Automated individual-level parcellation of Broca's region based on functional connectivity

Broca's region can be subdivided into its constituent areas 44 and 45 based on established differences in connectivity to superior temporal and inferior parietal regions. The current study builds on our previous work manually parcellating Broca's area on the individual-level by applying these anatomical criteria to functional connectivity data. Here we present an automated observer-independent and anatomy-informed parcellation pipeline with comparable precision to the manual labels at the individual-level. The method first extracts individualized connectivity templates of areas 44 and 45 by assigning to each surface vertex within the ventrolateral frontal cortex the partial correlation value of its functional connectivity to group-level templates of areas 44 and 45, accounting for other template connectivity patterns. To account for cross-subject variability in connectivity, the partial correlation procedure is then repeated using individual-level network templates, including individual-level connectivity from areas 44 and 45. Each node is finally labeled as area 44, 45, or neither, using a winner-take-all approach. The method also incorporates prior knowledge of anatomical location by weighting the results using spatial probability maps. The resulting area labels show a high degree of spatial overlap with the gold-standard manual labels, and group-average area maps are consistent with cytoarchitectonic probability maps of areas 44 and 45. To facilitate reproducibility and to demonstrate that the method can be applied to resting-state fMRI datasets with varying acquisition and preprocessing parameters, the labeling procedure is applied to two open-source datasets from the Human Connectome Project and the Nathan Kline Institute Rockland Sample. While the current study focuses on Broca's region, the method is adaptable to parcellate other cortical regions with distinct connectivity profiles

In praise of tedious anatomy

Functional neuroimaging is fundamentally a tool for mapping function to structure, and its success consequently requires neuroanatomical precision and accuracy. Here we review the various means by which functional activation can be localized to neuroanatomy and suggest that the gold standard should be localization to the individual’s or group’s own anatomy through the use of neuroanatomical knowledge and atlases of neuroanatomy. While automated means of localization may be useful, they cannot provide the necessary accuracy, given variability between individuals. We also suggest that the field of functional neuroimaging needs to converge on a common set of methods for reporting functional localization including a common “standard” space and criteria for what constitutes sufficient evidence to report activation in terms of Brodmann’s areas

Meta-analytic connectivity modeling of the left and right inferior frontal gyri

Background: Neurocognitive models of language processing highlight the role of the left inferior frontal gyrus (IFG) in the functional network underlying language. Furthermore, neuroscience research has shown that IFG is not a uniform region anatomically, cytoarchitectonically or functionally. However, no previous study explored the language-related functional connectivity patterns of IFG subdivisions using a meta-analytic connectivity modeling (MACM) approach. Purpose: The present MACM study aimed to identify language-related coactivation patterns of the left and right IFG subdivisions. Method: Six regions of interest (ROIs) were defined using a probabilistic brain atlas corresponding to pars opercularis, pars triangularis and pars orbitalis of IFG in both hemispheres. The ROIs were used to search the BrainMap functional database to identify neuroimaging experiments with healthy, right-handed participants reporting language-related activations in each ROI. Activation likelihood estimation analyses were then performed on the foci extracted from the identified studies to compute functional convergence for each ROI, which was also contrasted with the other ROIs within the same hemisphere. Results: A primarily left-lateralized functional network was revealed for the left and right IFG subdivisions. The left-hemispheric ROIs exhibited more robust coactivation than the right-hemispheric ROIs. Particularly, the left pars opercularis was associated with the most extensive coactivation pattern involving bilateral frontal, bilateral parietal, left temporal, left subcortical, and right cerebellar regions, while the left pars triangularis and orbitalis revealed a predominantly left-lateralized involvement of frontotemporal regions. Conclusion: The findings align with the neurocognitive models of language processing that propose a division of labor among the left IFG subdivisions and their respective functional networks. Also, the opercular part of left IFG stands out as a major hub in the language network with connections to diverse cortical, subcortical and cerebellar structures

Brain Modularity Mediates the Relation between Task Complexity and Performance

Recent work in cognitive neuroscience has focused on analyzing the brain as a network, rather than as a collection of independent regions. Prior studies taking this approach have found that individual differences in the degree of modularity of the brain network relate to performance on cognitive tasks. However, inconsistent results concerning the direction of this relationship have been obtained, with some tasks showing better performance as modularity increases and other tasks showing worse performance. A recent theoretical model (Chen & Deem, 2015) suggests that these inconsistencies may be explained on the grounds that high-modularity networks favor performance on simple tasks whereas low-modularity networks favor performance on more complex tasks. The current study tests these predictions by relating modularity from resting-state fMRI to performance on a set of simple and complex behavioral tasks. Complex and simple tasks were defined on the basis of whether they did or did not draw on executive attention. Consistent with predictions, we found a negative correlation between individuals' modularity and their performance on a composite measure combining scores from the complex tasks but a positive correlation with performance on a composite measure combining scores from the simple tasks. These results and theory presented here provide a framework for linking measures of whole brain organization from network neuroscience to cognitive processing.Comment: 47 pages; 4 figure

