Loss of tissue volume in the central nervous system may provide an index of fixed neurological dysfunction in multiple sclerosis. Recent magnetic resonance studies have shown a modest relationship between clinical disability rating scores and transverse sectional area of the cervical spinal cord. To explore further the relationship between atrophy and disability in multiple sclerosis, we estimated the volumes of infratentorial structures from MRIs in a cross-sectional study of 41 patients, 21 with relapsing-remitting multiple sclerosis and 20 with secondary progressive multiple sclerosis. We used the Cavalieri method of modern design stereology with point counting to estimate the volume of brainstem, cerebellum and upper cervical spinal cord from three-dimensional MRIs acquired with an MPRAGE (Magnetization-prepared Rapid Acquisition Gradient Echo) sequence. The volume of the upper (C1-C3) cervical spinal cord was significantly correlated with a composite spinal cord score derived from the appropriate Functional Scale scores of the Expanded Disability Status Scale (r = -0.50, P < 0.01). The cerebellar (r = 0.49, P < 0.01) and brainstem (r = 0.34, P < 0.05) volumes correlated with the Scripp's Neurological Disability Rating Scale scores. The upper cervical cord volumes (r = -0.39, P < 0.01), but not the brainstem or cerebellar volumes, were significantly associated with disease duration. MRI-estimated structural volumes may provide a simple index of axonal and/or myelin loss, the presumed pathological substrates of irreversible impairment and disability in multiple sclerosis.

Atrophy is confined to the supratentorial compartment early in the disease course corresponding to the RR stage, but becomes more pronounced in the brain and cervical spinal cord in the SP phase. The estimate of cervical cord volume for SP MS is relevant to functional disability and may be helpful in monitoring MS evolution in the progressive form of disease.

Diffusion tensor MRI is used to define trajectories that reflect the long-range order of in vivo white matter (WM) fiber tracts. Fiber tracking is particularly prone to cumulative error from noise and partial volume along the length of the trajectory paths, but the overall shape of each path is anatomically meaningful. By considering only the long-range similarity of path shapes, a method of constructing 3D maps of specific WM structures has been developed. A trajectory is first computed from an operator-selected seed voxel, located within the anatomical structure of interest (SOI). Voxels from the same structure are then automatically identified based on the similarity of trajectory path shapes, assessed using Pearson's correlation coefficient. The corpus callosum and pyramidal tracts in 14 patients with multiple sclerosis, and in 10 healthy controls were mapped by this method, and the apparent diffusion coefficient (ADC) was measured. The ADC was significantly higher in patients than in controls, and higher in the corpus callosum than in the pyramidal tracts for both groups. The diffusion tensor imaging (DTI) modality is a recent development in MRI. Data acquired by DTI is used to construct a diffusion tensor in each voxel (1-3). The tensor describes the quantitative apparent diffusion properties local to that voxel through its eigenvalues and eigenvectors. This is fundamentally different from other modalities wherein each voxel contains only a scalar signal intensity.Diagonalizing the diffusion tensor yields three eigenvalues, each of which measures the apparent diffusion coefficient (ADC) in one of three orthogonal directions. In the brain, the random motion of the water molecules in cerebrospinal fluid (CSF) and gray matter (GM) ensures largely isotropic diffusion. In the white matter (WM), restricted movement of water perpendicular to the direction of the fiber tracts results in anisotropic diffusion, so that one eigenvalue is significantly larger than the others. The largest eigenvalue is associated with the major diffusion vector, which has been shown to point parallel to the fiber tracts (4) and provides a means of mapping specific tract bundles (5-8). The method described here utilizes the major diffusion vector to define trajectory paths that reflect the underlying shape of these bundles.Analysis of specific WM structures is important because the functional role of each is different. The ability to identify and segment a single structure would allow a detailed assessment of damage within it, and a subsequent comparison with relevant clinical data (9). With conventional MRI, a brain atlas may be used to map the different regions (10). This is, however, subject to error due to intersubject anatomical variability (8,9). Since the advent of DTI, several groups have attempted to utilize the vectorial information available from the diffusion tensor to identify different structures. Specific WM tract bundles have been identified by computing trajectories initialized from within regions of interest (ROI) (5,1...

Objective: To ascertain the relative frequencies of the causes of non-traumatic paraparesis and tetraparesis in adults. Design: Survey of patients enrolled prospectively over a 3 year period between 1986 and 1989 and review of their case notes 1 year after enrollment ceased (mean duration of follow up 30 months). Setting: Regional neurosciences centre in the UK serving over three million people in Merseyside and North Wales. Patients: Experienced clinicians from the centre saw most patients in the region with non-traumatic spastic paraparesis or tetraparesis. Primary investigation of patients was by myelography, for which patients were admitted to the centre. 585 consecutive patients with spastic paraparesis or tetraparesis were identi®ed by daily screening of all 2104 patients undergoing myelography or radiculography during the 3 year period, ie selection by the intention to investigate them for myelopathy. Exclusions: age under 15 years, previous myelography for myelopathy. Interventions: None. Main outcome measures: Numbers and proportions of patients with each condition or category of disease. Results: Commonest diagnoses were cervical spondylotic myelopathy (23.6%), extrinsic neoplastic or developmental tumour (16.4%), multiple sclerosis (9.1% rising to 17.8% after MRI of a selected group), and motor neurone disease (4.1%). Diagnosis was uncertain in 27.4%, falling to 18.6% after MRI. Conclusions: This survey shows the pattern of diseases producing non-traumatic myelopathy in the Mersey Region and in North Wales. Changing patterns of referral, investigation in peripheral hospitals and by non neurologically trained practitioners, and increasing use of outpatient MRI for primary investigation may make comparable surveys impossible in the future.