Gholam-Ali Hossein-Zadeh

A signal subspace approach for modeling the hemodynamic response function in fMRI

Many fMRI analysis methods use a model for the hemodynamic response function (HRF). Common models of the HRF, such as the Gaussian or Gamma functions, have parameters that are usually selected a priori by the data analyst. A new method is presented that characterizes the HRF over a wide range of parameters via three basis signals derived using principal component analysis (PCA). Covering the HRF variability, these three basis signals together with the stimulation pattern define signal subspaces which are applicable to both linear and nonlinear modeling and identification of the HRF and for various activation detection strategies. Analysis of simulated fMRI data using the proposed signal subspace showed increased detection sensitivity compared to the case of using a previously proposed trigonometric subspace. The methodology was also applied to activation detection in both event-related and block design experimental fMRI data using both linear and nonlinear modeling of the HRF. The activated regions were consistent with previous studies, indicating the ability of the proposed approach in detecting brain activation without a priori assumptions about the shape parameters of the HRF. The utility of the proposed basis functions in identifying the HRF is demonstrated by estimating the HRF in different activated regions.

Directed Differential Connectivity Graph of Interictal Epileptiform Discharges

In this paper, we study temporal couplings between interictal events of spatially remote regions in order to localize the leading epileptic regions from intracerebral EEG (iEEG). We aim to assess whether quantitative epileptic graph analysis during interictal period may be helpful to predict the seizure onset zone of ictal iEEG. Using wavelet transform, cross-correlation coefficient, and multiple hypothesis test, we propose a differential connectivity graph (DCG) to represent the connections that change significantly between epileptic and nonepileptic states as defined by the interictal events. Post-processings based on mutual information and multiobjective optimization are proposed to localize the leading epileptic regions through DCG. The suggested approach is applied on iEEG recordings of five patients suffering from focal epilepsy. Quantitative comparisons of the proposed epileptic regions within ictal onset zones detected by visual inspection and using electrically stimulated seizures, reveal good performance of the present method.

A mutual information‐based metric for evaluation of fMRI data‐processing approaches

We propose a novel approach for evaluating the performance of activation detection in real (experimental) datasets using a new mutual information (MI)‐based metric and compare its sensitivity to several existing performance metrics in both simulated and real datasets. The proposed approach is based on measuring the approximate MI between the fMRI time‐series of a validation dataset and a calculated activation map (thresholded label map or continuous map) from an independent training dataset. The MI metric is used to measure the amount of information preserved during the extraction of an activation map from experimentally related fMRI time‐series. The processing method that preserves maximal information between the maps and related time‐series is proposed to be superior. The results on simulation datasets for multiple analysis models are consistent with the results of ROC curves, but are shown to have lower information content than for real datasets, limiting their generalizability. In real datasets for group analyses using the general linear model (GLM; FSL4 and SPM5), we show that MI values are (1) larger for groups of 15 versus 10 subjects and (2) more sensitive measures than reproducibility (for continuous maps) or Jaccard overlap metrics (for thresholded maps). We also show that (1) for an increasing fraction of nominally active voxels, both MI and false discovery rate (FDR) increase, and (2) at a fixed FDR, GLM using FSL4 tends to extract more voxels and more information than SPM5 using the default processing techniques in each package. Hum Brain Mapp, 2011.

Estimation of direct nonlinear effective connectivity using information theory and multilayer perceptron.

BACKGROUND Despite the variety of effective connectivity measures, few methods can quantify direct nonlinear causal couplings and most of them are not applicable to high-dimensional datasets. NEW METHOD In this paper, a novel approach (called βmRMR-MLP-GC) is proposed to estimate direct nonlinear effective connectivity of high-dimensional datasets. βmRMR is used to select a suitable subset of candidate regressors for approximating each neural (here EEG) signal. The multilayer perceptron (MLP) is used for multivariate characterization of EEG signals while the optimum MLP structure is selected using an iterative cross-validation scheme. Finally a causality measure is defined based on Granger Causality (GC) concept to quantify the casual relations among EEG channels. RESULTS Applying βmRMR-MLP-GC to high-dimensional simulated datasets with different linear and nonlinear structures yields sensitivity and specificity values higher than 95%. Also, applying it to eyes-closed resting state EEG of six normal subjects in the alpha frequency band yields significant net activity propagations from the posterior to anterior brain regions. This is in accordance with the most previous studies in this field. COMPARISON WITH EXISTING METHOD(S) βmRMR-MLP-GC is compared with Granger Causality Index, Conditional Granger Causality Index, and Transfer Entropy. It outperforms these methods in terms of sensitivity and specificity in simulated datasets. Also, βmRMR-MLP-GC detects the most number of significant and reproducible Back-to-Front net information flows among the specified brain regions and highlights the posterior brain regions as dominant source of alpha activity propagation. CONCLUSIONS βmRMR-MLP-GC provides a novel tool to estimate the direct nonlinear causal networks of high-dimensional datasets.

