Cerebral hemodynamic response to faces and emotions in infants at high risk for autism

Thesis (Ph. D.)--Harvard-MIT Program in Health Sciences and Technology, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 109-144).The incidence of autism spectrum disorders (ASD) has risen alarmingly in the United States, and is now thought to affect approximately 1 in 110 live births. Early diagnosis and intervention is the only treatment proven effective in cases of autism, however the behavioral tests currently available cannot make this diagnosis until at least two years of age. A lack of normal attention to faces and abnormal face processing is a cognitive deficit common to nearly all individuals with autism spectrum disorder, and this deficit is likely present from a very early age. The primary goal of this dissertation is therefore to characterize the specific neural response of face processing in infants with near-infrared spectroscopy (NIRS), and to then apply these measures to the study of abnormal face processing in infants at high risk for autism. In order to achieve these objectives, the work described herein aims to: 1) characterize the hemodynamic response to faces in normal infants at six months of age as measured by the Hitachi ETG-4000 functional Near-Infrared Spectroscopy (fNIRS) system; 2) Simultaneously measure orbitofrontal hemodynamic responses to social/emotional engagement and the response to faces in infants at high risk for autism as compared to low risk controls; and 3) Utilize a novel method of condition-related component selection and classification to identify waveforms associated with face and emotion processing in 6-7-month-old infants at high risk for ASD, and matched low-risk controls. Our results indicate similarities of response waveforms, but differences in both the spatial distribution, magnitude, and timing of oxy-hemoglobin and deoxy-hemoglobin responses between groups. Our findings represent the first identification of neuroimaging markers of a functional endophenotype at six months of age that may be associated with high risk of ASD. These results support a model of altered frontal lobe structure through evidence of altered hemodynamic response and/or functional activity in the high risk infant group, and these changes may, in turn, contribute to the development of ASD in specific individuals.by Sharon Elizabeth Fox.Ph.D

Precise measurements of radio-frequency magnetic susceptibility in (anti)ferromagnetic materials

Dynamic magnetic susceptibility, $\chi$, was studied in several intermetallic materials exhibiting ferromagnetic, antiferromagnetic and metamagnetic transitions. Precise measurements by using a 14 MHz tunnel diode oscillator (TDO) allow detailed insight into the field and temperature dependence of $\chi$. In particular, local moment ferromagnets show a sharp peak in $\chi(T)$ near the Curie temperature, $T_c$. The peak amplitude decreases and shifts to higher temperatures with very small applied dc fields. Anisotropic measurements of CeVSb$_3$ show that this peak is present provided the magnetic easy axis is aligned with the excitation field. In a striking contrast, small moment, itinerant ferromagnets (i.e., ZrZn$_2$) show a broad maximum in $\chi(T)$ that responds differently to applied field. We believe that TDO measurements provide a very sensitive way to distinguish between local and itinerant moment magnetic orders. Local moment antiferromagnets do not show a peak at the N\'eel temperature, $T_N$, but only a sharp decrease of $\chi$ below $T_N$ due to the loss of spin-disorder scattering changing the penetration depth of the ac excitation field. Furthermore, we show that the TDO is capable of detecting changes in spin order as well as metamagnetic transitions. Finally, critical scaling of $\chi(T,H)$ in the vicinity of $T_C$ is discussed in CeVSb$_3$ and CeAgSb$_2$

Microclimate affects landscape level persistence in the British Lepidoptera

Microclimate has been known to drive variation in the distribution and abundance of insects for some time. Until recently however, quantification of microclimatic effects has been limited by computing constraints and the availability of fine-scale biological data. Here, we tested fine-scale patterns of persistence/extinction in butterflies and moths against two computed indices of microclimate derived from Digital Elevation Models: a summer solar index, representing fine-scale variation in temperature, and a topographic wetness index, representing fine-scale variation in moisture availability. We found evidence of microclimate effects on persistence in each of four 20 × 20 km British landscapes selected for study (the Brecks, the Broads, Dartmoor, and Exmoor). Broadly, local extinctions occurred more frequently in areas with higher minimum or maximum solar radiation input, while responses to wetness varied with landscape context. This negative response to solar radiation is consistent with a response to climatic warming, wherein grid squares with particularly high minimum or maximum insolation values provided an increasingly adverse microclimate as the climate warmed. The variable response to wetness in different landscapes may have reflected spatially variable trends in precipitation. We suggest that locations in the landscape featuring cooler minimum and/or maximum temperatures could act as refugia from climatic warming, and may therefore have a valuable role in adapting conservation to climatic change

Core Mass Estimates in Strong Lensing Galaxy Clusters: A Comparison between Masses Obtained from Detailed Lens Models, Single-halo Lens Models, and Einstein Radii

