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Amyotrophic lateral sclerosis (ALS) is a fatal progressive neurodegenerative disorder. Current diagnosis time is about 12-months due to lack of objective methods. Previous brain white matter voxel based morphometry (VBM) studies in ALS reported inconsistent results. Fractal dimension (FD) has successfully been used to quantify brain WM shape complexity in various neurological disorders and aging, but not yet studied in ALS. Therefore, we investigated WM morphometric changes using FD analyses in ALS patients with different clinical phenotypes. We hypothesized that FD would better capture clinical features of the WM morphometry in different ALS phenotypes than VBM analysis. High resolution MRI T1-weighted images were acquired in controls (n = 11), and ALS patients (n = 89). ALS patients were assigned into four subgroups based on their clinical phenotypes.VBM analysis was carried out using SPM8. FD values were estimated for brain WM skeleton, surface and general structure in both controls and ALS patients using our previously published algorithm. No significant VBM WM changes were observed between controls and ALS patients and among the ALS subgroups. In contrast, significant (p<0.05) FD reductions in skeleton and general structure were observed between ALS with dementia and other ALS subgroups. No significant differences in any of the FD measures were observed between control and ALS patients. FD correlated significantly with revised ALS functional rating scale (ALSFRS-R) score a clinical measure of function. Results suggest that brain WM shape complexity is more sensitive to ALS disease process when compared to volumetric VBM analysis and FD changes are dependent on the ALS phenotype. Correlation between FD and clinical measures suggests that FD could potentially serve as a biomarker of ALS pathophysiology, especially after confirmation by longitudinal studies.

This paper studied the intellectual structure of urban studies through a co-citation analysis of its thirty-eight representative journals from 1992 to 2002. Relevant journal co-citation data were retrieved from Social SciSearch, and were subjected to cluster analysis, multidimensional scaling, and factor analysis. A cluster-enhanced two-dimensional map was created, showing a noticeable subject variation along the horizontal axis depicting four clusters of journals differentiated into mainstream urban studies, regional science and urban economics, transportation, and real estate finance. The cluster of the mainstream urban studies journals revealed a higher degree of interdisciplinarity than other clusters. The four-factor solution, though not a perfect match for the cluster solution, demonstrated the interrelationships among the overlapping journals loaded high on different factors. The results also showed a strong negative correlation between the coordinates of the horizontal axis and the mean journal correlation coefficients reflecting the subject variation, and a less revealing positive correlation between the coordinates of the vertical axis and the mean journal correlation coefficients.

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