Skull-stripping is the procedure of removing non-brain tissue from anatomical MRI data. This procedure can be useful for calculating brain volume and for improving the quality of other image processing steps. Developing new skull-stripping algorithms and evaluating their performance requires gold standard data from a variety of different scanners and acquisition methods. We complement existing repositories with manually corrected brain masks for 125 T1-weighted anatomical scans from the Nathan Kline Institute Enhanced Rockland Sample Neurofeedback Study.Skull-stripped images were obtained using a semi-automated procedure that involved skull-stripping the data using the brain extraction based on nonlocal segmentation technique (BEaST) software, and manually correcting the worst results. Corrected brain masks were added into the BEaST library and the procedure was repeated until acceptable brain masks were available for all images. In total, 85 of the skull-stripped images were hand-edited and 40 were deemed to not need editing. The results are brain masks for the 125 images along with a BEaST library for automatically skull-stripping other data.Skull-stripped anatomical images from the Neurofeedback sample are available for download from the Preprocessed Connectomes Project. The resulting brain masks can be used by researchers to improve preprocessing of the Neurofeedback data, as training and testing data for developing new skull-stripping algorithms, and for evaluating the impact on other aspects of MRI preprocessing. We have illustrated the utility of these data as a reference for comparing various automatic methods and evaluated the performance of the newly created library on independent data. One of the many challenges facing the analysis of magnetic resonance imaging (MRI) data is achieving accurate brain extraction from the data. Brain extraction, also known as skull-stripping, aims to remove all non-brain tissue from an image. This is commonly a preliminary step in preprocessing and the quality of its result affects the subsequent steps, such as image registration and brain matter segmentation. Many challenges surround the process of brain extraction. The manual creation and correction of brain masks is tedious, time-consuming, and susceptible to experimenter bias. On the other hand, fully automated brain extraction is not a simple image segmentation problem. Brains differ in orientation and morphology, especially pediatric, geriatric, and pathological brains. In addition, non-brain tissue may resemble brain in terms of voxel intensity. Differences in MRI scanner, acquisition sequence, and scan parameters can also have an effect on automated algorithms due to differences in image contrast, quality, and orientation. Image segmentation techniques with low computational time, high accuracy, and high flexibility are extremely desirable.Developing new automated skull-stripping methods, and comparing these with existing methods, requires large quantities of gold standard skull-stripped data acquired from a variety of scanners using a variety of sequences and parameters. This is due to the variation in performance of algorithms using different MRI data. Repositories containing gold standard skull-stripped data already exist: the Alzheimer’s Disease Neuroimaging Initiative (ADNI) ; BrainWeThe repository was constructed from defaced and anonymized anatomical data downloaded from the NFB . The NFBConsistent with the the Research Domain Criteria (RDoC) , the goa3 resolution with a 3D T1-weighted magnetization-prepared rapid acquisition gradient-echo (MPRAGE) [2 field of view (FOV), 2600 ms repetition time (TR), 3.02 ms echo time (TE), 900 ms inversion time (TI), 8° flip angle (FA), and generalized auto-calibrating partially parallel acquisition (GRAPPA) acceleration [Anatomical MRI data from the third visit of the NFB protocol were used to build the Neurofeedback Skull-stripped (NFBS) repository. MRI data were collected on a 3 T Siemens Magnetom TIM Trio scanner  using a 12-channel head coil. Anatomical images were acquired at 1×1×1 mm(MPRAGE)  sequenceleration , whose dMany researchers differ on the standard for what to include and exclude from the brain. Some brain extraction methods, such as brainwash, include the dura mater in the brain mask to use as a reference for measurements . The staThe BEaST method  was used to initially skull-strip the 125 anatomical T1-weighted images . This soManual edits were performed using the Freeview visualization tool from the FreeSurfer software package . The anaFor each of the 125 subjects, the repository contains the de-faced and anonymized anatomical T1-weighted image, skull-stripped brain image, and brain mask. Each of these are in compressed NIfTI file format (.nii.gz). The size of the entire data set is around 1.9 GB. The BEaST library created using these images is also available.The semi-automated skull-stripping procedure was repeated until all brain masks were determined to be acceptable by two raters (BP and ET). Once this was completed, the brain masks were used as gold standard data for comparing different automated skull-stripping algorithms. Additionally, we evaluated the performance of the newly created BEaST library by comparing it to other skull-stripping methods on data from the IBSR  and the BET is an algorithm incorporated in the FSL software that is based on a deformable model of the surface of the brain . First, 3dSkullStrip is a modified version of BET that is incorporated in the AFNI toolkit . The algHWA is a hybrid technique that uses a watershed algorithm in combination with a deformable surface algorithm . The watMany skullstripping algorithms have been developed , 18–22, 1 using AFNI’s 3dresample program [To illustrate the use of the NFBS as testing data, it was used to compare the performance of BET, 3dSkullStrip and HWA for automatically skull-stripping the original NFB data. In a second analysis we compared the performance of the NFBS BEaST library to the default BEaST library and the three aforementioned methods. Each of the methods was used to skull-strip data from the IBSR (version 2.0) and LPBA40 [ program . AdditioD=2·|A∩B|/(|A|+|B|), where A is the set of voxels in the test mask, B is the set of voxels in the gold standard data mask, A∩B is the intersection of A and B, and |·| is the number of voxels in a set. Dice was implemented in custom Python scripts that used the NiBabel neuroimaging package [The performance of the various methods was compared using the Dice similarity  between  package  for data package  for the  package .Figure Switching now from using NFBS as the repository of gold standard skull-stripped images to using the IBSR and LPBA40 repositories as the source of gold standard images, Fig. 2. Differences in Dice coefficients between datasets may be due the size and quality of the NFB study, as well as the pathology and age of the participants. In particular, the NFBS library of priors reflects a much wider range of individuals than does beast-library-1.1, which only contains 10 young individuals. There also may be differences in the standard of the masks, such as length of brainstem and inclusion of exterior nerves and sinuses.For the BEaST method, using NFBS as the prior library resulted in higher average Dice similarity coefficients and smaller standard deviationsPlacing our results in the context of other skull-stripping comparisons, differences between the Dice coefficients reported here and values already published in the literature may be due to the version and implementation of the skull stripping algorithms, a possibility that has received support in the literature . These dIn summary, we have created and shared the NFBS repository of high quality, skull-stripped T1-weighted anatomical images that is notable for its quality, its heterogeneity, and its ease of access. The procedure used to populate the repository combined the automated, state-of-the-art BEaST algorithm with meticulous hand editing to correct any residual brain extraction errors noticed on visual inspection. The manually corrected brain masks will be a valuable resource for improving the quality of preprocessing obtainable on the NFB data. The corresponding BEaST library will improve skull-stripping of future NFB releases and may outperform the default beast-library-1.1 on other datasets see Fig. . AdditioThe NFBS repository is larger and more heterogeneous than many comparable datasets. It contains 125 skull-stripped images, is composed of images from individuals with ages ranging from 21–45, and represents individuals diagnosed with a wide range of psychiatric disorders  . Researc1 This refers to the manner in which the 3D image data are saved in the file. With LPI orientation, the voxel at memory location  is located at the leftmost, posterior, inferior voxel in the image. As the indices increase, they scan the voxels from left-to-right, along lines that advance from posterior-to-anterior, and planes that advance from inferior-to-superior. Additional details concerning the orientation of MRI images are available online [e online .2 BEaST was unable to segment 1 subject, IBSR_11, in IBSR, only when using beast-library-1.1. For LPBA40, BEaST was also unable to segment 1 subject, S35, when using beast-library-1.1 and NFBS. These subjects were left out of the Dice calculations.


