Dataset of Multisensory Stimulation Responses for Neuroadaptive Interfaces and Connectographic Modeling
Description
Research Hypothesis Multisensory gamma-frequency stimulation (particularly around 40 Hz), when delivered through modalities such as visual flicker, auditory tone bursts, thermal infrared stimulation, or immersive virtual environments, can entrain neural oscillations, enhance functional brain connectivity, and improve cognitive and physiological outcomes in individuals with age-related or neurodegenerative vulnerabilities. What the Data Shows This dataset compiles data from multiple experimental paradigms involving human subjects who underwent multisensory neurostimulation protocols. The compiled results show measurable entrainment of gamma oscillations, modulation of microglial activity, improvement in cognitive functions (e.g., memory, semantic processing, executive function), enhancement in sleep parameters, and a reduction in Alzheimer’s-associated pathologies such as amyloid burden. The dataset also includes neuroimaging-derived functional connectivity maps and behavioral metrics associated with stimulation sessions. Notable Findings Gamma stimulation at 40 Hz can induce neuroprotective signaling distinct from classic neuroinflammation. Infrared thermal stimulation combined with high-field fMRI can non-invasively map mesoscale brain circuits. VR-based and BCI-integrated multisensory stimulation platforms demonstrate feasibility and early efficacy for cognitive rehabilitation. Gamma entrainment correlates with improved sleep architecture and daily living activities in Alzheimer's patients. Multisensory protocols can modulate both neural oscillatory dynamics and neuroimmune markers. Data Interpretation and Use The dataset includes structured metadata for each study, specifying the stimulation type (auditory, visual, IR, VR, or combined), frequency parameters, population demographics, cognitive status, and outcome measures (EEG/fMRI responses, behavioral data, sleep metrics, etc.). Researchers can use this dataset to: Train AI models for real-time connectomic reconstruction and prediction. Design personalized stimulation protocols based on patient-specific profiles. Conduct secondary analyses for meta-research or cross-protocol comparisons. Generate synthetic datasets or validation sets for neurotechnological development. Data Acquisition Summary All data was extracted from published studies and reformatted into a standardized structure compatible with Mendeley Data. Citations and source references are included for each entry. Where applicable, raw values were digitized from graphs using open-source extraction tools, and all entries were manually curated for consistency and quality.
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Data Acquisition and Reproducibility Statement The dataset was constructed through a systematic extraction of structured data from peer-reviewed publications and ongoing trials related to multisensory neurostimulation, particularly from studies affiliated with Cognito Therapeutics, a leading neurotechnology company focused on gamma frequency stimulation for neurodegenerative diseases. Additional metadata and study outputs were curated using Epistemic.ai, a specialized beta-phase software platform for scientific literature mining and semantic knowledge graph construction. The workflow followed these steps: Literature Identification and Screening A comprehensive set of publications was gathered from Cognito Therapeutics’ research portfolio, along with peer-reviewed articles retrieved via keyword-based semantic searches using Epistemic.ai’s proprietary NLP tools. Data Extraction Protocol For each study, key methodological and outcome parameters were extracted, including stimulation modality (e.g., auditory, visual, infrared), frequency (primarily 40 Hz), session duration, participant demographics, cognitive status, instrumentation (e.g., EEG, fMRI, VR setups), and results on cognitive, behavioral, or neuroimmunological metrics. Graphical data were digitized using WebPlotDigitizer when raw values were not available. Curation and Structuring The data were curated manually to ensure consistency and annotated according to experimental context. Variables were normalized across studies to enable interoperability and cross-study comparison. Data entries include reference identifiers, protocol details, and relevant outcomes. Tools and Software Used Epistemic.ai for semantic exploration and literature triage WebPlotDigitizer for digitizing numerical values from published figures Microsoft Excel and Python (Pandas) for data normalization and structuring Zotero for reference management Mendeley Data standard format for public deposition The methodology is reproducible for any team with access to scientific databases, the Epistemic.ai platform, and appropriate extraction and structuring tools. A reproducibility checklist and variable dictionary are included in the supplementary documentation.
Institutions
- Universidad Nacional Pedro Henriquez Urena Facultad de Ciencias de la Salud
- Massachusetts Institute of Technology
- Universidad de Puerto Rico - Ponce