We explored the effects of aging on 2 large-scale brain networks, the default mode network (DMN) and the task-positive network (TPN). During functional magnetic resonance imaging scanning, young and older participants carried out 4 visual tasks: detection, perceptual matching, attentional cueing, and working memory. Accuracy of performance was roughly matched at 80% across tasks and groups. Modulations of activity across conditions were assessed, as well as functional connectivity of both networks. Younger adults showed a broader engagement of the DMN and older adults a more extensive engagement of the TPN. Functional connectivity in the DMN was reduced in older adults, whereas the main pattern of TPN connectivity was equivalent in the 2 groups. Age-specific connectivity also was seen in TPN regions. Increased activity in TPN areas predicted worse accuracy on the tasks, but greater expression of a connectivity pattern associated with a right dorsolateral prefrontal TPN region, seen only in older adults, predicted better performance. These results provide further evidence for age-related differences in the DMN and new evidence of age differences in the TPN. Increased use of the TPN may reflect greater demand on cognitive control processes in older individuals that may be partially offset by alterations in prefrontal functional connectivity.

Research examining the cognitive consequences of bilingualism has expanded rapidly in recent years and has revealed effects on aspects of cognition across the lifespan. However, these effects are difficult to find in studies investigating young adults. One problem is that there is no standard definition of bilingualism or means of evaluating degree of bilingualism in individual participants, making it difficult to directly compare the results of different studies. Here, we describe an instrument developed to assess degree of bilingualism for young adults who live in diverse communities in which English is the official language. We demonstrate the reliability and validity of the instrument in analyses based on 408 participants. The relevant factors for describing degree of bilingualism are: (1) the extent of non-English language proficiency and use at home, and (2) non-English language use socially. We then use the bilingualism scores obtained from the instrument to demonstrate their association with: (1) performance on executive function tasks, and (2) previous classifications of participants into categories of monolinguals and bilinguals.

Here we review the neural correlates of cognitive control associated with bilingualism. We demonstrate that lifelong practice managing two languages orchestrates global changes to both the structure and function of the brain. Compared with monolinguals, bilinguals generally show greater gray matter volume, especially in perceptual/motor regions, greater white matter integrity, and greater functional connectivity between gray matter regions. These changes complement electroencephalography findings showing that bilinguals devote neural resources earlier than monolinguals. Parallel functional findings emerge from the functional magnetic resonance imaging literature: bilinguals show reduced frontal activity, suggesting that they do not need to rely on top-down mechanisms to the same extent as monolinguals. This shift for bilinguals to rely more on subcortical/posterior regions, which we term the bilingual anterior-to-posterior and subcortical shift (BAPSS), fits with results from cognitive aging studies and helps to explain why bilinguals experience cognitive decline at later stages of development than monolinguals.

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1AbstractTo extract patterns from neuroimaging data, various statistical methods and machine learning algorithms have been explored for the diagnosis of Alzheimer’s disease among older adults in both clinical and research applications; however, distinguishing between Alzheimer’s and healthy brain data has been challenging in older adults (age > 75) due to highly similar patterns of brain atrophy and image intensities. Recently, cutting-edge deep learning technologies have rapidly expanded into numerous fields, including medical image analysis. This paper outlines state-of-the-art deep learning-based pipelines employed to distinguish Alzheimer’s magnetic resonance imaging (MRI) and functional MRI (fMRI) from normal healthy control data for a given age group. Using these pipelines, which were executed on a GPU-based high-performance computing platform, the data were strictly and carefully preprocessed. Next, scale- and shift-invariant low- to high-level features were obtained from a high volume of training images using convolutional neural network (CNN) architecture. In this study, fMRI data were used for the first time in deep learning applications for the purposes of medical image analysis and Alzheimer’s disease prediction. These proposed and implemented pipelines, which demonstrate a significant improvement in classification output over other studies, resulted in high and reproducible accuracy rates of 99.9% and 98.84% for the fMRI and MRI pipelines, respectively. Additionally, for clinical purposes, subject-level classification was performed, resulting in an average accuracy rate of 94.32% and 97.88% for the fMRI and MRI pipelines, respectively. Finally, a decision making algorithm designed for the subject-level classification improved the rate to 97.77% for fMRI and 100% for MRI pipelines.

