Biology subjects

Shultz, S.

Publications and source records attributed to Shultz, S..

At least 19 recordsLinked to original sources

bioRxiv2026

Comparing Nonlinear Trajectories Across Brain Networks: A Key to Understanding Complex Brain Dynamics

This study explores the nonlinear developmental trajectories of brain networks in neurotypically developing infants during their first six months. Using a longitudinal dataset of 137 resting-state functional MRI scans from 74 infants, we analyzed five spatial metrics across 13 intrinsic connectivity networks, including motor, visual, subcortical, and prefrontal networks. A cubic model was specifically employed to capture distinct linear and non-linear trends in the networks developmental patterns, allowing for the identification of significant differences in cubic, quadratic, and linear slope parameters across networks. This model choice was driven by the need to examine how specific non-linear components (e.g., inflection points and acceleration rates) uniquely characterize each networks trajectory, which a generalized approach might smooth out without pinpointing such network-specific features. Notably, the subcortical network exhibited a distinct cubic growth pattern, while secondary motor and visual networks showed pronounced quadratic variations, suggesting network-specific shifts in spatial organization and connectivity. These findings highlight the unique maturation timelines and interactions between functional systems, such as early sensory-motor coordination and later cognitive integration. The results underscore the importance of network-specific growth patterns, providing deeper insights into how infant brain networks evolve and interact to support emerging cognitive and behavioral functions.

neuroscience↗

bioRxiv2026

Explicitly nonlinear fMRI networks reveal hidden trajectories of infant brain development

Nonlinearity is a hallmark of brain complexity at multiple scales. However, existing functional magnetic resonance imaging (fMRI) functional connectivity studies typically utilize linear methods. Therefore, links between nonlinear fMRI connectivity patterns and the development of the human brain during critical periods such as infancy remain unclear. To address this gap in knowledge, we developed a data-driven approach to capture brain intrinsic connectivity networks from explicitly nonlinear resting-state fMRI connectivity and profiled their developmental associations in a cohort of typically developing human infants. We identified neurobiologically structured nonlinear fMRI connectivity patterns during early postnatal life, indicating that macroscopic brain ensembles systematically participate in nonlinear relationships at birth. Furthermore, we found that linear and explicitly nonlinear network counterparts are linked to partially overlapping but complementary developmental profiles during this period of rapid brain maturation, with the explicitly nonlinear approach unveiling insights into the development of networks that have been associated with sensorimotor capacities, default mode processes, executive functioning, language production, and stimulus saliency. Our study marks the first comprehensive developmental investigation of whole-brain nonlinear fMRI networks in human infants and deepens contemporary perspectives on neuroimaging data discovery by emphasizing the informational richness of nonlinear relationships at fMRI scales of observation.

neuroscience↗

bioRxiv2026

Genetic insights on the mechanisms of human cortical folding

The unique and intricate pattern of human cortical folding is rooted in fetal neurodevelopmental processes and can now be comprehensively quantified by new neuroimaging-derived measures of sulcal complexity. Here, we provide the first genetic maps of human sulcal complexity. Beginning with large effects of rare variants, we survey nine different neurogenetic syndromes (n=615), detecting visible changes in sulcal complexity on a shared axis of sulcal change coupled to the prenatal timing of sulcation. Turning to common genetic variants, we use genome-wide association studies of complexity scores for 40 sulci in the UK Biobank (n[~]29,000) to (i) resolve variable heritability across sulci, (ii) reveal both local and remote shared genetic effects with cortical morphology, and (iii) identify complexity-associated genes and their embedding in brain maps of prenatal gene expression. These reference genetic maps uncover multiple new mechanistic pathways for cortical morphogenesis in health and disease.

