Relevant bibliographies by topics / FMRI signal

Academic literature on the topic 'FMRI signal'

Author: Grafiati
Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "FMRI signal"

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Choi, Uk-Su, Yul-Wan Sung, and Seiji Ogawa. "Effects of Physiological Signal Removal on Resting-State Functional MRI Metrics." Brain Sciences 13, no. 1 (December 20, 2022): 8. http://dx.doi.org/10.3390/brainsci13010008.

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Resting-state fMRIs (rs-fMRIs) have been widely used for investigation of diverse brain functions, including brain cognition. The rs-fMRI has easily elucidated rs-fMRI metrics, such as the fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), voxel-mirrored homotopic connectivity (VMHC), and degree centrality (DC). To increase the applicability of these metrics, higher reliability is required by reducing confounders that are not related to the functional connectivity signal. Many previous studies already demonstrated the effects of physiological artifact removal from rs-fMRI data, but few have evaluated the effect on rs-fMRI metrics. In this study, we examined the effect of physiological noise correction on the most common rs-fMRI metrics. We calculated the intraclass correlation coefficient of repeated measurements on parcellated brain areas by applying physiological noise correction based on the RETROICOR method. Then, we evaluated the correction effect for five rs-fMRI metrics for the whole brain: FC, fALFF, ReHo, VMHC, and DC. The correction effect depended not only on the brain region, but also on the metric. Among the five metrics, the reliability in terms of the mean value of all ROIs was significantly improved for FC, but it deteriorated for fALFF, with no significant differences for ReHo, VMHC, and DC. Therefore, the decision on whether to perform the physiological correction should be based on the type of metric used.
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Kim, Seong-Gi, and Seiji Ogawa. "Biophysical and Physiological Origins of Blood Oxygenation Level-Dependent fMRI Signals." Journal of Cerebral Blood Flow & Metabolism 32, no. 7 (March 7, 2012): 1188–206. http://dx.doi.org/10.1038/jcbfm.2012.23.

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After its discovery in 1990, blood oxygenation level-dependent (BOLD) contrast in functional magnetic resonance imaging (fMRI) has been widely used to map brain activation in humans and animals. Since fMRI relies on signal changes induced by neural activity, its signal source can be complex and is also dependent on imaging parameters and techniques. In this review, we identify and describe the origins of BOLD fMRI signals, including the topics of (1) effects of spin density, volume fraction, inflow, perfusion, and susceptibility as potential contributors to BOLD fMRI, (2) intravascular and extravascular contributions to conventional gradient-echo and spin-echo BOLD fMRI, (3) spatial specificity of hemodynamic-based fMRI related to vascular architecture and intrinsic hemodynamic responses, (4) BOLD signal contributions from functional changes in cerebral blood flow (CBF), cerebral blood volume (CBV), and cerebral metabolic rate of O2 utilization (CMRO2), (5) dynamic responses of BOLD, CBF, CMRO2, and arterial and venous CBV, (6) potential sources of initial BOLD dips, poststimulus BOLD undershoots, and prolonged negative BOLD fMRI signals, (7) dependence of stimulus-evoked BOLD signals on baseline physiology, and (8) basis of resting-state BOLD fluctuations. These discussions are highly relevant to interpreting BOLD fMRI signals as physiological means.
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Logothetis, Nikos K. "The neural basis of the blood–oxygen–level–dependent functional magnetic resonance imaging signal." Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 357, no. 1424 (August 29, 2002): 1003–37. http://dx.doi.org/10.1098/rstb.2002.1114.

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Magnetic resonance imaging (MRI) has rapidly become an important tool in clinical medicine and biological research. Its functional variant (functional magnetic resonance imaging; fMRI) is currently the most widely used method for brain mapping and studying the neural basis of human cognition. While the method is widespread, there is insufficient knowledge of the physiological basis of the fMRI signal to interpret the data confidently with respect to neural activity. This paper reviews the basic principles of MRI and fMRI, and subsequently discusses in some detail the relationship between the blood–oxygen–level–dependent (BOLD) fMRI signal and the neural activity elicited during sensory stimulation. To examine this relationship, we conducted the first simultaneous intracortical recordings of neural signals and BOLD responses. Depending on the temporal characteristics of the stimulus, a moderate to strong correlation was found between the neural activity measured with microelectrodes and the BOLD signal averaged over a small area around the microelectrode tips. However, the BOLD signal had significantly higher variability than the neural activity, indicating that human fMRI combined with traditional statistical methods underestimates the reliability of the neuronal activity. To understand the relative contribution of several types of neuronal signals to the haemodynamic response, we compared local field potentials (LFPs), single– and multi–unit activity (MUA) with high spatio–temporal fMRI responses recorded simultaneously in monkey visual cortex. At recording sites characterized by transient responses, only the LFP signal was significantly correlated with the haemodynamic response. Furthermore, the LFPs had the largest magnitude signal and linear systems analysis showed that the LFPs were better than the MUAs at predicting the fMRI responses. These findings, together with an analysis of the neural signals, indicate that the BOLD signal primarily measures the input and processing of neuronal information within a region and not the output signal transmitted to other brain regions.
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Hayward, Peter. "Ephemeral signal in fMRI." Lancet Neurology 2, no. 4 (April 2003): 204. http://dx.doi.org/10.1016/s1474-4422(03)00369-7.

