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Elliott, M. A.

Publications and source records attributed to Elliott, M. A..

15 recordsLinked to original sources
bioRxiv2026

The PMADS Project: A Longitudinal Multimodal Cohort Study to Understand Risk for Perinatal Mood and Anxiety Disorders

BackgroundPerinatal mood and anxiety disorders (PMADs) are among the most common and consequential complications of pregnancy. The perinatal period is also characterized by profound hormonal fluctuations and large-scale brain plasticity. However, the mechanisms linking these neurobiological changes to psychiatric risk are poorly understood. Prospective, clinically informed studies are needed to identify quantitative biomarkers and clarify pathways linking perinatal neurobiology to PMADs risk. MethodsThis report describes the design of a prospective, longitudinal cohort study integrating multimodal neuroimaging, biofluid sampling, and deep clinical phenotyping to enable precision characterization of neurobiological trajectories of PMADs risk. Twenty-five individuals at elevated risk for PMADs will be recruited prior to conception and followed across six in-person timepoints spanning the menstrual cycle, pregnancy, and early postpartum, with additional remote follow-ups through the first postpartum year. Data collection includes high-resolution structural MRI, functional brain mapping using multi-echo resting-state fMRI, diffusion MRI, arterial spin labeling, ultra-high field MR-based techniques for measuring glutamate (GluCEST and 1HMRS), biofluid sampling, and comprehensive clinical, behavioral, and cognitive assessments. Structured clinical interviews assess categorical diagnoses while dimensional symptom measures capture heterogeneity and transdiagnostic features of perinatal psychopathology. Longitudinal analyses will model nonlinear trajectories of brain and symptom change across the perinatal period as well as evaluate whether preconception network features and menstrual cycle-related brain changes are associated with subsequent perinatal symptom emergence. DiscussionThis cohort study establishes a longitudinal, multimodal framework for investigating neurobiological changes across the transition to pregnancy in individuals at elevated risk for PMADs. By anchoring pregnancy-related brain changes to preconception and menstrual cycle-related variability within the same individuals, this study is designed to evaluate associations between preconception hormone sensitivity, pregnancy-induced neuroplasticity, and PMADs risk. The resulting dataset will provide a deeply phenotyped longitudinal resource for investigating brain-behavior relationships across the perinatal period. Findings are expected to inform future larger-scale studies aimed at advancing mechanistic understanding of PMADs, improving individualized risk stratification, and supporting development of personalized preventive and neuromodulatory interventions.

neuroscience↗
bioRxiv2025

An Open, Fully-processed, Longitudinal Data Resource to Study Brain Development and Transdiagnostic Executive Function

Executive function (EF) develops rapidly during adolescence. However, deficits in EF also emerge in adolescence, representing a transdiagnostic symptom associated with many forms of psychopathology. To promote transdiagnostic research on EF during development, we introduce a new data resource - the Penn Longitudinal Executive functioning in Adolescent Development study (Penn LEAD) - that combines longitudinal multimodal imaging data with rich clinical and cognitive phenotyping. These data include 225 imaging sessions from 132 individuals (8-16 years old at the time of enrollment) who are typically developing (27.3%), or meet criteria for attention-deficit hyperactivity disorder (20.5%) or the psychosis-spectrum (52.3%). In addition to phenotypic data from multiple cognitive tasks focused on EF, the study includes data from structural MRI, diffusion MRI, n-back task fMRI, resting-state fMRI, and arterial spin-labeled MRI. Notably, all raw data, fully-processed derived data, and detailed quality control recommendations are publicly shared on OpenNeuro. We anticipate that such analysis-ready data will accelerate research on EF development in psychiatry.

neuroscience↗
bioRxiv2024

Quantification of NAD+ T1 and T2 relaxation times using downfield 1H MRS at 7 T in human brain in vivo

