This work was supported by Grant No. P50NS071675 (Morris K. Udall Center of Excellence in Parkinson's Disease Research at The Feinstein Institute for Medical Research) to D.E. from the National Institute of Neurological Disorders and Stroke. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Neurological Disorders and Stroke or the National Institutes of Health. The sponsor did not play a role in study design, collection, analysis and interpretation of data, writing of the report or in the decision to submit the paper for publication.

1. Data Collection and Preprocessing
<ol>
 <li>The SSM/PCA method can be applied to single volume images obtained from various sources and modalities. Specifically, for on-site PET imaging of metabolism, prepare a suitable radionuclide tracer such as [18F]-fluorodeoxyglucose (FDG) and administer to each patient. Patients are usually scanned at rest with eyes open, following a fast of at least 12 hr, off medications.</li>
 <li>Scan each subject for individual or group assessment. For pattern derivation, scan an equal number of gender- and age-matched patients and controls.</li>
 <li>Transfer data to a workstation and convert to an appropriate format for analysis in your platform. Our windows PC based MATLAB analysis software (scanvp, ssm_pca, <a href="http://www.feinsteinneuroscience.org" target="_blank">www.feinsteinneuroscience.org</a>) requires ANALYZE or nifti format images (Mayo Clinic, Rochester, MN). It provides a conversion routine to transform GE Advance (Milwaukee, WI, USA) scanner and other format images to ANALYZE format.</li>
 <li>Normalize each subject's image to a common stereotaxic space (e.g. Montreal Neurological Institute [MNI]) using a standard neuroimaging software package such as statistical parametric mapping (SPM) (<a href="http://www.fil.ion.ucl.ac.uk/spm" target="_blank">http://www.fil.ion.ucl.ac.uk/spm</a>) so that there is a one-to-one correspondence of voxel values between subjects (Figure 2). Masking to limit the analysis to gray matter areas (Figure 3) and log transformation are described in the next steps.</li>
</ol>
2. Perform Multivariate SSM/PCA
Operations for multivariate SSM/PCA (Figure 4) can be performed by external software. The steps itemized below reflect the procedures performed for the most part automatically by our in-house routines (Figure 5) (scanvp/ssmpca, <a href="http://www.feinsteinneuroscience.org" target="_blank">www.feinsteinneuroscience.org</a> also available as an SPM toolbox ssm_pca).
<ol>
 <li>Mask data using an available 0/1 image to remove unwanted areas of the voxel space such as white matter and ventricles. Using the ssmpca routine, the user is given an option to select the default or other external mask (Figure 3) or to have the program automatically create a mask by removing values lower than a selected fixed percent of each subject's data maximum. Individual subject masks are then multiplied to determine a composite mask. The areas within the mask define a common non-zero voxel space for the analysis.</li>
 <li>Convert each subject's 3D masked image voxel data to one continuous row vector by appending sequential scan lines from consecutive planes (Figure 4a). Form a group data matrix (D) so that each subject's data corresponds to a specific row of the matrix. Each column then represents a particular voxel across subjects. Ideally, for biomarker derivation, the matrix will be composed of an equal number of rows of normal control subject data and disease subject data.</li>
 <li>Transform each data entry to logarithmic form.</li>
 <li>Center the data matrix by subtracting each row average or subject mean from the row elements. The average of all centered rows represents a characteristic group mean log image termed a group mean profile (GMP, Figure 4a). Subtract the column means that are the elements of the GMP from the corresponding matrix column elements. Each row of the double centered matrix represents a 'residual' image termed a subject residual profile (SRP) whose elements represent deviations from both the subject s and voxel v group means (Figure 4b). 
 SRPsv = logDsv - means - GMPv</li>
 <li>Construct the subject-by-subject covariance matrix C of the composite double centered data matrix by computing the non-normalized covariance between each subject pair i, j of SRP matrix rows evaluated as a product of corresponding voxel elements summed over all voxels v (Figure 4b). 
 Cij =&sum;v (SRPiv x SRPjv)</li>
 <li>Apply PCA to the subject-by-subject covariance matrix C. The results will be a set of subject score eigenvectors with associated eigenvalues. Weight each vector by multiplying by the square root of its corresponding eigenvalue. The set of score eigenvectors is represented by the columns of the resulting matrix S in Figure 4c.</li>
