The global HIV response has been remarkably successful. More than 19 million persons living with HIV (PLHIV) have accessed life‐saving antiretroviral therapy (ART) Viral load measurement is a critical tool to assess the impact of HIV treatment efforts, and is now endorsed by the World Health Organization (WHO) as the primary methodology for monitoring response to ART Because viral load measurement is a laboratory assay, it is unfortunately easy to misunderstand the challenge of viral load scale‐up as one for laboratorians only. However, experience with other laboratory assays in resource‐limited settings has shown that it takes far more than assuring availability for a test to realize its potential. For example, even with the availability of testing for early infant HIV diagnosis, multiple challenges have been described in relation to coverage, quality and utilization of results. These include ensuring correct sampling methodologies for dried blood spot samples from HIV‐exposed infants, obtaining specimens at the recommended timeframes, transporting specimens to the laboratory, ensuring tests are done in a timely fashion, enabling providers to promptly access test results and providing results to the infant's caregiver to enable appropriate clinical decision‐making The experience of early infant diagnosis (EID) scale‐up highlights the importance of conceptualizing viral load testing as a continuum; a series of steps, each critical for achieving the ultimate goal – swift and appropriate clinical management to maximize the chance of sustained viral suppression Figure . The firOnce the sample is obtained, transportation systems are required to convey specimens, whether plasma or dried blood spot samples, to the appropriate laboratories. Laboratories need staff, equipment, laboratory information systems and quality assurance/improvement capacity to ensure that specimens are logged, tracked, tested and documented – and that results are transmitted back to the health facility, either electronically or physically. Point‐of‐care assays for viral load measurement offer the potential for overcoming the challenges encountered in transport of specimens and results Ultimately, the most critical step is for the providers to review the test results and to share them promptly with the recipients of services. Ensuring that clients are aware of the importance of viral load monitoring and viral suppression for both their own health as well as preventing onward transmission is critically important. Similarly, providers must be trained and supported to swiftly act upon viral load test results, reinforcing clients whose viral load is suppressed, and rapidly and accurately following national guidelines for patients with unsuppressed viral loads to increase adherence or adjust ART regimens if viral resistance is suspected.In most countries, the first step after an unsuppressed viral load result is to provide enhanced adherence counselling (EAC) based on a careful assessment of all the factors that may be impeding clients’ ability to take ART regularly, followed by repeat viral load measurement Findings to date indicate that many clients who have unsuppressed viral load achieve viral suppression after EAC, demonstrating that in a significant proportion, the unsuppressed viral load is largely due to non‐adherence This supplement aims to summarize a workshop focused on viral load scale‐up that took place from 27 to 30 June 2016 in Swaziland. The workshop, entitled “Reaching the Third 90: Implementing High Quality Viral Load Monitoring at Scale,” was attended by 150 participants from 16 sub‐Saharan African countries, including individuals from diverse backgrounds reflecting key elements of the viral load continuum et al. describe the efforts of the International Treatment Preparedness Coalition to mobilize communities of PLHIV to demand access to viral load testing and to empower them to advocate for such access in the countries where they live et al. describe the lessons learned from scale‐up of other laboratory tests, such as EID and CD4+ cell count assays, which can inform scale‐up of viral load testing et al. discuss the optimal viral load threshold to use when defining virological failure et al. describe the unique experience of providing viral load results to individuals participating in the PHIA Project et al. et al. et al. et al. et al. includes a systematic review of evidence related to the cost‐effectiveness of routine viral load monitoring in low‐ and middle‐income countries In their article, Killingo In summary, viral load measurement provides critical information for the management of individuals, as well as insight into the effectiveness of HIV programmes across the entire HIV care continuum. This one test serves as a unique measure of the coverage, quality and impact of HIV programmes. As access to viral load testing expands, it is critical to learn from the lessons of the past, and to take a systems approach to strengthening every step of the viral load continuum The authors cite no conflicts of interest.All authors contributed equally to the writing and editing of this manuscript.


Understanding local adaptation of tree populations to climate allows the development of assisted migration guidelines as a tool for forest managers to address climate change. Here, we study the relationship among climate, a wide range of physiological traits, and field performance of selected white spruce provenances originating from throughout the species range. Tree height, survival, cold hardiness, hydraulic, and wood anatomical traits were measured in a 32‐year‐old common garden trial, located in the center of the species range. Provenance performance included all combinations of high versus low survival and growth, with the most prevalent population differentiation for adaptive traits observed in cold hardiness. Cold hardiness showed a strong association with survival and was associated with cold winter temperatures at the site of seed origin. Tree height was mostly explained by the length of the growing season at the origin of the seed source. Although population differentiation was generally weak in wood anatomical and hydraulic traits, within‐population variation was substantial in some traits, and a boundary analysis revealed that efficient water transport was associated with vulnerable xylem and low wood density, indicating that an optimal combination of high water transport efficiency and high cavitation resistance is not possible. Our results suggest that assisted migration prescriptions may be advantageous under warming climate, but pronounced trade‐offs between survival and cold hardiness require a careful consideration of the distances of these transfers. This haOne way of accommodating changes in climate is the use of seed sources from areas already adapted to warmer temperatures as part of regular reforestation programs. This usually implies selecting seeds from southern areas to be planted in a more northern region  quantify genetic population differentiation among provenances from across the entire range of the species for a wide selection of hydraulic, anatomical, and cold hardiness traits, (2) detect possible relationships among resistance to climate (cold and drought), tree growth, and survival, and (3) analyze how these traits are related to the climate of origin of the provenances. The results of this range‐wide experiment could point to key traits for climate adaptation that could serve as a reference for more geographically limited studies with higher sample densities to support regional assisted migration prescriptions.22.1Picea glauca [Moench] Voss) trials in a single site in central Alberta, Canada . Both trials belong to a provenance trial experiment described in detail by Rweyongeza et al.  until it stabilized. The average of the last five measurements was used to calculate hydraulic conductivity (KH) with the following expression:Samples for hydraulic measurements were collected between 19 May and 8 June 2015. Distal branches from sun‐exposed parts of the tree crown were cut with a pole pruner. The branches were packed in plastic bags with wet paper towels to avoid desiccation and transported to a cold room (+4°C) the same day. All hydraulic measurements were made within 1 week after collection. We follow methodology described in Hacke and Jansen  and SchoKH was used to calculate xylem‐area‐specific conductivity (KS) and needle‐area‐specific conductivity (KL). Xylem area (AX) was measured in the center of the segments using a stereomicroscope . For the estimation of needle area (AL), we first measured the projected area of a subset of needles, and then, the same subset was weighted after drying the needles in an oven. Using the ratio of area to weight from the subset of needles, we could estimate the total area of the needles distal to the segment by drying and weighting the remaining needles. KS and KL were then calculated according to Tyree and Zimmermann  relative to the maximum conductivity. This procedure was repeated for six pressure levels from −2 to −7 MPa, and the results were fitted to two commonly used functions: the Weibull function  was also calculated as a measure of hydraulic efficiency.Lastly, the ratio of leaf area to xylem area (2.3Tdiam) was calculated using the Hagen–Poiseuille formula:d is tracheid diameter and n is the total number of tracheids measured. Tracheid length (Tlength) was measured using chemical macerations following Schoonmaker et al., The same segments used for hydraulic measurements were also used for wood anatomy analysis. Tracheid lumen diameter was measured for the most recent complete two rings of each segment using a radial file of three cells wide with images taken with a Leica DM3000 microscope at 20× magnification. Only noncompression wood was analyzed. The hydraulically weighted mean lumen diameter  in plastic bags with wet paper towels within the same day. The whole‐plant freeze testing method  and wood density   was associated with a wide range of both P50s and xylem densities, but the most hydraulically efficient branches were always characterized by vulnerable xylem and low densities, indicating that an ideal combination of high efficiency and high cavitation resistance is not possible  and cavitation resistance  originated slightly southeast of the study site which corresponds to a short northwest transfer as was recommended by Gray et al., 4.2The adaptive trait with the strongest genetic population differentiation was cold hardiness, and trait values showed strong associations to latitude and the mean coldest month temperature of seed origin. Cold hardiness was measured in branches collected around mid‐September, and at this time of year, only provenances from Ontario exhibited some frost damage after exposure to –30°C. Ontario provenances usually do not experience any frost at this time of year at their location of origin. Hence, there is no need to develop cold hardiness so early in the fall when trees grow in their native climate. The other provenances do regularly experience mild frost events earlier in September in their native climate and exhibited no significant damage when exposed to –30°C at the test site. The fact that cold hardiness patterns could largely be explained by the climate of the provenance origins indicates that cold hardiness was genetically differentiated among populations, presumably mediated by day length, and that there is little potential for phenotypic plasticity in response to climate conditions. These results conform to many other studies , that is, taller trees tended to show lower KL values than shorter trees. Lower values of KL corresponded to higher needle area distal to the section measured relative to its ability to transport water. KL was mostly regulated by needle area rather than by a change in hydraulic conductivity as we can see from the significant correlation between KL and AL:AX and the absence of a correlation between KL and KS (Table E), a branch with higher KL will be able to maintain a smaller water potential gradient (ΔΨ) than a branch with lower KL, because E = KL ΔΨ  was not correlated with growth or survival, but showed complex relationships with wood density and P50. The boundary analysis approach shown in Figure KS values were mutually exclusive. This points to a strength versus hydraulic efficiency trade‐off and is understandable given constraints arising from the geometry of tracheid‐based xylem  values also came at the expense of increased vulnerability to cavitation. The safety versus efficiency trade‐off is complex (Gleason et al., Hydraulic conductivity standardized by xylem area (5In this range‐wide common garden study, we identified traits that could play an important role in trade‐offs between growth potential and resistance to climatic stress. We should note that the growing conditions of the test site can affect what trade‐offs are revealed. Differences in adaptive traits that did not have an effect on growth and survival could be consequential under different growing environments. Wood density and leaf‐specific conductivity were identified as potentially important traits, but fall cold hardiness stood out as a key trait for tree survival and also showed easily interpretable associations with climate of the provenance origin. Furthermore, the relation between height and the end of the growing season points to the importance of the timing of growth cessation in the growth potential of white spruce. Therefore, regional studies that make use of a higher sampling density to develop assisted migration guidelines should focus on measuring the effects of seed transfers on fall phenology and match provenances to new locations so that their synchronization of the onset of frost hardiness matches new environmental conditions under climate change.None declared.AH, UH, and JS conceived the study and designed the methodology. JS collected the data and led the writing of the manuscript. All authors analyzed the data and contributed to draft versions of the manuscript.


