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Biomedical subjects

L Molinari

Publications and source records attributed to L Molinari.

At least 73 records · Page 4Linked to original sources
PubMed1984

EEG-based multivariate statistical analysis of sleep stages.

All-night sleep of 5 healthy male subjects was scored on the basis of EEG (paper records), EMG and EOG into the stages of wakefulness, REM sleep and slow sleep stages 1-4 according to Rechtschaffen and Kales. Spectral analysis for 8 EEG channels was then performed and spectral parameters (total power and coherence for certain frequency bands) extracted. Stepwise linear discriminant analysis was applied to these spectral parameters to see how well the polygraphically defined sleep stages were recognized. While the results of each intrasubject analysis were satisfactory (error rates between 10 and 15%), a more detailed analysis revealed that most errors occur between nearby slow sleep stages or between S1 and REM or wakefulness. This is related to the fact that Rechtschaffen and Kales classify into discrete stages an essentially continuous process and this is, to some extent, arbitrary. This interpretation is supported by examples. Cross-classification of each subject on the basis of the remaining 4 increases the error rates drastically. A simple way of avoiding this effect, by standardizing the individual data, is shown to work nicely in this case.

Adult↗
PubMed1983

[Application of beta-rhythm quantification to the treatment of epilepsy].

Abundance of beta activity in epileptic patients under clonazepam (Rivotril) treatment shows a significant correlation with the suppression of epileptic seizures and/or of epileptic EEG activity such as spikes and spike/wave complexes. In a longitudinal two-year study in 21 children with severe epilepsy treated with clonazepam, relative spectral peak power (RSPP) of beta activity was correlated with daily dose and serum concentration as well as with its effect on seizures and on epileptic EEG activity. Eight cases showed significant correlation of beta RSPP to daily dose, but only 4 to serum concentration. A significant (inverse) correlation was found between beta RSPP and the relative occurrence of epileptic EEG activity. Most illustrative were the individual follow-ups, with beta RSPP as an interesting additional parameter regarding the course of the epileptic condition. Quantitative measurement of beta activity by means of spectral analysis during long-term antiepileptic treatment promises important additional information on specific aspects in the individual case.

Benzodiazepinones↗
PubMed1983

Spectral analysis of all-night sleep EEG in healthy adults.

Power and coherence spectra were computed from all-night sleep EEG records in 6 healthy adult subjects. Derivations were from F3, F4, P3, P4, O1, O2, T3, and T4 to the vertex (Cz). Records were conventionally scored into sleep stages. Average power per sleep stage was maximal at frequencies 0.4-6 c/s in stage 4, at 6-10 c/s in either stage 3 or stage 4, at 12-14 c/s in stage 2 and at 14-30 c/s in stage 1. The average power range from highest values in the lowest frequency band to lowest values in the highest frequency band showed marked differences between sleep stages: It was lowest (12-14 dB) in stage 1, followed by stage 2 (20-22 dB), and stage 3 (16-28 dB), and largest in stage 4 (29-32 dB). REM sleep (15-16 sB) was between stage 1 and 2. The waking state showed an average power range of 11-15 dB. Alpha power at 8-10 c/s in occipital and parietal leads was remarkably constant during sleep, i.e. independent of sleep stage. Coherence showed maximal values at 2-8 c/s in REM sleep, at 8-12 c/s in stage 4, at 12-17 c/s in either stage 3 or 4, and at 17-30 c/s again in stage REM. There was significant coherence increase at 2-8 and 17-30 c/s from NREM to REM sleep, most pronounced between parietal to vertex derivations. Overall coherence between both occipital-to-vertex, or between occipital and parietal-to-vertex derivations, was essentially higher than in the other derivations. The results, essentially, give a comprehensive phenomenology of the dynamic spectral structure of all-night sleep EEG. They suggest that the different brain states during sleep (e.g. stage 1 NREM vs. REM) which are associated with different functions (e.g. hypnagogic hallucinations vs. dreams) differ in EEG spectral parameters if coherence is considered. Likewise, they suggest that studies of automatic sleep staging based exclusively on EEG spectral parameters appear promising.

