AIM: Statistical Parametric Mapping (SPM) has been applied for single-­subject evaluation of [18F]FDG uptake in Alzheimer Disease (AD). In a single-­subject framework, the patient is compared to a dataset of [18F]FDG PET images from healthy subjects (HS) evaluating brain metabolic abnormalities. No studies exist that assess the effects on SPM analysis of HS [18F]FDG PET datasets acquired from different subjects and using different PET scanners including the same or different PET scanners than those used for patients. This work aims to elucidate this issue from a methodological perspective. METHODS: We considered six different [18F]FDG PET datasets, from different HS populations, acquired by different PET scanners. We applied SPM5 procedures for single-­subject comparison with each of the six HS datasets in 10 probable AD patients showing the typical [18F]FDG pattern. We also implemented the same comparison in 3 probable AD patients and in 7 patients with a clinical diagnosis of Mild Cognitive Impariment (MCI), showing subtle changes on visual inspection of [18F]FDG distribution. RESULTS: Considering the 10 patients with the typical [18F]FDG pattern, the results were comparable for all the SPM maps. In the 3 probable AD patients with subtle changes in [18F]FDG distribution, no significant AD pattern emerged when a small number (<20) of HS was used, whereas a significant AD pattern appeared when a large (>50) HS image set was used. In the 7 considered MCI patients the use of a large (>50) HS image set allowed to assess significant hypo metabolic patterns related to a probable neurodegenerative pathology. CONCLUSION: The use of large HS datasets of PET scans (>50) is recommended for single-­subject SPM analysis. On condition that appropriate pre-­processing steps are provided, large HS datasets can include HS images acquired with different PET systems, not including images from the same scanner of that used for patients.

Alzheimer's Disease (AD) is an invalidating neurodegenerative disorders frequently affecting the aging population. In view of the increase of elderlies, not only in western countries, the related growing societal problems urge for identifying clinical biomarkers in view of potential treatments interfering or blocking the disease course. Among the plenty of anatomo-functional in vivo imaging techniques to inspect brain circuits and physiology, the Magnetic Resonance Imaging (MRI), the functional MRI (fMRI), the Electroencephalography (EEG) and Magnetoencephalography (MEG), have been extensively used for the study of AD, with different achievements and limitations. Eventually, the methodologies summoned by brain connectomics further strengthen the expectations in this field, as shown by recent results obtained with [F-18]2-fluoro-2-deoxyglucose (18)FDG-PET and fMRI in the prediction of the AD in early stages. However, the inherent complexity of the pathophysiology of the AD suggests that only integrative approaches combining different techniques and methodologies of brain scanning could produce significant breakthroughs in the study of AD. This review proposes a formal framework able to combine brain connectomic data from multimodal acquisitions by means of different in vivo neuroimaging techniques, briefly reporting their different advantages and drawbacks. Indeed, a specialized complex multiplex network, where nodes interact in layers linking the same pair of nodes and each layer reflects a distinct type of brain acquisition, can model the plurality of connectomes recommended in this framework.

The emergence of Alzheimer's Disease (AD) as a consequence of increasing aging population makes urgent the availability of methods for the early and accurate diagnosis. Magnetic Resonance Imaging (MRI) could be used as in vivo, non invasive tool to identify sensitive and specific markers of very early AD progression. In recent years, multivariate pattern analysis (MVPA) and machine- learning algorithms have attracted strong interest within the neuroimaging community, as they allow automatic classification of imaging data with higher performance than univariate statistical analysis. An exhaustive search of PubMed, Web of Science and Medline records was performed in this work, in order to retrieve studies focused on the potential role of MRI in aiding the clinician in early diagnosis of AD by using Support Vector Machines (SVMs) as MVPA automated classification method. A total of 30 studies emerged, published from 2008 to date. This review aims to give a state-of-the-art overview about SVM for the early and differential diagnosis of AD-related pathologies by means of MRI data, starting from preliminary steps such as image pre-processing, feature extraction and feature selection, and ending with classification, validation strategies and extraction of MRI-related biomarkers. The main advantages and drawbacks of the different techniques were explored. Results obtained by the reviewed studies were reported in terms of classification performance and biomarker outcomes, in order to shed light on the parameters that accompany normal and pathological aging. Unresolved issues and possible future directions were finally pointed out.

