Biology subjects

Scheinost, D.

Publications and source records attributed to Scheinost, D..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

Connectome caricatures: removing large-amplitude co-activation patterns in resting-state fMRI emphasizes individual differences

High-amplitude co-activation patterns are sparsely present during resting-state fMRI but drive functional connectivity1-5. Further, they resemble task activation patterns and are well-studied3,5-10. However, little research has characterized the remaining majority of the resting-state signal. In this work, we introduced caricaturing--a method to project resting-state data to a subspace orthogonal to a manifold of co-activation patterns estimated from the task fMRI data. Projecting to this subspace removes linear combinations of these co-activation patterns from the resting-state data to create Caricatured connectomes. We used rich task data from the Human Connectome Project (HCP)11 and the UCLA Consortium for Neuropsychiatric Phenomics12 to construct a manifold of task co-activation patterns. Caricatured connectomes were created by projecting resting-state data from the HCP and the Yale Test-Retest13 datasets away from this manifold. Like caricatures, these connectomes emphasized individual differences by reducing between-individual similarity and increasing individual identification14. They also improved predictive modeling of brain-phenotype associations. As caricaturing removes group-relevant task variance, it is an initial attempt to remove task-like co-activations from rest. Therefore, our results suggest that there is a useful signal beyond the dominating co-activations that drive resting-state functional connectivity, which may better characterize the brains intrinsic functional architecture.

neuroscience↗

bioRxiv2024

Brain-handedness associations depend on how and when handedness is measured

Hand preference is ubiquitous, intuitive, and often simplified to right- or left-handed. Accordingly, differences between right- and left-handed individuals in the brain have been established. Nevertheless, considering handedness as a binarized construct fails to capture the variability of brain-handedness associations across different domains or activities. Further, many cultures, environments, and generations impose right-handed norms, and handedness preferences can change over the lifespan. As a result, brain-handedness associations may depend on how and when handedness is measured. We used two large datasets, the Human Connectome Project-Development (HCP-D; n=465; age=5-21 years) and Human Connectome Project-Aging (HCP-A; n=368; age=36-100 years), to explore handedness preferences and brain-handedness associations. Nine items from the Edinburgh Handedness Inventory were associated with resting-state functional connectomes. We show that brain-handedness associations differed across the two cohorts. Moreover, these differences depended on the way handedness was measured. Given that brain-handedness associations differ across handedness measures and datasets, we caution against a one-size-fits-all approach to neuroimaging studies of this complex trait.

neuroscience↗

bioRxiv2024

Brain-phenotype predictions can survive across diverse real-world data

Recent work suggests that machine learning models predicting psychiatric treatment outcomes based on clinical data may fail when applied to unharmonized samples. Neuroimaging predictive models offer the opportunity to incorporate neurobiological information, which may be more robust to dataset shifts. Yet, among the minority of neuroimaging studies that undertake any form of external validation, there is a notable lack of attention to generalization across dataset-specific idiosyncrasies. Research settings, by design, remove the between-site variations that real-world and, eventually, clinical applications demand. Here, we rigorously test the ability of a range of predictive models to generalize across three diverse, unharmonized samples: the Philadelphia Neurodevelopmental Cohort (n=1291), the Healthy Brain Network (n=1110), and the Human Connectome Project in Development (n=428). These datasets have high inter-dataset heterogeneity, encompassing substantial variations in age distribution, sex, racial and ethnic minority representation, recruitment geography, clinical symptom burdens, fMRI tasks, sequences, and behavioral measures. We demonstrate that reproducible and generalizable brain-behavior associations can be realized across diverse dataset features with sample sizes in the hundreds. Results indicate the potential of functional connectivity-based predictive models to be robust despite substantial inter-dataset variability. Notably, for the HCPD and HBN datasets, the best predictions were not from training and testing in the same dataset (i.e., cross-validation) but across datasets. This result suggests that training on diverse data may improve prediction in specific cases. Overall, this work provides a critical foundation for future work evaluating the generalizability of neuroimaging predictive models in real-world scenarios and clinical settings.

neuroscience↗

bioRxiv2023

Power and reproducibility in the external validation of brain-phenotype predictions

