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Interprofessional practice (IPP) is the accepted standard of care for clients following a stroke. A brief, embedded and evidence-based IPP team simulation was designed to address stroke care knowledge and IPP competencies for students within limited curriculum space. Each team was required to construct a collaborative care plan for their patient during the simulation and submit the care plan for evaluation of best practice stroke care knowledge and implementation with evidence of interprofessional collaboration (IPC). A total of 302 students (274 on-site, 28 by distance technology) representing four professions comprised of 55 teams took part in this experience. Post-simulation, voluntary and anonymous programme evaluations were completed using the standardised interprofessional collaborative competency assessment scale (ICCAS) and open-ended free-text responses to five questions. There was a significant improvement for all pre-post ratings on the ICCAS regardless of profession or previous interprofessional experience. Additionally, the open-ended responses indicated perceived changes to role clarification, communication, and teamwork. The combined interpretation of the programme evaluation results supports interprofessional team simulation as an effective and efficient learning experience for students regardless of previous interprofessional experience, and demonstrated positive changes in stroke best-practice knowledge and IPC competencies.

Objectives: Major depression in the context of chronic pain has been conceptualized implicitly as a latent variable, in which symptoms are viewed as manifestations of an underlying disorder. A network approach provides an alternative model and posits that symptoms are causally connected, rather than merely correlated, and that disorders exist as systems, rather than as entities. The present study applied a network analysis to self-reported symptoms of major depression in patients with chronic pain. The goals of the study were to describe the network of depressive symptoms in individuals with chronic pain and to illustrate the potential of network analysis for generating new research questions and treatment strategies., Materials and Methods: Patients (N=216) admitted to an interdisciplinary chronic pain rehabilitation program provided symptom self-reports using the Patient Health Questionnaire-9. Well-established network analyses methods were used to illustrate the network of depressive symptoms and determine the centrality of each symptom (ie, the degree of connection with other symptoms in the network)., Results: The most central symptoms were difficulty concentrating, loss of interest or pleasure, depressed mood, and fatigue, although the relative position of each symptom varied slightly, depending on the centrality measure considered., Discussion: Consistent with past research with patients undergoing treatment for major depression, the current findings are supportive of a model in which depressive symptoms are causally connected within a network rather than being manifestations of a common underlying disorder. The research and clinical implications of the findings, such as developing treatments targeting the most central symptoms, are discussed.

Diffusion-weighted imaging (DWI) is based on the detection of water molecule movement in interstitial and intracellular space, and that motion may be restricted in ischemia and in tumors. An early diagnosis and characterization of several cancer related diseases is possible with DWI. Diffusion-weighted images are therefore important for patient management. Knowledge of the technical requirements for DWI, including a suitable selection of b-values for differentiating between perfusion and true diffusion, as well as an understanding of the advantages and limitations of different b-values selections, is necessary to obtain reliable diagnostic results. The aim of this article is to review the fundamentals of the DWI technique, the common protocols used, b-value selection, and DWI's main contribution to neoplasm detection and staging.

