Parcellation Method Research Articles

Sensitivity analysis of fine dust spreading from litter in poultry houses

Poultry houses are one of the major sources of agricultural particulate matter (PM) emissions into the environment. Litter properties, poultry activities, and indoor climate of poultry houses are the main parameters that influence the dust emission, but precise quantitative effects and their interactions are hardly known. The objective of this study was to gain quantitative insight into the dust spreading process from poultry litter by using the discrete parcel method (DPM) and global sensitivity analysis (GSA).The dust spreading process includes the creep, suspension, and saltation of fine particles was simulated with the DPM within the aerial region close to the litter. The simulations proved that a collision between an object and the litter should happen to release particles from the litter into the air. It was indicated that the released particles with a diameter ≥30 μm and particle density ≥1400 kg m −3 were deposited on the litter for all tested air velocities. Also, particles <10 μm diameter remained suspended for airflow velocities >1.5 m s −1 . The GSA indicated that the creep process has a direct relation with the airflow velocity and coefficient of restitution and an inverse relation with the particle density and the coefficient of friction. Overall, it was shown that the DPM is a suitable numerical technique to simulate the dust spreading process, and hence, it can be recommended for future studies to examine this process on larger scales. • The DPM is a suitable numerical technique to simulate the dust spreading process. • A collision with sufficient energy should happen to release dust from litter into the indoor air. • The fine dust release process is very sensitive to the litter density and the airflow velocity. • The fine dust with a diameter smaller than 10 μm will not settle in the presence of airflow. • The creep process is less sensitive to the coefficient of restitution between litter particles.

Particle-based coarse-grained approach for simulating dry powder inhaler

Drug delivery via dry powder inhaler (DPI) is a complex process affected by multiple factors involving gas and particles. The performance of a carrier-based formulation depends on the release of active pharmaceutical ingredient (API) particles, typically characterized by fine particle fraction (FPF) and dispersion fraction (DF). Computational Fluid Dynamics coupled with Discrete Element Method (CFD-DEM) can capture relevant gas and particle interactions but is computationally expensive, especially when tracking all carrier and API particles. This study assessed the efficacy of two coarse-grained CFD-DEM approaches, the Discrete Parcel Method and the representative particle approach, through highly-resolved CFD-DEM simulations. The representative particle approach simulates all carrier particles and a subset of API particles, whereas the Discrete Parcel Method tracks parcels representing a specified number of carrier or API particles. Both approaches are viable for a small carrier-API size ratio which requires modest degrees of coarse-graining, but the Discrete Parcel Method showed limitations for a large carrier-API size ratio. The representative particle approach can approximate CFD-DEM results with reasonable accuracies when simulations include at least 10 representative API particles per carrier. Using the representative particle approach, we probed powder characteristics that could affect FPF and DF in a model problem and correlated these fractions with the maximum carrier-API cohesive force per unit mass of API particles.

Identifying and characterizing different stages toward Alzheimer's disease using ordered core features and machine learning

Based on the joint HCPMMP parcellation method we developed before, which divides the cortical brain into 360 regions, the concept of ordered core features (OCF) is first proposed to reveal the functional brain connectivity relationship among different cohorts of Alzheimer's disease (AD), late mild cognitive impairment (LMCI), early mild cognitive impairment (EMCI) and healthy controls (HC). A set of core network features that change significantly under the specifically progressive relationship were extracted and used as supervised machine learning classifiers. The network nodes in this set mainly locate in the frontal lobe and insular, forming a narrow band, which are responsible for cognitive impairment as suggested by previous finding. By using these features, the accuracy ranged from 86.0% to 95.5% in binary classification between any pair of cohorts, higher than 70.1%–91.0% when using all network features. In multi-group classification, the average accuracy was 75% or 78% for HC, EMCI, LMCI or EMCI, LMCI, AD against baseline of 33%, and 53.3% for HC, EMCI, LMCI and AD against baseline of 25%. In addition, the recognition rate was lower when combining EMCI and LMCI patients into one group of mild cognitive impairment (MCI) for classification, suggesting that there exists a big difference between early and late MCI patients. This finding supports the EMCI/LMCI inclusion criteria introduced by ADNI based on neuropsychological assessments.

Volumetric parcellation of the cardiac right ventricle for regional geometric and functional assessment.

