Mentalization-Based Treatment

The concept of mentalizing has captured the interest and imagination of an astonishing range of people—from psychoanalysts to neuroscientists, from child development researchers to geneticists, from existential philosophers to phenomenologists—all of whom seem to have found it useful. According to the Thompson Reuter maintained Web of Science, the use of the term in titles and abstracts of scientific papers increased from 10 to 2,750 between 1991 and 2011. Clinicians in particular have enthusiastically embraced the idea, and have put it to innovative use in their practices. Mentalization-based treatment (MBT)—making mentalizing a core focus of therapy—was initially developed for the treatment of borderline personality disorder (BPD) in routine clinical services delivered in group and individual modalities. Therapy with mentalizing as a central component is currently being developed for treatment of numerous groups, including people with antisocial personality disorder, substance abuse, eating disorders, and at-risk mothers with infants and children (A. Bateman & Fonagy, 2011). It is also being used with families and adolescents, in schools, and in managing social groups (Asen & Fonagy, 2011; Fonagy et al., 2009; Twemlow, Fonagy, & Sacco, 2005a, 2005b). In this article, we focus on MBT in the treatment of BPD

Accommodating Asperger's: an autoethnography on the learning experience in an e-learning music education program

Thesis (D.M.A.)--Boston UniversityA student with Asperger's Syndrome faces a complex myriad of learning disabilities and social difficulties. The co-morbid conditions of dyslexia, Obsessive Compulsive Disorder, Attention Deficit Disorder, Attention Deficit Hyperactive Disorder and anxiety further complicate Asperger's Syndrome. Asperger's Syndrome and these conditions, singularly and in combination, have the potential to significantly hamper a student's achievement and success in learning environments. I am a person with Asperger's Syndrome, formerly diagnosed as Autism Spectrum Disorder-High Functioning, engaged in Boston University's Doctorate in Music Education Program delivered via E-learning modalities. The research question, "How does the E-learning modality serve the needs of a student with Asperger's Syndrome in the field of music education?" was a direct product of my personal experience with the convergence of E-learning, music education and Asperger's Syndrome. Autoethnography was employed as the research strategy to explore this convergence. The primary data source was a journal spanning almost three decades in conjunction with artifacts and other data sources. The data analysis and interpretation was completed through self-reflective and selfnarrative writing. The findings of this study, suggest that while E-learning modalities present both positives and negatives for students with Asperger's Syndrome; the potential to alleviate many of the challenges they face makes this is an excellent alternative to the traditional classroom educational delivery method in the field of music education. Further this research highlights the importance for educators to reflect on their own teaching methods and the profession to continually evaluate the methods utilized in delivering content and assessing achievement

Automatic Detection and Characterization of Pathological Fluid Regions in Optical Coherence Tomography Images

