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Alexander Leemans

Researcher at Utrecht University

Publications -  306
Citations -  21500

Alexander Leemans is an academic researcher from Utrecht University. The author has contributed to research in topics: Diffusion MRI & Fractional anisotropy. The author has an hindex of 64, co-authored 289 publications receiving 17932 citations. Previous affiliations of Alexander Leemans include Australian Catholic University & Cardiff University.

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Indirect frontocingulate structural connectivity predicts clinical response to accelerated rTMS in major depressive disorder

TL;DR: Structural connectivity between the patient-specific stimulation site and the caudal and posterior parts of the cingulate cortex had predictive potential for clinical response to aiTBS, and stronger structural frontocingular connections may be of essence to optimally benefit from left dlPFC rTMS treatment in MDD.
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Spherical deconvolution with tissue-specific response functions and multi-shell diffusion MRI to estimate multiple fiber orientation distributions (mFODs).

TL;DR: This study investigated whether performing spherical deconvolution with tissue specific models of both WM and GM can improve the characterization of the latter while retaining state-of-the-art performances in WM and developed a framework able to simultaneously accommodate multiple anisotropic response functions to estimate multiple, tissue-specific, fiber orientation distributions (mFODs).
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White matter organization in relation to upper limb motor control in healthy subjects: exploring the added value of diffusion kurtosis imaging

TL;DR: DKI provided additional information, but did not show increased sensitivity to detect relations between WM microstructure and bimanual performance in healthy controls.
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Automated characterization of noise distributions in diffusion MRI data.

TL;DR: Two new automated methods using the moments and maximum likelihood equations of the Gamma distribution to estimate noise distributions as they explicitly depend on the number of coils are introduced, making it possible to estimate all unknown parameters using only the magnitude data.