Shella Keilholz

Functional Magnetic Resonance Imaging (fMRI) is sensitive to changes in blood oxygenation levels. While fMRI has traditionally mapped changes in these levels that localize to brain areas activated by an external stimulus, recent work has focused on detecting correlated, non-stimulus-related fluctuations in the fMRI signal throughout the brain. These fluctuations are believed to arise from spontaneous variations in local neural

Understanding the function and connectivity of thalamic nuclei is critical for understanding normal and pathological brain function. The medial geniculate nucleus (MGN) has been studied mostly in the context of auditory processing and its connection to the auditory cortex. However, there is a growing body of evidence that the MGN and surrounding associated areas ('MGN/S') have a diversity of projections including those to the globus pallidus, caudate/putamen, amygdala, hypothalamus, and thalamus. Concomitantly, pathways projecting to the medial geniculate include not only the inferior colliculus but also the auditory cortex, insula, cerebellum, and globus pallidus. Here we expand our understanding of the connectivity of the MGN/S by using comparative diffusion weighted imaging with probabilistic tractography in both human and mouse brains (most previous work was in rats). In doing so, we provide the first report that attempts to match probabilistic tractography results betwe...

A multislice EPI sequence was used to obtain functional MR images of the entire rat brain with BOLD contrast at 11.7 T. Ten to 11 slices covering the rat brain, with an in-plane resolution of 300 microm, provided enough sensitivity to detect activation in brain regions known to be involved in the somatosensory pathway during stimulation of the forelimbs. These regions were identified by warping a digitized rat brain atlas to each set of images. Data analysis was constrained to four major areas of the somatosensory pathway: primary and secondary somatosensory cortices, thalamus, and cerebellum. Incidence maps were generated. Electrical stimulation at 3 Hz led to significant activation in the primary sensory cortex in all rats. Activation in the secondary sensory cortex and cerebellum was observed in 70% of the studies, while thalamic activation was observed in 40%. The amplitude of activation was measured for each area, and average response time courses were calculated. Finally, the ...

A multislice spin echo EPI sequence was used to obtain functional MR images of the entire rat brain with blood oxygenation level dependent (BOLD) and cerebral blood volume (CBV) contrast at 11.7 T. Maps of activation incidence were created by warping each image to the Paxinos rat brain atlas and marking the extent of the activated area. Incidence maps for BOLD and CBV were similar, but activation in draining veins was more prominent in the BOLD images than in the CBV images. Cerebellar activation was observed along the surface in BOLD images, but in deeper regions in the CBV images. Both effects may be explained by increased signal dropout and distortion in the EPI images after administration of the ferumoxtran-10 contrast agent for CBV fMRI. CBV-weighted incidence maps were also created for 10, 20, and 30 mg Fe/kg doses of ferumoxtran-10. The magnitude of the average percentage change during stimulation increased from 4.9% with the 10 mg Fe/kg dose to 8.7% with the 30-mg Fe/kg dose...

ABSTRACT Functional Magnetic Resonance Imaging (fMRI) is sensitive to changes in blood oxygenation levels. While fMRI has traditionally mapped changes in these levels that localize to brain areas activated by an external stimulus, recent work has focused on detecting correlated, non-stimulus-related fluctuations in the fMRI signal throughout the brain. These fluctuations are believed to arise from spontaneous variations in local neural activity, and so correlated fluctuations from different brain areas may indicate coordinated activity. Maps of ``functional connectivity'' based upon these fluctuations show reproducible patterns of correlated signals. To date, research has focused on steady-state networks that persist over the entire imaging session (minutes). We are exploring the possibility of detecting changes in network activity on much shorter time scales (seconds). Preliminary analysis shows that power in the frequency band used to map functional connectivity varies over time, and that power differences correspond to changes in correlation between areas. We also detected phase differences in fluctuations that are consistent with propagating waves. These results indicate that time-varying analysis of fMRI data may provide insight into the dynamics of functional networks in the brain.

ABSTRACT Methods to interpret data obtained from resting state functional magnetic imaging (rs-fMRI) must be developed to more thoroughly understand how network structure of the brain supports the body and the mind. To this end, we examine the use of agglomerative clustering (AC) as a method for rs-fMRI analysis. AC is a data driven approach for organizing spatially distinct clusters of temporally similar activity. Its application to rs-fMRI data produces spatial parcellation of brain areas that share similar temporal characteristics. The technique is scalable, enabling identification of local to widespread organization. Using a wavelet based filter bank, the technique is made amenable to frequency domain scaling as well. Comparisons drawn between AC and two alternative rs-fMRI analytics - seed-based correlation, and spatial independent component analysis - highlight the ability of the proposed technique to recognize well known functional brain regions.

Functional connectivity measurements from resting state blood-oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) are proving a powerful tool to probe both normal brain function and neuropsychiatric disorders. However, the neural mechanisms that coordinate these large networks are poorly understood, particularly in the context of the growing interest in network dynamics. Recent work in anesthetized rats has shown that the spontaneous BOLD fluctuations are tightly linked to infraslow local field potentials (LFPs) that are seldom recorded but comparable in frequency to the slow BOLD fluctuations. These findings support the hypothesis that long-range coordination involves low frequency neural oscillations and establishes infraslow LFPs as an excellent candidate for probing the neural underpinnings of the BOLD spatiotemporal patterns observed in both rats and humans. To further examine the link between large-scale network dynamics and infraslow LFPs, simultaneous fMRI and microelectrode recording were performed in anesthetized rats. Using an optimized filter to isolate shared components of the signals, we found that time-lagged correlation between infraslow LFPs and BOLD is comparable in spatial extent and timing to a quasi-periodic pattern (QPP) found from BOLD alone, suggesting that fMRI-measured QPPs and the infraslow LFPs share a common mechanism. As fMRI allows spatial resolution and whole brain coverage not available with electroencephalography, QPPs can be used to better understand the role of infraslow oscillations in normal brain function and neurological or psychiatric disorders.

