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Related Concept Videos

Data Collection by Experiments01:13

Data Collection by Experiments

Data collection is a systematic method of obtaining, observing, measuring, and analyzing accurate information. An experimental study is a standard method of data collection that involves the manipulation of the samples by applying some form of treatment prior to data collection. It refers to manipulating one variable to determine its changes on another variable. The sample subjected to treatment is known as “experimental units.”
An example of the experimental method is a public clinical trial...
Data Collection by Observations01:08

Data Collection by Observations

Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
An astronomer viewing the motion and brightness of stars in the sky and recording the data is an example of observational data collection. A botanist recording...
Data Reporting and Recording01:24

Data Reporting and Recording

Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
Biostatistics: Overview01:20

Biostatistics: Overview

Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...

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Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models
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Closing the loop in DBS: A data-driven approach.

Prerana Acharyya1, Kerry W Daley1, Jin Woo Choi1

  • 1Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA.

Parkinsonism & Related Disorders
|March 4, 2025
PubMed
Summary
This summary is machine-generated.

Adaptive deep brain stimulation (aDBS) offers personalized Parkinson's Disease treatment by using AI to interpret neural signals. Future systems integrating multiple data types promise enhanced therapeutic effects for movement disorders.

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Area of Science:

  • Neurology
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Deep brain stimulation (DBS) is a cornerstone in treating movement disorders like Parkinson's Disease (PD).
  • Adaptive DBS (aDBS) represents an advancement, utilizing closed-loop systems for personalized neuromodulation.
  • Innovations are driving the evolution of aDBS technology and its therapeutic applications.

Purpose of the Study:

  • To review recent advances in adaptive deep brain stimulation (aDBS).
  • To analyze progress in biomarker detection, control strategies, and efficacy mechanisms for aDBS.
  • To explore the role of artificial intelligence (AI) in decoding motor states for aDBS.

Main Methods:

  • Review of current literature on adaptive DBS technologies and applications.
  • Analysis of data-driven approaches, including AI for neural signal processing.
  • Investigation into multi-modal signal integration for enhanced biomarker detection.

Main Results:

  • AI-driven methods expand biomarker detection beyond traditional subcortical beta oscillations.
  • Leveraging diverse neural and kinematic signals improves motor state decoding.
  • Multi-modal input systems show potential for broader symptom management in aDBS.

Conclusions:

  • Adaptive DBS holds significant promise for personalized treatment of movement disorders.
  • AI and multi-modal sensing are key to advancing aDBS efficacy.
  • Further research is needed to overcome technical and computational challenges for clinical use.