NeuroImage
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In the present study, we report the results from a large sample of participants (N = 136), selected based on their EEG quality, to obtain event-related potential (ERP) normative data. All participants were tested in Simple Response Task (SRT) and Discriminative Response Task (DRT). A subset of 36 participants was tested also in Passive Vision task. ⋯ Spatiotemporal patterns of all the observed components were analyzed using source analysis. Beside the well-known ERP components, we also described recently identified prefrontal components: the pre-stimulus prefrontal negativity (pN) associated to proactive cognitive (mainly inhibitory) control within the inferior frontal gyrus (iFg); the post-stimulus prefrontal N1, P1 and P2 (pN1, pP1 and pP2) involved in perceptual and visual-motor awareness (pN1 and pP1), and in stimulus-response mapping and decision-making (pP2) localized within the insular cortex. The large sample of high-quality EEG datasets allowed to identify four additional components: the pre-stimulus visual negativity (vN) originating in extrastriate visual areas and interpreted as a visual readiness activity; the post-stimulus prefrontal N2 and N3 (pN2 and pN3) components interpreted as feedback reactivation of the anterior insular cortex; and the post-stimulus prefrontal P3 (pP3), interpreted as persisting inhibitory activity of the iFg for inhibited trials.
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Randomized Controlled Trial
Mediation analysis of triple networks revealed functional feature of mindfulness from real-time fMRI neurofeedback.
The triple networks, namely the default-mode network (DMN), the central executive network (CEN), and the salience network (SN), play crucial roles in disorders of the brain, as well as in basic neuroscientific processes such as mindfulness. However, currently, there is no consensus on the underlying functional features of the triple networks associated with mindfulness. In this study, we tested the hypothesis that (a) the partial regression coefficient (i.e., slope): from the SN to the DMN, mediated by the CEN, would be one of the potential mindfulness features in the real-time functional magnetic resonance imaging (rtfMRI) neurofeedback (NF) setting, and (b) this slope level may be enhanced by rtfMRI-NF training. ⋯ Our results indicated that the slope level from the SN to the DMN, mediated by the CEN, was associated with mindfulness score (rtfMRI-NF runs: r = 0.53, p = 0.007; p-value was corrected from 10,000 random permutations) and with task-performance feedback score (rtfMRI-NF run: r = 0.61, p = 0.001) in the experimental group only. In addition, during the rtfMRI-NF runs the level of the partial regression coefficient feature was substantially increased in the experimental group compared to the control group (p < 0.05 from the paired t-test; the p-value was corrected from 10,000 random permutations). To the best of our knowledge, this is the first study to demonstrate a partial regression coefficient feature of mindfulness in the rtfMRI-NF setting obtained by triple network mediation analysis, as well as the possibility of enhancement of the partial regression coefficient feature by rtfMRI-NF training.
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Non-invasive quantification of the in vivo myelin content may provide valuable information regarding healthy maturation of the brain, as well as insights into demyelination of several neurological disorders. However, these scans are often long thereby limiting acquisition of large brain parts in clinically feasible acquisition times. Therefore, fast acquisition of whole brain myelin content is important. ⋯ First, the multi-slice myelin-water maps showed good agreement with the single-slice reference method, with a bias of at most 1.2% in absolute MWF values. Second, we found an average within-subject coefficient of variation (CoV) of 5.9% and an average intra-class correlation coefficient (ICC) of 0.90 for myelin-water estimation using a multi-slice GRASE sequence without parallel acceleration (scan time 14:06 min), while acquisition with a parallel acceleration factor of 2 resulted in a slightly worse average within-subject CoV of 6.4% and an average ICC of 0.83 at half the scan time. Hence, a multi-slice GRASE acquisition with parallel acceleration factor 2 and a scan time of 7:30 min still provides an excellent reproducibility.
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The thalamus and its nuclei are largely indistinguishable on standard T1 or T2 weighted MRI. While diffusion tensor imaging based methods have been proposed to segment the thalamic nuclei based on the angular orientation of the principal diffusion tensor, these are based on echo planar imaging which is inherently limited in spatial resolution and suffers from distortion. We present a multi-atlas segmentation technique based on white-matter-nulled MP-RAGE imaging that segments the thalamus into 12 nuclei with computation times on the order of 10 min on a desktop PC; we call this method THOMAS (THalamus Optimized Multi Atlas Segmentation). ⋯ Lastly, we evaluated the potential of this method for targeting the Vim nucleus for deep brain surgery and focused ultrasound thalamotomy by overlaying the Vim nucleus segmented from pre-operative data on post-operative data. The locations of the ablated region and active DBS contact corresponded well with the segmented Vim nucleus. Our fast, direct structural MRI based segmentation method opens the door for MRI guided intra-operative procedures like thalamotomy and asleep DBS electrode placement as well as for accurate quantification of thalamic nuclear volumes to follow progression of neurological disorders.
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Real-time functional magnetic resonance imaging (rt-fMRI) enables the update of various brain-activity measures during an ongoing experiment as soon as a new brain volume is acquired. However, the recorded Blood-oxygen-level dependent (BOLD) signal also contains physiological artifacts such as breathing and heartbeat, which potentially cause misleading false positive effects especially problematic in brain-computer interface (BCI) and neurofeedback (NF) setups. The low temporal resolution of echo planar imaging (EPI) sequences (which is in the range of seconds) prevents a proper separation of these artifacts from the BOLD signal. ⋯ Functional connectivity analysis using partial correlation showed a reduced standard error in MREG compared to SMS-EPI. Also, direct time course comparisons by down-sampling the MREG signal to the SMS-EPI temporal resolution showed lower variance in MREG. In general, we show that the higher temporal resolution is beneficial for fMRI time course modeling and this aspect can be exploited in offline application but also, is especially attractive, for real-time BCI and NF applications.