Biology subjects

Fadnavis, S.

Publications and source records attributed to Fadnavis, S..

7 recordsLinked to original sources

bioRxiv2023

Data-driven characterization of Preterm Birth through intramodal Diffusion MRI

Preterm birth still represents a concrete emergency to be managed and addressed globally. Since cerebral white matter injury is the major form of brain impairment in survivors of premature birth, the identification of reliable, non-invasive markers of altered white matter development is of utmost importance in diagnostics. Diffusion MRI has recently emerged as a valuable tool to investigate these kinds of alterations. In this work, rather than focusing on a single MRI modality, we worked on a compound of beyond-DTI High Angular Resolution Diffusion Imaging (HARDI) techniques in a group of 46 preterm babies studied on a 3T scanner at term equivalent age and in 23 control neonates born at term. After extracting relevant derived parameters, we examined discriminative patterns of preterm birth through (i) a traditional voxel-wise statistical method such as the Tract-Based Spatial Statistics approach (TBSS); (ii) an advanced Machine Learning approach such as the Support Vector Machine (SVM) classification; (iii) establishing the degree of association between the two methods in voting white matter most discriminating areas. Finally, we applied a multi-set Canonical Correlation Analysis (CCA) in search for sources of linked alterations across modalities. TBSS analysis showed significant differences between preterm and term cohorts in several white matter areas for multiple HARDI features. SVM classification performed on skeletonized HARDI measures produced satisfactory accuracy rates, especially as for highly informative parameters about fibers directionality. Assessment of the degree of overlap between the relevant measures identified by the two methods exhibited a good, though parameter-dependent rate of agreement. Finally, CCA analysis identified joint changes precisely for those features exhibiting less correspondence between TBSS and SVM. Our results suggest that a data-driven intramodal imaging approach is crucial to extract deep and complementary information that cannot be extracted from a single modality.

bioengineering↗

bioRxiv2022

Mapping the "missing" pathways of the anterior cingulate cortex in the human brain

The anterior cingulate cortex (ACC) is functionally closely related with the insula and the ventral lateral prefrontal cortex (vlPFC). Extensive work on their functional relationships has led to the salience network theory and advanced understanding of value-based learning and decision making. However, the anatomical connections between the ACC and the two regions remain unknown in the human brain. Despite the anatomical ground truth established by nonhuman primate (NHP) tract-tracing, diffusion magnetic resonance imaging (dMRI) has not seen success identifying homologous pathways in humans. In this study we show that the negative finding does not reflect a cross-species discrepancy but rather a technical issue. We used NHP dMRI as a bridge to compare the ground-truth pathways in NHPs and dMRI-derived pathways in humans. The insight from NHP data helped pinpoint a bias in fiber orientation distribution functions (fODFs) caused by the disproportion of anterior-posterior vs. medial-lateral fibers in the human brain. Guided by this information, we successfully recovered the ACC-insula and ACC-vlPFC pathways that followed the same trajectories as in the NHP dMRI and tract-tracing data. Our findings provide an anatomical basis for the functional interactions among the ACC, the insula and the vlPFC.

neuroscience↗

bioRxiv2022

Patch2Self denoising of Diffusion MRI with Self-Supervision and Matrix Sketching

Diffusion-weighted magnetic resonance imaging (DWI) is the only noninvasive method for quantifying microstructure and reconstructing white-matter pathways in the living human brain. Fluctuations from multiple sources create significant additive noise in DWI data which must be suppressed before subsequent microstructure analysis. We introduce a self-supervised learning method for denoising DWI data, Patch2Self (P2S), which uses the entire volume to learn a full-rank locally linear denoiser for that volume. By taking advantage of the oversampled q-space of DWI data, P2S can separate structure from noise without requiring an explicit model for either. The setup of P2S however can be resource intensive, both in terms of running time and memory usage, as it uses all voxels (n) from all-but-one held-in volumes (d - 1) to learn a linear mapping {Phi} : [R]nx(d-1) [&map;] [R]n for denoising the held-out volume. We exploit the redundancy imposed by P2S to alleviate its performance issues and inspect regions that influence the noise disproportionately. Specifically we introduce P2S-sketch, which makes a two-fold contribution: (1) P2S-sketch uses matrix sketching to perform self-supervised denoising. By solving a sub-problem on a smaller sub-space, so called, coreset, we show how P2S can yield a significant speedup in training time while using less memory. (2) We show how the so-called statistical leverage scores can be used to interpret the denoising of dMRI data, a process that was traditionally treated as a black-box. Our experiments conducted on simulated and real data clearly demonstrate that P2S via matrix sketching (P2S-sketch) does not lead to any loss in denoising quality, while yielding significant speedup and improved memory usage by training on a smaller fraction of the data. With thorough comparisons on real and simulated data, we show that Patch2Self outperforms the current state-of-the-art methods for DWI denoising both in terms of visual conspicuity and downstream modeling tasks. We demonstrate the effectiveness of our approach via multiple quantitative metrics such as fiber bundle coherence, R2 via cross-validation on model fitting, mean absolute error of DTI residuals across a cohort of sixty subjects.

neuroscience↗

bioRxiv2021

MVD-Fuse: Detection of White Matter Degeneration via Multi-View Learning of Diffusion Microstructure

