Journal articles: 'FMRI signal'

1

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

2

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

3

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

4

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

6

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

7

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

8

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

9

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

10

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

11

Jego, Pierrick, Jesús Pacheco-Torres, Alfonso Araque, and Santiago Canals. "Functional MRI in Mice Lacking IP3-Dependent Calcium Signaling in Astrocytes." Journal of Cerebral Blood Flow & Metabolism 34, no. 10 (August 6, 2014): 1599–603. http://dx.doi.org/10.1038/jcbfm.2014.144.

Full text

Abstract:

Functional magnetic resonance imaging (fMRI) is a fundamental tool to investigate human brain networks. However, the cellular mechanisms underlying fMRI signals are not fully understood. One hypothetical mechanism is the putative vascular control exerted by cytosolic calcium in perivascular astrocytes. We have performed combined fMRI-electrophysiology experiments in mice lacking the inositol 1,4,5-triphosphate-type-2 receptor, with the primary pathway of cytosolic calcium increase eliminated into astrocytes. Our results show that evoked electrophysiologic activity and fMRI signals acquired during either transient or sustained neuronal activations occur independently of these large calcium signals. This result challenges the suggested intermediary role of astrocytic calcium surges in fMRI-signal generation.

APA, Harvard, Vancouver, ISO, and other styles

12

Wang, Xiao, Xiao-Hong Zhu, Yi Zhang, and Wei Chen. "Large Enhancement of Perfusion Contribution on fMRI Signal." Journal of Cerebral Blood Flow & Metabolism 32, no. 5 (March 7, 2012): 907–18. http://dx.doi.org/10.1038/jcbfm.2012.26.

Full text

Abstract:

The perfusion contribution to the total functional magnetic resonance imaging (fMRI) signal was investigated using a rat model with mild hypercapnia at 9.4 T, and human subjects with visual stimulation at 4 T. It was found that the total fMRI signal change could be approximated as a linear superposition of ‘true’ blood oxygenation level-dependent (BOLD; T2/T2*) effect and the blood flow-related ( T1) effect. The latter effect was significantly enhanced by using short repetition time and large radiofrequency pulse flip angle and became comparable to the ‘true’ BOLD signal in response to a mild hypercapnia in the rat brain, resulting in an improved contrast-to-noise ratio (CNR). Bipolar diffusion gradients suppressed the intravascular signals but had no significant effect on the flow-related signal. Similar results of enhanced fMRI signal were observed in the human study. The overall results suggest that the observed flow-related signal enhancement is likely originated from perfusion, and this enhancement can improve CNR and the spatial specificity for mapping brain activity and physiology changes. The nature of mixed BOLD and perfusion-related contributions in the total fMRI signal also has implication on BOLD quantification, in particular, the BOLD calibration model commonly used to estimate the change of cerebral metabolic rate of oxygen.

APA, Harvard, Vancouver, ISO, and other styles

13

He, Yi, Maosen Wang, and Xin Yu. "High spatiotemporal vessel-specific hemodynamic mapping with multi-echo single-vessel fMRI." Journal of Cerebral Blood Flow & Metabolism 40, no. 10 (November 7, 2019): 2098–114. http://dx.doi.org/10.1177/0271678x19886240.

Full text

Abstract:

High-resolution fMRI enables noninvasive mapping of the hemodynamic responses from individual penetrating vessels in animal brains. Here, a 2D multi-echo single-vessel fMRI (MESV-fMRI) method has been developed to map the fMRI signal from arterioles and venules with a 100 ms sampling rate at multiple echo times (TE, 3–30 ms) and short acquisition windows (<1 ms). The T2*-weighted signal shows the increased extravascular effect on venule voxels as a function of TE. In contrast, the arteriole voxels show an increased fMRI signal with earlier onset than venules voxels at the short TE (3 ms) with increased blood inflow and volume effects. MESV-fMRI enables vessel-specific T2* mapping and presents T2*-based fMRI time courses with higher contrast-to-noise ratios (CNRs) than the T2*-weighted fMRI signal at a given TE. The vessel-specific T2* mapping also allows semi-quantitative estimation of the oxygen saturation levels (Y) and their changes (ΔY) at a given blood volume fraction upon neuronal activation. The MESV-fMRI method enables vessel-specific T2* measurements with high spatiotemporal resolution for better modeling of the fMRI signal based on the hemodynamic parameters.

APA, Harvard, Vancouver, ISO, and other styles

14

Raut, Savita V., and Dinkar M. Yadav. "A decomposition model and voxel selection framework for fMRI analysis to predict neural response of visual stimuli." Biomedical Engineering / Biomedizinische Technik 63, no. 2 (March 28, 2018): 163–75. http://dx.doi.org/10.1515/bmt-2016-0194.

Full text

Abstract:

AbstractThis paper presents an fMRI signal analysis methodology using geometric mean curve decomposition (GMCD) and mutual information-based voxel selection framework. Previously, the fMRI signal analysis has been conducted using empirical mean curve decomposition (EMCD) model and voxel selection on raw fMRI signal. The erstwhile methodology loses frequency component, while the latter methodology suffers from signal redundancy. Both challenges are addressed by our methodology in which the frequency component is considered by decomposing the raw fMRI signal using geometric mean rather than arithmetic mean and the voxels are selected from EMCD signal using GMCD components, rather than raw fMRI signal. The proposed methodologies are adopted for predicting the neural response. Experimentations are conducted in the openly available fMRI data of six subjects, and comparisons are made with existing decomposition models and voxel selection frameworks. Subsequently, the effect of degree of selected voxels and the selection constraints are analyzed. The comparative results and the analysis demonstrate the superiority and the reliability of the proposed methodology.

