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Visual evoked potentials to motion-onset stimulation (M-VEPs) gradually attenuate in amplitude during examination. The observed decline in averaged responses can be caused by decreases in single response magnitudes and/or increased variability in a response delays, that is, latency jittering. To illuminate the origins of the suppression of M-VEPs during stimuli repetition, we used correlation technique to estimate an upper bound of possible latency jittering of single sweeps and we evaluated the effect of its correction on the amplitudes of three M-VEP dominant peaks P1, N2 and P3. During prolonged visual motion stimulation, the variability of corrective latency shifts in the occipital region increased (r = 0.35: 0.44) and the number of single responses corresponding to the average curve declined in occipital and parietal derivations (r = −0.48: −0.62). While the P1 peak amplitude did not exhibit any time-specific behaviour, the N2 amplitude exhibited a significant decay of 29.4 % that was partially reduced to 16.6 % in the central occipital derivation by the latency jitter and non-correspondence corrections. The strongest attenuation (32.7 %) was observed in the P3 amplitude and was less sensitive to the corrections, dropping only to 27.9 %. The main part of the response suppression to repeated motion stimulation was caused by amplitude drop and represents non-stationary process that likely correspond to a fatigue model. The rise of variability in latency jitter correction and the reduction in single responses correlated with the M-VEP were significant factors associated with prolonged motion stimulation. The relation of these parameters to a hypothetical veridical response is ambiguous and can be caused by a time shift of the response or by a change of signal-to-noise ratio. Using selective averaging and latency jitter correction, the effect of response suppression was partially removed.

Distributions of collagen fibrils and smooth muscle cells nuclei (SMC) orientations were investigated in histological sections obtained from medial layer of human thoracic aorta. The sections were stained with van Gieson. Digital image of the sections was converted to binary pixel map with target component represented in white (logical unity). Selected image was indicated to elucidate the sensitivity to threshold conditions and three different binary conversions were performed. Consecutively images were processed by in house developed software BinaryDirections which uses an algorithm of the rotation line segment to determine significant directions in digital images. The algorithm operates in the way that in each target pixel a line segment is rotated step by step to explore neighborhood of the pixel. Exploring the neighborhood the number of unity pixels in each rotating step is determined. The distribution of orientations in the entire image is gained after normalization either as averaged density distribution from all pixels or as an histogram of the most abundant directions in the image. It was found that both collagen fibrils and SMC nuclei analyses give significant peak in distributions. Its value ranges between 45° - 65° (defined as declination from longitudinal axis of an artery in a tubular configuration) depending on the method. It implies that preferred direction in aortic medial layer was oriented circumferentially rather than longitudinally. This conclusion was almost independent of the threshold setup. Results suggest that the orientations of SMC nuclei and collagen fibrils are mutually correlated and determination of collagen fibril orientations, which may be stained in insignificant manner, could be supported with SMC nuclei orientations to obtain more realistic models.

Realistic simulations and engineering innovations help to improve therapy of blood vessel diseases by involving detailed information about a blood vessel wall architecture. This information is especially needed in a constitutive modeling of tissues. This study presents a computational method for evaluating of arterial internal structure based on analysis of histological sections. Presented algorithm searches dominant directions in binary pixel maps of histological sections filtered according to a constituent, which was previously stained. Particular constituent is represented by pixels with the value one in a black and white image. Neighborhood of each non-zero pixel is analyzed by a rotating line segment. Dependence between the number of non-zero image pixels shared by the line segment and the segment angle is found. The rotation with maximum number of non-zero pixels determines local dominant direction for the pixel in the given neighborhood. By applying this procedure to all non-zero pixels of the binary image an empirical distribution function of preferred directions in the whole image is provided. This method was tested in four histological sections of human abdominal aorta. A distribution of collagen fibrils’ directions was evaluated for medial and adventitial layer. Two sections for every layer were analyzed. one with a long axis aligned to the axial direction of the aorta and one aligned circumferentially. Graphs of empirical probability density functions are presented. Results proved that collagen fibrils are not aligned in finite number of directions. However, the distribution in adventitial layer had two significant peaks almost symmetrically disposed to circumferential and axial direction of the artery wall. Peaks correspond to ≈67° and ≈112° (angle is measured from axial to circumferential direction). Sections of the medial layer did not provide unambiguous results.