Rostro-caudal Architecture of the Frontal Lobes in Humans

Abstract

The nature of the inputs and outputs of a brain region defines its functional specialization. The frontal portion of the brain is essential for goal-directed behaviors, however, the biological basis for its functional organization is unknown. Here, exploring structural connectomic properties, we delineated 12 frontal areas, defined by the pattern of their white matter connections. This result was highly reproducible across neuroimaging centers, acquisition parameters, and participants. These areas corresponded 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. These new anatomical signatures shed light onto the structural organization of the frontal lobes and could help strengthen the prediction or diagnosis of neurodevelopmental and neurodegenerative disorders.

Age‐related mapping of intracortical myelin from late adolescence to middle adulthood using T1‐weighted MRI

“…This matches a known structural gradient that exists in the rostral-caudal orientation of the PFC, such that in moving from caudodorsal to rostroventral, there is a gradual decrease in the number of fibers, in the caliber of fibers, and in cortical width [Nieuwenhuys, 2012]. There is also an increase in myelination in the rostrocaudal direction [Thiebaut de Schotten et al, 2016]. The functional significance of this gradient was extensively reviewed previously [Badre and D'Esposito, 2009] and it was suggested that hierarchical processing exists in the PFC, with more abstract thoughts being processed rostrally, and more concrete thoughts that are closer to producing a motor output being processed caudally.…”

“…The proton-density weighted image was registered to the T 1 -weighted image using a 6-parameter rigid transform (FSL) and filtered with a 3D median filter with a 5 3 5 3 5 mm kernel size. The T 1 -weighted image was then divided by the filtered proton-density-weighted image to create the ratio image, which is a strongly T 1 -weighted image with B 1 2 and some B 1 1 inhomogeneities removed [Marques and Gruetter, 2013;van de Moortele et al, 2009;Wang et al, 2005].…”

Modulation of network‐to‐network connectivity via spike‐timing‐dependent noninvasive brain stimulation

“…As visible in de Schotten et al, 2017;Nee & D'Esposito, 2016). As visible in de Schotten et al, 2017;Nee & D'Esposito, 2016).…”

The role of diffusion MRI in neuroscience

“…These connectivity‐based divisions have been shown to have functional relevance, and could be useful for individualized neurosurgical planning . Recently, tractography has revealed a division of the frontal lobes in 12 areas characterized by their connection with the rest of the brain and a clear‐cut functional specificity (Figure B) . Hence, a model of structural connectivity assessed with tractography seems to capture the functional organization of the brain.…”

“…Exploiting this difference, connectivity information gained from diffusion tractography can be used to define the boundary between these two regions based purely on connectivity, driven by the fact that SMA shows a closer relationship with the primary motor cortex, whereas pre-SMA is more connected to the prefrontal cortex. 27 Indeed, connectivity-based parcellation has been used to define subdivisions across a range of different human cortical areas, [119][120][121][122][123][124][125][126] as well as to demonstrate homologies between human and monkey cortex. [127][128][129][130][131][132][133] Recently, diffusion tractography has been used, together with other multimodal structural and functional MRI data, to generate a novel whole brain cortical parcellation using data from the Human Connectome Project ( Figure 6A).…”