Identifying reproducible and generalizable brain-phenotype associations is a central goal of neuroimaging. Consistent with this goal, prediction frameworks evaluate brain-phenotype models in unseen data. Most prediction studies train and evaluate a model in the same dataset. However, external validation, or the evaluation of a model in an external dataset, provides a better assessment of robustness and generalizability. Despite the promise of external validation and calls for its usage, the statistical power of such studies has yet to be investigated. In this work, we ran over 60 million simulations across several datasets, phenotypes, and sample sizes to better understand how the sizes of the training and external datasets affect statistical power. We found that prior external validation studies used sample sizes prone to low power, which may lead to false negatives and effect size inflation. Furthermore, increases in the external sample size led to increased simulated power directly following theoretical power curves, whereas changes in the training dataset size offset the simulated power curves. Finally, we compared the performance of a model within a dataset to the external performance. The within-dataset performance was typically within r=0.2 of the cross-dataset performance, which could help decide how to power future external validation studies. Overall, our results illustrate the importance of considering the sample sizes of both the training and external datasets when performing external validation.

neuroscience↗

bioRxiv2023

Normative Birth Weight Variation is Associated with White Matter Connectivity in Full-Term Neonates

ImportanceVariation in birth weight, an indicator of fetal growth, has been prospectively associated with both alterations in brain development and developmental delays in cognitive and language domains. However, studies examining birth weight variation and white matter development in the brain typically test these associations in infants that are preterm or very low birth weight, leaving potential normative associations in full term infants unclear. ObjectiveHere, we test prospective associations between birth weight variation in the normative range (> 2.5 kg and <4.5 kg) and white matter connectivity in full-term neonates. Further, the main objective includes testing associations between normative birth weight variation and cognitive and language developmental scores at 18 months, and if white matter connectivity that is related to birth weight variation is further associated with cognitive language development. We hypothesized, greater normative birth weight would be associated with higher white matter connectivity controlling for gestational age, particularly in thalamic, inferior frontal, and middle temporal connectivity. Further, we predicted greater connectivity for these tracts would be associated with higher scores for cognitive (thalamic) and language development (inferior and middle temporal) at 18 months of age. DesignThe study was an observational longitudinal design of data from the Developing Human Connectome Project (dHCP). Birth weight was measured at the birth of the child, white matter connectivity was measured as neonates (mean=40.07 weeks, SD=1.14), and cognitive/language outcomes were measured at 18 months of age. SettingThe dHCP data was collected at the Evelina Newborn Imaging Centre, Evalina London Childrens Hospital between 2015 and 2019. ParticipantsA sub-sample of the full dHCP was tested. These participants were full-term neonates with birth weight variation in the normative range (> 2.5 kg and < 4.5 kg). Participants also had to have usable diffusion-weighted imaging data as neonates and cognitive/language developmental assessments collected at 18 months (n=323). Exposure(s)The study participants were all born at full-term and in the normative birth weight range. Main Outcome(s) and Measure(s)The study had two main outcomes: white matter connectivity at the neonatal timepoint and cognitive/language developmental scores at 18 months. White matter connectivity was calculated from diffusion-weighted data for the whole-brain. Cognitive/language developmental scores were measured using the Bayley Scales of Infant and Toddler Development, Third Edition (Bayley-III) at 18 months. ResultsUsing a Network-Based Statistic (NBS) approach, we found widespread associations between normative birth weight variation and white matter connectivity in full-term neonates, primarily in the positive direction for the right middle occipital gyrus and left supplementary motor area. Conclusions and RelevanceWhile investigations have been focused on the extreme ends of the birth weight spectrum, we find evidence that there is a robust association between birth weight and white matter connectivity even within the normative birth weight range. As normative birth weight variation and regions of white matter associated with birth weight were further associated with language development scores at 18 months, our results suggest the birth weight to white matter pathway may be an underlying pathway between birth weights association with language development. Key PointsO_ST_ABSQuestionC_ST_ABSAre variations in normative birth weight associated with white matter connectivity and cognitive/language outcomes in infancy? FindingsGreater normative birth weight is associated with greater white matter organization across a widespread network of connections in the neonatal brain. Greater white matter organization in this network for neonates has a positive prospective with expressive language development at 18 months of age. MeaningEven variations within the normative birth weight range have robust associations with early white matter development and can be prospectively linked to language development.