3D echocardiography is an increasingly popular tool for assessing cardiac remodelling in the right ventricle (RV). It allows quantification of the cardiac chambers without any geometric assumptions, which is the main weakness of 2D echocardiography. However, regional quantification of geometry and function is limited by the lower spatial and temporal resolution and the scarcity of identifiable anatomical landmarks, especially within the ventricular cavity. We developed a technique for regionally assessing the volume of 3 relevant RV volumetric regions: apical, inlet and outflow. The proposed parcellation method is based on the geodesic distances to anatomical landmarks that are easily identifiable in the images: the apex and the tricuspid and pulmonary valves, each associated to a region. Based on these distances, we define a partition in the endocardium at end-diastole (ED). This partition is then interpolated to the blood cavity using the Laplace equation, which allows to compute regional volumes. For obtaining an end-systole (ES) partition, the endocardial partition is transported from ED to ES using a commercial image-based tracking software, and then the interpolation process is repeated. We assessed the intra- and inter-observer reproducibility using a 10-subjects dataset containing repeated quantifications of the same images, obtaining intra- and inter- observer errors (7-12% and 10-23% respectively). Finally, we propose a novel synthetic mesh generation algorithm that deforms a template mesh imposing a user-defined strain to a template mesh. We used this method to create a new dataset for involving distinct types of remodelling that were used to assess the sensitivity of the parcellation method to identify volume changes affecting different parts. We show that the parcellation method is adequate for capturing local circumferential and global circumferential and longitudinal RV remodelling, which are the most clinically relevant cases.

Corpus callosum morphology in major mental disorders: a magnetic resonance imaging study

Mental disorders diagnosis is based on specific clinical criteria. However, clinical studies found similarities and overlapping phenomenology across a variety of disorders, which suggests a common neurobiological substrate. Thus, there is a need to measure disease-related neuroanatomical similarities and differences across conditions. While structural alterations of the corpus callosum have been investigated in obsessive-compulsive disorder, schizophrenia, major depressive disorder and bipolar disorder, no study has addressed callosal aberrations in all diseases in a single study. Moreover, results from pairwise comparisons (patients vs. controls) show some inconsistencies, possibly related to the parcellation methods to divide the corpus callosum into subregions. The main aim of the present paper was to uncover highly localized callosal characteristics for each condition (i.e. obsessive-compulsive disorder, schizophrenia, major depressive disorder and bipolar disorder) as compared either to healthy control subjects or to each other. For this purpose, we did not rely on any sub-callosal parcellation method, but applied a well-validated approach measuring callosal thickness at 100 equidistant locations along the whole midline of the corpus callosum. One hundred and twenty patients (30 in each disorder) as well as 30 controls were recruited for the study. All groups were closely matched for age and gender, and the analyses were performed controlling for the impact of antipsychotic treatment and illness duration. There was a significant main effect of group along the whole callosal surface. Pairwise post hoc comparisons revealed that, compared to controls, patients with obsessive-compulsive disorder had the thinnest corpora callosa with significant effects almost on the entire callosal structure. Patients with schizophrenia also showed thinner corpora callosa than controls but effects were confined to the isthmus and the anterior part of the splenium. No significant differences were found in both major depressive disorder and bipolar disorder patients compared to controls. When comparing the disease groups to each other, the corpus callosum was thinner in obsessive-compulsive disorder patients than in any other group. The effect was evident across the entire corpus callosum, with the exception of the posterior body. Altogether, our study suggests that the corpus callosum is highly changed in obsessive-compulsive disorder, selectively changed in schizophrenia and not changed in bipolar disorder and major depressive disorder. These results shed light on callosal similarities and differences among mental disorders providing valuable insights regarding the involvement of the major brain commissural fibre tract in the pathophysiology of each specific mental illness.