Previous neuroanatomical research has shown that semantic processing of action-related language activates the premotor, motor, and sensory cortices somatotopically (e.g., Tettamanti et al., J Cognitive Neurosci. 2005;17(2): 273-281, using a listening task, and Hauk et al., Neuron. 2004;41:301-307 and Pulvermuller et al., Eur J Neurosci 2005;21:793-797; J Cognitive Neurosci 2005;17(6):884-892 using a silent reading task). We examined this somatotopic semantics hypothesis using an overt semantic generation task (i.e., participants generated aloud their own personal description of how they would interact with target object words), rather than semantic comprehension as examined in previous research, so as to provide a stronger test of the hypothesis under conditions that tap one's own semantic knowledge about interacting with objects. Experiment 1 used functional Magnetic Resonance Imaging (fMRI) to examine somatotopically organized activation in the premotor cortex for an overt semantic generation task, using targets that naturally involve either arm interactions or leg interactions. Consistent with previous research, our results showed that semantic processing related to object interaction involves the motor, premotor and sensory cortices in a somatotopic fashion. Previous behavioural research has shown a response advantage in lexical decision for words with multiple meanings or features, which diminishes with tasks that decrease semantic involvement (e.g., Borowsky and Masson, J Exp Psychol: Learn Memory Cognit 1996; 22(1):63-85; Pexman et al., Psychon Bull Rev 2002;9(3): 542-549). Experiment 2 evaluated whether semantic generation response times (total duration of response) display a complexity advantage (i.e., faster response times for more complex objects), and whether complexity ratings were related to the volume of brain activation during the task. Results from this behavioural experiment revealed a significant negative relationship between the total duration of response (i.e., the total amount of time taken to respond to the stimuli) and object complexity for leg objects (a semantic complexity advantage), but not for arms. This suggests that the smaller repertoire of possible interactions with leg objects requires a greater reliance on semantic knowledge in order to respond in the semantic generation task. This interpretation was further supported by a greater volume of brain activation in the premotor cortex for arm objects versus leg objects. The response times from Experiment 1 were also compared to the semantic complexity ratings gathered in Experiment 2 to determine if response times in the fMRI environment were affected by how complex an object is.

We examined the correlation between behavioural reaction time and functional imaging parameters of the blood oxygenated level dependent (BOLD) response in Broca's Area during a word identification task, and whether the correlation [Magnetic Resonance Imaging 22 (2004) 451-455] varies as a function of four stimulus types: regular words (REGs) (e.g., hint), irregular words (IRRs) (e.g., pint), nonwords (NWs) (e.g., bint), and pseudohomophones (PHs) (e.g., pynt). Participants named letter strings aloud during a functional magnetic resonance imaging study. Naming reaction times were recorded during regular gaps in image acquisition, and BOLD parameters were extracted via a Tikhonov regularized BOLD analysis technique. The results revealed that only PH reaction times were correlated with BOLD width, providing evidence that Broca's area supports phonetic decoding accompanied with phonological lexical access. In addition, we advanced the development of equation-based models of cognitive behaviour and neurophysiology, whereby we showed that the relationship of mathematical independence that exists for predicting REG accuracy, given IRR and NW or PH naming accuracy, was present for naming reaction time, BOLD width, BOLD time to peak, and BOLD intensity. Therefore, we provide converging behavioural and neuroanatomical evidence for a mathematically independent relationship between sight vocabulary and phonetic decoding systems, consistent with a dual-route model of reading.

Research on the modularity of perceptual and cognitive processes has often pointed to a ventral-dorsal distinction in cortical pathways that depend upon the nature of the stimuli and the task. However, it is not clear whether the dorsal, occipital-parietal stream specializes in locating visual objects (i.e., a "where" stream), or taking action toward objects (i.e., a "how" stream), although there is some consensus for a ventral, occipital-temporal "what" stream that specializes in the identification of visual objects. It is also not clear to what extent word and picture processing are modular along these streams, as functional imaging maps to date have not addressed the modularity question directly. Here we present two types of functional imaging maps that directly show modularity and intersection of processing function for word and picture stimuli in tasks that require decisions about "what is", "where is", or "how do you interact with" a stimulus (N=6 participants). Our results reveal a middle dorsal "how" stream with some modular regions of activation that are distinct from activation during "where" processing, and that words and pictures involve several modular regions of activation along these streams.

We analyzed image characteristics in T(1)-, T(2)-, and diffusion-weighted in vitro magnetic resonance (MR) images acquired at predefined stages of the ovarian cycle in 36 heifers to test the hypothesis that MR image attributes of the follicle wall reflect the physiologic status of ovarian follicles (viable, atretic, dominant, subordinate). Numerical pixel values (NPV), standard deviation of pixel values (heterogeneity), and area under the curve were used to assess images of follicle walls. Pixel values of the wall were used to calculate a regression line from which intercept, slope, and coefficient of determination were calculated. In T(1) images, NPV of dominant follicles were less likely to fit a regression line at the preovulatory phase than at any other phase (P < 0.1). Preovulatory dominant follicles had lower area under the curve in diffusion-weighted images than early and late static dominant follicles of the anovulatory wave (P < 0.02). Subordinate follicles in the presence of a preovulatory dominant follicle had lower mean NPV in T(1)- and T(2)-weighted images and lower intercepts in T(1)-weighted images than subordinate follicles of the anovulatory wave (P < 0.02). Early atresia of dominant follicles was identified at the late static phase by greater area, mean NPV, and slope in T(2)-weighted images (P < 0.02). Preovulatory dominant follicles had poor fit of NPV to a regression line in T(1)-weighted images and lower area under the curve in diffusion images. Atretic follicles had brighter walls with more acute transitions from follicular fluid to stroma in T(2)-weighted images and more heterogeneous walls in diffusion images. The MR image attributes of the follicle wall reflected the physiologic status of dominant and largest subordinate follicles.

