Frontiers in neuroscience
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Frontiers in neuroscience · Jan 2014
Single trial prediction of self-paced reaching directions from EEG signals.
Early detection of movement intention could possibly minimize the delays in the activation of neuroprosthetic devices. As yet, single trial analysis using non-invasive approaches for understanding such movement preparation remains a challenging task. We studied the feasibility of predicting movement directions in self-paced upper limb center-out reaching tasks, i.e., spontaneous movements executed without an external cue that can better reflect natural motor behavior in humans. ⋯ Comparison of the decoding accuracy across different frequency ranges (i.e., SCPs, delta, theta, alpha, and gamma) yielded the best accuracy using SCPs filtered between 0.1 to 1 Hz. Across all the subjects, including stroke subjects, the best selected features were obtained mostly from the fronto-parietal regions, hence consistent with previous neurophysiological studies on arm reaching tasks. In summary, we concluded that SCPs allow the possibility of single trial decoding of reaching directions at least 312.5 ms before onset of reach.
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Frontiers in neuroscience · Jan 2014
ReviewCerebral metabolism following traumatic brain injury: new discoveries with implications for treatment.
Because it is the product of glycolysis and main substrate for mitochondrial respiration, lactate is the central metabolic intermediate in cerebral energy substrate delivery. Our recent studies on healthy controls and patients following traumatic brain injury (TBI) using [6,6-(2)H2]glucose and [3-(13)C]lactate, along with cerebral blood flow (CBF) and arterial-venous (jugular bulb) difference measurements for oxygen, metabolite levels, isotopic enrichments and (13)CO2 show a massive and previously unrecognized mobilization of lactate from corporeal (muscle, skin, and other) glycogen reserves in TBI patients who were studied 5.7 ± 2.2 days after injury at which time brain oxygen consumption and glucose uptake (CMRO2 and CMRgluc, respectively) were depressed. By tracking the incorporation of the (13)C from lactate tracer we found that gluconeogenesis (GNG) from lactate accounted for 67.1 ± 6.9%, of whole-body glucose appearance rate (Ra) in TBI, which was compared to 15.2 ± 2.8% (mean ± SD, respectively) in healthy, well-nourished controls. ⋯ Use of a diagnostic to monitor BES to provide health care professionals with actionable data in providing nutritive formulations to fuel the body and brain and achieve exquisite glycemic control are discussed. In particular, the advantages of using inorganic and organic lactate salts, esters and other compounds are examined. To date, several investigations on brain-injured patients with intact hepatic and renal functions show that compared to dextrose + insulin treatment, exogenous lactate infusion results in normal glycemia.
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Frontiers in neuroscience · Jan 2014
An approach to identify microRNAs involved in neuropathic pain following a peripheral nerve injury.
Peripheral nerve injury alters the expression of hundreds of proteins in dorsal root ganglia (DRG). Targeting some of these proteins has led to successful treatments for acute pain, but not for sustained post-operative neuropathic pain. The latter may require targeting multiple proteins. ⋯ Bioinformatic analysis of how these miRs could affect the expression of some ion channels supports the view that, following a peripheral nerve injury, the increase of the seven miRs may contribute to the recovery from neuropathic pain while the decrease of four of them may contribute to the development of chronic neuropathic pain. The approach used resulted in the identification of a small number of potentially neuropathic pain relevant miRs. Additional studies are required to investigate whether manipulating the expression of the identified miRs in primary sensory neurons can prevent or ameliorate chronic neuropathic pain following peripheral nerve injuries.
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Frontiers in neuroscience · Jan 2014
High-resolution diffusion kurtosis imaging at 3T enabled by advanced post-processing.
Diffusion Kurtosis Imaging (DKI) is more sensitive to microstructural differences and can be related to more specific micro-scale metrics (e.g., intra-axonal volume fraction) than diffusion tensor imaging (DTI), offering exceptional potential for clinical diagnosis and research into the white and gray matter. Currently DKI is acquired only at low spatial resolution (2-3 mm isotropic), because of the lower signal-to-noise ratio (SNR) and higher artifact level associated with the technically more demanding DKI. Higher spatial resolution of about 1 mm is required for the characterization of fine white matter pathways or cortical microstructure. ⋯ We demonstrated the feasibility of high-quality, high-resolution DKI and its potential for delineating highly myelinated fiber pathways in the motor cortex. REMATCH performs robustly even at the low SNR level of high-resolution DKI, where standard EC and motion correction failed (i.e., produced incorrectly aligned images) and thus biased the diffusion model fit. We showed that the combination of REMATCH and msPOAS increased the contrast between gray and white matter in mean kurtosis (MK) maps by about 35% and at the same time preserves the original distribution of MK values, whereas standard Gaussian smoothing strongly biases the distribution.
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Frontiers in neuroscience · Jan 2014
An efficient automated parameter tuning framework for spiking neural networks.
As the desire for biologically realistic spiking neural networks (SNNs) increases, tuning the enormous number of open parameters in these models becomes a difficult challenge. SNNs have been used to successfully model complex neural circuits that explore various neural phenomena such as neural plasticity, vision systems, auditory systems, neural oscillations, and many other important topics of neural function. Additionally, SNNs are particularly well-adapted to run on neuromorphic hardware that will support biological brain-scale architectures. ⋯ A performance analysis comparing the GPU-accelerated implementation to a single-threaded central processing unit (CPU) implementation was carried out and showed a speedup of 65× of the GPU implementation over the CPU implementation, or 0.35 h per generation for GPU vs. 23.5 h per generation for CPU. Additionally, the parameter value solutions found in the tuned SNN were studied and found to be stable and repeatable. The automated parameter tuning framework presented here will be of use to both the computational neuroscience and neuromorphic engineering communities, making the process of constructing and tuning large-scale SNNs much quicker and easier.