This dataset contained two sessions of resting‐state fMRI from 214 adults aged 18–88. Functional connectivity between all regions was strongly related to vascular health, most likely reflecting respiratory and cardiac signals. These variations in mean connectivity limit the validity of between‐participant comparisons of connectivity estimates, and were best mitigated by regression of mean connectivity over participants. We also showed that high‐pass filtering, instead of band‐pass filtering, produced stronger and more reliable age‐effects. Head motion was correlated with gray‐matter volume in selected brain regions, and with various cognitive measures, suggesting that it has a biological (trait) component, and warning against regressing out motion over participants. Finally, we showed that the location of functional regions was more variable in older adults, which was alleviated by smoothing the data, or using a multivariate measure of connectivity. These results demonstrate that analysis choices have a dramatic impact on connectivity differences between individuals, ultimately affecting the associations found between connectivity and cognition. It is important that fMRI connectivity studies address these issues, and we suggest a number of ways to optimize analysis choices. Hum Brain Mapp 38:4125–4156, 2017. © 2017 Wiley Periodicals, Inc.Many studies report individual differences in functional connectivity, such as those related to age. However, estimates of connectivity from fMRI are confounded by other factors, such as vascular health, head motion and changes in the location of functional regions. Here, we investigate the impact of these confounds, and pre‐processing strategies that can mitigate them, using data from the Cambridge Centre for Ageing & Neuroscience ( Functional connectivity, as measured by functional magnetic resonance imaging (fMRI), has become a popular way to investigate age‐related differences in brain function and the implications of these differences for cognitive health in old age. There have been a number of consistent findings, including a reduction of connectivity within the default mode network (DMN) [Andrews‐Hanna et al., One important issue is separating the effects of age (or any other variable) on neural versus vascular components of the fMRI signal [Murphy et al., In addition to vascular effects, it has been shown that older participants tend to move more in the scanner [D'Esposito et al., Functional connectivity analyses are also affected by the regions of interest (ROIs) used to measure connectivity. The location or number of true functional regions may change with age [Chan et al., http://www.cam-can.org). These individuals were scanned twice (a few months up to a few years apart), allowing us to look at the effect of analysis choices on within‐participant reliability.In an attempt to address some of these issues, most studies use elaborate analysis pipelines. For example, studies may regress out signals from white matter (WM), cerebrospinal fluid (CSF), or global signal to reduce vascular and motion confounds. Given that respiratory and cardiac confounds may have different frequency distributions compared to true functional connectivity, band‐pass filtering of the data is also often used, while differences in the location of functional regions may be attenuated by spatial smoothing. However, the consequences of these analysis choices on the effects of aging on functional connectivity have not been explored systematically. Here, we aim to examine these analysis choices by exploiting a large dataset of resting‐state functional connectivity from over 200 individuals sampled uniformly across the adult‐lifespan from 18 to 88 years of age as part of the CamCAN project  were included in this study, from the population‐based sample of the Cambridge Centre for Ageing and Neuroscience (CamCAN). The sample used in this study was identical to the sample used in a previous publication [Geerligs et al., Two hundred and thirty‐six participants . These data were collected as part of more extensive scanning sessions in a 3T Siemens TIM Trio, with a 32 channel head‐coil. The first scan contained 261 volumes (lasting 8 min and 40 s) and the second scan contained 152 volumes (lasting 5 min). Each volume contained 32 axial slices (acquired in descending order), with slice thickness of 3.7 mm and interslice gap of 20% . In both sessions, a high‐resolution (1 mm × 1 mm × 1 mm) T1‐weighted Magnetization Prepared RApid Gradient Echo (MPRAGE) image was acquired. In the first session, we additionally acquired a T2‐weighted structural image (1 mm × 1 mm × 1 mm) using a Sampling Perfection with Application optimized Contrasts using different flip angle Evolution (SPACE) sequence.All participants underwent two separate sessions of eyes‐closed resting state fMRI scans, which were between three months and three years apart. The time difference between the two scanning session was independent of the participants' age (http://www.fil.ion.ucl.ac.uk/spm), as called by the automatic analysis (AA) batching system (http://imaging.mrc-cbu.cam.ac.uk/imaging/AA). The details of the pipeline can be found in Taylor et al. [The data were pre‐processed using the SPM12 software  kernel and signals were extracted from 748 of the 840 regions defined by Craddock et al. . Only 74The second step to reduce the effects of motion and other noise confounds on functional connectivity results was to apply a general linear model (GLM). This model included expansions of the six original motion parameters, as well as of average signals in the WM and CSF from the time courses of each voxel within each ROI. The WM and CSF signals were created by averaging across voxels in the associated mask image, after the wavelet‐despiking but before smoothing. The expansions included the first‐order temporal derivative, as well as their squares and squared derivatives, which reduces the effects of motion [Satterthwaite et al., In our analyses, we found evidence for residual physiological effects on connectivity estimates, after the default pre‐processing steps described above. Therefore, we investigated whether using the CompCor method [Behzadi et al., We compared two different methods of computing functional connectivity. One was the traditional Pearson correlation (Pcor), which was computed based on the average signal across all voxels within each ROI. The other was a multivariate method, called distance correlation (Dcor) [Székely et al., Previous work has shown that standardization methods, which correct individual differences in mean connectivity, may be necessary to minimize the influence of nuisance variables on inter‐individual variation in functional connectivity [Yan et al., y = x−1) to obtain a more Gaussian distribution across participants. Due to this inversion, higher scores are related to better performance for each of the cognitive measures. Data from 12 participants were missing for the choice RT task because of equipment error and 2 participants were excluded because they made too many errors .Participants performed several cognitive tasks outside the scanner as part of a larger test battery (for a full description) [see Shafto et al., We measured vascular health based on an electrocardiogram (ECG) collected during a separate magnetoencephalography (MEG) scanning session. The sampling frequency for the ECG data was 1 kHz. To detect the ECG R‐peaks corresponding to each heartbeat, we used the PeakFinder function in MatlabCentral and calculated the interbeat interval (IBI) as the time difference between each pair of subsequent R‐peaks. Next, we derived measures of mean heart rate and low (LF‐HRV) and high frequency (HF‐HRV) heart rate variability  using HRV Analysis Software Ramshur, . The dethttp://www.tnu-zurich.org/tapas), where detected IBIs were downsampled to EPI sampling rate . High quality pulseoxymetry data, in which the heartbeats could be detected clearly, was available for 138 participants. The pulseoxymetry data was used to create six physiological noise regressors (RETROICOR) using a well‐established model‐based approach  [Glover et al., Additional physiological data were acquired during our fMRI scans using a pulseoximeter with sampling frequency 50 Hz, placed on the left index finger of the participant. Preprocessing of the pulseoximetry data was performed in Matlab  using the Tapas PhysIO Toolbox  was used to estimate GM volume at each voxel [Ashburner and Friston, r > 0.5 was applied to correlation coefficients to exclude negative and weak correlations. Then the normalized cut spectral clustering (NCUT) algorithm was applied to identify clusters for each participant and each session. To make the parcellation comparable to the original Craddock parcellation which we have used in the remainder of this article, we pre‐specified the number of clusters as 750 and restricted the voxels to those covered by the ROIs that we used from the original set. It should be noted that the NCUT algorithm can result in empty clusters, leading to a number of ROIs that is less than 750. For each participant, we obtained a final clustering solution by combining the parcellations from the two sessions in a group clustering. In this group level clustering, the distances between voxels were determined by their cluster membership. Next, we also obtained an age‐representative parcellation by combining each participant's final parcellations in another group level clustering analysis. The similarity between different parcellations was assessed using normalized mutual information (NMI). NMI measures how much information is provided by one set of assignments about another set of assignments [Strehl and Ghosh, For each participant, we constructed parcellations based on their fMRI data using the pipeline developed by Craddock et al. . To makeWe used different approaches to compare the connectivity estimates for the different pre‐ and post‐processing options. All analyses were performed on data from session 1, except from analyses of reliability of connectivity.P‐value was determined by looking at the proportion of times that the absolute value of the random difference was larger than the absolute value of the real difference between options.First, we investigated the strength and distribution of associations between age and functional connectivity, as well as the association between head motion and