neuroscience↗

bioRxiv2023

The effects of data leakage on neuroimaging predictive models

Predictive modeling has now become a central technique in neuroimaging to identify complex brain-behavior relationships and test their generalizability to unseen data. However, data leakage, which unintentionally breaches the separation between data used to train and test the model, undermines the validity of predictive models. Previous literature suggests that leakage is generally pervasive in machine learning, but few studies have empirically evaluated the effects of leakage in neuroimaging data. Although leakage is always an incorrect practice, understanding the effects of leakage on neuroimaging predictive models provides insight into the extent to which leakage may affect the literature. Here, we investigated the effects of leakage on machine learning models in two common neuroimaging modalities, functional and structural connectomes. Using over 400 different pipelines spanning four large datasets and three phenotypes, we evaluated five forms of leakage fitting into three broad categories: feature selection, covariate correction, and lack of independence between subjects. As expected, leakage via feature selection and repeated subjects drastically inflated prediction performance. Notably, other forms of leakage had only minor effects (e.g., leaky site correction) or even decreased prediction performance (e.g., leaky covariate regression). In some cases, leakage affected not only prediction performance, but also model coefficients, and thus neurobiological interpretations. Finally, we found that predictive models using small datasets were more sensitive to leakage. Overall, our results illustrate the variable effects of leakage on prediction pipelines and underscore the importance of avoiding data leakage to improve the validity and reproducibility of predictive modeling.

neuroscience↗

bioRxiv2023

Rescuing missing data in connectome-based predictive modeling

Recent evidence suggests brain-behavior predictions may require very large sample sizes. However, as the sample size increases, the amount of missing data also increases. Conventional methods, like complete-case analysis, discard useful information and shrink the sample size. To address the missing data problem, we investigated rescuing these missing data through imputation. Imputation is the substitution of estimated values for missing data to be used in downstream analyses. We integrated imputation methods into the Connectome-based Predictive Modeling (CPM) framework. Utilizing four open-source datasets--the Human Connectome Project, the Philadelphia Neurodevelopmental Cohort, the UCLA Consortium for Neuropsychiatric Phenomics, and the Healthy Brain Network (HBN)--we validated and compared our framework with different imputation methods against complete-case analysis for both missing connectomes and missing phenotypic measures scenarios. Imputing connectomes exhibited superior prediction performance on real and simulated missing data as compared to complete-case analysis. In addition, we found that imputation accuracy was a good indicator for choosing an imputation method for missing phenotypic measures but not informative for missing connectomes. In a real-world example predicting cognition using the HBN, we rescued 628 individuals through imputation, doubling the complete case sample size and increasing explained variance by 45%. Together, our results suggest that rescuing data with imputation, as opposed to discarding subjects with missing information, improves prediction performance.

neuroscience↗

bioRxiv2023

Stacking multiple optimal transport policies to map functional connectomes

Connectomics is a popular approach for understanding the brain with neuroimaging data. However, a connectome generated from one atlas is different in size, topology, and scale compared to a connectome generated from another. Consequently, connectomes generated from different atlases cannot be used in the same analysis. This limitation hinders efforts toward increasing sample size and demonstrating generalizability across datasets. Recently, we proposed Cross Atlas Remapping via Optimal Transport (CAROT) to find a spatial mapping between a pair of atlases based on a set of training data. The mapping transforms timeseries fMRI data parcellated with an atlas to form a connectome based on a different one. Crucially, CAROT does not need raw fMRI data and thus does not require re-processing, which can otherwise be time-consuming and expensive. The current CAROT implementation leverages information from several source atlases to create robust mappings for a target atlas. In this work, we extend CAROT to combine existing mappings between a source and target atlas for an arbitrary number of mappings. This extension (labeled Stacking CAROT) allows mappings between a pair of atlases to be created once and re-used with other pre-trained mappings to create new mappings as needed. Reconstructed connectomes from Stacking CAROT perform as well as those from CAROT in downstream analyses. Importantly, Stacking CAROT significantly reduces training time and storage requirements compared to CAROT. Overall, Stacking CAROT improves previous versions of CAROT.

