Biology subjects

Barch, D.

Publications and source records attributed to Barch, D..

11 recordsLinked to original sources

bioRxiv2026

Infant Sensory Over-Responsivity: Relations to Early Life Adversity, Emerging Behavioral Problems, and Sensory Functional Connectivity

Background. Sensory over-responsivity (SOR) in mid- to late childhood is associated with early-life adversity (ELA) and psychopathology, yet the developmental origins and timing of these relationships remain unknown. This work leverages a longitudinal pediatric dataset enriched for ELA to characterize predictors and behavioral problems associated with infant/toddler SOR. Methods. Neonatal fMRI and longitudinal behavioral data were obtained from a sample (N=399) recruited during pregnancy and enriched for social disadvantage (SD). Negative binomial regression models were used to identify pre- and neonatal risk factors for and behavioral correlates of SOR at age two (Y2). Mediation analyses tested whether Y2 SOR mediates relationships between ELA and behavioral problems at age three (Y3). Relationships between neonatal functional connectivity (FC) measures and later SOR were also computed. Results. Two forms of prenatal ELA, SD and maternal psychosocial stress (PSS), positively predicted Y2 SOR, which in turn predicted Y2 and Y3 autism and anxiety problems, greater preschool internalizing symptoms, and greater preschool separation anxiety diagnoses. Positive relationships of prenatal PSS with Y3 autism, anxiety, and internalizing problems were partially mediated by Y2 SOR. Greater neonatal FC between the cortical somatomotor hand network and bilateral thalamus was also associated with SOR at one to two years later. Conclusions. The study results demonstrate the importance of infant/toddler SOR as an early predictor of autism and anxiety behaviors in early childhood and a mediating variable that links prenatal ELA with these behavioral outcomes. Results also implicate heightened neonatal signaling in sensory circuits as an early precursor of SOR.

neuroscience↗

bioRxiv2025

Patterns of brain-wide associations reflect socioeconomics

Previous brain-wide association studies (BWAS) cross-sectionally linked a specific behavioral trait, most commonly IQ or psychopathology, to variation in brain function or structure. Here, we expanded the focus of BWAS from effect sizes to interpretability and generalizability by mapping 649 variables to brain function and structure. We compared the resultant BWAS maps to other types of brain data to annotate the BWAS patterns. Socioeconomic status (SES) -- not IQ or psychopathology -- showed the strongest associations with both resting-state functional connectivity (RSFC) and cortical thickness in the Adolescent Brain Cognitive Development (ABCD) Study. A principal exposome brain pattern, anchored to sensory and motor cortex, captured 34% of the variance across all BWAS maps. This exposome pattern was strongly correlated with the SES and IQ BWAS maps and non-BWAS maps of sleep (EEG), norepinephrine (PET), and stimulants (drug trial), but not cognitive activation maps (task fMRI). Adjusting for SES, reduced brain-IQ associations by 40%. Brain with IQ associations did not generalize, as they could no longer be detected in subsamples drawn from only higher SES backgrounds, while brain with SES associations remained strong in higher-IQ-only subsamples. These findings reveal SES as the principal axis of population-level brain variation, possibly stemming from the sleep deprivation and heightened stress associated with lower SES, since socioeconomics can only indirectly affect the brain.

neuroscience↗

bioRxiv2025

Investigating Neural Correlates of Emotional Regulation as a Function of Age, Race, and Socioeconomic Status

Older adults often show improved emotional regulation with age, a phenomenon known as the aging paradox. This age-related increase in emotional regulation capacity is attributed to enhanced prefrontal cortex control over amygdala reactivity. However, because racial discrimination and economic disadvantage cause chronic stress, typical age-related neural associations may be altered in marginalized groups. Using task-functional MRI data from 8,711 UK Biobank participants aged 50-78, we investigated whether age-related associations in emotion-related brain function, specifically amygdala activation and vmPFC-amygdala connectivity, varied across racial and socioeconomic status (SES) groups. We found that older age was associated with decreased amygdala activation, which is consistent with improved emotional regulation. Yet, lower socioeconomic status was associated with increased amygdala activation, suggesting heightened stress-related reactivity. No significant age-related effects on vmPFC-amygdala connectivity were observed at the population level. Black participants showed a stronger age-related decline in functional connectivity compared to other racial groups. These findings call for more inclusive and diverse neuroimaging studies to better understand brain health across marginalized groups.