A robust standardized method for segmentation, quantification, and normalization of pediatric hippocampal volumes using magnetic resonance imaging is presented. The method will find application in time course measurements of hippocampal volumes in pediatric patients who suffer from temporal lobe epilepsy and was tested prospectively on six control patients (13-60 mo of age). The un-normalized hippocampal volumes obtained using our segmentation method ranged from 3.85 to 6.38 mL, in agreement with previously published results. Inter- and intraobserver variability of the segmentation method was determined to be 13.3% and 2.8%, respectively. Four different methods of volume normalization were tested. Normalization is required to adjust for age-related increases in hippocampal volume. The normalization approach that seemed to compensate best for growth-related hippocampal volume changes was based on a simple estimation of intracranial volumes. This is the first report of a consistent and reliable method for segmentation and normalization of hippocampi from pediatric patients that can be used to study the progression of neurologic diseases in children.

An algorithm of Dutt and Rokhlin (SIAM J Sci Comput 1993;14:1368-1383) for the computation of a fast Fourier transform (FFT) of nonuniformly-spaced data samples has been extended to two dimensions for application to MRI image reconstruction. The 2D nonuniform or generalized FFT (GFFT) was applied to the reconstruction of simulated MRI data collected on radially oriented sinusoidal excursions in k-space (ROSE) and spiral k-space trajectories. The GFFT was compared to conventional Kaiser-Bessel kernel convolution regridding reconstruction in terms of image reconstruction quality and speed of computation. Images reconstructed with the GFFT were similar in quality to the Kaiser-Bessel kernel reconstructions for 256(2) pixel image reconstructions, and were more accurate for smaller 64(2) pixel image reconstructions. Close inspection of the GFFT reveals it to be equivalent to a convolution regridding method with a Gaussian kernel. The Gaussian kernel had been dismissed in earlier literature as nonoptimal compared to the Kaiser-Bessel kernel, but a theorem for the GFFT, bounding the approximation error, and the results of the numerical experiments presented here show that this dismissal was based on a nonoptimal selection of Gaussian function.

The magnetic resonance images and maps of bovine ovaries acquired at defined phases of follicular development and regression were studied to determine whether magnetic resonance image attributes of the follicular antrum reflect the physiological status of dominant and subordinate ovarian follicles. Ovariectomies were performed at day 3 of wave one, day 6 of wave one, day 1 of wave two and at >/= day 17 after ovulation. The timings of ovariectomies were selected to acquire growing, early static, late static and regressing follicles of the first wave and preovulatory follicles of the ovulatory wave. Pre-selection and subordinate follicles were also available for analysis. Serum samples were taken on the day of ovariectomy and follicular fluid samples were taken after imaging. Numerical pixel value and pixel heterogeneity in a spot representing approximately 95% of the follicular antrum were quantified in T(1)- and T(2)-weighted images. T(1) and T(2) relaxation rates (T(1) and T(2)), proton density, apparent diffusion coefficients and their heterogeneities were determined from the computed magnetic resonance maps. The antra of early atretic dominant follicles showed higher T(2)-weighted mean pixel value (P < 0.008) and heterogeneity (P < 0. 01) and lower T(2) heterogeneity (P < 0.008) than growing follicles. Subordinate follicles in the presence of a preovulatory dominant follicle had higher T(1), T(1) heterogeneity, proton density, proton density heterogeneity, and lower mean pixel value in T(1)-weighted images than subordinate follicles of the anovulatory wave (P < 0.04). T(1) relaxation rate heterogeneity and proton density heterogeneity were positively correlated with follicular fluid oestradiol concentration (r = 0.4 and 0.3; P < 0.04). T(2) relaxation rate heterogeneity was positively correlated with follicular fluid progesterone concentration (r = 0.4; P < 0.008). Quantitative differences in magnetic resonance image attributes of the antrum observed among phases of follicular development and regression coincided with changes in the ability of the dominant follicle to produce steroid hormones and ovulate, and thus were indicative of physiological status and follicular health.

