When linguistic dogma rejects a neuroscientific hypothesis.

The term ``neurophilosophy'' is often used either implicitly or explicitly for characterizing the investigation of philosophical theories in relation to neuroscientific hypotheses. The exact methodological principles and systematic rules for a linkage between philosophical theories and neuroscientific hypothesis, however, remain to be clarified. The present contribution focuses on these principles, as well as on the relation between ontology and epistemology and the characterization of hypothesis in neurophilosophy. Principles of transdisciplinary methodology include the `principle of asymmetry', the `principle of bi-directionality' and the `principle of transdisciplinary circularity'. The `principle of asymmetry' points to an asymmetric relationship between logical and natural conditions. The `principle of bi-directionality' claims for the necessity of bi-directional linkage between natural and logical conditions. The `principle of transdisciplinary circularity' describes systematic rules for mutual comparison and cross-conditional exchange between philosophical theory and neuroscientific hypotheses. The relation between ontology and epistemology no longer is determined by ontological presuppositions i.e. ``ontological primacy''. Instead, there is correspondence between different `epistemological capacities' and different kinds of ontology which consecutively results in ``epistemic primacy'' and ``ontological pluralism''. The present contribution concludes by rejecting some so-called `standard-arguments' including the `argument of circularity', the `argument of categorical fallacy', the `argument of validity' and the `argument of necessity'.

This study addresses a central question in neuroscience: What is the physical nature of consciousness? Taking a neuroscientific approach, the study first establishes working definitions for its principal terms—qualia, consciousness, and information—to specify clearly the entities under investigation. It then analyzes the essential features of these defined terms. Information, in particular, is examined in detail across several aspects, including its carrier, nature, effects, interpretations, and meanings. The study next investigates the essential properties of consciousness and identifies potential entities that could underlie it. Candidate entities are drawn from two groups: physically established non-material entities in the brain, such as electrical fields, magnetic fields, electromagnetic waves, and neural information, and physically unestablished non-material entities proposed in various theoretical models. Each candidate is assessed for whether it can satisfy the required properties of consciousness. The analysis finds that the entity that most parsimoniously meets these criteria, without invoking new forces or physical laws, is neural information. Accordingly, the study proposes the hypothesis that consciousness is a form of neural information, specifically information encoded in the spatiotemporal patterns of electrochemical signaling within certain neural circuits. It presents empirically verifiable predictions derived from this hypothesis, making the hypothesis falsifiable. Further, it identifies a neural mechanism by which some information can manifest phenomenally as consciousness, enabling the occurrence of consciousness in the brain, and another mechanism underlying why this manifestation occurs only from the first-person perspective of some neural circuits. The study then compares its hypothesis and proposed mechanisms with existing theories of consciousness, clarifying how it differs in focus, explanatory scope, and thesis. Broader implications for neuroscience, clinical research, and the possibility of artificial consciousness are discussed, along with limitations of the present framework. Overall, because its evidence and arguments lie entirely within established neuroscience, with no novel entities, forces, or physical laws posited, this study advances a parsimonious and neuroscientifically grounded hypothesis on the physical nature of consciousness.

The process of developing a successful stroke rehabilitation methodology requires four key components: a good understanding of the pathophysiological mechanisms underlying this brain disease, clear neuroscientific hypotheses to guide therapy, adequate clinical assessments of its efficacy on multiple timescales, and a systematic approach to the application of modern technologies to assist in the everyday work of therapists. Achieving this goal requires collaboration between neuroscientists, technologists and clinicians to develop well-founded systems and clinical protocols that are able to provide quantitatively validated improvements in patient rehabilitation outcomes. In this article we present three new applications of complementary technologies developed in an interdisciplinary matrix for acute-phase upper limb stroke rehabilitation – functional electrical stimulation, arm robot-assisted therapy and virtual reality-based cognitive therapy. We also outline the neuroscientific basis of our approach, present our detailed clinical assessment protocol and provide preliminary results from patient testing of each of the three systems showing their viability for patient use.

Background: Culture can affect psychiatric disorders. Clinical Lycanthropy is a rare syndrome, described since Antiquity, within which the patient has the delusional belief of turning into a wolf. Little is known on its clinical or therapeutic correlates.Methods: We conducted a systematic review (PRISMA) on PubMed and Google Scholar, until January 2021. Case reports, data on neurobiological hypotheses, and cultural aspects were included. Language was not restricted to English.Results: Forty-three cases of clinical lycanthropy and kynanthropy (delusion of dog transformation) were identified. Associated diagnoses were: schizophrenia, psychotic depression, bipolar disorder, and other psychotic disorders. Antipsychotic medication may be an efficient treatment for this rare transnosographic syndrome. In case of depression or mania, the treatment included antidepressants or mood regulators. The neuroscientific hypotheses include the conception of clinical lycanthropy as a cenesthopathy, as a delusional misidentification of the self-syndrome, as impairments of sensory integration, as impairments of the belief evaluation system, and right hemisphere anomalies. Interestingly, there is a clinical overlap between clinical lycanthropy and other delusional misidentification syndromes. Clinical lycanthropy may be a culture-bound syndrome that happens in the context of Western cultures, myths, and stories on werewolves, and today's exposure to these narratives on cultural media such as the internet and the series. We suggest the necessity of a cultural approach for these patients' clinical assessment, and a narrative and patient-centered care.Conclusions: Psychiatric transtheoretical reflections are needed for complementaristic neurobiological and cultural approaches of complex delusional syndromes such as clinical lycanthropy. Future research should include integrative frameworks.

Functional magnetic resonance imaging (fMRI) has revolutionized cognitive neuroscience, but methodological barriers limit the generalizability of findings from the lab to the real world. Here, we present Neuroscout, an end-to-end platform for analysis of naturalistic fMRI data designed to facilitate the adoption of robust and generalizable research practices. Neuroscout leverages state-of-the-art machine learning models to automatically annotate stimuli from dozens of fMRI studies using naturalistic stimuli—such as movies and narratives—allowing researchers to easily test neuroscientific hypotheses across multiple ecologically-valid datasets. In addition, Neuroscout builds on a robust ecosystem of open tools and standards to provide an easy-to-use analysis builder and a fully automated execution engine that reduce the burden of reproducible research. Through a series of meta-analytic case studies, we validate the automatic feature extraction approach and demonstrate its potential to support more robust fMRI research. Owing to its ease of use and a high degree of automation, Neuroscout makes it possible to overcome modeling challenges commonly arising in naturalistic analysis and to easily scale analyses within and across datasets, democratizing generalizable fMRI research.

Philosophy of the Brain: The Brain Problem