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Neuroenhancement and Therapy in National Defense Contexts
Published in L. Syd M Johnson, Karen S. Rommelfanger, The Routledge Handbook of Neuroethics, 2017
Michael N. Tennison, Jonathan D. Moreno
Cutting-edge efforts to modulate neurological functioning begin with breakthroughs in modeling the brain and its operation in relevant circumstances. Brain modeling presents a dizzying array of possibilities and many corresponding ethical issues. As a dual-use field, it could create pathways for new therapies for persons with dementia. At another extreme, the notion of a reasonably complete brain simulation (whatever that would mean) has stimulated worries that an advanced machine intelligence could constitute a threat to human survival. Lying somewhere between therapy and existential risk, brain modeling might lead to cognitive enhancements. DARPA and IARPA have several projects funding this kind of research.
Diagnosing and managing post-stroke aphasia
Published in Expert Review of Neurotherapeutics, 2021
Shannon M. Sheppard, Rajani Sebastian
Noninvasive brain simulation techniques are increasingly used to modulate brain plasticity and accelerate language recovery. However, it remains unclear which area of the brain (left, right, or cerebellum), and which kind of stimulation (inhibitory or excitatory) is more effective in augmenting aphasia treatment. To know which region to target with NIBS, we need a better understanding of the mechanisms underlying recovery from aphasia. Similar to pharmacotherapy, there are wide variations in experimental factors including different types of aphasia, lesion site and location, NIBS stimulation parameters, different types of language therapy, therapy duration, and different outcome measures. This presents a major challenge to interpreting the findings. Finally, there are several practical issues that need to be addressed before we can adopt tDCS in clinical settings including training of clinicians, affordability, and reimbursement. It is also essential to include outcome measures that show that intervention (behavioral, brain stimulation, or pharmacotherapy) makes a difference in functional communication or quality of life, rather than simply focusing on impairment-based outcome measures.
Kathleen Mears Memorial Lecture: How We Can Solidify the Future of Neurodiagnostic Technology
Published in The Neurodiagnostic Journal, 2019
Another factor we need to consider is future medical advancements, which will undoubtedly create more demand for neurodiagnostic testing. Therapeutic interventions for progressive neurodegenerative diseases such as stem cell technology are emerging. It’s not legal in all states, but I believe eventually it will be and we’re going to see a big boom in stem cell research and utilization for treatment of neurological disorders, which will increase the demand for neurodiagnostic testing services. Another example is the use of interventions, such as neuro stimulation. Vagal nerve stimulation for the treatment of epilepsy and deep brain simulation to alleviate the symptoms of Parkinson’s disease are good examples of this, which will again, impact the need for neurodiagnostic testing. We have some great things coming, but we’re going to need staff to accommodate new advances in technology.
Construction of a risk model through the fusion of experimental data and finite element modeling: Application to car crash-induced TBI
Published in Computer Methods in Biomechanics and Biomedical Engineering, 2019
Seyed Saeed Ahmadisoleymani, Samy Missoum
The method is based on a multi-level framework constituted of a FE crash simulation with a dummy and a FE brain simulation. The kinematic data (e.g., accelerations) from the crash simulations are transferred to the FE brain model (SIMon (Takhounts et al. 2003, 2008)), which is used to provide a CSDM injury metric. The effect of parameters such as material properties and crash conditions on the probability of TBI – chosen as DAI in this work – is assessed through a metamodel (support vector machine (SVM) classifier (Basudhar et al. 2008; Lin et al. 2012)) which segregates the space of parameters (e.g., velocity, impact angle, material properties) into “safe” (uninjured) and “failure” (injured) regions. The failure region corresponds to cases where a critical threshold of the CSDM injury measure, obtained from the logistic regression model provided in (Takhounts et al. 2008), is reached. The SVM classifier is built based on an initial design of computer experiments followed by a specific adaptive sampling scheme (Basudhar and Missoum (2008)). The probability of DAI, for a given CSDM metric threshold, is then obtained in a straightforward manner using Monte-Carlo simulations (Melchers and Beck 2018; Zio 2013). The total probability of DAI, which combines information from the SVM and the logistic regression model, is finally obtained through a weighted average over CSDM metric thresholds.