Multi-atlas classification of autism spectrum disorder with hinge loss trained deep architectures: ABIDE I results

The Autism Brain Imaging Data Exchange (ABIDE) database has emerged as a benchmark cohort for training and testing machine learning models for classifying autism. Considering recent successes using deep learning, increasing the overall classification accuracy on this dataset is a challenging endeavor. In this paper, we propose a multi-input deep neural network model for classifying autism. Our model’s architecture is designed to incorporate neuroimaging data preprocessed using three different reference atlases. For each training example, the proposed deep neural network simultaneously receives connectome data originating from three different parcellation strategies and automatically learns discriminative features from the three input sets. This process results in learned features that are more general and less dependent on a single brain parcellation method. Moreover, the proposed neural network is trained using the hinge loss function, which strongly penalizes misclassifications. We validate our model under cross-validation frameworks, using a set of 1,038 real participants and an augmented set of 10,038 samples. We achieve a classification accuracy of 78.07% on real data and 79.13% on augmented data, which is about 9% higher than previously reported results. Our results reinforce the idea that discordance in resting-state network connectivity constitutes a major discriminatory feature between patients with autism and healthy individuals. Our proposed multi-atlas deep neural network classification framework is potentially applicable to additional brain disorders.

Development of New Particle Method Based on MPS and its Applicability for Friction Stir Welding of Dissimilar Joint

Friction stir welding (FSW) is one of the solid-state welding and it has been widely employed for joining aluminum alloys. In addition, as a result of R&amp;D efforts about FSW tool, this method is expected to join the steels and/or various dissimilar materials. In order to examine the thermal and mechanical behavior in FSW, many numerical studies have been conducted and the heat generation behavior near FSW tool is precisely demonstrated by using the moving particle semi-implicit (MPS) method which is one of the particle method. In this research, in order to reduce the computational time, a new parcel method based on MPS is developed and its applicability is examined for simulating the friction stir welded dissimilar joint between V-ally and austenite stainless steel SUS316L. From the serial computational results, it is revealed that the influence of rotational speed on the heat generation during FSW seems to be larger than that of traveling speed. Moreover, the numerical result indicates that the sound dissimilar joint might be fabricated when V-alloy is set to be the retreating side (RS), the FSW tool is inserted in RS and the rotational speed increased appropriately although the two materials have not been joined in this welding condition of FSW experimentally.

Differential early subcortical involvement in genetic FTD within the GENFI cohort

BackgroundStudies have previously shown evidence for presymptomatic cortical atrophy in genetic FTD. Whilst initial investigations have also identified early deep grey matter volume loss, little is known about the extent of subcortical involvement, particularly within subregions, and how this differs between genetic groups. Methods480 mutation carriers from the Genetic FTD Initiative (GENFI) were included (198 GRN, 202 C9orf72, 80 MAPT), together with 298 non-carrier cognitively normal controls. Cortical and subcortical volumes of interest were generated using automated parcellation methods on volumetric 3 T T1-weighted MRI scans. Mutation carriers were divided into three disease stages based on their global CDR® plus NACC FTLD score: asymptomatic (0), possibly or mildly symptomatic (0.5) and fully symptomatic (1 or more). ResultsIn all three groups, subcortical involvement was seen at the CDR 0.5 stage prior to phenoconversion, whereas in the C9orf72 and MAPT mutation carriers there was also involvement at the CDR 0 stage. In the C9orf72 expansion carriers the earliest volume changes were in thalamic subnuclei (particularly pulvinar and lateral geniculate, 9–10%) cerebellum (lobules VIIa-Crus II and VIIIb, 2–3%), hippocampus (particularly presubiculum and CA1, 2–3%), amygdala (all subregions, 2–6%) and hypothalamus (superior tuberal region, 1%). In MAPT mutation carriers changes were seen at CDR 0 in the hippocampus (subiculum, presubiculum and tail, 3–4%) and amygdala (accessory basal and superficial nuclei, 2–4%). GRN mutation carriers showed subcortical differences at CDR 0.5 in the presubiculum of the hippocampus (8%). ConclusionsC9orf72 expansion carriers show the earliest and most widespread changes including the thalamus, basal ganglia and medial temporal lobe. By investigating individual subregions, changes can also be seen at CDR 0 in MAPT mutation carriers within the limbic system. Our results suggest that subcortical brain volumes may be used as markers of neurodegeneration even prior to the onset of prodromal symptoms.

