Referral to post-acute care following traumatic brain injury (TBI) in the Australian context

The study aimed to describe the types of care allocated at the end of acute care to people diagnosed with TBI and to identify the factors associated with variations in referral to care. A retrospective analysis of medical records of 61 patients was conducted based on a sample from two hospitals. While 60.7% of the study sample were referred to formal rehabilitation care, this was primarily non-inpatient rehabilitation care (32.8%). Discriminant analysis was used to determine medical and non-medical predictors of referral. Results indicated that place of treatment and age contribute to group differences and were significant in separating the inpatient rehabilitation group from the non-inpatient and no rehabilitation groups. Review by a rehabilitation physician was associated with referral to inpatient rehabilitation but was not adequate to explain referral to non-inpatient rehabilitation. An in-depth exploration of post-acute referral is warranted to improve policy and practice in relation to continuity of care following TBI.

Context-free and context-sensitive dynamics in recurrent neural networks

Continuous-valued recurrent neural networks can learn mechanisms for processing context-free languages. The dynamics of such networks is usually based on damped oscillation around fixed points in state space and requires that the dynamical components are arranged in certain ways. It is shown that qualitatively similar dynamics with similar constraints hold for a(n)b(n)c(n), a context-sensitive language. The additional difficulty with a(n)b(n)c(n), compared with the context-free language a(n)b(n), consists of 'counting up' and 'counting down' letters simultaneously. The network solution is to oscillate in two principal dimensions, one for counting up and one for counting down. This study focuses on the dynamics employed by the sequential cascaded network, in contrast to the simple recurrent network, and the use of backpropagation through time. Found solutions generalize well beyond training data, however, learning is not reliable. The contribution of this study lies in demonstrating how the dynamics in recurrent neural networks that process context-free languages can also be employed in processing some context-sensitive languages (traditionally thought of as requiring additional computation resources). This continuity of mechanism between language classes contributes to our understanding of neural networks in modelling language learning and processing.

A context noise model of episodic word recognition

Item noise models of recognition assert that interference at retrieval is generated by the words from the study list. Context noise models of recognition assert that interference at retrieval is generated by the contexts in which the test word has appeared. The authors introduce the bind cue decide model of episodic memory, a Bayesian context noise model, and demonstrate how it can account for data from the item noise and dual-processing approaches to recognition memory. From the item noise perspective, list strength and list length effects, the mirror effect for word frequency and concreteness, and the effects of the similarity of other words in a list are considered. From the dual-processing perspective, process dissociation data on the effects of length, temporal separation of lists, strength, and diagnosticity of context are examined. The authors conclude that the context noise approach to recognition is a viable alternative to existing approaches.

MHCWeb: Converting a WWW database into a knowledge-based collaborative environment

The World Wide Web (WWW) is useful for distributing scientific data. Most existing web data resources organize their information either in structured flat files or relational databases with basic retrieval capabilities. For databases with one or a few simple relations, these approaches are successful, but they can be cumbersome when there is a data model involving multiple relations between complex data. We believe that knowledge-based resources offer a solution in these cases. Knowledge bases have explicit declarations of the concepts in the domain, along with the relations between them. They are usually organized hierarchically, and provide a global data model with a controlled vocabulary, We have created the OWEB architecture for building online scientific data resources using knowledge bases. OWEB provides a shell for structuring data, providing secure and shared access, and creating computational modules for processing and displaying data. In this paper, we describe the translation of the online immunological database MHCPEP into an OWEB system called MHCWeb. This effort involved building a conceptual model for the data, creating a controlled terminology for the legal values for different types of data, and then translating the original data into the new structure. The 0 WEB environment allows for flexible access to the data by both users and computer programs.

Recognition memory effects in event-based prospective memory

Prospective memory (ProM) is the memory for future actions. It requires retrieving content of anaction in response to an ambiguous cue. Currently, it is unclear if ProM is a distinct form of memory, or merely a variant of retrospective memory (RetM). While content retrieval in ProM appears analogous to conventional RetM, less is known about the process of cue detection. Using a modified version of the standard ProM paradigm, three experiments manipulated stimulus characteristics known to influence RetM, in order to examine their effects on ProM performance. Experiment 1 (N — 80) demonstrated that low frequency stimuli elicited significantly higher hit rates and lower false alarm rates than high frequency stimuli, comparable to the mirror effect in RetM. Experiment 2 (N = 80) replicated these results, and showed that repetition of distracters during the test phase significantly increased false alarm rates to second and subsequent presentations of low frequency distracters. Building on these results. Experiment 3 (AT = 40) showed that when the study list was strengthened, the repeated presentation of targets and distracters did not significantly affect response rates. These experiments demonstrate more overlap between ProM and RetM than has previously been acknowledged. The implications for theories of ProM are considered.