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Bednařík, Petr, Ivan Tkáč, Federico Giove, Mauro DiNuzzo, Dinesh K. Deelchand, Uzay E. Emir, Lynn E. Eberly, and Silvia Mangia. "Neurochemical and BOLD Responses during Neuronal Activation Measured in the Human Visual Cortex at 7 Tesla." Journal of Cerebral Blood Flow & Metabolism 35, no. 4 (January 7, 2015): 601–10. http://dx.doi.org/10.1038/jcbfm.2014.233.

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Several laboratories have consistently reported small concentration changes in lactate, glutamate, aspartate, and glucose in the human cortex during prolonged stimuli. However, whether such changes correlate with blood oxygenation level—dependent functional magnetic resonance imaging (BOLD-fMRI) signals have not been determined. The present study aimed at characterizing the relationship between metabolite concentrations and BOLD-fMRI signals during a block-designed paradigm of visual stimulation. Functional magnetic resonance spectroscopy (fMRS) and fMRI data were acquired from 12 volunteers. A short echo-time semi-LASER localization sequence optimized for 7 Tesla was used to achieve full signal-intensity MRS data. The group analysis confirmed that during stimulation lactate and glutamate increased by 0.26±0.06 μmol/g (∼30%) and 0.28±0.03 μmol/g (∼3%), respectively, while aspartate and glucose decreased by 0.20±0.04 μmol/g (∼5%) and 0.19±0.03 μmol/g (∼16%), respectively. The single-subject analysis revealed that BOLD-fMRI signals were positively correlated with glutamate and lactate concentration changes. The results show a linear relationship between metabolic and BOLD responses in the presence of strong excitatory sensory inputs, and support the notion that increased functional energy demands are sustained by oxidative metabolism. In addition, BOLD signals were inversely correlated with baseline γ-aminobutyric acid concentration. Finally, we discussed the critical importance of taking into account linewidth effects on metabolite quantification in fMRS paradigms.
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Gui, Renzhou, Tongjie Chen, and Han Nie. "Classification of Task-State fMRI Data Based on Circle-EMD and Machine Learning." Computational Intelligence and Neuroscience 2020 (August 1, 2020): 1–10. http://dx.doi.org/10.1155/2020/7691294.

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In the research work of the brain-computer interface and the function of human brain work, the state classification of multitask state fMRI data is a problem. The fMRI signal of the human brain is a nonstationary signal with many noise effects and interference. Based on the commonly used nonstationary signal analysis method, Hilbert–Huang transform (HHT), we propose an improved circle-EMD algorithm to suppress the end effect. The algorithm can extract different intrinsic mode functions (IMFs), decompose the fMRI data to filter out low frequency and other redundant noise signals, and more accurately reflect the true characteristics of the original signal. For the filtered fMRI signal, we use three existing different machine learning methods: logistic regression (LR), support vector machine (SVM), and deep neural network (DNN) to achieve effective classification of different task states. The experiment compares the results of these machine learning methods and confirms that the deep neural network has the highest accuracy for task-state fMRI data classification and the effectiveness of the improved circle-EMD algorithm.
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Wang, Maosen, Yi He, Terrence J. Sejnowski, and Xin Yu. "Brain-state dependent astrocytic Ca2+ signals are coupled to both positive and negative BOLD-fMRI signals." Proceedings of the National Academy of Sciences 115, no. 7 (January 30, 2018): E1647—E1656. http://dx.doi.org/10.1073/pnas.1711692115.

