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Dive into the research topics where MohammadMehdi Kafashan is active.

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Featured researches published by MohammadMehdi Kafashan.


Frontiers in Neural Circuits | 2016

Sevoflurane Alters Spatiotemporal Functional Connectivity Motifs That Link Resting-State Networks during Wakefulness

MohammadMehdi Kafashan; ShiNung Ching; Ben Julian A. Palanca

Background: The spatiotemporal patterns of correlated neural activity during the transition from wakefulness to general anesthesia have not been fully characterized. Correlation analysis of blood-oxygen-level dependent (BOLD) functional magnetic resonance imaging (fMRI) allows segmentation of the brain into resting-state networks (RSNs), with functional connectivity referring to the covarying activity that suggests shared functional specialization. We quantified the persistence of these correlations following the induction of general anesthesia in healthy volunteers and assessed for a dynamic nature over time. Methods: We analyzed human fMRI data acquired at 0 and 1.2% vol sevoflurane. The covariance in the correlated activity among different brain regions was calculated over time using bounded Kalman filtering. These time series were then clustered into eight orthogonal motifs using a K-means algorithm, where the structure of correlated activity throughout the brain at any time is the weighted sum of all motifs. Results: Across time scales and under anesthesia, the reorganization of interactions between RSNs is related to the strength of dynamic connections between member pairs. The covariance of correlated activity between RSNs persists compared to that linking individual member pairs of different RSNs. Conclusions: Accounting for the spatiotemporal structure of correlated BOLD signals, anesthetic-induced loss of consciousness is mainly associated with the disruption of motifs with intermediate strength within and between members of different RSNs. In contrast, motifs with higher strength of connections, predominantly with regions-pairs from within-RSN interactions, are conserved among states of wakefulness and sevoflurane general anesthesia.


international conference of the ieee engineering in medicine and biology society | 2014

Bounded-observation Kalman filtering of correlation in multivariate neural recordings

MohammadMehdi Kafashan; Ben Julian A. Palanca; ShiNung Ching

A persistent question in multivariate neural signal processing is how best to characterize the statistical association between brain regions known as functional connectivity. Of the many metrics available for determining such association, the standard Pearson correlation coefficient (i.e., the zero-lag cross-correlation) remains widely used, particularly in neuroimaging. Generally, the cross-correlation is computed over an entire trial or recording session, with the assumption of within-trial stationarity. Increasingly, however, the length and complexity of neural data requires characterizing transient effects and/or non-stationarity in the temporal evolution of the correlation. That is, to estimate dynamics in the association between brain regions. Here, we present a simple, data-driven Kalman filter-based approach to tracking correlation dynamics. The filter explicitly accounts for the bounded nature of correlation measurements through the inclusion of a Fisher transform in the measurement equation. An output linearization facilitates a straightforward implementation of the standard recursive filter equations, including admittance of covariance identification via an autoregressive least squares method. We demonstrate the efficacy and utility of the approach in an example of multivariate neural functional magnetic resonance imaging data.


advances in computing and communications | 2015

Controlling linear networks with minimally novel inputs

Gautam Kumar; Delsin Menolascino; MohammadMehdi Kafashan; ShiNung Ching

In this paper, we propose a novelty-based index for quantitative characterization of the controllability of complex networks. This inherently bounded index describes the average angular separation of an input with respect to the past input history. We use this index to find the minimally novel input that drives a linear network to a desired state using unit average energy. Specifically, the minimally novel input is defined as the solution of a continuous time, non-convex optimal control problem based on the introduced index. We provide conditions for existence and uniqueness, and an explicit, closed-form expression for the solution. We support our theoretical results by characterizing the minimally novel inputs for an example of a recurrent neuronal network.


IEEE Transactions on Control of Network Systems | 2018

Control Analysis and Design for Statistical Models of Spiking Networks

Anirban Nandi; MohammadMehdi Kafashan; ShiNung Ching

A popular approach to characterizing activity in neuronal networks is the use of statistical models that describe neurons in terms of their firing rates (i.e., the number of spikes produced per unit time). The output realization of a statistical model is, in essence, an


Journal of Neural Engineering | 2015

Optimal stimulus scheduling for active estimation of evoked brain networks.

MohammadMehdi Kafashan; ShiNung Ching

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advances in computing and communications | 2016

Controlling point process generalized linear models of neural spiking

Anirban Nandi; MohammadMehdi Kafashan; ShiNung Ching

dimensional binary time series, or pattern. While such models are commonly fit to data, they can also be postulated de novo, as a theoretical description of a given spiking network. More generally, they can model any network producing binary events as a function of time. In this paper, we rigorously develop a set of analyses that may be used to assay the controllability of a particular statistical spiking model, the point-process generalized linear model. Our analysis quantifies the ease or difficulty of inducing desired spiking patterns via an extrinsic input signal, thus providing a framework for basic network analysis, as well as for emerging applications such as neurostimulation design.


Neural Networks | 2016

Relating observability and compressed sensing of time-varying signals in recurrent linear networks

MohammadMehdi Kafashan; Anirban Nandi; ShiNung Ching

OBJECTIVE We consider the problem of optimal probing to learn connections in an evoked dynamic network. Such a network, in which each edge measures an input-output relationship between sites in sensor/actuator-space, is relevant to emerging applications in neural mapping and neural connectivity estimation. APPROACH We show that the problem of scheduling nodes to a probe (i.e., stimulate) amounts to a problem of optimal sensor scheduling. MAIN RESULTS By formulating the evoked network in state-space, we show that the solution to the greedy probing strategy has a convenient form and, under certain conditions, is optimal over a finite horizon. We adopt an expectation maximization technique to update the state-space parameters in an online fashion and demonstrate the efficacy of the overall approach in a series of detailed numerical examples. SIGNIFICANCE The proposed method provides a principled means to actively probe time-varying connections in neuronal networks. The overall method can be implemented in real time and is particularly well-suited to applications in stimulation-based cortical mapping in which the underlying network dynamics are changing over time.


Journal of Neuroscience Methods | 2018

Dimensionality reduction impedes the extraction of dynamic functional connectivity states from fMRI recordings of resting wakefulness

MohammadMehdi Kafashan; Ben Julian A. Palanca; ShiNung Ching

In brain networks, neurons communicate through action potentials or spikes. These spikes can be thought of as discrete events that constitute, in essence, a binary, time-varying spatial pattern over the entire network. A general hypothesis in neuroscience is that these patterns encode information, thus enabling function. Consequently, an emerging research direction in experimental neuroscience involves the use of neurostimulation technologies to artificially induce such patterns in a spatiotemporally precise manner - the so-called neurocontrol problem. In this work, we discuss the neurocontrol problem by means of statistical models, which, in contrast to more traditional dynamical-systems models, describe only the probability of spiking as a function of time. Thus, such models aggregate nonlinearity and uncertainty into a more tractable mathematical description. While statistical models are frequently used to describe experimental data, their use as tools for input construction is not as well explored. Here, we formulate an optimal control problem for spiking patterns via a weighted maximum likelihood approach and develop its solution. We demonstrate the design approach for a model network consisting of coupled stochastic integrate-and-fire neurons. Finally, we suggest how this overall framework can be used to develop a class of control analyses for point process models.


Neural Networks | 2017

Recurrent networks with soft-thresholding nonlinearities for lightweight coding

MohammadMehdi Kafashan; ShiNung Ching

In this paper, we study how the dynamics of recurrent networks, formulated as general dynamical systems, mediate the recovery of sparse, time-varying signals. Our formulation resembles the well-described problem of compressed sensing, but in a dynamic setting. We specifically consider the problem of recovering a high-dimensional network input, over time, from observation of only a subset of the network states (i.e., the network output). Our goal is to ascertain how the network dynamics may enable recovery, even if classical methods fail at each time instant. We are particularly interested in understanding performance in scenarios where both the input and output are corrupted by disturbance and noise, respectively. Our main results consist of the development of analytical conditions, including a generalized observability criterion, that ensure exact and stable input recovery in a dynamic, recurrent network setting.


BMC Neurology | 2017

EEG dynamical correlates of focal and diffuse causes of coma

MohammadMehdi Kafashan; Shoko Ryu; Mitchell J Hargis; Osvaldo Laurido-Soto; Debra E Roberts; Akshay Thontakudi; Lawrence N. Eisenman; Terrance T. Kummer; ShiNung Ching

BACKGROUND Resting wakefulness is not a unitary state, with evidence accumulating that spontaneous reorganization of brain activity can be assayed through functional magnetic resonance imaging (fMRI). The dynamics of correlated fMRI signals among functionally-related brain regions, termed dynamic functional connectivity (dFC), may represent nonstationarity arising from underlying neural processes. However, given the dimensionality and noise inherent in such recordings, seeming fluctuations in dFC could be due to sampling variability or artifacts. NEW METHOD Here, we highlight key methodological considerations when evaluating dFC in resting-state fMRI data. COMPARISON WITH EXISTING METHOD In particular, we demonstrate how dimensionality reduction of fMRI data, a common practice often involving principal component analysis, may give rise to spurious dFC phenomenology due to its effect of decorrelating the underlying time-series. CONCLUSION We formalize a dFC assessment that avoids dimensionality reduction and use it to show the existence of at least two FC states in the resting-state.

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ShiNung Ching

Washington University in St. Louis

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Anirban Nandi

Washington University in St. Louis

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Ben Julian A. Palanca

Washington University in St. Louis

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Akshay Thontakudi

Washington University in St. Louis

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Delsin Menolascino

Washington University in St. Louis

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Lawrence N. Eisenman

Washington University in St. Louis

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Osvaldo Laurido-Soto

Washington University in St. Louis

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