Allen T. Newton

Modulation of Steady State Functional Connectivity in the Default Mode and Working Memory Networks By Cognitive Load

Interregional correlations between blood oxygen level dependent (BOLD) magnetic resonance imaging (fMRI) signals in the resting state have been interpreted as measures of connectivity across the brain. Here we investigate whether such connectivity in the working memory and default mode networks is modulated by changes in cognitive load. Functional connectivity was measured in a steady‐state verbal identity N‐back task for three different conditions (N = 1, 2, and 3) as well as in the resting state. We found that as cognitive load increases, the functional connectivity within both the working memory the default mode network increases. To test whether functional connectivity between the working memory and the default mode networks changed, we constructed maps of functional connectivity to the working memory network as a whole and found that increasingly negative correlations emerged in a dorsal region of the posterior cingulate cortex. These results provide further evidence that low frequency fluctuations in BOLD signals reflect variations in neural activity and suggests interaction between the default mode network and other cognitive networks. Hum Brain Mapp, 2010.

Task demand modulation of steady‐state functional connectivity to primary motor cortex

Correlations in blood oxygen level‐dependent (BOLD) MRI signals from separate areas within the human brain have been used as a measure of functional connectivity. Steady‐state measures of interregional correlations are particularly useful because they do not depend on the specific design of a task nor on subtracting conditions in a blocked design task. However, the conditions under which such correlations are measured may influence these indices of functional connectivity. The aim of this study was to investigate the influence of task demand on interregional correlations within the motor system. Specifically, tapping rates in audibly paced finger‐tapping tasks were controlled and varied between runs in order to observe their effects on interregional correlations to contralateral primary motor cortex (PM). Regions of interest included the supplementary motor area, ipsilateral cerebellum, ipsilateral auditory cortex, and a control region. It was found that tapping rate was a significant factor in determining the mean correlation of some regions to PM, and that correlations measured during tapping in general increased relative to resting state. Furthermore, analysis of the percent of voxels in each region significantly correlated to PM suggested that changes in the mean correlation of that region to PM could be accounted for by changes in the fraction of significantly correlated voxels within a region. This provides insight into the manner in which steady‐state correlations are modified in response to different task demands and further evidence that low‐frequency fluctuations in BOLD signals reflect functional connectivity. Hum Brain Mapp, 2006.

Integrating Functional and Diffusion Magnetic Resonance Imaging for Analysis of Structure-Function Relationship in the Human Language Network

Background The capabilities of magnetic resonance imaging (MRI) to measure structural and functional connectivity in the human brain have motivated growing interest in characterizing the relationship between these measures in the distributed neural networks of the brain. In this study, we attempted an integration of structural and functional analyses of the human language circuits, including Wernickes (WA), Brocas (BA) and supplementary motor area (SMA), using a combination of blood oxygen level dependent (BOLD) and diffusion tensor MRI. Methodology/Principal Findings Functional connectivity was measured by low frequency inter-regional correlations of BOLD MRI signals acquired in a resting steady-state, and structural connectivity was measured by using adaptive fiber tracking with diffusion tensor MRI data. The results showed that different language pathways exhibited different structural and functional connectivity, indicating varying levels of inter-dependence in processing across regions. Along the path between BA and SMA, the fibers tracked generally formed a single bundle and the mean radius of the bundle was positively correlated with functional connectivity. However, fractional anisotropy was found not to be correlated with functional connectivity along paths connecting either BA and SMA or BA and WA. Conclusions/Significance These findings suggest that structure-function relations in the human language circuits may involve a number of confounding factors that need to be addressed. Nevertheless, the insights gained from this work offers a useful guidance for continued studies that may provide a non-invasive means to evaluate brain network integrity in vivo for use in diagnosing and determining disease progression and recovery.

Spatio-temporal correlation tensors reveal functional structure in human brain.

