Adaptive filtering and random variables coefficient for analyzing functional magnetic resonance imaging data

Efficient Variational Approach to Multimodal Registration of Anatomical and Functional Intra-Patient Tumorous Brain Data

This paper addresses the functional localization of intra-patient images of the brain. Functional images of the brain (fMRI and PET) provide information about brain function and metabolism whereas anatomical images (MRI and CT) supply the localization of structures with high spatial resolution. The goal is to find the geometric correspondence between functional and anatomical images in order to complement and fuse the information provided by each imaging modality. The proposed approach is based on a variational formulation of the image registration problem in the frequency domain. It has been implemented as a C/C[Formula: see text] library which is invoked from a GUI. This interface is routinely used in the clinical setting by physicians for research purposes (Inscanner, Alicante, Spain), and may be used as well for diagnosis and surgical planning. The registration of anatomic and functional intra-patient images of the brain makes it possible to obtain a geometric correspondence which allows for the localization of the functional processes that occur in the brain. Through 18 clinical experiments, it has been demonstrated how the proposed approach outperforms popular state-of-the-art registration methods in terms of efficiency, information theory-based measures (such as mutual information) and actual registration error (distance in space of corresponding landmarks).

Discrimination of schizophrenia auditory hallucinators by machine learning of resting-state functional MRI

Auditory hallucinations (AH) are a symptom that is most often associated with schizophrenia, but patients with other neuropsychiatric conditions, and even a small percentage of healthy individuals, may also experience AH. Elucidating the neural mechanisms underlying AH in schizophrenia may offer insight into the pathophysiology associated with AH more broadly across multiple neuropsychiatric disease conditions. In this paper, we address the problem of classifying schizophrenia patients with and without a history of AH, and healthy control (HC) subjects. To this end, we performed feature extraction from resting state functional magnetic resonance imaging (rsfMRI) data and applied machine learning classifiers, testing two kinds of neuroimaging features: (a) functional connectivity (FC) measures computed by lattice auto-associative memories (LAAM), and (b) local activity (LA) measures, including regional homogeneity (ReHo) and fractional amplitude of low frequency fluctuations (fALFF). We show that it is possible to perform classification within each pair of subject groups with high accuracy. Discrimination between patients with and without lifetime AH was highest, while discrimination between schizophrenia patients and HC participants was worst, suggesting that classification according to the symptom dimension of AH may be more valid than discrimination on the basis of traditional diagnostic categories. FC measures seeded in right Heschl's gyrus (RHG) consistently showed stronger discriminative power than those seeded in left Heschl's gyrus (LHG), a finding that appears to support AH models focusing on right hemisphere abnormalities. The cortical brain localizations derived from the features with strong classification performance are consistent with proposed AH models, and include left inferior frontal gyrus (IFG), parahippocampal gyri, the cingulate cortex, as well as several temporal and prefrontal cortical brain regions. Overall, the observed findings suggest that computational intelligence approaches can provide robust tools for uncovering subtleties in complex neuroimaging data, and have the potential to advance the search for more neuroscience-based criteria for classifying mental illness in psychiatry research.

A Predictive Modeling Approach to Analyze Data in EEG–fMRI Experiments

In this paper, a novel technique based on blind source extraction (BSE) using linear prediction is proposed to extract rolandic beta rhythm from electroencephalogram (EEG) recorded in a simultaneous EEG–fMRI experiment. We call this method CLP–BSE standing for constrained-linear-prediction BSE. Extracting event-related oscillations is a crucial task due to nonphase-locked nature and inter-trial variability of this event. The main objective of this work is to extract rolandic beta rhythm to measure event-related synchronization (ERS) with acceptable signal-to-noise ratio (SNR). The extracted rhythm is utilized for constructing a regressor to analyze functional magnetic resonance imaging (fMRI). The proposed method is a semi-blind technique which uses a spatio-temporal constraint for beta rhythm extraction. This constraint is derived from recorded EEG signals based on the prior knowledge about the frequency and location of the source of interest. The main reason of employing linear prediction as an effective algorithm to extract the EEG rhythm is the ability of extracting sources which have specific temporal structure. Performance of the proposed method is evaluated using both synthetic and real EEG data. The obtained results show that the proposed technique is able to extract ERS effectively. The maximum percentage of ERS obtained by filtering is 152% while the obtained ERS by CLP–BSE is 214%. In another experiment, the extracted event-related oscillations in beta band are used to make the necessary regressor for fMRI analysis. The results of EEG–fMRI coregistration confirm that there are correlation between the extracted rolandic beta rhythm and simultaneously recorded fMRI. This conclude that, the results of EEG–fMRI combination support the reliability of CLP–BSE output.

The Default Mode Network Differentiates Biological From Non-Biological Motion

The default mode network (DMN) has been implicated in an array of social-cognitive functions, including self-referential processing, theory of mind, and mentalizing. Yet, the properties of the external stimuli that elicit DMN activity in relation to these domains remain unknown. Previous studies suggested that motion kinematics is utilized by the brain for social-cognitive processing. Here, we used functional MRI to examine whether the DMN is sensitive to parametric manipulations of observed motion kinematics. Preferential responses within core DMN structures differentiating non-biological from biological kinematics were observed for the motion of a realistically looking, human-like avatar, but not for an abstract object devoid of human form. Differences in connectivity patterns during the observation of biological versus non-biological kinematics were additionally observed. Finally, the results additionally suggest that the DMN is coupled more strongly with key nodes in the action observation network, namely the STS and the SMA, when the observed motion depicts human rather than abstract form. These findings are the first to implicate the DMN in the perception of biological motion. They may reflect the type of information used by the DMN in social-cognitive processing.

Singular spectrum analysis and adaptive filtering enhance the functional connectivity analysis of resting state fMRI data

Sources of noise in resting-state fMRI experiments include instrumental and physiological noises, which need to be filtered before a functional connectivity analysis of brain regions is performed. These noisy components show autocorrelated and nonstationary properties that limit the efficacy of standard techniques (i.e. time filtering and general linear model). Herein we describe a novel approach based on the combination of singular spectrum analysis and adaptive filtering, which allows a greater noise reduction and yields better connectivity estimates between regions at rest, providing a new feasible procedure to analyze fMRI data.