Biology subjects

Eickhoff, S. B.

Publications and source records attributed to Eickhoff, S. B..

At least 73 records · Page 4Linked to original sources

bioRxiv2022

Accurate sex prediction of cisgender and transgender individuals without brain size bias

Brain size differs substantially between human males and females. This difference in total intracranial volume (TIV) can cause bias when employing machine-learning approaches for the investigation of sex differences in brain morphology. TIV-biased models will likely not capture actual qualitative sex differences in brain organization but rather learn to classify an individuals sex based on brain size differences, thus leading to spurious and misleading conclusions, for example when comparing brain morphology between cisgender- and transgender individuals. Here, TIV bias in sex classification models applied to cis- and transgender individuals was systematically investigated by controlling for brain size either through featurewise confound removal or by matching training samples for TIV. Our results provide evidence that non-TIV-biased models can classify the sex of both cis- and transgender individuals with high accuracy, highlighting the importance of appropriate modelling to avoid bias in automated decision making. TeaserAccurate non-biased structural sex classification in cis- and transgender individuals by matching training samples for TIV

neuroscience↗

bioRxiv2022

Whole-brain dynamical modeling for classification of Parkinson's disease

Simulated whole-brain connectomes demonstrate an enhanced inter-individual variability depending on data processing and modeling approach. By considering the human brain connectome as an individualized attribute, we investigate how empirical and simulated whole-brain connectome-derived features can be utilized to classify patients with Parkinsons disease against healthy controls in light of varying data processing and model validation. To this end, we applied simulated blood oxygenation level-dependent signals derived by a whole-brain dynamical model simulating electrical signals of neuronal populations to reveal differences between patients and controls. In addition to the widely used model validation via fitting the dynamical model to empirical neuroimaging data, we invented a model validation against behavioral data, such as subject classes, which we refer to as behavioral model fitting and show that it can be beneficial for Parkinsonian patient classification. Furthermore, the results of machine-learning reported in this study also demonstrated that performance of the patient classification can be improved when the empirical data are complemented by the simulation results. We also showed that temporal filtering of blood oxygenation level-dependent signals influences the prediction results, where the filtering in the low-frequency band is advisable for Parkinsonian patient classification. In addition, composing the feature space of empirical and simulated data from multiple brain parcellation schemes provided complementary features that improve prediction performance. Based on our findings, we suggest including the simulation results with empirical data is effective for inter-individual research and its clinical application.

neuroscience↗

bioRxiv2022

A topography-based predictive framework for naturalistic viewing fMRI

Recent work has shown great interest in understanding individual differences in complex brain function under naturalistic viewing (NV) conditions. However, methods specifically designed for achieving this goal remain limited. Here, we propose a novel approach, called TOpography-based Predictive Framework (TOPF), to investigate individual differences in evoked brain activity on NV fMRI data. Specifically, TOPF identifies individual-specific evoked activity topographies in a data- driven manner and examines their behavioural relevance using a machine learning predictive framework. Our results show that these topographies successfully predict individual phenotypes across cognition, emotion and personality on unseen subjects, and the identified predictive brain regions are neurobiologically interpretable. Further, the prediction accuracy exceeds that of the commonly-used functional connectivity-based features. Conceptually, we highlight the importance of examining multivariate evoked activity patterns for studying brain-behaviour relationships. In summary, we provide a powerful tool for understanding individual differences and brain-behaviour relationships on NV fMRI data.

neuroscience↗

bioRxiv2021

FAIRly big: A framework for computationally reproducible processing of large-scale data

Large-scale datasets present unique opportunities to perform scientific investigations with un-precedented breadth. However, they also pose considerable challenges for the findability, accessibility, interoperability, and reusability (FAIR) of research outcomes due to infrastructure limitations, data usage constraints, or software license restrictions. Here we introduce a DataLad-based, domain-agnostic framework suitable for reproducible data processing in compliance with open science mandates. The framework attempts to minimize platform idiosyncrasies and performance-related complexities. It affords the capture of machine-actionable computational provenance records that can be used to retrace and verify the origins of research outcomes, as well as be re-executed independent of the original computing infrastructure. We demonstrate the frameworks performance using two showcases: one highlighting data sharing and transparency (using the studyforrest.org dataset) and another highlighting scalability (using the largest public brain imaging dataset available: the UK Biobank dataset).