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Astrocytic Ca2+-mediated gliovascular interactions regulate the neurovascular network in situ and in vivo. However, it is difficult to measure directly both the astrocytic activity and fMRI to relate the various forms of blood-oxygen-level-dependent (BOLD) signaling to brain states under normal and pathological conditions. In this study, fMRI and GCaMP-mediated Ca2+ optical fiber recordings revealed distinct evoked astrocytic Ca2+ signals that were coupled with positive BOLD signals and intrinsic astrocytic Ca2+ signals that were coupled with negative BOLD signals. Both evoked and intrinsic astrocytic calcium signal could occur concurrently or respectively during stimulation. The intrinsic astrocytic calcium signal can be detected globally in multiple cortical sites in contrast to the evoked astrocytic calcium signal only detected at the activated cortical region. Unlike propagating Ca2+ waves in spreading depolarization/depression, the intrinsic Ca2+ spikes occurred simultaneously in both hemispheres and were initiated upon the activation of the central thalamus and midbrain reticular formation. The occurrence of the intrinsic astrocytic calcium signal is strongly coincident with an increased EEG power level of the brain resting-state fluctuation. These results demonstrate highly correlated astrocytic Ca2+ spikes with bidirectional fMRI signals based on the thalamic regulation of cortical states, depicting a brain-state dependency of both astrocytic Ca2+ and BOLD fMRI signals.
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Grössinger, Doris, Silvia Erika Kober, Stefan M. Spann, Rudolf Stollberger, and Guilherme Wood. "Real-Time Functional Magnetic Resonance Imaging as a Tool for Neurofeedback." Lernen und Lernstörungen 9, no. 3 (July 2020): 151–62. http://dx.doi.org/10.1024/2235-0977/a000300.

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Abstract. Neurofeedback allows participants to voluntarily control their own brain activity. Consequently, neurofeedback is a potential intervention tool in diverse clinical domains. Different brain signals can be fed back to the neurofeedback users, such as the hemodynamic response of the brain using functional magnetic resonance imaging (fMRI) and near-infrared spectroscopy (NIRS) or electrophysiological brain signals as measured with electroencephalography (EEG). Each of these neuroscientific methods has its advantages and disadvantages. For instance, using fMRI all brain regions can be targeted, while in EEG and NIRS signals from deeper regions cannot be precisely differentiated. Hence, fMRI-based neurofeedback allows treatment of mental and physical diseases, which are associated with activation patterns in deeper brain regions. Until now, only the blood oxygen level dependent signal (BOLD) has been used as feedback signal in fMRI-based neurofeedback studies. However, we have started to develop a neurofeedback pipeline using a different fMRI signal, namely arterial spin labeling (ASL), which will be introduced in this article. ASL neurofeedback enables a direct modulation of the cerebral blood flow and, consequently, might improve rehabilitation of disorders caused by perfusion imbalance in the future.
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Tong, Yunjie, Kimberly P. Lindsey, and Blaise deB Frederick. "Partitioning of Physiological Noise Signals in the Brain with Concurrent Near-Infrared Spectroscopy and fMRI." Journal of Cerebral Blood Flow & Metabolism 31, no. 12 (August 3, 2011): 2352–62. http://dx.doi.org/10.1038/jcbfm.2011.100.

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The blood–oxygen level dependent (BOLD) signals measured by functional magnetic resonance imaging (fMRI) are contaminated with noise from various physiological processes, such as spontaneous low-frequency oscillations (LFOs), respiration, and cardiac pulsation. These processes are coupled to the BOLD signal by different mechanisms, and represent variations with very different frequency content; however, because of the low sampling rate of fMRI, these signals are generally not separable by frequency, as the cardiac and respiratory waveforms alias into the LFO band. In this study, we investigated the spatial and temporal characteristics of the individual noise processes by conducting concurrent near-infrared spectroscopy (NIRS) and fMRI studies on six subjects during a resting state acquisition. Three time series corresponding to LFO, respiration, and cardiac pulsation were extracted by frequency from the NIRS signal (which has sufficient temporal resolution to critically sample the cardiac waveform) and used as regressors in a BOLD fMRI analysis. Our results suggest that LFO and cardiac signals modulate the BOLD signal independently through the circulatory system. The spatiotemporal evolution of the LFO signal in the BOLD data correlates with the global cerebral blood flow. Near-infrared spectroscopy can be used to partition these contributing factors and independently determine their contribution to the BOLD signal.
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Shen, Yuji, Risto A. Kauppinen, Rishma Vidyasagar, and Xavier Golay. "A Functional Magnetic Resonance Imaging Technique Based on Nulling Extravascular Gray Matter Signal." Journal of Cerebral Blood Flow & Metabolism 29, no. 1 (August 27, 2008): 144–56. http://dx.doi.org/10.1038/jcbfm.2008.96.

