Biology subjects

Eickhoff, S. B.

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

At least 37 records · Page 2Linked to original sources

bioRxiv2024

Connectivity profile and function of uniquely human cortical areas

Quantitative comparison of the white matter organization of the human neocortex with that of the chimpanzee and macaque shows a wide distribution of areas with a uniquely human connectivity profile, including the frontal-parietal fiber systems and the temporal visual pathway. Functional decoding of these areas shows their involvement in language, abstract reasoning, and social information processing. Overall, these results counter models that assign primacy to prefrontal cortex for human uniqueness.

neuroscience↗

bioRxiv2024

The transcriptomic architecture of the human cerebral cortex

For over a century, scientists have been attempting to map the human cerebral cortex, however, they have not taken into account the complex molecular structure of the cortex, which is only beginning to be understood. Here, we parcellate the human cerebral cortex using a machine learning (ML) approach to define its transcriptomic architecture, revealing a multi-resolution organization across individuals. The transcriptomically-derived spatial patterns of gene expression separate the cortex into three major regions, frontal, temporal and parietooccipital, with smaller subregions appearing at lower levels of the transcriptomic hierarchy. The core regions, which remain stable across different hierarchical levels, are physiologically associated with language, emotion regulation, social cognition, motor and visuospatial processing and planning. Importantly, some core regions cross structural and anatomical boundaries identified in previous parcellations of the cortex, revealing that the transcriptomic architecture of the cortex is closely linked to human-specific higher cognitive function.

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bioRxiv2024

A network correspondence toolbox for quantitative evaluation of novel neuroimaging results

Decades of neuroscience research has shown that macroscale brain dynamics can be reliably decomposed into a subset of large-scale functional networks, but the specific spatial topographies of these networks and the names used to describe them can vary across studies. Such discordance has hampered interpretation and convergence of research findings across the field. To address this problem, we have developed the Network Correspondence Toolbox (NCT) to permit researchers to examine and report spatial correspondence between their novel neuroimaging results and sixteen widely used functional brain atlases, consistent with recommended reporting standards developed by the Organization for Human Brain Mapping. The atlases included in the toolbox show some topographical convergence for specific networks, such as those labeled as "default" or "visual". Network naming varies across atlases, particularly for networks spanning frontoparietal association cortices. For this reason, quantitative comparison with multiple atlases is recommended to benchmark novel neuroimaging findings. We provide several exemplar demonstrations using the Human Connectome Project task fMRI results and UK Biobank independent component analysis maps to illustrate how researchers can use the NCT to report their own findings through quantitative evaluation against multiple published atlases. The NCT provides a convenient means for computing Dice coefficients with spin test permutations to determine the magnitude and statistical significance of correspondence among user-defined maps and existing atlas labels. The NCT also includes functionality to incorporate additional atlases in the future. The adoption of the NCT will make it easier for network neuroscience researchers to report their findings in a standardized manner, thus aiding reproducibility and facilitating comparisons between studies to produce interdisciplinary insights.

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bioRxiv2024

Adolescent maturation of cortical excitation-inhibition balance based on individualized biophysical network modeling

The balance of excitation and inhibition is a key functional property of cortical microcircuits which changes through the lifespan. Adolescence is considered a crucial period for the maturation of excitation-inhibition balance. This has been primarily observed in animal studies, yet human in vivo evidence on adolescent maturation of the excitation-inhibition balance at the individual level is limited. Here, we developed an individualized in vivo marker of regional excitation-inhibition balance in human adolescents, estimated using large-scale simulations of biophysical network models fitted to resting-state functional magnetic resonance imaging data from two independent cross-sectional (N = 752) and longitudinal (N = 149) cohorts. We found a widespread relative increase of inhibition in association cortices paralleled by a relative age-related increase of excitation, or lack of change, in sensorimotor areas across both datasets. This developmental pattern co-aligned with multiscale markers of sensorimotor-association differentiation. The spatial pattern of excitation-inhibition development in adolescence was robust to inter-individual variability of structural connectomes and modeling configurations. Notably, we found that alternative simulation-based markers of excitation-inhibition balance show a variable sensitivity to maturational change. Taken together, our study highlights an increase of inhibition during adolescence in association areas using cross sectional and longitudinal data, and provides a robust computational framework to estimate microcircuit maturation in vivo at the individual level.

