Biology subjects

Bernhardt, B.

Publications and source records attributed to Bernhardt, B..

At least 55 records · Page 3Linked to original sources

bioRxiv2023

Connectome-wide structure-function coupling models implicate polysynaptic alterations in autism

AO_SCPLOWBSTRACTC_SCPLOWAutism spectrum disorder (ASD) is one of the most common neurodevelopmental diagnoses. Although incompletely understood, structural and functional network alterations are increasingly recognized to be at the core of the condition. We utilized multimodal imaging and connectivity modeling to study structure-function coupling in ASD, and probed mono- and polysynaptic mechanisms on structurally-governed network function. We examined multimodal magnetic resonance imaging data in 47 ASD and 37 neurotypical controls from the Autism Brain Imaging Data Exchange (ABIDE) II initiative. We predicted intrinsic functional connectivity from structural connectivity data in each participant using a Riemannian optimization procedure that varies the times that simulated signals can unfold along tractography-derived personalized connectomes. In both ASD and neurotypical controls, we observed improved structure-function prediction at longer diffusion time scales, indicating better modeling of brain function when polysynaptic mechanisms are accounted for. Prediction improvements were marked in transmodal association systems, such as the default mode network, in both controls and ASD. Improvements were, however, lower in ASD in a polysynaptic regime at higher simulated diffusion times. Regional differences followed a sensory-to-transmodal cortical hierarchy, with an increased gap between groups in transmodal compared to sensory/motor systems. Multivariate associative techniques revealed that structure-function differences reflected inter-individual differences in autistic symptoms and verbal as well as non-verbal intelligence. Our network modeling approach sheds light on atypical structure-function coupling in autism, and suggests that polysynaptic network mechanisms are implicated in the condition and can help explain its wide range of associated symptoms.

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bioRxiv2023

GAN-MAT: Generative Adversarial Network-based Microstructural Profile Covariance Analysis Toolbox

AO_SCPLOWBSTRACTC_SCPLOWMultimodal magnetic resonance imaging (MRI) provides complementary information for investigating brain structure and function; for example, an in vivo microstructure-sensitive proxy can be estimated using the ratio between T1- and T2-weighted structural MRI. However, acquiring multiple imaging modalities is challenging in patients with inattentive disorders. In this study, we proposed a comprehensive framework to provide multiple imaging features related to the brain microstructure using only T1-weighted MRI. Our toolbox consists of (i) synthesizing T2-weighted MRI from T1-weighted MRI using a conditional generative adversarial network; (ii) estimating microstructural features, including intracortical covariance and moment features of cortical layer-wise microstructural profiles; and (iii) generating a microstructural gradient, which is a low-dimensional representation of the intracortical microstructure profile. We trained and tested our toolbox using T1- and T2-weighted MRI scans of 1,104 healthy young adults obtained from the Human Connectome Project database. We found that the synthesized T2-weighted MRI was very similar to the actual image and that the synthesized data successfully reproduced the microstructural features. The toolbox was validated using an independent dataset containing healthy controls and patients with episodic migraine as well as the atypical developmental condition of autism spectrum disorder. Our toolbox may provide a new paradigm for analyzing multimodal structural MRI in the neuroscience community, and is openly accessible at https://github.com/CAMIN-neuro/GAN-MAT.

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bioRxiv2023

Evaluation of surface-based hippocampal registration using ground-truth subfield definitions

