Acute painful diabetic neuropathy: an uncommon, remittent type of acute distal small fibre neuropathy.

INTRODUCTION: Acute painful diabetic neuropathy (APDN) is a distinctive diabetic polyneuropathy and consists of two subtypes: treatment-induced neuropathy (TIN) and diabetic neuropathic cachexia (DNC). The characteristics of APDN are (1.) the small-fibre involvement, (2.) occurrence paradoxically after short-term achievement of good glycaemia control, (3.) intense pain sensation and (4.) eventual recovery. In the face of current recommendations to achieve quickly glycaemic targets, it appears necessary to recognise and understand this neuropathy. METHODS AND RESULTS: Over 2009 to 2012, we reported four cases of APDN. Four patients (three males and one female) were identified and had a mean age at onset of TIN of 47.7 years (±6.99 years). Mean baseline HbA1c was 14.2% (±1.42) and 7.0% (±3.60) after treatment. Mean estimated time to correct HbA1c was 4.5 months (±3.82 months). Three patients presented with a mean time to symptom resolution of 12.7 months (±1.15 months). One patient had an initial normal electroneuromyogram (ENMG) despite the presence of neuropathic symptoms, and a second abnormal ENMG showing axonal and myelin neuropathy. One patient had a peroneal nerve biopsy showing loss of large myelinated fibres as well as unmyelinated fibres, and signs of microangiopathy. CONCLUSIONS: According to the current recommendations of promptly achieving glycaemic targets, it appears necessary to recognise and understand this neuropathy. Based on our observations and data from the literature we propose an algorithmic approach for differential diagnosis and therapeutic management of APDN patients.

Structured sparsity for spatially coherent fibre orientation estimation in diffusion MRI.

We propose a novel formulation to solve the problem of intra-voxel reconstruction of the fibre orientation distribution function (FOD) in each voxel of the white matter of the brain from diffusion MRI data. The majority of the state-of-the-art methods in the field perform the reconstruction on a voxel-by-voxel level, promoting sparsity of the orientation distribution. Recent methods have proposed a global denoising of the diffusion data using spatial information prior to reconstruction, while others promote spatial regularisation through an additional empirical prior on the diffusion image at each q-space point. Our approach reconciles voxelwise sparsity and spatial regularisation and defines a spatially structured FOD sparsity prior, where the structure originates from the spatial coherence of the fibre orientation between neighbour voxels. The method is shown, through both simulated and real data, to enable accurate FOD reconstruction from a much lower number of q-space samples than the state of the art, typically 15 samples, even for quite adverse noise conditions.