Altered functional and structural brain network organization in autism.

Structural and functional underconnectivity have been reported for multiple brain regions, functional systems, and white matter tracts in individuals with autism spectrum disorders (ASD). Although recent developments in complex network analysis have established that the brain is a modular network exhibiting small-world properties, network level organization has not been carefully examined in ASD. Here we used resting-state functional MRI (n = 42 ASD, n = 37 typically developing; TD) to show that children and adolescents with ASD display reduced short and long-range connectivity within functional systems (i.e., reduced functional integration) and stronger connectivity between functional systems (i.e., reduced functional segregation), particularly in default and higher-order visual regions. Using graph theoretical methods, we show that pairwise group differences in functional connectivity are reflected in network level reductions in modularity and clustering (local efficiency), but shorter characteristic path lengths (higher global efficiency). Structural networks, generated from diffusion tensor MRI derived fiber tracts (n = 51 ASD, n = 43 TD), displayed lower levels of white matter integrity yet higher numbers of fibers. TD and ASD individuals exhibited similar levels of correlation between raw measures of structural and functional connectivity (n = 35 ASD, n = 35 TD). However, a principal component analysis combining structural and functional network properties revealed that the balance of local and global efficiency between structural and functional networks was reduced in ASD, positively correlated with age, and inversely correlated with ASD symptom severity. Overall, our findings suggest that modeling the brain as a complex network will be highly informative in unraveling the biological basis of ASD and other neuropsychiatric disorders

Diagnostic Prediction Using Discomfort Drawings with IBTM

In this paper, we explore the possibility to apply machine learning to make diagnostic predictions using discomfort drawings. A discomfort drawing is an intuitive way for patients to express discomfort and pain related symptoms. These drawings have proven to be an effective method to collect patient data and make diagnostic decisions in real-life practice. A dataset from real-world patient cases is collected for which medical experts provide diagnostic labels. Next, we use a factorized multimodal topic model, Inter-Battery Topic Model (IBTM), to train a system that can make diagnostic predictions given an unseen discomfort drawing. The number of output diagnostic labels is determined by using mean-shift clustering on the discomfort drawing. Experimental results show reasonable predictions of diagnostic labels given an unseen discomfort drawing. Additionally, we generate synthetic discomfort drawings with IBTM given a diagnostic label, which results in typical cases of symptoms. The positive result indicates a significant potential of machine learning to be used for parts of the pain diagnostic process and to be a decision support system for physicians and other health care personnel.Comment: Presented at 2016 Machine Learning and Healthcare Conference (MLHC 2016), Los Angeles, C

Integrated Management of Multiple Sclerosis Spasticity and Associated Symptoms Using the Spasticity-Plus Syndrome Concept: Results of a Structured Specialists’ Discussion Using the Workmat® Methodology

Esclerosi múltiple; Espasticitat; CannabinoidesMúltiple esclerosis; Espasticidad; CannabidiolMultiple sclerosis; Muscle spasticity; CannabidiolBackground: Multiple sclerosis (MS) treatment has radically improved over the last years; however, MS symptom management is still challenging. The novel Spasticity-Plus syndrome was conceptualized to frame several spasticity-related symptoms that can be addressed together with broad-spectrum medication, such as certain cannabinoid-based drugs. The aim of this project was to gain insight into Spanish neurologists' clinical experience on MS spasticity and associated symptoms, and to assess the acknowledgment and applicability of the Spasticity-Plus syndrome concept in patients with MS. Methods: Ten online meetings were conducted using the Workmat® methodology to allow structured discussions. Fifty-five Spanish neurologists, experts in MS management, completed and discussed a set of predefined exercises comprising MS symptom assessment and its management in clinical practice, MS symptoms clustering in clinical practice, and their perception of the Spasticity-Plus syndrome concept. This document presents the quantitative and qualitative results of these discussions. Results: The specialists considered that polytherapy is a common concern in MS and that simplifying the management of MS spasticity and associated manifestations could be useful. They generally agreed that MS spasticity should be diagnosed before moderate or severe forms appear. According to the neurologists' clinical experience, symptoms commonly associated with MS spasticity included spasms/cramps (100% of the specialists), pain (85%), bladder dysfunction (62%), bowel dysfunction (42%), sleep disorders (42%), and sexual dysfunction (40%). The multiple correspondence analysis revealed two main symptom clusters: spasticity-spasms/cramps-pain, and ataxia-instability-vertigo. Twelve out of 16 symptoms (75%) were scored >7 in a 0-10 QoL impact scale by the specialists, representing a moderate-high impact. The MS specialists considered that pain, spasticity, spasms/cramps, bladder dysfunction, and depression should be a treatment priority given their frequency and chance of therapeutic success. The neurologists agreed on the usefulness of the new Spasticity-Plus syndrome concept to manage spasticity and associated symptoms together, and their experience with treatments targeting the cannabinoid system was satisfactory. Conclusions: The applicability of the new concept of Spasticity-Plus in MS clinical practice seems possible and may lead to an integrated management of several MS symptoms, thus reducing the treatment burden of disease symptoms.The study was funded by an investigational grant from Almirall S

