Biology subjects

Irfanoglu, M. O.

Publications and source records attributed to Irfanoglu, M. O..

7 recordsLinked to original sources

bioRxiv2025

Diffusion MRI Processing in the HEALthy Brain and Child Development Study: Innovations and Applications

The landmark ongoing HEALthy Brain and Cognitive Development (HBCD) study will longitudinally chart brain development in a large sample (projected n=7,200) of infants through age 10 years with multimodal neuroimaging that includes an advanced diffusion MRI (dMRI) acquisition. Here, we detail advances in dMRI image processing developed for HBCD, incorporated into the widely used QSIPrep pipeline. Major changes to preprocessing include improvements in infant brain extraction, distortion correction, and normalization to infant-specific templates. Additionally, we describe a new software package - QSIRecon - that yields rich derived data including diverse maps of tissue microstructure as well as person-specific white matter bundles. Using dMRI data from a subset of the HBCD 1.0 release where age information was available (n=529 sessions across two time points), we observe critical improvements in data quality with preprocessing and see expected developmental patterns. Moving forward, the publicly-available data from HBCD will rapidly grow to become the largest study of brain development in infancy and early childhood using dMRI. QSIPrep and QSIRecon are openly available and can be applied to other infant and pediatric dMRI datasets.

neuroscience↗

bioRxiv2025

Brain Microstructure and Obesity Risk in Early Childhood: Insights from Restriction Spectrum Imaging

Pediatric obesity is a growing public health concern, yet little is known about the neurobiological underpinnings of obesity risk in early childhood. Using restriction spectrum imaging (RSI), we examined associations between adiposity and brain microstructure in a cross-sectional sample of 159 children aged 4-7 years, including 81 with ADHD and 78 typically developing (TD) peers. We focused on RSI-derived measures of restricted diffusion--specifically restricted normalized isotropic (RNI), directional (RND), and total (RNT) signals--as indicators of cellular density in subcortical and cortical regions implicated in reward and salience processing. Body mass index (BMI), percent body fat, waist circumference, and obesity status were assessed. Higher BMI, but not other adiposity measures, was significantly associated with increased RNI in the right insula, nucleus accumbens (NAcc), and putamen, as well as increased RNT in the right insula and pallidum. These findings suggest early microstructural alterations in reward-related circuits, consistent with theories of diet-related neuroinflammation. Contrary to hypotheses, ADHD diagnosis did not moderate the associations, and anthropometric profiles were similar between groups. This suggests a shared neural pathway linking early adiposity and brain structure, independent of diagnostic status. Our findings replicate and extend prior work in older children, highlighting BMI as the most sensitive marker of obesity-related brain differences in early childhood. These results underscore the potential of RSI as a tool for identifying early neural risk markers of obesity and inform future efforts to design preventive interventions during critical developmental windows.

neuroscience↗

bioRxiv2024

Exploiting Four-way Phase-encoding Benefits for Robust Detection and Correction of EPI artifacts: Application to Residual Ghosts in Diffusion MRI.

PurposeTo propose and develop an image processing-based methodology for detecting and correcting echo planar imaging (EPI) artifact that employs data from 4-way phase-encoding acquisitions. ApproachPrevious studies have demonstrated that acquiring images with four different phase-encoding directions can improve the reproducibility of diffusion derived quantitative maps while retaining the same acquisition time. This improvement is achieved by averaging across phase-encoding directions (PEDs) to reduce the impact of EPI artifacts. Building on this principle, the proposed method further improves this 4-way encoding approach by leveraging the properties of signal distributions to detect artifactual regions. Additionally, the method can be tailored for specific artifact manifestations by considering their localization due to underlying acquisition parameters. The proposed method is applied to and validated using both simulated data and in-vivo diffusion MRI data affected by residual ghost artifacts. ResultsSimulations with known ground-truth images demonstrated high artifact detection accuracy, achieving a Dice score of 0.91 for reconstructions without parallel imaging. In the in-vivo dataset, the method also improved longitudinal reproducibility, reducing variability by 30% in ghost-affected regions. ConclusionThe proposed correction method effectively detected and corrected residual ghost artifacts without the need of any additional k-space data. This retrospective approach can be directly integrated into existing processing pipelines to further improve the quality of EPI images and enhancing image quality in studies that utilize 4-way PEDs acquisition.

bioengineering↗

bioRxiv2023

'Pscore' - A Novel Percentile-Based Metric to Accurately Assess Individual Deviations in Non-Gaussian Distributions of Quantitative MRI Metrics

BACKGROUNDQuantitative MRI metrics could be used in personalized medicine to assess individuals against normative distributions. Conventional Zscore analysis is inadequate in the presence of non-Gaussian distributions. Therefore, if quantitative MRI metrics deviate from normality, an alternative is needed. PURPOSETo confirm non-Gaussianity of diffusion MRI (dMRI) metrics on a publicly available dataset, and to propose a novel percentile-based method, Pscore to address this issue. STUDY TYPERetrospective cohort POPULATION961 healthy young-adults (age:22-35 years, Females:53%) from the Human Connectome Project FIELD STRENGTH/SEQUENCE3-T, spin-echo diffusion echo-planar imaging, T1-weighted: MPRAGE ASSESSMENTThe dMRI data were preprocessed using the TORTOISE pipeline. Forty-eight regions of interest (ROIs) from the JHU-atlas were redrawn on a study-specific diffusion tensor (DT) template and average values were computed from various DT and mean apparent propagator (MAP) metrics. For each ROI, percentile ranks across participants were computed to generate Pscores- which normalized the difference between the median and a participants value with the corresponding difference between the median and the 5th/95th percentile values. STATISTICAL TESTSROI-wise distributions were assessed using Log transformations, Zscore, and the Pscore methods. The percentages of extreme values above-95th and below-5th percentile boundaries (PEV>95(%),PEV<5(%)) were also assessed in the overall white matter. Bootstrapping was performed to test the reliability of Pscores in small samples (n=100) using 100 iterations. RESULTSThe dMRI metric distributions were systematically non-Gaussian, including positively skewed (e.g., mean and radial diffusivity) and negatively skewed (e.g., fractional and propagator anisotropy) metrics. This resulted in unbalanced tails in Zscore distributions (PEV>95=5%,PEV<5=5%) whereas Pscore distributions were symmetric and balanced (PEV>95=PEV<5=5%); even for small bootstrapped samples (average [Formula]. DATA CONCLUSIONThe inherent skewness observed for dMRI metrics may preclude the use of conventional Zscore analysis. The proposed Pscore method may help estimating individual deviations more accurately in skewed normative data, even from small datasets.