日本初のアイウォール貫通型航空機観測により捉えられた2017年台風第21号（ラン）の二重暖気核構造

Upper-tropospheric aircraft reconnaissance was carried out for Typhoon Lan (2017) using a civil jet with a newly developed dropsonde system. This was the first time a Japanese research group observed the inner core of an intense typhoon using dropsondes. This paper describes the warm-core structure in the eye and the associated thermodynamic and kinematic features of the eyewall. During two days of reconnaissance, the typhoon preserved its peak intensity in an environment with strengthening vertical shear. The dropsondes captured a double warm-core structure with a higher perturbation temperature in the middle and upper troposphere, which persisted between the two missions. The two warm cores showed a difference in equivalent potential temperature (θe) of more than 10 K, suggesting different air origins. Saturation-point analysis suggests that the air observed in the upper warm core was entrained from the eyewall. The eyewall updraft in the left-of-shear semicircle had a two-layer structure with a higher θe and lower absolute angular momentum on the inner side of the updraft core. Analyses of the saturation point and parcel method suggest that the warmer air with a θe exceeding 370 K on the inner side of the updrafts originated from the eye boundary layer and was eventually transported into the upper warm core. These results led us to hypothesize that the vertical transport of high-θe air from the eye boundary layer contributed to the continuous eye warming in the upper troposphere against the negative effect of strengthening environmental wind shear on storm intensity. This study demonstrates the significance of eyewallpenetrating upper-tropospheric reconnaissance for monitoring the warm-core structure in the present situation, where accurate measurements of both humidity and temperature for calculating θe can only be made with dropsonde-type expendables.

Parcellation of the human amygdala using recurrence quantification analysis

Several previous attempts have been made to divide the human amygdala into smaller subregions based on the unique functional properties of the subregions. Although these attempts have provided valuable insight into the functional heterogeneity in this structure, the possibility that spatial patterns of functional characteristics can quickly change over time has rarely been considered in previous studies. In the present study, we explicitly account for the dynamic nature of amygdala activity. Our goal was not only to develop another parcellation method but also to augment existing methods with novel information about amygdala subdivisions. We performed state-specific amygdala parcellation using resting-state fMRI (rsfMRI) data and recurrence quantification analysis (RQA). RsfMRI data from 102 subjects were acquired with a 3T Trio Siemens scanner. We analyzed values of several RQA measures across all voxels in the amygdala and found two amygdala subdivisions, the ventrolateral (VL) and dorsomedial (DM) subdivisions, that differ with respect to one of the RQA measures, Shannon's entropy of diagonal lines. Compared to the DM subdivision, the VL subdivision can be characterized by a higher value of entropy. The results suggest that VL activity is determined and influenced by more brain structures than is DM activity. To assess the biological validity of the obtained subdivisions, we compared them with histological atlases and currently available parcellations based on structural connectivity patterns (Anatomy Probability Maps) and cytoarchitectonic features (SPM Anatomy toolbox). Moreover, we examined their cortical and subcortical functional connectivity. The obtained results are similar to those previously reported on parcellation performed on the basis of structural connectivity patterns. Functional connectivity analysis revealed that the VL subdivision has strong connections to several cortical areas, whereas the DM subdivision is mainly connected to subcortical regions. This finding suggests that the VL subdivision corresponds to the basolateral subdivision of the amygdala (BLA), while the DM subdivision has some characteristics typical of the centromedial amygdala (CMA). The similarity in functional connectivity patterns between the VL subdivision and BLA, as well as between the DM subdivision and CMA, confirm the utility of our parcellation method. Overall, the study shows that parcellation based on BOLD signal dynamics is a powerful tool for identifying distinct functional systems within the amygdala. This tool might be useful for future research on functional brain organization.

Parcellation of the thalamus by using a dual-segment method based on resting-state functional connectivity: An application on autism spectrum disorder

BackgroundEvidence suggests thalamus is a key “information relay” center and all cortical areas receive inputs from the thalamus and each of the main nuclei of thalamus connects a single one or a few cortical areas. The traditional “winner-takes-all” thalamus parcellation method was then proposed based on this assumption. However, this method is based on the structural segments of the cortex which is not suitable for the functional parcellation of the thalamus. MethodHere we proposed a dual-segment method for thalamus functional parcellation based on the resting-state fMRI data. The traditional “winner-takes-all” and the proposed dual-segment methods were both applied to the dataset of 76 healthy controls (HCs) and 34 subjects with autism spectrum disorder. ResultsThe results showed that the thalamus was subdivided into two sub-regions by using the dual-segment method: one is located in the dorsomedial part of thalamus which connects the high-level cognitive cortical regions; the other is located in the ventrolateral part of thalamus which connects the low-level sensory cortical areas. The functional connectivity strength between thalamus sub-regions and the corresponding cortical regions based on the dual-segment method was higher than that of results from the traditional “winner-takes-all” method. The thalamo-cortical functional connectivity based on our proposed method also showed higher classification ability to distinguish subjects with autism spectrum disorder from HCs. ConclusionOur study will provide a new method for functional thalamus parcellation which might help understand the sub-regions functions of thalamus in neuroscience studies.