APA, Harvard, Vancouver, ISO, and other styles

15

Power, Jonathan D., Mark Plitt, Stephen J. Gotts, Prantik Kundu, Valerie Voon, Peter A. Bandettini, and Alex Martin. "Ridding fMRI data of motion-related influences: Removal of signals with distinct spatial and physical bases in multiecho data." Proceedings of the National Academy of Sciences 115, no. 9 (February 12, 2018): E2105—E2114. http://dx.doi.org/10.1073/pnas.1720985115.

Full text

Abstract:

“Functional connectivity” techniques are commonplace tools for studying brain organization. A critical element of these analyses is to distinguish variance due to neurobiological signals from variance due to nonneurobiological signals. Multiecho fMRI techniques are a promising means for making such distinctions based on signal decay properties. Here, we report that multiecho fMRI techniques enable excellent removal of certain kinds of artifactual variance, namely, spatially focal artifacts due to motion. By removing these artifacts, multiecho techniques reveal frequent, large-amplitude blood oxygen level-dependent (BOLD) signal changes present across all gray matter that are also linked to motion. These whole-brain BOLD signals could reflect widespread neural processes or other processes, such as alterations in blood partial pressure of carbon dioxide (pCO2) due to ventilation changes. By acquiring multiecho data while monitoring breathing, we demonstrate that whole-brain BOLD signals in the resting state are often caused by changes in breathing that co-occur with head motion. These widespread respiratory fMRI signals cannot be isolated from neurobiological signals by multiecho techniques because they occur via the same BOLD mechanism. Respiratory signals must therefore be removed by some other technique to isolate neurobiological covariance in fMRI time series. Several methods for removing global artifacts are demonstrated and compared, and were found to yield fMRI time series essentially free of motion-related influences. These results identify two kinds of motion-associated fMRI variance, with different physical mechanisms and spatial profiles, each of which strongly and differentially influences functional connectivity patterns. Distance-dependent patterns in covariance are nearly entirely attributable to non-BOLD artifacts.

APA, Harvard, Vancouver, ISO, and other styles

16

Salch, Andrew, Adam Regalski, Hassan Abdallah, Raviteja Suryadevara, Michael J. Catanzaro, and Vaibhav A. Diwadkar. "From mathematics to medicine: A practical primer on topological data analysis (TDA) and the development of related analytic tools for the functional discovery of latent structure in fMRI data." PLOS ONE 16, no. 8 (August 12, 2021): e0255859. http://dx.doi.org/10.1371/journal.pone.0255859.

Full text

Abstract:

fMRI is the preeminent method for collecting signals from the human brain in vivo, for using these signals in the service of functional discovery, and relating these discoveries to anatomical structure. Numerous computational and mathematical techniques have been deployed to extract information from the fMRI signal. Yet, the application of Topological Data Analyses (TDA) remain limited to certain sub-areas such as connectomics (that is, with summarized versions of fMRI data). While connectomics is a natural and important area of application of TDA, applications of TDA in the service of extracting structure from the (non-summarized) fMRI data itself are heretofore nonexistent. “Structure” within fMRI data is determined by dynamic fluctuations in spatially distributed signals over time, and TDA is well positioned to help researchers better characterize mass dynamics of the signal by rigorously capturing shape within it. To accurately motivate this idea, we a) survey an established method in TDA (“persistent homology”) to reveal and describe how complex structures can be extracted from data sets generally, and b) describe how persistent homology can be applied specifically to fMRI data. We provide explanations for some of the mathematical underpinnings of TDA (with expository figures), building ideas in the following sequence: a) fMRI researchers can and should use TDA to extract structure from their data; b) this extraction serves an important role in the endeavor of functional discovery, and c) TDA approaches can complement other established approaches toward fMRI analyses (for which we provide examples). We also provide detailed applications of TDA to fMRI data collected using established paradigms, and offer our software pipeline for readers interested in emulating our methods. This working overview is both an inter-disciplinary synthesis of ideas (to draw researchers in TDA and fMRI toward each other) and a detailed description of methods that can motivate collaborative research.

APA, Harvard, Vancouver, ISO, and other styles

17

Palmer, Helen S. "Optogenetic fMRI Sheds Light on the Neural Basis of the BOLD Signal." Journal of Neurophysiology 104, no. 4 (October 2010): 1838–40. http://dx.doi.org/10.1152/jn.00535.2010.

Full text

Abstract:

Blood oxygenation level dependent (BOLD) functional magnetic resonance imaging (fMRI) is widely used as a measure of neuronal activity, despite an incomplete understanding of the hemodynamic and neural bases for BOLD signals. Recent work by Lee and colleagues investigated whether activating genetically specified neurons elicits BOLD responses. Integrating optogenetic control of specific cells and fMRI showed that stimulating excitatory neurons triggers a positive BOLD signal with conventional kinetics locally and delayed weaker BOLD signals distally.

APA, Harvard, Vancouver, ISO, and other styles

18

Fischer, David, Otto Rapalino, Matteo Fecchio, and Brian L. Edlow. "Ictal fMRI: Mapping Seizure Topography with Rhythmic BOLD Oscillations." Brain Sciences 12, no. 12 (December 13, 2022): 1710. http://dx.doi.org/10.3390/brainsci12121710.

Full text

Abstract:

Functional magnetic resonance imaging (fMRI) has shown elevations in the blood-oxygen-level-dependent (BOLD) signal associated with, but insensitive for, seizure. Rather than evaluating absolute BOLD signal elevations, assessing rhythmic oscillations in the BOLD signal with fMRI may improve the accuracy of seizure mapping. We report a case of a patient with non-convulsive, right hemispheric seizures who underwent fMRI. Unbiased processing methods revealed a map of rhythmically oscillating BOLD signal over the cortical region affected by seizure, and synchronous BOLD signal in the contralateral cerebellum. High-resolution fMRI may help identify the spatial topography of seizure and provide insights into seizure physiology.