Parcellation of the human sensorimotor cortex: a resting-state fMRI study

The sensorimotor cortex is a brain region comprising the primary motor cortex (MI) and the primary somatosensory (SI) cortex. In humans, investigation into these regions suggests that MI and SI are involved in the modulation and control of motor and somatosensory processing, and are somatotopically organized according to a body plan (Penfield & Boldrey, 1937). Additional investigations into somatotopic mapping in relation to the limbs in the peripheral nervous system and SI in central nervous system have further born out the importance of this body-based organization (Wall & Dubner, 1972). Understanding the nature of the sensorimotor cortex‟s structure and function has broad implications not only for human development, but also motor learning (Taubert et al., 2011) and clinical applications in structural plasticity in Parkinson‟s disease (Sehm et al., 2014), among others. The aim of the present thesis is to identify functionally meaningful subregions within the sensorimotor cortex via parcellation analysis. Previously, cerebral subregions were identified in postmortem brains by invasive procedures based on histological features (Brodmann, 1909; Vogt. & Vogt., 1919; Economo, 1926; Sanides, 1970). One widely used atlas is based on Brodmann areas (BA). Brodmann divided human brains into several areas based on the visually inspected cytoarchitecture of the cortex as seen under a microscope (Brodmann, 1909). In this atlas, BA 4, BA 3, BA 1 and BA 2 together constitute the sensorimotor cortex (Vogt. & Vogt., 1919; Geyer et al., 1999; Geyer et al., 2000). However, BAs are incapable of delineating the somatotopic detail reflected in other research (Blankenburg et al., 2003). And, although invasive approaches have proven reliable in the discovery of functional parcellation in the past, such approaches are marked by their irreversibility which, according to ethical standards, makes them unsuitable for scientific inquiry. Therefore, it is necessary to develop non-invasive approaches to parcellate functional brain regions. In the present study, a non-invasive and task-free approach to parcellate the sensorimotor cortex with resting-state fMRI was developed. This approach used functional connectivity patterns of brain areas in order to delineate functional subregions as connectivity-based parcellations (Wig et al., 2014). We selected two adjacent BAs (BA 3 and BA 4) from a standard template to cover the area along the central sulcus (Eickhoff et al., 2005). Then subregions within this area were generated using resting-state fMRI data. These subregions were organized somatotopically from medial-dorsal to ventral-lateral (corresponding roughly to the face, hand and foot regions, respectively) by comparing them with the activity maps obtained by using independent motor tasks. Interestingly, resting-state parcellation map demonstrated higher correspondence to the task-based divisions after individuals had performed motor tasks. We also observed higher functional correlations between the hand area and the foot and tongue area, respectively, than between the foot and tongue regions. The functional relevance of those subregions indicates the feasibility of a wide range of potential applications to brain mapping (Nebel et al., 2014). In sum, the present thesis provides an investigation of functional network, functional structure, and properties of the sensorimotor cortex by state-of-art neuroimaging technology. The methodology and the results of the thesis hope to carry on the future research of the sensorimotor system

Colateralization of Broca's area and the visual word form area in left-handers: fMRI evidence

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Imaging-based parcellations of the human brain

A defining aspect of brain organization is its spatial heterogeneity, which gives rise to multiple topographies at different scales. Brain parcellation — defining distinct partitions in the brain, be they areas or networks that comprise multiple discontinuous but closely interacting regions — is thus fundamental for understanding brain organization and function. The past decade has seen an explosion of in vivo MRI-based approaches to identify and parcellate the brain on the basis of a wealth of different features, ranging from local properties of brain tissue to long-range connectivity patterns, in addition to structural and functional markers. Given the high diversity of these various approaches, assessing the convergence and divergence among these ensuing maps is a challenge. Inter-individual variability adds to this challenge but also provides new opportunities when coupled with cross-species and developmental parcellation studies

Connections of the human orbitofrontal cortex and inferior frontal gyrus

The direct connections of the orbitofrontal cortex (OFC) were traced with diffusion tractography imaging and statistical analysis in 50 humans, to help understand better its roles in emotion and its disorders. The medial OFC and ventromedial prefrontal cortex have direct connections with the pregenual and subgenual parts of the anterior cingulate cortex; all of which are reward-related areas. The lateral OFC (OFClat) and its closely connected right inferior frontal gyrus (rIFG) have direct connections with the supracallosal anterior cingulate cortex; all of which are punishment or nonreward-related areas. The OFClat and rIFG also have direct connections with the right supramarginal gyrus and inferior parietal cortex, and with some premotor cortical areas, which may provide outputs for the OFClat and rIFG. Another key finding is that the ventromedial prefrontal cortex shares with the medial OFC especially strong outputs to the nucleus accumbens and olfactory tubercle, which comprise the ventral striatum, whereas the other regions have more widespread outputs to the striatum. Direct connections of the OFC and IFG were with especially the temporal pole part of the temporal lobe. The left IFG, which includes Broca’s area, has direct connections with the left angular and supramarginal gyri