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Brain Struct Funct. 2016 Apr;221(3):1751-66. doi: 10.1007/s00429-015-1001-3. Epub 2015 Feb 15.

Atlasing the frontal lobe connections and their variability due to age and education: a spherical deconvolution tractography study.

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CNRS UMR 7225, Inserm, UPMC-Paris6, UMR_S 1127, CRICM, GH Pitié-Salpêtrière, 75013, Paris, France.
Natbrainlab, Brain and Spine Institute, Paris, France.
Service de Médecine et de Réadaptation Gériatrique et Neurologique, Hôpitaux de Saint-Maurice, Saint-Maurice, France.
Centre de Neuroimagerie de Recherche CENIR, Groupe Hospitalier Pitié-Salpêtrière, Paris, France.
Department of Neuroimaging, Institute of Psychiatry, Natbrainlab, King's College London, London, UK.
CNRS UMR 7225, Inserm, UPMC-Paris6, UMR_S 1127, CRICM, GH Pitié-Salpêtrière, 75013, Paris, France.
Natbrainlab, Brain and Spine Institute, Paris, France.
Natbrainlab, Sackler Institute of Translational Neurodevelopment, Institute of Psychiatry, King's College London, London, UK.


In neuroscience, there is a growing consensus that higher cognitive functions may be supported by distributed networks involving different cerebral regions, rather than by single brain areas. Communication within these networks is mediated by white matter tracts and is particularly prominent in the frontal lobes for the control and integration of information. However, the detailed mapping of frontal connections remains incomplete, albeit crucial to an increased understanding of these cognitive functions. Based on 47 high-resolution diffusion-weighted imaging datasets (age range 22-71 years), we built a statistical normative atlas of the frontal lobe connections in stereotaxic space, using state-of-the-art spherical deconvolution tractography. We dissected 55 tracts including U-shaped fibers. We further characterized these tracts by measuring their correlation with age and education level. We reported age-related differences in the microstructural organization of several, specific frontal fiber tracts, but found no correlation with education level. Future voxel-based analyses, such as voxel-based morphometry or tract-based spatial statistics studies, may benefit from our atlas by identifying the tracts and networks involved in frontal functions. Our atlas will also build the capacity of clinicians to further understand the mechanisms involved in brain recovery and plasticity, as well as assist clinicians in the diagnosis of disconnection or abnormality within specific tracts of individual patients with various brain diseases.


Aging; Atlas; Diffusion-weighted imaging; Fasciculi; Frontal lobe; Tractography; U-shaped tracts; White matter

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