PSYC 301 Final - New Frontiers 1

AI

What are the 5 features of Big Data?

1. Volume: Number of data points 

2. Variety: Data may cross different types (structured/unstructured)

3. Velocity: Pace of data generation 

4. Veracity: Data quality + accuracy 

5. Value: Potential to create benefits and insights 

Relate the 5 features of big data to health anxiety about COVID-19 on reddit

• Volume: posts from 826,961 users

• Variety: multiple boards (ex.  r/Depression, r/SuicideWatch, r/conspiracy) but all text posts

• Velocity: fixed dataset, collected once

• Veracity: automated data processing

• Value: could identify vulnerable groups and mental health themes in real-time

Artificial Intelligence

Artificial systems that appear to think like humans (decide, categorize, recognize) 

Machine Learning

Systems that can learn from experience/data without direct human programming 

• Involves training models on patterns in one set of data (training data) so they can apply what they learned to new data 

• Classify, predict, decide, etc. 

• Can produce “black box” results (difficult to interpret)

What are the 2 types of machine learning?

1. Supervised Learning 

   • Models are trained on known, labelled data 

   • Ex. medical records from patients who do vs. don't develop psychosis 

2. Unsupervised Learning 

   • Models learn from unlabeled data (ex. Twitter posts containing word “dementia”) 

   • Requires huge processing power

Generative AI

• Subset of machine learning 

• Systems that generate new text, images, video, or code based on prior input 

Large Language Models

Generate new text, subset of generative AI

• Essentially high high-powered auto correct

• Does not reflect on the output it produced 

What are AI applications in neuroscience?

1. Risk Prediction

2. Clinical Decision Making

3. Neurotech

4. Brain Modelling

5. Diagnosis + Prognostication

Risk Prediction

• Goal: Predict Alzheimer’s disease diagnosis using brain scans 

• Method: Train ML model using labelled MRI data (health vs. AD) to predict AS using neural activity 

• Identify most predictive brain regions

Clinical Decision Making

• Goal: Surgically remove epileptogenic brain region to treat seizures using intracranial EEG (like EEG, but electrodes implanted on surface of brain) 

• Proposed ML model uses unlabelled features of the raw iEEG output to identify seizure origin

Neurotech

• Goal: Create a device that can control limb prosthesis with neural activity 

• Train ML model on the mapping between neural activity and limb movement 

• Ex. what does brain activity look like when a monkey moves his hand? + use electrical activity to predict limb movement 

• Can later use the electric activity to control a separate device 

Brain Modelling

• Goal: Understand how rat brains represent space

• Trained ML model to “navigate space” with training data that stimulate real rodent behavior + neural activity 

• Model developed representations resembling real rat entorhinal cortex “grid cells”

Diagnosis + Prognostication

• Problem: Need to triage acute neurological illnesses quickly (ex. Hemorrhage, stoke) 

• Model Type: Supervised ML model trained on head CTs and radiology annotations 

• Result: Accelerated time to diagnosis in simulated clinical environment

Ethical Issues of AI in Neuroscience

1. Accountability

   • culpability (responsibility based on intention/knowledge/control)

     • Ex neurosurgery robot makes an error, culpability issues

   • moral accountability (duty to explain actions/reasoning)

2. Bias and Discrimination

   • groups that are underrepresented in AI models receive lower quality care

3. Privacy

4. Emerging Harms and Benefits

5. Lack of Transparency