Resting state functional magnetic resonance imaging (fMRI) has been commonly used to measure functional connectivity between cortical regions, while diffusion tensor imaging (DTI) can be used to characterize structural connectivity of white matter tracts. In principle combining resting state fMRI and DTI data could allow characterization of structure-function relations of distributed neural networks. However, due to differences in the biophysical origins of their signals and in the tissues to which they apply, there has been no direct integration of these techniques to date. We demonstrate that MRI signal variations and power spectra in a resting state are largely comparable between gray matter and white matter, that there are temporal correlations of fMRI signals that persist over long distances within distinct white matter structures, and that neighboring intervoxel correlations of low frequency resting state signals showed distinct anisotropy in many regions. These observations suggest that MRI signal variations from within white matter in a resting state may convey similar information as their corresponding fluctuations of MRI signals in gray matter. We thus derive a local spatio-temporal correlation tensor which captures directional variations of resting-state correlations and which reveals distinct structures in both white and gray matter. This novel concept is illustrated with in vivo experiments in a resting state, which demonstrate the potential of the technique for mapping the functional structure of neural networks and for direct integration of structure-function relations in the human brain.

Robust expertise effects in right FFA.

The fusiform face area (FFA) is one of several areas in occipito-temporal cortex whose activity is correlated with perceptual expertise for objects. Here, we investigate the robustness of expertise effects in FFA and other areas to a strong task manipulation that increases both perceptual and attentional demands. With high-resolution fMRI at 7T, we measured responses to images of cars, faces and a category globally visually similar to cars (sofas) in 26 subjects who varied in expertise with cars, in (a) a low load 1-back task with a single object category and (b) a high load task in which objects from two categories were rapidly alternated and attention was required to both categories. The low load condition revealed several areas more active as a function of expertise, including both posterior and anterior portions of FFA bilaterally (FFA1/FFA2, respectively). Under high load, fewer areas were positively correlated with expertise and several areas were even negatively correlated, but the expertise effect in face-selective voxels in the anterior portion of FFA (FFA2) remained robust. Finally, we found that behavioral car expertise also predicted increased responses to sofa images but no behavioral advantages in sofa discrimination, suggesting that global shape similarity to a category of expertise is enough to elicit a response in FFA and other areas sensitive to experience, even when the category itself is not of special interest. The robustness of expertise effects in right FFA2 and the expertise effects driven by visual similarity both argue against attention being the sole determinant of expertise effects in extrastriate areas.

Improving measurement of functional connectivity through decreasing partial volume effects at 7 T

Several applications of fMRI at high field have taken advantage of the increased BOLD contrast to increase spatial resolution, but the potential benefits of higher fields for detecting and analyzing functional connectivity have largely been unexplored. We measured the influence of spatial resolution at 7 T on estimates of functional connectivity through decreased partial volume averaging. Ten subjects were imaged at 7 T with a range of spatial resolutions (1×1×2 mm to 3×3×2 mm) during performance of a finger tapping task and in the resting state. We found that resting state correlations within the sensory-motor system increase as voxel dimensions decreased from 3×3×2 mm to 1×1×2 mm, whereas connectivity to other brain regions was unaffected. This improvement occurred even as overall signal to noise ratios decrease. Our data suggest that this increase may be due to decreased partial volume averaging, and that functional connectivity within the primary seed region is heterogeneous on the scale of single voxels.

Distinct fine-scale fMRI activation patterns of contra- and ipsilateral somatosensory areas 3b and 1 in humans.

Inter‐areal and ipsilateral cortical responses to tactile stimulation have not been well described in human S1 cortex. By taking advantage of the high signal‐to‐noise ratio at 7 T, we quantified blood oxygenation level dependent (BOLD) response patterns and time courses to tactile stimuli on individual distal finger pads at a fine spatial scale, and examined whether there are inter‐areal (area 3b versus area 1) and interhemispheric response differences to unilateral tactile stimulation in healthy human subjects. We found that 2‐Hz tactile stimulation of individual fingertips evoked detectable BOLD signal changes in both contralateral and ipsilateral area 3b and area 1. Contralateral digit activations were organized in an orderly somatotopic manner, and BOLD responses in area 3b were more digit selective than those in area 1. However, the area of cortex that was responsive to stimulation of a single digit (stimulus–response field) was similar across areas. In the ipsilateral hemisphere, response magnitudes in both areas 3b and 1 were significantly weaker than those of the contralateral hemisphere. Digit activations exhibited no clear somatotopic organizational pattern in either area 3b or area 1, yet digit selectivity was retained in area 1 but not in area 3b. The observation of distinct digit‐selective responses of contralateral area 3b versus area 1 supports a higher order function of contralateral area 1 in spatial integration. In contrast, ipsilateral cortices may play a less discriminative role in the perception of unilateral tactile sensation in humans. Hum Brain Mapp 35:4841–4857, 2014.

