Imaging the working brain – Issues in development and Learning
Professor Steven Swithenby – The Open University
Introduction by Helen Hare
As a teacher I spend my time teaching A level physics to mainly boys (although I am working on this). I don’t know whether it is the same in other mixed sex schools but the able girls in my school all seem to want to be medical doctors. This lecture shows that physics is important to medicine as well as other subjects such as psychology. In modern medicine we depend on imaging e.g. MRI, to locate some medical conditions and other physics based equipment to treat them, e.g. Proton therapy.
Magnetic resonance imaging (MRI), nuclear magnetic resonance imaging (NMRI), or magnetic resonance tomography (MRT) is a medical imaging technique used in radiology to investigate the anatomy and function of the body in both health and disease. MRI scanners use strong magnetic fields and radiowaves to form images of the body. The technique is widely used in hospitals for medical diagnosis, staging of disease and for follow-up without exposure to ionizing radiation.
Proton therapy is a type of particle therapy which uses a beam of protons to irradiate diseased tissue, most often in the treatment of cancer. The chief advantage of proton therapy is the ability to more precisely localize the radiation dosage when compared with other types of external beam radiotherapy.
I have heard it argued that a medical doctor will treat a relatively small quantity of patients during his/her career compared to a physicist’s creation which will treat many more. A medical doctor may prescribe the treatment but it took the physicist to come with the ideas for the treatment.
Can neuroscience help us to understand cognition – functional imaging
Cognition is a term referring to the mental processes involved in gaining knowledge and comprehension. These processes include thinking, knowing, remembering, judging, and problem-solving. These are higher-level functions of the brain and encompass language, imagination, perception, and planning.
A typical brain contains 100 billion cells and half of these are involved in information processing. There are up to 50000 connections per cell but this changes all the time.
Just looking at a brain tells us very little. Einstein’s brain didn’t look very different from lots of other brains although when it was dissected some differences were found. These apparent differences or irregularities in his brain have been used to support various ideas about correlations in neuroanatomy with general or mathematical intelligence. Scientific studies have suggested that regions involved in speech and language were smaller, while regions involved with numerical and spatial processing were larger. These conclusions were not completely accepted.
However as Einstein was dead it was difficult to know for sure what parts of his brain he was using when he was working on his theories. This is where modern imaging has the advantage. The person being investigated doesn’t have to be dead.
Functional imaging (or functional medical imaging), is a method of detecting or measuring changes in metabolism, blood flow, regional chemical composition, and absorption.
As opposed to structural imaging, functional imaging centres on revealing physiological activities within a certain tissue or organ by employing medical image modalities that very often use tracers or probes to reflect spatial distribution of them within the body. These tracers are often analogous to some chemical compounds, like glucose, within the body. To achieve this, isotopes are used because they have similar chemical and biological characteristics. By appropriate proportionality, the doctors or radiologists can determine the real intensity of certain substance within the body to evaluate the risk or danger of developing some diseases.
Functional magnetic resonance imaging or functional MRI (fMRI) is a functional neuroimaging procedure using MRI technology that measures brain activity by detecting associated changes in blood flow. This technique relies on the fact that cerebral blood flow and neuronal activation are coupled. When an area of the brain is in use, blood flow to that region also increases.
An MRI signal can also be considered as a function of blood oxygenation.
By repeating an MRI measurement oxygen levels in the brain can be compared several times.
To perform a study the patient is positioned within an MRI scanner which forms a strong magnetic field around the area to be imaged. Most medical applications rely on detecting a radio frequency signal emitted by excited hydrogen atoms in the body (present in any tissue containing water molecules) using energy from an oscillating magnetic field applied at the appropriate resonant frequency. The orientation of the image is controlled by varying the main magnetic field using gradient coils. As these coils are rapidly switched on and off they create the characteristic repetitive noises of an MRI scan. The contrast between different tissues is determined by the rate at which excited atoms return to the equilibrium state.
Before MRI is used to scan the brain the patient needs to let their mind go blank so that the stimulus used can have the maximum effect.
MRI imaging of blind people using braille have shown that they use their visual cortex even if they were blind from birth.
The images on the left of the picture show a blind person’s brain resting and the images on the right show the blind person’s brain when he is braille reading.
Cortivis Project – Universidad Miguel Hernández
The MRI shows that braille reading is decoded by the visual cortex.