bioinformatics↗

bioRxiv2021

Highly accurate local functional fingerprints and their stability

Precision medicine and the investigation of brain-behavior associations require biomarkers that are stable (low intraindividual variability) and unique (high interindividual variability) at the same time, hence calling them "fingerprints". The functional connectome (FC) has good "fingerprint properties", as individuals can be accurately identified in a database based on their FC. Importantly, research has shown lower intraindividual variability of more localized measures of brain function such as regional homogeneity (ReHo) and (fractional) amplitude of low-frequency fluctuations ((f)ALFF), compared to the FC. Here, with fMRI data from two publicly available datasets we demonstrate that individuals can be identified with near-perfect accuracies using local functional fingerprints, and especially the regional homogeneity (ReHo) fingerprint. Further analyses reveal that the dorsal attention network contributes most to the individual "uniqueness" of the ReHo fingerprint. Moreover, using a machine-learning setup, we show that the small intraindividual ReHo fingerprint variability across sessions is meaningful for explaining individual-level intelligence. Last, using two other publicly available datasets, clinical applicability is shown with high fingerprint accuracies and a significant correlation between fingerprint stability and intelligence in individuals with schizophrenia. Altogether, our findings suggest that the ReHo fingerprint is a good candidate for further exploration of applicability in precision medicine.

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bioRxiv2021

Genetic and phylogenetic uncoupling of structure and function in human transmodal cortex

Brain structure scaffolds intrinsic function, supporting cognition and ultimately behavioral flexibility. However, it remains unclear how a static, genetically controlled architecture supports flexible cognition and behavior. Here, we synthesize genetic, phylogenetic and cognitive analyses to understand how the macroscale organization of structure-function coupling across the cortex can inform its role in cognition. In humans, structure-function coupling was highest in regions of unimodal cortex and lowest in transmodal cortex, a pattern that was mirrored by a reduced alignment with heritable connectivity profiles. Structure-function uncoupling in non-human primates had a similar spatial distribution, but we observed an increased coupling between structure and function in association regions in macaques relative to humans. Meta-analysis suggested regions with the least genetic control (low heritable correspondence and different across primates) are linked to social cognition and autobiographical memory. Our findings establish the genetic and evolutionary uncoupling of structure and function in different transmodal systems may support the emergence of complex, culturally embedded forms of cognition.

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bioRxiv2021

Tract-specific statistics based on diffusion-weighted probabilistic tractography

Diffusion-weighted neuroimaging approaches provide rich evidence with which to estimate the structural integrity of white matter in vivo. Tract-based spatial statistics (TBSS), for instance, allows us to assess the spatial distribution of statistical tests performed on diffusion properties such as fractional anisotropy, which are proposed to reflect the integrity of white matter tracts. However, such methods do not provide a direct assessment of white matter integrity for connections between two specific regions of the brain. Here, we present a novel method for deriving tract-specific diffusion statistics, based upon arbitrarily-defined regions of interest. Our approach makes use of an empirically-derived population distribution based on probabilistic tractography, using the Nathan Kline Institute (NKI) Enhanced Rockland sample. We use a heuristic method to determine the most likely geometry of a path between two regions, if one exists, and express this as a spatial distribution. We then estimate the average orientation of streamlines traversing this path, at discrete distances along its trajectory, and compute the fraction of diffusion directed along this orientation for each participant. This allows us to obtain participant-wise metrics along the specific tract (tract-specific anisotropy; TSA), which can then be used to perform statistical analysis on any comparable population. Based on this method, we report both negative and positive associations between age and TSA for two networks derived from published meta-analytic studies (the "default mode" and "what-where" networks), along with more moderate sex differences and age-by-sex interactions in both networks.