The acquisition of unaliased ROSE (Radially Oriented Sinusoidal Excursions in k-space) data requires the appropriate selection of a gradient frequency, number of interleaves, and number of data samples per acquisition. When the data samples are uniformly distributed in time, they fall on irregularly spaced circles in k-space. Aliasing due to radial undersampling will be eliminated when the number of sample circles equals the number of pixels in one dimension of the desired reconstructed image. Azimuthal aliasing will be completely eliminated when the total number of ROSE petals is four times the number of pixels in one dimension, but acceptable reconstructions may be had with fewer petals. Magn Reson Med 44:129-136, 2000., (Copyright 2000 Wiley-Liss, Inc.)

Three-dimensional magnetic resonance imaging coupled with maximum intensity projection display, a technique usually reserved for magnetic resonance imaging angiography, is useful for the study of ovarian follicular growth. The ovaries of 19 cows were examined each day by transrectal ultrasonography. From these data, the precise phase of the ovarian cycle was determined and cows were ovariectomized on day 3 of wave one (n = 5), on day 6 of wave one (n = 4), on day 1 of wave two (n = 4), >/= 17 days after ovulation (n = 5), and on the day of ovulation (n = 1). The excised ovaries were examined by magnetic resonance imaging using a fast imaging with steady state precession imaging sequence with maximum intensity projection reconstruction, displayed as a cine-loop of the ovaries rotating in space. This provided the clearest view among the three principal three-dimensional steady state data acquisition approaches tried; the follicles and other ovarian structures could be distinguished unambiguously. Results from the bovine model indicate that the acuity of the three-dimensional fast imaging with steady state precession technique has potential application in in vivo intravaginal imaging in women for studying normal and pathological ovarian function.

The application of a knowledge-based segmentation method to the problem of automatically detecting the outer follicle wall boundary in ultrasonographic images of ovaries is presented. A combination of computer detection and interactive adjustment was used to define an approximate inner follicle-wall boundary, which was then used by the computer algorithm as a priori knowledge to automatically find the outer follicle-wall border. The segmentation algorithm was tested on ultrasonographic images of women's ovaries that were imaged in vivo. The semiautomatic segmentations were compared to segmentations by an expert human observer in terms of border placement differences and in terms of quantitative parameters relevant to the physiologic status of the follicles. These physiological parameters include total and specific signal intensity from the follicle and from the follicle wall. The computer-detected outer follicle wall boundaries correlated well with the human observer-defined wall boundaries, in terms of enclosed follicle area, specific and total follicle signal, enclosed wall area, and specific and total wall signal. The actual border placement differences were also small, with a maximum placement difference of 1.47 +/- 0.83 mm and a root mean square (r.m.s.) placement difference of 0.59 +/- 0.28 mm.

A wavelet transform was used to improve the signal-to-noise in NMR microimages of Phaseolus vulgaris cotyledonary tissues. Two cases were tested: a relatively clean image and, at longer TE, a relatively noisy image. Significant improvement in the image quality of both test cases was achieved, with the most dramatic improvement noted in the noisy image. The conditions were extended to include diffusion weighting with the aim of computing diffusion maps for the tissues. Again, the wavelet procedure was effective in improving the appearance of the (diffusion-weighted) images. Finally, diffusion maps from the raw and de-noised data were computed and compared. The de-noised images produced maps with better detail and more consistent features. It is concluded that wavelet de-noising is a very useful approach for enhancing the image quality of hard-to-image tissues.