neuroscience↗

bioRxiv2025

DECODING BRAIN DYNAMICS VIA CROSS-NETWORK NON-LINEAR TRAJECTORIES

Infant brain networks mature rapidly and nonlinearly, yet cross-network comparisons of developmental trajectories remain rare. We introduce cross-network non-linear trajectory characterization (CNTC), which fits cubic polynomials to age-related change in three spatial measures--network-averaged spatial similarity (NASS), network strength, and network size--derived from resting-state fMRI. Using 137 scans from 74 neurotypical infants spanning 0-6 months of age, we estimated networks via group ICA, modeled age trajectories within each network, and then contrasted coefficients across networks. Across seven canonical systems, trajectories showed robust age effects. Cross-network differences were dominated by linear terms, with selective quadratic effects and few cubic effects. Visual and cerebellar systems exhibited the steepest NASS slopes and pronounced increases in strength and size, whereas motor/attention changes were more gradual. CNTC provides a compact, coefficient-level summary of inter-network maturation and a basis for benchmarking atypical development in future work.

neuroscience↗

bioRxiv2025

Mapping the Developmental Trajectory of Functional Brain Networks in Early Infancy: Insights into Typical Maturation

Early infancy is a crucial period for brain development, during which fundamental functional and structural frameworks are established. Understanding the maturation of large-scale brain networks during this stage is essential for characterizing normative neurodevelopment and identifying potential deviations linked to neurodevelopmental disorders. In this study, we investigated developmental changes in the spatial organization of functional brain networks in infants using a longitudinal resting-state fMRI dataset comprising 137 scans from 74 low-likelihood developing infants aged 0-6 months. We applied independent component analysis to extract large-scale brain networks and utilized advanced spatial metrics, including network-averaged spatial similarity (NASS) to assess alignment with group-level patterns, network strength to quantify neural engagement based on voxel intensities, and network size to examine spatial distribution. Our findings reveal significant age-related increases in NASS across multiple networks, indicating greater consistency in functional organization over time. Additionally, most networks demonstrated increased network strength, reflecting heightened neural involvement, while network size exhibited distinct developmental trajectories, with some networks expanding and others remaining stable. These results highlight the dynamic evolution of functional brain architecture during early infancy, providing critical insights into neurodevelopmental processes. Clinical RelevanceThis study provides critical insights into early brain network development, which is essential for identifying biomarkers of neurodevelopmental disorders such as autism and schizophrenia. By mapping typical maturation patterns using advanced spatial metrics, our findings offer a foundation for early detection of atypical development. Deviations in network organization and strength could serve as early indicators, supporting neuroimaging-based screening and intervention strategies to optimize neurodevelopmental outcomes.

neuroscience↗

bioRxiv2025

SPATIAL DISSIMILARITY TRAJECTORIES REVEAL RAPID FUNCTIONAL NETWORK REFINEMENT DURING EARLY INFANCY

The first six months of life mark a critical window for human brain development, characterized by rapid synaptic formation, axonal growth, and network reorganization. During this period, large-scale functional networks progressively mature. Although prior infant fMRI studies have characterized temporal functional connectivity, the spatial organization and refinement of brain networks during early infancy remain underexplored. In this study, we analyzed longitudinal resting-state fMRI from 74 typically developing infants (0-6 months; 137 scans) using group independent component analysis (ICA) with group-information-guided ICA (GIG-ICA) back-reconstruction. Spatial dissimilarity between each infants spatial map and the group-level map was quantified using cosine dissimilarity and Euclidean distance. Generalized additive models were applied to model age-related trends. Both cosine and Euclidean dissimilarity metrics showed significant decreases with age, indicating a progressive convergence of individual network topographies toward the group-level architecture. These findings reveal increasing spatial coherence and refinement of large-scale brain networks during early infancy, suggesting that spatial maturation complements the well-established functional integration observed in temporal connectivity studies.

neuroscience↗

bioRxiv2025

The terrestrial-aquatic transition impacts endocranial shape in caniform carnivorans

