Publications by authors named "Chinta L"

The majority of patients with Alzheimer's disease have cerebral amyloid angiopathy, thus showing deposition of amyloid-β peptides in the walls of leptomeningeal and cortical arterioles. These deposits are believed to result from impaired clearance of parenchymal amyloid-β peptides. In the current work, we examined the changes in cortical microvascular structure and function in situ in TgCRND8, a transgenic mouse model of Alzheimer's disease.

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Schizophrenia is conceptualized as a failure of cognitive integration, and altered oscillatory properties of neurocircuits are associated with its symptoms. We hypothesized that abnormal characteristics of neural networks may alter functional connectivity and distort propagation of activation in schizophrenic brains. Thus, electroencephalography (EEG) responses to transcranial magnetic stimulation (TMS) of motor cortex were compared between schizophrenia and healthy subjects.

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Vascular transit time is an important indicator of microcirculatory health. We present a second-order-plus-dead-time (SOPDT) model for robust estimation of kinetic parameters characterizing microvascular bolus passage using two-photon fluorescence microscopy (2PFM) in anesthetized rats receiving somatosensory stimulation. This methodology enables quantification of transit time, time-to-peak, overshoot, and rate of bolus passage through the microvascular network.

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Functional hyperemia, or the increase in focal perfusion elicited by neuronal activation, is one of the primary functions of the neurovascular unit and a hallmark of healthy brain functioning. While much is known about the hemodynamics on the millimeter to tenths of millimeter-scale accessible by MRI, there is a paucity of quantitative data on the micrometer-scale changes in perfusion in response to functional stimulation. We present a novel methodology for quantification of perfusion and intravascular flow across the 3D microvascular network in the rat somatosensory cortex using two-photon fluorescence microscopy (2PFM).

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Many neural control systems are at least roughly optimized, but how is optimal control learned? There are algorithms for this purpose, but in their current forms, they are not suited for biological neural networks because they rely on a type of communication that is not available in the brain, namely, weight transport-transmitting the strengths, or "weights," of individual synapses to other synapses and neurons. Here we show how optimal control can be learned without weight transport. Our method involves a set of simple mechanisms that can compensate for the absence of weight transport in the brain and so may be useful for neural computation generally.

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Exercise electrocardiograms were registered in 50 patients with significant coronary artery stenosis (lumen narrowing greater than or equal to 70%) and in 20 controls without cardiac disease using automated registration (mapping). All patients had a normal ECG at rest and typical angina. ST-segment depression of more than 0.

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