neuroscience↗

bioRxiv2022

Trends in self-citation rates in neuroscience literature

Citation metrics influence academic reputation and career trajectories. Recent works have highlighted flaws in citation practices in the Neurosciences, such as the under-citation of women. However, self-citation rates--or how much authors cite themselves--have not yet been comprehensively investigated in the Neurosciences. This work characterizes self-citation rates in basic, translational, and clinical Neuroscience literature by collating 100,347 articles from 63 journals between the years 2000-2020. In analyzing over five million citations, we demonstrate four key findings: 1) increasing self-citation rates of Last Authors relative to First Authors, 2) lower self-citation rates in low- and middle-income countries, 3) gender differences in self-citation stemming from differences in the number of previously published papers, and 4) variations in self-citation rates by field. Our characterization of self-citation provides insight into citation practices that shape the perceived influence of authors in the Neurosciences, which in turn may impact what type of scientific research is done and who gets the opportunity to do it.

neuroscience↗

bioRxiv2022

Developmental trajectories of the default mode, executive control, and salience networks from the third trimester through the newborn period

Social cognition is critical to early learning. Functional imaging studies in adults and older children suggest the involvement of the default mode (DMN), executive control (ECN), and salience (SAL) networks in social cognition. These networks are vulnerable to environmental insults, and abnormalities of intra- and inter-network connectivity of the three are emerging as biomarkers of neurobehavioral disorders. However, the developmental trajectories of the DMN, ECN, and SAL across the third trimester of gestation and perinatal transition remain largely unknown. Employing resting-state functional MRI studies at 30-32, 34-36, and 40-44 weeks postmenstrual age (PMA), we tested the hypothesis that both intra- and inter-network functional connectivity in the DMN, ECN, and SAL develop across the 30-46 weeks PMA time interval in a longitudinal/cross-sectional sample of 84 fetuses and neonates. A secondary analysis addressed the impact of maternal mental health assessed at 28 weeks PMA on tri-network development from 30-46 weeks PMA. The DMN, ECN, and SAL develop across the third trimester of gestation and the first postnatal month. At the intra-network level, significant increases occurred between 36 to 44 weeks PMA for all three, with network strength values significantly different from 0 beginning at 40 weeks PMA for all. Functional connectivity increased less rapidly in the DMN than in the ECN and SAL networks, suggesting slower maturation of the network subserving social interactions. In contrast, significant inter-network DMN - ECN connectivity greater than 0 was found from 36 weeks PMA through the first postnatal month, suggesting the emergence of inter-network functional connectivity in the fetal brain. Finally, higher maternal mental health symptoms measured at the beginning of the third trimester negatively affected the developmental trajectory of the SAL network across the critical time interval of 30 weeks to 44 weeks PMA. Together, these data provide a framework to compare fetuses and neonates at risk for neurobehavioral disorders and assess the impact of the environment on the developing brain.

neuroscience↗

bioRxiv2022

The effects of ethnoracial-related stressors during pregnancy on the developing offspring brain.

We are increasingly aware of the effects of ethnoracial stress on health, with emerging interest in the potential for intergenerational transmission before birth. Here, we investigate the effect of maternal prenatal discrimination and acculturation experiences on fetal growth, birth outcomes, and functional connectivity in the infant brain. In pregnant adolescent women, we collected self-report measures of acculturation (tailored to an adolescent and Latinx population), discrimination, and maternal distress (i.e., stress and depressive symptoms; n=165). Fetal growth were obtained via electronic health records (n=92), and infant amygdala seed connectivity was assessed using functional magnetic resonance imaging (n=38). We found that greater maternal prenatal assimilation to the host culture was associated with slower fetal growth, lower gestational age at birth, and weaker amygdala-fusiform connectivity. Maternal prenatal discrimination was associated with weaker amygdala-prefrontal connectivity. Together, these results suggest intergenerational effects of ethnoracial stressors on the growth and neural development of future generations.