APA, Harvard, Vancouver, ISO, and other styles

19

Zhang, Jiang, Huafu Chen, Fang Fang, Hualin Liu, and Wei Liao. "A FREQUENCY SIGNAL METHOD FOR fMRI DATA ANALYSIS." Biomedical Engineering: Applications, Basis and Communications 22, no. 05 (October 2010): 377–83. http://dx.doi.org/10.4015/s1016237210002134.

Full text

Abstract:

Currently, all the data processing strategies for functional magnetic resonance imaging (fMRI) utilize temporal informationpaying little attention to or totally ignoring frequency information. In this paper, a new method is proposed to detect the functional activation regions in the brain by using the frequency information of fMRI time series. The main idea is that the frequency entropy information (FEI) difference of fMRI data between task and control states is specified as brain activation index. The validity of the proposed FEI approach is confirmed by analyzing the result of the simulated synthesized data. Additionally, the comparison of receiver operating characteristic (ROC) curves acquired respectively from the proposed scheme, the statistical parametric mapping (SPM), and the Support Vector Machine (SVM) methods of fMRI data analysis indicate an obvious superiority of the former. In vivo fMRI studies of subjects with event-related experiment reveal that FEI method can enable the effective detection of brain functional activation.

APA, Harvard, Vancouver, ISO, and other styles

20

Zhong, Yuan, Gang Zheng, Yijun Liu, and Guangming Lu. "Independent Component Analysis of Instantaneous Power-Based fMRI." Computational and Mathematical Methods in Medicine 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/579652.

Full text

Abstract:

In functional magnetic resonance imaging (fMRI) studies using spatial independent component analysis (sICA) method, a model of “latent variables” is often employed, which is based on the assumption that fMRI data are linear mixtures of statistically independent signals. However, actual fMRI signals are nonlinear and do not automatically meet with the requirement of sICA. To provide a better solution to this problem, we proposed a novel approach termed instantaneous power based fMRI (ip-fMRI) for regularization of fMRI data. Given that the instantaneous power of fMRI signals is a scalar value, it should be a linear mixture that naturally satisfies the “latent variables” model. Based on our simulated data, the curves of accuracy and resulting receiver-operating characteristic curves indicate that the proposed approach is superior to the traditional fMRI in terms of accuracy and specificity by using sICA. Experimental results from human subjects have shown that spatial components of a hand movement task-induced activation reveal a brain network more specific to motor function by ip-fMRI than that by the traditional fMRI. We conclude that ICA decomposition of ip-fMRI may be used to localize energy signal changes in the brain and may have a potential to be applied to detection of brain activity.

APA, Harvard, Vancouver, ISO, and other styles

21

Harel, Noam, Sang-Pil Lee, Tsukasa Nagaoka, Dae-Shik Kim, and Seong-Gi Kim. "Origin of Negative Blood Oxygenation Level—Dependent fMRI Signals." Journal of Cerebral Blood Flow & Metabolism 22, no. 8 (August 2002): 908–17. http://dx.doi.org/10.1097/00004647-200208000-00002.

Full text

Abstract:

Functional magnetic resonance imaging (fMRI) techniques are based on the assumption that changes in spike activity are accompanied by modulation in the blood oxygenation level—dependent (BOLD) signal. In addition to conventional increases in BOLD signals, sustained negative BOLD signal changes are occasionally observed and are thought to reflect a decrease in neural activity. In this study, the source of the negative BOLD signal was investigated using T2*-weighted BOLD and cerebral blood volume (CBV) techniques in isoflurane-anesthetized cats. A positive BOLD signal change was observed in the primary visual cortex (area 18) during visual stimulation, while a prolonged negative BOLD change was detected in the adjacent suprasylvian gyrus containing higher-order visual areas. However, in both regions neurons are known to increase spike activity during visual stimulation. The positive and negative BOLD amplitudes obtained at six spatial-frequency stimuli were highly correlated, and negative BOLD percent changes were approximately one third of the postitive changes. Area 18 with positive BOLD signals experienced an increase in CBV, while regions exhibiting the prolonged negative BOLD signal underwent a decrease in CBV. The CBV changes in area 18 were faster than the BOLD signals from the same corresponding region and the CBV changes in the suprasylvian gyrus. The results support the notion that reallocation of cortical blood resources could overcome a local demand for increased cerebral blood flow induced by increased neural activity. The findings of this study imply that caution should be taken when interpreting the negative BOLD signals as a decrease in neuronal activity.

APA, Harvard, Vancouver, ISO, and other styles

22

Zhu, David C., Takashi Tarumi, Muhammad Ayaz Khan, and Rong Zhang. "Vascular Coupling in Resting-State FMRI: Evidence from Multiple Modalities." Journal of Cerebral Blood Flow & Metabolism 35, no. 12 (July 15, 2015): 1910–20. http://dx.doi.org/10.1038/jcbfm.2015.166.

Full text

Abstract:

Resting-state functional magnetic resonance imaging (rs-fMRI) provides a potential to understand intrinsic brain functional connectivity. However, vascular effects in rs-fMRI are still not fully understood. Through multiple modalities, we showed marked vascular signal fluctuations and high-level coupling among arterial pressure, cerebral blood flow (CBF) velocity and brain tissue oxygenation at < 0.08 Hz. These similar spectral power distributions were also observed in blood oxygen level-dependent (BOLD) signals obtained from six representative regions of interest (ROIs). After applying brain global, white-matter, cerebrospinal fluid (CSF) mean signal regressions and low-pass filtering (< 0.08 Hz), the spectral power of BOLD signal was reduced by 55.6% to 64.9% in all ROIs ( P = 0.011 to 0.001). The coherence of BOLD signal fluctuations between an ROI pair within a same brain network was reduced by 9.9% to 20.0% ( P = 0.004 to < 0.001), but a larger reduction of 22.5% to 37.3% ( P = 0.032 to < 0.001) for one not in a same network. Global signal regression overall had the largest impact in reducing spectral power (by 52.2% to 61.7%) and coherence, relative to the other three preprocessing steps. Collectively, these findings raise a critical question of whether a large portion of rs-fMRI signals can be attributed to the vascular effects produced from upstream changes in cerebral hemodynamics.

