Protein biomarker studies are currently hampered by a lack of measurement standards to demonstrate quality, reliability and comparability across multiple assay platforms. This is especially pertinent for immunoassays where multiple formats for detecting target analytes are commonly used.® (MSD®) platform to illustrate reliable detection of the component proteins. The protein panel was also evaluated as a spike-in material for a model immunoassay involving detection of ovarian cancer biomarkers within individual human plasma samples. Our selected platform could discriminate between two panels of the proteins exhibiting small differences in abundance. Across distinct experiments, all component proteins exhibited reproducible signal outputs in pooled human plasma. When individual donor samples were used, half the proteins produced signals independent of matrix effects. These proteins may serve as a generic indicator of platform reliability.In this pilot study a generic panel of six non-human protein standards (50 - 10^7 pg/mL) of varying abundance was prepared as a quality control (QC) material. Simulated "normal" and "diseased" panels of proteins were prepared in pooled human plasma and incorporated into immunoassays using the Meso Scale DiscoveryEach of the remaining proteins exhibit differential signals across the distinct samples, indicative of sample matrix effects, with the three proteins following the same trend. This subset of proteins may be useful for characterising the degree of matrix effects associated with the sample which may impact on the reliability of quantifying target diagnostic biomarkers.We have demonstrated the potential utility of this panel of standards to act as a generic QC tool for evaluating the reproducibility of the platform for protein biomarker detection independent of serum matrix effects. Protein biomarkers for diagnosis of disease have formed the basis of clinical research proteomics for several decades -3. In spMany protein-based detection methods suffer from a lack of standardisation with the reagents and methods employed , in a siFor most protein biomarker assays, the diagnosis of diseases may be achieved by detecting the appropriate protein biomarker(s) above specified thresholds, alongside the generic QC proteins to indicate platform functionality. The change in the collective signal output profile of these QC proteins may indicate the presence of inhibitors within the biological matrix, and may infer that the robustness of detection of the target diagnostic biomarker(s) is also adversely affected.® platform. The scope of detecting each protein amidst the full panel of proteins was assessed, as well as the ability to identify small known changes in the protein composition. The panel of protein standards was also evaluated as a spike-in material, by supplementing individual donor plasma (ovarian cancer diseased and non-diseased) samples with the QC material. This pilot study revealed the value of the QC material as an indicator of platform robustness, as well as for highlighting any matrix effects associated with individual samples that may influence the reliability of detecting the target analytes within the test samples.In this paper we have prepared a panel of generic protein standards and evaluated its utility as a quality control (QC) material using the to MSDA generic panel of protein standards was prepared for use as a quality control (QC) material. The composition of this generic panel of proteins  is shown in Table 7 pg/mL) emulating the natural abundance of proteins in serological samples [Biological test samples were spiked with the QC material, extending over a broad dynamic range  and purified proteins  were reconstituted in PBS prior to gravimetric preparation of the 10× stock QC material  and stored at - 80°C. Refer to Additional file ®-based assays were constructed for each of the six spike proteins and four candidate ovarian cancer biomarkers, either alone or within serological samples. Capture antibodies , mouse anti-osteopontin, and mouse anti-interleukin-8 (IL-8) monoclonal antibodies (mAbs) and goat anti-caronte polyclonal antibody (pAb) (R&D Systems); anti-luciferase and anti-CA-125 mouse mAbs  and mouse anti-lysozyme mAb  diluted in PBS (at 1-8 μg/mL antibody), were added to wells on either MULTI-ARRAY™ standard bind or high bind MSD® plates  and incubated overnight at 4°C. The plate was decanted and each well incubated with 150 μL blocking buffer , pH 7.4; 1% (w/v) BSA, 0.05% (w/v) sodium azide) on a shaker for 1 hour at room temperature. After three washes with PBS, 0.05% (w/v) polyoxyethylenesorbitan monolaurate (Tween20), 25 μL of samples and standards ) diluted in either blocking buffer, pooled normal human plasma , or single donor normal and ovarian cancer diseased plasma  were added to each well and incubated for 1 hour at room temperature on the shaker. After three washes, 25 μL MSD® SULFO-TAG detection antibody  was added to each well and incubated for 1 hour at room temperature on a shaker.Direct MSDThe unreacted SULFO-TAG N-hydroxysuccinimide (NHS)-ester has molecular weight of 1141 Daltons, and after the labelling reaction, the conjugated SULFO-TAG adds 1027 Daltons to the protein. As the SULFO-TAG is a small hydrophilic molecule (approximately 1 kDa), it is not expected to affect the function of large protein conjugation partners such as antibodies, especially as the SULFO-TAG is much smaller than biotin (13 kDa). Biotin NHS-ester is the most commonly used method to label antibodies, which also binds to the antibody via primary amines in the same manner as the SULFO-TAG NHS-ester and is generally not anticipated to interfere with the binding of the antibody to the cognate target antigen.® Read Buffer T (with surfactant).For labelling, lyophilised antibodies were directly reconstituted in PBS, pH 7.9. For antibodies sourced in solution, buffer exchange was performed with PBS, pH 7.9 using Zeba Desalt Spin columns with a 7000 Dalton molecular weight cut-off threshold (Thermo Scientific). The concentration of the antibody was then determined by the BCA protein assay  using the microplate procedure. 3 nmol/μL Sulfo-Tag NHS-Ester tag  was added to the antibody for the labelling step using a molar challenge ratio of 12:1, and the sample was mixed for 2 hours in the dark at room temperature. The antibodies were then buffer exchanged into PBS, pH 7.4/0.05% sodium azide using Zeba Desalt Spin columns. The concentration of the labelled antibody was then determined by BCA protein assay using the microplate procedure. Absorbance of protein conjugate at 455 nm was measured by NanoDrop (Thermo Scientific) to establish labelling ratio of Sulfo-tag to antibody. The detection antibodies subjected to labelling were goat pAbs: anti-luciferase , anti-caronte, anti-CCL-6, anti-lungkine, anti-soggy, anti-osteopontin, anti-IL-8 and anti-EGFR (R&D Systems). Rabbit detection pAbs were anti-lysozyme , and anti-CA-125 . Wells were washed before addition of 150 μL of MSD® Discovery Workbench 3.0 software.A voltage was applied to the carbon electrodes integrated in the plate, initiating a redox reaction involving ruthenium chemistry, resulting in the emission of light detected by the cooled charge coupled device (CCD) camera. The raw data output was analysed by MSDEach of the spike mixtures with five proteins was prepared in pooled normal human plasma, maintaining the designated concentrations for each analyte as outlined in Table Two distinct panels of the six protein spikes were prepared, with the composition outlined in Table Uniplexed assays for each of the six proteins were performed with both panels of protein standards, incorporating triplicate determinants for all three separate experiments. The reliability of detecting known fold changes in concentrations of selected spike proteins diluted in pooled normal human plasma as the biological matrix, was evaluated by comparing the fold changes between the two protein panels. 7-point internal calibration curves were incorporated for the assay of each analyte.Single donor plasma samples  were supplemented with the QC material to incorporate a 1× working concentration of the six spike proteins and assayed for the six spike proteins and four putative ovarian cancer biomarkers, CA-125, EGFR, IL-8 and osteopontin. Experiments were performed with internal calibration curves  with triplicate determinants, in a randomised plate format for three separate experiments.The MSD Discovery Workbench analysis software version 3.0 was applied to process the data. A 4-parameter logistic model was used for curve-fitting. PCA was performed using SIMCA-P (Umetrics). Linear mixed-effects models were fitted by residual (or restricted) maximum likelihood using the program "R".Each assay was initially constructed using a background of buffer (blocking buffer) and then pooled normal human plasma as the sample diluent for each analyte, with the criteria that each analyte concentration used is within the linear working range of the assays (data not shown). The contribution to the signal output by each cognate analyte amidst the full panel of proteins was evaluated, to show that the presence of the five non-cognate proteins have no adverse effect on each assay. Each protein individually was removed from the mixture to gauge the contribution by the protein to the overall signal output Figure . A reducTwo distinct panels of protein standards were prepared with the same component proteins but differing (known) concentrations for three proteins; CCL6, soggy and luciferase. The ratios of these three analytes between the two protein panels were experimentally derived from three separate experiments by mean signal outputs and interpolated concentrations from internal calibration curves Table  with theGenerally, interpolation increased the variability in the ratios between the "normal" and "simulated diseased" panels, with CVs of 5.91 - 70.76%, compared with the CV range of 0.19 - 10.78% associated with the ratios between the two panels derived from the mean signal output data. Hence the material may be used to evaluate the robustness the platform by evaluating the performance of these assays in terms of the signal output, rather than by interpolation from the internal standard curves.The QC material was spiked into six single donor ovarian cancer plasma samples (termed as OC samples) and six single donor normal plasma samples. Three separate experiments were performed to evaluate the reproducibility of detecting the six spiked analytes, in addition to three putative ovarian cancer biomarkers, IL-8, EGFR, and osteopontin, alongside the FDA-approved marker CA-125. If all four candidate biomarkers are appropriate markers for ovarian cancer, differential expression of these cancer markers is anticipated between the normal and OC samples. It is anticipated that the spiked proteins are detectable at the same abundance if all samples are supplemented with the same quantity of QC material.et al., 2009 [The QC material when spiked into the plasma samples could serve as a useful indicator to verify the robustness of the platform if the signal outputs for its component proteins are reproducible between experimental runs and independent of matrix effects. From the plots of the signal outputs, it was