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bioRxiv2024

Cardiometabolic health, cortical thickness, and neurotransmitter systems: a large-scale multivariate study

The link between brain health and risk/protective factors for non-communicable diseases (such as high blood pressure, high body mass index, diet, smoking, physical activity, etc.) is increasingly acknowledged. However, the specific effects that these factors have on brain health are still poorly understood, delaying their implementation in precision brain health. Here, we studied the multivariate relationships between risk factors for non-communicable diseases and brain structure, including cortical thickness (CT) and grey matter volume (GMV). Furthermore, we adopted a systems-level perspective to understand such relationships, by characterizing the cortical patterns (yielded in association to risk factors) with regards to brain morphological and functional features, as well as with neurotransmitter systems. Similarly, we related the pattern of risk/protective factors dimensions with a peripheral marker of inflammation. First, we identified latent dimensions linking a broad set of risk factors for non-communicable diseases to parcel-wise CT and GMV across the whole cortex. Data was obtained from the UK Biobank (n=7370, age range=46-81 years). We used regularized canonical correlation analysis (RCCA) embedded in a machine learning framework. This approach allows us to capture inter-individual variability in a multivariate association and to assess the generalizability of the model. The brain patterns (captured in association with risk/protective factors) were characterized from a multi-level perspective, by performing correlations (spin tests) between them and different brain patterns of structure, function, and neurotransmitter systems. The association between the risk/protective factors pattern and C-reactive protein (CRP, a marker of inflammation) was examined using Spearman correlation. We found two significant and partly replicable latent dimensions. One latent dimension linked cardiometabolic health to brain patterns of CT and GMV and was consistent across sexes. The other latent dimension linked physical robustness (including non-fat mass and strength) to patterns of CT and GMV, with the association to GMV being consistent across sexes and the association to CT appearing only in men. The CT and GMV patterns of both latent dimensions were associated to the binding potentials of several neurotransmitter systems. Finally, the cardiometabolic health dimension was correlated to CRP, while physical robustness was only very weakly associated to it. We observed robust, multi-level and multivariate links between both cardiometabolic health and physical robustness with respect to CT, GMV, and neurotransmitter systems. Interestingly, we found that cardiometabolic health and physical robustness are associated with not only increases in CT or GMV, but also with decreases of CT or GMV in some brain regions. Our results also suggested a role for low-grade chronic inflammation in the association between cardiometabolic health and brain structural health. These findings support the relevance of adopting a holistic perspective in health, by integrating neurocognitive and physical health. Moreover, our findings contribute to the challenge to the classical conceptualization of neuropsychiatric and physical illnesses as categorical entities. In this perspective, future studies should further examine the effects of risk/protective factors on different brain regions in order to deepen our understanding of the clinical significance of such increased and decreased CT and GMV.

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bioRxiv2024

Lower motor performance is linked with poor sleep quality, depressive symptoms, and grey matter volume alterations

Motor performance (MP) is essential for functional independence and well-being, particularly in later life. However, the relationship between behavioural aspects such as sleep quality and depressive symptoms, which contribute to MP, and the underlying structural brain substrates of their interplay remains unclear. This study used three population-based cohorts of younger and older adults (n=1,950) from the Human Connectome Project-Young Adult (HCP-YA), HCP-Aging (HCP-A), and enhanced Nathan Kline Institute-Rockland sample (eNKI-RS). Several canonical correlation analyses were computed within a machine learning framework to assess the associations between each of the three domains (sleep quality, depressive symptoms, grey matter volume (GMV)) and MP. The HCP-YA analyses showed progressively stronger associations between MP and each domain: depressive symptoms (unexpectedly positive, r=0.13, SD=0.06), sleep quality (r=0.17, SD=0.05), and GMV (r=0.19, SD=0.06). Combining sleep and depressive symptoms significantly improved the canonical correlations (r=0.25, SD=0.05), while the addition of GMV exhibited no further increase (r=0.23, SD=0.06). In young adults, better sleep quality, mild depressive symptoms, and GMV of several brain regions were associated with better MP. This was conceptually replicated in young adults from the eNKI-RS cohort. In HCP-Aging, better sleep quality, fewer depressive symptoms, and increased GMV were associated with MP. Robust multivariate associations were observed between sleep quality, depressive symptoms and GMV with MP, as well as age-related variations in these factors. Future studies should further explore these associations and consider interventions targeting sleep and mental health to test the potential effects on MP across the lifespan.