Ceilometers as planetary boundary layer height detectors and a corrective tool for COSMO and IFS models

Abstract. The significance of planetary boundary layer (PBL) height detection is apparent in various fields, especially in air pollution dispersion assessments. Numerical weather models produce a high spatial and temporal resolution of PBL heights; however, their performance requires validation. This necessity is addressed here by an array of eight ceilometers; a radiosonde; and two models – the Integrated Forecast System (IFS) global model and COnsortium for Small-scale MOdeling (COSMO) regional model. The ceilometers were analyzed with the wavelet covariance transform method, and the radiosonde and models with the parcel method and the bulk Richardson method. Good agreement for PBL height was found between the ceilometer and the adjacent Bet Dagan radiosonde (33 m a.s.l.) at 11:00 UTC launching time (N=91 d, ME =4 m, RMSE =143 m, R=0.83). The models' estimations were then compared to the ceilometers' results in an additional five diverse regions where only ceilometers operate. A correction tool was established based on the altitude (h) and distance from shoreline (d) of eight ceilometer sites in various climate regions, from the shoreline of Tel Aviv (h=5 m a.s.l., d=0.05 km) to eastern elevated Jerusalem (h=830 m a.s.l., d=53 km) and southern arid Hazerim (h=200 m a.s.l., d=44 km). The tool examined the COSMO PBL height approximations based on the parcel method. Results from a 14 August 2015 case study, between 09:00 and 14:00 UTC, showed the tool decreased the PBL height at the shoreline and in the inner strip of Israel by ∼100 m and increased the elevated sites of Jerusalem and Hazerim up to ∼400 m, and ∼600 m, respectively. Cross-validation revealed good results without Bet Dagan. However, without measurements from Jerusalem, the tool underestimated Jerusalem's PBL height by up to ∼600 m.

Large-eddy simulations of a stratified-charge direct-injection spark-ignition engine: Comparison with experiment and analysis of cycle-to-cycle variations

Multiple-cycle large-eddy simulations (LES) have been performed for an optically accessible, single-cylinder, four-stroke-cycle, spray-guided direct-injection spark-ignition (SG-DISI) engine operating in a stratified globally fuel-lean mode. The simulations combine a standard Smagorinsky turbulence model, a stochastic Lagrangian parcel method for liquid fuel injection and fuel spray modeling, a simple energy-deposition spark-ignition model, and a modified thickened flame model for turbulent flame propagation through highly stratified reactant mixtures. Comparisons between simulations and experiments include individual-cycle and ensemble-average pressure and apparent-heat-release-rate traces, individual-cycle and ensemble-average indicated mean effective pressures (IMEP), and instantaneous two-dimensional vapor-equivalence-ratio contours. Although the number of LES cycles is small (35), the results show that the simulations are able to capture the global combustion behavior that is observed in the experiments, including cycle-to-cycle variations. The simulation results are then analyzed further to provide insight into the conditions that lead to misfire versus robust combustion. As has been reported in earlier experimental and LES studies for homogeneous-charge SI engines, local conditions in the vicinity of the spark gap at the time of ignition largely determine the subsequent flame development. However, in contrast to homogeneous-charge engines, no single local or global quantity correlates as strongly with the eventual peak pressure or IMEP for each cycle. Rather, it is the interplay among the early flame kernel, the velocity field that it experiences, and the fuel distribution that it encounters that ultimately determines the fate of each combustion event. Deeper analysis and quantitative statistical comparisons between experiments and simulations will require the simulation of larger numbers of engine cycles.

MRI-based Parcellation and Morphometry of the Individual Rhesus Monkey Brain: the macaque Harvard-Oxford Atlas (mHOA), a translational system referencing a standardized ontology.

