The Default Space Theory (DST) of consciousness claims that consciousness arises from bioelectric oscillations across brain and body which become time-locked to form an internal three-dimensionalized “default space” which continuously simulates external reality. Within this frame, we assert that fast rhythms created corticothalamically with the thalamic reticular nucleus (TRN) can become time-locked with regions throughout the brain via nascent oscillations as well as sensory organs and viscera. This proposed time-locking may render exteroceptive and interoceptive processed information one in the same as conscious experience. We review/integrate salient neuroscientific and physiological findings that validate DST. We reveal findings across the last decoding of the theory's detailed components from corticothalamic feedback loops to lateral/inhibition dynamics, from descending efferent projections to sensory organs. We utilize high cervical cord injury (HCCI) as syndromically supporting/denying DST's postulates regarding the necessity of bodily input to consciousness. Finally, we compare/contrast theories (Global Workspace, Integrated Information Theory, Predictive Processing) with DST to reveal overlaps/distinctions within theoretical scaffolds. At its heart, the DST is an all-encompassing biological apparatus through which typical oscillations create a “functional unity” of consciousness. Yet developments within the DST have brought about clarity that simultaneous connected long distances (i.e., gamma oscillations aligning cortex to sensory receptors) is a theory to be born out of experimentation, not reality. There is indirect support from visual studies for retinal rhythmic activity driving thalamocortical firing in a way consistent with modulation but direct phase-locking of periphery receptors with gamma activity in the cortex is aspirational, not reality. In addition, we corrected a prior disconnection regarding motor representations: focus of outputs occurs by surround inhibition (SI) eliminating superfluous irrelevant activations, not the cortical silent period—thus when SI fails there is motor “overflow.” We further clarify the importance of viscera in conscious experience with several examples: spontaneous breathing aligns electrical oscillations in limbo circuits, heart beats are shown to engage/disengage cortical activity and awareness, gastric slow waves correlate in-phase with what occurs in somatosensory and visual domains. All support DST positing that the neural and visceral systems operate as one oscillator network which, together, support consciousness. In an HCCI patient where somatic input below the neck is deceased, however, they experience retained consciousness, however. This DST predicts because oscillations of the head/brain and rhythms from the viscera allow continuity of internal space; yet its content/persona changes because the body map has been temporally truncated. This provides a distinction about how people can be consciously aware of their existence while feeling completely different at the same time—there is a core conscious sense vs. the schemas decorating it over timescales. The DST provides a bioelectric understanding of consciousness which connects brain-body-environment. Where we emphasize where theoretical estimations lie vs. what can be observed supports the DST. For example, if one excessively disrupts corticothalamic feedback loops or sensorimotor oscillations and fails to integrate things into consciousness, DST is supported. Conversely, these stimulus distortions will never come from any other theoretical premise. Prominent notions are shared with Global Workspace or Integrated Information-based systems regarding global integration, but no other theory has included the body's oscillatory components as part of consciousness; contrary to predictive processing, while prediction errors determine hypotheses, DST puts forward simultaneous synchronization as critical and afferent/efferent dynamics physicalized two-way brain-body communication. Testing factors inevitable in evolution include consistent electrophysiological studies with descending pathways, works decoding states of consciousness in paralysis. All citations updated for relevancy—new studies incorporated—non-self-citations limited to remove experimenter's bias—all to urge ongoing discourse between DST and contemporaneous science of consciousness research.
Spontaneous intracerebral hemorrhage (ICH) is a devastating cerebrovascular disorder associated with high mortality, severe disability, and substantial clinical burden. Stroke-associated pneumonia (SAP), one of the most common infectious complications after stroke, is increasingly recognized as a major contributor to mortality and poor neurological outcomes after ICH. Emerging evidence from studies of stroke-induced immunosuppression and the lung–brain axis suggests that SAP is not merely a secondary pulmonary complication, but rather a multifactorial consequence of central nervous system injury, peripheral immune remodeling, and impaired pulmonary host defense. Among these processes, T cells serve as a critical immunological relay between the central nervous system and peripheral immunity. During the acute phase of ICH, pro-inflammatory T-cell subsets infiltrate the injured brain and amplify neuroinflammation, blood–brain barrier disruption, cerebral edema, and neuronal injury. As the disease progresses, T-cell hyporesponsiveness and suppression of Th1/Th17-mediated antimicrobial immunity may increase increase susceptibility to SAP. Once SAP develops, pulmonary inflammation, microbiota dysbiosis, bacterial products, and systemic inflammatory mediators may further aggravate central immune dysregulation through the lung–brain axis, thereby establishing a vicious cycle involving ICH, T-cell dysfunction, SAP, and secondary brain injury. This review summarizes the temporal dynamics, trafficking patterns, and functional transitions of T-cell subsets after ICH, with particular emphasis on their potential roles in the bidirectional interactions between SAP and the injured brain along through the lung–brain axis. A better understanding of this T-cell-centered network may reveal potential targets for preventing SAP, restoring post-ICH immune homeostasis, and attenuating secondary neurological injury.
Kavinda Kehelella, Xiaomin Wu, Hamza Ali, Chen-Wei Yang, Jing Xie, Yaesop Lee, Rong Chen, Shuvra S. Bhattacharyya
Real-time neural decoding is critical for precise neuromodulation, which is of increasing interest in the treatment of brain disorders. This paper presents RNDC-Lab, a computational framework for designing, optimizing, and experimenting with real-time, calcium imaging-based, end-to-end neural decoding systems. RNDC-Lab integrates calcium imaging preprocessing with adaptive neural decoding using compact Deep Neural Networks (DNNs). RNDC-Lab supports dynamic retraining of a neural decoding model concurrently with inference, thereby providing robustness to changes in the statistical properties of input calcium imaging signals. We integrate NDSEP and NeuroGRS — two recently- developed tools for neural decoding and signal extraction, and optimized generation of DNN models, respectively—into RNDC-Lab. This results in the first real-time neural decoding system based on calcium imaging with a dynamic, DNN-based decoding model. This paper also introduces a number of simulated datasets to assist in system evaluation and experimentation using RNDC-Lab, and more broadly, as resources for experimentation with arbitrary neural decoding models and systems. The experimental capabilities of RNDC-Lab are further enhanced by four new datasets—the Brain Phantom, N80, N90 and Reward Learning (for multiclass classification) datasets—that we introduce in this work as companion resources for working with RNDC-Lab. To demonstrate the capabilities of RNDC-Lab, we conduct extensive experiments to evaluate the functionality and performance of the proposed framework. The experiments incorporate the aforementioned simulated datasets as well as real calcium imaging data collected from mouse models. Our results demonstrate that the RNDC-Lab system, combined with NDSEP and NeuroGRS, achieves efficient and accurate real-time neural decoding from both real and synthetic calcium imaging data. Moreover, the results demonstrate RNDC-Lab's capability for dynamic model retraining and characterize the associated trade-offs involving decoding performance and execution time. While the components within NDSEP, NGSynth, and NeuroGRS are used to concretely demonstrate the capabilities of RNDC-Lab in this paper, the rigorous dataflow- based architecture of RNDC-Lab enables these components to be selectively replaced with alternative modules that provide the same general functionality using different algorithms or implementations (e.g., a new module for motion correction or model retraining). This makes RNDC-Lab a powerful tool for experimenting with novel aspects of neural decoding in the context of an overall real-time framework.
