Research Ethics and EEG Principles

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Questions and Answers

What is the primary source of 50Hz noise in EEG recordings?

Power line alternating current (correct)

High-frequency electromagnetic waves

Magnetic interference from MRI machines

Battery-operated devices nearby

How can a Faraday cage help in EEG recordings?

By blocking external radio frequencies

By eliminating electrical noise in the environment (correct)

By amplifying the EEG signals

By enhancing the battery life of recording devices

Which ethical issue was highlighted by the Tuskegee Syphilis Study?

Lack of informed consent from participants (correct)

Disregard for patient confidentiality

Insufficient funding for research programs

Medical professionals being overworked

What was a significant consequence of the Tuskegee Syphilis Study for the participants?

They experienced long-term health complications and deaths. Signup and view all the answers

What is one key violation of ethics represented by the Tuskegee Syphilis Study?

Disregard for patient rights in research Signup and view all the answers

What does refinement in animal research primarily focus on?

Reducing pain and distress Signup and view all the answers

Which method is an example of replacement in animal research?

Using viral vectors for gene therapy Signup and view all the answers

What is a benefit of using nanoparticles in drug delivery systems?

Delivers medications directly to target tissues Signup and view all the answers

What is the primary objective of the ADNI database?

To understand the progression of Alzheimer's disease Signup and view all the answers

Which of the following best describes the purpose of power analysis in experimental design?

To ascertain the smallest group size for significance Signup and view all the answers

What is a major risk associated with 23andMe's genetic database?

Threats of cyberattacks Signup and view all the answers

Which imaging technique is NOT mentioned as part of the ADNI database?

CT Scan Signup and view all the answers

What is one of the outcomes of modifying research procedures according to refinement principles?

Reduced animal distress levels Signup and view all the answers

What is the purpose of de-identification in brain MRI scans?

To ensure patient confidentiality Signup and view all the answers

What are the consequences of a potential breach of the genetic data?

Exposure of sensitive information Signup and view all the answers

What type of interventions does the acute phase of treatment for traumatic brain injury emphasize?

Immediate medical interventions Signup and view all the answers

Which of the following is NOT mentioned as a benefit of data sharing?

Guarantees patient anonymity Signup and view all the answers

Which of the following best exemplifies environmental enrichment in animal research?

Providing larger cages with stimuli Signup and view all the answers

Which of the following techniques is used in the de-identification process described for brain MRI?

Facial feature removal Signup and view all the answers

What was the primary concern leading to the resignation of the independent directors of 23andMe?

Company's strategic direction Signup and view all the answers

Which technique represents a non-animal approach in research that aids in replacement?

Cell culture experiments Signup and view all the answers

What technology does the facial feature detector rely on?

Deep learning models Signup and view all the answers

What type of data does the ADNI database provide?

Large collection of longitudinal clinical and neuroimaging data Signup and view all the answers

What is one significant challenge in data sharing outlined in the content?

Balancing transparency with risk of misuse Signup and view all the answers

Which of the following BEST describes 'open data' in the context of the ADNI database?

Information available freely to the public for research purposes Signup and view all the answers

What institutional requirement is emphasized for funded research?

Mandates requiring data sharing Signup and view all the answers

What is an example of a biomarker that the ADNI database aims to identify?

Early signs of Alzheimer's within neuroimaging data Signup and view all the answers

What is a concern expressed by users of 23andMe in relation to their genetic data?

Desire to delete their genetic data Signup and view all the answers

Which of the following accurately reflects the unique characteristic of DNA data?

It is uniquely identifying and permanent Signup and view all the answers

What was the primary imaging system used for the rodent MRI scans?

7T preclinical MRI scanner Signup and view all the answers

Which MRI sequence was NOT used for training the model?

Diffusion-weighted Signup and view all the answers

What is the purpose of data augmentation in this study?

To enhance model robustness across multi-contrast MRI data Signup and view all the answers

How many brain images were used for testing the model?

5 images (129 slices) Signup and view all the answers

What approach was used for segmentation during image processing?

Auto-manual segmentation using 3D Slicer Signup and view all the answers

What size of convolutional filter is primarily used in the preprocessing?

3x3 Signup and view all the answers

Which atlas was followed for segmentation of the scans?

Tohoku Rat Brain Atlas Signup and view all the answers

What is the primary benefit of using a 3x3 convolutional kernel in image processing?

It captures fine details while maintaining computational efficiency. Signup and view all the answers

What is the primary purpose of AlphaFold2?

To predict protein structures from amino acid sequences Signup and view all the answers

Which component of AlphaFold2 is responsible for processing Multiple Sequence Alignments (MSA)?

Evoformer Signup and view all the answers

What complexity arises from predicting protein structures from amino acid sequences?

There are numerous possible configurations for protein folding Signup and view all the answers

How does AlphaFold2 utilize evolutionary information in its predictions?

It identifies conserved structural features from related sequences Signup and view all the answers

What is the role of the Structure Module in AlphaFold2?

To build 3D coordinates through iterative refinement Signup and view all the answers

What type of data does AlphaFold2 require for protein structure prediction?

Amino acid sequence and evolutionary data from MSAs Signup and view all the answers

Which of the following is NOT a feature predicted by AlphaFold2 during the structure building process?