The hippocampus is an archicortical structure, consisting of subfields with unique circuits. Understanding its microstructure, as proxied by these subfields, can improve our mechanistic understanding of learning and memory and has clinical potential for several neurological disorders. One prominent issue is how to parcellate, register, or retrieve homologous points between two hippocampi with grossly different morphologies. Here, we present a surface-based registration method that solves this issue in a contrast-agnostic, topology-preserving manner. Specifically, the entire hippocampus is first analytically unfolded, and then samples are registered in 2D unfolded space based on thickness, curvature, and gyrification. We demonstrate this method in seven 3D histology samples and show superior alignment with respect to subfields using this method over more conventional registration approaches. HighlightsO_LIHippocampal subfields contain microcircuits that are critical for memory and vulnerable to neurological disease. C_LIO_LIHippocampi have variable folding patterns between individuals, making them hard to register or parcellate. C_LIO_LIWe present a surface-based hippocampal registration method that is analogous to neocortical inflation to a sphere and registration. C_LIO_LITesting in seven detailed 3D histology samples revealed successful registration with respect to hippocampal subfields, and outperformed more conventional methods. C_LIO_LIThis method provides groundwork for detailed multimodal hippocampal mapping across subjects and datasets in the future. C_LI Data availabilityThe methodological advancements described here are made easily accessible in the latest version of open source software HippUnfold1. Code used in the development and testing of these methods, as well as preprocessed images, manual segmentations, and results, are openly available2.

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bioRxiv2023

The spatial arrangement of laminar thickness profiles in the human cortex scaffolds processing hierarchy

The human neocortex consists of tangentially organized layers with unique cytoarchitectural properties. These layers show spatial variations in thickness and cytoarchitecture across the neocortex, which is thought to support brain function through enabling targeted corticocortical connections. Here, leveraging maps of the six cortical layers in 3D human brain histology, we aimed to quantitatively characterize the systematic covariation of laminar structure in the cortex and its functional consequences. After correcting for the effect of cortical curvature, we identified a spatial pattern of changes in laminar thickness covariance from lateral frontal to posterior occipital regions, which differentiated the dominance of infra- versus supragranular layer thickness. Corresponding to the laminar regularities of cortical connections along cortical hierarchy, the infragranular-dominant pattern of laminar thickness was associated with higher hierarchical positions of regions, mapped based on resting-state effective connectivity in humans and tract-tracing of structural connections in macaques. Moreover, we show that regions with comparable laminar thickness patterns correspond to inter-regional structural covariance, maturational coupling, and transcriptomic patterning, indicating developmental relevance. In sum, here we characterize the association between organization of laminar thickness and processing hierarchy, anchored in ontogeny. As such, we illustrate how laminar organization may provide a foundational principle ultimately supporting human cognitive functioning.

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bioRxiv2023

Multimodal neural correlates of childhood psychopathology

AO_SCPLOWBSTRACTC_SCPLOWComplex structural and functional changes occurring in typical and atypical development necessitate multidimensional approaches to better understand the risk of developing psychopathology. Here, we simultaneously examined structural and functional brain network patterns in relation to dimensions of psychopathology in the Adolescent Brain Cognitive Development dataset. Several components were identified, recapitulating the psychopathology hierarchy, with the general psychopathology (p) factor explaining most covariance with multimodal imaging features, while the internalizing, externalizing, and neurodevelopmental dimensions were each associated with distinct morphological and functional connectivity signatures. Connectivity signatures associated with the p factor and neurodevelopmental dimensions followed the sensory-to-transmodal axis of cortical organization, which is related to the emergence of complex cognition and risk for psychopathology. Results were consistent in two separate data subsamples, supporting generalizability, and robust to variations in analytical parameters. Our findings help in better understanding biological mechanisms underpinning dimensions of psychopathology, and could provide brain-based vulnerability markers.

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bioRxiv2023

The Human Brain Connectome Weighted by the Myelin Content and Total Intra-Axonal Cross-Sectional Area of White Matter Tracts

