Biology subjects

Harms, M. P.

Publications and source records attributed to Harms, M. P..

9 recordsLinked to original sources

bioRxiv2026

HARMONY: A large-scale harmonized neuroimaging dataset for research on anxious misery disorders

Patterns of brain circuit dysfunction underlying depression and anxiety have been increasingly characterized, including dimensional and subtype variation. A key challenge is determining how such patterns generalize across populations and measurement frameworks. Here, we introduce HARMONY, a harmonized multimodal neuroimaging dataset supporting large-scale investigation of brain-behavior associations across symptom-defined dimensions. HARMONY integrates four Human Connectome Project-style Connectomes Related to Human Disease cohorts spanning adolescence to later adulthood and capturing anxious misery symptoms. The resource combines standardized HCP-style preprocessing, quality control, imaging-derived phenotypes, and harmonized symptom measures into a clinically enriched public dataset. Proof-of-concept analyses using HARMONY showed that pooling heterogeneous cohorts increased statistical power for detecting associations between imaging-derived phenotypes and anhedonia and depression severity. Effect sizes remained modest, consistent with symptom-based measures across heterogeneous samples. Functional imaging-derived phenotypes showed the strongest multivariate predictive performance. In summary, HARMONY provides a large multi-cohort resource for reproducible mental health neuroimaging research.

neuroscience↗

bioRxiv2026

Structural Brain Indicators of Cognitive Performance in Middle and Late Adulthood: The Human Connectome Project in Aging/Aging Adult Brain Connectome Cohort

IntroductionSignificant effort has been put towards mapping patterns of atrophy in the cerebral cortex that are related to pathological aging, including the characteristic patterns of neurodegeneration in Alzheimers disease (AD). In contrast, brain structural patterns that support preserved or even exceptional cognition throughout the adult age-span and especially in later life are much less known. It is possible that superior cognitive performance in late life is supported by a preservation of brain structures vulnerable to typical aging. Alternatively, elevated performance could be related to preservation of brain regions vulnerable to age-associated pathology. Examination of individuals that exhibit superior cognition throughout the adult lifespan may provide unique insights into neural mechanisms that support cognitive resilience in late life. MethodsWe examined cross-sectional associations between cortical brain structure and cognitive performance across three stages of adulthood: midlife (36-59), young-old (60-79), and older adults (80+) in typically aging individuals enrolled as part of the Human Connectome Project Lifespan-Aging (HCP-A)/Aging Adult Brain Connectome (AABC) studies. Participants were considered generally healthy and excluded for significant and/or atypical health conditions for their demographic category, including a clinical diagnosis of cognitive impairment or dementia. Domain-specific cognitive factor scores representing memory, fluid intelligence, and crystalized intelligence were sex-stratified and residualized relative to age, and participants were classified as high, middle, or low performers based on their unique performance relative to the study sample. ResultsIn the full sample, high performers demonstrated greater cortical thickness in regions of somatomotor, visual, and auditory cortices, as well as cortical areas in the frontal, parietal, and insular cortices (e.g., 5m, LIPv, MBelt). We also found associations between medial temporal and cingulate cortical thickness and cognitive performance, but only for select analyses. Group differences in cortical thickness were greatest when contrasting high and low performers for fluid intelligence compared to the other cognitive factors and were most prominent in the midlife participants compared to the other age strata. These group differences were primarily driven by reduced cortical thickness in the low performing individuals in the younger age bins relative to the typically performing sample. Effects were rather limited when contrasting high and low performers in the 80+ age group. The cortical areas of high statistical significance appeared to show a cross-sectional convergence effect, such that the differences in cortical thickness between high vs. low cognitive performance groups diminished with increasing age. Despite lower statistical power, effect sizes were greatest when contrasting individuals at the extremes of performance (e.g., top 10% vs. bottom 10% performers). These effects were robust to subsample replications using longitudinally defined cognitive classifications. DiscussionElevated cognitive performance was cross-sectionally associated with increased regional cortical thickness and effects were most prominent in mid-life compared to later ages. Notably, contrary to brain regions that may be expected to support such high-order cognitive performance, a significant portion of primary sensory, motor, and insular cortical areas exhibited group differences between the high- and low-performing groups in this relatively younger age group. Group differences were due to lower thickness in these regions in the low performing group relative to the typical performers. In contrast to the younger portion of the sample, regions typically considered vulnerable to Alzheimers disease (i.e., regions in the medial temporal lobe) were only infrequently implicated. These results suggest that specific patterns of cortical brain structural integrity including preserved thickness of primary motor and sensory cortical regions may be a necessary but not sufficient mechanism supporting superior cognitive abilities in earlier adulthood, while alternate neural mechanisms may support cognitive resilience later in life. These results must be interpreted with caution given the cross-sectional nature of this study. Cognitive capacity can only be estimated from a single timepoint, and several factors contribute to inter-individual variation that will not be accounted for in the models applied here. Longitudinal assessment of cognitive resilience in the HCP-A/AABC cohort will be performed in future work.

