Measure Of Temporal Variability Research Articles

Measurements of temporal variability of acoustic scattering from the seafloor in shallow-water sandy sites.

In the ocean, the performance of active sonar systems depends on the acoustic properties of the seafloor. Daily to monthly variations in near-bottom hydrodynamics and benthic biological activity may affect seafloor properties which then influence the acoustic response of the seafloor. The dependence of seafloor scatter on evolving environmental parameters was investigated using high-frequency active acoustic systems. Seafloor scattering measurements were analyzed in a series of experiments (two weeks to five months in duration) from downward-looking sonars oriented at 20° grazing angle with respect to the seafloor. Data were obtained in two shallow water locations near Portsmouth, New Hampshire, USA: a wave-dominated site and a site dominated by tidal currents. The bottom type for both sites was gravelly sand. The experimental set-up consisted of a tripod placed on the seafloor equipped with three transducers operating at 38, 70, and 200 kHz, a wave-sensing CTD, and underwater cameras. Scattering strength time series were obtained taking into account the local seafloor slope. Results show that there is variability in scattering strength (both in mean levels and distributions). Large variations often coincided with storm events, suggesting that this variability may be driven by changes in bottom roughness caused by storm-related hydrodynamics.

Reliability of variability and complexity measures for task and task-free BOLD fMRI.

Brain activity continuously fluctuates over time, even if the brain is in controlled (e.g., experimentally induced) states. Recent years have seen an increasing interest in understanding the complexity of these temporal variations, for example with respect to developmental changes in brain function or between-person differences in healthy and clinical populations. However, the psychometric reliability of brain signal variability and complexity measures-which is an important precondition for robust individual differences as well as longitudinal research-is not yet sufficiently studied. We examined reliability (split-half correlations) and test-retest correlations for task-free (resting-state) BOLD fMRI as well as split-half correlations for seven functional task data sets from the Human Connectome Project to evaluate their reliability. We observed good to excellent split-half reliability for temporal variability measures derived from rest and task fMRI activation time series (standard deviation, mean absolute successive difference, mean squared successive difference), and moderate test-retest correlations for the same variability measures under rest conditions. Brain signal complexity estimates (several entropy and dimensionality measures) showed moderate to good reliabilities under both, rest and task activation conditions. We calculated the same measures also for time-resolved (dynamic) functional connectivity time series and observed moderate to good reliabilities for variability measures, but poor reliabilities for complexity measures derived from functional connectivity time series. Global (i.e., mean across cortical regions) measures tended to show higher reliability than region-specific variability or complexity estimates. Larger subcortical regions showed similar reliability as cortical regions, but small regions showed lower reliability, especially for complexity measures. Lastly, we also show that reliability scores are only minorly dependent on differences in scan length and replicate our results across different parcellation and denoising strategies. These results suggest that the variability and complexity of BOLD activation time series are robust measures well-suited for individual differences research. Temporal variability of global functional connectivity over time provides an important novel approach to robustly quantifying the dynamics of brain function. PRACTITIONER POINTS: Variability and complexity measures of BOLD activation show good split-half reliability and moderate test-retest reliability. Measures of variability of global functional connectivity over time can robustly quantify neural dynamics. Length of fMRI data has only a minor effect on reliability.

A-111 Ecological Momentary Assessment of Mood after Moderate to Severe Traumatic Brain Injury

