Pictures are powerful tools for illustrating quantitative data and capturing public interest. Each year, NASA releases many beautiful images of Martian dunes and distant nebulae which help win public funding. Likewise, when it comes to grabbing headlines and commanding public attention, noninvasive studies of functional brain activity often do best when they beautifully illustrate said activity as colorful pixels dancing on the convoluted surface of the cerebral cortex.
However, these images are missing a critical dimension: time. Brain activity takes place on a millisecond timescale, yet functional MRI captures this activity at a rate of about one full brain scan per second. This is rather akin to watching a movie filmed at a rate of one frame per second.
EEG, short for electroencephalogram, is a comparatively old technology, first introduced by Hans Berger in 1929, in which electrodes placed on the scalp record the brain’s electrical activity (“brainwaves”) with millisecond resolution. By taking thousands of samples per second, EEG captures the time course of subsecond neural responses to stimuli. Furthermore, EEG is a direct measure of brain activity taking place at synapses and dendrites, whereas neuroimaging instead measures metabolic activity as a proxy for synaptic activity and neuronal firing.
While one might naively assume EEG to be a primitive technology given its relative antiquity and inability to produce sexy pictures, modern computers yield tremendous amounts of information from EEG recordings. Though originally recorded with a moving pen in seismograph-like fashion, digitized EEG data in the ’80s allowed for mathematical transformations of EEG recordings to show a spectrum of brain oscillations. Different brain oscillations are associated with different cognitive tasks and neurophysiological processes. The alpha rhythm, for instance, is associated with neural “idling” in the resting brain; the gamma rhythm is associated with cognition and temporal binding of brain regions processing different aspects of the same information. With the advent of faster computers, mathematics from the fields of chaos theory and information theory has recently been used to quantify the predictability and complexity of EEG recordings. These metrics offer promise for identifying biomarkers of brain disorders such as schizophrenia, autism, and Alzheimer’s. In normal clinical practice, EEG has been used for decades to diagnose epilepsy and sleep disorders; it is also an invaluable tool for observing comas and monitoring depth of anesthesia. Being cheap and highly portable, EEG tests are easier to administer than MRI brain scans and more practical for many purposes.
EEG and functional MRI are both useful tools for measuring functional brain activity, each with its own strengths and weaknesses. Being recorded from the scalp, EEG has poor spatial localization but addresses what questions with its high temporal resolution. Functional MRI has excellent spatial localization for addressing where questions, but often lacks the temporal resolution to tell us what is going on in the brain. Functional MRI is most appropriate when a researcher wishes to address an anatomical hypothesis. The brain treads a delicate balance between functional segregation and integration: cognitive tasks are partially localized to specific regions and partially distributed across cortical networks. EEG and functional MRI are both appropriate for testing different hypotheses in different contexts.
Written by Joel Frohlich.