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A new functional magnetic resonance imaging (fMRI) technique is proposed based on nulling the extravascular gray matter (GM) signal, using a spatially nonselective inversion pulse. The remaining MR signal provides cerebral blood volume (CBV) information from brain activation. A theoretical framework is provided to characterize the sources of GM-nulled (GMN) fMRI signal, effects of partial voluming of cerebrospinal fluid (CSF) and white matter, and behaviors of GMN fMRI signal during brain activation. Visual stimulation paradigm was used to explore the GMN fMRI signal behavior in the human brain at 3T. It is shown that the GMN fMRI signal increases by 7.2% ± 1.5%, which is two to three times more than that obtained with vascular space occupancy (VASO)-dependent fMRI (−3.2% ± 0.2%) or blood oxygenation level-dependent (BOLD) fMRI (2.9% ± 0.7%), using a TR of 3,000 ms and a resolution of 2 × 2 × 5 mm3. Under these conditions the fMRI signal-to-noise ratio (SNRfMRI) for BOLD, GMN, and VASO images was 4.97 ± 0.76, 4.56 ± 0.86, and 2.43 ± 1.06, respectively. Our study shows that both signal intensity and activation volume in GMN fMRI depend on spatial resolution because of partial voluming from CSF. It is shown that GMN fMRI is a convenient tool to assess CBV changes associated with brain activation.
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Dissertations / Theses on the topic "FMRI signal"

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Leach, Sean. "Physiological noise characterisation and signal analysis for fMRI." Thesis, University of Nottingham, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.437066.

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Kim, Junmo 1976. "Spatio-temporal fMRI signal analysis using information theory." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/8982.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.
Includes bibliographical references (p. 111-112).
Functional MRI is a fast brain imaging technique which measures the spatio-temporal neuronal activity. The development of automatic statistical analysis techniques which calculate brain activation maps from JMRI data has been a challenging problem due to the limitation of current understanding of human brain physiology. In previous work a novel information-theoretic approach was introduced for calculating the activation map for JMRI analysis [Tsai et al , 1999]. In that work the use of mutual information as a measure of activation resulted in a nonparametric calculation of the activation map. Nonparametric approaches are attractive as the implicit assumptions are milder than the strong assumptions of popular approaches based on the general linear model popularized by Friston et al [19941. Here we show that, in addition to the intuitive information-theoretic appeal, such an application of mutual information is equivalent to a hypothesis test when the underlying densities are unknown. Furthermore we incorporate local spatial priors using the well-known Ising model thereby dropping the implicit assumption that neighboring voxel time-series are independent. As a consequence of the hypothesis testing equivalence, calculation of the activation map with local spatial priors can be formulated as mincut/maxflow graph-cutting problem. Such problems can be solved in polynomial time by the Ford and Fulkerson method. Empirical results are presented on three JMRI datasets measuring motor, auditory, and visual cortex activation. Comparisons are made illustrating the differences between the proposed technique and one based on the general linear model.
by Junmo Kim.
S.M.
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Ambrose, Joseph Paul. "Dynamic field theory applied to fMRI signal analysis." Diss., University of Iowa, 2016. https://ir.uiowa.edu/etd/2035.

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In the field of cognitive neuroscience, there is a need for theory-based approaches to fMRI data analysis. The dynamic neural field model-based approach has been developing to meet this demand. This dissertation describes my contributions to this approach. The methods and tools were demonstrated through a case study experiment on response selection and inhibition. The experiment was analyzed via both the standard behavioral approach and the new model-based method, and the two methods were compared head to head. The methods were quantitatively comparable at the individual-level of the analysis. At the group level, the model-based method reveals distinct functional networks localized in the brain. This validates the dynamic neural field model-based approach in general as well as my recent contributions.
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Salloum, Jasmin B. "Behavioral modification of fMRI signal in studies of emotion." [S.l. : s.n.], 2001. http://deposit.ddb.de/cgi-bin/dokserv?idn=962689300.

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Riedel, Philipp, Mark J. Jacob, Dirk K. Müller, Nora C. Vetter, Michael N. Smolka, and Michael Marxen. "Amygdala fMRI Signal as a Predictor of Reaction Time." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2017. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-214196.