Vermivora chrysoptera) in managed forested landscapes of the western Great Lakes region. We studied a dense breeding population of Golden-winged Warblers at Rice Lake National Wildlife Refuge (NWR) in Minnesota, USA, where shrubs and young trees were sheared during the winter of 2014–2015 in a single treatment supported in part by the American Bird Conservancy (ABC) and in part by other funding source(s) to benefit Golden-winged Warblers and other species associated with young forest  and as part of maintenance of early successional forest cover on the refuge.Assessing outcomes of habitat management is critical for informing and adapting conservation plans. From 2013–2019, a multi-stage management initiative aims to create >26,000 ha of shrubland and early-successional vegetation to benefit Golden-winged Warblers (We monitored abundance of Golden-winged Warblers before (2013–2014) and after (2015–2016) management at the treatment site and a control site, and we estimated full-season productivity  on the treatment site from predictive, spatially explicit models, informed by nest and fledgling survival data collected at sites in the western Great Lakes region, including Rice Lake NWR, during 2011 and 2012. Then, using biologically informed models of Golden-winged Warbler response to observed and predicted vegetation succession, we estimated the cumulative change in population recruitment over various scenarios of vegetation succession and demographic response.We observed a 32% decline in abundance of Golden-winged Warbler breeding pairs on the treatment site and estimated a 27% decline in per-pair full-season productivity following management, compared to no change in a nearby control site. In models that ranged from highly optimistic to progressively more realistic scenarios, we estimated a net loss of 72–460 juvenile Golden-winged Warblers produced from the treatment site in the 10–20 years following management. Even if our well-informed and locally validated productivity models produced erroneous estimates and the management resulted in only a temporary reduction in abundance , our forecast models still predicted a net loss of 61–260 juvenile Golden-winged Warblers from the treatment site over the same time frame.Our study sites represent only a small portion of a large young-forest management initiative directed at Golden-winged Warblers in the western Great Lakes region; however, the brush management, or shearing of shrubs and small trees, that was applied at our study site is a common treatment applied by contractors funded by ABC and its partners on public lands across Minnesota with the expressed intent of benefiting Golden-winged Warblers and related species. Furthermore, the resulting vegetation structure at our treatment site is consistent with that of other areas managed under the initiative, and ABC documents include our study site as successful Golden-winged Warbler management based on observations of ≥1 Golden-winged Warbler at the treatment site since the management. Our assessment demonstrates that, at least for the only site for which pre- and post-management data on Golden-winged Warblers exist, the shearing of shrubs and small trees has had a substantial and likely enduring negative impact on Golden-winged Warblers. We suggest that incorporating region-specific, empirical information about Golden-winged Warbler—habitat relations into habitat management efforts would increase the likelihood of a positive response by Golden-winged Warblers and also suggest that management directed generically at young forest may not benefit Golden-winged Warblers. Vermivora chrysoptera) are a Nearctic-Neotropical migratory species experiencing one such decline . In January 2015, ABC announced a $10 M partnership with the US Department of Agriculture’s Natural Resource Conservation Service (NRCS) to incentivize and implement the same management prescriptions on private lands in Minnesota and Wisconsin (http://www.abcbirds.org). Together, these initiatives  have the stated goal of managing >26,000 ha within the primarily forested landscape of the western Great Lakes region focused on creating breeding habitat for Golden-winged Warblers (In 2013 (planned through 2019), multiple large-scale management initiatives began working to manage public and private forested areas to benefit Golden-winged Warblers and other young-forest-associated species in the western Great Lakes region. In July 2013, the American Bird Conservancy (ABC) and partners were awarded $4.7 M to implement management on public and private lands in Minnesota to benefit Golden-winged Warblers  in east-central Minnesota. Our study is the only assessment of habitat management, at least in part intended to benefit Golden-winged Warblers, with extensive before- and after-management information on habitat use and population dynamics, and therefore may provide insight into the potential response of Golden-winged Warblers to management intended to increase population size and productivity.We monitored abundance of breeding pairs of Golden-winged Warblers in a managed area within Rice Lake NWR for 42 years prior to management and two years after management, and in a nearby area that did not experience young-forest management, in a before-after-control-impact study design. In addition, because abundance alone can be a misleading indicator of habitat quality , we usedWe studied Golden-winged Warblers at two sites within Rice Lake NWR  in Aitkin County, Minnesota, USA. Rice Lake NWR encompasses 7,300 ha of diverse cover types including lakes, rivers, grassy and shrubby wetlands, bogs, upland and wetland forests of various successional stages, and minimal agriculture. The refuge hosts areas with some of the highest known density of breeding Golden-winged Warblers, including locations with >1 breeding pair/ha. Prior to this management action, our 80-ha main (treatment) study site included 41 ha of shrubland and early-successional forest cover types and supported 62 breeding pairs of Golden-winged Warblers, and our 30-ha control site included 16 ha of shrubland and early-successional forest cover types and supported 19 breeding pairs of Golden-winged Warblers. Our treatment site and control site were separated by 2.0 km and some of the area between sites was also occupied by Golden-winged Warblers. Both study sites combined supported one pair per 0.7 ha of shrubby cover types with which the species is typically associated as a habitat specialist , and we Acer saccharum), red maple (A. rubrum), quaking aspen (Populus tremuloides), bigtooth aspen (P. grandidentata), paper birch (Betula papyrifera), American basswood (Tilia americana), bur oak (Quercus macrocarpa), and red oak (Q. rubra). Shrubland and young forest stands were a complex mix of willow , hazel (Corylus spp.), dogwood (Cornus spp.), paper birch, quaking and bigtooth aspen, grasses, sedges, and forbs with individual and small groups of young and mature bur oak and red oak trees. The complexity and diversity of vegetation was reflective of the diversity of soil types including hamre muck, Dysler silt loam, and talmoon fine sandy loam  transitions between shrubby areas and later-successional forest . The vegndy loam . Due to During the winter of 2014–2015, several areas were managed in a single harvest prescription across Rice Lake NWR. This prescription included ∼40 ha of vegetation shearing of which 12 ha were in our treatment study site and none were within our control site. Management planning documents included maps with the area to be managed in our treatment study site described as “GWWA thinning” . The manScolopax minor) BMPs, and those portions of the management were funded by other sources. The management we assessed was described as Golden-winged Warbler management prior to the management implementation; the management was applied as a single consistent prescription across our treatment site; and the management technique was shearing, which is the method commonly used by ABC and its partners in an initiative aimed at benefiting Golden-winged Warblers across Minnesota.Sometime after the management was completed, employees of the ABC and Rice Lake NWR observed the resulting vegetation structure and determined that some sheared areas (primarily those outside of our treatment site) were consistent with Golden-winged Warbler BMPs, and ABC provided funding for those portions of the management . Those employees determined that other sheared areas (primarily those within our treatment site) were more consistent with American Woodcock  and fledgling survival (n = 190 fledglings) collected during a two-year study at three sites in northern Minnesota and southeastern Manitoba, Canada, including our study area at Rice Lake NWR  and 19017  during studies of nesting and post-fledging ecology of Golden-winged Warblers in 2011 and 2012. We applied spatially explicit models of full-season productivity  for GoldLake NWR . For fieLake NWR . We asseLake NWR , and we Step-by-step instructions and programming code for parameterization and implementation of full-season productivity models are detailed in Appendix C of We estimated Golden-winged Warbler full-season productivity for the treatment site at Rice Lake NWR under three scenarios of forest succession: (1) rapid succession, (2) moderate succession, and (3) slow succession. In each model scenario, we assumed that shrub and small tree density would regenerate to a state similar to pre-management vegetation within four years , 10 years , and 20 years . In addition, we estimated full-season productivity in the absence of management  to provide an estimate to which we could compare the net effects of the range of post-management scenarios. In the no-management scenario, we assumed the area would maintain similar, pre-management, vegetation structure for the entire period, which is likely for all three scenarios given the soil types and vegetation succession history of the managed area. It is possible that the complexity and vertical stratification of the pre-treatment vegetation structure may not return in any of the post-management scenarios, but we assumed that adequate shrub and small tree cover would regenerate by the end of each scenario to host pre-treatment numbers of breeding Golden-winged Warblers. For each succession scenario we modeled the response of Golden-winged Warbler productivity two ways. First, we assumed Golden-winged Warbler productivity responded linearly with time in each scenario, which is likely given the relationship between Golden-winged Warbler productivity and landscape composition and the slow and approximately linear vegetation succession that occurs in our study area. Second, although there is no evidence of density dependent changes in productivity in our study system, population theory predicts that productivity should increase in response to reduced density. Therefore, in a second set of scenarios, we assumed that after a brief initial negative response, Golden-winged Warbler productivity would increase rapidly to a level 25% greater than pre-management productivity, followed by a return to pre-management productivity by the end of each scenario period. Each of the various productivity scenarios included a linear response in density as described above. We did not consider models in which density responded in a parabolic fashion because our study sites hosted the greatest known densities of breeding Golden-winged Warblers prior to management, and any increase in density is unlikely.We assessed only abundance under post-management conditions for our control site  because we did not expect vegetation-based changes in productivity in the absence of management within any of our scenario time frames. Certainly, trees and shrubs will grow larger on the control site and in our no-management scenario, but past succession rates indicate it is unlikely that any shrub-dominated stands in our study area would succeed to forest-dominated stands in the time frame of our models.During the pre-management breeding seasons of 2013–2014, our treatment study site hosted 62 pairs of breeding Golden-winged Warblers each year  and