A Multivariate Surface-Based Analysis of the Putamen in Premature Newborns: Regional Differences within the Ventral Striatum

Many children born preterm exhibit frontal executive dysfunction, behavioral problems including attentional deficit/hyperactivity disorder and attention related learning disabilities. Anomalies in regional specificity of cortico-striato-thalamo-cortical circuits may underlie deficits in these disorders. Nonspecific volumetric deficits of striatal structures have been documented in these subjects, but little is known about surface deformation in these structures. For the first time, here we found regional surface morphological differences in the preterm neonatal ventral striatum. We performed regional group comparisons of the surface anatomy of the striatum (putamen and globus pallidus) between 17 preterm and 19 term-born neonates at term-equivalent age. We reconstructed striatal surfaces from manually segmented brain magnetic resonance images and analyzed them using our in-house conformal mapping program. All surfaces were registered to a template with a new surface fluid registration method. Vertex-based statistical comparisons between the two groups were performed via four methods: univariate and multivariate tensor-based morphometry, the commonly used medial axis distance, and a combination of the last two statistics. We found statistically significant differences in regional morphology between the two groups that are consistent across statistics, but more extensive for multivariate measures. Differences were localized to the ventral aspect of the striatum. In particular, we found abnormalities in the preterm anterior/inferior putamen, which is interconnected with the medial orbital/prefrontal cortex and the midline thalamic nuclei including the medial dorsal nucleus and pulvinar. These findings support the hypothesis that the ventral striatum is vulnerable, within the cortico-stiato-thalamo-cortical neural circuitry, which may underlie the risk for long-term development of frontal executive dysfunction, attention deficit hyperactivity disorder and attention-related learning disabilities in preterm neonates. © 2013 Shi et al

Feature selective temporal prediction of Alzheimer’s disease progression using hippocampus surface morphometry

IntroductionPrediction of Alzheimer’s disease (AD) progression based on baseline measures allows us to understand disease progression and has implications in decisions concerning treatment strategy. To this end, we combine a predictive multi‐task machine learning method (cFSGL) with a novel MR‐based multivariate morphometric surface map of the hippocampus (mTBM) to predict future cognitive scores of patients.MethodsPrevious work has shown that a multi‐task learning framework that performs prediction of all future time points simultaneously (cFSGL) can be used to encode both sparsity as well as temporal smoothness. The authors showed that this method is able to predict cognitive outcomes of ADNI subjects using FreeSurfer‐based baseline MRI features, MMSE score demographic information and ApoE status. Whilst volumetric information may hold generalized information on brain status, we hypothesized that hippocampus specific information may be more useful in predictive modeling of AD. To this end, we applied a multivariate tensor‐based parametric surface analysis method (mTBM) to extract features from the hippocampal surfaces.ResultsWe combined mTBM features with traditional surface features such as middle axis distance, the Jacobian determinant as well as 2 of the Jacobian principal eigenvalues to yield 7 normalized hippocampal surface maps of 300 points each. By combining these 7 × 300 = 2100 features together with the previous ~350 features, we illustrate how this type of sparsifying method can be applied to an entire surface map of the hippocampus that yields a feature space that is 2 orders of magnitude larger than what was previously attempted.ConclusionsBy combining the power of the cFSGL multi‐task machine learning framework with the addition of AD sensitive mTBM feature maps of the hippocampus surface, we are able to improve the predictive performance of ADAS cognitive scores 6, 12, 24, 36 and 48 months from baseline.In this work, we present our results of using machine learning to predict temporal behavior changes in Alzheimers Disease using entire topological feature maps of the hippocampus surface (2100 feature points). Our paper demonstrates that it is possible to use an entire topological map instead of just imaging derived volumetric measurements for predicting behavioral changes. We compare these results with previous results using only volumetric MR imaging features (309 features points) and show through repeated cross‐validation rounds that we are able to get better predictive power.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/137757/1/brb3733_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/137757/2/brb3733.pd