Diffusion kurtosis imaging discriminates patients with white matter lesions from healthy subjects

This work illustrates that DKI reveals white matter lesions and also discriminates healthy subjects from patientswith white matter lesions. To show this capability, we have investigated DKI images of a healthy subject and apatient with white matter lesions. The analysis was performed both between and within subjects. Regions of Interest (ROIs) for lesion and normal white matterin the patient images are selected manually (for within subject study) and also the corresponding ROIs in the healthy subject are defined (for between subject study). The results of comparing the estimated values for apparent diffusion and kurtosis parameters show that both Dapp and Kapp can distinguish normal and abnormal tissues. Kapp (Dapp) of the normal regions is greater (lower) than that ofthe abnormal regions. Another investigation over all voxels in the brain shows an important feature of kurtosis in determining white matter lesions.

Reconstruction of digit images from human brain fMRI activity through connectivity informed Bayesian networks

BACKGROUND Newly emerged developments in decoding of stimulus images from fMRI measurements have shown promising results. Decoding-classification has been the main concern of decoding studies, whereas the matter of reconstruction (decoding) of stimulus images from fMRI data, especially natural images, lacks adequate examination and it requires plenty of efforts to improve. NEW METHOD The present study employs Bayesian networks for decoding-reconstruction which is a novel application of this tool. Moreover, as a novel approach, we exploit the brain connectivity information in decoding-reconstruction procedure through Bayesian networks. RESULTS The proposed method was applied to reconstruct 100 images of digits 6 and 9 from the fMRI measurements obtained when showing some handwritten images of 6 and 9 to the subject. The information of only 10 brain voxels were exploited and an average (standard deviation) city-block distance error of 0.1071(0.0134) was obtained for all stimulis reconstruction. In comparison with current common methods: The results reveal that Bayesian networks are successful in decoding-reconstruction of handwritten digits and inclusion of brain connectivity information makes them perform even more efficiently and improves decoding-reconstruction as well (reducing average error by almost 5%). CONCLUSION In the task of decoding-reconstruction, the models including brain connectivity appear significantly superior to other existing models.

Long-Range Reduced Predictive Information Transfers of Autistic Youths in EEG Sensor-Space During Face Processing

The majority of previous functional/effective connectivity studies conducted on the autistic patients converged to the underconnectivity theory of ASD: “long-range underconnectivity and sometimes short-rang overconnectivity”. However, to the best of our knowledge the total (linear and nonlinear) predictive information transfers (PITs) of autistic patients have not been investigated yet. Also, EEG data have rarely been used for exploring the information processing deficits in autistic subjects. This study is aimed at comparing the total (linear and nonlinear) PITs of autistic and typically developing healthy youths during human face processing by using EEG data. The ERPs of 12 autistic youths and 19 age-matched healthy control (HC) subjects were recorded while they were watching upright and inverted human face images. The PITs among EEG channels were quantified using two measures separately: transfer entropy with self-prediction optimality (TESPO), and modified transfer entropy with self-prediction optimality (MTESPO). Afterwards, the directed differential connectivity graphs (dDCGs) were constructed to characterize the significant changes in the estimated PITs of autistic subjects compared with HC ones. By using both TESPO and MTESPO, long-range reduction of PITs of ASD group during face processing was revealed (particularly from frontal channels to right temporal channels). Also, it seemed the orientation of face images (upright or upside down) did not modulate the binary pattern of PIT-based dDCGs, significantly. Moreover, compared with TESPO, the results of MTESPO were more compatible with the underconnectivity theory of ASD in the sense that MTESPO showed no long-range increase in PIT. It is also noteworthy that to the best of our knowledge it is the first time that a version of MTE is applied for patients (here ASD) and it is also its first use for EEG data analysis.

