Posts tagged default mode network
Articles and news from the latest research reports.
Nicotine Withdrawal Weakens Brain Connections Tied to Self-Control Over Cigarette Cravings
People who try to quit smoking often say that kicking the habit makes the voice inside telling them to light up even louder, but why people succumb to those cravings so often has never been fully understood. Now, a new brain imaging study in this week’s JAMA Psychiatry from scientists in Penn Medicine and the National Institute on Drug Abuse (NIDA) Intramural Research Program shows how smokers suffering from nicotine withdrawal may have more trouble shifting from a key brain network—known as default mode, when people are in a so-called “introspective” or “self-referential” state— and into a control network, the so-called executive control network, that could help exert more conscious, self-control over cravings and to focus on quitting for good.
The findings help validate a neurobiological basis behind why so many people trying to quit end up relapsing—up to 80 percent, depending on the type of treatment—and may lead to new ways to identify smokers at high risk for relapse who need more intensive smoking cessation therapy.
The brain imaging study was led by researchers at University of Pennsylvania’s new Brain and Behavior Change Program, led by Caryn Lerman, PhD, who is also the deputy director of Penn’s Abramson Cancer Center, and Elliot Stein, PhD, and collaborators at NIDA. They found that smokers who abstained from cigarettes showed weakened interconnectivity between certain large-scale networks in their brains: the default mode network, the executive control network, and the salience network. They posit that this weakened connectivity reduces smokers’ ability to shift into or maintain greater influence from the executive control network, which may ultimately help maintain their quitting attempt.
“What we believe this means is that smokers who just quit have a more difficult time shifting gears from inward thoughts about how they feel to an outward focus on the tasks at hand,” said Lerman, who also serves as the Mary W. Calkins professor in the Department of Psychiatry. “It’s very important for people who are trying to quit to be able to maintain activity within the control network— to be able to shift from thinking about yourself and your inner state to focus on your more immediate goals and plan.”
Prior studies have looked at the effects of nicotine on brain interconnectivity in the resting state, that is, in the absence of any specific goal directed activity. This is the first study, however, to compare resting brain connectivity in an abstinent state and when people are smoking as usual, and then relate those changes to symptoms of craving and mental performance.
For the study, researchers conducted brain scans on 37 healthy smokers (those who smoke more than 10 cigarettes a day) ages 19 to 61 using functional magnetic resonance imaging (fMRI) in two different sessions: 24 hours after biochemically confirmed abstinence and after smoking as usual.
Imaging showed a significantly weaker connectivity between the salience network and default mode network during abstinence, compared with their sated state. Also, weakened connectivity during abstinence was linked with increases in smoking urges, negative mood, and withdrawal symptoms, suggesting that this weaker internetwork connectivity may make it more difficult for people to quit.
Establishing the strength of the connectivity between these large-scale brain networks will be important in predicting people’s ability to quit and stay quit, the authors write. Also, such connectivity could serve as a clinical biomarker to identify smokers who are most likely to respond to a particular treatment.
“Symptoms of withdrawal are related to changes in smokers’ brains, as they adjust to being off of nicotine, and this study validates those experiences as having a biological basis,” said Lerman. “The next step will be to identify in advance those smokers who will have more difficultly quitting and target more intensive treatments, based on brain activity and network connectivity.”
(Source: uphs.upenn.edu)
(Image caption: A daydreaming brain: the yellow areas depict the default mode network from three different perspectives; the coloured fibres show the connections amongst each other and with the remainder of the brain.)
The structure of the human brain is complex, reminiscent of a circuit diagram with countless connections. But what role does this architecture play in the functioning of the brain? To answer this question, researchers at the Max Planck Institute for Human Development in Berlin, in cooperation with colleagues at the Free University of Berlin and University Hospital Freiburg, have for the first time analysed 1.6 billion connections within the brain simultaneously. They found the highest agreement between structure and information flow in the “default mode network,” which is responsible for inward-focused thinking such as daydreaming.
Everybody’s been there: You’re sitting at your desk, staring out the window, your thoughts wandering. Instead of getting on with what you’re supposed to be doing, you start mentally planning your next holiday or find yourself lost in a thought or a memory. It’s only later that you realize what has happened: Your brain has simply “changed channels”—and switched to autopilot.
