How is brain activity measured

First, ECoG requires insertion of the electrode array under the scalp, and so requires surgery. For this reason, ECoG is only suitable for patients already scheduled for a medical surgery that involves opening of the scalp.

Second, ECoG allows significantly improved localisation of the activity source, as well as the recording of higher frequency electrical activity. Neuroscientists are justifiably limited in the sort of approaches they can use to study human brain activity. However, so far no technology exists that allows detailed neuron activity to be recorded through the human skull, meaning that the measures we can take give fairly coarse information as to how our brains work.

These spatial and temporal resolution limits will undoubtedly be improved in the near future, enabling more precise measurements and greater insights into human brain activity. Furthermore, complementary approaches that allow the temporary disruption of neuronal processing will help us to understand what sorts of regional brain dysfunction might lead to the cognitive deficits associated with mental disorders. QBI newsletters Subscribe. Help QBI research Give now. Skip to menu Skip to content Skip to footer.

Site search Search. Site search Search Menu. Moreover, the same problem applies to other data-rich measures of brain activity. The solution is not to limit our recordings, but to improve our analysis approaches to the multivariate problem that is the brain e.

There are many ways to analyse an fMRI dataset, so which do you choose? Especially when many of the available options make sense and can be easily justified, but different choices generate slightly different results. This dilemma will be familiar to anyone who has ever analysed fMRI.

A recent paper identified 6, slightly different paths through the analysis pipeline, resulting in 34, different sets of results. By fully exploiting this wiggle room, it should be possible to generate almost any kind of result you would like see here for further consideration.

Although this flexibility is not strictly a limit in fMRI and certainly not unique to fMRI , it is definitely something to keep in mind when interpreting what you read in the fMRI literature. Otherwise there is a danger that you will only see what you want to see i.

It is often pointed out the fMRI can only provide correlational evidence. The same can be said for any other measurement technique. Simply because a certain brain area lights up with a specific mental function, we cannot be sure that the observed activity actually caused the mental event see here. Only an interference approach can provide such causal evidence. Although this is strictly correct, this does not necessarily imply the causal methods are better. Neural recordings can provide enormously rich insights into how brain activity unfolds during normal behaviour.

In contrast, causal methods allow you to test how the system behaves without a specific area. Because there is likely to be redundancy in the brain multiple brain areas capable of performing the same function , interference approaches are susceptible to missing important contributions.

Moreover, perturbing the neural system is likely to have knock-on effects that are difficult to control for, thereby complicating positive effects. These issues probably deserve a dedicated post in the future. But the point for now is simply to note that one approach is not obviously superior to the other. It depends on the nature of the question. A final point worth raising is the spectre of reverse inference making.

In an influential review paper , Russ Poldrak outlines the problem:. Perusal of the discussion sections of a few fMRI articles will quickly reveal, however, an epidemic of reasoning taking the following form:. Reverse inferences are not a valid from of deductive reasoning, because there might be other cognitive functions that activate the brain area.

Nevertheless, the general form of reasoning can provide useful information, especially when the function of the particular brain area is relatively specific and particularly well-understood. Using accumulated knowledge to interpret new findings is necessary for theory building. However, in the asbence of a strict one-to-one mapping between structure and function, reverse inference is best approached from a Bayesian perspective rather than a logical inference.

Summary : fMRI is one of the most popular methods in cognitive neuroscience, and certainly the most headline grabbing. To appreciate these limits, it is important understand some of the basic principles of fMRI. We also need to consider fMRI as part of a broader landscape of available techniques, each with their unique strengths and weakness figure 6. The question is not so much: is fMRI useful? But rather: is fMRI the right tool for my particular question.

Fig 6. Oxford Sparks see below for video demo. Arthurs, O. Trends Neurosci, 25 1 , Logothetis, N. What we can do and what we cannot do with fMRI. Nature, , Poldrack, R. This dilemma will be familiar to anyone who has ever analysed fMRI.

A recent paper identified 6, slightly different paths through the analysis pipeline, resulting in 34, different sets of results. By fully exploiting this wiggle room, it should be possible to generate almost any kind of result you would like see here for further consideration. Although this flexibility is not strictly a limit in fMRI and certainly not unique to fMRI , it is definitely something to keep in mind when interpreting what you read in the fMRI literature.

Otherwise there is a danger that you will only see what you want to see i. It is often pointed out the fMRI can only provide correlational evidence. The same can be said for any other measurement technique.

Simply because a certain brain area lights up with a specific mental function, we cannot be sure that the observed activity actually caused the mental event see here. Only an interference approach can provide such causal evidence. Although this is strictly correct, this does not necessarily imply the causal methods are better. Neural recordings can provide enormously rich insights into how brain activity unfolds during normal behaviour.

In contrast, causal methods allow you to test how the system behaves without a specific area. Because there is likely to be redundancy in the brain multiple brain areas capable of performing the same function , interference approaches are susceptible to missing important contributions. Moreover, perturbing the neural system is likely to have knock-on effects that are difficult to control for, thereby complicating positive effects.