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bioRxiv2024

Inter- and intra-subject similarity in network functional connectivity across a full narrative movie

Naturalistic paradigms, such as watching movies during functional magnetic resonance imaging (fMRI), are thought to prompt the emotional and cognitive processes typically elicited in real life situations. Therefore, naturalistic viewing (NV) holds great potential for studying individual differences. However, in how far NV elicits similarity within and between subjects on a network level, particularly depending on emotions portrayed in movies, is currently unknown. We used the studyforrest dataset to investigate the inter- and intra-subject similarity in network functional connectivity (NFC) of 14 meta-analytically defined networks across a full narrative, audio-visual movie split into 8 consecutive movie segments. We characterized the movie segments by valence and arousal portrayed within the sequences, before utilizing a linear mixed model to analyze which factors explain inter- and intra-subject similarity. Our results showed that the model best explaining inter-subject similarity comprised network, movie segment, valence and a movie segment by valence interaction. Intra-subject similarity was influenced significantly by the same factors and an additional three-way interaction between movie segment, valence and arousal. Overall, inter- and intra-subject similarity in NFC were sensitive to the ongoing narrative and emotions in the movie. Lowest similarity both within and between subjects was seen in the emotional regulation network and networks associated with long-term memory processing, which might be explained by specific features and content of the movie. We conclude that detailed characterization of movie features is crucial for NV research.

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bioRxiv2024

Exploring sex-specific neuroendocrine influences on the sensorimotor-association axis in single individuals

Human neuroimaging studies consistently show multimodal patterns of variability along a key principle of macroscale cortical organization - the sensorimotor-association (S-A) axis. However, little is known about day-to-day fluctuations in functional activity along this axis within an individual, including sex-specific neuroendocrine factors contributing to such transient changes. We leveraged data from two densely sampled healthy young adults, one female and one male, to investigate intra-individual daily variability along the S-A axis, which we computed as our measure of functional cortical organization by reducing the dimensionality of functional connectivity matrices. Daily variability was greatest in temporal limbic and ventral prefrontal regions in both participants, and was more strongly pronounced in the male subject. Next, we probed local and system-level effects of steroid hormones and self-reported perceived stress on functional organization. Our findings revealed modest effects that differed between participants, hinting at subtle -potentially sex-specific- associations between neuroendocrine fluctuations and intra-individual variability along the S-A axis. In sum, our study points to neuroendocrine factors as possible modulators of intra-individual variability in functional brain organization, highlighting the need for further research in larger samples.

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bioRxiv2024

Microstructural asymmetry in the human cortex

While macroscale brain asymmetry and its relevance for human cognitive function have been consistently shown, the underlying neurobiological signatures remain an open question. Here, we probe layer-specific microstructural asymmetry of the human cortex using intensity profiles from post-mortem cytoarchitecture. An anterior-posterior cortical pattern of left-right asymmetry was found, varying across cortical layers. A similar anterior-posterior pattern was observed using in vivo microstructural imaging, with in vivo asymmetry showing the strongest similarity with layer III. Microstructural asymmetry varied as a function of age and sex and was found to be heritable. Moreover, asymmetry in microstructure corresponded to asymmetry of intrinsic function, in particular in sensory areas. Last, probing the behavioral relevance, we found differential association of language and markers of mental health with asymmetry, illustrating a functional divergence between inferior-superior and anterior-posterior microstructural axes anchored in microstructural development. Our study highlights the layer-based patterning of microstructural asymmetry of the human cortex and its functional relevance.