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bioRxiv2020

Tractography density affects whole-brain structural architecture and resting-state dynamical modeling

Dynamical modeling of the resting-state brain dynamics essentially relies on the empirical neuroimaging data utilized for the model derivation and validation. There is however still no standardized data processing for magnetic resonance imaging pipelines and the structural and functional connectomes involved in the models. In this study, we thus address how the parameters of diffusion-weighted data processing for structural connectivity (SC) can influence the validation results of the whole-brain mathematical models and search for the optimal parameter settings. On this way, we simulate the functional connectivity by systems of coupled oscillators, where the underlying network is constructed from the empirical SC and evaluate the performance of the models for varying parameters of data processing. For this, we introduce a set of simulation conditions including the varying number of total streamlines of the whole-brain tractography (WBT) used for extraction of SC, cortical parcellations based on functional and anatomical brain properties and distinct model fitting modalities. We observed that the graph-theoretical network properties of structural connectome can be affected by varying tractography density and strongly relate to the model performance. We explored free parameters of the considered models and found the optimal parameter configurations, where the model dynamics closely replicates the empirical data. We also found that the optimal number of the total streamlines of WBT can vary for different brain atlases. Consequently, we suggest a way how to improve the model performance based on the network properties and the optimal parameter configurations from multiple WBT conditions. Furthermore, the population of subjects can be stratified into subgroups with divergent behaviors induced by the varying number of WBT streamlines such that different recommendations can be made with respect to the data processing for individual subjects and brain parcellations. Author summaryThe human brain connectome at macro level provides an anatomical constitution of inter-regional connections through the white matter in the brain. Understanding the brain dynamics grounded on the structural architecture is one of the most studied and important topics actively debated in the neuroimaging research. However, the ground truth for the adequate processing and reconstruction of the human brain connectome in vivo is absent, which is crucial for evaluation of the results of the data-driven as well as model-based approaches to brain investigation. In this study we thus evaluate the effect of the whole-brain tractography density on the structural brain architecture by varying the number of total axonal fiber streamlines. The obtained results are validated throughout the dynamical modeling of the resting-state brain dynamics. We found that the tractography density may strongly affect the graph-theoretical network properties of the structural connectome. The obtained results also show that a dense whole-brain tractography is not always the best condition for the modeling, which depends on a selected brain parcellation used for the calculation of the structural connectivity and derivation of the model network. Our findings provide suggestions for the optimal data processing for neuroimaging research and brain modeling.

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bioRxiv2020

Behavioral, Anatomical and Genetic Convergence of Affect and Cognition in Superior Frontal Cortex

Affective experience and cognitive abilities are key human traits that are interrelated in behavior and brain. Individual variation of affective and cognitive traits, as well as brain structure, has been shown to partly underlie genetic effects. However, to what extent affect and cognition have a shared genetic relationship with local brain structure is incompletely understood. Here we studied phenotypic and genetic correlations of cognitive and affective traits in behavior and brain structure (cortical thickness, surface area and subcortical volumes) in the twin-based Human Connectome Project sample (N = 1091). Both affective and cognitive trait scores were highly heritable and showed significant phenotypic correlation on the behavioral level. Cortical thickness in the left superior frontal cortex showed a phenotypic association with both affect and cognition, which was driven by shared genetic effects. Quantitative functional decoding of this region yielded associations with cognitive and emotional functioning. This study provides a multi-level approach to study the association between affect and cognition and suggests a convergence of both in superior frontal cortical thickness.

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bioRxiv2020

Meta-matching: a simple framework to translate phenotypic predictive models from big to small data

There is significant interest in using brain imaging data to predict non-brain-imaging phenotypes in individual participants. However, most prediction studies are underpowered, relying on less than a few hundred participants, leading to low reliability and inflated prediction performance. Yet, small sample sizes are unavoidable when studying clinical populations or addressing focused neuroscience questions. Here, we propose a simple framework - "meta-matching" - to translate predictive models from large-scale datasets to new unseen non-brain-imaging phenotypes in boutique studies. The key observation is that many large-scale datasets collect a wide range inter-correlated phenotypic measures. Therefore, a unique phenotype from a boutique study likely correlates with (but is not the same as) some phenotypes in some large-scale datasets. Meta-matching exploits these correlations to boost prediction in the boutique study. We applied meta-matching to the problem of predicting non-brain-imaging phenotypes using resting-state functional connectivity (RSFC). Using the UK Biobank (N = 36,848), we demonstrated that meta-matching can boost the prediction of new phenotypes in small independent datasets by 100% to 400% in many scenarios. When considering relative prediction performance, meta-matching significantly improved phenotypic prediction even in samples with 10 participants. When considering absolute prediction performance, meta-matching significantly improved phenotypic prediction when there were least 50 participants. With a growing number of large-scale population-level datasets collecting an increasing number of phenotypic measures, our results represent a lower bound on the potential of meta-matching to elevate small-scale boutique studies.

