The effect of race and supplementation on maternal and umbilical cord plasma folates.

OBJECTIVE: The purpose of this study is to test the hypothesis that race and supplementation affect the concentration and correlation of various folate species in maternal and umbilical cord blood. METHODS: This is a single-center, prospective, cross-sectional cohort of cord blood samples obtained from 40 uncomplicated term pregnancies as a pilot study, following a protocol approved by the Institutional Review Board. High performance liquid chromatography mass spectrometry quantitated the following concentrations in extracted plasma samples: 5-methyltetrahydrofolate (5MTHF), 5,10-methenyl-tetrahydrofolate (5,10-MeTHF), tetrahydrofolate (THF), and unmetabolized folic acid. RESULTS: Folate concentrations in the umbilical cord plasma were consistently higher than maternal samples for 5MTHF (p < .001), 5,10-MeTHF (p < .001), and THF (p < .001); cord blood folic acid levels, however, were lower than maternal samples (p < .03). While 5MTHF was the most prevalent folate, ratios comparing cord blood to maternal blood folates suggests a fourfold preponderance of THF in cord blood folate signature, a trend unchanged by supplementation. Prenatal supplementation increased the concentrations of 5MTHF, for both maternal (p < .01) and cord blood samples (p < .005). In comparison to the other two racial groups, African American 5MTHF concentration demonstrated a lower total folate concentration in both maternal samples and cord blood samples, in addition to a relatively blunted response to supplementation. A significantly positive correlation between maternal and cord blood 5MTHF concentration was noted in all three racial groups. Supplementation resulted in a positive correlation between maternal and cord blood 5MTHF concentrations (r = 0.85, p < .0001). CONCLUSIONS: 5MTHF is the most prevalent folate in both cord and maternal plasma, and race and supplementation primarily affect variations in maternal and fetal 5MTHF concentrations and their correlation with each other. However, the greater concentration of THF in cord blood relative to maternal blood offers preliminary insight into the importance of how folate metabolism differs in the specific context of fetal development and physiology, with greater emphasis on DNA synthesis and stability. Furthermore, supplementation appeared to not have as great an impact on African American maternal or cord blood folates, suggesting a variable benefit of current repletion strategies to certain subsets of the population. Future studies that further elucidate these differences and their impact on birth outcomes may help inform supplementation protocols that are more personalized, with greater efficacy in promoting positive perinatal outcomes.

Neural Representation and Causal Models in Motor Cortex.

Dorsal premotor (PMd) and primary motor (M1) cortices play a central role in mapping sensation to movement. Many studies of these areas have focused on correlation-based tuning curves relating neural activity to task or movement parameters, but the link between tuning and movement generation is unclear. We recorded motor preparatory activity from populations of neurons in PMd/M1 as macaque monkeys performed a visually guided reaching task and show that tuning curves for sensory inputs (reach target direction) and motor outputs (initial movement direction) are not typically aligned. We then used a simple, causal model to determine the expected relationship between sensory and motor tuning. The model shows that movement variability is minimized when output neurons (those that directly drive movement) have target and movement tuning that are linearly related across targets and cells. In contrast, for neurons that only affect movement via projections to output neurons, the relationship between target and movement tuning is determined by the pattern of projections to output neurons and may even be uncorrelated, as was observed for the PMd/M1 population as a whole. We therefore determined the relationship between target and movement tuning for subpopulations of cells defined by the temporal duration of their spike waveforms, which may distinguish cell types. We found a strong correlation between target and movement tuning for only a subpopulation of neurons with intermediate spike durations (trough-to-peak ∼350 µs after high-pass filtering), suggesting that these cells have the most direct role in driving motor output.SIGNIFICANCE STATEMENT This study focuses on how macaque premotor and primary motor cortices transform sensory inputs into motor outputs. We develop empirical and theoretical links between causal models of this transformation and more traditional, correlation-based "tuning curve" analyses. Contrary to common assumptions, we show that sensory and motor tuning curves for premovement preparatory activity do not generally align. Using a simple causal model, we show that tuning-curve alignment is only expected for output neurons that drive movement. Finally, we identify a physiologically defined subpopulation of neurons with strong tuning-curve alignment, suggesting that it contains a high concentration of output cells. This study demonstrates how analysis of movement variability, combined with simple causal models, can uncover the circuit structure of sensorimotor transformations.

High-Resolution, Non-Invasive Imaging of Upper Vocal Tract Articulators Compatible with Human Brain Recordings.

A complete neurobiological understanding of speech motor control requires determination of the relationship between simultaneously recorded neural activity and the kinematics of the lips, jaw, tongue, and larynx. Many speech articulators are internal to the vocal tract, and therefore simultaneously tracking the kinematics of all articulators is nontrivial--especially in the context of human electrophysiology recordings. Here, we describe a noninvasive, multi-modal imaging system to monitor vocal tract kinematics, demonstrate this system in six speakers during production of nine American English vowels, and provide new analysis of such data. Classification and regression analysis revealed considerable variability in the articulator-to-acoustic relationship across speakers. Non-negative matrix factorization extracted basis sets capturing vocal tract shapes allowing for higher vowel classification accuracy than traditional methods. Statistical speech synthesis generated speech from vocal tract measurements, and we demonstrate perceptual identification. We demonstrate the capacity to predict lip kinematics from ventral sensorimotor cortical activity. These results demonstrate a multi-modal system to non-invasively monitor articulator kinematics during speech production, describe novel analytic methods for relating kinematic data to speech acoustics, and provide the first decoding of speech kinematics from electrocorticography. These advances will be critical for understanding the cortical basis of speech production and the creation of vocal prosthetics.

