The main aim of this works is to explore the relationship between language and analogical reasoning. In fact, despite the increasing interest about the neural substrates of reasoning, at present no fMRI study systematically investigates the contribution of stimulus format/context on reasoning network. In the present work three fMRI studies are developed in order to verify how much reasoning relies on verbal language using different stimuli format and within normal and a language impaired populations. The first study focus on how different verbal context, nameable pictures versus words, may influence brain activity related to analogical reasoning in healthy adults. In a second study the same fMRI paradigm was applied to investigate brain activity during analogical reasoning in young normal readers and young dyslexics in order to understand if the reading disorder may influence the reasoning in relation to the language load requested by the stimulus format. In the third study the contribution of language and semantic system to reasoning was investigate developing a new fMRI design where analogical reasoning had to be performed either on meaningful or on abstract geometrical pictures.

This thesis aims at evaluating, in two separate studies, strategies for physiological noise and head motion correction in resting state brain FC-fMRI. In particular, as a general marker of noise correction performance we use the test-retest reproducibility of the DMN. The guiding hypothesis is that methods that improve reproducibility should reflect more efficient corrections and thus be preferable in longitudinal studies. The physiological denoising study evaluated longitudinal changes in a 3T harmonized multisite fMRI study of healthy elderly participants from the PharmaCog Consortium (Jovicich et al., 2016). Retrospective physiological noise correction (rPNC) methods were here implemented to investigate their influence on several DMN reliability measures within and between 13 MRI sites. Each site involved five different healthy elderly participants who were scanned twice at least a week apart (5 participants per site). fMRI data analysis was performed once without rPNC and then with WM/CSF regression, with physiological estimation by temporal ICA (PESTICA) (Beall & Lowe, 2007) and FMRIB's ICA-based Xnoiseifier (FSL-FIX) (Griffanti et al., 2014; Salimi-Khorshidi et al., 2014). These methods differ for their data-based computational approach to identify physiological noise fluctuations and need to be applied at different stages of data preprocessing. As a working hypothesis, physiological denoising was in general expected to improve DMN reliability. The head motion study evaluated longitudinal changes in the DMN connectivity from a 4T single-site study of 24 healthy young volunteers who were scanned twice within a week. Within each scanning session, RS-fMRI scans were acquired once using interleaved and then sequential slice-order acquisition methods. Furthermore, brain volumes were corrected for motion using once rigid-body volumetric and then slice-wise methods. The effects of these choices were then evaluated computing multiple DMN reliability measures and investigating single regions within the DMN to assess the existence of inter-regional effects associated with head-motion. In this case, we expected to find slice-order acquisition effects in reliability estimates under standard volumetric motion correction and no slice-order acquisition effect under 2D slice-based motion correction. Both studies used ICA to characterize the DMN using group-ICA and dual regression procedures (Beckmann et al., 2009). This methodology proved successful at defining consistent DMN connectivity metrics in longitudinal and clinical RS-fMRI studies (Zuo & Xing, 2014). Automatic DMN selection procedures and other quality assurance analyses were made to supervise ICA performance. Both studies considered several test-retest (TRT) reliability estimates (Vilagut, 2014) for some DMN connectivity measurements: absolute percent error between the sessions, intraclass correlation coefficients (ICC) between sessions and multiple sites, the Jaccard index to evaluate the degree of voxel-wise spatial pattern actiavtion overlap between sessions.