The Emotional Edge of Financial Predators: a Four Group Longitudinal Study

En los últimos años, los inversionistas han sido engañados por sus propios expertos financieros. A pesar de las advertencias de las organizaciones reguladoras, como la Comisión de Seguridad de Valores Mobiliarios o los informes publicados por periódicos y revistas especializados, muchas personas se sienten atrapadas en los esquemas de Ponzi. La pregunta es ¿por qué? En este trabajo se plantea la hipótesis de que gran parte de los inversionistas basó sus decisiones en torno a los asesores o agentes financieros poco escrupulosos que capitalizaron la emoción primitiva. Se realiza una investigación longitudinal con cuatro grupos para un periodo de seis meses en donde se muestra que la gente se involucra en la negociación financiera con el corazón, no sólo con sus pensamientos y calculadoras.In the last few years, a number of investors from all walks of life have been duped by their once-trusted financial advisors. Despite warnings by regulatory bodies such as the Security Exchange Commission or educated reports published by newspapers and magazines, people still get caught in the likes of Ponzi schemes. The question is why? This paper hypothesizes that a large part of the blind eye turned onto financial advisors and brokers finds its source in primitive emotion. A four-group longitudinal study spread over six months shows that people engage in financial negotiation with their hearts and guts, not only with their thoughts and calculators

SPECTRAL CLUSTERING BASED PARCELLATION OF FETAL BRAIN MRI

Many neuroimaging studies are based on the idea that there are distinct brain regions that are functionally or micro-anatomically homogeneous. Obtaining such regions in an au-tomatic way is a challenging task for fetal data due to the lack of strong and consistent anatomical features at the early stages of brain development. In this paper we propose the use of an automatic approach for parcellating fetal cerebral hemi-spheric surfaces into K regions via spectral clustering. Unlike previous methods, our technique has the crucial advantage of only relying on intrinsic geometrical properties of the corti-cal surface and thus being unsupervised. Results on a data-set of fetal brain MRI acquired in utero demonstrated a convinc-ing parcellation reproducibility of the cortical surfaces across fetuses with varying gestational ages and folding magnitude

Atlas-Free Surface Reconstruction of the Cortical Grey-White Interface in Infants

BACKGROUND: The segmentation of the cortical interface between grey and white matter in magnetic resonance images (MRI) is highly challenging during the first post-natal year. First, the heterogeneous brain maturation creates important intensity fluctuations across regions. Second, the cortical ribbon is highly folded creating complex shapes. Finally, the low tissue contrast and partial volume effects hamper cortex edge detection in parts of the brain. METHODS AND FINDINGS: We present an atlas-free method for segmenting the grey-white matter interface of infant brains in T2-weighted (T2w) images. We used a broad characterization of tissue using features based not only on local contrast but also on geometric properties. Furthermore, inaccuracies in localization were reduced by the convergence of two evolving surfaces located on each side of the inner cortical surface. Our method has been applied to eleven brains of one- to four-month-old infants. Both quantitative validations against manual segmentations and sulcal landmarks demonstrated good performance for infants younger than two months old. Inaccuracies in surface reconstruction increased with age in specific brain regions where the tissue contrast decreased with maturation, such as in the central region. CONCLUSIONS: We presented a new segmentation method which achieved good to very good performance at the grey-white matter interface depending on the infant age. This method should reduce manual intervention and could be applied to pathological brains since it does not require any brain atlas

Cortical folding influences migraine aura symptoms in CADASIL

Migraine with aura is a hallmark of cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL). In contrast with the majority of CADASIL patients, some affected subjects never experience visual symptoms during their attacks of migraine with aura. The aim of this study was to determine whether specific morphology of the primary visual cortex is associated with the absence of visual symptoms during migraine aura in CADASIL

Optimizing contrastive learning for cortical folding pattern detection

The human cerebral cortex has many bumps and grooves called gyri and sulci. Even though there is a high inter-individual consistency for the main cortical folds, this is not the case when we examine the exact shapes and details of the folding patterns. Because of this complexity, characterizing the cortical folding variability and relating them to subjects' behavioral characteristics or pathologies is still an open scientific problem. Classical approaches include labeling a few specific patterns, either manually or semi-automatically, based on geometric distances, but the recent availability of MRI image datasets of tens of thousands of subjects makes modern deep-learning techniques particularly attractive. Here, we build a self-supervised deep-learning model to detect folding patterns in the cingulate region. We train a contrastive self-supervised model (SimCLR) on both Human Connectome Project (1101 subjects) and UKBioBank (21070 subjects) datasets with topological-based augmentations on the cortical skeletons, which are topological objects that capture the shape of the folds. We explore several backbone architectures (convolutional network, DenseNet, and PointNet) for the SimCLR. For evaluation and testing, we perform a linear classification task on a database manually labeled for the presence of the "double-parallel" folding pattern in the cingulate region, which is related to schizophrenia characteristics. The best model, giving a test AUC of 0.76, is a convolutional network with 6 layers, a 10-dimensional latent space, a linear projection head, and using the branch-clipping augmentation. This is the first time that a self-supervised deep learning model has been applied to cortical skeletons on such a large dataset and quantitatively evaluated. We can now envisage the next step: applying it to other brain regions to detect other biomarkers.Comment: 9 pages, 6 figures, 1 table, SPIE Imaging 202

Structural analysis of fMRI data revisited: improving the sensitivity and reliability of fMRI group studies.

