Reproducibility of the Structural Brain Connectome Derived from Diffusion Tensor Imaging.
BACKGROUND
Disruptions of brain anatomical connectivity are believed to play a central role in several neurological and psychiatric illnesses. The structural brain connectome is typically derived from diffusion tensor imaging (DTI), which may be influenced by methodological factors related to signal processing, MRI scanners and biophysical properties of neuroanatomical regions. In this study, we evaluated how these variables affect the reproducibility of the structural connectome.


METHODS
Twenty healthy adults underwent 3 MRI scanning sessions (twice in the same MRI scanner and a third time in a different scanner unit) within a short period of time. The scanning sessions included similar T1 weighted and DTI sequences. Deterministic or probabilistic tractography was performed to assess link weight based on the number of fibers connecting gray matter regions of interest (ROI). Link weight and graph theory network measures were calculated and reproducibility was assessed through intra-class correlation coefficients, assuming each scanning session as a rater.


RESULTS
Connectome reproducibility was higher with data from the same scanner. The probabilistic approach yielded larger reproducibility, while the individual variation in the number of tracked fibers from deterministic tractography was negatively associated with reproducibility. Links connecting larger and anatomically closer ROIs demonstrated higher reproducibility. In general, graph theory measures demonstrated high reproducibility across scanning sessions.


CONCLUSIONS
Anatomical factors and tractography approaches can influence the reproducibility of the structural connectome and should be factored in the interpretation of future studies. Our results demonstrate that connectome mapping is a largely reproducible technique, particularly as it relates to the geometry of network architecture measured by graph theory methods.
Department of Neurology, Medical University of South Carolina, Charleston, SC, United States of America.
Leonardo
L
Bonilha
Department of Neurology, Medical University of South Carolina, Charleston, SC, United States of America.
Ezequiel
E
Gleichgerrcht
Department of Communication Sciences and Disorders, University of South Carolina, Columbia, SC, United States of America.
Julius
J
Fridriksson
Department of Psychology, University of South Carolina, Columbia, SC, United States of America.
Chris
C
Rorden
Department of Neurology, Medical University of South Carolina, Charleston, SC, United States of America.
Jesse L
JL
Breedlove
Department of Neurology, Medical University of South Carolina, Charleston, SC, United States of America.
Travis
T
Nesland
Department of Clinical Neurophysiology, University of Göttingen, Göttingen, Germany.
Walter
W
Paulus
Department of Cognitive Neurology, MR-Research, University of Göttingen, Göttingen, Germany.
Gunther
G
Helms
Department of Neurology/Epileptology and Hertie Institute of Clinical Brain Research, University of Tübingen, Tübingen, Germany.
Niels K
NK
Focke
D000328:Adult; D063132:Connectome / Q000379:methods*; D056324:Diffusion Tensor Imaging / Q000379:methods*; D066128:Gray Matter / Q000503:physiopathology*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods; D009434:Neural Pathways / Q000502:physiology*; D009445:Neuroanatomy / Q000379:methods*; D015203:Reproducibility of Results