Quantifying attention span across the lifespan.

Introduction: Studies examining sustained attention abilities typically utilize metrics that quantify performance on vigilance tasks, such as response time and response time variability. However, approaches that assess the duration that an individual can maintain their attention over time are lacking. Methods: Here we developed an objective attention span metric that quantified the maximum amount of time that a participant continuously maintained an optimal "in the zone" sustained attention state while performing a continuous performance task. Results: In a population of 262 individuals aged 7-85, we showed that attention span was longer in young adults than in children and older adults. Furthermore, declines in attention span over time during task engagement were related to clinical symptoms of inattention in children. Discussion: These results suggest that quantifying attention span is a unique and meaningful method of assessing sustained attention across the lifespan and in populations with inattention symptoms.

Leveraging technology to personalize cognitive enhancement methods in aging.

As population aging advances at an increasing rate, efforts to help people maintain or improve cognitive function late in life are critical. Although some studies have shown promise, the question of whether cognitive training is an effective tool for improving general cognitive ability remains incompletely explored, and study results to date have been inconsistent. Most approaches to cognitive enhancement in older adults have taken a 'one size fits all' tack, as opposed to tailoring interventions to the specific needs of individuals. In this Perspective, we argue that modern technology has the potential to enable large-scale trials of public health interventions to enhance cognition in older adults in a personalized manner. Technology-based cognitive interventions that rely on closed-loop systems can be tailored to individuals in real time and have the potential for global testing, extending their reach to large and diverse populations of older adults. We propose that the future of cognitive enhancement in older adults will rely on harnessing new technologies in scientifically informed ways.

The Generation of Involuntary Mental Imagery in an Ecologically-Valid Task.

Laboratory tasks (e.g., the flanker task) reveal that incidental stimuli (e.g., distractors) can reliably trigger involuntary conscious imagery. Can such involuntary effects, involving competing representations, arise during dual-task conditions? Another concern about these laboratory tasks is whether such effects arise in highly ecologically-valid conditions. For example, do these effects arise from tasks involving dynamic stimuli (e.g., simulations of semi-automated driving experiences)? The data from our experiment suggest that the answer to our two questions is yes. Subjects were presented with video footage of the kinds of events that one would observe if one were seated in the driver's seat of a semi-automated vehicle. Before being presented with this video footage, subjects had been trained to respond to street signs according to laboratory techniques that cause stimulus-elicited involuntary imagery. After training, in the Respond condition, subjects responded to the signs; in the Suppress condition, subjects were instructed to not respond to the signs in the video footage. Subjects in the Suppress condition reported involuntary imagery on a substantive proportion of the trials. Such involuntary effects arose even under dual-task conditions (while performing the n-back task or psychomotor vigilance task). The present laboratory task has implications for semi-automated driving, because the safe interaction between driver and vehicle requires that the communicative signals from vehicle to driver be effective at activating the appropriate cognitions and behavioral inclinations. In addition, our data from the dual-task conditions provide constraints for theoretical models of cognitive resources.

Reconfiguration of Electroencephalography Microstate Networks after Breath-Focused, Digital Meditation Training.

Sustained attention and working memory were improved in young adults after they engaged in a recently developed, closed-loop, digital meditation practice. Whether this type of meditation also has a sustained effect on dominant resting-state networks is currently unknown. In this study, we examined the resting brain states before and after a period of breath-focused, digital meditation training versus placebo using an electroencephalography (EEG) microstate approach. We found topographical changes in postmeditation rest, compared with baseline rest, selectively for participants who were actively involved in the meditation training and not in participants who engaged with an active, expectancy-match, placebo control paradigm. Our results suggest a reorganization of brain network connectivity after 6 weeks of intensive meditation training in brain areas, mainly including the right insula, the superior temporal gyrus, the superior parietal lobule, and the superior frontal gyrus bilaterally. These findings provide an opening for the development of a novel noninvasive treatment of neuropathological states by low-cost, breath-focused, digital meditation practice, which can be monitored by the EEG microstate approach.

Focus on the Breath: Brain Decoding Reveals Internal States of Attention During Meditation.