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Reaction times (RTs) are a valuable measure for assessing cognitive processes. However, RTs are susceptible to confounds and therefore variable. Exposure to threat, for example, speeds up or slows down responses. Distinct task types to some extent account for differential effects of threat on RTs. But also do inter-individual differences like trait anxiety. In this functional magnetic resonance imaging (fMRI) study, we investigated whether activation within the amygdala, a brain region closely linked to the processing of threat, may also function as a predictor of RTs, similar to trait anxiety scores. After threat conditioning by means of aversive electric shocks, 45 participants performed a choice RT task during alternating 30 s blocks in the presence of the threat conditioned stimulus [CS+] or of the safe control stimulus [CS-]. Trait anxiety was assessed with the State-Trait Anxiety Inventory and participants were median split into a high- and a low-anxiety subgroup. We tested three hypotheses: (1) RTs will be faster during the exposure to threat compared to the safe condition in individuals with high trait anxiety. (2) The amygdala fMRI signal will be higher in the threat condition compared to the safe condition. (3) Amygdala fMRI signal prior to a RT trial will be correlated with the corresponding RT. We found that, the high-anxious subgroup showed faster responses in the threat condition compared to the safe condition, while the low-anxious subgroup showed no significant difference in RTs in the threat condition compared to the safe condition. Though the fMRI analysis did not reveal an effect of condition on amygdala activity, we found a trial-by-trial correlation between blood-oxygen-level-dependent signal within the right amygdala prior to the CRT task and the subsequent RT. Taken together, the results of this study showed that exposure to threat modulates task performance. This modulation is influenced by personality trait. Additionally and most importantly, activation in the amygdala predicts behavior in a simple task that is performed during the exposure to threat. This finding is in line with “attentional capture by threat”—a model that includes the amygdala as a key brain region for the process that causes the response slowing.
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Riedel, Philipp, Mark J. Jacob, Dirk K. Müller, Nora C. Vetter, Michael N. Smolka, and Michael Marxen. "Amygdala fMRI Signal as a Predictor of Reaction Time." Frontiers Research Foundation, 2016. https://tud.qucosa.de/id/qucosa%3A29972.

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Reaction times (RTs) are a valuable measure for assessing cognitive processes. However, RTs are susceptible to confounds and therefore variable. Exposure to threat, for example, speeds up or slows down responses. Distinct task types to some extent account for differential effects of threat on RTs. But also do inter-individual differences like trait anxiety. In this functional magnetic resonance imaging (fMRI) study, we investigated whether activation within the amygdala, a brain region closely linked to the processing of threat, may also function as a predictor of RTs, similar to trait anxiety scores. After threat conditioning by means of aversive electric shocks, 45 participants performed a choice RT task during alternating 30 s blocks in the presence of the threat conditioned stimulus [CS+] or of the safe control stimulus [CS-]. Trait anxiety was assessed with the State-Trait Anxiety Inventory and participants were median split into a high- and a low-anxiety subgroup. We tested three hypotheses: (1) RTs will be faster during the exposure to threat compared to the safe condition in individuals with high trait anxiety. (2) The amygdala fMRI signal will be higher in the threat condition compared to the safe condition. (3) Amygdala fMRI signal prior to a RT trial will be correlated with the corresponding RT. We found that, the high-anxious subgroup showed faster responses in the threat condition compared to the safe condition, while the low-anxious subgroup showed no significant difference in RTs in the threat condition compared to the safe condition. Though the fMRI analysis did not reveal an effect of condition on amygdala activity, we found a trial-by-trial correlation between blood-oxygen-level-dependent signal within the right amygdala prior to the CRT task and the subsequent RT. Taken together, the results of this study showed that exposure to threat modulates task performance. This modulation is influenced by personality trait. Additionally and most importantly, activation in the amygdala predicts behavior in a simple task that is performed during the exposure to threat. This finding is in line with “attentional capture by threat”—a model that includes the amygdala as a key brain region for the process that causes the response slowing.
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Thomas, Christopher G. "Signal optimization techniques and noise characterization in BOLD-based fMRI." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/NQ58241.pdf.

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Purdon, Patrick L. (Patrick Lee) 1974. "Signal processing in functional magnetic resonance imaging (fMRI) of the brain." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/50032.

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Maczka, Melissa May. "Investigations into the effects of neuromodulations on the BOLD-fMRI signal." Thesis, University of Oxford, 2013. http://ora.ox.ac.uk/objects/uuid:96d46d4d-480b-48d7-9f2d-060e76c5f8aa.