Diffusion Tensor imaging
Diffusion MRI (or dMRI) is a magnetic resonance imaging (MRI) method which came into existence in the mid-1980s. It allows the mapping of the diffusion process of molecules, mainly water, in biological tissues, in vivo and non-invasively. Molecular diffusion in tissues is not free, but reflects interactions with many obstacles, such as macromolecules, fibres, membranes, etc. Water molecule diffusion patterns can therefore reveal microscopic details about tissue architecture, either normal or in a diseased state.
Diffusion tensor imaging (DTI) is a magnetic resonance imaging technique that enables the measurement of the restricted diffusion of water in tissue in order to produce neural tract images instead of using this data solely for the purpose of assigning contrast or colours to pixels in a cross sectional image. It also provides useful structural information about muscle—including heart muscle—as well as other tissues such as the prostate.
Imaging the diffusion properties reveals the long distance connections. Imaging the biochemistry?
Tractographic reconstruction of neural connections via diffusion tensor imaging (DTI).
In neuroscience, tractography is a 3D modeling technique used to visually represent neural tracts using data collected by diffusion tensor imaging.
Positron emission tomography PET
Positron emission tomography (PET) is a nuclear medicine, functional imaging technique that produces a three-dimensional image of functional processes in the body. The system detects pairs of gamma rays emitted indirectly by a positron-emitting radionuclide (tracer), which is introduced into the body on a biologically active molecule. Three-dimensional images of tracer concentration within the body are then constructed by computer analysis. In modern PET-CT scanners, three dimensional imaging is often accomplished with the aid of a CT X-ray scan performed on the patient during the same session, in the same machine.
If the biologically active molecule chosen for PET is fludeoxyglucose (FDG), an analogue of glucose, the concentrations of tracer imaged will indicate tissue metabolic activity by virtue of the regional glucose uptake. Use of this tracer to explore the possibility of cancer metastasis (i.e., spreading to other sites) is the most common type of PET scan in standard medical care (90% of current scans). However, on a minority basis, many other radioactive tracers are used in PET to image the tissue concentration of many other types of molecules of interest.
As the radioisotope undergoes positron emission decay (also known as positive beta decay), it emits a positron, an antiparticle of the electron with opposite charge. The emitted positron travels in tissue for a short distance (typically less than 1 mm, but dependent on the isotope), during which time it loses kinetic energy, until it decelerates to a point where it can interact with an electron. The encounter annihilates both electron and positron, producing a pair of annihilation (gamma) photons moving in approximately opposite directions. These are detected when they reach a scintillator in the scanning device, creating a burst of light which is detected by photomultiplier tubes or silicon avalanche photodiodes (Si APD). The technique depends on simultaneous or coincident detection of the pair of photons moving in approximately opposite direction (it would be exactly opposite in their centre of mass frame, but the scanner has no way to know this, and so has a built-in slight direction-error tolerance). Photons that do not arrive in temporal “pairs” (i.e. within a timing-window of a few nanoseconds) are ignored.
By repeating a measurement the distribution of the radiochemical can be compared at two times.
Brain imaging techniques
Both fMRI and PET measure blood flow have revealed the functional anatomy of the brain but the time resolution is minutes for PET and ~1 second for fMRI
Is it possible to image activity at the rate at which the brain works?
Magnetoencephalography (MEG) is a functional neuroimaging technique for mapping brain activity by recording magnetic fields produced by electrical currents occurring naturally in the brain, using very sensitive magnetometers. Arrays of SQUIDs (superconducting quantum interference devices) are currently the most common magnetometer, and SERF being investigated for future machines. Applications of MEG include basic research into perceptual and cognitive brain processes, localizing regions affected by pathology before surgical removal, determining the function of various parts of the brain, and neurofeedback. This can be applied in a clinical setting to find locations of abnormalities as well as experimental setting to simply measure brain activity.
Red is the field in and blue is the field out.
Currents in the brain produce very weak magnetic fields
Superconducting sensors SQUIDs detect them
Map backwards from fields to the currents (head model and problem of skull)
Hence maps of currents indicate the brain activity.
There are infinite possibilities of situations so the process is not foolproof.
