Todd W. Thompson

Failure of Working Memory Training to Enhance Cognition or Intelligence

Fluid intelligence is important for successful functioning in the modern world, but much evidence suggests that fluid intelligence is largely immutable after childhood. Recently, however, researchers have reported gains in fluid intelligence after multiple sessions of adaptive working memory training in adults. The current study attempted to replicate and expand those results by administering a broad assessment of cognitive abilities and personality traits to young adults who underwent 20 sessions of an adaptive dual n-back working memory training program and comparing their post-training performance on those tests to a matched set of young adults who underwent 20 sessions of an adaptive attentional tracking program. Pre- and post-training measurements of fluid intelligence, standardized intelligence tests, speed of processing, reading skills, and other tests of working memory were assessed. Both training groups exhibited substantial and specific improvements on the trained tasks that persisted for at least 6 months post-training, but no transfer of improvement was observed to any of the non-trained measurements when compared to a third untrained group serving as a passive control. These findings fail to support the idea that adaptive working memory training in healthy young adults enhances working memory capacity in non-trained tasks, fluid intelligence, or other measures of cognitive abilities.

Optimizing real time fMRI neurofeedback for therapeutic discovery and development

While reducing the burden of brain disorders remains a top priority of organizations like the World Health Organization and National Institutes of Health, the development of novel, safe and effective treatments for brain disorders has been slow. In this paper, we describe the state of the science for an emerging technology, real time functional magnetic resonance imaging (rtfMRI) neurofeedback, in clinical neurotherapeutics. We review the scientific potential of rtfMRI and outline research strategies to optimize the development and application of rtfMRI neurofeedback as a next generation therapeutic tool. We propose that rtfMRI can be used to address a broad range of clinical problems by improving our understanding of brain–behavior relationships in order to develop more specific and effective interventions for individuals with brain disorders. We focus on the use of rtfMRI neurofeedback as a clinical neurotherapeutic tool to drive plasticity in brain function, cognition, and behavior. Our overall goal is for rtfMRI to advance personalized assessment and intervention approaches to enhance resilience and reduce morbidity by correcting maladaptive patterns of brain function in those with brain disorders.

Resting-state anticorrelations between medial and lateral prefrontal cortex: association with working memory, aging, and individual differences.

We examined how variation in working memory (WM) capacity due to aging or individual differences among young adults is associated with intrinsic or resting-state anticorrelations, particularly between (1) the medial prefrontal cortex (MPFC), a component of the default-mode network (DMN) that typically decreases in activation during external, attention-demanding tasks, and (2) the dorsolateral prefrontal cortex (DLPFC), a component of the fronto-parietal control network that supports executive functions and WM and typically increases in activation during attention-demanding tasks. We compared the magnitudes of MPFC-DLPFC anticorrelations between healthy younger and older participants (Experiment 1) and related the magnitudes of these anticorrelations to individual differences on two behavioral measures of WM capacity in two independent groups of young adults (Experiments 1 and 2). Relative to younger adults, older adults exhibited reductions in WM capacity and in MPFC-DLPFC anticorrelations. Within younger adults, greater MPFC-DLPFC anticorrelation at rest correlated with greater WM capacity. These findings show that variation in MPFC-DLPFC anticorrelations, whether related to aging or to individual differences, may reflect an intrinsic functional brain architecture supportive of WM capacity.

Computing moment-to-moment BOLD activation for real-time neurofeedback

Estimating moment-to-moment changes in blood oxygenation level dependent (BOLD) activation levels from functional magnetic resonance imaging (fMRI) data has applications for learned regulation of regional activation, brain state monitoring, and brain-machine interfaces. In each of these contexts, accurate estimation of the BOLD signal in as little time as possible is desired. This is a challenging problem due to the low signal-to-noise ratio of fMRI data. Previous methods for real-time fMRI analysis have either sacrificed the ability to compute moment-to-moment activation changes by averaging several acquisitions into a single activation estimate or have sacrificed accuracy by failing to account for prominent sources of noise in the fMRI signal. Here we present a new method for computing the amount of activation present in a single fMRI acquisition that separates moment-to-moment changes in the fMRI signal intensity attributable to neural sources from those due to noise, resulting in a feedback signal more reflective of neural activation. This method computes an incremental general linear model fit to the fMRI time series, which is used to calculate the expected signal intensity at each new acquisition. The difference between the measured intensity and the expected intensity is scaled by the variance of the estimator in order to transform this residual difference into a statistic. Both synthetic and real data were used to validate this method and compare it to the only other published real-time fMRI method.