Adult↗
PubMed1983

[Multivariate analysis of beta activity in clinical material].

Beta activity, as described by a number of parameters extracted from the power spectra, was submitted to several multivariate statistical techniques in search of interpretable subgroups. The difficulties of doing this on heterogeneous clinical material, as well as various shortcomings of the usual cluster analytical techniques, are outlined. Where longitudinal data are available, the analysis of beta activity may help in monitoring the effects of the therapy. Generally, the large variability between individuals and many sources of errors related to the processes of data collection and parameter extraction indicate that much work remains to be done before even a rough and clinically relevant typology of beta activity will become available.

Adolescent↗
PubMed1980

[The development of sleep behavior within the first 5 years of life].

Sleep behavior between 1/2 and 5 years in 320 Swiss children of the first Zurich longitudinal study is reported. The average length of night sleep between 1/2 and 5 years was 11 to 12 hours. Day sleep was noted in 96% of the children between 6 and 24 months, and in 20% at age 5. The total length of sleep between 6 and 18 months was 15 hours per 24 hours. It diminished to 12 hours at age 5. There was a large individual variability of day sleep and of night sleep at all ages. Positive correlations were found between the lengths of day sleep at different ages and between the lengths of night sleep at different ages. The length of day sleep and that of night sleep was negatively correlated between 2 and 5 years. Evening wakefulness was observed in 16% and night waking in 40 to 50% of all children. 18% woke up at least once each night. Night wakening between 2 and 5 years was positively correlated. In about half of the children with evening wakefulness and/or night wakening these sleep behaviors were considered as abnormal by the mothers.

Child, Preschool↗
PubMed1980

Analysis of the growth spurt at age seven (mid-growth spurt).

A statistical analysis of the mid-growth spurt from the data of the First Zurich Longitudinal Study is presented. A small but significant mid-growth spurt was found in most height and length measurements and in some girth and width measurements, such as chest circumference, bihumeral and biiliac width. The mid-growth spurt seemed to be slightly more pronounced in boys than in girls. The peak of the mid-growth spurt occurred between 6.5 and 8.5 years. The height of the peak varied from 0.3 to 0.7 cm/year for the different measurements (estimated from the smoothed median velocity curves). In a peak-centered analysis a mid-growth spurt of standing height was observed in two thirds of the children. It occurred about one year earlier in girls than in boys, and averaged 1.4 cm/year. The data indicate that the mid-growth spurt is due to a transient increased growth rate of the bones, particularly the long bones, and possibly of fat and muscle tissue. In contrast to the growth spurt of the extremities, the spurt of the rump height is not a true growth phenomenon, but the result of a postural change occurring at about 6.5 years of age.

Adolescent↗
PubMedDate not supplied

Contribution of growth phases to adult size.

Based on the data of the First Zurich Longitudinal Growth Study we investigate how interindividual differences in adult size arise in the variables leg height, sitting height and standing height, arm length, bi-iliac width and bihumeral width. Specifically, we are also interested in the question of whether across sexes and variables the same growth phases and the same parameters are predictive for achieving a certain adult size. A rather complex pattern emerges, demonstrating that regulation of growth is not the same for boys and girls and moreover is not the same for the six anthropometric variables studied. Prepubertal growth is characterized by its intensity (average velocity) and by its duration. Whereas duration has by itself no appreciable influence on adult size, prepubertal intensity determines adult size to a high degree across all variables and both sexes. The intensity of prepubertal growth determines adult size to a larger degree for boys than for girls. For a given size at the end of the prepubertal period, a small duration enhances the chance of obtaining a large adult size. Compared with prepubertal growth, the amount of variance of adult size explained is small for pubertal parameters, and--with respect to linear measures--significant for girls only. A small duration of prepubertal growth is in the following mainly compensated by a stronger pubertal spurt (PS), to a varying degree across variables. The overall picture which emerges indicates that sitting height--and to a lesser extent bihumeral width--develop in a more irregular fashion than the variables bi-iliac width and leg height.

