Biology subjects

Novello, L.

Publications and source records attributed to Novello, L..

4 recordsLinked to original sources

bioRxiv2023

Efficient PCA denoising of spatially correlated MRI data

Mar[c]enko-Pastur (MP) PCA denoising is emerging as an effective means for noise suppression in MRI acquisitions with redundant dimensions. However, MP-PCA performance is severely compromised by spatially correlated noise - an issue typically affecting most modern MRI acquisitions - almost to the point of returning the original images with little or no noise removal. In this study, we develop and apply two new strategies that enable efficient and robust denoising even in the presence of severe spatial correlations. This is achieved by measuring a-priori information about the noise variance and combing these estimates with PCA denoising thresholding concepts. The two denoising strategies developed here are: 1) General PCA (GPCA) denoising that uses a-priori noise variance estimates without assuming specific noise distributions; and 2) Threshold PCA (TPCA) denoising which removes noise components with a threshold computed from a-priori estimated noise variance to determine the upper bound of the MP distribution. These strategies were tested in simulations with known ground truth and applied for denoising diffusion MRI data acquired using pre-clinical (16.4T) and clinical (3T) MRI scanners. In synthetic phantoms, MP-PCA failed to denoise spatially correlated data, while GPCA and TPCA correctly classified all signal/noise components. In cases where the noise variance was not accurately estimated (as can be the case in many practical scenarios), TPCA still provides excellent denoising performance. Our experiments in pre-clinical diffusion data with highly corrupted by spatial correlated noise revealed that both GPCA and TPCA robustly denoised the data while MP-PCA denoising failed. In in vivo diffusion MRI data acquired on a clinical scanner in healthy subjects, MP-PCA weakly removed noised, while TPCA was found to have the best performance, likely due to misestimations of the noise variance. Thus, our work shows that these novel denoising approaches can strongly benefit future pre-clinical and clinical MRI applications.

neuroscience↗

bioRxiv2022

Head motion correction shapes functional network estimates: evidence from healthy and Parkinson's disease cohorts

An open discussion in studies of intrinsic brain functional connectivity is the mitigation of head motion-related artifacts, particularly in the presence of peculiar symptomatology such as in Parkinsons disease (PD). Previous studies show that Independent Component Analysis (ICA) denoising improves the reproducibility of functional connectivity findings by detecting sources of non-neural signals. However, there is still no consensus about which pre-processing pipeline should be applied in natural high motion populations such as PD, particularly in relation to novel functional network descriptions derived from dynamic connectivity analyses. In this study, we investigated how different pre-processing pipelines affect intrinsic brain connectivity metrics, both static and dynamic, derived from a group of young healthy controls (HC) and a group of PD participants. A total of 20 HC and 20 PD subjects participated in this 3 T MRI study. Resting-state functional MRI images were used to test the effects of the pre-processing pipeline of static (sFC) and temporal-varying functional connectivity (dFC) estimations. Both MRI datasets were pre-processed using three different workflows differing in the motion correction approach: (i) standard motion realignment (mc); (ii) motion outlier detection and deweighting based on image intensity change estimations (DVARS) and (iii) ICA-based noise removal using reference noise features (AROMA). Furthermore, the PD dataset was also processed with a fourth method by applying an ICA-based denoising (FIX), previously trained on the HC group. sFC analysis was performed using Group ICA, by temporally concatenating different pre-processing types in pairs of different runs. Two types of dFC analyses were considered: innovation-driven co-activation patterns (iCAPs) and co-activation patterns (CAPs). CAPs allow dFC estimations that do not require the deconvolution of the hemodynamic response function and its derivative, thus potentially being less sensitive to head-motion related noise. We found that regardless of substantial head motion differences in the two groups, sFC results were consistent across denoising strategies. Conversely, dFC was extremely sensitive to denoising strategies, particularly for the PD group with the transient-based dFC analyses. Indeed, the use of the peak-based dFC framework enables the detection of time-varying networks but in a way that is highly dependent on the motion correction pipeline. In conclusion, we show that dynamic functional network representations are highly sensitive to both head motion and to fMRI denoising methods. These findings stress the importance of considering and reporting these experimental aspects to help with the reproducibility and interpretation of different studies. Future work is needed to further investigate transient-based dFC strategies that are more robust to head motion.