Programa Oficial de Doutoramento en Computación. 5009V01[Abstract] Intraretinal fluid accumulation is both the common symptom and culprit of the main causes of blindness in developed countries: Age-related Macular Degeneration and Diabetic Macular Edema. For its diagnosis, experts of the domain employ Optical Coherence Tomography images (OCT), providing non-invasive cross-sectional representations of the retinal structures. However, like any medical imaging modality, OCT is influenced by multiple factors that impact its quality and subsequent interpretation. Coupled with the subjectiveness of the human experts, these factors can significantly affect the diagnostic process, treatment and quality of life for the affected individuals (particularly in these pathologies where early detection is crucial). To address these challenges, Computer-Aided Diagnosis (CAD) methodologies are developed, offering a layer of abstraction of the information present in the images. Still, in the particular scenario of these pathological fluid accumulations, the development of these methodologies is specially difficult due to their diffuse nature without defined boundaries. In this thesis, we proposed different CAD methodologies with the objective of helping expert clinicians to better detect and understand these pathologies. Furthermore, we expand the developed methodologies to other medical imaging modalities and conditions, such as macular neovascularizations in OCT Angiographies and COVID-19 diagnosis through the analysis of lung chest radiographs.[Resumen] La acumulación de líquido intrarretiniano es tanto síntoma común como culpable de las principales causas de ceguera en los países desarrollados: la degeneración macular asociada a la edad y el edema macular diabético. Para su diagnóstico, los expertos en el campo emplean imágenes de Tomografía de Coherencia Óptica (OCT), que proporcionan representaciones transversales no invasivas de las estructuras retinianas. Sin embargo, al igual que cualquier modalidad de imagen médica, OCT se ve influenciado por múltiples factores que afectan a su calidad y posterior interpretación. Junto con la subjetividad de los expertos humanos, estos factores pueden afectar significativamente el proceso diagnóstico, tratamiento y calidad de vida de las personas afectadas (particularmente en estas patologías donde una detección temprana es crucial). Para abordar estos desafíos, se desarrollan metodologías de diagnóstico asistido por ordenador (CAD), que ofrecen una capa de abstracción de la información presente en las imágenes. Sin embargo, en el escenario particular de estas acumulaciones patológicas de fluido, el desarrollo de estas metodologías es especialmente difícil debido a su naturaleza difusa, sin bordes definidos. En esta tesis doctoral proponemos diferentes metodologías CAD con el objetivo de ayudar a las personas expertas del dominio a detectar y comprender mejor estas patologías. Además, expandimos las metodologías desarrolladas a otras modalidades de imagen médica y afecciones, como al análisis de neovascularizaciones maculares en Angiografía OCT y al diagnóstico de COVID-19 mediante radiografías torácicas.[Resumo] A acumulación de líquido intrarretiniano é tanto o síntoma común como culpable das principais causas de cegueira nos países desenvolvidos: a dexeneración macular asociada á idade e o edema macular diabético. Para o seu diagnóstico, os expertos no campo empregan imaxes de tomografía de coherencia óptica (OCT), que proporcionan representacións transversais non invasivas das estruturas retinianas. Non obstante, ao igual que calquera modalidade de imaxe médica, a OCT vese influenciada por múltiples factores que afectan a s´ua calidade e a súa posterior interpretación. Xunto coa subxectividade dos expertos humanos, estes factores poden afectar significativamente ao proceso diagn´ostico, ao tratamento e á calidade de vida das persoas afectadas (particularmente nestas patoloxías onde unha detección precoz é crucial). Para abordar estes desafíos, desenvólvense metodoloxías de diagnóstico asistido por ordenador (CAD), que ofrecen unha capa de abstracción da información presente nas imaxes. Non obstante, no escenario particular das acumulacións patolóxicas de líquido, o desenvolvemento destas metodoloxías é especialmente difícil debido a súa natureza difusa, sen bordes definidos. Nesta tese de doutoramento propoñemos diferentes metodoloxías de CAD co obxectivo de axudar ás persoas expertas do campo a detectar e comprender mellor estas patoloxías. Ademais, expandimos as metodoloxías desenvoltas a outras modalidades de imaxe médica e patoloxías, como a an´alise de neovascularizacións maculares en Anxiografía OCT e ao diagnóstico da COVID-19 mediante a análise de radiografías torácicas

Deep Learning Techniques for Automated Analysis and Processing of High Resolution Medical Imaging

