The Biological Approach Flashcards
Brain Scanning Techniques: Structural imaging; MRI scans
An MRI scanner uses magnetic field and radio waves to map the activity of hydrogen molecules, which are present in different brain tissue to different degrees.
The image can either be viewed as a slice of the brain from any angle, or it can be used to create a three-dimensional image of the brain
The human body is mostly water. Water molecules contain hydrogen protons, which become aligned in a magnetic field. An MRI scanner applies a strong magnetic field which aligns the proton ‘spins’.
The scanner also produces a radio frequency current that creates a varying magnetic field. The protons absorb the energy from the magnetic field and flip their spins. When the field is turned off, the protons gradually return to their normal spin, a process called precession. The return process produces a radio signal that can be measured by receivers in the scanners and made into an image.
The MRI is a composite image of several images of the brain.
MRI scans: Holistic evaluation
Strengths:
- it is non-invasive, with minimal potential harm to the participant
- the image has a high spatial resolution; this gives the researchers a good sense of the actual structure of the brain.
Weaknesses:
- the MRI only indicates structure; it doesn’t actually map what is happening in the brain
- MRI research is correlational in research, not allowing researchers to establish a clear cause and effect relationship
Maguire et al. APFC (brain scanning techniques/ localisation of brain function/ neuroplasticity)
Aim:
The aim of the study was to investigate whether the brains of London taxi drivers would exhibit structural differences as a result of their extensive spatial navigation experience and exceptional knowledge of the city’s layout.
Procedure:
Sixteen right-handed male London taxi drivers, who had been licensed for at least 1.5 years and completed the “Knowledge” test, were compared with 50 right-handed male non-taxi drivers from an MRI database. The study used voxel-based morphometry (VBM) to measure grey matter density and pixel counting to assess the area of the hippocampus in MRI scans. This was a single-blind study, meaning the researcher analyzing the MRI data did not know whether the scan belonged to a taxi driver or a control participant.
Findings:
- Pixel Counting: Taxi drivers had significantly larger posterior hippocampi and smaller anterior hippocampi compared to controls.
- VBM: There was a positive correlation between the volume of the right posterior hippocampi and the number of years spent as a taxi driver. No significant differences were found in other brain regions.
Conclusion:
The study concluded that the hippocampus may undergo structural changes in response to the demands of spatial navigation. The posterior hippocampus appears to be involved in utilizing previously learned spatial information, while the anterior hippocampus may be more engaged in encoding new environmental layouts.
Maguire et al. evaluation (brain scanning techniques)
Evaluation:
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Strengths:
- The use of MRI scans allows for precise measurement of brain structure.
- The single-blind design reduces researcher bias.
- The study controls for age as a confounding variable.
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Limitations:
- As a quasi-experiment, it cannot establish a cause-and-effect relationship.
- The sample is gender-biased, limiting generalizability.
- Some may argue that individuals with naturally larger hippocampi might be more inclined to become taxi drivers, although this is countered by the correlation with driving experience
Maguire et al ethics
- The study is ethically sound; MRI scanning is non-invasive, and participants gave informed consent.
- The study maintained high standards of ethical research practices.
Maguire et al: Link paragraph for use of MRI to investigate human behaviour
This study illustrates the utility of MRI technology in exploring the relationship between environmental demands and brain structure. MRI provides high-resolution images of the brain, enabling researchers to measure grey matter density and specific brain regions’ volumes, such as the hippocampus, with great accuracy. Through techniques like voxel-based morphometry and pixel counting, researchers can identify correlations between brain anatomy and behavioral variables, such as the extensive navigation experience of taxi drivers in this study. By revealing structural changes in the brain associated with specific experiences, MRI technology contributes significantly to our understanding of brain plasticity and the localization of function in human behavior.
Localisation of brain function theory outline
Localisation of brain function refers to the theory that different parts of the brain are responsible for different aspects of human functioning, such as behaviours
This relates directly to the assumption of the biological approach that conditions, emotions and behaviours are products of the anatomy and physiology of our nervous and endocrine systems
Psychologists investigating localisation of brain function from the biological approach use brain-imaging techniques, brain surgeries and brain autopsies to investigate the correlation between brain processes and structures and human behaviour.
Draganski et al. (localisation of brain function/ neuroplasticity)
Aim
The aim of the study was to investigate whether learning a new skill, specifically juggling, would have an impact on the brain structure of the participants.
