Posts tagged cingulate cortex
Articles and news from the latest research reports.
Toward a clearer diagnosis of chronic fatigue syndrome
Chronic fatigue syndrome, which is also known as myalgic encephalomyelitis, is a debilitating condition characterized by chronic, profound, and disabling fatigue. Unfortunately, the causes are not well understood.
Neuroinflammation—the inflammation of nerve cells—has been hypothesized to be a cause of the condition, but no clear evidence has been put forth to support this idea. Now, in this clinically important study, published in the Journal of Nuclear Medicine, the researchers found that indeed the levels of neuroinflammation markers are elevated in CFS/ME patients compared to the healthy controls.
The researchers performed PET scanning on nine people diagnosed with CFS/ME and ten healthy people, and asked them to complete a questionnaire describing their levels of fatigue, cognitive impairment, pain, and depression. For the PET scan they used a protein that is expressed by microglia and astrocyte cells, which are known to be active in neuroinflammation.
The researchers found that neuroinflammation is higher in CFS/ME patients than in healthy people. They also found that inflammation in certain areas of the brain—the cingulate cortex, hippocampus, amygdala, thalamus, midbrain, and pons—was elevated in a way that correlated with the symptoms, so that for instance, patients who reported impaired cognition tended to demonstrate neuroinflammation in the amygdala, which is known to be involved in cognition. This provides clear evidence of the association between neuroinflammation and the symptoms experienced by patients with CFS/ME.
Though the study was a small one, confirmation of the concept that PET scanning could be used as an objective test for CFS/ME could lead to better diagnosis and ultimately to the development of new therapies to provide relief to the many people around the world afflicted by this condition. Dr. Yasuyoshi Watanabe, who led the study at RIKEN, stated, “We plan to continue research following this exciting discovery in order to develop objective tests for CFS/ME and ultimately ways to cure and prevent this debilitating disease.”
Study finds link between child’s obesity and cognitive function
A new University of Illinois study finds that obese children are slower than healthy-weight children to recognize when they have made an error and correct it. The research is the first to show that weight status not only affects how quickly children react to stimuli but also impacts the level of activity that occurs in the cerebral cortex during action monitoring.
“I like to explain action monitoring this way: when you’re typing, you don’t have to be looking at your keyboard or your screen to realize that you’ve made a keystroke error. That’s because action monitoring is occurring in your brain’s prefrontal cortex,” said Charles Hillman, a U of I professor of kinesiology and faculty member in the U of I’s Division of Nutritional Sciences.
As an executive control task that requires organizing, planning, and inhibiting, action monitoring requires people to be computational and conscious at all times as they process their behavior. Because these higher-order cognitive processes are needed for success in mathematics and reading, they are linked with success in school and positive life outcomes, he said.
“Imagine a child in a math class constantly checking to make sure she’s carrying the digit over when she’s adding. That’s an example,” he added.
In the study, the scientists measured the behavioral and neuroelectric responses of 74 preadolescent children, half of them obese, half at a healthy weight. Children were fitted with caps that recorded electroencephalographic activity and asked to participate in a task that presented left- or right-facing fish, predictably facing in either the same or the opposite direction. Children were asked to press a button based on the direction of the middle (that is, target) fish. The flanking fish either pointed in the same direction (facilitating) or in the opposite direction (hindering) their ability to respond successfully.
“We found that obese children were considerably slower to respond to stimuli when they were involved in this activity,” Hillman said.
The researchers also found that healthy-weight children were better at evaluating their need to change their behavior in order to avoid future errors.
“The healthy-weight kids were more accurate following an error than the obese children were, and when the task required greater amounts of executive control, the difference was even greater,” he reported.
A second evaluation measured electrical activity in the brain “that occurs at the intersection of thought and action,” Hillman said. “We can measure what we call error-related negativity (ERN) in the electrical pattern that the brain generates following errors. When children made an error, we could see a larger negative response. And we found that healthy-weight children are better able to upregulate the neuroelectric processes that underlie error evaluation.”
Scientists in the Hillman lab and elsewhere have seen a connection between healthy weight and academic achievement, “but a study like this helps us understand what’s happening. There are certainly physiological differences in the brain activity of obese and healthy-weight children. It’s exciting to be able to use functional brain imaging to see the way children’s weight affects the aspects of cognition that influence and underlie achievement,” said postdoctoral researcher and co-author Naiman Khan.
