Outcomes of ulnar nerve decompression for double crush syndrome.

BACKGROUND: Double crush syndrome (DCS) of the ulnar nerve, including cubital tunnel syndrome with ulnar tunnel syndrome (UTS), is uncommon. This study compares the postoperative outcomes of patients with isolated ulnar tunnel syndrome versus those with double crush syndrome of the elbow and ulnar tunnel. METHODS: This study enrolled 22 patients: 12 underwent cubital tunnel surgery and ulnar tunnel surgery (double crush group); and 10 underwent only ulnar tunnel decompression (isolated UTS group). Postoperative effect evaluation of patients in both groups after at least 2.6 years (mean, 5.1 years and 5.7 years, respectively). Statistical analysis compared postoperative function, physical examination, and patient-reported satisfaction between groups. RESULTS: In terms of postoperative grip strength, there was no difference between the postoperative states of the two groups (0.88 ± 0.04 versus 0.87 ± 0.05), while there was statistical difference in terms of the increment of the grip strength (p = 0.036); the two-point discrimination of isolated UTS group is better than the double crush group (90% versus 83.3%); double crush patients reported lower satisfaction than the UTS group (90% versus 83.3%). CONCLUSIONS: At a minimum of 2.6 years after the nerve decompression, the patients of isolated UTS group are likely to have superior grip strength increment than patients with a history of double crush surgery, and there is no big difference in the final recovery situation. The sensation and satisfaction of isolated UTS group after nerve release were better compared with patients following double crush surgery.

Recognizing mild cognitive impairment based on network connectivity analysis of resting EEG with zero reference.

The diagnosis of mild cognitive impairment (MCI) is very helpful for early therapeutic interventions of Alzheimer's disease (AD). MCI has been proven to be correlated with disorders in multiple brain areas. In this paper, we used information from resting brain networks at different EEG frequency bands to reliably recognize MCI. Because EEG network analysis is influenced by the reference that is used, we also evaluate the effect of the reference choices on the resting scalp EEG network-based MCI differentiation. The conducted study reveals two aspects: (1) the network-based MCI differentiation is superior to the previously reported classification that uses coherence in the EEG; and (2) the used EEG reference influences the differentiation performance, and the zero approximation technique (reference electrode standardization technique, REST) can construct a more accurate scalp EEG network, which results in a higher differentiation accuracy for MCI. This study indicates that the resting scalp EEG-based network analysis could be valuable for MCI recognition in the future.