Serum levels of brain-derived neurotrophicfactor correlate with the number of T2 MRI lesions in multiple sclerosis

The objective of the present study was to determine if there is a relationship between serum levels of brain-derived neurotrophic factor (BDNF) and the number of T2/fluid-attenuated inversion recovery (T2/FLAIR) lesions in multiple sclerosis (MS). The use of magnetic resonance imaging (MRI) has revolutionized the study of MS. However, MRI has limitations and the use of other biomarkers such as BDNF may be useful for the clinical assessment and the study of the disease. Serum was obtained from 28 MS patients, 18-50 years old (median 38), 21 women, 0.5-10 years (median 5) of disease duration, EDSS 1-4 (median 1.5) and 28 healthy controls, 19-49 years old (median 33), 19 women. BDNF levels were measured by ELISA. T1, T2/FLAIR and gadolinium-enhanced lesions were measured by a trained radiologist. BDNF was reduced in MS patients (median [range] pg/mL; 1160 [352.6-2640]) compared to healthy controls (1640 [632.4-4268]; P = 0.03, Mann-Whitney test) and was negatively correlated (Spearman correlation test, r = -0.41; P = 0.02) with T2/FLAIR (11-81 lesions, median 42). We found that serum BDNF levels were inversely correlated with the number of T2/FLAIR lesions in patients with MS. BDNF may be a promising biomarker of MS.

Brain tissue segmentation using q-entropy in multiple sclerosis magnetic resonance images

The loss of brain volume has been used as a marker of tissue destruction and can be used as an index of the progression of neurodegenerative diseases, such as multiple sclerosis. In the present study, we tested a new method for tissue segmentation based on pixel intensity threshold using generalized Tsallis entropy to determine a statistical segmentation parameter for each single class of brain tissue. We compared the performance of this method using a range of different q parameters and found a different optimal q parameter for white matter, gray matter, and cerebrospinal fluid. Our results support the conclusion that the differences in structural correlations and scale invariant similarities present in each tissue class can be accessed by generalized Tsallis entropy, obtaining the intensity limits for these tissue class separations. In order to test this method, we used it for analysis of brain magnetic resonance images of 43 patients and 10 healthy controls matched for gender and age. The values found for the entropic q index were 0.2 for cerebrospinal fluid, 0.1 for white matter and 1.5 for gray matter. With this algorithm, we could detect an annual loss of 0.98 percent for the patients, in agreement with literature data. Thus, we can conclude that the entropy of Tsallis adds advantages to the process of automatic target segmentation of tissue classes, which had not been demonstrated previously.