our For all post-management scenarios, we included the observed 45 breeding pairs for 2015, 45 breeding pairs for 2016, and then included linear responses from 45 back to 62 breeding pairs in the remainder of the scenario period. For example, in the rapid succession scenario, 2015 and 2016 were included as the first two years after management, and our models assumed 53 breeding pairs in 2017 and 62 in 2018, to complete the return to pre-treatment abundance in four years after management. We did the same for the moderate and slow succession scenarios including the first two years as observed and then return from 45 to 62 breeding pairs linearly over the subsequent eight years and 18 years to complete the scenario periods, respectively.Prior to management, our treatment site produced an estimated 1.45 (±0.11) juveniles recruited into the fall population per breeding pair each year . Given tUnder the rapid succession scenario, we estimated that 262 (±19) juvenile Golden-winged Warblers were recruited into the fall population from the treatment site during the 4 years following management, compared to 360 (±22) juveniles during the same period if management had not occurred. Under the moderate succession scenarios, our linear productivity model estimated that 662 (±48) juvenile Golden-winged Warblers were recruited into the fall population from the treatment site during the 10 years following management, compared to 825 (±59) juveniles estimated from the density-dependent, parabolic productivity model and 900 (±64) juveniles during the same period if management had not occurred. Under the slow succession scenario, we estimated that 1,340 (±96) juvenile Golden-winged Warblers were recruited into the fall population from the treatment site during the 20 years following management, compared to 1,674 (±120) juveniles estimated from the density-dependent, parabolic productivity model and 1,800 (±129) juveniles during the same period if management had not occurred. Therefore, in the rapid, moderate, and slow succession scenarios we estimated that 12 ha of shearing resulted in a net loss of 98, 238, and 460 juvenile Golden-winged Warblers under our linear productivity models, respectively, at the treatment site. Our density-dependent, parabolic productivity models estimated a net loss of 72 juveniles over 10 years or 96 juveniles over 20 years, compared to recruitment under our no-management scenario.Although our productivity models were informed by a sizeable, locally relevant, dataset and have been validated as predictive of Golden-winged Warbler productivity in our study area , we do nWe observed an immediate decline in breeding Golden-winged Warbler abundance following management consisting of shearing dense and diversely stratified vegetation surrounded by later-successional forest at Rice Lake NWR, while abundance remained unchanged at a nearby control site. Our observations are consistent with Spatially explicit models of Golden-winged Warbler full-season productivity indicated a decline in per-pair productivity concurrent with the decline in abundance after management. The initial decline in productivity was associated primarily with increased area of grassland on the landscape, which has negative effects on Golden-winged Warbler nest productivity and fledgling survival rate in most landscape configurations in Minnesota  despite It is unclear to us why large-scale vegetation management targeting Golden-winged Warblers was initiated in Minnesota in general and why management for young forest cover types would occur at a location with the highest recorded density of breeding Golden-winged Warblers at Rice Lake NWR in particular. During structured decision making in management and conservation planning a critical initial step is to determine if action is necessary ; the denIf management targeting Golden-winged Warblers was warranted in Minnesota, creating patches of diverse vegetation structure within otherwise contiguous mature forest areas would likely result in increased population size, based on extensive research on the species in Minnesota . Howeverhttp://www.uppermissgreatlakesjv.org/Priorities). Even in areas where the Although Minnesota hosts approximately half of the global breeding population of Golden-winged Warblers, only 4% (18 out of 412 nesting territories) of the data used to inform the Our study occurred at only a single treatment and control site in one study area, and it is possible that the vegetation management at our study area, or the response of Golden-winged Warblers at our study area, was unique and not representative of the response of Golden-winged Warblers to vegetation management implemented as part of the broader ABC-led Golden-winged Warbler initiative in the region. We also recognize that management decisions at the scale of a local refuge can be influenced by factors other than maximizing the benefit to a single species of conservation concern. However, a post-management report from individuals involved with the ABC initiative includes our treatment site as one of many sites where “shrub management” was used to “create habitat for Golden-winged Warblers” . This inIt is not clear whether results like ours could have been identified if management had not occurred on a study site where Golden-winged Warblers were already intensively studied, as the protocols followed by ABC and their partners in Minnesota did not include pre-management bird surveys. The post-management surveys being conducted as part of the ABC initiative are designed only to detect the presence of Golden-winged Warblers and other species purported to benefit from the management . WithoutWe suggest that there are several important implications from our assessment. First, we think this highlights the possible negative consequences of basing management prescriptions on plans developed in ecologically and geographically disparate systems. For example, it is possible that mechanical clearing of shrublands, as applied at our site and as applied by ABC and its partners across Minnesota, produces the initiative’s intended outcome in other regions, but is known not to do so in Minnesota e.g., . In addi10.7717/peerj.4319/supp-1Supplemental Information 1Pre-management document from Rice Lake National Wildlife Refuge identifying the management to take place in our study area as “Sheared sites” and “GWWA Thinning”, confirming that Golden-winged Warbler was the primary pre-management target species of the habitat management we assessed.Click here for additional data file.10.7717/peerj.4319/supp-2Supplemental Information 2Raw Golden-winged Warbler nest and fledgling survival data used to inform spatially explicit models of full-season productivity in Click here for additional data file.10.7717/peerj.4319/supp-3Supplemental Information 3Modeled post-management full-season productivity of Golden-winged Warblers in the area of Rice Lake NWR managed to benefit Golden-winged Warblers. Scenarios 1 (sheet 1), 2 (sheet 2), and 3 (sheet 3) include estimates of productivity assuming no management had occurred and assuming vegetation succeeds to pre-management structure in an unlikely 4 years (scenario 1), a realistic 10 years (scenario 2), and a likely 20 years (scenario 3).Click here for additional data file.


Traditional resting-state network concept is based on calculating linear dependence of spontaneous low frequency fluctuations of the BOLD signals of different brain areas, which assumes temporally stable zero-lag synchrony across regions. However, growing amount of experimental findings suggest that functional connectivity exhibits dynamic changes and a complex time-lag structure, which cannot be captured by the static zero-lag correlation analysis. Here we propose a new approach applying Dynamic Time Warping (DTW) distance to evaluate functional connectivity strength that accounts for non-stationarity and phase-lags between the observed signals. Using simulated fMRI data we found that DTW captures dynamic interactions and it is less sensitive to linearly combined global noise in the data as compared to traditional correlation analysis. We tested our method using resting-state fMRI data from repeated measurements of an individual subject and showed that DTW analysis results in more stable connectivity patterns by reducing the within-subject variability and increasing robustness for preprocessing strategies. Classification results on a public dataset revealed a superior sensitivity of the DTW analysis to group differences by showing that DTW based classifiers outperform the zero-lag correlation and maximal lag cross-correlation based classifiers significantly. Our findings suggest that analysing resting-state functional connectivity using DTW provides an efficient new way for characterizing functional networks. Resting-state fMRI  and the less restrictive independent component analysis (ICA), but several other valuable methods have been developed as well  indicated that coherence and phase difference  between and within identified resting-state networks is indeed variable in time  distance . Experiment 2 proves that DTW is applicable to real fMRI data. For that purpose we measured 20 runs of resting state fMRI on a single subject to study the reliability of connectivity patterns within subject. Experiment 2.A reveals how global signal regression effects seed based connectivity maps based on DTW, zero-lag correlation or maximal-lag cross-correlation calculation, while in Experiment 2.B we examine the three metrics robustness for multiple measurements by comparing the results from the 20 resting state runs that were measured within a week. In Experiment 3, we quantitatively compare the three metrics'  sensitivity for group differences in a classification pipeline on a public dataset containing five runs of resting state fMRI on 26 subjects.With these experiments we demonstrate that connectivity maps resulted from hypothesis-driven (seed-based) DTW connectivity analysis are consistent with previous findings using conventional approaches. We also show that DTW-based connectivity maps are more robust across preprocessing pipelines as well as across repetitions, than those calculated by means of SCA or maximal lag cross-correlation. Furthermore, our findings demonstrate that DTW reveals even subtle group differences that SCA cannot extract: the same (LASSO type) classifier achieved significantly higher accuracy when it relies on DTW distance features compared to correlation or cross-correlation values.x1 is matched to the peak in x2 when comparing the x1 and x2 with DTW  the replacement of an element of x1 to an element of x2 and (ii) elongation of an element of x1 or x2. In both cases, some elements of the two time series are matched. The cost of a single editing operation is the difference between the matched elements of the two time series.Dynamic Time Warping algorithm uses elastic matching, which can correct for phase-shifts as well as distortions of the shape of the signal, as illustrated in Figure l1-by-l2 matrix, called DTW matrix, where l1 and l2 refer to the length of time series x1 and x2, respectively. We use DTW to denote the -th entry of the aforementioned matrix. The -th entry of the DTW matrix corresponds to the distance between the prefix of length i of x1 and the prefix of length j of x2 and it can be calculated according to Equation (1).At the technical level, DTW distance is calculated by filling an i,j)-th entry of the DTW matrix only depends on the -th, -th and -th entries, the entries of the matrix can be calculated column-wise, beginning with the first entry of the first column, followed by the entries of the same column and the entries of subsequent columns, see Buza (As the (see Buza  for detax1(i) and x2(j) as x1 and x2 is the square root of the -th entry of the DTW matrix -th and (i+w)-th position, we calculate only the entries at most w positions far from the diagonal -th entry, we examine which entry of the DTW matrix lead to the minimum in Equation (1) and traverse the entries of the DTW matrix according to this minimum as long as we arrive at the -st entry. This optimal warping path gives