Continuous volumetric imaging via an optical phase-locked ultrasound lens

In vivo imaging at high spatiotemporal resolution is key to the understanding of complex biological systems. We integrated an optical phase-locked ultrasound lens into a two-photon fluorescence microscope and achieved microsecond-scale axial scanning, thus enabling volumetric imaging at tens of hertz. We applied this system to multicolor volumetric imaging of processes sensitive to motion artifacts, including calcium dynamics in behaving mouse brain and transient morphology changes and trafficking of immune cells

Gotta trace ‘em all: A mini-review on tools and procedures for segmenting single neurons toward deciphering the structural connectome

Decoding the morphology and physical connections of all the neurons populating a brain is necessary for predicting and studying the relationships between its form and function, as well as for documenting structural abnormalities in neuropathies. Digitizing a complete and high-fidelity map of the mammalian brain at the micro-scale will allow neuroscientists to understand disease, consciousness, and ultimately what it is that makes us humans. The critical obstacle for reaching this goal is the lack of robust and accurate tools able to deal with 3D datasets representing dense-packed cells in their native arrangement within the brain. This obliges neuroscientist to manually identify the neurons populating an acquired digital image stack, a notably time-consuming procedure prone to human bias. Here we review the automatic and semi-automatic algorithms and software for neuron segmentation available in the literature, as well as the metrics purposely designed for their validation, highlighting their strengths and limitations. In this direction, we also briefly introduce the recent advances in tissue clarification that enable significant improvements in both optical access of neural tissue and image stack quality, and which could enable more efficient segmentation approaches. Finally, we discuss new methods and tools for processing tissues and acquiring images at sub-cellular scales, which will require new robust algorithms for identifying neurons and their sub-structures (e.g., spines, thin neurites). This will lead to a more detailed structural map of the brain, taking twenty-first century cellular neuroscience to the next level, i.e., the Structural Connectome

Task-phase-specific dynamics of basal forebrain neuronal ensembles.

Cortically projecting basal forebrain neurons play a critical role in learning and attention, and their degeneration accompanies age-related impairments in cognition. Despite the impressive anatomical and cell-type complexity of this system, currently available data suggest that basal forebrain neurons lack complexity in their response fields, with activity primarily reflecting only macro-level brain states such as sleep and wake, onset of relevant stimuli and/or reward obtainment. The current study examined the spiking activity of basal forebrain neuron populations across multiple phases of a selective attention task, addressing, in particular, the issue of complexity in ensemble firing patterns across time. Clustering techniques applied to the full population revealed a large number of distinct categories of task-phase-specific activity patterns. Unique population firing-rate vectors defined each task phase and most categories of task-phase-specific firing had counterparts with opposing firing patterns. An analogous set of task-phase-specific firing patterns was also observed in a population of posterior parietal cortex neurons. Thus, consistent with the known anatomical complexity, basal forebrain population dynamics are capable of differentially modulating their cortical targets according to the unique sets of environmental stimuli, motor requirements, and cognitive processes associated with different task phases

Inversion using a new low-dimensional representation of complex binary geological media based on a deep neural network

Efficient and high-fidelity prior sampling and inversion for complex geological media is still a largely unsolved challenge. Here, we use a deep neural network of the variational autoencoder type to construct a parametric low-dimensional base model parameterization of complex binary geological media. For inversion purposes, it has the attractive feature that random draws from an uncorrelated standard normal distribution yield model realizations with spatial characteristics that are in agreement with the training set. In comparison with the most commonly used parametric representations in probabilistic inversion, we find that our dimensionality reduction (DR) approach outperforms principle component analysis (PCA), optimization-PCA (OPCA) and discrete cosine transform (DCT) DR techniques for unconditional geostatistical simulation of a channelized prior model. For the considered examples, important compression ratios (200 - 500) are achieved. Given that the construction of our parameterization requires a training set of several tens of thousands of prior model realizations, our DR approach is more suited for probabilistic (or deterministic) inversion than for unconditional (or point-conditioned) geostatistical simulation. Probabilistic inversions of 2D steady-state and 3D transient hydraulic tomography data are used to demonstrate the DR-based inversion. For the 2D case study, the performance is superior compared to current state-of-the-art multiple-point statistics inversion by sequential geostatistical resampling (SGR). Inversion results for the 3D application are also encouraging