Adolescent↗
PubMedDate not supplied

Sex dimorphism in growth.

While there is agreement that sex differences in height are small up to the onset of the pubertal spurt in girls, there has been some debate about the question of which, and to what extent, various growth phases contribute to the average adult sex difference of about 13 cm. There has been no consistent agreement between authors as to what extent this difference is due to the late onset of the pubertal spurt (PS) for boys and to what extent it is due to their more intense PS. In this paper, we investigate this question for the variables height, sitting and leg height, arm length, bihumeral and biiliac width. Biiliac width is a special case since both sexes have roughly the same adult size, but girls still have a shorter growing period. The gains for boys, when compared to girls, show a very different pattern across variables: for the legs, the additional growth due to the later spurt is responsible for most of the adult sex difference (64%). On the other hand, for bihumeral width and sitting height, the more intense PS contributes almost 50% to the adult sex difference. An analysis across variables indicates that increments from 1.5 to 6 years largely compensate for deviations in infant morphology from adult morphology.

Anthropometry↗
PubMedDate not supplied

Short-term and long-term variability of standard deviation scores for size in children.

PRIMARY OBJECTIVE: To quantify long-term and short-term variability in the standard deviation scores (SDS's) for six skeletal size variables and body mass index (BMI) in children and to compare average values of these quantities for boys with those of girls and to make comparisons across variables. METHODS AND PROCEDURES: The analysis is based on measurements made regularly for 120 boys and 112 girls from 1 month until 20 years for seven variables (standing height, sitting height, leg height, arm length, biiliac width, bihumeral width and BMI) as part of the first Zurich longitudinal growth study. Variation in these scores due to variablity in the timing of the pubertal spurt (PS) is separated out by rescaling the age axis on an individual basis and comparing children with the same developmental age rather than the same chronological age. For a given child, the relationship between the value of its SDS and age is modelled as the sum of an arbitrary (child dependent) smooth function plus an error term. The long-term variability for that child is defined to be the mean square of the departures of this smooth function from its mean level while the short-term variability is defined to be the variance of the error term. MAIN OUTCOMES AND RESULTS: Girls' SDS scores have significantly more long-term variability than those of boys, while there is no significant difference between the sexes for short-term variability. Bihumeral width, BMI and sitting height have significantly more long-term variation than the other variables. Bihumeral width and BMI have the largest short-term variability and standing height has the smallest. Correlations between long-term variability and adult size and timing and intensity of the PS were small. CONCLUSIONS: A useful way of assessing long-term and short-term variability of SDS's, which is widely applicable has been described and applied to data relating to the growth of children. The results of this analysis are intriguing. Why is the underlying growth process of girls more variable than that of boys? Differences across skeletal parameters are also interesting and deserve further consideration.

Body Height↗
PubMedDate not supplied

Growth processes leading to a large or small adult size.

BACKGROUND: The way in which a large size in anthropometric variables is achieved is a longstanding problem, since the pubertal spurt shows statistically and clinically little association with adult size (mostly studied for height). By analysing longitudinal growth of groups of subjects with a large or a small adult size separately for height, leg and sitting height, and bihumeral and biiliac width, we studied this problem in some detail. Of interest are growth patterns specific for these variables and for boys or girls. METHODS: The data consist of 120 boys and 112 girls followed longitudinally from 4 weeks until adulthood. Statistically, structural average velocity curves were computed for each variable and each subgroup separately for comparison. This velocity curve represents the average intensity and the average tempo of growth. Since the area under the velocity curve is adult size, differences in the growth process can be visualized. RESULTS: Both sexes show similar patterns in reaching a small or large adult size. The different variables, however, show marked differences. Only for legs is the pubertal spurt delayed for the large groups (with additional gains in prepubertal years). For sitting height and biiliac width, a slightly elevated velocity all along development (after 2 years) leads to a larger size and for bihumeral width the size of the pubertal peak is decisive. CONCLUSIONS: The steering of growth to a certain target size is qualitatively similar for boys and girls, but quite different for different anthropometric variables. This leads to questions about endocrinological control for various parts of the body and differential bone growth in development.