Procedure
- Participants: 24 volunteers aged 20-24 (21 females, 3 males), all non-jugglers at the start.
- Baseline MRI: All participants underwent an initial MRI scan to measure grey matter and brain structure.
- Condition Assignment: Participants were divided into two groups - jugglers and non-jugglers (control group).
- Juggling Training: The juggling group learned a three-ball cascade routine and practiced until mastery. Upon mastering, they had a second MRI scan.
- Post-Juggling Phase: After the second scan, jugglers were instructed to stop juggling. A third MRI scan was conducted three months later.
- Control Group: The non-jugglers did not learn juggling and served as a control group throughout the study.
Findings
- Baseline Comparison: Initial MRI scans showed no significant differences in grey matter between jugglers and non-jugglers.
- Post-Learning: After mastering juggling, jugglers exhibited a significant increase in grey matter in the mid-temporal area of both hemispheres, linked to visual memory.
- After Cessation: Three months after stopping juggling, the jugglers showed a decrease in grey matter in the same brain regions.
- Control Group: There were no changes in grey matter in the control group throughout the study.
Conclusion
Learning to juggle led to an increase in grey matter in the mid-temporal areas of the brain, suggesting that juggling primarily enhances visual memory areas rather than procedural memory regions like the cerebellum or basal ganglia. This increase in grey matter diminished after participants stopped practicing juggling. The study demonstrates the brain’s plasticity in response to acquiring new skills and the reversibility of these changes upon discontinuation of the activity.
Draganski et al evaluation (localisation of brain function/ neuroplasticity)
Strengths
- Clear Aim and Focus: The study has a specific aim to investigate structural brain changes resulting from learning a new skill, making the research question straightforward and focused.
- Controlled Design: By including a control group of non-jugglers, the study effectively controls for variables that might otherwise influence changes in grey matter, such as age or environment, helping to isolate the effects of learning to juggle.
- Longitudinal Approach: The study not only measured changes immediately after learning but also after a three-month cessation period, allowing researchers to observe both the increase and potential reversal of grey matter changes, which highlights the plasticity and adaptability of the brain.
- Objective Measures: MRI scans provide objective, quantifiable data on grey matter volume, which enhances the study’s validity and reduces potential biases associated with self-reported data.
Limitations
- Small Sample Size: With only 24 participants, the study’s sample size is relatively small. This may limit the generalizability of the findings, as small sample sizes increase the risk of sampling error.
- Gender Imbalance: The participant group is predominantly female (21 females, 3 males), which may affect the results if there are gender-specific differences in neuroplasticity or grey matter response to learning.
- Limited Skill and Practice Time Control: While participants practiced juggling until they mastered the skill, variations in time taken to achieve mastery or differences in individual practice routines could introduce variability in the results. Additionally, the study does not specify whether other factors (like sleep or lifestyle) were monitored, which could also influence grey matter changes.
- Absence of Other Neuroanatomical Regions: The study found changes in grey matter in the mid-temporal areas related to visual memory but did not observe changes in other areas associated with motor skills (e.g., cerebellum or basal ganglia). However, more extensive regions may be involved in complex motor tasks, and limiting the study to grey matter alone may overlook other relevant changes.
Implications and Future Research Directions
- Skill-Based Neuroplasticity: This study supports the idea that learning new skills, even briefly, can lead to structural changes in the brain. Future research could investigate whether other complex skills, such as learning a musical instrument or language, result in similar patterns of grey matter increase and reduction post-cessation.
- Long-Term Effects of Continuous Practice: While the study looked at grey matter changes three months after cessation, it would be insightful to examine the effects of continuous, long-term juggling practice to see if these structural changes stabilize or further increase.
- Diversity in Participants: Including a larger, more balanced sample size and controlling for potential confounding variables, such as participants’ physical activity or lifestyle, would strengthen the study’s applicability to a broader population.
- Exploration of Other Brain Regions and Techniques: Including scans or analysis techniques that measure changes in other brain areas, such as white matter or regions directly linked to motor skills, could yield a more comprehensive understanding of how learning juggling—or similar skills—affects the brain.
