Charting brain growth and aging at high spatial precision

eLife, Год журнала: 2022, Номер 11

Опубликована: Фев. 1, 2022

Defining reference models for population variation, and the ability to study individual deviations is essential understanding inter-individual variability its relation onset progression of medical conditions. In this work, we assembled a cohort neuroimaging data from 82 sites (N=58,836; ages 2-100) used normative modeling characterize lifespan trajectories cortical thickness subcortical volume. Models are validated against manually quality checked subset (N=24,354) provide an interface transferring new sources. We showcase clinical value by applying transdiagnostic psychiatric sample (N=1985), showing they can be quantify underlying multiple disorders whilst also refining case-control inferences. These will augmented with additional samples imaging modalities as become available. This provides common platform bind results different studies ultimately paves way personalized decision-making.

Язык: Английский

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Optical imaging and spectroscopy for the study of the human brain: status report

Neurophotonics, Год журнала: 2022, Номер 9(S2)

Опубликована: Авг. 30, 2022

This report is the second part of a comprehensive two-part series aimed at reviewing an extensive and diverse toolkit novel methods to explore brain health function. While first focused on neurophotonic tools mostly applicable animal studies, here, we highlight optical spectroscopy imaging relevant noninvasive human studies. We outline current state-of-the-art technologies software advances, most recent impact these neuroscience clinical applications, identify areas where innovation needed, provide outlook for future directions.

Язык: Английский

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Open and reproducible neuroimaging: From study inception to publication

NeuroImage, Год журнала: 2022, Номер 263, С. 119623 - 119623

Опубликована: Сен. 12, 2022

Empirical observations of how labs conduct research indicate that the adoption rate open practices for transparent, reproducible, and collaborative science remains in its infancy. This is at odds with overwhelming evidence necessity these their benefits individual researchers, scientific progress, society general. To date, information required implementing throughout different steps a project scattered among many sources. Even experienced researchers topic find it hard to navigate ecosystem tools make sustainable choices. Here, we provide an integrated overview community-developed resources can support collaborative, open, replicable, robust generalizable neuroimaging entire cycle from inception publication across modalities. We review supporting study planning, data acquisition, management, processing analysis, dissemination. An online version this resource be found https://oreoni.github.io. believe will prove helpful institutions successful move towards reproducible eventually take active role future development.

Язык: Английский

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An Open MRI Dataset For Multiscale Neuroscience

Scientific Data, Год журнала: 2022, Номер 9(1)

Опубликована: Сен. 15, 2022

Multimodal neuroimaging grants a powerful window into the structure and function of human brain at multiple scales. Recent methodological conceptual advances have enabled investigations interplay between large-scale spatial trends (also referred to as gradients) in microstructure connectivity, offering an integrative framework study multiscale organization. Here, we share multimodal MRI dataset for Microstructure-Informed Connectomics (MICA-MICs) acquired 50 healthy adults (23 women; 29.54 ± 5.62 years) who underwent high-resolution T1-weighted MRI, myelin-sensitive quantitative T1 relaxometry, diffusion-weighted resting-state functional 3 Tesla. In addition raw anonymized data, this release includes brain-wide connectomes derived from (i) imaging, (ii) diffusion tractography, (iii) covariance analysis, (iv) geodesic cortical distance, gathered across parcellation Alongside, gradients estimated each modality scale. Our will facilitate future research examining coupling microstructure, function. MICA-MICs is available on Canadian Open Neuroscience Platform data portal ( https://portal.conp.ca ) Science Framework https://osf.io/j532r/ ).

Язык: Английский

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THINGS-data, a multimodal collection of large-scale datasets for investigating object representations in human brain and behavior

eLife, Год журнала: 2023, Номер 12

Опубликована: Фев. 27, 2023

Understanding object representations requires a broad, comprehensive sampling of the objects in our visual world with dense measurements brain activity and behavior. Here, we present THINGS-data, multimodal collection large-scale neuroimaging behavioral datasets humans, comprising densely sampled functional MRI magnetoencephalographic recordings, as well 4.70 million similarity judgments response to thousands photographic images for up 1,854 concepts. THINGS-data is unique its breadth richly annotated objects, allowing testing countless hypotheses at scale while assessing reproducibility previous findings. Beyond insights promised by each individual dataset, multimodality allows combining much broader view into processing than previously possible. Our analyses demonstrate high quality provide five examples hypothesis-driven data-driven applications. constitutes core public release THINGS initiative (https://things-initiative.org) bridging gap between disciplines advancement cognitive neuroscience.