EEG-based Brain-Computer Interfaces: An Overview of Basic Concepts and Clinical Applications in Neurorehabilitation

Some patients are no longer able to communicate effectively or even interact with the outside world in ways that most of us take for granted. In the most severe cases, tetraplegic or post-stroke patients are literally `locked in` their bodies, unable to exert any motor control after, for example, a spinal cord injury or a brainstem stroke, requiring alternative methods of communication and control. But we suggest that, in the near future, their brains may offer them a way out. Non-invasive electroencephalogram (EEG)-based brain-computer interfaces (BCD can be characterized by the technique used to measure brain activity and by the way that different brain signals are translated into commands that control an effector (e.g., controlling a computer cursor for word processing and accessing the internet). This review focuses on the basic concepts of EEG-based BC!, the main advances in communication, motor control restoration and the down-regulation of cortical activity, and the mirror neuron system (MNS) in the context of BCI. The latter appears to be relevant for clinical applications in the coming years, particularly for severely limited patients. Hypothetically, MNS could provide a robust way to map neural activity to behavior, representing the high-level information about goals and intentions of these patients. Non-invasive EEG-based BCIs allow brain-derived communication in patients with amyotrophic lateral sclerosis and motor control restoration in patients after spinal cord injury and stroke. Epilepsy and attention deficit and hyperactive disorder patients were able to down-regulate their cortical activity. Given the rapid progression of EEG-based BCI research over the last few years and the swift ascent of computer processing speeds and signal analysis techniques, we suggest that emerging ideas (e.g., MNS in the context of BC!) related to clinical neuro-rehabilitation of severely limited patients will generate viable clinical applications in the near future.

Analyzing the connectivity between regions of interest: An approach based on cluster Granger causality for NRI data analysis

The identification, modeling, and analysis of interactions between nodes of neural systems in the human brain have become the aim of interest of many studies in neuroscience. The complex neural network structure and its correlations with brain functions have played a role in all areas of neuroscience, including the comprehension of cognitive and emotional processing. Indeed, understanding how information is stored, retrieved, processed, and transmitted is one of the ultimate challenges in brain research. In this context, in functional neuroimaging, connectivity analysis is a major tool for the exploration and characterization of the information flow between specialized brain regions. In most functional magnetic resonance imaging (fMRI) studies, connectivity analysis is carried out by first selecting regions of interest (ROI) and then calculating an average BOLD time series (across the voxels in each cluster). Some studies have shown that the average may not be a good choice and have suggested, as an alternative, the use of principal component analysis (PCA) to extract the principal eigen-time series from the ROI(s). In this paper, we introduce a novel approach called cluster Granger analysis (CGA) to study connectivity between ROIs. The main aim of this method was to employ multiple eigen-time series in each ROI to avoid temporal information loss during identification of Granger causality. Such information loss is inherent in averaging (e.g., to yield a single ""representative"" time series per ROI). This, in turn, may lead to a lack of power in detecting connections. The proposed approach is based on multivariate statistical analysis and integrates PCA and partial canonical correlation in a framework of Granger causality for clusters (sets) of time series. We also describe an algorithm for statistical significance testing based on bootstrapping. By using Monte Carlo simulations, we show that the proposed approach outperforms conventional Granger causality analysis (i.e., using representative time series extracted by signal averaging or first principal components estimation from ROIs). The usefulness of the CGA approach in real fMRI data is illustrated in an experiment using human faces expressing emotions. With this data set, the proposed approach suggested the presence of significantly more connections between the ROIs than were detected using a single representative time series in each ROI. (c) 2010 Elsevier Inc. All rights reserved.

Hypoactive sexual desire disorder in a population-based study of Brazilian women: associated factors classified according to their importance

Objective: The etiology of hypoactive sexual desire disorder (HSDD) is known to be multifactorial, involving biological, psychosexual, and context-related factors. The objective of the present study was to analyze the factors associated with female HSDD and to stratify these factors according to their importance. Methods: This was a population-based, hierarchical study conducted in Brazil, based on data from previous research on the Brazilian Sexual Life Study, conducted between November 2002 and February 2003 in various Brazilian cities. The primary study consisted of a self-administered and anonymous questionnaire, addressing sociodemographic parameters, general health, life habits, behavior, and complaints related to sexual function. The association between HSDD and various other factors was assessed. The data were evaluated by hierarchical multiple regression analysis. Results: The prevalence of HSDD in this sample was 9.5%. Associations were found with cardiovascular disease, breast cancer, posttraumatic stress, poorer education level, being older, being married, a lack of information on sexuality in childhood/adolescence, and a limited sexual repertoire. Women who consumed moderate amounts of alcohol were found to be less likely to have HSDD. Conclusions: Analysis of the associated factors classified in order of importance and analysis of the characteristics of the sexual relationships provide additional information to currently available data on the traditional concepts of HSDD.