 <li>Voxel eigenvectors for the same set of eigenvalues can be determined by applying PCA to the column by column covariance matrix or by this alternative computationally less demanding procedure depicted in (Figure 4c). Left multiply the previously derived score vector matrix by the transpose SRP matrix to derive an array P of voxel pattern eigenvectors in descending order of eigenvalues (Figure 4c, Figure 6, Figure 7a). 
 P = SRPT x S 
 Each column vector represents a principal component (PC) image pattern of the SSM/PCA analysis attributed to a percent of the total variance accounted for (vaf) corresponding to the relative size of its eigenvalue.</li>
</ol>
3. Pattern Biomarker Derivation
<ol>
 <li>Examine the results of the preceding analysis to determine PC patterns that are associated with high vaf values. Within our routine, voxel pattern vectors are Z-transformed so that their values represent positive and negative standard deviations from their mean value. They are then reshaped into image format prior to display (Figure 7a).</li>
 <li>In some cases patterns with known regional deviations associated with the disease can be visually identified. The mathematical formulation considers positive and negative forms of the derived PCs as equivalent solutions in which case both the PCs and their associated scores may be multiplied by minus one to conform to a physically correct interpretation.</li>
 <li>Scores corresponding to each PC pattern are displayed as bar graphs and scatter plots (Figure 7). An optional ROC plot can be generated (Figure 7d) . To identify a disease-specific pattern, notice the differentiation of subject scores between patients and controls reflected by the p-values of the corresponding two-sample t-tests and AUC values. Limit the analysis to those PCs associated with a high vaf and high differentiation for some fixed cutoff values (e.g., p &lt; 0.05 and vaf &gt; 5%). Typically, only one or two PCs satisfy this criterion for PET data.</li>
 <li>There are various other ways for PC selection that can be considered44. The scree plot of sequential eigenvalues (Figure 6) may give a sharp cutoff value represented by a bend in the curve and a sharp reduction in the slope of the curve where eigenvalues begin to degenerate. Another approach is to include all PCs that account for the top 50% of the variance. A widely accepted procedure is to compute the Akaike Information Criterion (AIC)45 to determine which combination of PCs define the model with the lowest AIC value that can distinguish between patients and controls.</li>
 <li>The selected PC(s) can be vector normalized and linearly combined to yield a single disease-related pattern. Our software optionally uses the MATLAB function glmfit to determine coefficients based on logistic or other regression models applied to subject scores. Although differentiation of patient and control groups usually improves with the additional PCs considered in the derivation group, the resultant patterns are a composite representation that may not correspond to a single physical network or may incorporate outlier deviations (Figures 7a and 7c).</li>
 <li>Further validation is required for reliability and prospective significance. Bootstrap resampling can be performed as discussed below8 to identify the most reliable voxels within the original derivation dataset associated with the least standard deviation in repeated pattern derivation. Forward validation is performed to test for the sensitivity and specificity of independent group discrimination by deriving scores for prospective groups of patients and controls using the single-subject score evaluation method (Figure 4d) described in the next segment of the protocol.</li>
</ol>
4. Single-subject Prospective Score Evaluation using a Predetermined Biomarker
<ol>
 <li>Once a significant SSM-GIS biomarker pattern has been identified, a score for its expression in a prospective subject can be evaluated from that individual's scan using a simple computation of the internal vector product of the subject's SRP vector and the GIS pattern vector (Figure 4d, Figure 7d). 
 SCORE = SRPs &bull; Pattern</li>
 <li>The previous operation is automated by our TPR routine. However, to independently derive the subject SRPs vector use the associated intrinsic GIS mask on the log transformed data and subtract both the subject mean and the corresponding voxel values of the prederived reference group GMP as in step 2.4. This insures that scores can be compared to the predetermined reference range. Scores for a new group can be similarly evaluated as a set of prospective single subject scores. For use with a different reference group or setting, GMP can be recalibrated while the pattern is unchanged.</li>
</ol>