Subtyping of common complex diseases and disorders by integrating heterogeneous data. Identifying clusters among women with lower urinary tract symptoms in the LURN study

We present a methodology for subtyping of persons with a common clinical symptom complex by integrating heterogeneous continuous and categorical data. We illustrate it by clustering women with lower urinary tract symptoms (LUTS), who represent a heterogeneous cohort with overlapping symptoms and multifactorial etiology. Data collected in the Symptoms of Lower Urinary Tract Dysfunction Research Network (LURN), a multi-center observational study, included self-reported urinary and non-urinary symptoms, bladder diaries, and physical examination data for 545 women. Heterogeneity in these multidimensional data required thorough and non-trivial preprocessing, including scaling by controls and weighting to mitigate data redundancy, while the various data types (continuous and categorical) required novel methodology using a weighted Tanimoto indices approach. Data domains only available on a subset of the cohort were integrated using a semi-supervised clustering approach. Novel contrast criterion for determination of the optimal number of clusters in consensus clustering was introduced and compared with existing criteria. Distinctiveness of the clusters was confirmed by using multiple criteria for cluster quality, and by testing for significantly different variables in pairwise comparisons of the clusters. Cluster dynamics were explored by analyzing longitudinal data at 3- and 12-month follow-up. Five clusters of women with LUTS were identified using the developed methodology. None of the clusters could be characterized by a single symptom, but rather by a distinct combination of symptoms with various levels of severity. Targeted proteomics of serum samples demonstrated that differentially abundant proteins and affected pathways are different across the clusters. The clinical relevance of the identified clusters is discussed and compared with the current conventional approaches to the evaluation of LUTS patients. The rationale and thought process are described for the selection of procedures for data preprocessing, clustering, and cluster evaluation. Suggestions are provided for minimum reporting requirements in publications utilizing clustering methodology with multiple heterogeneous data domains

From Affective Science to Psychiatric Disorder: Ontology as Semantic Bridge

Advances in emotion and affective science have yet to translate routinely into psychiatric research and practice. This is unfortunate since emotion and affect are fundamental components of many psychiatric conditions. Rectifying this lack of interdisciplinary integration could thus be a potential avenue for improving psychiatric diagnosis and treatment. In this contribution, we propose and discuss an ontological framework for explicitly capturing the complex interrelations between affective entities and psychiatric disorders, in order to facilitate mapping and integration between affective science and psychiatric diagnostics. We build on and enhance the categorisation of emotion, affect and mood within the previously developed Emotion Ontology, and that of psychiatric disorders in the Mental Disease Ontology. This effort further draws on developments in formal ontology regarding the distinction between normal and abnormal in order to formalize the interconnections. This operational semantic framework is relevant for applications including clarifying psychiatric diagnostic categories, clinical information systems, and the integration and translation of research results across disciplines