Family context and duration of untreated psychosis (DUP): Results from the Sao Paulo Study

Background: Duration of untreated psychosis (DUP) depends on several factors, including socio-demographic, socioeconomic, clinical and contextual circumstances, such as availability of mental health services. Living arrangements may also play a role, especially in low- and middle-income countries, where most people who develop psychosis live with their relatives. Methods: Population-based study of first-episode psychosis in Sao Paulo, Brazil. Participants were aged 18-64 years, lived in a defined geographic area of the city and had a first contact in life with mental health services due to a psychotic episode. Duration of untreated psychosis was defined as the period between onset of first psychotic symptom and first contact with health service due to psychosis. The median DUP was used to classify participants into short and long DUP. Psychopathology, social adjustment and psychiatric diagnoses were made with standardized assessments. Type of service sought and living arrangements were examined. Results: Two hundred participants were included (52% women, 61% non-affective psychoses). The median DUP was 4.1 weeks (inter-quartile range: 1.9-11.4), and was shorter for affective psychoses. Most participants had their first contact with psychiatric emergency services. Those who did not live with a relative (children older than 18 years, parents, partner) were more likely to present long DUP (OR: 2.63; 95%Cl: 0.98-7.04); p = 0.05). Conclusion: The DUP in Sao Paulo was shorter than expected. Living arrangements may play an important role in shortening the DUP in urban centres of low- and middle income countries that have a network of mental health services. (C) 2009 Elsevier B.V. All rights reserved.

Elevated left and reduced right orbitomedial prefrontal fractional anisotropy in adults with bipolar disorder revealed by tract-based spatial statistics

Context Diffusion tensor imaging (DTI) studies in adults with bipolar disorder (BD) indicate altered white matter (WM) in the orbitomedial prefrontal cortex (OMPFC), potentially underlying abnormal prefrontal corticolimbic connectivity and mood dysregulatioin in BD. Objective: To use tract-based spatial statistics (TBSS) to examine VVM skeleton (ie, the most compact whole-brain WM) in subjects with BD vs healthy control subjects. Design: Cross-sectional, case-control, whole-brain DTI using TBSS. Setting: University research institute. Participants: Fifty-six individuals, 31 having a DSM-IV diagnosis of BD type 1 (mean age, 35.9 years [age range, 24-52 years]) and 25 controls (mean age, 29.5 years [age range, 19-52 years]). Main Outcome Measures: Fractional anisotropy (FA) longitudinal and radial diffusivities in subjects with BD vs controls (covarying for age) and their relationships with clinical and demographic variables. Results: Subjects with BD vs controls had significantly greater FA (t > 3.0, P <=.05 corrected) in the left uncinate fasciculus (reduced radial diffusivity distally and increased longitudinal diffusivity centrally), left optic radiation (increased longitudinal diffusivity), and right anterothalamic radiation (no significant diffusivity change). Subjects with BD vs controls had significantly reduced FA (t > 3.0, P <=.05 corrected) in the right uncinate fasciculus (greater radial diffusivity). Among subjects with BD, significant negative correlations (P <.01) were found between age and FA in bilateral uncinate fasciculi and in the right anterothalamic radiation, as well as between medication load and FA in the left optic radiation. Decreased FA (P <.01) was observed in the left optic radiation and in the right anterothalamic radiation among subjects with BD taking vs those not taking mood stabilizers, as well as in the left optic radiation among depressed vs remitted subjects with BD. Subjects having BD with vs without lifetime alcohol or other drug abuse had significantly decreased FA in the left uncinate fasciculus. Conclusions: To our knowledge, this is the first study to use TBSS to examine WM in subjects with BD. Subjects with BD vs controls showed greater WM FA in the left OMPFC that diminished with age and with alcohol or other drug abuse, as well as reduced WM FA in the right OMPFC. Mood stabilizers and depressed episode reduced WM FA in left-sided sensory visual processing regions among subjects with BD. Abnormal right vs left asymmetry in FA in OMPFC WM among subjects with BD, likely reflecting increased proportions of left-sided longitudinally aligned and right-sided obliquely aligned myelinated fibers, may represent a biologic mechanism for mood dysregulation in BD.