Recommended guidelines for the diagnosis of dementia due to Alzheimer's Disease (AD) were revised in recent years, including Positron Emission Tomography (PET) as an in-vivo diagnostic imaging technique for the diagnosis of neurodegeneration. In particular PET, using 18Ffluorodeoxiglucouse ([18F]FDG), is able to detect very early changes of glucose consumption at the synaptic level, enabling to support both early and differential diagnosis of AD. In standard clinical practice, interpretation of [18F] FDG-PET images is usually achieved through qualitative assessment. Visual inspection although only reveals information visible at human eyes resolution, while information at a higher resolution is missed. Furthermore, qualitative assessment depends on the degree of expertise of the clinician, preventing from the definition of accurate and standardized imaging biomarkers. Automated and computerized image processing methods have been proposed to support the in-vivo assessment of brain PET studies. In particular, objective statistical image analyses, enabling the comparison of one patient's images to a group of control images have been shown to carry important advantages for detecting significant metabolic changes, including the availability of more objective, cross-center reliable metrics and the detectability of brain subtle functional changes, as occurring in prodromal AD. The purpose of the current review is to provide a systematic overview encompassing the frontiers recently reached by quantitative approaches for the statistical analysis of PET brain images in the study of AD, with a particular focus on Statistical Parametric Mapping. Main achievements, e.g. in terms of standardized biomarkers of AD as well as of sensitivity and specificity, will be discussed.

Hybrid PET/MRI represents an innovative diagnostic technology for non-invasive in vivo imaging of cancer. Although the current clinical experience is limited to few clinical centres investing in such an ambitious technology, preliminary results are showing potentials for hybrid PET/MRI to enter in the clinical setting as imaging technology that can profoundly impact on the pool of available in vivo multimodal imaging studies. The higher contrast increases detectability of oncological lesions in specific applications; the reduction of radiation exposure leads benefits for children and young adults and for follow-up studies; the multi-functionality of PET/MRI opens up to several opportunities for assessing cancer disease and for addressing oncological patients to optimal care.

18F-Fluorodeoxyglucose (18F-FDG) Positron Emission Tomography (PET) is a standard functional diagnostic technique to in vivo image cancer. Different quantitative paramters can be extracted from PET images and used as in vivo cancer biomarkers. Between PET biomarkers Metabolic Tumor Volume (MTV) has gained an important role in particular considering the development of patient-personalized radiotherapy treatment for non-homogeneous dose delivery. Different imaging processing methods have been developed to define MTV. The different proposed PET segmentation strategies were validated in ideal condition (e.g. in spherical objects with uniform radioactivity concentration), while the majority of cancer lesions doesn't fulfill these requirements. In this context, this work has a twofold objective: 1) to implement and optimize a fully automatic, threshold-based segmentation method for the estimation of MTV, feasible in clinical practice 2) to develop a strategy to obtain anthropomorphic phantoms, including non-spherical and non-uniform objects, miming realistic oncological patient conditions. The developed PET segmentation algorithm combines an automatic threshold-based algorithm for the definition of MTV and a k-means clustering algorithm for the estimation of the background. The method is based on parameters always available in clinical studies and was calibrated using NEMA IQ Phantom. Validation of the method was performed both in ideal (e.g. in spherical objects with uniform radioactivity concentration) and non-ideal (e.g. in non-spherical objects with a non-uniform radioactivity concentration) conditions. The strategy to obtain a phantom with synthetic realistic lesions (e.g. with irregular shape and a non-homogeneous uptake) consisted into the combined use of standard anthropomorphic phantoms commercially and irregular molds generated using 3D printer technology and filled with a radioactive chromatic alginate. The proposed segmentation algorithm was feasible in a clinical context and showed a good accuracy both in ideal and in realistic conditions.

