Directions: After students have had a chance to review the article “Flex time,” lead a classroom discussion based on the questions that follow.
1. What is magnetic resonance imaging and how does it generally work?
Magnetic resonance imaging (MRI), uses very powerful magnetic fields to interact with and measure the weak magnetic fields of atomic nuclei. Different types of atomic nuclei contain different numbers of protons and neutrons, and therefore have different magnetic fields, so MRI can distinguish between different elements (nuclei with different numbers of protons) and even different isotopes of the same element (nuclei with different numbers of neutrons but the same number of protons). Thus MRI can measure the amount and location of certain elements or isotopes within an object. The MRI’s strong magnetic fields can be used to create images of certain organs and tissues in live humans.
2. What is functional magnetic resonance imaging and how can it be used?
Functional magnetic resonance imaging (fMRI) uses MRI to create real-time maps of oxygen (or oxygenated blood) in a person. Like all human cells, neurons need oxygen, and the more active a neuron is, the more oxygen it draws from the blood. Therefore fMRI can measure the relative activity of different areas of the brain. For example, if a person is asked to do a specific mental task while in an fMRI scanner, regions of the brain involved in performing that task will light up on the image.
3. What is resting state functional magnetic imaging?
In resting state fMRI, the person whose brain is being imaged is not given a specific task to perform. Thus any brain areas that light up on the image indicate random thoughts or the natural resting state of human consciousness and personality. fMRI is so sensitive that it can detect signals that are in a very small region, very brief, and/or very weak. Sophisticated data processing algorithms can find correlations between different regions that experience a signal at the same time after filtering out uncorrelated signals, random noise, head motion and other factors.
Discussion questions: Depending on the level of your class, you may preface this discussion with this brief NBC Learn video describing how neurons process information.
1. Describe the structure of a neuron.
Like other human cell types, a neuron has a cell body with a nucleus. However, it also has outstretched tentacle-like structures that help it communicate with other neurons. Dendrites are tentacles that receive signals from other neurons, and neurons have many dendrites. Axons are tentacles that send signals to other neurons, and most neurons only have one axon.
2. How does a neuron send signals within itself, and from one neuron to another?
An electrical signal within a neuron is called an action potential, and it propagates down the axon away from the cell body. The action potential is binary — it either happens or it doesn’t happen, like a sneeze. The gap between the end of the axon of one neuron and a close neighboring dendrite of a second neuron is called a synapse. When the first neuron has an action potential, it releases chemical molecules called neurotransmitters from the end of its axon. Protein sensors called receptors on the dendrite of the second neuron detect the neurotransmitters. Some types of neurotransmitters are excitatory (urging the second neuron to fire its own action potential) and some types are inhibitory (urging the second neuron not to fire). Different types of neurons produce different neurotransmitters, and have receptors that specifically detect different kinds of neurotransmitters. The second neuron will react to the excitatory and inhibitory inputs that all of its dendrites receive from neighboring neurons and either fire an action potential or not.
3. What is long-term potentiation and how does it work?
Long-term potentiation is a process for forming long-lasting connections between neurons during learning. Pavlov’s dog can be used as an oversimplified example of this process to explain the basic concept. The dog learns that a bell is rung whenever food is given, and eventually will learn to drool with hunger when the bell is rung even without food because of long-term potentiation. To describe long-term potentiation, suppose that the dog’s brain has one neuron that causes the dog to slobber, a second neuron that lights up when the dog sees food, and a third neuron that lights up when the dog hears a bell. Initially there is only a strong connection from the food neuron to the slobber neuron. An action potential releasing neurotransmitters from the food neuron’s axon will activate dendritic receptors on the slobber neuron and trigger an action potential in the slobber neuron. There is no strong connection between the bell neuron and the other neurons, so ringing the bell and subsequent firing of the bell neuron has no effect on the other neurons. However, if the bell is rung each time that food is presented, eventually the food neuron and the bell neuron will always fire at the same time. Dendrites from the slobber neuron will recognize this association and become more sensitive to signals from the bell neuron. The dog’s brain has learned to associate the bell with food and slobbering.
4. How could too many connections interfere with learning?
“Flex time” gave an example where learners with a disability appeared to have more connections than fast learners. It is possible that having connections that are too many or too rigid interferes with the ability to learn new information and change connections between neurons.
5. How could unusually high amounts of brain flexibility be related to typical schizophrenic behaviors?
Brain flexibility is involved in learning new information and realizing that different things are related to each other. Unusually high amounts of brain flexibility, one could imagine, might make a person learn things that are not really there or experience hallucinations. It might also make a person mistakenly connect unconnected ideas, causing disjointed thoughts.
ENGINEERING AND EXPERIMENTAL DESIGN
1. In performing resting state functional magnetic imaging, or fMRI, to measure the connectivity between different brain regions, what factors would need to be screened out by the data processing algorithm to avoid irrelevant or erroneous results?
The algorithm would need to adjust for motion due to pulse, respiration and head motion. It would need to ignore neural firings that are not always accompanied by other simultaneous firings. It would also need to screen out neural firings that are correlated with each other simply because they are associated with a task the patient is currently performing, such as sensing or responding to distracting stimuli in the fMRI chamber. Yet the sensors and algorithm would need to capture correlated blips, no matter how brief, how spatially localized or how weak they might be.
2. What other applications could fMRI be used for?
Some possibilities include diagnosing diseases or measuring the influence of various drugs on the brain, or potentially creating a lie detector.
3. What are ways that one might improve the ability of the human mind to learn?
Possibilities include drugs that improve long-term potentiation or alter brain connectivity and flexibility, external brain stimulation by electrodes and mood-altering activities to make a person happier.
4. What are some technologies that are inspired by how brains learn?
Neural networks or neural nets are computers designed to emulate the human brain, automatically strengthening and weakening connections among artificial “neurons” as they learn to recognize patterns in data.