The behavioral variant frontotemporal dementia (bvFTD) usually emerges with behavioral changes similar to changes in late-life bipolar disorder (BD) especially in the early stages. According to the literature, a substantial number of bvFTD cases have been misdiagnosed as BD. Since the literature lacks studies comparing differential diagnosis ability of electrophysiological and neuroimaging findings in BD and bvFTD, we aimed to show their classification power using an artificial neural network and genetic algorithm based approach. Eighteen patients with the diagnosis of bvFTD and 20 patients with the diagnosis of late-life BD are included in the study. All patients' clinical magnetic resonance imaging (MRI) scan and electroencephalography recordings were assessed by a double-blind method to make diagnosis from MRI data. Classification of bvFTD and BD from total 38 participants was performed using feature selection and a neural network based on general algorithm. The artificial neural network method classified BD from bvFTD with 76% overall accuracy only by using on EEG power values. The radiological diagnosis classified BD from bvFTD with 79% overall accuracy. When the radiological diagnosis was added to the EEG analysis, the total classification performance raised to 87% overall accuracy. These results suggest that EEG and MRI combination has more powerful classification ability as compared with EEG and MRI alone. The findings may support the utility of neurophysiological and structural neuroimaging assessments for discriminating the 2 pathologies.

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Background and PurposeWe compared the motor-unit number estimation (MUNE) findings in patients who presented with signs and/or findings associated with carpal tunnel syndrome (CTS) and healthy controls, with the aim of determining if motor-unit loss occurs during the clinically silent period and if there is a correlation between clinical and MUNE findings in CTS patients.MethodsThe study investigated 60 hands of 35 patients with clinical CTS and 60 hands of 34 healthy controls. Routine median and ulnar nerve conduction studies and MUNE analysis according to the multipoint stimulation method were performed.ResultsThe most common electrophysiological abnormality was reduced conduction velocity in the median sensory nerve (100% of the hands). The MUNE value was significantly lower for the patient group than for the control group (p=0.0001). ROC analysis showed that a MUNE value of 121 was the optimal cutoff for differentiating between patients and controls, with a sensitivity of 63.3% and a specificity of 68.3%. MUNE values were lower in patients with complaints of numbness, pain, and weakness in the median nerve territory (p<0.05, for all comparisons), and lower in patients with hypoesthesia than in patients with normal neurological findings (p=0.023).ConclusionsThe MUNE technique is sensitive in detecting motor nerve involvement in CTS patients who present with sensorial findings, and it may be useful in detecting the loss of motor units during the early stages of CTS. Larger-scale prospective clinical trials assessing the effect of early intervention on the outcome of these patients would help in confirming the possible benefit of detecting subclinical motor-unit loss in CTS.