Mapping the MRI voxel volume in which thermal noise matches physiological noise--implications for fMRI

Phase vs. magnitude information in functional magnetic resonance imaging time series: toward understanding the noise

Although it has been shown that the phase of the MR signal from the brain is particularly prone to variation due to respiration, the overall physiological information contained in phase time series is not well understood. Here, we explore the different physiological processes contributing to the phase time series noise, identify their spatiotemporal characteristics and examine their relationship to BOLD-related and non-BOLD-related physiological noise in the magnitude time series. This was performed by manipulating the contribution of physiological noise to the total signal variance by modulating the TE and voxel volume, and using a short TR in order to adequately sample physiological signal fluctuations. The phase and magnitude signals were compared both before and after removal of signal fluctuations at the primary respiratory and cardiac frequencies with RETROICOR. We found that the temporal phase noise increased with TE at a faster rate than predicted by 1/TSNR as a result of physiological noise. As suggested by previous studies, the primary contributor to phase physiological noise was respiration-related effects which were manifested at a large scale (>1 cm). Notably, RETROICOR removed respiration-related large-scale artifacts and this resulted in considerable improvements in the temporal phase stability (7-90%). Physiological noise in the magnitude time series after RETROICOR consisted of low-frequency BOLD-related fluctuations (<0.13 Hz) localized to gray matter and the vasculature, and fluctuations in the vasculature correlated with slow (<0.1 Hz) variations in respiration volume and cardiac rhythm. Physiological noise in the phase signal after RETROICOR also occurred in frequencies below 0.13 Hz and was consistent with (1) residual large-scale magneto-mechanical effects correlated with slow variations in respiration volume and cardiac rhythm over time, and (2) local scale (<1 cm) effects localized in gray matter and vasculature most likely due to vascular dephasing mediated by a BOLD susceptibility change. While BOLD-related magnitude noise exhibited a TE dependence similar to BOLD, the 'BOLD-related' noise in the phase data increased with increasing TE and thus caused the overall phase noise to increase at a faster rate with TE than predicted by 1/TSNR. Interestingly, the spatial specificity of this effect was more evident for the higher resolution phase data, as opposed to the magnitude data, suggesting that at a higher spatial resolution the phase signal may contain more information on physiological processes than the magnitude signal.

Hippocampal volumetry and functional MRI of memory in temporal lobe epilepsy

This study examined the utility of structural and functional MRI at 1.5 and 3T in the presurgical evaluation and prediction of postsurgical cognitive outcome in temporal lobe epilepsy (TLE). Forty-nine patients undergoing presurgical evaluation for temporal lobe (TL) resection and 25 control subjects were studied. Patients completed standard presurgical evaluations, including the intracarotid amobarbital test (IAT) and neuropsychological testing. During functional imaging, subjects performed a complex visual scene-encoding task. High-resolution structural MRI scans were used to quantify hippocampal volumes. Both structural and functional imaging successfully lateralized the seizure focus and correlated with IAT memory lateralization, with improvement for functional imaging at 3T as compared with 1.5 T. Ipsilateral structural and functional MRI data were related to cognitive outcome, and greater functional asymmetry was related to earlier age at onset. These findings support continued investigation of the utility of MRI and fMRI in the presurgical evaluation of TLE.

fMRI in the presence of task-correlated breathing variations

Variations in the subject's heart rate and breathing pattern have been shown to result in significant fMRI signal changes, mediated in part by non-neuronal physiological mechanisms such as global changes in levels of arterial CO(2). When these physiological changes are correlated with a task, as may happen in response to emotional stimuli or tasks that change levels of arousal, a concern arises that non-neuronal physiologically-induced signal changes may be misinterpreted as reflecting task-related neuronal activation. The purpose of this study is to provide information that can help in determining whether task activation maps are influenced by task-correlated physiological noise, particularly task-correlated breathing changes. We also compare different strategies to reduce the influence of physiological noise. Two paradigms are investigated--1) a lexical decision task where some subjects showed task-related breathing changes, and 2) a task where subjects were instructed to hold their breath during the presentation of contrast-reversing checkerboard, an extreme case of task-correlated physiological noise. Consistent with previous literature, we find that MRI signal changes correlated with variations in breathing depth and rate have a characteristic spatial and temporal profile that is different from the typical activation-induced BOLD response. The delineation of activation in the presence of task correlated breathing changes was improved either by independent component analysis, or by including specific nuisance regressors in a regression analysis. The difference in the spatial and temporal characteristics of physiological-induced and neuronal-induced fluctuations exploited by these strategies suggests that activation can be studied even in the presence of task-correlated physiological changes.

