Traditional Posters : Functional MRI
Click on to view the abstract pdf and click on to view the pdf of the poster viewable in the poster hall.
Functional Connectivity Studies

Thursday May 12th
Exhibition Hall  13:30 - 15:30

1592.   Reliability of functional and effective connectivity of the resting state motor network in healthy subjects  
Tejaswini Kavallappa1, Steven Roys2, Anindya Roy3, Joel Greenspan2, Rao Gullapalli2, and Alan McMillan2
1Dept. of Nuclear Medicine and Diagnostic Radiology, Univeristy of Maryland School of Medicine, Baltimore, MD, United States, 2University of Maryland School of Medicine, 3University of Maryland Baltimore County

The reliability of functional and effective connectivity (using structural equation modeling [SEM]) was examined. Reliability assessment was performed on four datasets that were subject to various types of physiological and mean brain signal filtering. Path coefficients in effective connectivity demonstrated a higher degree of variability compared to correlation coefficients in functional connectivity, regardless of filtering method used. The high variability from test-retest results from SEM analysis suggests caution when interpreting results from such analysis.

1593.   Two new-discovered functional networks of resting brains 
Yi Chia Li1, and Jyh Horng Chen2
1Graduate Institute of Biological Engineering and Bioinformatics, National Taiwan University, Taipei, Taiwan, 2Interdisciplinary MRI/MRS Lab, Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan

Up to 10 functional networks contributed by low frequency fluctuations (LFFs) have been reliably identified to consistently exist in human resting brains. These networks consist of regions which are known to be involved in function of motor, vision, execution, auditory, pain perception, language, cerebellum, and the so called default-mode network (DMN). In our present work, we analyzed resting-state fMRI data of 11 healthy participants to further investigate functional networks which consistently exist in resting brains. The functional networks obtained in our work largely corresponded to the findings in prior literatures. Additionally, we discovered two new functional networks: spatial cognition network and facial sensory network. Spatial cognition network consisted predominantly of superior and inferior parietal gyrus (BA 7/40), which were crucial in visuo-spatial processing during cognition-Chinese-language paradigms (reading and writing). Facial sensory network covered pons and medial temporal pole, which served to process sensory information from human faces such as the sense of smell and taste.

1594.   Stimulating brain tissue with light - resting state fMRI analysis 
Tuomo Starck1,2, Juuso Nissilä3, Antti Aunio3, Ahmed Abou Elseoud1,2, Jukka Remes1, Juha Nikkinen1, Markku Timonen4,5, Timo Takala6, Osmo Tervonen1,2, and Vesa Kiviniemi1,2
1Diagnostic Radiology, Oulu University Hospital, Oulu, Finland, 2Diagnostic Radiology, Oulu University, Oulu, Finland, 3Valkee Ltd, Finland, 4Department of Psychiatry, Oulu University, Finland,5Institute of Health Sciences, Oulu University, Finland, 6ODL Health ltd, Oulu, Finland

Based on literature about opsin proteins and anecdotal evidence of inherent light-sensitivity of the human brain tissue we tested the hypothesis that brain activity would alter during bright light stimulation via ear canal. ICA dual regression analysis was performed for full band resting state BOLD fMRI data between constant light stimulus (n=24) and sham controls (n=26). Lateral visual IC was significantly different between the groups, light stimulus subjects demonstrated slowly increasing activity around the visual cortex and related regions. Results suggest brain tissue to be inherently light sensitive.

1595.   Self-organizing group level Independent Component Analysis reveals task-related activity as well as resting state networks during auditory stimulation 
Elizabeth Quattrocki Knight1,2, Xiaoying Fan3, Blaise Frederick4, Marc Kaufman4, and Bruce Cohen2,3
1Psychiatry, McLean Hospital, Belmont, MA, United States, 2Psychiatry, Harvard Medical School, Boston, MA, United States, 3Frazier Research Institute, McLean Hospital, Belmont, MA, United States, 4Brain Imaging Center, McLean Hospital, Belmont, MA, United States

Although numerous fMRI studies have examined visual processing, less work has focused on the auditory system. With the exception of sparse sampling techniques, interference from scanner noise can impede the study of auditory processing. Independent component analysis (ICA), by isolating and removing components in the data representing extraneous sources of noise, can facilitate fMRI data analysis. Here, we compare results of a self-organizing group level ICA (SogICA) to a random effects (RFX) general linear model in an auditory listening study. SogICA identifies not only more extensive task-related activity, but also reveals underlying resting state networks.

1596.   Interference of Default Mode Neural Network by Visual Stimulation and Subject’s Attention Depending on the Resting Functional MRI 
Yasuhiro Funakoshi1, Tomomi Sumiyoshi2, Masafumi Harada3, and Hitoshi Kubo3
1Medidcal Imaging, University of Tokushima, Tokushima, Tokushima, Japan, 2University of Tokushima, 3Health Biosciences, University of Tokushima

The default mode neural network would be interfered or localized in the smaller area by the stimulation from the outside and subject’s attention. In the case of clinical application to patients, the stimulation from the outside should be removed and it is considered that the psychological resting state is important to apply this technique for the clinical diagnosis.

1597.   Functional Network of Hand Prehension : validation by fMRI network connectivity 
Tzu-chen Yeh1,2, Chou-ming Cheng1, Bi-yu Hsu1, and Jo-mei Huang2
1Department of Medical Research and Education, Taipei Veterans General Hospital, Taipei, Taiwan, 2Insitute of Brain Science, National Yang-Ming University, Taipei, Taiwan

Prehension is defined as the capacity to reach and grasp which involves complex neuro-coginitive architectures. Hand prehension is composed of selection of grasp model and transformation of motor command. Wide network of motor hierarchy involves action initiation of parietal cortices, premotor, supplementary motor area and primary motor cortex. A visuo-motor flanker task was implemented for the fMRI study to validate the prehension control model via dorsal stream. By lag correlation of the prehension-related components derived from spatial independent component analysis, the functional network of hand prehension echoed the theoretical construct and demonstrated major and minor connectivity of functional correlates.

1598.   Hippocampal connectivity modulated by menstrual cycle:a resting state study 
Xinyuan Miao1, Thomas Zeffiro2, and Yan Zhuo1
1Institute of Biophysics, Chinese Academy of Sciences, Beijing, China, People's Republic of, 2Neural Systems Group, Massachusetts General Hospital, United States

In this preliminary study, we used ROI-based functional connectivity of resting-state functional MRI to investigate changes in inter-regional correlations in women’s different menstrual phases. Our results showed that the functional connectivity of the left hippocampus and parahippocampus with the bilateral superior occipital gyrus and cuneus, and the right middle frontal gyrus was higher in the early follicular phase than in the mid-luteal phase. The patterns of functional connectivity shown in this study may provide new clues for understanding the mechanism of how spatial abilities are modulated by hormone during menstrual cycle.

