Methods for cleaning the BOLD fMRI signal

Available online 9 December 2016 http://www.sciencedirect.com/science/article/pii/S1053811916307418?via%3Dihubhttp://www.sciencedirect.com/science/article/pii/S1053811916307418?via%3DihubBlood oxygen-level-dependent functional magnetic resonance imaging (BOLD fMRI) has rapidly become a popular technique for the investigation of brain function in healthy individuals, patients as well as in animal studies. However, the BOLD signal arises from a complex mixture of neuronal, metabolic and vascular processes, being therefore an indirect measure of neuronal activity, which is further severely corrupted by multiple non-neuronal fluctuations of instrumental, physiological or subject-specific origin. This review aims to provide a comprehensive summary of existing methods for cleaning the BOLD fMRI signal. The description is given from a methodological point of view, focusing on the operation of the different techniques in addition to pointing out the advantages and limitations in their application. Since motion-related and physiological noise fluctuations are two of the main noise components of the signal, techniques targeting their removal are primarily addressed, including both data-driven approaches and using external recordings. Data-driven approaches, which are less specific in the assumed model and can simultaneously reduce multiple noise fluctuations, are mainly based on data decomposition techniques such as principal and independent component analysis. Importantly, the usefulness of strategies that benefit from the information available in the phase component of the signal, or in multiple signal echoes is also highlighted. The use of global signal regression for denoising is also addressed. Finally, practical recommendations regarding the optimization of the preprocessing pipeline for the purpose of denoising and future venues of research are indicated. Through the review, we summarize the importance of signal denoising as an essential step in the analysis pipeline of task-based and resting state fMRI studies.This work was supported by the Spanish Ministry of Economy and Competitiveness [Grant PSI 2013–42343 Neuroimagen Multimodal], the Severo Ochoa Programme for Centres/Units of Excellence in R & D [SEV-2015-490], and the research and writing of the paper were supported by the NIMH and NINDS Intramural Research Programs (ZICMH002888) of the NIH/HHS

Medical imaging analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

A Survey on the Project in title

In this paper we present a survey of work that has been done in the project ldquo;Unsupervised Adaptive P300 BCI in the framework of chaotic theory and stochastic theoryrdquo;we summarised the following papers, (Mohammed J Alhaddad amp; 2011), (Mohammed J. Alhaddad amp; Kamel M, 2012), (Mohammed J Alhaddad, Kamel, amp; Al-Otaibi, 2013), (Mohammed J Alhaddad, Kamel, amp; Bakheet, 2013), (Mohammed J Alhaddad, Kamel, amp; Al-Otaibi, 2014), (Mohammed J Alhaddad, Kamel, amp; Bakheet, 2014), (Mohammed J Alhaddad, Kamel, amp; Kadah, 2014), (Mohammed J Alhaddad, Kamel, Makary, Hargas, amp; Kadah, 2014), (Mohammed J Alhaddad, Mohammed, Kamel, amp; Hagras, 2015).We developed a new pre-processing method for denoising P300-based brain-computer interface data that allows better performance with lower number of channels and blocks. The new denoising technique is based on a modified version of the spectral subtraction denoising and works on each temporal signal channel independently thus offering seamless integration with existing pre-processing and allowing low channel counts to be used. We also developed a novel approach for brain-computer interface data that requires no prior training. The proposed approach is based on interval type-2 fuzzy logic based classifier which is able to handle the usersrsquo; uncertainties to produce better prediction accuracies than other competing classifiers such as BLDA or RFLDA. In addition, the generated type-2 fuzzy classifier is learnt from data via genetic algorithms to produce a small number of rules with a rule length of only one antecedent to maximize the transparency and interpretability for the normal clinician. We also employ a feature selection system based on an ensemble neural networks recursive feature selection which is able to find the effective time instances within the effective sensors in relation to given P300 event. The basic principle of this new class of techniques is that the trial with true activation signal within each block has to be different from the rest of the trials within that block. Hence, a measure that is sensitive to this dissimilarity can be used to make a decision based on a single block without any prior training. The new methods were verified using various experiments which were performed on standard data sets and using real-data sets obtained from real subjects experiments performed in the BCI lab in King Abdulaziz University. The results were compared to the classification results of the same data using previous methods. Enhanced performance in different experiments as quantitatively assessed using classification block accuracy as well as bit rate estimates was confirmed. It will be shown that the produced type-2 fuzzy logic based classifier will learn simple rules which are easy to understand explaining the events in question. In addition, the produced type-2 fuzzy logic classifier will be able to give better accuracies when compared to BLDA or RFLDA on various human subjects on the standard and real-world data sets