Quantitative evaluation of optimal imaging parameters for single-cell detection in MRI using simulation.

Super-paramagnetic iron oxide (SPIO) nanoparticles are actively investigated to enhance disease detection through molecular imaging using magnetic resonance imaging (MRI). Detection of the cells labeled by SPIO depends on the MRI protocols and pulse sequence parameters that can be optimized. To evaluate the sensitivity and specificity of the image acquisition methods and to obtain optimal imaging parameters for single-cell detection, we further developed an MRI simulator. The simulator models an object (tissue) at a microscopic level to evaluate effects of spatial distribution and concentration of nanoparticles on the resulting image. In this study, the simulator was used to evaluate and compare imaging of the labeled cells by the gradient-echo (GE), true-FISP [fast imaging employing steady-state acquisition (FIESTA)] and echo-planar imaging (EPI) pulse sequences. Effects of the imaging and object parameters, such as field strength, imaging protocol and pulse sequence parameters, imaging resolution, cell iron load, position of SPIO within the voxel and cell division within the voxel, were investigated in the work. The results suggest that true-FISP has the highest sensitivity for single-cell detection by MRI.

Subspace-based Identification Algorithm for characterizing causal networks in resting brain

The resting brain has been extensively investigated for low frequency synchrony between brain regions, namely Functional Connectivity (FC). However the other main stream of brain connectivity analysis that seeks causal interactions between brain regions, Effective Connectivity (EC), has been little explored. Inherent complexity of brain activities in resting-state, as observed in BOLD (Blood Oxygenation-Level Dependant) fluctuations, calls for exploratory methods for characterizing these causal networks. On the other hand, the inevitable effects that hemodynamic system imposes on causal inferences in fMRI data, lead us toward the methods in which causal inferences can take place in latent neuronal level, rather than observed BOLD time-series. To simultaneously satisfy these two concerns, in this paper, we introduce a novel state-space system identification approach for studying causal interactions among brain regions in the absence of explicit cognitive task. This algorithm is a geometrically inspired method for identification of stochastic systems, purely based on output observations. Using extensive simulations, three aspects of our proposed method are investigated: ability in discriminating existent interactions from non-existent ones, the effect of observation noise, and downsampling on algorithm performance. Our simulations demonstrate that Subspace-based Identification Algorithm (SIA) is sufficiently robust against above-mentioned factors, and can reliably uncover the underlying causal interactions of resting-state fMRI. Furthermore, in contrast to previously established state-space approaches in Effective Connectivity studies, this method is able to characterize causal networks with large number of brain regions. In addition, we utilized the proposed algorithm for identification of causal relationships underlying anti-correlation of default-mode and Dorsal Attention Networks during the rest, using fMRI. We observed that Default-Mode Network places in a higher order in hierarchical structure of brain functional networks compared to Dorsal Attention Networks.

Assessment of functional and structural connectivity between motor cortex and thalamus using fMRI and DWI

Connectivity evaluations have been performed in a noninvasive manner by examining resting state fMRI alongside diffusion-weighted images (DWI). The spatial structures of coherent spontaneous BOLD fluctuations provided the most convincing preliminary evidence that the BOLD signal was predominantly of neuronal origin rather than non-neuronal, artifactual noise. In this study we have shown that in thalamocortical network, the results of functional connectivity analysis and DWI correspond well with each other, thereby providing cross-validation of the two techniques. We have used the resting state fMRI data of 3 subjects with 10 minute resting state functional images via a 3T Siemens scanner. we used cross correlation for functional analysis and reported thalamocortical results with pvalue=0.01 and cluster size=100, Then showed corresponding tracts connecting premotor cortex and thalamus. In addition, both techniques correspond well to histological delineation and invasive tract tracing, which provides a ‘gold standard’ validation of the two techniques. The degree of structural connectivity has been shown to correlate with the strength of functional connectivity, thereby providing a potentially straightforward structural explanation for many of the changes in functional connectivity in disease states.