Origin of the brain’s magnetic field
Sources (EEG and MEG)
Sources in brain are parallel neuronal tracts (pyramidal cells) in the cortex. They are simultaneously active and known as columns. Sources produce EEG and MEG signals. Pattern of EEG on the scalp is distorted by skull.
Electroencephalography (EEG) is the recording of electrical activity along the scalp. EEG measures voltage fluctuations resulting from ionic current flows within the neurons of the brain. In clinical contexts, EEG refers to the recording of the brain’s spontaneous electrical activity over a short period of time, usually 20–40 minutes, as recorded from multiple electrodes placed on the scalp. Diagnostic applications generally focus on the spectral content of EEG, that is, the type of neural oscillations that can be observed in EEG signals.
EEG is most often used to diagnose epilepsy, which causes obvious abnormalities in EEG readings. It is also used to diagnose sleep disorders, coma, encephalopathies, and brain death. EEG used to be a first-line method of diagnosis for tumours, stroke and other focal brain disorders, but this use has decreased with the advent of high-resolution anatomical imaging techniques such as MRI and CT. Despite limited spatial resolution, EEG continues to be a valuable tool for research and diagnosis, especially when millisecond-range temporal resolution (not possible with CT or MRI) is required.
A MEG ‘Scanner’
There are several hundred detectors at 4.2K. A helmet shaped detection area is used. It is non-invasive and produces continuous recording. There is considerable magnetic noise reduction and a modest spatial resolution with a high time resolution.
Superconducting coils linked to Superconducting Quantum Interference Devices.
How to probe the brain
Put the subject’s head into the imager.
Ask the subject to carry out a task.
Measure the brain activity as they do so.
Repeat and average.
You can find brain testing apps for your phone at http://www.thegreatbrainexperiment.com/
One of the activities that are used is the Stroop test.
In psychology, the Stroop effect is a demonstration of interference in the reaction time of a task. When the name of a colour (e.g., “blue,” “green,” or “red”) is printed in a colour not denoted by the name (e.g., the word “red” printed in blue ink instead of red ink), naming the colour of the word takes longer and is more prone to errors than when the colour of the ink matches the name of the colour. The effect is named after John Ridley Stroop who first published the effect in English in 1935. The effect had previously been published in Germany in 1929. The original paper has been one of the most cited papers in the history of experimental psychology, leading to more than 701 replications. The effect has been used to create a psychological test (Stroop test) that is widely used in clinical practice and investigation.
Green Red Blue
Purple Blue Purple
Blue Purple Red
Green Purple Green
The Stroop effect is the finding that naming the colour of the first set of words is easier and quicker than the second.
Have a go yourself
We are not programmed to read the actual colours
John Ridley Stroop first reported this effect in his Ph.D. dissertation published in 1935. Current research on the Stroop effect emphasizes the interference that automatic processing of words has on the more mentally effortful task of just naming the ink colour. The task of making an appropriate response – when given two conflicting signals – has tentatively been located in a part of the brain called the anterior cingulate. This is a region that lies between the right and left halves of the frontal portion of the brain. It is involved in a wide range of cognitive processes.
Although the functions of the anterior cingulate are very complex, broadly speaking it acts as a conduit between lower, somewhat more impulse-driven brain regions and higher, somewhat more thought-driven behaviours. The Stroop effect’s sensitivity to changes in brain function may be related to its association with the anterior cingulate.
Introduction to autism
Born on February 29, 1896 Klekotow, Austria-Hungary. Died on April 3, 1981 (aged 86) Sykesville, Maryland, U.S.
Leo Kanner was an American psychiatrist and physician known for his work related to autism. Kanner’s work formed the foundation of child and adolescent psychiatry in the U.S. and worldwide.
Bornon February 18, 1906 Hausbrunn, Austria-Hungary. Died on October 21, 1980 (aged 74) Vienna, Austria.
Hans Asperger was an Austrian paediatrician, medical theorist, and medical professor. He is best known for his early studies on mental disorders, especially in children. His work was largely unnoticed during his lifetime except for a few accolades in Vienna, and his studies on psychological disorders only acquired world renown posthumously.