functional connectivity, and vascular health and functional connectivity. To test whether differences between processing options were significant, we used permutation tests with 5,000 random assignments. In each permutation run, we randomly allocated the results of each ROI‐pair  to one of two groups. Then, the mean effect  was computed within each of these groups, and the difference between these groups compared to the real difference we measured between the processing options. The P‐value was determined by looking at the proportion of times that the absolute value of the random difference was larger than the absolute value of the real difference between processing options.Second, we computed three complementary indices of reliability. One index was the ICC between the (vectorized) connectivity matrices of the two sessions for each participant (within‐participant reliability). In this way, we tested whether the absolute values and the regional differences in connectivity strength remained stable across sessions. This reliability measure was compared across processing options using the Wilcoxon signed rank test. Another method focused on the stability of individual differences, by comparing individual differences in connectivity strength across sessions using the ICC, separately for each pair of ROIs (between‐participant reliability). We then averaged these ROI ICC values across all within‐network and all between‐network connections. For this measure of reliability, we again used permutation tests to determine the significance of the differences . The final reliability measure concerned the effect of age, by comparing the (vectorized) matrices of correlations between age and connectivity across sessions using the ICC. In this way, we tested whether the absolute values and the regional differences in the association between age and connectivity remained stable across sessions. For this measure, we computed the significance of the differences using a slightly different permutation test. We started with estimates of the association between age and connectivity for two sessions and two processing options. Next, we randomly shuffled age‐connectivity associations across processing options. Importantly, the same shuffle was performed for both sessions. Next, we computed the reliability of the age‐effect in both reshuffled datasets and we compared the difference in reliability between the two sets to the difference in reliability we observed in the unshuffled data. The The third approach was to compute the similarity of connectivity estimates between age‐matched participants. For each participant, we found the 30 participants that most closely matched their age. We computed the similarity between the connectivity of this participant and the average connectivity of the age matched participants, using Pearson correlations. We used Pearson correlations instead of the ICC because we did not expect perfect correspondence between absolute connectivity values across participants, but we did expect a similar network structure. Here, we used the Wilcoxon signed rank test to determine the significance of the differences across processing options.We also examined whether different pre‐and post‐processing choices affected how well connectivity measures could predict individual differences in cognitive function. Here, we focused specifically on the Cattell task of fluid intelligence as a general index of age‐related cognitive decline. For our connectivity measure, we looked at the average level of connectivity within the DMN because it has been consistently shown to decline with age, as well as mild cognitive impairment and Alzheimers's disease, and to be related to cognitive functioning [Hafkemeijer et al., Other analyses were specific to the question of interest in each section and the measures we used in each section are explained there. Results are reported with two decimals precision throughout the article, except when three decimals were needed to demonstrate significant differences.A summary of the analysis choices explored is shown in Table r = 0.75, P < 0.001). This measure declined with age , paralleling the decline in mean functional connectivity . Moreover, participants with better vascular health had higher connectivity estimates in cortical networks , even after accounting for the (linear) effects of age . The lack of cortical specificity of these effects suggests that vascular health is associated with transiently varying global physiological signals, related to fluctuations in breathing or blood flow for example [see also Power et al., First, we looked at the impact of vascular health and head motion on connectivity estimates after the typical nuisance regressions techniques were applied, including regression of motion parameters, CSF and WM signals and their derivate and quadratic terms. We estimated vascular health from ECG data collected during separate MEG scans [Tsvetanov et al., ty Figs. C and 5G,r = +0.77, P < 0.001) and strongly positively correlated with age . After partialing out effects of age, head motion was positively related to mean connectivity  and the effects of head motion on functional connectivity were more localized than effects of vascular health , CSF signal regression (C), CSF + WM signal regression (CW), and CompCor (CC). For the CompCor method [Behzadi et al., Across all participants, we observed that regressing out nuisance signals led to sparser connectivity matrices, with reduced number of between network connections Fig. A. MoreovWith regard to the effects of head motion, we again found a significant advantage of using between‐participant MR, regardless of the within‐participant nuisance regression option that was used see Fig.  and 3. TImportantly, we found a striking effect of nuisance regression on the association between age and functional connectivity Fig. B. When nr = 0.91; C‐CW, r = 0.96; CW‐CC, r = 0.81). These results are encouraging, as they suggest that the changes after different pre‐processing steps (at least C and CW) primarily affect the mean of connectivity estimates, while the pattern of age effects is less affected. This is supported by the correlations between results with and without MR, which were very high for all nuisance regression options .To determine whether this effect of nuisance regressors was only a shift in the distribution of age‐effects, rather than a change in the pattern of age‐related changes, we used Pearson correlations to compare the matrices of age‐effects in Figure To demonstrate how connectivity estimates of individual participants were affected by these different nuisance regression and MR steps, we show the connectivity matrices of four representative participants in Figure We also investigated complementary indices of reliability for these different nuisance regression options. First, we looked at the reliability of the complete connectivity matrix for each participant using the intraclass correlation coefficient (ICC). This analysis asks how similar the connectivity matrices for each participant are at timepoints 1 and 2. The reliability of connectivity matrices of individual participants improved from N to C and CW. However, a significant decrease was observed from CW to CC see Fig. B. When MWe also investigated the reliability of the age‐effect, by looking at the similarity of the age‐connectivity correlation matrices from both sessions. Interestingly, we observed that the reliability of the age‐effect behaved in line with the reliability of the individual connectivity matrices; it increased as more nuisance regression steps were added from N, to C, to CW. In contrast to the results for individual connectivity matrices, we also observed an increase for CC compared to CW. When MR was performed, the age‐effects were substantially more reliable for N, C, and CW, but less reliable for CC. Overall CW + MR was associated with the most reliable age effects. Finally, we examined the similarity between age‐matched participants (30 participants with most similar age). This index of reliability also increased significantly as more nuisance regression steps were added, and was also significantly higher when MR was performed, but significant differences were only observed for N and CC . After correcting for WM and CSF signals, RETROICOR resulted in a significant, although small, reduction in the association between vascular health and functional connectivity .A more sophisticated method to remove physiological artefacts is RETROICOR [Glover et al., These results suggest that regression of CSF and WM signals can substantially attenuate the effects of vascular health on functional connectivity, while RETROICOR provides only a small further attenuation. CompCor did result in a significant further attenuation of the effects of vascular health on functional connectivity, but at the expense of a decrease in reliability. In contrast, MR resulted in increased performance for most reliability indices, as well as reduced associations between connectivity, head motion, and vascular health. Overall, we found that the combination of CW and MR gave the best performance, with high reliability estimates and relatively low effects of vascular health and head motion on connectivity estimates. In the remainder of the article, we used connectivity estimates in which CSF and WM regression, as well as MR had been applied . We also observed that nuisance regression shifts the distribution of age effects to lower values (except for CompCor), but has little effect on the pattern of age effects across connections. It has been suggested previously that reliability of individual connections is most important for the reliability of group‐effects on connectivity [Varikuti et al., In the previous section, we have shown that the combination of CSF and WM nuisance signals and MR reduces the associations between vascular health, head motion, and functional connectivity, and generally leads to more reliable connectivity estimates. Here, we investigated which of the connections within‐ and between‐networks were most sensitive to changes in the nuisance regression procedures. These connections may also be the ones that are most vulnerable to confounds. To investigate this, we computed the correlation between the connectivity estimates before and after nuisance regression . This shows the extent to which differences in connectivity estimates