Abstract Objective In persons with moderate–severe traumatic brain injury (msTBI), we compared traditional measures of mood with dynamic measures of affect derived from ecological momentary assessment (EMA), for the purpose of validating the EMA indices and exploring their unique contributions to emotional assessment. Method 23 community-dwelling participants with chronic msTBI were enrolled in a treatment trial for anxiety and/ or depression. At baseline, participants completed the Brief Symptom Inventory-18 Depression and Anxiety subscales (BSI-D, BSI-A) and the Environmental Reward Observation Scale (EROS), a measure of everyday pleasure and reward. EMA data, including the Positive and Negative Affect Scale (PANAS), were collected via smartphone 5 times daily for 7–14 days prior to treatment (M = 8.65; SD = 1.87). Spearman correlations tested associations between baseline BSI-D, BSI-A, and EROS scores with both overall means and temporal variability measures for positive and negative affect (PA, NA). Results Mean PA was significantly correlated with BSI-D (rho −0.60, p < 0.05) and EROS (rho 0.72, p < 0.01). Mean NA and affect variability measures were uncorrelated with baseline scores. NA mean and variability were intercorrelated (rho 0.87, p < 0.001), but this was not the case for PA. Conclusion EMA measures of averaged positive affect showed robust relationships with retrospective measures of depression and environmental reward, providing support for the validity of EMA measures of PA, and for use of the EROS in msTBI. While negative findings must be interpreted with caution, the lack of association of affective variability with retrospective measures suggest a unique role for EMA in examining temporal dynamics of affect.

Drivers of Local Ocean Heat Content Variability in ECCOv4

AbstractVariation in upper-ocean heat content is a critical factor in understanding global climate variability. Using temperature anomaly budgets in a two-decade-long physically consistent ocean state estimate (ECCOv4r3; 1992–2015), we describe the balance between atmospheric forcing and ocean transport mechanisms for different depth horizons and at varying temporal and spatial resolutions. Advection dominates in the tropics, while forcing is most relevant at higher latitudes and in parts of the subtropics, but the balance of dominant processes changes when integrating over greater depths and considering longer time scales. While forcing is shown to increase with coarser resolution, overall the heat budget balance between it and advection is remarkably insensitive to spatial scale. A novel perspective on global ocean heat content variability was made possible by combining unsupervised classification with a measure of temporal variability in heat budget terms to identify coherent dynamical regimes with similar underlying mechanisms, which are consistent with prior research. The vast majority of the ocean includes significant contributions by both forcing and advection. However advection-driven regions were identified that coincide with strong currents, such as western boundary currents, the Antarctic Circumpolar Current, and the tropics, while forcing-driven regions were defined by shallower wintertime mixed layers and weak velocity fields. This identification of comprehensive dynamical regimes and the sensitivity of the ocean heat budget analysis to exact resolution (for different depth horizons and at varying temporal and spatial resolutions) should provide a useful orientation for future studies of ocean heat content variability in specific ocean regions.

Data-driven discovery of changes in clinical code usage over time: a case-study on changes in cardiovascular disease recording in two English electronic health records databases (2001–2015)

ObjectivesTo demonstrate how data-driven variability methods can be used to identify changes in disease recording in two English electronic health records databases between 2001 and 2015.DesignRepeated cross-sectional analysis that applied...

Altered resting-state dynamic functional brain networks in major depressive disorder: Findings from the REST-meta-MDD consortium

BackgroundMajor depressive disorder (MDD) is known to be characterized by altered brain functional connectivity (FC) patterns. However, whether and how the features of dynamic FC would change in patients with MDD are unclear. In this study, we aimed to characterize dynamic FC in MDD using a large multi-site sample and a novel dynamic network-based approach.MethodsResting-state functional magnetic resonance imaging (fMRI) data were acquired from a total of 460 MDD patients and 473 healthy controls, as a part of the REST-meta-MDD consortium. Resting-state dynamic functional brain networks were constructed for each subject by a sliding-window approach. Multiple spatio-temporal features of dynamic brain networks, including temporal variability, temporal clustering and temporal efficiency, were then compared between patients and healthy subjects at both global and local levels.ResultsThe group of MDD patients showed significantly higher temporal variability, lower temporal correlation coefficient (indicating decreased temporal clustering) and shorter characteristic temporal path length (indicating increased temporal efficiency) compared with healthy controls (corrected p < 3.14×10−3). Corresponding local changes in MDD were mainly found in the default-mode, sensorimotor and subcortical areas. Measures of temporal variability and characteristic temporal path length were significantly correlated with depression severity in patients (corrected p < 0.05). Moreover, the observed between-group differences were robustly present in both first-episode, drug-naïve (FEDN) and non-FEDN patients.ConclusionsOur findings suggest that excessive temporal variations of brain FC, reflecting abnormal communications between large-scale bran networks over time, may underlie the neuropathology of MDD.

