Jennifer L. Ferris

Alterations in Resting-State Functional Connectivity Link Mindfulness Meditation With Reduced Interleukin-6: A Randomized Controlled Trial

BACKGROUND Mindfulness meditation training interventions have been shown to improve markers of health, but the underlying neurobiological mechanisms are not known. Building on initial cross-sectional research showing that mindfulness meditation may increase default mode network (DMN) resting-state functional connectivity (rsFC) with regions important in top-down executive control (dorsolateral prefrontal cortex [dlPFC]), here we test whether mindfulness meditation training increases DMN-dlPFC rsFC and whether these rsFC alterations prospectively explain improvements in interleukin (IL)-6 in a randomized controlled trial. METHODS Stressed job-seeking unemployed community adults (n = 35) were randomized to either a 3-day intensive residential mindfulness meditation or relaxation training program. Participants completed a 5-minute resting-state scan before and after the intervention program. Participants also provided blood samples at preintervention and at 4-month follow-up, which were assayed for circulating IL-6, a biomarker of systemic inflammation. RESULTS We tested for alterations in DMN rsFC using a posterior cingulate cortex seed-based analysis and found that mindfulness meditation training, and not relaxation training, increased posterior cingulate cortex rsFC with left dlPFC (p < .05, corrected). These pretraining to posttraining alterations in posterior cingulate cortex-dlPFC rsFC statistically mediated mindfulness meditation training improvements in IL-6 at 4-month follow-up. Specifically, these alterations in rsFC statistically explained 30% of the overall mindfulness meditation training effects on IL-6 at follow-up. CONCLUSIONS These findings provide the first evidence that mindfulness meditation training functionally couples the DMN with a region known to be important in top-down executive control at rest (left dlPFC), which, in turn, is associated with improvements in a marker of inflammatory disease risk.

Cognitive and metacognitive activity in mathematical problem solving: prefrontal and parietal patterns

Students were taught an algorithm for solving a new class of mathematical problems. Occasionally in the sequence of problems, they encountered exception problems that required that they extend the algorithm. Regular and exception problems were associated with different patterns of brain activation. Some regions showed a Cognitive pattern of being active only until the problem was solved and no difference between regular or exception problems. Other regions showed a Metacognitive pattern of greater activity for exception problems and activity that extended into the post-solution period, particularly when an error was made. The Cognitive regions included some of parietal and prefrontal regions associated with the triple-code theory of (Dehaene, S., Piazza, M., Pinel, P., & Cohen, L. (2003). Three parietal circuits for number processing. Cognitive Neuropsychology, 20, 487–506) and associated with algebra equation solving in the ACT-R theory (Anderson, J. R. (2005). Human symbol manipulation within an 911 integrated cognitive architecture. Cognitive science, 29, 313–342. Metacognitive regions included the superior prefrontal gyrus, the angular gyrus of the triple-code theory, and frontopolar regions.

Neural imaging to track mental states while using an intelligent tutoring system

Hemodynamic measures of brain activity can be used to interpret a students mental state when they are interacting with an intelligent tutoring system. Functional magnetic resonance imaging (fMRI) data were collected while students worked with a tutoring system that taught an algebra isomorph. A cognitive model predicted the distribution of solution times from measures of problem complexity. Separately, a linear discriminant analysis used fMRI data to predict whether or not students were engaged in problem solving. A hidden Markov algorithm merged these two sources of information to predict the mental states of students during problem-solving episodes. The algorithm was trained on data from 1 day of interaction and tested with data from a later day. In terms of predicting what state a student was in during a 2-s period, the algorithm achieved 87% accuracy on the training data and 83% accuracy on the test data. The results illustrate the importance of integrating the bottom-up information from imaging data with the top-down information from a cognitive model.

Mindfulness meditation training alters stress-related amygdala resting state functional connectivity: a randomized controlled trial

Recent studies indicate that mindfulness meditation training interventions reduce stress and improve stress-related health outcomes, but the neural pathways for these effects are unknown. The present research evaluates whether mindfulness meditation training alters resting state functional connectivity (rsFC) of the amygdala, a region known to coordinate stress processing and physiological stress responses. We show in an initial discovery study that higher perceived stress over the past month is associated with greater bilateral amygdala-subgenual anterior cingulate cortex (sgACC) rsFC in a sample of community adults (n = 130). A follow-up, single-blind randomized controlled trial shows that a 3-day intensive mindfulness meditation training intervention (relative to a well-matched 3-day relaxation training intervention without a mindfulness component) reduced right amygdala-sgACC rsFC in a sample of stressed unemployed community adults (n = 35). Although stress may increase amygdala-sgACC rsFC, brief training in mindfulness meditation could reverse these effects. This work provides an initial indication that mindfulness meditation training promotes functional neuroplastic changes, suggesting an amygdala-sgACC pathway for stress reduction effects.