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The blood oxygen level dependent functional MRI (BOLD-fMRI) signal is an indirect measure of the neuronal activity that most BOLD studies are interested in. This thesis uses generative embedding algorithms to investigate some of the challenges and opportunities that this presents for BOLD imaging. It is standard practice to analyse BOLD signals using general linear models (GLMs) that assume fixed neurovascular coupling. However, this assumption may cause false positive or negative neural activations to be detected if the biological manifestations of brain diseases, disorders and pharmaceutical drugs (termed "neuromodulations") alter this coupling. Generative embedding can help overcome this problem by identifying when a neuromodulation confounds the standard GLM. When applied to anaesthetic neuromodulations found in preclinical imaging data, Fentanyl has the smallest confounding effect and Pentobarbital has the largest, causing extremely significant neural activations to go undetected. Half of the anaesthetics tested caused overestimation of the neuronal activity but the other half caused underestimation. The variability in biological action between anaesthetic modulations in identical brain regions of genetically similar animals highlights the complexity required to comprehensively account for factors confounding neurovascular coupling in GLMs generally. Generative embedding has the potential to augment established algorithms used to compensate for these variations in GLMs without complicating the standard (ANOVA) way of reporting BOLD results. Neuromodulation of neurovascular coupling can also present opportunities, such as improved diagnosis, monitoring and understanding of brain diseases accompanied by neurovascular uncoupling. Information theory is used to show that the discriminabilities of neurodegenerative-diseased and healthy generative posterior parameter spaces make generative embedding a viable tool for these commercial applications, boasting sensitivity to neurovascular coupling nonlinearities and biological interpretability. The value of hybrid neuroimaging systems over separate neuroimaging technologies is found to be greatest for early-stage neurodegenerative disease.
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Fisher, Julia Marie. "Classification Analytics in Functional Neuroimaging: Calibrating Signal Detection Parameters." Thesis, The University of Arizona, 2015. http://hdl.handle.net/10150/594646.

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Classification analyses are a promising way to localize signal, especially scattered signal, in functional magnetic resonance imaging data. However, there is not yet a consensus on the most effective analysis pathway. We explore the efficacy of k-Nearest Neighbors classifiers on simulated functional magnetic resonance imaging data. We utilize a novel construction of the classification data. Additionally, we vary the spatial distribution of signal, the design matrix of the linear model used to construct the classification data, and the feature set available to the classifier. Results indicate that the k-Nearest Neighbors classifier is not sufficient under the current paradigm to adequately classify neural data and localize signal. Further exploration of the data using k-means clustering indicates that this is likely due in part to the amount of noise present in each data point. Suggestions are made for further research.
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Books on the topic "FMRI signal"

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Chen, Jean, Garth John Thompson, Shella Keilholz, and Peter Herman, eds. Origins of the Resting-State fMRI Signal. Frontiers Media SA, 2020. http://dx.doi.org/10.3389/978-2-88966-285-2.

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Ramani, Ramachandran, ed. Functional MRI. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780190297763.001.0001.

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Functional MRI with BOLD (Blood Oxygen Level Dependent) imaging is one of the commonly used modalities for studying brain function in neuroscience. The underlying source of the BOLD fMRI signal is the variation in oxyhemoglobin to deoxyhemoglobin ratio at the site of neuronal activity in the brain. fMRI is mostly used to map out the location and intensity of brain activity that correlate with mental activities. In recent years, a new approach to fMRI was developed that is called resting-state fMRI. The fMRI signal from this method does not require the brain to perform any goal-directed task; it is acquired with the subject at rest. It was discovered that there are low-frequency fluctuations in the fMRI signal in the brain at rest. The signals originate from spatially distinct functionally related brain regions but exhibit coherent time-synchronous fluctuations. Several of the networks have been identified and are called resting-state networks. These networks represent the strength of the functional connectivity between distinct functionally related brain regions and have been used as imaging markers of various neurological and psychiatric diseases. Resting-state fMRI is also ideally suited for functional brain imaging in disorders of consciousness and in subjects under anesthesia. This book provides a review of the basic principles of fMRI (signal sources, acquisition methods, and data analysis) and its potential clinical applications.
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Seeck, Margitta, L. Spinelli, Jean Gotman, and Fernando H. Lopes da Silva. Combination of Brain Functional Imaging Techniques. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0046.

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Several tools are available to map brain electrical activity. Clinical applications focus on epileptic activity, although electric source imaging (ESI) and electroencephalography-coupled functional magnetic resonance imaging (EEG–fMRI) are also used to investigate non-epileptic processes in healthy subjects. While positron-emission tomography (PET) reflects glucose metabolism, strongly linked with synaptic activity, and single-photon-emission computed tomography (SPECT) reflects blood flow, fMRI (BOLD) signals have a hemodynamic component that is a surrogate signal of neuronal (synaptic) activity. The exact interpretation of BOLD signals is not completely understood; even in unifocal epilepsy, more than one region of positive or negative BOLD is often observed. Co-registration of medical images is essential to answer clinical questions, particularly for presurgical epilepsy evaluations. Multimodal imaging can yield information about epileptic foci and underlying networks. Co-registering MRI, PET, SPECT, fMRI, and ESI (or magnetic source imaging) provides information to estimate the epileptogenic zone and can help optimize surgical results.
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Stamatakis, Emmanuel A., Eleni Orfanidou, and Andrew C. Papanicolaou. Functional Magnetic Resonance Imaging. Edited by Andrew C. Papanicolaou. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199764228.013.7.

