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

Spin des niveaux à 0,46 et 0,67 MeV du 34Cl

L'étude de la réaction 32S(3He, pγ) 3Cl a été réalisée à l'aide d'un faisceau 3He++ aux énergies 9, 9,5 et 10 MeV. Les coïncidences pγ ont été effectuées entre protons détectés à 0° et rayonnements γ, conformément à la méthode II de corrélation angulaire de Litherland et Ferguson. L'exploitation des corrélations des groupes de protons menant aux deuxième et troisième niveaux excités du 34CI avec les rayonnements γ correspondants, détermine sans ambiguïté J = 1 pour les niveaux à 0,46 et 0,67 MeV de ce noyau. Ces résultats confirment les prévisions théoriques déduites du modèle en couches avec interaction à deux particules modifiée

Étude d'états excités de 22Ne a l'aide des résonances de capture radiative de particules alpha par 18O entre 1,6 et 5,0 MeV d'énergie incidente

La courbe d'excitation du rayonnement γ de 350 keV issu de la réaction 18O(α, nγ) 21Ne a été mesurée entre 1,6 et 5 MeV. Six nouvelles résonances ont été observées correspondant aux niveaux du 22Ne : Ex = 11,199 MeV (Eα = 1,873 MeV ; Γt = 7 keV) ; 11,271 (1,961 ; 7) ; 11,431 (2,156 ; 47) ; 11,519 (2,263 ; 6) ; 11,577 (2,335 ; 16) ; 11,745 (2,540 ; 41). Nous avons relevé les spectres de désexcitation par rayonnement γ de tous les niveaux mis en évidence et mesuré les distributions angulaires des rayonnements γ de la réaction 18O(α, γ) 22Ne chaque fois que l'intensité de transition le permettait. En fait seules trois résonances déjà observées se trouvaient dans ce cas. Pour le niveau Ex = 11,462 MeV (2,194 ; 9) la distribution angulaire a permis de fixer Jπ = 1 -. Pour les niveaux Ex = 11,682 MeV (2,463 ; 8) et Ex = 11,751 MeV (2,547 ; 8) les valeurs respectives J π = 2+ et Jπ = 1- ont été confirmées. Des rapports d'embranchement (γ0/γ1) ont pu être déterminés pour ces trois niveaux ainsi que les coefficients de mélange de multipolarité des transitions γ1. Des limites supérieures des intensités de transition ωγ pour les transitions γ0 + γ 1 sont données pour les autres niveaux

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

Automatically building morphometric anatomical atlases from 3D medical images : Application to a skull atlas

In this article, we present a method for building entirely automatically a morphometric anatomical atlas from 3D medical image s acquired by CT-Scan or MR . We detail each step of the method, including the non-rigid registration algorithm, 3D lines averaging , and statistical analysis processes . We apply the method to obtain a quantitative atlas of crest lines of the skull . Finally, we use the resulting atlas to study a craniofacia l disease : we show how we can obtain qualitative and quantitative results by contrasting a skull affected by a deformation of th e mandible with the atlas .Dans cet article, nous présentons une méthode pour construire de manière automatique des atlas anatomiques morphométriques à partir d'images médicales tridimensionnelles obtenues par scanographie ou imagerie par résonance magnétique. Nous en détaillons les différentes étapes, en particulier les algorithmes de mise en correspondance non-rigide, de moyenne et d'analyse statistique de lignes caractéristiques tridimensionnelles. Nous appliquons la méthode à la construction d'un atlas morphométrique des lignes de crête du crâne. Nous montrons alors comment la comparaison automatique entre l'atlas et un crâne présentant une déformation mandibulaire permet d'obtenir des résultats qualitatifs et quantitatifs utilisables par un médecin

Learning an atlas of a cognitive process in its functional geometry

Proceedings of the 22nd International Conference, IPMI 2011, Kloster Irsee, Germany, July 3-8, 2011.In this paper we construct an atlas that captures functional characteristics of a cognitive process from a population of individuals. The functional connectivity is encoded in a low-dimensional embedding space derived from a diffusion process on a graph that represents correlations of fMRI time courses. The atlas is represented by a common prior distribution for the embedded fMRI signals of all subjects. The atlas is not directly coupled to the anatomical space, and can represent functional networks that are variable in their spatial distribution. We derive an algorithm for fitting this generative model to the observed data in a population. Our results in a language fMRI study demonstrate that the method identifies coherent and functionally equivalent regions across subjects.National Science Foundation (U.S.) (IIS/CRCNS 0904625)National Science Foundation (U.S.) (CAREER grant 0642971)National Institutes of Health (U.S.) (NCRR NAC P41- RR13218)National Institute of Biomedical Imaging and Bioengineering (U.S.) (U54-EB005149)National Institutes of Health (U.S.) (U41RR019703)National Institutes of Health (U.S.) (P01CA067165)Seventh Framework Programme (European Commission) (n◦257528 (KHRESMOI)