The Main Injector Neutrino Oscillation Search (MINOS) experiment uses an acceleratorproduced neutrino beam to perform precision measurements of the neutrino oscillation parameters in the "atmospheric neutrino" sector associated with muon neutrino disappearance. This long-baseline experiment measures neutrino interactions in Fermilab's NuMI neutrino beam with a near detector at Fermilab and again 735 km downstream with a far detector in the Soudan Underground Laboratory in northern Minnesota. The two detectors are magnetized steel-scintillator tracking calorimeters. They are designed to be as similar as possible in order to ensure that differences in detector response have minimal impact on the comparisons of event rates, energy spectra and topologies that are essential to MINOS measurements of oscillation parameters. The design, construction, calibration and performance of the far and near detectors are described in this paper.

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The velocity of a ∼3 GeV neutrino beam is measured by comparing detection times at the Near and Far detectors of the MINOS experiment, separated by 734 km. A total of 473 Far Detector neutrino events was used to measure (v − c)/c = 5.1 ± 2.9 × 10 −5 (at 68% C.L.). By correlating the measured energies of 258 charged-current neutrino events to their arrival times at the Far Detector, a limit is imposed on the neutrino mass of mν < 50 MeV/c 2 (99% C.L.).

Recent studies indicate that functional connectivity using low-frequency BOLD fluctuations (LFBFs) is reduced between the bilateral primary sensorimotor regions in multiple sclerosis. In addition, it has been shown that pathway-dependent measures of the transverse diffusivity of water in white matter correlate with related clinical measures of functional deficit in multiple sclerosis. Taken together, these methods suggest that MRI methods can be used to probe both functional connectivity and anatomic connectivity in subjects with known white matter impairment. We report the results of a study comparing anatomic connectivity of the transcallosal motor pathway, as measured with diffusion tensor imaging (DTI) and functional connectivity of the bilateral primary sensorimotor cortices (SMC), as measured with LFBFs in the resting state. High angular resolution diffusion imaging was combined with functional MRI to define the transcallosal white matter pathway connecting the bilateral primary SMC. Maps were generated from the probabilistic tracking employed and these maps were used to calculate the mean pathway diffusion measures fractional anisotropy FA, mean diffusivity MD, longitudinal diffusivity lambda(1), and transverse diffusivity lambda(2). These were compared with LFBF-based functional connectivity measures (F(c)) obtained at rest in a cohort of 11 multiple sclerosis patients and approximately 10 age- and gender-matched control subjects. The correlation between FA and F(c) for MS patients was r = -0.63, P < 0.04. The correlation between all subjects lambda(2) and F(c) was r = 0.42, P < 0.05. The correlation between all subjects lambda(2) and F(c) was r = -0.50, P < 0.02. None of the control subject correlations were significant, nor were FA, lambda(1), or MD significantly correlated with F(c) for MS patients. This constitutes the first in vivo observation of a correlation between measures of anatomic connectivity and functional connectivity using spontaneous LFBFs.

10 ÿ3 eV 2 =c 4 and sin 2 2 > 0:87 at 68% confidence level.

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Head motion in functional MRI and resting-state MRI is a major problem. Existing methods do not robustly reflect the true level of motion artifact for in vivo fMRI data. The primary issue is that current methods assume motion is synchronized to the volume acquisition and thus ignore intra-volume motion. This manuscript covers three sections in the use of gold-standard motion-corrupted data to pursue an intra-volume motion correction. First, we present a way to get motion corrupted data with accurately known motion at the slice acquisition level. This technique simulates important data acquisition-related motion artifacts while acquiring real BOLD MRI data. It is based on a novel motion-injection pulse sequence that introduces known motion independently for every slice: Simulated Prospective Acquisition CorrEction (SimPACE). Secondly, with data acquired using SimPACE, we evaluate several motion correction and characterization techniques, including several commonly used BOLD signal- and motion parameter-based metrics. Finally, we introduce and evaluate a novel, slice-based motion correction technique. Our novel method, SLice-Oriented MOtion COrrection (SLOMOCO) performs better than the volumetric methods and, moreover, accurately detects the motion of independent slices, in this case equivalent to the known injected motion. We demonstrate that SLOMOCO can model and correct for nearly all effects of motion in BOLD data. Also, none of the commonly used motion metrics was observed to robustly identify motion corrupted events, especially in the most realistic scenario of sudden head movement. For some popular metrics, performance was poor even when using the ideal known slice motion instead of volumetric parameters. This has negative implications for methods relying on these metrics, such as recently proposed motion correction methods such as data censoring and global signal regression.

Huntington disease is a neurodegenerative disorder that involves preferential atrophy in the striatal complex and related subcortical nuclei. In this paper, which is based on a dataset extracted from the PREDICT-HD study, we use statistical shape analysis with deformation markers obtained through Large Deformation Diffeomorphic Metric Mapping of cortical surfaces to highlight specific atrophy patterns in the caudate, putamen, and globus pallidus, at different prodromal stages of the disease. Based on the relation to cortico-basal-ganglia circuitry, we propose that statistical shape analysis, along with other structural and functional imaging studies, may help expand our understanding of the brain circuitry affected and other aspects of the neurobiology of HD, and also guide the most effective strategies for intervention.