Week 1.3: Emerging Focuses in Affective Disorders Flashcards
These devices are revolutionizing data collection in affective disorders research.
Smartphones and Smartwatches
Uses accelerometers to track physical activity and assess sleep quality, amount, and timing.
Activity Monitoring (Actigraphy/Actimetry)
Gathers data on application usage, call and text information, and activity levels. This can help detect relapses in bipolar disorder by monitoring changes in activity and sleep patterns
Passive Data Collection
Analyzes speech and text for diagnostic accuracy, though privacy concerns limit its use
Speech/Text Data
Involves prompting participants several times a day to provide real-time data on their moods and behaviors
Ecological Momentary Assessment (EMA)
Are being developed to tailor data collection to specific research needs, improving the accuracy and relevance of the data collected
Smartphone Applications
Refers to the collection of detailed, high-quality, and high-integrity data that provides rich, actionable insights.
Deep Data
Is a type of artificial intelligence that uses algorithms to analyze data and make predictions.
Machine Learning
Occurs when a model is trained too closely to the training data, making it perform well on that data but poorly on new, unseen data
Overfitting
How is ML used in Affective Disorders?
1) Diagnostic Differentiation
2) Predicting Treatment Outcomes
3) Relapse Detection
Excessive or compulsive use of smartphones, which can lead to negative mental health outcomes
Problematic Smartphone Use
What is the link between Social Media and Depression?
Research indicates a correlation between time spent on social media and increased symptoms of depression, especially among young people.
Excessive social media use can lead to feelings of isolation, poor self-esteem, and disrupted sleep, all of which contribute to depression
Refers to a state of mental calmness, composure, and evenness of temper, especially in difficult situations.
It involves maintaining a balanced and stable mindset, regardless of external circumstances or emotional challenges.
Equanimity
Providing consistent, fair, and unbiased care to individuals from diverse cultural backgrounds, ensuring that everyone receives the same quality of treatment and support.
Equanimity of Care
An innovative mental health intervention developed in Zimbabwe to bridge the mental health treatment gap.
It uses a CBT-based approach, specifically problem-solving therapy, delivered by trained lay health workers
The Friendship Bench
Community volunteers, often referred to as “grandmothers,” who are trained to counsel patients
Lay Health Workers
The inability to feel pleasure, a common symptom in depression.
Anhedonia
Encourages looking at common features across different disorders to identify homogeneous sub-type
Transdiagnostic Approach
Helps disentangle the heterogeneity of affective disorders and improve mechanistic understanding using advanced technologies
Computational Models
Refers to the diversity and variability within affective disorders. This makes it difficult to achieve consistent research results.
Heterogeneity
Traditional VS Modern Approaches in Affective Disorders
Traditional Approach: Relied on singular experiments based on previous theories, often resulting in reductionist models that missed complex interactions.
Modern Approach: Uses sophisticated computational models to reproduce brain processes mathematically, providing a more nuanced understanding.
Mathematically explain relationships between neurobiology, environment, and symptoms.
Computational Psychiatry
Include serotonin, dopamine, and norepinephrine, which play key roles in mood regulation and cognitive functions.
Monoamine Neuotransmitters
Transdiagnostic VS Disorder-Specific Approaches to Affective Disorders
Transdiagnostic Approach: Focuses on common features across different disorders to identify homogeneous sub-types.
Example: Shared decision-making alterations in disorders like major depressive disorder (MDD), obsessive-compulsive disorder (OCD), and schizophrenia.
Disorder-Specific Approach: Targets unique features specific to each disorder.
Example: Specific connectome fingerprints that predict responses to antidepressants in depression.