IntroductionThe purpose of this study was to use a single-slice spectrally-selective sequence to measure T1 and T2 relaxation times of NAD+ proton resonances in the downfield 1H MRS spectrum in human brain at 7 T in vivo and assess the propagation of relaxation time uncertainty in NAD+ quantification. MethodsDownfield spectra from 7 healthy volunteers were acquired at multiple echo times in all subjects to measure T2 relaxation, and saturation recovery data were to measure T1 relaxation. The downfield acquisition used a spectrally-selective 90{degrees} sinc pulse for excitation centered at 9.1 ppm with a bandwidth of 2 ppm, followed by a 180{degrees} spatially-selective Shinnar-Le Roux refocusing pulse for localization. For the multiple echo experiment, spectra were collected with echo times ranging from 13 to 33 ms. For the saturation recovery experiment, saturation was performed prior to excitation using the same spectrally-selective sinc pulse as was used for excitation. Saturation delay times (TS) ranged from 100 to 600 ms. Uncertainty propagation analysis was performed analytically and with Monte Carlo simulation. ResultsThe mean {+/-} standard deviation of T1 relaxation times of the H2, H6, and H4 protons were 152.7 {+/-} 16.6, 163.6 {+/-} 22.3, and 169.9 {+/-} 11.2 ms, respectively. The mean {+/-} standard deviation of T2 relaxation times of the H2, H6, and H4 protons were 32.5 {+/-} 7.0, 27.4 {+/-} 5.2, and 38.1 {+/-} 11.7 ms, respectively. The mean R2 of the H2 and H6 T1 fits were 0.98. The mean R2 of the H4 proton T1 fit was 0.96. The mean R2 of the T2 fits of the H2 and H4 proton resonances were 0.98, while the mean R2 of the T2 fits of the H4 proton was 0.93. The relative uncertainty in NAD+ concentration due to relaxation time uncertainty was 8.5%-11%. ConclusionUsing downfield spectrally-selective spectroscopy with single-slice localization, we found NAD+ T1 and T2 relaxation times to be approximately 162 ms and 32 ms respectively in the human brain in vivo at 7 T.

bioengineering↗
bioRxiv2022

Diffusion MRI Head Motion Correction Methods are Highly Accurate but Impacted by Denoising and Sampling Scheme

Correcting head motion artifacts in diffusion-weighted MRI (dMRI) scans is particularly challenging due to the dramatic changes in image contrast at different gradient strengths and directions. Head motion correction is typically performed using a Gaussian Process model implemented in FSLs Eddy. Recently, the 3dSHORE-based SHORELine method was introduced to correct any dMRI sequence that has more than one shell. Here we perform a comprehensive evaluation of both methods on realistic simulations of a software fiber phantom that provides known ground-truth head motion. We demonstrate that both methods perform remarkably well, but that performance can be impacted by sampling scheme, the pervasiveness of head motion, and the denoising strategy applied before head motion correction. Our study also provides an open and fully-reproducible workflow that could be used to accelerate evaluation studies of other dMRI processing methods in the future. HIGHLIGHTSO_LIBoth Eddy and SHORELine head motion correction methods performed quite well on a large variety of simulated data C_LIO_LIDenoising with MP-PCA can improve head motion correction performance when Eddy is used C_LIO_LISHORELine effectively corrects motion in non-shelled acquisitions C_LI

neuroscience↗
bioRxiv2022

Inter-scanner brain MRI volumetric biases persist even in a harmonized multi-subject study of multiple sclerosis

Background/PurposeMulticenter study designs involving a variety of MRI scanners have become increasingly common. However, these present the issue of biases in image-based measures due to scanner or site differences. To assess these biases, we imaged 11 volunteers with multiple sclerosis (MS) with scan and rescan data at 4 sites. Materials and MethodsImages were acquired on Siemens or Philips scanners at 3-tesla. Automated white matter lesion detection and whole brain, gray and white matter, and thalamic volumetry were performed, as well as expert manual delineations of T1 and T2 (FLAIR) lesions. Random effect and permutation-based nonparametric modeling was performed to assess differences in estimated volumes within and across sites. ResultsRandom effect modeling demonstrated model assumption violations for most comparisons of interest. Non-parametric modeling indicated that site explained > 50% of the variation for most estimated volumes. This expanded to > 75% when data from both Siemens and Philips scanners were included. Permutation tests revealed significant differences between average inter- and intra-scanner differences in most estimated brain volumes (P < .05). The automatic activation of spine coil elements during some acquisitions resulted in a shading artifact in these images. Permutation tests revealed significant differences between thalamic volume measurements from acquisitions with and without this artifact. ConclusionDifferences in brain volumetry persisted across MR scanners despite protocol harmonization. These differences were not well explained by variance component modeling; however, statistical innovations for mitigating inter-scanner differences show promise in reducing biases in multi-center studies of MS.