1599.   Task modulation of intrinsic low-frequency temporal connectivity in the brain default mode network 
Jingyuan Chen1, Catie Chang2, Kui Ying1, Yan Zhu1, and Gary Glover2
1Tsinghua University, Beijing, Beijing, China, People's Republic of, 2Stanford University, Stanford, CA, United States

In our study, we used both cluster analysis and marginal/partial correlation analysis to quantify and compare how low-frequency temporal connectivity of the brain default mode network (DMN) changes during sustained tasks that activate and deactivate major regions in the network. We found that low-frequency temporal connectivity was not extinguished but attenuated within most major regions of DMN under tasks that deactivate its nodes relative to rest, and that more prefrontal regions were engaged in the network under such task modulation. Moreover, we noticed the persistence of low-frequency temporal connectivity in subjects whose DMN was activated by external task.

Traditional Posters : Functional MRI
Click on to view the abstract pdf and click on to view the pdf of the poster viewable in the poster hall.
Functional Connectivity Analysis

Monday May 9th
Exhibition Hall  14:00 - 16:00

1600.   Impact of the global average in resting state functional connectivity: quantification of anti-correlations 
Felix Carbonell1, Pierre Bellec2, and Amir Shmuel1
1Montreal Neurological Institute, Montreal, Quebec, Canada, 2Centre de recherche de l'institut de Gériatrie de Montréal

In the current work we introduce the notion of Impact of the Global Average in Functional Connectivity (IGAFC) for quantifying the sensitivity of seed-based correlation analysis to the inclusion of the global average signal as a confounding effect. The IGAFC index is defined as the correlation between the GA and the BOLD at a particular voxel times the correlation between the GA and the seed time course of interest. This definition enables the calculation of a threshold at which the impact of the GA would be large enough to artificially introduce negative correlations.

1601.   A graph-theory approach to study the effect of cognitive load on resting state networks 
Tommaso Gili1, Paolo Barucca2, Francesco De Santis2, Guido Caldarelli3, Emiliano Macaluso4, Bruno Maraviglia2, and Federico Giove2
1Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, Wales, United Kingdom, 2Dipartimento di Fisica, Università di Roma Sapienza, Roma, Italy, 3CNR-ISC Dipartimento di Fisica, Università di Roma Sapienza, Roma, Italy, 4Neuroimaging Laboratory, Santa Lucia Foundation, Roma, Italy

The interaction between different brain structures in the study of functional connectivity is a good conceptual match for considering the brain as a graph, or complex network, of nodes and links. In this representation, image voxels or parcellated brain regions represent the nodes and a measure of similarity in their responses defines the links between them. In this work we focused on a particular type of method that identifies nodes, which play central roles within the network structure. Specifically we calculated Eigenvector Centrality maps of the brain at rest and during a 2-back verbal working memory task.

1602.   Incorporation of regional homogeneity in seed definition for the resting-state functional MRI analysis 
Feng-Xian Yan1, Yuan-Yu Hsu2, Shi-Yu Cheng1, Kun-Eng Lim2, and Ho-Ling Liu1,3
1Department of Medical Imaging and Radiological Sciences, Chang Gung University, Kwei-Shan, Tao-Yuan, Taiwan, 2Department of Medical Imaging, Buddhist Tzu Chi General Hospital, Taipei, Taiwan, 3Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital, Tao-Yuan, Taiwan

This study proposed a method to improve the conventional seed-based correlation analysis (SCAC) of resting-state (RS) fMRI by incorporating the regional homogeneity information (SCAReHo) in seed selection. Data from twelve healthy subjects were analyzed with five seed locations found in literatures: three in posterior cingulate cortex for default mode network and two for amygdale (right and left). The results showed that SCAReHo was more sensitive in detecting functional connectivity and less subject to variations in seed locations. This method is applicable to all RS-fMRI analysis and may be particular helpful when subjects exhibit distinct functional anatomy compared with normal populations.

1603.   Beyond thresholding: fully-weighted graph representations of brain functional connectivity 
Adam J Schwarz1, and John McGonigle2
1Psychological and Brain Sciences, Indiana University, Bloomington, IN, United States, 2Computer Science, University of Bristol, Bristol, United Kingdom

Functional connectivity analyses of fMRI data have leveraged recent advances in complex network theory, but these approaches have conventionally used a cut-off inter-node connection strength to threshold the network. This results in a sparse adjacency matrix amenable to conventional graph theoretic treatment, but requires the choice of a hard threshold (and verification of results over a range of such thresholds). We characterize the properties of fully-weighted human brain networks obtained by retaining all edges along with connection strength information, including the parametric dependence of a power law adjacency function (replacing the hard thresholding operation).

1604.   A Resting-State Connectivity Index With No Dependence on SNR and CNR 
Ali Mohammad Golestani1, and Bradley G Goodyear1,2
1Biomedical Engineering, University of Calgary, Calgary, Alberta, Canada, 2Radiology & Clinical Neuroscience, University of Calgary, Calgary, Alberta, Canada

Resting-state fMRI analysis is often performed by averaging the time courses in seed and target ROIs and then computing the strength of connectivity using temporal cross-correlation. A good connectivity index should be sensitive to meaningful physiological changes (e.g. change in connectivity strength in response to a disease), but remain insensitive to SNR and CNR, which can change between sessions. We introduce a resting-state connectivity index that is normalized to the connectivity of the seed to itself. This normalization of connectivity within the given data set removes the dependence on changes in SNR and CNR.

1605.   Estimation of Resting State Network Activity Using Multivariate Prediction Analysis Regression (MVPA-R) 
Cameron Craddock1, and Stephen M LaConte1
1School of Biomedical Engineering and Sciences, Virginia Tech, Blacksburg, VA, United States

We propose a method for deriving functional connectivity maps using multivariate prediction analysis regression. This method provides accurate estimation of the time course of activity for a resting state network (RSN) of interest from a never-before-seen dataset. This approach is evaluated for 10 RSNs on a resting state test-retest dataset acquired from 26 subjects. The proposed method is able to accurately estimate RSN activity when at least 5 minutes of data are available for training. This method provides a framework for tracking RSN activity in real-time as well as comparing methodological tradeoffs inherent in resting state functional connectivity analyses.

1606.   Individual brain parcellation based on single subject ICA 
Erik van Oort1, and David Norris1
1MR Techniques in Brain Function, Radboud University Nijmegen, Donders Institute, Nijmegen, Gelderland, Netherlands

Standard brain atlases are commonly used in neurological applications of MRI, particularly fMRI studies. This abstract describes research attempting to develop a method to create an individual parcellation based on a single subject ICA. High quality (35 minutes) rs-fMRI data were acquired from 47 healthy subjects at 3T. A single subject ICA was performed. The components containing anatomical information were used to parcellate the brain, without the use of any other prior anatomical information. This parcellation was compared to the AAL template, and shows several anatomical features present in this template.

1607.   Principal Components Analysis Reveals the Correlation Structure of Resting-State fMRI Data 
Hongjian He1, and Thomas T Liu2
1Zhejiang University, Hangzhou, Zhejiang, China, People's Republic of, 2Center for Functional MRI and Department of Radiology, UC San Diego, La Jolla, California, United States

We use principal components analysis to generate low-dimensional approximations of resting-state fMRI correlation maps, where the components are ranked by their contribution to the original correlation map. Applying this approach to connectivity maps with a seed region in the posterior cingulate cortex, we find that the first ranked component map represents correlation with the global signal, while the second component shows the anti-correlated relation between the default mode network and task positive network. Our results support the general validity of global signal regression and the existence of anti-correlated resting-state networks.