Recommended guidelines for the diagnosis of dementia due to Alzheimer's Disease (AD) were revised in recent years, including Positron Emission Tomography (PET) as an in-vivo diagnostic imaging technique for the diagnosis of neurodegeneration. In particular PET, using 18Ffluorodeoxiglucouse ([18F]FDG), is able to detect very early changes of glucose consumption at the synaptic level, enabling to support both early and differential diagnosis of AD. In standard clinical practice, interpretation of [18F] FDG-PET images is usually achieved through qualitative assessment. Visual inspection although only reveals information visible at human eyes resolution, while information at a higher resolution is missed. Furthermore, qualitative assessment depends on the degree of expertise of the clinician, preventing from the definition of accurate and standardized imaging biomarkers. Automated and computerized image processing methods have been proposed to support the in-vivo assessment of brain PET studies. In particular, objective statistical image analyses, enabling the comparison of one patient's images to a group of control images have been shown to carry important advantages for detecting significant metabolic changes, including the availability of more objective, cross-center reliable metrics and the detectability of brain subtle functional changes, as occurring in prodromal AD. The purpose of the current review is to provide a systematic overview encompassing the frontiers recently reached by quantitative approaches for the statistical analysis of PET brain images in the study of AD, with a particular focus on Statistical Parametric Mapping. Main achievements, e.g. in terms of standardized biomarkers of AD as well as of sensitivity and specificity, will be discussed.

Prostate cancer (PC) includes several phenotypes, from indolent to highly aggressive cancer. Actual diagnostic and prognostic tools have several limitations, and there is a need for new biomarkers to stratify patients and assign them optimal therapies by taking into account potential genetic and epigenetic differences. MicroRNAs (miRNAs) are small sequences of non-coding RNA regulating specific genes involved in the onset and development of PC. Stable miRNAs have been found in biofluids, such as serum and plasma; thus, the measurement of PC-associated miRNAs is emerging as a non-invasive tool for PC detection and monitoring. In this study, we conduct an in-depth literature review focusing on miRNAs that may contribute to the diagnosis and prognosis of PC. The role of miRNAs as a potential theranostic tool in PC is discussed. Using a meta-analysis approach, we found a group of 29 miRNAs with diagnostic properties and a group of seven miRNAs with prognostic properties, which were found already expressed in both biofluids and PC tissues. We tested the two miRNA groups on The Cancer Genome Atlas dataset of PC tissue samples with a machine-learning approach. Our results suggest that these 29 miRNAs should be considered as potential panel of biomarkers for the diagnosis of PC, both as in vivo non-invasive test and ex vivo confirmation test.

The Cancer Genome Atlas (TCGA) research network has made public a large collection of clinical and molecular phenotypes of more than 10 000 tumor patients across 33 different tumor types. Using this cohort, TCGA has published over 20 marker papers detailing the genomic and epigenomic alterations associated with these tumor types. Although many important discoveries have been made by TCGA's research network, opportunities still exist to implement novel methods, thereby elucidating new biological pathways and diagnostic markers. However, mining the TCGA data presents several bioinformatics challenges, such as data retrieval and integration with clinical data and other molecular data types (e.g. RNA and DNA methylation). We developed an R/Bioconductor package called TCGAbiolinks to address these challenges and offer bioinformatics solutions by using a guided workflow to allow users to query, download and perform integrative analyses of TCGA data. We combined methods from computer science and statistics into the pipeline and incorporated methodologies developed in previous TCGA marker studies and in our own group. Using four different TCGA tumor types (Kidney, Brain, Breast and Colon) as examples, we provide case studies to illustrate examples of reproducibility, integrative analysis and utilization of different Bioconductor packages to advance and accelerate novel discoveries.

The human brain appears organized in compartments characterized by seemingly specific functional purposes on many spatial scales. A complementary functional state binds information from specialized districts to return what is called integrated information. These fundamental network dynamics undergoes to severe disarrays in diverse degenerative conditions such as Alzheimer's Diseases (AD). The AD represents a multifarious syndrome characterized by structural, functional, and metabolic landmarks. In particular, in the early stages of AD, adaptive functional modifications of the brain networks mislead initial diagnoses because cognitive abilities may result indiscernible from normal subjects. As a matter of facts, current measures of functional integration fail to catch significant differences among normal, mild cognitive impairment (MCI) and even AD subjects. The aim of this work is to introduce a new topological feature called Compression Flow (CF) to finely estimate the extent of the functional integration in the brain networks. The method uses a Monte Carlo-like estimation of the information integration flows returning the compression ratio between the size of the injected information and the size of the condensed information within the network. We analyzed the resting state connectomes of 75 subjects of the Alzheimer's Disease Neuroimaging Initiative 2 (ADNI) repository. Our analyses are focused on the 18FGD-PET and functional MRI (fMRI) acquisitions in several clinical screening conditions. Results indicated that CF effectively discriminate MCI, AD and normal subjects by showing a significant decrease of the functional integration in the AD and MCI brain connectomes. This result did not emerge by using a set of common complex network statistics. Furthermore, CF was best correlated with individual clinical scoring scales. In conclusion, we presented a novel measure to quantify the functional integration that resulted efficient to discriminate different stages of dementia and to track the individual progression of the impairments prospecting a proficient usage in a wide range of pathophysiological and physiological studies as well.