between participants remained the same for these different nuisance regression options. We observed particularly low correlation values for connections between subcortical  and higher‐order cortical networks see Fig. , indicatAnother way to reduce noise and nuisance signals in resting‐state fMRI analyses is to filter the data. High‐pass filtering is typically used to remove frequencies below ∼0.008 Hz, and band‐pass filtering is typically used to additionally remove frequencies above ∼0.1Hz. Although band‐pass filtering can reduce physiological noise, it also leads to less reliable estimates of functional connectivity [Shirer et al., r = 0.32) than subcortical (r = 0.16) regions. Permutation tests demonstrated that this lag‐1 autocorrelation showed a significantly greater reduction with age in cortical  than subcortical  regions (P < 0.001). The differential auto‐correlation across regions that is sensitive to age may therefore bias the effects of age on estimates of functional connectivity. After band‐pass filtering, the lag‐1 autocorrelation was very high in both cortical (r = 0.78) and subcortical regions (r = 0.77), but was not correlated with age in either cortical  or subcortical regions .First, we investigated the levels of lag‐1 autocorrelation and its relation to age after high‐pass or band‐pass filtering. After high‐pass filtering, lag‐1 autcorrelation was higher in cortical , high‐pass filtering (HP), and high‐pass filtering with pre‐whitening (HP‐PW). The pre‐whitening step removes auto‐correlation in the signals and may therefore lead to better estimates of functional connectivity, especially in younger adults with higher autocorrelation. We did not include the option of band‐pass filtering with pre‐whitening because we have previously found that the increased autocorrelation induced by band‐pass filtering cannot be fully removed by our standard pre‐whitening procedures [Geerligs et al., r = 0.96; HP‐BP, r = 0.92). However, effects of age were more pronounced  for HP and HP‐PW than BP  and more pronounced after HP than after BP , on the other hand, depended only minimally on how the data were filtered. The mean correlation remained around zero (because of the regression of mean connectivity), although the spread of correlation values across all ROIs was slightly more pronounced after HP‐PW than after HP  and considerably higher for HP than for BP  and between‐networks . Similarly, the reliability of the age‐effect declined with higher levels of autocorrelation . In addition, the similarity between age‐matched participants was higher for HP‐PW, less for HP  and least for BP .The reliability of connectivity matrices of individual participants improved as the autocorrelation decreased; and was higher for HP‐PW than HP , even after adjusting for effects of age . No significant relation with vascular health was observed . In contrast, the improvement in reliability between HP and PW was not related to head motion  or vascular health .Next, we investigated whether these changes in reliability could be related to confounding factors. We found that the improvement in the reliability of connectivity matrices of individual participants from BP to HP was higher for participants with high levels of head motion across both sessions , even after accounting for effects of age . No relation to vascular health was observed . The improvement in the between‐participant similarity from HP to PW was related to head motion  and vascular health , however after adjusting for effects of age, the effects of head motion and vascular health were not longer significant .For the similarity between age‐matched participants, we observed the same pattern, the improvement from BP to HP was higher for participants with high levels of head motion (n = 21) with the lowest amount of head motion, reliability and between‐participant similarity were significantly higher for HP than BP , suggesting that the improvement in reliability is also related to the lower levels of autocorrelation and increased degrees of freedom in HP compared to BP. This is confirmed by the comparison between HP and PW, where the improvement in reliability and between participant similarity does not appear to be driven by effects of head motion, but purely by the lower levels of autocorrelation and resultant decreases in autocorrelation.These results show that part of the improvement in the reliability indices from BP to HP is due to effects of head motion; it is likely that head motion had consistent effects on connectivity estimates across scanning sessions which were reduced by removing high‐frequency signals. However, we also found that even in the 10% of participants . Indeed when we regressed out individual differences in vascular health or head motion from our connectivity estimates before estimating the effects of age, we observed that the reliability of the age‐effect on connectivity was reduced significantly, compared to the results with MR . Here, we investigated whether vascular health and head motion could be related to other important dimensions of inter‐individual variability: GM volume and cognitive performance.In the previous section, we attempted to minimize the effects of vascular health and head motion on functional connectivity estimates. One way to reduce these effects would be to directly regress out effect of head motion or effects of vascular health from our connectivity estimates across participants. Indeed, in the case of head motion, this approach has been used in a number of papers [Cao et al., We used the average estimate across the two sessions as our trait measure of vascular health. We performed two VBM analyses, one for the T1 scan in each session, but we observed no significant clusters of main effects of vascular health, or interactions between vascular health and age, at a cluster corrected threshold of pFWE < 0.001. Next, we investigated the association between vascular health and cognitive functioning (as measured outside the scanner). We looked at fluid intelligence (as measured by the Cattell test), crystallized intelligence (as measured by the Spot‐the‐Word task), verbal memory , mean RTs as well as RT variability (in a choice reaction time task). We found no main effects of vascular health on cognition, nor any significant interaction between vascular health and age.The same analyses were performed for our measure of trait motion, which was based on the estimates of total motion from both sessions. Given that estimates of GMV can themselves be reduced by motion during a T1‐scan, we included TGM as a covariate of no interest [Reuter et al., t(213) = −2.49, P = 0.014) and had more variable RTs (t(213) = −2.28, P = 0.024)  These results support the idea that individual differences in head motion have both a biological basis and a relationship with cognitive performance, in which case, regressing out motion when examining the effects of age may not be advisable as it is could remove true age‐related changes in functional connectivity, rather than simply state‐related noise.Next, we investigated whether in‐scanner motion related to cognitive functioning. Although there was no significant effect of motion on crystallized intelligence or verbal memory, participants with high trait motion scored lower on the Cattell test of fluid intelligence  after averaging the time series of all voxels within ROIs. This may obscure true functional connectivity estimates if ROIs contain multiple sub‐regions with distinct connectivity. Previous papers have suggested that there may be an age‐related shift in the location of functional regions [Chan et al., P < 0.001), suggesting that there is a consistent age‐related shift in the location of functional regions. To investigate the localization of these effects, we constructed a binary vector for each voxel, containing ones for all the voxels in the same parcel and zeroes for voxels in another parcel. Next, we used Fisher transformed correlations as a measure of the similarity between participants for the parcellation information at each voxel. We tested for consistent age‐shifts in the location of functional regions by permuting age labels . We observed that consistent age‐differences were only present in a very small number of voxels, centered on the right lateral frontal regions (BA 45), suggesting that there is not much evidence for a widespread age‐related shift in the location of functional regions.To investigate this, we used the methods proposed by Craddock et al.  to creatr = −0.64, P < 0.001, see Fig. r < −0.26, P < 0.0001) or increased  significantly with age. We observed that the age‐related increase in idiosyncrasy reported above was mainly driven by differences in cortical regions, while some subcortical regions actually showed age‐related increases in similarity between age‐matched participants.Even though we did not find much evidence for a consistent shift in functional regions with age, there may still be age‐related changes in functional regions which vary between individuals. In this case, we would expect that the similarity between age‐matched participants would decrease with age . Therefore, we used NMI to measure the similarity between each participant's parcellation and that of the 30 participants that most closely matched their age. We found that this similarity with age‐peers decreased significantly with age .We examined whether this age‐related increase in idiosyncrasy could be explained by an age‐related reduction in the reliability of functional parcellations, by comparing the separate parcellations obtained from session 1 and 2. There was a reduction in the across‐scan reliability of functional parcellation in older adults  = 28.1, P < 0.001). Homogeneity was significantly reduced with advancing age in the original ROI set , while this decline was not significant in the participant‐specific ROI set . However, when we used a parametric test to compare the strength of these correlations [Steiger, z = 1.32, P = 0.19). We did not examine effects of participant‐specific ROIs on connectivity estimates, because it was not possible to match ROIs across participants.Next, we investigated whether participant‐specific ROIs improve the homogeneity of signals within ROIs, by computing the average correlation between the time‐series of different voxels within an ROI. In the participant‐specific set, the number of ROIs varied