APA, Harvard, Vancouver, ISO, and other styles

23

Nikolaou, F., C. Orphanidou, P. Papakyriakou, K. Murphy, R. G. Wise, and G. D. Mitsis. "Spontaneous physiological variability modulates dynamic functional connectivity in resting-state functional magnetic resonance imaging." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 374, no. 2067 (May 13, 2016): 20150183. http://dx.doi.org/10.1098/rsta.2015.0183.

Full text

Abstract:

It is well known that the blood oxygen level-dependent (BOLD) signal measured by functional magnetic resonance imaging (fMRI) is influenced—in addition to neuronal activity—by fluctuations in physiological signals, including arterial CO 2 , respiration and heart rate/heart rate variability (HR/HRV). Even spontaneous fluctuations of the aforementioned physiological signals have been shown to influence the BOLD fMRI signal in a regionally specific manner. Related to this, estimates of functional connectivity between different brain regions, performed when the subject is at rest, may be confounded by the effects of physiological signal fluctuations. Moreover, resting functional connectivity has been shown to vary with respect to time (dynamic functional connectivity), with the sources of this variation not fully elucidated. In this context, we examine the relation between dynamic functional connectivity patterns and the time-varying properties of simultaneously recorded physiological signals (end-tidal CO 2 and HR/HRV) using resting-state fMRI measurements from 12 healthy subjects. The results reveal a modulatory effect of the aforementioned physiological signals on the dynamic resting functional connectivity patterns for a number of resting-state networks (default mode network, somatosensory, visual). By using discrete wavelet decomposition, we also show that these modulation effects are more pronounced in specific frequency bands.

APA, Harvard, Vancouver, ISO, and other styles

24

Cifre, I., M. Zarepour, S. G. Horovitz, S. A. Cannas, and D. R. Chialvo. "Further results on why a point process is effective for estimating correlation between brain regions." Papers in Physics 12 (June 18, 2020): 120003. http://dx.doi.org/10.4279/pip.120003.

Full text

Abstract:

Signals from brain functional magnetic resonance imaging (fMRI) can be efficiently represented by a sparse spatiotemporal point process, according to a recently introduced heuristic signal processing scheme. This approach has already been validated for relevant conditions, demonstrating that it preserves and compresses a surprisingly large fraction of the signal information. Here we investigated the conditions necessary for such an approach to succeed, as well as the underlying reasons, using real fMRI data and a simulated dataset. The results show that the key lies in the temporal correlation properties of the time series under consideration. It was found that signals with slowly decaying autocorrelations are particularly suitable for this type of compression, where inflection points contain most of the information.

APA, Harvard, Vancouver, ISO, and other styles

25

Chang, Catie, David A. Leopold, Marieke Louise Schölvinck, Hendrik Mandelkow, Dante Picchioni, Xiao Liu, Frank Q. Ye, Janita N. Turchi, and Jeff H. Duyn. "Tracking brain arousal fluctuations with fMRI." Proceedings of the National Academy of Sciences 113, no. 16 (April 5, 2016): 4518–23. http://dx.doi.org/10.1073/pnas.1520613113.

Full text

Abstract:

Changes in brain activity accompanying shifts in vigilance and arousal can interfere with the study of other intrinsic and task-evoked characteristics of brain function. However, the difficulty of tracking and modeling the arousal state during functional MRI (fMRI) typically precludes the assessment of arousal-dependent influences on fMRI signals. Here we combine fMRI, electrophysiology, and the monitoring of eyelid behavior to demonstrate an approach for tracking continuous variations in arousal level from fMRI data. We first characterize the spatial distribution of fMRI signal fluctuations that track a measure of behavioral arousal; taking this pattern as a template, and using the local field potential as a simultaneous and independent measure of cortical activity, we observe that the time-varying expression level of this template in fMRI data provides a close approximation of electrophysiological arousal. We discuss the potential benefit of these findings for increasing the sensitivity of fMRI as a cognitive and clinical biomarker.

APA, Harvard, Vancouver, ISO, and other styles

26

Kashyap, Amrit, and Shella Keilholz. "Dynamic properties of simulated brain network models and empirical resting-state data." Network Neuroscience 3, no. 2 (January 2019): 405–26. http://dx.doi.org/10.1162/netn_a_00070.

Full text

Abstract:

Brain network models (BNMs) have become a promising theoretical framework for simulating signals that are representative of whole-brain activity such as resting-state fMRI. However, it has been difficult to compare the complex brain activity obtained from simulations to empirical data. Previous studies have used simple metrics to characterize coordination between regions such as functional connectivity. We extend this by applying various different dynamic analysis tools that are currently used to understand empirical resting-state fMRI (rs-fMRI) to the simulated data. We show that certain properties correspond to the structural connectivity input that is shared between the models, and certain dynamic properties relate more to the mathematical description of the brain network model. We conclude that the dynamic properties that explicitly examine patterns of signal as a function of time rather than spatial coordination between different brain regions in the rs-fMRI signal seem to provide the largest contrasts between different BNMs and the unknown empirical dynamical system. Our results will be useful in constraining and developing more realistic simulations of whole-brain activity.

APA, Harvard, Vancouver, ISO, and other styles

27

Mitra, A., A. Z. Snyder, C. D. Hacker, and M. E. Raichle. "Lag structure in resting-state fMRI." Journal of Neurophysiology 111, no. 11 (June 1, 2014): 2374–91. http://dx.doi.org/10.1152/jn.00804.2013.