1608.   On connectivity within the Default Mode Network: an ICA and tractography approach 
Erik van Oort1, and David Norris1
1MR Techniques in Brain Function, Radboud University Nijmegen, Donders Institute, Nijmegen, Gelderland, Netherlands

This abstract describes detailed connectivity within the DMN. High quality rs-fMRI (35 minutes) and DWI (256 directions) data of 47 healthy subjects was acquired at 3T. The rs-fMRI data was examined using ICA methods on single subject level. The DMN was extracted using a group-ICA. Its regions were examined for local connectivity using sub-regions from the single-subject ICA using a partial correlation analysis. This analysis found nodes of connectivity in the PCC region of the DMN. The locations of these nodes show a good correspondence to the fiber tracking results.

1609.   Dynamic functional connectivity measures using fcMRI 
Thomas W. Allan1, Matthew J. Brookes1, Susan T. Francis1, and Penny A. Gowland1
1SPMMRM, University of Nottingham, Nottingham, United Kingdom

Electrophysiological measurements of functional connectivity (fc) have shown marked changes in fc over the time scale of typical resting state fcMRI recordings. Here we investigate whether these dynamic changes are observable in fcMRI. We 1) describe a technique to derive the statistical significance of fc maps constructed using varying length time windows; 2) measure dynamic changes in motor and default mode network connectivity showing a significant change in fc over time; 3) confirm that networks are active on both a short (20s) and long (300s) time scale.

1610.   The spectral power of brain oscillations predicts the functions of brain networks 
Yi Chia Li1, and Jyh Horng Chen2
1Graduate Institute of Biological Engineering and Bioinformatics, National Taiwan University, Taipei, Taiwan, 2Interdisciplinary MRI/MRS Lab, Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan

Up to 10 functional networks contributed by low frequency fluctuations (LFFs) have been reliably identified to consistently exist in human resting brains. These networks consist of regions which are known to be involved in function of motor, vision, execution, auditory, pain perception, language, cerebellum, and the so called default-mode network (DMN). Based on the concept proposed by Weisskoff et al. that the baseline of LFF power spectrums followed a 1/f curve, we analyzed resting-state fMRI data of 11 healthy participants with 1/f model, to further investigate the spectral characteristics of brain oscillations across different networks. The parameter ¡§b¡¨ in 1/f model was discovered to predict the functions of these networks, which illustrated the spectral power of brain oscillations differed across networks which served different functions such as sensory, active, cognition, and default-mode. The result was supported by the discovery of the prior literature that the spectral characteristics of brain oscillations linked with neural processes which were modulated by the functions of brain networks.

Traditional Posters : Functional MRI
Click on to view the abstract pdf and click on to view the pdf of the poster viewable in the poster hall.
fMRI Analysis

Tuesday May 10th
Exhibition Hall  13:30 - 15:30

1611.   Complex and magnitude-only preprocessing of 2D and 3D BOLD fMRI data at 7 Tesla 
Robert L Barry1,2, Stephen C Strother3,4, and John C Gore1,2
1Vanderbilt University Institute of Imaging Science, Nashville, TN, United States, 2Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States, 3Rotman Research Institute, Baycrest, Toronto, ON, Canada, 4Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada

A challenge with ultra-high-field fMRI is the predominance of noise associated with physiological processes unrelated to tasks of interest. This degradation in data quality may be reversed using post-acquisition algorithms designed to estimate and remove the effects of these noise sources. BOLD fMRI data acquired using 2D EPI and 3D PRESTO at 7T were processed using the Stockwell transform filter, retrospective image correction (RETROICOR), and phase regression. Data quality was evaluated via metrics of prediction and reproducibility using NPAIRS. Results demonstrate the pseudo-complementation of these algorithms and maximization of prediction and reproducibility through synergistic interactions between RETROICOR and phase regression.

1612.   Detecting fMRI Activation in K-Space for High Acceleration Factors 
Gigi Galiana1, and Robert Todd Constable1
1Diagnostic Radiology, Yale University, New Haven, CT, United States

Significant improvements have been realized in the reconstruction of undersampled MR angiography data simply by switching the order of the subtraction and the unfolding steps. We report a similar improvement in fMRI reconstructions simply by switching the order of the analogous steps in fMRI processing. Rather than reconstructing the individual time point images and analyzing those images for activation, we perform the GLM analysis on the undersampled k-space. The sparse activation images are then easier to unfold, allowing for better quality reconstructions at high acceleration factors.

1613.   The Bleeding Artifact of Spatially Constrained Canonical Correlation Analysis in Functional MRI 
Dietmar Cordes1, Mingwu Jin1, Tim Curran2, and Rajesh Nandy3
1C-TRIC and Dept. of Radiology, University of Colorado-Denver, Aurora, CO, United States, 2Dept. of Psychology and Neuroscience, University of Colorado-Boulder, Boulder, CO, United States,3Depts. of Biostatistic and Psychology, University of California-Los Angeles, Los Angeles, CA, United States

An improved method to detect activations in fMRI uses local canonical correlation analysis (CCA) to encompass a group of voxels in a 3x3 pixel neighborhood. It is customary to assign the value of the test statistic to the center voxel of the local neighborhood. However, without spatial constraints such an assignment introduces smoothing artifacts in regions of strong localized activation, which we refer to as “bleeding artifacts”. To reduce this artifact we propose different spatial constraints in CCA to enforce dominance of the center voxel and introduce a method based on mixture modeling to further reduce this artifact.

1614.   Investigation of Efficient Implementation of Local Constrained Canonical Correlation Analysis for fMRI 
Mingwu Jin1, Rajesh Nandy2, and Dietmar Cordes1
1University of Colorada Denver, Aurora, CO, United States, 2UCLA, Los Angeles, CA, United States

In previous work, local constrained canonical correlation analysis (cCCA) methods were proposed in order to avoid model overfitting and loss of specificity. In this work, we further investigate the performance, efficiency and possible improvement of region-growing based cCCA (cCCA-RG) methods. Using simulated data, we compare the estimation power of different cCCA-RG methods as well as the exhaustive search method (cCCA-ES). The detection power is also investigated upon real fMRI data. Our results demonstrate that cCCA-RG can significantly improve the detection power within an acceptable period of computation time.

1615.   A multivariate regression framework for the analysis of fMRI data accounting for spatial correlation 
Rajesh Ranjan Nandy1
1Psychology and Biostatistics, University of California, Los Angeles, CA, United States

Local canonical correlation analysis (CCA) is a multivariate method that simultaneously analyzes the timecourses of a group of neighboring voxels and is more sensitive than the conventional univariate GLM approach. However, unlike the general linear model (GLM), an arbitrary linear contrast of the temporal regressors has not been so far incorporated in the CCA formalism. To address the first problem, a multivariate regression model is presented. Multivariate regression model is equivalent to CCA, but easier to interpret. Arbitrary contrasts can be used in the multivariate regression model (MRM) approach including contrasts on voxels which is impossible in the univariate framework.