A speech measure for early stratification of fast and slow progressors of bulbar amyotrophic lateral sclerosis: lip movement jitter

Objective: To assess the utility of novel measures derived from a rapid syllable repetition task (i.e. oral dysdiadochokinesis [DDK]) in early stratification of fast and slow progressive bulbar amyotrophic lateral sclerosis (ALS) and prediction of bulbar disease progression rate. Methods: Fifty-four individuals with ALS were tracked longitudinally on their oral DDK and global bulbar/speech performance (i.e. bulbar subscore on the ALS Functional Rating Scale-Revised [ALSFRS-R]; articulation rate during passage reading) for a four-month average duration. Based on the bulbar deterioration rate over the tracked period, the participants were stratified as 14 fast bulbar progressors and 40 slow bulbar progressors using a posteriori classification approach. To determine if oral DDK performance predicts the differential bulbar disease progression trajectories in these individuals during the early stages of the tracked period (prior to significant bulbar/speech signs), twenty-two measures of lip motor performance in an oral DDK task were derived to (1) differentiate fast and slow bulbar progressors using the Receiver Operating Characteristic analysis and (2) predict bulbar disease progression rates across all individuals using linear regressions. Results: Movement jitter, a measure of temporal variability of alternating lip movement during DDK, showed 80% sensitivity and 95% specificity in differentiating fast and slow bulbar progressors early in the disease, and outperformed the ALSFRS-R bulbar subscore and articulation rate. Movement jitter also predicted bulbar disease progression rates across participants. Conclusion: Findings provided preliminary validation of the clinical value of movement jitter during oral DDK in patient stratification and bulbar disease prognosis.

Assessing spatiotemporal variability of brain spontaneous activity by multiscale entropy and functional connectivity

Brain signaling occurs across a wide range of spatial and temporal scales, and analysis of brain signal variability and synchrony has attracted recent attention as markers of intelligence, cognitive states, and brain disorders. However, current technologies to measure brain signals in humans have limited resolutions either in space or in time and cannot fully capture spatiotemporal variability, leaving it untested whether temporal variability and spatiotemporal synchrony are valid and reliable proxy of spatiotemporal variability in vivo. Here we used optical voltage imaging in mice under anesthesia and wakefulness to monitor cortical voltage activity at both high spatial and temporal resolutions to investigate functional connectivity (FC, a measure of spatiotemporal synchronization), Multi-Scale Entropy (MSE, a measure of temporal variability), and their relationships to Regional Entropy (RE, a measure of spatiotemporal variability). We observed that across cortical space, MSE pattern can largely explain RE pattern at small and large temporal scales with high positive and negative correlation respectively, while FC pattern strongly negatively associated with RE pattern. The time course of FC and small scale MSE tightly followed that of RE, while large scale MSE was more loosely coupled to RE. fMRI and EEG data simulated by reducing spatiotemporal resolution of the voltage imaging data or considering hemodynamics yielded MSE and FC measures that still contained information about RE based on the high resolution voltage imaging data. This suggested that MSE and FC could still be effective measures to capture spatiotemporal variability under limitation of imaging modalities applicable to human subjects. Our results support the notion that FC and MSE are effective biomarkers for brain states, and provide a promising viewpoint to unify these two principal domains in human brain data analysis.

The consecutive disparity index, D: a measure of temporal variability in ecological studies

AbstractTemporal variability in ecological processes has attracted the attention of many disciplines in ecology, which has resulted in the development of several quantitative indices. The coefficient of variation (CV = standard deviation × mean−1) is still one of the most commonly used indices to assess temporal variability, despite being known to present several problems on its assessment (e.g., mean dependence or high sensitivity to rare events). The proportional variability (PV) index was developed to solve some of the CV's drawbacks, but, so far, no variability index takes into account the chronological order of the values in time series. In this paper, we introduce the consecutive disparity index (D), a temporal variability index that takes into account the chronological order of the values, assessing the average rate of change between consecutive values. We used computer simulations and empirical data for fruit production in trees, bird counts, and rodent captures to compare the behavior of D, PV, and CV under different scenarios. D was sensitive to changes in temporal autocorrelation in the negative autocorrelation range, and CV and PV were sensitive in the positive autocorrelation range despite not considering the chronological order of the values. The CV, however, was highly dependent on the mean of the time series, while D and PV were not. Our results demonstrate that, like PV, D solves many of the problems of the CV index while taking into account the chronological order of values in time series. The mathematical and statistical features of D make it a suitable index for analyzing temporal variability in a wide range of ecological studies.

