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

Exploratory fMRI analysis without spatial normalization

Author Manuscript received 2010 March 11. 21st International Conference, IPMI 2009, Williamsburg, VA, USA, July 5-10, 2009. ProceedingsWe present an exploratory method for simultaneous parcellation of multisubject fMRI data into functionally coherent areas. The method is based on a solely functional representation of the fMRI data and a hierarchical probabilistic model that accounts for both inter-subject and intra-subject forms of variability in fMRI response. We employ a Variational Bayes approximation to fit the model to the data. The resulting algorithm finds a functional parcellation of the individual brains along with a set of population-level clusters, establishing correspondence between these two levels. The model eliminates the need for spatial normalization while still enabling us to fuse data from several subjects. We demonstrate the application of our method on a visual fMRI study.McGovern Institute for Brain Research at MIT. Neurotechnology ProgramNational Science Foundation (U.S.) (CAREER Grant 0642971)National Institutes of Health (U.S.) (NIBIB NAMIC U54-EB005149)National Institutes of Health (U.S.) (NCRR NAC P41-RR13218

Littérature scientifique et formation à l'information, la situation des bioingénieurs à Gembloux Agro-Bio Tech (ULg) (synthèse bibliographique)

Scholarly publication and education in Information Literacy within the bioengineering curriculum, the Gembloux Agro-Bio Tech (ULg) case. A review. This article is based on a doctoral study on the role of scientific literature in the teaching of bioengineering at Gembloux. It is essentially a summary incorporating recent advances in Information Literacy. Data analysis indicates that the bioengineers working at Gembloux publish at least as much as research as other scientists in Belgium. These bioengineers choose to publish articles in journals with a high impact factor, preferring to read articles rather than books and using all the electronic resources available to them. Their fields of research, and reading, go beyond the bounds of agronomy in the strictest sense. The bioengineering courses provided at Gembloux are based on the concept of Information Literacy. This concept refers to a set of skills that allow individuals to recognize an information need and enable them to locate, evaluate and use the required information. The area of Information Literacy has evolved over the last two decades. The scope of education of this area goes well beyond the bounds of the library. In addition to intellectual skills, Information Literacy also involves social and cultural skills. These include an understanding of media and new information technologies, without being reduced to technical or technological skills. At Gembloux, education in Information Literacy is included in the student's timetable. It incorporates the production of scientific papers and is based on a methodological approach with its own didactic and specific content

Parametric oscillator based on non-linear vortex dynamics in low resistance magnetic tunnel junctions

Radiofrequency vortex spin-transfer oscillators based on magnetic tunnel junctions with very low resistance area product were investigated. A high power of excitations has been obtained characterized by a power spectral density containing a very sharp peak at the fundamental frequency and a series of harmonics. The observed behaviour is ascribed to the combined effect of spin transfer torque and Oersted-Amp\`ere field generated by the large applied dc-current. We furthermore show that the synchronization of a vortex oscillation by applying a ac bias current is mostly efficient when the external frequency is twice the oscillator fundamental frequency. This result is interpreted in terms of a parametric oscillator.Comment: 4 pages, 4 figure

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

Notes

Notes by Charles M. Urruela, Norman B. Thirion, R. F. Swisher, Peter Francis Nemeth, Walter C. Ivansevic, Charles M. Boynton, Theodore P. Frericks, Hal Hunter, and J. D. Kelly

Josephson junctions and superconducting quantum interference devices made by local oxidation of niobium ultrathin films

We present a method for fabricating Josephson junctions and superconducting quantum interference devices (SQUIDs) which is based on the local anodization of niobium strip lines 3 to 6.5 nm-thick under the voltage-biased tip of an Atomic Force Microscope. Microbridge junctions and SQUID loops are obtained either by partial or total oxidation of the niobium layer. Two types of weak link geometries are fabricated : lateral constriction (Dayem bridges) and variable thickness bridges. SQUIDs based on both geometries show a modulation of the maximum Josephson current with a magnetic flux periodic with respect to the superconducting flux quantum h/2e. They persist up to 4K. The modulation shape and depth for SQUIDs based on variable thickness bridges indicate that the weak link size becomes comparable to the superconducting film coherence length which is of the order of 10nm.Comment: 12 page

Principal Component Regression predicts functional responses across individuals

International audienceInter-subject variability is a major hurdle for neuroimaging group-level inference, as it creates complex image patterns that are not captured by standard analysis models and jeopardizes the sensitivity of statistical procedures. A solution to this problem is to model random subjects effects by using the redundant information conveyed by multiple imaging contrasts. In this paper, we introduce a novel analysis framework, where we estimate the amount of variance that is fit by a random effects subspace learned on other images; we show that a principal component regression estimator outperforms other regression models and that it fits a significant proportion (10% to 25%) of the between-subject variability. This proves for the first time that the accumulation of contrasts in each individual can provide the basis for more sensitive neuroimaging group analyzes