Brain segmentation using endogenous contrast mechanism using breath holding fMRI signal for tissue characterization

MRL has fast become the modality of choice for the analysis of the complexity of the human brain. MRJ is a non-invasive method and gives high spatial resolution maps of the brain with soft tissue contrast. Conventional MRI technique modified to be used to image the functionality at high temporal resolution is known as fMRI. In fMRI the BOLD signal we measure is the hemodynamic response to neuronal and vascular changes at rest or in response to a stimulus where the various tissue types will have a different response. While fMRI has been traditionally been used to detect and identify eloquent regions of the cortex corresponding to specific tasks/stimulus, a number of groups have also used tMRI to study cerebrovascular changes and its consequence on the BOLD signal. A number of different perturbation methods including breath holding, hypercapnia, inhalation of various gas mixtures, and injection of acetozolamyde has been used to study spatio-temporal changes in the fMRI signal intensity. Spatiotemporal changes corresponding to changes in cerebral blood flow (CBF), cerebral blood volume (CBV), oxygen extraction fraction (OEF), and other physiological factors are then estimated and differences between diseased regions and healthy regions are then elucidated

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

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

Resolving the projecjion of an occluded stimulus on the human cortical surface

The human visual system is capable of tracking multiple visual targets under a variety of task constraints and configurations. For nearly two decades, the psychophysical literature has shown that moving, occluded visual targets -- targets that are momentarily invisible as they pass behind an occluding bar -- are differentially represented by the visual system compared to their moving, non-occluded counterparts. Here, I sought to examine the neurophysiological basis of this behavioral difference in response to occluded versus non-occluded visual targets. I used brain imaging to conduct modern retinotopic mapping experiments in human participants. Once· their early visual cortices were mapped, I was able characterize the neural representations for both targets and distractors as well as during moments of occlusion and non-occlusion. The results show that, using our method, we can distinguish visual targets from distractors; furthermore, there appears to be a representation in retinotopically organized early visual cortex for visual targets that have momentarily disappeared from the visual field due to occlusion

Within-Subject Joint Independent Component Analysis of Simultaneous fMRI/ERP in an Auditory Oddball Paradigm

The integration of event-related potential (ERP) and functional magnetic resonance imaging (fMRI) can contribute to characterizing neural networks with high temporal and spatial resolution. This research aimed to determine the sensitivity and limitations of applying joint independent component analysis (jICA) within-subjects, for ERP and fMRI data collected simultaneously in a parametric auditory frequency oddball paradigm. In a group of 20 subjects, an increase in ERP peak amplitude ranging 1–8 μV in the time window of the P300 (350–700 ms), and a correlated increase in fMRI signal in a network of regions including the right superior temporal and supramarginal gyri, was observed with the increase in deviant frequency difference. JICA of the same ERP and fMRI group data revealed activity in a similar network, albeit with stronger amplitude and larger extent. In addition, activity in the left pre- and post-central gyri, likely associated with right hand somato-motor response, was observed only with the jICA approach. Within-subject, the jICA approach revealed significantly stronger and more extensive activity in the brain regions associated with the auditory P300 than the P300 linear regression analysis. The results suggest that with the incorporation of spatial and temporal information from both imaging modalities, jICA may be a more sensitive method for extracting common sources of activity between ERP and fMRI

Distributed Activity Patterns for Objects and Their Features: Decoding Perceptual and Conceptual Object Processing in Information Networks of the Human Brain

How are object features and knowledge-fragments represented and bound together in the human brain? Distributed patterns of activity within brain regions can encode distinctions between perceptual and cognitive phenomena with impressive specificity. The research reported here investigated how the information within regions\u27 multi-voxel patterns is combined in object-concept networks. Chapter 2 investigated how memory-driven activity patterns for an object\u27s specific shape, color, and identity become active at different stages of the visual hierarchy. Brain activity patterns were recorded with functional magnetic resonance imaging (fMRI) as participants searched for specific fruits or vegetables within visual noise. During time-points in which participants were searching for an object, but viewing pure noise, the targeted object\u27s identity could be decoded in the left anterior temporal lobe (ATL). In contrast, top-down generated patterns for the object\u27s specific shape and color were decoded in early visual regions. The emergence of object-identity information in the left ATL was predicted by concurrent shape and color information in their respective featural regions. These findings are consistent with theories proposing that feature-fragments in sensory cortices converge to higher-level identity representations in convergence zones. Chapter 3 investigated whether brain regions share fluctuations in multi-voxel information across time. A new analysis method was first developed, to measure dynamic changes in distributed pattern information. This method, termed informational connectivity (IC), was then applied to data collected as participants viewed different types of man-made objects. IC identified connectivity between object-processing regions that was not apparent from existing functional connectivity measures, which track fluctuating univariate signals. Collectively, this work suggests that networks of regions support perceptual and conceptual object processing through the convergence and synchrony of distributed pattern information