Temporal Variability of Primary Production Explains Marine Ecosystem Structure and Function

Understanding drivers of ecosystem structure and function is a pervasive goal in academic and applied research. We used 24 synthetic ecosystem-level indices derived from trophic models, and independently derived data for Net Primary Productivity, to investigate drivers of ecosystem structure and function for 43 marine ecosystems distributed in all oceans of the world and including coastal, estuaries, mid-ocean islands, open-ocean, coral reef, continental shelf, and upwelling ecosystems. Of these indices, ecosystem Biomass, Primary Production, Respiration, the ratio of Biomass to Total System Throughput (sum of total energy flow into and out of an ecosystem as well as between ecosystem components), the ratio of Production to Biomass, Residence Time (mean time that a unit of energy remains in the ecosystem), Average Trophic Level, and Relative Ascendency (index of organization and complexity of a food web) displayed relationships with measures of Net Primary Productivity (NPP). Across all ecosystems, relationships were stronger with seasonal and interannual variability of NPP as compared to mean NPP. Both measures of temporal variability were combined into multivariate predictive relationships for each ecosystem index, with r2 values ranging from 0.14 to 0.49 and Akaike’s information criteria values from − 8.44 to 3.26. Our results indicate that despite large geographic and environmental differences, temporal variability of NPP is strongly linked to the structure and function of marine ecosystems.

Listeners use temporal information to identify French- and English-accented speech

Which acoustic cues can be used by listeners to identify speakers’ linguistic origins in foreign-accented speech? We investigated accent identification performance in signal-manipulated speech, where (a) Swiss German listeners heard native German speech to which we transplanted segment durations of French-accented German and English-accented German, and (b) Swiss German listeners heard 6-band noise-vocoded French-accented and English-accented German speech to which we transplanted native German segment durations. Therefore, the foreign accent cues in the stimuli consisted of only temporal information (in a) and only strongly degraded spectral information (in b). Findings suggest that listeners were able to identify the linguistic origin of French and English speakers in their foreign-accented German speech based on temporal features alone, as well as based on strongly degraded spectral features alone. When comparing these results to previous research, we found an additive trend of temporal and spectral cues: identification performance tended to be higher when both cues were present in the signal. Acoustic measures of temporal variability could not easily explain the perceptual results. However, listeners were drawn towards some of the native German segmental cues in condition (a), which biased responses towards ‘French’ when stimuli featured uvular /r/s and towards ‘English’ when they contained vocalized /r/s or lacked /r/.

Enhanced resting-state dynamics of the hemoglobin signal as a novel biomarker for detection of breast cancer.

