CO-CONSTRUCTING A LEARNING EXPERIENCE TO APPROACH MENTAL ILLNESS IN THE CLASSROOM: A TEACHING MICRO-SCENARIO

Social distancing, isolation, stress, and fear in the times of the COVID-19 pandemic are factors that trigger or exacerbate mental health conditions. Further to this, mental health literacy is particularly relevant to secondary education as puberty is a common age of onset of mental disorders. Nevertheless, the topic is somewhat overlooked due to teaching challenges that involve potential emotional triggers, the complex and sensitive nature of the issue, as well as a limited pool of educational resources. Here, we propose a teaching micro-scenario that addresses the topic of mental health literacy via an interdisciplinary approach that promotes active learning. First, students are introduced to the neurobiology of mental disorders by means of engaging with interactive audiovisual materials and a 3D brain simulation. Then, students work on their statistics skills by calculating estimates on affected populations including the school community. Finally, art and creativity are employed to explore healing and public health. The evaluation of the proposed learning intervention is achieved in the form of plenary discussion.

Bridging Scales in Alzheimer's Disease: Biological Framework for Brain Simulation With The Virtual Brain

Despite the acceleration of knowledge and data accumulation in neuroscience over the last years, the highly prevalent neurodegenerative disease of AD remains a growing problem. Alzheimer's Disease (AD) is the most common cause of dementia and represents the most prevalent neurodegenerative disease. For AD, disease-modifying treatments are presently lacking, and the understanding of disease mechanisms continues to be incomplete. In the present review, we discuss candidate contributing factors leading to AD, and evaluate novel computational brain simulation methods to further disentangle their potential roles. We first present an overview of existing computational models for AD that aim to provide a mechanistic understanding of the disease. Next, we outline the potential to link molecular aspects of neurodegeneration in AD with large-scale brain network modeling using The Virtual Brain (www.thevirtualbrain.org), an open-source, multiscale, whole-brain simulation neuroinformatics platform. Finally, we discuss how this methodological approach may contribute to the understanding, improved diagnostics, and treatment optimization of AD.

Can We Learn Anything from Brain Simulation?

If you figure out how machines learn, then you will figure out how the brain works, and what the brain’s functions are. Such an idea is widespread among philosophers and computer scientists who agree with a functionalist reductionist point of view of consciousness. This theory leads to hold that the more accurate the simulation of cognitive behavior is, the more the math behind it must be true – when true means what really happens in our brain. In this article, we aim to show that, on one hand, brain simulation is nothing more than just another simulation, and it offers very little help to understand – nor to produce – the vivid experiences (qualia) of cognitive functions. On the other hand, we would like to emphasize that when it succeeds at predicting a mechanism with less ambiguity and more accuracy than without a simulation nor direct observation, it really develops the knowledge of our brain. As long as brain simulation follows scientific principles, it should be regarded as valuable, even though the knowledge it brings to science must not be confused for the real phenomenon. Brain simulation, like all simulation, cannot fill any reality or epistemic gap. It is a consolation prize.

From genes to network models of Alzheimer's disease: Biological framework for multi-scale brain simulation with The Virtual Brain

While the knowledge in neuroscience and the possibilities in clinical neurology have improved for many years, neurogenerative diseases and associated dementia remain a growing problem. Alzheimer’s Disease (AD) is the most common cause of dementia and also represents the most prevalent type of neurodegenerative diseases. For AD, disease-modifying treatments are presently lacking and the understanding of disease mechanisms remain incomplete. In the present review, we consider candidate contributing factors leading to AD and we evaluate novel computational brain simulation methods to further disentangle their potential roles. We first discuss existing computational models of AD that aim to provide a mechanistic understanding of the disease. Next, we outline the potential to link molecular aspects of neurodegeneration in AD with large-scale brain network modeling using The Virtual Brain (TVB, www.thevirtualbrain.org), an open-source, multi- scale, whole-brain simulation neuroinformatics platform. Finally, we discuss how this methodological approach may contribute to the understanding, improved diagnostics and treatment of AD.

Anaesthetic management of patients undergoing deep brain simulation: A retrospective review of 8 cases from a tertiary care center of Pakistan

Objectives: To review anaesthesia related outcome, perioperative complications and overall length of stay (LOS) in hospital for patients who had deep brain stimulation (DBS). Methods: The study was retrospective review of patients medical records diagnosed with Parkinson disease (PD) and underwent DBS at The Aga Khan University Hospital, Karachi from 2017-2019. Data was reviewed from file notes and patient chart and recorded on predesigned Performa. Frequency and percentages were used to present the data. Results: All patients were anaesthetized using Sleep-Awake-Sleep technique (SAS). Dexmedetomidine was mainly used for conscious sedation. Bispectral index monitor (BIS) was used to monitor the depth of sedation, and kept between 70-85 during sedative phase. All patients had successful intraoperative neurological monitoring, stimulation, and placement of electrodes. Total duration of anesthesia varied significantly in between the patients. Maximum duration was 600 minutes. None of our patient had any intraoperative event related to anaesthetic management. Overall five patients had some adverse events during ward stay. Mean LOS in hospital was four days. Conclusion: Anaesthetic management of DBS is well-tolerated. It requires dedicated team. The SAS technique is excellent for intraoperative neurophysiological monitoring. Careful selection of sedative agents and monitoring depth of anaesthesia using BIS would be beneficial in terms of improving related outcomes. doi: https://doi.org/10.12669/pjms.36.7.2870 How to cite this:Ahmed U, Shafiq F, Kumar D, Ahsan K, Ghaffar W, Bari E. Anaesthetic management of patients undergoing deep brain simulation: A retrospective review of 8 cases from a tertiary care center of Pakistan. Pak J Med Sci. 2020;36(7):---------. doi: https://doi.org/10.12669/pjms.36.7.2870 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.