Meditation practices are often used to cultivate interoception or internally-oriented attention to bodily sensations, which may improve health via cognitive and emotional regulation of bodily signals. However, it remains unclear how meditation impacts internal attention (IA) states due to lack of measurement tools that can objectively assess mental states during meditation practice itself, and produce time estimates of internal focus at individual or group levels. To address these measurement gaps, we tested the feasibility of applying multi-voxel pattern analysis (MVPA) to single-subject fMRI data to: (1) learn and recognize internal attentional states relevant for meditation during a directed IA task; and (2) decode or estimate the presence of those IA states during an independent meditation session. Within a mixed sample of experienced meditators and novice controls (N = 16), we first used MVPA to develop single-subject brain classifiers for five modes of attention during an IA task in which subjects were specifically instructed to engage in one of five states [i.e., meditation-related states: breath attention, mind wandering (MW), and self-referential processing, and control states: attention to feet and sounds]. Using standard cross-validation procedures, MVPA classifiers were trained in five of six IA blocks for each subject, and predictive accuracy was tested on the independent sixth block (iterated until all volumes were tested, N = 2,160). Across participants, all five IA states were significantly recognized well above chance (>41% vs. 20% chance). At the individual level, IA states were recognized in most participants (87.5%), suggesting that recognition of IA neural patterns may be generalizable for most participants, particularly experienced meditators. Next, for those who showed accurate IA neural patterns, the originally trained classifiers were applied to a separate meditation run (10-min) to make an inference about the percentage time engaged in each IA state (breath attention, MW, or self-referential processing). Preliminary group-level analyses demonstrated that during meditation practice, participants spent more time attending to breath compared to MW or self-referential processing. This paradigm established the feasibility of using MVPA classifiers to objectively assess mental states during meditation at the participant level, which holds promise for improved measurement of internal attention states cultivated by meditation.

ß-amyloid and tau drive early Alzheimer's disease decline while glucose hypometabolism drives late decline.

Clinical trials focusing on therapeutic candidates that modify ß-amyloid (Aß) have repeatedly failed to treat Alzheimer's disease (AD), suggesting that Aß may not be the optimal target for treating AD. The evaluation of Aß, tau, and neurodegenerative (A/T/N) biomarkers has been proposed for classifying AD. However, it remains unclear whether disturbances in each arm of the A/T/N framework contribute equally throughout the progression of AD. Here, using the random forest machine learning method to analyze participants in the Alzheimer's Disease Neuroimaging Initiative dataset, we show that A/T/N biomarkers show varying importance in predicting AD development, with elevated biomarkers of Aß and tau better predicting early dementia status, and biomarkers of neurodegeneration, especially glucose hypometabolism, better predicting later dementia status. Our results suggest that AD treatments may also need to be disease stage-oriented with Aß and tau as targets in early AD and glucose metabolism as a target in later AD.

Closed-loop digital meditation improves sustained attention in young adults.

Attention is a fundamental cognitive process that is critical for essentially all aspects of higher-order cognition and real-world activities. Younger generations have deeply embraced information technology and multitasking in their personal lives, school and the workplace, creating myriad challenges to their attention. While improving sustained attention in healthy young adults would be beneficial, enhancing this ability has proven notoriously difficult in this age group. Here we show that 6 weeks of engagement with a meditation-inspired, closed-loop software program (MediTrain) delivered on mobile devices led to gains in both sustained attention and working memory in healthy young adults. These improvements were associated with positive changes in key neural signatures of attentional control (frontal theta inter-trial coherence and parietal P3b latency), as measured by electroencephalography. Our findings suggest the utility of delivering aspects of the ancient practice of focused-attention meditation in a modern, technology-based approach and its benefits on enhancing sustained attention.

Differential Impact of Interference on Internally- and Externally-Directed Attention.

Attention can be oriented externally to the environment or internally to the mind, and can be derailed by interference from irrelevant information originating from either external or internal sources. However, few studies have explored the nature and underlying mechanisms of the interaction between different attentional orientations and different sources of interference. We investigated how externally- and internally-directed attention was impacted by external distraction, how this modulated internal distraction, and whether these interactions were affected by healthy aging. Healthy younger and older adults performed both an externally-oriented visual detection task and an internally-oriented mental rotation task, performed with and without auditory sound delivered through headphones. We found that the addition of auditory sound induced a significant decrease in task performance in both younger and older adults on the visual discrimination task, and this was accompanied by a shift in the type of distractions reported (from internal to external). On the internally-oriented task, auditory sound only affected performance in older adults. These results suggest that the impact of external distractions differentially impacts performance on tasks with internal, as opposed to external, attentional orientations. Further, internal distractibility is affected by the presence of external sound and increased suppression of internal distraction.