1616.   Model-free fMRI group analysis using FENICA 
Veronika Schöpf1,2, Christian Windischberger1,2, Simon Robinson1,3, Christian Kasess1,4, Florian Ph.S. Fischmeister1,5, Rupert Lanzenberger4, Jessica Albrecht6, Anna M Kleemann6, Rainer Kopietz6, Martin Wiesmann6,7, and Ewald Moser1,2
1MR Centre of Excellence, Medical University Vienna, Vienna, Vienna, Austria, 2Center of Medical Physics and Biomedical Engineering, Medical University Vienna, Vienna, Vienna, Austria,3Department of Radiology, Division of Neuroradiology, Medical University Vienna, Vienna, Vienna, Austria, 4Division of Biological Psychiatry, Department of Psychiatry and Psychotherapy, Medical University Vi, Vienna, Vienna, Austria, 5Faculty of Psychology, University of Vienna, Vienna, Vienna, Austria, 6Department of Neuroradiology, Ludwig-Maximilians-University, Munich, Munich, Germany, 7Department of Neuroradiology, Technical University Aachen RWTH, Aachen, Germany

In this study we were able to show that the new ICA method FENICA reliably identifies activation in a wide variety of paradigms and stimuli types. Activation maps are in excellent agreement with those established in previous, model-based analyses. FENICA has the potential to become a valuable tool for group fMRI studies, eliminating a priori assumptions including model and HRF, and without the need to downsample data in large studies, define spatial templates or manually identify single-subjector group components. Using FENICA it is possible to analyze functional MRI data of experiments using complex stimulus design involving different modalities on a truly data-driven basis. FENICA is a single-subject based technique allowing for group statistics to be applied in a well-established framework and provides a truly exploratory, data-driven, operator independent and therefore unbiased way of identifying common patterns of activation.

1617.   Model-based and data-driven analysis of whole brain EVI demonstrates increased statistical power compared to EPI at 3 T 
Radu Mutihac1,2, Elena Ackley1, Jochen Rick3, Akio Yoshimoto4, Maxim Zaitsev3, Oliver Speck5, and Stefan Posse1,6
1Department of Neurology, University of New Mexico, Albuquerque, New Mexico, United States, 2Department of Electricity & Biophysics, University of Bucharest, Bucharest, Romania,3Department of Radiology - Medical Physics, University Medical Center Freiburg, Freiburg, Germany, 4Polytechnic Institute of New York University, New York, New York, United States,5Department Biomedical Magnetic Resonance, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany, 6Department of Physics and Astronomy, University of New Mexico, Albuquerque, New Mexico, United States

Whole brain multiple-slab echo-volumar-imaging (EVI) is a novel methodology that provides up to an order of magnitude higher temporal resolution compared to multi-slice echo-planar imaging (EPI). However, fMRI sensitivity of EVI and EPI has not yet been systematically compared using neither hypothesis driven inferential statistics like statistical parametric mapping (SPM) nor exploratory methods like spatial independent component analysis (ICA) or temporal fuzzy clustering analysis (FCA). In this study, we statistically assess the extent and maximum T-score of activation elicited by an auditory-gated visual-motor task for both modalities using SPM8. Furthermore, the finer time course information available in EVI lends itself to data-driven analysis to identify physiological noise sources and spurious activation investigated by spatial ICA.

1618.   Use of independent component analysis to define regions of interest for fMRI studies 
Jolinda Carol Smith1, and Scott H Frey1,2
1Lewis Center for Neuroimaging, University of Oregon, Eugene, OR, United States, 2Department of Psychology, University of Oregon, Eugene, OR, United States

Regions of interest (ROIs) are frequently used in fMRI. When defining functional ROIs, investigators face a number of arbitrary choices concerning the statistical threshold to employ and the method for delineating ROI boundaries. We propose a method for defining ROIs using independent component analysis (ICA). This method avoids many of the shortcomings of general linear model based ROI definition, and is robust and easy to implement. As a demonstration, we apply this method to define ROIs in the cortex and cerebellum that respond selectively to aurally paced movements of the lips, hands, and feet.

1619.   One-step Thresholding for BOLD Signal Detection in Accelerated fMRI 
Samir D Sharma1, Bosco S Tjan2, and Krishna S Nayak1
1Electrical Engineering, University of Southern California, Los Angeles, CA, United States, 2Psychology, University of Southern California, Los Angeles, CA, United States

Functional magnetic resonance imaging (fMRI) with blood-oxygenation-level-dependent (BOLD) signal is fundamentally limited by the time required to acquire each volume. Standard EPI sequences with statistically advantageous TRs of 1-2s are typically restricted to acquiring no more than 16-32 slices with a typical 3 mm3 isotropic resolution. This covers only about 40-80% of the cerebral cortex. A fast imaging technique is needed for full volumetric coverage at short TRs. This work proposes a simple and fast one-step thresholding (OST) algorithm for the detection of BOLD signal activation. Results from the proposed method at 2x-acceleration demonstrate close agreement with the fully-sampled reference.

1620.   Development of a reasonable lateralization index for functional magnetic resonance imaging 
Kayako Matsuo1, Annabel S.-H. Chen2, and Wen-Yih Isaac Tseng1
1Center for Optoelectronic Biomedicine, National Taiwan University College of Medicine, Taipei, Taipei, Taiwan, 2Division of Psychology, School of Humanities and Social Sciences, Nanyang Technological University, Singapore

Laterality index (LI) is often applied in functional magnetic resonance imaging (fMRI) studies to determine functional hemispheric lateralization. However, conventional LI methods could suffer from an outlier bias if t-values were extreme for a small number of voxels. We developed an improved method to calculate LI for fMRI called AveLI. This method considers laterality of activation at each and every t-value threshold for the task (thus this index is not limited by over stringent thresholds), and allows an intuitive comprehension of overall asymmetry. We examined the feasibility and reproducibility of AveLI by applying it to two fMRI language tasks.

1621.   Multivariate discrimination in natural and urban scene viewing 
Scott James Peltier1,2, Marc G Berman3, Yash Shah2, Stephen Kaplan3, and John Jonides4
1Functional MRI Laboratory, University of Michigan, Ann Arbor, MI, United States, 2Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States, 3Psychology, University of Michigan, Ann Arbor, MI, United States, 4Psychology, University of Michigan, Ann Arbor, MI

Multivariate analysis offers an alternative way to analyze functional MRI data, and allows applications such as biofeedback. In this study, we use support vector machines to analyze subjects viewing natural or urban scenes. We demonstrate high accuracy, even when using minimal amount of data.