Dr. Eidelberg is listed as co-inventor of two U.S. patents on the use of imaging markers to screen patients for nervous system dysfunction (without financial gain). There are no further disclosures.

The SSM/PCA model originally presented by Moeller et al.4 has evolved1-3 into a straightforward and robust technique for the analysis of neuroimaging data. However, there have been ambiguities in the application of this methodology that we have attempted to clarify here and in previous publications5-7,10. Some of these issues have been addressed in the text but are reemphasized here because of their importance. As detailed in the Introduction, SSM/PCA is primarily effective in re...

Neurodegenerative disorders have been extensively studied using techniques that localize and quantify abnormalities of brain metabolism as well as non-inferential methods that study regional interactions17. Data-driven multivariate analytical strategies such as principal component analysis (PCA)1,2,4,18 and independent component analysis (ICA)19,20, as well as supervised techniques such as partial least squares (PLS)21 and ordinal trends canonical variates analysis (OrT/CVA)22 can reveal characteristic patterns or "networks" of interrelated activity. The basics of multivariate procedures, particularly the scaled subprofile model (SSM)1,2,4-6,18 have been previously described in JoVE3. This PCA-based approach was originally developed to examine abnormal functional covariance relationships between brain regions in steady-state single volume images of cerebral blood flow and metabolism acquired in the resting state of modalities such as PET and SPECT that exhibit high signal-to-noise characteristics. Disease-specific SSM patterns are imaging biomarkers that reflect overall differences in the regional topography in patients compared to normal subjects7,16 and may reflect a single network process or the assimilation of several complex abnormal functions23. Metabolic covariance pattern brain networks are associated with expression values (subject scores) that can distinguish between normal and disease groups and provide network-based measures that correlate with clinical ratings of disease severity. Typically, subject scores for such patterns increase with disease progression and may even be expressed before symptom onset14,24. Indeed, disease-related network biomarkers have been characterized for neurodegenerative disorders such as Parkinson's disease10 (PD), Huntington's disease25(HD), and Alzheimer's disease8 (AD). Importantly, disease-related metabolic topographies have also been identified for atypical parkinsonian movement disorders such as multiple system atrophy (MSA) and progressive supranuclear palsy (PSP). These patterns have been used in concert for the differential diagnosis of individuals with clinically similar "look-alike" syndromes12,13,26.
In contrast, typical fMRI voxel-based univariate methods assess the significance of differences between patients and controls in isolated brain clusters. More recently, methods have been developed to measure functional connectivity between variously defined brain regions27-29. This definition of functional connectivity is restricted to subject and region specific interactions and deviates from the original SSM/PCA concept that refers to the cross-sectional interconnectivity of intrinsic spatially distributed brain network regions1,2,23,30. To their advantage, MRI platforms are easily installed, widely available, non-invasive and typically require shorter scanning time than traditional radiotracer imaging modalities such as PET or SPECT resulting in an upsurge of potential methodologies described in recent literature. However, the resulting time-dependent fMRI signals provide indirect measures of local neural activity31,32. The generally complex analytical algorithms employed have been limited by the large size of datasets, the physiological noise inherent in fMRI signals, as well as the high variability in brain activity that exists between subjects and regions19,23. Although interesting information regarding brain organization can be inferred from the properties of fMRI "networks", they have not been sufficiently stable to be used as reliable disease biomarkers. Furthermore, the resulting network topographies are not necessarily equivalent to those identified using established functional imaging methodologies such as SSM/PCA. For the most part, rigorous cross-validation of the resulting fMRI topographies has been lacking with few examples of successful forward application of derived patterns in prospective scan data from single cases.
An advantage of PCA covariance analysis lies in its capacity to identify the most significant sources of data variation in the first few principal components but it is ineffective if the prominent eigenvectors represent random noise factors rather than actual intrinsic network response. By selecting only the first few eigenvectors and limiting to those that show significant differences in patient versus normal control scores, we greatly reduce the influence of noise elements. However, for the basic approach described here, these measures may not be adequate to generate robust estimators in a typical fMRI dataset with the exception of the modalities described below.
Thus, because of the stable direct relationship of regional glucose metabolism and synaptic activity33, this methodology has been applied primarily to the analysis of rest state FDG PET data. However, given that cerebral blood flow (CBF) is closely coupled to metabolic activity in the resting state10,11,34, SPECT35,36 and more recently arterial spin labeling (ASL) MRI perfusion imaging methods37,38, have been used to assess abnormal metabolic activity in individual cases. That said, the derivation of reliable spatial covariance patterns with resting state fMRI (rsfMRI) is as previously noted not straightforward 31,32. Even so, preliminary SSM/PCA analysis of rsfMRI data from PD patients and control subjects has revealed some topographical homologies between disease-related patterns identified using the two modalities, PET and amplitude of low-frequency fluctuations (ALFF) of BOLD fMRI39,40. Lastly, we also note that this approach has been applied successfully in voxel based morphometry (VBM) structural MRI data41,42, revealing distinctive spatial covariance patterns associated with age-related volume loss and in further comparisons of VBM and ASL patterns in the same subjects43. The relationship between SSM/PCA spatial covariance topographies and analogous brain networks identified using different analytical approaches and imaging platforms is a topic of ongoing investigation.

