Geometric constraints on human brain function

Nature, Journal Year: 2023, Volume and Issue: 618(7965), P. 566 - 574

Published: May 31, 2023

The anatomy of the brain necessarily constrains its function, but precisely how remains unclear. classical and dominant paradigm in neuroscience is that neuronal dynamics are driven by interactions between discrete, functionally specialized cell populations connected a complex array axonal fibres

Language: Английский

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Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review

NeuroImage, Journal Year: 2022, Volume and Issue: 249, P. 118870 - 118870

Published: Jan. 1, 2022

Diffusion magnetic resonance imaging (dMRI) tractography is an advanced technique that enables in vivo reconstruction of the brain's white matter connections at macro scale. It provides important tool for quantitative mapping structural connectivity using measures or tissue microstructure. Over last two decades, study brain dMRI has played a prominent role neuroimaging research landscape. In this paper, we provide high-level overview how used to enable analysis health and disease. We focus on types analyses tractography, including: 1) tract-specific refers typically hypothesis-driven studies particular anatomical fiber tracts, 2) connectome-based more data-driven generally entire brain. first review methodology involved three main processing steps are common across most approaches including methods correction, segmentation quantification. For each step, aim describe methodological choices, their popularity, potential pros cons. then have matter, focusing applications neurodevelopment, aging, neurological disorders, mental neurosurgery. conclude that, while there been considerable advancements technologies breadth applications, nevertheless remains no consensus about "best" researchers should remain cautious when interpreting results clinical applications.

Language: Английский

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Cross-ethnicity/race generalization failure of behavioral prediction from resting-state functional connectivity

Science Advances, Journal Year: 2022, Volume and Issue: 8(11)

Published: March 16, 2022

Algorithmic biases that favor majority populations pose a key challenge to the application of machine learning for precision medicine. Here, we assessed such bias in prediction models behavioral phenotypes from brain functional magnetic resonance imaging. We examined using two independent datasets (preadolescent versus adult) mixed ethnic/racial composition. When predictive were trained on data dominated by white Americans (WA), out-of-sample errors generally higher African (AA) than WA. This toward WA corresponds more WA-like brain-behavior association patterns learned models. AA only, compared training only or an equal number and participants, accuracy improved but stayed below Overall, results point need caution further research regarding current minority populations.

Language: Английский

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Comparison of individualized behavioral predictions across anatomical, diffusion and functional connectivity MRI

NeuroImage, Journal Year: 2022, Volume and Issue: 263, P. 119636 - 119636

Published: Sept. 16, 2022

A fundamental goal across the neurosciences is characterization of relationships linking brain anatomy, functioning, and behavior. Although various MRI modalities have been developed to probe these relationships, direct comparisons their ability predict behavior lacking. Here, we compared anatomical T1, diffusion functional (fMRI) at an individual level. Cortical thickness, area volume were extracted from T1 images. Diffusion Tensor Imaging (DTI) approximate Neurite Orientation Dispersion Density (NODDI) models fitted The resulting metrics projected Tract-Based Spatial Statistics (TBSS) skeleton. We also ran probabilistic tractography for images, which stream count, average length, each DTI NODDI metric tracts connecting pair regions. Functional connectivity (FC) was both task resting-state fMRI. Individualized prediction a wide range behavioral measures performed using kernel ridge regression, linear regression elastic net regression. Consistency results investigated with Human Connectome Project (HCP) Adolescent Brain Cognitive Development (ABCD) datasets. In datasets, FC-based gave best performance, regardless model or measure. This especially true cognitive component. Furthermore, all able cognition better than other components. Combining improved cognition, but not Finally, behaviors, combining resting FC yielded performance similar modalities. Overall, our study suggests that in case healthy children young adults, behaviorally-relevant information features might reflect subset variance captured by FC.

Language: Английский

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Micapipe: A pipeline for multimodal neuroimaging and connectome analysis

NeuroImage, Journal Year: 2022, Volume and Issue: 263, P. 119612 - 119612

Published: Sept. 6, 2022

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

Language: Английский

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The Burden of Reliability: How Measurement Noise Limits Brain-Behaviour Predictions

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: Feb. 10, 2023

Abstract Major efforts in human neuroimaging strive to understand individual differences and find biomarkers for clinical applications by predicting behavioural phenotypes from brain imaging data. An essential prerequisite identifying generalizable replicable brain-behaviour prediction models is sufficient measurement reliability. However, the selection of targets predominantly guided scientific interest or data availability rather than reliability considerations. Here we demonstrate impact low phenotypic on out-of-sample performance. Using simulated empirical Human Connectome Projects, found that levels common across many can markedly limit ability link behaviour. Next, using 5000 subjects UK Biobank, show only highly reliable fully benefit increasing sample sizes hundreds thousands participants. Overall, our findings highlight importance brain–behaviour associations differences.

