The ulam Programming Language for Artificial Life.

Traditional digital computing demands perfectly reliable memory and processing, so programs can build structures once then use them forever-but such deterministic execution is becoming ever more costly in large-scale systems. By contrast, living systems, viewed as computations, naturally tolerate fallible hardware by repairing and rebuilding structures even while in use-and suggest ways to compute using massive amounts of unreliable, merely best-effort hardware. However, we currently know little about programming without deterministic execution, in architectures where traditional models of computation-and deterministic ALife models such as the Game of Life-need not apply.

Diffusion tensor spectroscopic imaging of the human brain in children and adults.

Purpose: We developed diffusion tensor spectroscopic imaging (DTSI), based on proton-echo-planar-spectroscopic imaging (PEPSI), and evaluated the feasibility of mapping brain metabolite diffusion in adults and children.

Methods: PRESS prelocalized DTSI at 3 Tesla (T) was performed using navigator-based correction of movement-related phase errors and cardiac gating with compensation for repetition time (TR) related variability in T saturation. Mean diffusivity (MD) and fractional anisotropy (FA) of total N-acetyl-aspartate (tNAA), total creatine (tCr), and total choline (tCho) were measured in eight adults (17-60 years) and 10 children (3-24 months) using b  = 1734 s/mm , 1 cc and 4.

Amygdala Regulation Following fMRI-Neurofeedback without Instructed Strategies.

Within the field of functional magnetic resonance imaging (fMRI) neurofeedback, most studies provide subjects with instructions or suggest strategies to regulate a particular brain area, while other neuro-/biofeedback approaches often do not. This study is the first to investigate the hypothesis that subjects are able to utilize fMRI neurofeedback to learn to differentially modulate the fMRI signal from the bilateral amygdala congruent with the prescribed regulation direction without an instructed or suggested strategy and apply what they learned even when feedback is no longer available. Thirty-two subjects were included in the analysis.

Spatially aggregated multiclass pattern classification in functional MRI using optimally selected functional brain areas.

In previous works, boosting aggregation of classifier outputs from discrete brain areas has been demonstrated to reduce dimensionality and improve the robustness and accuracy of functional magnetic resonance imaging (fMRI) classification. However, dimensionality reduction and classification of mixed activation patterns of multiple classes remain challenging. In the present study, the goals were (a) to reduce dimensionality by combining feature reduction at the voxel level and backward elimination of optimally aggregated classifiers at the region level, (b) to compare region selection for spatially aggregated classification using boosting and partial least squares regression methods and (c) to resolve mixed activation patterns using probabilistic prediction of individual tasks.

Enhancement of temporal resolution and BOLD sensitivity in real-time fMRI using multi-slab echo-volumar imaging.

In this study, a new approach to high-speed fMRI using multi-slab echo-volumar imaging (EVI) is developed that minimizes geometrical image distortion and spatial blurring, and enables nonaliased sampling of physiological signal fluctuation to increase BOLD sensitivity compared to conventional echo-planar imaging (EPI). Real-time fMRI using whole brain 4-slab EVI with 286 ms temporal resolution (4mm isotropic voxel size) and partial brain 2-slab EVI with 136 ms temporal resolution (4×4×6 mm(3) voxel size) was performed on a clinical 3 Tesla MRI scanner equipped with 12-channel head coil. Four-slab EVI of visual and motor tasks significantly increased mean (visual: 96%, motor: 66%) and maximum t-score (visual: 263%, motor: 124%) and mean (visual: 59%, motor: 131%) and maximum (visual: 29%, motor: 67%) BOLD signal amplitude compared with EPI.