neuroscience↗

bioRxiv2022

Sex differences in default mode network connectivity in healthy aging adults

Women show an increased lifetime risk of Alzheimers disease (AD) compared to men. Characteristic brain connectivity changes, particularly within the default mode network (DMN), have been associated with both symptomatic and preclinical AD, but the impact of sex on DMN function throughout aging is poorly understood. We investigated sex differences in DMN connectivity over the lifespan in 595 cognitively healthy participants from the Human Connectome Project - Aging cohort. We used the intrinsic connectivity distribution (a robust voxel-based metric of functional connectivity) and a seed connectivity approach to determine sex differences within the DMN and between the DMN and whole brain. Compared with men, women demonstrated increased connectivity with age in posterior DMN nodes and decreased connectivity in the medial prefrontal cortex. Differences were most prominent in the decades surrounding menopause. Seed-based analysis revealed increased connectivity in women from the posterior cingulate to angular gyrus and parahippocampal gyrus, which correlated with neuropsychological measures of declarative memory. Taken together, we show significant sex differences in DMN subnetworks over the lifespan, including patterns in aging women that resemble changes previously seen in preclinical AD. These findings highlight the importance of considering sex in neuroimaging studies of aging and neurodegeneration.

neuroscience↗

bioRxiv2022

Cross Atlas Remapping via Optimal Transport (CAROT): Creating connectomes for any atlas when raw data is not available

Open-source, publicly available neuroimaging datasets--whether from large-scale data collection efforts or pooled from multiple smaller studies--offer unprecedented sample sizes and promote generalization efforts. Releasing data can democratize science, increase the replicability of findings, and lead to discoveries. Due to patient privacy and data storage concerns, researchers typically release preprocessed data with the voxelwise time series parcellated into a map of predefined regions, known as an atlas. However, releasing preprocessed data also limits the choices available to the end-user. This is especially true for connectomics, as connectomes created from different atlases are not directly comparable. Since there exist several atlases with no gold standards, it is unrealistic to have processed, open-source data available from all atlases. Together, these limitations directly inhibit the potential benefits of open-source neuroimaging data. To address these limitations, we introduce Cross Atlas Remapping via Optimal Transport (CAROT) to find a mapping between two atlases. This approach allows data processed from one atlas to be directly transformed into a connectome based on another atlas without the need for raw data access. To validate CAROT, we compare reconstructed connectomes against their original counterparts (i.e., connectomes generated directly from an atlas), demonstrate the utility of transformed connectomes in downstream analyses, and show how a connectome-based predictive model can generalize to publicly available data that was processed with different atlases. Overall, CAROT can reconstruct connectomes from an extensive set of atlases--without ever needing the raw data--allowing already processed connectomes to be easily reused in a wide range of analyses while eliminating redundant processing efforts. We share this tool as both source code and as a stand-alone web application (http://carotproject.com/).

neuroscience↗

bioRxiv2022

Hypoconnectivity between anterior insula and amygdala in neonates with familial history of autism

BackgroundAltered resting state functional connectivity (FC) involving the anterior insula (aINS), a key node in the salience network, has been reported consistently in autism. MethodHere we examined, for the first time, FC between the aINS and the whole brain in a sample of full-term, postmenstrual age (PMA) matched neonates (mean 44.0 weeks, SD=1.5) who due to family history have high likelihood (HL) for developing autism (n=12) and in controls (n=41) without family history of autism (low likelihood, LL). Behaviors associated with autism were evaluated between 12 and 18 months (M=17.3 months, SD=2.5) in a subsample (25/53) of participants using the First Year Inventory (FYI). ResultsCompared to LL controls, HL neonates showed hypoconnectivity between left aINS and left amygdala. Lower connectivity between the two nodes was associated with higher FYI risk scores in the social domain (r(25) = -.561, p=.003) and this association remained robust when maternal mental health factors were considered. Considering that a subsample of LL participants (n=14/41) underwent brain imaging during the fetal period at PMA 31 and 34 weeks, in an exploratory analysis, we evaluated prospectively development of the LaINS-Lamy connectivity and found that the two areas strongly coactivate throughout the third trimester of pregnancy. ConclusionsThe study identifies left lateralized anterior insula - amygdala connectivity as a potential target of further investigation into neural circuitry that enhances likelihood of future onset of social behaviors associated with autism during neonatal and potentially prenatal periods.