A central goal in neuroscience is the development of a comprehensive mapping between structural and functional brain features. Computational models support in vivo investigation of the mechanisms mediating this relationship but currently lack the requisite biological detail. Here, we characterize human structural brain networks weighted by multiple white matter microstructural features to assess their potential joint utilization in computational models. We report edge-weight-dependent spatial distributions, variance, small-worldness, rich club, hubs, as well as relationships with function, edge length and myelin. Contrasting networks weighted by the total intra-axonal cross-sectional area and myelin content of white matter tracts, we find opposite relationships with functional connectivity, an edge-length-independent inverse relationship with each other, and the lack of a canonical rich club in myelin-weighted networks. When controlling for edge length, tractometry-derived networks weighted by either tensor-based metrics or neurite density show no relationship with whole-brain functional connectivity. We conclude that structure-function brain models are likely to be improved by the co-utilization of structural networks weighted by total intra-axonal cross-sectional area and myelin content. We anticipate that the proposed microstructure-weighted computational modeling approach will support mechanistic understanding of the structure-function relationship of the human brain. AUTHOR SUMMARYFor computational network models to provide mechanistic links between brain structure and function, they must be informed by networks in which edge weights quantify structural features relevant to brain function. Here, we characterized several weighted structural networks capturing multiscale features of white matter connectivity. We describe these networks in terms of edge weight distribution, variance and network topology, as well as their relationships with each other, edge length and function. Overall, these findings support the joint use of structural networks weighted by the total intra-axonal cross-sectional area and myelin content of white matter tracts in structure-function models. This thorough characterization serves as a benchmark for future investigations of weighted structural brain networks.

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bioRxiv2023

Variation in spatial dependencies across the cortical mantle discriminates the functional behaviour of primary and association cortex

Recent theories of cortical organisation maintain that important features of brain function emerge through the spatial arrangement of regions of cortex. For example, areas of association cortex are located in regions of cortex furthest from sensory and motor cortex. Association cortex is also interdigitated since adjacent regions can have relatively different patterns of functional connectivity. It is assumed that topographic properties such as distance between cortical regions constrain their functions. For example, large distances between association and sensory and motor systems may enable these areas of cortex to maintain differentiable neural patterns, while an interdigitated organisation may enable association cortex to contain many functional systems in a relatively compact space. We currently lack a formal understanding of how spatial organisation impacts brain function, limiting the ability to leverage cortical topography to facilitate better interpretations of a regions function. Here we use variograms, a quantification of spatial autocorrelation, to develop a cortex-wide profile of how functional similarity changes as a function of the distance between regions. We establish that function changes gradually within sensory and motor cortex as the distance between regions increases, while in association cortex function changes rapidly over shorter distances. Subsequent analysis suggests these differential classes of spatial dependency are related to variation in intracortical myelin between sensory motor and association cortex. Our study suggests primary and association cortex are differentiated by the degree to which function varies over space, emphasising the need to formally account for spatial properties when estimating a systems contribution to cognition and behaviour. Significance statementThe spatial arrangements of regions in the human brain are hypothesised to underpin important features of a brain regions function. Currently, however, we lack a formal understanding of how topography shapes brain function, limiting our ability to leverage topographical perspectives to inform better theories of brain function. Here we use a formal mathematical approach to establish that in regions of association cortex function varies across the cortex more rapidly than in sensory and motor cortex, a phenomenon linked to levels of intracortical myelin. This result highlights how topographical features distinguish between cortical regions with different functional profiles and provides a formal account of how spatial differences support different features of brain function.

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bioRxiv2022

DIFFERENTIAL MEMORY IMPAIRMENT ACROSS RELATIONAL DOMAINS IN TEMPORAL LOBE EPILEPSY

BO_SCPLOWACKGROUNDC_SCPLOWTemporal lobe epilepsy (TLE) is typically associated with pathology of the hippocampus, a key structure involved in relational memory processes, including episodic, semantic, and spatial memory. While it is widely accepted that TLE-associated hippocampal alterations may underlie global deficits in memory, it remains poorly understood whether TLE may present with shared or unique impairment across distinct relational memory domains. MO_SCPLOWETHODSC_SCPLOWWe administered a recently validated behavioral paradigm to evaluate episodic, semantic, and spatial memory in 20 pharmacoresistant TLE patients and 53 age- and sex-matched healthy controls. We implemented linear mixed effects models to identify memory deficits in individuals with TLE relative to controls, and used partial least squares analysis to identify factors contributing to overall variations in relational memory performance across both cohorts. RO_SCPLOWESULTSC_SCPLOWTLE patients showed marked impairment in episodic memory compared to controls, while spatial and semantic memory remained relatively intact. Findings were robust, with slight decreases in effect sizes after controlling for performance on executive function tests. Via partial least squares analysis, we identified group, age, and bilateral hippocampal volumes as important variables relating to relational memory impairment. CO_SCPLOWONCLUSIONC_SCPLOWOur behavioral framework provides a granular approach for assessing relational memory deficits in people with TLE and may inform future prognostic strategies in patients with hippocampal pathology. Our work warrants further investigations into the underlying neural substrates of relational memory.

