One-class support vector machines identify the language and default mode regions as common patterns of structural alterations in young children with autism spectrum disorders

The identification of reliable brain endophenotypes of autism spectrum disorders (ASD) has been hampered to date by the heterogeneity in the neuroanatomical abnormalities detected in this condition. To handle the complexity of neuroimaging data and to convert brain images in informative biomarkers of pathology, multivariate analysis techniques based on Support Vector Machines (SVM) have been widely used in several disease conditions. They are usually trained to distinguish patients from healthy control subjects by making a binary classification. Here, we propose the use of the One-Class Classification (OCC) or Data Description method that, in contrast to two-class classification, is based on a description of one class of objects only. This approach, by defining a multivariate normative rule on one class of subjects, allows recognizing examples from a different category as outliers. We applied the OCC to 314 regional features extracted from brain structural Magnetic Resonance Imaging (MRI) scans of young children with ASD (21 males and 20 females) and control subjects (20 males and 20 females), matched on age [range: 22-72 months of age; mean = 49 months] and non-verbal intelligence quotient (NVIQ) [range: 31-123; mean = 73]. We demonstrated that a common pattern of features characterize the ASD population. The OCC SVM trained on the group of ASD subjects showed the following performances in the ASD vs. controls separation: the area under the receiver operating characteristic curve (AUC) was 0.74 for the male and 0.68 for the female population, respectively. Notably, the ASD vs. controls discrimination results were maximized when evaluated on the subsamples of subjects with NVIQ = 70, leading to AUC = 0.81 for the male and AUC = 0.72 for the female populations, respectively. Language regions and regions from the default mode network-posterior cingulate cortex, pars opercularis and pars triangularis of the inferior frontal gyrus, and transverse temporal gyrus-contributed most to distinguishing individuals with ASD from controls, arguing for the crucial role of these areas in the ASD pathophysiology. The observed brain patterns associate preschoolers with ASD independently of their age, gender and NVIQ and therefore they are expected to constitute part of the ASD brain endophenotype

Convolutional Neural Networks for Breast Density Classification: Performance and Explanation Insights

We propose and evaluate a procedure for the explainability of a breast density deep learning based classifier. A total of 1662 mammography exams labeled according to the BI-RADS categories of breast density was used. We built a residual Convolutional Neural Network, trained it and studied the responses of the model to input changes, such as different distributions of class labels in training and test sets and suitable image pre-processing. The aim was to identify the steps of the analysis with a relevant impact on the classifier performance and on the model explainability. We used the grad-CAM algorithm for CNN to produce saliency maps and computed the Spearman's rank correlation between input images and saliency maps as a measure of explanation accuracy. We found that pre-processing is critical not only for accuracy, precision and recall of a model but also to have a reasonable explanation of the model itself. Our CNN reaches good performances compared to the state-of-art and it considers the dense pattern to make the classification. Saliency maps strongly correlate with the dense pattern. This work is a starting point towards the implementation of a standard framework to evaluate both CNN performances and the explainability of their predictions in medical image classification problems

Inter-method reliability of brainstem volume segmentation algorithms in preschoolers with ASD

Introduction: The brainstem has a potential role in the pathophysiology of Autism Spectrum Disorders (ASD) (Roger, 2013). In particular, alterations in brainstem volume and their relationship with sensory/motor abnormalities have been suggested (Trevarthen & Delafield-Butt, 2013). However, the findings in volume alterations of subjects with ASD with respect to matched controls are controversial both in adults and children cohorts (Hardan, 2001; Piven, 1992; Kleiman, 1992). Moreover, the contribution to variability of brainstem volume measurements performed with different automated methods is still unclear. Methods: T1-weighted MRI brain scans of a cohort of 80 preschoolers (20 male controls, 20 male subjects with ASD, 20 female controls, 20 female subjects with ASD, mean age controls 49 months, std 12 months, mean age ASD 49 months, std 14) were processed with three different automated methods to measure the brainstem volume: Freesurfer 5.3 (Fischl, 2002), FSL-FIRST (Patenaude, 2011) and ANTs (Avants, 2011). Analysis of variance was then carried out taking into account gender and total brain volume in order to investigate potential brainstem volume differences between controls/ASD subjects for each method. A direct comparison of brainstem volume assessments in native space was then performed to assess inter-method reliability (correlation has been calculated by Pearson coefficient) and Dice similarity indexes were calculated to evaluate the segmentation agreement across methods. Results:The brainstem volume measurements are reported in scatter plots in Fig. 1 to show the agreement in terms of volume (in mm3) between different methods. The color represents the Dice similarity index (range 0-1 were 1 means total agreement) of the brainstem segmentations obtained by the methods under investigation. In Fig. 2 four examples of brainstem segmentations with the different methods are shown in sagittal view (brainstem segmentations are reported in red, green, blue for Freesurfer, FSL-FIRST and ANTs respectively). Pearson correlation coefficient between FSL-FIRST and Freesurfer brainstem volume assessments was 0.27 (p-value=0.02). It was 0.51 (p-value0.05).Conclusions:The inter-method reliability of automated algorithms for brainstem volume assessment is limited (the mean Dice similarity index barely reaches 0.8 in just one out of 3 comparisons). Inconsistencies across previous studies on brainstem and more in general the lack of evidence for brain biomarkers in ASD may in part be a result of this poor agreements in the extraction of structural features with different methods. Inter-method brainstem volume differences can be attributed to varying definitions of brainstem structure, the use of different templates (e.g. in our study only ANTs processed the brain scans by using an age-specific brain template) and the varying effects of imaging artifacts and acquisition settings. This study suggests that research on brain structure alterations should cross-validate findings across multiple methods before providing biological interpretations