Characterizing spatiotemporal properties of visual motion in the human middle temporal cortex at 7T

Tese de mestrado integrado em Engenharia Biomédica e Biofísica, apresentada à Universidade de Lisboa, através da Faculdade de Ciências, em 2018O ser humano é diariamente exposto a situações onde é necessária a deteção de movimento. Esta capacidade de deteção da velocidade e direção de um objeto em movimento é considerada vital para a perceção, reação e adaptação a todos os eventos dinâmicos com que nos deparamos no meio ambiente próximo. O middle temporal cortex (hMT+) é conhecido como a região central de processamento e deteção de eventos visuais em movimento, cuja velocidade pode ser descrita como resultado de propriedades inerentes: frequência espacial e temporal. Estudos prévios apresentam resultados conflituosos no que diz respeito ao mecanismo de codificação e resposta a estímulos visuais em movimento. Em primatas foi observada uma predominância de neurónios do MT+ cuja resposta apresenta preferência para combinações específicas de frequências espaciais e temporais. Ou seja, a resposta para estímulos visuais no MT+ é dependente das propriedades da velocidade e não da velocidade em si. Contudo, outros estudos em humanos demonstram exatamente o contrário: a existência de uma resposta preferencial para certos valores de velocidade independentemente das frequências espaciotemporais usadas para gerar tal velocidade. Este projeto tem como objetivo a caracterização da resposta neuronal a estímulos com diferentes frequências espaciotemporais no hMT+ por uso de uma técnica não invasiva, utilizando ressonância magnética funcional de modo a avaliar padrões de ativação BOLD (Blood-oxygen-level dependent). Pretende-se inferir se a resposta das populações neuronais depende da velocidade do estímulo em questão ou das suas componentes (frequência temporal e espacial). As aquisições de resposta BOLD foram feitas num scanner 7 Tesla a voluntários sem patologias durante uma tarefa específica perante um estímulo visual. Este estímulo consistia num alvo subdividido em porções pretas e brancas bem distintas que se expande a partir de um ponto central durante 1 segundo. O estímulo foi repetido em seis scans, cada scan apresentando combinações diferentes de frequências espaciotemporais. O estímulo foi criado via script de MATLAB e é apresentado ao voluntário através da sua reflexão num ecrã espelhado presente junto da porção posterior do voluntário. O primeiro dos seis scans obtido durante a aquisição de scans de ressonância magnética funcional é designado de localizer devido à sua utilização na localização funcional do hMT+ de uma maneira independente e não regressiva. Assim, ao realizar o pré-processamento nestes primeiros scans localizer é possível obter clusters de ativação que representarão a área de estudo dos restantes cinco scans adquiridos. Nos restantes cinco scans de ressonância magnética funcional foram apresentados estímulos com três velocidades diferentes (3 graus/segundo, 9 graus/segundo e 15 graus/segundo), sendo que duas destas velocidades estão codificadas por duas combinações diferentes de frequências espaciais e temporais: 3 graus/segundo é codificado utilizando 1 grau/ciclo e 3 Hz e 3 graus/ciclo e 1 Hz; 15 graus/segundo é codificado utilizando tanto 3 graus/ciclo e 5 Hz como 5 graus/ciclo e 3 Hz. Desta forma, após o pré-processamento da data adquirida e a extração da resposta hemodinâmica e a análise do sinal BOLD podem-se tecer conclusões relativamente ao perfil seguido pelo hMT+. Inicialmente, após extração da resposta hemodinâmica, a média da amplitude da mesma sobre todos os clusters de ativação identificados pelo scan localizer foi calculada e comparada entre os vários scans com diferentes características spaciotemporais. Contudo, não foi conclusivo quer a teoria de um perfil no hMT+ baseado na seleção de frequências espaciotemporais, nem um perfil baseado na seleção de velocidades do estímulo. Contudo, foi possível verificar a existência de certas variações entre as várias respostas hemodinâmicas de