Neuroimaging studies support the involvement of the cerebello-cortical and striato-cortical motor loops in motor sequence learning. Here, we investigated whether the gain of motor sequence learning could depend on a priori resting-state functional connectivity (rsFC) between motor areas and structures belonging to these circuits. Fourteen healthy subjects underwent a resting-state fMRI session. Afterward, they were asked to reproduce a verbally-learned sequence of finger opposition movements as fast and accurate as possible. All subjects increased their movement rate with practice, by reducing touch duration and/or inter tapping interval. The rsFC analysis showed that at rest left and right M1 and left and right supplementary motor cortex (SMA) were mainly connected with other motor areas. The covariate analysis taking into account the different kinematic parameters indicated that the subjects achieving greater movement rate increase were those showing stronger rsFC of the left M1 and SMA with the right lobule VIII of the cerebellum. Notably, the subjects with greater inter tapping interval reduction showed stronger rsFC of the left M1 and SMA with the association nuclei of the thalamus. Conversely, the regression analysis with the right M1 and SMA seeds showed only few significant clusters for the different covariates not located in the cerebellum and thalamus. No common clusters were found between right M1 and SMA. All these findings indicate important functional connections at rest of those neural circuits responsible of motor learning improvement, involving the motor areas related to the hemisphere directly controlling the finger movements, the thalamus and the cerebellum.

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Abstract. One of the main challenges in content-based image retrieval still remains to bridge the gap between low-level features and semantic information. In this paper, we present our first results concerning a medical image retrieval approach using a semantic medical image and report indexing within a fusion framework, based on the Unified Medical Language System (U M LS) metathesaurus. We propose a structured learning framework based on Support Vector Machines to facilitate modular design and extract medical semantics from images. We developed two complementary visual indexing approaches within this framework: a global indexing to access image modality, and a local indexing to access semantic local features. Visual indexes and textual indexes -extracted from medical reports using M etaM ap software application -constitute the input of the late fusion module. A weighted vectorial norm fusion algorithm allows the retrieval system to increase its meaningfulness, efficiency and robustness. First results on the CLEF medical database are presented. The important perspectives of this approach in terms of semantic query expansion and data-mining are discussed.

Structured descriptions attached to medical image series conforming to the DZCOM standard make possible to fit the collections of existing digitized images into an educational and research framework. This paper presents the milestones in our work to provide a simple yet robust structured reporting method for echocardiographic investigations. Our initial reporting solution and the underlying healthcare information system are introduced first, and the results of the system's use in a clinical environment are also pointed out. Latest development in the domain of DICOM structured reporting norms is outlined next. Accordingly, our recent improvements are covered then: the upgraded structured reporting method is introduced and several new or mod$ed components of the system are brought into view. The paper concludes by summarizing similarities and differences between the old and the new approach, highlighting future development tracks.