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bioRxiv2024

MRI economics: Balancing sample size and scan duration in brain wide association studies

A pervasive dilemma in brain-wide association studies (BWAS) is whether to prioritize functional MRI (fMRI) scan time or sample size. We derive a theoretical model showing that individual-level phenotypic prediction accuracy increases with sample size and total scan duration (sample size x scan time per participant). The model explains empirical prediction accuracies extremely well across 76 phenotypes from nine resting-fMRI and task-fMRI datasets (R2 = 0.89), spanning a wide range of scanners, acquisitions, racial groups, disorders and ages. For scans [≤]20 mins, prediction accuracy increases linearly with the logarithm of total scan duration, suggesting interchangeability of sample size and scan time. However, sample size is ultimately more important than scan time in determining prediction accuracy. Nevertheless, when accounting for overhead costs associated with each participant (e.g., recruitment costs), to boost prediction accuracy, longer scans can yield substantial cost savings over larger sample size. To achieve high prediction performance, 10-min scans are highly cost inefficient. In most scenarios, the optimal scan time is [≥]20 mins. On average, 30-min scans are the most cost-effective, yielding 22% cost savings over 10-min scans. Overshooting is cheaper than undershooting the optimal scan time, so we recommend aiming for [≥]30 mins. Compared with resting-state whole-brain BWAS, the most cost-effective scan time is shorter for task-fMRI and longer for subcortical-cortical BWAS. Standard power calculations maximize sample size at the expense of scan time. Our study demonstrates that optimizing both sample size and scan time can boost prediction power while cutting costs. Our empirically informed reference is available for future study planning: WEB_APPLICATION_LINK

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bioRxiv2024

Simulated brain networks reflecting progression of Parkinson's disease

Neurodegenerative progression of Parkinsons disease affects brain structure and function and, concomitantly, alters topological properties of brain networks. The network alteration accompanied with motor impairment and duration of the disease is not yet clearly demonstrated in the disease progression. In this study, we aim at resolving this problem with a modeling approach based on large-scale brain networks from cross-sectional MRI data. Optimizing whole-brain simulation models allows us to discover brain networks showing unexplored relationships with clinical variables. We observe that simulated brain networks exhibit significant differences between healthy controls (n=51) and patients with Parkinsons disease (n=60) and strongly correlate with disease severity and disease duration of the patients. Moreover, the modeling results outperform the empirical brain networks in these clinical measures. Consequently, this study demonstrates that utilizing simulated brain networks provides an enhanced view on network alterations in the progression of motor impairment and potential biomarkers for clinical indices.

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bioRxiv2024

Translating phenotypic prediction models from big to small anatomical MRI data using meta-matching

Individualized phenotypic prediction based on structural MRI is an important goal in neuroscience. Prediction performance increases with larger samples, but small-scale datasets with fewer than 200 participants are often unavoidable. We have previously proposed a "meta-matching" framework to translate models trained from large datasets to improve the prediction of new unseen phenotypes in small collection efforts. Meta-matching exploits correlations between phenotypes, yielding large improvement over classical machine learning when applied to prediction models using resting-state functional connectivity as input features. Here, we adapt the two best performing meta-matching variants ("meta-matching finetune" and "meta-matching stacking") from our previous study to work with T1-weighted MRI data by changing the base neural network architecture to a 3D convolution neural network. We compare the two meta-matching variants with elastic net and classical transfer learning using the UK Biobank (N = 36,461), Human Connectome Project Young Adults (HCP-YA) dataset (N = 1,017) and HCP-Aging dataset (N = 656). We find that meta-matching outperforms elastic net and classical transfer learning by a large margin, both when translating models within the same dataset, as well as translating models across datasets with different MRI scanners, acquisition protocols and demographics. For example, when translating a UK Biobank model to 100 HCP-YA participants, meta-matching finetune yielded a 136% improvement in variance explained over transfer learning, with an average absolute gain of 2.6% (minimum = -0.9%, maximum = 17.6%) across 35 phenotypes. Overall, our results highlight the versatility of the meta-matching framework.

