829 research outputs found

    Statistical Analysis of fMRI Time-Series: A Critical Review of the GLM Approach.

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    Functional magnetic resonance imaging (fMRI) is one of the most widely used tools to study the neural underpinnings of human cognition. Standard analysis of fMRI data relies on a general linear model (GLM) approach to separate stimulus induced signals from noise. Crucially, this approach relies on a number of assumptions about the data which, for inferences to be valid, must be met. The current paper reviews the GLM approach to analysis of fMRI time-series, focusing in particular on the degree to which such data abides by the assumptions of the GLM framework, and on the methods that have been developed to correct for any violation of those assumptions. Rather than biasing estimates of effect size, the major consequence of non-conformity to the assumptions is to introduce bias into estimates of the variance, thus affecting test statistics, power, and false positive rates. Furthermore, this bias can have pervasive effects on both individual subject and group-level statistics, potentially yielding qualitatively different results across replications, especially after the thresholding procedures commonly used for inference-making

    Detecting language activations with functional magnetic resonance imaging

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    This thesis investigates a number of factors that affect sensitivity to language activations in functional Magnetic Resonance Imaging (fMRI). In the first part, I investigate the impact of experimental design parameters on the ability to detect language activations. These parameters include stimulus rate, stimulus duration, stimulus amplitude, epoch length and stimulus ordering. Crucially, they may affect sensitivity in multiple ways that include neurophysiological, efficiency-mediated and BOLD saturation effects. I illustrate and discuss these effects by presenting biophysical simulations and fMRI studies of single word and pseudoword reading. In addition, I focus on the differential effects of the above parameters in Positron Emission Tomography and fMRI studies. In the second part, I investigate the impact of the analysis used to estimate effects of interest from the data. I compare event-related and epoch analyses and show that, even in the context of blocked design fMRI, an event-related model may provide greater sensitivity than an epoch model. I then address the notion that experimentally-induced effects may be detected not only as task-dependent changes in regional responses but also as changes in connectivity amongst functionally connected regions. These two complementary approaches are motivated by two fundamental principles of brain organisation: functional specialisation and functional integration. I present two fMRI studies investigating the neural correlates of reading words and pseudowords in terms of functional specialisation and functional integration. Furthermore, in both studies I address the issue of inter-subject variability, which may be a critical determinant of sensitivity. Men

    BOLD Neurovascular Coupling Does Not Change Significantly with Normal Aging

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    Studies of cognitive function that compare the blood oxygenation level dependent (BOLD) signal across age groups often require the assumption that neurovascular coupling does not change with age. Tests of this assumption have produced mixed results regarding the strength of the coupling and its relative time course. Using deconvolution, we found that age does not have a significant effect on the time course of the hemodynamic impulse response function or on the slope of the BOLD versus stimulus duration relationship. These results suggest that in cognitive studies of healthy aging, group differences in BOLD activation are likely due to age-related changes in cognitive-neural interactions and information processing rather than to impairments in neurovascular coupling

    Mapping the Integration of Sensory Information across Fingers in Human Sensorimotor Cortex

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    The integration of somatosensory signals across fingers is essential for dexterous object manipulation. Previous experiments suggest that this integration occurs in neural populations in the primary somatosensory cortex (S1). However, the integration process has not been fully characterized, as previous studies have mainly used 2-finger stimulation paradigms. Here, we addressed this gap by stimulating all 31 single- and multifinger combinations. We measured population-wide activity patterns evoked during finger stimulation in human S1 and primary motor cortex (M1) using 7T fMRI in female and male participants. Using multivariate fMRI analyses, we found clear evidence of unique nonlinear interactions between fingers. In Brodmann area (BA) 3b, interactions predominantly occurred between pairs of neighboring fingers. In BA 2, however, we found equally strong interactions between spatially distant fingers, as well as interactions between finger triplets and quadruplets. We additionally observed strong interactions in the hand area of M1. In both M1 and S1, these nonlinear interactions did not reflect a general suppression of overall activity, suggesting instead that the interactions we observed reflect rich, nonlinear integration of sensory inputs from the fingers. We suggest that this nonlinear finger integration allows for a highly flexible mapping from finger sensory inputs to motor responses that facilitates dexterous object manipulation.SIGNIFICANCE STATEMENT Processing of somatosensory information in primary somatosensory cortex (S1) is essential for dexterous object manipulation. To successfully handle an object, the sensorimotor system needs to detect complex patterns of haptic information, which requires the nonlinear integration of sensory inputs across multiple fingers. Using multivariate fMRI analyses, we characterized brain activity patterns evoked by stimulating all single- and multifinger combinations. We report that progressively stronger multifinger interactions emerge in posterior S1 and in the primary motor cortex (M1), with interactions arising between inputs from neighboring and spatially distant fingers. Our results suggest that S1 and M1 provide the neural substrate necessary to support a flexible mapping from sensory inputs to motor responses of the hand

    Investigation of the modulation of spatial frequency preferences with attentional load within human visual cortex

