Surface-based mixed effects multilevel analysis of grouped human electrocorticography

Kadipasaoglu, Cihan; Baboyan, Vatche G.; Conner, Christopher R.; Chen, G.; Saad, Ziad S.; Tandon, Nitin

doi:10.1016/j.neuroimage.2014.07.006

Cited by 50 publications

(81 citation statements)

References 60 publications

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“…S8 and S9. To obtain a precise spatial localization for continuous maps, we minimized the circular kernel size by using the geodesic distance (38) and logistic weighting function. Of note, masks centered at 1.5-cm geodesic distance were completely independent, having no shared nodes.…”

Section: Discussionmentioning

confidence: 99%

Four-dimensional maps of the human somatosensory system

Avanzini

Abdollahi

Sartori

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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A fine-grained description of the spatiotemporal dynamics of human brain activity is a major goal of neuroscientific research. Limitations in spatial and temporal resolution of available noninvasive recording and imaging techniques have hindered so far the acquisition of precise, comprehensive four-dimensional maps of human neural activity. The present study combines anatomical and functional data from intracerebral recordings of nearly 100 patients, to generate highly resolved four-dimensional maps of human cortical processing of nonpainful somatosensory stimuli. These maps indicate that the human somatosensory system devoted to the hand encompasses a widespread network covering more than 10% of the cortical surface of both hemispheres. This network includes phasic components, centered on primary somatosensory cortex and neighboring motor, premotor, and inferior parietal regions, and tonic components, centered on opercular and insular areas, and involving human parietal rostroventral area and ventral medial-superior-temporal area. The technique described opens new avenues for investigating the neural basis of all levels of cortical processing in humans.A detailed description of the spatiotemporal dynamics of human brain activity is a major goal of neuroscientific research. However, it has been impossible so far to attain both high spatial and temporal resolution using the available noninvasive recording and imaging techniques. Hence, a precise and comprehensive four-dimensional cartography of human neural activity has not yet been obtained. High spatial resolution, provided by neuroimaging techniques such as functional magnetic resonance imaging (fMRI), is crucial for highlighting the topographical organization of specific areas (e.g., somatotopy of sensorimotor areas) as well as identifying the nodes of brain networks endowed with specific functional properties (1). It is not sufficient, however, to know which nodes are active; information is also needed about the local dynamics of the nodes, as well as the relative timing of their activity, to fully understand human brain functions (2, 3). Even if the temporal resolution of electroencephalography (EEG) and magnetoencephalography (MEG) allowed one to observe the intra-and interareal dynamics, to date such recordings remain too poor in localization power (1-2 cm) (3, 4). Combining EEG and fMRI has been suggested as a solution, using EEG to determine the temporal dynamics within and between the areas identified with fMRI (5). However, the disparate nature of the two signals recorded (hemodynamic for fMRI, electrical for EEG) creates discrepancies in the results that prevent precise matching of these methods (3).Invasive intracranial EEG offers a unique opportunity to observe human brain activity with an unparalleled combination of spatial and temporal resolution. Depending on the electrodes used, two kinds of recordings can be made: (i) intraparenchymal recordings, also called stereo-EEG (sEEG) (6), obtained using stereotactically inserted needle-like electrodes...

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Section: Discussionmentioning

confidence: 99%

Four-dimensional maps of the human somatosensory system

Avanzini

Abdollahi

Sartori

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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“…An omnibus test was run to assess whether there were differences between brain regions and specific post-hoc tests quantified the differences between selected brain regions, after false discovery rate (FDR) correction was applied to account for multiple comparisons across brain regions (Benjamini and Hochberg, 1995). The combination of LMEM and surface-based realignment has been shown to be highly accurate and sensitive to the underlying effect of interest (Kadipasaoglu et al, 2014; Mak-McCully et al, 2015). …”