neuroscience↗

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↗

bioRxiv2024

Arterial spin labelling perfusion MRI analysis for the Human Connectome Project Lifespan Ageing and Development studies

The Human Connectome Project Lifespan studies cover the Development (5-21) and Aging (36-100+) phases of life. Arterial spin labelling (ASL) was included in the imaging protocol, resulting in one of the largest datasets collected to-date of high spatial resolution multiple delay ASL covering 3,000 subjects. The HCP-ASL minimal processing pipeline was developed specifically for this dataset to pre-process the image data and produce perfusion estimates in both volumetric and surface template space. Applied to the whole dataset, the outputs of the pipeline revealed significant and expected differences in perfusion between the Development and Ageing cohorts. Visual inspection of the group average surface maps showed that cortical perfusion often followed cortical areal boundaries, suggesting differential regulation of cerebral perfusion within brain areas at rest. Group average maps of arterial transit time also showed differential transit times in core and watershed areas of the cerebral cortex, which are useful for interpreting haemodynamics of functional MRI images. The pre-processed dataset will provide a valuable resource for understanding haemodynamics across the human lifespan.

neuroscience↗

bioRxiv2021

Graded variation in cortical T1w/T2w myelination during adolescence

Myelination influences brain connectivity during sensitive periods of development by enhancing neural signaling speed and regulating synapse formation to reduce plasticity. However, in vivo studies characterizing the maturational timing of cortical myelination during human development remain scant. Here, we take advantage of recent advances in high-resolution cortical T1w/T2w myelin mapping methods, including principled correction of B1+ transmit field effects, using data from the Human Connectome Project in Development (N=628, ages 8-21) to characterize the maturational timing of myelination from childhood through early adulthood throughout the cerebral neocortex. We apply Bayesian spline models and functional latent clustering analysis to demonstrate graded variation in the rate of cortical T1w/T2w myelin growth in neocortical areas that is strongly correlated with the sensorimotor-association (S-A) axis of cortical organization reported by others. In sensorimotor areas T1w/T2w myelin starts at high levels at early ages, increases at a fast pace, and decelerates at later ages (18-21). In intermediate multimodal areas along the S-A axis, T1w/T2w myelin tends to start at intermediate levels and increase linearly at an intermediate pace. In transmodal/paralimbic association areas high along the S-A axis, T1w/T2w myelin tends to start at low levels and increase linearly at the slowest pace. These data provide evidence for graded variation along the S-A axis in the rate of cortical myelination during adolescence, which could reflect ongoing plasticity underlying the development of complex information processing and psychological functioning. Significance StatementMyelin is a lipid membrane that is essential to healthy brain function. Myelin wraps axons to increase neural signaling speed, enabling complex neuronal functioning underlying learning and cognition. Here we characterize the developmental timing of myelination across the cerebral cortex during adolescence using recent advances in non-invasive myelin mapping. Our results provide new evidence demonstrating graded variation across the cortex in the timing of myelination during adolescence, with rapid myelination in lower-order sensory areas and gradual myelination in higher-order association areas. This spatial pattern of microstructural brain development closely parallels the sensorimotor-to-association axis of cortical organization and plasticity during ontogeny.

neuroscience↗

bioRxiv2021

Reliability and Stability Challenges in ABCD Task fMRI Data

Trait stability of measures is an essential requirement for individual differences research. Functional MRI has been increasingly used in studies that rely on the assumption of trait stability, such as attempts to relate task related brain activation to individual differences in behavior and psychopathology. However, recent research using adult samples has questioned the trait stability of task-fMRI measures, as assessed by test-retest correlations. To date, little is known about trait stability of task fMRI in children. Here, we examined within-session reliability and longitudinal stability of task-fMRI using data from the Adolescent Brain Cognitive Development (ABCD) Study using its tasks focused on reward processing, response inhibition, and working memory. We also evaluated the effects of factors potentially affecting reliability and stability. Reliability and stability [quantified via an intraclass correlation (ICC) that focuses on rank consistency] was poor in virtually all brain regions, with an average ICC of .078 and .054 for short (within-session) and long-term (between-session) ICCs, respectively, in regions of interest (ROIs) historically-recruited by the tasks. ICC values in ROIs did not exceed the poor cut-off of .4, and in fact rarely exceeded .2 (only 5.9%). Motion had a pronounced effect on estimated ICCs, with the lowest motion quartile of participants having a mean reliability/stability three times higher (albeit still poor) than the highest motion quartile. Regions with stronger activation tended to show higher ICCs, with the absolute value of activity and reliability/stability correlating at .53. Across regions, the magnitude of age-related longitudinal (between-session) changes positively correlated with the longitudinal stability of individual differences, which suggests developmental change was not necessarily responsible for poor stability. Poor reliability and stability of task-fMRI, particularly in children, diminishes potential utility of fMRI data due to a drastic reduction of effect sizes and, consequently, statistical power for the detection of brain-behavior associations. This essential issue needs to be addressed through optimization of preprocessing pipelines and data denoising methods.