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bioRxiv2023

Stimulus selection influences prediction of individual phenotypes in naturalistic conditions

Understanding individual differences and brain-behaviour relationships is an essential goal of human neuroscience. Recent studies have shown the great potential of naturalistic stimuli, e.g., movie clips, in advancing this pursuit. While the use of naturalistic stimuli attracts increasing interest, the influence of stimulus selection remains largely unclear. In this study, we show that brain activity is generally sensitive to the choice of movie stimuli at both group and individual subject levels. Using sex classification as an example, we demonstrate that brain activity elicited by different stimuli can lead to distinct prediction performance and unique predictive features. The stimuli that yield better classification performance often elicit stronger synchrony of brain activity across all subjects and are mostly derived from Hollywood films with rich social content and cohesive narratives. Our results highlight the importance of stimulus selection and provide practical guidance for choosing appropriate stimuli, opening up new avenues for future studies on individual differences and brain-behaviour relationships.

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bioRxiv2023

Multilayer meta-matching: translating phenotypic prediction models from multiple datasets to small data

Resting-state functional connectivity (RSFC) is widely used to predict phenotypic traits in individuals. Large sample sizes can significantly improve prediction accuracies. However, for studies of certain clinical populations or focused neuroscience inquiries, small-scale datasets often remain a necessity. We have previously proposed a "meta-matching" approach to translate prediction models from large datasets to predict new phenotypes in small datasets. We demonstrated large improvement of meta-matching over classical kernel ridge regression (KRR) when translating models from a single source dataset (UK Biobank) to the Human Connectome Project Young Adults (HCP-YA) dataset. In the current study, we propose two meta-matching variants ("meta-matching with dataset stacking" and "multilayer meta-matching") to translate models from multiple source datasets across disparate sample sizes to predict new phenotypes in small target datasets. We evaluate both approaches by translating models trained from five source datasets (with sample sizes ranging from 862 participants to 36,834 participants) to predict phenotypes in the HCP-YA and HCP-Aging datasets. We find that multilayer meta-matching modestly outperforms meta-matching with dataset stacking. Both meta-matching variants perform better than the original "meta-matching with stacking" approach trained only on the UK Biobank. All meta-matching variants outperform classical KRR and transfer learning by a large margin. In fact, KRR is better than classical transfer learning when less than 50 participants are available for finetuning, suggesting the difficulty of classical transfer learning in the very small sample regime. The multilayer meta-matching model is publicly available at GITHUB_LINK.

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bioRxiv2023

Sex differences in intrinsic functional cortical organization reflect differences in network topology rather than cortical morphometry

Brain size robustly differs between sexes. However, the consequences of this anatomical dimorphism on sex differences in intrinsic brain function remain unclear. We investigated the extent to which sex differences in intrinsic cortical functional organization may be explained by differences in cortical morphometry, namely brain size, microstructure, and the geodesic distances of connectivity profiles. For this, we computed a low dimensional representation of functional cortical organization, the sensory-association axis, and identified widespread sex differences. Contrary to our expectations, observed sex differences in functional organization were not fundamentally associated with differences in brain size, microstructural organization, or geodesic distances, despite these morphometric properties being per se associated with functional organization and differing between sexes. Instead, functional sex differences in the sensory-association axis were associated with differences in functional connectivity profiles and network topology. Collectively, our findings suggest that sex differences in functional cortical organization extend beyond sex differences in cortical morphometry. TeaserInvestigating sex differences in functional cortical organization and their association to differences in cortical morphometry.

