50 research outputs found

    Higher Frequency Network Activity Flow Predicts Lower Frequency Node Activity in Intrinsic Low-Frequency BOLD Fluctuations

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    The brain remains electrically and metabolically active during resting conditions. The low-frequency oscillations (LFO) of the blood oxygen level-dependent (BOLD) signal of functional magnetic resonance imaging (fMRI) coherent across distributed brain regions are known to exhibit features of this activity. However, these intrinsic oscillations may undergo dynamic changes in time scales of seconds to minutes during resting conditions. Here, using wavelet-transform based timefrequency analysis techniques, we investigated the dynamic nature of default-mode networks from intrinsic BOLD signals recorded from participants maintaining visual fixation during resting conditions. We focused on the default-mode network consisting of the posterior cingulate cortex (PCC), the medial prefrontal cortex (mPFC), left middle temporal cortex (LMTC) and left angular gyrus (LAG). The analysis of the spectral power and causal flow patterns revealed that the intrinsic LFO undergo significant dynamic changes over time. Dividing the frequency interval 0 to 0.25 Hz of LFO into four intervals slow- 5 (0.01–0.027 Hz), slow-4 (0.027–0.073 Hz), slow-3 (0.073–0.198 Hz) and slow-2 (0.198–0.25 Hz), we further observed significant positive linear relationships of slow-4 in-out flow of network activity with slow-5 node activity, and slow-3 in-out flow of network activity with slow-4 node activity. The network activity associated with respiratory related frequency (slow- 2) was found to have no relationship with the node activity in any of the frequency intervals. We found that the net causal flow towards a node in slow-3 band was correlated with the number of fibers, obtained from diffusion tensor imaging (DTI) data, from the other nodes connecting to that node. These findings imply that so-called resting state is not ‘entirely’ at rest, the higher frequency network activity flow can predict the lower frequency node activity, and the network activity flow can reflect underlying structural connectivity

    Is the Brain’s Inertia for Motor Movements Different for Acceleration and Deceleration?

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    The brain’s ability to synchronize movements with external cues is used daily, yet neuroscience is far from a full understanding of the brain mechanisms that facilitate and set behavioral limits on these sequential performances. This functional magnetic resonance imaging (fMRI) study was designed to help understand the neural basis of behavioral performance differences on a synchronizing movement task during increasing (acceleration) and decreasing (deceleration) metronome rates. In the MRI scanner, subjects were instructed to tap their right index finger on a response box in synchrony to visual cues presented on a display screen. The tapping rate varied either continuously or in discrete steps ranging from 0.5 Hz to 3 Hz. Subjects were able to synchronize better during continuously accelerating rhythms than in continuously or discretely decelerating rhythms. The fMRI data revealed that the precuneus was activated more during continuous deceleration than during acceleration with the hysteresis effect significant at rhythm rates above 1 Hz. From the behavioral data, two performance measures, tapping rate and synchrony index, were derived to further analyze the relative brain activity during acceleration and deceleration of rhythms. Tapping rate was associated with a greater brain activity during deceleration in the cerebellum, superior temporal gyrus and parahippocampal gyrus. Synchrony index was associated with a greater activity during the continuous acceleration phase than during the continuous deceleration or discrete acceleration phases in a distributed network of regions including the prefrontal cortex and precuneus. These results indicate that the brain’s inertia for movement is different for acceleration and deceleration, which may have implications in understanding the origin of our perceptual and behavioral limits

    Awareness regarding radiation knowledge among clinicians practicing in Bharatpur, Nepal

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    Missed lung lesions are one of the most frequent causes of malpractice issues, caused by several reasons; among them suboptimal radiography. When radiographers interpret acquired images of a patient, an acceptance or rejection must be decided. When a retake is required, radiographers need to know how to improve the image quality. Improvements in image quality properties as contrast, sharpness and noise often lead to improved perception, which in turn should enable more information to the observer and also allow computer-assisted detection (CAD) to be more successful

    Detection of Methicillin Resistant Staphylococcus aureus and Determination of Minimum Inhibitory Concentration of Vancomycin for Staphylococcus aureus Isolated from Pus/Wound Swab Samples of the Patients Attending a Tertiary Care Hospital in Kathmandu, Ne

