Alexandra Tran-Van-Minh

SignificanceAchieving adequate brain sensitivity remains a significant obstacle to noninvasive optical measurements of pulsatile cerebral blood flow. Increasing source-detector separation (SDS), utilizing time-of-flight (ToF) information, and/or increasing acquisition frequency can increase brain sensitivity. However, optimizing these parameters is nontrivial and must balance the need to achieve sufficient signal-to-noise ratio.AimWe aim to guide hardware optimization by evaluating the benefits of ToF gating, autocorrelation time-lag gating, and increased source-detector separation in recovering pulsatile cerebral blood flow.ApproachMonte Carlo simulations of ToF-resolved pulsatile blood flow index at 1064 nm were performed. A simulation and analysis pipeline is presented and validated against phantom and in vivo measurements.ResultsBrain sensitivity deteriorates for autocorrelation time-lags >1 to 10 μs. Continuous-wave diffuse correlation spectroscopy (CW-DCS) at 40 mm SDS achieves equivalent brain sensitivity to time-resolved measurements with a 1.2 ns ToF gate. For median adult brain depths, a 31 mm SDS (CW-DCS), 35 mm SDS (speckle contrast optical spectroscopy), or 0.9 ns ToF are needed for brain-dominated signals. For the 85th percentile of brain depth, these thresholds increase to 40 mm, 46 mm, and 1.2 ns, respectively.ConclusionsWe quantitatively compare source-detector separation, ToF gating, and acquisition frequency and identify crucial performance requirements for clinically viable optical monitors of cerebral blood flow.

SignificanceContinuous, noninvasive monitoring of cerebral blood flow (CBF) at the bedside is a critical unmet clinical need and a long-standing goal of biomedical optics. Despite extensive development, no optical measurement of CBF has achieved routine clinical use.AimWe aim to demonstrate and validate CoMind R1, a time-resolved interferometric neuromonitoring system designed to noninvasively measure CBF at pulsatile rates.ApproachCoMind R1 integrates numerous advances, including a high-power, linearly swept 1064 nm laser, multimode collection, parallelized detection, and real-time processing. Performance is evaluated with homogeneous and dynamic bi-layer phantoms and in vivo studies of adults at rest and during visual stimulation.ResultsCoMind R1 achieves an instrument response function width of 120 ps and exhibits no significant drift over 24 h. In vivo data from 25 adults show consistent pulsatile blood flow waveforms at times-of-flight averaging 1.2 ns, exceeding the prior art. Visual stimulation produces significant, time-of-flight-dependent hyperaemic responses, demonstrating brain sensitivity and selectivity.ConclusionsCoMind R1 consistently achieves time-of-flight resolved measurements of pulsatile blood flow at the late times-of-flight necessary to ensure a brain-dominated signal in the typical adult. CoMind R1 thus provides cerebral sensitivity and tissue specificity in a scalable, cost-effective architecture and advances optical monitoring of CBF toward clinical translation.

Theory predicts that nonlinear summation of synaptic potentials within dendrites allows neurons to perform linearly non-separable computations (LNSCs). Using Boolean analysis approaches, we predicted that both supralinear and sublinear synaptic summation could allow single neurons to implement a type of LNSC, the feature binding problem (FBP), which does not require inhibition contrary to the XOR. Notably, sublinear dendritic operations enable LNSCs when scattered synaptic activation generates increased somatic spike output. However, experimental demonstrations of scatter-sensitive neuronal computations have not yet been described. Using glutamate uncaging onto cerebellar molecular layer interneurons, we show that scattered synaptic-like activation of dendrites evoked larger compound EPSPs than clustered synaptic activation, generating a higher output spiking probability. Moreover, we also demonstrate that single interneurons can indeed implement the FBP. We use a biophysical model to predict under what conditions a neuron can implement the FBP and what leads to failures. Experimental results agree with the model-determined conditions and hence validate our protocol as a solid benchmark for a neuron to implement linearly non-separable computations. Since sublinear synaptic summation is a property of passive dendrites we expect that many different neuron types can implement LNSCs.

