2013
DOI: 10.1523/jneurosci.4882-12.2013
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Connectivity-Based Parcellation of the Human Frontal Pole with Diffusion Tensor Imaging

Abstract: The human frontal pole (FP) approximately corresponds to Brodmann's area 10 and is a highly differentiated cortical area with unique cytoarchitectonic characteristics. However, its functional diversity is highly suggestive of the existence of functional subregions. Based on anatomical connection patterns derived from diffusion tensor imaging data, we applied a spectral clustering algorithm to parcellate the human right FP into orbital (FPo), lateral (FPl), and medial (FPm) subregions. This parcellation scheme … Show more

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Cited by 101 publications
(124 citation statements)
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“…The negative correlation between FA and neuroticism is consistent with previous findings showing a link between altered DTI metrics in the fronto-cingulate and limbic regions and neuroticism (96)(97)(98). There is also evidence of an association between abnormal DTI metrics in the cingulum-hippocampus tracts and increased anxiety scores (99).…”
Section: Discussionsupporting
confidence: 89%
“…The negative correlation between FA and neuroticism is consistent with previous findings showing a link between altered DTI metrics in the fronto-cingulate and limbic regions and neuroticism (96)(97)(98). There is also evidence of an association between abnormal DTI metrics in the cingulum-hippocampus tracts and increased anxiety scores (99).…”
Section: Discussionsupporting
confidence: 89%
“…The fronto-polar cortex (BA 10) -a structurally and functionally heterogeneous region (Kringelbach and Rolls, 2004;Gilbert et al, 2010a) has been consistently subdivided based on cytoarchitectonic features, on anatomical and functional resting state connectivity patterns, and on meta-analyses of fMRI activation experiments, in two distinct interconnected subdivisions -a medial one as part of a network underlying affective processing and social cognition and a lateral one implicated in working memory, cognition and perception (Blundau et al, 2014;Moayedi et al, 2014;Kringelbach and Rolls 2004;Gilbert et al, 2006;Gilbert et al, 2010). Some studies distinguished three subdomains (Ongür et al, 2003;Liu H. et al 2013; although less evident in some subjects, see Moayedi et al, 2014), the medial part being further subdivided (Liu H. et al, 2013) in the orbital region (FPo) showing greater connection probability to social-emotional network (OFC, temporal pole, amygdala) and more medial part (FPm) showing stronger connection with midline regions of the default mode network (ACC, PCC/precuneus, MPFC) involved in internally focused tasks such as self-referential thought (Gusnard et al, 2001) and autobiographical memory retrieval and prospecting the future (Schacter et al, 2007). The meta-analysis showed that high NE-trait individuals tend to exhibit a lower GM within the medial OFC part overlapping mainly the above-mentioned orbital subdivision and increased GM in the left amygdala/ anterior parahippocampal regions (i.e.…”
Section: Discussionmentioning
confidence: 99%
“…One study examined the area (Bjornebekk et al, 2013), others focused on the whole amygdalar volume (manually delineated, e.g. Cherbuin et al, 2008), thus overlooking possible variations between the nuclei, or investigated only restricted subregions (Wang et al, 2013).…”
Section: Amygdalamentioning
confidence: 99%
“…MAI); RAI can be an order of magnitude faster than AMI Thirion et al, 2014 Akaike's information criterion (AIC) derived from information theory, it finds the best number of clusters by acknowledging the trade-off between goodnessof-fit and model complexity (i.e, number of clusters); it is based on a probabilistic model and a measure of complexity; this penalty for the cluster numbers is aimed at preventing overfitting, yet is independent of the sample size; as it is a relative measure, it judges the absolute quality of the finally selected model Zalesky et al, 2010 Bayesian information criterion (BIC) despite many similarities to AIC, BIC is motivated by a Bayesian approach to model selection; it penalizes the model complexity (i.e, number of clusters, 5AIC); in comparison to AIC, BIC imposes higher costs on more complex models (i.e, small cluster numbers are privileged) Thirion et al, 2014 Cram er's V assesses the statistical correlation between two groups of discrete values (i.e, Liu et al, 2013;Solano-Castiella et al, 2011 r Eickhoff et al r r 4784 r…”
Section: Table I Main Characteristics Of Cluster Validity Criteria Imentioning
confidence: 99%