Ragini Verma

PIGMENT: A deep learning framework for Porcine Immunohistochemistry seGMENTation

PIGMENT is a deep learning framework that automates the segmentation and quantification of APP-positive pathology in porcine white matter histology, achieving high detection rates with limited expert annotations.

Big Data, Small Bias: Harmonizing Diffusion MRI-Based Structural Connectomes to Mitigate Site-Related Bias in Data Integration

This paper develops a harmonization framework for structural connectomes to reduce site-related biases, showing that gamma-GLM improves generalizability and statistical power in multi-site analyses.

Choice of parcellation atlas might not be too critical for connectomic analysis

The paper investigates how different parcellation atlases affect deep learning tasks on structural connectomes for age and sex prediction, finding that atlas choice is generally not critical.

Analysis of structural network topology alterations in autism and its relationship with autism severity

This study uses a novel connectomic measure to identify structural connectivity alterations in ASD and links network topology changes to autism severity.

Connectomic Assessment of Injury Burden and Longitudinal Structural Network Alterations in Moderate-to-severe Traumatic Brain Injury

This study introduces a novel connectomic measure, NAS, to quantify structural network disruptions in TBI patients and shows its superiority over existing graph theory measures.

Tractography dissection variability: what happens when 42 groups dissect 14 white matter bundles on the same dataset?

This study evaluates variability in white matter bundle segmentation across 42 groups using the same dataset, finding protocol variability exceeds other sources of inconsistency.

Tractography-based targeting of ventral intermediate nucleus: A comparison between conventional stereotactic targeting and diffusion tensor imaging-based targeting

This study compares probabilistic and deterministic diffusion tensor imaging tractography with stereotactic coordinates for targeting the ventral intermediate nucleus in DBS, finding probabilistic tractography most accurate.

Structural and functional brain parameters related to cognitive performance across development: Replication and extension of the parieto-frontal integration theory in a single sample.

The study replicates and extends the Parieto-Frontal Integration Theory (PFIT) in youths, showing age-related strengthening of brain parameter-cognitive performance associations and suggesting an expanded network incorporating motivation and affect regions.

Connectomic consistency: a systematic stability analysisof structural and functional connectivity

This study systematically analyzes the consistency of structural and functional connectomes across different generation methods and populations, finding structural connectomes more consistent than functional ones.

Tractography Reproducibility Challenge with Empirical Data (TraCED): The 2017 ISMRM Diffusion Study Group Challenge

The paper evaluates tractography reproducibility using empirical data from a single healthy volunteer scanned on two scanners, finding high consistency among top algorithms but variability due to tract volume containment.

Structural connectivity analysis using Finsler geometry

This paper introduces a Finsler geometry-based method for analyzing brain structural connectivity, showing improved accuracy compared to previous geodesic methods using HCP and autism data.

Limits to anatomical accuracy of diffusion tractography using modern approaches

This study evaluates modern diffusion tractography methods using a standardized challenge, finding no significant improvement in anatomical accuracy despite methodological advances.

Multifactorial Dynamics of White Matter Connectivity During Adolescence

The paper constructs a multifactorial model of white matter connectivity during adolescence using Bayesian networks and diffusion imaging data, showing that architectural specifications become more influential with age.

The Impact of In-Scanner Head Motion on Structural Connectivity Derived from Diffusion Tensor Imaging

This paper evaluates how in-scanner head motion affects structural connectivity estimates from diffusion tractography in a developmental cohort, showing motion biases connectivity strength in a consistency and length-dependent manner.

Harmonization Of Multi-Site Diffusion Tensor Imaging Data

This paper shows that diffusion tensor imaging data are site-specific and demonstrates that ComBat effectively harmonizes them while preserving biological variability.