Programa Oficial de Doutoramento en Computación . 5009V01[Abstract] Medical imaging plays a prominent role in modern clinical practice for numerous medical specialties. For instance, in ophthalmology, different imaging techniques are commonly used to visualize and study the eye fundus. In this context, automated image analysis methods are key towards facilitating the early diagnosis and adequate treatment of several diseases. Nowadays, deep learning algorithms have already demonstrated a remarkable performance for different image analysis tasks. However, these approaches typically require large amounts of annotated data for the training of deep neural networks. This complicates the adoption of deep learning approaches, especially in areas where large scale annotated datasets are harder to obtain, such as in medical imaging. This thesis aims to explore novel approaches for the automated analysis of medical images, particularly in ophthalmology. In this regard, the main focus is on the development of novel deep learning-based approaches that do not require large amounts of annotated training data and can be applied to high resolution images. For that purpose, we have presented a novel paradigm that allows to take advantage of unlabeled complementary image modalities for the training of deep neural networks. Additionally, we have also developed novel approaches for the detailed analysis of eye fundus images. In that regard, this thesis explores the analysis of relevant retinal structures as well as the diagnosis of different retinal diseases. In general, the developed algorithms provide satisfactory results for the analysis of the eye fundus, even when limited annotated training data is available.[Resumen] Las técnicas de imagen tienen un papel destacado en la práctica clínica moderna de numerosas especialidades médicas. Por ejemplo, en oftalmología es común el uso de diferentes técnicas de imagen para visualizar y estudiar el fondo de ojo. En este contexto, los métodos automáticos de análisis de imagen son clave para facilitar el diagnóstico precoz y el tratamiento adecuado de diversas enfermedades. En la actualidad, los algoritmos de aprendizaje profundo ya han demostrado un notable rendimiento en diferentes tareas de análisis de imagen. Sin embargo, estos métodos suelen necesitar grandes cantidades de datos etiquetados para el entrenamiento de las redes neuronales profundas. Esto complica la adopción de los métodos de aprendizaje profundo, especialmente en áreas donde los conjuntos masivos de datos etiquetados son más difíciles de obtener, como es el caso de la imagen médica. Esta tesis tiene como objetivo explorar nuevos métodos para el análisis automático de imagen médica, concretamente en oftalmología. En este sentido, el foco principal es el desarrollo de nuevos métodos basados en aprendizaje profundo que no requieran grandes cantidades de datos etiquetados para el entrenamiento y puedan aplicarse a imágenes de alta resolución. Para ello, hemos presentado un nuevo paradigma que permite aprovechar modalidades de imagen complementarias no etiquetadas para el entrenamiento de redes neuronales profundas. Además, también hemos desarrollado nuevos métodos para el análisis en detalle de las imágenes del fondo de ojo. En este sentido, esta tesis explora el análisis de estructuras retinianas relevantes, así como el diagnóstico de diferentes enfermedades de la retina. En general, los algoritmos desarrollados proporcionan resultados satisfactorios para el análisis de las imágenes de fondo de ojo, incluso cuando la disponibilidad de datos de entrenamiento etiquetados es limitada.[Resumo] As técnicas de imaxe teñen un papel destacado na práctica clínica moderna de numerosas especialidades médicas. Por exemplo, en oftalmoloxía é común o uso de diferentes técnicas de imaxe para visualizar e estudar o fondo de ollo. Neste contexto, os métodos automáticos de análises de imaxe son clave para facilitar o diagn ostico precoz e o tratamento adecuado de diversas enfermidades. Na actualidade, os algoritmos de aprendizaxe profunda xa demostraron un notable rendemento en diferentes tarefas de análises de imaxe. Con todo, estes métodos adoitan necesitar grandes cantidades de datos etiquetos para o adestramento das redes neuronais profundas. Isto complica a adopción dos métodos de aprendizaxe profunda, especialmente en áreas onde os conxuntos masivos de datos etiquetados son máis difíciles de obter, como é o caso da imaxe médica. Esta tese ten como obxectivo explorar novos métodos para a análise automática de imaxe médica, concretamente en oftalmoloxía. Neste sentido, o foco principal é o desenvolvemento de novos métodos baseados en aprendizaxe profunda que non requiran grandes cantidades de datos etiquetados para o adestramento e poidan aplicarse a imaxes de alta resolución. Para iso, presentamos un novo paradigma que permite aproveitar modalidades de imaxe complementarias non etiquetadas para o adestramento de redes neuronais profundas. Ademais, tamén desenvolvemos novos métodos para a análise en detalle das imaxes do fondo de ollo. Neste sentido, esta tese explora a análise de estruturas retinianas relevantes, así como o diagnóstico de diferentes enfermidades da retina. En xeral, os algoritmos desenvolvidos proporcionan resultados satisfactorios para a análise das imaxes de fondo de ollo, mesmo cando a dispoñibilidade de datos de adestramento etiquetados é limitada

A self-management programme to reduce falls and improve safe mobility in people with secondary progressive MS: the BRiMS feasibility RCT