Detecting neuro-degenerative disorders in early-stage and asymptomatic patients is challenging. Diffusion MRI (dMRI) has shown great success in generating biomarkers for cellular organization at the microscale level using complex biophysical models, but there has never been a consensus on a clinically usable standard model. Here, we propose a new framework (MVD-Fuse) to integrate measures of diverse diffusion models to detect alterations of white matter microstructure. The spatial maps generated by each measure are considered as a different diffusion representation (view), the fusion of these views being used to detect differences between clinically distinct groups. We investigate three different strategies for performing intermediate fusion: neural networks (NN), multiple kernel learning (MKL) and multi-view boosting (MVB). As a proof of concept, we applied MVD-Fuse to a classification of premanifest Huntingtons disease (pre-HD) individuals and healthy controls in the TRACK-ON cohort. Our results indicate that the MVD-Fuse boosts predictive power, especially with MKL (0.90 AUC vs 0.85 with the best single diffusion measure). Overall, our results suggest that an improved characterization of pathological brain microstructure can be obtained by combining various measures from multiple diffusion models.

neuroscience↗

bioRxiv2021

On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: chronicles of the MEMENTO challenge.

Diffusion MRI (dMRI) has become an invaluable tool to assess the microstructural organization of brain tissue. Depending on the specific acquisition settings, the dMRI signal encodes specific properties of the underlying diffusion process. In the last two decades, several signal representations have been proposed to fit the dMRI signal and decode such properties. Most methods, however, are tested and developed on a limited amount of data, and their applicability to other acquisition schemes remains unknown. With this work, we aimed to shed light on the generalizability of existing dMRI signal representations to different diffusion encoding parameters and brain tissue types. To this end, we organized a community challenge - named MEMENTO, making available the same datasets for fair comparisons across algorithms and techniques. We considered two state-of-the-art diffusion datasets, including single-diffusion-encoding (SDE) spin-echo data from a human brain with over 3820 unique diffusion weightings (the MASSIVE dataset), and double (oscillating) diffusion encoding data (DDE/DODE) of a mouse brain including over 2520 unique data points. A subset of the data sampled in 5 different voxels was openly distributed, and the challenge participants were asked to predict the remaining part of the data. After one year, eight participant teams submitted a total of 80 signal fits. For each submission, we evaluated the mean squared error, the variance of the prediction error and the Bayesian information criteria. Most predictions predicted either multi-shell SDE data (37%) or DODE data (22%), followed by cartesian SDE data (19%) and DDE (18%). Most submissions predicted the signals measured with SDE remarkably well, with the exception of low and very strong diffusion weightings. The prediction of DDE and DODE data seemed more challenging, likely because none of the submissions explicitly accounted for diffusion time and frequency. Next to the choice of the model, decisions on fit procedure and hyperparameters play a major role in the prediction performance, highlighting the importance of optimizing and reporting such choices. This work is a community effort to highlight strength and limitations of the field at representing dMRI acquired with trending encoding schemes, gaining insights into how different models generalize to different tissue types and fiber configurations over a large range of diffusion encodings.

neuroscience↗

bioRxiv2020

QSIPrep: An integrative platform for preprocessing and reconstructing diffusion MRI

Diffusion-weighted magnetic resonance imaging (dMRI) has become the primary method for non-invasively studying the organization of white matter in the human brain. While many dMRI acquisition sequences have been developed, they all sample q-space in order to characterize water diffusion. Numerous software platforms have been developed for processing dMRI data, but most work on only a subset of sampling schemes or implement only parts of the processing workflow. Reproducible research and comparisons across dMRI methods are hindered by incompatible software, diverse file formats, and inconsistent naming conventions. Here we introduce QSIPrep, an integrative software platform for the processing of diffusion images that is compatible with nearly all dMRI sampling schemes. Drawing upon a diverse set of software suites to capitalize upon their complementary strengths, QSIPrep automatically applies best practices for dMRI preprocessing, including denoising, distortion correction, head motion correction, coregistration, and spatial normalization. Throughout, QSIPrep provides both visual and quantitative measures of data quality as well as "glass-box" methods reporting. Taken together, these features facilitate easy implementation of best practices for processing of diffusion images while simultaneously ensuring reproducibility.

bioinformatics↗

bioRxiv2020

Pandora: 4-D white matter bundle population-based atlasesderived from diffusion MRI fiber tractography

Brain atlases have proven to be valuable neuroscience tools for localizing regions of interest and performing statistical inferences on populations. Although many human brain atlases exist, most do not contain information about white matter structures, often neglecting them completely or labelling all white matter as a single homogenous substrate. While few white matter atlases do exist based on diffusion MRI fiber tractography, they are often limited to descriptions of white matter as spatially separate "regions" rather than as white matter "bundles" or fascicles, which are well-known to overlap throughout the brain. Additional limitations include small sample sizes, few white matter pathways, and the use of outdated diffusion models and techniques. Here, we present a new population-based collection of white matter atlases represented in both volumetric and surface coordinates in a standard space. These atlases are based on 2443 subjects, and include 216 white matter bundles derived from 6 different state-of-the-art tractography techniques. This atlas is freely available and will be a useful resource for parcellation and segmentation.

neuroscience↗