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Functional magnetic resonance imaging (fMRI) is the most frequently used functional neuroimaging method and the one that accounts for most of the neuroimaging literature. It measures the blood oxygen level-dependent (BOLD) signal in different parts of the brain during rest and during task-induced activation of functional networks mediating basic and higher functions. A basic understanding of the various instruments and techniques of recording the hemodynamic responses of different brain regions and the manner in which we establish activation and connectivity patterns out of these responses is necessary for an appreciation of the contemporary functional neuroimaging literature. To facilitate such an understanding is the purpose of this chapter.
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Meijer, Ewout H., and Bruno Verschuere. Detection Deception Using Psychophysiological and Neural Measures. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190612016.003.0010.

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The use of physiological signals to detect deception can be traced back almost a century. Historically, the polygraph has been used—and debated. This chapter discusses the merits of polygraph testing, and to what extent the introduction of measures of brain activity—most notably functional magnetic imaging (fMRI)—have solved the problems associated with polygraph testing. It discusses the different question formats used with polygraph and brain activity measures, and argues that these formats are the main factor contributing to the tests’ validity. Moreover, the authors argue that erroneous test outcomes are caused by errors in logical inferences, and that these errors will not be remedied by new technology. The biggest challenge for the field is to find a question format that isolates deception, and to corroborate laboratory data with methodologically sound field studies.
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Book chapters on the topic "FMRI signal"

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Kayser, Christoph, and Nikos K. Logothetis. "The Electrophysiological Background of the fMRI Signal." In fMRI, 25–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-34342-1_4.

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Kayser, Christoph, and Nikos K. Logothetis. "The Electrophysiological Background of the fMRI Signal." In fMRI, 23–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-68132-8_4.

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Kayser, Christoph, and Nikos K. Logothetis. "The Electrophysiological Background of the fMRI Signal." In fMRI, 15–27. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41874-8_3.

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Lei, Xu. "Simultaneous EEG-fMRI." In EEG Signal Processing and Feature Extraction, 377–405. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9113-2_18.

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Turner, Robert. "Signal Sources in Bold Contrast FMRI." In Advances in Experimental Medicine and Biology, 19–25. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4899-0056-2_2.

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Uludağ, Kâmil, and Kâmil Uğurbil. "Physiology and Physics of the fMRI Signal." In fMRI: From Nuclear Spins to Brain Functions, 163–213. Boston, MA: Springer US, 2015. http://dx.doi.org/10.1007/978-1-4899-7591-1_8.

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Ylipaavalniemi, Jarkko, Seppo Mattila, Antti Tarkiainen, and Ricardo Vigário. "Brains and Phantoms: An ICA Study of fMRI." In Independent Component Analysis and Blind Signal Separation, 503–10. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11679363_63.

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Gruber, P., C. Kohler, and F. J. Theis. "A Toolbox for Model-Free Analysis of fMRI Data." In Independent Component Analysis and Signal Separation, 209–17. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74494-8_27.

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Bazán, Paulo Rodrigo, and Edson Amaro. "fMRI and fNIRS Methods for Social Brain Studies: Hyperscanning Possibilities." In Social and Affective Neuroscience of Everyday Human Interaction, 231–54. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08651-9_14.

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AbstractRecently, the “social brain” (i.e., how the brain works in social context and the mechanisms for our social behavior) has gained focus in neuroscience literature – largely due to the fact that recently developed techniques allow studying different aspects of human social cognition and its brain correlates. In this context, hyperscanning techniques (Montague et al., Neuroimage 16(4):1159–1164, 2002) open the horizon for human interaction studies, allowing for the evaluation of interbrain connectivity. These techniques represent methods for simultaneously recording signals from different brains when subjects are interacting. In this chapter, we will explore the potentials of functional magnetic resonance imaging (fMRI) and functional near-infrared spectroscopy (fNIRS), which are techniques based on blood-oxygen-level-dependent (BOLD) signal. We will start with a brief explanation of the BOLD response basic principles and the mechanisms involved in fMRI and fNIRS measurements related to brain function. We will then discuss the foundation of the social brain, based on the first studies, with one subject per data acquisition, to allow for understanding the new possibilities that hyperscanning techniques offer. Finally, we will focus on the scientific literature reporting fMRI and fNIRShyperscanning contribution to understand the social brain.
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Chatzichristos, Christos, Eleftherios Kofidis, Yiannis Kopsinis, Manuel Morante Moreno, and Sergios Theodoridis. "Higher-Order Block Term Decomposition for Spatially Folded fMRI Data." In Latent Variable Analysis and Signal Separation, 3–15. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53547-0_1.