This is the final version, also available from NIHR journals library via the DOI in this record.Abstract Background Balance, mobility impairments and falls are common problems for people with multiple sclerosis (MS). Our ongoing research has led to the development of Balance Right in MS (BRiMS), a 13-week home- and group-based exercise and education programme intended to improve balance and encourage safer mobility. Objective This feasibility trial aimed to obtain the necessary data and operational experience to finalise the planning of a future definitive multicentre randomised controlled trial. Design Randomised controlled feasibility trial. Participants were block randomised 1 : 1. Researcher-blinded assessments were scheduled at baseline and at 15 and 27 weeks post randomisation. As is appropriate in a feasibility trial, statistical analyses were descriptive rather than involving formal/inferential comparisons. The qualitative elements utilised template analysis as the chosen analytical framework. Setting Four sites across the UK. Participants Eligibility criteria included having a diagnosis of secondary progressive MS, an Expanded Disability Status Scale (EDSS) score of between ≥ 4.0 and ≤ 7.0 points and a self-report of two or more falls in the preceding 6 months. Interventions Intervention – manualised 13-week education and exercise programme (BRiMS) plus usual care. Comparator – usual care alone. Main outcome measures Trial feasibility, proposed outcomes for the definitive trial (including impact of MS, mobility, quality of life and falls), feasibility of the BRiMS programme (via process evaluation) and economic data. Results A total of 56 participants (mean age 59.7 years, standard deviation 9.7 years; 66% female; median EDSS score of 6.0 points, interquartile range 6.0–6.5 points) were recruited in 5 months; 30 were block randomised to the intervention group. The demographic and clinical data were broadly comparable at baseline; however, the intervention group scored worse on the majority of baseline outcome measures. Eleven participants (19.6%) withdrew or were lost to follow-up. Worsening of MS-related symptoms unrelated to the trial was the most common reason (n = 5) for withdrawal. Potential primary and secondary outcomes and economic data had completion rates of > 98% for all those assessed. However, the overall return rate for the patient-reported falls diary was 62%. After adjusting for baseline score, the differences between the groups (intervention compared with usual care) at week 27 for the potential primary outcomes were MS Walking Scale (12-item) version 2 –7.7 [95% confidence interval (CI) –17.2 to 1.8], MS Impact Scale (29-item) version 2 (MSIS-29vs2) physical 0.6 (95% CI –7.8 to 9) and MSIS-29vs2 psychological –0.4 (95% CI –9.9 to 9) (negative score indicates improvement). After the removal of one outlier, a total of 715 falls were self-reported over the 27-week trial period, with substantial variation between individuals (range 0–93 falls). Of these 715 falls, 101 (14%) were reported as injurious. Qualitative feedback indicated that trial processes and participant burden were acceptable, and participants highlighted physical and behavioural changes that they perceived to result from undertaking BRiMS. Engagement varied, influenced by a range of condition- and context-related factors. Suggestions to improve the utility and accessibility of BRiMS were highlighted. Conclusions The results suggest that the trial procedures are feasible and acceptable, and retention, programme engagement and outcome completion rates were sufficient to satisfy the a priori progression criteria. Challenges were experienced in some areas of data collection, such as completion of daily diaries.National Institute for Health Research (NIHR

