Table_1_Development of an Online and Offline Integration Hypothesis for Healthy Internet Use: Theory and Preliminary Evidence.DOCX

<p>The Internet has become an integral part of our daily life, and how to make the best use of the Internet is important to both individuals and the society. Based on previous studies, an Online and Offline Integration Hypothesis is proposed to suggest a framework for considering harmonious and balanced Internet use. The Integration Hypothesis proposes that healthier patterns of Internet usage may be achieved through harmonious integration of people’s online and offline worlds. An online/offline integration is proposed to unite self-identity, interpersonal relationships, and social functioning with both cognitive and behavioral aspects by following the principles of communication, transfer, consistency, and “offline-first” priorities. To begin to test the hypothesis regarding the relationship between integration level and psychological outcomes, data for the present study were collected from 626 undergraduate students (41.5% males). Participants completed scales for online and offline integration, Internet addiction, pros and cons of Internet use, loneliness, extraversion, and life satisfaction. The findings revealed that subjects with higher level of online/offline integration have higher life satisfaction, greater extraversion, and more positive perceptions of the Internet and less loneliness, lower Internet addiction, and fewer negative perceptions of the Internet. Integration mediates the link between extraversion and psychological outcomes, and it may be the mechanism underlying the difference between the “rich get richer” and social compensation hypotheses. The implications of the online and offline integration hypothesis are discussed.</p

Opposite Modulation of Brain Functional Networks Implicated at Low vs. High Demand of Attention and Working Memory

<div><p>Background</p><p>Functional magnetic resonance imaging (fMRI) studies indicate that the brain organizes its activity into multiple functional networks (FNs) during either resting condition or task-performance. However, the functions of these FNs are not fully understood yet.</p><p>Methodology/Principal Findings</p><p>To investigate the operation of these FNs, spatial independent component analysis (sICA) was used to extract FNs from fMRI data acquired from healthy participants performing a visual task with two levels of attention and working memory load. The task-related modulations of extracted FNs were assessed. A group of FNs showed increased activity at low-load conditions and reduced activity at high-load conditions. These FNs together involve the left lateral frontoparietal cortex, insula, and ventromedial prefrontal cortex. A second group of FNs showed increased activity at high-load conditions and reduced activity at low-load conditions. These FNs together involve the intraparietal sulcus, frontal eye field, lateral frontoparietal cortex, insula, and dorsal anterior cingulate, bilaterally. Though the two groups of FNs showed opposite task-related modulations, they overlapped extensively at both the lateral and medial frontoparietal cortex and insula. Such an overlap of FNs would not likely be revealed using standard general-linear-model-based analyses.</p><p>Conclusions</p><p>By assessing task-related modulations, this study differentiated the functional roles of overlapping FNs. Several FNs including the left frontoparietal network are implicated in task conditions of low attentional load, while another set of FNs including the dorsal attentional network is implicated in task conditions involving high attentional demands.</p></div

ICs activated at low-load conditions.

<p>A. Colors on the Montreal Neurological Institute (MNI) T1 templates show the spatial distributions of positive sub-networks from ICs exhibiting increased activity at low- relative to high-load conditions. Only clusters surviving corrections for voxel-wise whole-brain analyses are shown. The numbers at the bottom right of each brain image indicate Z coordinates in MNI space. The color bar indicates t values. The “Beta-weight” column shows values of beta-weights at low- and high-load conditions. Error bars indicate standard errors (SEs) of the mean. The p value on each panel indicates the statistical significance of the main effect of task load on beta-weight. The “Timecourse” column shows task-load-related modulations in the timecourses of related ICs within 30 s after the onset of task blocks in the four task conditions. For x-axis, 0 represents the onset of task blocks and the block duration is 19.2 s. B. Yellow-red colors on T1 templates indicate brain regions covered by one or more ICs. The color bar indicates the number of overlapping ICs. The number below each brain image indicates the Z coordinates in MNI space. Abbreviations: L: low load without distractors; LD: low load with distractors; H: high load without distractors; HD: high load with distractors; R: right.</p

Overlap of ICs activated at low- and high-load conditions.

<p>The yellow color on the Montreal Neurological Institute (MNI) T1 templates indicates brain regions covered by overlap of ICs activated at low- and high-load conditions. The blue color indicates brain regions covered by ICs activated at low-load conditions, while the red color indicates brain regions covered by ICs activated at high-load conditions. The number below each brain image indicates the Z coordinates in MNI space. Abbreviation: R: right.</p

ICs activated at high-load conditions.