apparent that three proteins, caronte, soggy and lysozyme, fulfilled this criteria Figure . A lineal., 2009 ), in spiOf the four candidate biomarkers, the major contributor to the separation of the data for the two patient populations was IL-8 (data not shown). In addition to evaluating the IL-8 data in terms of the mean signal outputs Figure  for eachTo demonstrate platform robustness, the signal output of the component proteins should not exhibit significant statistical difference between the individual donor plasma samples, or between technical replicates and separate experimental runs. The component proteins exhibiting differential signals between distinct samples would be indicative of the existence of matrix effects that may also influence credibility in the data derived for the detection of the target analytes. In this pilot study, the purpose of the generic QC material was illustrated by immunoassay using a single set of antibodies. These data form a preliminary finding, and all aspects of variability inducing parameters should be subjected to a full validation. A larger scale study with a greater number of individual donor plasma samples, platforms and antibodies may be required to determine the true criteria for acceptable variability in the signal outputs for the component proteins. Generic protein standards have not previously been applied to immunodetection methods, however, it is anticipated that this QC material may be applied to all protein-based detection methods, including mass spectrometry and other immunoassays.per se, it was most probable that the variable protein composition of the biological matrix bound non-specifically to some of the antibodies within these assays.One limitation with immunoassays is the potential for cross-reactivity between proteins and non-cognate antibodies that may impair the detection of the target analyte. We have shown that each protein was detectable amidst the presence of the five non-cognate target proteins. However, with the ovarian cancer pilot study involving single donor plasma samples, the CCL6, lungkine and luciferase assays exhibited differential degrees of cross reactivity, giving rise to the variable signal outputs observed when the QC material was spiked in different patient samples. Interestingly, there was concordance among these three assays in terms of their signal profiles across the twelve donor samples, in spite of the concern regarding the robustness of the CCL6 assay. As this cross-reactivity was not due to the presence of the five other proteins within the spike panel Lungkine as well as CCL6 and luciferase, at their current designated spike concentrations could not be used as the QC material of platform reliability for immunoassays with single donor plasma samples, with the current set of antibody pairs. However, this does not exclude their use as spike materials using distinct antibody pairs or for assays without antibodies (e.g. mass spectrometry), or as a spike material for other biological samples. These alternative uses will require further investigation on a case-specific basis. Nonetheless, with the existing assay conditions for these three component proteins, their signal outputs are valuable indicators of matrix effects associated with individual samples. Identifying matrix effects is of importance as this phenomenon may also adversely affect the accuracy of quantifying the presence of the target analyte.Hence, this subset of proteins may collectively highlight the need for caution when evaluating the level of robustness for the test analyte data. Conversely, soggy, lysozyme and possibly caronte do not exhibit sensitivity to sample matrix effects. Hence these latter three proteins may serve as indicators of the robustness of the platform.Another limitation of many antibody-based assays is the narrow linear range of the working assays, especially when calibrants are spiked into a biological matrix (e.g. plasma) rather than a buffer. Assays may not always exhibit a sufficiently broad linear range encompassing the full concentration coverage for physiological status, leading to interpolation of some data from a non-linear portion of the fitted curve. Albeit curve-fitting within an experiment may be robust, calibration curves may be susceptible to changes in the trendline thus increasing variability between distinct experiments. This subsequently reduces the capacity for robust inter-experiment comparison. High variability with the interpolated analyte concentrations via internal calibration curves from different experimental runs is observed when evaluating the the normal and "simulated disease" protein panels, as well as for IL-8 detection.in lieu of internal calibrants for each analyte may demonstrate the platform is fit-for-purpose and improve on assay throughput by reducing the number of wells consumed by calibrants.In this instance, IL-8 concentration in the OC samples coincided with the linear portion of the IL-8 standard curve, whereas the concentrations of IL-8 associated with normal plasma fell outside this robust range. This brings into question the need for a standard curve, given that detection above an assigned cut-off threshold  may suffice for the diagnosis of disease. The incorporation of internal standard curves also utilises numerous additional wells to ensure there are sufficient datapoints for robust curve fitting, with adequate technical replication. However, using the QC material We have shown the utility of the generic protein QC material as an indicator of platform performance and matrix effects, using immunoassays on the MSD Sector 6000 Imager platform. The QC material may also be used alone to evaluate bias introduced by variable instruments, operators or reagents. This panel of proteins has also exhibited suitable homogeneity and stability for this application. The material can be incorporated into a "quality metrics" toolkit for assessing protein biomarker platform performance, addressing issues associated with insufficient assay robustness delineated in the FDA's Critical Path Initiative in 2004.®): Meso Scale Discovery®; (MFI): median fluorescent intensity; (OC): ovarian cancer; (pAb): polyclonal antibody; (PBS): phosphate buffered saline; (Tween20): polyoxyethylenesorbitan monolaurate; (QC): quality control.(BSA): Bovine serum albumin; (EGFR): epidermal growth factor receptor; (IL-8): interleukin-8; (mAb): monoclonal antibody; , as well as the overall variability of the platform.Click here for fileEvaluation of the short term stability of the 10× stock QC material. The isochronous assay for each of the six protein components of the QC material was performed, following exposure of the 10× stock to room temperature, for 1 h, 1 day, 3 days and 7 days prior to the assays.Click here for fileEvaluation of the long term stability of the 10× stock QC material. Uniplexed assays were performed for each the six protein components of the diluted 1× QC material at monthly intervals, following storage of the 10× stock QC material in the -20°C freezer, -80°C freezer, or as a lyophilised powder stored at -20°C.Click here for fileStandard error estimated associated to each ratio of normal and simulated diseased signal output for each analyte. The formula used to calculate the uncertainty associated with the derivation of the ratios between the normal and simulated diseased panels for each analyte is shown.Click here for fileTabulated variability of the assays. Intra- and inter-assay variability for the 6 spike proteins and 4 candidate ovarian cancer biomarkers derived from three separate experiments conducted to evaluate the implementation of the QC spike protein in a model system.Click here for file


Surgical treatment of focal epilepsy in patients with focal cortical dysplasia (FCD) is most successful if all epileptogenic tissue is resected. This may not be evident on structural magnetic resonance imaging (MRI), so intracranial electroencephalography (icEEG) is needed to delineate the seizure onset zone (SOZ). EEG-functional MRI (fMRI) can reveal interictal discharge (IED)-related hemodynamic changes in the irritative zone (IZ). We assessed the value of EEG-fMRI in patients with FCD-associated focal epilepsy by examining the relationship between IED-related hemodynamic changes, icEEG findings, and postoperative outcome.Twenty-three patients with FCD-associated focal epilepsy undergoing presurgical evaluation including icEEG underwent simultaneous EEG-fMRI at 3T. IED-related hemodynamic changes were modeled, and results were overlaid on coregistered T1-weighted MRI scans fused with computed tomography scans showing the intracranial electrodes. IED-related hemodynamic changes were compared with the SOZ on icEEG and postoperative outcome at 1 year.Twelve of 23 patients had IEDs during recording, and 11 of 12 had significant IED-related hemodynamic changes. The fMRI results were concordant with the SOZ in 5 of 11 patients, all of whom had a solitary SOZ on icEEG. Four of 5 had >50% reduction in seizure frequency following resective surgery. The remaining 6 of 11 patients had widespread or discordant regions of IED-related fMRI signal change. Five of 6 had either a poor surgical outcome (<50% reduction in seizure frequency) or widespread SOZ precluding surgery.Comparison of EEG-fMRI with icEEG suggests that EEG-fMRI may provide useful additional information about the SOZ in FCD. Widely distributed discordant regions of IED-related hemodynamic change appear to be associated with a widespread SOZ and poor postsurgical outcome. ANN NEUROL 2011 Focal cortical dysplasia (FCD) results from abnormal neuronal migration and is commonly associated with pharmacoresistant focal epilepsy.EEG-functional MRI (fMRI) recordings have been used to study cerebral neural activity associated with interictal discharges (IEDs),17Comparisons between IED-correlated fMRI results and icEEG, considered the gold standard technique for localization of epileptic foci, have generally been limited to case descriptions in the context of studies of patients with epilepsy of mixed etiology.19We aimed to prospectively compare presurgical IED-related BOLD signal changes with the results of icEEG and postoperative outcome in patients with FCD. We also aimed to assess whether regions of IED-related BOLD signal change relate to epileptogenic regions in FCD, which would have potential implications for surgical efficacy where multiple regions of signal change were detected.Sixty-five patients with refractory focal epilepsy undergoing presurgical evaluation and awaiting icEEG, from 4 centers , underwent EEG-fMRI at the Epilepsy Society MRI Unit, Chalfont St Peter, United Kingdom. Twenty-three had a diagnosis of FCD made on structural MRI, histology, or both and were selected for analysis. All procedures were subject to the relevant local and national research ethics committees' approval.Patients had scalp EEG video-telemetry, MRI, and in some cases positron emission tomography (PET) and/or single photon emission computed tomography (SPECT) according to their local treatment center protocol. All underwent structural MRI at the Epilepsy Society MRI Unit, according to the Epilepsy Society protocol, at 3T.multifocal SOZ refers to situations where seizure onset was recorded at several discrete sites, as opposed