Faculty Bibliography

The objective of this study was to determine the clinical characteristics of multiple sclerosis (MS) in African American (AA) patients in the New York State Multiple Sclerosis Consortium (NYSMSC) patient registry. The NYSMSC is a group of 18 MS centers throughout New York State organized to prospectively assess clinical characteristics of MS patients. AAs comprise 6% (329) of the total NYSMSC registrants (5602). Demographics, disease course, therapy, and socioeconomic status were compared in AA registrants versus nonAfrican Americans (NAA). There was an increased female preponderance and a significantly younger age at diagnosis in the AA group. AA patients were more likely to have greater disability with increased disease duration. No differences were seen in types of MS and use of disease modifying therapies. Our findings suggest a racial influence in MS. Further genetic studies that consider race differences are warranted to elucidate mechanisms of disease susceptibility

Fatigue is a common disabling symptom of multiple sclerosis (MS). It is often considered a state of exhaustion distinct from depressed mood or physical weakness. Fatigue can be assessed by either self-report scales or performance-based measures; however, neither method captures all features of fatigue. Fatigue in MS frequently leads to unemployment. It is associated with a sense of loss of control over one's environment, low positive affect, psychological distress and neurological impairment. To date there is no reproducible neuroimaging marker or biological correlate that has been identified. Proposed pathological mechanisms of fatigue in MS include neuronal factors such as dysfunction of premotor, limbic, basal ganglia or hypothalamic areas; disruption of the neuroendrocrine axis leading to low arousal; alteration in serotoninergic pathways; changes in neurotransmitter levels; and altered CNS functioning caused by a disruption of the immune response. Treatment of fatigue is best approached in a multidisciplinary fashion that incorporates nonpharmacological interventions as well as medication. Amantadine and modafinil are among the most commonly used medications for fatigue associated with MS. Both medications have been studied with positive results in controlled clinical trials. Additional research towards measurement and pathogenesis of fatigue will hopefully lead to improved therapies.

We present an automatic mixture-based algorithm for segmentation of brain tissues (white and gray matters-WM and GM), cerebral spinal fluid (CSF), and brain lesions to quantitatively analyze multiple sclerosis. The method performs intensity-based tissue classification using multispectral magnetic resonance (MR) images based on a stochastic model. With the existence of white Gaussian noise and spatially invariant blurring in acquired MR images, a Karhunen-Loeve (K-L) domain Wiener filter is applied for accurate noise reduction and resolution restoration on blurred and noisy images to minimize the partial volume effect (PVE), which is a major limiting factor for the quantitative analysis. Following that, we utilize a Markov random field Gibbs model to integrate the local spatial information into the well-established expectation-maximization model-fitting algorithm. Each voxel is then classified by a maximum a posterior (MAP) criterion, indicating its probabilities of belonging to each class, i.e., each voxel is labeled as a mixel with different tissue percentages, leading to further minimization of the PVE. The volumes of WM, GM, CSF, and brain lesions are extracted from the mixture-based segmentation and the corresponding brain atrophies are computed. In this study, we have investigated the accuracy and repeatability of the algorithm with inclusion of noise analysis and point spread function for image resolution enhancement. Experimental results on phantom, healthy volunteer, and patient studies are presented.

We present a fully automatic mixture-based algorithm for segmentation of brain tissues (white and gray matters - WM and GM), cerebral spinal fluid (CSF) and brain lesion to quantitatively analyze multiple sclerosis. The method performs intensity-based tissue classification using multispectral magnetic resonance (MR) images based on a stochastic model. With the existence of white Gaussian noise and spatially invariant blurring in acquired MR images, a Karhunen-Loeve (K-L) domain Wiener filter is applied for an accurate noise reduction and resolution restoration on blurred and noisy images to minimize the partial volume effect (PVE), which is a major limiting factor for the quantitative analysis. Following that, we utilize a Markov random field Gibbs model to integrate the local spatial information into the established expectation-maximization model-fitting algorithm. Each voxel is then classified by a mixture-based maximum a posterior (MAP) criterion, indicating its probabilities of belonging to each class, i.e., each voxel is labeled as a mixel with different tissue percentages, leading to further minimization of the PVE. The volumes of WM, GM and CSF are extracted from the mixture-based segmentation and the corresponding brain atrophies are computed. In this study, we have investigated the accuracy and repeatability of the algorithm with inclusion of noise analysis and point spread function for image resolution enhancement. Experimental results on both phantom and healthy volunteer studies are presented.