Lateral inferior prefrontal cortex and anterior cingulate cortex are engaged at different stages in the solution of insight problems

Two studies used puzzles that required participants to find a word that satisfied a set of constraints. The first study used a remote-association task, where participants had to find a word that would form compound words with 3 other words. The second study required participants to complete a word fragment with an associate of another word. Both studies produced distinct patterns of activity in the lateral inferior prefrontal cortex (LIPFC) and the anterior cingulate cortex (ACC). Activation in the LIPFC rose only as long as the participants were trying to retrieve the solution and dropped off as soon as the solution was obtained. However, activation in the ACC increased upon the retrieval of a solution, reflecting the need to process that solution. The data of the second experiment are fit by an information-processing model that interprets the activity in the LIPFC as reflecting retrieval operations and the activity in the ACC as reflecting subgoal setting.

Brain networks supporting execution of mathematical skills versus acquisition of new mathematical competence.

This fMRI study examines how students extend their mathematical competence. Students solved a set of algebra-like problems. These problems included Regular Problems that have a known solution technique and Exception Problems that but did not have a known technique. Two distinct networks of activity were uncovered. There was a Cognitive Network that was mainly active during the solution of problems and showed little difference between Regular Problems and Exception Problems. There was also a Metacognitive Network that was more engaged during a reflection period after the solution and was much more engaged for Exception Problems than Regular Problems. The Cognitive Network overlaps with prefrontal and parietal regions identified in the ACT-R theory of algebra problem solving and regions identified in the triple-code theory as involved in basic mathematical cognition. The Metacognitive Network included angular gyrus, middle temporal gyrus, and anterior prefrontal regions. This network is mainly engaged by the need to modify the solution procedure and not by the difficulty of the problem. Only the Metacognitive Network decreased with practice on the Exception Problems. Activity in the Cognitive Network during the solution of an Exception Problem predicted both success on that problem and future mastery. Activity in the angular gyrus and middle temporal gyrus during feedback on errors predicted future mastery.

Retrograde facilitation under midazolam : The role of general and specific interference

In a double-blind, placebo-controlled experiment that usedmidazolam, a benzodiazepine that creates temporary amnesia, we compared acquisition and retention of paired associates of different types. Some word pairs were studied before the injection of saline or midazolam, and two lists of word pairs were studied after the injection. Critical comparisons involved retention of pairs that were practiced on all three lists, pairs studied on only one list, and pairs that involved recombining cue and response terms from one list to the next, as a function of drug condition. Previous research with benzodiazepines had found retrograde facilitation for material acquired prior to injection, compared with the control condition. One explanation for this facilitation is that the anterograde amnesia produced by the benzodiazepine frees up the hippocampus to better consolidate previously learned material (Wixted, 2004, 2005). We accounted for a rich data set using a simple computational model that incorporated interference effects (cue overload) at retrieval for both general (experimental context) interference and specific (stimulus term) interference without the need to postulate a role for consolidation. The computational model as an Excel spreadsheet may be downloaded from www.psychonomic.org/archive.

Mindfulness Meditation Training and Executive Control Network Resting State Functional Connectivity: A Randomized Controlled Trial

Objective Mindfulness meditation training has been previously shown to enhance behavioral measures of executive control (e.g., attention, working memory, cognitive control), but the neural mechanisms underlying these improvements are largely unknown. Here, we test whether mindfulness training interventions foster executive control by strengthening functional connections between dorsolateral prefrontal cortex (dlPFC)—a hub of the executive control network—and frontoparietal regions that coordinate executive function. Methods Thirty-five adults with elevated levels of psychological distress participated in a 3-day randomized controlled trial of intensive mindfulness meditation or relaxation training. Participants completed a resting state functional magnetic resonance imaging scan before and after the intervention. We tested whether mindfulness meditation training increased resting state functional connectivity (rsFC) between dlPFC and frontoparietal control network regions. Results Left dlPFC showed increased connectivity to the right inferior frontal gyrus (T = 3.74), right middle frontal gyrus (MFG) (T = 3.98), right supplementary eye field (T = 4.29), right parietal cortex (T = 4.44), and left middle temporal gyrus (T = 3.97, all p < .05) after mindfulness training relative to the relaxation control. Right dlPFC showed increased connectivity to right MFG (T = 4.97, p < .05). Conclusions We report that mindfulness training increases rsFC between dlPFC and dorsal network (superior parietal lobule, supplementary eye field, MFG) and ventral network (right IFG, middle temporal/angular gyrus) regions. These findings extend previous work showing increased functional connectivity among brain regions associated with executive function during active meditation by identifying specific neural circuits in which rsFC is enhanced by a mindfulness intervention in individuals with high levels of psychological distress. Clinical Trial Registration Clinicaltrials.gov, NCT01628809.

Using Brain Imaging to Interpret Student Problem Solving

We have been exploring whether multi voxel pattern analysis (MVPA) of functional magnet resonance imaging (fMRI) data can be used to infer the mental states of students learning mathematics. This approach has shown considerable success in tracking static mental states such as whether a person is thinking about a location or an animal. Applying this to our case involves significant challenges not faced in many MVPA applications because it is necessary to track changing student states over time. The paths of states that students take in solving problems can be quite variable. Nevertheless, we have achieved relatively high accuracy in determining what step a student is on when solving a sequence of problems and whether that step is being performed correctly. Hidden Markov models can then be used to combine behavioral and brain-imaging data from an intelligent tutoring system to track mental states during students problem-solving episodes.