to propagation of seizure activity from a solitary SOZ.The SOZ was identified on icEEG by experienced observers and described by the electrode(s) at which low-amplitude fast activity was first seen at seizure onset. The irritative zone (IZ) was defined as the region giving rise to IEDs on icEEG (described by the electrodes where IEDs were recorded). Regions to which the seizure onset rhythms rapidly (<3 seconds) propagated -compatible system . Further details can be found in our previous publications.MR gradient and pulse-related artifact were removed from the EEGp = 0.05 (family-wise error [FWE] correction for multiple comparisons), but effects seen at a lower level of significance  were also reported. The sign of the BOLD change for each cluster was determined by plotting the fitted response at the most significant voxel within the cluster.fMRI time series data were analyzed using a general linear model to determine the presence of regional IED-related BOLD changes in SPM5 (spm.fil.ac.uk). For this purpose, IEDs were represented as zero-duration events (unit impulse δ functions), and convolved with the canonical hemodynamic response function and its temporal and dispersion derivatives, resulting in 3 regressors for each event type.Individual T1-weighted MRI scans were coregistered and fused with a postimplantation computed tomography (CT) scan acquired with the icEEG electrodes in situConcordant (C): All IED-related BOLD signal clusters colocalized (within 2cm and in the same lobe) with the SOZ as identified on icEEG.Concordant Plus (C+): Some clusters of significant IED-related BOLD signal change were localized with the SOZ. All other significant BOLD clusters were within the same lobe or touching the edge of the same lobe as the SOZ .Discordant Plus (D+): Some clusters of significant IED-related BOLD signal change were localized within the SOZ, with other significant BOLD clusters in other lobes.Discordant (D): All clusters of IED-related BOLD signal change were remote from the SOZ.Null: There was no cluster of significant IED-related BOLD change.IEDs were recorded during EEG-fMRI scanning in 12 of 23 cases, of whom 11 had statistically significant IED-related BOLD changes. Ten of 11 patients had IED-related BOLD increases, of whom 2 were classified C (Patients 18 and 21), 3 were classified C+ ; in Patient 19, clusters lay outside the region of rapid seizure propagation, but colocalized with the IZ.Three of 4 patients  had clusters of BOLD increase colocalized with the IZ in addition to clusters colocalized with the SOZ. In Patient 1, all IED-associated BOLD changes were decreases, 1 of which colocalized with the SOZ and IZ. In all 4 patients, there were IED-related BOLD clusters outside the icEEG coverage.In Patient 2, a single BOLD cluster colocalized in part with the IZ, which was extensive on icEEG, but remote from the SOZ. In Patient 23, a single BOLD cluster was revealed deep in the (widespread) IZ but remote from the SOZ, which was presumed to lie outside the region covered by icEEG.Of the patients classified C, Patient 18 was seizure free 12 months after resection (ILAE 1), and Patient 21 had a >50% reduction in seizure frequency (ILAE 4), following a resection that was limited because of the risk of a visual field deficit. The surgical resection included the region of most significant BOLD increase in both patients.Two of the 3 patients classified C+ (Patients 3 and 22) had a good outcome , and the resection included the cluster of BOLD signal increase colocalized with the SOZ. In the remaining patient (Patient 19), a single seizure focus was identified around the central sulcus, and the patient underwent gamma knife surgery with ILAE outcome of 5 at 1 year.Of the patients classified D+, Patients 9 and 12 had a poor outcome (ILAE class 5 or 6), and there were regions of significant BOLD signal increase remote from the resection area. Two of 4 did not undergo resection: Patient 1, who had a highly localized single seizure focus, owing to overlap with primary motor cortex; and Patient 20, due to multiple independent sites of seizure onset.One patient classified D (Patient 2) had a poor outcome (ILAE class 5) following surgical resection with no overlap with the EEG-fMRI result. The remaining patient (Patient 23), who had IED-related BOLD change remote from the SOZ, did not undergo resection owing to a diffuse SOZ.Six of 11 patients with no recorded IEDs had a good outcome (ILAE 1-4) following resection of a solitary focus of seizure onset on icEEG, whereas in the remaining 5 patients, resection was precluded or a poor outcome was recorded.We report the first prospective systematic evaluation of the potential role of EEG-fMRI in the presurgical evaluation of patients with FCD by systematic comparison with icEEG.EEG-fMRI revealed significant IED-related BOLD clusters in 11 of 12 cases in whom IED were recorded (12 of 23 patients). Comparison with icEEG and consideration of the surgical outcome suggests that in addition to the presence of IED-related clusters within the SOZ, the extent of clusters remote from the SOZ may also be important.In 9 of 11 patients , at least 1 cluster colocalized with the SOZ, and all clusters colocalized with the SOZ at a lobar level in 5 of these 9. Two of 5 had normal structural MRI, and a cluster concordant with a solitary seizure focus, not previously identified during noninvasive evaluation, was found in all 5. These results are comparable with previous EEG-fMRI studies of mixed pathologies and nonlesional cases in focal epilepsy.When multiple IED-related BOLD clusters of positive IED-related BOLD signal change were identified remote from and particularly contralateral to the region of FCD, multiple or diffuse epileptic foci were usually seen on icEEG, precluding successful resective surgery.Our results support evidence from icEEG studies that FCD may be associated with multiple areas of epileptogenicity,16The observation of IED-related regions of BOLD signal change remote from the seizure onset zone, but concordant with the IZ or regions of rapid propagation on icEEG supports electrospray ionization (ESI) studies, which suggest that EEG-fMRI may be a useful tool to image the epileptic network.28In 1 patient (Patient 1), the IED-related BOLD signal cluster that colocalized with the SOZ was classified as a decrease, consistent with previous studies reporting regions of IED-related BOLD decrease in the IZ or SOZ in a only a small proportion of cases.It has been suggested that noninvasive localization techniques such as scalp EEG-fMRI could reduce the need for invasive tests such as icEEG, which remains the gold standard for the localization of epileptic foci, but has limited spatial sampling, and is both expensive and not without risk. Although EEG-fMRI is unable to provide the same information as icEEG, it benefits from relatively uniform whole-brain coverage. The finding that multiple widespread IED-related BOLD signal clusters were apparently associated with a widespread SOZ or multiple sites of seizure onset suggests a potential use for EEG-fMRI in determining which patients are likely to benefit from icEEG and those in whom results are likely to be poor.The size of the group studied here precludes statistically meaningful calculations of sensitivity and specificity with regard to postsurgical outcome. It is of note that in those classified C or C+, surgical outcome was good (ILAE class 1-4) in the majority of cases, whereas surgical outcome was poor (or resection was precluded) in 5 of 6 (83%) of the cases classified D or D+. It should be noted that of those patients classified C or C+, the patients who had the poorest outcomes  underwent modified procedures despite a solitary SOZ  to avoid functional deficit. This may explain in part why only 2 of 5 of this group were completely seizure free (ILAE class 1) following resection.Six of the 11 FCD patients who did not have IED in the EEG-fMRI study had a good seizure outcome. It is not known what BOLD activations would have been found if IED had occurred during these studies. It is evident that the occurrence of IED during EEG-fMRI is not in itself a predictive factor for outcome. What is evident is that in those with FCD, the finding of widespread BOLD activations with IED appears to be associated with widespread epileptic abnormalities and may be a useful factor to include in the decision to undertake invasive EEG studies. EEG-fMRI, however, with the very limited temporal sampling possible, cannot replace icEEG in identifying a target for resection.The finding of IED-related BOLD changes in patients with normal structural MRI is not new,There was no relationship between the extent of IED-related BOLD changes and histological subtype, although the majority of the patients had FCD type 2.EEG-fMRI is among several evolving techniques including magnetoencephalography (MEG), ESI (used to inform EEG-fMRI), and isotope imaging used in the noninvasive evaluation of epilepsy. MEG appears to have a higher predictive value for surgical outcome and better sensitivity for SOZ localization than PET and SPECT.Due to the difficulty of studying seizures using fMRI, EEG-fMRI typically focuses on IEDs, whereas the gold standard is the SOZ identified on ictal icEEG, a mismatch common to many noninvasive presurgical localization tests41The difficulties in validating EEG-fMRI results, which may reveal multifocal and widespread patterns, are similar to those encountered in other tomographic mapping techniques such as PET and ictal SPECT, and are generally more complex than those in MEG/EEG-based localization, particularly for single-dipole source models. Validation requires summarizing the correspondence between whole-brain maps and the SOZ on icEEG, expressed as concordance. Features of the fMRI maps used for the assessment of concordance include the amplitude, size, and sign of observed BOLD signal change. The concordance scheme used in this study is modified from our previous studies3 around individual contacts, are used,The comparison of EEG-fMRI and icEEG is also limited by differences between the signals that can lead to decoupling of hemodynamic and electrophysiological effects over scales on the order of 1cm.Coregistration, particularly between EPI and T1-weighted MRI, is limited by EPI signal dropout effects at the brain–cerebrospinal fluid–air interfaces, and intraoperative cortical shifts during electrode placement, estimated by some authors to be up to 24mm.EEG-fMRI relies on the recording of IEDs during the scanning period. Events were captured in only 52% of this group, as we deliberately included all patients regardless of their resting EEG to avoid selection. Recent developments promise to increase the technique's sensitivity, particularly by using information derived from routine EEG.Our results add to the increasing body of evidence that FCD may be multifocal, with areas of epileptogenic tissue remote from the dysplastic lesion in some cases. We found that EEG-fMRI may be useful in identifying those patients who are more likely to have multiple epileptic foci and are therefore less likely to benefit from icEEG and resective surgery, although larger groups and longer outcome data are required to refine the role of EEG-fMRI in the presurgical evaluation of FCD.


