Sleep
Conditions
Keywords
TMS-EEG, consciousness, complexity
Brief summary
This study aims to validate a novel real-time algorithm (Presence-IP1.0) designed to detect consciousness from TMS-evoked EEG responses using a reduced electrode montage. Thirty healthy adult participants will undergo TMS-EEG recordings during wakefulness and sleep. The algorithm's ability to differentiate conscious from unconscious states will be evaluated against behavioral and physiological state classification. The goal is to determine whether Presence-IP1.0 achieves clinically useful accuracy for detecting consciousness using a portable, reduced-channel system.
Interventions
DEVICETMS combined with EEG
Structural MRI is used to help determine coil placement, before TMS-EEG visit.
DEVICEPresence-IP1.0
novel algorithm
Sponsors
University of Wisconsin, Madison
National Institute of Neurological Disorders and Stroke (NINDS)
Intrinsic Powers, Inc
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
DIAGNOSTIC
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
18 Years to 85 Years
Healthy volunteers
Yes
Inclusion criteria
* Healthy adults greater than or equal to 18 years * Able to provide informed consent * Able to undergo TMS and EEG recordings * Able to sleep in laboratory setting
Exclusion criteria
* History of neurological or psychiatric disorders * Pregnancy * Sleep disorders affecting normal sleep architecture * Current history of poorly controlled headaches including intractable or poorly controlled migraines * Any systemic illness or unstable medical condition that may cause a medical emergency in case of a provoked seizure (cardiac malformation, cardiac dysrhythmia, asthma, etc.) * Possible pregnancy or plan to become pregnant in the next 6 months * Any metal in the head * Any metal in the body * Any medical devices or implants (i.e. cardiac pacemaker, medication infusion pump, cochlear implant, vagal nerve stimulator) unless otherwise approved by the responsible MD * Dental implants * Permanent retainers * Claustrophobia (a fear of small or closed places) * Back problems that would prevent lying flat for several hours * Regular night-shift work (second or third shift)
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Accuracy of Presence-IP1.0 to classify consciousness state (AUC) | data collected during experimental sessions (2 visits, up to 8 hours each) | Area under the receiver operating characteristic curve (AUC) for discriminating conscious versus unconscious states using Presence-IP1.0 |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Sensitivity of Presence-IP1.0 | data collected during experimental sessions (2 visits, up to 8 hours each) | Sensitivity will refer to TP/(TP+FN) Where TP is Presence-IP1.0 correctly diagnosing consciousness and FN is Presence-IP1.0 falsely diagnosing unconsciousness. |
| Specificity of Presence-IP1.0 | data collected during experimental sessions (2 visits, up to 8 hours each) | Specificity is TN/(TN+FP) Where TN is Presence-IP1.0 correctly diagnosing unconsciousness and FP is Presence-IP1.0 falsely diagnosing consciousness. |
| Signal to Noise Ratio | data collected during experimental sessions (2 visits, up to 8 hours each) | To assess the signal quality of reduced EEG montage, the signal-to-noise ratio will be reported. |
| Stability of TMS-evoked responses | data collected during experimental sessions (2 visits, up to 8 hours each) | To assess the signal quality of reduced EEG montage, the stability of TMS-evoked responses will be reported. This refers to the reproducibility of the TMS response components across the first vs the second half of stimulation runs. |
| Agreement with standard high-density EEG metrics (if available | data collected during experimental sessions (2 visits, up to 8 hours each) | Comparison of average performance of PresenceIP1.0 on the lower-density EEG dataset acquired to this study compared to a reference, previously acquired high-density EEG dataset. |
Countries
United States
Contacts
CONTACTMelanie Boly, MD, PhD
CONTACTUrszula Gorska, PhD
PRINCIPAL_INVESTIGATORMelanie Boly, MD, PhD
UW School of Medicine and Public Health
Outcome results
None listed