neuroscience↗

bioRxiv2021

Transmit Field Bias Correction of T1w/T2w Myelin Maps

T1-weighted divided by T2-weighted (T1w/T2w) myelin maps were initially developed for neuroanatomical analyses such as identifying cortical areas, but they are increasingly used in statistical comparisons across individuals and groups with other variables of interest. Existing T1w/T2w myelin maps contain radiofrequency transmit field (B1+) biases, which may be correlated with these variables of interest, leading to potentially spurious results. Here we propose two empirical methods for correcting these transmit field biases using either explicit measures of the transmit field or alternatively a pseudo-transmit approach that is highly correlated with the transmit field at 3T. We find that the resulting corrected T1w/T2w myelin maps are both better neuroanatomical measures (e.g., for use in cross-species comparisons), and more appropriate for statistical comparisons of relative T1w/T2w differences across individuals and groups (e.g., sex, age, or body-mass-index) within a consistently acquired study at 3T. We recommend that investigators who use the T1w/T2w approach for mapping cortical myelin use these B1+ transmit field corrected myelin maps going forward.

neuroscience↗

bioRxiv2018

Image processing and analysis methods for the Adolescent Brain Cognitive Development Study

The Adolescent Brain Cognitive Development (ABCD) Study is an ongoing, nationwide study of the effects of environmental influences on behavioral and brain development in adolescents. The ABCD Study is a collaborative effort, including a Coordinating Center, 21 data acquisition sites across the United States, and a Data Analysis and Informatics Center (DAIC). The main objective of the study is to recruit and assess over eleven thousand 9-10-year-olds and follow them over the course of 10 years to characterize normative brain and cognitive development, the many factors that influence brain development, and the effects of those factors on mental health and other outcomes. The study employs state-of-the-art multimodal brain imaging, cognitive and clinical assessments, bioassays, and careful assessment of substance use, environment, psychopathological symptoms, and social functioning. The data will provide a resource of unprecedented scale and depth for studying typical and atypical development. Here, we describe the baseline neuroimaging processing and subject-level analysis methods used by the ABCD DAIC in the centralized processing and extraction of neuroanatomical and functional imaging phenotypes. Neuroimaging processing and analyses include modality-specific corrections for distortions and motion, brain segmentation and cortical surface reconstruction derived from structural magnetic resonance imaging (sMRI), analysis of brain microstructure using diffusion MRI (dMRI), task-related analysis of functional MRI (fMRI), and functional connectivity analysis of resting-state fMRI.\n\nHighlightsO_LIAn overview of the MRI processing pipeline for the ABCD Study\nC_LIO_LIA discussion on the challenges of large, multisite population studies\nC_LIO_LIA methodological reference for users of publicly shared data from the ABCD Study\nC_LI

neuroscience↗

bioRxiv2017

Using Temporal ICA to Selectively Remove Global Noise While Preserving Global Signal in Functional MRI Data

Temporal fluctuations in functional Magnetic Resonance Imaging (fMRI) have been profitably used to study brain activity and connectivity for over two decades. Unfortunately, fMRI data also contain structured temporal \"noise\" from a variety of sources, including subject motion, subject physiology, and the MRI equipment. Recently, methods have been developed to automatically and selectively remove spatially specific structured noise from fMRI data using spatial Independent Components Analysis (ICA) and machine learning classifiers. Spatial ICA is particularly effective at removing spatially specific structured noise from high temporal and spatial resolution fMRI data of the type acquired by the Human Connectome Project and similar studies. However, spatial ICA is mathematically, by design, unable to separate spatially widespread \"global\" structured noise from fMRI data (e.g., blood flow modulations from subject respiration). No methods currently exist to selectively and completely remove global structured noise while retaining the global signal from neural activity. This has left the field in a quandary--to do or not to do global signal regression--given that both choices have substantial downsides. Here we show that temporal ICA can selectively segregate and remove global structured noise while retaining global neural signal in both task-based and resting state fMRI data. We compare the results before and after temporal ICA cleanup to those from global signal regression and show that temporal ICA cleanup removes the global positive biases caused by global physiological noise without inducing the network-specific negative biases of global signal regression. We believe that temporal ICA cleanup provides a \"best of both worlds\" solution to the global signal and global noise dilemma and that temporal ICA itself unlocks interesting neurobiological insights from fMRI data.

neuroscience↗