Mammals exhibit remarkable diversity in brain size and morphology, shaped by numerous ecological radiations throughout the Cenozoic. Although previous studies have demonstrated the influence of phylogeny, allometry, and various ecological variables on endocranial morphology in certain mammalian lineages, the extent to which locomotor behaviour influences brain shape evolution remains unclear. The suborder Caniformia, or 'dog-like' carnivorans, is an ecologically diverse clade, containing terrestrial, arboreal, fossorial, and semiaquatic species, each facing a unique set of sensory and motor challenges. Here, we evaluate the impact of phylogeny, allometry, and locomotor mode on brain shape evolution in caniforms. We examine endocranial morphology in 73 species using high-density 3D geometric morphometrics, principal component analysis and phylogenetic comparative methods. Our findings indicate that the terrestrial-to-aquatic transition significantly impacts endocranial shape across caniforms, which is associated with maximum dive duration. This effect is more pronounced in pinnipeds than in other semiaquatic fissipeds, such as mustelids and ursids, likely due to their more derived ecology and greater commitment to aquatic life. Aquatic caniforms tend to exhibit expanded neocortices alongside reduced paleocortices and olfactory bulbs. These morphological changes likely reflect selection pressures acting on both the brain and the surrounding cranial architecture in aquatic environments.

evolutionary biology↗

bioRxiv2025

aNy-way ICA and its application to estimate cortico-thalamo-cerebellar functional links in schizophrenia

Disrupted large-scale brain network interactions are a hallmark of schizophrenia, yet the circuit-level mechanisms underlying cognitive deficits remain incompletely understood. In particular, cortico-thalamo-cerebellar circuitry has been implicated in "cognitive dysmetria," but its functional organization across subdomains has not been fully characterized. Here, we apply a flexible multimodal data fusion framework (aNy-way independent component analysis) to integrate 4D domain-specific resting-state fMRI data from the cortex, thalamus, and cerebellum. Using a large discovery cohort (FBIRN, N=311) and two independent replication datasets (COBRE, N=148; MPRC, N=364), we identify a robust cortico-thalamo-cerebellar circuit involving higher-order thalamic nuclei, visual cortex, default mode network, and cerebellar posterior regions. This circuit shows altered connectivity in schizophrenia, characterized by reduced anticorrelation between thalamus and cortex, and is consistently associated with cognitive impairments, including processing speed, working memory, and reasoning deficits across cohorts. Importantly, these findings are largely replicated across independent cohorts. Together, these results provide data-driven evidence for a cortico-thalamo-cerebellar mechanism underlying cognitive dysfunction in schizophrenia, supporting the cognitive dysmetria hypothesis. This work also demonstrates the utility of aNy-way independent component analysis for identifying clinically relevant brain circuits in psychiatric disorders. HighlightsO_LIIdentified cortico-thalamo-cerebellar circuits in schizophrenia C_LIO_LIAltered thalamocortical interactions differentiated schizophrenia C_LIO_LICircuit alterations were associated with cognitive impairment C_LIO_LIFindings are replicated across three independent multisite datasets C_LIO_LIMultidomain fusion enabled subdomain-specific circuit characterization C_LI

neuroscience↗

bioRxiv2025

Insectivorans and Carnivorans Exhibit the Same Social Brain Relationship as Primates

It has previously been thought that the social brain hypothesis applies in its full quantitative form only to primates. Using more appropriate brain indices and less error-prone statistical methods, we here show that it does in fact apply to both Insectivorans and Carnivorans, as well as to elephants and orcas. While Insectivorans form a grade on their own, reflecting a simple transition from small-brained solitary social systems to slightly larger-brained pairbonded monogamy, the Carnivorans map directly onto the primate socio-cognitive grades, dividing between loose sociality and more bonded sociality paralleling the pattern observed in monkeys. In addition, the subgroupings of species with multilevel social systems (including hyaenids elephants and orcas) map across the grades in the same way as do primates with similar societies. This suggests that common principles and trajectories underlie social evolution in mammals.

animal behavior and cognition↗

bioRxiv2025

Dynamics of infant white matter maturation from birth to 6 months

The first months after a babys birth encompass the most rapid period of postnatal change in the human lifespan, but longitudinal trajectories of white matter maturation in this period remain uncharted. Using densely sampled diffusion tensor images collected longitudinally at a mean rate of 1 scan per 1.55 days, we measured non-linear growth and growth rate trajectories of major white matter tracts from birth to 6 months. Growth rates at birth were 6 to 11 times faster than at 6 months, with tracts less mature at birth developing fastest. When matched on chronological age, shorter gestation infants had less mature white matter at birth but faster growth rates than their longer gestation peers; however, growth trajectories were highly similar when corrected for gestational age. This is the first study to estimate white matter trajectories using dense sampling in the first 6 post-natal months, which can inform the study of neurodevelopmental disorders beginning in infancy.

