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Cortical thickness gradients in structural hierarchies.

Wagstyl K, Ronan L, Goodyer IM, Fletcher PC - Neuroimage (2015)

Bottom Line: Our results suggest that an easily measurable macroscopic brain parameter, namely, cortical thickness, is systematically related to cytoarchitecture and to the structural hierarchical organisation of the cortex.We argue that the measurement of cortical thickness gradients may become an important way to develop our understanding of brain structure-function relationships.The identification of alterations in such gradients may complement the observation of regionally localised cortical thickness changes in our understanding of normal development and neuropsychiatric illnesses.

View Article: PubMed Central - PubMed

Affiliation: Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, CB2 3EB, UK. Electronic address: kw350@cam.ac.uk.

No MeSH data available.


Related in: MedlinePlus

Boundaries and parcellation. (a) Addressing individual variability in atlas-defined boundaries. The dashed red line represents an atlas boundary between the orange area with hierarchical level 1 and the blue area with hierarchical level two. Randomly parcellated regions crossed by the red line are given the mean of the hierarchical levels.(b) The random parcellation process is repeated 10 times, averaging cortical thickness values across parcellations to mitigate gyral–sulcal thickness differences (Fig. 3).
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f0015: Boundaries and parcellation. (a) Addressing individual variability in atlas-defined boundaries. The dashed red line represents an atlas boundary between the orange area with hierarchical level 1 and the blue area with hierarchical level two. Randomly parcellated regions crossed by the red line are given the mean of the hierarchical levels.(b) The random parcellation process is repeated 10 times, averaging cortical thickness values across parcellations to mitigate gyral–sulcal thickness differences (Fig. 3).

Mentions: Automated parcellation schemes are to some extent limited by individual variability. For example, there is a two-fold intersubject variability in the surface area of V1 (Andrews et al., 1997), and many borders are not consistently associated with large-scale morphological landmarks (Welker, 1990; Amunts et al., 2007). To address the uncertainty over precise border locations, the cortex was randomly parcellated into 100 regions of approximately equal surface area; any region containing a border between different hierarchical levels was assigned the average value of the levels (Fig. 2a). By repeating random parcellation 10 times and averaging the thicknesses (Fig. 2b), we minimise the effect of folding (Fig. 3) and the bias of each individual random parcellation scheme.


Cortical thickness gradients in structural hierarchies.

Wagstyl K, Ronan L, Goodyer IM, Fletcher PC - Neuroimage (2015)

Boundaries and parcellation. (a) Addressing individual variability in atlas-defined boundaries. The dashed red line represents an atlas boundary between the orange area with hierarchical level 1 and the blue area with hierarchical level two. Randomly parcellated regions crossed by the red line are given the mean of the hierarchical levels.(b) The random parcellation process is repeated 10 times, averaging cortical thickness values across parcellations to mitigate gyral–sulcal thickness differences (Fig. 3).
© Copyright Policy - CC BY
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4401442&req=5

f0015: Boundaries and parcellation. (a) Addressing individual variability in atlas-defined boundaries. The dashed red line represents an atlas boundary between the orange area with hierarchical level 1 and the blue area with hierarchical level two. Randomly parcellated regions crossed by the red line are given the mean of the hierarchical levels.(b) The random parcellation process is repeated 10 times, averaging cortical thickness values across parcellations to mitigate gyral–sulcal thickness differences (Fig. 3).
Mentions: Automated parcellation schemes are to some extent limited by individual variability. For example, there is a two-fold intersubject variability in the surface area of V1 (Andrews et al., 1997), and many borders are not consistently associated with large-scale morphological landmarks (Welker, 1990; Amunts et al., 2007). To address the uncertainty over precise border locations, the cortex was randomly parcellated into 100 regions of approximately equal surface area; any region containing a border between different hierarchical levels was assigned the average value of the levels (Fig. 2a). By repeating random parcellation 10 times and averaging the thicknesses (Fig. 2b), we minimise the effect of folding (Fig. 3) and the bias of each individual random parcellation scheme.

Bottom Line: Our results suggest that an easily measurable macroscopic brain parameter, namely, cortical thickness, is systematically related to cytoarchitecture and to the structural hierarchical organisation of the cortex.We argue that the measurement of cortical thickness gradients may become an important way to develop our understanding of brain structure-function relationships.The identification of alterations in such gradients may complement the observation of regionally localised cortical thickness changes in our understanding of normal development and neuropsychiatric illnesses.

View Article: PubMed Central - PubMed

Affiliation: Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, CB2 3EB, UK. Electronic address: kw350@cam.ac.uk.

No MeSH data available.


Related in: MedlinePlus