Leo Kanner and Hans Asperger independently came up with similar descriptions of children displaying severe social deficits and unusual behaviours. Leo Kanner (1943) in Baltimore, USA, described 11 children with ‘early infantile autism’ in his seminal paper ‘Autistic disturbances of affective contact’. In the same year, October 1943, Hans sperger, in Vienna, Austria, submitted his thesis on ‘Autistic psychopathy in childhood’, which was published in 1944, describing four children with ‘autistic psychopathy’. Both authors used the term ‘autistic’ which was coined by Bleuler, a Swiss psychiatrist, who used this label to describe the characteristics of individuals with schizophrenia. However, only Asperger acknowledged the fact that he had adopted Bleuler’s terminology in his doctoral thesis, whereas no references to Bleuler are evident in Kanner’s paper.
Autism is a childhood disorder of social interaction, aloofness and stereotyped behaviour (e.g. as seen in Rainman movie). It is not caused by ‘bad parents’, specific brain lesions or simple environmental triggers (e.g. heavy metals, MMR etc.)
It is believed to be caused by a complex genetic predisposition – link to developmentally significant proteins but more work needed.
Autism MEG study – strategy
The aim was to find the neurophysiological basis of autism. Tasks were chosen where there are clear differences in behaviour, e.g. face processing.
Escalate the demands of the tasks on high functioning subjects (adults & children) until the differences are seen by MEG. Identify the neurophysiological differences.
Face processing studies
The above bottom right image (left in the picture) shows the location in the brain that is responsive to faces in typical individuals. This region, called the “Fusiform Face Area” (FFA) is located in a particular location in the temporal lobe called fusiform gyrus and is shown in this functional activation map. Although both sides of the brain are commonly active in response to faces, it is the right side that is usually more active in response to faces (note radiological convention where left and right are reversed in the image).
This image was taken from early face processing research conducted by Karen Pierce and colleagues at the ACE.
The above bottom right image on the right of the picture is of the human brain, post mortem, where the fusiform face area is coloured in pink.
Face responses (autistic adults)
Psychologist Robert Schultz and colleagues presented images of faces to individuals with and without autism. Using fMRI, they discovered that the brains of people with autism reacted diﬀerently to faces than the brains of those without the disorder. In normal brains, the sight of a face activates a region known as the fusiform face area. In brains of those with autism, Schultz and his colleagues found, that area doesn’t show much activity but a nearby area involved in recognizing objects does. There is more activity at the back of the head (the visual cortex) and less activity on the right side.
When children with autism do look people in the eye, says psychologist Kim Dalton, they often see threats where none exist.
In one study, Dalton and her colleagues combined fMRI and eye-tracking technology to see what happens during eye contact. They found that the amygdala—a part of the brain associated with negative emotions—becomes abnormally active when children with autism gaze at a nonthreatening face. Thanks to the over-excited amygdala, even the most familiar face—their mum or dad’s face, for instance—can seem scary. As a result, most people with autism avoid eye contact.
This diﬃculty in looking people in the eye can result in extreme social disability, according to psychologist Simon Baron-Cohen. He has found that people with autism have a hard time interpreting the subtle and even not-so-subtle behaviour of others.
In one study, Baron-Cohen and his colleagues showed photos of eyes to people with and without autism while they lay inside an fMRI scanner. As each set of eyes ﬂashed by, the researchers asked participants to choose between two possible interpretations for what the person was feeling or thinking. Do scowling eyes mean someone is sympathetic or unsympathetic? For the most part, the people with autism couldn’t say. And the scans revealed that regions of the brain that seem to govern so-called “social intelligence” became more active when people without autism searched the eyes for meaning, but stayed quiet for those with autism.
Recognising face and motor bikes
It is well documented that individuals with ASD show impairments that are face linked, for example, in patterns of eye contact and response to gaze. During development, individuals with ASD typically show reduced memory for faces and a deficit in recognizing facially expressed emotion. Autism, however, is also associated with a heightened ability to recognise upside down faces and to extract information from some face features compared to typically developing individuals. Such observations support the suggestion that individuals with autism attend to individual features rather than process faces as a whole. Most of the neuroimaging literature on face processing in autism has concentrated on the fusiform face area (FFA) located in ventral occipitotemporal cortices. The FFA is part of the brain’s face processing system and is selectively activated by images of faces in typically developing subjects. It is generally accepted that FFA activation is atypical in individuals with ASD. However, there is debate as to whether this is intrinsic or is connected with the details of the task, such as the degree of engagement. Despite the relevance of face processing to the study of autism, there have been only a few published MEG studies in individuals with ASD so far.