the matching between the positions of the two time series , the 100 s warping window size is sufficient to detect anticorrelations of the lowest frequencies even with their potential time-delay fluctuations. On the individual subject's data, we examined how the choice of the warping window size effects the results. If we wish only to detect (supposedly) highly positively correlated areas, a window size of 20 s would be long enough, these experiments result in clearly visible homotopic and within-network connections. However, connections traditionally classified as anticorrelations  usually exhibited time delay of 20–60 s, thus the proposed 100 s warping window is indeed long enough to capture these relationships as well.t-test can be used to find statistically significant voxels. From now on, we refer to the transformed DTW values as DTW similarities throughout the paper.There are important differences if we measure the strength of the relationship of two time series with DTW distance or linear correlation coefficients. Correlation coefficients ranges from −1 to +1, and the strength of the connection is determined by the coefficient's absolute value ranging from 0 (no correlation) to 1 (maximum correlation). DTW distance, on the other hand, is inherently positive and strong connection results in a distance close to 0 (no distance). To make the DTW distance measures comparable to that of the correlation coefficients, as well as to be able to perform parametric tests, we transformed our calculated DTW distances to similarity values. In practicality, the maximal DTW distance of two time-series is constrained by the range of the values in the time series, and the number of time points, i.e., when comparing time-series with zero mean and standard deviation of one, one can expect larger DTW distances, if the compared time-series are longer. In our case, for example, DTW values ranged between 0 and 14  with 2 s sampling rate and the time courses were filtered from white Gaussian noise with a band-pass filter using a combination of temporal high-pass (based on the regression of ninth-order discrete cosine transform basis set) and low-pass  filters to retain signals only within the range of 0.009 and 0.08 Hz, the same frequency filter that is traditionally used for preprocessing resting state fMRI data. The empirical distribution of the DTW distance and correlation coefficient values of independent signals were constructed from 1,000 independently simulated signals' pairwise DTW distance and correlation coefficient value. In case of correlation coefficients we choose the 95 and 5 percentiles of the empirical distribution as significance threshold, while for DTW distances the threshold is the 5 percentile, as strong connection is reflected by DTW distance close to zero. We modeled transient interaction of time courses by copying a piece of the original signal into another independently generated noise signal, either in synchrony or with a given time delay. In the simulation we examined the significance of the calculated correlation coefficient and DTW values as a function of the length of the interaction and time delay.Experiment 1.B demonstrates the robustness of DTW distance in the presence of superimposed common noise . We generated two perfectly anticorrelated sinusoid signal with 0.08 Hz frequency with independent frequency filtered white noise added, so the level of correlation is reduced from −1 to −0.86. We examined the changes in both correlation coefficient and DTW distance values as the function of the weight of linearly added common noise in the range of 0–50%. The noise signals, and the empirical null distribution were calculated from frequency filtered independent white Gaussian noise signals with the same method as in the previous simulation.A right-handed female subject (aged 28 years) participated in the experiment and gave informed written consent in accordance with the protocols approved by Health Registration and Training Center (ENKK 006641/2016/OTIG), Budapest, Hungary. She did not have any history of neurological or psychiatric diseases, and had normal visual acuity.Ten fMRI sessions were spread over 5 days (one morning and one afternoon). In each session two runs of 10-min-long closed-eyes resting-state runs were conducted.1-weighted 3D-MPRAGE sequence . A total of 340 functional images over 10 min and 1.8 s were collected with a BOLD-sensitive Data were collected at the Brain Imaging Centre of the Research Centre for Natural Sciences of the Hungarian Academy of Sciences  on a 3 T Siemens MAGNETOM Prisma scanner . High-resolution anatomical images were acquired using a T1 image was coregistered to the mean functional 1 images were segmented using the unified segmentation and normalization tool of SPM12. The resulting gray matter (GM) mask was used to restrict statistical analysis of the EPI images to GM voxels; while white matter (WM) and cerebrospinal fluid (CSF) masks were used to extract nuisance signals that are unlikely to reflect neural activity in resting-state data. After regressing out the head-motion parameters, mean WM and mean CSF signals and their first five components obtained from PCA  as well as custom-made scripts running on MATLAB 2015a . In the single subject study all functional images were motion-corrected: EPI images from all sessions were spatially realigned to the mean image of the fourth session in order to register functional images from all sessions into a common space. Then, EPI images were spatially smoothed using an 8 mm full-width half maximum Gaussian filter. The anatomical TAs our aim was to study effects of GSReg, we preprocessed each dataset twice. In one case, in addition to the abovementioned preprocessing steps, we regressed mean time series of brain voxels out from the GM voxel time courses before band-pass filtering.Seed-based connectivity analysis was used to compare the within subject variance of three different metrics: DTW distance, conventional correlation, and cross-correlation analysis. Thirteen seed voxels were selected according to previous relevant studies . From the calculated cross-correlation values the maximal lagged correlation coefficient was selected as a descriptor of connectivity, similarly to the functional network connectivity (FCN) method described by Jafri et al. , except Before DTW distance calculation the compared time series were normalized, so that the mean of the normalized time series equals to zero and the standard deviation is one. This is in accordance with correlation and cross-correlation calculations, as these metrics perform normalization by definition.t-test of the resulted connectivity maps using fixed-effect analysis.Statistical parametric maps were derived via performing a voxel-wise one-sample SD): 24.2 ± 1.85 years]. Each subject participated in one fMRI session, containing at least five 455-s-long resting-state runs.Publicly available data from Consortium for Reliability and Reproducibility . A total of 180 functional images over 455 s were collected with a BOLD-sensitive T2* weighted GRE-EPI sequence . Fifty-two axial slices were acquired in ascending acquisition order covering the whole brain. Further details are available on the website of the dataset (http://fcon_1000.projects.nitrc.org/indi/CoRR/html/lmu_1.html).The LMU1 dataset was collected at the Institute of Clinical Radiology, Ludwig-Maximilians-University, Munich, Germany, on a 3 T Philips Achieva scanner . High-resolution anatomical images were acquired for each subject using a TIn the group study, preprocessing, and data analysis were performed with the pipeline described in Section Preprocessing of Experiment 2 except that the realigned and coregistered images were transformed to the MNI-152 space using the transformation matrices generated during the segmentation and normalization of the anatomical images.To quantify the discrimination ability of functional connectivity metrics, we performed a whole-brain connectivity pattern based classification on the group study data. To calculate ROI based whole-brain functional connectivity we used a functional atlas of FIND Lab, consisting of 90 functional regions of interest , and calculated the accuracy values of these random classifications. The threshold of significance, i.e., the 95 percentile of accuracy values of this random classifier is 55.4%.For a stricter threshold, we can train and test our classifier with the original datasets, but with permuted labels  and time delay. As expected, correlation can efficiently capture even short term relationships between time courses, but only if the interaction is perfectly synchronized. As Figure With this simulation we demonstrated that with appropriate parameters DTW distance is able to capture a wide range of dynamically changing relationships, which are undetectable, or have very low significance if examined with linear correlation.Experiment 1.B demonstrates the robustness of DTW distance when common noise is present. If we consider the case of a measurement taken in the presence of relatively high level of common noise, e.g., global signal from respiration and cardiac movements, it is often the case  is known to produce unreliable significance in anticorrelations.p = 0.05, and in case of both correlation maps, we calculated the number of voxels that were significantly negatively correlated when preprocessing included GSReg, but were no more significant when we skipped this step. With DTW based T-maps we simply counted voxels, which passed the threshold with only one preprocessing pipeline  in cross-correlation based maps is usually much lower than the number of questionable voxels selected by zero-lag correlation, DTW produces 80% less spurious statistics than cross-correlation in almost all seeds.T-test to compare DTW similarity, correlation coefficient and maximal lag cross-correlation values between each dataset pair from the forty datasets , e.g., we compare two PCC seed based DTW similarity maps by calculating the T-value from the paired T-test on the two significance thresholded DTW similarity maps. The absolute T-values should be low, if the connectivity strength is the same between measurements, or different preprocessing pipelines. After calculating the absolute T-values from the paired T-tests, we can plot these differences on forty-by-forty matrices  for DTW distance calculation was determined, and an apt transformation was developed for DTW distance values, that yields DTW similarity values interpretable with standard statistical analysis tools used in traditional correlation based studies. The obtained DTW value as a single scalar measure is suitable to describe functional connectivity strength similarly to traditional SCA or ICA methods. Next, we showed that seed based statistical maps produced based on DTW similarity are very similar to those obtained based on correlation or cross-correlation values. Due to the fact that, as our simulations demonstrated (Experiment 1.A), DTW distance and similarity is capable to capture more complex relationships than linear correlation, DTW based T-maps showed more clusters that are slightly better localized.The strength of static functional connectivity between brain regions in case of an individual brain is expected to be stable across repeated measurements  in the time series datasets strongly influences the functional connectivity calculated from correlation coefficients, while connectivity measured with DTW is less affected. Also, DTW can effectively capture intermittent irregular positive (in-phase) and negative  coherence between network nodes characterized by weak negative  correlation coefficients as demonstrated in Experiment 1 with simulated data. Based on these properties, functional connectivity calculated from DTW similarity is shown to be more robust, and significantly less sensitive to global signal regression than standard SCA and maximal lag cross-correlation analysis. While—in accordance with the literature  and DTW differentiates well between strong connections Figure . The supAnother advantage of DTW that should be a subject of future research is that through the in-depth analysis of the warping paths we gain information of the dynamic changes in the relationship of the voxels. Neural signals have been shown to form dynamically changing adaptive patterns of activation over various time scales , Budapest, Hungary, and the subject gave informed written consent in accordance with the Declaration of Helsinki.All coauthors contributed to this study: RM, PH, KB, VG, and ZV.This work was supported by a grant from the Hungarian Brain Research Program (KTIA_13_NAP–A–I/18) to ZV. KB was supported by the National Research, Development and Innovation Office—NKFIH PD 111710 and the János Bolyai Research Scholarship of the Hungarian Academy of Sciences.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