Presently, there are no valid biomarkers to identify individuals with eating disorders (ED). The aim of this work was to assess the feasibility of a machine learning method for extracting reliable neuroimaging features allowing individual categorization of patients with ED. Support Vector Machine (SVM) technique, combined with a pattern recognition method, was employed utilizing structural magnetic resonance images. Seventeen females with ED (six with diagnosis of anorexia nervosa and 11 with bulimia nervosa) were compared against 17 body mass index-matched healthy controls (HC). Machine learning allowed individual diagnosis of ED versus HC with an Accuracy >= 0.80. Voxel-based pattern recognition analysis demonstrated that voxels influencing the classification Accuracy involved the occipital cortex, the posterior cerebellar lobule, precuneus, sensorimotor/premotor cortices, and the medial prefrontal cortex, all critical regions known to be strongly involved in the pathophysiological mechanisms of ED. Although these findings should be considered preliminary given the small size investigated, SVM analysis highlights the role of well-known brain regions as possible biomarkers to distinguish ED from HC at an individual level, thus encouraging the translational implementation of this new multivariate approach in the clinical practice.

Naming abilities are typically preserved in amnestic Mild Cognitive Impairment (aMCI), a condition associated with increased risk of progression to Alzheimer's disease (AD). We compared the functional correlates of covert picture naming and word reading between a group of aMCI subjects and matched controls. Unimpaired picture naming performance was associated with more extensive activations, in particular involving the parietal lobes, in the aMCI group. In addition, in the condition associated with higher processing demands (blocks of categorically homogeneous items, living items), increased activity was observed in the aMCI group, in particular in the left fusiform gyrus. Graph analysis provided further evidence of increased modularity and reduced integration for the homogenous sets in the aMCI group. The functional modifications associated with preserved performance may reflect, in the case of more demanding tasks, compensatory mechanisms for the subclinical involvement of semantic processing areas by AD pathology.

BACKGROUND: Development of human cancer can proceed through the accumulation of different genetic changes affecting the structure and function of the genome. Combined analyses of molecular data at multiple levels, such as DNA copy-number alteration, mRNA and miRNA expression, can clarify biological functions and pathways deregulated in cancer. The integrative methods that are used to investigate these data involve different fields, including biology, bioinformatics, and statistics. RESULTS: These methodologies are presented in this review, and their implementation in breast cancer is discussed with a focus on integration strategies. We report current applications, recent studies and interesting results leading to the identification of candidate biomarkers for diagnosis, prognosis, and therapy in breast cancer by using both individual and combined analyses. CONCLUSION: This review presents a state of art of the role of different technologies in breast cancer based on the integration of genetics and epigenetics, and shares some issues related to the new opportunities and challenges offered by the application of such integrative approaches.

Determination of sensitive and specific markers of very early AD progression is intended to aid researchers and clinicians to develop new treatments and monitor their effectiveness, as well as to lessen the time and cost of clinical trials. Magnetic Resonance (MR)-related biomarkers have been recently identified by the use of machine learning methods for the in vivo differential diagnosis of AD. However, the vast majority of neuroimaging papers investigating this topic are focused on the difference between AD and patients with mild cognitive impairment (MCI), not considering the impact of MCI patients who will (MCIc) or not convert (MCInc) to AD. Morphological T1-weighted MRIs of 137 AD, 76 MCIc, 134 MCInc, and 162 healthy controls (CN) selected from the Alzheimer's disease neuroimaging initiative (ADNI) cohort, were used by an optimized machine learning algorithm. Voxels influencing the classification between these AD-related pre-clinical phases involved hippocampus, entorhinal cortex, basal ganglia, gyrus rectus, precuneus, and cerebellum, all critical regions known to be strongly involved in the pathophysiological mechanisms of AD. Classification accuracy was 76% AD vs. CN, 72% MCIc vs. CN, 66% MCIc vs. MCInc (nested 20-fold cross validation). Our data encourage the application of computer-based diagnosis in clinical practice of AD opening new prospective in the early management of AD patients