neuroscience↗

bioRxiv2020

The Aging Brain and Executive Functions Revisited: Implications from Meta-Analytic and Functional-Connectivity Evidence

Healthy aging is associated with changes in cognitive performance including executive functions (EFs) and their associated brain activation patterns. However, it has remained unclear which EF-related brain regions are affected consistently, because the results of pertinent neuroimaging studies and earlier meta-analyses vary considerably. We, therefore, conducted new rigorous meta-analyses of published age differences in EF-related brain activity. Out of a larger set of regions associated with EFs, only left inferior frontal junction (IFJ) and left anterior cuneus/precuneus (aC/PrC) were found to show consistent age differences. To further characterize these two age-sensitive regions, we performed seed-based resting-state functional connectivity (RS-FC) analyses using fMRI data from a large adult sample with a wide age range. We also assessed associations of the two regions whole-brain RS-FC patterns with age and EF performance. Although functional profiling and RS-FC analyses point towards a domain-general role of left IFJ in EFs, the pattern of individual study contributions to the meta-analytic results suggests process-specific modulations by age. Our analyses further indicate that left aC/PrC is recruited differently by older (compared to younger) adults during EF tasks, potentially reflecting inefficiencies in switching the attentional focus. Overall, our findings question earlier meta-analytic results and suggest a larger heterogeneity of age-related differences in brain activity associated with EFs. Hence, they encourage future research that pays greater attention to replicability, investigates age-related differences in deactivation, and focuses on more narrowly defined EF subprocesses, combining multiple behavioral assessments with multi-modal imaging. Highlights- Healthy aging is linked to deterioration in executive functions (EFs) - ALE meta-analyses examined consistent age differences in brain activity linked to EFs - In a larger set of EF regions, only left IFJ and (pre)cuneus were sensitive to age - Advanced age was linked to weaker functional coupling within EF-related networks - Our findings question earlier meta-analytic findings

neuroscience↗

bioRxiv2020

Connectivity patterns of task-specific brain networks allow individual prediction of cognitive symptom dimension of schizophrenia and link to molecular architecture

BackgroundDespite the marked inter-individual variability in the clinical presentation of schizophrenia, it remains unclear the extent to which individual dimensions of psychopathology may be reflected in variability across the collective set of functional brain connections. Here, we address this question using network-based predictive modeling of individual psychopathology along four data-driven symptom dimensions. Follow-up analyses assess the molecular underpinnings of predictive networks by relating them to neurotransmitter-receptor distribution patterns. MethodsWe investigated resting-state fMRI data from 147 schizophrenia patients recruited at seven sites. Individual expression along negative, positive, affective, and cognitive symptom dimensions was predicted using relevance vector machine based on functional connectivity within 17 meta-analytic task-networks following a repeated 10-fold cross-validation and leave-one-site-out analyses. Results were validated in an independent sample. Networks robustly predicting individual symptom dimensions were spatially correlated with density maps of nine receptors/transporters from prior molecular imaging in healthy populations. ResultsTen-fold and leave-one-site-out analyses revealed five predictive network-symptom associations. Connectivity within theory-of-mind, cognitive reappraisal, and mirror neuron networks predicted negative, positive, and affective symptom dimensions, respectively. Cognitive dimension was predicted by theory-of-mind and socio-affective-default networks. Importantly, these predictions generalized to the independent sample. Intriguingly, these two networks were positively associated with D1 dopamine receptor and serotonin reuptake transporter densities as well as dopamine-synthesis-capacity. ConclusionsWe revealed a robust association between intrinsic functional connectivity within networks for socio-affective processes and the cognitive dimension of psychopathology. By investigating the molecular architecture, the present work links dopaminergic and serotonergic systems with the functional topography of brain networks underlying cognitive symptoms in schizophrenia.

neuroscience↗

bioRxiv2020

Linking cerebellar functional gradients to transdiagnostic behavioral dimensions of psychopathology