In this study, we present an algorithm called i-rDNA that can facilitate the rapid detection of 16S rDNA fragments from amongst millions of sequences in metagenomic data sets with high detection sensitivity.Obtaining accurate estimates of microbial diversity using rDNA profiling is the first step in most metagenomics projects. Consequently, most metagenomic projects spend considerable amounts of time, money and manpower for experimentally cloning, amplifying and sequencing the rDNA content in a metagenomic sample. In the second step, the entire genomic content of the metagenome is extracted, sequenced and analyzed. Since DNA sequences obtained in this second step also contain rDNA fragments, rapid Performance evaluation with data sets/database variants simulating typical metagenomic scenarios indicates the significantly high detection sensitivity of i-rDNA. Moreover, i-rDNA can process a million sequences in less than an hour on a simple desktop with modest hardware specifications.In addition to the speed of execution, high sensitivity and low false positive rate, the utility of the algorithmic approach discussed in this paper is immense given that it would help in bypassing the entire experimental step of primer-based rDNA amplification, cloning and sequencing. Application of this algorithmic approach would thus drastically reduce the cost, time and human efforts invested in all metagenomic projects.http://metagenomics.atc.tcs.com/i-rDNA/A web-server for the i-rDNA algorithm is available at  The majority of microorganisms present in natural ecosystems cannot be cultured in the laboratory and hence remain unexplored. To exploit the potential of this unexplored diversity, the new field, called metagenomics, has been initiated in the recent years.The first step in a typical metagenomics project involves estimating the microbial diversity present in the environmental sample under study. Obtaining accurate estimates of this diversity is an important step and forms the first objective of any metagenomics project. Analyzing 16S ribosomal RNA (16S rRNA) gene (commonly referred to as 16S rDNA) sequences is the most commonly used method for rapidly estimating microbial diversity . This meIn the second step of any metagenomics project, the entire genomic content of the environmental sample under study is extracted and sequenced. Millions of DNA sequences originating from the genomes of various microbes in the sample are thus obtained. Using computational techniques, the genes harbored in these DNA sequences are identified and functionally characterized.et al. (2008), the process of 16S rDNA extraction and amplification  took approximately 6 months and required three persons and the cost incurred was more than $70,000 of materials and equipments [Since the first step, i.e the 16S rDNA profiling, is expected to provide a near comprehensive snapshot of microbial diversity, almost all metagenomic projects spend considerable amount of time, money and manpower for completing the various experimental procedures involved in this step. For instance, in a metagenomics study performed by Manichanh in silico approach to identify 16S rDNA fragments directly from the completely sequenced metagenomes will be immense, since it would not only save huge amount of time, efforts and money , but would also circumvent various experimental limitations associated with 16S rDNA profiling [in silico approach. As a pre-processing step, this program uses the HMMER program [Since the sequences obtained from the complete DNA content of an environmental sample (in the second step mentioned above) contains rDNA fragments, it is possible (in theory) to computationally identify these rDNA fragments directly from the sequenced environmental DNA. Considering the availability of faster and cheaper sequencing technologies, such as 454 , the approfiling -10. The rofiling  represen program  for firsIn spite of having significantly high detection sensitivity (even with metagenomic sequences as short as 100 bp), enormous compute time is needed by the meta-rna program for analyzing huge metagenomic data sets. Our experiments  with simulated data sets indicated that the meta-rna program takes approximately 19, 49, 84 and 156 milliseconds for analyzing a query sequence of length 100, 250, 400, and 800 base pairs (bp), respectively. At this rate, approximately 325 hours (i.e. greater than 13 days) would be needed for analyzing the 7,521,215 sequences (average length around 800 bp) constituting the Global Ocean Sampling Expedition Microbial Metagenomic data sets , the totidentification of ribosomal DNA) that can rapidly identify a small subset of metagenomic sequences  that, in high probability, harbor portions of the rDNA gene. Sequences belonging to only this  subset can further be analyzed using the meta-rna program. We demonstrate that adopting the i-rDNA program as a precursor step to the meta-rna program reduces the overall detection time by several fold. Importantly, this significant reduction in analysis time does not affect the overall detection sensitivity.A careful examination of the analysis procedure reveals that the meta-rna program needs to analyze every individual sequence in a given metagenomic data set to identify rDNA fragments. Besides being time consuming, analyzing every sequence is also not an ideal procedure  given the following observation. Several recent studies on metagenomic data sets obtained from diverse environments such as Sargasso sea , soil 1 and sludThe i-rDNA algorithm is based on the following premise. Major portions of the 16S rDNA gene sequence are highly conserved across all bacterial and archaeal species. Consequently, 16S rDNA sequences are expected to have an oligonucleotide composition distinct as compared to the oligonucleotide composition of DNA sequences originating from other portions of the genome. Therefore if genome fragments obtained from all known bacterial and archaeal genomes are clustered based on oligonucleotide usage patterns, fragments harboring portions of the 16S rDNA gene sequence (irrespective of their taxonomic origin) will get spatially localized to a 'few' clusters in feature vector space with high frequency. In the pre-processing step, the method presented in this paper identifies these 'few' clusters  and tags them as 'probable' rDNA clusters. While performing the actual analysis , the i-rDNA algorithm first identifies a set of clusters  whose sequences have an oligonucleotide composition similar to that of the query sequence being analyzed. Subsequently, i-rDNA algorithm classifies a given query sequence as a 'probable 16S rDNA fragment' if the overlap between the set of clusters  and the set of clusters pre-tagged as 'probable 16S rDNA clusters' exceeds a pre-determined threshold percentage. Given that the run time steps of the i-rDNA algorithm only involves finding the closest clusters (in terms of oligonucleotide composition) for a given query sequence, and subsequently checking if the overlap percentage (between the closest clusters and the pre-tagged 'probable 16S rDNA clusters') exceeds a pre-determined threshold, the i-rDNA algorithm is able to rapidly analyze huge metagenomic data sets within a short span of time and identify  probable rDNA fragments from amongst millions of sequences constituting the complete metagenomic data set. The meta-rna algorithm can then be employed for analyzing this small set of candidate 16S rDNA query fragments.Figure http://fames.jgi-psf.org/. These simulated data sets of varying taxonomic complexity  were constructed by combining real time sequencing reads from 113 organisms listed in Additional File Twelve simulated metagenomic data sets were used for evaluating the performance of the i-rDNA algorithm. Three of these data sets  were downloaded from These 12 simulated data sets were first given as input to the meta-rna algorithm. The number of rDNA fragments identified in each data set by the meta-rna algorithm was noted down. The same data sets were then given as input to the i-rDNA algorithm. Sequences identified by the i-rDNA algorithm as 'probable' 16S rDNA fragments (in each data set) were consequently given as input to the meta-rna algorithm. The number of rDNA fragments identified in this two step procedure (involving both i-rDNA and meta-rna) and the overall analysis time taken for this alternative approach was noted down. Results of these experiments are given in Table The overall pattern of results indicates that the two-step procedure (i-rDNA followed by the meta-rna) is able to detect around 85-94% of 16S rDNA fragments in a given data set. Around 9-17% of sequences in the data sets are observed to be predicted by the i-rDNA algorithm as 'probable 16S rDNA sequences'. As expected, providing this relatively small subset of 'probable' sequences as input to the meta-rna algorithm (instead of the whole input data set) results in 6-11 fold reduction in the overall processing time. It is significant to note that negligible time is required by the i-rDNA algorithm for identifying the initial 16S rDNA candidate set. The i-rDNA algorithm is able to process approximately 35,000 sequences/minute. For example, i-rDNA algorithm processed 1,744,283 mouse gut metagenome sequences -15 indicGiven that the final objective of 'computationally' identifying 16S rDNA fragments is to obtain a taxonomic profile of any given metagenomic data set, any loss in detection sensitivity by the i-rDNA approach will be 'acceptable', only if the 'reduced detection sensitivity' does not significantly alter the taxonomic profile of a given metagenomic data set. Using a recently published metagenomic data set, the following experiment was performed to verify if 'similar' taxonomic profiles are obtained by providing input sequences to (1) i-rDNA or (2) directly to the meta-RNA program. Approximately 1.3 million sequences constituting the metagenomic data set sampled from a malnourished child  were proResults of the above experiments , it is important to test the present algorithm's ability in detecting the rDNA fragments originating from organisms belonging to new (or hitherto unknown) taxonomic clades. For this purpose, the following 'leave one clade out' testing strategy was adopted. In an iterative manner, 16S rDNA sequences belonging to a species or genus or family or order or class or phylum were not considered while pre- computing the 'probable 16S rDNA clusters' (section B in methods). These six different simulated scenarios were referred to as 'NEW SPECIES', 'NEW GENUS', 'NEW FAMILY', 'NEW ORDER', 'NEW CLASS' and 'NEW PHYLUM', respectively. Subsequently, these left out sequences (which now simulate sequences from an unknown species or genus or family or order or class or phylum respectively) were tested against the respective modified set of 'probable 16S rDNA clusters'. These tests were also carried out using four sequence lengths  that mimicked sequences obtained using the four commonly used sequencing technologies mentioned above.Table in silico approach suggested in this paper has the potential to completely bypass the first phase of experimentation, thereby eliminating the costs associated with this phase.A typical metagenomic analysis comprises of two major phases of experimentation. The first phase involves isolation, amplification and sequencing of the rDNA content of the environmental sample to obtain estimates of taxonomic diversity. In the second phase, the entire genomic content of the environmental sample is sequenced and analyzed. Both phases therefore involve costs with respect to time, money and manpower for carrying out experimentation and analysis. Given the ability of the approach  to accurately and rapidly identify rDNA fragments directly from the sequenced genomic content (obtained in the second phase), the It should be noted that conservation of oligonucleotide usage patterns within rDNA genes forms the premise of the i-rDNA algorithm. Consequently, the subset of sequences predicted by the i-rDNA algorithm as probable rDNA fragments is a mix of true 16S rDNA fragments and other genomic fragments (from non-rDNA gene regions) sharing an oligonucleotide composition similar to the rDNA genes. However, the size of the predicted subset (by i-rDNA) as a proportion of the total data set is observed to be around 10-15%. This indicates that the oligonucleotide composition of approximately 10-15% of genomic regions in prokaryotic organisms resembles that of the 16S rDNA gene. This region is probably well conserved and would be interesting for further analysis. However it should be noted that the subsequent use of the meta-rna program of this predicted subset helps in identifying the true set of rDNA fragments in the given data set. As demonstrated in this paper, using the meta-rna program on the subset of the sequences (predicted by i-rDNA), rather than the whole data set results in significant saving of time with minimal loss in detection sensitivity.Furthermore, given the premise of the i-rDNA algorithm is based on conservation of oligonucleotide usage patterns, the i-rDNA approach can, in principle, be applied for the identification of any phylogenetic marker gene exhibiting universal sequence conservation at nucleotide level. The current study involving 16S rDNA sequences (as marker genes) can be taken as a proof of concept for the above assertion. However, the following aspects need to be considered before extending the i-rDNA approach to other phylogenetic marker genes. First, the marker gene should be conserved across all phylogenetic clades. Second, sequences belonging to the marker gene should also be available in sufficient numbers. This is important given that the training process (in the i-rDNA approach) involves identification of a set of ‘probable marker gene clusters’. The higher the number of marker gene sequences available, the more robust is the training process.The initial step in the i-rDNA approach involves (a one-time) pre-clustering of genomics fragments generated from completely sequenced microbial genomes. In the present study, the time taken for performing this initial step was only about 3 hours on a simple desktop . This includes the time taken for (1) generating all the vectors (2) the actual clustering step, as well as, for (3) obtaining the vectors corresponding to the final cluster centroids. However, it should be noted that the initial clustering step (as well as the steps followed by i-rDNA for identifying a set of ‘probable rDNA clusters’) is a 'one-time' process. The actual run-time steps of the i-rDNA approach do not involve a repetition of the initial clustering steps.Mycobacterium sp. 1764, was observed to be highly distinct as compared to 16S rDNA sequences of other species belonging to genus Mycobacterium. The resulting tree  for these sequences is shown in Figure Mycobacterium sp. 1764) is observed to be placed at a relatively higher distance from the other 16S rDNA sequences belonging to genus Mycobacterium. Given that the meta-RNA program uses universal HMM models (one each for Bacteria and Archaea) for predicting 16S rDNA fragments, it is expected that the meta-RNA program will miss such 16S rDNA sequences having deviant sequence architectures.The final detection of 16S rDNA fragments within the i-rDNA approach is dependent on the detection sensitivity of the downstream meta-RNA program. Results in the original published paper  of the mThe paper presents an algorithmic approach that can rapidly identify probable 16S rDNA sequences from metagenomic sequence data sets typically constituted of millions of sequences. The detection sensitivity of this algorithmic approach has been validated using simulated sequence data sets generated using four different sequencing technologies (with sequence lengths ranging from 100 bp to 800 bp). Validation results, even using simulated metagenomic data sets (wherein 16S rDNA sequences originate from entirely new taxonomic clades), indicate high detection sensitivity by the approach discussed in this paper. Furthermore, the i-rDNA algorithm is able to process a million metagenomic sequences in less than an hour.In addition to the speed of execution and the high detection sensitivity, the utility of the approach discussed in this paper would be immense since it would bypass the entire experimental step of primer-based rDNA amplification, cloning and sequencing. This would result in drastically reducing the cost, time and human efforts invested in all metagenomic projects.http://ncbi.nlm.nih.gov/). Each genome was split into 1,000 base pair fragments. Frequencies of all possible tetra-nucleotides were computed for each fragment and stored as 256 dimensional vectors. Using k-means clustering approach [DNA sequences from microbial genomes can be clustered based on compositional characteristics such as oligonucleotide usage patterns. For this purpose, fna files (which contain entire genome sequences) corresponding to 237 completely sequenced microbial genomes (one representative from each genera) were downloaded from NCBI  clustering genomic fragments and (B) identification of ‘probable rDNA clusters’ is provided in Figure There are two run-time values needed by the i-rDNA algorithm. First is the optimal number of closest clusters to be identified for a given query sequence. Second is a value for 'overlap percentage' between the closest clusters and the pre-tagged 'probable 16S rDNA clusters'. The objective was to find the right combination of these values to ensure that the i-rDNA algorithm achieves highest detection sensitivity in the least possible time. For this purpose, 55 genomes  was varied by progressively increasing the value of an encoded parameter, which we termed as 'cumulative sequence count'. For a given query sequence, the value of cumulative sequence count is obtained by progressively adding the number of sequences in the identified closest clusters . Therefore, the number of closest clusters identified for a given query sequence increases with increasing value of 'cumulative sequence count'. The i-rDNA algorithm was applied on the four training data sets using varying values of (a) 'cumulative sequence count' (ranging from 20K to 80K sequences) and (b) overlap percentage (ranging between 20% to 80%). For each combination of parameter values, two results (A and B) were noted down. While value 'A' represented the percentage of training data set sequences reported by i-rDNA as 'probable' 16S rDNA fragments, value 'B' represented the percentage of 'true' 16S rDNA fragments found within the subset of sequences reported by i-rDNA as 'probable' 16S rDNA fragment. The idea was to find the right combination of A and B to ensure that the i-rDNA algorithm achieves highest detection sensitivity in the least possible time. In an ideal scenario, the value of A should be the lowest possible, and the value of B should be the highest possible. A low 'A' value will result in only a small subset of query sequences being redirected to meta-rna program for further analysis. At the same time, the right subset of query sequences should also be picked by the i-rDNA program to ensure minimal loss in detection sensitivity .Results of the above experiments are summarized in Additional File identification of ribosomal DNA; MetaSim: Metagenomic sequence Simulator.i-rDNA: The authors declare that they have no competing interests.MMH, TSG and SSM have conceived the idea and designed the detailed methodology. SC implemented the algorithm. MMH and SC created validation data sets, carried out detailed validation and testing of the algorithm. MMH, TSG and SSM have analyzed the data and finally drafted the complete paper.List of the 112 organisms constituting the simLC, simMC and the simHC data sets along with their representation status with respect to the 55 genomes of training data set.Click here for fileLlist of of 55 organisms used for generating four different training data sets corresponding to the sequence lengths of Sanger, 454-Titanium, 454-Standard, 454-GS20 sequencing technologies.Click here for fileThe training results obtained using various combinations of 'cumulative sequence count'  and 'overlap percentage' . The four tables 3A-D (in this file) show results obtained with Sanger, 454-400, 454-250 and 454-100 training data sets respectively.Click here for fileSummary of the methodology used for computing the true positive/false positive rates and the corresponding analysis on the generated ROC curvesClick here for file


Arabidopsis thaliana. We compare these models against first-order HMMs and other existing methods using a reference of known deletions and sequence deviations. We find that parsimonious higher-order HMMs clearly improve the identification of these polymorphisms. Moreover, we perform a functional analysis of identified polymorphisms revealing novel details of genomic differences between C24 and Col-0. Additional model evaluations are done on widely considered Array-CGH data of human cell lines indicating that parsimonious HMMs are also well-suited for the analysis of non-plant specific data. All these results indicate that parsimonious higher-order HMMs are useful for Array-CGH analyses. An implementation of parsimonious higher-order HMMs is available as part of the open source Java library Jstacs (www.jstacs.de/index.php/PHHMM).Array-based comparative genomic hybridization (Array-CGH) is an important technology in molecular biology for the detection of DNA copy number polymorphisms between closely related genomes. Hidden Markov Models (HMMs) are popular tools for the analysis of Array-CGH data, but current methods are only based on first-order HMMs having constrained abilities to model spatial dependencies between measurements of closely adjacent chromosomal regions. Here, we develop parsimonious higher-order HMMs enabling the interpolation between a mixture model ignoring spatial dependencies and a higher-order HMM exhaustively modeling spatial dependencies. We apply parsimonious higher-order HMMs to the analysis of Array-CGH data of the accessions C24 and Col-0 of the model plant  Arabidopsis thaliana, we find that parsimonious higher-order HMMs clearly improve the identification of copy number polymorphisms in comparison to standard first-order HMMs and other frequently used methods. Functional analysis of identified polymorphisms revealed details of genomic differences between the accessions C24 and Col-0 of Arabidopsis thaliana. An additional study on human cell lines further indicates that parsimonious HMMs are well-suited for the analysis of Array-CGH data.Array-based comparative genomics is a standard approach for the identification of DNA copy number polymorphisms between closely related genomes. The huge amounts of data produced by these experiments require efficient and accurate bioinformatics tools for the identification of copy number polymorphisms. Hidden Markov Models (HMMs) are frequently used for analyzing such data sets, but current models are based on first-order HMMs only having limited capabilities to model spatial dependencies between measurements of closely adjacent chromosomal regions. We develop parsimonious higher-order HMMs enabling the interpolation between a mixture model ignoring spatial dependencies and a higher-order HMM exhaustively modeling these dependencies to overcome this limitation. In an in-depth case study with  Arabidopsis thalianaIn recent years, the method of array-based comparative genomic hybridization (Array-CGH) Over the last years, a large number of different methods for the identification of copy number polymorphisms from Array-CGH data have been developed including approaches based on Gaussian mixture models Despite these different methods, the identification of copy number polymorphisms by methods based on Hidden Markov Models (HMMs) is very popular A characteristic of all these HMMs is that they are based on the mathematical theory of standard first-order HMMs In contrast to the broad usage of first-order HMMs in applied sciences To enable the usage of improved modeling characteristics of greater model orders by simultaneously overcoming the exponential increase of transition parameters, a fast incremental training has been developed in the domain of speech recognition Here, we develop the novel model class of parsimonious higher-order HMMs enabling the interpolation between a mixture model ignoring spatial dependencies and a higher-order HMM exhaustively modeling spatial dependencies between measurements of closely adjacent chromosomal regions. This interpolation is realized by incorporating a dynamic programming approach Arabidopsis thaliana, we apply parsimonious higher-order HMMs to compare the genomes of the accessions C24 and Col-0 based on a publicly available Array-CGH data set. This enables the identification of DNA polymorphisms  in C24 with respect to the reference genome of Col-0 In an in-depth case study with the model plant In the materials part of this section, the Arabidopsis Array-CGH data set comparing the genomes of C24 and Col-0 is introduced and candidate regions of deletions or sequence deviations for model evaluation determined by an independent public resequencing experiment are considered. The model class of parsimonious higher-order HMMs is developed in the methods part of this section.In this section, the Arabidopsis Array-CGH data set is introduced and candidate regions of deletions or sequence deviations for model evaluation identified in resequencing data are considered.A. thaliana is used to investigate the identification of DNA polymorphisms  by different methods. This data set was measured on a NimbleGen tiling array representing the five chromosomes of the Col-0 reference genome An Array-CGH data set by The measurement of tile Spatial dependencies between log-ratios on chromosomes are characterized in The distribution of log-ratios in the Array-CGH data set is shown in An array-based Affymetrix resequencing experiment of C24 was performed in We used the determined candidate regions of deletions or sequence deviations from the resequencing experiment to identify each tile in the Array-CGH data set for which at least 75% of its nucleotides  that does not model spatial dependencies between log-ratios in Array-CGH profiles. Complete trees are underlying a higher-order HMM exhaustively modeling spatial dependencies. Parsimonious trees provide the basis for a parsimonious higher-order HMM interpolating between a mixture model and a higher-order HMM. This interpolation poses the problem of selecting optimal state-context trees for an HMM. For a fixed set of states, the number of different state-context trees grows super-exponentially for increasing model order  does not model dependencies between log-ratios in any chromosomal distance. The first-order HMM shows a clear improvement in comparison to the mixture model, but especially HMMs of order three up to five reached the best, nearly identical approximation of the partial autocorrelation function of Array-CGH profiles. A better modeling of the partial autocorrelation function by higher-order HMMs is expected from theory because of their more complex state-transition processes enabling an improved modeling of spatial dependencies compared to HMMs with a smaller model order. Still, none of these HMMs was able to perfectly approximate the partial autocorrelation structure of the Array-CGH profiles. But, despite of that, this study helped to determine a range of model orders for further analyses. The results of this study are summarized in Figure S3 in Based on this initial study with higher-order HMMs, parsimonious HMMs of order one up to five are subsequently investigated in detailed studies to analyze their abilities to identify DNA polymorphisms between C24 and Col-0.A. thaliana is used to identify polymorphic regions between both genomes by parsimonious higher-order HMMs. These models are evaluated based on deletions or sequence deviations determined in An Array-CGH data set by Parsimonious higher-order HMMs of different model complexities were adapted to the Array-CGH data using the developed Bayesian Baum-Welch training. For each model, all chromosomal regions in the Array-CGH data set were ranked in decreasing order of their state-posterior probabilities of state ‘Similar results are shown in Figure S6a in Parsimonious higher-order HMMs have initially been compared against the standard first-order HMM and higher-order HMMs at a stringent FPR of 1%. Next, these models are compared at a less stringent FPR of 2.5%. That leads to an identification of deletions or sequence deviations comparable with those obtained by applying the state-posterior decoding algorithm Generally, parsimonious higher-order HMMs reach a higher accuracy for the identification of deletions or sequence deviations than the standard first-order HMM. The best parsimonious higher-order HMMs also reach an accuracy that is comparable or slightly better than that of corresponding higher-order HMMs. This accuracy is obtained at much lower model complexities than for higher-order HMMs. That can become particularly useful for avoiding overfitting in small data.In comparison to the results at 1% FPR, the complexity of the best models is more shifted into the range of 9 to 27 leaves at 2.5% FPR . This inA tree structure of one of the best models is shown in Generally, similar tendencies like shown in A. thaliana.Here, the well-performing parsimonious fourth-order HMM is compared against other existing methods on the Arabidopsis data set. Then, another widely considered human cell lines data set by Next, the parsimonious fourth-order HMM with underlying tree structure shown in The identification of deletions or sequence deviations by these methods is compared against the predictions of the parsimonious fourth-order HMM with respect to the known potential deletions or sequence deviations characterized in The ROC curves are shown in Comparing the different HMM-based methods by the number of hidden states required for modeling chromosomal aberrations in the Arabidopsis data set, wuHMM and FHMM both determined seven states, RJaCGH used six, and GHMM and the parsimonious HMM required only three states for reaching the reported performance. Supported by the best identification of deletions or sequence deviations, this indicates that the three states of the parsimonious HMM are flexible enough for modeling complex Arabidopsis Array-CGH profiles.The two good-performing Bayesian HMMs RJaCGH and GHMM had substantially different run-times. RJaCGH required 30 hours and 42 minutes for analyzing the Arabidopsis data set, while GHMM only required about 24 minutes. An overview of run-times of all methods is given in In summary, this study further illustrated that parsimonious higher-order HMMs can outperform existing methods and are well-suited for analyzing Arabidopsis Array-CGH data. It should also be noted that experts of specific methods might be able to improve the results of individual methods by fine-tuning of specific parameters. Still, parsimonious higher-order HMMs represent an important contribution to the field of Array-CGH data analysis because they combine improved modeling of spatial dependencies with the integration of prior knowledge and because these models have reached a good performance on the Arabidopsis Array-CGH data.Additional model evaluations were also done on Array-CGH data of human cell lines Considering the run-times on the human data set with about 17.5 times less measurements than in the Arabidopsis data set, RJaCGH required the longest time with about seventy minutes. Both, the GHMM and the parsimonious first-order HMM required only about one minute for analyzing the human cell lines. A summary of run-times of the ten different tested methods is given in The genome annotation of the reference genome of Col-0 provides the opportunity to investigate what is functionally behind chromosomal regions where the genomes of C24 and Col-0 differ. The parsimonious fourth-order HMM with underlying parsimonious tree structure in In total, about 4.7%  of all chromosomal regions of the reference genome of Col-0 were identified as being affected by deletions or sequence deviations in the genome of C24, and about 0.2%  of all chromosomal regions were identified as amplified in C24 . This asArabidopsis Information Resource (TAIR8) genome annotation of Col-0 Chromosomal regions identified as being affected by deletions or sequence deviations and regions identified as being affected by amplifications were analyzed separately using the A. thalianaBy definition, the TAIR8 categories are not completely disjoint meaning that each chromosomal region can have annotations in more than one category . Comparisons of the identified polymorphic regions in C24 to randomly chosen control sets revealed that a significant proportion of chromosomal regions affected by deletions or sequence deviations and also that regions affected by amplifications are caused by transposons. Such mobile genomic elements were also identified to be involved in rearrangements of the genomes of other accessions of Thus, genomic differences between C24 and Col-0 do not occur randomly because transposons differ more than other parts of the genome. These results are also supported by the finding that transposons change faster than genes Ontology classification was performed for genes affected by amplifications and for genes affected by deletions or sequence deviations using the MIPS Functional Catalogue No prevalence of any functional category was found for the 39 genes affected by amplifications. In contrast to this, among the 1,675 genes affected by deletions or sequence deviations, five significantly over-represented functional clusters of genes with p-values less than 5A. thalianaA. thaliana grown under different temperature conditions In coincidence with these findings, over-representations of sequence polymorphisms in defense-related genes or genes involved in signaling were previously identified in different accessions of In summary, the five identified gene clusters with increased rate of deletions or sequence deviations indicate a rapid evolutionary change between C24 and Col-0. All genes affected by deletions or sequence deviations are provided in A superfamily classification of transposons affected by deletions or sequence deviations and of transposons affected by amplifications was performed using the TAIR8 transposon annotation of Col-0 to identify under- or over-representations of specific transposon superfamilies in C24. This analysis was done in comparison to randomly sampled control sets of transposons. The results are summarized in Retrotransposons  moving by a RNA-mediated copy-and-paste mechanism and DNA transposons  moving by a DNA-mediated cut-and-paste mechanism are significantly over-represented among the 2,695 transposons identified as being affected by deletions or sequence deviations in C24 