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bioRxiv2025

Dense Longitudinal Precision Neuroimaging of Recovery from Traumatic Brain Injury

Traumatic brain injury (TBI) disrupts white matter tracts essential for cognition and emotion. Diffusion tensor imaging (DTI) can noninvasively measure white matter integrity. However, DTI has been inconsistent in predicting patient recovery from TBI, possibly due to the complex, dynamic, and individual-specific process of post-TBI white matter remodeling. Here, we employed dense longitudinal neuroimaging to track white matter recovery weekly over six months after a TBI within a single patient and a control in a similar age group (21 vs. 24 y.o.). In the patient, but not in the control, DTI metrics precisely tracked parabolic trajectories across time, with early structural alterations continuing for more than 15 weeks before reversing direction. The extent of alteration in each tract was correlated with the time until reversal. These continuous DTI changes also mediated recovery of cognitive and emotional function, suggesting they are not passive markers of damage but dynamic processes underlying functional improvement. Complementary diffusion basis spectrum imaging (DBSI) revealed an initial phase of cellular loss followed by inflammatory remodeling, vascular adaptations, and persistent metabolic activity. Our findings indicate that recovery does not follow predefined phases but rather individualized transition points, which could define optimal windows for rehabilitation. Identifying these inflection points may enable personalized interventions aligned with biologically relevant structural shifts, rather than broad recovery periods.

neuroscience↗

bioRxiv2025

Longitudinal Trajectories of Cognition and Neural Metrics as Predictors of Persistent Distressing Psychotic-Like Experiences Across Middle Childhood and Early Adolescence

ObjectivesPsychotic-like experiences (PLEs) may arise from genetic and environmental risk leading to worsening cognitive and neural metrics over time, which in turn lead to worsening PLEs. Persistence and distress are factors that distinguish more clinically significant PLEs. Analyses used three waves of unique longitudinal Adolescent Brain Cognitive Development Study data (ages 9-13) to test whether changes in cognition and structural neural metrics attenuate associations between genetic and environmental risk with persistent distressing PLEs. MethodsMultigroup univariate latent growth models examined three waves of cognitive metrics and global structural neural metrics separately for three PLE groups: persistent distressing PLEs (n=356), transient distressing PLEs (n=408), and low-level PLEs (n=7901). Models then examined whether changes in cognitive and structural neural metrics over time attenuated associations between genetic liability (i.e., schizophrenia polygenic risk scores/family history) or environmental risk scores (e.g., poverty) and PLE groups. ResultsPersistent distressing PLEs showed greater decreases (i.e., more negative slopes) of cognition and neural metrics over time compared to those in low-level PLE groups. Associations between environmental risk and persistent distressing PLEs were attenuated when accounting for lowered scores over time on cognitive (e.g., picture vocabulary) and to a lesser extent neural (e.g., cortical thickness, volume) metrics. ConclusionsAnalyses provide novel evidence for extant theories that worsening cognition and global structural metrics may partially account for associations between environmental risk with persistent distressing PLEs.

neuroscience↗

bioRxiv2024

Evidence for shallow cognitive maps in schizophrenia

Individuals with schizophrenia can have marked deficits in goal-directed decision making. Prominent theories differ in whether schizophrenia (SZ) affects the ability to exert cognitive control, or the motivation to exert control. An alternative explanation is that schizophrenia negatively impacts the formation of cognitive maps, the internal representations of the way the world is structured, necessary for the formation of effective action plans. That is, deficits in decision-making could also arise when goal-directed control and motivation are intact, but used to plan over ill-formed maps. Here, we test the hypothesis that individuals with SZ are impaired in the construction of cognitive maps. We combine a behavioral representational similarity analysis technique with a sequential decision-making task. This enables us to examine how relationships between choice options change when individuals with SZ and healthy age-matched controls build a cognitive map of the task structure. Our results indicate that SZ affects how people represent the structure of the task, focusing more on simpler visual features and less on abstract, higher-order, planning-relevant features. At the same time, we find that SZ were able to display similar performance on this task compared to controls, emphasizing the need for a distinction between cognitive map formation and changes in goal-directed control in understanding cognitive deficits in schizophrenia.

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bioRxiv2022

Motion Impact Score for Detecting Spurious Brain-Behavior Associations

Between-participant differences in head motion introduce systematic bias to resting state fMRI brain-wide association studies (BWAS) that is not completely removed by denoising algorithms. Researchers who study traits, or phenotypes associated with in-scanner head motion (e.g. psychiatric disorders) need to know if trait-functional connectivity (FC) effects are biased by residual motion artifact in order to avoid reporting false positive results. We devised an adaptable method, Split Half Analysis of Motion Associated Networks (SHAMAN), to assign a motion impact score to specific trait-FC effects. The SHAMAN approach distinguishes between motion artifact causing overestimation or underestimation of trait-FC effects. SHAMAN was > 95% specific at sample sizes of n = 100 and above. SHAMAN was powered to detect motion overestimation scores 80% of the time at sample sizes of n = 5,000 but could detect motion underestimation scores only 50% of the time at n = 5000, making it most useful for researchers seeking to avoid overestimating trait-FC effects in large BWAS. We computed motion impact scores for trait-FC effect with 45 demographic, biophysical, cognitive, and personality traits from n = 7,270 participants in the Adolescent Brain Cognitive Development (ABCD) Study. After standard denoising with ABCD-BIDS and without motion censoring, 42% (19/45) of traits had significant (p < 0.05) motion overestimation scores and 38% (17/45) of traits had significant motion underestimation scores. Censoring at framewise displacement (FD) < 0.2 mm reduced the proportion of traits with significant motion overestimation scores from 42% to 2% (1/45) but did not decrease the number of traits with significant motion underestimation scores.