High co-morbidity and substantial overlap across psychiatric disorders encourage a transition in psychiatry research from categorical to dimensional approaches that integrate neuroscience and psychopathology. Cerebellum is involved in a wide range of nonmotor cognitive functions and mental disorders. An important question thus centers on the extent to which cerebellar function can be linked to transdiagnostic dimensions of psychopathology. Here, this question is investigated using partial least squares to identify latent dimensions linking cerebellar connectome properties as assessed by macroscale spatial gradients of connectivity to a large set of clinical and behavioral measures in 198 participants across diagnostic categories. This analysis reveals significant correlated patterns of cerebellar connectivity gradients and behavioral measures that could be represented into four latent dimensions: general psychopathology, general lack of attention regulation, internalizing symptoms, and dysfunctional memory. Each dimension is associated with a distinct spatial pattern of cerebellar connectivity gradients. These findings highlight the relevance of cerebellar connectivity as a necessity for the study and classification of transdiagnostic dimensions of psychopathology.

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bioRxiv2020

Chimpanzee Brain Morphometry Utilizing Standardized MRI Preprocessing and Macroanatomical Annotations

Chimpanzees are among the closest living relatives to humans and, as such, provide a crucial comparative model for investigating primate brain evolution. In recent years, human brain mapping has strongly benefited from enhanced computational models and image processing pipelines that could also improve data analyses in animals by using species-specific templates. In this study, we use structural MRI data from the National Chimpanzee Brain Resource (NCBR) to develop the chimpanzee brain reference template Juna.Chimp for spatial registration and the macro-anatomical brain parcellation Davi130 for standardized whole-brain analysis. Additionally, we introduce a ready-to-use image processing pipeline built upon the CAT12 toolbox in SPM12, implementing a standard human image preprocessing framework in chimpanzees. Applying this approach to data from 178 subjects, we find strong evidence for age-related GM atrophy in multiple regions of the chimpanzee brain, as well as, a human-like anterior-posterior pattern of hemi-spheric asymmetry in medial chimpanzee brain regions.

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bioRxiv2020

JuSpace: A tool for spatial correlation analyses of magnetic resonance imaging data with positron emission tomography derived neurotransmitter maps

Recent studies have shown that drug-induced spatial alteration patterns in resting state functional activity as measured using magnetic resonance imaging (rsfMRI) are associated with the distribution of specific receptor systems targeted by respective compounds. Based on this approach, we introduce a toolbox (JuSpace) allowing for cross-modal correlation of MRI- based measures with nuclear imaging derived estimates covering various neurotransmitter systems including dopaminergic, serotonergic, noradrenergic, and GABAergic (gamma- aminobutric acid) neurotransmission. We apply JuSpace to two datasets covering Parkinsons disease patients (PD) and risperidone-induced changes in rsfMRI and cerebral blood flow (CBF). Consistently with the predominant neurodegeneration of dopaminergic and serotonergic system in PD, we find significant spatial associations between rsfMRI activity alterations in PD and dopaminergic (D2) and serotonergic systems (5-HT1b). Risperidone induced CBF alterations were correlated with its main targets in serotonergic and dopaminergic systems. JuSpace provides a biologically meaningful framework for linking neuroimaging to underlying neurotransmitter information.

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bioRxiv2020

Altered Sensorimotor-to-Transmodal Hierarchical Organization in Schizophrenia

For decades, schizophrenia has been primarily conceptualized as a disorder of high-order cognitive functions with deficits in executive brain regions. Yet due to the increasing reports of early sensory processing deficit, recent models focus more on the developmental effects of impaired sensory process on high-order functions. The present study examined whether this pathological interaction relates to an overarching system-level imbalance, specifically a disruption in macroscale hierarchy affecting integration and segregation of unimodal and transmodal networks. We applied a novel combination of connectome gradient and stepwise connectivity analysis to resting-state functional magnetic resonance imaging (rsfMRI) to characterize the sensorimotor-to-transmodal cortical hierarchy organization (96 patients vs. 122 controls). Using these techniques, we demonstrated compression of the cortical hierarchy organization in schizophrenia, with a prominent compression from the sensorimotor region and a less prominent compression from the frontal-parietal region, resulting in a diminished separation between sensory and fronto-parietal cognitive systems. Further analyses suggested reduced differentiation related to atypical functional connectome transition from unimodal to transmodal brain areas. Specifically, we found hypo-connectivity within unimodal regions and hyper-connectivity between unimodal regions and frontoparietal and ventral attention regions along the classical sensation-to-cognition continuum established in prior neuroanatomical work. The compression of cortical hierarchy organization represents a novel and integrative system-level substrate underlying the pathological interaction of early sensory and cognitive function in schizophrenia. This abnormal cortical hierarchy organization suggests cascaded impairments stemming from the disrupted somatosensory-motor system and inefficient integration of bottom-up sensory information with attentional demands and executive control processes partially account for high-level cognitive deficits characteristic of schizophrenia.