cada scan dentro das regiões de interesse criadas pelos localizers. Devido à natureza do projeto (o estudo é efetuado num scanner de 7T com a aplicação de uma coil de superfície capaz de trazer grande resolução espacial e rácio signal-to-noise elevado) foi-nos possível partir para uma análise da amplitude máxima da resposta hemodinâmica em cada voluntário dentro das regiões de interesse (clusters). Ao comparar os máximos da resposta hemodinâmica para cada run foram identificadas diferenças significativas na resposta hemodinâmica entre runs cuja velocidade do seu estímulo visual em movimento é a mesma, simplesmente codificada com combinações diferentes de frequência espacial e temporal. Ou seja, entre os 10 hemisférios cerebrais presentes no estudo (cada scan pode ser dividido por hemisfério, perfazendo os 10 hemisférios mencionados) foi possível observar uma diferença significativa de atividade entre as mesmas velocidades codificadas com frequências diferentes, em pelo menos 7 (na velocidade 3 graus/segundo) e 5 (na velocidade 15 graus/segundo). Esta diferença de resposta em hemisférios expostos à mesma velocidade de estímulos visuais, ainda que insuficiente para uma conclusão acerca do perfil de codificação presente no hMT+, aponta para uma codificação em torno das componentes da velocidade (frequência espacial e temporal). Isto significaria que o córtex temporal médio apresenta preferência para processar estímulos com certas frequências espaciotemporais. Com o intuito de investigar o modo como esta preferência por determinadas frequências espaciais e temporais estaria disposta no hMT+ uma análise de voxel por voxel foi de seguida efetuada. Cada voxel presente nos clusters de atividade identificados pelo scan localizer foi classificado conforme o estímulo visual apresentado que resultaria numa diferença maior de atividade (maior amplitude na resposta hemodinâmica). Após representação dos clusters de atividade em superfícies inflacionadas representativas do córtex individual do voluntario foi possível tecer considerações acerca da presença de um mapa geral para a preferência de frequências espaciais e temporais especificas dentro do hMT+. Deste modo foi-nos possível identificar uma organização espacial dentro do hMT+ com possibilidade de separação do mesmo em subáreas previamente sugeridas noutros estudos mesentéricos do córtex visual: MT (área médio temporal) e MST (área superior temporal). Estes resultados vão contra as conclusões retiradas de estudos prévios em torno do hMT+ com uso de ressonância magnética funcional: Chawla et al e Lingnau et al. Contudo, no caso de Chawla et al, as diferentes conclusões podem ser justificadas pela natureza dos estímulos utilizados durante o seu estudo (pontos em movimento aleatório). Este tipo de estímulos é conhecido por não permitir selecionar a frequência espacial nas várias velocidades demonstradas nos estímulos, eliminando a possibilidade de fazer conclusões quanto ao perfil de codificação baseado em frequência do hMT+. No caso de Lingnau et al. o paradigma para adaptação foi baseado em estímulos de pouco contraste o que leva a que outras populações neuronais tenham sido incitadas ao invés das propostas (hMT+). Contudo, os resultados apresentados vão de encontro a estudos feitos por Gaglianese, A. com electrocorticografia em pacientes. O que torna este estudo uma extensão dos resultados prévios obtidos em electrocorticografia, mas utilizando agora um método não invasivo (fMRI) numa população saudável. No entanto, mesmo após resultados promissores, o projeto encontra ainda uma panóplia de desafios que têm prioridade em ser corrigidos de modo a tornar os resultados mais robustos e melhores. Em primeiro lugar a seleção das regiões de interesse onde a analise do estudo se baseou pode ser de futuro efetuada de maneira menos restrita e com a ajuda de atlas anatómicos. Isto poderia permitir a utilização dos voxeis que foram selecionados de modo a obter esquemas de distribuição de clusters de ativação sobre o córtex humano motor mais completos. Para além disso o tempo dispensado