Language: Английский

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Human brain dynamics are shaped by rare long-range connections over and above cortical geometry

Proceedings of the National Academy of Sciences, Journal Year: 2025, Volume and Issue: 122(1)

Published: Jan. 3, 2025

A fundamental topological principle is that the container always shapes content. In neuroscience, this translates into how brain anatomy dynamics. From neuroanatomy, topology of mammalian can be approximated by local connectivity, accurately described an exponential distance rule (EDR). The compact, folded geometry cortex shaped and geometric harmonic modes reconstruct much functional However, ignores role rare long-range (LR) cortical connections, crucial for improving information processing in brain, but not captured folding geometry. Here, we show superiority combining LR connectivity with EDR (EDR+LR) capturing dynamics (specifically task-evoked activity) compared to representations. Importantly, orchestration carried out a more efficient manifold made up low number EDR+LR modes. Our results importance complexity activity through low-dimensional

Language: Английский

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Machine learning prediction of cognition from functional connectivity: Are feature weights reliable?

NeuroImage, Journal Year: 2021, Volume and Issue: 245, P. 118648 - 118648

Published: Oct. 20, 2021

Cognitive performance can be predicted from an individual's functional brain connectivity with modest accuracy using machine learning approaches. As yet, however, predictive models have arguably yielded limited insight into the neurobiological processes supporting cognition. To do so, feature selection and weight estimation need to reliable ensure that important connections circuits high utility reliably identified. We comprehensively investigate test-retest reliability for various of cognitive built resting-state networks in healthy young adults (n=400). Despite achieving prediction accuracies (r=0.2–0.4), we find is generally poor all (ICC< 0.3), significantly poorer than overt biological attributes such as sex (ICC≈0.5). Larger sample sizes (n=800), Haufe transformation, non-sparse selection/regularization smaller spaces marginally improve 0.4). elucidate a tradeoff between univariate statistics are more weights models. Finally, show measuring agreement cross-validation folds provides inflated estimates reliability. thus recommend estimated out-of-sample, if possible. argue rebalancing focus model may facilitate mechanistic understanding cognition

Language: Английский

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Brain structure-function coupling provides signatures for task decoding and individual fingerprinting

NeuroImage, Journal Year: 2022, Volume and Issue: 250, P. 118970 - 118970

Published: Feb. 4, 2022

Brain signatures of functional activity have shown promising results in both decoding brain states, meaning distinguishing between different tasks, and fingerprinting, that is identifying individuals within a large group. Importantly, these do not account for the underlying anatomy on which function takes place. Structure-function coupling based graph signal processing (GSP) has recently revealed meaningful spatial gradient from unimodal to transmodal regions, average healthy subjects during resting-state. Here, we explore specificity structure-function distinct states (tasks) individual subjects. We used multimodal magnetic resonance imaging 100 unrelated Human Connectome Project rest seven tasks adopted support vector machine classification approach with various cross-validation settings. found measures allow accurate classifications task fingerprinting. In particular, key information fingerprinting more liberal portion signals, contributions strikingly localized fronto-parietal network. Moreover, signals showed strong correlation cognitive traits, assessed partial least square analysis, corroborating its relevance By introducing new perspective GSP-based filtering FC decomposition, show provides class cognition organization at tasks. Further, they provide insights clarifying role low high frequencies structural connectome, leading understanding where characterizing can be across connectome spectrum.

Language: Английский

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Exploring MEG brain fingerprints: Evaluation, pitfalls, and interpretations

NeuroImage, Journal Year: 2021, Volume and Issue: 240, P. 118331 - 118331

Published: July 5, 2021

Individual characterization of subjects based on their functional connectome (FC), termed "FC fingerprinting", has become a highly sought-after goal in contemporary neuroscience research. Recent magnetic resonance imaging (fMRI) studies have demonstrated unique and accurate identification individuals as an accomplished task. However, FC fingerprinting magnetoencephalography (MEG) data is still widely unexplored. Here, we study resting-state MEG from the Human Connectome Project to assess its relationship with several factors including amplitude- phase-coupling connectivity measures, spatial leakage correction, frequency bands, behavioral significance. To this end, first employ two scoring methods, differential identifiability success rate, provide quantitative fingerprint scores for each measurement. Secondly, explore edgewise nodal patterns across different bands (delta, theta, alpha, beta, gamma). Finally, investigate cross-modality obtained fMRI recordings same subjects. We significance measures modalities using partial least square correlation analyses. Our results suggest that performance heavily dependent measure, band, method, correction. report higher performances central (alpha beta), visual, frontoparietal, dorsal-attention, default-mode networks. Furthermore, comparisons reveal certain degree concordance between data, especially visual system. multivariate analyses show connectomes strong significance, which however depends considered measure temporal scale. This comprehensive, albeit preliminary investigation test-retest offers relation methodological electrophysiological contributes understanding cross-modal relationships. hope will contribute setting grounds identification.

Language: Английский

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