Discovery Potential for Doubly Charged Higgs Bosons in e^+e^- Collisions at LEP

We study the discovery limits for doubly charged Higgs bosons, Delta^{--}, obtainable at the LEP e^+e^- collider. We expect that the LEP2 collaborations can rule out the existence of a doubly charged Higgs boson of mass below about 190 GeV for Yukawa couplings greater than 0.1. However, even for larger values of M_Delta, evidence for the Delta can be seen due to contributions from virtual intermediate Delta's provided they have relatively large values of the Yukawa couplings.Comment: 10 pages including 3 figures. Uses Revtex. Typos corrected. References adde

3-3-1 Models at Electroweak Scale

We show that in 3-3-1 models there exist a natural relation among the $SU(3)_L$ coupling constant $g$, the electroweak mixing angle $\theta_W$, the mass of the $W$, and one of the vacuum expectation values, which implies that those models can be realized at low energy scales and, in particular, even at the electroweak scale. So that, being that symmetries realized in Nature, new physics may be really just around the corner.Comment: 10 pages, version to be published in Physics Letters

DTI based structural damage characterization for Disorders of Consciousness

peer reviewe

S, T, U parameters in SU(3)C×SU(3)L×U(1)SU(3)_C\times SU(3)_L\times U(1) model with right-handed neutrinos

The S, T, U parameters in the $SU(3)_C\times SU(3)_L\times U(1)$ model with right -handed neutrinos are calculated. Explicit expressions for the oblique and Z - Z' mixing contributions are obtained. We show that the bilepton oblique contributions to S and T parameters are bounded : $- 0.085 \stackrel{<}{\sim} S \stackrel{<}{\sim} 0.05$ and $- 0.001 \stackrel{<}{\sim} T \stackrel{<}{\sim} 0.08$. The Z - Z' mixing contribution is positive and above 10%, but it will increase fastly with the higher Z' mass. %can be negative. The consequent mass splitting of the bilepton is derived and to be 15%. The limit on the mass of the neutral bilepton in this model is obtained.Comment: Latex, axodraw.sty used, 3 figures, 18 page

New Limits on Doubly Charged Bileptons from CERN LEP Data and the Search at Future Electron-Positron and Electron-Photon Colliders

We show that the accumulated LEP-II data taken at $\sqrt{s} =$ 130 to 206 GeV can establish more restrictive bounds on doubly charged bileptons couplings and masses than any other experiment so far. We also analyze the discovery potential of a prospective linear collider operating in both $e^+ e^-$ and $e \gamma$ modes.Comment: Revised version with 14 pages, 7 figures, RevTex. To appear in Phys. Rev.

Using magnetic resonance imaging to assess visual deficits : a review

PURPOSE: Over the last two decades, magnetic resonance imaging (MRI) has been widely used in neuroscience research to assess both structure and function in the brain in health and disease. With regard to vision research, prior to the advent of MRI, researchers relied on animal physiology and human post-mortem work to assess the impact of eye disease on visual cortex and connecting structures. Using MRI, researchers can non-invasively examine the effects of eye disease on the whole visual pathway, including the lateral geniculate nucleus, striate and extrastriate cortex. This review aims to summarise research using MRI to investigate structural, chemical and functional effects of eye diseases, including: macular degeneration, retinitis pigmentosa, glaucoma, albinism, and amblyopia. RECENT FINDINGS: Structural MRI has demonstrated significant abnormalities within both grey and white matter densities across both visual and non-visual areas. Functional MRI studies have also provided extensive evidence of functional changes throughout the whole of the visual pathway following visual loss, particularly in amblyopia. MR spectroscopy techniques have also revealed several abnormalities in metabolite concentrations in both glaucoma and age-related macular degeneration. GABA-edited MR spectroscopy on the other hand has identified possible evidence of plasticity within visual cortex. SUMMARY: Collectively, using MRI to investigate the effects on the visual pathway following disease and dysfunction has revealed a rich pattern of results allowing for better characterisation of disease. In the future MRI will likely play an important role in assessing the impact of eye disease on the visual pathway and how it progresses over time