TEST-RETEST RELIABILITY OF FRACTIONAL ANISOTROPY IN 5-YEAR-OLDS

Diffusion tensor imaging (DTI) has provided great insights to the microstructural features of developing brain and has been shown to be reliable in infants. However, the repeatability of the DTI scalars for older pediatric age groups has not been thoroughly addressed. In this study, DTI scans of 5-year-olds were used to investigate the test-retest reliability of three different measurements with both voxel-wise and region of interest (ROI) analysis. Out of 96 diffusion encoding directions, divided into three parts, 20 unique diffusion encoding directions were chosen per measurement from 48 subjects. Tract based spatial analysis (TBSS) was used to extract fractional anisotropy (FA) values from those images and using the FA values the repeatability of the measurements was assessed by intraclass correlation coefficient (ICC) and standard error of measurement (SEM). Overall, FA values had high repeatability both in voxel-based analysis (ICC>0.73) and ROI analysis (for non-skeletonized ROI type 88% of the ROI labels: ICC>0.75, for skeletonized ROI type 87% of the ROI labels: ICC>0.75). Using a skeleton in the ROI analysis did not contribute to the repeatability and the volume size was found to be a contributing factor for repeatability. Interscanner reliability as well as reliability measured by using different atlases are yet to be investigated in 5-year-old data

Dear reviewers: Responses to common reviewer critiques about infant neuroimaging studies

The field of adult neuroimaging relies on well-established principles in research design, imaging sequences, processing pipelines, as well as safety and data collection protocols. The field of infant magnetic resonance imaging, by comparison, is a young field with tremendous scientific potential but continuously evolving standards. The present article aims to initiate a constructive dialog between researchers who grapple with the challenges and inherent limitations of a nascent field and reviewers who evaluate their work. We address 20 questions that researchers commonly receive from research ethics boards, grant, and manuscript reviewers related to infant neuroimaging data collection, safety protocols, study planning, imaging sequences, decisions related to software and hardware, and data processing and sharing, while acknowledging both the accomplishments of the field and areas of much needed future advancements. This article reflects the cumulative knowledge of experts in the FIT\u27NG community and can act as a resource for both researchers and reviewers alike seeking a deeper understanding of the standards and tradeoffs involved in infant neuroimaging

Test-retest reliability of diffusion tensor imaging scalars in 5-year-olds

Diffusion tensor imaging (DTI) has provided great insights into the microstructural features of the developing brain. However, DTI images are prone to several artifacts and the reliability of DTI scalars is of paramount importance for interpreting and generalizing the findings of DTI studies, especially in the younger population. In this study, we investigated the intrascan test-retest repeatability of four DTI scalars: fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) in 5-year-old children (N = 67) with two different data preprocessing approaches: a volume censoring pipeline and an outlier replacement pipeline. We applied a region of interest (ROI) and a voxelwise analysis after careful quality control, tensor fitting and tract-based spatial statistics. The data had three subsets and each subset included 31, 32, or 33 directions thus a total of 96 unique uniformly distributed diffusion encoding directions per subject. The repeatability of DTI scalars was evaluated with intraclass correlation coefficient (ICC(3,1)) and the variability between test and retest subsets. The results of both pipelines yielded good to excellent (ICC(3,1) > 0.75) reliability for most of the ROIs and an overall low variability (<10%). In the voxelwise analysis, FA and RD had higher ICC(3,1) values compared to AD and MD and the variability remained low (<12%) across all scalars. Our results suggest high intrascan repeatability in pediatric DTI and lend confidence to the use of the data in future cross-sectional and longitudinal studies

Clinically feasible brain morphometric similarity network construction approaches with restricted magnetic resonance imaging acquisitions

Morphometric similarity networks (MSNs) estimate organization of the cortex as a biologically meaningful set of similarities between anatomical features at the macro-and microstructural level, derived from multiple structural MRI (sMRI) sequences. These networks are clinically relevant, predicting 40% variance in IQ. However, the sequences required (T1w, T2w, DWI) to produce these networks are longer acquisitions, less feasible in some populations. Thus, estimating MSNs using features from T1w sMRI is attractive to clinical and developmental neuroscience. We studied whether reduced-feature approaches approximate the original MSN model as a potential tool to investigate brain structure. In a large, homogenous dataset of healthy young adults (from the Human Connectome Project, HCP), we extended previous investigations of reduced-feature MSNs by comparing not only T1w-derived networks, but also additional MSNs generated with fewer MR sequences, to their full acquisition counterparts. We produce MSNs that are highly similar at the edge level to those generated with multimodal imaging; however, the nodal topology of the networks differed. These networks had limited predictive validity of generalized cognitive ability. Overall, when multimodal imaging is not available or appropriate, T1w-restricted MSN construction is feasible, provides an appropriate estimate of the MSN, and could be a useful approach to examine outcomes in future studies