<table border="1">
 <tbody>
 <tr>
 <td>Name of Equipment</td>
 <td>Company</td>
 <td>Catalog Number</td>
 <td>Comments</td>
 </tr>
 <tr>
 <td></td>
 <td></td>
 <td></td>
 <td>Image Acquisition </td>
 </tr>
 <tr>
 <td>PET Scanner</td>
 <td>GE Medical Systems</td>
 <td>GE Advance</td>
 <td>Any PET, PET/CT and PET/MRI Scanners from GE, Siemens and Philips </td>
 </tr>
 <tr>
 <td>PC Workstations</td>
 <td>Lenovo</td>
 <td>Any</td>
 <td><a href="http://www.lenovo.com/us/en/" target="_blank">http://www.lenovo.com/us/en/</a></td>
 </tr>
 <tr>
 <td></td>
 <td></td>
 <td></td>
 <td>Radiopharmaceuticals </td>
 </tr>
 <tr>
 <td>[18F]-fluorodeoxyglucose</td>
 <td>Feinstein Institute for Medical Research</td>
 <td>Routine Production</td>
 <td>Also distributed by Cardinal Health <a href="http://www.cardinal.com/" target="_blank">http://www.cardinal.com/</a> </td>
 </tr>
 <tr>
 <td></td>
 <td></td>
 <td></td>
 <td>Software </td>
 </tr>
 <tr>
 <td>ScanVP</td>
 <td>Feinstein Institute for Medical Research</td>
 <td>Version 5.9.1, Version 6.2, To be released</td>
 <td><a href="http://www.feinsteinneuroscience.org/" target="_blank">www.feinsteinneuroscience.org</a></td>
 </tr>
 <tr>
 <td>SPM</td>
 <td>The UCL Institute of Neurology</td>
 <td>spm99-spm8</td>
 <td><a href="http://www.fil.ion.ucl.ac.uk/spm" target="_blank">http://www.fil.ion.ucl.ac.uk/spm</a></td>
 </tr>
 <tr>
 <td>Windows</td>
 <td>Microsoft</td>
 <td>Any</td>
 <td></td>
 </tr>
 <tr>
 <td>Matlab</td>
 <td>Mathworks</td>
 <td>Matlab Version 7.0, 7.3</td>
 <td><a href="http://www.mathworks.com/" target="_blank">http://www.mathworks.com/</a></td>
 </tr>
 <tr>
 <td>JMP</td>
 <td>SAS</td>
 <td>Version 5</td>
 <td><a href="http://www.jmp.com/" target="_blank">http://www.jmp.com/</a></td>
 </tr>
 </tbody>
</table>

identification of disease-related spatial covariance patterns using neuroimaging data

The scaled subprofile model (SSM)1-4 is a multivariate PCA-based algorithm that identifies major sources of variation in patient and control group brain image data while rejecting lesser components (Figure 1). Applied directly to voxel-by-voxel covariance data of steady-state multimodality images, an entire group image set can be reduced to a few significant linearly independent covariance patterns and corresponding subject scores. Each pattern, termed a group invariant subprofile (GIS), is an orthogonal principal component that represents a spatially distributed network of functionally interrelated brain regions. Large global mean scalar effects that can obscure smaller network-specific contributions are removed by the inherent logarithmic conversion and mean centering of the data2,5,6. Subjects express each of these patterns to a variable degree represented by a simple scalar score that can correlate with independent clinical or psychometric descriptors7,8. Using logistic regression analysis of subject scores (i.e. pattern expression values), linear coefficients can be derived to combine multiple principal components into single disease-related spatial covariance patterns, i.e. composite networks with improved discrimination of patients from healthy control subjects5,6. Cross-validation within the derivation set can be performed using bootstrap resampling techniques9. Forward validation is easily confirmed by direct score evaluation of the derived patterns in prospective datasets10. Once validated, disease-related patterns can be used to score individual patients with respect to a fixed reference sample, often the set of healthy subjects that was used (with the disease group) in the original pattern derivation11. These standardized values can in turn be used to assist in differential diagnosis12,13 and to assess disease progression and treatment effects at the network level7,14-16. We present an example of the application of this methodology to FDG PET data of Parkinson's Disease patients and normal controls using our in-house software to derive a characteristic covariance pattern biomarker of disease.

A simple application of multivariate SSM/PCA analysis to derive a neuroimaging biomarker pattern for PD is illustrated below. PET FDG images of ten clinically diagnosed PD patients (6M/4F, 59y &plusmn; 7y sd) of variable diseased duration (9y &plusmn; 5y sd) and ten age and gender matched normal controls (6M/4F, 58y &plusmn;7y sd) were analyzed using our ssmpca routine. The twenty corresponding spatially pre-normalized images were initially selected under the categories disease subjects or controls along with th...

