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The locus coeruleus is critical for selective information processing by modulating the brain’s connectivity configuration. Increasingly, studies have suggested that LC controls sensory inputs at the sensory gating stage. Furthermore, accumulating evidence has shown that young children and older adults are more prone to distraction and filter out irrelevant information less efficiently, possibly due to the unoptimized LC connectivity. However, the LC connectivity pattern across the life span is not fully examined yet, hampering our ability to understand the relationship between LC development and the distractibility. In this study, we examined the intrinsic network connectivity of the LC using a public fMRI dataset with wide-range age samples. Based on LC-seed functional connectivity maps, we examined the age-related variation in the LC connectivity with a quadratic model. The analyses revealed two connectivity patterns explicitly. The sensory-related brain regions showed a positive quadratic age effect (u-shape), and the frontal regions for the cognitive control showed a negative quadratic age effect (inverted u-shape). Our results imply that such age-related distractibility is possibly due to the impaired sensory gating by the LC and the insufficient top-down controls by the frontal regions. We discuss the underlying neural mechanisms and limitations of our study.

The locus coeruleus is critical for selective information processing by modulating brain connectivity configuration. Increasingly studies have suggested that LC controls sensory inputs at the sensory gating stage. Furthermore, accumulating evidence has examined that young children and older adults are more prone to distraction and filter out irrelevant information less efficiently, possibly due to the impaired LC connectivity. However, the LC connectivity pattern across the life span is not fully examined yet, hampering our ability to understand the relationship between LC development and the distractibility. In this study, we examined the intrinsic network connectivity of the LC using resting-state fMRI from the enhanced NKI dataset with wide-range age samples. Based on LC-seed functional connectivity maps, we examined the age-related variation in the LC connectivity with a quadratic model. The analyses revealed two connectivity patterns explicitly. The sensory-related brain regions showed a positive quadratic age effect (u-shape), and the frontal regions for the cognitive control showed a negative quadratic age effect (inverted u-shape). Our results imply that such age-related distractibility is possibly due to the impaired sensory gating by the LC and the insufficient top-down controls by the frontal regions. We discuss the underlying neural mechanisms and limitations of our study.

The LC is a brainstem region associated with broad physiological and neural arousal as part of the release of norepinephrine, but it has increasingly been associated with multiple specific cognitive processes. These include sustained attention, deficits in which are associated with a variety of neuropsychological disorders. Neural models of attention deficits to date have focused on interrupted dynamics between the salience network (SAL) with the fronto-parietal network (FPN), which has been associated with task-switching and processing of external stimuli, respectively. Conflicting findings based on these regions suggest the possibility of upstream signaling leading to attention dysfunction, and recent research suggest the LC may play this role. In this study, resting-state functional connectivity (FC) and behavioral performance on an attention task was examined within 584 individuals. Analysis revealed significant clusters connected to the LC activity in the bilateral insula, anterior cingulate cortex (ACC), and bilateral ventral striatum, all regions associated with the SAL. Given previous findings that attention deficits may be caused by dysfunctions in network switching by the SAL, our findings here further suggest that dysfunction in LC signaling to the SAL may interfere with attention.

The think-aloud protocol provides researchers an insight into the designer's mental state, but little is understood about how thinking aloud influences design. The study presented in this paper sets out to measure the cognitive and neurocognitive changes in designers when thinking aloud. Engineering students (n=50) were randomly assigned to the think-aloud or control group. Students were outfitted with a functional near-infrared spectroscopy band. Students were asked to design a personal entertainment system. The think-aloud group spent significantly less time designing. Their design sketches included significantly fewer words. The think-aloud group also required significantly more resources in the left and right dorsolateral prefrontal cortex (DLPFC). The left DLPFC is often recruited for language processing, and the right DLPFC is involved in visual representation and problem-solving. The faster depletion of neurocognitive resources may have contributed to less time designing. Thinking aloud influences design cognition and neurocognition, but these effects are only now becoming apparent. More research and the adoption of neuroscience techniques can help shed light on these differences.

The locus coeruleus (LC) is a brainstem region associated with broad neural arousal because of norepinephrine production, but it has increasingly been associated with specific cognitive processes. These include sustained attention, with deficits associated with various neuropsychological disorders. Neural models of attention deficits have focused on interrupted dynamics between the salience network (SAL) with the frontoparietal network, which has been associated with task-switching and processing of external stimuli, respectively. Conflicting findings for these regions suggest the possibility of upstream signaling leading to attention dysfunction, and recent research suggests LC involvement. In this study, resting-state functional connectivity and behavioral performance on an attention task was examined within 584 individuals. Analysis revealed significant clusters connected to LC activity in the SAL. Given previous findings that attention deficits may be caused by SAL network switching dysfunctions, findings here further suggest that dysfunction in LC–SAL connectivity may impair attention.

The brain connectivity of resting-state fMRI (rs-fMRI) represents an intrinsic state of brain architecture, and it has been used as a useful neural marker for detecting psychiatric conditions as well as for predicting psychosocial characteristics. However, most studies using brain connectivity have focused more on the strength of functional connectivity over time (static-FC) but less attention to temporal characteristics of connectivity changes (FC-variability). The primary goal of the current study was to investigate the effectiveness of using the FC-variability in classifying an individual's pathological characteristics from others and predicting psychosocial characteristics. In addition, the current study aimed to prove that benefits of the FC-variability are reliable across various analysis procedures. To this end, three open public large resting-state fMRI datasets including individuals with Autism Spectrum Disorder (ABIDE; N = 1249), Schizophrenia disorder (COBRE; N = 145), and typical development (NKI; N = 672) were utilized for the machine learning (ML) classification and prediction based on their static-FC and the FC-variability metrics. To confirm the robustness of FC-variability utility, we benchmarked the ML classification and prediction with various brain parcellations and sliding window parameters. As a result, we found that the ML performances were significantly improved when the ML included FC-variability features in classifying pathological populations from controls (e.g., individuals with autism spectrum disorder vs. typical development) and predicting psychiatric severity (e.g., score of autism diagnostic observation schedule), regardless of parcellation selection and sliding window size. Additionally, the ML performance deterioration was significantly prevented with FC-variability features when excessive features were inputted into the ML models, yielding more reliable results. In conclusion, the current finding proved the usefulness of the FC-variability and its reliability.

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