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Sliding Window SENSE Calibration for Reducing Noise in fMRI

Law, Christine S.; Liu, Chunlei; Glover, Gary H.
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Publicado em /11/2008 Português
Relevância na Pesquisa
26.73%
Functional magnetic resonance imaging (fMRI) at high magnetic field with parallel imaging (PI) has become increasingly popular for high resolution imaging. We present a method of self-calibrated PI-fMRI in which sensitivity profiles are calculated using a sliding window of fully-sampled multi-shot imaging data. We show that by updating these sensitivity profiles in a sliding fashion, thermal noise is reduced in the reconstructed image time series. This is accomplished by retaining thermal noise in the sensitivity profiles; no spatial smoothing is performed. These noisy profiles actually provide a closer match to those required for thermal noise-free reconstruction than conventional sensitivity map generation. Our proposed technique is especially applicable in acquiring high spatial resolution images, where thermal noise exceeds physiological noise. With conventional sensitivity calculation, PI-fMRI sensitivity is preserved only when using a voxel size large enough such that physiological noise predominates. With small voxel size, our technique reveals activation from visual stimulation where conventional sensitivity calculation techniques falter. Our technique enhances fMRI detection especially when higher spatial resolution is desired.

The Stroop Effect in Kana and Kanji Scripts in Native Japanese Speakers: An fMRI Study

Coderre, Emily L.; Filippi, Christopher G.; Newhouse, Paul A.; Dumas, Julie A.
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
26.73%
Prior research has shown that the two writing systems of the Japanese orthography are processed differently: kana (syllabic symbols) are processed like other phonetic languages such as English, while kanji (a logographic writing system) are processed like other logographic languages like Chinese. Previous work done with the Stroop task in Japanese has shown that these differences in processing strategies create differences in Stroop effects. This study investigated the Stroop effect in kanji and kana using functional magnetic resonance imaging (fMRI) to examine the similarities and differences in brain processing between logographic and phonetic languages. Nine native Japanese speakers performed the Stroop task both in kana and kanji scripts during fMRI. Both scripts individually produced significant Stroop effects as measured by the behavioral reaction time data. The imaging data for both scripts showed brain activation in the anterior cingulate gyrus, an area involved in inhibiting automatic processing. Though behavioral data showed no significant differences between the Stroop effects in kana and kanji, there were differential areas of activation in fMRI found for each writing system. In fMRI, the Stroop task activated an area in the left inferior parietal lobule during the kana task and the left inferior frontal gyrus during the kanji task. The results of the present study suggest that the Stroop task in Japanese kana and kanji elicits differential activation in brain regions involved in conflict detection and resolution for syllabic and logographic writing systems.

A Review of Challenges in the Use of fMRI for Disease Classification / Characterization and A Projection Pursuit Application from Multi-site fMRI Schizophrenia Study

Demirci, Oguz; Clark, Vincent P.; Magnotta, Vincent A.; Andreasen, Nancy C.; Lauriello, John; Kiehl, Kent A.; Pearlson, Godfrey D.; Calhoun, Vince D.
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Publicado em 01/09/2008 Português
Relevância na Pesquisa
26.73%
Functional magnetic resonance imaging (fMRI) is a fairly new technique that has the potential to characterize and classify brain disorders such as schizophrenia. It has the possibility of playing a crucial role in designing objective prognostic/diagnostic tools, but also presents numerous challenges to analysis and interpretation. Classification provides results for individual subjects, rather than results related to group differences. This is a more complicated endeavor that must be approached more carefully and efficient methods should be developed to draw generalized and valid conclusions out of high dimensional data with a limited number of subjects, especially for heterogeneous disorders whose pathophysiology is unknown. Numerous research efforts have been reported in the field using fMRI activation of schizophrenia patients and healthy controls. However, the results are usually not generalizable to larger data sets and require careful definition of the techniques used both in designing algorithms and reporting prediction accuracies. In this review paper, we survey a number of previous reports and also identify possible biases (cross-validation, class size, e.g.) in class comparison/prediction problems. Some suggestions to improve the effectiveness of the presentation of the prediction accuracy results are provided. We also present our own results using a projection pursuit algorithm followed by an application of independent component analysis proposed in an earlier study. We classify schizophrenia versus healthy controls using fMRI data of 155 subjects from two sites obtained during three different tasks. The results are compared in order to investigate the effectiveness of each task and differences between patients with schizophrenia and healthy controls were investigated.

