MRI evaluation of brain iron in earlier- and later-onset Parkinson's disease and normal subjects.

Tissue iron levels in the extrapyramidal system of earlier- and later-onset Parkinson's disease (PD) subjects were evaluated in vivo using a magnetic resonance imaging (MRI) method. The method involves scanning subjects in both high- and low-field MRI instruments, measuring tissue relaxation rate (R2), and calculating the field-dependent R2 increase (FDRI) which is the difference between the R2 measured with the two MRI instruments. In tissue, only ferritin iron is known to increase R2 in a field-dependent manner and the FDRI measure is a specific measure of this tissue iron pool. Two groups of male subjects with PD and two age-matched groups of normal control males were studied. The two groups of six subjects with PD consisted of subjects with earlier- or later-onset (before or after age 60) PD. FDRI was measured in five subcortical structures: the substantia nigra reticulata (SNR), substantia nigra compacta (SNC), globus pallidus, putamen, and caudate nucleus, and in one comparison region; the frontal white matter. Earlier-onset PD subjects had significant (p < 0.05) increases in FDRI in the SNR, SNC, putamen, and globus pallidus, while later-onset PD subjects had significantly decreased FDRI in the SNR when compared to their respective age-matched controls. Controlling for illness duration or structure size did not meaningfully alter the results. Published post-mortem studies on SN iron levels indicate decreased ferritin levels and increased free iron levels in the SN of older PD subjects, consistent with the decreased FDRI observed in our later-onset PD sample, which was closely matched in age to the post-mortem PD samples. The FDRI results suggest that disregulation of iron metabolism occurs in PD and that this disregulation may differ in earlier- versus later-onset PD.

Quantifying deficiencies associated with Parkinson's disease by use of time-series analysis.

In order to assess quantitatively the state of the disease or the effect of drugs in parkinsonian patients, it would be helpful to have at our disposal mathematical models that reflect in their parameter values the deficiencies associated with the disease. This paper proposes a class of such models that are easily obtained in practice and lend themselves to useful interpretations. A pursuit manual tracking experiment is used to derive these models for patients with Parkinson's disease undergoing drug therapy and for normal controls. The input (one-dimensional visual target) and operator's output (manual tracking) are analyzed using a time series approach aiming at obtaining an auto-regressive moving-average (ARMA) model that minimizes the mean square error between the actual and model response. This mathematical model takes the form of a difference equation expressing, in discrete time, the present output value as a linear combination of past output values and past and present input values. Our experimental results indicate that a difference equation (ARMA model) involving the two previous output values and the present and past input values fits best both patient and control data. A comparison between the mean estimated model parameters for patients and controls shows a statistically significant difference in two of these parameters. The first parameter, which is significantly increased in patients, relates the current response of the patient to the immediately preceding response which represents an increased 'damping' of the motor dynamics, reflecting the muscular rigidity associated with the disease (motor disorder). The other parameter, which is significantly decreased in patients, represents the relative degree to which the current response of the patient is influenced by the target position information at the previous point in time which points to a deficiency in sensing/processing of this information (possible a sensory disorder). Our results also showed a marked reduction in the mean-square error of a second trial of the experiment in normal subjects but failed to do so for the patients, possibly indicating learning deficiencies associated with the disease.

Spatial distribution of potential in a flat cell. Application to the catfish horizontal cell layers.

An analytical solution is obtained for the three-dimensional spatial distribution of potential inside a flat cell, such as the layer of horizontal cells, as a function of its geometry and resistivity characteristics. It was found that, within a very large range of parameter values, the potential is given by [Formula: see text] where r = rho/rho(0), z = z/rho(0), rho = (R(i)/R(m)).rho(0), delta = h/rho(0); K is a constant; J is the assumed synaptic current; rho, z are cylindrical coordinates; rho(0) is the radius of the synaptic area of excitation; h is the cell thickness; and R(i), R(m) are the intracellular and membrane resistivities, respectively. Formula A closely fits data for the spatial decay of potential which were obtained from the catfish internal and external horizontal cells. It predicts a decay which is exponential down to about 40% of the maximum potential but is much slower than exponential below that level, a characteristic also exhibited by the data. Such a feature in the decay mode allows signal integration over the large retinal areas which have been observed experimentally both at the horizontal and ganglion cell stages. The behavior of the potential distribution as a function of the flat cell parameters is investigated, and it is found that for the range of the horizontal cell thicknesses (10-50 mu) the decay rate depends solely on the ratio R(m)/R(i). Data obtained from both types of horizontal cells by varying the diameter of the stimulating spot and for three widely different intensity levels were closely fitted by equation A. In the case of the external horizontal cell, the fit for different intensities was obtained by varying the ratio R(m)/R(i); in the case of the internal horizontal cell it was found necessary, in order to fit the data for different intensities, to vary the assumed synaptic current J.

White-noise analysis of a neuron chain: an application of the Wiener theory.

The Wiener theory of nonlinear system identification was applied to a three-stage neuron chain in the catfish retina in order to determine the functional relationship between the artificial polarization of the horizontal cell membrane potential and the resulting discharge of the ganglion cell. A mathematical model was obtained that can predict quantitatively, with reasonable accuracy, the nonlinear, dynamic behavior of the neuron chain. The applicability of the method is discussed. We conclude that this is a very powerful method in the analysis of information transfer in the central nervous system.