Associating Multi-modal Brain Imaging Phenotypes and Genetic Risk Factors via A Dirty Multi-task Learning Method

Brain imaging genetics becomes more and more important in brain science, which integrates genetic variations and brain structures or functions to study the genetic basis of brain disorders. The multi-modal imaging data collected by different technologies, measuring the same brain distinctly, might carry complementary information. Unfortunately, we do not know the extent to which the phenotypic variance is shared among multiple imaging modalities, which further might trace back to the complex genetic mechanism. In this paper, we propose a novel dirty multi-task sparse canonical correlation analysis (SCCA) to study imaging genetic problems with multi-modal brain imaging quantitative traits (QTs) involved. The proposed method takes advantages of the multi-task learning and parameter decomposition. It can not only identify the shared imaging QTs and genetic loci across multiple modalities, but also identify the modality-specific imaging QTs and genetic loci, exhibiting a flexible capability of identifying complex multi-SNP-multi-QT associations. Using the state-of-the-art multi-view SCCA and multi-task SCCA, the proposed method shows better or comparable canonical correlation coefficients and canonical weights on both synthetic and real neuroimaging genetic data. In addition, the identified modality-consistent biomarkers, as well as the modality-specific biomarkers, provide meaningful and interesting information, demonstrating the dirty multi-task SCCA could be a powerful alternative method in multi-modal brain imaging genetics

Graph-Based Fusion of Imaging, Genetic and Clinical Data for Degenerative Disease Diagnosis

Graph learning methods have achieved noteworthy performance in disease diagnosis due to their ability to represent unstructured information such as inter-subject relationships. While it has been shown that imaging, genetic and clinical data are crucial for degenerative disease diagnosis, existing methods rarely consider how best to use their relationships. How best to utilize information from imaging, genetic and clinical data remains a challenging problem. This study proposes a novel graph-based fusion (GBF) approach to meet this challenge. To extract effective imaging-genetic features, we propose an imaging-genetic fusion module which uses an attention mechanism to obtain modality-specific and joint representations within and between imaging and genetic data. Then, considering the effectiveness of clinical information for diagnosing degenerative diseases, we propose a multi-graph fusion module to further fuse imaging-genetic and clinical features, which adopts a learnable graph construction strategy and a graph ensemble method. Experimental results on two benchmarks for degenerative disease diagnosis (Alzheimer's Disease Neuroimaging Initiative and Parkinson's Progression Markers Initiative) demonstrate its effectiveness compared to state-of-the-art graph-based methods. Our findings should help guide further development of graph-based models for dealing with imaging, genetic and clinical data

Advanced photonic and electronic systems WILGA 2016

Young Researchers Symposium WILGA on Photonics Applications and Web Engineering has been organized since 1998, two times a year. Subject area of the Wilga Symposium are advanced photonic and electronic systems in all aspects: theoretical, design and application, hardware and software, academic, scientific, research, development, commissioning and industrial, but also educational and development of research and technical staff. Each year, during the international Spring edition, the Wilga Symposium is attended by a few hundred young researchers, graduated M.Sc. students, Ph.D. students, young doctors, young research workers from the R&D institutions, universities, innovative firms, etc. Wilga, gathering through years the organization experience, has turned out to be a perfect relevant information exchange platform between young researchers from Poland with participation  of international guests, all active in the research areas of electron and photon technologies, electronics, photonics, telecommunications, automation, robotics and information technology, but also technical physics. The paper summarizes the achievements of the 38th Spring Edition of 2016 WILGA Symposium, organized in Wilga Village Resort owned by Warsaw University of technology

Mechanisms for Taste Sensation of Carbonation

Carbonation, or the presence of carbon dioxide (CO2) dissolved in solution, is a commonly encountered feature of beverages in the contemporary human diet. While the popularity of carbonation may be attributed to its distinct sensory qualities, the specific orosensory pathways mediating CO2 detection in mammals have not previously been delineated. This dissertation describes the identification of specific cellular and molecular mechanisms that mediate taste sensation of carbonation, using the mouse as a model system. The mammalian gustatory system is sensitive to CO2, and these responses are sensitive to inhibition of carbonic anhydrases, enzymes that catalyze the interconversion of carbon dioxide with carbonic acid. Through gene expression profiling I discovered that the gene carbonic anhydrase IV (Car4), encoding an extracellular enzyme, is specifically expressed in acid sensing taste receptor cells (TRCs). Genetic ablation of the Car4 locus resulted in a major deficit in gustatory CO2 sensation that is stimulus specific, not affecting responses to acid. Ablation or silencing of acid sensing TRCs likewise produced a profound deficit in taste responses to CO2. These studies identified a primary pathway of the gustatory carbonation response, substantiating acid sensing TRC and the Car4 enzyme as key mediators. A smaller gustatory neural response to carbonation remains even in the absence of sour-sensing TRC and/or Car4. To identify additional carbonation sensing pathways, I applied an in vivo calcium-imaging assay to define the ensemble of primary gustatory neurons activated by CO2. These studies revealed that in addition to robust activation of sour sensing neurons, a secondary gustatory pathway for CO2 detection is mediated by subpopulations of bitter and sweet responsive neurons. I identified carbonic anhydrase VII (Car7) as an intracellular carbonic anhydrase specifically expressed by sweet, bitter and umami sensing TRC. Pharmacological and gene expression data support a role for Car7 in transducing the secondary CO2 sensing pathway. These studies suggested that carbonation acts as a complex gustatory stimulus, stimulating sour, sweet and bitter taste qualities simultaneously. The rules governing peripheral encoding of multi-modal taste stimuli are not well understood. To address this issue, I examined the peripheral gustatory response to binary mixtures of taste qualities. I found that most combinations of taste qualities are represented as a superimposition of the component responses. However, neural responses to attractive stimuli, including natural sugars, artificial sweeteners and umami tastants, are selectively suppressed by simultaneous co-stimulation with a sour (acidic) stimulus. Acid-mediated suppression of sweet is cell autonomous, occurring even in the absence of gustatory acid sensing. Remarkably, carbonation stimulates sour signaling without suppressing sweet taste response. These studies suggest that cross-modal interactions at the periphery modulate the sensory response to complex taste stimuli