neuroscience↗

bioRxiv2023

In vivo disentanglement of diffusion frequency-dependence, tensor shape, and relaxation using multidimensional MRI

Diffusion MRI with free gradient waveforms, combined with simultaneous relaxation encoding, referred to as multidimensional MRI (MD-MRI), offers microstructural specificity in complex biological tissue. This approach delivers intravoxel information about the microstructure, local chemical composition, and importantly, how these properties are coupled within heterogeneous tissue containing multiple microenvironments. Recent theoretical advances incorporated diffusion time dependency and integrated MD-MRI with concepts from oscillating gradients. This framework probes the diffusion frequency,{omega} , in addition to the diffusion tensor, D, and relaxation, R1, R2, correlations. A D({omega})-R1-R2 clinical imaging protocol was then introduced, with limited brain coverage and 3 mm3 voxel size, which hinder brain segmentation and future cohort studies. In this study, we introduce an efficient, sparse in vivo MD-MRI acquisition protocol providing whole brain coverage at 2 mm3 voxel size. We demonstrate its feasibility and robustness using a well-defined phantom and repeated scans of five healthy individuals. Additionally, we test different denoising strategies to address the sparse nature of this protocol, and show that efficient MD-MRI encoding design demands a nuanced denoising approach. The MD-MRI framework provides rich information that allows resolving the diffusion frequency dependence into intravoxel components based on their D({omega})-R1-R2 distribution, enabling the creation of microstructure-specific maps in the human brain. Our results encourage the broader adoption and use of this new imaging approach for characterizing healthy and pathological tissues.

neuroscience↗

bioRxiv2019

A framework for spatial normalization and voxelwise analysis of diffusion propagators in multiple MAP-MRI data sets

We describe a pipeline for constructing a study-specific template of diffusion propagators measured with mean apparent propagator (MAP) MRI that supports direct voxelwise analysis of differences between propagators across multiple data sets. The pipeline leverages the fact that MAP-MRI is a generalization of diffusion tensor imaging (DTI) and combines simple and robust processing steps from existing tensor-based image registration methods. First, we compute a DTI study template which provides the reference frame and scaling parameters needed to construct a standardized set of MAP-MRI basis functions at each voxel in template space. Next, we transform each subjects diffusion data, including diffusion weighted images (DWIs) and gradient directions, from native to template space using the corresponding tensor-based deformation fields. Finally, we fit MAP coefficients in template space to the transformed DWIs of each subject using the standardized template of MAP basis functions. The consistency of MAP basis functions across all data sets in template space allows us to: 1. compute a template of propagators by directly averaging MAP coefficients and 2. quantify voxelwise differences between co-registered propagators using the angular dissimilarity, or a probability distance metric, such as the Jensen-Shannon Divergence. We illustrate the application of this method by generating a template of MAP propagators for a cohort of healthy volunteers and show a proof-of-principle example of how this pipeline may be used to detect subtle differences between propagators in a single-subject longitudinal clinical data set. The ability to standardize and analyze multiple clinical MAP-MRI data sets could improve assessments in cross-sectional and single-subject longitudinal clinical studies seeking to detect subtle microstructural changes, such as those occurring in mild traumatic brain injury (mTBI), or during the early stages of neurodegenerative diseases, or cancer.

neuroscience↗

bioRxiv2018

Limits to anatomical accuracy of diffusion tractography using modern approaches

Diffusion MRI fiber tractography is widely used to probe the structural connectivity of thebrain, with a range of applications in both clinical and basic neuroscience. Despite widespread use, tractography has well-known pitfalls that limits the anatomical accuracy of this technique. Numerous modern methods have been developed to address these shortcomings through advances in acquisition, modeling, and computation. To test whether these advances improve tractography accuracy, we organized the ISBI 2018 3D Validation of Tractography with Experimental MRI (3D-VoTEM) challenge. We made available three unique independent tractography validation datasets - a physical phantom and two ex vivo brain specimens - resulting in 176 distinct submissions from 9 research groups. By comparing results over a wide range of fiber complexities and algorithmic strategies, this challenge provides a more comprehensive assessment of tractographys inherent limitations than has been reported previously. The central results were consistent across all sub-challenges in that, despite advances in tractography methods, the anatomical accuracy of tractography has not dramatically improved in recent years. Taken together, our results independently confirm findings from decades of tractography validation studies, demonstrate inherent limitations in reconstructing white matter pathways using diffusion MRI data alone, and highlight the need for alternative or combinatorial strategies to accurately map the fiber pathways of the brain.

neuroscience↗