Connectivity alterations assessed by combining fMRI and MR-compatible hand robots in chronic stroke

Mintzopoulos, Dionyssios; Astrakas, Loukas G.; Khanicheh, Azadeh; Konstas, Angelos A.; Singhal, Aneesh; Moskowitz, Michael A.; Rosen, Bruce R.; Aria Tzika, A.
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
26.73%
The aim of this study was to investigate functional reorganization of motor systems by probing connectivity between motor related areas in chronic stroke patients using functional magnetic resonance imaging (fMRI) in conjunction with a novel MR-compatible hand-induced, robotic device (MR_CHIROD). We evaluated data sets obtained from healthy volunteers and right-hand-dominant patients with first-ever left-sided stroke ≥6 months prior and mild to moderate hemiparesis affecting the right hand. We acquired T1-weighted echo planar and fluid attenuation inversion recovery MR images and multi-level fMRI data using parallel imaging by means of the GeneRalized Autocalibrating Partially Parallel Acquisitions (GRAPPA) algorithm on a 3 T MR system. Participants underwent fMRI while performing a motor task with the MR_CHIROD in the MR scanner. Changes in effective connectivity among a network of primary motor cortex (M1), supplementary motor area (SMA) and cerebellum (Ce) were assessed using dynamic causal modeling. Relative to healthy controls, stroke patients exhibited decreased intrinsic neural coupling between M1 and Ce, which was consistent with a dysfunctional M1 to Ce connection. Stroke patients also showed increased SMA to M1 and SMA to cerebellum coupling...

Enhanced False Discovery Rate using Gaussian Mixture Models for thresholding fMRI statistical maps

Pendse, Gautam; Borsook, David; Becerra, Lino
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
26.73%
A typical fMRI data analysis proceeds via the generalized linear model (GLM) with Gaussian noise using a model based on the experimental paradigm. This analysis ultimately results in the production of z-statistic images corresponding to the contrasts of interest. Thresholding such z-statistic images at uncorrected thresholds suitable for testing activation at a single voxel results in the problem of multiple comparisons. A number of methods which account for the problem of multiple comparisons have been proposed including Gaussian random field theory, mixture modeling and false discovery rate (FDR). The focus of this paper is on the development of a generalized version of FDR (GFDR) in an empirical Bayesian framework, specially adapted for fMRI thresholding, that is more robust to modeling violations as compared to traditional FDR. We show theoretically as well as by simulation that for real fMRI data various factors lead to a mixture of Gaussians (MOG) density for the “null” distribution. Artificial data was used to systematically study the bias of FDR and GFDR under varying intensity of modeling violations, signal to noise ratios and activation fractions for a range of q-values. GFDR was able to handle modeling violations and produce good results when FDR failed. Real fMRI data was also used to confirm GFDR capabilities. Our results indicate that it is very important to account for the form and fraction of the “null” hypothesis adaptively from the data in order to obtain valid inference.

A comparison of fMRI adaptation and multivariate pattern classification analysis in visual cortex

Sapountzis, Panagiotis; Schluppeck, Denis; Bowtell, Richard; Peirce, Jonathan W.
Fonte: Academic Press Publicador: Academic Press
Tipo: Artigo de Revista Científica
Publicado em 15/01/2010 Português
Relevância na Pesquisa
26.73%
Functional magnetic resonance imaging (fMRI) has become a ubiquitous tool in cognitive neuroscience. The technique allows noninvasive measurements of cortical responses in the human brain, but only on the millimeter scale. Because a typical voxel contains many thousands of neurons with varied properties, establishing the selectivity of their responses directly is impossible. In recent years, two methods using fMRI aimed at studying the selectivity of neuronal populations on a ‘subvoxel’ scale have been heavily used. The first technique, fMRI adaptation, relies on the observation that the blood oxygen level-dependent (BOLD) response in a given voxel is reduced after prolonged presentation of a stimulus, and that this reduction is selective to the characteristics of the repeated stimuli (adapters). The second technique, multivariate pattern analysis (MVPA), makes use of multivariate statistics to recover small biases in individual voxels in their responses to different stimuli. It is thought that these biases arise due to the uneven distribution of neurons (with different properties) sampled by the many voxels in the imaged volume. These two techniques have not been compared explicitly, however, and little is known about their relative sensitivities. Here...