Head fixation allows the recording and presentation of controlled stimuli and is used to study neural processes underlying spatial navigation. However, it disrupts the head direction system because of the lack of vestibular stimulation. To overcome this limitation, we developed a novel rotation platform which can be driven by the experimenter (open-loop) or by animal movement (closed-loop). The platform is modular, affordable, easy to build and open source. Additional modules presented here include cameras for monitoring eye movements, visual virtual reality and a micro-manipulator for positioning various probes for recording or optical interference. We demonstrate the utility of the platform by recording eye movements and showing the robust activation of head-direction cells. This novel experimental apparatus combines the advantages of head fixation and intact vestibular activity in the horizontal plane. The open-loop mode can be used to study e.g. vestibular sensory representation and processing, while the closed-loop mode allows animals to navigate in rotational space, providing a better substrate for 2-D navigation in virtual environments. Documentation is available at ( https://ranczlab.github.io/RPM/ ).

Monosynaptically restricted rabies viruses have been used for more than a decade for synaptic connectivity tracing. However, the verisimilitude of quantitative conclusions drawn from these experiments is largely unknown. The primary reason is the simple metrics commonly used, which generally disregard the effect of starter cell numbers. Here we present an experimental dataset with a broad range of starter cell numbers and explore their relationship with the number of input cells across the brain using descriptive statistics and modelling. We show that starter cell numbers strongly affect input fraction and convergence index measures, making quantitative comparisons unreliable. Furthermore, we suggest a principled way to analyse rabies derived connectivity data by taking advantage of the starter vs input cell relationship that we describe and validate across independent datasets.

Voltage-gated calcium channels are essential regulators of brain function where they support depolarization-induced calcium entry into neurons. They consist of a pore-forming subunit (Ca_vα₁) that requires co-assembly with ancillary subunits to ensure proper functioning of the channel. Among these ancillary subunits, the Ca_vβ plays an essential role in regulating surface expression and gating of the channels. This regulation requires the direct binding of Ca_vβ onto Ca_vα₁ and is mediated by the alpha interacting domain (AID) within the Ca_vα₁ subunit and the α binding pocket (ABP) within the Ca_vβ subunit. However, additional interactions between Ca_vα₁ and Ca_vβ have been proposed. In this study, we analyzed the importance of Ca_vβ₃ surface charged residues in the regulation of Ca_v2.1 channels. Using alanine-scanning mutagenesis combined with electrophysiological recordings we identified several amino acids within the Ca_vβ₃ subunit that contribute to the gating of the channel. These findings add to the notion that additional contacts besides the main AID/ABP interaction may occur to fine-tune the expression and properties of the channel.

Nonlinear dendritic integration is thought to increase the computational ability of neurons. Most studies focus on how supralinear summation of excitatory synaptic responses arising from clustered inputs within single dendrites result in the enhancement of neuronal firing, enabling simple computations such as feature detection. Recent reports have shown that sublinear summation is also a prominent dendritic operation, extending the range of subthreshold input-output transformations conferred by dendrites. Like supralinear operations, sublinear dendritic operations also increase the repertoire of neuronal computations, but feature extraction requires different synaptic connectivity strategies for each of these operations. In this article we will review the experimental and theoretical findings describing the biophysical determinants of the three primary classes of dendritic operations: linear, sublinear, and supralinear. We then review a Boolean algebra-based analysis of simplified neuron models, which provides insight into how dendritic operations influence neuronal computations. We highlight how neuronal computations are critically dependent on the interplay of dendritic properties (morphology and voltage-gated channel expression), spiking threshold and distribution of synaptic inputs carrying particular sensory features. Finally, we describe how global (scattered) and local (clustered) integration strategies permit the implementation of similar classes of computations, one example being the object feature binding problem.