Geochemistry of neritic carbonates of ODP Hole 134-831B

Drilling at Bougainville Guyot (Ocean Drilling Program Site 831), New Hebrides Island Arc during Leg 134 revealed that 727.5 m of carbonate overlies an andesite basement. The carbonate cap at Site 831 consists of 20 m of pelagic carbonate overlying 707.5 m of neritic carbonates. The neritic section consists of ~230 m of largely unaltered aragonite sediment that overlies ~497 m of totally calcitized limestone. The deeper portion of the calcitized interval has been pervasively altered by diagenesis. Prior to this study the age distribution of sediments at Bougainville Guyot was poorly known because age diagnostic fossils are sparsely and discontinuously distributed in the sequence. We have used Sr isotopes to provide temporal constraints on the deposition of carbonates at Site 831; these constraints are critical in reconstructing the vertical movement of Bougainville Guyot before its collision with the New Hebrides Island Arc. Overall, the chronostratigraphy of Bougainville Guyot can be subdivided into three intervals: (1) a Pleistocene interval (102.4 to 391.11 meters below sea floor [mbsf]); (2) a Miocene interval (410.31 to 669.53 mbsf); and (3) an Oligocene interval (678.83 to 727.50 mbsf). Strontium isotopic ages of samples increase with increasing depth in the carbonate sequence, except near the bottom of the sequence, where several samples exhibit a consistent reversed age vs. depth trend. Such age reversals are most likely the product of post-depositional rock-water interaction. Preliminary stable isotope data are consistent with diagenetic alteration in the marine and meteoric environments. Several abrupt decreases in d87Sr, and hence age, of sediments are recognized in the carbonate cap at Bougainville Guyot. These disconformities are most likely the product of subaerial exposure in response to relative sea-level fall. Indeed, Sr-isotope ages indicate that 2 to 9 m.y. of sediment deposition is missing across these d87Sr disconformities.

Submicrosecond pacemaker precision is behaviorally modulated: The gymnotiform electromotor pathway

What are the limits and modulators of neural precision? We address this question in the most regular biological oscillator known, the electric organ command nucleus in the brainstem of wave-type gymnotiform fish. These fish produce an oscillating electric field, the electric organ discharge (EOD), used in electrolocation and communication. We show here that the EOD precision, measured by the coefficient of variation (CV = SD/mean period) is as low as 2 × 10−4 in five species representing three families that range widely in species and individual mean EOD frequencies (70–1,250 Hz). Intracellular recording in the pacemaker nucleus (Pn), which commands the EOD cycle by cycle, revealed that individual Pn neurons of the same species also display an extremely low CV (CV = 6 × 10−4, 0.8 μs SD). Although the EOD CV can remain at its minimum for hours, it varies with novel environmental conditions, during communication, and spontaneously. Spontaneous changes occur as abrupt steps (250 ms), oscillations (3–5 Hz), or slow ramps (10–30 s). Several findings suggest that these changes are under active control and depend on behavioral state: mean EOD frequency and CV can change independently; CV often decreases in response to behavioral stimuli; and lesions of one of the two inputs to the Pn had more influence on CV than lesions of the other input.

Blind separation of auditory event-related brain responses into independent components

Averaged event-related potential (ERP) data recorded from the human scalp reveal electroencephalographic (EEG) activity that is reliably time-locked and phase-locked to experimental events. We report here the application of a method based on information theory that decomposes one or more ERPs recorded at multiple scalp sensors into a sum of components with fixed scalp distributions and sparsely activated, maximally independent time courses. Independent component analysis (ICA) decomposes ERP data into a number of components equal to the number of sensors. The derived components have distinct but not necessarily orthogonal scalp projections. Unlike dipole-fitting methods, the algorithm does not model the locations of their generators in the head. Unlike methods that remove second-order correlations, such as principal component analysis (PCA), ICA also minimizes higher-order dependencies. Applied to detected—and undetected—target ERPs from an auditory vigilance experiment, the algorithm derived ten components that decomposed each of the major response peaks into one or more ICA components with relatively simple scalp distributions. Three of these components were active only when the subject detected the targets, three other components only when the target went undetected, and one in both cases. Three additional components accounted for the steady-state brain response to a 39-Hz background click train. Major features of the decomposition proved robust across sessions and changes in sensor number and placement. This method of ERP analysis can be used to compare responses from multiple stimuli, task conditions, and subject states.