no pre-processamento poderia ser estandardizado pelo uso de funções automáticas no que diz respeito à remoção de artefactos fisiológicos. No que diz respeito à direção do projeto para um futuro próximo: devido aos resultados demonstrados neste relatório de tese de mestrado, existem fortes possibilidades de efetuar estudos completos no sentido a obter clusters suficientemente definidos, que possibilitam a separação do córtex temporal médio em duas subdivisões que podem estar relacionadas com funcionamentos específicos e especificidade no que diz respeito a padrões de codificação de estímulos: MT e MST. Esta divisão poderá permitir tecer novas conclusões no que diz respeito à importância do hMT+ no que diz respeito a casos em que o V1 (córtex visual primário) se encontra danificado, mas a codificação de movimento ainda é efetuada (blindsight).Motion detection comes to humans as an important component in our daily life. Knowing the direction and speed of a moving object helps in understanding, reacting, and adapting to sudden dynamic events in our environment. Among other regions, the middle temporal cortex hMT+ in the human brain is the core region involved in the detection and processing of moving stimuli. The speed of a moving object depends on the ratio of the change in position in between time samples, or, the spatial and temporal frequencies of a moving object. Therefore, motion can be encoded by speed per se or by separate and independent tuning of the specific different spatial and temporal frequencies components. A recent study using ECoG in humans and complex visual stimuli using square wave gratings at different spatial and temporal frequencies has proven that specific recorded hMT+ neuronal populations exhibited separable selectivity for spatial and temporal frequencies rather than speed tuning. However, due to specific confined localization of the ECoG grid it remains elusive whether this selectivity comprises a spatial organization within the hMT+. Thanks to the advent of new neuroimaging techniques such as ultra-high field MRI at 7T it is now possible to visualize in unprecedent detail the human brain in vivo (0.8mm). Compared to commonly used field strengths, 7T allows for a gain in sensitivity and signal-to-noise, allowing to map for the first time non-invasively, the mesoscopic architecture of brain regions and measuring non-invasively neuronal responses via BOLD. The aim of this project is to characterize the neural response to stimuli with different temporal and spatial frequencies in the hMT+ in a non-invasive method with the use of 7T fMRI via evaluation of patterns of blood-oxygen-level dependent imaging activation. We investigated whether the response preferences of the neural populations present in the human middle temporal cortex depend on the stimulus speed or to the independent spatial and temporal frequency components. The 7 Tesla blood-oxygen-level dependent functional magnetic resonance imaging responses were collected from healthy human volunteers on a Philips 7 Tesla scanner using advanced channels and techniques such as two 16-channel surface coils and gradient echo-planar imaging sequence. This was done during a specific task that consists on a visual stimulus (a high-contrast black-and-white dartboard) that’s expanded from the fixation point for one second, presented in six scans (one used to localize the activity region – hence designated localizer – and the remaining five to study said activity), while having a baseline of a homogeneous grey background. Each run consisted on different spatial and temporal frequencies of the dartboard that have been previously used in the mentioned ECoG study. After computation of the BOLD signal using a deconvolution approach, the results showed that the human middle temporal cortex separates motion into its spatial and temporal components rather than decoding speed directly. Moreover, clusters of activity for specific combinations of spatial and temporal frequencies suggest a spatial organization within the human middle temporal cortex