neuroscience↗
bioRxiv2021

Functional Differences in the Neural Substrates of Auditory Cognition as a Consequence of Music Training

Previous studies have demonstrated that musical deviants (syntactically irregular chords) elicit event related potentials/fields with negative polarity; specifically, the early right anterior negativity and the right anterior temporal negativity responses with peak latencies at ~200 ms and ~350 ms, respectively, post stimulus onset. Here, we investigated differences in the neural dynamics of the auditory perceptual system of individuals with music training compared to those with no music training. Magnetoencephalography was used to examine the neural response to a deviant sound when the auditory system was primed using stimulus entrainment to evoke an auditory gamma-band response between 31 Hz and 39 Hz, in 2 Hz steps. Participants responded to the harmonic relationship between the entrainment stimulus and the subsequent target stimulus. Gamma frequencies carry stimulus information; thus, the paradigm primed the auditory system with a known gamma frequency and evaluated any improvement in the brains response to a deviant stimulus. The entrainment stimuli did not elicit an early right anterior negativity response. Furthermore, the source location of the event-related field difference during the later right anterior temporal negativity response time-window varied depending on group and entrainment condition. In support of previous findings from research using this, and a functionally similar visual-priming paradigm, a 7 Hz phase modulation of gamma amplitude was found for non-musicians following 33 Hz stimulus entrainment. Overall, significant effects of gamma entrainment were found more frequently in the non-music brain. By contrast, musicians demonstrated a greater range of interactions with slower brain rhythms, indicative of increased top-down control. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=130 SRC="FIGDIR/small/446932v1_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@1097a58org.highwire.dtl.DTLVardef@13f3c74org.highwire.dtl.DTLVardef@99fae1org.highwire.dtl.DTLVardef@125d351_HPS_FORMAT_FIGEXP M_FIG C_FIG ConclusionO_LIMusicians auditory cognition relies more on top-down processes, while non-musicians rely on bottom-up processing, and therefore, their auditory cognition is facilitated by the entrained gamma-band response. C_LIO_LIFirst neural evidence in support of faster reaction-time responses due to an interaction in phase of an entrained gamma-band response of 33 Hz and a slower endogenous theta rhythm (~7 Hz) - consistently reported in previous research. C_LI

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

Indoleamine 2,3-dioxygenase-1 expressing aggregate bone marrow dendritic cell populations are associated with systemic T cell compartment changes in chronic myelomonocytic leukemia

Systemic immune tolerance is not well-characterized in chronic myelomonocytic leukemia (CMML). Due to the presence of clonal plasmacytoid dendritic cells (pDC) in CMML, and the established association of lymph node indoleamine 2,3-dioxygenase-1 (IDO1)-positive (+) DC populations (IDC) with systemic immune tolerance in other malignant contexts, we sought to determine the association of IDO1 expression and bone marrow (BM) DC populations with systemic T-cell compartment changes using primary CMML patient samples (BM, plasma, and peripheral blood mononuclear cells) via immunohistochemistry (IHC), liquid chromatography-mass spectrometry (LC-MS), and time-of-flight mass cytometry (CyTOF). Our results highlight that aggregate BM IDC (CD123 and/or CD11c positive) occur in 33% CMML patients at any disease time-point (IHC), correlate with accentuated tryptophan catabolism (LC-MS, increased kynurenine level, median 4.7 versus 3 microM, P=0.049*), systemic regulatory T-cell expansion (CyTOF, %parent cell type, 14.5 versus 4.9%, P=0.04*) and play a role in disease progression, as evidenced by a higher rate of transformation to acute myeloid leukemia (41 versus 13%, P=0.002**), when compared to CMML patients without BM IDC. Our data also highlight a perturbed immune system in CMML with specific systemic immune signatures, particularly type 1, IL-17 producing helper T, CD4 terminal effector and natural killer cell suppression. Key PointsO_LIAggregate IDO1+ dendritic cell populations occur in the CMML bone marrow microenvironment, and their presence correlates with disease progression. C_LIO_LISystemic immune microenvironment signatures in CMML indicate an altered T- and natural killer (NK)-cell balance. Specifically, suppression of type 1 helper T (Th1), IL-17 producing helper T (Th17), CD4 terminal effector and NK cells. C_LIO_LIIDO1+ bone marrow dendritic cell populations in CMML are associated with a T-cell compartment shift towards a regulatory T cell phenotype. C_LI