Functional networks, defined by synchronous spontaneous blood oxygenation level-dependent (BOLD) oscillations between spatially distinct brain regions, appear to be essential to brain function and have been implicated in disease states, cognitive capacity, and sensing and motor processes. While the topographical extent and behavioral function of these networks has been extensively investigated, the neural functions that create and maintain these synchronizations remain mysterious. In this work callosotomized rodents are examined, providing a unique platform for evaluating the influence of structural connectivity via the corpus callosum on bilateral resting state functional connectivity. Two experimental groups were assessed, a full callosotomy group, in which the corpus callosum was completely sectioned, and a sham callosotomy group, in which the gray matter was sectioned but the corpus callosum remained intact. Results indicated a significant reduction in interhemispheric connectivity in the full callosotomy group as compared with the sham group in primary somatosensory cortex and caudate-putamen regions. Similarly, electrophysiology revealed significantly reduced bilateral correlation in band limited power. Bilateral gamma Band-limited power connectivity was most strongly affected by the full callosotomy procedure. This work represents a robust finding indicating the corpus callosum's influence on maintaining integrity in bilateral functional networks; further, functional magnetic resonance imaging (fMRI) and electrophysiological connectivity share a similar decrease in connectivity as a result of the callosotomy, suggesting that fMRI-measured functional connectivity reflects underlying changes in large-scale coordinated electrical activity. Finally, spatiotemporal dynamic patterns were evaluated in both groups; the full callosotomy rodents displayed a striking loss of bilaterally synchronous propagating waves of cortical activity.

Different regions in the resting brain exhibit non-stationary functional connectivity (FC) over time. In this paper, a simple and efficient framework of clustering the variability in FC of a rat's brain at rest is proposed. This clustering process reveals areas that are always connected with a chosen region, called seed voxel, along with the areas exhibiting variability in the FC. This addresses an issue common to most dynamic FC analysis techniques, which is the assumption that the spatial extent of a given network remains constant over time. We increase the voxel size and reduce the spatial resolution to analyze variable FC of the whole resting brain. We hypothesize that the adjacent voxels in resting state functional magnetic resonance imaging (rsfMRI), just as in task-based fMRI, exhibit similar intensities, so they can be averaged to obtain larger voxels without any significant loss of information. Sliding window correlation is used to compute variable patterns of the rat's whole brain FC with the seed voxel in the sensorimotor cortex. These patterns are grouped based on their spatial similarities using binary transformed feature vectors in k-means clustering, not only revealing the variable and nonvariable portions of FC in the resting brain but also detecting the extent of the variability of these patterns.

ABSTRACT This work presents a new data-driven method for the identification of functionally connected regions in the rat brain, using agglomerative clustering based on the discrete wavelet transform (DWT). The proposed approach is evaluated on resting state fMRI data and no a priori assumptions about the distribution of the signals or anatomical ROIs are made. The coefficients of the DWT are used as features in the clustering algorithm, and the performance of the classifier is evaluated as the capability to produce clusters that best correlate with known anatomical regions in the sensorimotor cortex of the brain. Wavelet features that best represent salient characteristics in the spectrum of the voxel signals are found to produce best clustering results.

The brain is organized into networks composed of spatially separated anatomical regions exhibiting coherent functional activity over time. Two of these networks (the default mode network, DMN, and the task positive network, TPN) have been implicated in the performance of a number of cognitive tasks. To directly examine the stable relationship between network connectivity and behavioral performance, high temporal resolution functional magnetic resonance imaging (fMRI) data were collected during the resting state, and behavioral data were collected from 15 subjects on different days, exploring verbal working memory, spatial working memory, and fluid intelligence. Sustained attention performance was also evaluated in a task interleaved between resting state scans. Functional connectivity within and between the DMN and TPN was related to performance on these tasks. Decreased TPN resting state connectivity was found to significantly correlate with fewer errors on an interrupter task pres...

The slow fluctuations of the blood-oxygenation-level dependent (BOLD) signal in resting-state fMRI are widely utilized as a surrogate marker of ongoing neural activity. Spontaneous neural activity includes a broad range of frequencies, from infraslow (<0.5 Hz) fluctuations to fast action potentials. Recent studies have demonstrated a correlative relationship between the BOLD fluctuations and power modulations of the local field potential (LFP), particularly in the gamma band. However, the relationship between the BOLD signal and the infraslow components of the LFP, which are directly comparable in frequency to the BOLD fluctuations, has not been directly investigated. Here we report a first examination of the temporal relation between the resting-state BOLD signal and infraslow LFPs using simultaneous fMRI and full-band LFP recording in rat. The spontaneous BOLD signal at the recording sites exhibited significant localized correlation with the infraslow LFP signals as well as with the slow power modulations of higher-frequency LFPs (1-100 Hz) at a delay comparable to the hemodynamic response time under anesthesia. Infraslow electrical activity has been postulated to play a role in attentional processes, and the findings reported here suggest that infraslow LFP coordination may share a mechanism with the large-scale BOLD-based networks previously implicated in task performance, providing new insight into the mechanisms contributing to the resting state fMRI signal.