Motor impulsivity in Parkinson disease: associations with COMT and DRD2 polymorphisms.

Parkinson disease (PD) is an age-related degenerative disease of the brain, characterized by motor, cognitive, and psychiatric symptoms. Neurologists and neuroscientists now understand that several symptoms of the disease, including hallucinations and impulse control behaviors, stem from the dopaminergic medications used to control the motor aspects of PD. Converging evidence from animals and humans suggests that individual differences in the genes that affect the dopamine system influence the response of PD patients to dopaminergic medication. In this study, we tested the hypothesis that patients taking dopamine replacement therapy who carry candidate alleles that increase dopamine signaling, exhibit greater amounts of motor impulsivity. We examined the relation between inhibitory ability (measured by the Stop Signal Task) and polymorphisms of COMT Val158Met and DRD2 C957T in patients with idiopathic PD. On the Stop Signal Task, carriers of COMT Val/Met and Met/Met genotypes were more impulsive than Val/Val carriers, but we did not find a link between DRD2 polymorphisms and inhibitory ability. These results support the hypothesis that the Met allele of COMT confers an increased risk for behavioral impulsivity in PD patients, whereas DRD2 polymorphisms appear to be less important in determining whether PD patients exhibit a dopamine overdose in the form of motor impulsivity.

A cognitive framework for understanding and improving interference resolution in the brain.

All of us are familiar with the negative impact of interference on achieving our task goals. We are referring to interference by information, which either impinges on our senses from an external environmental source or is internally generated by our thoughts. Informed by more than a decade of research on the cognitive and neural processing of interference, we have developed a framework for understanding how interference impacts our neural systems and especially how it is regulated and suppressed during efficient on-task performance. Importantly, externally and internally generated interferences have distinct neural signatures, and further, distinct neural processing emerges depending on whether individuals must ignore and suppress the interference, as for distractions, or engage with them in a secondary task, as during multitasking. Here, we elaborate on this cognitive framework and how it changes throughout the human lifespan, focusing mostly on research evidence from younger adults and comparing these findings to data from older adults, children, and cognitively impaired populations. With insights gleaned from our growing understanding, we then describe three novel translational efforts in our lab directed at improving distinct aspects of interference resolution using cognitive training. Critically, these training approaches were specifically developed to target improved interference resolution based on neuroplasticity principles and have shown much success in randomized controlled first version evaluations in healthy aging. Our results show not only on-task training improvements but also robust generalization of benefit to other cognitive control abilities. This research showcases how an in-depth understanding of neural mechanisms can then inform the development of effective deficit-targeted interventions, which can in turn benefit both healthy and cognitively impaired populations.

New MRI Biomarkers Advance the Characterization of Parkinson Disease.

The pathophysiology of idiopathic Parkinson disease (PD) is traditionally characterized as substantia nigra degeneration, but careful examination of the widespread neuropathological changes suggests individual differences in neuronal vulnerability. A major limitation to studies of disease progression in PD has been that conventional MRI techniques provide relatively poor contrast for the structures that are affected by the disease, and thus are not typically used in experimental or clinical studies. Here, we review the current state of structural MRI as applied to the analysis of the PD brain. We also describe a new multispectral MRI method that provides improved contrast for the substantia nigra and basal forebrain, which we recently used to show that these structures display different trajectories of volume loss early in the disease.

Substantia nigra volume loss before basal forebrain degeneration in early Parkinson disease.