Maguire et al link paragraph (localisation of brain function)
Link to Localisation of Brain Function
This study’s findings contribute to the understanding of brain function localization by highlighting how specific brain regions adapt structurally in response to skill learning. The increase in grey matter in the mid-temporal regions, associated with visual memory, supports the idea that certain brain areas specialize in specific types of processing—in this case, visual and spatial memory needed for juggling. The absence of structural changes in other regions, such as the cerebellum or basal ganglia (areas traditionally associated with procedural memory and motor coordination), suggests that mastering juggling engages visual memory and perception more than motor skills, reinforcing the theory of functional localization. This insight aligns with previous research showing that distinct brain regions are responsible for particular cognitive functions, and it demonstrates how localized brain plasticity can occur in response to targeted learning experiences.
Maguire localisation of brain function link paragraph
This study provides insight into the localization of brain function, specifically within the hippocampus. The findings suggest that different regions of the hippocampus have distinct roles: the posterior hippocampus is involved in retrieving and using previously learned spatial information, while the anterior hippocampus may be more crucial for encoding new spatial information. This distinction aligns with the theory of localized brain functions, where specific brain areas are responsible for particular cognitive processes. By demonstrating structural differences in the hippocampi of taxi drivers, who rely heavily on spatial navigation skills, the study underscores how environmental demands can shape and potentially localize brain function.
Localisation of brain function holistic evaluation
Localisation research is often limited to very specific behaviours.
Most of the research is of two types; animal research, where there can be a direct manipulation of the IV. Or correlational research, such as many of the quasi experiments that are done. The correlational nature of the research may lead to drawing indirect conclusions.
Neuroplasticity (neural networks and synaptic pruning)
Neuroplasticity is the term used to describe the changes in neural pathways and synapses due to changes in behaviour, environment, thinking, emotions, as well as changing from bodily injury.
- It is fundamentally the ability of the brain to change through the making and breaking of synaptic connections between neurons. It occurs on different scales from synaptic plasticity to cortical remapping
- Synaptic plasticity is neuroplasticity occurring on the level of a separate neuron, construction of new synaptic connections (neural networks) and elimination of the ones that are not used (synaptic pruning)
- Cortical remapping is when neurons remap to new areas of the brain
Neuroplasticity is the brain’s ability to reorganise itself by forming new neural connections. It allows neurons in the brain to compensate for injury or to respond to changes in the environment.
Neural networks
- The process by which neural networks are formed is called neuroplasticity. This is known as synaptic plasticity
- When a neuron is stimulated, an action potential travels down the axon. Neural networks are created when a neuron, or set of neurons are repeatedly stimulated
- This repeated firing of the neurons, called long term potentiation, results in gene expression which causes the neurons to sprout new dendrites - known as dendritic branching
- This increases the number of synapses available for the behaviour
Synaptic pruning
Another way that our brain can change is through synaptic pruning- which is a decrease in the number of synapses as a result of removing dendritic branches
Synaptic pruning refers to the process by which extra neurons and synaptic connections are eliminated in order to increase the efficiency of neuronal transmissions
Synaptic pruning is a natural process that occurs in the brain between early childhood and adulthood. During synaptic pruning, the brain eliminates extra synapses. Synaptic pruning is our body’s way of maintaining more efficient brain function as we get older and learn new complex information.
- pruning can be the result of neuronal cell death, hormones such as cortisol or the lack of use of a neural pathway
- The exact mechanism of synaptic pruning is not yet fully understood.
Draganski link to neural networks
Link to Neural Networks
This study underscores the role of neural networks in skill acquisition by showing that learning juggling engages specific regions within the brain’s network of interconnected neurons. The increase in grey matter within the mid-temporal area suggests that neural networks in this region strengthen and adapt in response to the visual and spatial demands of juggling. By stimulating connections that support visual memory, learning to juggle appears to enhance the efficiency and density of neural pathways involved in processing movement and spatial information, reflecting the brain’s ability to reorganize and reinforce networks based on experience.
Draganski et al link to synaptic pruning
Link to Synaptic Pruning
The findings also relate to synaptic pruning, the process by which unused neural connections are reduced to increase efficiency in the brain. The decrease in grey matter observed three months after participants stopped juggling suggests that, once a skill is no longer practiced, the brain may prune or reduce the synaptic density in regions previously adapted for that skill. This pruning helps the brain allocate resources to more active networks, illustrating how experience-dependent changes in the brain can be reversible and emphasizing the importance of continued practice to maintain structural changes.