1622.   Assessing (fMRI) Brain-Computer Interface Stability in ALS with Support Vector Machine 
Robert Cary Welsh1, Laura Jelsone-Swain1, Veronika Schoepf2, and Scott J Peltier3
1Radiology, University of Michigan, Ann Arbor, MI, United States, 2Radiology, Division of Neuroradiology, Medical University of Vienna, Vienna, Austria, 3Functional MRI Laboratory, University of Michigan, Ann Arbor, MI, United States

We used fMRI data in conjunction with support vector machine (SVM) classification of brain state to examine (fMRI-based) brain-computer interface stability in the amyotrophic lateral sclerosis (ALS) patient population.

1623.   Class-wise contributions to spatio-temporal SVM classification of fMRI data 
Rainer Boegle1,2, Carolin Cyran3, Stefan Glasauer1,2, and Marianne Dieterich2,3
1Center for Sensorimotor Research, Ludwig-Maximilians University, Munich, Germany, 2Integrated Center for Research and Treatment of Vertigo, Ludwig-Maximilians University (IFBLMU), Munich, Germany, 3Department of Neurology, Ludwig-Maximilians-University, Munich, Germany

Nowadays it is assumed that most brain functions involve a network of cortical areas. Thus it should be anticipated that the temporal dynamics of the task-related BOLD responses of the respective areas may vary. Previous studies used spatio-temporal support vector classification to produce functional brain maps revealing responses discriminating between tasks (''discrimination maps''), without having to assume a model for the task related BOLD response. Building on these we present a method for determining the class-wise contributions to these brain maps which can reveal the class(task)-wise similarities in addition to the class(task)-wise differences revealed by the ''discrimination maps''.

1624.   Automated classification of SLE and APL patients and normal controls using fMRI and DTI features 
An Vo1, Aziz M. Ulug1,2, E Kozora3,4, G Ramon5, J Vega5, R D Zimmerman6, D Erkan5, and M D Lockshin5
1The Feinstein Institute for Medical Research, Manhasset, New York, United States, 2Department of Radiology, Albert Einstein School of Medicine, Bronx, New York, United States, 3National Jewish Health, Denver, Colorado, United States, 4University of Colorado Medical Center, Denver, Colorado, United States, 5Hospital for Special Surgery, New York, New York, United States,6Weill Medical College of Cornell University, New York, New York, United States

Most patients with systemic lupus erythematosus (SLE) have cognitive dysfunction suggesting but clinically important central nervous system involvement. Antiphospholipid syndrome is another autoimmune disorder defined as the presence of arterial or venous thromboses and/or pregnancy morbidity with persistent antiphospholipid antibodies (APL). Diffusion tensor imaging (DTI) and fMRI has been used to study the SLE and APL patients. The purpose of this study is to use both fMRI and DTI features to automatically classify SLEs, APLs and normal controls.

1625.   Sub millimiter coregistration of functional maps across imaging sessions
Jeremy Lecoeur1, Feng Wang2, Li Min Chen2, Benoit M. Dawant1, and Malcolm J. Avison2
1Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, United States, 2Radiology and Radiological Science, Vanderbilt University Medical Center, Nashville, Tennessee, United States

A pipeline for the automated coregistration of sub-millimeter resolution functional maps from brain sub-volumes was implemented and tested in a non human primate model of focal somatosensory cortical activation. Preliminary studies demonstrate an accuracy of coregistration of about 30 micrometers for structural and 100 micrometers for functional maps across separate sessions.

1626.   Spatial modeling of phMRI data with a functional basis set 
Adam J Schwarz1, Vesa Kiviniemi2, Sara de Simoni3, Steven CR Williams3, and Mitul A Mehta3
1Translational Medicine, Eli Lilly and Company, Indianapolis, IN, United States, 2Diagnostic Radiology, Oulu University Hospital, Oulu, Finland, 3Centre for Neuroimaging Sciences, Institute of Psychiatry, London, United Kingdom

PhMRI responses are often widespread, but the stability of the spatial localization of responses across subjects and cohorts at the voxel scale may be affected by neuroanatomical variation, motion and residual differences in spatial normalization. We show that the phMRI response to ketamine can be accurately and sensitively modeled as a linear superposition of stable, independently derived, functional units. The concept is illustrated using a high model order ICA segmentation of the brain. Such functional units can be spatially distributed and overlapping, unlike anatomical VOIs, and may be robust to high spatial frequency differences across subjects.

1627.   BOLD susceptibility map reconstruction from fMRI by 3D total variation regularization 
zikuan chen1, Arvind Caprihan1, and Vince Calhoun1,2
1Mind Research Network, Albuquerque, NM, United States, 2Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, United States

In BOLD fMRI, the BOLD activity can be delineated in terms of susceptibility map reconstructed from BOLD complex image, which is a 3D ill-posed inverse problem involving 3D deconvolution and denosing. In this work, we report a solution by the split Bregman algorithm of total variation (TV) regularization, which is an iterative regularization (implemented by a 3-subproblems iteration) for image restoration from noisy blurred image. Numerical simulation and phantom experiment show that this novel TV technique outperforms the filter-truncated Fourier inverse solution.

Traditional Posters : Functional MRI
Click on to view the abstract pdf and click on to view the pdf of the poster viewable in the poster hall.
fMRI Acquisition & Artifacts

Wednesday May 11th
Exhibition Hall  13:30 - 15:30

1628.   Sensitivity and Specificity of mHASTE BOLD fMRI on MT/V5 activation 
Yongquan Ye1, Jiani Hu1, Jie Yang1, and Mark Haacke1
1Radiology, WSU, Detroit, MI, United States

The novel single shot mHASTE BOLD fMRI technique was tested using a more subtle stimuli pattern than the flashing checkerboard, i.e. the MT/V5 activation, which induced highly localized activation with medium level significance. mHASTE was demonstrated to be sensitive to the BOLD response in MT/V5 area with significantly improved functional specificity than EPI methods, indicating that mHASTE is mainly sensitive to micro-vasculature BOLD signals.

1629.   T2- and T2*-weighted high-resolution fMRI at 7T using non-balanced SSFP 
Pål Erik Goa1,2, Peter Jan Koopmans2,3, Benedikt Andreas Poser2,3, Markus Barth2,3, and David Gordon Norris2,3
1Department of Medical Imaging, St.Olav University Hospital, Trondheim, Norway, 2Erwin L. Hahn Institute for Magnetic Resonance Imaging, University Duisburg-Essen, Essen, Germany,3Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands

Non-balanced Steady-state free precession (nb-SSFP) might be an alternative for BOLD-fMRI at 7T. Here we compare the functional signal change at the pial surface and within grey matter for S1 and S2 using multi-echo nb-SSFP at high spatial resolution. Eight subjects were scanned using a checkerboard paradigm, and tissue-specific signal changes were extracted based on segmentation of MP-RAGE images. Results show that the signal change is largest at the pial surface in S1 at both short and long TE as well as in S2. This might indicate intravascular blood is contributing to the functional contrast also at 7T.