The work presented here demonstrates an application of diffuse optical tomography (DOT) to the problem of breast-cancer diagnosis. The potential for using spatial and temporal variability measures of the hemoglobin signal to identify useful biomarkers was studied. DOT imaging data were collected using two instrumentation platforms the authors developed, which were suitable for exploring tissue dynamics while performing a simultaneous bilateral exam. For each component of the hemoglobin signal (e.g., total, oxygenated), the image time series was reduced to eight scalar metrics that were affected by one or more dynamic properties of the breast microvasculature (e.g., average amplitude, amplitude heterogeneity, strength of spatial coordination). Receiver-operator characteristic (ROC) analyses, comparing groups of subjects with breast cancer to various control groups (i.e., all noncancer subjects, only those with diagnosed benign breast pathology, and only those with no known breast pathology), were performed to evaluate the effect of cancer on the magnitudes of the metrics and of their interbreast differences and ratios. For women with known breast cancer, simultaneous bilateral DOT breast measures reveal a marked increase in the resting-state amplitude of the vasomotor response in the hemoglobin signal for the affected breast, compared to the contralateral, noncancer breast. Reconstructed 3D spatial maps of observed dynamics also show that this behavior extends well beyond the tumor border. In an effort to identify biomarkers that have the potential to support clinical aims, a group of scalar quantities extracted from the time series measures was systematically examined. This analysis showed that many of the quantities obtained by computing paired responses from the bilateral scans (e.g., interbreast differences, ratios) reveal statistically significant differences between the cancer-positive and -negative subject groups, while the corresponding measures derived from individual breast scans do not. ROC analyses yield area-under-curve values in the 77%-87% range, depending on the metric, with sensitivity and specificity values ranging from 66% to 91%. An interesting result is the initially unexpected finding that the hemodynamic-image metrics are only weakly dependent on the tumor burden, implying that the DOT technique employed is sensitive to tumor-induced changes in the vascular dynamics of the surrounding breast tissue as well. Computational modeling studies serve to identify which properties of the vasomotor response (e.g., average amplitude, amplitude heterogeneity, and phase heterogeneity) principally determine the values of the metrics and their codependences. Findings from the modeling studies also serve to clarify the influence of spatial-response heterogeneity and of system-design limitations, and they reveal the impact that a complex dependence of metric values on the modeled behaviors has on the success in distinguishing between cancer-positive and -negative subjects. The authors identified promising hemoglobin-based biomarkers for breast cancer from measures of the resting-state dynamics of the vascular bed. A notable feature of these biomarkers is that their spatial extent encompasses a large fraction of the breast volume, which is mainly independent of tumor size. Tumor-induced induction of nitric oxide synthesis, a well-established concomitant of many breast cancers, is offered as a plausible biological causal factor for the reported findings.

Relationships between biodiversity and the stability of marine ecosystems: Comparisons at a European scale using meta-analysis

The relationship between biodiversity and stability of marine benthic assemblages was investigated through meta-analyses using existing data sets (n=28) covering various spatial (m–km) and temporal (1973–2006; ranging from 5 to >250months) scales in different benthic habitats (emergent rock, rock pools and sedimentary habitats) over different European marine systems (North Atlantic and western Mediterranean). Stability was measured by a lower variability in time, and variability was estimated as temporal variance of species richness, total abundance (density or % cover) and community structure (using Bray–Curtis dissimilarities on species composition and abundance). Stability generally decreased with species richness. Temporal variability in species richness increased with the number of species at both quadrat (<1m2) and site (~100m2) scales, while no relationship was observed by multivariate analyses. Positive relationships were also observed at the scale of site between temporal variability in species richness and variability in community structure with evenness estimates. This implies that the relationship between species richness or evenness and species richness variability is slightly positive and depends on the scale of observation. Thus, species richness does not stabilize temporal fluctuations in species number, rather species rich assemblages are those most likely to undergo the largest fluctuations in species numbers and abundance from time to time. Changes within community assemblages in terms of structure are, however, generally independent of biodiversity. Except for sedimentary and rock pool habitats, no relationship was observed between temporal variation of total abundances and diversity at either scale. Overall, our results emphasize that the relation between species richness and species-level measures of temporal variability depends on scale of measurements, type of habitats and the marine system (North Atlantic and Mediterranean) considered.

Quantifying the Extent of and Factors Associated with the Temporal Variability of Physical Stream Habitat in Headwater Streams in the Interior Columbia River Basin