Investigations of the rhesus monkey (Macaca mulatta) brain have shed light on the function and organization of the primate brain at a scale and resolution not yet possible in humans. A cornerstone of the linkage between non-human primate and human studies of the brain is magnetic resonance imaging, which allows for an association to be made between the detailed structural and physiological analysis of the non-human primate and that of the human brain. To further this end, we present a novel parcellation method and system for the rhesus monkey brain, referred to as the macaque Harvard-Oxford Atlas (mHOA), which is based on the human Harvard-Oxford Atlas (HOA) and grounded in an ontological and taxonomic framework. Consistent anatomical features were used to delimit and parcellate brain regions in the macaque, which were then categorized according to functional systems. This system of parcellation will be expanded with advances in technology and, like the HOA, will provide a framework upon which the results from other experimental studies (e.g., functional magnetic resonance imaging (fMRI), physiology,connectivity, graph theory) can be interpreted.

Investigation of local white matter abnormality in Parkinson’s disease by using an automatic fiber tract parcellation

The deficits of white matter (WM) microstructure are involved during Parkinson’s disease (PD) progression. Most current methods identify key WM tracts relying on cortical regions of interest (ROIs). However, such ROI methods can be challenged due to low diffusion anisotropy near the gray matter (GM), which could result in a low sensitivity of tract identification. This work proposes an automatic WM parcellation method to improve the accuracy of WM tract identification and locate abnormal tracts by using sensitive features. The proposed method consists of 1) whole brain WM parcellation using an established fiber clustering method, without using any ROIs, 2) features of fasciculus were calculated to quantify diffusion measures at each equal cross-section along the whole cluster. Then, we use the proposed features to investigate the WM difference in PD compared with healthy controls (HC). We also use these features to investigate the relationship of clinical symptoms and specific fiber tracts. The novelty of the proposed method is that it automatically identifies the abnormal WM fibers in cluster degree. Experiment results indicated that the proposed method had advantage in detecting the local WM abnormality by performing between-group statistical analysis in 30 patients with PD and 28 HC. We found 13 hemisphere clusters and 8 commissural clusters had significant group difference (p < 0.05, corrected by FDR method) in local regions, which belonged to multiple fiber tracts including cingulum bundle (CB), inferior occipito-frontal fasciculus (IoFF), corpus callosum (CC), external capsule (EC), uncinate fasciculus (UF), superior longitudinal fasciculus (SLF) and thalamo front (TF). We also found clusters that had relevance with clinical indices of cognitive function (2 clusters), athletic function (6 clusters), and depressive state (2 clusters) in these significant clusters. From the experiment results, it confirmed the ability of the proposed method to identify potential WM microstructure abnormality.

An automated toolchain for quantitative characterisation of structural connectome from MRI based on non-anatomical cortical parcellation.

Brain connectivity analysis is a new multidisciplinary approach in neuroscience for determining neurological disorders from brain imaging data. But, there is no end-to-end toolchain that processes raw MRI data and extracts brain connectivity network metrics. Again, the existing method of cortical parcellation from MRI data is mainly based on fixed Brodmann atlas; which does not support neonate's brain or adult's brain with neuroplasticity anomalies. In this work, we design an end-to-end toolchain that processes raw MRI data and generates network metrics for brain connectivity analysis using non-anatomical equal-area parcellation. We process the structural and diffusion MRI data to generate the parcellated and segmented image, extract white matter tracks and build structural connectome and then interface it with Brain Connectivity Toolbox to extract graph theory measures.Clinical relevance An automated tool for end-to-end processing of MRI data to brain connectivity pattern extraction and its quantitative characterisation for diagnosing brain disorder.

GeoSP: A parallel method for a cortical surface parcellation based on geodesic distance.

We present GeoSP, a parallel method that creates a parcellation of the cortical mesh based on a geodesic distance, in order to consider gyri and sulci topology. The method represents the mesh with a graph and performs a K-means clustering in parallel. It has two modes of use, by default, it performs the geodesic cortical parcellation based on the boundaries of the anatomical parcels provided by the Desikan-Killiany atlas. The other mode performs the complete parcellation of the cortex. Results for both modes and with different values for the total number of sub-parcels show homogeneous sub-parcels. Furthermore, the execution time is 82s for the whole cortex mode and 18s for the Desikan-Killiany atlas subdivision, for a parcellation into 350 sub-parcels. The proposed method will be available to the community to perform the evaluation of data-driven cortical parcellations. As an example, we compared GeoSP parcellation with Desikan-Killiany and Destrieux atlases in 50 subjects, obtaining more homogeneous parcels for GeoSP and minor differences in structural connectivity reproducibility across subjects.