The core mass of galaxy clusters is both an important anchor of the radial mass distribution profile and a probe of structure formation. With thousands of strong lensing galaxy clusters being discovered by current and upcoming surveys, timely, efficient, and accurate core mass estimates are needed. We assess the results of two efficient methods to estimate the core mass of strong lensing clusters: the mass enclosed by the Einstein radius (M(<θE), where θE is approximated from arc positions, and a single-halo lens model (MSHM), compared with measurements from publicly available detailed lens models (MDLM) of the same clusters. We use data from the Sloan Giant Arc Survey, the Reionization Lensing Cluster Survey, the Hubble Frontier Fields, and the Cluster Lensing and Supernova Survey with Hubble. We find a scatter of 18.1% (8.2%) with a bias of −7.1% (1.0%) between ${M}_{\mathrm{corr}}\left(\lt {\theta }_{\mathrm{arcs}}\right)$ (MSHM) and MDLM. Last, we compare the statistical uncertainties measured in this work to those from simulations. This work demonstrates the successful application of these methods to observational data. As the effort to efficiently model the mass distribution of strong lensing galaxy clusters continues, we need fast, reliable methods to advance the field

Dirac Gauginos and Kinetic Mixing

We present formulae for the calculation of Dirac gaugino masses at leading order in the supersymmetry breaking scale using the methods of analytic continuation in superspace and demonstrate a link with kinetic mixing, even for non-abelian gauginos. We illustrate the result through examples in field and string theory. We discuss the possibility that the singlet superfield that gives the U(1) gaugino a Dirac mass may be a modulus, and some consequences of the D-term coupling to the scalar component. We give examples of possible effects in colliders and astroparticle experiments if the modulus scalar constitutes decaying dark matter.Comment: 17 pages, no figures. Published version, one reference adde

A Novel Sparse Graphical Approach for Multimodal Brain Connectivity Inference

International audienceDespite the clear potential benefits of combining fMRI and diffusion MRI in learning the neural pathways that underlie brain functions, little methodological progress has been made in this direction. In this paper, we propose a novel multimodal integration approach based on sparse Gaussian graphical model for estimating brain connectivity. Casting functional connectivity estimation as a sparse inverse covariance learning problem, we adapt the level of sparse penalization on each connection based on its anatomical capacity for functional interactions. Functional connections with little anatomical support are thus more heavily penalized. For validation, we showed on real data collected from a cohort of 60 subjects that additionally modeling anatomical capacity significantly increases subject consistency in the detected connection patterns. Moreover, we demonstrated that incorporating a connectivity prior learned with our multimodal connectivity estimation approach improves activation detection

Metabolic drift in the aging brain.

Brain function is highly dependent upon controlled energy metabolism whose loss heralds cognitive impairments. This is particularly notable in the aged individuals and in age-related neurodegenerative diseases. However, how metabolic homeostasis is disrupted in the aging brain is still poorly understood. Here we performed global, metabolomic and proteomic analyses across different anatomical regions of mouse brain at different stages of its adult lifespan. Interestingly, while severe proteomic imbalance was absent, global-untargeted metabolomics revealed an energymetabolic drift or significant imbalance in core metabolite levels in aged mouse brains. Metabolic imbalance was characterized by compromised cellular energy status (NAD decline, increased AMP/ATP, purine/pyrimidine accumulation) and significantly altered oxidative phosphorylation and nucleotide biosynthesis and degradation. The central energy metabolic drift suggests a failure of the cellular machinery to restore metabostasis (metabolite homeostasis) in the aged brain and therefore an inability to respond properly to external stimuli, likely driving the alterations in signaling activity and thus in neuronal function and communication

Chaos and Quantum-Classical Correspondence via Phase Space Distribution Functions

Quantum-classical correspondence in conservative chaotic Hamiltonian systems is examined using a uniform structure measure for quantal and classical phase space distribution functions. The similarities and differences between quantum and classical time-evolving distribution functions are exposed by both analytical and numerical means. The quantum-classical correspondence of low-order statistical moments is also studied. The results shed considerable light on quantum-classical correspondence.Comment: 16 pages, 5 figures, to appear in Physical Review

Dirac Gauginos in General Gauge Mediation

We extend the formulation by Meade, Seiberg and Shih of general gauge mediation of supersymmetry breaking to include Dirac masses for the gauginos. These appear through mixing of the visible sector gauginos with additional states in adjoint representations. We illustrate the method by reproducing the existing results in the literature for the gaugino and sfermion masses when preserving R-symmetry. We then explain how the generation of same sign masses for the two propagating degrees of freedom in the adjoint scalars can be achieved. We end by commenting on the use of the formalism for describing U(1) mixing.Comment: 22 pages, no figures. V2: minor corrections