Examining the performance of trend surface models for inference on Functional Magnetic Resonance Imaging (fMRI) data

The current predominant approach to neuroimaging data analysis is to use voxels as units of computation in a mass univariate approach which does not appropriately account for the existing spatial correlation and is plagued by problems of multiple comparisons. Therefore, there is a need to explore alternative approaches for inference on neuroimaging data that accurately model spatial autocorrelation, potentially providing better type I error control and more sensitive inference. In this project we examine the performance of a trend surface modeling (TSM) approach that is based on a biologically relevant parcellation of the brain. We present our results from applying the TSM to both task fMRI and resting-state fMRI and compare the latter to the results from the parametric software, FSL. We demonstrate that the TSM provides better Type I error control, as well as sensitive inference on task data.The current predominant approach to neuroimaging data analysis is to use voxels as units of computation in a mass univariate approach which does not appropriately account for the existing spatial correlation and is plagued by problems of multiple comparisons. Therefore, there is a need to explore alternative approaches for inference on neuroimaging data that accurately model spatial autocorrelation, potentially providing better type I error control and more sensitive inference. In this project we examine the performance of a trend surface modeling (TSM) approach that is based on a biologically relevant parcellation of the brain. We present our results from applying the TSM to both task fMRI and resting-state fMRI and compare the latter to the results from the parametric software, FSL. We demonstrate that the TSM provides better Type I error control, as well as sensitive inference on task data

Cortical depth dependent functional responses in humans at 7T: improved specificity with 3D GRASE

Ultra high fields (7T and above) allow functional imaging with high contrast-to-noise ratios and improved spatial resolution. This, along with improved hardware and imaging techniques, allow investigating columnar and laminar functional responses. Using gradient-echo (GE) (T2* weighted) based sequences, layer specific responses have been recorded from human (and animal) primary visual areas. However, their increased sensitivity to large surface veins potentially clouds detecting and interpreting layer specific responses. Conversely, spin-echo (SE) (T2 weighted) sequences are less sensitive to large veins and have been used to map cortical columns in humans. T2 weighted 3D GRASE with inner volume selection provides high isotropic resolution over extended volumes, overcoming some of the many technical limitations of conventional 2D SE-EPI, whereby making layer specific investigations feasible. Further, the demonstration of columnar level specificity with 3D GRASE, despite contributions from both stimulated echoes and conventional T2 contrast, has made it an attractive alternative over 2D SE-EPI. Here, we assess the spatial specificity of cortical depth dependent 3D GRASE functional responses in human V1 and hMT by comparing it to GE responses. In doing so we demonstrate that 3D GRASE is less sensitive to contributions from large veins in superficial layers, while showing increased specificity (functional tuning) throughout the cortex compared to GE

Post-learning hippocampal dynamics promote preferential retention of rewarding events

Reward motivation is known to modulate memory encoding, and this effect depends on interactions between the substantia nigra/ventral tegmental area complex (SN/VTA) and the hippocampus. It is unknown, however, whether these interactions influence offline neural activity in the human brain that is thought to promote memory consolidation. Here we used fMRI to test the effect of reward motivation on post-learning neural dynamics and subsequent memory for objects that were learned in high- and low-reward motivation contexts. We found that post-learning increases in resting-state functional connectivity between the SN/VTA and hippocampus predicted preferential retention of objects that were learned in high-reward contexts. In addition, multivariate pattern classification revealed that hippocampal representations of high-reward contexts were preferentially reactivated during post-learning rest, and the number of hippocampal reactivations was predictive of preferential retention of items learned in high-reward contexts. These findings indicate that reward motivation alters offline post-learning dynamics between the SN/VTA and hippocampus, providing novel evidence for a potential mechanism by which reward could influence memory consolidatio