Fine-scale functional connectivity in somatosensory cortex revealed by high-resolution fMRI.

High-resolution functional magnetic resonance imaging (fMRI) at high field (9.4 T) has been used to measure functional connectivity between subregions within the primary somatosensory (SI) cortex of the squirrel monkey brain. The hand-face region within the SI cortex of the squirrel monkey has been previously well mapped with functional imaging and electrophysiological and anatomical methods, and the orderly topographic map of the hand region is characterized by a lateral to medial representation of individual digits in four subregions of areas 3a, 3b, 1 and 2. With submillimeter resolution, we are able to detect not only the separate islands of activation corresponding to vibrotactile stimulations of single digits but also, in subsequent acquisitions, the degree of correlation between voxels within the SI cortex in the resting state. The results suggest that connectivity patterns are very similar to stimulus-driven distributions of activity and that connectivity varies on the scale of millimeters within the same primary region. Connectivity strength is not a reflection of global larger-scale changes in blood flow and is not directly dependent on distance between regions. Preliminary electrophysiological recordings agree well with the fMRI data. In human studies at 7 T, high-resolution fMRI may also be used to identify the same subregions and assess responses to sensory as well as painful stimuli, and to measure connectivity dynamically before and after such stimulations.

On the Origins of Signal Variance in FMRI of the Human Midbrain at High Field

Functional Magnetic Resonance Imaging (fMRI) in the midbrain at 7 Tesla suffers from unexpectedly low temporal signal to noise ratio (TSNR) compared to other brain regions. Various methodologies were used in this study to quantitatively identify causes of the noise and signal differences in midbrain fMRI data. The influence of physiological noise sources was examined using RETROICOR, phase regression analysis, and power spectral analyses of contributions in the respiratory and cardiac frequency ranges. The impact of between-shot phase shifts in 3-D multi-shot sequences was tested using a one-dimensional (1-D) phase navigator approach. Additionally, the effects of shared noise influences between regions that were temporally, but not functionally, correlated with the midbrain (adjacent white matter and anterior cerebellum) were investigated via analyses with regressors of ‘no interest’. These attempts to reduce noise did not improve the overall TSNR in the midbrain. In addition, the steady state signal and noise were measured in the midbrain and the visual cortex for resting state data. We observed comparable steady state signals from both the midbrain and the cortex. However, the noise was 2–3 times higher in the midbrain relative to the cortex, confirming that the low TSNR in the midbrain was not due to low signal but rather a result of large signal variance. These temporal variations did not behave as known physiological or other noise sources, and were not mitigated by conventional strategies. Upon further investigation, resting state functional connectivity analysis in the midbrain showed strong intrinsic fluctuations between homologous midbrain regions. These data suggest that the low TSNR in the midbrain may originate from larger signal fluctuations arising from functional connectivity compared to cortex, rather than simply reflecting physiological noise.

A Comparison of Distributional Considerations with Statistical Analysis of Resting State fMRI at 3T and 7T

Ultra-high field 7T magnetic resonance imaging (MRI) offers potentially unprecedented spatial resolution of functional activity within the human brain through increased signal and contrast to noise ratios over traditional 1.5T and 3T MRI scanners. However, the effects physiological and imaging artifacts are also greatly increased. Traditional statistical parametric mapping theories based on distributional properties representative of data acquired at lower fields may be inadequate for new 7T data. Herein, we investigate the model fitting residuals based on two 7T and one 3T protocols. We find that model residuals are substantively more non-Gaussian at 7T relative to 3T. Imaging slices that passed through regions with peak inhomogeneity problems (e.g., mid-brain acquisitions for the 7T hippocampus) exhibited visually higher degrees of distortion along with spatially correlated and extreme values of kurtosis (a measure of non- Gaussianity). The impacts of artifacts have been previously addressed for 3T data by estimating the covariance matrix of the regression errors. We further extend the robust estimation approach for autoregressive models and evaluate the qualitative impacts of this technique relative to traditional inference. Clear differences in statistical significance are shown between inferences based on classical versus robust assumptions, which suggest that inferences based on Gaussian assumptions are subject to practical (as well as theoretical) concerns regarding their power and validity. Hence, modern statistical approaches, such as the robust autoregressive model posed herein, are appropriate and suitable for inference with ultra-high field functional magnetic resonance imaging.