OBJECTIVE: To test the hypothesis that degeneration of the substantia nigra pars compacta (SNc) precedes that of the cholinergic basal forebrain (BF) in Parkinson disease (PD) using new multispectral structural magnetic resonance (MR) imaging tools to measure the volumes of the SNc and BF. DESIGN: Matched case-control study. SETTING: The Athinoula A. Martinos Imaging Center at the McGovern Institute for Brain Research, Massachusetts Institute of Technology (MIT), and the Massachusetts General Hospital/MIT Morris Udall Center of Excellence in Parkinson Disease Research. PATIENTS: Participants included 29 patients with PD (Hoehn and Yahr [H&Y] stages 1-3) and 27 matched healthy control subjects. MAIN OUTCOME MEASURES: We acquired multiecho T1-weighted, multiecho proton density, T2-weighted, and T2-weighted fluid-attenuated inversion recovery (FLAIR) sequences from each participant. For the SNc, we created a weighted mean of the multiple echoes, yielding a single volume with a high ratio of contrast to noise. We visualized the BF using T2-weighted FLAIR images. For each participant, we manually labeled the 2 structures and calculated their volumes. RESULTS: Relative to the controls, 13 patients with H&Y stage 1 PD had significantly decreased SNc volumes. Sixteen patients with H&Y stage 2 or 3 PD showed little additional volume loss. In contrast, the BF volume loss occurred later in the disease, with a significant decrease apparent in patients having H&Y stage 2 or 3 PD compared with the controls and the patients having H&Y stage 1 PD. The latter group did not differ significantly from the controls. CONCLUSION: Our results support the proposed neuropathological trajectory in PD and establish novel multispectral methods as MR imaging biomarkers for tracking the degeneration of the SNc and BF.

Statistical adjustments for brain size in volumetric neuroimaging studies: some practical implications in methods.

Volumetric magnetic resonance imaging (MRI) brain data provide a valuable tool for detecting structural differences associated with various neurological and psychiatric disorders. Analysis of such data, however, is not always straightforward, and complications can arise when trying to determine which brain structures are "smaller" or "larger" in light of the high degree of individual variability across the population. Several statistical methods for adjusting for individual differences in overall cranial or brain size have been used in the literature, but critical differences exist between them. Using agreement among those methods as an indication of stronger support of a hypothesis is dangerous given that each requires a different set of assumptions be met. Here we examine the theoretical underpinnings of three of these adjustment methods (proportion, residual, and analysis of covariance) and apply them to a volumetric MRI data set. These three methods used for adjusting for brain size are specific cases of a generalized approach which we propose as a recommended modeling strategy. We assess the level of agreement among methods and provide graphical tools to assist researchers in determining how they differ in the types of relationships they can unmask, and provide a useful method by which researchers may tease out important relationships in volumetric MRI data. We conclude with the recommended procedure involving the use of graphical analyses to help uncover potential relationships the ROI volumes may have with head size and give a generalized modeling strategy by which researchers can make such adjustments that include as special cases the three commonly employed methods mentioned above.

Transformations in oscillatory activity and evoked responses in primary somatosensory cortex in middle age: a combined computational neural modeling and MEG study.

Oscillatory brain rhythms and evoked responses are widely believed to impact cognition, but relatively little is known about how these measures are affected by healthy aging. The present study used MEG to examine age-related changes in spontaneous oscillations and tactile evoked responses in primary somatosensory cortex (SI) in healthy young (YA) and middle-aged (MA) adults. To make specific predictions about neurophysiological changes that mediate age-related MEG changes, we applied a biophysically realistic model of SI that accurately reproduces SI MEG mu rhythms, containing alpha (7-14 Hz) and beta (15-30 Hz) components, and evoked responses. Analyses of MEG data revealed a significant increase in prestimulus mu power in SI, driven predominately by greater mu-beta dominance, and a larger and delayed M70 peak in the SI evoked response in MA. Previous analysis with our computational model showed that the SI mu rhythm could be reproduced with a stochastic sequence of rhythmic approximately 10 Hz feedforward (FF) input to the granular layers of SI (representative of lemniscal thalamic input) followed nearly simultaneously by approximately 10 Hz feedback (FB) input to the supragranular layers (representative of input from high order cortical or non-specific thalamic sources) (Jones et al., 2009). In the present study, the model further predicted that the rhythmic FF and FB inputs become stronger with age. Further, the FB input is predicted to arrive more synchronously to SI on each cycle of the 10 Hz input in MA. The simulated neurophysiological changes are sufficient to account for the age-related differences in both prestimulus mu rhythms and evoked responses. Thus, the model predicts that a single set of neurophysiological changes intimately links these age-related changes in neural dynamics.

Cognition in healthy aging is related to regional white matter integrity, but not cortical thickness.

