Examining Functional Activity in an Individual’s Brain

The ability to examine the structure of the human brain has been available for many years. Structural brain imaging techniques are used routinely to diagnose conditions such as brain tumors, strokes, and atrophy. But structural imaging is unable to measure the actual functioning of brain regions.

Over the last decade, sophisticated techniques have been developed to examine and analyze the functional activity of specific brain regions and the ways brain regions interact with each other to accomplish specific tasks. One such technique, blood oxygenation level dependent functional magnetic resonance imaging (bold fMRI), can determine changes in blood flow in brain regions. Changes in blood flow reflect changes in energy utilization and are a measure of the activity of brain cells.

Years ago, Marc Raichle and colleagues made a significant discovery while examining bold fMRI activity in individuals who were not engaged in any specific tasks while they were in a brain scanner, i.e., while they were resting. Raichle’s group found that fluctuations in blood flow in certain brain areas were synchronous with blood flow changes in other areas. Thus, they discovered that specific regions were linked together functionally during the resting state despite the fact that the brain regions were not physically next to each other.

Raichle named this group of synchronously active brain regions the “default mode network.” This network of functionally connected brain areas demonstrated high energy use when a person wasn’t doing any specific task while in the scanner. In fact, energy utilization in these regions actually decreased when the person started attending to specific tasks as the brain regions involved in the tasks increased their energy utilization.

Over time, about 15 different functional brain networks have been characterized. Each shows synchronous activity during resting states and responds differently when a person performs different tasks. For instance, certain networks respond in synchrony when a person shifts attention from one task to another. Other networks show increased blood flow when a person participates in a visual task. Some networks are involved in emotional regulation.

Recently, it has been demonstrated that some of these functional networks are affected by specific illnesses. It appears that the regions of the brain that make up the default mode network are targeted early in Alzheimer’s disease. Another type of dementia—behavioral variant frontotemporal dementia—specifically involves a functional network called the emotional salience network.

Because of methodological limitations, the ability to demonstrate functional brain networks has required averaging data from many individuals. This has limited the ability to make specific conclusions regarding any single individual. If techniques could be developed that would provide reliable data from one person’s scans, an individual’s way of processing information could be examined. In addition, patterns of abnormal function related to specific illnesses, such as bipolar disorder or schizophrenia, could be determined.

A paper recently published in the journal Neuron by Steven Nelson, Nico Dosenbach, Evan Gordon, Timothy Laumann, and colleagues describes techniques that allow the generation of highly reliable data from single individuals. Their approach involved scanning the same person repeatedly during rest and various activities. Depending on the task being studied, the total amount of scanning time necessary to discern activity in specific networks ranged from 10 minutes to several hours.

Functional MRI is relatively safe, but scanning time is expensive. Nevertheless, the techniques described in this paper are non-invasive and yield information about the working of an individual’s brain networks. It is likely that the techniques will be refined over time and costs will come down. Eventually, it will be possible to determine how a specific psychiatric illness influences the way an individual’s brain processes information. This could lead to better ways of categorizing these disorders and developing personalized therapeutic interventions.

This column was written by Eugene Rubin MD, Ph.D. and Charles Zorumski MD.