NEXT GENERATION
EEG NEUROANALYTICS
TOMOGRAPHY
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TELEMETRY
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ELECTRICAL SOURCE NEUROIMAGING
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QEEG-SLEEP
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NEUROTRANSMITTER DOMINANCE
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BRAIN MAPS
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SPECTRAL ANALYSES
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STATISTICS
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ARTIFICIAL INTELLIGENCE
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MACHINE LEARNING
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SLEEP SCORING
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TOMOGRAPHY · TELEMETRY · ELECTRICAL SOURCE NEUROIMAGING · QEEG-SLEEP · NEUROTRANSMITTER DOMINANCE · BRAIN MAPS · SPECTRAL ANALYSES · STATISTICS · ARTIFICIAL INTELLIGENCE · MACHINE LEARNING · SLEEP SCORING ·
DRUG
DEVELOPMENT
qEEG BIOMARKERS
Quantitative Electroencephalography (qEEG)
qEEG measures changes in the power and cortical distribution of different EEG frequency bands (e.g., delta, theta, alpha, beta, gamma). Drugs may increase or decrease the power in these bands, in specific patterns indicating their stimulant or depressant effects on neural activity.
Pharmacokinetic - Pharmacodynamic Correlations (PK/PD)
qEEG provides detailed insights into the type of effect a drug has on brain oscillations and their temporal dynamics.
By correlating qEEG data with plasma drug levels, researchers can establish sensitive PK/PD models, guiding dosage adjustments and timing for optimal therapeutic effect.
Event-Related Potentials (ERPs)
ERPs can determine how drugs affect the brain's response to specific sensory or cognitive events. Changes in the amplitude or latency of these potentials provide insights into the drug's impact on cognitive or sensory function.
sleep-qEEG
By integrating qEEG with sleep analytics, researchers gain a deeper understanding of the effects of drugs on both sleep architecture and underlying oscillatory brain activity, increasing the translational relevance of preclinical findings.
Drug Efficacy
Combining qEEG with sleep scoring enables researchers to analyze the time spent in each of the sleep stages and quantify changes in each EEG frequency across specific sleep stages, thus increasing the predictive value of EEG for drug efficacy and providing insights into the neural mechanisms underlying drug effects on sleep.
Integrating qEEG with sleep metrics increases the predictive validity of animal models for clinical trials. Sleep - qEEG biomarkers identified in preclinical studies may serve as translational endpoints, helping to predict clinical efficacy and to optimize dosing regimens in human trials.
Sleep Spindle Analysis
Sleep spindles are a hallmark of non-REM sleep and play a fundamental role in memory consolidation. Sleep spindles have been linked to various aspects of cognitive function, including memory consolidation, learning, and executive function. Therefore, monitoring changes in sleep spindle activity in preclinical studies can provide insights into the cognitive effects of drugs targeting sleep or related neurochemical pathways.
Sleep spindles are modulated by various neurotransmitter systems, including the cholinergic and serotonergic systems, which are targets for many pharmacological interventions. Assessing the effects of drug candidates on sleep spindle activity in preclinical studies can provide valuable information for the development of more selective and effective therapeutics.
Sleep spindle activity has been proposed as a potential biomarker for predicting individual differences in treatment response to sleep interventions or drugs targeting sleep disorders. By examining baseline spindle parameters in preclinical models, researchers can assess the predictive value of spindle activity in identifying responders and non-responders to pharmacological interventions, thereby optimizing patient selection and treatment strategies in clinical trials.
CROSS-SPECIES
EEG SOURCE
IMAGING
EEG source imaging or qEEG tomography (qEEGt) is a method of mapping the 3D distribution of neuronal sources responsible for generating the EEG signal and for computing changes in brain network connectivity metrics reflective of synaptic plasticity.
qEEGt is eminently translatable from preclinical non-human primate (NHP) data to human clinical trials, as brain oscillatory activity and the connectome are preserved across mammalian species.