Unsupervised Spatiotemporal fMRI Data Analysis using Support Vector Machines

Song, Xiaomu; Wyrwicz, Alice M.
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
26.73%
In this work we present a new support vector machine (SVM)-based method for fMRI data analysis. SVM has been shown to be a powerful, efficient data-driven tool in pattern recognition, and has been applied to the supervised classification of brain cognitive states in fMRI experiments. We examine the unsupervised mapping of activated brain regions using SVM. Specifically, the mapping process is formulated as an outlier detection problem of one-class SVM (OCSVM) that provides initial mapping results. These results are further refined by applying prototype selection and SVM reclassification. Multiple spatial and temporal features are extracted and selected to facilitate SVM learning. The proposed method was compared with correlation analysis (CA), t-test (TT), and spatial independent component analysis (SICA) methods using synthetic and experimental data. Our results show that the proposed method can provide more accurate and robust activation mapping than CA, TT and SICA, and is computationally more efficient than SICA. Besides its applicability to typical fMRI experiments, the proposed method is also a powerful tool in fMRI studies where a reliable quantification of activated brain regions is required.

Interhemispheric neuroplasticity following limb deafferentation detected by resting-state functional connectivity magnetic resonance imaging (fcMRI) and functional magnetic resonance imaging (fMRI)

Pawela, Christopher P.; Biswal, Bharat B.; Hudetz, Anthony G.; Li, Rupeng; Jones, Seth R.; Cho, Younghoon R.; Matloub, Hani S.; Hyde, James S.
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
26.73%
Functional connectivity magnetic resonance imaging (fcMRI) studies in rat brain show brain reorganization following peripheral nerve injury. Subacute neuroplasticity was observed two weeks following transection of the four major nerves of the brachial plexus. Direct functional magnetic resonance imaging (fMRI) stimulation of the intact radial nerve reveals an activation pattern in the forelimb regions of the sensory and motor cortices that is significantly different from that observed in normal rats. Results of this fMRI experiment were used to determine seed voxel regions for fcMRI analysis. Intrahemispheric connectivities in the sensorimotor forelimb representations in both hemispheres are largely unaffected by deafferentation, whereas substantial disruption of interhemispheric sensorimotor cortical connectivity occurs. In addition, significant intra- and interhemispheric changes in connectivities of thalamic nuclei were found. These are the central findings of the study. They could not have been obtained from fMRI studies alone—both fMRI and fcMRI are needed. The combination provides a general marker for brain plasticity. The rat visual system was studied in the same animals as a control. No neuroplastic changes in connectivities were found in the primary visual cortex upon forelimb deafferentation. Differences were noted in regions responsible for processing multisensory visual-motor information. This incidental discovery is considered to be significant. It may provide insight into phantom limb epiphenomena.

Exploratory fMRI Analysis without Spatial Normalization

Lashkari, Danial; Golland, Polina
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Publicado em //2009 Português
Relevância na Pesquisa
26.73%
We present an exploratory method for simultaneous parcellation of multisubject fMRI data into functionally coherent areas. The method is based on a solely functional representation of the fMRI data and a hierarchical probabilistic model that accounts for both inter-subject and intra-subject forms of variability in fMRI response. We employ a Variational Bayes approximation to fit the model to the data. The resulting algorithm finds a functional parcellation of the individual brains along with a set of population-level clusters, establishing correspondence between these two levels. The model eliminates the need for spatial normalization while still enabling us to fuse data from several subjects. We demonstrate the application of our method on a visual fMRI study.

Changes in fMRI magnitude data and phase data observed in block-design and event-related tasks

Arja, Sunil Kumar; Feng, Zhaomei; Chen, Zikuan; Caprihan, Arvind; Kiehl, Kent A.; Adali, Tülay; Calhoun, Vince D.
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
26.73%
Functional magnetic resonance imaging (fMRI) data are acquired as a complex image pair including magnitude and phase information. The vast majority of fMRI experiments do not attempt to take advantage of the time varying phase information. The phase of the MRI signal is related to the local magnetic field changes, suggesting it may contain useful information about the source of hemodynamic activity. Analysis of phase data acquired from different fMRI experiments has shown the presence of activity in response to various stimuli. However, there have been no studies that have examined phase data in a larger group of subjects for multiple types of fMRI tasks nor have studies examined phase changes due to event-related stimuli. In this paper, we evaluate the correspondence between the magnitude and phase changes at a group level in a block-design motor tapping task and in an event-related auditory oddball task. The results for both block-design and event-related tasks indicate the presence of task-related information in the phase data with phase-only and magnitude-only approaches showing signal changes in the expected brain regions. Although there is more overall activity detected with magnitude data, the phase-only analysis also reveals activity in regions expected to be involved in the task...