Superficial White Matter Analysis: An Efficient Point-cloud-based Deep Learning Framework with Supervised Contrastive Learning for Consistent Tractography Parcellation across Populations and dMRI Acquisitions

Diffusion MRI tractography is an advanced imaging technique that enables in vivo mapping of the brain's white matter connections. White matter parcellation classifies tractography streamlines into clusters or anatomically meaningful tracts. It enables quantification and visualization of whole-brain tractography. Currently, most parcellation methods focus on the deep white matter (DWM), whereas fewer methods address the superficial white matter (SWM) due to its complexity. We propose a novel two-stage deep-learning-based framework, Superficial White Matter Analysis (SupWMA), that performs an efficient and consistent parcellation of 198 SWM clusters from whole-brain tractography. A point-cloud-based network is adapted to our SWM parcellation task, and supervised contrastive learning enables more discriminative representations between plausible streamlines and outliers for SWM. We train our model on a large-scale tractography dataset including streamline samples from labeled SWM clusters and anatomically implausible streamline samples, and we perform testing on six independently acquired datasets of different ages and health conditions (including neonates and patients with space-occupying brain tumors). Compared to several state-of-the-art methods, SupWMA obtains highly consistent and accurate SWM parcellation results on all datasets, showing good generalization across the lifespan in health and disease. In addition, the computational speed of SupWMA is much faster than other methods.Comment: 12 pages, 7 figures. Extension of our ISBI 2022 paper (arXiv:2201.12528) (Best Paper Award Finalist

Dear reviewers: responses to common reviewer critiques about infant neuroimaging studies

The field of adult neuroimaging relies on well-established principles in research design, imaging sequences, processing pipelines, as well as safety and data collection protocols. The field of infant magnetic resonance imaging, by comparison, is a young field with tremendous scientific potential but continuously evolving standards. The present article aims to initiate a constructive dialog between researchers who grapple with the challenges and inherent limitations of a nascent field and reviewers who evaluate their work. We address 20 questions that researchers commonly receive from research ethics boards, grant, and manuscript reviewers related to infant neuroimaging data collection, safety protocols, study planning, imaging sequences, decisions related to software and hardware, and data processing and sharing, while acknowledging both the accomplishments of the field and areas of much needed future advancements. This article reflects the cumulative knowledge of experts in the FIT'NG community and can act as a resource for both researchers and reviewers alike seeking a deeper understanding of the standards and tradeoffs involved in infant neuroimaging.R01 MH104324 - NIMH NIH HHS; UL1 TR001863 - NCATS NIH HHS; P50 MH115716 - NIMH NIH HHS; K01 MH108741 - NIMH NIH HHS; TL1 TR001864 - NCATS NIH HHS; R01 MH118285 - NIMH NIH HHS; U01 MH110274 - NIMH NIH HHS; P50 MH100029 - NIMH NIH HHS; ZIA MH002782 - Intramural NIH HHS; R01 EB027147 - NIBIB NIH HHS; R01 MH119251 - NIMH NIH HHS; UL1 TR003015 - NCATS NIH HHS; F31 HD102156 - NICHD NIH HHS; KL2 TR003016 - NCATS NIH HHS; T32 MH018268 - NIMH NIH HHSPublished versio

Typical and Atypical Development of Functional Human Brain Networks: Insights from Resting-State fMRI

Over the past several decades, structural MRI studies have provided remarkable insights into human brain development by revealing the trajectory of gray and white matter maturation from childhood to adolescence and adulthood. In parallel, functional MRI studies have demonstrated changes in brain activation patterns accompanying cognitive development. Despite these advances, studying the maturation of functional brain networks underlying brain development continues to present unique scientific and methodological challenges. Resting-state fMRI (rsfMRI) has emerged as a novel method for investigating the development of large-scale functional brain networks in infants and young children. We review existing rsfMRI developmental studies and discuss how this method has begun to make significant contributions to our understanding of maturing brain organization. In particular, rsfMRI has been used to complement studies in other modalities investigating the emergence of functional segregation and integration across short and long-range connections spanning the entire brain. We show that rsfMRI studies help to clarify and reveal important principles of functional brain development, including a shift from diffuse to focal activation patterns, and simultaneous pruning of local connectivity and strengthening of long-range connectivity with age. The insights gained from these studies also shed light on potentially disrupted functional networks underlying atypical cognitive development associated with neurodevelopmental disorders. We conclude by identifying critical gaps in the current literature, discussing methodological issues, and suggesting avenues for future research

