M
Mohamed L. Seghier
Researcher at Emirates College for Advanced Education
Publications - 115
Citations - 10728
Mohamed L. Seghier is an academic researcher from Emirates College for Advanced Education. The author has contributed to research in topics: Functional magnetic resonance imaging & Lateralization of brain function. The author has an hindex of 52, co-authored 109 publications receiving 9356 citations. Previous affiliations of Mohamed L. Seghier include French Institute of Health and Medical Research & Khalifa University.
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Visualising inter-subject variability in fMRI using threshold-weighted overlap maps
TL;DR: A simple way of visualising whole-brain consistency and variability in brain responses across subjects using threshold-weighted voxel-based overlap maps, which can be particularly useful when it comes to explaining typical (or atypical) compensatory mechanisms used by patients following brain damage.
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Sensory-to-motor integration during auditory repetition: a combined fMRI and lesion study
Ōiwi Parker Jones,Ōiwi Parker Jones,Susan Prejawa,Thomas M.H. Hope,Marion Oberhuber,Mohamed L. Seghier,Alexander P. Leff,David W. Green,Cathy J. Price +8 more
TL;DR: The results were most consistent with the neurological tradition that emphasizes the importance of the arcuate fasciculus in the non-semantic integration of auditory and motor speech processing and the lesion sites in eight patients who had selective difficulties repeating heard words but with preserved word comprehension, picture naming and verbal fluency.
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How distributed processing produces false negatives in voxel-based lesion-deficit analyses
Andrea Gajardo-Vidal,Andrea Gajardo-Vidal,Diego L Lorca-Puls,Jennifer T. Crinion,Jitrachote White,Mohamed L. Seghier,Mohamed L. Seghier,Alexander P. Leff,Thomas M.H. Hope,Philipp Ludersdorfer,David W. Green,Howard Bowman,Howard Bowman,Cathy J. Price +13 more
TL;DR: The results illustrate how false negative results arise when the same deficit can be caused by different lesion sites; how univariate voxel‐based lesion‐deficit mappings cannot, in isolation, be used to predict outcome in other patients.
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Detecting subject-specific activations using fuzzy clustering
TL;DR: A non-iterative fuzzy clustering algorithm (FCP) for characterizing inter-subject variability in between-subject or second-level analyses of fMRI data and can identify atypical activation patterns in a quantitative and unsupervised manner.
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Inter- and Intrahemispheric Connectivity Differences When Reading Japanese Kanji and Hiragana
Keith J. Kawabata Duncan,Tae Twomey,Ōiwi Parker Jones,Mohamed L. Seghier,Tomoki Haji,Katsuyuki Sakai,Cathy J. Price,Joseph T. Devlin +7 more
TL;DR: Functional magnetic resonance imaging with dynamic causal modeling is used to investigate competing theories regarding the neural processing of Kanji and Hiragana during a visual lexical decision task and finds that Kanji significantly increased the connection strength from right-to-left vOT.