Improving delay and strength maps derived from resting-state fMRI using PCA-based denoising and group data from the HCP dataset.

There is a huge heterogeneity of magnetic resonance imaging findings in Parkinson's disease (PD) with impulse control disorders (ICDs) studies. Here, we hypothesized that brain regions identified by structural and functional imaging studies of PD with ICDs could be reconciled in a common network. In this study, an initial systematic literature review was conducted to collect and evaluate whole-brain functional and structural magnetic resonance imaging studies related to PD with ICDs. We subsequently utilized the Human Connectome Project (HCP) dataset (n = 1,093) and a novel functional connectivity network mapping (FCNM) technique to identify a common brain network affected in PD with ICDs. A total of 19 studies with 25 contrasts, incorporating 345 individuals with PD and ICDs, and 787 individuals with PD without ICDs were included in the analysis. By using the HCP dataset and a novel FCNM technique, we ultimately identified that the aberrant neural networks predominantly involve the default mode network (middle and inferior temporal gyrus, anterior cingulate cortex, angular gyrus) and subcortical network (caudate nucleus). This study suggests that the heterogeneous neuroimaging findings in PD with ICDs can be attributed to shared abnormalities in the default mode and subcortical networks. These dysfunctions are associated with impaired self-regulation, decision-making, and heightened impulsivity in PD with ICDs. Our findings integrate diverse neuroimaging results from previous studies, providing a clearer understanding of the neurobiological mechanisms underlying PD with ICDs at a network level.

Recent work in our laboratory has suggested that human caudal lateral frontal cortex contains four interleaved regions in each hemisphere that exhibit strong sensory-specific biases in attention tasks (Michalka et al, 2014). Two visually-biased attention regions, superior and inferior pre-central sulcus (sPCS, iPCS), anatomically alternate with two auditory-biased attention regions, caudal inferior frontal sulcus (cIFS) and the transverse gyrus intersection the precentral sulcus (tgPCS). These small regions were identified in fMRI studies in a small number of individual subjects. Here, we have investigated these regions and their putative networks by mining the WashU-Minn Human Connectome Project (HCP) dataset. We used data from the 482 HCP participants with both diffusion-weighted imaging and resting-state fMRI. We defined seed regions from our individual subject data in a task that contrasted auditory and visual spatial attention. Probabilistic activation maps were constructed and thresholded to generate ROIs. These ROIs served as seed regions for resting state and tractography analyses of the HCP dataset. Stronger functional connectivity was observed for the sPCS and iPCS than for tgPCS and cIFS with superior parietal lobule visual attention regions, and conversely stronger connectivity was observed for the tgPCS and cIFS than for sPCS and iPCS with superior temporal lobe auditory attention regions. A similar pattern was observed with tractography for all ROIs, except for tgPCS. We next analyzed the whole-brain connectivity patterns of these ROIs using a multivariate approach; we found that the modality of sensory-bias can be predicted well above chance in both hemispheres at a voxelwise scale (L:71%, R:80%), using only the connectivity pattern of an individual voxel. A long-term goal of this analysis is to develop reliable methods for identifying fine-scale brain networks in large population datasets, which could have important clinical applications. Our preliminary results reveal both successes and challenges of these efforts. Meeting abstract presented at VSS 2015

