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Embedding Ethics in Neural Engineering
Published in Evelyn Brister, Robert Frodeman, A Guide to Field Philosophy, 2020
Although we were just feeling our way that first summer, in retrospect, what we developed was a bottom-up approach to understanding the needs of our Center. Starting with interviews with the PIs gave us important scientific grounding in the area and a good sense of the range of projects housed within the Center, but it also positioned us as potential collaborators on ethical issues, rather than as ethics “police.” By reaching out to researchers early on we demonstrated our commitment to understanding what they do, and our commitment to helping to shape technology development with them. In the interviews, we treated the PIs not just as experts in their own areas (e.g., electrode design, neurosurgery, computational neuroscience, bioengineering) but also as people well-positioned to help us recognize and think through potentially troubling ethical matters. Of course, we also brought our own expertise to the exchange. We raised issues related to human identity, privacy, responsibility, and security, inviting the PIs to explore with us how these fundamental human values intersect with the kinds of work undertaken in their labs and beyond. We emphasized our philosophical training, to make clear what we could offer in the collaboration. In so doing, we also proclaimed what we were not: people who would take over all the applications to the institutional review board (IRB) for projects using human subjects, or with the expertise to help navigate through regulatory processes of device approval (e.g., with the Food and Drug Administration).
The Autism Phenotype
Published in Elizabeth B. Torres, Caroline Whyatt, Autism, 2017
Given the mounting evidence at both a behavioral and a physiological level for an underlying etiology of ASD, questions are raised over the continued use of a psychologically driven framework. As noted, a social-economic model is prevalent and beneficial within the clinical arena, facilitating diagnosis and enabling the timely provision of services. However, this restricted interpretation and conceptualization is arguably limiting. By utilizing diagnostic tools and criteria drawn from a clinical and psychological perspective, the academic community is restricted in their scope and exploration of ASD. Moreover, with a reliance on subjective behavioral examination and relatively broad psychological interpretation, we fail to progress to a patient-oriented and personalized approach to both diagnosis and intervention. With recent developments ranging from the amalgamation of a range of neurodevelopmental disorders into an umbrella term of ASD, through to a new conceptualization of both the nosology and epidemiology since the seminal works of Kanner (1943) and Asperger (1944), a modified framework seems apt. In addition, viewed within the context of today’s precision medicine platform and health agenda, a patient-oriented, physiological perspective to approach and define ASD seems timely. Yet, barriers remain with disconnect between academic disciplines. The distinction between the fields of psychology and the new burgeoning area of neuroscience—particularly computational neuroscience—although closing, is pertinent to this struggle. The move from behavioral observation using arguably subjective psychologically driven tools and metrics, to the use of robust objective empirical estimation for individualized patient-oriented models is a large one*—yet a model for this transition can be followed.
Computational Neuroscience and Compartmental Modeling
Published in Bahman Zohuri, Patrick J. McDaniel, Electrical Brain Stimulation for the Treatment of Neurological Disorders, 2019
Bahman Zohuri, Patrick J. McDaniel
Computational neuroscience describes the nervous system through computational models. Although this research program is grounded in mathematical modeling of individual neurons, the distinctive focus of computational neuroscience is systems of interconnected neurons. Computational neuroscience usually models these systems as neural networks. In that sense, it is a variant, offshoot, or descendant of connectionism. However, most computational neuroscientists do not self-identify as connectionists. There are several differences between connectionism and computational neuroscience: Neural networks employed by computational neuroscientists are much more biologically realistic than those employed by connectionists are. The computational neuroscience literature is filled with talk about firing rates, action potentials, tuning curves, etc. These notions play at best a limited role in connectionist research, such as most of the research canvassed in Rogers and McClelland.70Computational neuroscience is driven in large measure by knowledge about the brain, and it assigns a huge importance to neurophysiological data (e.g., cell recordings). Connectionists place much less emphasis upon such data. Their research is primarily driven by behavioral data (although more recent connectionist writings cite neurophysiological data with somewhat greater frequency).Computational neuroscientists usually regard individual nodes in neural networks as idealized descriptions of actual neurons. Connectionists usually instead regard nodes as neuron-like processing units,70 while remaining neutral about how exactly these units map onto actual neurophysiological entities.
Advances in the pharmacotherapeutic management of post-traumatic stress disorder
Published in Expert Opinion on Pharmacotherapy, 2021
Ansab Akhtar, Sangeeta Pilkhwal Sah
Interestingly, computational neuroscience could play a vital role in detecting the exact diagnosis or research findings [99]. For the same computational models extracting data through abstraction and mathematical paradigms can be applied. Thus, changes in brain function and structure can be inferred. However, genetic models here show little relevance [100]. The reason could be the exclusion of hereditary or congenital causes in PTSD generation. Five to ten years down the line seems to come through hardships in PTSD research and treatment. Still, scientists and clinicians are trying at their peak level to gather the opportunities, remove barriers, improve patient’s consent, resource and funding acquisition, heightened effectiveness, and lessened toxicity. These struggles could be in the form of a different combination of drugs to synergize effects, cutting dosage or dosage regimen, and creating a more conspicuous disease paradigm.
Privacy Concerns in Brain–Computer Interfaces
Published in AJOB Neuroscience, 2019
The interests of the target article are “neuroscience-based techniques that ‘detect’ mental states or subjective phenomena” (XX). I suggest drawing a distinction between neural and mental data for ethical and legal purposes, as both might—and should—be subjected to different regulations in some cases. NTAs primarily detect neurophysiological (neural) signals, that is, electric or chemical processes in the central nervous system. Further processed and computed, the signals may afford to draw inferences about mental states. Detection of mental states through NTAs is thus inferential and faces a range of problems. A foundational epistemic one is that inferences require knowledge of the relation between neural and mental states. Their interplay is part of the unsolved mysteries of the mind–brain relation. It is unclear how well neural and mental states and processes can be mapped onto each other, and the degree to which mental states are multiple realizable. It should also be noted that the approach works by “reverse inference”: Through testing, correlations between a particular mental state and some neural state or activation pattern might be established. But of course this does not establish that the correlation holds vice versa, or in the future (Poldrack 2006). This problem is aggravated by intraindividual differences. Brains and minds may differ, so correlative patterns observed in one person may not obtain in another. These are the metaphysical problems of mind reading, which dim the chances of NTA’s success. However, progress in computational neuroscience and machine learning may overcome them.
Preclinical stress research: where are we headed? An early career investigator’s perspective
Published in Stress, 2018
Anand Gururajan, Aron Kos, Juan Pablo Lopez
Computational neuroscience clearly conveys a significant benefit in being able to weigh the importance of specific relationships between structures, sub-structures, circuits, microcircuits as well as the cellular, molecular and genetic systems that underpin behavior. This is consistent with the Research Domain Criteria (RDoC) framework for the study of psychiatric disorders which integrates data from multiple levels of research (i.e. from genetic analyses to patient self-reports) to understand the basis of normal and abnormal human behavior (Insel et al., 2010). In the context of stress research, such an understanding may reveal novel insights into the pathophysiology of various psychopathologies as well as strategies for their treatment which seek to rescue or normalize network-level disruptions, perhaps akin to the effects of deep-brain stimulation.