<p>A. Colors on the Montreal Neurological Institute (MNI) T1 templates show the spatial distribution of positive sub-networks from ICs exhibiting increased activity at high- relative to low-load conditions. B. Yellow-red colors on T1 templates indicate brain regions covered by one or more ICs. Please see fig. 1 legend for additional details.</p

Diagram demonstrating elements of the visual target identification task.

<p>A) A block sequence in the <i>low perceptual load without distractor</i> condition. First, the instruction screen shows “Please Identify” above an elongated nose, for 5 seconds. The elongated nose informs the participants to identify targets in the next task block based on nose shape. During the task block, 16 faces are presented one at a time, for 100 ms each (with 1.1 second inter-stimulus interval). B) A block sequence in the <i>high perceptual load without distractor</i> condition. The instruction screen shows “Please Identify” above two faces without a nose, which informs the participants to identify targets matching the unique combination of face color, and shapes of face, eyes, and mouth in the next task block. C. An example of a face overlaid on a background distractor (i.e., scene picture). The same 64 scene pictures were used in task blocks of both low and high perceptual load. The same instruction screens for low and high perceptual load were used in the distractor and no-distractor conditions.</p

Distractor related changes in BOLD signal at different perceptual load.

<p>Color on T1 template images from SPM2 indicates significant distractor-related increases (red color) or decreases (blue color) in BOLD signal in the conditions of low (A) and high perceptual load (B), and differences in distractor-related changes in BOLD signal at low vs. high perceptual load (C). The color bar indicates t value. The number under each brain image at the bottom row indicates the Z coordinate of the brain image in the MNI (Montreal Neurological Institute) template space. The only voxels displayed on the brain images are those surviving voxel threshold <.01 and cluster level p<.05, FWE corrected for multiple comparisons of voxel-wise whole brain analysis. D) Bar graphs show changes in BOLD signal in the labeled ROIs. The level of BOLD signal at the condition of low load without distractor was defined as baseline (0). Error bars indicates standard errors of means. Abbreviation: H: high load without distractor; HD: high load with distractors; IFG: inferior frontal gyrus; L: low load without distractors; LD: low load with distractors; MOG: middle occipital gyrus; MPFC: medial prefrontal cortex; PAG: periaqueductal gray; R: right; SFG: superior frontal gyrus; VTA/SN: ventral tegmental area and adjacent substantia nigra.</p

Distractor-related changes in BOLD signal.

<p>All clusters in this table were generated at FWE corrected cluster p<.05 with voxel level p<.01. Abbreviations: ACC: anterior cingulate; BA: Brodmann area; G: Gyrus; L: left; MPFC: medial prefrontal cortex; R: right; Size: number of voxels in the cluster; VTA/SN: ventral tegmental area/substantia nigra; Z-value: the Z value of peak voxel in the cluster.</p

Performance data and BOLD signal changes on the task.

<p>A). Error bar indicates standard error of means. Perceptual load showed main effects on commission and omission errors and on RT. Distractors showed significant main effects on commission and omission errors, but only a trend on RT. Perceptual loads and distractors showed a significant two-way interaction with respect to commission errors, and trended towards a significant two-way interaction with respect to omission errors. B) Color on T1 template image from SPM2 indicates significant increases (red color) and decreases (blue color) in BOLD signal in the condition of high relative to low perceptual load, collapsed across distractor conditions. The color bar indicates t value. The number under each brain image indicates the Z coordinate of the image in the MNI (Montreal Neurological Institute) template space. The only voxels displayed on the brain images are those surviving voxel threshold <.01 and cluster level p<.05, FWE corrected for multiple comparisons of voxel-wise whole brain analysis. Abbreviations: L: low load; H: high load; R: right side; RT: reaction time.</p

Relationships Between Impulsivity, Anxiety, and Risk-Taking and the Neural Correlates of Attention in Adolescents

<p>Although impulsivity, anxiety, and risk-taking may relate to attentional processes, little research has directly investigated how each may be associated with specific facets of attentional processes and their underlying neural correlates. Nineteen adolescents performed a functional magnetic resonance imaging task involving simple, selective, and divided attention. Out-of-scanner-assessed impulsivity, anxiety, and risk-taking scores were not correlated with each other and showed task-phase-specific patterns of association. Results are discussed in light of research and theory suggesting a relationship between these domains and attention and may serve to focus future research aiming to understand these relationships.</p