Multi-scale analysis of lung computed tomography images

A computer-aided detection (CAD) system for the identification of lung internal nodules in low-dose multi-detector helical Computed Tomography (CT) images was developed in the framework of the MAGIC-5 project. The three modules of our lung CAD system, a segmentation algorithm for lung internal region identification, a multi-scale dot-enhancement filter for nodule candidate selection and a multi-scale neural technique for false positive finding reduction, are described. The results obtained on a dataset of low-dose and thin-slice CT scans are shown in terms of free response receiver operating characteristic (FROC) curves and discussed.Comment: 18 pages, 12 low-resolution figure

Analysis of a Flexible Dual-Channel Octagonal Coil System for UHF MRI

Nowadays, MRI is focused on using ultra-high static magnetic fields (> 7 T) to increase the signal-to-noise ratio. The use of high fields, on the other hand, requires novel technical solutions as well as more stringent design criteria for specific absorption rate levels, reducing radiative effect and coil resistance. In this paper, two flexible RF coils for 7 T human magnetic resonance, and 298 MHz ultra-high frequency operations were analyzed and characterized. Imaging of lower human limbs is regarded as a case study. The lumped element theory and subsequent numerical simulations were used to fine-tune the single-coil element and the dual-coil array design, respectively. Here, we demonstrate how the shape, size, configuration, and presence of the sample influence the coil performance. The penetration depth of the B 1 -field and the specific absorption rate values have been determined numerically using two numerical surface phantoms: saline and a multilayer human tissue. A preliminary study in the presence of a saline solution phantom has been carried out to develop and validate the dual-coil system. The frequency response of the dual-coil array was measured to assess its robustness when coupled to twelve human volunteers. We found that our design is robust to variations in the anatomical properties of the human thighs, and hence to coil bending. The presented approach can be useful for the implementation of flexible devices with high sensitivity levels and low specific absorption rat

B_{s,d} -> l^+ l^- and K_L -> l^+ l^- in SUSY models with non-minimal sources of flavour mixing

We present a general analysis of B_{s,d}-> l^+ l^- and K_L -> l^+ l^- decays in supersymmetric models with non-minimal sources of flavour mixing. In spite of the existing constraints on off-diagonal squark mass terms, these modes could still receive sizeable corrections, mainly because of Higgs-mediated FCNCs arising at large tan(beta). The severe limits on scenarios with large tan(beta) and non-negligible {tilde d}^i_{R(L)}-{d-tilde}^j_{R(L)} mixing imposed by the present experimental bounds on these modes and Delta B=2 observables are discussed in detail. In particular, we show that scalar-current contributions to K_L -> l^+ l^- and B-{bar B} mixing set non-trivial constraints on the possibility that B_s -> l^+ l^- and B_d -> l^+ l^- receive large corrections.Comment: 18 pages, 4 figures (v2: minor changes, published version

