Spatial maps in frontal and prefrontal cortex

Hagler, Donald J.; Sereno, Martin I.

doi:10.1016/j.neuroimage.2005.08.058

Cited by 224 publications

(207 citation statements)

References 38 publications

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“…Our methods for cortical surface-based group analysis of fMRI data have been described previously (Hagler and Sereno, 2006), but they are briefly summarized here. For each subject, cortical surface models were generated from high-resolution (1 mm 3 ) structural MRI scans using FreeSurfer (Dale et al, 1999;Fischl et al, 1999a).…”

Section: Acquisition and Analysis Of Fmri Datamentioning

confidence: 99%

Smoothing and cluster thresholding for cortical surface-based group analysis of fMRI data

2006

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Cortical surface-based analysis of fMRI data has proven to be a useful method with several advantages over 3-dimensional volumetric analyses. Many of the statistical methods used in 3D analyses can be adapted for use with surface-based analyses. Operating within the framework of the FreeSurfer software package, we have implemented a surface-based version of the cluster-size exclusion method used for multiple comparisons correction. Furthermore, we have a developed a new method for generating regions of interest on the cortical surface using a sliding threshold of cluster exclusion followed by cluster growth. Cluster size limits for multiple probability thresholds were estimated using random field theory and validated with Monte Carlo simulation. A prerequisite of RFT or cluster-size simulation is an estimate of the smoothness of the data. In order to estimate the intrinsic smoothness of group analysis statistics, independent of true activations, we conducted a group analysis of simulated noise datasets. Because smoothing on a cortical surface mesh is typically implemented using an iterative method, rather than directly applying a Gaussian blurring kernel, it is also necessary to determine the width of the equivalent Gaussian blurring kernel as a function of smoothing steps. Iterative smoothing has previously been modeled as continuous heat diffusion, providing a theoretical basis for predicting the equivalent kernel width, but the predictions of the model were not empirically tested. We generated an empirical heat diffusion kernel width function by performing surface-based smoothing simulations and found a large disparity between the expected and actual kernel widths.

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Section: Acquisition and Analysis Of Fmri Datamentioning

confidence: 99%

Smoothing and cluster thresholding for cortical surface-based group analysis of fMRI data

2006

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“…Various gradients in patterns of connectivity and function have been reported within these three regions (Petrides, 1987, 1991; Wilson et al, 1993; Ó Scalaidhe et al, 1997; Hirsch et al, 2001; Denys et al, 2004; Barbas et al, 2005; Hagler and Sereno, 2006; Nelissen et al, 2005; Noppeney et al, 2005). However, there are no standardized quantitative data on pyramidal cell structure within these different gradients in the gPFC.…”

Section: Introductionmentioning

confidence: 99%

Pyramidal cells in prefrontal cortex of primates: marked differences in neuronal structure among species

Elston¹,

Benavides-Piccione

Elston³

et al. 2011

Front. Neuroanat.

103

105

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The most ubiquitous neuron in the cerebral cortex, the pyramidal cell, is characterized by markedly different dendritic structure among different cortical areas. The complex pyramidal cell phenotype in granular prefrontal cortex (gPFC) of higher primates endows specific biophysical properties and patterns of connectivity, which differ from those in other cortical regions. However, within the gPFC, data have been sampled from only a select few cortical areas. The gPFC of species such as human and macaque monkey includes more than 10 cortical areas. It remains unknown as to what degree pyramidal cell structure may vary among these cortical areas. Here we undertook a survey of pyramidal cells in the dorsolateral, medial, and orbital gPFC of cercopithecid primates. We found marked heterogeneity in pyramidal cell structure within and between these regions. Moreover, trends for gradients in neuronal complexity varied among species. As the structure of neurons determines their computational abilities, memory storage capacity and connectivity, we propose that these specializations in the pyramidal cell phenotype are an important determinant of species-specific executive cortical functions in primates.

