Meeting abstract: iMap 4: An Open Source Toolbox for the Statistical Fixation Mapping of Eye Movement data with Linear Mixed Modeling.

A major challenge in modern eye movement research is to statistically map where observers are looking at, as well as isolating statistical significant differences between groups and conditions. Compared to signals of contemporary neuroscience measures, such as M/EEG and fMRI, eye movement data are sparse with much larger variations across trials and participants. As a result, the implementation of a conventional Hierarchical Linear Model approach on two-dimensional fixation distributions often returns unstable estimations and underpowered results, leaving this statistical problem unresolved. Here, we tackled this issue by using the statistical framework implemented in diverse state-of-the-art neuroimaging data processing toolboxes: Statistical Parametric Mapping (SPM), Fieldtrip and LIMO EEG. We first estimated the mean individual fixation maps per condition by using trimmean to account for the sparseness and the high variations of fixation data. We then applied a univariate, pixel-wise linear mixed model (LMM) on the smoothed fixation data with each subject as a random effect, which offers the flexibility to code for multiple between- and within- subject comparisons. After this step, our approach allows to perform all the possible linear contrasts for the fixed effects (main effects, interactions, etc.). Importantly, we also introduced a novel spatial cluster test based on bootstrapping to assess the statistical significance of the linear contrasts. Finally, we validated this approach by using both experimental and computer simulation data with a Monte Carlo approach. iMap 4 is a freely available MATLAB open source toolbox for the statistical fixation mapping of eye movement data, with a user-friendly interface providing straightforward, easy to interpret statistical graphical outputs and matching the standards in robust statistical neuroimaging methods. iMap 4 represents a major step in the processing of eye movement fixation data, paving the way to a routine use of robust data-driven analyses in this important field of vision sciences. Meeting abstract presented at VSS 2015

iMap4: An Open Source Toolbox for the Statistical Fixation Mapping of Eye Movement data with Linear Mixed Modeling.

A major challenge in modern eye movement research is to statistically map where observers are looking, by isolating the significant differences between groups and conditions. Compared to signals of contemporary neuroscience measures, such as M/EEG and fMRI, eye movement data are sparser with much larger variations in space across trials and participants. As a result, the implementation of a conventional linear modeling approach on two-dimensional fixation distributions often returns unstable estimations and underpowered results, leaving this statistical problem unresolved (Liversedge, Gilchrist, & Everling. 2011). Here, we present a new version of the iMap toolbox (Caldara and Miellet, 2011) which tackles this issue by implementing a statistical framework comparable to those developped in state-of the- art neuroimaging data processing toolboxes. iMap4 uses univariate, pixel-wise Linear Mixed Models (LMM) on the smoothed fixation data, with the flexibility of coding for multiple between- and within- subject comparisons and performing all the possible linear contrasts for the fixed effects (main effects, interactions, etc.). Importantly, we also introduced novel nonparametric tests based on resampling to assess statistical significance. Finally, we validated this approach by using both experimental and Monte Carlo simulation data. iMap4 is a freely available MATLAB open source toolbox for the statistical fixation mapping of eye movement data, with a user-friendly interface providing straightforward, easy to interpret statistical graphical outputs. iMap4 matches the standards of robust statistical neuroimaging methods and represents an important step in the data-driven processing of eye movement fixation data, an important field of vision sciences

Cluster-based computational methods for mass univariate analyses of event-related brain potentials/fields:A simulation study

Background In recent years, analyses of event related potentials/fields have moved from the selection of a few components and peaks to a mass-univariate approach in which the whole data space is analyzed. Such extensive testing increases the number of false positives and correction for multiple comparisons is needed. Method Here we review all cluster-based correction for multiple comparison methods (cluster-height, cluster-size, cluster-mass, and threshold free cluster enhancement – TFCE), in conjunction with two computational approaches (permutation and bootstrap). Results Data driven Monte-Carlo simulations comparing two conditions within subjects (two sample Student's t-test) showed that, on average, all cluster-based methods using permutation or bootstrap alike control well the family-wise error rate (FWER), with a few caveats. Conclusions (i) A minimum of 800 iterations are necessary to obtain stable results; (ii) below 50 trials, bootstrap methods are too conservative; (iii) for low critical family-wise error rates (e.g. p = 1%), permutations can be too liberal; (iv) TFCE controls best the type 1 error rate with an attenuated extent parameter (i.e. power < 1)