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Conference papers on the topic "FMRI signal"

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Zhang, Nanyin, Xiao-Hong Zhu, Zhongming Liu, Bin He, and Wei Chen. "Quantitatively interpreting fMRI signal." In 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2008. http://dx.doi.org/10.1109/iembs.2008.4650190.

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Parker, David, Raphael T. Gerraty, and Qolamreza R. Razlighi. "Optimal signal recovery from interleaved FMRI data." In 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI 2015). IEEE, 2015. http://dx.doi.org/10.1109/isbi.2015.7164131.

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Soltanian-Zadeh, Hamid, Gholam-Ali Hossein-Zadeh, and Babak A. Ardekani. "fMRI activation detection in wavelet signal subspace." In Medical Imaging 2002, edited by Anne V. Clough and Chin-Tu Chen. SPIE, 2002. http://dx.doi.org/10.1117/12.463602.

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Seghouane, Abd-Krim. "FMRI: Principles and analysis." In 2013 8th InternationalWorkshop on Systems, Signal Processing and their Applications (WoSSPA). IEEE, 2013. http://dx.doi.org/10.1109/wosspa.2013.6602328.

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Oberlin, Thomas, Christian Barillot, Remi Gribonval, and Pierre Maurel. "Symmetrical EEG-FMRI imaging by sparse regularization." In 2015 23rd European Signal Processing Conference (EUSIPCO). IEEE, 2015. http://dx.doi.org/10.1109/eusipco.2015.7362708.

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Xu, Hao, Alexander Lorbert, Peter J. Ramadge, J. Swaroop Guntupalli, and James V. Haxby. "Regularized hyperalignment of multi-set fMRI data." In 2012 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2012. http://dx.doi.org/10.1109/ssp.2012.6319668.

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Weizman, L., K. L. Miller, Y. C. Eldar, O. Maayan, and M. Chiew. "PEAR: PEriodic and ApeRiodic signal separation for fast FMRI." In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2017. http://dx.doi.org/10.1109/embc.2017.8036872.

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Seiyama, Akitoshi, Yasuhiro Ooi, and Junji Seki. "Implication of output signal from Optical Topography and fMRI." In 2007 IEEE/ICME International Conference on Complex Medical Engineering. IEEE, 2007. http://dx.doi.org/10.1109/iccme.2007.4381879.

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Yoshida, Shinichi. "Decoding of emotional visual stimuli using fMRI brain signal." In 2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS). IEEE, 2016. http://dx.doi.org/10.1109/icis.2016.7550878.

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Mogultay, Hazal, Sarper Alkan, and Fatos T. Yarman-Vural. "Classification of fMRI data by using clustering." In 2015 23th Signal Processing and Communications Applications Conference (SIU). IEEE, 2015. http://dx.doi.org/10.1109/siu.2015.7130360.

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Reports on the topic "FMRI signal"

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Pizarro, Rodrigo, Raúl Delgado, Huáscar Eguino, and Carlos Pimenta. Marco conceptual para la clasificación del gasto público en cambio climático en América Latina y el Caribe. Banco Interamericano de Desarrollo, September 2022. http://dx.doi.org/10.18235/0004449.

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Para alinear el gasto público con las estrategias nacionales de cambio climático y con los compromisos de mitigación asumidos en referencia a las contribuciones determinadas a nivel nacional (NDC, por sus siglas en inglés), los países de América Latina y el Caribe (ALC) necesitan identificar periódicamente los gastos presupuestarios relacionados con el clima, sean de incidencia positiva o negativa. En función del hecho de que todavía no existe una metodología consensuada a nivel internacional para identificar estos gastos, el objetivo de esta publicación es proponer un marco conceptual y orientaciones metodológicas para los países de ALC, que permitan la identificación, clasificación y evaluación del gasto público verde relacionado con el cambio climático, de forma integrada con los sistemas estadísticos vigentes. La metodología planteada es coherente con el Sistema de Cuentas Ambientales de Naciones Unidas (Naciones Unidas, 2008) y con la clasificación de funciones de gobierno presente en el Marco de Estadísticas de Finanzas Públicas (FMI, 2014), ambos reconocidos estándares estadísticos internacionales. La metodología apunta a usar una matriz de clasificación funcional de doble entrada, que atiende el propósito principal y secundario del gasto ligado al clima, y emplea un enfoque analítico de cuentas satélite. No se trata de un modelo único sino de definiciones metodológicas para que los países construyan sus propios sistemas de clasificación funcional presupuestaria relacionada con el cambio climático, efectúen un seguimiento permanente y sostenible de este tipo de gasto verde, y puedan así evaluar sus resultados e impactos, retroalimentando el proceso de formulación presupuestaria relacionado con el clima.
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