International audienceGroup studies of functional magnetic resonance imaging datasets are usually based on the computation of the mean signal across subjects at each voxel (random effects analyses), assuming that all subjects have been set in the same anatomical space (normalization). Although this approach allows for a correct specificity (rate of false detections), it is not very efficient for three reasons: i) its underlying hypotheses, perfect coregistration of the individual datasets and normality of the measured signal at the group level are frequently violated; ii) the group size is small in general, so that asymptotic approximations on the parameters distributions do not hold; iii) the large size of the images requires some conservative strategies to control the false detection rate, at the risk of increasing the number of false negatives. Given that it is still very challenging to build generative or parametric models of intersubject variability, we rely on a rule based, bottom-up approach: we present a set of procedures that detect structures of interest from each subject's data, then search for correspondences across subjects and outline the most reproducible activation regions in the group studied. This framework enables a strict control on the number of false detections. It is shown here that this analysis demonstrates increased validity and improves both the sensitivity and reliability of group analyses compared with standard methods. Moreover, it directly provides information on the spatial position correspondence or variability of the activated regions across subjects, which is difficult to obtain in standard voxel-based analyses

Towards Deciphering the Fetal Foundation of Normal Cognition and Cognitive Symptoms From Sulcation of the Cortex.

Growing evidence supports that prenatal processes play an important role for cognitive ability in normal and clinical conditions. In this context, several neuroimaging studies searched for features in postnatal life that could serve as a proxy for earlier developmental events. A very interesting candidate is the sulcal, or sulco-gyral, patterns, macroscopic features of the cortex anatomy related to the fold topology-e.g., continuous vs. interrupted/broken fold, present vs. absent fold-or their spatial organization. Indeed, as opposed to quantitative features of the cortical sheet (e.g., thickness, surface area or curvature) taking decades to reach the levels measured in adult, the qualitative sulcal patterns are mainly determined before birth and stable across the lifespan. The sulcal patterns therefore offer a window on the fetal constraints on specific brain areas on cognitive abilities and clinical symptoms that manifest later in life. After a global review of the cerebral cortex sulcation, its mechanisms, its ontogenesis along with methodological issues on how to measure the sulcal patterns, we present a selection of studies illustrating that analysis of the sulcal patterns can provide information on prenatal dispositions to cognition (with a focus on cognitive control and academic abilities) and cognitive symptoms (with a focus on schizophrenia and bipolar disorders). Finally, perspectives of sulcal studies are discussed

A novel method for quantification of sulfolane (a metabolite of busulfan) in plasma by gas chromatography-tandem mass spectrometry

The role of busulfan (Bu) metabolites in the adverse events seen during hematopoietic stem cell transplantation and in drug interactions is not explored. Lack of availability of established analytical methods limits our understanding in this area. The present work describes a novel gas chromatography-tandem mass spectrometric assay for the analysis of sulfolane (Su) in plasma of patients receiving high-dose Bu. Su and Bu were extracted from a single 100 μL plasma sample by liquid-liquid extraction. Bu was separately derivatized with 2,3,5,6-tetrafluorothiophenolfluorinated agent. Mass spectrometric detection of the analytes was performed in the selected reaction monitoring mode on a triple quadrupole instrument after electronic impact ionization. Bu and Su were analyzed with separate chromatographic programs, lasting 5min each. The assay for Su was found to be linear in the concentration range of 20-400ng/mL. The method has satisfactory sensitivity (lower limit of quantification, 20ng/mL) and precision (relative standard deviation less than 15%) for all the concentrations tested with a good trueness (100 ± 5%). This method was applied to measure Su from pediatric patients with samples collected 4h after dose 1 (n = 46), before dose 7 (n = 56), and after dose 9 (n = 54) infusions of Bu. Su (mean ± SD) was detectable in plasma of patients 4h after dose 1, and higher levels were observed after dose 9 (249.9 ± 123.4ng/mL). This method may be used in clinical studies investigating the role of Su on adverse events and drug interactions associated with Bu therapy. Figure Overall sample preparation procedure for quantification of sulfolane and busulfan in plasma from patients receiving higher doses of busulfa