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    Performance in visual tasks improves with attention, and this improvement has been shown to stem, in part, from changes in sensory processing. However, the mechanism by which attention affects perception remains unclear. Considering that neurons within the visual areas are selective for basic image statistics, such as orientation or spatial frequency (SF), it is plausible that attention modulates these sensory preferences by altering their so-called ‘tuning curves’. The goal of this project is to investigate this possibility by measuring and comparing the SF tuning curves across a range of attentional states in humans. In Experiment 1, a model-driven approach to fMRI analysis was introduced that allows for fast and efficient estimation of population spatial frequency tuning (pSFT) for individual voxels within human visual cortices. Using this method, I estimated pSFTs within early visual cortices of 8 healthy, young adults. Consistent with previous studies, the estimated SF optima showed a decline with retinotopic eccentricity. Moreover, my results suggested that the bandwidth of pSFT depends on eccentricity, and that populations with lower SF peaks possess broader bandwidths. In Experiment 2, I proposed a new visual task, coined the Numerosity Judgement Paradigm (NJP), for fine-grained parametric manipulation of attentional load. Eight healthy, young adults performed this task in an MRI scanner, and the analysis of the BOLD signal indicated that the activity within the putative dorsal attention network was precisely modulated as a function of the attentional load of the task. In Experiment 3, I used the NJP to modulate attentional load, and exploited the model-based approach to estimate pSFTs under different attentional states. fMRI results of 9 healthy, young adults did not reveal any changes in either peak or the bandwidth of the pSFTs with attentional load. This study yields a full visuocortical map of spatial frequency sensitivity and introduces a new paradigm for modulating attentional load. Although under this paradigm I did not find any changes in SF preferences within human visual areas with attentional load, I cannot preclude the possibility that changes emerge under different attentional manipulations

    Parameter estimation efficiency using nonlinear models in fMRI

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    There is an increasing interest in using physiologically plausible models in fMRI analysis. These models do raise new mathematical problems in terms of parameters estimation and interpretation of the measured data. We present some theoretical contributions in this area, using different variations of the Balloon Model (Buxton98,Friston00,Buxton04) as example models. We propose 1) a method to analyze the models dynamics and their stability around equilibrium, 2) a new way to derive least square energy gradient for parameter estimation, 3) a quantitative measurement of parameter estimation efficiency, and 4) a statistical test for detecting voxel activations. We use these methods in a visual perception checker-board experiment. It appears that the different hemodynamic models considered better capture some features of the response than linear models. In particular, they account for small nonlinearities observed for stimulation durations between 1 and 8 seconds. Nonlinearities for stimulation shorter than one second can also be explained by a neural habituation model (Buxton04), but further investigations should assess whether they are rather not due to nonlinear effects of the flow response. Moreover, the tools we have developed prove that statistical methods that work well for the GLM can be nicely adapted to nonlinear models. The activation maps obtained in both frameworks are comparable

    Multivoxel codes for representing and integrating acoustic features in human cortex

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    Using fMRI and multivariate pattern analysis, we determined whether acoustic features are represented by independent or integrated neural codes in human cortex. Male and female listeners heard band-pass noise varying simultaneously in spectral (frequency) and temporal (amplitude-modulation [AM] rate) features. In the superior temporal plane, changes in multivoxel activity due to frequency were largely invariant with respect to AM rate (and vice versa), consistent with an independent representation. In contrast, in posterior parietal cortex, neural representation was exclusively integrated and tuned to specific conjunctions of frequency and AM features. Direct between-region comparisons show that whereas independent coding of frequency and AM weakened with increasing levels of the hierarchy, integrated coding strengthened at the transition between non-core and parietal cortex. Our findings support the notion that primary auditory cortex can represent component acoustic features in an independent fashion and suggest a role for parietal cortex in feature integration and the structuring of acoustic input. Significance statement A major goal for neuroscience is discovering the sensory features to which the brain is tuned and how those features are integrated into cohesive perception. We used whole-brain human fMRI and a statistical modeling approach to quantify the extent to which sound features are represented separately or in an integrated fashion in cortical activity patterns. We show that frequency and AM rate, two acoustic features that are fundamental to characterizing biological important sounds such as speech, are represented separately in primary auditory cortex but in an integrated fashion in parietal cortex. These findings suggest that representations in primary auditory cortex can be simpler than previously thought and also implicate a role for parietal cortex in integrating features for coherent perception

    Representational Similarity Analysis – Connecting the Branches of Systems Neuroscience

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    A fundamental challenge for systems neuroscience is to quantitatively relate its three major branches of research: brain-activity measurement, behavioral measurement, and computational modeling. Using measured brain-activity patterns to evaluate computational network models is complicated by the need to define the correspondency between the units of the model and the channels of the brain-activity data, e.g., single-cell recordings or voxels from functional magnetic resonance imaging (fMRI). Similar correspondency problems complicate relating activity patterns between different modalities of brain-activity measurement (e.g., fMRI and invasive or scalp electrophysiology), and between subjects and species. In order to bridge these divides, we suggest abstracting from the activity patterns themselves and computing representational dissimilarity matrices (RDMs), which characterize the information carried by a given representation in a brain or model. Building on a rich psychological and mathematical literature on similarity analysis, we propose a new experimental and data-analytical framework called representational similarity analysis (RSA), in which multi-channel measures of neural activity are quantitatively related to each other and to computational theory and behavior by comparing RDMs. We demonstrate RSA by relating representations of visual objects as measured with fMRI in early visual cortex and the fusiform face area to computational models spanning a wide range of complexities. The RDMs are simultaneously related via second-level application of multidimensional scaling and tested using randomization and bootstrap techniques. We discuss the broad potential of RSA, including novel approaches to experimental design, and argue that these ideas, which have deep roots in psychology and neuroscience, will allow the integrated quantitative analysis of data from all three branches, thus contributing to a more unified systems neuroscience
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