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bioRxiv2022

Macro-scale patterns in functional connectivity associated with ongoing thought patterns and dispositional traits

Complex macro-scale patterns of brain activity that emerge during periods of wakeful rest provide insight into the organisation of neural function, how these differentiate individuals based on their traits, and the neural basis of different types of self-generated thoughts. Although brain activity during wakeful rest is valuable for understanding important features of human cognition, its unconstrained nature makes it difficult to disentangle neural features related to personality traits from those related to the thoughts occurring at rest. Our study builds on recent perspectives from work on ongoing conscious thought that highlight the interactions between three brain networks - ventral and dorsal attention networks, as well as the default mode network. We combined measures of personality with state-of-the-art indices of ongoing thoughts at rest and brain imaging analysis and explored whether this tri-partite view can provide a framework within which to understand the contribution of states and traits to observed patterns of neural activity at rest. To capture macro-scale relationships between different brain systems, we calculated cortical gradients to describe brain organisation in a low dimensional space. Our analysis established that for more introverted individuals, regions of the ventral attention network were functionally more aligned to regions of the somatomotor system and the default mode network. At the same time, a pattern of detailed self-generated thought was associated with a decoupling of regions of dorsal attention from regions in the default mode network. Our study, therefore, establishes that interactions between attention systems and the default mode network are important influences on ongoing thought at rest and highlights the value of integrating contemporary perspectives on conscious experience when understanding patterns of brain activity at rest.

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bioRxiv2022

Conserved whole-brain spatiomolecular gradients shape adult brain functional organization

Cortical arealization arises during neurodevelopment from the confluence of molecular gradients representing patterned expression of morphogens and transcription factors. However, how these gradients relate to adult brain function, and whether they are maintained in the adult brain, remains unknown. Here we uncover three axes of topographic variation in gene expression in the adult human brain that specifically capture previously identified rostral-caudal, dorsal-ventral and medial-lateral axes of early developmental patterning. The interaction of these spatiomolecular gradients i) accurately predicts the location of unseen brain tissue samples, ii) delineates known functional territories, and iii) explains the topographical variation of diverse cortical features. The spatiomolecular gradients are distinct from canonical cortical functional hierarchies differentiating primary sensory cortex from association cortex, but radiate in parallel with the axes traversed by local field potentials along the cortex. We replicate all three molecular gradients in three independent human datasets as well as two non-human primate datasets, and find that each gradient shows a distinct developmental trajectory across the lifespan. The gradients are composed of several well known morphogens (e.g., PAX6 and SIX3), and a small set of genes shared across gradients are strongly enriched for multiple diseases. Together, these results provide insight into the developmental sculpting of functionally distinct brain regions, governed by three robust transcriptomic axes embedded within brain parenchyma.