between 696 and 746 (average 719), while the original Craddock set contained 746 ROIs. Despite the smaller number of ROIs for participant‐specific ROIs (and hence more voxels per ROI on average), we found that the similarity of voxel time‐series within an ROI improved significantly with respect to the set of original Craddock ROIs  = 131.05, P < 0.001). This improvement was larger than for the participant‐specific ROI set, although this may owe to the larger number of ROIs (750 versus ∼719). The association between homogeneity and age was similar in the original Craddock ROIs  and the age‐representative ROI set .These results suggest that the use of participant‐specific parcellations may lead to more valid and somewhat less age‐biased results. While participant‐specific ROIs may seem optimal, group‐based ROIs will be more accurate for those functional regions that are consistently located across participants (by virtue of pooling over more data to define them), and most importantly, a common ROI set facilitates connectivity analyses across participants. We therefore tested whether an improvement was also achieved using a group ROI set generated from our own sample, which is more age‐representative than the original Craddock sample. Indeed, this sample‐specific set of 750 ROIs showed higher homogeneity across participants than the original Craddock ROIs , compared to the original Craddock ROIs (r = 0.700). These results suggest that, at least in this specific case (using the Cradock method with 750 ROIs), the use of an age‐representative, group ROI set did not appear to be advantageous.The age‐representative ROIs did not reveal a change in the distribution of age‐effects compared to the original Craddock ROIs. There was also no improvement in the reliability of the individual connectivity matrices, using the age‐representative ROI set (ICC = 0.641) as compared to the original Craddock set (ICC = 0.645), nor in the reliability of individual connections , nor in the reliability of the effects of age on functional connectivity . Finally, the similarity between age‐matched participants was slightly lower for the age‐representative ROIs (r = 0.83) compared to the original Craddock ROIs  = 178.6, P < 0.001). However, we also observed a greater age‐related decline in homogeneity for the Power ROI set , compared to the Craddock ROIs . The difference between these correlations was significant .We also examined how age affects homogeneity in a different set of ROIs, which were based on results from meta‐analyses [Power et al., r = −0.33, P < 0.001; S6: r = −0.17, P = 0.011 and S8: r = −0.13, P = 0.049). When we compared these correlations we found that there was a significant increase in the association between age and homogeneity from S8, to S6 and S0 . The similarity of connectivity estimates between age‐matched participants also increased with smoothing. Interestingly, both the positive and the negative associations between age and functional connectivity were amplified as smoothing increased, as evidenced by a significant increase in mean absolute association between age and connectivity . In addition, the age‐effects were significantly more reliable after smoothing and the reliability of individual connectivity matrices increased after smoothing. The reliability of individual differences in connectivity strength increased with smoothing for between‐network connections, while for within‐network connections an increase was observed for S6 and S8 compared to S0, but a decrease was observed for S8 compared to S6 . The data in all previous analyses were smoothed with an 8 mm FWHM Gaussian kernel (S8). Here we examined how smoothing affected the homogeneity, the associations between age and connectivity and the reliability of connectivity estimates. Averages and statistical tests that are not reported in the text can be found in Tables The downside of smoothing is that it reduces the ability to localize functional connectivity to specific ROIs. Another potential solution to this problem of misalignment of functional regions that is less likely to reduce spatial specificity is to use a multivariate measure of functional connectivity. Multivariate measures utilize data from all the voxels in an ROI, instead of just the average. We have recently shown that distance correlation (Dcor) is well equipped to deal with inhomogenous ROIs [Geerligs et al., Smoothing also improved the reliability of individual connectivity matrices and single connections for both Pcor and unsigned Pcor, but not Dcor see Fig. . Here, wFor many studies of functional connectivity, the ultimate aim is to relate connectivity to cognitive function. Given prior associations reported between the DMN and cognition [Hafkemeijer et al., This interaction effects between age and DMN connectivity on Cattell scores was strengthened further using CompCor, instead of the mean CSF and WM signals. A similar increase in the interaction affect was observed after MR. However, for MR (in combination with C or CW) we additionally found that the association between DMN connectivity and fluid intelligence in the older tertile became significant. Interestingly, for the different filtering and pre‐whitening options (for CW + MR), we found that the association between DMN connectivity and Cattell was stronger for HP‐PW, weaker for HP and no longer significant for BP.Smoothing the data slightly weakened the DMN‐age interaction. However, using multivariate Dcor increased the strength of this interaction when 8 mm smoothing was applied compared to Pcor. But this was not true when no smoothing was used.z = 2.08, P = 0.038); the difference between N and CW + MR for 0 mm smoothing  and the difference between N and CW + MR for 8 mm smoothing with Dcor (z = 2.29. P = 0.022). These results suggest that that pre‐ and post‐processing optimization does affect the type of outcome that is most important in many studies of functional connectivity.We also investigated whether these differences between pre‐ and post‐processing choices in their association between DMN connectivity and fluid intelligence in the older group were significant. Using parametric tests Steiger, , we obseAging is likely to have important effects on functional connectivity in the brain, but measuring connectivity with fMRI raises several challenges, such as the confounding effects of age on vascular health, head movement and functional organization. We have demonstrated that different analysis choices, designed to address some of these confounding effects, dramatically alter the effect of age observed on resting‐state fMRI connectivity. More specifically, we examined, among others, the choice of (i) nuisance signal regression and connection strength standardization, (ii) temporal filtering and pre‐whitening, (iii) group‐level motion correction, and (iv) functional parcellation  attributed to differences in neural factors. Studies on aging are one case where vascular effects may be an important systematic confound. Tsvetanov et al.  for examIndeed, we observed that vascular health was strongly related to the functional connectivity across all ROI‐pairs. Our results demonstrate that valid comparisons between participants cannot be made when these differences in mean connectivity have not been accounted for; without accounting for differences in the mean we would conclude that aging is associated with brain‐wide decreases in neural connectivity, while these results are actually driven by physiological signals. While we have focused on effects of aging, we also observed variations in vascular health and mean connectivity in young participants  is to regress out signals that come from voxels outside the GM ROIs, such as in WM or CSF, which are likely to contain global physiological artefacts without any neural signal [Jo et al., Several methods can be used to reduce the effects of widespread differences in the mean connectivity. In a subset of participants, we investigated whether adding RETROICOR regressors could further reduce these effects of vascular health, but we found only minimal changes after RETROICOR, suggesting that the residual effects of vascular health  are not solely due to cardiac pulsation artifacts. These results are in line with previous findings, showing that variations in heart rate  are linked to variations in respiration [Power et al., Another method that is often been applied is global signal regression, in which the mean across all brain voxels for each volume is used as a nuisance regressor within the analysis of each participant. Global signal regression shifts the distribution of connectivity values to have zero mean. Although global signal regression may reduce effects of physiological signals and head motion [Power et al., We found that including CompCor signals as nuisance regressors indeed reduced the effects of vascular health on functional connectivity. However, it also resulted in a strong reduction in the reliability of connectivity estimates, both across participants and across sessions. Moreover, it increased the effects of head motion on functional connectivity. This is in line with the results of previous studies: Varikuti et al. found that CompCor was disadvantageous for both within or between‐participant reliability [Varikuti et al., In contrast, MR resulted in more reliable connectivity estimates while simultaneously eliminating differences in mean connectivity between participants and reducing associations between connectivity and vascular health. It also reduced the effects of head motion on connectivity estimates; improved the reliability of the effects of age, the reliability of single connections (across participants), the reliability of the connectivity matrix (across sessions) and the similarity between participants. In addition, stronger associations between DMN connectivity and performance on the Cattell test of fluid intelligence were observed after MR. While prior to MR, the association between age and connectivity relied heavily on the choice of nuisance regressors, this was no longer the case after MR was performed. MR does make the assumption that individual differences in average levels of functional connectivity across the brain reflect physiological noise, rather than neural signal. If this assumption is not valid, the estimated age‐differences may be inaccurate. In addition, in smaller samples , within‐participant correction methods, such as mean subtraction [Yan et al., These findings may explain some of the striking differences in the literature about the effects of age on functional connectivity. Studies using global signal regression have typically observed both increases and decreases in connectivity with age because global signal regression shifts the distribution of connectivity values toward mean zero [Andrews‐Hanna et al., Many studies are interested in the association between connectivity and other characteristics of an individual, such as cognitive function, personality traits or disease symptoms. Here we have shown that the choices made in pre‐ and posts‐processing also affect the strength of the associations observed between cognition and connectivity. WM and CSF regression strengthened the association between connectivity and cognition, as did MR. In addition, we have shown that nuisance signals affected between‐network connectivity estimates most, while within‐network cortical connections were less affected. Together, these results demonstrate that pre‐ and post‐processing choices do not only affect the reliability of our results but also the conclusions that can be drawn with regards to important other variables such as cognition. Most importantly, we have demonstrated that differences between participants in mean connectivity strength need to be accounted for before valid comparisons can be made.Higher frequency components of the fMRI signal have been thought to reflect physiological noise, which is why many studies apply band‐pass filtering prior to calculating functional connectivity. Recently however, some studies have shown that band‐pass filtering can also impair the efficiency of functional connectivity estimates [Shirer et al., The current results reinforce those findings, by showing that the effects of band‐pass filtering are two sided. On the one hand, we observed that band‐passing filtering reduced the effects of vascular health and head motion on functional connectivity. However, we also found that band‐pass filtering strongly reduced the reliability of connectivity estimates and reduced the reliability of the effects age on functional connectivity. Part of this change in reliability was associated with the amount of head motion during the scan, suggesting that consistent head motion across sessions could impact functional connectivity in a consistent fashion. However, we also observed reduced reliability after band‐pass filtering in the lowest moving participants and we found that band–pass filtering weakens the association between DMN and fluid intelligence in older adults. Together, these results suggest that band‐pass filtering reduces the effects of vascular health and head motion on functional connectivity, while simultaneously impairing the efficiency of the connectivity estimation and the reliability of connectivity estimates by increasing the amount of autocorrelation and reducing the effective degrees of freedom in the data.When the data were high‐pass filtered, we found that the dominant source of autocorrelation, at Lag 1, was stronger in younger than in older adults. This residual autocorrelation can be reduced by pre‐whitening the data. When we did this, we found that pre‐whitening (after high‐pass filtering) further improved the reliability of the age‐effect, as well as the reliability of the estimated connectivity matrices in individual participants and the similarity of connectivity estimates of age‐matched participants. These results confirm that autocorrelation is an important factor to consider when optimizing functional connectivity estimates. In some cases, high‐pass filtering combined with pre‐whitening may be a better analysis choice than band‐pass filtering, at least in studies where individual differences in head motion and other nuisance signals are not a major concern.Head motion is another confound that is particularly relevant in the study of healthy aging, as older adult often move more than younger participants [Cao et al., One might think that it is safer to examine effects of age after possible vascular health and motion artifacts have been removed from the functional connectivity estimates by group‐wide correction. However, the danger is that important effects of age on true neural connectivity are also removed. Both vascular health and head motion were stable across the two scans, suggesting a trait component above any random, state‐related noise. For vascular health, there was no evidence that this trait was related to GM volume or cognitive profile. In contrast, we found evidence that head motion has both neural and cognitive correlates. Specifically, we observed that participants who moved more had smaller GM volume in areas 8 and 9 of the cerebellum, and lower scores on tests of general cognitive decline, fluid intelligence and crystallized intelligence, as well as higher variability of RTs, even after adjusting for age. While it is difficult to disentangle true correlates of trait motion from motion artefacts, as motion during the structural scan itself may have compromised the segmentation and spatial normalization of the cerebellum, we attempted to mitigate these concerns using TGM as a covariate [Reuter et al., If there is a neurobiological foundation for individual differences in head motion, this may also cause differences in functional connectivity [Zeng et al., Functional connectivity analyses are typically based on an a priori set of ROIs, often defined on young adults. If the location of true functional regions in the brain changes with advancing age, this approach could lead to worse overlap between functional regions and ROIs in older adults, as well as biased connectivity estimates [Sohn et al., In this article, we specifically investigated the set of Craddock ROIs and used their methods to create participant‐specific parcellations. Other ROI sets exist that are based on different approaches/assumptions, such as identification of regions from meta‐analyses of previous task‐related studies [Power et al., We therefore examined two other approaches that may mitigate the age‐related change in the location of functional regions. The first is approach is simply to smooth the data across nearby voxels. This rendered the effects of age on functional connectivity more pronounced and more reliable. The disadvantage of smoothing however is that it reduces the spatial specificity of the functional connectivity results . The second approach we examined was to use a multivariate method to compute functional connectivity called distance correlation [Geerligs et al., Our measure of vascular health was based on heart rate variability, derived from ECG data during a separate MEG scan. Age‐related reductions in heart rate variability have previously been linked to changes in the regulation of the autonomous nervous system, and low frequency oscillations in the heart rate have been shown to contribute around 20% to the cerebral hemodynamics [Katura et al., In this article, we have focused on a range of methods to optimize the validity of connectivity estimates. One category of de‐noising methods that we did not consider are those based on independent component analysis (ICA). These methods, such as FMRIB's ICA‐based X‐noiseifier (FIX) and ICA‐based strategy for Automatic Removal of Motion Artifacts (ICA‐AROMA), separate the data of each individual into multiple independent components and use either classifiers (FIX) or matching with template features (ICA‐AROMA) to segregate signal from noise components [Pruim et al., We focused specifically on correlation‐based measures of connectivity. It is not obvious how all of these recommendations translate to other approaches, such as group‐level spatial ICA analyses. The effects of smoothing we reported here would be expected to be comparable across correlation‐based and group‐level ICA‐based methods, as the misalignment of functional regions does not depend on the connectivity method that is used. For both types of analyses, we would expect to see reduced effects of age with lower levels of smoothing. It is less clear how the effects of physiological signals we reported here would translate to group ICA analyses. In contrast to correlation‐based analyses, studies using ICA typically do not regress out nuisance signals before the analysis. Instead, the assumption is made that the ICA will separate signal and noise components. Spatial ICA separates the data into spatially‐independent components. Because this independence is forced, it does not detect components related to brain‐wide physiological signals, such as those related to vascular health. This is unlike temporal‐ICA where such brain‐wide signals can be detected [Smith et al., Our results demonstrate that different pre‐processing choices can substantially alter the effects that age has on fMRI resting‐state functional connectivity. First, the effects of age on the distribution of connectivity values are strongly modulated by the choice of nuisance regressors, suggesting that changes in mean connectivity may not be very meaningful. Second, the connectivity values were strongly related to vascular health, which decreases with age. This effect of vascular health can be reduced by including nuisance regressors that capture the physiological signals present in WM and CSF voxels. In addition, we propose that it is more appropriate to focus on the relative pattern of age‐related changes across ROIs; MR increases the interpretability of comparisons between participants and leads to more reliable connectivity estimates that are less affected by head motion and vascular health. We also recommend researchers to consider in each study whether band‐pass filtering is required, because it is likely to decrease the reliability of connectivity estimates. Regarding effects of head motion, we would suggest that researchers attempt to optimally reduce motion effects on the single subject level, as group level regression may not account for all head motion effects [Power et al., Although this article is specifically about aging, most studies comparing different populations , and even studies in healthy younger populations, will be affected by similar issues. Therefore, it is imperative that future studies consider carefully the various analysis choices they make, and ideally ensure that their conclusions hold across a range of such choices.