neuroscience↗

bioRxiv2025

Deciphering Multiway Multiscale Brain Network Connectivity:Insights from Birth to 6 Months

Converging evidence suggests that understanding the human brain requires more than just examining pairwise functional brain interactions. The human brain is a complex, nonlinear system, and focusing solely on linear pairwise functional connectivity often overlooks important nonlinear and higher-order relationships. Infancy is a critical period marked by significant brain development that could contribute to future learning, health, and life success. Exploring higher-order functional relationships in the brain can provide insight into brain function and development. To the best of our knowledge, there is no existing research on multiway, multiscale brain network interactions in infants. In this study, we comprehensively investigate the interactions among brain intrinsic connectivity networks (ICNs), including both pairwise (pair-FNC) and triple relationships (tri-FNC). We focused on an infant dataset collected between birth and six months, a critical period for brain maturation. Our results revealed significant hierarchical, multiway, multiscale brain functional network interactions in the infant brain. These findings suggest that tri-FNC provide additional insights beyond what pairwise interactions reveal during early brain development. The tri-FNC predominantly involve the default mode, sensorimotor, visual, limbic, language, salience, and central executive domains. Notably, these triplet networks align with the classical triple network model of the human brain, which includes the default mode network, the salience network, and the central executive network. This suggests that the brain network system might already be initially established during the first six months of infancy. Interestingly, tri-FNC in the default mode and salience domains showed significantly stronger nonlinear interactions with age compared to pair-FNC. We also found that pair-FNC were less effective at detecting these networks. The present study suggests that exploring tri-FNC can offer additional insights beyond pair-FNC by capturing higher-order nonlinear interactions, potentially yielding more reliable biomarkers to characterize developmental trajectories.

neuroscience↗

bioRxiv2024

Born Connected: Do Infants Already Have Adult-Like Multi-Scale Connectivity Networks?

The human brain undergoes remarkable development during the first six postnatal months, a period of dramatic structural and functional change critical for understanding neurodevelopmental trajectories. Previous infant resting-state functional MRI (rsfMRI) studies identified intrinsic connectivity networks (ICNs) but reported widely varying network numbers and organization, limiting cross-study comparisons. A recent analysis of over 100,000 subjects spanning from adolescents to adults generated a 105-network multi-scale template that greatly enhanced replicability, but the presence of these canonical ICNs in infants has not been investigated. We analysed resting-state scans from infants aged 0-6 months using two complementary approaches: burst independent component analysis (burstICA), a fully data-driven, model-order-agnostic method, and the reference-informed NeuroMark framework. burstICA successfully recovered the full set of canonical ICNs directly from infant data, and simulation-based validation confirmed that the recovered networks were biologically specific rather than methodological biases. The NeuroMark framework enhanced spatial correspondence with the reference template and maintained distinct, reproducible network profiles across scans, demonstrating high reliability for cross-study and cross-age comparisons. Networks critical for higher-order cognition, such as the default mode and salience networks, displayed adult-like topography comparable to that of primary sensory networks even within the first 6 months after birth. Subcortical networks also exhibited precise spatial organization, underscoring early maturation of deep brain structures. Functional network connectivity analyses revealed close similarity to adult profiles and clear antagonistic patterns between sensory systems and higher-order networks, indicating that foundational aspects of mature brain organization are already emerging in early infancy. Together, these findings establish a methodological foundation for early network mapping in infancy and lay critical groundwork for longitudinal and translational studies of brain development.

neuroscience↗

bioRxiv2024

Spontaneous Brain Dynamics Associated With Acceleration Of Longterm Functional Connectome In Postnatal Development