Язык: Английский

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Quantitative approaches to guide epilepsy surgery from intracranial EEG

Brain, Год журнала: 2023, Номер 146(6), С. 2248 - 2258

Опубликована: Янв. 8, 2023

Over the past 10 years, drive to improve outcomes from epilepsy surgery has stimulated widespread interest in methods quantitatively guide intracranial EEG (iEEG). Many patients fail achieve seizure freedom, part due challenges subjective iEEG interpretation. To address this clinical need, quantitative analytics have been developed using a variety of approaches, spanning studies seizures, interictal periods, and their transitions, encompass range techniques including electrographic signal analysis, dynamical systems modeling, machine learning graph theory. Unfortunately, many generalize new data are sensitive differences pathology electrode placement. Here, we critically review selected literature on computational identifying epileptogenic zone iEEG. We highlight shared methodological common field propose ways that they can be addressed. One fundamental pitfall is lack open-source, high-quality data, which specifically by sharing centralized high-quality, well-annotated, multicentre dataset consisting >100 support larger more rigorous studies. Ultimately, provide road map help these tools reach trials hope lives future patients.

Язык: Английский

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brainlife.io: a decentralized and open-source cloud platform to support neuroscience research

Nature Methods, Год журнала: 2024, Номер 21(5), С. 809 - 813

Опубликована: Апрель 11, 2024

Neuroscience is advancing standardization and tool development to support rigor transparency. Consequently, data pipeline complexity has increased, hindering FAIR (findable, accessible, interoperable reusable) access. brainlife.io was developed democratize neuroimaging research. The platform provides standardization, management, visualization processing automatically tracks the provenance history of thousands objects. Here, described evaluated for validity, reliability, reproducibility, replicability scientific utility using four modalities 3,200 participants.

Язык: Английский

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Brain tumor segmentation using synthetic MR images - A comparison of GANs and diffusion models

Scientific Data, Год журнала: 2024, Номер 11(1)

Опубликована: Фев. 29, 2024

Abstract Large annotated datasets are required for training deep learning models, but in medical imaging data sharing is often complicated due to ethics, anonymization and protection legislation. Generative AI such as generative adversarial networks (GANs) diffusion can today produce very realistic synthetic images, potentially facilitate sharing. However, order share images it must first be demonstrated that they used different with acceptable performance. Here, we therefore comprehensively evaluate four GANs (progressive GAN, StyleGAN 1–3) a model the task of brain tumor segmentation (using two networks, U-Net Swin transformer). Our results show trained on reach Dice scores 80%–90% when real memorization problem models if original dataset too small. conclusion viable option further work required. The generated shared AIDA hub.

Язык: Английский

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A survey on deep learning in medical image registration: New technologies, uncertainty, evaluation metrics, and beyond

Medical Image Analysis, Год журнала: 2024, Номер 100, С. 103385 - 103385

Опубликована: Ноя. 10, 2024

Язык: Английский

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Functional Connectivity of the Brain Across Rodents and Humans

Frontiers in Neuroscience, Год журнала: 2022, Номер 16

Опубликована: Март 8, 2022

Resting-state functional magnetic resonance imaging (rs-fMRI), which measures the spontaneous fluctuations in blood oxygen level-dependent (BOLD) signal, is increasingly utilized for investigation of brain's physiological and pathological activity. Rodents, as a typical animal model neuroscience, play an important role studies that examine neuronal processes underpin BOLD signal connectivity results. Translating this knowledge from rodents to humans requires basic similarities differences across species terms both resulting connectivity. This review begins by examining anatomical features, acquisition parameters, preprocessing techniques, factors contribute Homologous networks are compared species, aspects such topography global relationship between structural examined. Time-varying features connectivity, obtained sliding windowed approaches, quasi-periodic patterns, coactivation species. Applications demonstrating use rs-fMRI translational tool cross-species analysis discussed, with emphasis on neurological psychiatric disorders. Finally, open questions presented encapsulate future direction field.

Язык: Английский

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