The Human Connectome Project (HCP) has become a keystone dataset in human neuroscience, with a plethora of important applications in advancing brain imaging methods and an understanding of the human brain. We focused on tractometry of HCP diffusion-weighted MRI (dMRI) data. We used an open-source software library (pyAFQ; https://yeatmanlab.github.io/pyAFQ) to perform probabilistic tractography and delineate the major white matter pathways in the HCP subjects that have a complete dMRI acquisition (n = 1,041). We used diffusion kurtosis imaging (DKI) to model white matter microstructure in each voxel of the white matter, and extracted tract profiles of DKI-derived tissue properties along the length of the tracts. We explored the empirical properties of the data: first, we assessed the heritability of DKI tissue properties using the known genetic linkage of the large number of twin pairs sampled in HCP. Second, we tested the ability of tractometry to serve as the basis for predictive models of individual characteristics (e.g., age, crystallized/fluid intelligence, reading ability, etc.), compared to local connectome features. To facilitate the exploration of the dataset we created a new web-based visualization tool and use this tool to visualize the data in the HCP tractometry dataset. Finally, we used the HCP dataset as a test-bed for a new technological innovation: the TRX file-format for representation of dMRI-based streamlines. We released the processing outputs and tract profiles as a publicly available data resource through the AWS Open Data program's Open Neurodata repository. We found heritability as high as 0.9 for DKI-based metrics in some brain pathways. We also found that tractometry extracts as much useful information about individual differences as the local connectome method. We released a new web-based visualization tool for tractometry-"Tractoscope" (https://nrdg.github.io/tractoscope). We found that the TRX files require considerably less disk space-a crucial attribute for large datasets like HCP. In addition, TRX incorporates a specification for grouping streamlines, further simplifying tractometry analysis.

The multilayer dynamic network model has been proposed as an effective method to understand the brain function. In particular, derived from the definition of clustering coefficient in static networks, the temporal clustering coefficient provides a direct measure of the topological stability of dynamic brain networks and shows potential in predicting altered brain functions. However, test-retest reliability and demographic-related effects on this measure remain to be evaluated. Using a data set from the Human Connectome Project (157 male and 180 female healthy adults; 22-37 years old), the present study investigated: (1) the test-retest reliability of temporal clustering coefficient across four repeated resting-state functional magnetic resonance imaging scans as measured by intraclass correlation coefficient (ICC); and (2) sex- and age-related effects on temporal clustering coefficient. The results showed that (1) the temporal clustering coefficient had overall moderate test-retest reliability (ICC > 0.40 over a wide range of densities) at both global and subnetwork levels, (2) female subjects showed significantly higher temporal clustering coefficient than males at both global and subnetwork levels, particularly within the default-mode and subcortical regions, and (3) temporal clustering coefficient of the subcortical subnetwork was positively correlated with age in young adults. The results of sex effects were robustly replicated in an independent REST-meta-MDD data set, while the results of age effects were not. Our findings suggest that the temporal clustering coefficient is a relatively reliable and reproducible approach for identifying individual differences in brain function, and provide evidence for demographically related effects on the human brain dynamic connectomes.

