rats. Tracer-specific templates were constructed by averaging the scans, and by spatial normalization to a widely used MRI-based template. The added value of tracer-specific templates was evaluated by quantification of the residual error between original and realigned voxels after random misalignments of the data set. Additionally, the impact of strain differences, disease uptake patterns (focal and diffuse lesion), and the effect of image and template size on the registration errors were explored. Mean registration errors were 0.70 ± 0.32 mm for [18F]FDG (n = 25), 0.23 ± 0.10mm for [11C]flumazenil (n = 13), 0.88 ± 0.20 mm for [11C]MeDAS (n = 15), 0.64 ± 0.28 mm for [11C]PK11195 (n = 19), 0.34 ± 0.15 mm for [11C]raclopride (n = 6), and 0.40 ± 0.13 mm for [99mTc]HMPAO (n = 15). These values were smallest with tracer-specific templates, when compared to the use of [18F]FDG as reference template (p<0.001). Additionally, registration errors were smallest with strain-specific templates (p<0.05), and when images and templates had the same size (p ≤ 0.001). Moreover, highest registration errors were found for the focal lesion group (p<0.005) and the diffuse lesion group (p = n.s.). In the voxel-based analysis, the reported coordinates of the focal lesion model are consistent with the stereotaxic injection procedure. The use of PET/SPECT strain- and tracer-specific templates allows accurate registration of functional rat brain data, independent of disease specific uptake patterns and with registration error below spatial resolution of the cameras. The templates and the SAMIT package will be freely available for the research community [corrected].

Imaging biomarkers play a wide-ranging role in clinical trials for neurological disorders. This includes selecting the appropriate trial participants, establishing target engagement and mechanism-related pharmacodynamic effect, monitoring safety, and providing evidence of disease modification. In the early stages of clinical drug development, evidence of target engagement and/or downstream pharmacodynamic effect—especially with a clear relationship to dose—can provide confidence that the therapeutic candidate should be advanced to larger and more expensive trials, and can inform the selection of the dose(s) to be further tested, i.e., to “de-risk” the drug development program. In these later-phase trials, evidence that the therapeutic candidate is altering disease-related biomarkers can provide important evidence that the clinical benefit of the compound (if observed) is grounded in meaningful biological changes. The interpretation of disease-related imaging markers, and comparability across different trials and imaging tools, is greatly improved when standardized outcome measures are defined. This standardization should not impinge on scientific advances in the imaging tools per se but provides a common language in which the results generated by these tools are expressed. PET markers of pathological protein aggregates and structural imaging of brain atrophy are common disease-related elements across many neurological disorders. However, PET tracers for pathologies beyond amyloid β and tau are needed, and the interpretability of structural imaging can be enhanced by some simple considerations to guard against the possible confound of pseudo-atrophy. Learnings from much-studied conditions such as Alzheimer's disease and multiple sclerosis will be beneficial as the field embraces rarer diseases.