The first six postnatal months are a critical period for brain development, marked by rapid changes in functional neural circuits. However, long-term changes in neonatal functional connectome lacks an interpretive imaging indicator for the future development due to the non-linearity characteristics. In this study, we introduce an approach to extract intrinsic brain states from short-term brain dynamics to study the long-term (longitudinal) development. We found a high association (r=0.460) between the co-activated pattern of specific brain state and the acceleration pattern of non-linear development of static functional connectome. The fractional occupancy, self-sustaining probability of this short-term state share the similar age tendency with the long-term change rate within the majority of the function connectome. These findings suggest that short-term brain dynamics could serve as potential biomarkers for predicting the long-term development of functional connectome.

neuroscience↗

bioRxiv2024

Spatial Development of Brain Networks During The First Six Postnatal Months

The initial months of life constitute a crucial period for human development. A comprehensive understanding of this early phase is essential for unraveling the origins of neurodevelopmental disorders and promoting infant brain health. This study uniquely focuses on the spatial development of intrinsic brain connectivity networks during infancy, which has been less explored compared to functional connectivity. We utilized independent component analysis on resting-state fMRI data from 74 infants to assess how the spatial organization of infant brain networks evolves between birth and six months. Our findings reveal significant changes in spatial characteristics, including an a notable rise in the network-averaged spatial similarity across age, reflecting how closely each participant-specific spatial map aligns with the group-level map for each network. We also observed a marked reduction in the network engagement range by age, representing the extent of voxel intensity range fluctuation within each network. This suggests a continuing process of consolidation, where voxel contributions to the network become more uniform, as indicated by the narrowing of intensity values. The network strength, calculated as the average of all the voxel intensities in the network, indicating the degree of involvement to the specific functional network, increased across age in several networks, such as frontal-mPFC, primary, and secondary visual networks. The network size, along with the network center of mass, illustrating spatial distribution alterations of brain networks by age, varied across different networks. For instance, both metrics increased across age in the secondary visual network but decreased in the temporal network. Additionally, we examined the networks in relation to their linear versus non-linear developmental trajectories across all spatial characteristics, providing a deeper understanding of how these patterns evolve during early infancy. These findings contribute to early brain development understanding and offer insights into potential markers of consolidation and spatial reorganization in large-scale brain networks during infancy.

neuroscience↗

bioRxiv2024

A Novel Registration Framework for Aligning Longitudinal Infant Brain Tensor Images

Registering longitudinal infant brain images is challenging, as the infant brain undergoes rapid changes in size, shape and tissue contrast in the first months and years of life. Diffusion tensor images (DTI) have relatively consistent tissue properties over the course of infancy compared to commonly used T1 or T2- weighted images, presenting great potential for infant brain registration. Moreover, groupwise registration has been widely used in infant neuroimaging studies to reduce bias introduced by predefined atlases that may not be well representative of samples under study. To date, however, no methods have been developed for groupwise registration of tensor-based images. Here, we propose a novel registration approach to groupwise align longitudinal infant DTI images to a sample-specific common space. Longitudinal infant DTI images are first clustered into more homogenous subgroups based on image similarity using Louvain clustering. DTI scans are then aligned within each subgroup using standard tensor-based registration. The resulting images from all subgroups are then further aligned onto a sample-specific common space. Results show that our approach significantly improved registration accuracy both globally and locally compared to standard tensor-based registration and standard fractional anisotropy-based registration. Additionally, clustering based on image similarity yielded significantly higher registration accuracy compared to no clustering, but comparable registration accuracy compared to clustering based on chronological age. By registering images groupwise to reduce registration bias and capitalizing on the consistency of features in tensor maps across early infancy, our groupwise registration framework facilitates more accurate alignment of longitudinal infant brain images.

bioengineering↗

bioRxiv2023

The adaptive immune response to Trichuris in wild versus laboratory mice: An established model system in context