Frontal aslant tracts as correlates of lexical retrieval in MS

ABSTRACT Introduction Previous studies reveal that a newly described white matter pathway, the frontal aslant tract (FAT), connecting inferior and superior frontal gyri has a role in speech and language functions. We explored the role of this tract in a phonemic and semantic fluency tasks in a cohort of multiple sclerosis patients diagnosed with cognitive impairment. Methods Thirty-five MS patients with varying degrees of cognitive impairment underwent diffusion tensor imaging and the Controlled Associated Word Test. Fractional anisotropy (FA) of FAT and arcuate fasciculus (AF) were obtained through a supervised, atlas-based tissue segmentation and parcellation method. Phonemic and semantic fluency scores were obtained from COWAT. We ran a multivariate regression model, and partial correlation analyses adjusted for age, education, and lesion load, and corrected for multiple comparisons. False discovery rate (FDR) was used for the correction of multiple comparisons. Results Bilateral FAT FA showed significant association with phonemic verbal fluency task (Left; r = 0.46, p = 0.0058 and right; r = 0.46, p = 0.0059) but not semantic fluency task and this relation remained significant after FDR correction (p = 0.02 bilaterally). Although left AF showed some significant association with phonemic fluency task, this relation was insignificant after FDR correction. Conclusion We show that bilateral FAT are correlates of phonemic verbal fluency task but not semantic in an MS cohort with cognitive impairment. This finding suggests that FAT is more specialized in lexical retrieval function as semantic fluency test encompasses all the functions except the lexical retrieval.

M149. THE TOPOGRAPHY OF STRIATAL DOPAMINE AND SYMPTOMS IN PSYCHOSIS: AN INTEGRATIVE PET AND MRI STUDY

BackgroundStriatal dopamine dysfunction is proposed to underlie symptoms in psychosis, yet it is not known how changes in a single neurotransmitter could underlie the heterogenous presentations that are seen clinically. One hypothesis is that the symptomatic consequences of aberrant dopamine signalling may depend on where within the striatum dysfunction occurs. Positron emission tomography (PET) allows for the measurement of dopamine function across the striatum. However, when using typical atlas-based parcellation methods, the collinearity between measures of dopamine function within each striatal subdivision precludes investigation of this hypothesis.MethodsWe use a novel and data-driven parcellation method to address the above, and investigate relationships between spatial variability in dopamine synthesis capacity and psychotic symptoms. We employ a multimodal imaging approach combining 18F-DOPA PET and resting state MRI in 29 unmedicated and minimally-treated patients with first episode psychosis and 21 healthy controls. In each participant, we use resting state functional connectivity maps to quantify the functional connectivity of each striatal voxel to cortical networks. Network-specific striatal dopamine synthesis capacity (Kicer) was calculated for the resulting connectivity defined parcellations.ResultsConnectivity defined parcellations generated Kicer values with equivalent reliability, and significantly greater orthogonality to standard anatomical parcellation methods. Dopamine function within striatal areas connected to the default mode network is strongly associated with negative symptoms (p<0.001).DiscussionThese findings suggest that individual differences in the topography of striatal dopamine dysfunction contribute to shaping psychotic symptomatology.

Estimations of the Mexicali Valley (Mexico) Mixing Height

We report estimations of the Mexicali Valley (Mexico) mixing height for three seasons. Surface and upper air meteorological measurements were carried out nearby Cerro Prieto geothermal power plant during July 2010 (summer), January 2012 (winter), and October 2016 (autumn). Four different methods were applied to estimate the convective boundary layer (CBL) height from radiosonde (RS) profiles: the parcel method, the gradients method, the least-squares variational approach based on the slab model of the CBL structure, and a covariance method. For nocturnal conditions, we used diagnostic models based on friction velocity and Monin–Obukhov length. Under unstable conditions, we obtained (on average) mixing heights of 497 m at 06:00 LST, 1242 m at 12:00 LST, 1404 m at 15:00 LST, and 482 at 18:00 LST during summer; 754 m at 12:00 LST during winter; and 1195 m at 12:00 LST and 13:00 m between the 14:00 and 15:00 LST during the autumn. The results allowed adjusting a semiempirical model to evaluate mixing height from turbulent sensible heat flux and friction velocity data. Our results provide practical tools that could facilitate the application of regulatory dispersion models to assess air quality in the region.