For some time now, experts have been interested in the competition among different networks of the brain, which are able to suppress one another’s activity. If one of these approximately 20 networks is active, the others remain more or less silent. So if you’re thinking about your next holiday, it is almost impossible to follow the content of a text at the same time.
To find out how the anatomical structure of the brain impacts its functional networks, a team of researchers at the Max Planck Institute for Human Development in Berlin, in cooperation with colleagues at the Free University of Berlin and the University Hospital Freiburg, have analysed the connections between a total of 40,000 tiny areas of the brain. Using functional magnetic resonance imaging, they examined a total of 1.6 billion possible anatomical connections between these different regions in 19 participants aged between 21 and 31 years. The research team compared these connections with the brain signals actually generated by the nerve cells.
Their results showed the highest agreement between brain structure and brain function in areas forming part of the “default mode network“, which is associated with daydreaming, imagination, and self-referential thought. “In comparison to other networks, the default mode network uses the most direct anatomical connections. We think that neuronal activity is automatically directed to level off at this network whenever there are no external influences on the brain,” says Andreas Horn, lead author of the study and researcher in the Center for Adaptive Rationality at the Max Planck Institute for Human Development in Berlin.
Living up to its name, the default mode network seems to become active in the absence of external influences. In other words, the anatomical structure of the brain seems to have a built-in autopilot setting. It should not, however, be confused with an idle state. On the contrary, daydreaming, imagination, and self-referential thought are complex tasks for the brain.
“Our findings suggest that the structural architecture of the brain ensures that it automatically switches to something useful when it is not being used for other activities,” says Andreas Horn. “But the brain only stays on autopilot until an external stimulus causes activity in another network, putting an end to the daydreaming. A buzzing fly, a loud bang in the distance, or focused concentration on a text, for example.”
The researchers hope that their findings will contribute to a better understanding of brain functioning in healthy people, but also of neurodegenerative disorders such as Alzheimer’s disease and psychiatric conditions such as schizophrenia. In follow-up studies, the research team will compare the brain structures of patients with neurological disorders with those of healthy controls.
Study finds altered brain connections in epilepsy patients
Patients with the most common form of focal epilepsy have widespread, abnormal connections in their brains that could provide clues toward diagnosis and treatment, according to a new study published online in the journal Radiology.
(Image: MP-RAGE volumes are segmented into 83 ROIs, which are further parcellated into 1000 cortical and 15 subcortical ROIs. Whole-brain white matter tractography is performed after voxelwise tensor calculation, and the density of fibers that connect each pair of cortical ROIs is used to calculate structural connectivity. T1w = T1-weighted. Credit: Courtesy of Radiology and RSNA)
Temporal lobe epilepsy is characterized by seizures emanating from the temporal lobes, which sit on each side of the brain just above the ear. Previously, experts believed that the condition was related to isolated injuries of structures within the temporal lobe, like the hippocampus. But recent research has implicated the default mode network (DMN), the set of brain regions activated during task-free introspection and deactivated during goal-directed behavior. The DMN consists of several hubs that are more active during the resting state.
To learn more, researchers performed diffusion tensor imaging, a type of MRI that tracks the movement, or diffusion, of water in the brain’s white matter, the nerve fibers that transmit signals throughout the brain. The study group consisted of 24 patients with left temporal lobe epilepsy who were slated for surgery to remove the site from where their seizures emanated. The researchers compared them with 24 healthy controls using an MRI protocol dedicated to finding white matter tracts with diffusion imaging at high resolution. The data was analyzed with a new technique that identifies and quantifies structural connections in the brain.
Patients with left temporal lobe epilepsy exhibited a decrease in long-range connectivity of 22 percent to 45 percent among areas of the DMN when compared with the healthy controls.
"Using diffusion MRI, we found alterations in the structural connectivity beyond the medial temporal lobe, especially in the default mode network," said Steven M. Stufflebeam, M.D., from the Athinoula A. Martinos Center for Biomedical Imaging at Massachusetts General Hospital in Boston.
In addition to reduced long-range connectivity, the epileptic patients had an 85 percent to 270 percent increase in local connectivity within and beyond the DMN. The researchers believe this may be an adaptation to the loss of the long-range connections.
"The increase in local connections could represent a maladaptive mechanism by which overall neural connectivity is maintained despite the loss of connections through important hub areas," Dr. Stufflebeam said.