neuroscience↗

bioRxiv2021

Leveling up: improving power in fMRI by moving beyond cluster-level inference

Inference in neuroimaging commonly occurs at the level of "clusters" of neighboring voxels or connections, thought to reflect functionally specific brain areas. Yet increasingly large studies reveal effects that are shared throughout the brain, suggesting that reported clusters may only reflect the "tip of the iceberg" of underlying effects. Here, we empirically compare power of traditional levels of inference (edge and cluster) with broader levels of inference (network and whole-brain) by resampling functional connectivity data from the Human Connectome Project (n=40, 80, 120). Only network- and whole brain-level inference attained or surpassed "adequate" power ({beta}=80%) to detect an average effect, with almost double the power for network-compared with cluster-level procedures at more typical sample sizes. Likewise, effects tended to be widespread, and more widespread pooling resulted in stronger magnitude effects. Power also substantially increased when controlling FDR rather than FWER. Importantly, there may be similar implications for task-based activation analyses where effects are also increasingly understood to be widespread. However, increased power with broader levels of inference may diminish the specificity to localize effects, especially for non-task contexts. These findings underscore the benefit of shifting the scale of inference to better capture the underlying signal, which may unlock opportunities for discovery in human neuroimaging.

neuroscience↗

bioRxiv2021

(Un)common space in infant neuroimaging studies: a systematic review of infant templates

In neuroimaging, spatial normalization is an important step that maps an individuals brain onto a template brain permitting downstream statistical analyses. Yet, in infant neuroimaging, there remain several technical challenges that have prevented the establishment of a standardized template for spatial normalization. Thus, many different approaches are used in the literature. To quantify the popularity and variability of these approaches in infant neuroimaging studies, we performed a systematic review of infant MRI studies from 2000 to 2020. Here, we present results from 833 studies meeting inclusion criteria. Studies were classified into 1) processing data in single subject space, 2) using a predefined, or "off the shelf", template, 3) creating a study specific template or 4) using a hybrid of these methods. We found that across the studies in the systematic review, single subject space was the most used (no common space). This was the most used common space for DWI and structural MRI studies while fMRI studies preferred off the shelf atlases. We found a pattern such that more recently published studies are more commonly using off the shelf atlases. When considering special populations, preterm studies most used single subject space while, when no special populations were being analyzed, an off the shelf template was most common. The most used off the shelf templates were the UNC Infant Atlases (26.1%). Using a systematic review of infant neuroimaging studies, we highlight a lack of an established "standard" template brain in these studies.

neuroscience↗

bioRxiv2021

Large-scale differences in functional organization of left- and right-handed individuals using whole-brain, data-driven analysis of connectivity

Handedness influences differences in lateralization of language areas as well as dominance of motor and somatosensory cortices. However, differences in whole brain functional organization due to handedness have been relatively understudied beyond pre-specified networks of interest. Functional connectivity offers the ability to unravel differences in the functional organization of the whole brain. Here, we compared connectivity profiles of left- and right-handed individuals using data-driven parcellations of the whole brain. We explored differences in connectivity profiles of previously established regions of interest, and showed functional organization differences between primarily left- and primarily right-handed individuals in the motor, somatosensory, and language areas using functional connectivity. We then proceeded to investigate these differences in the whole brain and found that the functional organization of left- and right-handed individuals are not specific to regions of interest. In particular, we found that connections between and within-hemispheres and the cerebellum show distinct patterns of connectivity. Together these results shed light on regions of the brain beyond those traditionally explored that contribute to differences in the functional organization of left- and right-handed individuals.

neuroscience↗

bioRxiv2021

Arousal impacts distributed hubs modulating the integration of brain functional connectivity

Even when subjects are at rest, it is thought that brain activity is organized into distinct brain states during which reproducible patterns are observable. Yet, it is unclear how to define or distinguish different brain states. A potential source of brain state variation is arousal, which may play a role in modulating functional interactions between brain regions. Here, we use simultaneous resting state functional magnetic resonance imaging and pupillometry to study the impact of arousal levels indexed by pupil area on the integration of large-scale brain networks. We employ a novel sparse dictionary learning-based method to identify hub regions participating in between-network integration stratified by arousal, by measuring k-hubness, the number (k) of functionally overlapping networks in each brain region. We show evidence of a brain-wide decrease in between-network integration and inter-subject variability at low relative to high arousal, with differences emerging across regions of the frontoparietal, default mode, motor, limbic, and cerebellum networks. State-dependent changes in k-hubness relate to the actual patterns of network integration within these hubs, suggesting a brain state transition from high to low arousal characterized by global synchronization and reduced network overlaps. We demonstrate that arousal is not limited to specific brain areas known to be directly associated with arousal regulation, but instead has a brain-wide impact that involves high-level between-network communications.

neuroscience↗