AbstractThe quality and quantity of stream habitat can have profound impacts on the distribution and abundance of aquatic species. Stream networks, however, are dynamic in their response to natural‐ and human‐induced disturbance regimes, which results in spatially explicit patterns of temporal variability. Quantifying spatial patterns in habitat (temporal) variability across different sites and identifying those factors associated with different levels of variability are important steps for stream habitat assessments. We evaluated the temporal variability in stream habitat over a 9‐year period for 47headwater streams of the interior Columbia River basin. We used repeat‐measures analyses to calculate temporal variability as root mean square error for six habitat attributes at each site. Multiple linear regression analyses with root mean square error as the response were then used to quantify which landscape, climate, and disturbance attributes were associated with different levels of temporal variability among habitat attributes. Our results indicated a considerable range of temporal variability in physical stream attributes across sites and an almost fourfold difference in the overall variability at sites. Landscape factors affecting stream power, land management activities, and recent fire regimes were all factors associated with the different levels of temporal variability across sites; surprisingly, we found little association with the different climatic attributes considered herein. The observed differences in temporal variability across sites suggest that a “one‐size‐fits‐all” approach to monitoring stream habitat in response to restoration and management activities may be misleading, particularly in terms of sampling intensity, required resources, and statistical power; thus, in situ measures of temporal variability may be required for accurate assessments of statistical power.

Variability in Nucleus Accumbens Activity Mediates Age-Related Suboptimal Financial Risk Taking

As human life expectancy continues to rise, financial decisions of aging investors may have an increasing impact on the global economy. In this study, we examined age differences in financial decisions across the adult life span by combining functional neuroimaging with a dynamic financial investment task. During the task, older adults made more suboptimal choices than younger adults when choosing risky assets. This age-related effect was mediated by a neural measure of temporal variability in nucleus accumbens activity. These findings reveal a novel neural mechanism by which aging may disrupt rational financial choice.

Ageing and gait variability--a population-based study of older people

gait variability may be an important predictor of falls risk, but its characteristics are poorly understood. to examine the relationship between age and gait variability in a population-based sample of older people. cross-sectional study. in people aged 60-86 years (n = 412), temporal and spatial gait variability measures were recorded with a GAITRite walkway. Regression analysis was used to model the relationship between age and gait variability adjusting for height, weight and self-reported chronic disease. Further adjustment was made for gait speed to examine its influence on the associations. older age was associated with greater variability (P < 0.05) in all gait measures. All relationships were linear, except that between age and step time variability, which was curvilinear in women. Adjusting for gait speed changed the magnitude of the age coefficient by 62-86% for temporal variability measures, 25% for step length variability and 5-12% for step width variability. age is linearly associated with greater intra-individual gait variability for most gait measures, except for step time variability in women. Gait speed may mediate the association between age and temporal variability measures. Further study is needed to understand the factors responsible for the greater gait variability with ageing.

On energetic particles in space

On energetic particles in spaceParticles energized above the Earth's atmosphere provide unique informations about specific physical processes in the outer space. This is an attempt to present a short review of the knowledge of the characteristics of the cosmic energetic particles in wide energy range as observed on the ground, in the atmosphere, on Earth's satellites and on space probes. However, the review is related only to selected problems of cosmic ray physics and space physics. The bias is especially towards lower energies. After the historical introduction the features of primary cosmic rays is described. The heliosphere, in which the direct measurements of cosmic energetic particles takes place, is modulating the primary flux by magnetic fields controlled by the processes on solar surface and, is contributing to the low energy population by acceleration via transient processes as well as by solar flares. Important processes occur near the heliospheric outer boundary from where recently the space probes provided new information. Heliospheric influence is summarized in the third chapter. Another important object, the magnetosphere, is changing trajectories of incoming charged particles by "magnetospheric optics". Magnetosphere iself by the acceleration, transport, trapping as well as losses of lower energy particles alternates significantly the radiation environment near the Earth. The knowledge of particle population for which the condition of trapping are suitable in magnetospheres of giant planets of solar system, have increased thanks to space probes and planetary orbiters. Chapter four summarizes few important points of the magnetospheric influence on energetic particles. The measurements of temporal variability of the flux, energy spectra and angular distribution of cosmic ray particles influenced by solar-terrestrial effects, provides a unique tool for monitoring and eventual prediction of space weather effects, in addition to the investigations of the photon flux of various wavelength from the Sun, solar wind plasma and the magnetic field in interplanetary space. This is the main part of the chapter five.