A unified framework for group independent component analysis for multi-subject fMRI data

Guo, Ying; Pagnoni, Giuseppe
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
26.73%
Independent component analysis (ICA) is becoming increasingly popular for analyzing functional magnetic resonance imaging (fMRI) data. While ICA has been successfully applied to single-subject analysis, the extension of ICA to group inferences is not straightforward and remains an active topic of research. Current group ICA models, such as the GIFT (Calhoun et al., 2001) and tensor PICA (Beckmann and Smith, 2005), make different assumptions about the underlying structure of the group spatio-temporal processes and are thus estimated using algorithms tailored for the assumed structure, potentially leading to diverging results. To our knowledge, there are currently no methods for assessing the validity of different model structures in real fMRI data and selecting the most appropriate one among various choices. In this paper, we propose a unified framework for estimating and comparing group ICA models with varying spatio-temporal structures. We consider a class of group ICA models that can accommodate different group structures and include existing models, such as the GIFT and tensor PICA, as special cases. We propose a maximum likelihood (ML) approach with a modified Expectation-Maximization (EM) algorithm for the estimation of the proposed class of models. Likelihood ratio tests (LRT) are presented to compare between different group ICA models. The LRT can be used to perform model comparison and selection...

The fMRI signal, slow cortical potential and consciousness

He, Biyu J.; Raichle, Marcus E.
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
26.73%
As functional magnetic resonance imaging (fMRI) has become a driving force in cognitive neuroscience, it is crucial to understand the neural basis of the fMRI signal. Here, we discuss a novel neurophysiological correlate of the fMRI signal, the slow cortical potential (SCP), which also seems to modulate the power of higher-frequency activity, the more established neurophysiological correlate of the fMRI signal. We further propose a hypothesis for the involvement of the SCP in the emergence of consciousness, and review existing data that lend support to our proposal. This hypothesis, unlike several previous theories of consciousness, is firmly rooted in physiology and as such is entirely amenable to empirical testing.

fMRI-Compatible Registration of Jaw Movements Using a Fiber-Optic Bend Sensor

Sörös, Peter; MacIntosh, Bradley J.; Tam, Fred; Graham, Simon J.
Fonte: Frontiers Research Foundation Publicador: Frontiers Research Foundation
Tipo: Artigo de Revista Científica
Publicado em 22/03/2010 Português
Relevância na Pesquisa
26.73%
A functional magnetic resonance imaging (fMRI)-compatible fiber-optic bend sensor was investigated to assess whether the device could be used effectively to monitor opening and closing of the jaw during an fMRI experiment at 3 T. In contrast to surface electromyography, a bend sensor fixed to the chin of the participant is fast and easy to use and is not affected by strong electromagnetic fields. Bend sensor recordings are characterized by high validity (compared with concurrent video recordings of mouth opening) and high reliability (comparing two independent measurements). The results of this study indicate that a bend sensor is able to record the opening and closing of the jaw associated with different overt speech conditions (producing the utterances /a/, /pa/, /pataka/) and the opening of the mouth without speech production. Data post-processing such as filtering was not necessary. There are several potential applications for bend sensor recordings of speech-related jaw movements. First, bend sensor recordings are a valuable tool to assess behavioral performance, such as response latencies, accuracies, and completion times, which is particularly important in children, seniors, or patients with various neurological or psychiatric conditions. Second...

Improving robustness and reliability of phase-sensitive fMRI analysis using Temporal Off-resonance Alignment of Single-echo Timeseries (TOAST)

Hahn, Andrew D.; Nencka, Andrew S.; Rowe, Daniel B.
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
26.73%
Echo Planar Imaging (EPI), often utilized in functional MRI (fMRI) experiments, is well known for its vulnerability to inconsistencies in the static magnetic field (B0). Correction for these field inhomogeneities usually involves measuring the magnetic field at a single time point, and using this static information to correct a series of images collected over the course of one or multiple experiments. However, common phenomena, such as respiration and motion, change the characteristics of the B0 field homogeneity in a time-dependent and often unpredictable manner, rendering previous field measurements invalid. The effects of these changes are particularly large in the image phase, due to its direct and sensitive relationship to the magnetic field, and methods utilizing complex information can suffer enormously. This dependence can be exploited to estimate the temporal dynamics of the B0 field. Use of this information to correct fMRI data can provide more effective motion correction, reduce temporal “noise,” and can substantially restore statistically significant power to complex fMRI data analysis. All of the necessary information is embedded in complex EPI images, and results indicate this is a robust way to improve the quality of fMRI data...