neuroscience↗

bioRxiv2021

In vivo Correlation Tensor MRI reveals microscopic kurtosis in the human brain on a clinical 3T scanner

Diffusion MRI (dMRI) has become one of the most important imaging modalities for noninvasively probing tissue microstructure. Diffusion Kurtosis MRI (DKI) quantifies the degree of non-gaussian diffusion, which in turn has been shown to increase sensitivity towards, e.g., disease and orientation mappings in neural tissue. However, the specificity of DKI is limited as different microstructural sources can contribute to the total diffusional kurtosis, including: variance in diffusion tensor magnitudes (Kiso), variance due to intravoxel diffusion anisotropy (Kaniso), and microscopic kurtosis (K) related to restricted diffusion and/or microstructural disorder. The latter in particular is typically ignored in diffusion MRI signal modeling as it is assumed to be negligible. Recently, Correlation Tensor MRI (CTI) based on Double-Diffusion-Encoding (DDE) was introduced for kurtosis source separation and revealed non negligible K in preclinical imaging. Here, we implemented CTI for the first time on a clinical 3T scanner and investigated the kurtosis sources in healthy subjects. A robust framework for kurtosis source separation in humans is introduced, followed by estimation of the relative importance of K in the healthy brain. Using this clinical CTI approach, we find that K significantly contributes to total diffusional kurtosis both in gray and white matter tissue but, as expected, not in the ventricles. The first K maps of the human brain are presented. We find that the spatial distribution of K provides a unique source of contrast, appearing different from isotropic and anisotropic kurtosis counterparts. We further show that ignoring K - as done by many contemporary methods based on multiple gaussian component approximation for kurtosis source estimation - biases the estimation of other kurtosis sources and, perhaps even worse, compromises their interpretation. Finally, a twofold acceleration of CTI is discussed in the context of potential future clinical applications. We conclude that CTI has much potential for future in vivo microstructural characterizations in healthy and pathological tissue. HighlightsO_LICorrelation Tensor MRI (CTI) was recently proposed to resolve kurtosis sources C_LIO_LIWe implemented CTI on a 3T scanner to study kurtosis sources in the human brain C_LIO_LIIsotropic, anisotropic, and microscopic kurtosis sources were successfully resolved C_LIO_LIMicroscopic kurtosis (K) significantly contributes to overall kurtosis in human brain C_LIO_LIK provides a novel source of contrast in the human brain in vivo C_LI

neuroscience↗

bioRxiv2018

White matter connectivity between occipital and temporal regions involved in face and voice processing in hearing and early deaf individuals.

Neuroplasticity following sensory deprivation has long inspired neuroscience research in the quest of understanding how sensory experience and genetics interact in developing the brain functional and structural architecture. Many studies have shown that sensory deprivation can lead to cross-modal functional recruitment of sensory deprived cortices. Little is known however about how structural reorganization may support these functional changes. In this study, we examined early deaf, hearing signer and hearing non-signer individuals using diffusion MRI to evaluate the potential structural connectivity linked to the functional recruitment of the temporal voice area by face stimuli in deaf individuals. More specifically, we characterized the structural connectivity between occipital, fusiform and temporal regions typically supporting voice- and face-selective processing. Despite the extensive functional reorganization for face processing in the temporal cortex of the deaf, macroscopic properties of these connections did not differ across groups. However, both occipito- and fusiform-temporal connections showed significant microstructural changes between groups (fractional anisotropy reduction, radial diffusivity increase). We propose that the reorganization of temporal regions after early auditory deprivation builds on intrinsic and mainly preserved anatomical connectivity between functionally specific temporal and occipital regions.\n\nHighlightsO_LIMacrostructural connectivity of the face-voice system is preserved in early deafness\nC_LIO_LIEarly deafness impacts on the microstructural connectivity of the face-voice system\nC_LIO_LIBoth genetics and experience shape structural connections in the face-voice system\nC_LIO_LIInnate anatomical networks might constrain the expression of cross-modal plasticity\nC_LI\n\nAbbreviations

neuroscience↗