A new technology (BlazePod™) that measures response time (RT) is currently on the market and has been used by strength and conditioning professionals. Nevertheless, to trust in the measurement, before the use of a new device to measure any outcome in the research or clinical setting, a reliability analysis of its measurement must be established (Koo and Li, 2016). Hence, we assessed the test-retest reliability (repeatability) of the BlazePod™ (Play Coyotta Ltd., Aviv, Israel) technology during a pre-defined activity to provide information about the level of agreement and the magnitude of errors incurred when using the technology. This information can assist practitioners and researchers in the use of BlazePod™ technology. We recruited 24 physically active young adults (age = 23.9 ± 4.0 years; height = 1.67 ± 0.09 m; body mass = 68.2 ± 13.1 kg), who were free of injuries, and any orthopedic, or cardiorespiratory diseases. Participants reported to the laboratory on two occasions, separated by one week. One week before, participants performed a familiarization session with the instrument. During the first session, the one-leg balance activity (OLBA) was performed. This activity was chosen randomly among all BlazePod™ pre-defined activities. We conducted all sessions in a physiology laboratory at the same time for each participant and under similar environmental conditions (~23° C; ~60% humidity). The OLBA consisted of a unipedal balance activity performed with four pods arranged in a square on the floor. Participants stood up in the center of the square, and the OLBA aim was to tap out as many lights as possible with the dominant foot during 30 seconds. The system lighted up in a random order not known by the participants neither the researchers. The distance between the Pods was the individual lower limb length. Three trials were performed. The best value obtained was recorded. A one-minute rest interval between all trials was given. The total number of taps and average RT of all taps in the OLBA were recorded for further analysis. Data are presented as mean ± SD or 95% confidence interval (CI). We confirmed the normal data distribution using the Shapiro-Wilk test. A paired t-test, Cohen’s d effect size (ES) and its 95% CI were calculated to assess the magnitude of the mean difference between sessions. The interpretation of the ES was: trivial (<0.20), small (0.20-0.59), moderate (0.60-1.19), large (1.2-2.0) and very large (>2.0) effect (Hopkins et al., 2009). The intraclass correlation coefficient (ICC) and its 95% CI was used to assess the reliability based on a single measurement, absolute-agreement, two-way mixed-effects model. The ICC value was interpreted as follows: poor (<0.5), moderate (0.5-0.75), good (0.75-0.9), and excellent (>0.9) reliability (Koo and Li, 2016). We also calculated the standard error of measurement (SEM), the coefficient of variation (CV), the smallest detectable change (SDC), the level of agreement between sessions by a Bland-Altman plot, the systematic bias, and its 95% limits of agreement (LoA = bias ± 1.96 SD) (Bland and Altman, 1986). We observed a small to moderate increase between sessions for the number of taps (Day 1 = 20 ± 3 taps, Day 2 = 22 ± 4 taps; t(23) = -4.121; p < 0.001; ES = 0.55, 95% CI = 0.43 to 0.67) and a trivial to small decrease for the RT (Day 1 = 1418 ± 193 ms, Day 2 = 1358 ± 248 ms; t(23) = 1.721; p = 0.099; ES = -0.27, 95% CI = -0.15 to -0.38 CI). All reliability indexes for both outcome measures are shown in Table 1. Moderate to excellent levels of reliability were found by the ICC (95% CI) values and acceptable reliability by the CV for both measures. Bland-Altman plots are depicted in Figure 1. The systematic bias that we found showed that on average in the second day, participants achieved two taps more than the first day and were 59 ms faster than the first day. The LoA showed that the number of taps measured in the first day might be 7 units below or 3 units above Day 2. Besides, the RT measured in Day 1 might be 272 ms below or 391 ms above Day 2. In conclusion, the BlazePod™ technology provides reliable information during its OLBA in physically active young adults. We considered the measurement error as acceptable for practical use since low systematic biases and errors of measurement were reported in this study, besides a moderate ICC and excellent CV. These results suggest that practitioners can use the information provided by the BlazePod™ technology to monitor performance changes during cognitive training and to evaluate the effects of a training intervention.

Previous studies showed that radiologists can detect the gist of an abnormality in a mammogram based on a half-second image presentation through global processing of screening mammograms. This study investigated the intra- and inter-observer reliability of the radiologists' initial impressions about the abnormality (or "gist signal"). It also examined if a subset of radiologists produced more reliable and accurate gist signals. Thirty-nine radiologists provided their initial impressions on two separate occasions, viewing each mammogram for half a second each time. The intra-class correlation (ICC) values showed poor to moderate intra-reader reliability. Only 13 radiologists had an ICC of 0.6 or above, which is considered the minimum standard for reliability, and only three radiologists had an ICC exceeding 0.7. The median value for the weighted Cohen's Kappa was 0.478 (interquartile range = 0.419-0.555). The Mann-Whitney U-test showed that the "Gist Experts", defined as those who outperformed others, had significantly higher ICC values (p = 0.002) and weighted Cohen's Kappa scores (p = 0.026). However, even for these experts, the intra-radiologist agreements were not strong, as an ICC of at least 0.75 indicates good reliability and the signal from none of the readers reached this level of reliability as determined by ICC values. The inter-reader reliability of the gist signal was poor, with an ICC score of 0.31 (CI = 0.26-0.37). The Fleiss Kappa score of 0.106 (CI = 0.105-0.106), indicating only slight inter-reader agreement, confirms the findings from the ICC analysis. The intra- and inter-reader reliability analysis showed that the radiologists' initial impressions are not reliable signals. In particular, the absence of an abnormal gist does not reliably signal a normal case, so radiologists should keep searching. This highlights the importance of "discovery scanning," or coarse screening to detect potential targets before ending the visual search.