Mixture Models for Image Analysis

International audienc

The Emotional Brain in Obsessive-Compulsive Disorder

Background Obsessive-compulsive disorder (OCD) is characterized by distressing obsessions and time-consuming compulsions. The disorder affects 1-3% and can be highly impairing to daily functioning and detrimental to the quality of life. Cognitive behavioral therapy is an effective treatment for 50-75% of people with OCD, leaving a considerable minority who do not benefit from the best available treatments we have today. Neuroimaging has related the disorder to the function and structure of cortico-striato-thalamo-cortical and fronto-limbic circuits. A better understanding of these circuits might contribute to a better understanding of the disorder, how current treatments change the brain, and how we can help non-responders with better treatments in the future. This is likely particularly true for fronto-limbic and affective circuits, given their role in the formation, maintenance, and extinction of fear as well as motivating behavior. The aim of this dissertation was, first, to investigate how OCD is related to brain activation during emotional processing of aversive stimuli. Secondly, we wanted to examine if unaffected siblings of OCD patients showed similar anxiety, brain activation, and connectivity during emotion provocation and regulation as their OCD-affected siblings compared to unrelated healthy controls. Lastly, we wanted to investigate if the resting-state network structure changes in OCD patients directly after the Bergen 4-Day Treatment (B4DT), a concentrated and exposure-based psychological therapy. Methods Paper I was a meta-analysis of 25 functional neuroimaging studies comparing OCD patients and healthy controls during emotion processing, when participants were exposed to aversive or neutral stimuli. In Paper II we used functional magnetic resonance imaging (fMRI) to investigate distress, brain activation, and fronto-limbic connectivity during emotion provocation and regulation of neutral, fear-related, and OCD-related stimuli in 43 unmedicated OCD patients, 19 unaffected siblings, and 38 healthy controls. In Paper III we used resting-state fMRI to study the network structure of 28 OCD patients (21 unmedicated) and 19 healthy controls the day before and three days after B4DT. We examined static and dynamic graph metrics at the global, subnetwork, and regional levels, as well as between-subnetwork connectivity. Results In Paper I, we found that OCD patients showed more activation than healthy controls in the orbitofrontal cortex (OFC), extending into the subgenual anterior cingulate cortex (sgACC) and ventromedial prefrontal cortex (vmPFC), bilateral amygdala (extending into the right putamen), left inferior occipital cortex, and right middle temporal gyrus during aversive versus neutral stimuli. Meta-regressions showed that medication status and comorbidity moderated amygdala, occipital and ventromedial prefrontal cortex hyperactivation, while symptom severity moderated hyperactivation in medial frontal prefrontal and superior parietal regions. In Paper II we showed that unaffected siblings resembled healthy controls in task-related distress, less amygdala activation/altered timing than OCD patients during emotion provocation. During OCD-related emotion regulation siblings showed no significant difference in dmPFC activation versus either OCD patients or healthy controls, but showed more temporo-occipital activation and dmPFC-amygdala connectivity compared to healthy controls. In Paper III we found that unmedicated OCD patients showed more frontoparietal-limbic connectivity before treatment than healthy controls. This, along with sgACC flexibility, was reduced in OCD patients directly after B4DT. Conclusions OCD patients show hyperactivation of the amygdala and related structures, but this characteristic is not directly shared with unaffected siblings during provocation or regulation of emotional information. However, siblings seem to show compensatory activation and connectivity in other areas. The rapid changes in frontoparietal-limbic connectivity and subgenual ACC flexibility suggests that concentrated treatment leads to a more independent and stable network state. OCD is related to subtle alterations in limbic activation and fronto-limbic connectivity during both emotional tasks and resting-state, which seems to vary with comorbidity and is sensitive to treatment

The Technological Emergence of AutoML: A Survey of Performant Software and Applications in the Context of Industry

With most technical fields, there exists a delay between fundamental academic research and practical industrial uptake. Whilst some sciences have robust and well-established processes for commercialisation, such as the pharmaceutical practice of regimented drug trials, other fields face transitory periods in which fundamental academic advancements diffuse gradually into the space of commerce and industry. For the still relatively young field of Automated/Autonomous Machine Learning (AutoML/AutonoML), that transitory period is under way, spurred on by a burgeoning interest from broader society. Yet, to date, little research has been undertaken to assess the current state of this dissemination and its uptake. Thus, this review makes two primary contributions to knowledge around this topic. Firstly, it provides the most up-to-date and comprehensive survey of existing AutoML tools, both open-source and commercial. Secondly, it motivates and outlines a framework for assessing whether an AutoML solution designed for real-world application is 'performant'; this framework extends beyond the limitations of typical academic criteria, considering a variety of stakeholder needs and the human-computer interactions required to service them. Thus, additionally supported by an extensive assessment and comparison of academic and commercial case-studies, this review evaluates mainstream engagement with AutoML in the early 2020s, identifying obstacles and opportunities for accelerating future uptake