Protein synthesis efficiency Signup and view all the answers

What is a significant challenge in the protein folding problem?

The final structure is determined by complex biochemistry Signup and view all the answers

Study Notes

X-ray and CT Scans

X-rays use radiation to image the body.
Areas with high calcium density (bones and teeth) appear white.
Soft tissues are visible as gray or black.
X-rays won't show subtle bone, soft tissue injuries, or inflammation.
CT scans provide more detailed images than X-rays.

Artificial intelligence (AI) and machine/deep learning are used for diagnostics (e.g., image analysis for cancer detection) and predictive analytics for patient outcomes.
Wearable and implantable devices for health monitoring (e.g., heart rate, glucose levels).
Brain-computer interfaces (BCIs) for rehabilitation.
Biomedical imaging and advanced imaging techniques (functional and molecular imaging, real-time imaging advancements, and portable imaging devices for remote healthcare).
Nanotechnology in medicine (nanomedicine for targeted drug delivery, nanosensors for disease detection, and nanomaterials for regenerative medicine).
Biomechanics and bio-robotics (robotic surgery advancements, prosthetics with sensory feedback, and exoskeletons for rehabilitation).

Machine Learning in Medical Imaging

Pattern Recognition: Identifying anomalies or disease-specific features in imaging data.
Predictive Modeling: Using imaging data to predict outcomes or disease progression.
Automation: Assisting in repetitive tasks such as image segmentation, registration, or classification

Popular ML Algorithms in Medical Imaging

Support Vector Machines (SVMs): For binary classification tasks (separating diseased from non-diseased tissues).
Random Forests: For robust feature selection and multi-class classification.
K-Nearest Neighbors (KNN): For identifying similar patterns in images.
Linear Regression/Logistic Regression: For predictive modeling, based on image-derived features.

Linear SVM

Used for linearly separable data (data can be classified using a single straight line)
The classifier is called a Linear SVM classifier.

Non-Linear SVM

Used for non-linear data (data cannot be classified using a straight line)

Importance of Training Set/Labeling

The training set's data enables tumor detection and distinguishes healthy tissue from abnormalities.
The training set includes tumor-specific patterns (shape, size, texture, and contrast) in medical images (MRI, CT, X-rays).
The dataset is pivotal in discriminating between normal tissues and abnormalities.

Multi-Parametric MRI (mpMRI)

Evaluates quantitative features from mpMRI images using a multiregion-of-interest approach in machine-learning-based glioma grading.

Assessment of Alzheimer's Disease Using SVM Classification

SVM classification from whole-brain MRI data to diagnose Alzheimer's disease
Analyzing and summarizing the role of ML/SVM in MRI-based diagnosis
Including applications, advantages, and challenges/limitations in the reading.

Data Privacy and Patient Confidentiality

Wearable devices and apps collect vast amounts of sensitive personal health data, including heart rate, activity levels, sleep patterns, and even menstrual cycle tracking.
This data is shared with third-party companies for further analysis, marketing, or research without explicit consent.
Genomic and biobanking risks (collection, storage, and use of genomic data, data breaches).
Institutional mandates by NIH, EU Horizon, and other agencies for data sharing in funded research.
Data breaches pose a significant ethical concern regarding individual privacy.

Data sharing is important in science for collaboration, and verifying results.
However, sharing data, raises several ethical issues, such as informed consent, de-identification, and data misuse.

The 3 Rs of Animal Research

Reduce the number of animals used in research.
Refine tests to cause animals minimal stress.
Replace animal studies with other methods where appropriate (e.g., cell culture, simulation).

Drug Delivery Systems

Nanoparticles: Testing nanoparticle-based drug carriers for targeted drug delivery (e.g., to tumors).
Controlled Release: Developing implants or hydrogels for sustained drug delivery.
Gene Therapy: Using viral vectors to deliver therapeutic genes to specific organs or systems.

Deep Learning in Medicine

Deep learning is used to improve diagnostics, such as identifying conditions like cancer, pneumonia, and bone fractures from images (X-rays, CT scans, and MRIs) reducing manual effort from histopathology slides.
Deep learning is also used in drug discovery, to predict interactions between drugs and their targets and their properties.
A system like AlphaFold uses deep learning to increase the prediction accuracy of protein structure.

Key Components of Deep Learning

Neural networks are organized into layers (input, hidden layers, and output layers).
The input layer takes raw data like images or numerical values.
Hidden layers use weights and biases to refine data and activation functions.
The output layer makes predictions.
Deep learning models require large datasets for accurate training.

Open Data

ADNI Database (Alzheimer's Disease Neuroimaging Initiative) provides access to research data including structural MRI, functional MRI, PET scans, and EEG data. This is public data.

Automated Brain Extraction for Multi-Contrast MRI

A deep learning model (U-Net) is used to automate the brain extraction from multi-contrast MRI scans of rats.

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Description

This quiz covers fundamental principles in research ethics, focusing on historical studies such as the Tuskegee Syphilis Study, and explores technical aspects of EEG recordings. Participants will be challenged to consider ethical implications and scientific methodologies critical to research design and data integrity.