We found that expression of a 15-kb human RENIN (hREN) transgene was aberrantly upregulated (>4.2-fold), while the endogenous mouse renin (mRen) gene was suppressed (>1.7-fold) in Tsukuba hypertensive mice (THM), a model for genetically induced hypertension. We then generated transgenic mice using a 13-kb mRen gene fragment that was homologous to the 15-kb hREN transgene and found that its expression was also upregulated (>3.1-fold) in THM, suggesting that putative silencing elements of the renin genes were distally located in the loci. We next examined the possible role of a previously identified mouse distal enhancer (mdE) located outside of the 13-kb mRen gene fragment. Deletion of the mdE in the context of a 156-kb mRen transgene did not affect its transcriptional repression in THM, implying that although the silencing element of the mRen gene is located within the 156-kb fragment tested, it is distinct from the mdE. Consistent with these results, deletion of the 63-kb region upstream of the mdE from the endogenous mRen gene locus abrogated its transcriptional repression in THM. We finally tested whether dysregulation of the short renin transgenes also occurred in the fetal or neonatal kidneys of THM and found that their expression was not aberrantly upregulated, demonstrating that aberrant regulation of short renin transgenes commences sometime between neonate and adult periods.Renin, a rate-limiting enzyme in the renin–angiotensin system, is regulated to maintain blood pressure homeostasis:  Tg copy numbers were determined by comparison with a standard curve generated by spiking non-Tg mouse genomic DNA with varying amounts of plasmids containing the mRen gene sequence. Signal intensities of the bands were quantified by phosphorimager and ImageQuant software .For structural analysis of Tgs, agarose-embedded thymus DNA was treated with BbsI site of pX330  for genEcoRV and PvuII, respectively, and fractionated by conventional agarose gel electrophoreses. Following capillary transfer onto nylon membranes, blots were hybridized with [α-32P]-labeled DNA probes corresponding to nt 86,373–86,793 and nt 98,147–98,713 sequences, respectively [AC068906].For structural analysis of mutant alleles, tail (5'-large-del) or thymus (pseudo-WT) DNA was treated with Mice were housed in a pathogen-free barrier facility in a 12-hour light/12-hour dark cycle, and fed standard rodent chow. Adult (8-week old) and neonatal (1-day old) mice were sacrificed by cervical dislocation or beheading, respectively, and the kidneys were immediately removed and flash-frozen in liquid nitrogen. For fetal studies, female mice were sacrificed at 17.5 days of gestation , and fetuses were removed from the uterine sacs. Fetal kidneys were immediately flash-frozen in liquid nitrogen.Animal experiments were performed in a humane manner under approval from the Institutional Animal Experiment Committee of the University of Tsukuba. Experiments were performed in accordance with the Regulation of Animal Experiments of the University of Tsukuba and the Fundamental Guidelines for Proper Conduct of Animal Experiments and Related Activities in Academic Research Institutions under the jurisdiction of the Ministry of Education, Culture, Sports, Science and Technology of Japan.KpnI-NcoI), corresponding to exons 3 to 9 of the Ren-1c cDNA  was [α-32P]-labeled and used as a probe. Mouse Gapdh gene expression analyzed by a mouse cDNA  probe was used as the internal control.Total RNA was isolated from mouse kidneys using ISOGEN  and analyzed as described elsewhere . A DNA frenin gene (5'-GCCCTCTGCCACCCAGTAA-3' and 5'-CAAAGCCAGACAAAATGGCCC-3'), mRen Tg (5'-CATCCACCGGATCTAGATAAC-3' and 5'-CAAAGCCAGACAAAATGGCCC-3'), hREN Tg (5'-GCTTTCTCAGCCAGGACATC-3' and 5'-TGCCAATGGCCTGTTCAATG-3'), and mouse Gapdh gene (5'-AAAATGGTGAAGGTCGGTGTG-3' and 5'-TGAGGTCAATGAAGGGGTCGT-3').Total RNA from kidneys was converted to cDNA using ReverTra Ace qPCR RT Master Mix with gDNA Remover . Quantitative amplification of cDNA was performed with the Thermal Cycler Dice  using SYBR Premix EX Taq II (TaKaRa Bio). PCR primer sequences are as follows: endogenous mouse Systolic blood pressure was measured by a programmable sphygmomanometer  using the tail-cuff method, as previously described .P<0.05 were considered statistically significant.Values are expressed as mean±SD. Number of animals analyzed is shown in each panel. All data were analyzed using an unpaired t-test. Results with renin gene expression in THM appropriately regulated the maintenance of blood pressure homeostasis. To this end, we crossed female homozygous hREN TgM with male heterozygous hAGT TgM and obtained progeny carrying either hREN Tg alone  or both hREN and hAGT Tgs  in THM than in control mice  that is required for its proper regulation in THM. In possible accord with the latter hypothesis, it has been reported that a 140-kb hREN P1 artificial chromosome Tg was appropriately downregulated in a hypertensive environment [Two possible explanations for dysregulation of the hironment .REN and mRen genes share common regulatory mechanisms in maintaining blood pressure homeostasis, we generated TgM carrying a mRen gene fragment that was homologous to the 15-kb hREN Tg. The homologous sequence was determined using the Ensembl genome browser  were inserted into the 3'-untranslated region of the mouse Ren-1c gene, which allows distinction between the endogenous and transgenic mRen genes , and two TgM lines (244 and 179) were established. The integrity of the Tg was confirmed by the long-range structural analysis of high-molecular-weight thymic DNA  and defRen gene , bottom.en genes . The 13-ymic DNA . Quantifymic DNA .Ren transgene in the TgM suggested that it could be overexpressed. Therefore, we conducted Northern blot analysis using mRen cDNA probe to roughly quantify the overall (endogenous plus transgenic) mRen gene expression  was not suitable for the analysis of its regulation in hypertensive environment. We then measured the systolic blood pressure of non-Tg and Tg animals and found their levels did not differ significantly, as expected from the Tg expression level  environment  and hREN Tg  expression was significantly upregulated in the same RNA samples. These results suggest that the dysregulation of hREN Tg in THM was not due to species-specific differences in transcriptional regulation, but rather to the lack of putative silencing elements in both the 13-kb mRen and 15-kb hREN Tgs.To examine its regulation in THM, the 13-kb mironment . Total R  environment, as was the case for the 13-kb mRen Tg , demonstrating that the mdE sequence is dispensable for the observed transcriptional downregulation in the THM. Endogenous mRen  resided within the 156-kb region of the locus examined, yet outside of the 13-kb region of the mT allele , bottom,Ren  environment . Total RRen , endogenRen  and female hAGT TgM produced a severe hypertension phenotype during pregnancy of the mother . It was therefore predicted that fetoplacentally-produced hREN enters the maternal circulation where it catalyzes maternally produced hAGT to cause blood pressure elevation [REN Tg expression in the fetus by examining renin gene expression in the fetal kidney of THM. To obtain control and THM fetuses in the same litter, we set up the mating as shown in renin gene expression was analyzed by qRT-PCR. Expression of the endogenous mRen gene was suppressed in THM when compared with that in the control littermates  THM, analyzed  qRT-PCR , and fouREN gene [Ren gene was also distally located to its protein-coding region, suggesting that transcriptional mechanisms, at least in adult kidney, are conserved between the two species  and eveREN gene , equival species .renin genes [et al. reported that CREs in hdE and proximal promoter region of the hREN gene were essential for its correct transcriptional regulation in response to administration of β-adrenergic receptor agonist and low sodium diet in TgM [Ren gene transcription [cis-elements responsible for transcriptional repression of renin genes in THM  is in fact modulating renin gene transcription in THM remains to be determined.Both the human distal enhancer (hdE) and mdE are distally located to the in genes , 19 and t in TgM . Althougcription , its poss in THM . Previous in THM  demonstrREN Tg is the basis for pathogenesis of hypertension in adult THM. Furthermore, we hypothesized that the same dysregulation may be associated with the expression of short renin genes in the fetal kidney, which may be, at least in part, a cause of the maternal hypertension in PAH model animals [REN (15 kb) nor mRen (13 kb) Tgs were upregulated  and red fetuses , we assul kidney  was suppREN Tg in the placenta of THM during late gestation [REN mRNA levels are higher during early gestation than those during term in human placenta [REN Tg caused its aberrant expression in the placenta and pathogenesis of PAH. Germain et al. identified a cis-regulatory element between −5.8 and −5.5 kb upstream from the transcription start site of hREN in primary human chorionic cells [REN Tg may account for the dysregulation.We previously reported an overexpression of the 15-kb hestation , while iplacenta . It is tic cells . A lack REN Tg expression was appropriately suppressed in the kidney of THM neonates (1-day-old), demonstrating that regulation of short renin Tg expression in THM changed during two months period after birth. Alteration in transcriptional environment of the kidney or epigenetic change in proximal promoter region of renin genes by long-term exposure to high concentration of AII and/or high blood pressure, in combination with a lack of distal sequences may account for the phenotype. Although exact reason for developmental difference of the renin gene regulation in THM remains to be determined, it is certain that distal silencing element is essential for proper regulation of renin genes in the adult THM, because expression of the endogenous mRen gene in THM was properly regulated throughout life.In the same way as in THM fetuses, hREN and mRen genes in the kidney of adult THM are located at long distance from their protein-coding regions. In the mRen gene, mdE was dispensable for the repression in THM and approximate location of a putative silencing element was determined, by genome editing, to be upstream of mdE and inside a 156-kb mRen gene locus. Meanwhile, transcriptional responses of the short renin transgenes in the fetal/neonatal vs adult kidneys were apparently different, although endogenous mRen gene behaved similarly at all these stages. We therefore conclude that distal silencing element of the mRen gene  was essential for proper regulation of renin genes in adult THM.In summary, our results demonstrate that putative silencing elements of both h