with respect to the reference genome of Col-0. DNA transposons  are significantly under-represented among the 2,695 affected transposons.For transposons affected by amplifications, retrotransposons (LTR/Gypsy) and DNA transposons (DNA/En-Spm) are significantly over-represented among the 114 transposons identified as being affected by amplifications in C24. DNA transposons  are significantly under-represented among these 114 transposons.A. thaliana genomes contributing to size, structure, and variation of genomes Thus, these results indicate that some transposon superfamilies tend to play a more prevalent role for driving the evolution of genomic differences between C24 and Col-0. All these transposons represent fundamental components of The development of parsimonious higher-order HMMs for the analysis of Array-CGH data has been motivated by the observation of strong spatial dependencies between measurements in close chromosomal proximity. A parsimonious higher-order HMM represents an interpolation between a mixture model ignoring spatial dependencies and a higher-order HMM exhaustively modeling spatial dependencies. To enable this interpolation, the mathematical theory of widely used first-order HMMs has been extended. A central point is the extension of the Bayesian Baum-Welch training by incorporating a dynamic programming approach Arabidopsis thaliana accessions C24 and Col-0, parsimonious higher-order HMMs clearly improved the identification of deletions or sequence deviations in comparison to typically used first-order HMMs and other existing methods. Especially, parsimonious HMMs of order three up to five with clearly reduced model complexities in comparison to corresponding higher-order HMMs reached the best results.In a detailed study based on Array-CGH data for comparing the genomes of the In-depth functional analyses of identified DNA polymorphisms revealed that most of these genomic differences between C24 and Col-0 are caused by transposons. Genic regions as well as 5′ and 3′ untranslated regions are less affected, but still genes with functions in ATP-binding, cellular signaling, or cell pathogen defense have been found to be specifically affected by deletions or sequence deviations in C24 in comparison to the reference genome of Col-0. These findings are in accordance with other studies Additional model evaluations performed on widely considered human cell lines showed that parsimonious HMMs are also well-suited for the analysis of non-plant-specific Array-CGH data sets.All these results indicate that parsimonious higher-order HMMs are useful tools for the analysis of Array-CGH data. Potential future applications could include other domains in which standard first-order HMMs are frequently used. This might include the HMM -based analysis of ChIP-chip data Table S1Arabidopsis Array-CGH data set including detected DNA polymorphisms identified by the parsimonious fourth-order HMM using the state-posterior decoding algorithm.(TXT)Click here for additional data file.Table S2Genes affected by deletions or sequence deviations in C24.(TXT)Click here for additional data file.Table S3Genes affected by amplifications in C24.(TXT)Click here for additional data file.Table S4Transposons affected by deletions or sequence deviations in C24.(TXT)Click here for additional data file.Table S5Transposons affected by amplifications in C24.(TXT)Click here for additional data file.Text S1Mathematical basics of prior distributions for initial state and emission parameters and details to chosen prior parameters are given in the section ‘Prior distribution’. A detailed derivation of the Bayesian Baum-Welch algorithm for a parsimonious higher-order HMM is given in the section ‘Bayesian Baum-Welch algorithm’. Details to the case study on human cell lines are given in the section ‘Model evaluations on human cell lines’. The supporting Figures S1, S2, S3, S4, S5, S6, S7, S8, S9 are provided in the section ‘Supporting Figures’.(PDF)Click here for additional data file.


The right ventricular (RV) morphology and function are important parameters for the diagnosis of cardiopulmonary diseases and serve as predictors for the long-term outcome. To date there are little data about right heart characteristics due to its complex anatomy. Since cardiac magnetic resonance (CMR) is the gold-standard for the evaluation of left and right cardiac anatomy and function we sought to investigate and provide reference values for the entire right heart acquired in a large population of healthy volunteers.We studied a group 119 healthy volunteers  which consisted of three age groups of nearly equal size . CMR images were acquired on a 1.5 T whole body MRI applying a standard cine SSFP sequence. Right atrial (RA) function and morphology as well as RV sizes were assessed in a 4-chamber view. For the measurement of the RV volumes and function short axis views were used. End-diastolic and end-systolic volumes and diameters , RV stroke volume and ejection fraction  were measured. As the emptying of the RA is a combination of active and passive mechanisms we tried to quantify the contribution of the contraction by measuring the RA contractive stroke volume (RA CSV) and ejection fraction (RA CEF). Furthermore the RV end-diastolic and end-systolic length  and the RA end-diastolic and end-systolic area  were quantified. Body surface area (BSA) was calculated using the Mosteller formula. A p-value <0.05 was regarded as statistically significant.The reference values are shown in figure In this study we provide age- and gender specific reference values for the right heart morphology and function in a large group of healthy volunteers applying a standard clinical SSFP sequence. These values may contribute to define a reference frame to detect pathological alterations. Yet, further studies are needed confirm our results and to define cut-off values applicable for clinical routine.none


Improving performance in motor skill acquisition is proposed to be supported by tuning of neural networks. To address this issue we investigated changes of phase-amplitude cross-frequency coupling (paCFC) in neuronal networks during motor performance improvement. We recorded intracranially from subdural electrodes  from 6 patients who learned 3 distinct motor tasks requiring coordination of finger movements with an external cue . Performance improved in all subjects and all tasks during the first block and plateaued in subsequent blocks. Performance improvement was paralled by increasing neural changes in the trial-to-trial paCFC between theta ( Phase-amplitude cross-frequency coupling (paCFC) of oscillations in different frequency bands has been proposed as an effective mechanism to form functional networks that recruit local neuronal populations across a global spatial scale A stronger link between paCFC and behavior in humans would be supported by a correlation between paCFC and trial-by-trial variations in performance. To address this, we examined the relation between paCFC and motor performance improvement. We recorded the electrocorticogram (ECoG) in human patients  undergoing epilepsy diagnosis while they learned skilled motor behaviors. To assess the link between paCFC and behavior we compared changes in paCFC to changes in performance over an extended time scale during motor skill acquisition, and correlated performance and paCFC at the single trial level. We show that paCFC in intracranial subdural recordings between We investigated potential links between paCFC and motor performance in six subjects each performing one of three repetitive motor tasks described next. The three different behavioral tasks  were a snals see . We calcWe first asked whether the amplitude envelope of the local HG oscillations is phase coupled to the local ct 1 see . The freerror  =  (Nsubj -1)*Ntrialbin). The next question was whether TPR exhibits the same behavior (To investigate if cross frequency coupling (paCFC) covaries with motor performance variations, we first calculated the trough to peak ratio  to test for this correlation and (ii) to disentangle cortical regions showing varying paCFC with performance, we pooled the data in six anterior and frontal regions of interest (ROIs); the anterior and posterior medial frontal gyrus, the anterior and the posterior inferior frontal gyrus, and the superior and inferior sensorimotor cortex see  for all To disentangle these two potential and functionally distinct causes of paCFC-performance covariations and disentangle the spatially wide ROIs we performed two different correlation analyses separately for each recording electrode. First, we calculated the partial correlation of TPR with performance. Second, we calculated the standard Pearson correlation between TPR and motor performance. Pearson correlation captured the performance improvement related plus the performance improvement unrelated correlation. Combined with partial correlation this was used to distinguish between the two effects. Electrodes that capture performance improvement related TPR with performance correlations should show a partial correlation different from zero and a Pearson correlation different from the partial correlation see . Importament see .Phase-amplitude cross-frequency coupling has been proposed to support interaction within functional networks During motor performance improvement Oscillatory dynamics are proposed We identified cross-frequency coupling in the human cortex which is associated with motor performance variability per se. In this network a smaller area is integrated whose oscillatory dynamics reflect the progress in performance improvement. This learning related network suggests the establishment of a memory trace which is accumulated during practice and which is represented in a mutually adapted level of activity of Six epilepsy patients undergoing pre-surgical monitoring with subdural electrodes participated in the experiments after providing their written informed consent. Experimental and clinical recordings were taken in parallel. Recordings took place at the University of California San Francisco (UCSF), CA, USA (4 Patients), Johns Hopkins University, Baltimore, USA (1 Patient) and the Epilepsy Center Bethel (ECB), Bielefeld (1 Patient), Germany and were approved by the local ethics committees .We carried out three different motor tasks  with six different patients . Each paThe serial reaction task (SRT) consisted of a series of visually cued finger taps. The subjects had their fingers placed on different keys of a laptop keyboard . Trials started with one of the numbers 1, 2, 3, or 5 appearing on a laptop-screen cueing the movement of thumb, index finger, middle finger, or little finger, respectively. Numbers were presented until a key was pressed but maximally for 2 seconds. In each subject the four numbers were presented in a fixed sequence (six items long) or random order depending of the block number but only fixed blocks were used. Each block took approximately 10 minutes. Two patients participated in this task (SRT01 - 02). They were instructed to press keys as fast and accurate as possible. One block took approximately six minutes.The second motor-paradigm was an auditory-motor coordination task (AMCT). One patient participated and was instructed to respond as accurately as possible halfway between successive auditory clicks presented at a constant rate. Seven click sequences, each 60 s long, were presented in a block. The inter-click-interval in a sequence was either 500 ms, 1000 ms or 2000 ms and the participant was informed that the interclick-interval changes. The seven click sequences with the differing interclick-interval