Linear versus non-linear measures of temporal variability in finger tapping and their relation to performance on open- versus closed-loop motor tasks: Comparing standard deviations to Lyapunov exponents

The nature of temporal variability during speeded finger tapping was examined using linear (standard deviation) and non-linear (Lyapunov exponent) measures. Experiment 1 found that right hand tapping was characterised by lower amounts of both linear and non-linear measures of variability than left hand tapping, and that linear and non-linear measures of variability were often negatively correlated with one another. Experiment 2 found that increased non-linear variability was associated with relatively enhanced performance on a closed-loop motor task (mirror tracing) and relatively impaired performance on an open-loop motor task (pointing in a dark room), especially for left hand performance. The potential uses and significance of measures of non-linear variability are discussed.

Measuring temporal variability in residential magnetic field exposures

Considerable interest has developed during the past ten years regarding the hypothesis that living organisms may respond to temporal variability in ELF magnetic fields to which they are exposed. Consequently, methods to measure various aspects of temporal variability are of interest. In this paper, five measures of temporal variability were examined: Arithmetic means (Dmean) and rms values (Drms) of the first differences (i.e., absolute value of the difference between consecutive measurements) of magnetic field recordings; “standardized” forms of Drms, denoted RCMS, obtained by dividing Drms by the standard deviations of the magnetic field data; and mean (Fmean) and rms (Frms) values of fractional first differences. Theoretical investigations showed that Dmean and Drms are virtually unaffected by long-term systematic trends (changes) in exposure. These measures thus provide rather specific measures of short-term temporal variability. This was also true to a lesser extent for Fmean and Frms. In contrast, the RCMS metric was affected by both short-term and long-term exposure variabilities. The metrics were also investigated using a data set consisting of twice-repeated two-calendar-day recordings of bedroom magnetic fields and personal exposures of 203 women residing in the western portion of Washington State. The predominant source of short-term temporal variability in magnetic field exposures arose from the movement of subjects through spatially varying magnetic fields. Spearman correlations between TWA bedroom magnetic fields or TWA personal exposures and five measures of temporal variability were relatively low. Weak to moderate levels of correlation were observed between temporal variability measured during two different sessions separated in time by 3 or 6 months. We conclude that first difference and fractional difference metrics provide specific and fairly independent measures of short-term temporal variability. The RCMS metric does not provide an easily interpreted measure of short-term or long-term temporal variability. This last result raises uncertainties about the interpretation of published studies that use the RCMS metric. Bioelectromagnetics 22:232–245, 2001. © 2001 Wiley-Liss, Inc.

Measuring temporal variability in residential magnetic field exposures

Considerable interest has developed during the past ten years regarding the hypothesis that living organisms may respond to temporal variability in ELF magnetic fields to which they are exposed. Consequently, methods to measure various aspects of temporal variability are of interest. In this paper, five measures of temporal variability were examined: Arithmetic means (D(mean)) and rms values (D(rms)) of the first differences (i.e., absolute value of the difference between consecutive measurements) of magnetic field recordings; "standardized" forms of D(rms), denoted RCMS, obtained by dividing D(rms) by the standard deviations of the magnetic field data; and mean (F(mean)) and rms (F(rms)) values of fractional first differences. Theoretical investigations showed that D(mean) and D(rms) are virtually unaffected by long-term systematic trends (changes) in exposure. These measures thus provide rather specific measures of short-term temporal variability. This was also true to a lesser extent for F(mean) and F(rms). In contrast, the RCMS metric was affected by both short-term and long-term exposure variabilities. The metrics were also investigated using a data set consisting of twice-repeated two-calendar-day recordings of bedroom magnetic fields and personal exposures of 203 women residing in the western portion of Washington State. The predominant source of short-term temporal variability in magnetic field exposures arose from the movement of subjects through spatially varying magnetic fields. Spearman correlations between TWA bedroom magnetic fields or TWA personal exposures and five measures of temporal variability were relatively low. Weak to moderate levels of correlation were observed between temporal variability measured during two different sessions separated in time by 3 or 6 months. We conclude that first difference and fractional difference metrics provide specific and fairly independent measures of short-term temporal variability. The RCMS metric does not provide an easily interpreted measure of short-term or long-term temporal variability. This last result raises uncertainties about the interpretation of published studies that use the RCMS metric.