Interestingly, metformin, a glucose lowering drug associated with moderate weight loss, is also associated with an increased colonic 18F-FDG uptake. Consequently, increased colonic glucose use might partly explain the weight losing effect of metformin when this results in an increased energy expenditure and/or core body temperature. Therefore, we aimed to determine whether metformin modifies the metabolic activity of the colon by increasing glucose uptake.Physiological colonic 18F-FDG uptake on PET-CT, energy expenditure and core body temperature before and after the use of metformin. The maximal colonic 18F-FDG uptake was measured in 5 separate segments .In this open label, non-randomized, prospective mechanistic study, we included eight lean and eight overweight males. We measured colonic 18F-FDG uptake increased significantly in all separate segments after the use of metformin. There was no significant difference in energy expenditure or core body temperature after the use of metformin. There was no correlation between maximal colonic 18F-FDG uptake and energy expenditure or core body temperature.The maximal colonic 18F-FDG uptake, but this increased uptake is not associated with an increase in energy expenditure or core body temperature. Although the colon might be an important site of the glucose plasma lowering actions of metformin, this mechanism of action does not explain directly any associated weight loss.Metformin significantly increases colonic  Obesity and diabetes mellitus type 2 (DM2) are health problems with a tremendous impact and a still increasing prevalence. Many attempts have been made to combat obesity and DM2, however the current therapies are lacking in effectivity .18F-fluorodeoxyglucose (18F-FDG) positron emission tomography computed tomography (PET-CT) pinpoints areas with high glucose turnover. In a retrospective analyses, approximately 50% of the patients that underwent a diagnostic 18F-FDG PET-CT showed high 18F-FDG uptake in the colon, suggesting that the colon has a comparatively high glucose consumption [18F-FDG PET-CT [sumption , 3. IndeG PET-CT .18F-FDG uptake in the colon but this association has not yet been prospectively evaluated [Metformin, a drug widely used in the treatment of DM2, is associated with moderate weight loss–7. Intervaluated . In addi18F-FDG uptake. This might cause an increase in core temperature  as increased muscle activity results in the production of heat. Apart from metformin there are no other known modulators that increase 18F-FDG uptake in the colon.If the colon increases the glucose consumption under influence of metformin, energy expenditure is expected to increase. Putatively, an increased muscle peristalsis would increase glucose demand and thereby 18F-FDG uptake in the colon. We further wanted to assess whether the 18F-FDG uptake was associated with an increase in energy expenditure and/or core body temperature.Therefore, the aim of this study was to determine prospectively whether metformin increases nd 2015 and the study was conducted according to the Declaration of Helsinki. All subjects gave written informed consent after oral and written explanation of the procedures performed during the study. Participants were recruited by public advertisement. This study was registered on www.clinicaltrials.gov with the registration number NCT02783053. The registry of this study was completed after the first recruitment of subjects because of a delay in finalizing the protocol on the website. However, the authors confirm that all ongoing and related trials for this drug/intervention are registered.The Medical Ethics Committee of the Academic Medical Center of the University of Amsterdam approved the study protocol  on July The manuscript was written according to the trend statement checklist .2) and eight lean . Volunteers were recruited and finalized the study protocol between October the 15th 2015 and June the 9th 2016. Healthy subjects were defined as subjects without any current or ongoing diseases  In addition subjects were required not to use any medication or drugs, including anti-DM drugs. Baseline characteristics, including length, weight, waist circumference, medical history (including medication use), laboratory measurements (kidney function and liver function), were obtained during the baseline visit. All subjects completed all study visits and all subjects were included in the analysis , assuming a standard deviation of differences of 2,24, using a paired t-test with a 0,05 two-sided significance level.We based our sample size on a retrospective study, we aime18F-FDG uptake in the colon resulted in energy disposal by increasing energy expenditure and/or core temperature. Subjects were investigated on two study visits, one before and one after using metformin. Thousand mg of metformin orally was used for seven days, 500 mg in the morning and 500 mg in the evening. We based our dosing regimen on the fact that discontinuation of metformin for two days have been shown to significantly reduce colonic 18F-FDG uptake.[This was an open label, non-randomized, prospective mechanistic study investigating whether an increase in G uptake..When subjects were eligible for inclusion, the two study visits were planned. The study visits were separated by a two week interval. On the first visit, subjects received the metformin tablets from the PhD student along with the metformin instructions and clear oral instructions how to use the tablets and the potential side effects. On both study visits, subjects arrived after at least a six hour fast at the Clinical Trial Unit. The evening before the last visit, subjects used their last metformin tablet. Compliance was checked by pill count and anamnesis. After arrival, the equipment to measure the core body temperature was applied. Weight was measured with the subjects wearing only underwear and on the same calibrated mechanical scale (SECA) to the nearest 100 g. Height and waist circumference were recorded to the nearest 0.01 m.18F-FDG was administered intravenously. One hour after the administration of 18F-FDG a PET-CT imaging of the abdomen was performed.Subsequently, subjects were rested on a bed and energy expenditure (EE) was measured for 20 minutes. After the EE measurement, subjects were again rested on a bed and 100 MBq of TM Manager  core temperature pill. The mean transit time of the colon was estimated at approximately 30 hours [Core body temperature was measured using a VitalSense® Core temperature capsule, and data were analysed using Equivital30 hours , 13. The2 during expiration / O2 usage) was also obtained as an indirect measure of calories used. Subjects were not allowed to move or talk during the measurements.Energy expenditure  was measured via indirect calorimetry, using a ventilated hood system  for approximately 20 minutes in a supine position. During these measurements, the respiratory quotient . Subjects were rested on a bed in a warm room (25°C) an half hour after the administration of 18F-FDG in order to minimize the uptake of 18F-FDG in muscles and brown adipose tissue. PET images were acquired one hour after the administration of 18F-FDG and included diaphragm to the pelvis. For attenuation correction and anatomical colocation, a low dose CT imaging  was consecutively performed.18F-FDG uptake in the colon on the PET-CT images was scored visually and by generating volumes of interest (VOI) using the software program Hybrid Viewer . For the analysis, the colon was divided into 5 segments: the distal ileum (later referred to as ileum), the cecum and ascending colon (later referred to as cecum), the hepatic flexure and the transverse colon (later referred to as transverse colon), the splenic flexure and the descending colon (later referred to as colon descendens) and the sigmoid colon ending at the recto sigmoid junction (later referred to as sigmoid), according to our previously published method [The d method .18F-FDG was performed according to the 4-point scale described by Gontier et al.[18F-FDG uptake as a reference .Visual assessment of the colonic uptake of er et al., using t18F-FDG uptake  were determined. In colonic segments with a visual grading of ≤ 2, the software program was not able to generate VOIs. In these segments, SUVmax was assessed using the option “quick-ROI”. The SUVmax is defined as 18F-FDG activity in becquerel per milliliter within the ROI divided by injected dose in becquerel per gram of body weight. As a reference the 18F-FDG uptake in the liver was determined (i.e. SUVmax in a VOI of 10 consecutive transverse liver 18F-FDG PET-CT slices), according to our previously published method [In colonic segments with a visual grading of ≥ 3, volumes of interest (VOIs) were generated. After generating the VOI, the volume (mL), maximal and mean 18F-FDG uptake in the colon. Our secondary aims were to assess whether the increased 18F-FDG uptake was correlated with an increase in energy expenditure and/or core body temperature.Our first aim was to confirm prospectively that metformin significantly increases 18F-FDG uptake in the colon. Furthermore, we hypothesized that this increase in 18F-FDG uptake in the colon would result in an increased energy disposal in the form of an increase in energy expenditure and/or core body temperature.We hypothesized that metformin could increase 18F-FDG uptake in the colon after the use of metformin to confirm the role of metformin in 18F-FDG uptake in the colon prospectively.Our primary outcome measure was therefore the increase in 18F-FDG uptake and the increase in energy expenditure and/or core body temperature.Our secondary outcomes were the correlation between the increase in Data are represented as median and interquartile range (IQR). A p-value <0.05 was considered as statistically significant. Differences in paired analysis were calculated using the Wilcoxon signed rank test. Differences between groups were calculated using the Mann Whitney U test.18F-FDG uptake was calculated as (18F-FDG uptake post-exposure * 100%) / 18F-FDG uptake pre-exposure. For the statistical analysis, SPSS 20.0 was used. The database can be found as supporting file  and eight overweight males . There was no significant difference in age between the two groups. As expected, BMI, waist circumference and fasting plasma glucose were significantly higher in the overweight group .At baseline the max values of the liver between the lean and overweight subjects .There was, however, a significant difference in the baseline SUV18F-FDG uptake increased in all subjects after metformin administration. In the remaining segments, 18F-FDG uptake increased in almost every subject  and BMI, core body temperature  or EE. Post-exposure, there was a significant inverse correlation between BMI and the visual assessment of the colon , and between EE and the visual assessment of the colon , and EE and the maximal uptake of 18F-FDG in the colon . After correction for BMI, none of the correlations remained significant.  did not significantly correlate with any of the parameters of 18F-FDG uptake in the colon, but there was a significant correlation between the difference in EE and the difference in core temperature  after the use of metformin, were inversely correlated. However, when considering the role of BMI in EE, these correlations might have been driven mainly by BMI. This assumption was confirmed by the fact that after correction for BMI none of the correlations remained significant.The lack of change in EE after metformin treatment is conform the results of an earlier study. However,Interestingly, we found a strong correlation between the difference in EE and core body temperature whereby in approximately half of the subjects, metformin caused a decrease in EE and core body temperature whereas in the other half, metformin caused an increase in EE and core body temperature. This might again reflect the differences in effectiveness of metformin between individuals .18F-FDG uptake in the colon. The colon is characterized by a complexity of systems amongst others, the gut microbiome. Recent reports show that metformin influences the composition of the microbiome,[18F-FDG uptake. Especially the role of butyrate producing bacteria and/or short chain fatty acids might be interesting to evaluate. However, while the gut microbiome is different between lean and obese subjects with type 2 diabetes mellitus[18F-FDG is administered intravenously. Therefore 18F-FDG uptake in the colon reflects glucose uptake from the blood whereas the gut microbiome is likely to mainly influence the luminal side of the colon. Nonetheless, metformin modifies both the 18F-FDG uptake in the colon and influences the gut microbiome composition. If and how these system influence each other would be an interesting topic for future research.Apart from these factors, there are many other factors might influence the crobiome,–29 which mellitus we did n18F-FDG since the uptake of 18F-FDG did not increase with increased colonic 18F-FDG uptake.