Diagnostic Palpation in Osteopathic Medicine: A Putative Neurocognitive Model of Expertise

This thesis examines the extent to which the development of expertise in diagnostic palpation in osteopathic medicine is associated with changes in cognitive processing. Chapter 2 and Chapter 3 review, respectively, the literature on the role of analytical and non-analytical processing in osteopathic and medical clinical decision making; and the relevant research on the use of vision and haptics and the development of expertise within the context of an osteopathic clinical examination. The two studies reported in Chapter 4 examined the mental representation of knowledge and the role of analogical reasoning in osteopathic clinical decision making. The results reported there demonstrate that the development of expertise in osteopathic medicine is associated with the processes of knowledge encapsulation and script formation. The four studies reported in Chapters 5 and 6 investigate the way in which expert osteopaths use their visual and haptic systems in the diagnosis of somatic dysfunction. The results suggest that ongoing clinical practice enables osteopaths to combine visual and haptic sensory signals in a more efficient manner. Such visuo-haptic sensory integration is likely to be facilitated by top-down processing associated with visual, tactile, and kinaesthetic mental imagery. Taken together, the results of the six studies reported in this thesis indicate that the development of expertise in diagnostic palpation in osteopathic medicine is associated with changes in cognitive processing. Whereas the experts’ diagnostic judgments are heavily influenced by top-down, non-analytical processing; students rely, primarily, on bottom-up sensory processing from vision and haptics. Ongoing training and clinical practice are likely to lead to changes in the clinician’s neurocognitive architecture. This thesis proposes an original model of expertise in diagnostic palpation which has implications for osteopathic education. Students and clinicians should be encouraged to appraise the reliability of different sensory cues in the context of clinical examination, combine sensory data from different channels, and consider using both analytical and nonanalytical reasoning in their decision making. Importantly, they should develop their skills of criticality and their ability to reflect on, and analyse their practice experiences in and on action

The efficacy of an online learning tool in improving EEG analysis and interpretation skills of Technologists, Neurology Registrars and Neurologists

Background Scalp electroencephalography (EEG) remains an invaluable neurophysiological tool in supporting the diagnosis and management of epilepsy and encephalopathy, however, most sub-Saharan countries have very few neurologists per population for EEG analysis and training. Web-based, distance learning programs may provide effective electroencephalogram (EEG) training in resource-poor settings. EEGonline is an interactive, web-based, 6-month multi-modality, learning program designed to teach basic principles and clinical application of EEG. This study aimed to determine the effectiveness of EEGonline in improving EEG analysis and interpretation skills for neurologists, neurology residents and technologists, particularly in resource-limited settings. Methods Between June 2017 and November 2018, 179 learners were registered on the EEGonline course. Of these, 128 learners originating from 20 African countries, Europe, the UK and USA participated in the study. Pre- and post-course multiplechoice question (MCQ) test results and EEGonline user logs were analyzed. Differences in pre- and post-test performance were correlated with quantified exposure to various EEGonline learning modalities. Participants' impressions of EEGonline efficacy and usefulness were assessed through pre- and post-course perception surveys. Results Ninety-one participants attempted both pre- and post-course tests. Mean scores improved from 46.7% ± 17.6% to 64.1% ± 18% respectively (p< 0.001, Cohen's d 0.974). Almost all participants improved regardless of the amount of course material used, however those who used more, tended to have higher scores. The largest percentage-improvement was in the correct identification of normal features (43.2% to 59.1%, p< 0.001, Cohen's d 0.664) and artefacts (43.3% to 61.6%, p< 0.001, Cohen's d 0.836). Improvement in competence was associated with improvement in subjective confidence in EEG analysis. Overall confidence among 72 survey respondents improved significantly from 25.3% to 64.8% (p< 0.001). Lecture notes, end-of-module self-assessment quizzes and discussion forums were the most utilised learning modalities. The majority of survey respondents (97.2%) concluded that EEGonline was a useful learning tool and 93% recommended that similar courses should be included in EEG training curricula. Discussion Almost all participants showed significant improvement in EEG analysis competence (MCQ test scores) and confidence (survey responses) following the educational intervention, regardless of the amount of course material used. Improved identification of normal features and artefacts is particularly useful as it reduces the risk of misdiagnosis which can cause harm. The EEGonline course employed several learning techniques, through its multi-modality format, that may have contributed to the improvement observed, including, self-directed learning, cognitivism, collaborative learning, contextual learning and reflective learning. Subjective confidence likely correlates with competence and may be useful to gauge learners' needs and levels of understanding about a subject. Learning preferences vary among adult learners, it is unclear if one learning modality (that is, video, audio, lecture notes, epoch activities, discussion forums) is superior to others, but it seems as though a multi-modal approach may be the most sensible. Conclusions This study demonstrated that a multi-modal, online EEG teaching tool was effective in improving EEG analysis and interpretation skills and may be a useful supplement for EEG teaching especially in resource-poor settings. Given the optimistic findings of this study, we encourage the development and evaluation of further online neurology teaching tools

Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research

By promising more accurate diagnostics and individual treatment recommendations, deep neural networks and in particular convolutional neural networks have advanced to a powerful tool in medical imaging. Here, we first give an introduction into methodological key concepts and resulting methodological promises including representation and transfer learning, as well as modelling domain-specific priors. After reviewing recent applications within neuroimaging-based psychiatric research, such as the diagnosis of psychiatric diseases, delineation of disease subtypes, normative modeling, and the development of neuroimaging biomarkers, we discuss current challenges. This includes for example the difficulty of training models on small, heterogeneous and biased data sets, the lack of validity of clinical labels, algorithmic bias, and the influence of confounding variables