Rostro-caudal architecture of the frontal lobes in humans
Michel Thiebaut de Schotten1, Marika Urbanski1, Leonardo Cerliani1, and Emmanuelle Volle1

1BCBlab, Institut du Cerveau et de la Moelle, Paris, France


Functional models of the frontal lobes suggest a rostro-caudal organization that is essential for goal-directed behaviour and cognitive control, in which higher processing-level anterior regions send control signals to lower processing-level posterior regions. Here we show that tractography can divide the frontal lobes into 12 regions organized in a rostro-caudal axis showing a gradient of cortical thickness, myelination and cell body density.


Functional models of the frontal lobes suggest a rostro-caudal organization that is essential for goal-directed behavior and cognitive control, in which higher processing-level anterior regions send control signals to lower processing-level posterior regions 1-5. While these studies support the existence of a set of frontal regions arranged in an anterior-posterior progression of functional specialization, spatial and structural anatomical definition of these regions are unknown. To assess whether an architectural gradient supports this functional organization, we employed diffusion weighted imaging tractography based on Qball imaging 6-7 to segregate frontal areas based on their connectivity. We further examine the microstructural properties of these areas with recent developments in neuroimaging and post-mortem histology to reveal the anatomical signature of the frontal rostro-caudal organization.

Material and methods.

T1w and Diffusion weighted imaging were obtained from 12 healthy controls (2 males and 10 females; 26 to 35 year-old) from HCP (humanconnectome.org). Data was pre-processed using the default HCP pipeline (V.2). A tractography algorithm based on Qball imaging (20,000 samples, step length 0.5 mm, curvature threshold of 0.2) optimized for multishell 6 was used to propagate streamlines from ‘seed’ (in the frontal lobe) to ‘target’ (in the whole left hemisphere) regions of interest (ROIs) situated in the white matter that was the closest to the grey matter. A ‘connectivity’ matrix was fed to a PCA performed using SPSS software 8. Functional specificity across the connectivity based regions (CBR) identified was defined using the decode tool provided in Neurosynth (neurosynth.org). Additionally, each CBR was anatomically characterized extracting cortical thickness, T1/T2 signal, Big Brain silver staining intensity (bigbrain.loris.ca), and entropy measures derived from fix-corrected resting state functional MRI.


PCA identified 12 connectivity-based regions (CBRs) in the left frontal lobe of each participant (Fig. 1a). The comparison with a meta-analysis of functional imaging studies revealed a functional specialization for each of the identified CBRs (Fig. 1b). Cortical thickness analyses 9-10 revealed that anterior CBRs were progressively thicker than posterior CBRs in the left (LH) an the right (RH) hemispheres (Fig. 2a, LH R2 = 0.856 ; P < 0.001 ; RH R2 = 0.642 ; P = 0.002). Cortical myelin content was progressively lower from posterior to anterior CBRs (Fig. 2b LH R2 = 0.880 ; P < 0.001 ; RH R2 = 0.900 ; P < 0.001). There was also a gradient of cell body density with anterior CBRs containing smaller cells that are less densely packed than in posterior CBRs (Fig. 2c LH R2=0.372; P=0.035, RH R2=0.477 ; P=0.013). Anterior CBRs had a higher entropy than posterior CBRs for the left and right hemispheres (Fig. 2d; LH R2=0.689 ; P=0.003, RH R2=0.426 ; P=0.041)


Tractography-based parcellation using the PCA statistical framework divided the frontal lobe into 12 areas corresponding to regions functionally engaged in specific tasks, organized along a rostro-caudal axis from the most complex high-order association areas to the simplest idiotopic areas. The rostro-caudal axis along which the 12 regions were organized also reflected a gradient of cortical thickness, myelination and cell body density. Importantly, across the identified regions, this gradient of microstructural features was strongly associated with the varying degree of information processing complexity.


This work provides a new framework, and subsequently, new hypotheses to test regarding the anatomical bases of frontal functions. This approach may strengthen the prediction or diagnosis of neurodevelopmental and neurodegenerative disorders.


Data was provided in part by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University. The BigBrain dataset is the result of a collaborative effort between the teams of Dr Katrin Amunts, Dr Karl Zilles (Forschungszentrum Jülich) and Dr Alan Evans (Montreal Neurological Institute). Additional support comes from the “Agence Nationale de la Recherche” [grants number ANR-09-RPDOC-004-01 and number ANR-13- JSV4-0001-01] and from the Fondation pour la Recherche Médicale (FRM). The research leading to these results received funding from the program “Investissements d’avenir” ANR-10- IAIHU-06.


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Fig. 1: Brain CBRs of the frontal lobes defined by their anatomical connectivity. a) Lateral and medial views b) Montreal Neurological Institute stereotaxic coronal sections c) Function most likely activated in functional imaging studies for each CBR. Left panel, z-score indicates the likeliness for each CBR to be activated for the term indicated in ordinate (compared with 2912 other term-related activations provided in Neurosynth, http://www.neurosynth.org). Right panel, spatial correlations between each CBR and functional maps.

Fig. 2: Correlations between the posterior–anterior position of each region’s centroid and (a) cortical thickness, (b) T1w/T2w myelin ratio, (c) Silver staining of cortical cell bodies (decreased values indicate an increase in the staining of tissue, http://bigbrain.loris.ca) and (d) functional magnetic resonance imaging entropy. LH, left hemisphere; RH, right hemisphere; MNI, Montreal Neurological Institute (http://www.bic.mni.mcgill.ca) *P < 0.05, **P < 0.01, ***P < 0.001.

Proc. Intl. Soc. Mag. Reson. Med. 26 (2018)