Quantitation of the Grey Matter Flow Response during visual activation: Partial volume correction by dynamic fitting

Abstract& Human and nonhuman animal research has outlined the neural regions that support saccadic eye movements. The aim of the current work was to outline the sequence by which distinct neural regions come on-line to support goal-directed saccade execution and error-related feedback. To achieve this, we obtained behavioral responses via eye movement recordings and neural responses via magnetoencephalography (MEG), concurrently, while participants performed an antisaccade task. Neural responses were examined with respect to the onset of the saccadic eye movements. Frontal eye field and visual cortex activity distinguished subsequently successful goal-directed saccades from (correct and erroneous) reflexive saccades prior to the deployment of the eye movement. Activity in the same neural regions following the saccadic movement distinguished correct from incorrect saccadic responses. Error-related activity in the frontal eye fields preceded that from visual regions, suggesting a potential feedback network that may drive corrective eye movements. This work provides the first empirical demonstration of simultaneous remote eyetracking and MEG recording. The coupling of behavioral and neuroimaging technologies, used here to characterize dynamic brain networks underlying saccade execution and error-related feedback, demonstrates a novel within-paradigm converging evidence approach by which to outline the neural underpinnings of cognition. &

Section: Discussionmentioning

confidence: 55%

Section: Er-sam Source Activitymentioning

confidence: 99%

See 1 more Smart Citation

Spatio-temporal Brain Dynamics Underlying Saccade Execution, Suppression, and Error-related Feedback

Herdman

Ryan

2007

“…Nonetheless, the early TMS effect (50 msec) and the relatively simple left-right judgment required in this task do not support this hypothesis. A second plausible explanation for the present results lies in the fact that virtual lesions of the OC could have altered an eye position signal, known to be present in the dorsal extrastriate occipital region (Rosenbluth & Allman, 2002;Trotter & Celebrini, 1999;Law, Svarer, Rostrup, & Paulson, 1998;Galletti, Battaglini, & Fattori, 1995) and further used in the processing of auditory spatial cues. We know from behavioral experiments that eye position influences the localization of sounds (Lewald, 1998;Lewald & Ehrenstein, 1996).…”

Section: Contribution Of the Oc In Spatial Sound Processingmentioning

confidence: 82%

Time-course of Posterior Parietal and Occipital Cortex Contribution to Sound Localization

Collignon

Davare²,

Volder³

et al. 2008

It has been suggested that both the posterior parietal cortex (PPC) and the extrastriate occipital cortex (OC) participate in the spatial processing of sounds. However, the precise time-course of their contribution remains unknown, which is of particular interest, considering that it could give new insights into the mechanisms underlying auditory space perception. To address this issue, we have used event-related transcranial magnetic stimulation (TMS) to induce virtual lesions of either the right PPC or right OC at different delays in subjects performing a sound lateralization task. Our results confirmed that these two areas participate in the spatial processing of sounds. More precisely, we found that TMS applied over the right OC 50 msec after the stimulus onset significantly impaired the localization of sounds presented either to the right or to the left side. Moreover, right PPC virtual lesions induced 100 and 150 msec after sound presentation led to a rightward bias for stimuli delivered on the center and on the left side, reproducing transiently the deficits commonly observed in hemineglect patients. The finding that the right OC is involved in sound processing before the right PPC suggests that the OC exerts a feedforward influence on the PPC during auditory spatial processing.

“…Cuneus has been primarily associated with visual processing, although it has been also involved in various cognitive functions including cognitive control (Haldane, Cunningham, Androutsos, & Frangou, 2008) and working memory (Bluhm et al, 2010). In particular, the cuneus participates in monitoring changes in ocular position in response to self-generated eye movements (Law, Svarer, Rostrup, & Paulson, 1988) and is involved in the bottom-up control of visuospatial selective attention (Hahn, Ross, & Stein, 2006), two processes that might be involved in the processing and implicit detection of grammatical violations in the sequence. Although cuneus activation was previously reported during sequential information processing at wake (e.g., Schubotz & von Cramon, 2001), it is not commonly seen as a critical component of sequence learning, and further studies are needed to clarify its implication in probabilistic sequence processing.…”

Section: Discussionmentioning

confidence: 99%

Sleep-dependent Neurophysiological Processes in Implicit Sequence Learning

Urbain

Schmitz

Schmidt

et al. 2013

Abstract■ Behavioral studies have cast doubts about the role that posttraining sleep may play in the consolidation of implicit sequence learning. Here, we used event-related fMRI to test the hypothesis that sleep-dependent functional reorganization would take place in the underlying neural circuits even in the possible absence of obvious behavioral changes. Twenty-four healthy human adults were scanned at Day 1 and then at Day 4 during an implicit probabilistic serial RT task. They either slept normally (RS) or were sleep-deprived (SD) on the first posttraining night. Unknown to them, the sequential structure of the material was based on a probabilistic finite-state grammar, with 15% chance on each trial of replacing the rules-based grammatical (G) stimulus with a nongrammatical (NG) one. Results indicated a gradual differentiation across sessions between RTs (faster RTs for G than NG), together with NG-related BOLD responses reflecting sequence learning. Similar behavioral patterns were observed in RS and SD participants at Day 4, indicating time-but not sleep-dependent consolidation of performance. Notwithstanding, we observed at Day 4 in the RS group a diminished differentiation between G-and NG-related neurophysiological responses in a set of cortical and subcortical areas previously identified as being part of the network involved in implicit sequence learning and its offline processing during sleep, indicating a sleep-dependent processing of both regular and deviant stimuli. Our results suggest the sleep-dependent development of distinct neurophysiological processes subtending consolidation of implicit motor sequence learning, even in the absence of overt behavioral differences. ■