Preface

DAMSS-2018 is the jubilee 10th international workshop on data analysis methods for software systems, organized in Druskininkai, Lithuania, at the end of the year. The same place and the same time every year. Ten years passed from the first workshop. History of the workshop starts from 2009 with 16 presentations. The idea of such workshop came up at the Institute of Mathematics and Informatics. Lithuanian Academy of Sciences and the Lithuanian Computer Society supported this idea. This idea got approval both in the Lithuanian research community and abroad. The number of this year presentations is 81. The number of registered participants is 113 from 13 countries. In 2010, the Institute of Mathematics and Informatics became a member of Vilnius University, the largest university of Lithuania. In 2017, the institute changes its name into the Institute of Data Science and Digital Technologies. This name reflects recent activities of the institute. The renewed institute has eight research groups: Cognitive Computing, Image and Signal Analysis, Cyber-Social Systems Engineering, Statistics and Probability, Global Optimization, Intelligent Technologies, Education Systems, Blockchain Technologies. The main goal of the workshop is to introduce the research undertaken at Lithuanian and foreign universities in the fields of data science and software engineering. Annual organization of the workshop allows the fast interchanging of new ideas among the research community. Even 11 companies supported the workshop this year. This means that the topics of the workshop are actual for business, too. Topics of the workshop cover big data, bioinformatics, data science, blockchain technologies, deep learning, digital technologies, high-performance computing, visualization methods for multidimensional data, machine learning, medical informatics, ontological engineering, optimization in data science, business rules, and software engineering. Seeking to facilitate relations between science and business, a special session and panel discussion is organized this year about topical business problems that may be solved together with the research community. This book gives an overview of all presentations of DAMSS-2018.DAMSS-2018 is the jubilee 10th international workshop on data analysis methods for software systems, organized in Druskininkai, Lithuania, at the end of the year. The same place and the same time every year. Ten years passed from the first workshop. History of the workshop starts from 2009 with 16 presentations. The idea of such workshop came up at the Institute of Mathematics and Informatics. Lithuanian Academy of Sciences and the Lithuanian Computer Society supported this idea. This idea got approval both in the Lithuanian research community and abroad. The number of this year presentations is 81. The number of registered participants is 113 from 13 countries. In 2010, the Institute of Mathematics and Informatics became a member of Vilnius University, the largest university of Lithuania. In 2017, the institute changes its name into the Institute of Data Science and Digital Technologies. This name reflects recent activities of the institute. The renewed institute has eight research groups: Cognitive Computing, Image and Signal Analysis, Cyber-Social Systems Engineering, Statistics and Probability, Global Optimization, Intelligent Technologies, Education Systems, Blockchain Technologies. The main goal of the workshop is to introduce the research undertaken at Lithuanian and foreign universities in the fields of data science and software engineering. Annual organization of the workshop allows the fast interchanging of new ideas among the research community. Even 11 companies supported the workshop this year. This means that the topics of the workshop are actual for business, too. Topics of the workshop cover big data, bioinformatics, data science, blockchain technologies, deep learning, digital technologies, high-performance computing, visualization methods for multidimensional data, machine learning, medical informatics, ontological engineering, optimization in data science, business rules, and software engineering. Seeking to facilitate relations between science and business, a special session and panel discussion is organized this year about topical business problems that may be solved together with the research community. This book gives an overview of all presentations of DAMSS-2018

Preparing to Teach, Committing to Learn: An Introduction to Educating Children Who Are Deaf/Hard of Hearing

Preparing to Teach is an open-source eBook on deaf education for students and faculty in professional preparation programs, practitioners in deaf education, and families of children who are DHH. Although the focus is on deaf-education teachers who are providing services to students who are DHH, the text is useful to students and faculty in related fields including speech-language pathology, audiology, and special education. Because the text is introductory in nature it covers a broad range of topics and does not replace texts that go into more depth on a particular aspect of deaf education. The text includes references and additional recommended readings and resources for further study. The text addresses the range of communication options used by students in deaf education with an emphasis on listening and spoken language approaches and strategies that have often been only minimally addressed in introductory texts. References and resources for further study of approaches that are primarily visual are included.https://griffinshare.fontbonne.edu/oer-books/1000/thumbnail.jp

Management of cannabis use disorder and related issues. A clinician’s guide.

This guide provides the knowledge essential to help people reduce, cease, or manage their cannabis-use problems. The manual aims to provide facts, figures, and useful techniques to assist clinicians in providing evidence-based treatments for cannabis users wishing to change the patterns of their use. The manual also provides a number of worksheets to use with cannabis clients. The manual is divided into 12 sections: Chapter 1: Background information Chapter 2: Working with cannabis users Chapter 3: Screening for cannabis use Chapter 4: Assessment Chapter 5: Withdrawal management Chapter 6: Brief interventions Chapter 7: Special considerations Chapter 8: Family interventions Chapter 9: Psycho-education and social support Chapter 10: Treating mental health in cannabis users Chapter 11: Screening tools Chapter 12: Worksheet