were presented randomly within each block. Only sequences with a interclick-interval of 1000 and 2000 ms entered the analysis. The clicks were presented with speakers plugged into the laptop's sound card and placed in front of the patient at 1 m distance. One block lasted 7 min.Three patients participated in the go/no-go (GNG) task (GNG01 - 03). In each trial the patients were presented with either a green or red square of 100 ms duration and 900 - 1200 ms stimulus onset asynchrony. The subjects were instructed to respond as quickly as possible to green squares by pressing a key on a laptop keyboard but to withhold responses when a red square was presented . The participants were familiarized with the task in an initial short practice session. Only correct Go-trials of the first two blocks entered the analysis. Each block lasted approximately eight minutes.www.psychtoolbox.org). In Bielefeld (AMCT) the ECoG signal was recorded at 1000 Hz sampling frequency (16 Bit resolution) with a Nihon Kohden system  equipped with auxiliary analogue channels for synchronous recording of the trigger signals and the output from the sound card. Here 5 stripes were implanted each equipped with two parallel rows of 5 electrodes each (see At UCSF the electrocorticogram (ECoG) was recorded either from 64 platin-iridium-electrode grids arranged in an 8×8 array with 10 mm center-to-center spacing  or from a 256 electrode grid  arranged in a 16×16 array with 4 mm center-to-center spacing (GNG02). Exposed electrode diameter was 2.3 mm in the 64 electrodes grid and 1.8 mm in the 256 electrodes grid. The electrode signals were recorded with a 256 channel preamplifier , Inc) with the electrode furthest from the motor cortex used as a reference. The data from the pre-amplifier were sampled at 3051.7 Hz on a digital signal processor , Inc) with 16-bit resolution and stored to hard disk. Trigger signals indicating button presses and stimulus onsets were sent from the stimulus laptop via a USB-1208FS DAQ  plus a photodiode attached to the screen and recorded on the DSP synchronized to the brain data. Trigger timing was additionally recorded on the stimulus laptop by querying the computers performance counter using the Psychophysics Toolbox  for all offline data processing. We first preprocessed the recorded brain data and then we derived measures quantifying adaptation of oscillatory neural dynamics during motor skill learning. All filtering was done using IIR filters (Butterworth filter of order 4). Preprocessing served to remove non-physiological artifacts from the recorded data and to prepare them for further analysis. First we excluded channels exhibiting ictal activity or excessive noise from further analysis. In the remaining good channels we then excluded time intervals containing artifactual signal distortions such as steps of pulses by visual inspection. Finally, we re-referenced the remaining electrode time-series by subtracting the common average referenceFor each trial starting at stimulus onset we calculated the TPR to quantify the evolution of phase-amplitude cross-frequency interactions of cortical oscillations during motor skill learning. Therefore, we band-pass filtered each electrode's time series at two frequency bands, in the us onset . We obtaTaking the log of the ratio makes the distribution of TPRs symmetric. Note that the TPR includes both stimulus-locked and non-stimulus-locked brain activity. It summarizes the global cross frequency interaction on the grids.paCFC was tested by splitting the In each patient we grouped electrodes according to the same anatomical landmarks in 6 regions of interest see : The antWe separated performance from learning effects by applying a permutation test statistic. The reasoning for applying a permutation test was two-fold. First, we sought to correct the p-values for each electrode due to the many individual correlation tests applied. We tested this against a distribution which did not rely on the same temporal interval (500 ms following the stimulus presentation) for which the correlation coefficient was calculated. Second, we wanted to identify electrodes in which Pearsons correlation coefficient r was significantly higher than the partial correlation coefficient Appendix S1Supplementary Material.(PDF)Click here for additional data file.Figure S1Prediction of behavior changes as a function of the phase. Red and black asterisks show the number of signicant electrodes for each of the 20 phase bins for the performance/paCFC correlation and partial correlation, respectively.(TIF)Click here for additional data file.Figure S2We grouped electrodes into 6 regions of interest.Each outline denotes the grid coverage of one subject. The bold outline shows the summed coverage across all subjects. The anterior and posterior medial frontal gyrus , the anterior and the posterior inferior frontal gyrus , and the superior and inferior sensorimotor cortex . The outline of the grid location of the AMCT participant is given in (TIF)Click here for additional data file.


Aaron Kesselheim discusses Natalie Vernaz and colleagues' paper on the effects on health care costs of “evergreening” strategies pursued by drug manufacturers.Please see later in the article for the Editors' Summary PLOS Medicine:This Perspective discusses the following new study published in doi:10.1371/journal.pmed.1001460Vernaz N, Haller G, Girardin F, Huttner B, Combescure C, et al. (2013) Patented Drug Extension Strategies on Healthcare Spending: A Cost-Evaluation Analysis. PLoS Med 10(6): e1001460. In a cost-evaluation analysis of pharmacy invoice data in one Canton in Switzerland Nathalie Vernaz and colleagues find that “evergreening” strategies pursued by drug manufacturers have been successful in maintaining market share and contribute to increased overall healthcare costs.PLOS Medicine, Nathalie Vernaz and colleagues study the consequences of this delicate balancing act going awry due to the cumulative effects of so-called life-cycle management (or “evergreening”) strategies employed by the drug's manufacturer The problem of rising prescription drug costs has emerged as a critical policy issue around the world, particularly in the United States. These costs strain the budgets of patients and health insurers, and directly contribute to adverse health outcomes by reducing adherence to important medications. Drug spending is driven by brand-name drugs, which currently account for about 20% of all prescriptions in the US, but 80% of costs. Brand-name drugs generally are granted periods of market exclusivity during which they charge high prices to account for the initial investment in research and development. In the case of small molecule drugs, once the market exclusivity period ends, competitors producing bioequivalent generic drugs can enter the market, and the drug price quickly falls. In this week's The term “life-cycle management” refers to the practice of brand-name manufacturers seeking to further extend the market exclusivity periods for their drugs to maintain revenue streams. Market exclusivity extensions may be achieved through a number of different strategies. Some evergreening strategies offer scant public health benefits, including slight changes in formulation protected by later-issued patents Life-cycle management may contribute to rising costs at a time when government insurance programs are cutting back on important areas of medical coverage, but their impact on costs or health care delivery is not often subject to empirical analysis. In a 2006 publication, my colleagues and I studied three brand-name pharmaceutical products  whose market exclusivities were extended through tactics such as lawsuits against generic competitors and patents on peripheral aspects of products. We identified US$1.5 billion in revenue that Medicaid, the US drug insurance program for low-income patients, could have saved if generic alternatives to these three medications had been available and widely used when the patent on the active ingredient expired Research that assesses the implications of life-cycle management in the pharmaceutical market is rare, and the practice continues as health care costs continue to accelerate, which make the analysis by Nathalie Vernaz and colleagues particularly timely. Studying prescription rates in the Swiss canton of Geneva, the authors focused on eight drugs that demonstrate a wide array of evergreening strategies, including single-isomer versions marketed in place of enantiomeric drugs , combination products marketed in place of unitary products , and slow-release formulations . Among all of these examples, the follow-on product had no proven clinical advantage. Vernaz et al. found that the canton, which represents about 5% of Switzerland's total population, would have saved over 30 million Euros between 2001 and 2008 if physicians had avoided the high-cost follow-on products and instead prescribed generic versions of the original products.In their analysis, Vernaz and colleagues also provide some empirical insight into two possible anti-evergreening tactics. First, they studied the effect on community-based prescribing of a restrictive drug formulary imposed by the region's large academic medical center. They found that requiring prescription of a generic drug (enantiomeric cetirizine) over the follow-on product (single-isomer levocetirizine) in the hospital had “spill-over” effects in encouraging prescribing of the lower-cost product by community physicians, although the policy only saved the health system a small amount. By contrast, listing the follow-on product (single-isomer esomeprazole) over the generic precursor (enantiomeric omeprazole) in the restrictive formulary led to substantial extra costs through the same spill-over mechanism.Second, their analysis covers a country-wide policy enacted in 2006 to slightly increase out-of-pocket expenses for patients selecting brand-name drugs over generic alternatives. Though they did not specifically test changes in prescription rates before and after this policy change, their results show the limited overall impact that the policy had in stemming prescribing of the second-generation products in their sample.Given the modest effects of restrictive formularies and co-payment changes in combating excessive spending related to drug life-cycle management, what other possible solutions could reduce the negative effects of these tactics on health care systems? Vernaz and colleagues suggest greater investment in comparative efficacy testing of follow-on products to better inform physicians and patients about the benefits—or lack thereof—of higher-cost follow-on products like levocetirizine or simvastatin/ezetimibe. Another more fundamental option would be to treat second-generation products differently under patent statutes or other laws conferring market exclusivity such that less innovative follow-on products earn market exclusivity periods shorter than that of the original products. This pathway was recently upheld in India, where drug patent laws require drugs to demonstrate advances in clinical efficacy, and not merely minor physiochemical differences, to earn market exclusivity With rigorously collected and analyzed data, the study by Vernaz and colleagues highlights an area of wasteful spending in the health care market. While their manuscript did not directly address patient outcomes, their results suggest that addressing life-cycle management through rational regulatory oversight or alterations in patent or market exclusivity laws will be an important way that policymakers can achieve cost savings without adversely affecting public health.