Watch this Scientific Journal Video about Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data at JoVE.com

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Multivariate techniques including principal component analysis (PCA) have been used to identify signature patterns of regional change in functional brain images. We have developed an algorithm to identify reproducible network biomarkers for the diagnosis of neurodegenerative disorders, assessment of disease progression, and objective evaluation of treatment effects in patient populations.

The scaled subprofile model (SSM)1-4 is a multivariate PCA-based algorithm that identifies major sources of variation in patient and control group brain ...

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15.5K Views.  The Feinstein Institute for Medical Research. Multivariate techniques including principal component analysis (PCA) have been used to identify signature patterns of regional change in functional brain images. We have developed an algorithm to identify reproducible network biomarkers for the diagnosis of neurodegenerative disorders, assessment of disease progression, and objective evaluation of treatment effects in patient populations.

Video: Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Sleeping Beauty Transposon Insertions in Solid Tumors using Linker-mediated PCR

Genomic, proteomic, transcriptomic, and epigenomic analyses of human tumors indicate that there are thousands of anomalies within each cancer genome compared to matched normal tissue. Based on these analyses it is evident that there are many undiscovered genetic drivers of cancer1. Unfortunately these drivers are hidden within a much larger number of passenger anomalies in the genome that do not directly contribute to tumor formation. Another aspect of the cancer genome is that there is considerable genetic heterogeneity within similar tumor types. Each tumor can harbor different mutations that provide a selective advantage for tumor formation2. Performing an unbiased forward genetic screen in mice provides the tools to generate tumors and analyze their genetic composition, while reducing the background of passenger mutations. The Sleeping Beauty (SB) transposon system is one such method3. The SB system utilizes mobile vectors (transposons) that can be inserted throughout the genome by the transposase enzyme. Mutations are limited to a specific cell type through the use of a conditional transposase allele that is activated by Cre Recombinase. Many mouse lines exist that express Cre Recombinase in specific tissues. By crossing one of these lines to the conditional transposase allele (e.g. Lox-stop-Lox-SB11), the SB system is activated only in cells that express Cre Recombinase. The Cre Recombinase will excise a stop cassette that blocks expression of the transposase allele, thereby activating transposon mutagenesis within the designated cell type. An SB screen is initiated by breeding three strains of transgenic mice so that the experimental mice carry a conditional transposase allele, a concatamer of transposons, and a tissue-specific Cre Recombinase allele. These mice are allowed to age until tumors form and they become moribund. The mice are then necropsied and genomic DNA is isolated from the tumors. Next, the genomic DNA is subjected to linker-mediated-PCR (LM-PCR) that results in amplification of genomic loci containing an SB transposon. LM-PCR performed on a single tumor will result in hundreds of distinct amplicons representing the hundreds of genomic loci containing transposon insertions in a single tumor4. The transposon insertions in all tumors are analyzed and common insertion sites (CISs) are identified using an appropriate statistical method5. Genes within the CIS are highly likely to be oncogenes or tumor suppressor genes, and are considered candidate cancer genes. The advantages of using the SB system to identify candidate cancer genes are: 1) the transposon can easily be located in the genome because its sequence is known, 2) transposition can be directed to almost any cell type and 3) the transposon is capable of introducing both gain- and loss-of-function mutations6. The following protocol describes how to devise and execute a forward genetic screen using the SB transposon system to identify candidate cancer genes (Figure 1).

Identification of Sleeping Beauty Transposon Insertions in Solid Tumors using Linker-mediated PCR

A method of identifying unknown drivers of carcinogenesis using an unbiased approach is described. The method uses the Sleeping Beauty transposon as a random mutagen directed to specific tissues. Genomic mapping of transposon insertions that drive tumor formation identifies novel oncogenes and tumor suppressor genes

Genomic, proteomic, transcriptomic, and epigenomic analyses of human tumors indicate that there are thousands of anomalies within each cancer genome ...

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

We demonstrate methods for the detection of architectural distortion in prior mammograms of interval-cancer cases based on analysis of the orientation of breast tissue patterns in mammograms. We hypothesize that architectural distortion modifies the normal orientation of breast tissue patterns in mammographic images before the formation of masses or tumors. In the initial steps of our methods, the oriented structures in a given mammogram are analyzed using Gabor filters and phase portraits to detect node-like sites of radiating or intersecting tissue patterns. Each detected site is then characterized using the node value, fractal dimension, and a measure of angular dispersion specifically designed to represent spiculating patterns associated with architectural distortion.
Our methods were tested with a database of 106 prior mammograms of 56 interval-cancer cases and 52 mammograms of 13 normal cases using the features developed for the characterization of architectural distortion, pattern classification via quadratic discriminant analysis, and validation with the leave-one-patient out procedure. According to the results of free-response receiver operating characteristic analysis, our methods have demonstrated the capability to detect architectural distortion in prior mammograms, taken 15 months (on the average) before clinical diagnosis of breast cancer, with a sensitivity of 80% at about five false positives per patient.

