NODDI-SH: a computational efficient NODDI extension for fODF estimation in diffusion MRI

Diffusion Magnetic Resonance Imaging (DMRI) is the only non-invasive imaging technique which is able to detect the principal directions of water diffusion as well as neurites density in the human brain. Exploiting the ability of Spherical Harmonics (SH) to model spherical functions, we propose a new reconstruction model for DMRI data which is able to estimate both the fiber Orientation Distribution Function (fODF) and the relative volume fractions of the neurites in each voxel, which is robust to multiple fiber crossings. We consider a Neurite Orientation Dispersion and Density Imaging (NODDI) inspired single fiber diffusion signal to be derived from three compartments: intracellular, extracellular, and cerebrospinal fluid. The model, called NODDI-SH, is derived by convolving the single fiber response with the fODF in each voxel. NODDI-SH embeds the calculation of the fODF and the neurite density in a unified mathematical model providing efficient, robust and accurate results. Results were validated on simulated data and tested on \textit{in-vivo} data of human brain, and compared to and Constrained Spherical Deconvolution (CSD) for benchmarking. Results revealed competitive performance in all respects and inherent adaptivity to local microstructure, while sensibly reducing the computational cost. We also investigated NODDI-SH performance when only a limited number of samples are available for the fitting, demonstrating that 60 samples are enough to obtain reliable results. The fast computational time and the low number of signal samples required, make NODDI-SH feasible for clinical application

Image Perception

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Cross-linguistic similarity affects L2 cognate representation: blu vs. blue in Italian-English bilinguals

In a psycholinguistic study we explored semantic shifts of focal colours for ‘blue’ terms in Italian-English bilinguals. Italian speakers require more than one basic colour term to name blue colours: blu ‘dark blue’ and azzurro ‘light/medium blue’; celeste ‘sky/light blue’ is salient, too [1-2]. Participants were Italian-English bilinguals residing in Liverpool (N=13). Their naming data, collected in two languages (L1, L2), were compared to those of Italian (N=13) and English (N=16) monolinguals. An unconstrained colour naming method was used to name each Munsell chip (M=237) embracing the BLUE area of colour space. Participants also indicated the best example focal colour) of blu, azzurro and celeste(Italian) or blue and light blue (English). Here we report two main findings: (i) Lightness shift: for the majority of the bilinguals, their L2 blue foci are semantically down-shifted towards L1 blu ‘dark blue’ foci. The semantic shift is thought to result from cross-linguistic similarity between the homophone Italian blu and English blue, facilitating asymmetric L1–L2 mediation in favour of the dominant language representation; (ii) Hue shift: proficient bilinguals revealed a hue shift of the L1 azzurro focus from azure, characteristic of Italian monolinguals, towards that of English monolinguals’ blue, with a purplish hint. The findings indicate Whorfian effects, or modulation of semantic-lexical representations, in proficient bilinguals immersed in L2 and, in addition, point to their integrated mental lexicon

‘Italian blues’: A challenge to the universal inventory of basic colour terms

‘Blue’ is one of the 11 basic colour terms (BCTs) in languages with a developed colour term inventory [1]. In a challenge to the Berlin-Kay model, Italian appears to require more than one BCT to name the blue area: blu ‘dark blue’, azzurro ‘light (-and-medium) blue’ and celeste ‘light blue’. We addressed the proposition of multiple Italian ‘blue’ BCTs in a psycholinguistic study. Eight Munsell charts embracing the BLUE area of colour space (7.5BG-5PB, Value 2-9, Chroma 2-12) were employed to explore colour name mapping in Italian speakers compared to English speakers. Participants were Italian monolinguals (N=13, Alghero; N=15, Verona) and English monolinguals (N=19; Liverpool). An unconstrained colour naming method was used; this was followed by indicating the best example (focal colour) of blu, azzurro and celeste (Italian) or blue and light blue (English). Choices of focal colours, in Munsell notation, are reported for each of the terms. In addition, distances between centroids of the focal colours, in CIELAB notation, are reported for each of the three participant groups. The dominant focal English blue and Italian blu appeared to concur in Hue (2.5PB, 5PB), but not in lightness (blue: Value 5; blu: Value 2-3). Italian speakers required, in addition, the azzurro term for naming light/medium blue colours. Notably, for the Algherese, azzurro indicates the ‘medium blue’ and is complemented by celeste for denoting light blue shades, similar to English light blue. In contrast, the Veronese use azzurro for ‘light-and-medium blue’; celeste was named conspicuously less frequently, overlapping with azzurro. The present study adds to psycholinguistic evidence that Italian possesses two BCTs, blu and azzurro, differentiating ‘blues’ along the lightness dimension. Celeste is a contender for a third BCT for the Alghero speakers. Cognitive representation (i.e. prototype) of azzurro as well as the status of celeste appear to vary markedly across Italian dialects