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bioRxiv2022

Atypical intrinsic neural timescales in temporal lobe epilepsy

ObjectiveTemporal lobe epilepsy (TLE) is the most common drug-resistant epilepsy in adults. Here, we aimed to profile local neural function in TLE in vivo, building on prior evidence that has identified widespread structural alterations. Using multimodal MRI, we mapped intrinsic neural timescales (INT) at rest, examined associations to TLE-related structural compromise, and evaluated the clinical utility of INT. MethodsWe studied 46 TLE patients and 44 healthy controls from two independent sites, and mapped INT changes in patients relative to controls across hippocampal, subcortical, and neocortical regions. We examined region-specific associations to structural alterations and explored effects of age and epilepsy duration. A supervised machine learning paradigm assessed utility of INT for classifying patients-vs-controls and seizure focus lateralization. ResultsRelative to controls, TLE showed marked INT reductions across multiple regions bilaterally, indexing faster changing resting activity, with strongest effects in ipsilateral medial and lateral temporal regions, and sensorimotor cortices. Findings were consistent in each site and robust, albeit with reduced effect sizes, when correcting for structural alterations. TLE-related INT reductions increased with advancing disease duration, yet findings differed from aging effects seen in controls. Classifiers based on INT distinguished patients-vs-controls (balanced accuracy, 5-fold: 76{+/-}2.65%; cross-site, 72-83%) and lateralized the focus in TLE (balanced accuracy, 5-fold: 96{+/-}2.10%; cross-site, 95-97%) with high accuracies and generalization. ConclusionsOur findings robustly demonstrate atypical macroscale function in TLE in a topography that extends beyond mesiotemporal epicenters. INT measurements can assist in TLE diagnosis, seizure focus lateralization, and monitoring of disease progression, which suggests clinical utility.

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bioRxiv2022

Micapipe: a pipeline for multimodal neuroimaging and connectome analysis

AO_SCPLOWBSTRACTC_SCPLOWMultimodal magnetic resonance imaging (MRI) has accelerated human neuroscience by fostering the analysis of brain structure, function, and connectivity across multiple scales and in living brains. The richness and complexity of multimodal neuroimaging, however, demands processing methods to integrate information across modalities and different spatial scales. Here, we present micapipe, an open processing pipeline for BIDS-conform multimodal MRI datasets. micapipe can generate i) structural connectomes derived from diffusion tractography, ii) functional connectomes derived from resting-state signal correlations, iii) geodesic distance matrices that quantify cortico-cortical proximity, and iv) microstructural profile covariance matrices that assess inter-regional similarity in cortical myelin proxies. These matrices are routinely generated across established 18 cortical parcellations (100-1000 parcels), in addition to subcortical and cerebellar parcellations. Results are represented on three different surface spaces (native, conte69, fsaverage5), and outputs are BIDS-conform. Processed outputs can be quality controlled at the individual and group level. micapipe was tested on several datasets and is available at https://github.com/MICA-MNI/micapipe, documented at https://micapipe.readthedocs.io/, and containerized as a BIDS App http://bids-apps.neuroimaging.io/apps/. We hope that micapipe will foster robust and integrative studies of human brain microstructure, morphology, and connectivity.

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bioRxiv2022

BrainStat: A toolbox for brain-wide statistics and neuroscientific contextualization

AO_SCPLOWBSTRACTC_SCPLOWAnalysis and interpretation of neuroimaging datasets has become a multidisciplinary endeavor, relying not only on statistical methods, but increasingly on associations with respect to other brain-derived features such as gene expression, histological data, and functional as well as cognitive architectures. Here, we introduce BrainStat - a toolbox for (i) univariate and multivariate linear models in volumetric and surface-based brain imaging datasets, and (ii) multidomain feature association of results with respect to spatial maps of post-mortem gene expression and histology, task-based fMRI meta-analysis, as well as resting-state fMRI motifs across several common surface templates. The combination of statistics and feature associations into a turnkey toolbox streamlines analytical processes and accelerates cross-modal research. The toolbox is implemented in both Python and MATLAB, two widely used programming languages in the neuroimaging and neuroinformatics communities. BrainStat is openly available and complemented by an expandable documentation.

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bioRxiv2021

The Unique Cytoarchitecture and Wiring of the Human Default Mode Network

The default mode network (DMN) is implicated in many aspects of complex thought and behavior. Here, we leverage post-mortem histology and in vivo neuroimaging to characterise the anatomy of the DMN to better understand its role in information processing and cortical communication. Our results show that the DMN is cytoarchitecturally heterogenous, containing cytoarchitectural types that are variably specialised for unimodal, heteromodal, and memory-related processing. Studying diffusion-based structural connectivity in combination with cytoarchitecture, we found the DMN contains regions receptive to input from sensory cortex and a core that is relatively insulated from environmental input. Finally, analysis of signal flow with effective connectivity models showed that the DMN is unique amongst cortical networks in balancing its output across the levels of sensory hierarchies. Together, our study establishes an anatomical foundation from which mechanistic accounts of the broad role the DMN plays in human brain function and cognition can be developed.