1630.   FMRI using high flip-angle alternating steady state balanced SSFP supported by Monte Carlo studies 
Steven Andrew Patterson1,2, Steven Donald Beyea1,3, and Chris Van Bowen1,3
1Institute for Biodiagnostics (Atlantic), National Research Council Canada, Halifax, Nova Scotia, Canada, 2Physics, Dalhousie University, Halifax, Nova Scotia, Canada, 3Physics, Biomedical Engineering and Radiology, Dalhousie University, Halifax, Nova Scotia, Canada

To achieve artifact-free whole brain coverage and good temporal resolution using passband balanced SSFP (pbSSFP) functional MRI (fMRI), alternating between two steady states to eliminate banding artifact is necessary. Monte Carlo simulations of alternating steady state pbSSFP (altSSFP) fMRI were conduced to characterize BOLD signal and contrast. Results suggest altSSFP can provide artifact-free whole brain coverage with 2-3 s temporal resolution and up to 90% of the BOLD contrast observed in conventional pbSSFP if high (45-60°) flip angles and linearly increasing flip angle RF catalyzation is used. Despite off-resonance signal intensity variation, spatially-uniform sensitivity fMRI maps are anticipated.

1631.   A real-time feedback optimization method for automatic calibration of functional sensitivity-band of transition-band bSSFP fMRI sequence 
Yu-Wei Tang1, and Teng-Yi Huang1
1Electrical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan

To calibrate the sensitivity-band of transition-band SSFP fMRI, a SSFP angle adjustment method based on a sweep scan with increment of SSFP angle has been proved its potential in previous studies. In this study, we proposed a real-time feedback optimization method to calibrate the SSFP angle automatically and rapidly. Through network connection of MRI scanner and an external PC, a feedback loop was created. An optimization method is proposed to search the optimal SSFP angle automatically. The results demonstrate that it exhibits similar effectiveness of the previous sweep scan method and requires much shorter calibration time.

1632.   A novel approach to investigate the impact of RF pulses on the BOLD contrast in steady-state pulse sequences 
Ute Goerke1, and Kamil Ugurbil1
1Radiology, Center for Magnetic Resonance Research, Minneapolis, Minnesota, United States

In recent research efforts, fMRI contrast in steady-state sequence has been investigated. Potential contributions to the fMRI signal changes in these sequences, in which a large fraction of the imaging time is used for excitation pulse, include magnetization transfer effects and/or relaxation during RF pulses. In this paper, we propose a novel approach using non-slice selective spoiled 3D GRE with chirp pulse excitation and multiple echo acquisition. Activation maps are obtained from the undersampled fMRI signal changes using the spectral side band analysis (SSBA). Initial results indicate differences in amplitude of the fMRI signal change depending on pulse bandwidth.

1633.   Spectral-Spatial Pulse Design with Spectral Decomposition 
Cungeng Yang1, and Victor Andrew Stenger1
1University of Hawaii, Honolulu, Hawaii, United States

Signal loss caused by susceptibility induced intravoxel dephasing is a major limitation in high field MRI applications such as BOLD fMRI. Spectral-spatial (SPSP) pulses have been shown to be very effective at reducing through-plane signal loss in axial slices using a single excitation. SPSP pulse design assumes a linear relationship between off-resonance frequency and through-plane susceptibility gradient Gs(f)=αf. Previous studies show empirically α=-2.0 μT/m/Hz works well for many brain regions at 3T, however, no detailed measurement of α was investigated. We propose spectral decomposition technique using a spiral spectroscopic imaging sequence to directly measure α at all locations in the brain. Resultant SPSP pulses were demonstrated in T2*-weighted brain images showing reduced signal loss at 3T. Inferior slices were found to require α values of opposite sign and smaller magnitude. This indicates that using more than one pulse may improve the efficacy of the SPSP technique.

1634.   Matched Filter EPI Increases BOLD-Sensitivity in Human Functional MRI 
Lars Kasper1,2, Maximilian Häberlin1, Christoph Barmet1, Bertram Jakob Wilm1, Christian C. Ruff2,3, Klaas Enno Stephan2,3, and Klaas Paul Prüssmann1
1University and ETH Zurich, Institute for Biomedical Engineering, Zurich, Zurich, Switzerland, 2University of Zurich, Laboratory for Social and Neural Systems Research, Zurich, Zurich, Switzerland, 3University College of London, Wellcome Trust Centre for Neuroimaging, London, London, United Kingdom

Filtering is a common post-processing step in MRI. Specifically, the analysis of fMRI data frequently includes a Gaussian smoothing of the raw images. This application of a “matched filter” improves sensitivity at the spatial scale of the BOLD response. We show that the argument already holds at the acquisition stage and propose a 2D gradient-velocity modulated EPI sequence providing a Gaussian k-space density weighting. In the case of phantom and resting state fMRI, temporal SNR is thus raised by 60-80% while preserving overall measurement duration. Furthermore, for visual stimulation, t-contrast images reveal both increases in cluster sizes and peak t-values.

1635.   Improved Partial Fourier EPI using Tissue Susceptibility Matched Pyrolytic Graphite Foams 
Gary Chiaray Lee1, Caroline Jordan2, Carlos Ruiz3, Pamela Tiet3, Brian Hargreaves2, Ben Inglis4, and Steven Conolly1
1Berkeley/UCSF Bioengineering Joint Graduate Group, Berkeley, CA, United States, 2Radiology, Stanford University, 3Bioengineering, UC Berkeley, Berkeley, CA, United States, 4Helen Wills Neuroscience Institute, Berkeley, CA

One difficulty with current EPI BOLD fMRI is poor T2*-weighted analysis near areas of strong field inhomogeneities. The magnetic susceptibility difference between air and tissue produces B0 field perturbations in the head, and may cause significant MRI artifacts, expecially for EPI. Here we develop in vivo susceptibility matching pyrolytic graphite foams for improving EPI near external air/tissue interfaces. We have verified these that foams are safe for patient use, and here we demonstrate that the foams can make partial Fourier acquisition techniques more robust, which can ultimately lead to faster acquisitions or higher resolutions for EPI BOLD fMRI.

1636.   Human fMRI at 9.4 T: Preliminary Results 
Juliane Budde1, Frank Mühlbauer1, G. Shajan1, Maxim Zaitsev2, and Rolf Pohmann1
1Max Planck Institute for Biological Cybernetics, Tuebingen, Germany, 2University Hospital Freiburg, Freiburg, Germany

EPI data in humans were acquired at 9.4 T, using a GRE EPI sequence with distortion correction, resulting in 1.1 mm isotropic voxels. A simple finger tapping paradigm was employed for functional testing. In addition, high resolution (0.2 mm x 0.2 mm x 1.1 mm) phase and susceptibility-weighted images were acquired and co-registered to the functional data. Highly significant BOLD response was found in the left motor cortex, mostly located within gray matter, with activation amplitudes up to 20%. Co-registration to susceptibility-weighted data shows strong signal contributions even from vein-free regions.

