This work presents design, fabrication and test of a microfluidic device which employs Fahraeus-Lindqvist and Zweifach-Fung effects for cell concentration and blood cell-plasma separation. The device design comprises a straight main channel with a series of branched channels placed symmetrically on both sides of the main channel. The design implements constrictions before each junction (branching point) in order to direct cells that would have migrated closer to the wall (naturally or after liquid extraction at a junction) towards the centre of the main channel. Theoretical and numerical analysis are performed for design of the microchannel network to ensure that a minimum flow rate ratio (of 2.5:1, main channel-to-side channels) is maintained at each junction and predict flow rate at the plasma outlet. The dimensions and location of the constrictions were determined using numerical simulations. The effect of presence of constrictions before the junctions was demonstrated by comparing the performances of the device with and without constrictions. To demonstrate the performance of the device, initial experiments were performed with polystyrene microbeads (10 and 15 μm size) and droplets. Finally, the device was used for concentration of HL60 cells and separation of plasma and cells in diluted blood samples. The cell concentration and blood-plasma purification efficiency was quantified using Haemocytometer and Fluorescence-Activated Cell Sorter (FACS). A seven-fold cell concentration was obtained with HL60 cells and a purification efficiency of 70 % and plasma recovery of 80 % was observed for diluted (1:20) blood sample. FACS was used to identify cell lysis and the cell viability was checked using Trypan Blue test which showed that more than 99 % cells are alive indicating the suitability of the device for practical use. The proposed device has potential to be used as a sample preparation module in lab on chip based diagnostic platforms.

Neurovascular coupling is typically considered as a master-slave relationship between the neurons and the cerebral vessels: the neurons demand energy which the vessels supply in the form of glucose and oxygen. In the recent past, both theoretical and experimental studies have suggested that the neurovascular coupling is a bidirectional system, a loop that includes a feedback signal from the vessels influencing neural firing and plasticity. An integrated model of bidirectionally connected neural network and the vascular network is hence required to understand the relationship between the informational and metabolic aspects of neural dynamics. In this study, we present a computational model of the bidirectional neurovascular system in the whisker barrel cortex and study the effect of such coupling on neural activity and plasticity as manifest in the whisker barrel map formation. In this model, a biologically plausible self-organizing network model of rate coded, dynamic neurons is nourished by a network of vessels modeled using the biophysical properties of blood vessels. The neural layer which is designed to simulate the whisker barrel cortex of rat transmits vasodilatory signals to the vessels. The feedback from the vessels is in the form of available oxygen for oxidative metabolism whose end result is the adenosine triphosphate (ATP) necessary to fuel neural firing. The model captures the effect of the feedback from the vascular network on the neuronal map formation in the whisker barrel model under normal and pathological (Hypoxia and Hypoxia-Ischemia) conditions.

Artificial feedforward neural networks perform a wide variety of classification and function approximation tasks with high accuracy. Unlike their artificial counterparts, biological neural networks require a supply of adequate energy delivered to single neurons by a network of cerebral microvessels. Since energy is a limited resource, a natural question is whether the cerebrovascular network is capable of ensuring maximum performance of the neural network while consuming minimum energy? Should the cerebrovascular network also be trained, along with the neural network, to achieve such an optimum? In order to answer the above questions in a simplified modeling setting, we constructed an Artificial Neurovascular Network (ANVN) comprising a multilayered perceptron (MLP) connected to a vascular tree structure. The root node of the vascular tree structure is connected to an energy source, and the terminal nodes of the vascular tree supply energy to the hidden neurons of the MLP. The energy delivered by the terminal vascular nodes to the hidden neurons determines the biases of the hidden neurons. The “weights” on the branches of the vascular tree depict the energy distribution from the parent node to the child nodes. The vascular weights are updated by a kind of “backpropagation” of the energy demand error generated by the hidden neurons. We observed that higher performance was achieved at lower energy levels when the vascular network was also trained along with the neural network. This indicates that the vascular network needs to be trained to ensure efficient neural performance. We observed that below a certain network size, the energetic dynamics of the network in the per capita energy consumption vs. classification accuracy space approaches a fixed-point attractor for various initial conditions. Once the number of hidden neurons increases beyond a threshold, the fixed point appears to vanish, giving place to a line of attractors. The model also showed that when there is a limited resource, the energy consumption of neurons is strongly correlated to their individual contribution to the network’s performance.