Non-human Primate qEEG Tomography
Neuroimaging research is advancing rapidly yet the implementation of cutting edge brain mapping discoveries is lagging in the preclinical field. Despite these advances, there are currently no standardized preclinical non-human primate (NHP) models of source localization and brain network function. In addition, there are no streamlined methods for obtaining network biomarkers using human EEG and for predicting targets, as applicable to the drug development environment.
To close this gap, we standardized EEG recordings from NHPs, specifically designed as addons to preclinical toxicology studies and we developed heuristics for EEG tomography-derived biomarkers.
Clinical Trial qEEG Tomography
PCEC has developed robust and streamlined methods for conducting fast qEEGt analyses specifically designed for clinical trials. The type and timecourse of drug effects are assessed based on topographic surface spectral maps and on tomographic maps including cortical, subcortical and cerebellar areas, and also based on network connectivity changes. The precision and depth of our qEEG clinical trial outcomes are unmatched in the industry.
Reducing Heterogeneity of Response in Clinical Trials to Improve Patient Selection
PCEC offers a platform for robust AI/ML biomarker methodology specific to high-dimensional datasets with small sample size. These capabilities allow identifying responders in early stages of clinical trials and further biomarker-based enrollment, thus increasing the success of a clinical trial.
FUNCTIONAL
CONNECTOMICS
Functional brain connectivity (“connectomics”) refers to statistical relationships between different regions of the brain, i.e., measuring how their activity patterns are synchronized over time. Essentially, it identifies which parts of the brain are "talking" to each other and how this activity differs from normal.
CONNECTOMICS APPLICATIONS
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Brain connectivity changes due to CNS-active drugs reflect synaptic plasticity and drug activity at the level of the network.
Functional connectomics generate clinically relevant hypotheses related to brain network changes due to a drug or disease, and can be aligned with contemporary cytoarchitecture or gene expression brain maps.
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Connectomics can help in diagnosing and understanding neurological and psychiatric conditions. For instance, alterations in functional connectivity patterns have been observed in conditions like Alzheimer's disease, schizophrenia, and autism spectrum disorders.
A vast body of literature correlating symptoms to known functional networks in humans is available, as well as standardized qEEG clinical databases, positioning connectomics at the cutting edge of precision personalized medicine.
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Researchers use functional connectivity to explore the brain's network organization, understand how information is processed across different regions, and identify how these connections change with different cognitive states or tasks. Our platforms accelerate and integrate the results.
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Functional connectivity analyses help in examining the impact of various interventions on quantitative brain function and their correlations with cognitive or behavioral outcomes.
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Understanding functional connectivity can improve the design and functionality of BCIs, which rely on decoding brain signals to control external devices, potentially aiding individuals with mobility or communication impairments.
NEUROTRANSMITTER
PREDICTIONS
Aligning functional and molecular brain-mapping
Relating Neural Oscillations to Neurotransmitter Dominance
To relate neurotransmitter or transporter contribution to changes in neural oscillations due to a drug or therapeutic intervention, we use models which predict the cortical power distribution of each EEG frequency band using normative PET or autoradiography-derived neurotransmitter cortical densities and normative EEG databases. We then apply dominance analyses to identify the neurotransmitters that contributed most to the fit (e.g., comparing before and after treatment). The differences between conditions highlight changes in NT dominance which provide powerful insight into the target.
Stratified Neurotransmitter Dominance by Receptor Class
It is known that inhibitory, non-monoamine and Gi-coupled receptors are more dominant than excitatory, monoamine and Gs-/ Gq-coupled receptors. With our platform, neurotransmitter dominance related to a drug or intervention effect can be stratified by class, narrowing down the target hypothesis and gaining insight into excitation/inhibition or specific mechanisms.
Expert QEEG
Clinical Report Services
Solutions for Precision Neurofeedback
We offer a fast turnaround, reliable and affordable qEEG report services including z-score topographic and swLORETA tomographic metrics (CSD power and connectomics), as well as custom statistics across clinical databases and correlations with symptom-based metrics. Offering powerful insights into network dysregulation and potential correlations with multiple neurotransmitter systems, our reports provide unparalleled precision in choosing the correct neurofeedback target.
We are currently developing applications of EEG analytics for companion animal training for enhanced performance and wellbeing.
