Background Patients with hemiplegic migraine (HM) may sometimes develop progressive neurological deterioration of which the pathophysiology is unknown.Patient We report a 16-year clinical and... Show moreBackground Patients with hemiplegic migraine (HM) may sometimes develop progressive neurological deterioration of which the pathophysiology is unknown.Patient We report a 16-year clinical and neuroradiological follow-up of a patient carrying a de novo p.Ser218Leu CACNA1A HM mutation who had nine severe HM attacks associated with seizures and decreased consciousness between the ages of 3 and 12 years.Results Repeated ictal and postictal neuroimaging revealed cytotoxic oedema during severe HM attacks in the symptomatic hemisphere, which later showed atrophic changes. In addition, progressive cerebellar atrophy was observed. Brain atrophy halted after cessation of severe attacks, possibly due to prophylactic treatment with flunarizine and sodium valproate.Conclusion Severe HM attacks may result in brain atrophy and prophylactic treatment of these attacks might be needed in an early stage of disease to prevent permanent brain damage. Show less
Wierenga, L.M.; Van den Heuvel, M.P.; Oranje, B.; Giedd, J.N.; Durston, S.; Peper, J.S.; ... ; Crone, E.A. 2018
Magnetic resonance imaging (MRI) is sensitive to structural and functional changes in the brain caused by Alzheimer's disease (AD), and can therefore be used to help in diagnosing the disease.... Show moreMagnetic resonance imaging (MRI) is sensitive to structural and functional changes in the brain caused by Alzheimer's disease (AD), and can therefore be used to help in diagnosing the disease. Improving classification of AD patients based on MRI scans might help to identify AD earlier in the disease's progress, which may be key in developing treatments for AD. In this study we used an elastic net classifier based on several measures derived from the MRI scans of mild to moderate AD patients (N = 77) from the prospective registry on dementia study and controls (N = 173) from the Austrian Stroke Prevention Family Study. We based our classification on measures from anatomical MRI, diffusion weighted MRI and resting state functional MRI. Our unimodal classification performance ranged from an area under the curve (AUC) of 0.760 (full correlations between functional networks) to 0.909 (grey matter density). When combining measures from multiple modalities in a stepwise manner, the classification performance improved to an AUC of 0.952. This optimal combination consisted of grey matter density, white matter density, fractional anisotropy, mean diffusivity, and sparse partial correlations between functional networks. Classification performance for mild AD as well as moderate AD also improved when using this multimodal combination. We conclude that different MRI modalities provide complementary information for classifying AD. Moreover, combining multiple modalities can substantially improve classification performance over unimodal classification. (C) 2016 The Authors. Published by Elsevier Inc. Show less
Schouten, T.M.; Koini, M.; Vos, F. de; Seiler, S.; Van der Grond, J.; Lechner, A.; ... ; Rombouts, S.A.R.B. 2016