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    The present study was conducted to evaluate the performance of cefoxitin disc diffusion method and oxacillin broth microdilution method for detection of methicillin resistant S. aureus (MRSA), taking presence of mecA gene as reference. In addition, inducible clindamycin resistance and beta-lactamase production were studied and minimum inhibitory concentration (MIC) of vancomycin for S. aureus isolates was determined. A total of 711 nonrepeated pus/wound swab samples from different anatomic locations were included in the study. The Staphylococcus aureus was identified on the basis of colony morphology, Gram's stain, and biochemical tests. A total of 110 (15.47%) S. aureus isolates were recovered, of which 39 (35.50%) isolates were identified as MRSA by cefoxitin disc diffusion method. By oxacillin broth microdilution method, 31.82% of the Staphylococcus aureus isolates were found to be MRSA. However, mecA gene was present in only 29.1% of the isolates. Further, beta-lactamase production was observed in 71.82% of the isolates, while inducible clindamycin resistance was found in 10% of S. aureus isolates. The MIC value of vancomycin for S. aureus ranged from 0.016 g/mL to 1 g/mL. On the basis of the absolute sensitivity (100%), both phenotypic methods could be employed for routine diagnosis of MRSA in clinical microbiology laboratory; however cefoxitin disc diffusion could be preferred over MIC method considering time and labour factor

    Design and implementation of an affordable, public sector electronic medical record in rural Nepal

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    IntroductionGlobally, electronic medical records are central to the infrastructure of modern healthcare systems. Yet the vast majority of electronic medical records have been designed for resource-rich environments and are not feasible in settings of poverty. Here we describe the design and implementation of an electronic medical record at a public sector district hospital in rural Nepal, and its subsequent expansion to an additional public sector facility.DevelopmentThe electronic medical record was designed to solve for the following elements of public sector healthcare delivery: 1) integration of the systems across inpatient, surgical, outpatient, emergency, laboratory, radiology, and pharmacy sites of care; 2) effective data extraction for impact evaluation and government regulation; 3) optimization for longitudinal care provision and patient tracking; and 4) effectiveness for quality improvement initiatives.ApplicationFor these purposes, we adapted Bahmni, a product built with open-source components for patient tracking, clinical protocols, pharmacy, laboratory, imaging, financial management, and supply logistics. In close partnership with government officials, we deployed the system in February of 2015, added on additional functionality, and iteratively improved the system over the following year. This experience enabled us then to deploy the system at an additional district-level hospital in a different part of the country in under four weeks. We discuss the implementation challenges and the strategies we pursued to build an electronic medical record for the public sector in rural Nepal.DiscussionOver the course of 18 months, we were able to develop, deploy and iterate upon the electronic medical record, and then deploy the refined product at an additional facility within only four weeks. Our experience suggests the feasibility of an integrated electronic medical record for public sector care delivery even in settings of rural poverty

    Temporal-order judgment of audiovisual events involves network activity between parietal and prefrontal cortices. Brain Connect. 3, 536–545. doi: 10.1089/brain.2013.0163

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    Abstract Our perception of the temporal order of everyday external events depends on the integrated sensory information in the brain. Our understanding of the brain mechanism for temporal-order judgment (TOJ) of unisensory events, particularly in the visual domain, is advanced. In case of multisensory events, however, there are unanswered questions. Here, by using physically synchronous and asynchronous auditory-visual events in functional magnetic resonance imaging (fMRI) experiments, we identified the brain network that is associated with the perception of the temporal order of multisensory events. The activation in the right temporo-parietal junction was modulated by the perception of asynchronous audiovisual events. During this perception of temporal order, the right dorsolateral prefrontal cortex coordinated activity with the right temporo-parietal and the left inferior parietal cortices. These results suggest that the TOJ in the multisensory domain underlies a network activity between parietal and prefrontal cortices unlike the regional activity in the right temporo-parietal junction in the unisensory visual domain

    Time-varying nature of power in 6 sample participants.

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    <p>Slow-4 frequency band activity fluctuates significantly (A–F). Overall, there is a significant power variation across different time windows, each of size 38 sec in the slow-4 band (0.0027–0.073 Hz) for 11 out of 17 participants.</p

    DTI fiber tracts from a representative participant.

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    <p>Fiber pathways between: (A) PCC (R1) and mPFC (R2), (B) PCC (R1) and LMTC (R3), and (C) PCC (R1) and LAG (R4).</p

    Voxel-averaged time-series for four brain regions and correlation coefficients between pairs.

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    <p>(A) Times-series averaged over all the trials for four nodes, and (B) positive significant correlation coefficients (c) were found to be significantly varying from one window to next.</p
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