Psychologically informed vestibular rehabilitation for persistent dizziness

Vertigo or dizziness is not a disease, but rather a leading symptom of various underlying conditions. These include disorders of the vestibular system, which is responsible for our sense of balance and motion. Many people with vestibular system disorders experience persistent dizziness that can be particularly hard to treat. Vestibular rehabilitation therapy (VRT) is an established physiotherapy-based treatment for people with vestibular dysfunction, which tries to reduce dizziness and improve postural and gaze stability. However, the benefits are not universal and do not always correlate with physiological findings. Poor response to VRT may be because there are concomitant psychosocial factors contributing to the chronicity of the symptoms which are not addressed during VRT. Previous studies have shown a correlation between dizziness and anxiety and depression. Studies of cognitive-behavioural therapy (CBT) for persistent dizziness have to date focused on reducing generalised anxiety to determine if this in turn relieves the dizziness symptoms. This appears to have limited or only short-term success. This may be because although anxiety and depression likely play a role in exacerbating symptoms, mood is only one factor in this multifactorial condition. Understanding a broader range of psychosocial factors specific to vestibular disorders may be needed to provide more tailored and targeted CBT. Combining this with VRT would provide an integrated approach to treating both physiological and psychological features of the disorder. The central question of this thesis was to see whether we could design and evaluate the feasibility of an empirically derived theory-based ‘CBT informed’ vestibular rehabilitation intervention for people with persistent dizziness. The project followed the Medical Research Council framework for developing and evaluating complex interventions. A systematic review of 89 studies using meta-analysis and narrative synthesis identified potentially modifiable psychosocial factors from existing research related to dizziness handicap and symptom severity. A longitudinal survey (n =185 pre diagnosis) was conducted to test the relationship between relevant psychological variables, clinical tests of vestibular deficits and dizziness handicap and subjective symptoms. The cross-sectional results showed that the psychological factors which included distress, negative illness perceptions, and unhelpful cognitive-behavioural responses to symptoms explained >50% of the variance in self-reported handicap and around 30% of the variance in symptom severity. Following diagnosis (n=135) an all-or-nothing erratic pattern of behaviour and experiencing symptoms for a longer time predicted higher dizziness handicap, although baseline ‘handicap’ was the strongest predictor. The results of the review and the survey, together with patient-public representation, informed the development of an integrated manual-based programme of ‘CBT informed’ vestibular rehabilitation, called INVEST, combining cognitive behavioural therapy and physical rehabilitation. A parallel group randomised controlled pilot-feasibility trial was then conducted, with 40 participants with persistent dizziness who were randomly assigned to receive 6 sessions of INVEST (n=20) or current ‘gold standard’ VRT (n=20). Participants were individually randomised using a minimisation procedure with allocation concealment. Both interventions were delivered by specialist physiotherapists. Primary feasibility and self-report outcomes were collected at baseline and 4 months post randomisation. A nested qualitative study was also conducted post-intervention to explore the acceptability of the intervention and identify any areas in need of improvement. This study demonstrated excellent acceptability and feasibility. The study met all the a-priori criteria to progress to a full-scale efficacy trial, including 80% of eligible patients participating (pre-defined criteria >70%), 15% therapy and 2.5% trial drop-out rates (criteria <20%), comparable acceptability ratings to current gold standard VRT, and 80% adherence to sessions (criteria >60%). Fifty-nine percent of patients screened met the selection criteria and the enrolment rate was 80%. According to the qualitative data and exploratory treatment effect sizes, the intervention appeared to be both acceptable and potentially beneficial. This thesis improves our understanding of chronic vestibular symptoms. It provides invaluable information to inform a larger scale trial of an intervention that could potentially improve the quality of life of sufferers, above and beyond standard physiotherapy care. By delivering the intervention as part of physiotherapy, this in turn will improve the access to psychological therapies and use of available resources; reduce the need for patients to see more than one healthcare professional and allow clinicians to respond to patient preference. Improving the outcome of rehabilitation may have additional beneficial social and economic implications as the patient is able to better manage their condition. Given the high prevalence of persistent dizziness in audio-vestibular, neuro-otology, and VRT clinics there is a sufficient need, and number of patients, to run a fully powered RCT

Deep Learning in Medical Image Analysis

The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis