Further, strength and stiffness parameters of bio-cemented sands are determined. Medium-grained bio-cemented sand yields compressive strengths up to 12 MPa while, surprisingly, fine-grained sand yields up to 2.5 MPa for similar bond contents. To understand the observed discrepancies, we undertake a systematic study of the bio-cemented material’s microstructure, by combining a series of microstructural inspection tools. The study extends beyond conventional qualitative and textural characterization and provides with new insight into the material’s peculiar 3D micro-architecture. We apply a new methodology towards quantifying crucial microscopic characteristics such as the particle sizes of the crystalline bond lattice, the bond-grain contacts and particle orientations. Bonds are found to exhibit distinctive geometries and morphologies when MICP applies to different base materials. We thus contribute to the debate on the importance of factors affecting: (i) MICP efficiency, (ii) the mechanical response and (iii) peculiar micro-architecture of bio-improved geo-materials.We introduce the application of microbial-induced calcite precipitation via the ureolytic soil bacterium Sporosarcina Pasteurii in freeze-dried form, as a means of enhancing overall MICP efficiency and reproducibility for geotechnical engineering applications. We show that the execution of urea hydrolysis and CaCO The quest for novel, performant materials and solutions in engineering problems is driven nowadays by coupling technical innovation with economic efficiency and positive environmental impact. This quest is more current and pertinent than ever in the construction industry. Especially in urban areas “good” soils are already taken and the remaining land often requires extensive soil stabilization and strengthening works to secure the integrity of engineering projects and mitigate long-term risks related to failures and environmental threats.3) mineral crystals which act as binders among particles of granular soils. Quite unlike most applications targeting the artificial cementation of soils for improving their overall mechanical properties and bearing capacity2 microbial-induced calcite precipitation (MICP)7 via the ureolytic soil bacterium Sporosarcina Pasteurii8 is applied via non-erosive, low-pressure propagation of bio-chemical solutions. MICP at its core is urea hydrolysis with the catalyzing activity of the enzyme urease, which is found in several bacterial strains9 or other fungi and algae10. Due to the presence and activity of urease, urea hydrolysis completes 1014 times faster compared to the non-catalysed reaction11. The bio-improved geo-material can be, hence, produced in-situ at ambient temperature, with limited energy requirements from the relatively inexpensive combination of urea and calcium. The new, enhanced, lattice of mineral bonds among soil particles forms between several hours and few days, depending on the desired final bond content. It comprises calcite crystals which grow in number and size following hierarchical plane expansion with their dimensions ranging from few, up to hundreds of micrometers12. The resulting material is lightweight cemented soil which retains the permeability of its matrix13 exhibits significant enhancement of strength and stiffness14 and is difficult to produce by any other synthetic means.This study puts the focus on bio-mediated soil cementation, an emerging technology which alters substantially the structure of geo-materials and endows the subsurface with improved overall mechanical behaviour. The technique integrates bacterial metabolic activity in soil-permeation solutions, to ultimately induce the formation of calcite . More precisely, the chemical composition of treatment solutions27, the environmental conditions28 and the degree of saturation29 during MICP improvement are all found to yield distinctive precipitation behaviors and correspond to varying obtained mechanical properties. Moreover, microstructural evidence provided in the literature is often limited to the scale of few grains and bonds and thus it remains unclear whether such evidence can be considered representative of the macro-scale.Finally, although the bio-cemented geo-material was brought into focus over a decade ago30 where micro-CT is mobilized for capturing microstructural quantities and their evolution with respect to changes in rheological properties, such as the porosity, permeability and effective diffusion. In the same work, comparison between the experimentally measured bond volumetric fraction and that obtained through micro-CT image processing is reported, with the mean error between the two reaching 20%. Contrary to30, the work presented herein deals with the micro-architecture of bio-cemented materials by implementing micro-CT to isolate and analyze individual bond particles which compose the calcite lattice in two different base materials. Results on the geometrical and spatial characteristics are interpreted and evaluated with respect to the captured strength and stiffness parameters. Moreover, complementary microstructural inspection tools are mobilized to further shed light on the bond growth mechanism and on the previously underexplored internal state of calcite bonds.To overcome such uncertainties regarding the limited validity of textural observations, micro-computed tomography (micro-CT) is considered herein as the ultimate tool which allows providing with a robust determination of micro-scale quantities. The goal is to capture spatial and morphological heterogeneities of micro-scale properties. Recent work in this direction has been reported in24 have utilized extensively whole vegetative cells of Sporosarcina Pasteurii which are introduced in soils to ultimately induce their bio-cementation. Herein, we obtain ureolytic cells in dry powder form through a freeze-drying process and evaluate their ureolytic capacity upon rehydration, as described in the methods section. When re-suspended in urea- and calcium-rich solutions, the lyophilized cells of S. Pasteurii are found to reactivate their hydrolysis mechanism and CaCO3 precipitates are immediately formed.Previous studies reported in the literature9. A maximum urea hydrolysis rate of 104 mmol/L−1/h−1 is obtained  of the former is almost half of that of the latter. Both materials are subjected to MICP to achieve various calcite bond contents in the range between 3–10%, by altering the number of the provided reactant batches which are rich in urea and calcium. Further details on specimen preparation are available in12. Same external treatment conditions are provided to sand columns where the materials are packed as a means of minimizing uncertainties related to the influence of bio-chemical and environmental conditions on the precipitated bond lattice. Thus, the morphology of the crystalline assembly of bonds, result of MICP, will be attributed only to the influence of the base materials’ initial porosity.We provide with results referring to the unconfined compressive strength (UCS) of fine- and medium-grained sand, with their identification properties listed in Table 32 under confinement. The UCS of medium-grained bio-improved sand falls between 3 and 12 MPa for calcite bond contents in the range of 5% to 10%. Meanwhile, the fine bio-improved sand reaches approximately 2.5 MPa for similar bond contents  scale to identify microstructural characteristics which are considered representative of the material’s macroscale. To this purpose, the analyzed volumes via micro-CT refer to dimensions at least ten-times the Dls Table .34 allows splitting individual soil particles. Further, by observing the intersection of bond particles with the produced split lines which divide sand particles, it becomes possible to identify the number of active bonds and the active area of contact  and mean maximum (D50 max) diameters are determined as the cumulative 50% points of diameter. These values are plotted for both materials in Fig. 50 diameters which incorporate the notion of the cumulative volume of the bond phase. It is noteworthy to consider herein that active bonds are those which are found to bridge neighbouring soil particles. The remaining bonds are characterized as inactive since they do not contribute with their planes to inter-granular force transmission. A typical example of a real geometry and texture of active and inactive bonds is captured through scanning electron microscopy (SEM) and illustrated in Fig. We further suggest a bond particle analysis which expresses the particle size distribution with respect to the cumulative bond volume. This analysis follows the principle of the well-established experimental procedure for determining the grain size distribution of soilsume Fig. . In Fig.The mean minimum and maximum diameters of bonds precipitated in medium-grained sand are found to range between 80–370 micrometres for calcite volumetric contents in the range between 3–7.5% Figs f and 4d.236 and thus to lower overall shear resistance.The particle orientation theta of the major inertia axis of individual bond particles is used to determine the spatial orientations of population of bonds Fig. . For MICResults further reveal that the fraction of active bonds is estimated between 77–84% for the 6 samples analyzed through micro-CT and reconstructed numerically. The bonds per soil particle ratios are found to range between 4.3 and 3.2 for fine- and medium-grained sand respectively, independently of the overall calcite content.38 physical model of a microporous medium. Observations reveal the immediate precipitation of CaCO3 nuclei upon the introduction of reactants and their gradual deposition on the PDMS grains’ surfaces ces Fig. . This fiond Fig. . These oond Fig. . On the 34, shown in dashed red lines in Fig. 12, for ultimately determining directly the crucial calcite bond content. When comparing the indirect, numerical measurements of the bond contents with direct measurements performed in the laboratory on the same samples, results reveal an error range between 2.2% and 15.1% in the total calcite content. Finally, this error between direct and indirect measurements of the calcite content confirms similar range as that reported in30, found to reach 20%. Such discrepancies are expected given the limitations in the maximum resolution achieved by the micro-CT equipment.As far as the validity of the obtained results from micro-CT and image processing is concerned, we compare the geometrical characteristics determined via micro-CT and subsequent 3D volume reconstruction with parameters measured directly in the laboratory under standardized experimental procedures. More precisely we generate the 3D packing of sand particles and analyze their grain size distribution. Results, shown in solid red lines in Fig. The study condenses a multi-disciplinary approach to the investigation of bio-cemented geo-materials. More precisely notions of: (i) kinetics of precipitation, (ii) mechanics and (iii) microstructural inspection are brought together to provide with new insight into the contributing mechanisms and efficiency of the MICP process. The contributions of this study are summarized below based on these three principal axes.Firstly, an alternative application of MICP for soils was introduced and discussed. Kinetics analyses showed that urea hydrolysis and calcium carbonate precipitation persist in a “cell-free” mechanism, after complete breakdown of the cell clusters of rehydrated calcifying bacteria. Additionally, the efficiency of the urease enzyme is not found to vary after application of freezing-drying and rehydration cycles or after the degradation of the cell cluster. This fundamental evidence provides with a preliminary step towards the conception of an efficient and reproducible mechanism for soil improvement based on MICP where given dry biomass yields known ranges of hydrolysis and precipitation efficiencies.Moreover, strength and stiffness parameters are obtained for fine- and medium-grained bio-cemented samples of various overall bond contents. By applying two base materials, which yield similar response in their untreated state, to identical external treatment conditions we minimize uncertainties related to the influence of bio-chemical factors on the obtained mechanical response. Results reveal a more pronounced improvement for the medium-grained material, for similar range of the final calcite bond content. This finding confirms that the average bulk mass of bonds is not sufficient to determine the level of improvement and thus the contribution of the crystalline skeleton of bonds needs to be better understood.In light of the captured mechanical response the study extended to cover the investigation of the three-dimensional micro-architecture of bio-cemented geo-materials. A new approach was presented which allows estimating the crucial contact area between bonds and sand grains through micro-CT analyses and subsequent 3D volume reconstruction. Microscopic parameters, such as bond sizes, numbers and orientations are determined in the representary elementary volume scale. This allows considering that the quantified parameters reflect the fabric of the material at the macroscale. Distinctive trends were obtained for the geometrical and spatial distributions of microbial-induced calcite precipitates, forming the crystalline lattice of bonds within fine- and medium-grained soils. These distinctive morphological characteristics were discussed in relation to the mechanical behaviour of the material at the macroscale.Moreover, microscale inspection goes beyond traditional textural observations and provides with new, real-time insight into the nature of the growth mechanism and into the internal state of crystalline bonds. Deposition of precipitated nuclei was identified as a principal bond growth mechanism.41 on 3D particle packings where bonds represent realistic geometries and microstructural characteristics of the material. Finally, the introduced workflow to investigate, reproduce and analyse the fabric of the material can be adopted and extended to similar, natural or composite, materials with distinctive fabric properties.Overall, the study contributes towards an advanced characterization of the peculiar bio-cemented geo-material. A passing from qualitative approaches towards the quantification of critical parameters of its micro-architecture was presented. The improved understanding provided herein forms a new basis for the conception of modelling formulations which incorporate notions of contact mechanics or other parameters to unify microscopic quantities as a means of interpreting macroscale phenomena. Thus, the provided insight can be used to improve predictive theoretical modelling and numerical simulations. For example, a practical application of the provided insight into the peculiar architecture of the bio-improved geo-material would be to design and perform simulations using DEMSporosarcina Pasteurii8, which was obtained from the ATCC bacteria culture collection, is grown under sterile conditions in a liquid broth medium with its composition listed in ATCC Medium 1376. Images are collected via photomicroscopy (Nikon NI-U Microscope) at several intervals during the growth phase to evaluate the cell state and associate it with the optical density that was measured at 600 nm and ensure that no contamination occurs. An optical density equal to 1 yields 108 colony-forming units (cfu) per millilitre (ml), which is determined via plating an aliquot of the liquid culture on an agar plate (with its composition mentioned in ATCC Medium 1376) using the spread plate technique.4Cl and 10% sucrose. NH4Cl is provided at the same concentration as in the culture broth to alter the osmotic neutrality of Milli-Q water and to avoid cell lysis upon resuspension of the pellet. Sucrose is provided to maintain the integrity of the bacterial cells upon dehydration during the freeze-drying process. Upon resuspension in the lyophilisation solution, cells are stored at −80 °C for 1 hour. Subsequently, the frozen stock is placed in a vacuum chamber (Christ LCG Lyo Chamber Guard) at a minimum pressure of 0.014 bars for 24 hours. The obtained freeze-dried cultures in powder form are subsequently maintained in closed vials to protect them from atmospheric humidity and stored upon rehydration for application of MICP.Subsequently, the cells are centrifuged at 4 °C at 4000 g for 20 minutes. The supernatant is removed and the pellet is resuspended in liquid yeast extract medium and stored at 4 °C. Alternatively, the pellet is resuspended in the lyophilisation solution, which includes Milli-Q water, 10% NH4+), which results in increased electrical conductivity and alkalinity of the solution. The relationship between the total urea hydrolysed and total increase in electrical conductivity of the solution is given in9:Batch experiments are carried out for measuring the electrical conductivity which corresponds to the production of ammonium and can thus be associated with the mass of urea hydrolysed. The hydrolysis of 1 mole of urea corresponds to the release of two moles of ammonium  which is equipped with a Hamamatsu 130/300X-ray beam source and a camera detector which yields images of resolutions equal to 2,240 × 2,240 pixels. Samples that are subjected to micro-CT have diameters up to 5 mm and heights up to 15 mm (micro-columns). The voxel size of the obtained images is 6.41 μm and 7.12 μm for samples of fine- and medium-grained MICP-treated sands, respectively.34 for ultimately obtaining the lines that split individual sand particles. An advantage of the particle separation operation performed herein is that there is no deletion of pixels, thus the particle volume remains unchanged. Upon particle separation, we perform a logical operation on the obtained binary images referring to the lines separating sand grains (namely A) and that of the segmented bond phase (namely B). For every pixel x which belongs to images A and B we obtain a new image C upon the logical operation described as follows:A step adopted during the image processing approach shown in Figs Image C, resulted from the superposition of A and B, allows determining in quantitative terms the total active contact area by analyzing the length of the split lines intersecting with bond particles in the two-dimensional space and the total area in the three-dimensional space Fig. . Bonds tThe datasets generated and analysed during the current study related to the microstructural characterization of the materials will become available on Re3data prior to publication date. Data series obtained and analysed for the kinetics and mechanics studies are available from the corresponding author on reasonable request.Supplementary Video


Rare cell populations play a pivotal role in the initiation and progression of diseases such as cancer. However, the identification of such subpopulations remains a difficult task. This work describes CellCnn, a representation learning approach to detect rare cell subsets associated with disease using high-dimensional single-cell measurements. Using CellCnn, we identify paracrine signalling-, AIDS onset- and rare CMV infection-associated cell subsets in peripheral blood, and extremely rare leukaemic blast populations in minimal residual disease-like situations with frequencies as low as 0.01%. While rare cell subpopulations frequently make the difference between health and disease, their detection remains a challenge. Here, the authors devise CellCnn, a representation learning approach to detecting such rare cell populations from high-dimensional single cell data, and, among other examples, demonstrate its capacity for detecting rare leukaemic blasts in minimal residual disease. The health and disease status of multicellular organisms pivotally depends on rare cell populations, such as haematopoietic stem cells or tumour-initiating cell subsets56representation of a cell population, defined in terms of cluster-based features such as cluster mediansunsupervised machine learning constitutes a powerful exploratory tool, the identification of disease-associated cell subsets requires a further supervised learning step to associate the clustering-derived representation with disease status. Unsupervised approaches have been extended to the classification of single-cell samples and have been successful where disease association manifested itself in condition-specific differences of abundant cell subpopulations8Such multiparametric single-cell snapshots have been used to define heterogeneous cell population structure using unsupervised clustering techniques that generate a overfitting or even precluding the identification of disease-associated rare cell populations. As this study will demonstrate, this situation severely limits the capacity of existing approaches to take advantage of novel highly multiparametric single-cell measurements to yield insights into the subpopulation-origin of diseases such as minimal residual disease (MRD) or tumour-initiating cellsUnsupervised approaches describe general population features that are not necessarily associated with disease status. Typically a large number of cell population features thousands are requmultiple instance learningconvolutional neural networksCellCnn overcomes this critical limitation and facilitates the detection of rare disease-associated cell subsets. Unlike previous methods, CellCnn does not separate the steps of extracting a cell population representation and associating it with disease status. Combining these two tasks requires an approach that (1) is capable of operating on the basis of a set of unordered single-cell measurements, (2) specifically learns representations of single-cell measurements that are associated with the considered phenotype and (3) takes advantage of the possibly large number of such observations. We bring together concepts from In this study, we apply CellCnn in a classification setting to reconstruct cell-type-specific signalling responses in samples of peripheral blood mononuclear cells (PBMCs). We additionally apply CellCnn in a regression setting to identify abundant cell populations associated with disease onset after HIV infection, and achieve comparable prediction accuracy to a state-of-the-art analysis performed recentlya priori unknown cell subsets. To address this difficulty, CellCnn associates a multi-cell input with the considered phenotype by means of a convolutional neural network. The network automatically learns a concise cell population representation in terms of molecular profiles (filters) of individual cells whose presence or frequency is associated with a phenotype (CellCnn takes as input a set of observations of cellular populations (multi-cell inputs) each associated with a phenotype, for example, patient blood or tissue samples with associated disease status or survival information. It is difficult to learn the molecular basis of this association since it possibly manifests itself by differences of Convolutional neural networks were originally designed to process the two-dimensional structure of images and typically consist of one or more sets of convolutional and pooling layers1212Trained filter weights correspond to molecular profiles of relevant cell subsets and allow for assignment of the cell subset membership of individual cells . Specifically, we analysed mass cytometry data sets of healthy bone marrow samples with autoencoder-based sample representations by projecting the full sample representations to their first two principal components (for details see Methods). The two-dimensional representations for the training, validation and test samples obtained by the different methods are visualized in Due to the limited number of test samples available, we assessed the ability of CellCnn to correctly predict the phenotype of new samples on the basis of the characteristics of the learned representation. A good representation should clearly separate healthy, CN AML and CBF AML samples. To this end, we computed a two-dimensional projection of each mass cytometry sample by projecting it to the two most relevant AML-specific filters. We refer to this projection as the CellCnn-based representation. In a similar fashion, we computed a two-dimensional Citrus-based representation by projecting each mass cytometry sample to the two most relevant AML-specific clusters. Finally, we derived two-dimensional moment-based and Additionally, CellCnn was used for single-cell classification, i.e., to identify individual cells constituting the disease-associated cell subset. We compared CellCnn with (1) a state-of-the-art distance-based outlier detection algorithm21The above study is designed to identify disease-associated cell subsets that generalize to new patients not seen during CellCnn training. We further evaluated a personalized-medicine scenario where the same patient's bone marrow samples are assessed with respect to coherent cell population changes across different conditions. On the basis of the AML and acute lymphoblastic leukaemia (ALL) samples provided in refs CellCnn achieves this unprecedented high precision in the rare cell type setting by overcoming the inherent limitations of the unsupervised feature engineering strategies of state-of-the-art approaches. When analysing samples from modern single-cell techniques with increasing multiparametricity, such as mass cytometry, these approaches enumerate thousandsCellCnn analysis allows for description of the molecular makeup of phenotype-associated cell populations. While a direct interpretation of the learned filter weights might not be sufficient for defining properties of the phenotype-associated cell populations, we suggest using the learned filters to select and interpret the phenotype-associated cell populations in the analysed single-cell data. Specifically, we evaluate the underlying potential multiplicity of cell types by clustering and report statistics such as histograms on their marker profiles. It is conceivable that CellCnn does not identify the entirety of phenotype-associated cell types. Such a situation can arise if the association with the phenotype manifests itself by the activity of multiple processes spanning various distinct cell subsets, as it is the case for AIDS onset in HIV patients. In this case, CellCnn's regularization mechanisms will favour a simple and yet effective association via a fraction of these cell subsets. Iterative application of CellCnn with explicit exclusion of already selected cell populations could be explored as a means to identify further redundant, but possibly biologically relevant, phenotype-associated cell populations. However, iteratively applying CellCnn to the HIV cohort data set did not reveal further AIDS onset-associated cell populations.CellCnn can be used in conjunction with other cell population detection methods such as Citrus. Since these follow a conceptually different approach, detection of a cell population by both methods would be a strong indicator of this population's association with the studied phenotype. However, in addition to rare cell types, further situations are conceivable where CellCnn would correctly detect a cell population that Citrus does not detect. In particular, CellCnn should be better at finding cells that do not form distinct subset clusters in the space defined by all measured markers .ab initio identify phenotype-associated cell subsets in publicly available data sets with known ground truth. Given the expected increase in patient cohort sizes in concerted initiatives as the The Cancer Genome Atlas (TCGA) and concomitant rise in their analysis with single-cell technologiesFurthermore, neural network training is efficient, scaling linearly with the number of measured components. Consequently, CellCnn is applicable to a variety of highly multiparametric single-cell data sources beyond flow and mass cytometry, such as single-cell RNA sequencing or imaging data. In this study, we have demonstrated the ability of CellCnn to et al.The mass cytometry data set of PBMCs and the flow cytometry data set of HIV-infected patients were adopted, respectively, from Bodenmiller et al.et al.The mass cytometry data set for the first rare cell type study is based on the data set from Horowitz training set comprises cells from two healthy bone marrows  as well as from patients SJ10 (CN) and SJ1 (CBF). Cells from each healthy BM were split at random into two mutually exclusive sets:  and , respectively. BM1a and BM2a are the training samples from the healthy class. BM1b and BM2b are used to create synthetic MRD samples. For each patient, we randomly drew six subsets of blast cells and computationally mixed each of these subsets with either BM1b or BM2b. In total, we have two healthy samples, six samples from class CN and six samples from class CBF. The validation set comprises cells from one healthy bone marrow (BM3) as well as from patients SJ12 (CN) and SJ2 (CBF). Cells from BM3 were split at random into two mutually exclusive sets: . BM3a is the validation sample from the healthy class. BM3b is used to create synthetic MRD samples. For each patient, we randomly drew one subset of blast cells and mixed it with BM3b. In total, we have one healthy sample, one sample from class CN and one sample from class CBF. The test set comprises cells from two healthy bone marrows  as well as from patients SJ13 (CN), SJ3 (CBF), SJ4 (CBF) and SJ5 (CBF). BM4 and BM5 are the test samples from the healthy class. BM4 and BM5 are also used to create synthetic MRD samples for testing. For each patient, we randomly drew six subsets of blast cells and mixed each of these subsets with either BM4 or BM5. In total, we have 2 healthy samples, 6 samples from class CN and 18 samples from class CBF. Using the procedure described above, three benchmark data sets were created with blast spike-in frequencies of 0.1% (500 cells), 0.05% (250 cells) and 0.01% (50 cells).The et al.al.The mass cytometry data set for the ‘personalized medicine' study is based on the healthy and AML bone marrow samples provided by Levine multiple instance learning task12CellCnn takes as input groups of single-cell measurements , each group annotated with a phenotype and aims at identification of phenotype-associated cell subpopulations. This is an example of a L2 weight decay=1e-04). Network weights were randomly initialized from a uniform distribution. Mean squared error was minimized for regression problems and categorical cross entropy for classification  in order to define filter-specific cell subsets. It is expected that, in some cases, a filter-specific cell subset may comprise more than one cell type. To resolve such a situation, density-based clustering using the DBSCAN algorithmTo take advantage of high-content single-cell techniques like flow or mass cytometry, CellCnn optionally takes multiple random cell subsets of a specific cytometry sample as input to increase the effective number of data points for association.outlierness score. We define the outlierness score of each cell on the basis of the distances between this cell and its closest neighbours from the control samplesIn all our experiments, random cell subsets, drawn with replacement from the original cytometry samples, were used as multi-cell input training examples of CellCnn. In all cases, we generated an equal number of multi-cell inputs associated with each label. The number of multi-cell inputs and the number of cells in each multi-cell input were chosen on the basis of the validation set. Our experiments on mass cytometry data showed that CellCnn is not very sensitive to these two hyperparameters, as long as the multi-cell inputs are chosen sufficiently large to contain cells with the molecular profile of interest . To detePBMC data set. Each sample was initially split into a training (80%) and a validation (20%) set of cells. We trained 10 models, each comprising two filters, with initial weights drawn from a uniform distribution and selected the model with highest predictive accuracy on the validation set. The filter with highest weight connection to the output node corresponding to stimulation was chosen for the detection of stimulated cells.HIV-cohort data set. The full patient cohort was randomly split into a training (2/3) and a test (1/3) cohort. We used fivefold CV on the training cohort resulting in five models, each trained on a different subset of the training cohort. For each CV fold, random search was used to optimize over different hyperparameter settings and the model achieving best predictive performance on the corresponding validation samples was chosen. The hyperparameters finally adopted are the following: 3–5 filters (varying among CV folds), no dropout regularization, learning rate=0.01. Finally, an ensemble model, consisting of the five best networks (one from each CV fold), was used to predict survival times for the individuals in the test cohort. For the test phase, one subset of 3,000 cells was used per individual. The output of CellCnn corresponded to predicted disease-free survival time for each patient and was used to split the test cohort into a low-risk and a high-risk group. The threshold used for defining the two groups was the median predicted survival time. The survival distributions of the low- and high-risk groups were compared using a log-rank test, as the data set contained several right-censored observations.Information from all CV runs was used to select frequently occurring filters. We compiled a matrix of all filter weights from the five networks and performed hierarchical clustering using cosine similarity as metric . For eacNK-cell benchmark data set. A Monte Carlo CV procedure was used to randomly split the available mass cytometry samples  into training and test sets. We performed 100 Monte Carlo CV repetitions, each time using 7 CMV− and 7 CMV+ samples in the training set. The predictive performance of each trained model was evaluated on the basis of the six left-out samples (test set). The area under the ROC curve for the test set (test ROC AUC) was used as metric to quantify classifier performance. The test ROC AUC values from the 100 Monte Carlo CV repetitions are summarized in the boxplots of Within each Monte Carlo CV repetition, the corresponding training set was further split into a training and a validation set using a threefold CV scheme (we refer to this scheme as nested CV). For each nested CV fold, random search was used to optimize over different hyperparameter settings and the model achieving best predictive performance on the corresponding validation samples was chosen. The hyperparameters finally adopted are the following: 3,000 cells per multi-cell input, 200 multi-cell inputs per sample, 3–5 filters (varying among CV folds), no dropout regularization, learning rate=0.01, mean-pooling of the top 1% of cells in each multi-cell input. The network achieving the highest validation accuracy in the nested CV runs was used for the final predictions on the test set. For the test phase, one subset of 20,000 cells was used per individual.To interpret the trained models, we seeked to identify cell populations frequently selected by the CellCnn filters across the 100 Monte Carlo CV runs. For each filter, a filter-specific cell subset was defined on the basis of cell-filter responses, using a cutoff threshold of 0.5*(maximum cell-filter response achieved for this filter). Each filter-specific cell subset is compactly represented by its centroid. Therefore, we computed filter-specific centroids and used hierarchical clustering to group these centroids into clusters. By clustering the filter-specific centroids, we effectively assigned the filter-specific cell populations into similarity groups. For each cluster of centroids, we then computed the number of occurrences of any of its members in the 100 Monte Carlo CV runs. We followed a similar procedure to analyse the trained Citrus models: we computed the centroids of cell clusters with non-zero logistic regression coefficients, grouped these centroids via hierarchical clustering and counted the number of occurrences of each cluster in the 100 Monte Carlo CV runs. For both CellCnn and Citrus, we reported the centroids belonging to clusters that occurred in at least 20 of the 100 Monte Carlo CV repetitions. Centroids were projected to a t-SNE map obtained from all samples by computing the nearest neighbour cell in marker space and using the projection of that cell as the projection of the centroid . For CelAML benchmark data set. The training, validation and test sets were defined as described previously (see section Data sets in Methods). Training samples were used to fit the model, validation samples were used to optimize over hyperparameters using random search and test samples were used to evaluate the generalization performance of the best fitted model. The hyperparameters finally adopted are the following: 20 filters with dropout regularization, learning rate=0.01.The validation samples were used to select AML-specific filters from the best performing model. For each AML subgroup (CN and CBF), we selected the filter achieving the highest difference of maximum cell-filter responses (averaged over the top 30 cells) between healthy and AML subgroup validation samples. Subsequently, the selected filters were used to (a) obtain a representation of each sample in terms of maximum cell-filter response, averaged over the top 30 cells  compute The following models were used for comparison with CellCnn.Outlier detection. We used a state-of-the-art distance-based outlier detection methodS of s observations (single-cell profiles) is randomly sampled from the inlier class  and then used to evaluate the outlierness of single-cell profiles in the test samples. The outlierness of an observation is defined as the L1 distance between this observation and its closest neighbour in S. Results for different values of s are given in s=200,000.Single-cell input logistic regression/SVM/random forest. A logistic regression/support vector machine/random forests classifier that takes as input single-cell profiles from the multi-cell inputs generated for CellCnn. Each single-cell profile is labelled with the label  of its corresponding cytometry sample. Random search was used to optimize over the hyperparameters of the classifiers.Moment-based representation. A moment-based summary of a set of single-cell measurements. The first four moments of the marker abundance distributions are computed.Denoising autoencoder. An unsupervised representation learning modelCitrus. A state-of-the-art approach for detecting phenotype-associated cell subpopulationsL1 regularized predictor that detects phenotype-associated differentially abundant features. See L1 regularized classifier. For the moment-based sample representation, we used the first four moments of the marker abundance distributions.Due to the limited number of test samples available, the ability of different methods to correctly predict the phenotype of new samples was assessed on the basis of the characteristics of the learned whole-sample representation. A good representation should clearly separate healthy, CN AML and CBF AML samples. For CellCnn and the denoising autoencoder, a sample representation was computed as the vector of maximum cell-filter responses, averaged over the top 30 cells. For Citrus, a sample representation was computed as the vector of cluster abundances for all clusters with non-zero coefficients, as computed by the The high-dimensional representations computed by the different methods were visualized via two-dimensional projections on the first two principal components see . The twoTechnical terms are displayed in italic font when first introduced and are exemplified in https://github.com/fchollet/keras).CellCnn is implemented in Python 2.7 and uses the neural network libraries Theanohttp://www.imsb.ethz.ch/research/claassen/Software/cellcnn.html.It is available for download at http://www.imsb.ethz.ch/research/claassen/Software/cellcnn.html. The corresponding studies are cited in the respective sections.This study uses previously published data sets. These data sets are available at How to cite this article: Arvaniti, E. & Claassen, M. Sensitive detection of rare disease-associated cell subsets via representation learning. Nat. Commun.8, 14825 doi: 10.1038/ncomms14825 (2017).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Supplementary figures, supplementary note, supplementary methods and supplementary references.


The purpose of this single case report was to report the use of anterior segment optical coherence tomography for the diagnosis and management of a retained vegetal intraocular foreign body.A 23-year-old otherwise healthy male presented with a progressive vision loss in the right eye (RE). He reported a mild ocular trauma with a tree leaf 1 year ago followed by recurrent episodes of redness and pain in the RE that partially resolved after a self-medication with topical steroids. Visual acuity of the RE was limited to light perception. Slit-lamp examination of the RE showed an iris granuloma with overlying exudate and associated anterior chamber inflammatory reaction. Film X-rays, contact B-scan ultrasonography, and CT scan showed no abnormalities. Anterior segment optical coherence tomography revealed an enclaved iris foreign body. The foreign body was removed after a short course of local antibio-corticosteroid therapy. This was followed 2 months later by cataract surgery with intraocular lens implantation, with subsequent improvement of visual acuity to 20/40.A missed intraocular foreign body can lead to sight-threatening complications. Anterior segment optical coherence tomography may be useful for detecting non-clinically evident intraocular foreign body involving the anterior segment masquerading as chronic anterior uveitis. Intraocular foreign bodies (IOFBs) account for 18 to 41% of all open-globe injuries , 3.We herein report a case of small missed retained vegetal intracameral foreign body masquerading as chronic intraocular inflammation that could be detected only by anterior segment optical coherence tomography (AS-OCT).A 23-year-old otherwise healthy male presented with a progressive visual loss in the right eye (RE). He reported a mild ocular trauma with a tree leaf 1 year ago followed by recurrent episodes of redness and pain in the RE that partially resolved after a self-medication with topical steroids. On ocular examination, his visual acuity was of light perception in the RE and 20/20 in the left eye (LE). Slit-lamp examination of the RE showed a normal cornea, fine keratic precipitates, 2+ cells in the anterior chamber, and an iris granuloma with adjacent inflammatory exudate. The anterior chamber depth was irregular with irido-capsular lens adhesions, and a mature cataract was noted  demonstrated on the iris surface a highly reflective linear structure with marked posterior shadowing suggesting a foreign body, and associated adjacent moderately reflective round lesion corresponding to the inflammatory exudate seen clinically Fig. . The patThe postoperative period was uneventful and a second AS-OCT showed no IOFB scarps Fig. . Two monIOFB may be responsible for various signs and symptoms according to its size, composition, location, and ballistics . It may We report here a delayed presentation of a vegetal IOFB located in the anterior segment. The size and the composition of this IOFB and the inflammatory reaction prevented its detection by ophthalmologic examination and the various standard imaging modalities. Only AS-OCT allowed detecting the IOFB.Previous reports assessed the role of retinal OCT in the detection of small retinal IOFB . AS-OCT Reflectivity of IOFB on AS-OCT may vary according to its type . IOFB ofTo the best of our knowledge, our case report is the first to demonstrate the ability of AS-OCT to detect vegetal IOFB that can be missed on clinical examination and by other imaging modalities.AS-OCT is a safe and non-contact, non-invasive method of anterior ocular tissue imaging . It is sThe rationale for using AS-OCT in this case is to reveal unexpected IOFB because neither routine slit-lamp examination nor other imaging techniques were able to demonstrate the presence of such a small IOFB and to visualize how it is positioned in relation to the iris and the ciliary body. Thus, AS-OCT helped us to choose the most appropriate technique for IOFB removal avoiding unexpected intraoperative issues. AS-OCT can be used to prepare high-quality cross-sectional and 3D images of the anterior segment.In conclusion, IOFB is rather variable in presentation, outcomes, and prognosis. AS-OCT is a valuable imaging modality for detecting and managing vegetal anterior segment IOFB.


Hepatocellular carcinoma (HCC) is a major cause of cancer-related death worldwide. Due to inadequate screening methods and the common coexistence of limited functional liver reserves, curative treatment options are limited. Liver transplantation is the only curative treatment modality for early HCC. There are multidisciplinary treatment options like ablative treatments, radiation and systemic therapy available for more advanced patients or those that are inoperable. Treatment resistance and progression is inevitable for these HCC patients. Newer therapeutics need to be explored for better management of HCC. HCC is a hypervascular tumor and many pro-angiogenic proteins are found significantly overexpressed in HCC. Here we explored the therapeutic potential of the anti-angiogenic, anti-lymphangiogenic, and directly anti-tumorigenic biomimetic collagen IV-derived peptide developed by our group. Human HCC cell lines HuH7, Hep3b and HepG2 showed significant disruption of cell adhesion and migration upon treatment with the peptide. Consistent with previously described multimodal inhibitory properties, the peptide was found to inhibit both c-Met and IGF1R signaling in HepG2 cells and blocked HepG2 conditioned media stimulation of microvascular endothelial cell (MEC) tube formation. Furthermore, the peptide treatment of mouse HepG2 tumor xenografts significantly inhibited growth relative to untreated controls. The peptide was also found to improve the survival of autochthonous Myc-induced HCC in a transgenic mouse model. Mechanistically, we found that the peptide treatment reduced microvascular density in the autochthonous liver tumors with increased apoptosis. This study shows the promising therapeutic potential of our biomimetic peptide in the treatment of HCC. Liver cancer is one of the top causes of cancer related mortality in the world and the incidence has been increasing in the US for decades . SurgicaCompared to normal liver cells, HCC tumor cells are often associated with the dysregulation of numerous cellular signaling pathways. Expression changes or mutations of growth factor signaling components, specifically c-Met and insulin-like growth factor 1 receptor (IGF1R) are well documented in the pathogenesis of HCC . Overexpc-myc  is the concentration of FAM- AXT050, [R] is the concentration of integrin, and rCell adhesion, migration, proliferation, and Western blot data were analyzed by ANOVA in experimental triplicates. The paired Student's t-test was carried out to compare tumor growth delay and analyze statistical difference between mean tumor volume in tumor growth delay and mean tumor weight at the end of the treatment. The statistical difference in Kaplan Meier survival curve between control and AXT050 treatment animals was assessed by the log-rank (Mantel-Cox) test and the Gehan-Breslow-Wilcoxon test. All the statistical analysis were carried out using the Graphpad Prism v 5.0 package unless mentioned otherwise in the methods.


The perceptual representation of individual faces is often explained with reference to a norm-based face space. In such spaces, individuals are encoded as vectors where identity is primarily conveyed by direction and distinctiveness by eccentricity. Here we measured human fMRI responses and psychophysical similarity judgments of individual face exemplars, which were generated as realistic 3D animations using a computer-graphics model. We developed and evaluated multiple neurobiologically plausible computational models, each of which predicts a representational distance matrix and a regional-mean activation profile for 24 face stimuli. In the fusiform face area, a face-space coding model with sigmoidal ramp tuning provided a better account of the data than one based on exemplar tuning. However, an image-processing model with weighted banks of Gabor filters performed similarly. Accounting for the data required the inclusion of a measurement-level population averaging mechanism that approximates how fMRI voxels locally average distinct neuronal tunings. Our study demonstrates the importance of comparing multiple models and of modeling the measurement process in computational neuroimaging. Humans recognize conspecifics by their faces. Understanding how faces are recognized is an open computational problem with relevance to theories of perception, social cognition, and the engineering of computer vision systems. Here we measured brain activity with functional MRI while human participants viewed individual faces. We developed multiple computational models inspired by known response preferences of single neurons in the primate visual cortex. We then compared these neuronal models to patterns of brain activity corresponding to individual faces. The data were consistent with a model where neurons respond to directions in a high-dimensional space of faces. It also proved essential to model how functional MRI voxels locally average the responses of tens of thousands of neurons. The study highlights the challenges in adjudicating between alternative computational theories of visual information processing. Humans are expert at recognizing individual faces, but the mechanisms that support this ability are poorly understood. Multiple areas in human occipital and temporal cortex exhibit representations that distinguish individual faces, as indicated by successful decoding of face identity from functional magnetic resonance imaging (fMRI) response patterns –10. DecoMany cognitive and neuroscientific models of face processing do not make quantitative predictions about the representation of particular faces ,13. HoweFace-space coding models define high-level representational spaces, which are assumed to arise through unspecified low-level featural processing. An alternative possibility is that some cortical face-space representations can be explained directly by low-level visual features, which typically covary with position in PCA-derived face spaces. To explore this possibility, we evaluated a Gabor-filter model, which receives stimulus images rather than face-space coordinates as input, and has previously been used to model response preferences of individual voxels in early visual cortex .We found that cortical face responses measured with fMRI strongly reflect face-space eccentricity: a step along the radial axis in face space results in a much larger pattern change than an equal step along the tangential axis. These effects were consistent with either a sigmoidal-ramp-tuning or a Gabor-filter model. The performance of these winning models depended on the inclusion of a measurement-level population-averaging mechanism, which accounted for local averaging of neuronal tunings in fMRI voxel measurements.In order to elicit strong percepts of the 3D shape of each individual face, we generated a set of photorealistic animations of face exemplars. Each 2s animation in the main experiment featured a face exemplar in left or right half profile, which rotated outward continuously . The aniIn order to sample human cortical and perceptual face representations, human participants (N = 10) participated in a perceptual judgment task followed by fMRI scans. The perceptual judgment task . The samWe observed a strong group-average correlation between distance matrices estimated from perceptual dissimilarity judgments and Euclidean distances in the reference PCA space (mean(r) = 0.83, mean(Z(r)) = 1.20, standard error = 0.05, p<0.001, Figs We quantified the apparent warps in the cortical face spaces by constructing a multiple-regression RSA model, with separate distance-matrix predictors for eccentricity and direction, and for within and across face viewpoints . These pEccentricity changes had a consistently greater effect on representational patterns than direction changes, suggesting that a step change along the radial axis resulted in a larger pattern change than an equivalent step along the tangential axis . The oveAlthough these findings suggest a larger contribution of face-space eccentricity than direction in visual cortex, we also observed clear evidence for greater-than-chance discrimination performance among faces that differed only in face-space direction. Group-average cross-validated discriminant distances for faces that differed in direction but not eccentricity exceeded chance-level performance (p<0.05) for typical and caricatured faces in both the early visual cortex and the fusiform face area . Sub-carCortical face-space warping could not be explained by regional-mean activation preferences for caricatures. We performed a regional-mean analysis of responses in each cortical area, which confirmed previous reports that fMRI responses increase with distinctiveness across much of visual cortex , S6 Fig We developed multiple computational models, each of which predicts a representational distance matrix and a regional-mean activation profile. These models can be divided into three classes: the sigmoidal-ramp tuning and exemplar tuning models receive face-space coordinates as input, while the Gabor-filter model receives gray-scale pixel intensities from the stimulus images as input. We evaluated each of these three model classes with and without a measurement-level population-averaging mechanism, which approximates how fMRI voxels locally average underlying neural activity.The sigmoidal-ramp-tuning model proposes that the representational space is covered with randomly oriented ramps, each of which exhibits a monotonically increasing response along its preferred direction in face space . This moIn the exemplar model, each unit prefers a location in face space, rather than a direction, and its tuning is described by a Gaussian centered on the preferred location. The representational space is covered by a population of units whose preferred locations are sampled from a Gaussian centered on the norm face . We fittThe Gabor-filter model differs from the previous model classes in that it receives gray-scale image intensities as input, rather than PCA space position . Such moWe fitted each of the computational models so as to best predict the representational distance matrices from cortical regions and perceptual judgments using a grid search over all free parameters. We used a leave-one-participant-out cross-validation approach, in which model performance was evaluated on participants and face identities not used in fitting the parameters . Model pWe found that all evaluated models explained almost all the explainable variance for the perceptual face space , with onUnlike the perceptual judgments data, the cortical face spaces exhibited substantial differences between the model fits, with a robust advantage for measurement-level population averaging in most cases. In the following, we focus on generalization performance for fits to the early visual cortex and the fusiform face area . Cortical regions varied in their sensitivity to face viewpoint. We fitted multiple computational models to the data. Considered collectively, the cortical face spaces in the fusiform face area were most consistent with a PCA-space-based sigmoidal-ramp-tuning model and an image-based Gabor-filter model, and less consistent with models based on exemplar coding. As expected, effects in the early visual cortex were consistent primarily with the Gabor-filter model. In all cases, the winning models’ performance depended on the inclusion of a measurement-level population-averaging mechanism, which approximates how individual model units are locally averaged in functional imaging measurements.Out of the models we considered, the best accounts for the fusiform face area were a PCA-space-based sigmoidal-ramp-tuning model and an image-based Gabor-filter model. Exemplar-coding models exhibited relatively lower generalization performance, or made inaccurate predictions for regional-mean fMRI activation profiles. Importantly, the advantage for both the sigmoidal-ramp and Gabor-filter model depended on the measurement-level population averaging mechanism. The key contribution of our modeling effort is to narrow the set of plausible representational models for the fusiform face area to two models that can explain both representational distances and the regional-mean activation profile.It may appear surprising that a PCA-space coding model based on sigmoidal-ramp tuning and an image-based model based on Gabor filters should perform so similarly when fitted to face spaces in the fusiform face area. However, the sigmoidal-ramp-tuning model captures continuous variation in face shape and texture, which covaries with low-level image similarity. For instance, local curvature likely increases with face space eccentricity and is encoded in a ramp-like manner at intermediate stages of visual processing in macaque V4 . ConversThe models we evaluate here raise the provocative possibility that in some cases, fMRI effects that might conventionally be attributed to high-level featural coding could instead arise from the neuroimaging measurement process. Such an explanation appears possible for two effects in our data. First, even though the Gabor-filter model is a single-layer network with limited representational flexibility, this model nevertheless exhibited near-complete tolerance to mirror-symmetric viewpoint changes, when coupled with measurement-level population averaging. Second, both this model and the sigmoidal-ramp-tuning model showed greater over-representation of eccentricity when measurement-level population averaging was enabled, suggesting that this over-representation in the fMRI data might also plausibly arise through local averaging in voxels.Previous studies have tended to interpret view-tolerant fMRI effects in terms of cortical processing to support invariant object recognition ,34,35. TThe winning models in this study exemplify how sensitivity to face-space eccentricity at the regional-mean activation level can arise as an artifact of averaging, with no individual neuron encoding distinctiveness or an associated psychological construct. Previous functional imaging studies often interpreted response modulations with face eccentricity as evidence for coding of distinctiveness or related social perception attributes –23,42. HA simple model of measurement-level population averaging was sufficient here to substantially improve the generalization performance of multiple computational models for multiple cortical regions. The precise way that fMRI voxels sample neuronal activity patterns remains a topic of debate ,31,33,44Here we approximated measurement effects for models with nonlinear parameters by mixing the population average into the predicted representational feature space. Despite the simplicity of this method, our noise-ceiling estimates indicate that the winning models captured nearly all the explainable variance in the current dataset. For model representations without nonlinear parameters, this measurement model can be implemented more easily by linearly combining the model’s original distance matrix and the distance matrix obtained for the population average dimension of the space . This simplifying assumption is problematic for early visual areas, where there is a well-established retinotopic organization with a strong contralateral response preference. For the retinotopic Gabor filter model, we therefore added a hemifield-specific pooling stage, which helped account for strong view-sensitivity in occipital regions of interest. Accounting for measurement effects in the presence of topographic organization is likely to prove more challenging for naturalistic stimulus sets. One solution is to account for local averaging in fMRI voxels by local averaging of the model’s internal representational map . This loThis study demonstrates the importance of considering multiple alternative models to guide progress in computational neuroimaging. In particular, the finding that practically every model we evaluated exhibited significantly greater-than-zero generalization performance strongly suggests how studies that only evaluate a limited set of candidate models can arrive at misleading conclusions (see e.g. ).Representational similarity analysis has two key advantages for model comparison relative to alternative approaches. First, competing model predictions can be easily visualized at the level of the best-fitting distance matrix for a given cortical region e.g., . By contAlthough the central goal of model comparison is to select the best account of the data, the finding that some models are not dissociable under the current experimental context also has important implications for the design of future studies. Here we demonstrated that Gaussian-distributed exemplar-coding models are less likely to account for human face coding, while accounts based on sigmoidal ramp tuning and Gabor filter outputs perform very similarly. This suggests the need to design stimulus sets that generate distinct predictions from these winning models. For example, presenting face stimuli on naturalistic textured backgrounds may be sufficient to adjudicate between the two models, because the Gabor-filter model lacks a mechanism for figure-ground separation. In conclusion, our study exemplifies the need to test and compare multiple models and suggests routes by which the sigmoidal-ramp-tuning model of face-space coding could be further evaluated.All procedures were performed under a protocol approved by the Cambridge Psychology Research Ethics Committee (CPREC). Human participants provided written informed consent at the beginning of each data recording day.10 healthy human participants participated in a similarity judgment task and fMRI scans. The psychophysical task comprised 4 separate days of data collection which were completed prior to 4 separate days of fMRI scans. Participants were recruited from the local area  and were naïve with regard to the purposes of the study. Five additional participants participated in data collection up to the first MRI data recording day, but were not invited to complete the study due to difficulties with vigilance, fixation stability, claustrophobia and/or head movements inside the scanner. The analyses reported here include all complete datasets that were collected for the study.We generated faces using a norm-based model of 3D face shape and texture, which has been described in detail previously ,25. BrieWe developed a method for sampling faces from the reference PCA space in a manner that would maximize dissimilarity variance. This is related to the concept of design efficiency in univariate general linear modeling , and invWe used Matlab software to generate a 3D face mesh for each exemplar. This mesh was rendered at each of the orientations of interest in the study in a manner that centered the axis of rotation on the bridge of the nose for each face. This procedure ensured that the eye region remained centered on the fixation point throughout each animation in order to discourage eye movements. Renders were performed at sufficient increments to enable 24 frames per second temporal resolution in the resulting animations. Frames were converted to gray-scale and cropped with a feathered oval aperture to standardize the outline of each face and to remove high-contrast mesh edges from the stimulus set. Finally, we performed a frame-by-frame histogram equalization procedure where the average histogram for each frame was imposed on each individual face. Thus, the histogram was allowed to vary across time but not across faces. Note that histogram matching implies that the animations also have identical mean gray-scale intensity and root-mean-square contrast.A potential concern with these matching procedures is that they could affect the validity of the comparison to the reference PCA space. However, we found that the opposite appeared to be true: distances in the reference PCA space were more predictive of pixelwise correlation distances in the matched images than in the original images. Thus, the matching procedure did not remove features that were encoded in the PCA space and may in fact have acted to emphasize such features.We used a pair-of-pairs task to characterize perceptual similarity . ParticiWe measured brain response patterns evoked by faces in a rapid event-related fMRI experiment . ParticiThe experiment was divided into 16 runs where each run comprised 156 trials bookended by 10s fixation intervals. Each scanner run comprised two experimental runs, which were modeled independently in all subsequent analyses. The data was collected on 4 separate MRI data recording days (2 scanner runs per recording day). The trial order in each run was first-order counterbalanced over the 12 faces using a De Bruijn sequence  with 1 aFunctional and structural images were collected at the MRC Cognition and Brain Sciences Unit  using a 3T Siemens Tim Trio system and a 32-channel head coil. There were 4 separate MRI data recording days for each participant. Each recording day comprised 2 runs of the main experiment followed by 2 runs of the functional localizer experiment. All functional runs used a 3D echoplanar imaging sequence  with GRAPPA acceleration . Each participant’s functional dataset (7376 volumes over 8 scanner runs for the main experiment) was converted to NIFTI format and realigned to the mean of the first recording day’s first experimental run using standard functionality in SPM8 (fil.ion.ucl.ac.uk/spm/software/spm8/). A structural T1-weighted volume was collected in the first recording day using a multi-echo MPRAGE sequence (1mm isotropic voxels). The strWe estimated the neural discriminability of each face pair for each region of interest using a cross-validated version of the Mahalanobis distance . This anWe developed a variant of this discriminant analysis where effects that might be broadly described as region-mean-related are removed . This coWe used a multiple regression model to estimate the relative contribution of eccentricity and direction to cortical and perceptual face-space representations. Multiple regression fits to distance estimates can be performed after a square transform, since squared distances sum according to the Pythagorean theorem. We partitioned the squared distances in the reference PCA space into variance associated with eccentricity changes by creating a distance matrix where each entry reflected the minimum distance for its eccentricity group in the squared reference PCA matrix . The direction matrix was then constructed as the difference between the squared reference PCA matrix and the eccentricity matrix . These pWe used a conventional block-based functional localizer experiment to identify category-selective and visually-responsive regions of interest in human visual cortex. Participants fixated a central cross on the screen while blocks of full-color images were presented . Participants were instructed to respond to exact image repetitions within the block. Each run comprised 3 blocks each of faces, scenes, objects and phase-scrambled versions of the scene images. Each participant’s data  was smoothed with a Gaussian kernel  and responses to each condition were estimated using a standard SPM8 first-level model. Regions of interest were identified using a region-growing approach, where a peak coordinate for each region was identified in individual participants, and a region of interest was grown as a contiguous set of the most selective 100 voxels extending from this coordinate. We defined the face-selective occipital and fusiform face areas with the minimum-statistic conjunction contrast of faces over objects and faces over baseline, and the scene-selective parahippocampal place area and transverse occipital sulcus as the minimum-statistic conjunction contrast of scenes over objects and scenes over baseline, and the early visual cortex as the contrast of scrambled stimuli over the fixation baseline. We also attempted to localize a face-selective region in the posterior superior temporal sulcus, a face-selective region in anterior inferotemporal cortex and a scene-selective region in retrosplenial cortex, but do not report results for these regions here since they could only be identified in a minority of the participants. All regions of interest were combined into bilateral versions before further analysis since we did not have distinct predictions concerning functional lateralization. Example regions of interest can be viewed in y at position x along the preferred direction is described by the sigmoido, which specifies the horizontal offset of the response function , and s, which defines response function saturation . The raw output of each model unit is then translated toward the population-mean responsep is a free parameter that defines the strength of measurement-level population averaging .The sigmoidal ramp model comprises 1000 model units, each of which exhibits a monotonically increasing response in a random direction extending from the origin of the face space . The resw, which controls the full width at half-maximum tuning width of the Gaussian response function, and d, which controls the width of the Gaussian distribution of tuning centers (0.1 places Z = 2.32 at 10% of the eccentricity of the caricatures while 3 places this tail at 300% of the eccentricity of the caricatures).The exemplar model comprises 1000 model units, each of which prefers a Cartesian coordinate in the face space with response fall-off captured by an isotropic Gaussian. The free parameters are We also constructed an inverted-Gaussian variant of this model where the distribution of distances was inverted at Z = 2.32 and negative distances truncated to zero (1% of exemplars). This model was fitted with similar parameters as the original Gaussian exemplar model.github.com/kendrickkay/knkutils/tree/master/imageprocessing). The model is composed of 5 banks of Gabor filters varying in spatial position, phase  and orientation . Correlation coefficients were Z-transformed prior to statistical testing. Average Z statistics were reverse-transformed before visualization for illustrative purposes.Fold-wise generalization performance estimates are partially dependent, which can lead to sample variance underestimates ,59 and gS1 FigEach stimulus set shares the same underlying distance matrix in the reference PCA space, while the randomization of the orientation of the plane on which the faces are sampled ensures that each set is visually distinct.(PDF)Click here for additional data file.S2 Fig(PDF)Click here for additional data file.S3 FigEach point reflects the mean performance for all directions at a given eccentricity level Click here for additional data file.S4 FigThe top row shows original distance matrices, while the bottom row shows distance matrices after removing additive and multiplicative mean pattern effects . The cit(PDF)Click here for additional data file.S5 FigPlotted as (PDF)Click here for additional data file.S6 FigPlotted as in (PDF)Click here for additional data file.S7 FigPlotted as in Figs (PDF)Click here for additional data file.S8 FigRegions are color-coded and labels are provided in the bottom row. Abbreviations and colors: EVC—early visual cortex, green; FFA—fusiform face area, red; OFA—occipital face area, yellow; PPA—parahippocampal place area, purple; TOS—transverse occipital sulcus, blue.(PDF)Click here for additional data file.S9 FigWe simulated out-of-sample generalization performance for two arbitrary models, using methods that closely matched the ones described in this manuscript Click here for additional data file.S10 FigEach point represents the mean rejection probability for a unique set of simulation parameters Click here for additional data file.S1 CodeRequires Matlab R2013a.(M)Click here for additional data file.S1 TableWe report the group-average correlation coefficient (mean_r), the group-average Z-transformed correlation (mean_zr), standard error for the Z-transformed correlation (sterr_zr), one-tailed p values for the Z-transformed correlation (ppara_zr) and sample sizes (n). Related to (CSV)Click here for additional data file.S2 TableFor details, see main text. Related to (XLSX)Click here for additional data file.S3 TableMean, standard error (sterr), one-tailed p values (ppara) and sample sizes (n) are included on separate rows. See also (CSV)Click here for additional data file.S4 TableRelated to (CSV)Click here for additional data file.S5 TableSee (CSV)Click here for additional data file.S6 TableRelated to (CSV)Click here for additional data file.S7 TableSee (CSV)Click here for additional data file.S8 TableSee (CSV)Click here for additional data file.S9 TableWe identified the peak voxel in each region of interest using the region-defining contrast in the localizer experiment, and converted these native-space voxel indices to standard mm coordinates in the MNI template brain using transformations obtained from SPM8 structural T1 normalization routines. We report means and standard deviations across participants for each region. See also (CSV)Click here for additional data file.S1 Movie(MOV)Click here for additional data file.S2 Movie(MOV)Click here for additional data file.


Existing evidence suggests that the default-mode network (DMN) and fronto-pariatal network (FPN) play an important role in altered states of consciousness. However, the brain mechanisms underlying impaired consciousness and the specific network interactions involved are not well understood. We studied the topological properties of brain functional networks using resting-state functional MRI data acquired from 18 patients  and compared these properties with those of healthy controls. We identified that the topological properties in DMN and FPN are anti-correlated which comes, in part, from the contribution of interactions between dorsolateral prefrontal cortex of the FPN and precuneus of the DMN. Notably, altered nodal connectivity strength was distance-dependent, with most disruptions appearing in long-distance connections within the FPN but in short-distance connections within the DMN. A multivariate pattern-classification analysis revealed that combination of topological patterns between the FPN and DMN could predict conscious state more effectively than connectivity within either network. Taken together, our results imply distinct interactions between the FPN and DMN, which may mediate conscious state. Previous neuroimaging studies have shown that a core set of brain regions within the fronto-parietal network (FPN) and the default mode network (DMN) is centrally involved in conscious processes13et al. found decreased local efficiency in several brain regions  when comparing minimally conscious and unconscious patientset al. examined functional networks in comatose patients shortly after brain injury and demonstrated extensive reorganization of central hubs, including precuneus of the DMNUnlike traditional task-evoked paradigms, functional magnetic resonance imaging in the resting state (R-fMRI) is particularly suitable for studying the brain functions of individuals with disorders of consciousness (DOC). Multiple R-fMRI studies demonstrated that nodal topology is disrupted in impaired consciousness565The purpose of this study was to more closely examine differences in connectivity patterns across conscious states and identify specific interactions between the FPN and DMN. This was accomplished by quantifying network level topological properties using R-fMRI in a cohort of DOC patients, including those in a VS and MCS. We hypothesized the differences would exist between conscious states in the topological properties of the FPN and DMN. We also hypothesized that the functional connectivity patterns between these two networks could discriminate conscious state.p = 0.53) and gender (p = 0.28) (Twenty-four DOC patients (13 VS/UWS and 11 MCS) and 13 control subjects were recruited for this study. Six DOC patients (2 VS/UWS and 4 MCS) and 2 control subjects had to be excluded from the fMRI analysis due to excessive head movement (see the criteria described below) = 0.28) . In pati3, 36 axial interleaved slices covering the whole brain, and 240 volumes acquired in 8 min). High-resolution brain structural images were acquired using a T1-weighted 3D MP-RAGE sequence  for co-registration with the functional images. R-fMRI data were preprocessed using SPM8 (http://www.fil.ion.ucl.ac.uk/spm/). The first five volumes for each subject were excluded before performing slice timing, head-motion correction, co-registration with the T1-weighted structural images, and normalization to the MNI template without spatial smoothing. Data from subjects with >3 mm/° of head movement/rotation during scan acquisition were excluded. Preprocessed images were de-trended to abandon linear trends and then temporally filtered with a Chebyshev band-pass filter (0.01–0.08 Hz).Brain functional and high-resolution structural images were acquired via a GE 3-T MR scanner housed in the Guangzhou General Hospital of Guangzhou Military Command. All subjects were instructed to lie motionless in the scanner with their eyes open. During data acquisition, head movements were minimized using customized cushions. Eye opening could not be fully controlled during the entire scanning procedure involving patients because of their cognitive and physical impairmentset al. according to a fMRI meta-analysis, to define nodes11121112z transformed) between each pair of regions to obtain inter-nodal connectivity, and to construct a brain network for each subject g = , where V corresponds to the set of the 264 nodes and E to the set of edges.To construct whole-brain functional networks for each subject, we used a brain partition scheme, Power-264 template that included 264 putative functional regions proposed by Power The brain partition scheme was obtained from a fMRI meta-analysis adopted in previous studies1066https://sites.google.com/site/bctnet/). For each of these topological metrics, its average value across all nodes within any of the ten networks was calculated and defined as the network level topological metric. Connectivity strength for the ith node is defined as follows:To characterize the ten networks and their specific interactions, topological metrics were computed including connectivity strength, betweenness, and degree, which are suggested to be important measures for examining the interactions between network elements  in network topology analysis18K is the number of nodes in the whole-brain network G, and zi,j is the z-score of the temporal correlation coefficient between the time courses of nodes i and j. For the jth  network (Gj), its connectivity strength can be calculated as:where Kj is the number of nodes in the jth network Gj.where The global metric of characteristic path length and the local metric of clustering coefficient were calculated to examine the small-world property of a whole-brain network with a selected threshold. In accordance with previous work, these two metrics were first normalized to their corresponding values obtained by averaging 100 random networks with matched size and degree distribution of a brain network566Other metrics  were also normalized to their corresponding metrics estimated in the randomized networks to characterize the topology of the ten networks and their specific interactions. For each of the above mentioned network metrics, the area-under-the-curve (AUC) method was used to summarize the results across the whole range of the selected connection density5Connectivity strength is suggested to be strongly associated with the spatial distance of a given inter-nodal connection2123A multi-voxel pattern analysis (MVPA) procedure was applied to classify conscious state in all the subjects. For each brain region of the whole-brain network, three nodal topological metrics were obtained: connectivity strength, betweenness, and degree. For each subject, a feature vector was constructed for pattern classification by concatenating the three nodal topological metrics of all brain regions within each of the ten functional networks. All subjects were divided into non-overlapped training and testing samples. Using the feature vectors, we applied a linear support vector machine (SVM) approach to perform classifier training and generalization testingp-value represented the proportion of the accuracies in the null distribution that were greater than or equal to the actual classification accuracy, which was obtained using the non-permutated training samples.The leave-one-out-cross-validation (LOOCV) strategy was used to evaluate performance of the three SVM classifiers. In the calculations, each subject was used as the test sample in turns while the remaining subjects were used as training samples to train the three SVM classifiers. Classification performance of the classifiers was quantified using the accuracy obtained from the cross-validation estimations. Statistical significance of classification accuracy was assessed by conducting a nonparametric permutation test13p < 0.05 based on the nonparametric permutation test as above. The sensitivity and specificity of the classification were also calculated. The sensitivity represents the proportion of MCS/VS/VS correctly classified, while the specificity represented the proportion of control/control/MCS correctly classified between groups of MCS and control/VS and control/VS and MCS.Classification performance was most optimal when features in the FPN and DMN were combined (see Results). Therefore, the same MVPA steps were performed with these features for classification between groups  based on the LOOCV strategy. The level of significance for classification accuracy was set at The contribution of each node to the prediction of conscious states was dependent on its absolute weight in the feature vectorsTwo-way analysis of variance (ANOVA) was used to examine the effect of conscious state  and brain network on the topological metrics of network connectivity strength, betweenness, and degree. One-way ANOVA was performed to determine the effect of conscious state on nodal connectivity strength within the FPN and DMN at different spatial distances. Tukey post-hoc tests were used to carry out significant comparisons. Pearson’s correlation analysis was used to assess the relationship of topological metrics between the FPN and DMN or between different brain regions within these two networks in the DOC patients while using the average topological metrics in controls as the baseline (Bonferroni corrections were used for multiple comparisons).F-value from one-way ANOVA. The average F-value across the three topological metrics for each node  was used to perform nonparametric permutation tests. Initially, all subjects in the three groups were combined together and randomly re-sampled into three new groups with the same number of subjects in each group. A new average F-value for each nodal topological metric within the FPN and DMN was then obtained after performing the ANOVA among the 3 re-sampled groups. This process was repeated 10,000 times resulting in 10,000 average F-values corresponding to each node within the FPN and DMN. These values were used to build a null distribution. The p-value of the nodal average F-value was calculated as a percentile in the null distribution. Multiple comparisons were corrected using FDR correction . The p-values of the correlation analysis are presented as the corrected value after Bonferroni corrections. Group data are presented as the mean ± standard deviation in the text.A nonparametric permutation test was performed to determine which brain regions exhibited altered nodal topological metrics within the FPN or DMN13F2,260 = 52.21, p < 0.001), but not for brain network , on network connectivity strength . Post-hoc tests indicated increased network connectivity strength within the FPN (p < 0.01) and decreased strength within the DMN (p < 0.01) in both the VS/UWS and MCS groups compared to controls. In addition, network connectivity strength for the VS/UWS group was significantly increased within the FPN (p < 0.01) and decreased within the DMN (p < 0.01) compared to the MCS group. Network connectivity strength was also increased within the FPN but decreased within DMN for the VS/UWS and MCS groups compared to the control group. Similar trends were evident in the other two topological metrics, betweenness  and bilateral precuneus (BA 7) of the DMN (p < 0.05). The nodal topological metrics between the DLPFC and the precuneus regions were negatively correlated after Bonferroni corrections: strength , betweenness , and degree .To determine if anti-correlated properties are also observed in specific regions within the FPN and DMN, we initially identified brain regions exhibiting significantly altered topological metrics within the FPN and DMN in DOC patients. These regions are located in the bilateral dorsolateral prefrontal cortices  of the FPN  and stronger for the VS/UWS group relative to the MCS group (p < 0.01). Nodal connectivity strength in short-distance connections (10–40 mm) within the DMN was also different between groups  and weaker for the VS/UWS group compared to the MCS group (p < 0.01). Taken together, these results demonstrate inverse trends for distance and connectivity strength between FPN and DMN networks, suggesting different patterns of signal integration in individuals with impaired consciousness. That is, distant FPN connections exhibit stronger connectivity and closer DMN connections show less connectivity in VS/UWS and MCS patients compared to controls or in MCS compared to VS/UWS patients.Results of the one-way ANOVA showed that network interactions related to connectivity strength are spatially distance dependent. This revealed that nodal connectivity strength in long-distance connections (120–150 mm) within the FPN was different between groups  but also for the DMN . Classification accuracy was not above chance level (33.3%) for any of the other eight networks  discriminate conscious state, we applied a multivariate pattern analysis (MVPA) classification algorithm to each of the 10 functional networks. The specific objective was to determine if functional connectivity patterns within the FPN and DMN, and not the other eight networks, could be used to discriminate conscious state. Results indicated classification accuracy was significantly above chance level (33.3%) not only for the FPN  for all brain regions within FPN and DMN . Brain rFindings from the present study reveal abnormal FPN and DMN topological properties in DOC that are anti-correlated between networks. This anti-correlation was observed between nodal topological metrics in the dorsolateral prefrontal cortex of the FPN and precuneus of the DMN. In addition, we found architectural differences between networks in subjects with DOC. Specifically, altered nodal connectivity within the FPN mainly occurred in long-distance connections, while it primarily occurred in short-distance connections within the DMN. Classification analysis indicated that FPN and DMN connectivity patterns discriminate conscious state. These results confirm previous findings implicating the involvement of FPN and DMN in DOC and highlight specific interactions in network-level topology that may play a role in mediating consciousness.In this study, DOC patients exhibited abnormal FPN and DMN functional connectivity, a finding that is consistent with results of previous inter-nodal correlation analyses in MCS467Some studies have not detected differences in global level topological properties between comatose patients and healthy controlsThe FPN and DMN exhibit antiphasic activation patterns22930A novel finding from the current study is that the disruption in nodal connectivity strength within FPN and DMN is spatially distance-dependent. FPN regions with altered connectivity strength corresponded to long-distance connections, while DMN regions with altered connectivity strength related to short-distance connections. These findings are consistent with previous work that shows connectivity strength is decreased in short-distance connections but increased in long-distance connections for subjects in an anesthetic-induced unconscious stateet al. reported a significant reduction in the precuneus of coma patientset al. found decreased local efficiency of the precuneus in impaired consciousnessDistinct anti-correlated brain regions within either network were also evident in this study. Classification analysis showed that dorsolateral prefrontal cortex within the FPN and precuneus within the DMN made the largest contribution to predicting conscious state. The dorsolateral prefrontal cortex, a region within the FPN, has been suggested to play a key role in conscious processes3334et al.et al.et al. conducted a series of experiments to examine the influence of different acquisition parameters on rs-fMRI, such as run length (2–12 min), temporal resolution (2.5 or 5s), spatial resolution (2 or 3 mm), and subject state The present study has some noted limitations. First, segmentation of the brain image of DOC patients is complicated by distortions in different types of tissues  during automated segmentation40425In summary, this study provides evidence of network-level abnormalities contributing to DOC. Our results demonstrate that there are inverse trends in the relationships between conscious impairment and the respective topological properties of the FPN and DMN. These relationships were particularly evident in nodal topological metrics of the dorsolateral prefrontal cortex of the FPN and precuneus of the DMN. Altered connectivity strength in DOC was distance dependent, with most disruptions appearing in long-distance connections within the FPN but occurring in short-distance connections within the DMN. The combination of FPN and DMN network-level topology patterns strongly predicted conscious state. Collectively, these findings imply distinct interactions between FPN and DMN that may play a role in mediating consciousness and, therefore, provide a potential biomarker of conscious state.How to cite this article: Long, J. et al. Distinct Interactions between Fronto-Parietal and Default Mode Networks in Impaired Consciousness. Sci. Rep.6, 38866; doi: 10.1038/srep38866 (2016).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.