Connectivity-Based Parcellation of Broca's Area

Abstract

It is generally agreed that the cerebral cortex can be segregated into structurally and functionally distinct areas. Anatomical subdivision of Broca's area has been achieved using different microanatomical criteria, such as cytoarchitecture and distribution of neuroreceptors. However, brain function also strongly depends upon anatomical connectivity, which therefore forms a sensible criterion for the functio-anatomical segregation of cortical areas. Diffusion-weighted magnetic resonance (MR) imaging offers the opportunity to apply this criterion in the individual living subject. Probabilistic tractographic methods provide excellent means to extract the connectivity signatures from diffusion-weighting MR data sets. The correlations among these signatures may then be used by an automatic clustering method to identify cortical regions with mutually distinct and internally coherent connectivity. We made use of this principle to parcellate Broca's area. As it turned out, 3 subregions are discernible that were identified as putative Brodmann area (BA) 44, BA45, and the deep frontal operculum. These results are discussed in the light of previous evidence from other methods in both human and nonhuman primates. We conclude that plausible results can be achieved by the proposed technique, which cannot be obtained by any other method in vivo. For the first time, there is a possibility to investigate the anatomical subdivision of Broca's area noninvasively in the individual living human subject.

MR connectomics: Principles and challenges

“…If human structural connection networks can be reliably partitioned into tens or hundreds of distinct network modules, then it is expected that these modules will correspond to functionally localized subdivisions of the brain. Indeed, connectionbased subdivisions of premotor cortex (Johansen-Berg et al, 2004) have been found to correspond closely to the subdivisions identified using functional imaging of motor and linguistic tasks in the same individuals, and connection-based subdivisions of thalamus (Zhang et al, 2008) as well as inferior frontal cortex (Anwander et al, 2007) and cingulate cortices (Beckmann et al, 2009) have been found to match the canonical functional parcellations of these areas. In a less function-specific vein, other groups have investigated the relationship, across the brain, between so-called "functional connectivity" which is assessed using correlations in resting-state BOLD signal with structural connectivity identified using diffusion imaging (Greicius et al, 2009;Hagmann et al, 2008;Honey et al, 2009;Koch et al, 2002;Skudlarski et al, 2008;van den Heuvel et al, 2008van den Heuvel et al, , 2009van den Heuvel et al, 2009;Van Dijk et al, 2010).…”

Empirical approaches to the study of language evolution

“…Prominent among these advances is the use of diffusion tensor imaging (DTI) and related techniques to map and measure the white-matter tracts connecting distant brain regions (Anwander, Tittgemeyer, von Cramon, Friederici, & Knösche, 2007;Catani & Mesulam, 2008;Friederici, 2009;Melhem et al, 2002). This allows in vivo analysis of the anatomical connectivity of different regions, and how they vary between species (e.g., Rilling et al, 2008).…”

Double-dissociation between the mechanism leading to impulsivity and inattention in Attention Deficit Hyperactivity Disorder: A resting-state functional connectivity study

“…We used K-means cluster analysis (Steinhaus 1957;Forgy 1965;MacQueen 1967;Hartigan and Wong 1979;Lloyd 1982) on the ratio between the hyperactivity-impulsivity and inattention CPRS-R scores to fractionate our sample into three subgroups: predominantly inattentive, predominantly hyperactive and combined. K-means clustering analysis is a commonly used approach to identify relatively homogeneous groups of cases or variables based on selected characteristics (Johansen-Berg et al 2004;Anwander et al 2007;Catani et al 2007;). This identified the following: 53 children with a predominantly inattentive CPRS score (32%, 7-17 year old, 11.28 ± 2.75 years, 34 males and 19 females); 44 children (27%, 7-17 year old, 11.36 ± 2.59 years, 34 males and 10 females) with a predominantly hyperactive-impulsive CPRS score and 68 children (41%, 7-16 year old, 10.39 ± 2.24 years, 53 males and 15 females) with a combined symptom profile.…”