Seeing it all: Convolutional network layers map the function of the human visual system

International audienceConvolutional networks used for computer vision represent candidate models for the computations performed in mammalian visual systems. We use them as a detailed model of human brain activity during the viewing of natural images by constructing predictive models based on their different layers and BOLD fMRI activations. Analyzing the predictive performance across layers yields characteristic fingerprints for each visual brain region: early visual areas are better described by lower level convolutional net layers and later visual areas by higher level net layers, exhibiting a progression across ventral and dorsal streams. Our predictive model generalizes beyond brain responses to natural images. We illustrate this on two experiments, namely retinotopy and face-place oppositions, by synthesizing brain activity and performing classical brain mapping upon it. The synthesis recovers the activations observed in the corresponding fMRI studies, showing that this deep encoding model captures representations of brain function that are universal across experimental paradigms

Singular Spectrum Analysis and Adaptive Filtering: A Novel Approach for Assessing the Functional Connectivity in fMRI Resting State Experiments

Functional Magnetic Resonance Imaging (fMRI) is used to investigate brain functional connectivity at rest after filtering out non-neuronal components related to cardiac and respiratory processes and to the instrumental noise of MRI scanner. These components are generally removed at their fundamental frequencies through band-pass filtering of the Blood-Oxygen-Level-Dependent (BOLD) signal (low-frequency band – LFB: 0.01–0.10 Hz) while General Linear Model (GLM) is usually employed to suppress slow variations of physiological noise in the LFB, using a signal template derived from non-neuronal regions (e.g. brain ventricles). However, these sources of noise exhibit a non-stationary nature due to the intrinsic time variability of physiological activities or to the nonlinear characteristics of MRI scanner drifts: at present, the standard procedure (band-pass filtering and GLM) does not take into account these noise properties in the processing of BOLD signal. This thesis proposes the joint usage of two methods (Singular Spectrum Analysis – SSA – and adaptive filtering) that takes advantage of their statistical and time flexibility features, respectively. Indeed SSA is a nonparametric technique capable of extracting amplitude and phase modulated components against a null hypothesis of autocorrelated noise, while the adaptive filter removes the noise correlated to a reference signal, exploiting its non-stationary properties. The novel procedure (SSA and adaptive filtering) was applied to eight resting state recordings and compared to the standard procedure. The functional connectivity between homologous contralateral regions was then estimated in the LFB using a multivariate correlation index (the RV coefficient) and assessed on preselected grey (GM) and white matter (WM) regions of interest (ROIs). A corrected version of the RV coefficient for the number of voxels was developed and used to compare the functional connectivity estimates obtained by the standard procedure (using all available voxels) and from the novel procedure based on the voxel time courses with significant SSA components in the LFB (active voxels). The adaptive filtering showed a greater reduction of noise compared to GLM (average signal variance decrease in all ROIs: −43.9% vs. −10.1%), using a non-stationary noise template obtained from brain ventricles signals in the LFB. The functional connectivity quantified by the RV coefficient and estimated on the active voxels identified by SSA showed a higher contrast between GM and WM regions with respect to the standard procedure (35% vs. 28%). These results suggest that SSA and adaptive filtering may be a feasible procedure for properly removing the physiological noise in the LFB of BOLD signal and for highlighting resting state functional networks

Detecting diverse types of cardiovascular brain pulses in Alzheimer’s disease simultaneously with fNIRS and MREG