Overwriting and rebinding: Why feature-switch detection tasks underestimate the binding capacity of visual working memory

In these two experiments, we explored the ability to store bound representations of colour and location information in visual working memory using three different tasks. In the location-cue task, we probed how well colour information could be recalled when observers are given a location cue. In the feature-cue task, we probed how well location information could be recalled when observers are given a colour cue. Finally, in the feature-switch detection task, we tested how well observers could detect a recombination of features (e.g., switching the locations of the red and green items). We hypothesized that these tasks might reveal differences in binding capacity limits between switching and nonswitching tests of visual working memory. We also hoped the tasks could provide an explanation for those differences in terms of the component processes of working memory—do failures occur in the encoding, maintenance, or retrieval stages of the task? Experiment 1 showed that performance in the two cued-recall tasks was equally high, and was significantly better than performance in the feature-switch detection task. Thus, the feature-switch detection task underestimates the number of colour–location bindings that can be remembered, but is a useful task for examining the fragile nature of feature binding in working memory. Experiment 2 explored why feature-switch detection underestimates the binding capacity of visual working memory by examining whether the feature switch errors occur at the level of encoding, maintaining, or retrieving binding information from visual working memory. The results suggest that feature switch errors reflect failures to maintain bound objects in working memory, perhaps due to the automatic rewriting and rebinding of information in the face of new perceptual input.

Roles of Default-Mode Network and Supplementary Motor Area in Human Vigilance Performance: Evidence from Real-Time fMRI

We used real-time functional magnetic resonance imaging (fMRI) to determine which regions of the human brain have a role in vigilance as measured by reaction time (RT) to variably timed stimuli. We first identified brain regions where activation before stimulus presentation predicted RT. Slower RT was preceded by greater activation in the default-mode network, including lateral parietal, precuneus, and medial prefrontal cortices; faster RT was preceded by greater activation in the supplementary motor area (SMA). We examined the roles of these brain regions in vigilance by triggering trials based on brain states defined by blood oxygenation level-dependent activation measured using real-time fMRI. When activation of relevant neural systems indicated either a good brain state (increased activation of SMA) or a bad brain state (increased activation of lateral parietal cortex and precuneus) for performance, a target was presented and RT was measured. RTs on trials triggered by a good brain state were significantly faster than RTs on trials triggered by a bad brain state. Thus human performance was controlled by monitoring brain states that indicated high or low vigilance. These findings identify neural systems that have a role in vigilance and provide direct evidence that the default-mode network has a role in human performance. The ability to control and enhance human behavior based on brain state may have broad implications.

Intensive working memory training produces functional changes in large-scale frontoparietal networks

Working memory is central to human cognition, and intensive cognitive training has been shown to expand working memory capacity in a given domain. It remains unknown, however, how the neural systems that support working memory are altered through intensive training to enable the expansion of working memory capacity. We used fMRI to measure plasticity in activations associated with complex working memory before and after 20 days of training. Healthy young adults were randomly assigned to train on either a dual n-back working memory task or a demanding visuospatial attention task. Training resulted in substantial and task-specific expansion of dual n-back abilities accompanied by changes in the relationship between working memory load and activation. Training differentially affected activations in two large-scale frontoparietal networks thought to underlie working memory: the executive control network and the dorsal attention network. Activations in both networks linearly scaled with working memory load before training, but training dissociated the role of the two networks and eliminated this relationship in the executive control network. Load-dependent functional connectivity both within and between these two networks increased following training, and the magnitudes of increased connectivity were positively correlated with improvements in task performance. These results provide insight into the adaptive neural systems that underlie large gains in working memory capacity through training.