Learning from failure

We study decentralized learning in organizations. Decentralization is captured through a symmetry constraint on agents’ strategies. Among such attainable strategies, we solve for optimal and equilibrium strategies. We model the organization as a repeated game with imperfectly observable actions. A fixed but unknown subset of action profiles are successes and all other action profiles are failures. The game is played until either there is a success or the time horizon is reached. For any time horizon, including infinity, we demonstrate existence of optimal attainable strategies and show that they are Nash equilibria. For some time horizons, we can solve explicitly for the optimal attainable strategies and show uniqueness. The solution connects the learning behavior of agents to the fundamentals that characterize the organization: Agents in the organization respond more slowly to failure as the future becomes more important, the size of the organization increases and the probability of success decreases.Game theory

Parametric study of EEG sensitivity to phase noise during face processing

&lt;b&gt;Background: &lt;/b&gt; The present paper examines the visual processing speed of complex objects, here faces, by mapping the relationship between object physical properties and single-trial brain responses. Measuring visual processing speed is challenging because uncontrolled physical differences that co-vary with object categories might affect brain measurements, thus biasing our speed estimates. Recently, we demonstrated that early event-related potential (ERP) differences between faces and objects are preserved even when images differ only in phase information, and amplitude spectra are equated across image categories. Here, we use a parametric design to study how early ERP to faces are shaped by phase information. Subjects performed a two-alternative force choice discrimination between two faces (Experiment 1) or textures (two control experiments). All stimuli had the same amplitude spectrum and were presented at 11 phase noise levels, varying from 0% to 100% in 10% increments, using a linear phase interpolation technique. Single-trial ERP data from each subject were analysed using a multiple linear regression model. &lt;b&gt;Results: &lt;/b&gt; Our results show that sensitivity to phase noise in faces emerges progressively in a short time window between the P1 and the N170 ERP visual components. The sensitivity to phase noise starts at about 120–130 ms after stimulus onset and continues for another 25–40 ms. This result was robust both within and across subjects. A control experiment using pink noise textures, which had the same second-order statistics as the faces used in Experiment 1, demonstrated that the sensitivity to phase noise observed for faces cannot be explained by the presence of global image structure alone. A second control experiment used wavelet textures that were matched to the face stimuli in terms of second- and higher-order image statistics. Results from this experiment suggest that higher-order statistics of faces are necessary but not sufficient to obtain the sensitivity to phase noise function observed in response to faces. &lt;b&gt;Conclusion: &lt;/b&gt; Our results constitute the first quantitative assessment of the time course of phase information processing by the human visual brain. We interpret our results in a framework that focuses on image statistics and single-trial analyses

Testing for the Dual-Route Cascade Reading Model in the Brain: An fMRI Effective Connectivity Account of an Efficient Reading Style

Neuropsychological data about the forms of acquired reading impairment provide a strong basis for the theoretical framework of the dual-route cascade (DRC) model which is predictive of reading performance. However, lesions are often extensive and heterogeneous, thus making it difficult to establish precise functional anatomical correlates. Here, we provide a connective neural account in the aim of accommodating the main principles of the DRC framework and to make predictions on reading skill. We located prominent reading areas using fMRI and applied structural equation modeling to pinpoint distinct neural pathways. Functionality of regions together with neural network dissociations between words and pseudowords corroborate the existing neuroanatomical view on the DRC and provide a novel outlook on the sub-regions involved. In a similar vein, congruent (or incongruent) reliance of pathways, that is reliance on the word (or pseudoword) pathway during word reading and on the pseudoword (or word) pathway during pseudoword reading predicted good (or poor) reading performance as assessed by out-of-magnet reading tests. Finally, inter-individual analysis unraveled an efficient reading style mirroring pathway reliance as a function of the fingerprint of the stimulus to be read, suggesting an optimal pattern of cerebral information trafficking which leads to high reading performance

Feasibility, acceptability and effectiveness of integrated care for COPD patients: a mixed methods evaluation of a pilot community-based programme.