neuroscience↗

bioRxiv2020

The cue-reactivity paradigm: An ensemble of networks driving attention and cognition when viewing drug-related and natural-reward stimuli

BackgroundThe cue-reactivity paradigm is a widely adopted neuroimaging probe engendering brain activity linked with attentional, affective, and reward processes following presentation of appetitive stimuli. Given the multiple mental operations invoked, we sought to decompose cue-related brain activity into constituent components employing emergent meta-analytic techniques when considering drug and natural reward-related cues. MethodsWe conducted multiple coordinate-based meta-analyses delineating common and distinct brain activity convergence across cue-reactivity studies (N=196 articles) involving drug (n=133) or natural reward-related (n=63) visual stimuli. Subsequently, we characterized the connectivity profiles of identified brain regions by using them as seeds in task-independent and task-dependent functional connectivity analyses. Using hierarchical clustering on these connectivity profiles, we grouped cue-related brain regions into subnetworks. Functional decoding was then employed to characterize mental operations linked with each subnetwork. ResultsAcross all studies, pooled activity convergence was observed in the striatum, amygdala, thalamus, cingulate, insula, and multiple frontal, parietal, and occipital regions. Drug-distinct convergence (drug>natural) was observed notably in the posterior cingulate cortex (PCC), dorsolateral prefrontal cortex (dlPFC), and temporal and parietal regions, whereas distinct natural reward convergence (natural>drug) was observed in thalamic, insular, orbitofrontal, and occipital regions. Hierarchical clustering using each regions connectivity profiles identified six subnetworks, involving: 1) occipital and thalamic (lateral geniculate nucleus) regions functionally linked with early visual processing, 2) occipital-temporal regions associated with higher level visual association, 3) parietal-frontal regions linked with cognitive control mechanisms, 4) posterior and ventral insula as well as anterior cingulate cortex (ACC) functionally linked with salient event detection, 5) nucleus accumbens, PCC, precuneus, ACC, and thalamus (mediodorsal) associated with subjective valuation, and 6) bilateral amygdalae, orbitofrontal, and dorsal insula regions linked with affective processes. ConclusionsThese outcomes suggest multifaceted brain activity during the cue-reactivity paradigm can be decomposed into more elemental processes and indicate that while drugs of abuse usurp the brains natural reward processing system, some regions appear distinctly related to drug cue-reactivity (e.g., PCC, dlPFC).

neuroscience↗

bioRxiv2020

Shaping Brain Structure: Genetic and Phylogenetic Axes of Macro Scale Organization of Cortical Thickness

Structural and functional characteristics of the cortex systematically vary along global axes as a function of cytoarchitecture, gene expression, and connectivity. The topology of the cerebral cortex has been proposed to be a prerequisite for the emergence of human cognition and explain both the impact and progression of pathology. However, the neurogenetic origin of these organizational axes in humans remains incompletely understood. To address this gap in the literature our current study assessed macro scale cortical organization through an unsupervised machine learning analysis of cortical thickness covariance patterns and used converging methods to evaluate its genetic basis. In a large-scale sample of twins (n=899) we found structural covariance of thickness to be organized along both an anterior-to-posterior and inferior-to-superior axis. We found that both axes showed a high degree of correspondence in pairs of identical twins, suggesting a strong heritable component in humans. Furthermore, comparing these dimensions in macaques and humans highlighted similar organizational principles in both species demonstrating that these axes of cortical organization are phylogenetically conserved within primate species. Finally, we found that in both humans and macaques the inferior-superior dimension of cortical organization was aligned with the predictions of the dual-origin theory, highlighting the possibility that the macroscale organization of primate brain structure is subject to multiple distinct neurodevelopmental trajectories. Together, our study establishes the genetic basis of natural axes in the cerebral cortex along which structure is organized and so provides important insights into the organization of human cognition that will inform both our understanding of how structure guides function and for the progression of pathology in diseases.

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