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

We demonstrate methods for the detection of architectural distortion in prior mammograms. Oriented structures are analyzed using Gabor filters and phase portraits to detect sites of radiating tissue patterns. Each site is characterized and classified using measures to represent spiculating patterns. The methods should assist in the detection of breast cancer.

We demonstrate methods for the detection of architectural distortion in prior mammograms of interval-cancer cases based on analysis of the orientation of ...

Using Continuous Data Tracking Technology to Study Exercise Adherence in Pulmonary Rehabilitation

Pulmonary rehabilitation (PR) is an important component in the management of respiratory diseases. The effectiveness of PR is dependent upon adherence to exercise training recommendations. The study of exercise adherence is thus a key step towards the optimization of PR programs. To date, mostly indirect measures, such as rates of participation, completion, and attendance, have been used to determine adherence to PR. The purpose of the present protocol is to describe how continuous data tracking technology can be used to measure adherence to a prescribed aerobic training intensity on a second-by-second basis.
In our investigations, adherence has been defined as the percent time spent within a specified target heart rate range. As such, using a combination of hardware and software, heart rate is measured, tracked, and recorded during cycling second-by-second for each participant, for each exercise session. Using statistical software, the data is subsequently extracted and analyzed. The same protocol can be applied to determine adherence to other measures of exercise intensity, such as time spent at a specified wattage, level, or speed on the cycle ergometer. Furthermore, the hardware and software is also available to measure adherence to other modes of training, such as the treadmill, elliptical, stepper, and arm ergometer. The present protocol, therefore, has a vast applicability to directly measure adherence to aerobic exercise.

Pulmonary rehabilitation is widely recognized in the management of respiratory diseases. A key component to successful pulmonary rehabilitation is adherence to the recommended exercise training. The purpose of the present protocol is to describe how continuous data tracking technology can be used to precisely measure adherence to a prescribed aerobic training intensity.

Pulmonary rehabilitation (PR) is an important component in the management of respiratory diseases. The effectiveness of PR is dependent upon adherence to ...

Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer

The prostate-specific antigen (PSA) is the main diagnostic biomarker for prostate cancer in clinical use, but it lacks specificity and sensitivity, particularly in low dosage values1&#8203;&#8203;. &#8216;How to use PSA&#39; remains a current issue, either for diagnosis as a gray zone corresponding to a concentration in serum of 2.5-10 ng/ml which does not allow a clear differentiation to be made between cancer and noncancer2 or for patient follow-up as analysis of post-operative PSA kinetic parameters can pose considerable challenges for their practical application3,4. Alternatively, noncoding RNAs (ncRNAs) are emerging as key molecules in human cancer, with the potential to serve as novel markers of disease, e.g. PCA3 in prostate cancer5,6 and to reveal uncharacterized aspects of tumor biology. Moreover, data from the ENCODE project published in 2012 showed that different RNA types cover about 62% of the genome. It also appears that the amount of transcriptional regulatory motifs is at least 4.5x higher than the one corresponding to protein-coding exons. Thus, long terminal repeats (LTRs) of human endogenous retroviruses (HERVs) constitute a wide range of putative/candidate transcriptional regulatory sequences, as it is their primary function in infectious retroviruses. HERVs, which are spread throughout the human genome, originate from ancestral and independent infections within the germ line, followed by copy-paste propagation processes and leading to multicopy families occupying 8% of the human genome (note that exons span 2% of our genome). Some HERV loci still express proteins that have been associated with several pathologies including cancer7-10. We have designed a high-density microarray, in Affymetrix format, aiming to optimally characterize individual HERV loci expression, in order to better understand whether they can be active, if they drive ncRNA transcription or modulate coding gene expression. This tool has been applied in the prostate cancer field (Figure 1).

Human endogenous retroviruses (HERV), which occupy 8% of the human genome, retain scarce coding capacities but a hundred thousand long terminal repeats (LTRs). A custom Affymetrix microarray was designed to identify individual HERV locus expression and was used on prostate cancer tissues as a proof of concept for future clinical studies.

The prostate-specific antigen (PSA) is the main diagnostic biomarker for prostate cancer in clinical use, but it lacks specificity and sensitivity, ...

