92 research outputs found
How often we need to measure brain natriuretic peptide (BNP) serum levels in patients hospitalised for heart failure?
No full textQT dispersion on ECG Holter monitoring and risk of ventricular arrhythmias in patients with dilated cardiomyopathy
No full textAtlas for RecobundlesX
No full textMulti-atlas bundle segmentation
This data is made to be used with the following script: scil_recognize_multi_bundles.py
This script is in fact a multi-atlas, multi-parameters version of Garyfallidis et al. (2018) with labels fusions. We name this algorithm RecobundlesX. If you have any questions, email [email protected].
Garyfallidis, Eleftherios, et al. "Recognition of white matter bundles using local and global streamline-basedregistration and clustering." NeuroImage 170 (2018): 283-295.</p
Test Bundles for DIPY
No full textTwo uncinate left bundles for the BundleWarp[1] tutorial in DIPY[2].
[1] Chandio, B. Q., Olivetti, E., Romero, D., Harezlak, J., & Garyfallidis, E. (2023). BundleWarp, streamline-based nonlinear registration of white matter tracts. bioRxiv, 2023-01.
[2] Garyfallidis, E., Brett, M., Amirbekian, B., Rokem, A., Van Der Walt, S., Descoteaux, M., ... & Dipy Contributors. (2014). Dipy, a library for the analysis of diffusion MRI data. Frontiers in neuroinformatics, 8, 8.</p
p-values for all combinations between EEG data and structural data.
No full textSmallest value was 0.06.</p
Diagram showing QA calculation from a spin distribution function.
No full text<p>The red outer contour represents the spin distribution function calculated by generalized q-sampling imaging, whereas the sphere at the center is the isotropic component estimated by its minimum value. A QA value is defined for each peak orientation, and it serves as an index to differentiate less salient peaks and to selectively remove them. (Eleftherios Garyfallidis, "Towards an accurate brain tractography", PhD thesis, University of Cambridge, 2012, use with permission).</p
Reduced Brain White Matter Integrity in Trichotillomania
No full textContext: trichotillomania is an Axis I disorder characterized by repetitive, pathological hair pulling. Objective: to assess the integrity of white matter tracts in subjects with the disorder. Design: between-group comparison using permutation cluster analysis, with stringent correction for multiple comparisons. Setting: academic psychiatry department. Participants: eighteen volunteers meeting DSM-IV criteria for trichotillomania and 19 healthy control subjects. Main outcome measures: fractional anisotropy (measured using diffusion tensor imaging), trichotillomania disease severity (Massachusetts General Hospital Hairpulling Scale score), and dysphoria (Montgomery-Asberg Depression Rating Scale score). Results: subjects with trichotillomania exhibited significantly reduced fractional anisotropy in anterior cingulate, presupplementary motor area, and temporal cortices. Fractional anisotropy did not correlate significantly with trichotillomania disease severity or depressive mood scores.Conclusions: these data implicate disorganization of white matter tracts involved in motor habit generation and suppression, along with affective regulation, in the pathophysiology of trichotillomania.</p
A Lagrangian study of globally emitted aviation NOx and associated short-term O3 radiative forcing effects
No full textThe resilient growth of air travel demands a comprehensive understanding of the climate effects from aviation emissions. The current level of knowledge of the environmental repercussions of CO2 emissions is considerably higher than that of non-CO2 emissions, which includes nitrogen oxides (NOx), sulfur oxides (SOx), other aerosols like black carbon (BC), water vapor and contrails. Aircraft NOx emissions not only possess a high degree of uncertainty because of the non-linearity of the NOx – O3 chemistry, but are also responsible for producing the second strongest net warming effect out of all non-CO2 climate forcers from aviation, right after contrails [1]. This study employs global-scale simulations to characterize the transport patterns of nitrogen oxides and assess their climate effects across several regions (North America, South America, Africa, Eurasia and Australasia) from January to March and July to September in 2014. Radiative forcing effects from the short-term increase in O3, which