Publications (61) View all
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Article: Hyperfrontality and hypoconnectivity during refreshing in schizophrenia.
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ABSTRACT: Anomalous activations of the prefrontal cortex (PFC) and posterior cerebral areas have been reported in previous studies of working memory in schizophrenia. Several interpretations have been reported: e.g., neural inefficiency, the use of different strategies and differences in the functional organization of the cerebral cortex. To better understand these abnormal activations, we investigated the cerebral bases of a working memory component process, namely refreshing (i.e., thinking briefly of a just-activated representation). Fifteen patients with schizophrenia and 15 control subjects participated in this fMRI study. Participants were told that whenever they saw a word on the screen, they had to read it silently to themselves (read and repeat conditions), and when they saw a dot, they had to think of the just-previous word (refresh condition). The refresh condition (in comparison with the read condition) was associated with increased activation in the left inferior frontal gyrus (T=3.55, p=0.009) and decreased connectivity within the prefrontal cortex and between the prefrontal and parietal cortices (T(s)>4.09, p(s)<0.05) in patients with schizophrenia in comparison with control subjects. These results suggest that prefrontal dysfunctions in schizophrenia might be related to a defective ability to initiate (rather than to execute) specific cognitive processes.Psychiatry research. 11/2012; -
Article: Light-pulse atom interferometry in microgravity
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ABSTRACT: We describe the operation of a light pulse interferometer using cold 87Rb atoms in reduced gravity. Using a series of two Raman transitions induced by light pulses, we have obtained Ramsey fringes in the low gravity environment achieved during parabolic flights. With our compact apparatus, we have operated in a regime which is not accessible on ground. In the much lower gravity environment and lower vibration level of a satellite, our cold atom interferometer could measure accelerations with a sensitivity orders of magnitude better than the best ground based accelerometers and close to proven spaced-based ones.The European Physical Journal D 04/2012; 53(3):353-357. · 1.48 Impact Factor -
Article: I.C.E.: a transportable atomic inertial sensor for test in microgravity
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ABSTRACT: We present the construction of an atom interferometer for inertial sensing in microgravity, as part of the I.C.E. (Interférométrie Cohérente pour l’Espace) collaboration. On-board laser systems have been developed based on fibre-optic components, which are insensitive to mechanical vibrations and acoustic noise, have sub-MHz line width, and remain frequency stabilised for weeks at a time. A compact, transportable vacuum system has been built, and used for laser cooling and magneto-optical trapping. We will use a mixture of quantum degenerate gases, bosonic 87Rb and fermionic 40K, in order to find the optimal conditions for precision and sensitivity of inertial measurements. Microgravity will be realised in parabolic flights lasting up to 20s in an Airbus. We investigate the experimental limits of our apparatus, and show that the factors limiting the sensitivity of a long-interrogation-time atomic inertial sensor are the phase noise in reference-frequency generation for Raman-pulse atomic beam splitters and acceleration fluctuations during free fall.Applied Physics B 04/2012; 84(4):673-681. · 2.19 Impact Factor -
Article: Scale-Free and Multifractal Time Dynamics of fMRI Signals during Rest and Task.
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ABSTRACT: Scaling temporal dynamics in functional MRI (fMRI) signals have been evidenced for a decade as intrinsic characteristics of ongoing brain activity (Zarahn et al., 1997). Recently, scaling properties were shown to fluctuate across brain networks and to be modulated between rest and task (He, 2011): notably, Hurst exponent, quantifying long memory, decreases under task in activating and deactivating brain regions. In most cases, such results were obtained: First, from univariate (voxelwise or regionwise) analysis, hence focusing on specific cognitive systems such as Resting-State Networks (RSNs) and raising the issue of the specificity of this scale-free dynamics modulation in RSNs. Second, using analysis tools designed to measure a single scaling exponent related to the second order statistics of the data, thus relying on models that either implicitly or explicitly assume Gaussianity and (asymptotic) self-similarity, while fMRI signals may significantly depart from those either of those two assumptions (Ciuciu et al., 2008; Wink et al., 2008). To address these issues, the present contribution elaborates on the analysis of the scaling properties of fMRI temporal dynamics by proposing two significant variations. First, scaling properties are technically investigated using the recently introduced Wavelet Leader-based Multifractal formalism (WLMF; Wendt et al., 2007). This measures a collection of scaling exponents, thus enables a richer and more versatile description of scale invariance (beyond correlation and Gaussianity), referred to as multifractality. Also, it benefits from improved estimation performance compared to tools previously used in the literature. Second, scaling properties are investigated in both RSN and non-RSN structures (e.g., artifacts), at a broader spatial scale than the voxel one, using a multivariate approach, namely the Multi-Subject Dictionary Learning (MSDL) algorithm (Varoquaux et al., 2011) that produces a set of spatial components that appear more sparse than their Independent Component Analysis (ICA) counterpart. These tools are combined and applied to a fMRI dataset comprising 12 subjects with resting-state and activation runs (Sadaghiani et al., 2009). Results stemming from those analysis confirm the already reported task-related decrease of long memory in functional networks, but also show that it occurs in artifacts, thus making this feature not specific to functional networks. Further, results indicate that most fMRI signals appear multifractal at rest except in non-cortical regions. Task-related modulation of multifractality appears only significant in functional networks and thus can be considered as the key property disentangling functional networks from artifacts. These finding are discussed in the light of the recent literature reporting scaling dynamics of EEG microstate sequences at rest and addressing non-stationarity issues in temporally independent fMRI modes.Frontiers in physiology. 01/2012; 3:186. -
Article: A novel sparse graphical approach for multimodal brain connectivity inference.
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ABSTRACT: Despite the clear potential benefits of combining fMRI and diffusion MRI in learning the neural pathways that underlie brain functions, little methodological progress has been made in this direction. In this paper, we propose a novel multimodal integration approach based on sparse Gaussian graphical model for estimating brain connectivity. Casting functional connectivity estimation as a sparse inverse covariance learning problem, we adapt the level of sparse penalization on each connection based on its anatomical capacity for functional interactions. Functional connections with little anatomical support are thus more heavily penalized. For validation, we showed on real data collected from a cohort of 60 subjects that additionally modeling anatomical capacity significantly increases subject consistency in the detected connection patterns. Moreover, we demonstrated that incorporating a connectivity prior learned with our multimodal connectivity estimation approach improves activation detection.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 01/2012; 15(Pt 1):707-14.