Full text

Abstract:

The discovery that spontaneous fluctuations in blood oxygen level-dependent (BOLD) signals contain information about the functional organization of the brain has caused a paradigm shift in neuroimaging. It is now well established that intrinsic brain activity is organized into spatially segregated resting-state networks (RSNs). Less is known regarding how spatially segregated networks are integrated by the propagation of intrinsic activity over time. To explore this question, we examined the latency structure of spontaneous fluctuations in the fMRI BOLD signal. Our data reveal that intrinsic activity propagates through and across networks on a timescale of ∼1 s. Variations in the latency structure of this activity resulting from sensory state manipulation (eyes open vs. closed), antecedent motor task (button press) performance, and time of day (morning vs. evening) suggest that BOLD signal lags reflect neuronal processes rather than hemodynamic delay. Our results emphasize the importance of the temporal structure of the brain's spontaneous activity.

APA, Harvard, Vancouver, ISO, and other styles

28

Ekstrom, Arne D. "Regional variation in neurovascular coupling and why we still lack a Rosetta Stone." Philosophical Transactions of the Royal Society B: Biological Sciences 376, no. 1815 (November 16, 2020): 20190634. http://dx.doi.org/10.1098/rstb.2019.0634.

Full text

Abstract:

Functional magnetic resonance imaging (fMRI) is the dominant tool in cognitive neuroscience although its relation to underlying neural activity, particularly in the human brain, remains largely unknown. A major research goal, therefore, has been to uncover a ‘Rosetta Stone’ providing direct translation between the blood oxygen level-dependent (BOLD) signal, the local field potential and single-neuron activity. Here, I evaluate the proposal that BOLD signal changes equate to changes in gamma-band activity, which in turn may partially relate to the spiking activity of neurons. While there is some support for this idea in sensory cortices, findings in deeper brain structures like the hippocampus instead suggest both regional and frequency-wise differences. Relatedly, I consider four important factors in linking fMRI to neural activity: interpretation of correlations between these signals, regional variability in local vasculature, distributed neural coding schemes and varying fMRI signal quality. Novel analytic fMRI techniques, such as multivariate pattern analysis (MVPA), employ the distributed patterns of voxels across a brain region to make inferences about information content rather than whether a small number of voxels go up or down relative to baseline in response to a stimulus. Although unlikely to provide a Rosetta Stone, MVPA, therefore, may represent one possible means forward for better linking BOLD signal changes to the information coded by underlying neural activity. This article is part of the theme issue ‘Key relationships between non-invasive functional neuroimaging and the underlying neuronal activity’.

APA, Harvard, Vancouver, ISO, and other styles

29

Autio, Joonas A., Artem Shatillo, Rashid Giniatullin, and Olli H. Gröhn. "Parenchymal Spin-Lock fMRI Signals Associated with Cortical Spreading Depression." Journal of Cerebral Blood Flow & Metabolism 34, no. 5 (February 5, 2014): 768–75. http://dx.doi.org/10.1038/jcbfm.2014.16.

Full text

Abstract:

We found novel types of parenchymal functional magnetic resonance imaging (fMRI) signals in the rat brain during large increases in metabolism. Cortical spreading depression (CSD), a self-propagating wave of cellular activation, is associated with several pathologic conditions such as migraine and stroke. It was used as a paradigm to evoke transient neuronal depolarization leading to enhanced energy consumption. Activation of CSD was investigated using spin-lock (SL), diffusion, blood oxygenation level-dependent and cerebral blood volume fMRI techniques. Our results show that the SL-fMRI signal is generated by endogenous parenchymal mechanisms during CSD propagation, and these mechanisms are not associated with hemodynamic changes or cellular swelling. Protein phantoms suggest that pH change alone does not explain the observed SL-fMRI signal changes. However, increased amounts of inorganic phosphates released from high-energy phosphates combined with pH changes may produce SL- power-dependent longitudinal relaxation in the rotating frame ( R1ρ) changes in protein phantoms that are similar to those observed during CSD, as seen before in acute ischemia under our experimental conditions. This links SL-fMRI changes intimately to energy metabolism and supports the use of the SL technique as a new, promising functional approach for noninvasive imaging of metabolic transitions in the active or pathologic brain.

APA, Harvard, Vancouver, ISO, and other styles

30

Caballero-Gaudes, César, and Richard C. Reynolds. "Methods for cleaning the BOLD fMRI signal." NeuroImage 154 (July 2017): 128–49. http://dx.doi.org/10.1016/j.neuroimage.2016.12.018.

Full text

APA, Harvard, Vancouver, ISO, and other styles

31

Parrish, Todd B., Darren R. Gitelman, Kevin S. LaBar, and M. Marsel Mesulam. "Signal to noise influence on clinical fMRI." NeuroImage 11, no. 5 (May 2000): S532. http://dx.doi.org/10.1016/s1053-8119(00)91463-0.

Full text

APA, Harvard, Vancouver, ISO, and other styles

32

Power, Jonathan D., Bradley L. Schlaggar, and Steven E. Petersen. "Studying Brain Organization via Spontaneous fMRI Signal." Neuron 84, no. 4 (November 2014): 681–96. http://dx.doi.org/10.1016/j.neuron.2014.09.007.

Full text

APA, Harvard, Vancouver, ISO, and other styles

33

Hansen, Lars Kai, Finn Årup Nielsen, and Jan Larsen. "Exploring fMRI data for periodic signal components." Artificial Intelligence in Medicine 25, no. 1 (May 2002): 35–44. http://dx.doi.org/10.1016/s0933-3657(02)00007-6.

Full text

APA, Harvard, Vancouver, ISO, and other styles

34

Hyde, James S., and Andrzej Jesmanowicz. "Cross-correlation: An fMRI signal-processing strategy." NeuroImage 62, no. 2 (August 2012): 848–51. http://dx.doi.org/10.1016/j.neuroimage.2011.10.064.

