White matter organization in relation to upper limb motor control in healthy subjects: exploring the added value of diffusion kurtosis imaging
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Diffusion tensor imaging (DTI) characterizes white matter (WM) microstructure. In many brain regions, however, the assumption that the diffusion probability distribution is Gaussian may be invalid, even at low b values. Recently, diffusion kurtosis imaging (DKI) was suggested to more accurately estimate this distribution. We explored the added value of DKI in studying the relation between WM microstructure and upper limb coordination in healthy controls (N = 24). Performance on a complex bimanual tracking task was studied with respect to the conventional DTI measures (DKI or DTI derived) and kurtosis metrics of WM tracts/regions carrying efferent (motor) output from the brain, corpus callosum (CC) substructures and whole brain WM. For both estimation models, motor performance was associated with fractional anisotropy (FA) of the CC-genu, CC-body, the anterior limb of the internal capsule, and whole brain WM (r s range 0.42–0.63). Although DKI revealed higher mean, radial and axial diffusivity and lower FA than DTI (p < 0.001), the correlation coefficients were comparable. Finally, better motor performance was associated with increased mean and radial kurtosis and kurtosis anisotropy (r s range 0.43–0.55). In conclusion, DKI provided additional information, but did not show increased sensitivity to detect relations between WM microstructure and bimanual performance in healthy controls.
KeywordsBimanual motor control Diffusion tensor imaging Excess kurtosis Non-Gaussian diffusion
This work was supported by a grant from the Research Programme of the Research Foundation—Flanders (Fonds Wetenschappelijk Onderzoek—FWO) (G.0482.010, G0483.10, G.A114.11, G0721.12), from the Research Fund of the Katholieke Universiteit Leuven, Belgium (OT/11/071), and Grant P7/11 from the Interuniversity Attraction Poles program of the Belgian federal government, awarded to S.P. Swinnen. J. Gooijers is funded by an aspirant fellowship of the Research Foundation—Flanders (FWO).
Conflict of interest
There are no conflicts of interest.
- Caeyenberghs K, Leemans A, Geurts M, Taymans T, Vander Linden C, Smits-Engelsman BCM, Sunaert S, Swinnen SP (2010b) Brain-behavior relationships in young traumatic brain injury patients: fractional anisotropy measures are highly correlated with dynamic visuomotor tracking performance. Neuropsychologia 48:1472–1482PubMedCrossRefGoogle Scholar
- Callaghan PT (1991) Principles of nuclear magnetic resonance microscopy. Oxford University Press, OxfordGoogle Scholar
- Falangola MF, Jensen JH, Tabesh A, Hu C, Deardorff RL, Babb JS, Ferris S, Helpern JA (2013) Non-Gaussian diffusion MR assessment of brain microstructure in mild cognitive impairment and Alzheimer’s disease. Magn Reson Imaging 31:840–846Google Scholar
- Grossman EJ, Ge YL, Jensen JH, Babb JS, Miles L, Reaume J, Silver JM, Grossman RI, Inglese M (2012) Thalamus and cognitive impairment in mild traumatic brain injury: a diffusional kurtosis imaging study. J Neurotrauma 29:2318–2327Google Scholar
- Helpern JA, Adisetiyo V, Falangola MF, Hu CX, Di Martino A, Williams K, Castellanos FX, Jensen JH (2011) Preliminary evidence of altered gray and white matter microstructural development in the frontal lobe of adolescents with attention-deficit hyperactivity disorder: a diffusional kurtosis imaging study. J Magn Reson Imaging 33:17–23PubMedCentralPubMedCrossRefGoogle Scholar
- Jeurissen B, Leemans A, Tournier JD, Jones DK, Sijbers J (2012) Investigating the prevalence of complex fiber configurations in white matter tissue with diffusion magnetic resonance imaging. Hum Brain Mapp. doi: 10.1002/hbm.22099
- Leemans A et al (2009) ExploreDTI: A graphical toolbox for processing, analyzing, and visualizing diffusion MR data. In: 17th annual meeting of Intl Soc Mag Reson Med, p 3537. Hawaii, USAGoogle Scholar
- Mori S, Wakana S, Van Zijl PCM, Nagae-Poetscher LM (2005) MRI atlas of the human white matter. Elsevier, AmsterdamGoogle Scholar
- Raz E, Bester M, Sigmund EE, Tabesh A, Babb JS, Jaggi H, Helpern J, Mitnick RJ, Inglese M (2013) A better characterization of spinal cord damage in multiple sclerosis: a diffusional kurtosis imaging study. Am J Neuroradiol. doi: 10.3174/ajnr.A3512
- Rosenkrantz AB, Sigmund EE, Johnson G, Babb JS, Mussi TC, Melamed J, Taneja SS, Lee VS, Jensen JH (2012) Prostate cancer: feasibility and preliminary experience of a diffusional kurtosis model for detection and assessment of aggressiveness of peripheral zone cancer. Radiology 264:126–135PubMedCrossRefGoogle Scholar
- Sisti HM, Geurts M, Clerckx R, Gooijers J, Coxon JP, Heitger MH, Caeyenberghs K, Beets IAM, Serbruyns L, Swinnen SP (2011) Testing multiple coordination constraints with a novel bimanual visuomotor task. Plos One 6:e23619Google Scholar
- Vanhoutte G, Pereson S, Delgado y Palacios R, Guns P, Asselbergh B, Veraart J, Sijbers J, Verhoye M, Van Broeckhoven C, Van der Linden A (2013) Diffusion kurtosis imaging to detect amyloidosis in an APP/PS1 mouse model for Alzheimer’s Disease. Magn Reson Med 69:1115–1121Google Scholar
- Veraart J, Rajan J, Peeters RR, Leemans A, Sunaert S, Sijbers J (2012) Comprehensive framework for accurate diffusion MRI parameter estimation. Magn Reson Med. doi: 10.1002/mrm.24529