BackgroundCurrent wound assessment practices are lacking on several measures. For example, the most common method for measuring wound size is using a ruler, which has been demonstrated to be crude and inaccurate. An increase in periwound temperature is a classic sign of infection but skin temperature is not always measured during wound assessments. To address this, we have developed a smartphone application that enables non-contact wound surface area and temperature measurements. Here we evaluate the inter-rater reliability and accuracy of this novel point-of-care wound assessment tool.Methods and findingsThe wounds of 87 patients were measured using the Swift Wound app and a ruler. The skin surface temperature of 37 patients was also measured using an infrared FLIR™ camera integrated with the Swift Wound app and using the clinically accepted reference thermometer Exergen DermaTemp 1001. Accuracy measurements were determined by assessing differences in surface area measurements of 15 plastic wounds between a digital planimeter of known accuracy and the Swift Wound app. To evaluate the impact of training on the reproducibility of the Swift Wound app measurements, three novice raters with no wound care training, measured the length, width and area of 12 plastic model wounds using the app. High inter-rater reliabilities (ICC = 0.97–1.00) and high accuracies were obtained using the Swift Wound app across raters of different levels of training in wound care. The ruler method also yielded reliable wound measurements (ICC = 0.92–0.97), albeit lower than that of the Swift Wound app. Furthermore, there was no statistical difference between the temperature differences measured using the infrared camera and the clinically tested reference thermometer.ConclusionsThe Swift Wound app provides highly reliable and accurate wound measurements. The FLIR™ infrared camera integrated into the Swift Wound app provides skin temperature readings equivalent to the clinically tested reference thermometer. Thus, the Swift Wound app has the advantage of being a non-contact, easy-to-use wound measurement tool that allows clinicians to image, measure, and track wound size and temperature from one visit to the next. In addition, this tool may also be used by patients and their caregivers for home monitoring.

Current behavioral evidence suggests that attention regulation in humans varies with a circadian arousal rhythm that is influenced by age. However it is not known whether functional BOLD activation also varies with performance across the day in older adults. We used fMRI to compare activity in the control network in older adults tested in the morning and older and younger adults tested in the afternoon. Using a 1-back task with simultaneously presented stimuli (words superimposed on pictures), we show that older adults tested in the morning are not only more able to ignore the unattended stimulus than older adults in the afternoon, but activate similar cognitive control regions to young adults (rostral prefrontal and superior parietal cortex). We conclude that time of day modulates task-related fMRI signal in older adults and that age differences are reduced when older adults are tested at peak times of day. Keywordstime of day; circadian arousal; aging; attention; cognitive control; distraction; implicit memory; control network There are well known circadian fluctuations in cognitive alertness Yoon et al., 1999;Paradee et al., 2005;Blatter and Cajochen, 2007;Murray et al., 2009), fluctuations measurable with paper and pencil inventories highly correlated with physiological arousal (Horne and Ostberg, 1976;Roenneberg et al., 2003;Zavada et al., 2005). Additionally, there are age and individual differences in alertness patterns, such that the majority of older adults are shifted towards morningness, with younger adults falling into neutral and evening type ranges of alertness. Similar effects have also been demonstrated in animal studies which report robust age by synchrony interactions for arousal and memory Hasher, 1999, 2002 CIHR Author Manuscript CIHR Author Manuscript CIHR Author ManuscriptConcerning cognitive functioning, there is a substantial literature showing a synchrony effect (May et al., 1993;May and Hasher, 1998) such that performance, particularly on tasks requiring effortful (top-down) executive control or attention regulation are best performed at one's better times of day (May and Hasher, 1998;May, 1999;Yoon et al., 1999; Hasher et al., 2005;Ramírez et al., 2006Ramírez et al., , 2012Goldstein et al., 2007;Rowe et al., 2009; Hahn et al., 2012;Lehmann et al., 2013).Despite a rich behavioral literature, the influence of circadian rhythms and time of testing is largely unexplored in the neuroimaging literature and what research there is has focused on young adults (e.g. Marek et al., 2010) showing time of day differences in the ability to regulate strong but incorrect responses in the orienting attentional network -a division of the task-positive network that regulates where and when attention is directed in response to external cues (Schmidt et al., 2012).These findings are suggestive for young adults but an open and critical question is the impact of different times of testing for older adults -whose behavioral data shows substantially larger fluctuations, including in regulation of di...

We examined the influence of emotional valence and type of item to be remembered on brain activity during recognition, using faces and scenes. We used multivariate analyses of event-related fMRI data to identify whole-brain patterns, or networks of activity. Participants demonstrated better recognition for scenes vs faces and for negative vs neutral and positive items. Activity was increased in extrastriate cortex and inferior frontal gyri for emotional scenes, relative to neutral scenes and all face types. Increased activity in these regions also was seen for negative faces relative to positive faces. Correct recognition of negative faces and scenes (hits vs correct rejections) was associated with increased activity in amygdala, hippocampus, extrastriate, frontal and parietal cortices. Activity specific to correctly recognized emotional faces, but not scenes, was found in sensorimotor areas and rostral prefrontal cortex. These results suggest that emotional valence and type of visual stimulus both modulate brain activity at recognition, and influence multiple networks mediating visual, memory and emotion processing. The contextual information in emotional scenes may facilitate memory via additional visual processing, whereas memory for emotional faces may rely more on cognitive control mediated by rostrolateral prefrontal regions.