Section: Methodsmentioning

confidence: 99%

Spatiotemporal characteristics of sleep spindles depend on cortical location

2017

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Since their discovery almost one century ago, sleep spindles, 0.5–2 s long bursts of oscillatory activity at 9–16 Hz during NREM sleep, have been thought to be global and relatively uniform throughout the cortex. Recent work, however, has brought this concept into question but it remains unclear to what degree spindles are global or local and if their properties are uniform or location-dependent. We addressed this question by recording sleep in eight patients undergoing evaluation for epilepsy with intracranial electrocorticography, which combines high spatial resolution with extensive cortical coverage. We find that spindle characteristics are not uniform but are strongly influenced by the underlying cortical regions, particularly for spindle density and fundamental frequency. We observe both highly isolated and spatially distributed spindles, but in highly skewed proportions: while most spindles are restricted to one or very few recording channels at any given time, there are spindles that occur over widespread areas, often involving lateral prefrontal cortices and superior temporal gyri. Their co-occurrence is affected by a subtle but significant propagation of spindles from the superior prefrontal regions and the temporal cortices towards the orbitofrontal cortex. This work provides a brain-wide characterization of sleep spindles as mostly local graphoelements with heterogeneous characteristics that depend on the underlying cortical area. We propose that the combination of local characteristics and global organization reflects the dual properties of the thalamo-cortical generators and provides a flexible framework to support the many functions ascribed to sleep in general and spindles specifically.

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“…Surface based normalization has been well established in functional imaging (Saad et al, ), and we borrow heavily from these methods. Normalizing surface anatomy in this manner has also been previously adapted in one study for iEEG data (Kadipasaoglu et al, ). We used surface based normalization to standardize all surfaces to a standard brain (Kadipasaoglu et al, ), then used icosahedral standardized sampling to establish an anatomical correspondence of vertex sets across patients (Saad et al, ).…”

Section: Discussionmentioning

confidence: 99%

“…We reconstructed surfaces from preoperative MRI images using FreeSurfer (Fischl, ). We created surface models using “recon‐all.” After creating a surface for each participant, we created a smoothed pial surface using “localGI” that traverses cortical sulci (Figure b) (Kadipasaoglu et al, ; Schaer et al, ). The smoothed pial surface is a smoothed version of the surface which follows the gyral crowns and traverses cortical sulci.…”

Section: Methodsmentioning

confidence: 99%

“…We compare the accuracy of localization between registration algorithms and confirm that the inclusion of anchor points enables accurate electrode localization. We then address the group level analysis problem by defining uniformly distributed ROI center points on a standard template brain, then normalizing each participant's brain to the template using surface‐based registration (Dykstra et al, ; Fischl, Sereno, & Dale, ; Kadipasaoglu et al, ) and a standardized mesh resampling (Saad & Reynolds, ). Consequently, each ROI center has a spatially analogous location across participants which is independent of a priori assumptions regarding function, and we are able to aggregate electrode data into ROIs for comparison across participants.…”

Section: Introductionmentioning

confidence: 99%

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Surface based electrode localization and standardized regions of interest for intracranial EEG

Trotta

Cocjin

Whitehead

et al. 2017

Human Brain Mapping

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Intracranial recordings captured from subdural electrodes in patients with drug resistant epilepsy offer clinicians and researchers a powerful tool for examining neural activity in the human brain with high spatial and temporal precision. There are two major challenges, however, to interpreting these signals both within and across individuals. Anatomical distortions following implantation make accurately identifying the electrode locations difficult. In addition, because each implant involves a unique configuration, comparing neural activity across individuals in a standardized manner has been limited to broad anatomical regions such as cortical lobes or gyri. We address these challenges here by introducing a semi-automated method for localizing subdural electrode contacts to the unique surface anatomy of each individual, and by using a surface-based grid of regions of interest (ROIs) to aggregate electrode data from similar anatomical locations across individuals. Our localization algorithm, which uses only a postoperative CT and preoperative MRI, builds upon previous spring-based optimization approaches by introducing manually identified anchor points directly on the brain surface to constrain the final electrode locations. This algorithm yields an accuracy of 2 mm. Our surface-based ROI approach involves choosing a flexible number of ROIs with different spatial resolutions. ROIs are registered across individuals to represent identical anatomical locations while accounting for the unique curvature of each brain surface. This ROI based approach therefore enables group level statistical testing from spatially precise anatomical regions.

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Surface-based mixed effects multilevel analysis of grouped human electrocorticography

Cited by 50 publications

References 60 publications

Four-dimensional maps of the human somatosensory system

Four-dimensional maps of the human somatosensory system

Spatiotemporal characteristics of sleep spindles depend on cortical location

Surface based electrode localization and standardized regions of interest for intracranial EEG

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