Laboratory model organisms have provided a window into how the immune system functions. An increasing body of evidence, however, suggests that the immune responses of naive laboratory animals may differ substantially to those of their wild counterparts. Past exposure, environmental challenges and physiological condition may all impact on immune responsiveness. Chronic infections of soil-transmitted helminths impose significant health burdens on humans, livestock and wildlife, with limited treatment success. In laboratory mice, Th1 versus Th2 immune polarisation is the major determinant of helminth infection outcome. Here we compared antigen-specific immune responses to the soil-transmitted whipworm Trichuris muris between controlled laboratory and wild free-ranging populations of house mice (Mus musculus domesticus). Wild mice harbouring chronic, low-level infections produced lower levels of cytokines in response to Trichuris antigen than laboratory-housed C57BL/6 mice. Wild mouse effector/memory CD4+ T cell phenotype reflected the antigen-specific cytokine response across the Th1/Th2 spectrum. Increasing worm burden was associated with body condition loss. However, local Trichuris-specific Th1/Th2 balance was positively associated with worm burden only in older wild mice. Thus, although the fundamental relationships between the CD4+ T helper cell response and resistance to T. muris infection are similar in both laboratory and wild M.m.domesticus, there are quantitative differences and age-specific effects that are analogous to human immune responses. These context-dependent immune responses demonstrate the fundamental importance of understanding the differences between model and natural systems for translating mechanistic models to real world immune function.

immunology↗

bioRxiv2022

Disease-modifying effects of sodium selenate in a model of drug-resistant, temporal lobe epilepsy.

There are no pharmacological disease-modifying treatments that can mitigate the seizures and comorbidities associated with established chronic temporal lobe epilepsy (TLE). This study evaluated the effect of sodium selenate in the post-status epilepticus (SE) rat model of chronic drug resistant TLE. Wistar rats underwent kainic acid-induced SE or sham. Ten-weeks post-SE, rats were randomly assigned to receive either sodium selenate, levetiracetam, or vehicle treatments continuously for 4 weeks. To evaluate the effects of the treatments, 1 week of continuous video-EEG was acquired before, during, and 4, 8 weeks post-treatment, followed by behavioral tests. Targeted and untargeted proteomics and metabolomics were performed on post-mortem brain tissue to identify potential pathways associated with modified disease outcomes. Telomere length was investigated as a novel surrogate marker of disease severity. Sodium selenate treatment was able to mitigate disease severity, reducing the number of spontaneous seizures (p< 0.05), cognitive dysfunction (p< 0.05 in both novel object placement and recognition tasks), and sensorimotor deficits (p< 0.01) 8 weeks post-treatment cessation. Moreover, increased protein phosphatase 2A (PP2A) expression, reduced hyperphosphorylated tau, and reversed telomere length shortening caused by SE (p< 0.05). Network medicine integration of multi-omics/ pre-clinical outcomes identified protein-metabolite modules positively correlated with the TLE phenotype. Our results provide evidence that treatment with sodium selenate results in a sustained disease modifying effect in chronically epileptic rats in the post-KA SE model of TLE, including improved comorbid learning and memory deficits.

neuroscience↗

bioRxiv2020

The Evolution of Self-Control

The capacity to inhibit prepotent actions (inhibitory self-control) plays an important role in many aspects of the behaviour of birds and mammals. Although a number of studies have used it as an index of foraging skills, inhibition is also crucial for maintaining the temporal and spatial coherence of bonded social groups. Using three different sets of comparative data, we show that, across primate species, the capacity for self-control correlates better with the demands of social contexts than with the demands of foraging contexts, whereas a more generalised capacity for causal reasoning correlates better with foraging contexts. In addition, we confirm the Passingham-Wise Conjecture that the capacity for self-control is unique to anthropoid primates. These results suggest that the capacity for self-control most likely evolved because it was crucial for the evolution of bonded social groups. Significance StatementThe capacity for self-control has commonly been viewed as an index of foraging skills. In fact, it plays a much more important role in the social domain by enabling groups of animals to maintain social cohesion as they travel through time and space. In this respect, it is particularly important for species that live in stable bonded social groups (congregations). We show that, in this respect, it is uniquely characteristic of the anthropoid primates, in contrast to other kinds of reasoning tasks such as causal reasoning on which primates often perform no better than other birds and mammals.

animal behavior and cognition↗