1637.   Improved detection of functional connectivity MRI with 32-channel phased array head coil 
Sheeba Arnold1, Susan Whitfield-Gabrieli2, Steven Shannon1, John DE Gabrieli2, and Christina Triantafyllou1,3
1A.A. Martinos Imaging Center, McGovern Institute for Brain Research, MIT, Cambridge, MA, United States, 2Deparment of Brain and Cognitive Sciences, Cambridge, MA, United States, 3A.A. Martinos Center for Biomedical Imaging, Department of Radiology, MGH, Charlestown, MA, United States

Amongst the existing acquisition tools for functional connectivity MRI (fcMRI), 32-channel phased array head coil is yet to be evaluated at 3T. Our results demonstrate that in comparison to most commonly used receive coils (e.g. 12-channel) highly parallel multiple channel arrays offer increased sensitivity in detecting detailed connections in resting state networks such as the default mode network. Furthermore, the 32-channel coil proved to be significantly better than the 12-channel as revealed by graph theory analysis in terms of both global and local network efficiency.

1638.   Resting-State Networks at Higher Frequencies: a Preliminary Study 
Hsu-Lei Lee1, Benjamin Zahneisen1, Thimo Grotz1, Pierre LeVan1, and Jürgen Hennig1
1Medical Physics, University Medical Center Freiburg, Freiburg, Germany

Resting-state network analysis looks for coherent spontaneous BOLD signal fluctuations at frequencies lower than 0.1 Hz, where the most signal energy is stored. However hemodynamic signal change can occur at a faster rate and can also have different characteristics at different time scales. By using a highly under-sampled shell trajectory we were able to resolve a frequency spectrum up to 5 Hz. Preliminary tests found both coherent networks that are similar to those at 0.01~0.1 Hz frequency band, and some that differ.

Traditional Posters : Functional MRI
Click on to view the abstract pdf and click on to view the pdf of the poster viewable in the poster hall.
fMRI: Respiratory Challenges

Thursday May 12th
Exhibition Hall  13:30 - 15:30

1639.   Characterization of Static Field Effects of Paramagnetic Molecular Oxygen on BOLD-Modulated Hyperoxic Contrast Studies of the Human Brain 
David Thomas Pilkinton1,2, Santosh R Gaddam2, and Ravinder Reddy1,2
1Biochemistry & Molecular Biophysics, University of Pennsylvania, Philadelphia, Pennsylvania, United States, 2Center for Magnetic Resonance and Optical Imaging, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, United States

The inhalation of hyperoxic gas mixtures is known to be an effective positive contrast agent in T2*-weighted images, with minimal associated physiological and biochemical alterations. However, the quality of oxygen as an intravascular contrast agent depends not only on it having minimal effects on the underlying physiology, but also on it having minimal non-BOLD relaxation effects. In this study, we show that inhaled oxygen in the upper airway near the brain substantially increases the static field inhomogeneity in the frontal lobes as a function of the concentration of inhaled oxygen and field strength.

1640.   Field Shift due to Paramagnetic Effect of Molecular Oxygen 
Kejia Cai1, Kalli Grasley1, Anup Singh1, David Pilkinton1, Mohammad Haris1, Hari Hariharan1, Mark Elliott1, and Ravinder Reddy1
1CMROI, Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States

Oxygen inhalation has been shown to be a simple and effective positive contrast agent in T2*-weighted images based on the BOLD effect. While paramagnetic effect of molecular oxygen could shift the static field, which is unrelated to blood oxygenation. In this study, we have characterized the static field shift due to oxygen inhalation at 9.4T by using SVS and B0 field maps. Proton Larmor frequency is shifted by ~1Hz per 1% of oxygen at 9.4T. One must be cautious on this confounding factor when conducting fMRI and field sensitive MRI studies with oxygen inhalation.

1641.   Quantifying the artefacts caused by hyperoxic challenges 
Ian Driver1, Jack Harmer1, Emma Hall1, Susan Pritchard1, Susan Francis1, and Penny Gowland1
1Sir Peter Mansfield Magnetic Resonance Centre, University of Nottingham, Nottingham, United Kingdom

Increased oxygen in the oral cavity and sinuses during hyperoxia causes local changes in magnetic field homogeneity. This study dynamically maps these field changes, and quantifies the hyperoxia-induced frequency shift in the frontal sinus, as well as more distant brain regions of most interest to studies using hyperoxia for cerebral blood volume estimation and BOLD calibration. A hyperoxia challenge was found to induce a ~ 20 Hz shift close to the sinus, away from this region this reduced, but not to zero. Although not significantly affecting transverse relaxation, hyperoxia will modulate EPI distortions, an effect that should be dynamically monitored.

1642.   Quantitation of changes in cerebral blood flow and longitudinal relaxation rate (R1 =1/T1) induced by mild hyperoxia 
Hajime Tamura1, Tatsuo Nagasaka2, Kazuki Shimada2, Junki Nishikata1, Miho Shidahara1, Shunji Mugikura3, and Yoshio Machida4
1Department of Medical Physics, Tohoku University, Graduate School of Medicine, Sendai, Miyagi, Japan, 2Department of Radiology, Tohoku University Hospital, Sendai, Miyagi, Japan,3Department of Diagnostic Radiology, Tohoku University Hospital, Sendai, Miyagi, Japan, 4Department of Medical Imaging and Applied Radiology, Tohoku University, Graduate School of Medicine, Sendai, Miyagi, Japan

Decreases in blood flow (CBF) and T1 during hyperoxia are affected by arterial pressure of carbon dioxide as well as that of oxygen (PaO2). To extract those effects of PaO2 and to examine if the changes in R1 relate to the changes in CBF, serial R1 and CBF maps were obtained during normoxia-hyperoxia epochs and analyzed with a linear model. Thus the effects of PaO2 on R1 and CBF were obtained. However, no significant correlation was observed between them (P = 0.18), which might imply that the ratio of CBF to metabolic rate of oxygen does not change during hyperoxia.

1643.   Venous Vessel Size MRI in the Human Brain Using Transient Hyperoxia 
Yuji Shen1, Trevor Ahearn1, Matthew Clemence2, and Christian Schwarzbauer1
1Aberdeen Biomedical Imaging Centre, University of Aberdeen, Aberdeen, United Kingdom, 2Clinical Science MRI, Philips Healthcare, Surrey, United Kingdom

Hyperoxia-induced BOLD contrast was employed to measure the mean venous vessel size in the human brain using a combined GE and SE EPI sequence. The experimental paradigm consisted of two 3-minute blocks of breathing 100% O2 interleaved with three 2-minute blocks of breathing room air. The vessel size index q = ÄR2*/ÄR2 was calculated on a pixel-by-pixel basis and then converted to a map of the vessel radius based on a calibration curve obtained from a biophysical tissue model.

1644.   Quantitative Evaluation of the Dynamic BOLD and CBF Responses to Breath Hold in Different Brain Territories 
Wen-Cheng Chu1, Yuan-Yu Hsu2, Kun-Eng Lim2, and Ho-Ling Liu1,3
1Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan County, Taiwan, 2Buddhist Tzu Chi General Hospital, Taipei County, Taiwan, 3Division of Medical Imaging and Intervention, Chang Gung Memorial Hospital, Taoyuan County, Taiwan

Blood oxygen level-dependent (BOLD) signal reflects a complex combination of cerebral blood function (CBF), cerebral blood volume (CBV), and oxygen consumption changes. Therefore, this study aimed to quantitatively evaluate the dynamics of CBF changes in the territories supplied by anterior cerebral artery, middle cerebral artery, and posterior cerebral artery obtained from the arterial spin labeling (ASL) experiment during breath-holding tasks. The averaged signal time courses were fitted using gamma-variate function, including three quantified parameters, such as onset time, FWHM, and maximum signal change. Here, this study demonstrated substantial differences between the BOLD and ASL responses to the breath-holding tasks.

