From single-neuron dynamics to higher-order circuit motifs in control and pathological brain networks.

The convergence of advanced single-cell in vivo functional imaging techniques, computational modelling tools and graph-based network analytics has heralded new opportunities to study single-cell dynamics across large-scale networks, providing novel insights into principles of brain communication and pointing towards potential new strategies for treating neurological disorders. A major recent finding has been the identification of unusually richly connected hub cells that have capacity to synchronize networks and may also be critical in network dysfunction. While hub neurons are traditionally defined by measures that consider solely the number and strength of connections, novel higher-order graph analytics now enables the mining of massive networks for repeating subgraph patterns called motifs. As an illustration of the power offered by higher-order analysis of neuronal networks, we highlight how recent methodological advances uncovered a new functional cell type, the superhub, that is predicted to play a major role in regulating network dynamics. Finally, we discuss open questions that will be critical for assessing the importance of higher-order cellular-scale network analytics in understanding brain function in health and disease.

Inhibitory plasticity supports replay generalization in the hippocampus.

Memory consolidation assimilates recent experiences into long-term memory. This process requires the replay of learned sequences, although the content of these sequences remains controversial. Recent work has shown that the statistics of replay deviate from those of experience: stimuli that are experientially salient may be either recruited or suppressed from sharp-wave ripples. In this study, we found that this phenomenon can be explained parsimoniously and biologically plausibly by a Hebbian spike-time-dependent plasticity rule at inhibitory synapses. Using models at three levels of abstraction-leaky integrate-and-fire, biophysically detailed and abstract binary-we show that this rule enables efficient generalization, and we make specific predictions about the consequences of intact and perturbed inhibitory dynamics for network dynamics and cognition. Finally, we use optogenetics to artificially implant non-generalizable representations into the network in awake behaving mice, and we find that these representations also accumulate inhibition during sharp-wave ripples, experimentally validating a major prediction of our model. Our work outlines a potential direct link between the synaptic and cognitive levels of memory consolidation, with implications for both normal learning and neurological disease.

Maximally selective single-cell target for circuit control in epilepsy models.

Neurological and psychiatric disorders are associated with pathological neural dynamics. The fundamental connectivity patterns of cell-cell communication networks that enable pathological dynamics to emerge remain unknown. Here, we studied epileptic circuits using a newly developed computational pipeline that leveraged single-cell calcium imaging of larval zebrafish and chronically epileptic mice, biologically constrained effective connectivity modeling, and higher-order motif-focused network analysis. We uncovered a novel functional cell type that preferentially emerged in the preseizure state, the superhub, that was unusually richly connected to the rest of the network through feedforward motifs, critically enhancing downstream excitation. Perturbation simulations indicated that disconnecting superhubs was significantly more effective in stabilizing epileptic circuits than disconnecting hub cells that were defined traditionally by connection count. In the dentate gyrus of chronically epileptic mice, superhubs were predominately modeled adult-born granule cells. Collectively, these results predict a new maximally selective and minimally invasive cellular target for seizure control.

A miniature multi-contrast microscope for functional imaging in freely behaving animals.

Neurovascular coupling, cerebrovascular remodeling and hemodynamic changes are critical to brain function, and dysregulated in neuropathologies such as brain tumors. Interrogating these phenomena in freely behaving animals requires a portable microscope with multiple optical contrast mechanisms. Therefore, we developed a miniaturized microscope with: a fluorescence (FL) channel for imaging neural activity (e.g., GCaMP) or fluorescent cancer cells (e.g., 9L-GFP); an intrinsic optical signal (IOS) channel for imaging hemoglobin absorption (i.e., cerebral blood volume); and a laser speckle contrast (LSC) channel for imaging perfusion (i.e., cerebral blood flow). Following extensive validation, we demonstrate the microscope's capabilities via experiments in unanesthetized murine brains that include: (i) multi-contrast imaging of neurovascular changes following auditory stimulation; (ii) wide-area tonotopic mapping; (iii) EEG-synchronized imaging during anesthesia recovery; and (iv) microvascular connectivity mapping over the life-cycle of a brain tumor. This affordable, flexible, plug-and-play microscope heralds a new era in functional imaging of freely behaving animals.

Brain tumors disrupt the resting-state connectome.

Brain tumor patients often experience functional deficits that extend beyond the tumor site. While resting-state functional MRI (rsfMRI) has been used to map such functional connectivity changes in brain tumor patients, the interplay between abnormal tumor vasculature and the rsfMRI signal is still not well understood. Therefore, there is an exigent need for new tools to elucidate how the blood­oxygenation-level-dependent (BOLD) rsfMRI signal is modulated in brain cancer. In this initial study, we explore the utility of a preclinical model for quantifying brain tumor-induced changes on the rsfMRI signal and resting-state brain connectivity. We demonstrate that brain tumors induce brain-wide alterations of resting-state networks that extend to the contralateral hemisphere, accompanied by global attenuation of the rsfMRI signal. Preliminary histology suggests that some of these alterations in brain connectivity may be attributable to tumor-related remodeling of the neurovasculature. Moreover, this work recapitulates clinical rsfMRI findings from brain tumor patients in terms of the effects of tumor size on the neurovascular microenvironment. Collectively, these results lay the foundation of a preclinical platform for exploring the usefulness of rsfMRI as a potential new biomarker in patients with brain cancer.