Incremental parsing in language-based editors: user needs and how to meet them

Incremental parsing has long been recognized as a technique of great utility in the construction of language-based editors, and correspondingly, the area currently enjoys a mature theory. Unfortunately, many practical considerations have been largely overlooked in previously published algorithms. Many user requirements for an editing system necessarily impact on the design of its incremental parser, but most approaches focus only on one: response time. This paper details an incremental parser based on LR parsing techniques and designed for use in a modeless syntax recognition editor. The nature of this editor places significant demands on the structure and quality of the document representation it uses, and hence, on the parser. The strategy presented here is novel in that both the parser and the representation it constructs are tolerant of the inevitable and frequent syntax errors that arise during editing. This is achieved by a method that differs from conventional error repair techniques, and that is more appropriate for use in an interactive context. Furthermore, the parser aims to minimize disturbance to this representation, not only to ensure other system components can operate incrementally, but also to avoid unfortunate consequences for certain user-oriented services. The algorithm is augmented with a limited form of predictive tree-building, and a technique is presented for the determination of valid symbols for menu-based insertion. Copyright (C) 2001 John Wiley & Sons, Ltd.

An evidence-based medicine fellowship in a children's teaching hospital

This paper reports the introduction of an evidence-based medicine fellowship in a children’s teaching hospital. The results are presented of a self-reported ‘evidence-based medicine’ questionnaire, the clinical questions requested through the information retrieval service are outlined and the results of an information retrieval service user questionnaire are reported. It was confirmed that clinicians have frequent clinical questions that mostly remain unanswered. The responses to four questions with ‘good quality’ evidence-based answers were reviewed and suggest that at least one-quarter of doctors were not aware of the current best available evidence. There was a high level of satisfaction with the information retrieval service; 19% of users indicated that the information changed their clinical practice and 73% indicated that the information confirmed their clinical practice. The introduction of an evidence-based medicine fellowship is one method of disseminating the practice of evidence-based medicine in a tertiary children’s hospital.

A Context Noise Model of Episodic Recognition Memory

Item noise models of recognition assert that interference at retrieval is generated by the words from the study list. Context noise models of recognition assert that interference at retrieval is generated by the contexts in which the test word has appeared. The authors introduce the bind cue decide model of episodic memory, a Bayesian context noise model, and demonstrate how it can account for data from the item noise and dual-processing approaches to recognition memory. From the item noise perspective, list strength and list length effects, the mirror effect for word frequency and concreteness, and the effects of the similarity of other words in a list are considered. From the dual-processing perspective, process dissociation data on the effects of length. temporal separation of lists, strength, and diagnosticity of context are examined. The authors conclude that the context noise approach to recognition is a viable alternative to existing approaches. (PsycINFO Database Record (c) 2008 APA, all rights reserved)

The Building Partnerships Program: An Approach to Community-based Learning for Medical Students in Australia

The Building Partnerships Program at the University of Queensland, Australia seeks to address the dual challenge of preparing doctors who are responsive to the community while providing a meaningful context for social sciences learning. Through partnerships with a diverse range of community agencies, the program offers students opportunities to gain non-clinical perspectives on health and illness through structured learning activities including: family visits; community agency visits and attachments; and interview training. Students learn first-hand about psychosocial influences on health and how people manage health problems on a day-to-day basis. They also gain insights into the work of community agencies and how they as future doctors might work in partnership with them to enhance patient care. We outline the main components of the program, identify challenges and successes from student and community agency perspectives, and consider areas that invite further development.

Links between attachment orientations and dispositional and diary-based measures of disclosure in dating couples: A study of actor and partner effects

Issues concerning the influence of attachment characteristics on own and partner's disclosure were addressed using a sample of 113 couples in medium-term dating relationships. Individual differences in attachment were assessed in terms of relationship anxiety and avoidance. Disposition to disclose was assessed using questionnaire measures of self-disclosure, relationship-focused disclosure, and the ability to elicit disclosure from the partner; in addition, structured diaries were used to assess aspects of disclosure (amount, intimacy, emotional tone, and satisfaction) in the context of couples' everyday interactions. Couple-level analyses showed that avoidance strongly predicted dispositional measures of disclosure, whereas anxiety (particularly partner's anxiety) was related to negative evaluations of everyday interactions. Interactive effects of attachment dimensions and gender were also obtained, highlighting the complexity of communication behavior. The results are discussed in terms of the goals and strategies associated with working models of attachment.