Darian Hadjiabadi

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.

Summary: Memory consolidation assimilates recent experiences into long-term memory. This process requires the replay of learned sequences, though the content of these sequences remains controversial. Recent work has shown that the statistics of replay deviate from those of experience: stimuli which are experientially salient may be either selected or suppressed. We find that this phenomenon can be explained parsimoniously and biologically plausibly by a Hebbian spike time-dependent plasticity rule at inhibitory synapses. Using spiking networks at three levels of abstraction–leaky integrate-and-fire, biophysically detailed, and abstract binary–we show that this rule enables efficient inference of a model of the structure of the world. We present analytical results that these replayed sequences converge to ground truth under a mathematical model of replay. Finally, we make specific predictions about the consequences of intact and perturbed inhibitory dynamics for network dynamics and cognition. 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.

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. image

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. We studied epileptic circuits using a newly developed integrated computational pipeline applied to cellular resolution functional imaging data. Control and preseizure neural dynamics in larval zebrafish and in chronically epileptic mice were captured using large-scale cellular-resolution calcium imaging. Biologically constrained effective connectivity modeling extracted the underlying cell-cell communication network. Novel analysis of the higher-order network structure revealed the existence of ‘superhub’ cells that are unusually richly connected to the rest of the network through feedforward motifs. Instability in epileptic networks was causally linked to superhubs whose involvement in feedforward motifs critically enhanced downstream excitation. Disconnecting individual superhubs was significantly more effective in stabilizing epileptic networks compared to disconnecting hub cells defined traditionally by connection count. Collectively, these results predict a new, maximally selective and minimally invasive cellular target for seizure control. Highlights: Higher-order connectivity patterns of large-scale neuronal communication networks were studied in zebrafish and mice Control and epileptic networks were modeled from in vivo cellular resolution calcium imaging data Rare ‘superhub’ cells unusually richly connected to the rest of the network through higher-order feedforward motifs were identified Disconnecting single superhub neurons more effectively stabilized epileptic networks than targeting conventional hub cells defined by high connection count. These data predict a maximally selective novel single cell target for minimally invasive seizure control

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.

Dendritic size and branching patterns are important features of neural form and function. However, current computational models of neuronal networks use simplistic cylindrical geometries to mimic dendritic arborizations. One reason for this is that current methods to generate dendritic trees have rigid a priori constraints. To address this, a deep convolutional generative adversarial network (DCGAN) trained on images of rodent hippocampal granule and pyramidal dendritic trees. Image features learned by the network were used to generate realistic dendritic morphologies. Results show that DCGANs achieved greater stability ^∗ and high generalization, as quantified by kernel maximum mean discrepancy, when exposed to instance noise and/or label smoothing during training. Trained models successfully generated realistic morphologies for both neuron types, with high false positive rate reported by expert reviewers. Collectively, DCGANs offer a unique opportunity to advance the geometry of neural modeling, and, therefore, to propel our understanding of neuronal function. ∗ A “stable/stabilized DCGAN”, as mentioned throughout this work, is a DCGAN which was stable throughout training.

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 imaging, respectively in a critical-sized calvarial defect model. Defects that were 4mm in diameter were made in the calvarial regions of mice followed by the implantation of osteoconductive scaffolds loaded with human adipose-derived stem cells (hASCs) 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 (HUVECs). Finally, we employed 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 in order 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.

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.