immunology↗
bioRxiv2019

Multimodal parameters of brain structure and function related to sex differences in cognitive performance.

The Parieto-Frontal Integration Theory (PFIT) identified a fronto-parietal network of regions where individual differences in brain parameters most strongly relate to cognitive performance. PFIT was supported and extended in adult samples, but not in youths or within single-scanner well-powered multimodal studies. We performed multimodal neuroimaging in 1601 youths age 8-22 on the same 3-Tesla scanner with contemporaneous neurocognitive assessment, measuring volume, gray matter density (GMD), mean diffusivity (MD), cerebral blood flow (CBF), resting-state functional MRI measures of amplitude of low frequency fluctuations (ALFF) and regional homogeneity (ReHo), and activation to a working memory and a social cognition task. Across age and sex groups, better performance was associated with higher volumes, greater GMD, lower MD, lower CBF, higher ALFF and ReHo and greater activation for the working memory task in PFIT regions. However, additional cortical, striatal, limbic and cerebellar regions showed comparable effects, hence PFIT needs expansion into an Extended PFIT (ExtPFIT) network incorporating nodes that support motivation and affect. Associations of brain parameters became stronger with advancing age group from childhood to adolescence to young adulthood, effects occurring earlier in females. This ExtPFIT network is developmentally fine-tuned, optimizing abundance and integrity of neural tissue while maintaining low resting energy state.

neuroscience↗
bioRxiv2019

Advantages of Multi-shell Diffusion Models for Studies of Brain Development in Youth

Diffusion tensor imaging (DTI) has advanced our understanding of how brain microstructure evolves over development. However, the proliferation of multi-shell diffusion imaging sequences has coincided with notable advances in the modeling of neuronal diffusion patterns, such as Neurite Orientation Dispersion and Density Imaging (NODDI) and Laplacian-regularized Mean Apparent Propagator MRI (MAPL). The relative utility of these newer diffusion models for understanding brain maturation remains sparsely investigated. Additionally, despite evidence that motion artifact is a major confound for studies of development, the relative vulnerability of these models to in-scanner motion has not been described. Accordingly, in a sample of 123 youth (ages 12-30) we evaluated DTI, NODDI, and MAPL for associations with age and in-scanner head motion at multiple scales, including mean white matter values, voxelwise analyses, and tractography-based structural brain networks. Our results reveal that multi-shell diffusion imaging sequences can be leveraged to robustly characterize neurodevelopment, even within the framework of DTI. However, these metrics of diffusion are variably impacted by motion, highlighting the importance of modeling choices for studies of movement-prone populations. Our findings suggest that while traditional DTI is sensitive to neurodevelopmental trends, contemporary modeling techniques confer key advantages for neurodevelopmental inquiries.

neuroscience↗
bioRxiv2019

Accelerated Cortical Thinning within Structural Brain Networks is Associated with Irritability in Youth

