TMS Prediction System Using EEG Brain Network Analysis
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Solution Overview
Problem
Current treatments for neuropsychiatric conditions like ADHD lack effective prediction methods for treatment outcome, leading to inefficiencies and intolerance in many patients.
Innovation Solution
A system combining transcranial magnetic stimulation (TMS) with electrophysiological signal detection using EEG, where a computer processor applies TMS pulses, detects signals, and predicts treatment outcomes by analyzing frequency bands and brain network activity patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional treatment methods for neuropsychiatric conditions are used, then treatment can be applied to patients, but there is no effective prediction method for treatment outcome leading to inefficiencies and intolerance
Solution Approach 1:
The system performs preliminary assessment of brain electrical activity patterns before treatment to predict treatment outcome. By analyzing EEG spectra and brain network activity in advance, the system determines whether a patient is likely to respond to TMS therapy, enabling preliminary classification of treatment responders vs. non-responders before actual treatment begins.
Solution Approach 2:
The system uses feedback from measured brain electrical activity patterns to adjust and optimize treatment predictions. By continuously monitoring EEG spectra, frequency bands, and brain network activity, the system refines its prediction of treatment outcome based on actual physiological responses, creating a closed-loop feedback mechanism for treatment prediction.
2Reliability
If transcranial magnetic stimulation is applied to treat neuropsychiatric conditions, then therapeutic effect can be achieved, but treatment outcome cannot be predicted accurately
Solution Approach 1:
The system replaces complex clinical judgment and trial-and-error treatment approaches with automated computational analysis of brain electrical activity. By using algorithms to process EEG spectra, frequency band analysis, and brain network activity patterns, the system substitutes mechanical/clinical complexity with computational processing, achieving reliable treatment predictions without proportionally increasing physical device complexity.
Solution Approach 2:
The prediction system is designed to be universally applicable across different neuropsychiatric conditions (ADHD, depression, anxiety) and different TMS treatment protocols. By analyzing fundamental brain electrical activity patterns that are common across various conditions, the system achieves multi-functional prediction capability without requiring condition-specific or protocol-specific customization, reducing overall system complexity.
3Measurement precision
If multiple frequency bands and brain network activity patterns are analyzed, then treatment outcome prediction accuracy improves, but measurement and analysis complexity increases
Solution Approach 1:
The system segments the complex EEG signal into distinct frequency bands (delta, theta, alpha, beta, gamma) for separate analysis. By dividing the broad spectral range into discrete bands, the system can independently analyze characteristic patterns in each band without being overwhelmed by the full complexity of the raw signal, making the measurement and analysis process more manageable while maintaining high prediction accuracy.
Solution Approach 2:
The system focuses analysis on specific, most-relevant frequency bands and brain network activity patterns that have been identified as particularly predictive of treatment outcome. Rather than analyzing every aspect of the EEG signal equally, the system applies partial action by concentrating computational resources on the most informative features, achieving high prediction accuracy without the excessive complexity of comprehensive signal analysis.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate prediction of treatment responses for neuropsychiatric conditions, such as ADHD, depression, and major depressive disorder, improving treatment efficacy and patient tolerance.
Implementation Method 1
The magnetic fields cause electric conduction in brain cells, and, as a consequence, generation of action potentials
Implementation Method 2
detect an electrophysiological signal of the subject, using the electrophysiological signal detecting electrodes
Data Source
Figure 1
Figure 2
Figure 3A~3C
AI summary
Apparatus and methods are described for use with electrophysiological signal detecting electrodes (14), and a transcranial magnetic stimulation device (10). A computer processor (16) drives the transcranial stimulation device to apply one or more pulses of transcranial magnetic stimulation to a subject. Within a given time period of applying one of the one or more pulses of transcranial magnetic stimulation to the subject, the computer processor detects an electrophysiological signal of the subject, using the electrophysiological signal detecting electrodes (14). At least partially in response thereto, the computer processor predicts an outcome of treating the subject for a neuropsychiatric condition, using a given therapy, and generates an output on an output device (18) in response to the predicted outcome. Other applications are also described.