Introduction Large‐fiber neuropathy is rare in neurofibromatosis type 1, but small‐fiber neuropathy has not been studied. Methods Patients with neurofibromatosis type 1 underwent nerve conduction studies for large‐fiber assessment. Small‐fiber tests included quantitative thermal thresholds, laser Doppler flare imaging, intraepidermal nerve fiber density, and corneal nerve fiber length. Results Of the 52 patients enrolled, 31 (60%) were female and the mean age was 33.0 ± 12.3 years. Four (8%) patients had abnormal nerve conduction studies. Small‐fiber tests were frequently abnormal: thermal thresholds in 7 (13%); laser Doppler flare imaging in 10 (19%); intraepidermal nerve fiber density in 11 (22%); and corneal nerve fiber length in 27 (52%). The mean corneal nerve fiber length was below normative level (10.1 ± 2.7 mm/mm3). Discussion Small‐fiber neuropathy may be common in neurofibromatosis type 1, and should be investigated in symptomatic patients.

Background and purpose Advanced analysis of electroencephalography (EEG) data has become an essential tool in brain research. Based solely on resting state EEG signals, a data‐driven, predictive and explanatory approach is presented to discriminate painful from non‐painful diabetic polyneuropathy (DPN) patients. Methods Three minutes long, 64 electrode resting‐state recordings were obtained from 180 DPN patients. The analysis consisted of a mixture of traditional, explanatory and machine learning analyses. First, the 10 functional bivariate connections best differentiating between painful and non‐painful patients in each EEG band were identified and the relevant receiver operating characteristic was calculated. Later, those connections were correlated with selected clinical parameters. Results Predictive analysis indicated that theta and beta bands contain most of the information required for discrimination between painful and non‐painful polyneuropathy patients, with area under the receiver operating characteristic curve values of 0.93 for theta and 0.89 for beta bands. Assessing statistical differences between the average magnitude of functional connectivity values and clinical pain parameters revealed that painful DPN patients had significantly higher cortical functional connectivity than non‐painful ones (p = 0.008 for theta and p = 0.001 for alpha bands). Moreover, intra‐band analysis of individual significant functional connections revealed a positive correlation with average reported pain in the previous 3 months in all frequency bands. Conclusions Resting state EEG functional connectivity can serve as a highly accurate biomarker for the presence or absence of pain in DPN patients. This highlights the importance of the brain, in addition to the peripheral lesions, in generating the clinical pain picture. This tool can probably be extended to other pain syndromes.

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In this chapter, we describe the prevalence, diagnostic methods, and treatment efficacy of compressive neuropathies of the median and the ulnar nerves in patients with diabetes mellitus (DM). Median neuropathy at the wrist is found in up to onethird of patients with DM, when demonstrated electrophysiologically, but is symptomatic as carpal tunnel syndrome (CTS) in a smaller proportion of these patients. It is clear that diabetes increases the risk of having clinical CTS. Diagnosis of CTS using nerve conduction studies is difficult in patients with DM and diabetic sensorimotor polyneuropathy (DSP) as median nerve conduction studies are affected predominantly by the diabetes state. We will discuss different electrodiagnostic and ultrasonography techniques for diagnosis and the outcomes of carpal tunnel release decompressive surgery in this special patient population. It is controversial whether DM is a risk factor for cubital tunnel syndrome or ulnar neuropathy at the elbow (UNE) or at the wrist (UNW). In this chapter, we will review the ultrasonographic and electrophysiological diagnostic techniques used in UNE and UNW and the efficacy of cubital tunnel release in DM patients.