In this work an integrated approach was used to identify functional miRNAs regulating gene pathway cross-talk in breast cancer (BC). We first integrated gene expression profiles and biological pathway information to explore the underlying associations between genes differently expressed among normal and BC samples and pathways enriched from these genes. For each pair of pathways, a score was derived from the distribution of gene expression levels by quantifying their pathway cross-talk. Random forest classification allowed the identification of pairs of pathways with high cross-talk. We assessed miRNAs regulating the identified gene pathways by a mutual information analysis. A Fisher test was applied to demonstrate their significance in the regulated pathways. Our results suggest interesting networks of pathways that could be key regulatory of target genes in BC, including stem cell pluripotency, coagulation, and hypoxia pathways and miRNAs that control these networks could be potential biomarkers for diagnostic, prognostic, and therapeutic development in BC. This work shows that standard methods of predicting normal and tumor classes such as differentially expressed miRNAs or transcription factors could lose intrinsic features; instead our approach revealed the responsible molecules of the disease.

Dysregulation of microRNAs (miRNAs) is involved in the initiation and progression of several human cancers, including breast cancer (BC), as strong evidence has been found that miRNAs can act as oncogenes or tumor suppressor genes. This review presents the state of the art on the role of miRNAs in the diagnosis, prognosis, and therapy of BC. Based on the results obtained in the last decade, some miRNAs are emerging as biomarkers of BC for diagnosis (i.e., miR-9, miR-10b, and miR-17-5p), prognosis (i.e., miR-148a and miR-335), and prediction of therapeutic outcomes (i.e., miR-30c, miR-187, and miR-339-5p) and have important roles in the control of BC hallmark functions such as invasion, metastasis, proliferation, resting death, apoptosis, and genomic instability. Other miRNAs are of interest as new, easily accessible, affordable, non-invasive tools for the personalized management of patients with BC because they are circulating in body fluids (e.g., miR-155 and miR-210). In particular, circulating multiple miRNA profiles are showing better diagnostic and prognostic performance as well as better sensitivity than individual miRNAs in BC. New miRNA-based drugs are also promising therapy for BC (e.g., miR-9, miR-21, miR34a, miR145, and miR150), and other miRNAs are showing a fundamental role in modulation of the response to other non-miRNA treatments, being able to increase their efficacy (e.g., miR-21, miR34a, miR195, miR200c, and miR203 in combination with chemotherapy).

Objective. The aim of this work was to assess robustness and reliability of an adaptive thresholding algorithm for the biological target volume estimation incorporating reconstruction parameters. Method. In a multicenter study, a phantom with spheres of different diameters (6.5-57.4 mm) was filled with (18)F-FDG at different target-to-background ratios (TBR: 2.5-70) and scanned for different acquisition periods (2-5 min). Image reconstruction algorithms were used varying number of iterations and postreconstruction transaxial smoothing. Optimal thresholds (TS) for volume estimation were determined as percentage of the maximum intensity in the cross section area of the spheres. Multiple regression techniques were used to identify relevant predictors of TS. Results. The goodness of the model fit was high (R (2): 0.74-0.92). TBR was the most significant predictor of TS. For all scanners, except the Gemini scanners, FWHM was an independent predictor of TS. Significant differences were observed between scanners of different models, but not between different scanners of the same model. The shrinkage on cross validation was small and indicative of excellent reliability of model estimation. Conclusions. Incorporation of postreconstruction filtering FWHM in an adaptive thresholding algorithm for the BTV estimation allows obtaining a robust and reliable method to be applied to a variety of different scanners, without scanner-specific individual calibration.

In the present work, we have undertaken a proof-of-concept study to determine whether a simple upper-limb movement could be useful to accurately classify low-functioning children with autism spectrum disorder (ASD) aged 2-4. To answer this question, we developed a supervised machine-learning method to correctly discriminate 15 preschool children with ASD from 15 typically developing children by means of kinematic analysis of a simple reach-to-drop task. Our method reached a maximum classification accuracy of 96.7 % with seven features related to the goal-oriented part of the movement. These preliminary findings offer insight into a possible motor signature of ASD that may be potentially useful in identifying a well-defined subset of patients, reducing the clinical heterogeneity within the broad behavioral phenotype.