Comparison of multivariate classifiers and response normalizations for pattern-information fMRI

Misaki, Masaya; Kim, Youn; Bandettini, Peter A.; Kriegeskorte, Nikolaus
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
26.73%
A popular method for investigating whether stimulus information is present in fMRI response patterns is to attempt to “decode” the stimuli from the response patterns with a multivariate classifier. The sensitivity for detecting the information depends on the particular classifier used. However, little is known about the relative performance of different classifiers on fMRI data. Here we compared six multivariate classifiers and investigated how the response-amplitude estimate used (beta or t-value) and different pattern normalizations affect classification performance. The compared classifiers were a pattern-correlation classifier, a k-nearest-neighbors classifier, Fisher’s linear discriminant, Gaussian naïve Bayes, and linear and nonlinear (radial-basis-function-kernel) support vector machines. We compared these classifiers’ accuracy at decoding the category of visual objects from response patterns in human early visual and inferior temporal cortex acquired in an event-related design with BOLD fMRI at 3T using SENSE and isotropic voxels of about 2-mm width. Overall, Fisher’s linear discriminant (with an optimal-shrinkage covariance estimator) and the linear support vector machine performed best. The pattern-correlation classifier often performed similarly as those two classifiers. The nonlinear classifiers never performed better and sometimes significantly worse than the linear classifiers...

Echo-Planar BOLD fMRI of Mice on a Narrow-Bore 9.4 T Magnet

Nair, Govind; Duong, Timothy Q.
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Publicado em /08/2004 Português
Relevância na Pesquisa
26.73%
The feasibility of BOLD fMRI in association with electrical somatosensory stimulation on spontaneously breathing, isoflurane-anesthetized mice was investigated using spin-echo, echo-planar imaging (EPI) on a vertical narrow-bore 9.4 T magnet. Three experiments were performed to derive an optimal fMRI protocol. In Experiment 1 (n = 9), spin-echo BOLD responses to 10% CO2 challenge under graded isoflurane (0.25–1.25%) ranged from 10 ± 2% to 3.5 ± 0.9%; the optimal BOLD contrast-to-noise ratio peaked at 0.75% isoflurane. In Experiment 2 (n = 6), hindpaw somatosensory stimulations using 1–7 mA under 0.75% isoflurane revealed the optimal BOLD response was at 6 mA. In Experiment 3 (n = 5), BOLD responses to 4 and 6 mA stimulation under 0.75% and 1% isoflurane were evaluated in detail, confirming the optimal conditions in Experiment 2. These results demonstrated that BOLD fMRI using single-shot, spin-echo EPI in a mouse somatosensory stimulation model could be routinely performed on high-field, vertical, narrow-bore magnets. This protocol might prove useful for fMRI studies of transgenic mice.

Multivariate dynamical systems models for estimating causal interactions in fMRI

Ryali, Srikanth; Supekar, Kaustubh; Chen, Tianwen; Menon, Vinod
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
26.73%
Analysis of dynamical interactions between distributed brain areas is of fundamental importance for understanding cognitive information processing. However, estimating dynamic causal interactions between brain regions using functional magnetic resonance imaging (fMRI) poses several unique challenges. For one, fMRI measures Blood Oxygenation Level Dependent (BOLD) signals, rather than the underlying latent neuronal activity. Second, regional variations in the hemodynamic response function (HRF) can significantly influence estimation of casual interactions between them. Third, causal interactions between brain regions can change with experimental context over time. To overcome these problems, we developed a novel state-space Multivariate Dynamical Systems (MDS) model to estimate intrinsic and experimentally-induced modulatory causal interactions between multiple brain regions. A probabilistic graphical framework is then used to estimate the parameters of MDS as applied to fMRI data. We show that MDS accurately takes into account regional variations in the HRF and estimates dynamic causal interactions at the level of latent signals. We develop and compare two estimation procedures using maximum likelihood estimation (MLE) and variational Bayesian (VB) approaches for inferring model parameters. Using extensive computer simulations...

