HC11 Genetics

Question 1

Imaging genetics

Answer 1

• Combines brain imaging and genetics
• Identifies genetic variants (genotype) linked to differences in brain structure & function (phenotype)
• Examines how genetics relate to behavioral traits (e.g., cognition, psychiatric disorders)

Question 2

DNA

Answer 2

Deoxyribonucleic Acid
Spiral-shaped, complex molecule with base pairs
Contains genetic instructions

Question 3

Gene

Answer 3

• Segment of DNA that codes for a specific protein
• Located on chromosomes

Question 4

Chromosome

Answer 4

• Long strand of coiled DNA
• Contains multiple genes

Question 5

Polymorphism

Answer 5

Variation in a gene or DNA segment

Question 6

Single Nucleotide Polymorphism (SNP)

Answer 6

• Variation of a single base pair in the genome
• Some individuals may have G, others A at a specific location
• Can result from mutations, cell division, or sexual reproduction

Question 7

Family linkage studies

Answer 7

• Identify genes in families with high disease prevalence
• Success: Diseases caused by a single gene mutation (e.g., cystic fibrosis)
• Failure: Common diseases with multiple interacting genes

Question 8

Twin studies

Answer 8

Compare monozygotic (MZ) twins (100% genetic similarity) and dizygotic (DZ) twins (50% genetic similarity)

Question 9

Discordant MZ Twin Design

Answer 9

Discordant means that the trait is not shared between the twins.
For example, only one twin scores high on ADHD, OCD, generalized anxiety, major depression, etc. These differences also reflect environmental effects.

Question 10

Candidate-gene approach

Answer 10

• Tests specific hypotheses linking gene variation to brain structure/function
• Focuses on one or more SNPs

Strength: High statistical power
Weakness: Cannot discover new genes or interactions

Question 11

Genome-wide association studies (GWAS)

Answer 11

• Examines large sets of SNPs across the genome
• Can identify unknown genetic links

Weakness: Low power due to multiple comparisons

Question 12

Consortia & Meta-Analytic Approaches

Answer 12

• Multi-center consortia: Large datasets from multiple centers (e.g., IMAGEMEND)
• Meta-analyses: Combine findings from different studies (e.g., ENIGMA)

Question 13

Polygenic Risk Scoring

Answer 13

Polygenic risk scoring looks at many small genetic differences (SNPs) across a person’s entire DNA. It combines them into a single score using data from previous large studies (GWAS). This score shows how a person’s genetic risk for a certain disease compares to others. The score is based only on genetics and does not depend on other factors you are studying.

Question 14

Polygenetic risk scoring - advantages

Answer 14

Maximization of power even with small samples. - Replicate findings.

Question 15

Computational modeling

Answer 15

Uses math, physics, and computer science to study behaviour of complex systems. It wants to find mechanistic explanations of how the nervous system processes information to give rise to cognitive function and behaviour using computer stimulations

Question 16

Why use computational modeling?

Answer 16

• Test theories of cognition
• Integrate knowledge from multiple fields
• Observe complex interactions
• Generate empirical predictions
• Perform artificial lesioning to test validity

Question 17

Computer simulations

Answer 17

An imitation or reproduction of behaviour in an alternative medium, simulated cognitive processes. These simulated cognitive processes form the model is called artificial intelligence.

Question 18

Computer simulation - simplify

Answer 18

Simulations allow you to simplify things to make it easier to look at how things work. If you wouldn’t do this, then it is very hard to examine the human brain. The human brain by itself is too complex to simulate. An equally complex model not helpful: not easier than studying the brain.

Question 19

Computer simulations - animate

Answer 19

Dynamic presentations: they are able to capture and reveal how dynamic processes unfold over time.

Question 20

Computer simulations - emerge

Answer 20

study how psychological properties emerge from brain-like models.

Question 21

Why do we build models?

Answer 21

• explicit hypotheses and assumptions necessary to test theories of cognition.
• provides a framework for integrating knowledge from various fields, you can bring all the knowledge together within one model.
• possible to observe complex interactions among hypotheses.
• provides ultimate control.
• leads to empirical predictions.
• artificial lesioning makes it possible to test a model’s validity, if you make a lesion in the model, you can see the effect.

Question 22

Neural network

Answer 22

A biologically inspired computational model patterned after network of neurons present in the human brain. It has different levels of complexity, from single neurons and synapses up to abstract connectionist-type or population-level descriptions of neural networks.

Question 23

The basic form of a neural network has 3 different layers:

Answer 23

Input Layer: Receives information
Hidden Layers: Processes information
Output Layer: Produces results

Question 24

Deep neural networks (DNNs)

Answer 24

A neural network with multiple layers between the input and the output layers, so a network with more hidden layers.

Question 25

Constraints to make the neural network biological plausible:

Answer 25

- Multi-layered neural architecture. - Each processing node (which reflects a neuron) is connected to many others. - Each connection (which reflects a synapse) is characterized by a connection weight. o positive values = reflect the degrees of excitation. o negative values = reflect the degrees of inhibition. - Each simulated neuron receives multiple inputs (which reflect dendrites). - If the sum of the inputs is above the threshold: an output is triggered from the receiving node (according to a transfer function). - Memory and learning depend heavily on changing connection weights (which reflects synaptic properties) → experience-dependent plasticity mechanisms.

Question 26

Supervised learning

Answer 26

Develop a predictive model based on both input and desired output data. Input and output is given so known. The weights are adjusted until the output has the desired value: classification/regression. The idea is that training data can be generalized, the model can be used on new data. All information is given, input and output.

Question 27

Unsupervised learning

Answer 27

Group the data and interpret the data based only on input data. A model that might have generated that set: clustering. You get data (input) without any information (labels) and you don’t know the output, the output becomes a structured dataset by using clustering.

Question 28

Reinforcement learning

Answer 28

the value of the output is unknown beforehand but the network provides feedback whether the output is right or wrong: sequential decision problem / trial-and-error. This is done to train animals.

Question 29

Human brain project

Answer 29

Using simulation of multi-scale models to uncover the neural mechanisms underlying cognitive processes, such as learning, perception, sleep and consciousness. Construct neural network simulators from neuromorphic chips that use transistors to simulate neurons and synapses.

Question 30

Advantages of neuromorphic computing

Answer 30

- Energy efficiency. - Execution speed. - Robustness against local failures. - Ability to learn, it is possible to access the different timescales involved in learning and development.