Adolescent↗
PubMedDate not supplied

Growth of early and late maturers.

BACKGROUND: This is a study on the growth of subgroups of normal children, maturing early or late, in the variables height, leg and sitting height, arm length, biiliac and bihumeral width. While a longer growth period affects adult height only marginally, less is known about the other variables. It is also of interest to see in what way a shorter growth period is compensated by a higher velocity. METHODS: Out of 120 boys and 112 girls followed from 4 weeks until adulthood, subgroups of 40 boys and 37 girls were formed with respect to the average timing (across variables) of the pubertal spurt as an indicator of maturity. RESULTS: Only leg height shows a smaller adult size for early maturers. The shorter growth period is compensated by a higher prepubertal velocity and a higher level in pubertal years. The pubertal peak is a little larger for early maturing boys but not for girls. CONCLUSIONS: There is an inherent pacemaker for growth that leads to the same adult size for a shorter growth period via a higher basic intensity. Legs are an exception since late maturers have, on average, longer legs as adults.

Adolescent↗
PubMedDate not supplied

TW3 bone age: RUS/CB and gender differences of percentiles for score and score increments.

BACKGROUND: Longitudinal data on bone age progression is scarce. AIM: The study aimed to present reference values for Tanner-Whitehouse 3 (TW3) bone age score and score increments, and to provide means and standard deviations of appearance time for all TW3 stages. Gender differences and differences between radio ulna and short bones (RUS) and carpal bone (CB) scores were studied. SUBJECTS AND METHODS: Bone age data collected for ages 3 months to 20 years in 232 subjects during the First Zurich Longitudinal Study (1954-1976) were used. Smoothed empirical percentiles of TW3 RUS and CB scores for age, of score increments for age and of score increments for attained score are presented. Means and standard deviations of the appearance times are calculated by parametric censored regression. RESULTS: There are clear differences between the RUS and CB scores and between the genders. Boys are delayed with respect to girls, with different delays for RUS and CB. For RUS, differences in maturation reflect the known differences of physical growth, with a later and more intense peak in boys. For CB, there is little difference in timing and intensity. However, girls reach the final score about 2 years earlier than boys. The consistently earlier mean appearance times in girls indicate that skeletal maturation is, already in childhood, more rapid in girls than in boys. There are significant gender differences in the sequence of appearance. CONCLUSION: Reference values for TW3 score and score increments and mean appearance times for stages add to existing knowledge and indicate important RUS/CB and gender differences, whose sources are largely unknown.

Adolescent↗
PubMedDate not supplied

Shape-invariant modelling of human growth.

A new approach to modelling human height growth is presented which is also suitable for other variables. In a mathematical algorithm, some guess about the functional form of this growth process is improved consistently using the data; the resulting shape-invariant model (SIM) allows an approximately bias-free fitting for longitudinal data from 1 to 20 years with six parameters assigned to each individual. The SIM approach and the use of velocities rather than distances proved to be suitable for biomathematical modelling in order to answer questions qualitative in nature. In comparison of an additive two-component SIM and one where the appearance of puberty inhibits further growth of the non-pubertal component ('switch-off model'), the latter proved to be superior in various aspects. Among the qualitative features found are notable: a pronounced midspurt, and a dip before the onset of puberty, as well as the asymmetry of the pubertal peak. A preliminary analysis of individual parameters confirmed some results found previously by other methods regarding sex differences and relations between parameters and with adult height.

Adolescent↗