The Reliability of the Associate Platinum Digital Foot Scanner in Measuring Previously Developed Footprint Characteristics: A Technical Note

Ultrasonic cleaning equipment

In the last years in-vivo tractography has assumed an important role in neurosciences, for both research and clinical applications such as non-invasive investigation of brain connectivity and presurgical planning in neurosurgery. In more recent years there has been a growing interest in the applications of diffusion tractography for target identification in functional neurological disorders for an increasingly tailored approach. The growing diffusion of well-established neurosurgical procedures, such as deep brain stimulation or trans-cranial Magnetic Resonance-guided Focused Ultrasound, favored this trend. Tractography can indeed provide more accurate, patient-specific, information about the targeted region if compared to stereotactic atlases. On the other hand, this tractography-based approach is not very physician-friendly, and its heavily time consuming since it needs several hours for Magnetic Resonance Imaging data processing. In this study we propose a novel open-source deep learning framework called DeLTA-BIT (acronym of Deep-learning Local TrActography for BraIn Targeting) for fast target predictions, based on probabilistic tractography. The proposed framework exploits a convolutional neural network (CNN) to predict the location of the Ventral Intermediate Nucleus of the thalamus (VIM). The CNN was trained on the Human Connectome Project (HCP) dataset. The model capability in predicting the VIM location was tested both on the HCP (internal validation) and clinical data (external validation). Results from the internal validation have shown good capability in predicting the VIM region (mean DSC = 0.62+- 0.15, mean sDSC=0.76+- 0.17) by using just T1 images as input, in a time scale of fraction of second per subject. As for the clinical data, results have been compared with an atlas-based method demonstrating similar performance, but within a significantly shorter timeframe.

ObjectiveAlthough metabolic profiles have been associated with chronic disease risk, lack of temporal stability of metabolite levels could limit their use in epidemiological investigations. The present study aims to evaluate the reliability over a two-year period of 158 metabolites and compare reliability over time in fasting and non-fasting serum samples.MethodsMetabolites were measured with the AbsolueIDQp180 kit (Biocrates, Innsbruck, Austria) by mass spectrometry and included acylcarnitines, amino acids, biogenic amines, hexoses, phosphatidylcholines and sphingomyelins. Measurements were performed on repeat serum samples collected two years apart in 27 fasting men from Turin, Italy, and 39 non-fasting women from Utrecht, The Netherlands, all participating in the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Reproducibility was assessed by estimating intraclass correlation coefficients (ICCs) in multivariable mixed models.ResultsIn fasting samples, a median ICC of 0.70 was observed. ICC values were <0.50 for 48% of amino acids, 27% of acylcarnitines, 18% of lysophosphatidylcholines and 4% of phosphatidylcholines. In non-fasting samples, the median ICC was 0.54. ICC values were <0.50 for 71% of acylcarnitines, 48% of amino acids, 44% of biogenic amines, 36% of sphingomyelins, 34% of phosphatidylcholines and 33% of lysophosphatidylcholines. Overall, reproducibility was lower in non-fasting as compared to fasting samples, with a statistically significant difference for 19–36% of acylcarnitines, phosphatidylcholines and sphingomyelins.ConclusionA single measurement per individual may be sufficient for the study of 73% and 52% of the metabolites showing ICCs >0.50 in fasting and non-fasting samples, respectively. ICCs were higher in fasting samples that are preferable to non-fasting.

This work addresses the problem of constructing a unified, topologically optimal connectivity-based brain atlas. The proposed approach aggregates an ensemble partition from individual parcellations without label agreement, providing a balance between sufficiently flexible individual parcellations and intuitive representation of the average topological structure of the connectome. The methods exploit a previously proposed dense connectivity representation, first performing graph-based hierarchical parcellation of individual brains, and subsequently aggregating the individual parcellations into a consensus parcellation. The search for consensus—based on the hard ensemble (HE) algorithm—approximately minimizes the sum of cluster membership distances, effectively estimating a pseudo-Karcher mean of individual parcellations. Computational stability, graph structure preservation, and biological relevance of the simplified representation resulting from the proposed parcellation are assessed on the Human Connectome Project data set. These aspects are assessed using (1) edge weight distribution divergence with respect to the dense connectome representation, (2) interhemispheric symmetry, (3) network characteristics' stability and agreement with respect to individually and anatomically parcellated networks, and (4) performance of the simplified connectome in a biological sex classification task. Ensemble parcellation was found to be highly stable with respect to subject sampling, outperforming anatomical atlases and other connectome-based parcellations in classification as well as preserving global connectome properties. The HE-based parcellation also showed a degree of symmetry comparable with anatomical atlases and a high degree of spatial contiguity without using explicit priors.