[max values of the liver were significantly higher in overweight subjects than lean subjects. Thus, the colonic 18F-FDG uptake in overweight subjects might be underestimated as compared to the lean subjects in the visual assessment. Although speculative, this difference in18F-FDG liver uptake between lean and overweight may be seen as an early marker of the metabolic syndrome in the overweight subjects.We previously showed that the liver was a reliable reference for the visual assessment of the colonic uptake of G uptake. However,18F-FDG uptake in the colon after the administration metformin both in lean and the overweight subjects. All segments showed a significant increase in 18F-FDG after the use of metformin. By performing multiple tests, we might have introduced a type I error. However, as our data are in line with the results of previous studies type I errors seem unlikely.[Though the sample size of the groups was rather small, we found a very convincing increase in unlikely., 9, 10.18F-FDG uptake increased significantly after metformin, we did not find a correlation with energy expenditure and/or core body temperature. The effect of metformin on EE and/or core body temperature might have been subtle considering the relatively small mass of smooth muscle in the colon. Also, energy expenditure measurements do not take into account anaerobic glucose utilization. So, to further clarify the effect, in vitro studies have to be performed to assess the effect of metformin on anaerobic glucose utilization and its association with 18F-FDG uptake.Even though the 18F-FDG uptake and energy expenditure and/or core body temperature.Furthermore, the effect of metformin might have been too diverse  and therefore a larger sample size might have revealed an association between 18F-FDG uptake, so if there was an association with EE or core body temperature, we would have found this.Our subjects used metformin for two weeks. This might have been too short to induce significant changes. It is thought that the actions of metformin are attributed to phenotypic modifications in gut cells, occurring after a relatively long time rather than to the presence of therapeutic drug concentrations. NonethelAnother important limitation of this study is the failure of the core body temperature measurements in one out of eight lean subjects and in six out of eight overweight subjects, and therefore we cannot draw firm conclusions from our data and our findings are in need of replication. Interestingly, most of the core body temperature measurements failed in obese subjects. This might imply that the signal of the core body temperature pill was disturbed due to an increased BMI. If this was the case, in future, a core body temperature pill might not be the best method to measure core body temperature in obese subjects. However, up until now, the core body temperature pill is still the most reliable method to measure the colonic temperature. Rectal t18F-FDG uptake in the colon and this might potentially play an important role in the glucose lowering actions of metformin. However, the increase in 18F-FDG uptake in the colon is not associated with either a significant increase in EE or in core body temperature.Metformin significantly increases S1 FileSupporting file containing the research protocol.(PDF)Click here for additional data file.S2 FileSupporting file containing the trend statement checklist.(PDF)Click here for additional data file.S3 FileSupporting file containing the database.(SAV)Click here for additional data file.


Severe health anxiety (hypochondriasis), or illness anxiety disorder according to the Diagnostic and Statistical Manual of Mental Disorders, 5th edition, is characterized by preoccupation with fear of suffering from a serious illness in spite of medical reassurance. It is a debilitating, prevalent disorder associated with increased health care utilization. Still, there is a lack of easily accessible specialized treatment for severe health anxiety.The aims of this paper were to (1) describe the development and setup of a new internet-delivered acceptance and commitment therapy (iACT) program for patients with severe health anxiety using self-referral and a video-based assessment; and (2) examine the feasibility and potential clinical efficacy of iACT for severe health anxiety.Self-referred patients (N=15) with severe health anxiety were diagnostically assessed by a video-based interview. They received 7 sessions of clinician-supported iACT comprising self-help texts, video clips, audio files, and worksheets over 12 weeks. Self-report questionnaires were obtained at baseline, post-treatment, and at 3-month follow-up. The primary outcome was Whiteley-7 Index (WI-7) measuring health anxiety severity. Depressive symptoms, health-related quality of life (HRQoL), life satisfaction, and psychological flexibility were also assessed. A within-group design was employed. Means, standard deviations, and effect sizes using the standardized response mean (SRM) were estimated. Post-treatment interviews were conducted to evaluate the patient experience of the usability and acceptability of the treatment setup and program.11= 3.66, P=.004) with a large within-group effect size of 1.06 as measured by the SRM.The self-referral and video-based assessments were well received. Most patients  completed the treatment, and only 1  dropped out. Post-treatment  and 3-month follow-up  data were available for almost all patients. Paired t tests showed significant improvements on all outcome measures both at post-treatment and 3-month follow-up, except on one physical component subscale of HRQoL. Health anxiety symptoms decreased with 33.9 points at 3-month follow-up  Severe health anxiety is characterized by the preoccupation with fear of having a serious illness, which interferes with daily functions and persists despite medical reassurance . In the Despite the high prevalence and the existence of effective psychological treatments , the disPatient self-referral in combination with internet-delivered psychological treatment is a new approach that may facilitate treatment accessibility and broaden availability. As used here, internet-delivered treatment is a highly structured treatment that the patient accesses through a secure online platform. The patient is granted gradual access to content such as video clips, texts, audio files, and work sheets, and the treatment is guided and supported by a clinician through an embedded asynchronous message system. The basic principle is that internet-delivered treatment should target the same core treatment processes as face-to-face treatment, with only the mode of delivery changing . interneinternet-delivered treatment for common mental disorders has been investigated in well over 100 randomized trials, and a recent meta-analysis showed that internet-delivered cognitive-behavioral therapy (iCBT) for depressive and anxiety disorders can be just as effective as face-to-face-treatment . In 3 raPoor adherence to internet-delivered treatment is a widespread and well-documented concern ,20, whicThe aims of this study were to (1) describe the development of a new treatment concept for patients with severe health anxiety encompassing patient self-referral, video-based diagnostic assessment, and treatment with iACT over 12 weeks; and (2) evaluate the feasibility and overall outcome of this new treatment concept. The feasibility evaluation included patients’ feedback provided both at the video-based assessment and post-treatment interview, self-reported questionnaires, and the patients’ attrition and adherence to the treatment. The evaluation did not apply a priori defined criteria of success, since this study was a proof of concept prior to conducting a larger, RCT.Both the technical platform and the contents of the treatment were developed de novo . The treSelf-referral was developed and tested as a new treatment entry through the clinic’s webpage, where patients could log into a portal using their unique Danish personal identification number. The self-referral procedure consisted of the following steps: (1) patients gave written consent for the clinician to access information on their health status through the electronic patient record prior to assessment, (2) patients were asked to describe their problem in an open text field “Please describe your health anxiety in your own words and how you feel at the moment,” and (3) patients completed baseline screening questionnaires measuring health anxiety symptoms among others. Video-based assessment was implemented to support nationwide recruitment. A shortened version of the semi-structured psychiatric interview, Schedules for Clinical Assessment in Neuropsychiatry (SCAN) , was adaAccessibility, interactivity, contact, and technical solution are some of the focal points that define the functionality and appearance of internet-delivered treatment programs. The decisions regarding these focal points as they relate to the present study are described below.Accessibility can range from full, open access to health care integrated programs with clinician-administered inclusion. Since this platform was developed and operated in a hospital setting, access was administered by health professionals to secure that the hospital’s legal responsibility was ensured. Treatment progress was monitored and access to treatment modules was granted consecutively by the clinician.Interactivity can range from fixed content with no interaction to more responsive content such as online exercises or games that are adapted based on the patient’s interaction. The platform’s content was designed to be relatively fixed , but included interactive work sheets which were completed by the individual patient using embedded text fields. All sheets were automatically collected and stored in a folder on the front page and shared with the patient’s personal clinician.Contact can range from pure self-help to varying degrees and formats of contact . The current platform enabled written, asynchronous clinician guidance . This format was chosen to minimize interference with the patient’s daily activities. Guidance was provided throughout the 12 weeks of treatment. Every patient had a designated personal clinician and messages were answered within 48 hours on weekdays. The content of the written guidance was not predetermined or restricted, and no limit was established concerning contact frequency and length.Technical solution can range from tools designed for mobile apps to extensive Web-based programs. The current platform was developed as a Web-based app built upon Drupal, a flexible open source content management system (CMS) and development framework. This solution was chosen because the treatment material is comprehensive and specialized functionality had to be added to the platform. The following solutions were developed: (1) a 2-factor authentification login which interfaces with the Danish public login system (NemID); (2) an embedded, securely encrypted message system for written communication; (3) an automatic text message  notification system ; and (4) a clinician-monitored control panel with automatic notifications with information about patient activity . In addition, the responsive design allows access to the treatment program through mobile devices and tablets.The treatment content was based on an empirically tested group-based ACT program for patients with severe health anxiety ,30, and The number of modules in the original face-to-face manual was shortened from 10 to 7 . Since tThis feasibility study took place from April 2015 to October 2015 at the Research Clinic for Functional Disorders and Psychosomatics at Aarhus University Hospital in Denmark. It was an open trial with no control group. Self-report questionnaires were obtained at baseline prior to diagnostic assessment, at post-treatment, and at 3-month follow-up. Patients not completing any modules were