These issues probably deserve a dedicated post in the future. But the point for now is simply to note that one approach is not obviously superior to the other. It depends on the nature of the question.

A final point worth raising is the spectre of reverse inference making. In an influential review paper , Russ Poldrak outlines the problem:. Perusal of the discussion sections of a few fMRI articles will quickly reveal, however, an epidemic of reasoning taking the following form:.

Reverse inferences are not a valid from of deductive reasoning, because there might be other cognitive functions that activate the brain area. Nevertheless, the general form of reasoning can provide useful information, especially when the function of the particular brain area is relatively specific and particularly well-understood.

Using accumulated knowledge to interpret new findings is necessary for theory building. However, in the asbence of a strict one-to-one mapping between structure and function, reverse inference is best approached from a Bayesian perspective rather than a logical inference.

Summary : fMRI is one of the most popular methods in cognitive neuroscience, and certainly the most headline grabbing. To appreciate these limits, it is important understand some of the basic principles of fMRI. We also need to consider fMRI as part of a broader landscape of available techniques, each with their unique strengths and weakness figure 6. The question is not so much: is fMRI useful? But rather: is fMRI the right tool for my particular question.

Fig 6. Oxford Sparks see below for video demo. Arthurs, O. Trends Neurosci, 25 1 , Logothetis, N. What we can do and what we cannot do with fMRI.

Nature, , Poldrack, R. Can cognitive processes be inferred from neuroimaging data? Trends Cogn Sci, 10 2 , Sejnowski, T. Putting big data to good use in neuroscience. Nat Neurosci, 17 11 , Electricity is the language of the brain, but fMRI only measures changes in blood flow that are coupled to these electrical signals.

This coupling is complex, therefore fMRI can only provide a relatively indirect measure of neural activity. Electroencephalography EEG is a classic method for measuring actual electrical activity. It has been around for more than years, but again, as every student should know: EEG has poor spatial resolution. It is difficult to know exactly where the activity is coming from.

Developed more recently, MEG is better at localising the source of brain activity. But the fundamental laws of physics mean that any measure of electromagnetic activity from outside the head will always be spatially ambiguous the inverse problem.

The best solution is to record directly from the surface of the brain. Here we discuss the unique opportunities in that arise in the clinic to measure electrical activity directly from the human brain using electrocorticography ECoG. Epilepsy can be a seriously debilitating neurological condition. Although the symptoms can often be managed with medication, some patients continue to have major seizures despite a cocktail of anti-epileptic drugs.

So-called intractable epilepsy affects every aspect of life, and can even be life-threatening. Sometimes the only option is neurosurgery : careful removal of the specific brain area responsible for seizures can dramatically improve quality of life.

Psychology students should be familiar with the case of Henry Molaison aka HM. Probably the most famous neuropsychology patient in history, HM suffered intractable epilepsy until the neurosurgeon William Scoville removed two large areas of tissue in the medial temporal lobe, including left and right hippocampus.

This pioneering surgery successfully treated his epilepsy, but this is not why the case became so famous in neuropsychology. Unfortunately, the treatment also left HM profoundly amnesic. It turns out that removing both sides of the medial temporal lobe effectively removes the brain circuitry for forming new memories.

This lesson in functional neuroanatomy is what made the case of HM so important, but there was also a important lesson for neurosurgery - be careful which parts of the brain you remove!

The best way to plan a neurosurgical resection of epileptic tissue is to identify exactly where the seizure is comping from. The best way to map out the affected region is to record activity directly from the surface of the brain. This typically involves neurosurgical implantation of recording electrodes directly in the brain to be absolutely sure of the exact location of the seizure focus. Activity can then be monitored over a number of days, or even weeks, for seizure related abnormalities.

This invasive procedure allows neurosurgeons to monitor activity in specific areas that could be the source of epileptic seizures, but also provides a unique opportunity for neuroscientific research. This observation period provides a unique opportunity to also explore healthy brain function.

If patients are interested, they can perform some simple experiments using computer based tasks to determine how different parts of the brain perform different functions. Previous studies from some of the great pioneers in neuroscience mapped out the motor cortex by stimulating different brain areas during neurosurgery. Current experiments are continuing in this tradition to explore less well charted brain areas involved in high-level thought.

For example, in a recent study from Berkeley, researchers used novel brain decoding algorithms to convert brain activity associated with internal speech into actual words listen to the audio reconstructions here.

This research helps us understand the fundamental neural code for the internal dialogue that underlies much of conscious thought, but could also help develop novel tools for providing communication to those otherwise unable to general natural speech.

In Stanford, researchers were recently able to identify a brain area that codes for numbers and quantity estimation read study here. Critically, they were even able to show that this area is involved in everyday use for numerical cognition, rather than just under their specific experimental conditions.

See video below. The great generosity of these patients vitally contributes to the broader understanding of brain function. They have dedicated their valuable time in otherwise adverse circumstances to help neuroscientists explore the very frontiers of the brain.

These patients are true pioneers. Pasley, B. Reconstructing speech from human auditory cortex. PLoS Biol, 10, e The two data figures are from the two papers cited above.