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.
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.
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.
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.
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►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.
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.
Whole-genome sequencing (WGS) shows promise as a first-line genetic test for acutely ill infants, but widespread adoption and implementation requires evidence of an effect on clinical management.
To ...determine the effect of WGS on clinical management in a racially and ethnically diverse and geographically distributed population of acutely ill infants in the US.
This randomized, time-delayed clinical trial enrolled participants from September 11, 2017, to April 30, 2019, with an observation period extending to July 2, 2019. The study was conducted at 5 US academic medical centers and affiliated children's hospitals. Participants included infants aged between 0 and 120 days who were admitted to an intensive care unit with a suspected genetic disease. Data were analyzed from January 14 to August 20, 2020.
Patients were randomized to receive clinical WGS results 15 days (early) or 60 days (delayed) after enrollment, with the observation period extending to 90 days. Usual care was continued throughout the study.
The main outcome was the difference in the proportion of infants in the early and delayed groups who received a change of management (COM) 60 days after enrollment. Additional outcome measures included WGS diagnostic efficacy, within-group COM at 90 days, length of hospital stay, and mortality.
A total of 354 infants were randomized to the early (n = 176) or delayed (n = 178) arms. The mean participant age was 15 days (IQR, 7-32 days); 201 participants (56.8%) were boys; 19 (5.4%) were Asian; 47 (13.3%) were Black; 250 (70.6%) were White; and 38 (10.7%) were of other race. At 60 days, twice as many infants in the early group vs the delayed group received a COM (34 of 161 21.1%; 95% CI, 15.1%-28.2% vs 17 of 165 10.3%; 95% CI, 6.1%-16.0%; P = .009; odds ratio, 2.3; 95% CI, 1.22-4.32) and a molecular diagnosis (55 of 176 31.0%; 95% CI, 24.5%-38.7% vs 27 of 178 15.0%; 95% CI, 10.2%-21.3%; P < .001). At 90 days, the delayed group showed a doubling of COM (to 45 of 161 28.0%; 95% CI, 21.2%-35.6%) and diagnostic efficacy (to 56 of 178 31.0%; 95% CI, 24.7%-38.8%). The most frequent COMs across the observation window were subspecialty referrals (39 of 354; 11%), surgery or other invasive procedures (17 of 354; 4%), condition-specific medications (9 of 354; 2%), or other supportive alterations in medication (12 of 354; 3%). No differences in length of stay or survival were observed.
In this randomized clinical trial, for acutely ill infants in an intensive care unit, introduction of WGS was associated with a significant increase in focused clinical management compared with usual care. Access to first-line WGS may reduce health care disparities by enabling diagnostic equity. These data support WGS adoption and implementation in this population.
ClinicalTrials.gov Identifier: NCT03290469.
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 18FFDG, 11Cflumazenil, 11CMeDAS, 11CPK11195 and 11Craclopride, and SPECT 99mTcHMPAO 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 18FFDG (n = 25), 0.23 ± 0.10mm for 11Cflumazenil (n = 13), 0.88 ± 0.20 mm for 11CMeDAS (n = 15), 0.64 ± 0.28 mm for 11CPK11195 (n = 19), 0.34 ± 0.15 mm for 11Craclopride (n = 6), and 0.40 ± 0.13 mm for 99mTcHMPAO (n = 15). These values were smallest with tracer-specific templates, when compared to the use of 18FFDG 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.
Abstract
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
Copy number variations (CNV) involving multiple genes are ideal models to study polygenic neuropsychiatric disorders. Since 22q11.2 deletion is regarded as the most important single genetic risk ...factor for developing schizophrenia, characterizing the effects of this CNV on neural networks offers a unique avenue towards delineating polygenic interactions conferring risk for the disorder. We used a Df(h22q11)/+ mouse model of human 22q11.2 deletion to dissect gene expression patterns that would spatially overlap with differential resting-state functional connectivity (FC) patterns in this model (N = 12 Df(h22q11)/+ mice, N = 10 littermate controls). To confirm the translational relevance of our findings, we analyzed tissue samples from schizophrenia patients and healthy controls using machine learning to explore whether identified genes were co-expressed in humans. Additionally, we employed the STRING protein-protein interaction database to identify potential interactions between genes spatially associated with hypo- or hyper-FC. We found significant associations between differential resting-state connectivity and spatial gene expression patterns for both hypo- and hyper-FC. Two genes, Comt and Trmt2a, were consistently over-expressed across all networks. An analysis of human datasets pointed to a disrupted co-expression of these two genes in the brain in schizophrenia patients, but not in healthy controls. Our findings suggest that COMT and TRMT2A form a core genetic component implicated in differential resting-state connectivity patterns in the 22q11.2 deletion. A disruption of their co-expression in schizophrenia patients points out a prospective cause for the aberrance of brain networks communication in 22q11.2 deletion syndrome on a molecular level.
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