Motor variability arises from a slow random walk in neural state.

Even well practiced movements cannot be repeated without variability. This variability is thought to reflect "noise" in movement preparation or execution. However, we show that, for both professional baseball pitchers and macaque monkeys making reaching movements, motor variability can be decomposed into two statistical components, a slowly drifting mean and fast trial-by-trial fluctuations about the mean. The preparatory activity of dorsal premotor cortex/primary motor cortex neurons in monkey exhibits similar statistics. Although the neural and behavioral drifts appear to be correlated, neural activity does not account for trial-by-trial fluctuations in movement, which must arise elsewhere, likely downstream. The statistics of this drift are well modeled by a double-exponential autocorrelation function, with time constants similar across the neural and behavioral drifts in two monkeys, as well as the drifts observed in baseball pitching. These time constants can be explained by an error-corrective learning processes and agree with learning rates measured directly in previous experiments. Together, these results suggest that the central contributions to movement variability are not simply trial-by-trial fluctuations but are rather the result of longer-timescale processes that may arise from motor learning.

The Neural Code for Motor Control in the Cerebellum and Oculomotor Brainstem.

A single extra spike makes a difference. Here, the size of the eye velocity in the initiation of smooth eye movements in the right panel depends on whether a cerebellar Purkinje cell discharges 3 (red), 4 (green), 5 (blue), or 6 (black) spikes in the 40-ms window indicated by the gray shading in the rasters on the left. Spike trains are rich in information that can be extracted to guide behaviors at millisecond time resolution or across longer time intervals. In sensory systems, the information usually is defined with respect to the stimulus. Especially in motor systems, however, it is equally critical to understand how spike trains predict behavior. Thus, our goal was to compare systematically spike trains in the oculomotor system with eye movement behavior on single movements. We analyzed the discharge of Purkinje cells in the floccular complex of the cerebellum, floccular target neurons in the brainstem, other vestibular neurons, and abducens neurons. We find that an extra spike in a brief analysis window predicts a substantial fraction of the trial-by-trial variation in the initiation of smooth pursuit eye movements. For Purkinje cells, a single extra spike in a 40 ms analysis window predicts, on average, 0.5 SDs of the variation in behavior. An optimal linear estimator predicts behavioral variation slightly better than do spike counts in brief windows. Simulations reveal that the ability of single spikes to predict a fraction of behavior also emerges from model spike trains that have the same statistics as the real spike trains, as long as they are driven by shared sensory inputs. We think that the shared sensory estimates in their inputs create correlations in neural spiking across time and across each population. As a result, one or a small number of spikes in a brief time interval can predict a substantial fraction of behavioral variation.

Sensory population decoding for visually guided movements.

We have used a new approach to study the neural decoding function that converts the population response in extrastriate area MT into estimates of target motion to drive smooth pursuit eye movement. Experiments reveal significant trial-by-trial correlations between the responses of MT neurons and the initiation of pursuit. The preponderance of significant correlations and the relatively low reduction in noise between MT and the behavioral output support the hypothesis of a sensory origin for at least some of the trial-by-trial variation in pursuit initiation. The finding of mainly positive MT-pursuit correlations, whether the target speed is faster or slower than the neuron's preferred speed, places strong constraints on the neural decoding computation. We propose that decoding is based on normalizing a weighted population vector of opponent motion responses; normalization comes from neurons uncorrelated with those used to compute the weighted population vector.

A neurally efficient implementation of sensory population decoding.

A sensory stimulus evokes activity in many neurons, creating a population response that must be "decoded" by the brain to estimate the parameters of that stimulus. Most decoding models have suggested complex neural circuits that compute optimal estimates of sensory parameters on the basis of responses in many sensory neurons. We propose a slightly suboptimal but practically simpler decoder. Decoding neurons integrate their inputs across 100 ms, incoming spikes are weighted by the preferred stimulus of the neuron of origin, and a local, cellular nonlinearity approximates divisive normalization without dividing explicitly. The suboptimal decoder includes two simplifying approximations. It uses estimates of firing rate across the population rather than computing the total population response, and it implements divisive normalization with local cellular mechanisms of single neurons rather than more complicated neural circuit mechanisms. When applied to the practical problem of estimating target speed from a realistic simulation of the population response in extrastriate visual area MT, the suboptimal decoder has almost the same accuracy and precision as traditional decoding models. It succeeds in predicting the precision and imprecision of motor behavior using a suboptimal decoding computation because it adds only a small amount of imprecision to the code for target speed in MT, which is itself imprecise.