excluded from the final analyses as the primary aim of the study was to evaluate the feasibility of the new treatment set-up and program. Treatment completion was defined a priori as 3 or more modules completed since the first 2 modules primarily consisted of psycho-education and did not explicitly include behavior change strategies. Semi-structured interviews with patients after end of treatment were conducted by the clinician assigned to the individual patient to explore the usability and acceptability of the procedure with self-referral, video-based assessment, as well as the treatment program. All participants provided written informed consent, and the study was approved by the Danish Data Protection Agency, Central Denmark Region (ID no. 1-16-02-427-14).A total of 15 patients were recruited, 10% of the projected sample size for the RCT. This was considered sufficient for the feasibility evaluation of the treatment concept including self-referral, assessment, and treatment. Patients were recruited through the clinic’s webpage, which announced information about the study, and presented a patient video about health anxiety. Patient self-referrals were screened for eligibility by a psychologist (first author) and then allocated to a video-based clinical assessment. The assessments were conducted by psychologists or psychiatrists trained in the use of the shortened version of SCAN . Health Treatment targets for each moduleModule 1: The first important stepWhat are health anxiety and acceptance and commitment therapy?Overview and introduction to the treatmentMotivation and values for participationModule 2: Your experiences managing health anxietyModel of health anxiety and introduction to valuesControl, avoidance and creative hopelessnessMindfulness and rumination about illnessModule 3: Challenging and accepting anxietyEvolutionary function of bodily symptoms of anxietyThe workability of control and avoidance behaviorChallenging (exposure) and accepting anxietyModule 4: You get to know your values when taking stepsValues sortingValues compassValues-based exposure-hierarchyModule 5: What is stopping me?Inner barriers Defusion and acceptanceMindfulness and self-as-contextModule 6: Self-compassion and boundariesHow stress affects bodily symptomsBalancing maladaptive “all-or-nothing” behaviorPersonal boundaries, open up and self-careModule 7: Relapse preventionRepetition of treatment principlesConsolidating personal insights from the treatmentNormalization of relapse and relapse preventionThe mean sum score of the scales presented below were transformed into a 0 to 100 score point scale according to equation 1:This was chosen to facilitate the comparison of changes between measures in this and previous studies ,32.The WI-7  is a 7-iThe Symptom Checklist-92 (SCL-92)  subscaleThe Short-Form Health Survey (SF-12) ,37 with Life satisfaction  is a 1-iThe Acceptance and Action Questionnaire-II (AAQ-II) ,40 is a t tests. The assumptions behind the paired t test were assessed by graphical inspection of a quantile-quantile (Q-Q) plot of the differences and a Bland-Altman plot. An estimate of within group effect sizes was calculated using the standardized response mean (SRM) according to equation 2, by the formula, where X1 and X2 are the means of the measurements at time points 1 and 2, respectively. SDDIFF is the standard deviation of the variable containing the difference between the 2 measurements.Data were summarized using means and standard deviations. The mean differences from baseline to post-treatment and from baseline to 3-month follow-up were analyzed using paired Adherence was measured as the mean number of modules completed, and exchanged messages in the program were summarized using means and standard deviations. All analyses were performed using Stata version 13 for Windows. The post-treatment interviews were conducted using semi-structured questions. The answers were not analyzed quantitatively.A total of 18 patients were self-referred of which 3  withdrew prior to assessment . Of the Of the patients, 12  agreed to participate in the semi-structured interviews at post-treatment. Due to cancellations, only 10 interviews were conducted. The patient who dropped out and 1 of the non-completers participated in the interview ensuring a varied evaluation.Online patient recruitment through the clinic’s webpage attracted the patient population of interest. All self-referred patients were judged to have health anxiety as their primary problem based on the first screening and they were invited for assessment. Patients reported that it was easy to find the trial and go through the steps of self-referral.At the end of the video-based interview, patients commented on their user experience. Overall, patients found it convenient, and the majority expressed feeling more secure at home than having to go to the hospital. A few patients felt anxious prior to the assessment due to the new video-format, and technical issues did interrupt a few interviews. In spite of this, most patients expressed that the video-format did not interfere negatively with their sense of contact with the clinician during the assessment. All clinicians had no prior experience with the video-format, and they reported positively about the amount of clinical information the assessment allowed for.The post-treatment patient interviews revealed overall satisfaction with relevance and progression of treatment themes and a positive response to the use of patient videos. There was no consistent pattern in content feedback . Patients sent an average of 12.9 (SD 7.76) messages (range 4-29) to their clinician, and clinicians sent an average of 18.5 (SD 3.46) messages (range 12-25) to their patient. Some patients expressed that this form of communication allowed for more honesty compared with earlier experiences with face-to-face therapy, whereas for others it was a hindrance. Therefore, some patients wrote many extensive messages where others consulted the clinician only to a limited extent. The clinicians reported that it was possible to have a good clinical sense of the patient. However, for those patients who did not use the clinician, the therapeutic reinforcement was missing to a larger extent. Furthermore, the patients were generally surprised by the amount of time they needed to complete a module.P values, and effect sizes are shown in t13= 6.11, P<.001), and at 3-month follow-up with a difference of 33.9 points  with large within-group effect sizes (SRM 1.06-1.63).Mean, standard deviation, and percent changes at baseline, post-treatment, and 3-month follow-up are shown in the There were no significant changes in scores from post-treatment to 3-month follow-up which suggests stability of clinical outcome. Furthermore, patients reported significant reductions in symptoms of anxiety and depression, and significant increases in life satisfaction and psychological flexibility . Effect The patient interviews gave rise to revision and further extension of some treatment themes and exercises. Furthermore, we learned that patients need to not only be motivated for internet-delivered treatment, but also to be able to work independently, prioritize their time with regard to treatment engagement, and have a minimum of technical know-how. To further improve adherence prior to the RCT, a patient recruitment video was developed to provide a clear picture of the treatment format, emphasizing the time and effort needed to participate in this type of treatment. This was also further addressed in the assessment interview. Patients who were ambivalent had up to 2 weeks to decide whether to enter the randomization for the following trial. Based on the experience with the drop-out of 1 patient due to technical obstacles, we included a question about computer skills in the baseline questionnaire. We also enquired more about this in the assessment interview. Finally, due to discussions raised about changes in medication during the pilot study, an extra inclusion criterion was added for the RCT to ensure that patients had been stable on anxiety medication for at least 2 months prior to inclusion.The main finding of this pilot feasibility study was that iACT encompassing online self-referral, video-based assessment, and internet-delivered treatment was a feasible treatment concept for patients suffering from severe health anxiety. We found overall positive outcomes with large improvements in health anxiety symptoms and medium to large improvements in anxiety, depression, HRQoL, and life satisfaction, which improved further during the 3-month follow-up period. The observed reductions in health anxiety symptoms were similar to those reported in a previous, uncontrolled, face-to-face pilot study, and a subsequent RCT on group-delivered ACT for patients with health anxiety ,32. SincThe thorough standardized clinical assessment and well-defined diagnostic inclusion criteria were strengths of this pilot study. Moreover, both completers and non-completers participated in the post-treatment interview and most data were available for patients at follow-up. Lastly, the study employed validated clinical outcome measures. Even though this study did not employ a randomized design, most outcomes pointed in the same direction: symptoms improved following iACT. The most important limitation of this study was the uncontrolled study design and small sample size. The design was chosen based on the aims of this study, and the sample size did not limit the feasibility evaluation, which was the primary aim. The preliminary observed clinical changes should be replicated in a larger RCT to establish clinical relevance. Another limitation was that the video-based assessment was not evaluated systematically. We did discuss the technical and clinical implications at supervision, and asked the patients about their experience at the interview, but we did not collect our experiences in a structured manner. Lastly, the post-treatment patient interviews were not analyzed systematically and since they were conducted by the dedicated clinician, there was a risk of bias due to socially desirable answers.Patient self-referral is rarely applied in a publicly funded health care system based on gatekeeping, where patients are referred primarily through the general practitioner. We found that the majority of those who self-referred fulfilled criteria for severe health anxiety, which may indicate that self-referral is a sensible recruitment procedure in patients with severe health anxiety. One explanation may be that patients with health anxiety often seek medical information via the internet, and may have come across the clinic’s webpage while searching for explanations for their symptoms. The patient video on the webpage was likely to have further increased recruitment sensitivity as many patients reported that they recognized their own health anxiety from the patient’s stories. Former studies support that patient self-referral is a feasible recruitment method for internet-delivered psychological treatment ,43. HoweThe overall aim of iACT for health anxiety is to target and improve a patient's psychological flexibility, which previously has shown to be a central process of change ,45. ThisClinician guidance was added to this treatment program because studies generally find that unguided internet-delivered treatment has higher attrition ,46. The Severe health anxiety is a disabling and costly disorder with a lack of accessible, specialized treatment options. Here, we showed that online self-referral, video-based assessment, and iACT may be a feasible treatment set-up for patients suffering from severe health anxiety. The promising outcome results also suggest potential efficacy for this iACT program which is currently being tested in a larger RCT. If proven effective, iACT may increase availability and accessibility of specialized treatment for health anxiety. Future studies need to evaluate the sensitivity of patient self-referral, and to compare characteristics in patients self-referred or referred through a health care professional, to determine whether different patient populations are reached.