MEG measurements were made using a whole head magnetometer covering all cortical regions to study the neuronal response in 12 able adults with ASD and 22 adult controls performing image categorization and 1-back image memory task.
It was used to study the neural mechanisms underlying face and gaze processing of autistic and non-autistic participants. The participants performed a task in which they had to decide whether images presented sequentially in pairs, depicted the same person or the same motorbike (the control object). In this task, the participants saw pictures of faces in which the eyes were either open or shut and pictures of motorbikes.
Face responses – control adults
Three very obvious peaks
Face responses – autistic adults
Still three peaks but much less obvious
NF = Non autistic subjects looking at faces
NB = Non autistic subjects looking at bikes
AF = Autistic subject looking at faces
AB = Autistic subjects looking at bikes
The ‘graph’ shows the range of locations of face and general object (e.g. motor bike) processing in a horizontal cross section through the brain. It reflects biological diversity but does demonstrate that control subjects have different locations for faces but autistic subjects don’t. They don’t process faces in a different way.
The graph seems to show that the autistic subjects see faces differently from non-autistic subjects but there are huge error bars. Could it be that autistic subjects see the face simply as an object?
The above wikipedia article will help you identify the different parts of the brain. mentioned in the article.
Those with ASD generated responses to images of faces in right extrastriate cortices around 145 ms after the start of the stumulus that were signiﬁcantly weaker, less lateralised and less affected by stimulus repetition. Early latency (30–60 ms) responses to face images over right anterior temporal regions were different for the two groups in the image identiﬁcation task. Overall the study suggests that those with ASD develop differently located and functionally different extra-striate processing pathways. These pathways are functionally competent for some aspects of face processing. However, it is currently unresolved whether such processing routes may provide advantage in socially linked cognition.
Face responses – control children
Face and gaze processing were studied using magnetoencephalography in 10 children with autism and 10 normally developing children, aged between 7 and 12 years. The children performed two tasks in which they had to discriminate whether images of faces presented sequentially in pairs were identical. The images showed four different categories of gaze: direct gaze, eyes averted (left or right) and closed eyes but there was no instruction to focus on the direction of gaze. Images of motorbikes were used as control stimuli. Faces evoked strong activity over posterior brain regions at about 100 ms in both groups of children. A response at 140 ms to faces observed over extrastriate cortices, thought to be homologous to the adults, was weak and bilateral in both groups and somewhat weaker (approaching significance) in the children with autism than in the control children. The response to motorbikes differed between the groups at 100 and 140 ms. Averted eyes evoked a strong right lateralized component at 240 ms in the normally developing children that was weak in the clinical group. By contrast, direct gaze evoked a left lateralized component at 240 ms only in children with autism. The findings suggest that face and gaze processing in children with autism follows a trajectory somewhat similar to that seen in normal development but with subtle differences. There is also a possibility that other categories of object may be processed in an unusual way. The inter-relationships between these findings remain to be elucidated.
There are profound differences in early responses in all the children. 140ms face-specific signal weaker but strong face signal at 100ms. Autistic children are similar but 140 ms even weaker.
In children there was no evidence of the face sensitive, low amplitude short latency (30–60 ms) activity seen previously in adults. A strong, midline posterior response at approximately 100 ms was observed in children, which was earlier and somewhat stronger to faces than to motorbikes; in adults the signal at this latency was weak. A clear face sensitive response was seen in adults at 135 ms, predominantly over the right inferior occipito-temporal regions. Although activity was observed in the children at the same latency, it was less prominent, not lateralized and was evoked similarly by faces and motorbikes. Averted gaze conditions evoked strong right-lateralized activity at approximately 245 ms in children only. These ﬁndings indicate that even in middle childhood the neural mechanisms underlying face processing are less specialized than in adults, with greater early activation of posterior occipital cortices and less speciﬁc activation of ventral occipito-temporal cortex.
Overall, the study demonstrated that the response differences between the two groups of children were less marked than between the adult groups and between children and adults.
Both groups showed similarity in immaturity of face processing though there were subtle differences with, apparently, greater recruitment of extrastriate cortex in processing non-face (motorbike) stimuli and a less face-speciﬁc response. Together these ﬁndings suggest that there is divergence between the developing face and object processing systems. An interesting though as yet inconclusive observation was differences in longer latency responses to averted and direct gaze images with those with ASD responding more strongly to direct gaze.