In Vivo Tracking of Edema Development and Microvascular Pathology in a Model of Experimental Cerebral Malaria Using Magnetic Resonance Imaging

Cerebral malaria is a sign of severe malarial disease and is often a harbinger of death. While aggressive management can be life-saving, the detection of cerebral malaria can be difficult. We present an experimental mouse model of cerebral malaria that shares multiple features of the human disease, including edema and microvascular pathology. Using magnetic resonance imaging (MRI), we can detect and track the blood-brain barrier disruption, edema development, and subsequent brain swelling. We describe multiple MRI techniques that can visualize these pertinent pathological changes. Thus, we show that MRI represents a valuable tool to visualize and track pathological changes, such as edema, brain swelling, and microvascular pathology, in vivo.

In Vivo Tracking of Edema Development and Microvascular Pathology in a Model of Experimental Cerebral Malaria Using Magnetic Resonance Imaging

We describe a mouse model of experimental cerebral malaria and show how inflammatory and microvascular pathology can be tracked in vivo using magnetic resonance imaging.

Cerebral malaria is a sign of severe malarial disease and is often a harbinger of death. While aggressive management can be life-saving, the detection of ...

High-Throughput Analysis of Optical Mapping Data Using ElectroMap

Optical mapping is an established technique for high spatio-temporal resolution study of cardiac electrophysiology in multi-cellular preparations. Here we present, in a step-by-step guide, the use of ElectroMap for analysis, quantification, and mapping of high-resolution voltage and calcium datasets acquired by optical mapping. ElectroMap analysis options cover a wide variety of key electrophysiological parameters, and the graphical user interface allows straightforward modification of pre-processing and parameter definitions, making ElectroMap applicable to a wide range of experimental models. We show how built-in pacing frequency detection and signal segmentation allows high-throughput analysis of entire experimental recordings, acute responses, and single beat-to-beat variability. Additionally, ElectroMap incorporates automated multi-beat averaging to improve signal quality of noisy datasets, and here we demonstrate how this feature can help elucidate electrophysiological changes that might otherwise go undetected when using single beat analysis. Custom modules are included within the software for detailed investigation of conduction, single file analysis, and alternans, as demonstrated here. This software platform can be used to enable and accelerate the processing, analysis, and mapping of complex cardiac electrophysiology.

This protocol describes the setup and use of ElectroMap, a MATLAB-based open-source software platform for analysis of cardiac optical mapping data. ElectroMap provides a versatile high-throughput tool for analysis of optical mapping voltage and calcium datasets across a wide range of cardiac experimental models.

Optical mapping is an established technique for high spatio-temporal resolution study of cardiac electrophysiology in multi-cellular preparations. Here we ...

Virtual Reality Tools for Assessing Unilateral Spatial Neglect: A Novel Opportunity for Data Collection

Unilateral spatial neglect (USN) is a syndrome characterized by inattention to or inaction in one side of space and affects between 23-46% of acute stroke survivors. The diagnosis and characterization of these symptoms in individual patients can be challenging and often requires skilled clinical staff. Virtual reality (VR) presents an opportunity to develop novel assessment tools for patients with USN.
We aimed to design and build a VR tool to detect and characterize subtle USN symptoms, and to test the tool on subjects treated with inhibitory repetitive transcranial magnetic stimulation (TMS) of cortical regions associated with USN.
We created three experimental conditions by applying TMS to two distinct regions of cortex associated with visuospatial processing- the superior temporal gyrus (STG) and the supramarginal gyrus (SMG) - and applied sham TMS as a control. We then placed subjects in a virtual reality environment in which they were asked to identify the flowers with lateral asymmetries of flowers distributed across bushes in both hemispaces, with dynamic difficulty adjustment based on each subject's performance. 
We found significant differences in average head yaw between subjects stimulated at the STG and those stimulated at the SMG and marginally significant effects in the average visual axis.
VR technology is becoming more accessible, affordable, and robust, presenting an exciting opportunity to create useful and novel game-like tools. In conjunction with TMS, these tools could be used to study specific, isolated, artificial neurological deficits in healthy subjects, informing the creation of VR-based diagnostic tools for patients with deficits due to acquired brain injury. This study is the first to our knowledge in which artificially generated USN symptoms have been evaluated with a VR task.

The goal was to design, build, and pilot a novel virtual reality task to detect and characterize unilateral spatial neglect, a syndrome affecting 23-46% of acute stroke survivors, expanding the role of virtual reality in the study and management of neurologic disease.

