A time-varying source connectivity approach to reveal human somatosensory information processing

Hu, Li; Zhang, Zhiguo; Hu, Yong

doi:10.1016/j.neuroimage.2012.03.094

Cited by 69 publications

(63 citation statements)

References 86 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Our estimation approach differs in a number of important respects from similar approaches (in, for instance, Havlicek et al, 2011; Hu et al, 2012; Milde et al, 2010). Firstly, use is made of the sEKFS instead of the first-order extended Kalman filter (Milde et al, 2010) or the cubature Kalman filter (Havlicek et al, 2011; Hu et al, 2012).…”

Section: Methodsmentioning

confidence: 99%

“…Models for electroencephalography (EEG) and magnetoencephalography (MEG) data have also been estimated with Kalman filters and smoothers (for a description, see Bar-Shalom & Fortmann, 1988), which provide more noise suppression than sliding windows and more stable estimates than recursive least squares (Milde et al, 2010; Vedel-Larsen et al, 2010). Importantly, work using time-varying VARs has similarly found that dynamic relations are present in task-related effective connectivity maps (Hemmelmann et al, 2009; Hu et al, 2012; Milde et al, 2010; Wacker et al, 2011). …”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

State space modeling of time-varying contemporaneous and lagged relations in connectivity maps

et al. 2016

View full text Add to dashboard Cite

Most connectivity mapping techniques for neuroimaging data assume stationarity (i.e., network parameters are constant across time), but this assumption does not always hold true. The authors provide a description of a new approach for simultaneously detecting time-varying (or dynamic) contemporaneous and lagged relations in brain connectivity maps. Specifically, they use a novel raw data likelihood estimation technique (involving a second-order extended Kalman filter/smoother embedded in a nonlinear optimizer) to determine the variances of the random walks associated with state space model parameters and their autoregressive components. The authors illustrate their approach with simulated and blood oxygen level-dependent functional magnetic resonance imaging data from 30 daily cigarette smokers performing a verbal working memory task, focusing on seven regions of interest (ROIs). Twelve participants had dynamic directed functional connectivity maps: Eleven had one or more time-varying contemporaneous ROI state loadings, and one had a time-varying autoregressive parameter. Compared to smokers without dynamic maps, smokers with dynamic maps performed the task with greater accuracy. Thus, accurate detection of dynamic brain processes is meaningfully related to behavior in a clinical sample.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

State space modeling of time-varying contemporaneous and lagged relations in connectivity maps

et al. 2016

View full text Add to dashboard Cite

show abstract

“…The permutation was executed 5000 times. The distributions of the pseudo- t -statistics from the reference population and the bootstrap p -value for the null hypothesis were generated.This procedure yielded time–frequency distributions in which the brain responses within the post-stimulus interval were significantly different from the responses in the reference interval (Hu et al, 2012; Peng et al, 2012). To address the problem of multiple comparisons, the significance level ( p -value) was corrected using a false discovery rate (FDR) procedure (Durka et al, 2004).…”

Section: Methodsmentioning

confidence: 99%

The temporal dynamics of visual working memory guidance of selective attention

Tan

Zhao

et al. 2014

Front. Behav. Neurosci.

View full text Add to dashboard Cite

The biased competition model proposes that there is top-down directing of attention to a stimulus matching the contents of working memory (WM), even when the maintenance of a WM representation is detrimental to target relevant performance. Despite many studies elucidating that spatial WM guidance can be present early in the visual processing system, whether visual WM guidance also influences perceptual selection remains poorly understood. Here, we investigated the electrophysiological correlates of early guidance of attention by WM in humans. Participants were required to perform a visual search task while concurrently maintaining object representations in their visual WM. Behavioral results showed that response times (RTs) were longer when the distractor in the visual search task was held in WM. The earliest WM guidance effect was observed in the P1 component (90–130 ms), with match trials eliciting larger P1 amplitude than mismatch trials. A similar result was also found in the N1 component (160–200 ms). These P1 and N1 effects could not be attributed to bottom-up perceptual priming from the presentation of a memory cue, because there was no significant difference in early event-related potential (ERP) component when the cue was merely perceptually identified but not actively held in WM. Standardized Low Resolution Electrical Tomography Analysis (sLORETA) showed that the early WM guidance occurred in the occipital lobe and the N1-related activation occurred in the parietal gyrus. Time-frequency data suggested that alpha-band event-related spectral perturbation (ERSP) magnitudes increased under the match condition compared with the mismatch condition only when the cue was held in WM. In conclusion, the present study suggests that the reappearance of a stimulus held in WM enhanced activity in the occipital area. Subsequently, this initial capture of attention by WM could be inhibited by competing visual inputs through attention re-orientation, reflecting by the alpha-band rhythm.