Engagement of the rat hindlimb motor cortex across natural locomotor behaviors

Contrary to cats and primates, cortical contribution to hindlimb locomotor movements is not critical in rats. However, the importance of the motor cortex to regain locomotion after neurological disorders in rats suggests that cortical engagement in hindlimb motor control may depend on the behavioral context. To investigate this possibility, we recorded whole-body kinematics, muscle synergies, and hindlimb motor cortex modulation in freely moving rats performing a range of natural locomotor procedures. We found that the activation of hindlimb motor cortex preceded gait initiation. During overground locomotion, the motor cortex exhibited consistent neuronal population responses that were synchronized with the spatiotemporal activation of hindlimb motoneurons. Behaviors requiring enhanced muscle activity or skilled paw placement correlated with substantial adjustment in neuronal population responses. In contrast, all rats exhibited a reduction of cortical activity during more automated behavior, such as stepping on a treadmill. Despite the facultative role of the motor cortex in the production of locomotion in rats, these results show that the encoding of hindlimb features in motor cortex dynamics is comparable in rats and cats. However, the extent of motor cortex modulations appears linked to the degree of volitional engagement and complexity of the task, reemphasizing the importance of goal-directed behaviors for motor control studies, rehabilitation, and neuroprosthetics. © 2016 the authors

Constraints and spandrels of interareal connectomes

Interareal connectomes are whole-brain wiring diagrams of white-matter pathways. Recent studies have identified modules, hubs, module hierarchies and rich clubs as structural hallmarks of these wiring diagrams. An influential current theory postulates that connectome modules are adequately explained by evolutionary pressures for wiring economy, but that the other hallmarks are not explained by such pressures and are therefore less trivial. Here, we use constraint network models to test these postulates in current gold-standard vertebrate and invertebrate interareal-connectome reconstructions. We show that empirical wiring-cost constraints inadequately explain connectome module organization, and that simultaneous module and hub constraints induce the structural byproducts of hierarchies and rich clubs. These byproducts, known as spandrels in evolutionary biology, include the structural substrate of the default-mode network. Our results imply that currently standard connectome characterizations are based on circular analyses or double dipping, and we emphasize an integrative approach to future connectome analyses for avoiding such pitfalls.M.R. was funded by the NARSAD Young Investigator Award, the Isaac Newton Grant for Research Purposes, and the Parke Davis Exchange Fellowship. The BCNI was funded by the MRC and the Wellcome Trust

Anterior Intraparietal Area: a Hub in the Observed Manipulative Action Network.

Current knowledge regarding the processing of observed manipulative actions (OMAs) (e.g., grasping, dragging, or dropping) is limited to grasping and underlying neural circuitry remains controversial. Here, we addressed these issues by combining chronic neuronal recordings along the anteroposterior extent of monkeys\u2019 anterior intraparietal (AIP) area with tracer injections into the recorded sites. We found robust neural selectivity for 7 distinct OMAs, particularly in the posterior part of AIP (pAIP), where it was associated with motor coding of grip type and own-hand visual feedback. This cluster of functional properties appears to be specifically grounded in stronger direct connections of pAIP with the temporal regions of the ventral visual stream and the prefrontal cortex, as connections with skeletomotor related areas and regions of the dorsal visual stream exhibited opposite or no rostrocaudal gradients. Temporal and prefrontal areas may provide visual and contextual information relevant for manipulative action processing. These results revise existing models of the action observation network, suggesting that pAIP constitutes a parietal hub for routing information about OMA identity to the other nodes of the network