BackgroundIrritability is an important dimension of psychopathology that spans multiple clinical diagnostic categories, yet its relationship to patterns of brain development remains sparsely explored. Here, we examined how trans-diagnostic symptoms of irritability relate to the development of structural brain networks.\n\nMethodsAll participants (n=144, 87 females) completed structural brain imaging with 3 Tesla MRI at two timepoints (mean age at follow-up: 20.9 years, mean inter-scan interval: 5.1 years). Irritability at follow-up was assessed using the Affective Reactivity Index, and cortical thickness was quantified using Advanced Normalization Tools software. Structural covariance networks were delineated using non-negative matrix factorization, a multivariate analysis technique. Both cross-sectional and longitudinal associations with irritability at follow-up were evaluated using generalized additive models with penalized splines. The False Discovery Rate (q<0.05) was used to correct for multiple comparisons.\n\nResultsCross-sectional analysis of follow-up data revealed that 11 of the 24 covariance networks were associated with irritability, with higher levels of irritability being associated with thinner cortex. Longitudinal analyses further revealed that accelerated cortical thinning within 9 networks was related to irritability at follow-up. Effects were particularly prominent in brain regions implicated in emotion regulation, including the orbitofrontal, lateral temporal, and medial temporal cortex.\n\nConclusionsCollectively, these findings suggest that irritability is associated with widespread cortical thickness reductions and accelerated cortical thinning, particularly within frontal and temporal cortex. Aberrant structural maturation of regions important for emotional regulation may in part underlie symptoms of irritability.

neuroscience↗
bioRxiv2018

Brain Iron Mediates the Relationship Between Cognition and Neighborhood Socioeconomic Status in Youth

Non-heme brain iron is a critical metabolic cofactor essential for healthy brain development.1 Iron deficiency is the most common nutritional disorder in the world, with greater prevalence of non-heme iron deficiency among individuals of lower socioeconomic status (SES). However, it remains unknown how brain iron accumulation during development may impact cognition. Brain iron can be measured in vivo using R2* weighted magnetic resonance imaging (MRI); prior work has established that higher R2* is associated with higher iron content.2,3 We hypothesized that more iron in the basal ganglia (BG) regions of the caudate, putamen, and pallidum would be associated with improved cognitive performance and potentially mediate the known relationship between neighborhood-level socioeconomic status (SES) and cognition.4

neuroscience↗
bioRxiv2017

Multifactorial Dynamics of White Matter Connectivity During Adolescence

Studying developmental changes in white matter connectivity is critical for understanding neurobiological substrates of cognition, learning, and neuropsychiatric disorders. This becomes especially important during adolescence when a rapid expansion of the behavioral repertoire occurs. Several factors such as brain geometry, genetic expression profiles, and higher level architectural specifications such as the presence of segregated modules have been associated with the observed organization of white matter connections. However, we lack understanding of the extent to which such factors jointly describe the brain network organization, nor have insights into how their contribution changes developmentally. We constructed a multifactorial model of white matter connectivity using Bayesian network analysis and tested it with diffusion imaging data from a large community sample. We investigated contributions of multiple factors in explaining observed connectivity, including architectural specifications, which promote a modular yet integrative organization, and brains geometric and genetic features. Our results demonstrated that the initially dominant geometric and genetic factors become less influential with age, whereas the effect of architectural specifications increases. The identified structural modules are associated with well-known functional systems, and the level of association increases with age. This integrative analysis provides a computational characterization of the normative evolution of structural connectivity during adolescence.

neuroscience↗
bioRxiv2017

Harmonization Of Multi-Site Diffusion Tensor Imaging Data

Diffusion tensor imaging (DTI) is a well-established magnetic resonance imaging (MRI) technique used for studying microstructural changes in the white matter. As with many other imaging modalities, DTI images suffer from technical between-scanner variation that hinders comparisons of images across imaging sites, scanners and over time. Using fractional anisotropy (FA) and mean diffusivity (MD) maps of 205 healthy participants acquired on two different scanners, we show that the DTI measurements are highly site-specific, highlighting the need of correcting for site effects before performing downstream statistical analyses. We first show evidence that combining DTI data from multiple sites, without harmonization, is counter-productive and negatively impacts the inference. Then, we propose and compare several harmonization approaches for DTI data, and show that ComBat, a popular batch-effect correction tool used in genomics, performs best at modeling and removing the unwanted inter-site variability in FA and MD maps. Using age as a biological phenotype of interest, we show that ComBat both preserves biological variability and removes the unwanted variation introduced by site. Finally, we assess the different harmonization methods in the presence of different levels of confounding between site and age, in addition to test robustness to small sample size studies.

neuroscience↗