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“…However, in the last decade or so, several additional retinotopic maps were discovered in frontal cortex [Hagler and Sereno, 2006; Hagler et al, 2007; Kastner et al, 2007], parietal cortex [Huk et al, 2002; Schluppeck et al, 2005; Sereno and Huang, 2006; Sereno et al, 2001, 2003; Silver et al, 2005; Silver and Kastner, 2009; Swisher et al, 2007] and temporal cortex [Huang and Sereno, 2013; Sereno et al, 2003]. The higher‐level retinotopic maps in frontal cortex were originally discovered using working memory and spatial attention paradigms [Hagler and Sereno, 2006; Kastner et al, 2007]. Those studies suggested that topological maps may serve as a convenient method of allocating working memory, or maintaining pointers to specific content, even (or more so!)…”

Section: Discussionmentioning

confidence: 99%

“…The time reversed scans were time‐shifted to compensate for hemodynamic delay before averaging. The averaged time courses were analyzed using linear Fourier methods [Bandettini et al, 1992; Engel et al, 1994, 1997; Hagler and Sereno, 2006; Sereno et al, 1995], which can be exactly recast as a general linear model. Voxels preferentially responding to a particular point in the stimulus cycle will show higher amplitude at the stimulus frequency than at any other 'noise' frequency, after excluding (i.e., linearly regressing out) the three lowest temporal frequencies as motion artifact.…”

Section: Methodsmentioning

confidence: 99%

“…In contrast to lower‐level maps, however, localized activity in higher‐level maps is affected as much by spatial attention as by the spatial characteristics of the stimuli [Saygin and Sereno, 2008]. In frontal cortex, there is evidence that topological maps may serve as a convenient method of allocating working memory, or maintaining pointers to specific content [Hagler and Sereno, 2006], even for tasks not overtly referencing position; for example, the exact areas that showed more robust activity during an identity two‐back task (in which location was ignored), than during a location two‐back task (in which identity was ignored) turned out to contain retinotopic maps. A significant role for topological maps in other complex mental operations has been suggested before [Simmons and Barsalou, 2003; Thivierge and Marcus, 2007], but direct neuroimaging evidence supporting this idea has been scant, and mostly confined to somatosensory cortex.…”

Section: Introductionmentioning

confidence: 99%

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Areas activated during naturalistic reading comprehension overlap topological visual, auditory, and somatotomotor maps

Sood

Sereno

2016

Human Brain Mapping

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Cortical mapping techniques using fMRI have been instrumental in identifying the boundaries of topological (neighbor‐preserving) maps in early sensory areas. The presence of topological maps beyond early sensory areas raises the possibility that they might play a significant role in other cognitive systems, and that topological mapping might help to delineate areas involved in higher cognitive processes. In this study, we combine surface‐based visual, auditory, and somatomotor mapping methods with a naturalistic reading comprehension task in the same group of subjects to provide a qualitative and quantitative assessment of the cortical overlap between sensory‐motor maps in all major sensory modalities, and reading processing regions. Our results suggest that cortical activation during naturalistic reading comprehension overlaps more extensively with topological sensory‐motor maps than has been heretofore appreciated. Reading activation in regions adjacent to occipital lobe and inferior parietal lobe almost completely overlaps visual maps, whereas a significant portion of frontal activation for reading in dorsolateral and ventral prefrontal cortex overlaps both visual and auditory maps. Even classical language regions in superior temporal cortex are partially overlapped by topological visual and auditory maps. By contrast, the main overlap with somatomotor maps is restricted to a small region on the anterior bank of the central sulcus near the border between the face and hand representations of M‐I. Hum Brain Mapp 37:2784–2810, 2016. © 2016 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

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Spatial maps in frontal and prefrontal cortex

Cited by 224 publications

References 38 publications

Smoothing and cluster thresholding for cortical surface-based group analysis of fMRI data

Smoothing and cluster thresholding for cortical surface-based group analysis of fMRI data

Pyramidal cells in prefrontal cortex of primates: marked differences in neuronal structure among species

Areas activated during naturalistic reading comprehension overlap topological visual, auditory, and somatotomotor maps

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