are triggered by NOx emissions, are estimated. These emissions, which are introduced at a typical cruising altitude, are modelled as Lagrangian air parcels that are transported within the ECHAM5/MESSy Atmospheric Chemistry (EMAC) model [2]. In order to summarize the dynamical and radiative forcing characteristics of more than 10,000 simulated trajectories, a clustering approach with an adapted distance metric is applied. The method itself is an unsupervised machine learning algorithm, called QuickBundles [3], that is most commonly used in the field of neuroscience. A strong seasonal dependence is found for the contribution of NOx emissions to O3. In terms of residence times, NOx emitted in Northern regions resides mainly in the upper mid-latitudes while those initiated in the South remain mostly in the Tropics. Due to pronounced zonal jets, the location of emission does not necessarily correspond to the region that will be most affected, i.e., an emission starting in N. America in July will induce the greatest warming in Europe.[1] Lee, D.S., Fahey, D.W., Skowron, A., Allen, M.R., Burkhardt, U., Chen, Q., Doherty, S.J., Freeman, S., Forster, P.M., Fuglestvedt, J., Gettelman, A., De León, R.R., Lim, L.L., Lund, M.T., Millar, R.J., Owen, B., Penner, J.E., Pitari, G., Prather, M.J., Sausen, R., Wilcox, L.J.: The contribution of global aviation to anthropogenic climate forcing for 2000 to 2018, Atmospheric Environment, Volume 244, 2021, 117834, ISSN 1352-2310, https://doi.org/10.1016/j.atmosenv.2020.117834.[2] Jöckel, P., Kerkweg, A., Pozzer, A., Sander, R., Tost, H., Riede, H., Baumgaertner, A., Gromov, S., Kern, B., Development cycle 2 of the Modular Earth Submodel System (MESSy2), Geoscientific Model Development, 3, 717-752, doi: 10.5194/gmd-3-717-2010, 2010.[3] Garyfallidis, E., Brett, M., Correia, M. M., Williams, G. B., Nimmo-Smith, I. QuickBundles, a Method for Tractography Simplification. Frontiers in neuroscience, 6, 175. https://doi.org/10.3389/fnins.2012.00175, 2012.Aircraft Noise and Climate Effect
Metric scores used in <b>Multi-scale V-net architecture with deep feature CRF layers for brain extraction</b>
No full textPark et al. investigate adding major improvements to a base segmentation model to increase robustness and go beyond the provided ground truth segmentation. Results indicate an overall improvement in the metrics and provide further discussion towards improving brain extraction models. This is the data that were used in creating the table and the plots.</p
Semi-Automatic Segmentation of Optic Radiations and LGN, and Their Relationship to EEG Alpha Waves.
No full textAt rest, healthy human brain activity is characterized by large electroencephalography (EEG) fluctuations in the 8-13 Hz range, commonly referred to as the alpha band. Although it is well known that EEG alpha activity varies across individuals, few studies have investigated how this may be related to underlying morphological variations in brain structure. Specifically, it is generally believed that the lateral geniculate nucleus (LGN) and its efferent fibres (optic radiation, OR) play a key role in alpha activity, yet it is unclear whether their shape or size variations contribute to its inter-subject variability. Given the widespread use of EEG alpha in basic and clinical research, addressing this is important, though difficult given the problems associated with reliably segmenting the LGN and OR. For this, we employed a multi-modal approach and combined diffusion magnetic resonance imaging (dMRI), functional magnetic resonance imaging (fMRI) and EEG in 20 healthy subjects to measure structure and function, respectively. For the former, we developed a new, semi-automated approach for segmenting the OR and LGN, from which we extracted several structural metrics such as volume, position and diffusivity. Although these measures corresponded well with known morphology based on previous post-mortem studies, we nonetheless found that their inter-subject variability was not significantly correlated to alpha power or peak frequency (p >0.05). Our results therefore suggest that alpha variability may be mediated by an alternative structural source and our proposed methodology may in general help in better understanding the influence of anatomy on function such as measured by EEG or fMRI
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