Phenotyping the Microvasculature in Critical-Sized Calvarial Defects via Multimodal Optical Imaging.

Tissue-engineered scaffolds are a powerful means of healing craniofacial bone defects arising from trauma or disease. Murine models of critical-sized bone defects are especially useful in understanding the role of microenvironmental factors such as vascularization on bone regeneration. Here, we demonstrate the capability of a novel multimodality imaging platform capable of acquiring in vivo images of microvascular architecture, microvascular blood flow, and tracer/cell tracking via intrinsic optical signaling (IOS), laser speckle contrast (LSC), and fluorescence (FL) imaging, respectively, in a critical-sized calvarial defect model. Defects that were 4 mm in diameter were made in the calvarial regions of mice followed by the implantation of osteoconductive scaffolds loaded with human adipose-derived stem cells embedded in fibrin gel. Using IOS imaging, we were able to visualize microvascular angiogenesis at the graft site and extracted morphological information such as vessel radius, length, and tortuosity two weeks after scaffold implantation. FL imaging allowed us to assess functional characteristics of the angiogenic vessel bed, such as time-to-peak of a fluorescent tracer, and also allowed us to track the distribution of fluorescently tagged human umbilical vein endothelial cells. Finally, we used LSC to characterize the in vivo hemodynamic response and maturity of the remodeled microvessels in the scaffold microenvironment. In this study, we provide a methodical framework for imaging tissue-engineered scaffolds, processing the images to extract key microenvironmental parameters, and visualizing these data in a manner that enables the characterization of the vascular phenotype and its effect on bone regeneration. Such multimodality imaging platforms can inform optimization and design of tissue-engineered scaffolds and elucidate the factors that promote enhanced vascularization and bone formation.

Implications of neurovascular uncoupling in functional magnetic resonance imaging (fMRI) of brain tumors.

Functional magnetic resonance imaging (fMRI) serves as a critical tool for presurgical mapping of eloquent cortex and changes in neurological function in patients diagnosed with brain tumors. However, the blood-oxygen-level-dependent (BOLD) contrast mechanism underlying fMRI assumes that neurovascular coupling remains intact during brain tumor progression, and that measured changes in cerebral blood flow (CBF) are correlated with neuronal function. Recent preclinical and clinical studies have demonstrated that even low-grade brain tumors can exhibit neurovascular uncoupling (NVU), which can confound interpretation of fMRI data. Therefore, to avoid neurosurgical complications, it is crucial to understand the biophysical basis of NVU and its impact on fMRI. Here we review the physiology of the neurovascular unit, how it is remodeled, and functionally altered by brain cancer cells. We first discuss the latest findings about the components of the neurovascular unit. Next, we synthesize results from preclinical and clinical studies to illustrate how brain tumor induced NVU affects fMRI data interpretation. We examine advances in functional imaging methods that permit the clinical evaluation of brain tumors with NVU. Finally, we discuss how the suppression of anomalous tumor blood vessel formation with antiangiogenic therapies can "normalize" the brain tumor vasculature, and potentially restore neurovascular coupling.

Computational analysis of LDDMM for brain mapping.

One goal of computational anatomy (CA) is to develop tools to accurately segment brain structures in healthy and diseased subjects. In this paper, we examine the performance and complexity of such segmentation in the framework of the large deformation diffeomorphic metric mapping (LDDMM) registration method with reference to atlases and parameters. First we report the application of a multi-atlas segmentation approach to define basal ganglia structures in healthy and diseased kids' brains. The segmentation accuracy of the multi-atlas approach is compared with the single atlas LDDMM implementation and two state-of-the-art segmentation algorithms-Freesurfer and FSL-by computing the overlap errors between automatic and manual segmentations of the six basal ganglia nuclei in healthy subjects as well as subjects with diseases including ADHD and Autism. The high accuracy of multi-atlas segmentation is obtained at the cost of increasing the computational complexity because of the calculations necessary between the atlases and a subject. Second, we examine the effect of parameters on total LDDMM computation time and segmentation accuracy for basal ganglia structures. Single atlas LDDMM method is used to automatically segment the structures in a population of 16 subjects using different sets of parameters. The results show that a cascade approach and using fewer time steps can reduce computational complexity as much as five times while maintaining reliable segmentations.