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bioRxiv2023

Resting state changes in aging and Parkinson's disease are shaped by underlying neurotransmission - a normative modeling study

Human healthy and pathological aging is linked to a steady decline in brain resting state activity and connectivity measures. The neurophysiological mechanisms underlying these changes remain poorly understood. Making use of recent developments in normative modeling and availability of in vivo maps for various neurochemical systems, we test in the UK Biobank cohort (N=25,917) if and how age- and Parkinsons disease related resting state changes in commonly applied local and global activity and connectivity measures co-localize with underlying neurotransmitter systems. We find the distributions of several major neurotransmitter systems including serotonergic, dopaminergic, noradrenergic and glutamatergic neurotransmission to explain age-related changes as observed across functional activity and connectivity measures. Co-localization patterns in Parkinsons disease deviate from normative aging trajectories for these, as well as for cholinergic and GABAergic neurotransmission. The deviation from normal co-localization of brain function and GABAa correlates with disease duration. These findings provide new insights into molecular mechanisms underlying age- and Parkinsons related brain functional changes. Combining normative modeling and neurotransmitter mapping may aid future research and drug development through deeper understanding of neurophysiological mechanisms underlying specific clinical conditions.

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bioRxiv2023

Commentary on Pang et al. (2023) Nature

Pang et al. (2023) observe that the geometric eigenmodes, derived from the shape of the cortical surface, are better at reconstructing patterns of both spontaneous and stimulus-evoked activity, when contrasted with three alternative connectome-based models including structural connectome derived eigenmodes. Based on this observation they propose that geometric eigenmodes offer a good model for explaining brain function, noting that "wave dynamics offer a more accurate and parsimonious mechanistic account of macroscale, spontaneous cortical dynamics captured by fMRI". They then question the prevailing view that brain activity is "localized to focal, spatially isolated clusters" and it is driven by "intricate patterns of anatomical connections". While the observation that geometric properties fit brain activity well is intriguing, we argue that accepting geometric eigenmodes as a model for brain function risks the logical fallacy of "affirming the consequent". A representation that effectively describes the underlying geometry is inherently adept at fitting patterns within that geometric space; it does not necessarily shed light on mechanisms of the brains functional attributes. To this end, we provide two lines of empirical results: (a) Basic parcel-based representations, which capture localized structures, can reconstruct activity patterns as well. (b) Geometric eigenmodes demonstrate a high flexibility when fit to a range of manipulated patterns, which evokes the danger of overfitting. Based on those results, theoretical considerations, and previous data we argue that more consideration is needed regarding "parsimony, robustness and generality of geometric eigenmodes as a basis set for brain function". While we recognize the potential role of the brains geometry in influencing its dynamics, assertions regarding its efficacy should be weighed against the performance of simpler models, an inherent risk of overfitting and anatomical evidence. Pang et al.1 put forth harmonic modes derived from the brains geometry as a previously underrecognized model to explain brain-wide dynamics. Their reconstruction framework relies on multiple linear regression to fit brain patterns using a basis set and then calculating Pearsons correlation between original data and fitted data, both parcellated using an atlas with 180 parcels in each hemisphere2. In addition to the overarching challenge of accepting any model as an actual reflection of the real world3, we here provide empirical results and theoretical arguments that highlight the need for further consideration regarding geometric eigenmodes as a model of macroscale brain activity.

neuroscience↗

bioRxiv2023

Sex classification from functional brain connectivity: Generalization to multiple datasets

Machine learning (ML) approaches are increasingly being applied to neuroimaging data. Studies in neuroscience typically have to rely on a limited set of training data which may impair the generalizability of ML models. However, it is still unclear which kind of training sample is best suited to optimize generalization performance. In the present study, we systematically investigated the generalization performance of sex classification models trained on the parcelwise connectivity profile of either single samples or a compound sample containing data from four different datasets. Generalization performance was quantified in terms of mean across-sample classification accuracy and spatial consistency of accurately classifying parcels. Our results indicate that generalization performance of pwCs trained on single dataset samples is dependent on the specific test samples. Certain datasets seem to "match" in the sense that classifiers trained on a sample from one dataset achieved a high accuracy when tested on the respected other one and vice versa. The pwC trained on the compound sample demonstrated overall highest generalization performance for all test samples, including one derived from a dataset not included in building the training samples. Thus, our results indicate that a big and heterogenous training sample comprising data of multiple datasets is best suited to achieve generalizable results.

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