Color naming in Italian language

The present study investigated Italian basic color terms (BCTs). It is an extension of our previous work that explored Italian basic color categories (BCCs) using a constrained color-naming method, with 11 Italian BCTs allowed, including blu for naming the BLUE area. Since a latter outcome indicated a categorization bias, here monolexemic color-naming method was employed, enabling also use of azzurro, deeply entrenched Italian term that designates light blue. In Experiment 1, colors (N=367), sampling the Munsell Mercator projection, were presented on a CRT; color names and reaction times of vocalization onset were recorded. Naming consistency and consensus were estimated. Consistency was obtained for 12 CTs, including the two blue terms; consensus was found for 11 CTs, excluding rosso ‘red’. For each consensus category, color with the shortest RT was considered focal. In Experiment 2, consensus stimuli (N=72) were presented; on each trial, observers indicated the focal color (“best example”) in an array of colors comprising a consensus category. For each of the 12 Italian CCs, centroid was calculated and focal color (two measures) estimated. Compared to English color terms, two outcomes are specific to Italian color naming: (i) naming of the RED-PURPLE area is highly refined, with consistent use of emergent non-BCTs; (ii) azzurro and blu both perform as BCTs dividing the BLUE area along the lightness dimension. The findings are considered in the framework of the weak relativity hypothesis. Historico-linguistic, environmental and pragmatic communication factors are discussed that conceivably have driven the extension of the BCT inventory in Italian

Lossy to lossless object-based coding of 3-D MRI data

We propose a fully three-dimensional object-based coding system exploiting the diagnostic relevance of the different regions of the volumetric data for rate allocation. The data are first decorrelated via a 3D discrete wavelet transform. The implementation via the lifting steps scheme allows to map integer-to-integer values, enabling lossless coding, and facilitates the definition of the object-based inverse transform. The coding process assigns disjoint segments of the bitstream to the different objects, which can be independently accessed and reconstructed at any up-to-lossless quality. Two fully 3D coding strategies are considered: Embedded Zerotree Coding (EZW-3D) and Multidimensional Layered Zero Coding (MLZC), both generalized for Region of Interest (ROI) based processing. In order to avoid artifacts along region boundaries, some extra coefficients must be encoded for each object. This gives rise to an overheading of the bitstream with respect to the case where the volume is encoded as a whole. The amount of such extra information depends on both the filter length and the decomposition depth. The system is characterized on a set of head magnetic resonance images. Results show that MLZC and EZW-3D have competitive performances. In particular, the best MLZC mode outperforms the other state-of-the-art techniques on one of the datasets for which results are available in the literature

Imaging Genetics through Brain Age Estimation and Image Derived Phenotypes

In this thesis, we investigated brain aging using different simple and complex models through brain age estimation using IDPs extracted from brain MRI.We have also applied simple methods and machine learning explainability models to identify the most informative features to model brain age. We further estimated brain age for fiber groups within brain white matter tracts. In addition, we revealed the effects of daily life style, cardiac risk factors and morbidity in brain aging. Finally, we used causal models to explore the role of TL in healthy aging and Alzheimer’s disease in unhealthy aging to cause alterations within brain structures and functions