Brodmann's Areas 17 and 18 Brought into Stereotaxic Space—Where and How Variable?

Amunts, Katrin; Maliković, Aleksandar; Mohlberg, Hartmut; Schormann, Thorsten; Zilles, Karl

doi:10.1006/nimg.1999.0516

Cited by 628 publications

(606 citation statements)

References 86 publications

Supporting

Mentioning

575

Contrasting

Unclassified

Order By: Relevance

“…The layout of the cortical areas is in agreement with previous fMRI (DeYoe et al, 1996;Engel et al, 1997;Sereno et al, 1995;Smith et al, 1998;Tootell et al, 1997), positron emission tomography (Fox et al, 1987;Hasnain et al, 1998Hasnain et al, , 2001), lesion studies (Holmes, 1945;Horton and Hoyt, 1991;Wong and Sharpe, 1999), and cytoarchitectonic maps of Brodmann's area 17 and 18 (Amunts et al, 2000;Clarke and Miklossy, 1990).…”

Section: Discussionsupporting

confidence: 88%

“…6 provides a validation of the method described here. In the calcarine sulcus a large region of negative field sign (mirror image) is present, around which regions opposite field sign can be distinguished corresponding to the known layout of areas V1 and V2 (Amunts et al, 2000;Clarke and Miklossy, 1990;Gilissen et al, 1995;Gilissen and Zilles, 1996;Holmes, 1945;Horton and Hoyt, 1991;Rademacher et al, 1993;Stensaas et al, 1974;Wong Fig. 6.…”

Section: Volumetric Visual Field Sign Identificationmentioning

confidence: 99%

See 1 more Smart Citation

Automatic volumetric segmentation of human visual retinotopic cortex

et al. 2003

View full text Add to dashboard Cite

Previous identification of early visual cortical areas in humans with phase-encoded retinotopic mapping techniques have relied on an accurate cortical surface reconstruction. Here a 3D phase-encoded retinotopic mapping technique that does not require a reconstruction of the cortical surface is demonstrated. The visual field sign identification is completely automatic and the method directly supplies volumes for a region-of-interest analysis, facilitating the application of cortical mapping to a wider population. A validation of the method is provided by simulations and comparison to cortical surface-based methodology.

show abstract

Section: Discussionsupporting

confidence: 88%

Section: Volumetric Visual Field Sign Identificationmentioning

confidence: 99%

Automatic volumetric segmentation of human visual retinotopic cortex

et al. 2003

View full text Add to dashboard Cite

show abstract

“…However, it has been recommended that a probabilistic model be used when characterizing the location of an activated voxel (Fox et al, 1998;Amunts et al, 2000;Rademacher et al, 2001a) due in part to the inherent variability involved in functional neuroimaging data. For instance, variability is introduced during steps such as postprocessing, filtering, motion correction, and normalization.…”

Section: Introductionmentioning

confidence: 99%

“…The ROIs included MPMC (pre-SMA and SMA), LPMC (PMd and PMv) and SMC (M1 and S1). Because it has been recommended that a probabilistic model be used when characterizing the location of an activated voxel (Fox et al, 1998;Amunts et al, 2000;Rademacher et al, 2001a), we then used the activation likelihood estimation (ALE) method developed by Turkeltaub et al (2002) to localize the distributions of the activation foci in stereotaxic space. This technique is based on the assumption that a single coordinate represents a probability distribution, which allows us to determine the likelihood that at least one activation focus lies within any given voxel.…”

Section: Introductionmentioning

confidence: 99%

Three-dimensional locations and boundaries of motor and premotor cortices as defined by functional brain imaging: A meta-analysis

et al. 2006

View full text Add to dashboard Cite

The mesial premotor cortex (pre-supplementary motor area and supplementary motor area proper), lateral premotor cortex (dorsal premotor cortex and ventral premotor cortex), and primary sensorimotor cortex (primary motor cortex and primary somatosensory cortex) have been identified as key cortical areas for sensorimotor function. However, the three-dimensional (3-D) anatomic boundaries between these regions remain unclear. In order to clarify the locations and boundaries for these six sensorimotor regions, we surveyed 126 articles describing pre-supplementary motor area, supplementary motor area proper, dorsal premotor cortex, ventral premotor cortex, primary motor cortex, and primary somatosensory cortex. Using strict inclusion criteria, we recorded the reported normalized stereotaxic coordinates (Talairach and Tournoux or MNI) from each experiment. We then computed the probability distributions describing the likelihood of activation, and characterized the shape, extent, and area of each sensorimotor region in 3-D. Additionally, we evaluated the nature of the overlap between the six sensorimotor regions. Using the findings from this meta-analysis, along with suggestions and guidelines of previous researchers, we developed the Human Motor Area Template (HMAT) that can be used for ROI analysis.

show abstract

“…Using anatomical landmarks such as the lingual gyrus and the calcarine sulcus, aided by peak coordinates cited in other studies (Büchel and Friston, 1997;Amunts et al, 2000;Sunaert et al, 2000;Kastner et al, 2001), we defined regions of activation in the left hemisphere for V1, V2 and V5.…”

Section: Image Analysismentioning

confidence: 99%

Connectivity analysis with structural equation modelling: an example of the effects of voxel selection

Gonçalves

Hall

2003

NeuroImage

View full text Add to dashboard Cite

Structural equation modelling (SEM) of neuroimaging data is commonly applied to a network of distributed brain regions. We applied SEM to an fMRI dataset to identify condition-specific effects in a simple experiment composed of visual stimulation and baseline conditions. The visual network was composed of three well-defined anatomical regions (V1, V2 and V5) and three path connections (V1→V2, V1→V5 and V2→V5). This network was used to test four hypotheses: (1) whether the condition-specific effects for all three connections vary according to the data selected for modelling; (2) whether the 'summary' measures that are often used are indeed appropriate; (3) whether measures taken from the voxel timecourse can reliably predict the condition-specific effects for each one of the three path connections; (4) whether all voxels within an anatomical region yield equivalent SEM outcomes. There was some variability in the significance of the condition-specific effects across randomly-selected voxels within regions. However, the SEM outcome from the 'summary' measures was comparable to the most frequent pattern of conditionspecific effects. Magnitude, delay, spread and goodness-of-fit measures taken from a gamma fit to the voxel timecourses predicted reliably the significance of the SEM condition-specific effects for each connection. This result enabled us to identify spatially coherent regions at the boundaries of V2 that displayed different condition-specific effects from those seen in the majority of the voxels.Although the generality of these results awaits further investigation, this example highlights a number of important issues for SEM. We have provided further evidence that the SEM outcome does vary somewhat according to the voxels selected and that, while the use of summary measures can give a generalised view of the connectivity pattern, they could fail to capture functional differences within specialised areas.

show abstract

Brodmann's Areas 17 and 18 Brought into Stereotaxic Space—Where and How Variable?

Cited by 628 publications

References 86 publications

Automatic volumetric segmentation of human visual retinotopic cortex

Automatic volumetric segmentation of human visual retinotopic cortex

Three-dimensional locations and boundaries of motor and premotor cortices as defined by functional brain imaging: A meta-analysis

Connectivity analysis with structural equation modelling: an example of the effects of voxel selection

Contact Info

Product

Resources

About