Brain structural connectivity and neurodevelopment in post-Fontan adolescents

Congenital heart disease (CHD) is the most common congenital anomaly, with single ventricle (SV) defects accounting for nearly 10% of all CHD. SV defects tend to be the most severe forms of CHD: all patients born with SV require multiple open heart surgeries, often beginning in the neonatal period, ultimately leading to the Fontan procedure. Due to improvements in surgical procedures and medical care, more patients are surviving into adolescence and adulthood. Brain imaging and pathology studies have shown that patients with SV have differences in brain structure and metabolism even before the first surgery, and as early as in utero. Furthermore, a significant number of patients have new or more severe lesions after the initial surgery, and many still have brain abnormalities into early childhood. However, there are no detailed brain structural data of SV patients in adolescence. Our group recruited a large cohort of post-Fontan SV patients aged 10-19 years. Separate analyses of neuropsychological and behavioral outcomes in these patients show deficits in multiple areas of cognition, increased rates of attention deficit-hyperactivity disorder (ADHD), and increased use of remedial and/or special education services compared to a control group. Post-Fontan adolescents have more gross brain abnormalities, including evidence of chronic ischemic stroke. Furthermore, there are widespread reductions in cortical and subcortical gray matter volume and cortical thickness, some of which are associated with medical and surgical variables. Diffusion tensor imaging (DTI) analyses show widespread areas of altered white matter microstructure in deep subcortical and cerebellar white matter. In this dissertation, I use graph theory methods to characterize structural connectivity based on gray matter (cortical thickness covariance) and white matter (DTI tractography), and examine associations between brain structure and neurodevelopment. I found that brain network connectivity differs in post-Fontan patients compared with controls, both at the global and regional level. Additionally, deficits in overall network structure were associated with impaired neurodevelopment in several domains, including general intelligence, executive function, and visuospatial skills. These data suggest that early neuroprotection should be a major focus in the care of SV patients, with the goal of improving long-term neurodevelopmental outcomes

Effect of preterm birth on white matter tracts and infant cognition

Preterm birth (defined as birth before 37 weeks) is a leading cause of neurocognitive impairment in childhood, including difficulties in social cognition and executive function. Microstructural divergence from typical brain development in the preterm brain can be quantified using diffusion magnetic resonance imaging (dMRI) tractography during the neonatal period. The relationship between dMRI tractography metrics and later cognitive difficulties remains inconclusive. A general measure of white matter microstructure (gWM) offers a neural basis for cognitive processes in adults, however it remains unclear when gWM is first detectable in the developmental trajectory. Eye-tracking is a technique which assesses eye-gaze behaviour in response to visual stimuli, which permits inference about underlying cognitive processes, such as social cognition and executive function in infancy. The primary aims of this thesis were to test the hypotheses: dMRI tractography reveals significant differences in tract-average fractional anisotropy (FA) and mean diffusivity (MD) between preterm and term infants, and variance in tract-average FA and MD is shared across major tracts. Secondly, infants born preterm have altered social cognition and executive function compared to term born peers, assessed by eye-tracking and finally, neonatal MRI gWM is associated with cognitive function in infancy. Preterm (birth weight ≤ 1500g) and term infants (born ≥ 37 weeks’ post-menstrual age [PMA]) were recruited and underwent a MRI scan at term equivalent age (between 38 - 42 weeks’ PMA) and an eye-tracking assessment six to nine months later. Preterm infants were assessed at two years using the Bayley Scales of Infant and Toddler Development, Third Edition (BSID-III). dMRI tractography metrics were generated using probabilistic neighbourhood tractography (PNT) in eight pre-defined tracts-of-interest. Principal component analyses (PCA) were used to determine the correlations between the eight tracts-of-interest for four tract-averaged water diffusion parameters. dMRI metrics were compared to the eye-tracking performance and two year outcome data. Quantitative microstructural changes were identifiable within the preterm brain when compared to infants born at term. PCA revealed a single variable that accounts for nearly 50% of shared variance between tracts-of-interest, and all tracts showed positive loadings. Eye-tracking revealed group-wise differences in infant social cognition, attributable to preterm birth, but executive functions inferred from eye-tracking did not differ between groups. dMRI tractography metrics within the neonatal period did not relate to later outcome measures. This thesis shows that variance in dMRI parameters is substantially shared across white matter tracts of the developing brain and suggests that anatomical foundations of later intelligence are present by term equivalent age. Social cognition is altered by preterm birth, however social cognitive ability in infancy is independent of gWM