(Source: news.aces.illinois.edu)
Brain scans show what makes us drink water and what makes us stop drinking
Drinking water when you’re thirsty is a pleasurable experience. Continuing to drink when you’re not, however, can be very unpleasant. To understand why your reaction to water drinking changes as your thirst level changes, Pascal Saker of the University of Melbourne and his colleagues performed fMRI scans on people as they drank water. They found that regions of the brain associated with positive feelings became active when the subjects were thirsty, while regions associated with negative feelings and with controlling and coordinating movement became active after the subjects were satiated. The research appears in the Proceedings of the National Academy of Sciences.
Overlapping Neural Systems Represent Cognitive Effort and Reward Anticipation
Anticipating a potential benefit and how difficult it will be to obtain it are valuable skills in a constantly changing environment. In the human brain, the anticipation of reward is encoded by the Anterior Cingulate Cortex (ACC) and Striatum. Naturally, potential rewards have an incentive quality, resulting in a motivational effect improving performance. Recently it has been proposed that an upcoming task requiring effort induces a similar anticipation mechanism as reward, relying on the same cortico-limbic network. However, this overlapping anticipatory activity for reward and effort has only been investigated in a perceptual task. Whether this generalizes to high-level cognitive tasks remains to be investigated. To this end, an fMRI experiment was designed to investigate anticipation of reward and effort in cognitive tasks. A mental arithmetic task was implemented, manipulating effort (difficulty), reward, and delay in reward delivery to control for temporal confounds. The goal was to test for the motivational effect induced by the expectation of bigger reward and higher effort. The results showed that the activation elicited by an upcoming difficult task overlapped with higher reward prospect in the ACC and in the striatum, thus highlighting a pivotal role of this circuit in sustaining motivated behavior.
Brain Structure Shows Who is Most Sensitive to Pain
Everybody feels pain differently, and brain structure may hold the clue to these differences.
In a study published in the current online issue of the journal Pain, scientists at Wake Forest Baptist Medical Center have shown that the brain’s structure is related to how intensely people perceive pain.
“We found that individual differences in the amount of grey matter in certain regions of the brain are related to how sensitive different people are to pain,” said Robert Coghill, Ph.D., professor of neurobiology and anatomy at Wake Forest Baptist and senior author of the study.
The brain is made up of both grey and white matter. Grey matter processes information much like a computer, while white matter coordinates communications between the different regions of the brain.
The research team investigated the relationship between the amount of grey matter and individual differences in pain sensitivity in 116 healthy volunteers. Pain sensitivity was tested by having participants rate the intensity of their pain when a small spot of skin on their arm or leg was heated to 120 degrees Fahrenheit. After pain sensitivity testing, participants underwent MRI scans that recorded images of their brain structure.
“Subjects with higher pain intensity ratings had less grey matter in brain regions that contribute to internal thoughts and control of attention,” said Nichole Emerson, B.S., a graduate student in the Coghill lab and first author of the study. These regions include the posterior cingulate cortex, precuneus and areas of the posterior parietal cortex, she said.
The posterior cingulate cortex and precuneus are part of the default mode network, a set of connected brain regions that are associated with the free-flowing thoughts that people have while they are daydreaming.
“Default mode activity may compete with brain activity that generates an experience of pain, such that individuals with high default mode activity would have reduced sensitivity to pain,” Coghill said.
Areas of the posterior parietal cortex play an important role in attention. Individuals who can best keep their attention focused may also be best at keeping pain under control, Coghill said.
“These kinds of structural differences can provide a foundation for the development of better tools for the diagnosis, classification, treatment and even prevention of pain,” he said.
To predict, perchance to update: Neural responses to the unexpected
Among the brain’s many functions is the use of predictive models to processing expected stimuli or actions. In such a model, we experience surprise when presented with an unexpected stimulus – that is, one which the model evaluates as having a low probability of occurrence. Interestingly, there can be two distinct – but often experimentally correlated – responses to a surprising event: reallocating additional neural resources to reprogram actions, and updating the predictive model to account for the new environmental stimulus. Recently, scientists at Oxford University used brain imaging to identify separate brain systems involved in reprogramming and updating, and created a mathematical and neuroanatomical model of how brains adjust to environmental change, Moreover, the researchers conclude that their model may also inform models of neurological disorders, such as extinction, Balint syndrome and neglect, in which this adaptive response to surprise fails.