The images below are of the MEG autism research. (Top) The ECD locations for responses to images of human faces at about 145 ms after stimulus onset in a typically developing subject and an individual with ASD (circle indicates the volume conductor sphere). These images illustrate locations in the right posterior cortices of the generators, where, on average, dipole locations are more lateral in TD compared to ASD. (Below) Grand root-mean-square signals following face images. The curves have been obtained by summation over all participants within a participant group (blue, boys with ASD; red, typically developing boys; and stimulus onset at 0) and channels. Even in middle childhood, the neural mechanisms underlying face processing are less specialized than in adults (inset) with greater early activation of posterior occipital cortices (I, II) and less specific activation of ventral occipitotemporal cortex (III), particularly in boys with ASD.
Autism study – conclusions
1) The Autistic group display very different activity. No evidence that the Autistic group have developed face specific systems
2) Even as late as 12 years old, face processing systems are not well established.
3) The evidence is that people with autism spectrum disorders have developed less specialised neural networks. Less specialised neural networks
4) This may underlie less ‘tuned’ social interactions and awareness.
Psychology, neuroscience & education
Can measurements on the brain help us to understand how people develop?
Can MEG provide insights into educational problems?
Yes but beware myths and exaggerations such as:
1) Male/female brain equated to right/left brain
2) Focus on early learning. ‘It is clear that by the time most children start preschool, the architecture of the brain has essentially been constructed.’ Hilary Clinton
Areas of interest to Professor Swithenby
1) The role of repetition in deeper learning
2) The balance of novelty, rote and engagement
3) The transition from being able to being expert
a) The maths problem
b) Symbolic maths as an acquired language
c) The value of standard problem exercises
Expertise in algebra
Previous work by Anderson et al. (2008)
fMRI – quasi algebra – data Interpreted using Adaptive Control of
Thought – Rational mode
7x + 1 = 29
ENCODE +1 = 29 Visual
RETRIEVE inverse of +1 Retrieve
RETRIEVE 29-1 Retrieve
TRANSFORM =28 Imaginal
ENCODE 7x Visual
RETRIEVE 28/7 is 4 Retrieval
TRANSFORM X = 4 Imaginal
KEY 4 Manual
Declarative knowledge of rules of algebra- lateral inferior pre- frontal cortex – study the rules
Perception and recognition of algebraic form
fusiform cortex – practice algebraic reading
Reasoning within the domain of algebra – anterior prefrontal cortex – justify transformations used
MEG can be used longitudinally and can separate out the steps in reasoning
Arithmetic versus Algebra
30 volunteer subjects (students)
Look at screen – view arithmetic expression or algebraic equation
View 2 solutions – choose one and press corresponding button
(Arithmetic) 6- 2 is the answer 3 or 4?
(Algebra) 4x – 7 = 5 is x 2 or 3?
60% to 3100% correct (split into 2 groups – expert & ok))
Reaction time (to the prompt) much longer for algebraic problems 1.1s compared with 0.65s
Expert group quicker (p< 0.05)
Arithmetic processing – every 20 ms for 0.8 secs
Algebraic processing – every 20 ms for 0.6 secs
Algebraic processing – every 20 ms for 0.6 secs
Evidence for strong (algebra and arithmetic linked) perceptual processes in experts (fusiform)
Evidence for declarative arithmetic knowledge retrieval in non-experts but generally greater activity in experts. Non-experts suffer from a cognitive overload and don’t leap the perception barrier for algebra. They don’t perceive the form and can’t unscramble it. They can identify the types of equation but not the procedure.
Little sign of semantic (language) activity in either group
Little sign of number relationship processing in either group (parietal cortex)
Follow up on ~14 year old maths students
Longitudinal (repeated) study through learning process
Additional protocols (e.g. mechanics problems or chemical structures)
Postscript by Helen Hare
I would like to thank professor Swithenby for answering my questions about his presentation. I have to be honest that I don’t entirely understand some of the data from the autism research and I’m not very familiar with the parts of the brain mentioned. As a teacher I am aware that autistic children see the world differently from other children and I was pleased that physics is being used to investigate these differences.
I’m also pleased that physics is also being used to investigate how children learn, especially maths. Hopefully in the future it will lead us to better teaching methods.