Abstract. One of the risk factors for Alzheimer’s disease is hypertension. Hypertension alters the brain’s blood vessel structure due to increased arterial pressure. Structural changes in the blood vessels are seen in the cardiovascular pulse, which is formed by blood velocity, blood flow rate, blood pressure, and infrequently blood flow. By simultaneously applying magnetic resonance encephalography (MREG) and functional near-infrared spectroscopy (fNIRS), this study discovered cardiovascular brain pulses from the blood flow within patients with Alzheimer’s disease and healthy controls. This study detects specific parameters within diverse types of cardiovascular brain pulses. The results detected changes in parameters for diverse types of cardiovascular brain pulses in patients with Alzheimer’s disease within MREG and fNIRS. In addition, the results present an alternative method for finding cardiovascular brain pulse from the blood flow, which might reflect the structural changes of a blood vessel in patients with Alzheimer’s disease. In conclusion, diverse types of cardiovascular brain pulses represent an approximation of arterial, venous, and tissue pulses, which is beneficial for distinguishing the effect of venous and arterial hypertension in Alzheimer’s disease. Furthermore, altered blood flow may potentially be associated with the impaired glymphatic system in Alzheimer’s disease

Magnetoencephalography

This is a practical book on MEG that covers a wide range of topics. The book begins with a series of reviews on the use of MEG for clinical applications, the study of cognitive functions in various diseases, and one chapter focusing specifically on studies of memory with MEG. There are sections with chapters that describe source localization issues, the use of beamformers and dipole source methods, as well as phase-based analyses, and a step-by-step guide to using dipoles for epilepsy spike analyses. The book ends with a section describing new innovations in MEG systems, namely an on-line real-time MEG data acquisition system, novel applications for MEG research, and a proposal for a helium re-circulation system. With such breadth of topics, there will be a chapter that is of interest to every MEG researcher or clinician

Machine Learning Methods with Noisy, Incomplete or Small Datasets

In many machine learning applications, available datasets are sometimes incomplete, noisy or affected by artifacts. In supervised scenarios, it could happen that label information has low quality, which might include unbalanced training sets, noisy labels and other problems. Moreover, in practice, it is very common that available data samples are not enough to derive useful supervised or unsupervised classifiers. All these issues are commonly referred to as the low-quality data problem. This book collects novel contributions on machine learning methods for low-quality datasets, to contribute to the dissemination of new ideas to solve this challenging problem, and to provide clear examples of application in real scenarios

A Novel Synergistic Model Fusing Electroencephalography and Functional Magnetic Resonance Imaging for Modeling Brain Activities

Study of the human brain is an important and very active area of research. Unraveling the way the human brain works would allow us to better understand, predict and prevent brain related diseases that affect a significant part of the population. Studying the brain response to certain input stimuli can help us determine the involved brain areas and understand the mechanisms that characterize behavioral and psychological traits. In this research work two methods used for the monitoring of brain activities, Electroencephalography (EEG) and functional Magnetic Resonance (fMRI) have been studied for their fusion, in an attempt to bridge together the advantages of each one. In particular, this work has focused in the analysis of a specific type of EEG and fMRI recordings that are related to certain events and capture the brain response under specific experimental conditions. Using spatial features of the EEG we can describe the temporal evolution of the electrical field recorded in the scalp of the head. This work introduces the use of Hidden Markov Models (HMM) for modeling the EEG dynamics. This novel approach is applied for the discrimination of normal and progressive Mild Cognitive Impairment patients with significant results. EEG alone is not able to provide the spatial localization needed to uncover and understand the neural mechanisms and processes of the human brain. Functional Magnetic Resonance imaging (fMRI) provides the means of localizing functional activity, without though, providing the timing details of these activations. Although, at first glance it is apparent that the strengths of these two modalities, EEG and fMRI, complement each other, the fusion of information provided from each one is a challenging task. A novel methodology for fusing EEG spatiotemporal features and fMRI features, based on Canonical Partial Least Squares (CPLS) is presented in this work. A HMM modeling approach is used in order to derive a novel feature-based representation of the EEG signal that characterizes the topographic information of the EEG. We use the HMM model in order to project the EEG data in the Fisher score space and use the Fisher score to describe the dynamics of the EEG topography sequence. The correspondence between this new feature and the fMRI is studied using CPLS. This methodology is applied for extracting features for the classification of a visual task. The results indicate that the proposed methodology is able to capture task related activations that can be used for the classification of mental tasks. Extensions on the proposed models are examined along with future research directions and applications