Research Fellow Jill X. O’Reilly discussed the research she and her colleagues conducted with Medical Xpress. “Sometimes we think of the brain as an input-output device which takes sensory information, processes it, and produces actions appropriately – but in fact, brains don’t passively ‘sit around’ waiting for sensory input,” O’Reilly explains. “Rather, they actively predict what is going to happen next, because by being prepared, they can process stimuli more efficiently.”
O’Reilly cites an important example of predictive processing, which the researchers used in their study: the control of eye movements. “You can actually only process quite a small portion of visual space accurately at any one time, which is why people tend to actively look at interesting objects,” O’Reilly tells Medical Xpress. “Parts of the brain that control eye movements – for example, the parietal cortex – are actively involved in trying to predict where visual objects that are worth looking at will occur next, in order to respond to them quickly and effectively.” Since the scientists were interested in how the brain forms predictions – such as where eye movements should be directed – they designed an experiment in which people’s expectations about where they should make eye movements were built up over time and then suddenly changed. (They did this moving the stimuli participants’ were instructed to fixate on to a different part of the computer screen.)
"However," notes O’Reilly, "we know from previous work that activity in many brain areas is evoked when people are expecting to make an eye movement to one place, and actually they have to make an eye movement to another. A lot of this brain activity has to do with reprogramming the eye movement itself, rather than learning about the changed environment. That means we needed to design an experiment in which re-planning of eye movements was sometimes accompanied by learning, and sometimes not." The researchers accomplished this by color-coding stimuli: participants knew that colorful stimuli indicated a real change in the environment, while grey stimuli were to be ignored.
To quantify how much participants learned on each trial of the experiment, the team constructed a computer participant that learned about the environment in the same way the real, human participants did. Because they could determine exactly what the computer participant knew or believed about the environment – that is, where it would need to look – on each trial, we could get mathematical measures of how surprising it found each stimulus (defined as how far the stimulus location was from where the computer participant expected it to be) and how much it learned on each trial.
Therefore, the computer participant allowed the scientists to separately measure the degree to which human participants had to respond to surprise in terms of reprogramming eye movements, and how much they learned on each trial. “We then needed to work out whether some parts of the brain were specifically involved in each of these processes,” O’Reilly continues. “To do this we used fMRI and looked for areas that increased their activity in proportion to how much the computer participant, and thereby the real participants, would need to reprogram their eye movements for each surprising stimulus – as well as the extent to which they’d have to update their predictions about future stimulus locations – on each trial.”
O’Reilly stresses that the computer participant was critical to addressing the challenges they encountered. “We had access to a complete model of what participants could know or should believe about where stimuli were expected to appear on each trial. That meant we could make very specific predictions about how much they should be surprised by certain stimuli and how much they learned from each stimulus.” The team checked these predictions by looking at behavioral measures like reaction time (participants were slower to move their eyes to surprising stimuli) and gaze dwell time (participants looked at stimuli for longer when the stimuli carried information about the possible locations of future stimuli).
O’Reilly describes how their study may inform understanding of neurological disorders in which this adjustment process fails by observing that a second saccade-sensitive region in the inferior posterior parietal cortex was activated by surprise and modulated by updating. “Some stroke victims are unable to move their eyes in order to look at stimuli that show up in their visual periphery, which turns out to be similar to the process of reprogramming to surprising stimuli in our model. In contrast,” she continues, “people with brain lesions in a slightly different brain region are able to move their eyes to look at stimuli, but seem unable to learn that stimuli could occur in some parts of space – usually towards the left of the body – even if given lots of hints and training.” Because the brain regions damaged in these two patient groups map onto the regions of parietal cortex active in the experiment’s reprogramming and updating conditions, the researchers think these two processes could be differentially affected in the two patient groups.
Moving forward, the researchers would like to test their paradigm in patients who have had strokes that damaged the different brain regions activated in their study. “We’d expect to find a difference between patients with damage in different parts of parietal cortex, such that one group might be slower to reprogram eye movements to all surprising stimuli whether these stimuli are informative about future stimulus locations or not,” O’Reilly concludes, “whereas the other group might have trouble learning that the location where stimuli are going to appear has changed.”