Multiplexed Echo Planar Imaging for Sub-Second Whole Brain FMRI and Fast Diffusion Imaging

Feinberg, David A.; Moeller, Steen; Smith, Stephen M.; Auerbach, Edward; Ramanna, Sudhir; Glasser, Matt F.; Miller, Karla L.; Ugurbil, Kamil; Yacoub, Essa
Fonte: Public Library of Science Publicador: Public Library of Science
Tipo: Artigo de Revista Científica
Publicado em 20/12/2010 Português
Relevância na Pesquisa
26.73%
Echo planar imaging (EPI) is an MRI technique of particular value to neuroscience, with its use for virtually all functional MRI (fMRI) and diffusion imaging of fiber connections in the human brain. EPI generates a single 2D image in a fraction of a second; however, it requires 2–3 seconds to acquire multi-slice whole brain coverage for fMRI and even longer for diffusion imaging. Here we report on a large reduction in EPI whole brain scan time at 3 and 7 Tesla, without significantly sacrificing spatial resolution, and while gaining functional sensitivity. The multiplexed-EPI (M-EPI) pulse sequence combines two forms of multiplexing: temporal multiplexing (m) utilizing simultaneous echo refocused (SIR) EPI and spatial multiplexing (n) with multibanded RF pulses (MB) to achieve m×n images in an EPI echo train instead of the normal single image. This resulted in an unprecedented reduction in EPI scan time for whole brain fMRI performed at 3 Tesla, permitting TRs of 400 ms and 800 ms compared to a more conventional 2.5 sec TR, and 2–4 times reductions in scan time for HARDI imaging of neuronal fibertracks. The simultaneous SE refocusing of SIR imaging at 7 Tesla advantageously reduced SAR by using fewer RF refocusing pulses and by shifting fat signal out of the image plane so that fat suppression pulses were not required. In preliminary studies of resting state functional networks identified through independent component analysis...

A 4-channel 3 Tesla phased array receive coil for awake rhesus monkey fMRI and diffusion MRI experiments

Khachaturian, Mark Haig
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Publicado em 01/01/2010 Português
Relevância na Pesquisa
26.73%
Awake monkey fMRI and diffusion MRI combined with conventional neuroscience techniques has the potential to study the structural and functional neural network. The majority of monkey fMRI and diffusion MRI experiments are performed with single coils which suffer from severe EPI distortions which limit resolution. By constructing phased array coils for monkey MRI studies, gains in SNR and anatomical accuracy (i.e., reduction of EPI distortions) can be achieved using parallel imaging. The major challenges associated with constructing phased array coils for monkeys are the variation in head size and space constraints. Here, we apply phased array technology to a 4-channel phased array coil capable of improving the resolution and image quality of full brain awake monkey fMRI and diffusion MRI experiments. The phased array coil is that can adapt to different rhesus monkey head sizes (ages 4–8) and fits in the limited space provided by monkey stereotactic equipment and provides SNR gains in primary visual cortex and anatomical accuracy in conjunction with parallel imaging and improves resolution in fMRI experiments by a factor of 2 (1.25 mm to 1.0 mm isotropic) and diffusion MRI experiments by a factor of 4 (1.5 mm to 0.9 mm isotropic).

Physiological Noise Effects on the Flip Angle Selection in BOLD fMRI

Gonzalez-Castillo, J.; Roopchansingh, V.; Bandettini, P.A.; Bodurka, J.
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
26.73%
This work addresses the choice of imaging flip angle in blood oxygenation level dependent (BOLD) functional magnetic resonance imaging (fMRI). When noise of physiological origin becomes the dominant noise source in fMRI timeseries, it causes a nonlinear dependence of the temporal signal-to-noise ratio (TSNR) versus signal-to-noise ratio (SNR) that can be exploited to perform BOLD fMRI at angles well below the Ernst angle without any detrimental effect on our ability to detect sites of neuronal activation. We show, both experimentally and theoretically, that for situations where available SNR is high and physiological noise dominates over system/thermal noise, although TSNR still reaches it maximum for the Ernst angle, reduction of imaging flip angle well below this angle results in negligible loss in TSNR. Moreover, we provide a way to compute a suggested imaging flip angle, which constitutes a conservative estimate of the minimum flip angle that can be used under given experimental SNR and physiological noise levels. For our experimental conditions, this suggested angle equals 7.63° for the grey matter compartment, while the Ernst angle = 77°. Finally, using data from eight subjects with a combined visual-motor task we show that imaging at angles as low as 9° introduces no significant differences in observed hemodynamic response time-course...