The aim of this study was to assess the feasibility, acceptability and effectiveness of a pilot COPD integrated care programme implemented in Valais, Switzerland. The programme was adapted from the self-management programme Living Well with COPD, and included the following elements: self-management patient-education group sessions, telephone and medical follow-ups, multidisciplinary teams, training of healthcare professionals, and evidence-based COPD care. A process and outcome evaluation of the pilot phase of the programme was conducted by means of qualitative and quantitative methods. Reach (coverage, participation rates), dosage (interventions carried out), fidelity (delivered as intended) and stakeholders' acceptance of the programme were evaluated through data monitoring and conduct of focus groups with patients and healthcare professionals. Effectiveness was assessed with pre-post analyses (before and after the intervention). The primary outcome measures were; (1) generic and disease-specific quality of life (36-Item Short Form Health Survey, Chronic Respiratory Questionnaire); and (2) hospitalisations (all-cause and for acute exacerbations) in the past 12 months. Secondary outcomes included self-efficacy, number of exacerbations and exercise capacity. Finally, controlled pre-post comparisons were also made with patients from the Swiss COPD Cohort for three common outcome measures (dyspnoea [mMRC score], number of exacerbations and smoking status). During the first 2 years of the programme, eight series of group-based education sessions were delivered to 57 patients with COPD in three different locations of the canton of Valais. Coverage objectives were achieved and attendance rate at the education sessions was high (83.6%). Patients' and healthcare professionals' reported a high degree of satisfaction, except for multidisciplinarity and transfer of information. Exploration of the effectiveness of this pilot programme suggested positive pre-post results at 12 months, with improvements in terms of health-related quality of life, self-efficacy, exercise capacity, immunisation coverage and Patient Assessment of Chronic Illness Care score. No other outcome, including the number of hospital admissions, differed significantly after 12 months. We observed no differences from the control group. The evaluation demonstrated the feasibility and acceptability of the programme and confirmed the relevance of mixed method process evaluation to adjust and improve programme implementation. The introduction of multidisciplinary teams in a context characterised by fragmentation of care was identified as the main challenge in the programme implementation and could not be achieved as expected. Despite this area for improvement, patients' feedback and early effectiveness results confirmed the benefits of COPD integrated care programmes emphasising self-management education

Imaging learned fear circuitry in awake mice using fMRI

Functional magnetic resonance imaging (fMRI) of learned behaviour in ‘awake rodents’ provides the opportunity for translational preclinical studies into the influence of pharmacological and genetic manipulations on brain function. fMRI has recently been employed to investigate learned behaviour in awake rats. Here, this methodology is translated to mice, so that future fMRI studies may exploit the vast number of genetically modified mouse lines that are available. One group of mice was conditioned to associate a flashing light (conditioned stimulus, CS) with foot shock (PG; paired group), and another group of mice received foot shock and flashing light explicitly unpaired (UG; unpaired group). The blood oxygen level-dependent signal (proxy for neuronal activation) in response to the CS was measured 24 h later in awake mice from the PG and UG using fMRI. The amygdala, implicated in fear processing, was activated to a greater degree in the PG than in the UG in response to the CS. Additionally, the nucleus accumbens was activated in the UG in response to the CS. Because the CS signalled an absence of foot shock in the UG, it is possible that this region is involved in processing the safety aspect of the CS. To conclude, the first use of fMRI to visualise brain activation in awake mice that are completing a learned emotional task is reported. This work paves the way for future preclinical fMRI studies to investigate genetic and environmental influences on brain function in transgenic mouse models of disease and aging

Machine learning of neuroimaging for assisted diagnosis of cognitive impairment and dementia: A systematic review

pp. 519-535Los métodos avanzados de aprendizaje automático pueden ayudar a identificar el riesgo de demencia de la neuroimagen, pero su precisión hasta la fecha no está clara. Revisamos sistemáticamente la literatura, desde 2006 hasta finales de 2016, para los estudios de aprendizaje automático que diferencian el envejecimiento saludable de la demencia de varios tipos, evaluamos la calidad del estudio y comparamos la precisión en diferentes límites de enfermedades. De los 111 estudios relevantes, la mayoría evaluó la enfermedad de Alzheimer en comparación con los controles sanos, utilizando datos de la Iniciativa de neuroimagen AD, máquinas de vectores de soporte y solo secuencias ponderadas en T1. La precisión fue más alta para diferenciar la enfermedad de Alzheimer de los controles sanos y pobre para diferenciar los controles sanos versus deterioro cognitivo leve versus enfermedad de Alzheimer o conversores de deterioro cognitivo leve versus no conversores. La precisión aumentó con los tipos de datos combinados, pero no con la fuente de datos, el tamaño de la muestra o el método de aprendizaje automático. El aprendizaje automático todavía no distingue categorías de enfermedades clínicamente relevantes. Los conjuntos de datos más diversos, las combinaciones de diferentes tipos de datos y la estrecha integración clínica del aprendizaje automático ayudarían a avanzar en este campo.S