A group model for stable multi-subject ICA on fMRI datasets

Spatial Independent Component Analysis (ICA) is an increasingly used data-driven method to analyze functional Magnetic Resonance Imaging (fMRI) data. To date, it has been used to extract sets of mutually correlated brain regions without prior information on the time course of these regions. Some of these sets of regions, interpreted as functional networks, have recently been used to provide markers of brain diseases and open the road to paradigm-free population comparisons. Such group studies raise the question of modeling subject variability within ICA: how can the patterns representative of a group be modeled and estimated via ICA for reliable inter-group comparisons? In this paper, we propose a hierarchical model for patterns in multi-subject fMRI datasets, akin to mixed-effect group models used in linear-model-based analysis. We introduce an estimation procedure, CanICA (Canonical ICA), based on i) probabilistic dimension reduction of the individual data, ii) canonical correlation analysis to identify a data subspace common to the group iii) ICA-based pattern extraction. In addition, we introduce a procedure based on cross-validation to quantify the stability of ICA patterns at the level of the group. We compare our method with state-of-the-art multi-subject fMRI ICA methods and show that the features extracted using our procedure are more reproducible at the group level on two datasets of 12 healthy controls: a resting-state and a functional localizer study

Confidence intervals for performance estimates in 3D medical image segmentation

Medical segmentation models are evaluated empirically. As such an evaluation is based on a limited set of example images, it is unavoidably noisy. Beyond a mean performance measure, reporting confidence intervals is thus crucial. However, this is rarely done in medical image segmentation. The width of the confidence interval depends on the test set size and on the spread of the performance measure (its standard-deviation across of the test set). For classification, many test images are needed to avoid wide confidence intervals. Segmentation, however, has not been studied, and it differs by the amount of information brought by a given test image. In this paper, we study the typical confidence intervals in medical image segmentation. We carry experiments on 3D image segmentation using the standard nnU-net framework, two datasets from the Medical Decathlon challenge and two performance measures: the Dice accuracy and the Hausdorff distance. We show that the parametric confidence intervals are reasonable approximations of the bootstrap estimates for varying test set sizes and spread of the performance metric. Importantly, we show that the test size needed to achieve a given precision is often much lower than for classification tasks. Typically, a 1% wide confidence interval requires about 100-200 test samples when the spread is low (standard-deviation around 3%). More difficult segmentation tasks may lead to higher spreads and require over 1000 samples.Comment: 10 page

Statistical Learning for Resting-State fMRI: Successes and Challenges

International audienceIn the absence of external stimuli, fluctuations in cerebral activity can be used to reveal intrinsic structures. Well-conditioned probabilistic models of this so-called resting-state activity are needed to support neuroscientific hypotheses. Exploring two specific descriptions of resting-state fMRI, namely spatial analysis and connectivity graphs, we discuss the progress brought by statistical learning techniques, but also the neuroscientific picture that they paint, and possible modeling pitfalls

Tapered-amplified AR-coated laser diodes for Potassium and Rubidium atomic-physics experiments

We present a system of room-temperature extended-cavity grating-diode lasers (ECDL) for production of light in the range 760-790nm. The extension of the tuning range towards the blue is permitted by the weak feedback in the cavity: the diodes are anti-reflection coated, and the grating has just 10% reflectance. The light is then amplified using semiconductor tapered amplifiers to give more than 400mW of power. The outputs are shown to be suitable for atomic physics experiments with potassium (767nm), rubidium (780nm) or both, of particular relevance to doubly-degenerate boson-fermion mixtures

Population modeling with machine learning can enhance measures of mental health

Background: Biological aging is revealed by physical measures, e.g., DNA probes or brain scans. In contrast, individual differences in mental function are explained by psychological constructs, e.g., intelligence or neuroticism. These constructs are typically assessed by tailored neuropsychological tests that build on expert judgement and require careful interpretation. Could machine learning on large samples from the general population be used to build proxy measures of these constructs that do not require human intervention? Results: Here, we built proxy measures by applying machine learning on multimodal MR images and rich sociodemographic information from the largest biomedical cohort to date: the UK Biobank. Objective model comparisons revealed that all proxies captured the target constructs and were as useful, and sometimes more useful, than the original measures for characterizing real-world health behavior (sleep, exercise, tobacco, alcohol consumption). We observed this complementarity of proxy measures and original measures at capturing multiple health-related constructs when modeling from, both, brain signals and sociodemographic data. Conclusion: Population modeling with machine learning can derive measures of mental health from heterogeneous inputs including brain signals and questionnaire data. This may complement or even substitute for psychometric assessments in clinical populations

Markov models for fMRI correlation structure: is brain functional connectivity small world, or decomposable into networks?

Correlations in the signal observed via functional Magnetic Resonance Imaging (fMRI), are expected to reveal the interactions in the underlying neural populations through hemodynamic response. In particular, they highlight distributed set of mutually correlated regions that correspond to brain networks related to different cognitive functions. Yet graph-theoretical studies of neural connections give a different picture: that of a highly integrated system with small-world properties: local clustering but with short pathways across the complete structure. We examine the conditional independence properties of the fMRI signal, i.e. its Markov structure, to find realistic assumptions on the connectivity structure that are required to explain the observed functional connectivity. In particular we seek a decomposition of the Markov structure into segregated functional networks using decomposable graphs: a set of strongly-connected and partially overlapping cliques. We introduce a new method to efficiently extract such cliques on a large, strongly-connected graph. We compare methods learning different graph structures from functional connectivity by testing the goodness of fit of the model they learn on new data. We find that summarizing the structure as strongly-connected networks can give a good description only for very large and overlapping networks. These results highlight that Markov models are good tools to identify the structure of brain connectivity from fMRI signals, but for this purpose they must reflect the small-world properties of the underlying neural systems

Fast human activity recognition in lifelogging

This paper addresses the problem of fast Human Activity Recognition (HAR) in visual lifelogging. We identify the importance of visual features related to HAR and we specifically evaluate the HAR discrimination potential of Colour Histograms and Histogram of Oriented Gradients. In our evaluation we show that colour can be a low-cost and effective means of low-cost HAR when performing single-user classification. It is also noted that, while much more efficient, global image descriptors perform as well or better than local descriptors in our HAR experiments. We believe that both of these findings are due to the fact that a user’s lifelog is rich in reoccurring scenes and environments

Light-shift tomography in an optical-dipole trap for neutral atoms

We report on light-shift tomography of a cloud of 87 Rb atoms in a far-detuned optical-dipole trap at 1565 nm. Our method is based on standard absorption imaging, but takes advantage of the strong light-shift of the excited state of the imaging transition, which is due to a quasi-resonance of the trapping laser with a higher excited level. We use this method to (i) map the equipotentials of a crossed optical-dipole trap, and (ii) study the thermalisation of an atomic cloud by following the evolution of the potential-energy of atoms during the free-evaporation process