Running enhances neurogenesis, learning, and long-term potentiation in mice

Running increases neurogenesis in the dentate gyrus of the hippocampus, a brain structure that is important for memory function. Consequently, spatial learning and long-term potentiation (LTP) were tested in groups of mice housed either with a running wheel (runners) or under standard conditions (controls). Mice were injected with bromodeoxyuridine to label dividing cells and trained in the Morris water maze. LTP was studied in the dentate gyrus and area CA1 in hippocampal slices from these mice. Running improved water maze performance, increased bromodeoxyuridine-positive cell numbers, and selectively enhanced dentate gyrus LTP. Our results indicate that physical activity can regulate hippocampal neurogenesis, synaptic plasticity, and learning.

A universal scaling law between gray matter and white matter of cerebral cortex

Neocortex, a new and rapidly evolving brain structure in mammals, has a similar layered architecture in species over a wide range of brain sizes. Larger brains require longer fibers to communicate between distant cortical areas; the volume of the white matter that contains long axons increases disproportionally faster than the volume of the gray matter that contains cell bodies, dendrites, and axons for local information processing, according to a power law. The theoretical analysis presented here shows how this remarkable anatomical regularity might arise naturally as a consequence of the local uniformity of the cortex and the requirement for compact arrangement of long axonal fibers. The predicted power law with an exponent of 4/3 minus a small correction for the thickness of the cortex accurately accounts for empirical data spanning several orders of magnitude in brain sizes for various mammalian species, including human and nonhuman primates.

Knowledge-based analysis of microarray gene expression data by using support vector machines

We introduce a method of functionally classifying genes by using gene expression data from DNA microarray hybridization experiments. The method is based on the theory of support vector machines (SVMs). SVMs are considered a supervised computer learning method because they exploit prior knowledge of gene function to identify unknown genes of similar function from expression data. SVMs avoid several problems associated with unsupervised clustering methods, such as hierarchical clustering and self-organizing maps. SVMs have many mathematical features that make them attractive for gene expression analysis, including their flexibility in choosing a similarity function, sparseness of solution when dealing with large data sets, the ability to handle large feature spaces, and the ability to identify outliers. We test several SVMs that use different similarity metrics, as well as some other supervised learning methods, and find that the SVMs best identify sets of genes with a common function using expression data. Finally, we use SVMs to predict functional roles for uncharacterized yeast ORFs based on their expression data.

Spatially independent activity patterns in functional MRI data during the Stroop color-naming task

A method is given for determining the time course and spatial extent of consistently and transiently task-related activations from other physiological and artifactual components that contribute to functional MRI (fMRI) recordings. Independent component analysis (ICA) was used to analyze two fMRI data sets from a subject performing 6-min trials composed of alternating 40-sec Stroop color-naming and control task blocks. Each component consisted of a fixed three-dimensional spatial distribution of brain voxel values (a “map”) and an associated time course of activation. For each trial, the algorithm detected, without a priori knowledge of their spatial or temporal structure, one consistently task-related component activated during each Stroop task block, plus several transiently task-related components activated at the onset of one or two of the Stroop task blocks only. Activation patterns occurring during only part of the fMRI trial are not observed with other techniques, because their time courses cannot easily be known in advance. Other ICA components were related to physiological pulsations, head movements, or machine noise. By using higher-order statistics to specify stricter criteria for spatial independence between component maps, ICA produced improved estimates of the temporal and spatial extent of task-related activation in our data compared with principal component analysis (PCA). ICA appears to be a promising tool for exploratory analysis of fMRI data, particularly when the time courses of activation are not known in advance.