10The neurovascular coupling is mostly considered as a master-slave relationship between 11 the neurons and the cerebral vessels: the neurons demand energy which the vessels supply in 12 the form of glucose and oxygen. In the recent past both theoretical and experimental studies 13 have suggested that the neurovascular coupling is a bidirectional system, a loop that includes a 14 feedback signal from the vessels influencing neural firing and plasticity. An integrated model 15 of bidirectionally connected neural network and vascular network is hence required to 16 understand the relationship between the informational and metabolic aspects of neural 17 dynamics . In this study, we present a computational model of the bidirectional neurovascular 18 system in the whisker barrel cortex and study the effect of such coupling on neural activity and 19 plasticity as manifest in the map formation. In this model, a biologically plausible self-20 organizing network model of rate coded, dynamic neurons is nourished by a network of vessels 21 modeled using the biophysical properties of blood vessels. The neural layer which is designed 22 to simulate the whisker barrel cortex of rat transmits the vasodilatory signals to the vessels. 23The feedback from the vessels is in the form of available oxygen for oxidative metabolism 24 whose end result is the ATP necessary to fuel neural firing. The model captures the effect of 25 the feedback from the vascular network on the neuronal map formation in the whisker barrel 26 model under normal and pathological (Hypoxia and Hypoxia-Ischemia) conditions.27 28 29 30 Author Summary 31 32Although neurovascular coupling is typically depicted as a unidirectional influence 33 originating from the neurons and acting on the cerebral vessels, in reality it forms a 34 bidirectional system, consisting of neuronal energy demand signals transmitted to the vessels, 35 and a feedback of metabolic substrates from the vessels, that influence the neural firing and 36 plasticity. We present a computational model of the bidirectional neurovascular coupling in the 37 whisker barrel cortex of rats and study the effect of such coupling on neural activity and 38 plasticity as manifest in the map formation. The model consists of a biologically plausible self-39 organizing dynamic, neural network model and a biophysical vascular network model that 40 nourishes the neural network in the form of oxygen necessary for neural oxidative metabolism. 41The model reproduces the spatio-temporal hemodynamic responses observed in rat whisker 42 barrel cortex. It also demonstrates the essentiality of the vascular feedback on the whisker 43 barrel map formation under normal and pathological conditions. al(9), desynchronized vascular rhythms were found to improve the efficiency of 106 learning in a neural network trained as an autoencoder. Among the aforementioned set of 107 models, there are either detailed biophysical models at the single unit level or abstract models 108 at a network level. There is a need to create detail...

Normal functioning of the brain relies on a continual and efficient delivery of energy by a vast network of cerebral blood vessels. The bidirectional coupling between neurons and blood vessels consists of vasodilatory energy demand signals from neurons to blood vessels, and the retrograde flow of energy substrates from the vessels to neurons, which fuel neural firing, growth and other housekeeping activities in the neurons. Recent works indicate that, in addition to the functional coupling observed in the adult brain, the interdependence between the neural and vascular networks begins at the embryonic stage, and continues into subsequent developmental stages. The proposed Vascular Arborization Model (VAM) captures the effect of neural cytoarchitecture and neural activity on vascular arborization. The VAM describes three important stages of vascular tree growth: (i) The prenatal growth phase, where the vascular arborization depends on the cytoarchitecture of neurons and non-neural cells, (ii) the post-natal growth phase during which the further arborization of the vasculature depends on neural activity in addition to neural cytoarchitecture, and (iii) the settling phase, where the fully grown vascular tree repositions its vascular branch points or nodes to ensure minimum path length and wire length. The vasculature growth depicted by VAM captures structural characteristics like vascular volume density, radii, mean distance to proximal neurons in the cortex. VAM-grown vasculature agrees with the experimental observation that the neural densities do not covary with the vascular density along the depth of the cortex but predicts a high correlation between neural areal density and microvascular density when compared over a global scale (across animals and regions). To explore the influence of neural activity on vascular arborization, the VAM was used to grow the vasculature in neonatal rat whisker barrel cortex under two conditions: (i) Control, where the whiskers were intact and (ii) Lesioned, where one row of whiskers was cauterized. The model captures a significant reduction in vascular branch density in lesioned animals compared to control animals, concurring with experimental observation.