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↗

bioRxiv2020

Associations Between Resting State Functional Connectivity and a Hierarchical Dimensional Structure of Psychopathology in Middle Childhood

BackgroundPrevious research from the Adolescent Brain Cognitive Development (ABCD) study delineated and validated a hierarchical 5-factor structure with a general psychopathology ( p) factor at the apex and five specific factors (internalizing, somatoform, detachment, neurodevelopmental, externalizing) using parent-reported child symptoms. The current study is the first examining associations between dimensions from a hierarchical structure and resting state functional connectivity (RSFC) networks. MethodsUsing 9-11-year-old children from the ABCD baseline sample, we compared the variance explained by each hierarchy level (p-factor, 2-factor, 3-factor, 4-factor, and 5-factor models) in RSFC. Analyses were first conducted in a discovery dataset (n=3790) with significant associations examined in a replication dataset (n=3791). ResultsThe current study found associations between p-factor and lower connectivity within default mode network (DMN), although stronger effects emerged for the neurodevelopmental factor. Neurodevelopmental impairments were related to variation in RSFC networks associated with attention to internal states and external stimuli. These networks included within DMN, DMN with cingulo-opercular (CON) and Other (Unassigned) networks, CON with ventral attention and Other network, and dorsal attention with Other network. Results held when accounting for parental psychopathology. ConclusionThe hierarchical structure of psychopathology showed replicable links to RSFC alterations in middle childhood. The p-factor had minimal association with altered connectivity, while the specific neurodevelopmental dimension showed robust associations with multiple RSFC impairments. Results show the utility of examining associations between intrinsic brain architecture and specific dimensions of psychopathology, revealing associations specifically with neurodevelopmental impairments.

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bioRxiv2019

Dynamic Reorganization of the Frontal Parietal Network During Cognitive Control and Episodic Memory

Higher cognitive functioning is supported by adaptive reconfiguration of large-scale functional brain networks. Cognitive control (CC), which plays a vital role in flexibly guiding cognition and behavior in accordance with our goals, supports a range of executive functions via distributed brain networks. These networks process information dynamically and can be represented as functional connectivity changes between network elements.\n\nUsing graph theory, we explored context-dependent network reorganization in 56 healthy adults performing fMRI tasks from two cognitive domains that varied in CC and episodic-memory demands. We examined whole-brain modular structure during the DPX task, which engages proactive CC in the frontal-parietal cognitive control network (FPN), and the RiSE task, which manipulates CC demands at encoding and retrieval during episodic-memory processing, and engages FPN, the medial-temporal lobe and other memory related networks in a context dependent manner.\n\nAnalyses revealed different levels of network integration and segregation. High CC conditions exhibited greater integration across tasks. Moreover, nodes associated with higher cognitive functioning displayed the greatest amount of dynamic module reorganization across tasks. Specifically, the FPN displayed a high level of segregation in the DPX task, where only demands for proactive control varied, and more complex network integration with default mode and salience networks in the RiSE task, where CC is differentially integrated during memory encoding and retrieval. These findings provide insight into how brain networks reorganize to support differing task contexts, suggesting that the FPN flexibly segregates during focused proactive control and integrates to support control in other domains such as episodic-memory.

neuroscience↗

bioRxiv2019

Brain age prediction reveals aberrant brain white matter in schizophrenia and bipolar disorder: A multi-sample diffusion tensor imaging study

BackgroundSchizophrenia (SZ) and bipolar disorders (BD) share substantial neurodevelopmental components affecting brain maturation and architecture. This necessitates a dynamic lifespan perspective in which brain aberrations are inferred from deviations from expected lifespan trajectories. We applied machine learning to diffusion tensor imaging (DTI) indices of white matter structure and organization to estimate and compare brain age between patients with SZ, BD, and healthy controls across 10 cohorts. MethodsWe trained six cross-validated models using different combinations of DTI data from 927 healthy controls (HC, 18-94 years), and applied the models to the test sets including 648 SZ (18-66 years) patients, 185 BD patients (18-64 years), and 990 HC (17-68 years), estimating brain age for each participant. Group differences were assessed using linear models, accounting for age, sex, and scanner. A meta-analytic framework was applied to assess the heterogeneity and generalizability of the results. Results10-fold cross-validation revealed high accuracy for all models. Compared to controls, the model including all feature sets significantly over-estimated the age of patients with SZ (d=-.29) and BD (d=.18), with similar effects for the other models. The meta-analysis converged on the same findings. Fractional anisotropy (FA) based models showed larger group differences than the models based on other DTI-derived metrics. ConclusionsBrain age prediction based on DTI provides informative and robust proxies for brain white matter integrity. Our results further suggest that white matter aberrations in SZ and BD primarily consist of anatomically distributed deviations from expected lifespan trajectories that generalize across cohorts and scanners.

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