This process is characterized by a series of cell–state transitions, in which normal epithelial cells acquire first a senescent state which is later surpassed to attain a mesenchymal stem–like phenotype with a potentially tumorigenic behavior. In this paper we aim to provide a system–level mechanistic explanation to the emergence of these cell types, and to the time–ordered transition patterns that are common to neoplasias of epithelial origin. To this end, we first integrate published functional and well–curated molecular data of the components and interactions that have been found to be involved in such cell states and transitions into a network of 41 molecular components. We then reduce this initial network by removing simple mediators , and formalize the resulting regulatory core into logical rules that govern the dynamics of each of the network components as a function of the states of its regulators.Tumorigenic transformation of human epithelial cells in vitro has been described experimentally as the potential result of Computational dynamic analysis shows that our proposed Gene Regulatory Network model recovers exactly three attractors, each of them defined by a specific gene expression profile that corresponds to the epithelial, senescent, and mesenchymal stem–like cellular phenotypes, respectively. We show that although a mesenchymal stem–like state can be attained even under unperturbed physiological conditions, the likelihood of converging to this state is increased when pro–inflammatory conditions are simulated, providing a systems–level mechanistic explanation for the carcinogenic role of chronic inflammatory conditions observed in the clinic. We also found that the regulatory core yields an epigenetic landscape that restricts temporal patterns of progression between the steady states, such that recovered patterns resemble the time–ordered transitions observed during the spontaneous immortalization of epithelial cells, both in vivo and in vitro.Our study strongly suggests that the in vitro tumorigenic transformation of epithelial cells, which strongly correlates with the patterns observed during the pathological progression of epithelial carcinogenesis in vivo, emerges from underlying regulatory networks involved in epithelial trans–differentiation during development.The online version of this article (doi:10.1186/s12918-017-0393-5) contains supplementary material, which is available to authorized users. Nearly 84% of cancers diagnosed in human adults are carcinomas . Although epithelial carcinogenesis has been strongly associated with a chronic inflammatory process and aging , the preIn agreement with this developmental view of cancer, numerous experimental findings in molecular and cell biology of cancer research have revealed that it is possible to recover cells with cancer–like phenotypes through the induction of de–differentiation events. This has been shown particularly in carcinomas , 7, sincattractors and correspond to configurations of gene or protein activation states that underlie the cellular fates or phenotypes. Therefore, dynamic developmental processes – particularly, cellular differentiation and morphogenetic patterning – can be formalized in temporal terms as transitions between attractors . Here, we adopt such approach to study how tumorigenic transformation due to spontaneous immortalization via EMT emerges from the regulatory interactions between different molecular players that are known to contribute to the tumorigenic transformation of epithelial cells. We hypothesize that: (1) a generic series of cell state transitions describing the phenotypic transformation of epithelial cells first to senescent cells and finally to mesenchymal stem–like cells, that is widely observed in epithelial cell cultures during spontaneous immortalization, naturally result from the self–organized behavior emerging from an underlying intracellular GRN; and (2) that pro–inflammatory tissue–level conditions, which are associated with a bad prognosis, increase the likelihood of this tumorigenic process, promoting the emergence and progression of epithelial cancer.In systems biology it is common to study cell differentiation processes that underlie development and pathogenesis from the point of view of dynamical systems theory. In this framework, the information encoded by the genome, in addition to epigenetic mechanisms, can be mapped to a gene regulatory network (GRN), that shows multiple stable steady states, each of which corresponds to a particular phenotypic cellular state. Further, the GRN also underlies the epigenetic landscape (EL), that restricts the time–ordered patterns of transition between the phenotypes –13. ThusTo test our hypothesis, we propose here a cellular–level GRN that, for the first time, integrates those molecular components and their interactions that have been experimentally shown to play an important role during the emergence and progression of carcinomas. It includes the key molecular regulators that: (1) characterize the cellular phenotypes of epithelial, mesenchymal stem–like, and senescent cells; (2) are involved in the induction of the cellular processes of replicative senescence, cellular inflammation, and epithelial–mesenchymal transition (EMT); and (3) are involved in the phenotypic changes undergone by cells emerging from these processes . We then obtained a reduced regulatory core for further dynamical analyses by removing linear cascades while maintaining the feedback loops. We show that the proposed regulatory core module displays an orchestrating robust behavior akin to that seen in other developmental regulatory modules previously characterized with similar modeling approaches Our results suggest that the proposed core GRN incorporates a set of necessary and sufficient components and interactions to explain the emergence of gene configurations characteristic of epithelial, senescent and mesenchymal cells, as well as the time–ordered sequence of cellular transformations observed in the spontaneous immortalization process that, in turn, underlies the tumorigenic transformation of epithelial cells.Following a bottom–up approach, we performed an extensive literature search to gather the most relevant experimental functional molecular data describing the cellular–level processes involved in epithelial carcinogenesis, namely: replicative cellular senescence, inflammation, and EMT . While both E2F [senescence of epithelial cells is mainly controlled by the two tumor supressors p53 and p16 [Cellular inflammation is characterized by the increased activity of the TF NF– κB. Many (micro)environmental stimuli, including pathogens, cytokines, interleukins, and antigens; trigger the expression of NF– κB, resulting in an immune response characterized by increased levels of cytokines and enzymes such as phospholipase A2, cyclooxygenase, and lipoxygenase [as Snail . ESE–2 aas Snail , and it as Snail . The difeb-FOXC2 . The proboth E2F  and cyclboth E2F  are requboth E2F . The enzboth E2F . Indeed,both E2F , 25. The and p16 . They bo and p16  and by i and p16 . Cellulaxygenase .κB) are tightly interconnected by the following regulatory interactions: NF–κB positively controls its own expression via the induction of pro-inflammatory cytokines [ESE–2 forms an auto–activation feedback loop, inhibits Snai2 [κB [Snai2 induces its own expression [κB [p16 promotes its own activity forming a positive feedback loop [κB [p53 inhibits E2F [Rb inhibits E2F [E2F induces cyclin [Cyclin inhibits Rb activity [TELase inhibits the senescence markers p16 [The above described molecular players  and of the senescent (from 25.78 to 18.75%) phenotypes  phenotype, the other corresponding to an epithelial senescent phenotype. The first prediction is consistent with the immortalization and apoptosis–resistance shown by mesenchymal stem–like cells, as well as with the capability of mesenchymal TFs to abrogate senescence [To further validate our model, we tested if the GRN Fig. b is ablobserved   canOur proposed integrative, quantitative, and dynamical framework contributes to the understanding of the mechanisms underlying the onset and progression of epithelial cancer, which is necessary for devising new and more effective preventive strategies that halt or slow the progression of these diseases.A total of 159 references, considering both references in the main text and in the Supplementary Information [see Additional file Following a bottom–up and an expert knowledge approach we propose a set of cellular dynamical processes ubiquitous to spontaneous immortalization , namely: replicative cellular senescence EMT driven by inflammation. The cellular phenotypes, epithelial, senescent, and mesenchymal stem–like produced by EMT induction have been largely characterized as biological observables involved in such processes. We take this information as a methodological basis to integrate a generic dynamical network model of spontaneous immortalization. As a first step in network integration, we assembled a set of TFs and additional molecular species involved in the establishment and regulation of these cellular states and processes. Subsequently, we manually retrieved documented regulatory interactions among the molecular species, considering only those supported by experimental evidence. The resulting nodes and regulatory interactions were then assembled manually into the network represented in Fig. The bioinformatics tools EnrichNet  and TopoκB), since different activity configurations of these molecular players define the transcriptional identity of the three cellular phenotypes we sought to reproduce with the proposed model.The regulatory core underlying spontaneous immortalization Fig. b was obA Boolean network models a dynamical system assuming both discrete–time and discrete–state variables. This is expressed formally with the mapping: Fi are logical propositions (or truth tables) expressing the relationship between the genes that share regulatory interactions with the gene i, and where the state variables xi(t) can take the discrete values 1 or 0 indicating whether the gene i is expressed or not at a certain discrete–time t, respectively. An experimentally grounded Boolean GRN model is then completely specified by the set of genes proposed to be involved in the process of interest and the associated set of logical functions derived from experimental data [BoolNet [where the set of functions tal data . The set[BoolNet  within twt  is determined or not by its associated logical function ξ or ξ, respectively.The probability that the value of the now random variable Π with components: An attractor transition probability matrix j is reached from an attractor i, was estimated by numerical simulation following [i in the state space (2n) a stochastic one–step transition was simulated a large number of times . The probability of transition from an attractor i to an attractor j was then estimated as the frequency of times the states belonging to the basin of the attractor i were stochastically mapped into a state within the basin of the attractor j.representing the probability that an attractor ollowing . SpecifiFollowing the Discrete Time Markov Chains  theoretiPA(t) is the probability distribution over the attractors at time t, and Π is the transition probability matrix (Additional file where M was calculated which reflects the transition barrier between any two states based on the MFPT. Here, the MFPT was numerically estimated. Using the transition probabilities among attractors, a large number sample paths of a finite Markov chain were simulated. The MFPT from attractor i to attractor j corresponds to the averaged value of the number of steps taken to visit attractor j for the fist time, given that the entire probability mass was initially localized at the attractor i. The average is taken over the realizations. Following [i and j can be defined as follows: In addition to the calculation of the most probable temporal cell–fate pattern , a discrollowing , based oThis quantity effectively measures the ease of transition as a net probability flow, and is shown for our GRN in Additional file The consistent global ordering of all attractors uncovered with the core GRN was defined based on the formula proposed in . Briefly