Full text

APA, Harvard, Vancouver, ISO, and other styles

35

Boynton, Geoffrey M., Stephen A. Engel, and David J. Heeger. "Linear systems analysis of the fMRI signal." NeuroImage 62, no. 2 (August 2012): 975–84. http://dx.doi.org/10.1016/j.neuroimage.2012.01.082.

Full text

APA, Harvard, Vancouver, ISO, and other styles

36

Erné, S. N., H. P. Müller, H. G. Kammrath, R. Tomczak, and A. Wunderlich. "Signal to Noise Improvement in fMRI Analysis." Biomedizinische Technik/Biomedical Engineering 44, s2 (1999): 181–83. http://dx.doi.org/10.1515/bmte.1999.44.s2.181.

Full text

APA, Harvard, Vancouver, ISO, and other styles

37

Al Zoubi, Obada, Ahmad Mayeli, Masaya Misaki, Aki Tsuchiyagaito, Vadim Zotev, Hazem Refai, Martin Paulus, and Jerzy Bodurka. "Canonical EEG microstates transitions reflect switching among BOLD resting state networks and predict fMRI signal." Journal of Neural Engineering 18, no. 6 (December 1, 2021): 066051. http://dx.doi.org/10.1088/1741-2552/ac4595.

Full text

Abstract:

Abstract Objective. Electroencephalography (EEG) microstates (MSs), which reflect a large topographical representation of coherent electrophysiological brain activity, are widely adopted to study cognitive processes mechanisms and aberrant alterations in brain disorders. MS topographies are quasi-stable lasting between 60–120 ms. Some evidence suggests that MS are the electrophysiological signature of resting-state networks (RSNs). However, the spatial and functional interpretation of MS and their association with functional magnetic resonance imaging (fMRI) remains unclear. Approach. In a cohort of healthy subjects (n = 52), we conducted several statistical and machine learning (ML) approaches analyses on the association among MS spatio-temporal dynamics and the blood-oxygenation-level dependent (BOLD) simultaneous EEG-fMRI data using statistical and ML approaches. Main results. Our results using a generalized linear model showed that MS transitions were largely and negatively associated with BOLD signals in the somatomotor, visual, dorsal attention, and ventral attention fMRI networks with limited association within the default mode network. Additionally, a novel recurrent neural network (RNN) confirmed the association between MS transitioning and fMRI signal while revealing that MS dynamics can model BOLD signals and vice versa. Significance. Results suggest that MS transitions may represent the deactivation of fMRI RSNs and provide evidence that both modalities measure common aspects of undergoing brain neuronal activities. These results may help to better understand the electrophysiological interpretation of MS.

APA, Harvard, Vancouver, ISO, and other styles

38

Wang, Kainan S., David V. Smith, and Mauricio R. Delgado. "Using fMRI to study reward processing in humans: past, present, and future." Journal of Neurophysiology 115, no. 3 (March 1, 2016): 1664–78. http://dx.doi.org/10.1152/jn.00333.2015.

Full text

Abstract:

Functional magnetic resonance imaging (fMRI) is a noninvasive tool used to probe cognitive and affective processes. Although fMRI provides indirect measures of neural activity, the advent of fMRI has allowed for 1) the corroboration of significant animal findings in the human brain, and 2) the expansion of models to include more common human attributes that inform behavior. In this review, we briefly consider the neural basis of the blood oxygenation level dependent signal to set up a discussion of how fMRI studies have applied it in examining cognitive models in humans and the promise of using fMRI to advance such models. Specifically, we illustrate the contribution that fMRI has made to the study of reward processing, focusing on the role of the striatum in encoding reward-related learning signals that drive anticipatory and consummatory behaviors. For instance, we discuss how fMRI can be used to link neural signals (e.g., striatal responses to rewards) to individual differences in behavior and traits. While this functional segregation approach has been constructive to our understanding of reward-related functions, many fMRI studies have also benefitted from a functional integration approach that takes into account how interconnected regions (e.g., corticostriatal circuits) contribute to reward processing. We contend that future work using fMRI will profit from using a multimodal approach, such as combining fMRI with noninvasive brain stimulation tools (e.g., transcranial electrical stimulation), that can identify causal mechanisms underlying reward processing. Consequently, advancements in implementing fMRI will promise new translational opportunities to inform our understanding of psychopathologies.

APA, Harvard, Vancouver, ISO, and other styles

39

Ramsey, Nick F., Brenda S. Kirkby, Peter Van Gelderen, Karen F. Berman, Jeff H. Duyn, Joe A. Frank, Venkata S. Mattay, et al. "Functional Mapping of Human Sensorimotor Cortex with 3D BOLD fMRI Correlates Highly with H215O PET rCBF." Journal of Cerebral Blood Flow & Metabolism 16, no. 5 (September 1996): 755–64. http://dx.doi.org/10.1097/00004647-199609000-00001.

Full text

Abstract:

Positron emission tomography (PET) functional imaging is based on changes in regional cerebral blood flow (rCBF). Functional magnetic resonance imaging (fMRI) is based on a variety of physiological parameters as well as rCBF. This study is aimed at the cross validation of three-dimensional (3D) fMRI, which is sensitive to changes in blood oxygenation, with oxygen-15-labeled water (H215O) PET. Nine normal subjects repeatedly performed a simple finger opposition task during fMRI scans and during PET scans. Within-subject statistical analysis revealed significant (“activated”) signal changes ( p < 0.05, Bonferroni corrected for number of voxels) in contralateral primary sensorimotor cortex (PSM) in all subjects with fMRI and with PET. With both methods, 78% of all activated voxels were located in the PSM. Overlap of activated regions occurred in all subjects (mean 43%, SD 26%). The size of the activated regions in PSM with both methods was highly correlated ( rho = 0.87, p < 0.01). The mean distance between centers of mass of the activated regions in the PSM for fMRI versus PET was 6.7 mm (SD 3.0 mm). Average magnitude of signal change in activated voxels in this region, expressed as z-values adapted to timeseries, zt, was similar (fMRI 5.5, PET 5.3). Results indicate that positive blood oxygen level-dependent (BOLD) signal changes obtained with 3D principles of echo shifting with a train of observations (PRESTO) fMRI are correlated with rCBF, and that sensitivity of fMRI can equal that of H215O PET.