Optimized non-invasive MRI protocols for characterizing tissue microstructures: applications in humans to prostate cancer and fetal brain development

This PhD project was aimed to optimize MRI protocols for pelvis imaging, in particular for the diagnosis of prostate cancer (PCa) and for the fetal brain development. Different non-invasive MRI techniques were employed to investigate biological tissues, with the purpose to obtain information on microstructures and potentially metabolism. Prostate cancer is the second most common malignancy and the fifth leading cause of death in men worldwide. Due to the high incidence of PCa and the limitations of current diagnostic methods, the primary goal of this work was to develop an MRI protocol able to improve the sensitivity of the diagnostic. The investigation of prostate cancer started with ex-vivo experiments conducted on specimens of human prostate gland, obtained after radical prostatectomy, with the 9.4T scanner at the NMR and Medical Physics Laboratory of CNR-ISC (Sapienza). Diffusion Tensor Imaging (DTI) and Diffusion Kurtosis Imaging (DKI) were performed at high-resolution (70x70 micrometers in plane) to evaluate diffusion metrics in the different prostate compartment and directly compare measurements with the histopathology results. This study proceeded with in-vivo experiments with a 3T clinical MR scanner (Philips Achieva at Policlinico Tor Vergata) on subjects with diagnosed PCa. DTI was performed with the purpose to assess its diagnostic ability in individuating and classifying PCa with different ranges of diffusion weightings, i.e. b-values. Our results showing that the diagnostic accuracy of DTI is improved with high diffusion weightings motivated our interest in performing DKI, a technique that captures water diffusion features when high b-values are employed, providing additional information on tissue microstructures, inaccessible to DTI technique. The second part of this PhD project was conducted at the Center for Magnetic Resonance Research (CMRR) in Minneapolis and was funded by the European Union's Horizon 2020 research and innovation program under the Marie Curie grant agreement No 691110 (MICROBRADAM). The study was dedicated to perform prostate cancer imaging with new contrast mechanisms, based on T1rho and T2rho relaxation times. T1rho and T2rho characterize the relaxation of the nuclear magnetization in the rotating frame and they are sensitive to molecular dynamics occurring at frequencies in the range of kHz, characteristic of several in-vivo processes, enabling the access to important information on tissue microenvironment. T1rho and T2rho imaging is limited by the intensive energy deposited by the acquisition sequence, which it is usually overcome by increasing the acquisition time, preventing the possibility of diagnostic applications. Therefore the aim of this work was to develop a new approach to perform imaging in the rotating frame with a three-dimensional acquisition method, recently developed at the CMRR, in order to address the aforementioned shortcomings. Given the incidence of PCa, this research has international interest and potentially contributes to improve not only the sensitivity of PCa diagnostic but also the knowledge of the tissue micro-changing caused by the tumor development. A part of this project was dedicated to Diffusion MRI application in woman pelvis to image fetuses during gestation. The aim of this work was to develop a fast and reliable protocol for fetal imaging to minimize mother-fetal motion artifact and perfusion effects. The protocol designed for acquisition and post-processing was employed to successfully study fetal brain development during the second and third trimester of gestation, in normal cases and in fetuses affected by ventriculomegaly disease. These preliminary data can contribute to delineate a reference standard to assess the normal progress of sulcation and myelination as well as the normative biometry of the fetal brain, improving the knowledge of brain maturation. Globally, the impact of this research lies in having demonstrated that the sensitivity of DMRI for microstructural changes in body tissue caused by cancer, brain disease or normal condition like brain maturation can be fruitfully utilized in combination with artifact correction methods. Moreover, new strategy of image reconstruction, such as 3D gradient echo, can be successfully employed to perform abdominal imaging, enriching the investigation of in-vivo systems with information on tissue microenvironment and metabolism