1645.   Characterizing the BOLD response to transient respiratory challenges at 7 Tesla 
Molly Gallogly Bright1,2, Daniel P Bulte2, Peter Jezzard2, and Jeff H Duyn1
1Advanced MRI Section, LFMI, NINDS, National Institutes of Health, Bethesda, MD, United States, 2FMRIB Centre, University of Oxford, Oxford, Oxfordshire, United Kingdom

Cerebrovascular reactivity to changes in arterial gas tensions offers clinical insight into vessel health and compliance. The BOLD response to a new respiratory challenge of Cued Deep Breathing enables accessible mapping of reactivity and response dynamics. At 7 Tesla, the enhanced magnitude of the BOLD response to this challenge enables voxelwise fitting for % signal change, time-to-peak (TTP), onset time, and full-width-half-minimum of the response throughout the brain. TTP is a robust surrogate for the other, more subtle temporal parameters, and results further establish, in individual subjects, the regional heterogeneity observed previously across a healthy population at 3 Tesla.

1646.   Determination of R2* across multiple postlabeling delays in ASL and comparison with flow, arterial volume and transit times in physiological challenges 
Yi-Ching Lynn Ho1,2, Esben Thade Petersen3, and Xavier Golay4
1Center for Functionally Integrative Neuroscience, Aarhus, Denmark, 2Neuroradiology, National Neuroscience Institute, Singapore, Singapore, 3Clinical Imaging Research Centre, Singapore,4UCL Institute of Neurology, United Kingdom

In Look-Locker-based ASL sequences, the transverse relaxation time R2* is assumed to be constant across the multiple postlabeling delay times, permitting accurate cerebral blood flow (CBF) quantification. Using dual echoes, R2* was found to be consistent across the multiple time points, validating the assumption. Secondly, the R2* results allowed a regional determination of oxygenation levels for arterial input function sampling. Finally, it is seen that R2* changes are tightly coupled with CBF changes, rather than with arterial blood volume or transit time changes and this during neuronal activity alone. The presence of hypercapnia increases the complexity of the relationships.

1647.   Hemodynamic changes can be detected in rat white matter using a hypercapnic challenge 
Erin Lindsay Mazerolle1,2, Chris V Bowen1,3, Drew R DeBay1, Kirk W Feindel1, James A Rioux1, Douglas D Rasmusson4, Kazue Semba5, and Ryan C D'Arcy1,6
1Institute for Biodiagnostics (Atlantic), National Research Council, Halifax, Nova Scotia, Canada, 2Neuroscience Graduate Program, Dalhousie University, Halifax, Nova Scotia, Canada, 3Physics, Dalhousie University, Halifax, Nova Scotia, Canada, 4Physiology and Biophysics, Dalhousie University, Halifax, Nova Scotia, Canada, 5Anatomy and Neurobiology, Dalhousie University, Halifax, Nova Scotia, Canada, 6Neuroscience, Dalhousie University, Halifax, Nova Scotia, Canada

White matter (WM) functional magnetic resonance imaging (fMRI) activation has potential to expand current approaches for studying brain connectivity and improve assessment of WM disorders. We took the first steps towards investigating the hemodynamic events that underlie WM fMRI activation. A hypercapnic challenge was used to elicit whole brain activation in the rat. We demonstrated that rat WM has the capacity to support hemodynamic changes due to hypercapnia. This is the first demonstration of hemodynamic changes in rat WM, and will serve as the foundation for future investigations of the neurophysiologic bases of WM fMRI activation.

1648.   Comparison of physiologic modulators in event-related fMRI 
Peiying Liu1, Andrew C. Hebrank2, Blair Flicker2, Denise C. Park2, and Hanzhang Lu1
1Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Texas, United States, 2Center for Vital Longevity, University of Texas at Dallas, Dallas, Texas, United States

In order for fMRI to be possibly used as a personalized diagnostic tool, it is critical to understand and account for the considerable inter-subject variations in fMRI responses. A few physiologic modulators have been reported that could explain such variations and may potentially be used for fMRI normalization, including baseline venous oxygenation, cerebrovascular reactivity, resting state BOLD signal fluctuation and baseline cerebral blood flow. This work extends previous findings in block-design to event-related design which are more widely used in cognitive neuroscience and clinical studies, and showed the modulation effect of physiologic parameters on fMRI signals in different brain regions.

1649.   Dynamics of cerebral lactate during acute hypoxia 
Ashley D Harris1, Richard AE Edden2,3, Kevin Murphy1, C John Evans1, Victoria Roberton1, Danielle Huckle4, Judith E Hall4, Neeraj Saxena4, Damian M Bailey5, and Richard G Wise1
1CUBRIC - School of Psychology, Cardiff University, Cardiff, United Kingdom, 2Russell H Morgan Department of Radiology and Radiological Science, The Johns Hopkins University, Baltimore, Maryland, United States, 3FM Kirby Research Centre for Functional MRI, Kennedy Krieger Institute, Baltimore, Maryland, United States, 4Department of Anaethetics, Cardiff University, Cardiff, United Kingdom, 5Department of Health, Sport and Science, University of Glamorgan, Pontypridd, United Kingdom

The role of cerebral lactate is unclear, as there is emerging evidence that it is a neural energy source, not just a by-product of anaerobic metabolism. There are also questions about cerebral metabolism during hypoxia, with some groups showing a non-intuitive result of increased cerebral metabolism during hypoxia. Here, the dynamics of lactate during an acute exposure to hypoxia and then return to normoxia are examined in 3 healthy humans with multiple repeat sessions. We show the dynamics and complexities of lactate accumulation.

1650.   T1 and T2* responses to hypercapnic and hyperoxic gases in normal tissue are independent of the order of gas delivery 
Jeff D Winter1,2, Marvin Estrada1, and Hai-Ling Margaret Margaret Cheng1,3
1Physiology and Experimental Medicine, The Hospital for Sick Children, Toronto, Ontario, Canada, 2Research and Development, IMRIS, Winnipeg, Manitoba, Canada, 3Medical Biophysics, University of Toronto, Toronto, Ontario, Canada

Quantitative MRI measures of T1 and T2* offer noninvasive means to indirectly monitor tissue O2. This study characterized T1 and T2* responses to randomized hypercapnic and hyperoxic gas challenges in normal rabbit liver, kidney and paraspinal muscle in comparison with pilot invasive tissue O2 and perfusion changes. All between-gas ΔT1 and ΔT2* transitions exhibited expected trends, especially in liver and kidney. However, T1 changes were much less predictable. Invasive measures demonstrated consistent trends in tissue perfusion and oxygenation but considerable variability. In summary, we demonstrated independence of T1 and T2* transitions on gas order, and organ-specific pO2 and perfusion dynamics.