Short-term test–retest reliability of resting state fMRI metrics in children with and without attention-deficit/hyperactivity disorder

The reproducibility of 99m Tc-pertechnetate single photon emission computed tomography (SPECT) for measurement of gastric accommodation in healthy humans: Evaluation of the test results performed at the same time and different time of the day (

Sensory measurement underlies sensory science. Sensory analysis and decision‐making heavily depend on the quality of sensory data, which is determined by the performance of trained sensory panels and panelists. Various methods have been proposed for monitoring and assessing the performance. A weakness of the currently used methods is lack of a unified framework for various criteria and a variety of experiments with different types of data. This paper proposes to use accuracy, validity and reliability as general terminologies to describe sensory measurement and to apply the intraclass correlation coefficient (ICC) as a framework for monitoring and assessing performance.ICCcan measure both similarity among panelists and sensitivity of panels and panelists. Hence, ICC can handle the problems of both reliability and validity.ICCcan be obtained from different types of data for diverse experiments. This paper provides the equations andRandS‐Plus functions for estimations ofICCs from continuous data (ratings), multivariate continuous data, ordinal data, ranking data, binary‐choice data, multiple‐choice data and forced‐choice data. Confidence intervals, variances of the estimators, comparison with a fixed value and difference and similarity tests for multipleICCs are also provided. The relationship betweenCronbach's coefficient alpha andICCis discussed.Practical ApplicationsIntraclass correlation coefficient (ICC) may play a framework role in monitoring and assessing performance of trained sensory panels and panelists. It can be used as an index of the quality of sensory data. The larger theICCorCronbach's coefficient alpha value, the better the performance of panels and panelists. If and only if anICCis significantly larger than a specified value, can the quality of the data for that attribute be regarded as acceptable. A statistical test that fails to show that anICCorCronbach's coefficient alpha is significantly larger than a specified lowest limit, e.g., 0.1, suggests that the products are undiscriminating, or the sensory data, at least for that attribute, might not be valid and reliable. We should use that data with caution.

Aim The Rett Syndrome Gross Motor Scale (RSGMS) is an observational measurement, assessing gross motor skills in individuals with Rett syndrome. A Dutch version is lacking. The current study aims to translate and cross-culturally adapt the original RSMGS to Dutch and assess its inter-rater and intra-rater reliability. Design Translation and cross-cultural adaptation were performed in concordance with internationally accepted guidelines. A pretest was performed, and expert validation was assured. Video data of three girls with Rett syndrome was independently assessed by 27 physiotherapists via an online webinar to measure inter-rater reliability. Additionally, videos of 17 individuals with Rett syndrome were scored twice by two raters to assess intra-rater reliability. The reliability of the total score, the three subscale scores and the new items were analyzed using Intraclass Correlation Coefficients (ICC). Results Good comprehensibility and expert validation of the RSMGS-NL was achieved, and four items were added to the original scale. Inter-rater reliability for the total score was excellent (ICC 0.97, 95% CI 0.89–0.99), and good to perfect inter-rater reliability for the three subscales; Sitting, Standing & walking and Challenge was found (ICC values 1.0 (95% CI 0), 0.98 (95% CI 0.91–0.99) and 0.82 (95% CI 0.93–0.99) respectively). The intra-rater reliability was excellent for the total test score (ICC 0.98, 95% CI 0.97–0.99) and good to excellent for the subscale scores (ICC values 0.87 (95% CI 0.75–0.94), 0.99 (95% CI 0.98–1.0) and 0.97 (95% CI 0.95–0.99) respectively). The four new items (Standing to sitting, walking down a slope, ascending the stairs, descending the stairs) showed good to excellent intra-rater reliability. Conclusion The RSGMS-NL is a reliable measure of gross motor skills in Dutch individuals with Rett syndrome.