APA, Harvard, Vancouver, ISO, and other styles

40

Wen, Jie, Manu S. Goyal, Serguei V. Astafiev, Marcus E. Raichle, and Dmitriy A. Yablonskiy. "Genetically defined cellular correlates of the baseline brain MRI signal." Proceedings of the National Academy of Sciences 115, no. 41 (September 25, 2018): E9727—E9736. http://dx.doi.org/10.1073/pnas.1808121115.

Full text

Abstract:

fMRI revolutionized neuroscience by allowing in vivo real-time detection of human brain activity. While the nature of the fMRI signal is understood as resulting from variations in the MRI signal due to brain-activity-induced changes in the blood oxygenation level (BOLD effect), these variations constitute a very minor part of a baseline MRI signal. Hence, the fundamental (and not addressed) questions are how underlying brain cellular composition defines this baseline MRI signal and how a baseline MRI signal relates to fMRI. Herein we investigate these questions by using a multimodality approach that includes quantitative gradient recalled echo (qGRE), volumetric and functional connectivity MRI, and gene expression data from the Allen Human Brain Atlas. We demonstrate that in vivo measurement of the major baseline component of a GRE signal decay rate parameter (R2t*) provides a unique genetic perspective into the cellular constituents of the human cortex and serves as a previously unidentified link between cortical tissue composition and fMRI signal. Data show that areas of the brain cortex characterized by higher R2t* have high neuronal density and have stronger functional connections to other brain areas. Interestingly, these areas have a relatively smaller concentration of synapses and glial cells, suggesting that myelinated cortical axons are likely key cortical structures that contribute to functional connectivity. Given these associations, R2t* is expected to be a useful signal in assessing microstructural changes in the human brain during development and aging in health and disease.

APA, Harvard, Vancouver, ISO, and other styles

41

Tian, Lixia, Juejing Ren, and Yufeng Zang. "Regional homogeneity of resting state fMRI signals predicts Stop signal task performance." NeuroImage 60, no. 1 (March 2012): 539–44. http://dx.doi.org/10.1016/j.neuroimage.2011.11.098.

Full text

APA, Harvard, Vancouver, ISO, and other styles

42

Lotze, M., W. Grodd, F. A. Rodden, E. Gut, P. W. Schönle, B. Kardatzki, and L. G. Cohen. "Neuroimaging Patterns Associated with Motor Control in Traumatic Brain Injury." Neurorehabilitation and Neural Repair 20, no. 1 (March 2006): 14–23. http://dx.doi.org/10.1177/1545968305282919.

Full text

Abstract:

Objective. To determine if patients with traumatic brain injury (TBI) and motor deficits show differences in functional activation maps during repetitive hand movements relative to healthy controls. Are there predictors for motor outcome in the functional maps of these patients? Methods. In an exploratory cross-sectional study, functional magnetic resonance imaging (fMRI) was used to study the blood-oxygenation-level-dependent (BOLD) response in cortical motor areas of 34 patients suffering from moderate motor deficits after TBI as they performed unilateral fist-clenching motions. Twelve of these patients with unilateral motor deficits were studied 3 months after TBI and a 2nd time approximately 4 months later. Results. Compared to age-matched, healthy controls performing the same task, TBI patients showed diminished fMRI-signal change in the primary sensorimotor cortex contralateral to the moving hand (cSM1), the contralateral dorsal premotor cortex, and bilaterally in the supplementary motor areas (SMAs). Clinical impairment and the magnitude of the fMRI-signal change in cSM1 and SMA were negatively correlated. Patients with poor and good motor recovery showed comparable motor impairment at baseline. Only patients who evolved to “poor clinical outcome” had decreased fMRI-signal change in the cSM1 during baseline. Conclusions. These observations raise the hypothesis that the magnitude of the fMRI-signal change in the cSM1 region could have prognostic value in the evaluation of patients with TBI.

APA, Harvard, Vancouver, ISO, and other styles

43

Feige, Bernd, Klaus Scheffler, Fabrizio Esposito, Francesco Di Salle, Jürgen Hennig, and Erich Seifritz. "Cortical and Subcortical Correlates of Electroencephalographic Alpha Rhythm Modulation." Journal of Neurophysiology 93, no. 5 (May 2005): 2864–72. http://dx.doi.org/10.1152/jn.00721.2004.

Full text

Abstract:

Neural correlates of electroencephalographic (EEG) alpha rhythm are poorly understood. Here, we related EEG alpha rhythm in awake humans to blood-oxygen-level-dependent (BOLD) signal change determined by functional magnetic resonance imaging (fMRI). Topographical EEG was recorded simultaneously with fMRI during an open versus closed eyes and an auditory stimulation versus silence condition. EEG was separated into spatial components of maximal temporal independence using independent component analysis. Alpha component amplitudes and stimulus conditions served as general linear model regressors of the fMRI signal time course. In both paradigms, EEG alpha component amplitudes were associated with BOLD signal decreases in occipital areas, but not in thalamus, when a standard BOLD response curve (maximum effect at ∼6 s) was assumed. The part of the alpha regressor independent of the protocol condition, however, revealed significant positive thalamic and mesencephalic correlations with a mean time delay of ∼2.5 s between EEG and BOLD signals. The inverse relationship between EEG alpha amplitude and BOLD signals in primary and secondary visual areas suggests that widespread thalamocortical synchronization is associated with decreased brain metabolism. While the temporal relationship of this association is consistent with metabolic changes occurring simultaneously with changes in the alpha rhythm, sites in the medial thalamus and in the anterior midbrain were found to correlate with short time lag. Assuming a canonical hemodynamic response function, this finding is indicative of activity preceding the actual EEG change by some seconds.