As electronic mental health records become more widely available, several approaches have been suggested to automatically extract information from free‐text narrative aiming to support epidemiological research and clinical decision‐making. In this paper, we explore extraction of explicit mentions of symptom severity from initial psychiatric evaluation records. We use the data provided by the 2016 CEGS N‐GRID NLP shared task Track 2, which contains 541 records manually annotated for symptom severity according to the Research Domain Criteria.We designed and implemented 3 automatic methods: a knowledge‐driven approach relying on local lexicalized rules based on common syntactic patterns in text suggesting positive valence symptoms; a machine learning method using a neural network; and a hybrid approach combining the first 2 methods with a neural network.The results on an unseen evaluation set of 216 psychiatric evaluation records showed a performance of 80.1% for the rule‐based method, 73.3% for the machine‐learning approach, and 72.0% for the hybrid one.Although more work is needed to improve the accuracy, the results are encouraging and indicate that automated text mining methods can be used to classify mental health symptom severity from free text psychiatric notes to support epidemiological and clinical research. ADEadverse drug eventsAIartificial intelligenceAUDIT C‐SCOREalcohol use disorders identification test—consumption scoreCBTcognitive behavioural therapyDBTdialectical behavioural therapyDUIdriving under the influenceDWIdriving while intoxicatedEMRelectronic medical recordEMSemergency medical servicesERemergency roomETOHethanolGATEGeneral Architecture for Text EngineeringGSgold standardIOPintensive outpatient programMAEMmean absolute error measureMJmarijuanaMMSEmini mental state examinationN‐GRIDneuropsychiatric genome‐scale and RDoC individualized domainsOCDobsessive compulsive disorderOCSDobsessive compulsive spectrum disordersOUIoperating under the influencePTpatientRDoCResearch Domain CriteriaSMIsevere mental illnessTMtext miningYBOCSYale‐Brown obsessive–compulsive scale1Recent developments in the use and availability of electronic medical records (EMRs) have triggered a number of opportunities for more efficient clinical decision support and epidemiological research. EMRs contain information such as the patient's clinical history, treatments, and laboratory results , 1 (mild = symptoms present but not a focus of treatment), 2 (moderate = a focus of treatment), and 3 . Through a thorough process, each report has been manually categorized in terms of symptom severity by two expert psychiatrists from the Massachusetts General Hospital and the Harvard Medical School; because psychiatrists may interpret the notes differently (which in this case happened in around 40% of cases), a third annotator adjudicated to generate the gold standard data  Individualized Domains (N‐GRID) project of the Harvard Medical School. The organizers released 325 psychiatric records that were used as the training set, and a set of 216 unseen psychiatric records for validation purposes. Each patient was represented by one record that included information from their initial psychiatric evaluation, containing unstructured free‐text narrative  and structured question‐answer pairs . Each report  has been classified with regard to the severity of experienced symptoms as: 0 . Any mention of a positive valence symptom that has been described in free text is labelled as unstructured . Table A sample of 50 records from the training data set randomly selected for each severity  was manually inspected by a domain expert (C. H. K.) to identify terms indicative of each severity level. The domain expert classified them into structured and unstructured mentions in text. In particular, structured mentions are represented by questions referring to particular symptoms followed by a “yes” or a “no” answer  that describe symptom mentions in text. The lexical patterns use (a) frozen lexical expressions as anchors for certain symptom mentions  based on verbs, noun phrases, and prepositions; and/or (b) semantic place holders (through dictionary mentions) suggesting the presence of a positive valence symptom . We have also implemented concept enumeration as it appears quite frequently in the training data, particularly for the reporting of various positive valence symptom mentions . Table We created rules for each of the symptom severity classes for the recognition of mentions of positive valence symptoms. The rules are based on common lexical patterns identified in clinical notes  determined the rating of the whole patient document regardless of the number of lower severity events present .2.3In our machine learning approach, we implemented a NN approach because NNs are flexible for integrating different input data types into the same architecture, such as word counts, extracted values , outputs coming from other text processing pipelines , or even raw text in the form of word embeddings. Additionally, in the last 5 years, due to recent developments in hardware and software, NNs have become the state of the art in imaging, sound processing, and in certain areas of natural language processing  was used . This metrics works well with imbalanced data by assigning the same importance to each severity class regardless of its frequency in the corpus by calculating how close the classification of each report was to the gold standard one. The following formula is usedj is a set of records with score j; xi is a record; h(xi) represents the predicted score with yi being the correct one.The system has been formally evaluated as part of the 2016 N‐GRID challenge on a test‐set containing 216 unseen reports. In order to reduce the complexity of the N‐GRID task, only a single severity score per record was used, instead of having various scores for each positive valence symptom mention. As for the metric of evaluation, the macroaverage mean absolute error measure  by observing the relevant unstructured text. For example, despite having positively answered some structured questions indicating severe and moderate severity, the overall record would still not be classified as severe or moderate. For example, certain structured questions indicating the severe class could be present in reports that were classified as moderate or mild . Consequently, we chose to base our rules for all severity classes on unstructured symptom mentions in order to avoid the generation of false positives. Still, we included processing of structured questions in the mild class aiming to avoid overclassification of the remaining records as absent .We performed manual examination of the errors in the evaluation data set with 74 records assigned the wrong symptom severity; 24 records were assigned a higher class than the correct one; and 50 records were assigned a lower severity class.304.03 Opioid dependence,” “History of drug use: Yes”) in the unstructured part does not necessarily agree with the classification provided at the gold standard (which was mild in the above cases). Also in a quarter of cases, the rules did not deal with negated context . There were few cases associated with symptoms of family members that were wrongly associated with the patient . Finally, another source of errors were treatment plans that were not identified as such .We note that in more than half of cases where a higher class was assigned, the reports mention symptoms that could indicate such higher severity. For example, correct recognition of a moderate symptom  was overwritten as mild severity by identifying the structured mention of “Marijuana: Yes”). In five records, mentions of specific drugs indicated a certain severity level, but were not included in our rules . In a similar fashion, another source of errors  was due to expression patterns that have not been spotted in the training data .The cases where lower severity was wrongly assigned are mainly due to the fact that we have focused on capturing unstructured symptom mentions with the exception of structured mentions of the mild class. This choice led to several falsely classified documents to less severe classes. For example, four records had a structured question suggesting a symptom but we have decided that it was best for our system's performance to focus on unstructured symptom mentions only. Additionally, an unstructured mention of “4.2The data‐driven approaches exemplify what is, as of now, the biggest challenge for deep NNs—namely, the dependence on having to use a large prelabelled corpus. Although deep NNs are becoming the state of the art in NLP when enough annotated text exists . Perhaps, a larger set might have helped engineering more rules that would able to cast a wider net to identify mentions of positive valence symptoms in text. Our emphasis on unstructured symptom mentions might also be a limitation as information from structured questions is disregarded instead of being taken into consideration for the classification of a record (with the exception of the mild class). Initially, we considered structured questions for all classes but we noted that they were not discriminative and would produce a number of errors. Perhaps, they can be useful in cases where there is not a single unstructured mention that can help for the recognition of the class severity.Our precedence rules might be another limitation. Choosing the highest severity symptom to characterize  a record (and ignoring mentions of other symptoms of lower severity) has led to some misclassifications. Combining different severity signals could be an interesting task to explore in the future.All the methods developed here have been trained on initial psychiatric evaluation records. Although both rules and the data‐driven approaches may work on reports from other organizations, it is likely that they would need adjustments both in lexical and expression coverage. However, the rules and language models developed here focus on severity of symptom expressions and therefore may be used to process other types of mental health records.5We have designed and explored three methods of the identification of positive valence symptom severity in initial psychiatric evaluation records. The performance is promising, ranging from 72% (hybrid) to 80% (rule‐based). We noted that the lexicalized rules managed to generalize automatic classification of symptom severity relatively well. However, NNs, at least as implemented in this study, failed to generalize in the final evaluation set, even when 10‐fold cross‐validation results with the training set suggested that no severe overfitting was being produced. Combining the NN with rules in a hybrid serial system did not ameliorate the lack of generalization. Although there is still significant room for improvement, the results are encouraging and indicate that automated text mining methods can be used to classify mental symptom severity from psychiatric notes with a range of reasonably promising performance.The authors have no conflicts of interest to declare.