show abstract

“…To assess the group-level significance of the SEP waveforms obtained in the different conditions, a bootstrapping method was used to compare the signal amplitude within the poststimulus interval to the signal amplitude within the prestimulus interval (Delorme and Makeig 2004;Durka et al 2004;Hu et al 2012). To address the problem of multiple comparisons, the significance level was corrected by a false discovery rate (FDR) procedure (Durka et al 2004).…”

Section: Methodsmentioning

confidence: 99%

“…In addition, short-lasting epochs (Ͻ10 consecutive time bins) were discarded. See Hu et al (2012) for additional details on this procedure.…”

Section: Methodsmentioning

confidence: 99%

Unmasking the obligatory components of nociceptive event-related brain potentials

Mouraux

Paepe

Marot

et al. 2013

Journal of Neurophysiology

View full text Add to dashboard Cite

-It has been hypothesized that the human cortical responses to nociceptive and nonnociceptive somatosensory inputs differ. Supporting this view, somatosensory-evoked potentials (SEPs) elicited by thermal nociceptive stimuli have been suggested to originate from areas 1 and 2 of the contralateral primary somatosensory (S1), operculo-insular, and cingulate cortices, whereas the early components of nonnociceptive SEPs mainly originate from area 3b of S1. However, to avoid producing a burn lesion, and sensitize or fatigue nociceptors, thermonociceptive SEPs are typically obtained by delivering a small number of stimuli with a large and variable interstimulus interval (ISI). In contrast, the early components of nonnociceptive SEPs are usually obtained by applying many stimuli at a rapid rate. Hence, previously reported differences between nociceptive and nonnociceptive SEPs could be due to differences in signal-to-noise ratio and/or differences in the contribution of cognitive processes related, for example, to arousal and attention. Here, using intraepidermal electrical stimulation to selectively activate A␦-nociceptors at a fast and constant 1-s ISI, we found that the nociceptive SEPs obtained with a long ISI are no longer identified, indicating that these responses are not obligatory for nociception. Furthermore, using a blind source separation, we found that, unlike the obligatory components of nonnociceptive SEPs, the obligatory components of nociceptive SEPs do not receive a significant contribution from a contralateral source possibly originating from S1. Instead, they were best explained by sources compatible with bilateral operculo-insular and/or cingulate locations. Taken together, our results indicate that the obligatory components of nociceptive and nonnociceptive SEPs are fundamentally different.pain; nociception; event-related potentials; primary somatosensory cortex; laser-evoked potentials; intraepidermal stimulation FOR THE PAST 30 YEARS, a large number of studies have aimed at understanding the neural mechanisms underlying the processing of nociceptive input and the perception of pain in the human cortex. Most of these studies have relied on noninvasive techniques to sample brain activity, such as electroencephalography (EEG), magnetoencephalography (MEG), functional magnetic resonance imaging (fMRI), and positron emission tomography (PET) (Bushnell and Apkarian 2005;Garcia-Larrea et al. 2003;Kakigi et al. 2005;Peyron et al. 2002;Treede et al. 1999). With these techniques, it has been shown repeatedly that nociceptive stimuli elicit responses in a wide array of brain areas, including postcentral, operculo-insular, and cingulate areas. A number of investigators have considered that the combined activation of these brain regions is responsible for the transformation of nociceptive input into a conscious perception of pain, in particular, the coding of pain intensity and pain unpleasantness (Boly et al.

show abstract

A time-varying source connectivity approach to reveal human somatosensory information processing

Cited by 69 publications

References 86 publications

State space modeling of time-varying contemporaneous and lagged relations in connectivity maps

State space modeling of time-varying contemporaneous and lagged relations in connectivity maps

The temporal dynamics of visual working memory guidance of selective attention

Unmasking the obligatory components of nociceptive event-related brain potentials

Contact Info

Product

Resources

About