Matching categorical object representations in inferior temporal cortex of man and monkey

Inferior temporal (IT) object representations have been intensively studied in monkeys and humans, but representations of the same particular objects have never been compared between the species. Moreover, IT's role in categorization is not well understood. Here, we presented monkeys and humans with the same images of real-world objects and measured the IT response pattern elicited by each image. In order to relate the representations between the species and to computational models, we compare response-pattern dissimilarity matrices. IT response patterns form category clusters, which match between man and monkey. The clusters correspond to animate and inanimate objects; within the animate objects, faces and bodies form subclusters. Within each category, IT distinguishes individual exemplars, and the within-category exemplar similarities also match between the species. Our findings suggest that primate IT across species may host a common code, which combines a categorical and a continuous representation of objects.

Cluster analysis of resting-state fMRI time series

Functional MRI (fMRI) has become one of the leading methods for brain mapping in neuroscience. Recent advances in fMRI analysis were used to define the default state of brain activity, functional connectivity and basal activity. Basal activity measured with fMRI raised tremendous interest among neuroscientists since synchronized brain activity pattern could be retrieved while the subject rests (resting state fMRI). During recent years, a few signal processing schemes have been suggested to analyze the resting state blood oxygenation level dependent (BOLD) signal from simple correlations to spectral decomposition. In most of these analysis schemes, the question asked was which brain areas "behave" in the time domain similarly to a pre-specified ROI. In this work we applied short time frequency analysis and clustering to study the spatial signal characteristics of resting state fMRI time series. Such analysis revealed that clusters of similar BOLD fluctuations are found in the cortex but also in the white matter and sub-cortical gray matter regions (thalamus). We found high similarities between the BOLD clusters and the neuro-anatomical appearance of brain regions. Additional analysis of the BOLD time series revealed a strong correlation between head movements and clustering quality. Experiments performed with T1-weighted time series also provided similar quality of clustering. These observations led us to the conclusion that non-functional contributions to the BOLD time series can also account for symmetric appearance of signal fluctuations. These contributions may include head motions, the underling microvasculature anatomy and cellular morphology.

System stability of a 3T-MRI during continuous EPI scan

The purpose of this study is to evaluate the general stability and image properties of a 3T MRI system newly installed at the ATR-Brain Activity Imaging Center (ATR-BAIC), in addition to a conventional 1.5T system. In this study, we focused on the echo planar imaging (EPI) sequence since continuous EPI with a relatively long duration of up to 30 min is routinely used, and the stabilization of EPI is always a concern. The following five results were obtained: (1) Significant image shifts along the phase direction were observed in the 1.5T data but not in the 3T data, although B0 shifts in both the 1.5T and 3T systems were the same level (1.3 Hz/min); (2) The signal fluctuations were 1/2-1/3 smaller in the 3T system compared with the 1.5T system; (3) The temporal signal-to-noise ratio (TSNR) of the 3T system was 1.7-2.0 (CP-coil) and 2.5-4.0 (12ch-coil) greater than the 1.5T system; (4) We found a low frequency periodic fluctuation (cycles of approximately 30-40 sec), and an increase in noise in the latter half of the long term series, which might originate from the 3T MRI scanner; and (5) Spatial non-uniformity of TSNR and voxels with a linear-trend were observed in the 3T data.

The neural basis of human social values: evidence from functional MRI

Social values are composed of social concepts (e.g., "generosity") and context-dependent moral sentiments (e.g., "pride"). The neural basis of this intricate cognitive architecture has not been investigated thus far. Here, we used functional magnetic resonance imaging while subjects imagined their own actions toward another person (self-agency) which either conformed or were counter to a social value and were associated with pride or guilt, respectively. Imagined actions of another person toward the subjects (other-agency) in accordance with or counter to a value were associated with gratitude or indignation/anger. As hypothesized, superior anterior temporal lobe (aTL) activity increased with conceptual detail in all conditions. During self-agency, activity in the anterior ventromedial prefrontal cortex correlated with pride and guilt, whereas activity in the subgenual cingulate solely correlated with guilt. In contrast, indignation/anger activated lateral orbitofrontal-insular cortices. Pride and gratitude additionally evoked mesolimbic and basal forebrain activations. Our results demonstrate that social values emerge from coactivation of stable abstract social conceptual representations in the superior aTL and context-dependent moral sentiments encoded in fronto-mesolimbic regions. This neural architecture may provide the basis of our ability to communicate about the meaning of social values across cultural contexts without limiting our flexibility to adapt their emotional interpretation.