APA, Harvard, Vancouver, ISO, and other styles

44

Schabdach, Jenna, Rafael Ceschin, Vanessa Schmithorst, M. Dylan Tisdall, Aaron Alexander-Bloch, and Ashok Panigrahy. "A Descriptive Review of the Impact of Patient Motion in Early Childhood Resting-State Functional Magnetic Resonance Imaging." Diagnostics 12, no. 5 (April 20, 2022): 1032. http://dx.doi.org/10.3390/diagnostics12051032.

Full text

Abstract:

Resting-state functional magnetic images (rs-fMRIs) can be used to map and delineate the brain activity occurring while the patient is in a task-free state. These resting-state activity networks can be informative when diagnosing various neurodevelopmental diseases, but only if the images are high quality. The quality of an rs-fMRI rapidly degrades when the patient moves during the scan. Herein, we describe how patient motion impacts an rs-fMRI on multiple levels. We begin with how the electromagnetic field and pulses of an MR scanner interact with a patient’s physiology, how movement affects the net signal acquired by the scanner, and how motion can be quantified from rs-fMRI. We then present methods for preventing motion through educational and behavioral interventions appropriate for different age groups, techniques for prospectively monitoring and correcting motion during the acquisition process, and pipelines for mitigating the effects of motion in existing scans.

APA, Harvard, Vancouver, ISO, and other styles

45

Yarmish, Gail, and Michael L. Lipton. "Functional Magnetic Resonance Imaging: From Acquisition to Application." Einstein Journal of Biology and Medicine 20, no. 1 (March 2, 2016): 2. http://dx.doi.org/10.23861/ejbm200320103.

Full text

Abstract:

Functional magnetic resonance imaging (fMRI) is a technique that exploits magnetic resonance imaging (MRI) to detect regional brain activity through measurement of the hemodynamic response that is coupled to electrical neuronal activity. The most common fMRI method detects blood oxygen level dependent (BOLD) contrast. The BOLD effect represents alteration in the ratio of deoxygenated to oxygenated hemoglobin within brain tissue following neuronal activity. Alterations in this hemoglobin ratio result from changes in cerebral oxygen extraction, cerebral blood flow, and cerebral blood volume that occur in response to neuronal activity. The small, but detectable, change in magnetics resonance signal intensity is due to the sensitivity of magnetic resonance (MR) images to the paramagnetic deoxygenated state of hemoglobin that is the basis of contrast in fMRI applications. This review describes the physical and physiological bases of the MR signal, the principle of the BOLD effect, technical issues related to fMRI implementation, and fMRI experimental design. Research and clinical applications of fMRI are presented, including the use of fMRI in neurosurgical planning. Since it provides an individualized map of brain function, fMRI enables accurate localization of eloquent brain regions prior to surgery, allowing assessment of surgical risk and prognosis, as well as planning surgical approach.

APA, Harvard, Vancouver, ISO, and other styles

46

Cao, Benchun, Yanchun Liang, Shinichi Yoshida, and Renchu Guan. "Facial Expression Decoding based on fMRI Brain Signal." International Journal of Computers Communications & Control 14, no. 4 (August 5, 2019): 475–88. http://dx.doi.org/10.15837/ijccc.2019.4.3433.

Full text

Abstract:

The analysis of facial expressions is a hot topic in brain-computer interface research. To determine the facial expressions of the subjects under the corresponding stimulation, we analyze the fMRI images acquired by the Magnetic Resonance. There are six kinds of facial expressions: "anger", "disgust", "sadness", "happiness", "joy" and "surprise". We demonstrate that brain decoding is achievable through the parsing of two facial expressions ("anger" and "joy"). Support vector machine and extreme learning machine are selected to classify these expressions based on time series features. Experimental results show that the classification performance of the extreme learning machine algorithm is better than support vector machine. Among the eight participants in the trials, the classification accuracy of three subjects reached 70-80%, and the remaining five subjects also achieved accuracy of 50-60%. Therefore, we can conclude that the brain decoding can be used to help analyzing human facial expressions.

APA, Harvard, Vancouver, ISO, and other styles

47

Deng, Fan, Dajiang Zhu, Jinglei Lv, Lei Guo, and Tianming Liu. "FMRI Signal Analysis Using Empirical Mean Curve Decomposition." IEEE Transactions on Biomedical Engineering 60, no. 1 (January 2013): 42–54. http://dx.doi.org/10.1109/tbme.2012.2221125.

Full text

APA, Harvard, Vancouver, ISO, and other styles

48

Liu, Thomas T. "Noise contributions to the fMRI signal: An overview." NeuroImage 143 (December 2016): 141–51. http://dx.doi.org/10.1016/j.neuroimage.2016.09.008.

Full text

APA, Harvard, Vancouver, ISO, and other styles

49

Liu, Thomas T., Alican Nalci, and Maryam Falahpour. "The global signal in fMRI: Nuisance or Information?" NeuroImage 150 (April 2017): 213–29. http://dx.doi.org/10.1016/j.neuroimage.2017.02.036.

Full text

APA, Harvard, Vancouver, ISO, and other styles

50

Lipschutz, Brigitte, John Ashburner, Karl Friston, and Cathy Price. "Assessing study-specific regional variation in fMRI signal." NeuroImage 11, no. 5 (May 2000): S460. http://dx.doi.org/10.1016/s1053-8119(00)91391-0.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Journal articles on the topic 'FMRI signal'

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles