Adnan Shah

HRF Estimation in fMRI Data With an Unknown Drift Matrix by Iterative Minimization of the Kullback–Leibler Divergence

Hemodynamic response function (HRF) estimation in noisy functional magnetic resonance imaging (fMRI) plays an important role when investigating the temporal dynamic of a brain region response during activations. Nonparametric methods which allow more flexibility in the estimation by inferring the HRF at each time sample have provided improved performance in comparison to the parametric methods. In this paper, the mixed-effects model is used to derive a new algorithm for nonparametric maximum likelihood HRF estimation. In this model, the random effect is used to better account for the variability of the drift. Contrary to the usual approaches, the proposed algorithm has the benefit of considering an unknown and therefore flexible drift matrix. This allows the effective representation of a broader class of drift signals and therefore the reduction of the error in approximating the drift component. Estimates of the HRF and the hyperparameters are derived by iterative minimization of the Kullback-Leibler divergence between a model family of probability distributions defined using the mixed-effects model and a desired family of probability distributions constrained to be concentrated on the observed data. The performance of proposed method is demonstrated on simulated and real fMRI data, the latter originating from both event-related and block design fMRI experiments.

Consistent hemodynamic response function estimation in functional MRI by first order differencing

Non-parametric hemodynamic response function (HRF) estimation in noisy functional Magnetic Resonance Imaging (fMRI) plays an important role when investigating the temporal dynamic of a brain region response during activations. Assuming the drift Lipschitz continuous; a new algorithm for non-parametric HRF estimation is derived in this paper. The proposed algorithm estimates the HRF by applying a first order differencing to the fMRI time series samples. It is shown that the proposed HRF estimator is √(N) consistent. Its performance is assessed using both simulated and a real fMRI data sets obtained from an event-related fMRI experiment. The application results reveal that the proposed HRF estimation method is efficient both computationally and in term of accuracy.

An Integrated Framework for Joint HRF and Drift Estimation and HbO/HbR Signal Improvement in fNIRS Data

Nonparametric hemodynamic response function (HRF) estimation in functional near-infrared spectroscopy (fNIRS) data plays an important role when investigating the temporal dynamics of a brain region response during activations. Assuming the drift arising from both physical and physiological effects in fNIRS data is Lipschitz continuous; a novel algorithm for joint HRF and drift estimation is derived in this paper. The proposed algorithm estimates the HRF by applying a first-order differencing to the fNIRS time series samples in order to remove the drift effect. An estimate of the drift is then obtained using a wavelet thresholding technique applied to the residuals generated by removing the estimated induced activation response from the fNIRS time-series. It is shown that the proposed HRF estimator is √N consistent whereas the estimator of the drift is asymptotically optimal. The de-drifted fNIRS oxygenated (HbO) and deoxygenated (HbR) hemoglobin responses are then obtained by removing the corresponding estimated drifts from the fNIRS time-series. Its performance is assessed using both simulated and real fNIRS data sets. The application results reveal that the proposed joint HRF and drift estimation method is efficient both computationally and in terms of accuracy. In comparison to traditional model based methods used for HRF estimation, the proposed novel method avoids the selection of a model to remove the drift component. As a result, the proposed method finds an optimal estimate of the fNIRS drift and offers a model-free approach to de-drift the HbO/HbR responses.

Consistent estimation of the hemodynamic response function in fNIRS

Non-parametric hemodynamic response function (HRF) estimation in noisy functional near-infrared spectroscopy (fNIRS) plays an important role when investigating the temporal dynamics of a brain region response during activations. Assuming the drift Lipschitz continuous; a new algorithm for non-parametric HRF estimation from the oxygenated (HbO) and deoxygenated (HbR) fNIRS time-series is derived in this paper. The proposed algorithm estimates the HRF by applying a first order differencing to the fNIRS time series samples. It is shown that the proposed HRF estimator is √N consistent. Its performance is assessed using both simulated and a real fNIRS data set obtained from a motor activity experiment. The application results reveal that the proposed HRF estimation method is efficient both computationally and in terms of accuracy.

Connectivity in Language Areas of the Brain in Cochlear Implant Users as Revealed by fNIRS

Many studies, using a variety of imaging techniques, have shown that deafness induces functional plasticity in the brain of adults with late-onset deafness, and in children changes the way the auditory brain develops. Cross modal plasticity refers to evidence that stimuli of one modality (e.g. vision) activate neural regions devoted to a different modality (e.g. hearing) that are not normally activated by those stimuli. Other studies have shown that multimodal brain networks (such as those involved in language comprehension, and the default mode network) are altered by deafness, as evidenced by changes in patterns of activation or connectivity within the networks. In this paper, we summarise what is already known about brain plasticity due to deafness and propose that functional near-infra-red spectroscopy (fNIRS) is an imaging method that has potential to provide prognostic and diagnostic information for cochlear implant users. Currently, patient history factors account for only 10 % of the variation in post-implantation speech understanding, and very few post-implantation behavioural measures of hearing ability correlate with speech understanding. As a non-invasive, inexpensive and user-friendly imaging method, fNIRS provides an opportunity to study both pre- and post-implantation brain function. Here, we explain the principle of fNIRS measurements and illustrate its use in studying brain network connectivity and function with example data.

Functional brain connectivity as revealed by singular spectrum analysis

Correlation based measures have widely been used to characterize brain connectivity. In this paper, a new approach based on singular spectrum analysis is proposed to characterize brain connectivity. It is obtained by deriving the common basis vector of two or more trajectory matrices associated with functional brain responses. This approach has the advantage illustrating the existence of joint variations of the functional brain responses and to characterize the correlation structure. The performance of the method are illustrated on both simulated autoregressive data and real fMRI data.

Sparse dictionary learning for fMRI analysis using autocorrelation maximization.

In this paper, the effect of temporal autocorrelations in functional magnetic resonance imaging (fMRI) data on sparse dictionary learning (SDL) is addressed. For sparse general linear model (sGLM), the fMRI time-series is modeled as a linear mixture of several signals such as neural dynamics, structured noise, random noise and unexplained signal variations on the basis of spatial sparseness. These signals are considered as underlying sources and SDL is used to estimate them. However, the sparse GLM model does not take into account the autocorrelations in fMRI data. To address this shortcoming, a new model is proposed to incorporate the prior knowledge about lag-1 autocorrelation into dictionary update stage. This helps improve the sensitivity and specificity of the fMRI data during statistical analysis. Using a simulation study, the effect of the proposed dictionary update on sGLM is compared to conventional sGLM by utilizing various detrending techniques. Furthermore, the proposed update is validated in an sGLM framework for real fMRI datasets, which shows its better capability to estimate neural dynamics in presence of spatiotemporal dependencies.

A Model-Free De-Drifting Approach for Detecting BOLD Activities in fMRI Data

A model-free method for efficiently capturing drifts in functional magnetic resonance imaging (fMRI) data is presented. The proposed algorithm applies a first order differencing to the fMRI time series samples in order to remove the drift effect. Initially, a consistent hemodynamic response function (HRF) of the fMRI voxel is estimated using linear least-squares. An optimal estimate of the drift is then obtained based on a wavelet thresholding technique applied to the generated residuals after eliminating the induced activation response. Finally, the de-drifted fMRI voxel response is acquired by removing the estimated drift from the fMRI time-series. Its performance is assessed using simulated and motor-task real fMRI data sets obtained from both block and event-related designs. The application results reveal that the proposed method, which avoids the selection of a model to remove the drift component unlike traditional methods, is efficient in de-drifting the fMRI time-series and offers blood oxygen level-dependent (BOLD)-fMRI signal improvement and enhanced activation detection.

SPARSE ESTIMATION OF THE HEMODYNAMIC RESPONSE FUNCTION IN FUNCTIONAL NEAR INFRARED SPECTROSCOPY

Functional near-infrared spectroscopy (fNIRS) signals offer an interesting alternative to functional magnetic resonance imaging (fMRI) when investigating the temporal dynamics of brain region responses during activations. The hemodynamic response function (HRF) is the object of primary interest to neuroscientists in this case. Making use of a semiparametric model to characterize the oxygenated (HbO) and deoxygenated (HbR) fNIRS time-series and a sparsity assumption on the HRF, a new method for non-parametric HRF estimation from a single fNIRS signal is derived in this paper. The proposed method consistently estimates the HRF using a profile least square estimator obtained using the local polynomial smoothing technique applied to estimate the drift and introducing a regularization penalty in the minimization problem to promote sparsity of the HRF coefficients. The performance of the proposed method is assessed on both simulated and fNIRS data from a finger tapping experiment.

Model-free optimal de-drifting and enhanced detection in fMRI data

Discriminating between active and non-active brain voxels in noisy functional magnetic resonance imaging (fMRI) data plays an important role when investigating task-related activations of the neuronal sites. A novel method for efficiently capturing drifts in the functional magnetic resonance imaging (fMRI) data is presented that leads to enhanced fMRI activation detection. The proposed algorithm apply a first order differencing to the fMRI time series samples in order to remove the drift effect. Using linear least-squares, a consistent hemodynamic response function (HRF) of the fMRI voxel is estimated as a first-step that leads to an optimal estimate of the drift based on a wavelet thresholding technique. The de-drifted fMRI voxel response is then obtained by removing the estimated drift from the fMRI time-series. Its performance is assessed using a visual task real fMRI data set. The application results reveal that the proposed method, which avoids the selection of a model to remove the drift component, leads to an improved activation detection performance in fMRI data.