Unilateral spatial neglect (USN) is a syndrome characterized by inattention to or inaction in one side of space and affects between 23-46% of acute stroke ...

Detection and Removal of Tooth-Colored Composite Resin Using the Fluorescence-Aided Identification Technique

The detection and removal of tooth-colored filling materials is a major challenge for every dentist. The Fluorescence-aided Identification Technique (FIT) is a noninvasive tool to facilitate the distinction of composite resin material from sound tooth substance. Compared to conventional illumination, FIT is a very accurate, reliable, and fast diagnostic method. When composite resin is illuminated with a wavelength of approximately 398 &plusmn; 5 nm, certain fluorescent components make the composite resin appear brighter than the tooth structure. Any fluorescence-inducing light source with the appropriate wavelength can be used for this method. Optimally, this technique is used without additional natural or artificial lighting. The application of FIT can be used for diagnostic purposes, for example, dental charts, and additionally for the complete and minimally invasive removal of composite resin restorations, bracket debonding, and trauma splint removal. The assessment of volumetric changes after composite removal can be provided by overlapping pre- and postoperative scans and subsequent calculation using suitable software.

The Fluorescence-aided Identification Technique is a practicable, fast, and reliable approach for the differentiation of composite resin restorations from tooth substance, and facilitates the minimally invasive and complete removal of composite resin restorations and composite bonded trauma splints.

The detection and removal of tooth-colored filling materials is a major challenge for every dentist. The Fluorescence-aided Identification Technique (FIT) ...

Visualization of Metabolites Identified in the Spatial Metabolome of Traditional Chinese Medicine Using DESI-MSI

The medicinal use of traditional Chinese medicine is mainly due to its secondary metabolites. Visualization of the distribution of these metabolites has become a crucial topic in plant science. Mass spectrometry imaging can extract huge volumes of data and provide spatial distribution information about these by analyzing tissue slices. With the advantage of high throughput and higher accuracy, desorption electrospray ionization mass spectrometry imaging (DESI-MSI) is often used in biological research and in the study of traditional Chinese medicine. However, the procedures used in this research are complicated and not affordable. In this study, we optimized sectioning and DESI imaging procedures and developed a more cost-effective method to identify the distribution of metabolites and categorize these compounds in plant tissues, with a special focus on traditional Chinese medicines. The study will promote the utilization of DESI in metabolite analysis and standardization of traditional Chinese medicine/ethnic medicine for research-related technologies.

In this study, a series of methods are presented to prepare DESI-MSI samples from plants, and a procedure of DESI assembly installation, MSI data acquisition, and processing is described in detail. This protocol can be applied in several conditions for acquiring spatial metabolome information in plants.

The medicinal use of traditional Chinese medicine is mainly due to its secondary metabolites. Visualization of the distribution of these metabolites has ...

A Comprehensive 3D-Liver Fat Function Distribution Model Using Dixon MRI

A 3D Quantification Technique for Liver Fat Fraction Distribution Analysis Using Dixon Magnetic Resonance Imaging

This study presents a 3D quantification methodology for the distribution of liver fat fraction (LFF) through the utilization of Dixon MRI image analysis. The central aim is to offer a highly accurate and non-invasive means of evaluating liver fat content. The process involves the acquisition of In-phase and Water-phase images from a Dixon sequence. LFF maps are then meticulously computed voxel by voxel by dividing the Lipid Phase images by the In-phase images. Simultaneously, 3D liver contours are extracted from the In-phase images. These crucial components are seamlessly integrated to construct a comprehensive 3D-LFF distribution model. This technique is not limited to healthy livers but extends to those afflicted by hepatic steatosis. The results obtained demonstrate the remarkable effectiveness of this approach in both visualizing and quantifying liver fat content. It distinctly discerns patterns that differentiate between normal and steatotic livers. By harnessing Dixon MRI to extract the 3D structure of the liver, this method offers precise LFF assessments spanning the entirety of the organ, thereby holding great promise for the diagnosis of hepatic steatosis with remarkable effectiveness.

Author Spotlight: A Non-Invasive Tool to Assess and Differentiate Fat Patterns in Liver Using 3D Dixon MRI 

This study introduces a unique 3D quantification method for liver fat fraction (LFF) distribution using Dixon Magnetic Resonance Imaging (Dixon MRI). LFF maps, derived from in-phase and water-phase images, are integrated with 3D liver contours to differentiate LFF patterns between normal and steatotic livers, enabling precise assessment of liver fat content.

This study presents a 3D quantification methodology for the distribution of liver fat fraction (LFF) through the utilization of Dixon MRI image analysis. ...