The results are supported by prior functional MRI studies that have shown decreased functional connectivity in DMN areas in temporal lobe epilepsy. Researchers are not certain if the structural changes cause the functional changes, or vice versa.
"It’s probably a breakdown of myelin, which is the insulation of neurons, causing a slowdown in the propagation of information, but we don’t know for sure," Dr. Stufflebeam said.
Dr. Stufflebeam and colleagues plan to continue their research, using structural and functional MRI with electroencephalography and magnetoencephalography to track diffusion changes and look at real-time brain activity.
"Our long-term goal is to see if we can we predict from diffusion studies who will respond to surgery and who will not," he said.
(Source: eurekalert.org)
Similar connectivity profiles in humans and monkeys used to generate a Theory of Mind
The ability to infer emotion or intention in others from their outward appearance and behavior, has been called a “Theory of Mind” (TOM). While cognitive scientists have debated whether animals other than humans possess a TOM, many animals (like monkeys) clearly react to facial expression or body movements. One area of the human brain that has received considerable attention in discussions of TOM, is the temporo-parietal junction (TPJ). If each half of the brain is viewed as a boxing glove, the TPA corresponds to the junction between the “thumb” and body of the glove. To explore whether the TPJ regions of humans and monkeys have similar “functional connectivity” profiles, a group of Oxford researchers turned to high resolution at-rest fMRI. The researchers generated correlation maps between each time series obtained for specific voxel regions of interest. Their results, just published in PNAS, show that the most similar TPJ connectivity profiles correspond to areas that process, among other things, faces and social stimuli within the temporal cortex.
When the brain first begins to develop in the womb, the cortex is basically a smooth sheet. The most noticeable topological feature in the cortex of all higher vertebrates, the lateral or Sylvian fissure, begins to take shape as an invagination in the side that proceeds from front to back. This fold, with the TPA at its apex, remains as the primary feature of the cortex even as it grows increasingly convoluted. It is little wonder that many of the most interesting mental phenomena, and malady, are often attributed to this region. Stimulation of this area has produced effects as widespread as out of body experiences, impostor syndromes, and even phantom body doubles with precise geometrically offsets to the primary body position.
It is a bit of a paradox perhaps, that many studies which look for uniform or predictable features in the brain have instead hit upon the very region where any such pigeonholing is most labile. In other words, when the brain folds, the TPA is precisely the region where the most scrunching happens, with the result the mature structure typically shows the most variance. In animals like cats and many monkeys, the cortical gyri and sulci, have virtually the same pattern in each individual. In humans however, attempts to assign names to specific folds of the TPA region is like playing a game of pin the tail on the donkey. For example, the Angular gyrus, Wernicke’s area, Supramarginal gyrus, and Inferior parietal area, can all be variously designated as part of the TPA.
Recent attempts to define a default mode network (DMN) using fMRI have included this same region. In theory, the DMN can be used to distinguish sleep from arousal. It was noted that neurons which project out of the cortex in this region have, in effect, more options open to them than those virtually anywhere else in the brain. For example, directly under the angular gyrus is the area known as the temporo-parietal fiber association area. It includes at least seven long range white matter superhighways. That is not to say TPA neurons have free reign to board any tract they choose, (especially those like the optic radiations whose foundations are strongly and quickly set by myelin), but certainly the wide variance in behavioral correlates of these cells has an anatomical basis.
The Oxford study used Macaques, a monkey which has been on a separate evolutionary path from humans for around 30 million years. They note that the superior temporal (STS) region of the Macaque contains face cells that have been found to be more responsive to social cues rather than to identity. The researchers included the STS in their MRI meta-analysis, and also incorporated information from the BrainMap database, a large repository of neuroimaging data. While it is encouraging to see big data being put to use, it is often difficult to follow exactly how the data is processed to yield the so-called “activation likelihood estimation maps for activity elicited by theory of mind paradigms and by face discrimination or processing.”
As various federal projects begin to assemble connectomes for the human brain, functional connectivity studies that use highly processed MRI data, will need to be made as simple and straightforward as possible if they are to be put to widespread use. MRI tractography is a related technology that can assign physical connectivity by performing a meta-analysis on diffusion tensor data. Using scans and connectomes to generate theories to explain some of the strange mental phenomena generated secondary to stroke or by various kinds of electromagnetic stimulation are the best approaches we have at the moment. New technologies generated by the BRAIN Initiative will hopefully allow a finer-grained exploration of theory of mind.