Temporal lobe connects regression and macrocephaly to autism spectrum disorders

Interictal electroencephalogram (EEG) abnormalities are frequently associated with autism spectrum disorders (ASD), although their relationship with the clinical features of ASD, particularly the regressive onset, remains controversial. The aim of this study was to investigate whether the characteristics of interictal EEG abnormalities might help to distinguish and predict definite phenotypes within the heterogeneity of ASD. We reviewed the awake and sleep interictal EEGs of 220 individuals with idiopathic ASD, either with or without a history of seizures. EEG findings were analyzed with respect to a set of clinical variables to explore significant associations. A brain morphometry study was also carried out on a subgroup of patients. EEG abnormalities were seen in 154/220 individuals (70 %) and were mostly focal (p < 0.01) with an anterior localization (p < 0.001). They were detected more frequently during sleep (p < 0.01), and were associated with a regressive onset of ASD (p < 0.05), particularly in individuals with focal temporal localization (p < 0.05). This association was also stronger in regressive patients with concurrent macrocephaly, together with a relative volumetric reduction of the right temporal cortex (p < 0.05). Indeed, concurrence of temporal EEG abnormalities, regression and macrocephaly might possibly define a distinct endophenotype of ASD. EEG-based endophenotypes could be useful to untangle the complexity of ASD, helping to establish anatomic or pathophysiologic subtypes of the disorder

T-odd correlations in charged Kl4 decays

We analyse the sensitivity to physics beyond the SM of T-odd correlations in $K_{\ell 4}$ decays, which do not involve the lepton polarization. We show that a combined analysis of $K^+_{\mu 4}$ and $K^-_{\mu 4}$ decays can lead to new constraints about CP violation in $\Delta S=1$ charged-current interactions, complementary to those obtained from the transverse muon polarization in $K_{\mu 3}$ and of comparable accuracy.Comment: 6 pages (LaTeX

Brainstem morphometric differences in children with autism spectrum disorder, developmental coordination disorder, and those typically developing

Background: The brainstem is a neglected topic in autism research, despite major lines of evidence indicating its active involvement in sensory, motor, affect, arousal, and social regulation (Dadalko & Travers, 2018). It is the substrate of what affective neuroscience identifies as the ‘Core Self’ (Alcaro, Carta, & Panksepp, 2017), and disruption to its growth and function appears to disturb core conscious experience in autism (Delafield-Butt & Trevarthen, 2017; Trevarthen & Delafield-Butt, 2013). Yet, although evidence indicates brainstem growth is disrupted in early childhood (Bosco et al., 2018), how these growth differences compare to closely related neurodevelopmental disorders, such a Developmental Coordination Disorder (DCD), is not yet understood. Objectives: To determine brainstem morphometric differences between children with ASD, DCD, and those typically developing (TD). Methods: Study participants were 87 youths ages 8 to 17 assigned to the ASD (n = 30, 7 female), DCD (n =24, 12 female) or TD (n = 33, 12 female) group. Exclusion criteria for all groups included IQ <80. TD were excluded if they had any neuropsychological or psychopathological disorder. DCD eligibility additionally included performance 16th percentile on the MABC-2 and no concern about an ASD diagnosis. ASD participants had a previous clinical diagnosis confirmed by ADOS-2 and ADI-R. Individuals were excluded if they had another neuropsychological disorder, except attention deficit or anxiety disorder. T1-weighted MPRAGE (1mm isotropic resolution) MRI data were acquired on a 3T MAGNETOM Prisma (Siemens). Brainstem morphology was analysed using SPHARM-MAT (http://lishenlab.com/spharm/), a 3D Fourier surface representation method¬¬¬. A typical surface was calculated for the TD group, and distances from this norm computed for each vertex. Mean distances at each vertex were computed for each group (ASD, DCD, TD) and compared, taking into account age, gender and supratentorial volume as covariates. Results: Significant brainstem morphological differences were identified between all three (TD, ASD and DCD; Figure 1). Significant differences between TD and ASD (p<0.01) were identified in a large region of the anterior-most surface, extending caudally along the right posterior surface. Differences between TD and DCD groups were similar with reduced significance (p0.01), and the pattern diverged with more inclusion of the anterior ventricular surface and less pronouncement at the right anterior border. Finally, significant differences were found between ASD and DCD groups (p<0.01), specifically at the anterior midline either side of the ventricular surface, and especially in two long anteroposterior columns on the left side adjacent and parallel to the fourth ventricle. Conclusions: Surface morphology differences indicate alterations in local nuclei and/or tract growth within the brainstem, especially approaching the anterior surface in ASD and DCD children, and differentially between them at the ventricular surface. The former may relate to specific nerve growth of the pons, and the latter to cerebellar peduncle connectivity differences, superficial nuclei growth such as the hypoglossal, intercalatus, or vagus and associated tracts, or deeper nuclei such as the inferior olivary nucleus. Brainstem structural differences likely disturbs the integrative function of the Core Self. Higher resolution 7T MRI is required to resolve the underlying differential composition