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bioRxiv2021

A Riemannian approach to predicting brain function from the structural connectome

AO_SCPLOWBSTRACTC_SCPLOWOngoing brain function is largely determined by the underlying wiring of the brain, but the specific rules governing this relationship remain unknown. Emerging literature has suggested that functional interactions between brain regions emerge from the structural connections through mono-as well as polysynaptic mechanisms. Here, we propose a novel approach based on diffusion maps and Riemannian optimization to emulate this dynamic mechanism in the form of random walks on the structural connectome and predict functional interactions as a weighted combination of these random walks. Our proposed approach was evaluated in two different cohorts of healthy adults (Human Connectome Project, HCP; Microstructure-Informed Connectomics, MICs). Our approach outperformed existing approaches and showed that performance plateaus approximately around the third random walk. At macroscale, we found that the largest number of walks was required in nodes of the default mode and frontoparietal networks, underscoring an increasing relevance of polysynaptic communication mechanisms in transmodal cortical networks compared to primary and unimodal systems.

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bioRxiv2021

Adolescent development of multiscale cortical wiring and functional connectivity in the human connectome

AO_SCPLOWBSTRACTC_SCPLOWAdolescence is a time of profound changes in the physical wiring and function of the brain. Here, we analyzed structural and functional brain network development in an accelerated longitudinal cohort spanning 14-25 years (n = 199). Core to our work was an advanced in vivo model of cortical wiring incorporating MRI features of cortico-cortical proximity, microstructural similarity, and white matter tractography. Longitudinal analyses assessing age-related changes in cortical wiring identified a continued differentiation of multiple cortico-cortical structural networks in youth. Studying resting-state functional MRI measures in the same participants at baseline, we found that regions with more similar structural wiring were more likely to be functionally coupled. Moreover, longitudinal structural wiring changes, particularly between sensory/unimodal and default mode networks, were reflected in tendencies for increased differentiation in brain function. These longitudinal findings provide new insights into adolescent development of human brain structure and function, illustrating how structural wiring interacts with the maturation of macroscale functional hierarchies.

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bioRxiv2021

Time-resolved structure-function coupling in brain networks

The relationship between structural and functional connectivity in the brain is a key question in systems neuroscience. Modern accounts assume a single global structure-function relationship that persists over time. Here we show that structure-function coupling is dynamic and regionally heterogeneous. We use a temporal unwrapping procedure to identify moment-to-moment co-fluctuations in neural activity, and reconstruct time-resolved structure-function coupling patterns. We find that patterns of dynamic structure-function coupling are highly organized across the cortex. These patterns reflect cortical hierarchies, with stable coupling in unimodal and transmodal cortex, and dynamic coupling in intermediate regions, particularly in insular cortex (salience network) and frontal eye fields (dorsal attention network). Finally, we show that the variability of structure-function coupling is shaped by the distribution of connection lengths. The time-varying coupling of structural and functional connectivity points towards an informative feature of the brain that may reflect how cognitive functions are flexibly deployed and implemented.

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bioRxiv2021

The Cost of Untracked Diversity in Brain-Imaging Prediction

Brain-imaging research enjoys increasing adoption of supervised machine learning for singlesubject disease classification. Yet, the success of these algorithms likely depends on population diversity, including demographic differences and other factors that may be outside of primary scientific interest. Here, we capitalize on propensity scores as a composite confound index to quantify diversity due to major sources of population stratification. We delineate the impact of population heterogeneity on the predictive accuracy and pattern stability in two separate clinical cohorts: the Autism Brain Imaging Data Exchange (ABIDE, n=297) and the Healthy Brain Network (HBN, n=551). Across various analysis scenarios, our results uncover the extent to which cross-validated prediction performances are interlocked with diversity. The instability of extracted brain patterns attributable to diversity is located preferentially to the default mode network. Our collective findings highlight the limitations of prevailing deconfounding practices in mitigating the full consequences of population diversity.

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