Investigation of spatial resolution, partial volume effects and smoothing in functional MRI using artificial 3D time series

This work addresses the balance between temporal signal-to-noise ratio (tSNR) and partial volume effects (PVE) in functional magnetic resonance imaging (fMRI) and investigates the impact of the choice of spatial resolution and smoothing. In fMRI, since physiological time courses are monitored, tSNR is of greater importance than image SNR. Improving SNR by an increase in voxel volume may be of negligible benefit when physiological fluctuations dominate the noise. Furthermore, at large voxel volumes, PVE are more pronounced, leading to an overall loss in performance. Artificial fMRI time series, based on high-resolution anatomical data, were used to simulate BOLD activation in a controlled manner. The performance was subsequently quantified as a measure of how well the resulted activation matched the simulated activation. The performance was highly dependent on the spatial resolution. At high contrast-to-noise ratio (CNR), the optimal voxel volume was small, i.e. in the region of 2(3) mm(3). It was also shown that using a substantially larger voxel volume in this case could potentially negate the CNR benefits. The optimal smoothing kernel width was dependent on the CNR, being larger at poor CNR. At CNR >1, little or no smoothing proved advantageous. The use of artificial time series gave an opportunity to quantitatively investigate the effects of partial volume and smoothing in single subject fMRI. It was shown that a proper choice of spatial resolution and smoothing kernel width is important for fMRI performance.

Three-dimensional spiral technique for high-resolution functional MRI

For high-resolution functional MRI (fMRI) studies, signal-to-noise ratio (SNR) plays an important role. Any method that results in an improvement in SNR will be able to improve the quality of activation maps. Three-dimensional (3D) acquisition methods in general can provide higher SNR than that of 2D methods due to volume excitation. To demonstrate the superiority of 3D methods for high-resolution fMRI scans, a comparison study between 3D and 2D spiral methods was performed using a contrast-reversing checkerboard visual stimulus. A 3-inch surface coil was used to limit the in-plane FOV to 14 cm x 14 cm so that 32 1-mm slices with an in-plane voxel size of 1.1 mm x 1.1 mm could be acquired within 5.76 seconds. Results showed that average numbers of activated voxels were 407 and 841 for 2D and 3D methods, respectively (P < 0.01). Therefore, the 3D technique may be a useful alternative to the conventional 2D method for high resolution fMRI studies.

Analyzing for information, not activation, to exploit high-resolution fMRI

High-resolution functional magnetic resonance imaging (hi-res fMRI) promises to help bridge the gap between the macro- and the microview of brain function afforded by conventional neuroimaging and invasive cell recording, respectively. Hi-res fMRI (voxel volume Collapse

Key Words Collapse

MESH Headings

• Brain/anatomy & histology
• Brain/physiology
• Humans
• Image Processing, Computer-Assisted/methods
• Individuality
• Information Theory
• Magnetic Resonance Imaging/methods
• Magnetic Resonance Imaging/statistics & numerical data
• Neurons/physiology

Collapse

Grants

• NIH0011638520 Intramural NIH HHS

Collapse

Affiliation(s)

Nikolaus Kriegeskorte Section on Functional Imaging Methods, Lab of Brain and Cognition, National Institute of Mental Health, USA.
Peter Bandettini

Collapse

Collaborators Collapse

Model-free analysis of brain fMRI data by recurrence quantification

We propose a novel model-free univariate strategy for functional magnetic resonance imaging (fMRI) studies based upon recurrence quantification analysis (RQA). RQA is an auto-regressive method, which identifies recurrences in signals without any a priori assumptions. The performance of RQA is compared to that of univariate statistics based on a general linear model (GLM) and probabilistic independent component analysis (P-ICA) for a set of simulated and real fMRI data. RQA provides an appealing alternative to conventional GLM techniques, due to its exclusive feature of being model-free and of detecting potentially both linear and nonlinear dynamic processes, without requiring signal stationarity. The overall performance of the method compares positively also with P-ICA, another well-known model-free algorithm, which requires prior information to discriminate between different spatio-temporal processes. For simulated data, RQA is endowed with excellent accuracy for contrast-to-noise ratios greater than 0.2, and has a performance comparable to that of GLM for t(CNR)>or=0.8. For cerebral fMRI data acquired from a group of healthy subjects performing a finger-tapping task, (i) RQA reveals activations in the primary motor area contra-lateral to the employed hand and in the supplementary motor area, in agreement with the outcome of GLM analysis and (ii) identifies an additional brain region with transient signal changes. Moreover, RQA identifies signal recurrences induced by physiological processes other than BOLD (movement-related or of vascular origin). Finally, RQA is more robust than the GLM with respect to variations in the shape and timing of the underlying neuronal and hemodynamic responses which may vary between brain regions, subjects and tasks.