It is well established that healthy aging is accompanied by structural changes in many brain regions and functional decline in a number of cognitive domains. The goal of this study was to determine (1) whether the regional distribution of age-related brain changes is similar in gray matter (GM) and white matter (WM) regions, or whether these two tissue types are affected differently by aging, and (2) whether measures of cognitive performance are more closely linked to alterations in the cerebral cortex or in the underlying WM in older adults (OA). To address these questions, we collected high-resolution magnetic resonance imaging (MRI) data from a large sample of healthy young adults (YA; aged 18-28) and OA (aged 61-86 years). In addition, the OA completed a series of tasks selected to assess cognition in three domains: cognitive control, episodic memory, and semantic memory. Using advanced techniques for measuring cortical thickness and WM integrity, we found that healthy aging was accompanied by deterioration of both GM and WM, but with distinct patterns of change: Cortical thinning occurred primarily in primary sensory and motor cortices, whereas WM changes were localized to regions underlying association cortices. Further, in OA, we found a striking pattern of region-specific correlations between measures of cognitive performance and WM integrity, but not cortical thickness. Specifically, cognitive control correlated with integrity of frontal lobe WM, whereas episodic memory was related to integrity of temporal and parietal lobe WM. Thus, age-related impairments in specific cognitive capacities may arise from degenerative processes that affect the underlying connections of their respective neural networks.

Amygdala volume associated with alcohol abuse relapse and craving.

OBJECTIVE: Amygdala volume has been associated with drug craving in cocaine addicts, and amygdala volume reduction is observed in some alcohol-dependent subjects. This study sought an association in alcohol-dependent subjects between volumes of reward-related brain regions, alcohol craving, and the risk of relapse. METHOD: Besides alcohol craving, the authors assessed amygdala, hippocampus, and ventral striatum volumes in 51 alcohol-dependent subjects and 52 age- and education-matched healthy comparison subjects after detoxification. After imaging and clinical assessment, patients were followed for 6 months and alcohol intake was recorded. RESULTS: Alcohol-dependent subjects showed reduced amygdala, hippocampus, and ventral striatum volumes and reported stronger craving in relation to healthy comparison subjects. However, only amygdala volume and craving differentiated between subsequent relapsers and abstainers. A significant decrease of amygdala volume in alcohol-dependent subjects was associated with increased alcohol craving before imaging and an increased alcohol intake during the 6-month follow-up period. CONCLUSIONS: These findings suggest a relationship between amygdala volume reduction, alcohol craving, and prospective relapse into alcohol consumption.

Adjustment for whole brain and cranial size in volumetric brain studies: a review of common adjustment factors and statistical methods.

In this article we address analytic challenges inherent in brain volumetrics (i.e., the study of volumes of brains and brain regions). It has sometimes been assumed in the literature that deviations in regional brain size in clinical samples are directly related to maldevelopment or pathogenesis. However, this assumption may be incorrect; such volume differences may, instead, be wholly or partly attributable to individual differences in overall dimension (e.g., for head, brain, or body size). What quantitative approaches can be used to take these factors into account? Here, we provide a review of volumetric and nonvolumetric adjustment factors. We consider three examples of common statistical methods by which one can adjust for the effects of body, head, or brain size on regional volumetric measures: the analysis of covariance, the proportion, and the residual approaches. While the nature of the adjustment will help dictate which method is most appropriate, the choice is context sensitive, guided by numerous considerations-chiefly the experimental hypotheses, but other factors as well (including characteristic features of the disorder and sample size). These issues come into play in logically framing the assessment of putative abnormalities in regional brain volumes.

Volumetric neuroimaging and low-dose early-life exposures: loose coupling of pathogenesis-brain-behavior links.

The interface of developmental neuroimaging with developmental neurotoxicology can, broadly speaking, address two complementary concerns. The first is to study the impact of specific exposures on brain development. The second is to study known neurobehavioral disorders with an eye to discerning toxicological contributions to pathogenesis. Pathogenesis targets brain based upon physical properties (receptors, growth factors, etc.) while behavior is modulated by regional and neural systems alterations. The distribution of pathogenesis-brain relationships overlaps only partially with that of brain-behavior relationships. The goal of this paper is to highlight methodological issues involved in designing and interpreting volumetric neuroimaging studies in the light of this loose coupling.