Brain Imaging Genomics: Integrated Analysis and Machine Learning

Proceedings of the IEEE, Journal Year: 2019, Volume and Issue: 108(1), P. 125 - 162

Published: Oct. 29, 2019

Brain imaging genomics is an emerging data science field, where integrated analysis of brain and data, often combined with other biomarker, clinical environmental performed to gain new insights into the phenotypic, genetic molecular characteristics as well their impact on normal disordered function behavior. It has enormous potential contribute significantly biomedical discoveries in science. Given increasingly important role statistical machine learning biomedicine rapidly growing literature genomics, we provide up-to-date comprehensive review methods for a practical discussion method selection various applications.

Language: Английский

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Neuroimaging in Functional Neurological Disorder: State of the Field and Research Agenda

NeuroImage Clinical, Journal Year: 2021, Volume and Issue: 30, P. 102623 - 102623

Published: Jan. 1, 2021

Functional neurological disorder (FND) was of great interest to early clinical neuroscience leaders. During the 20th century, neurology and psychiatry grew apart – leaving FND a borderland condition. Fortunately, renaissance has occurred in last two decades, fostered by increased recognition that is prevalent diagnosed using "rule-in" examination signs. The parallel use scientific tools bridge brain structure - function relationships helped refine an integrated biopsychosocial framework through which conceptualize FND. In particular, growing number quality neuroimaging studies variety methodologies have shed light on emerging pathophysiology This renewed with enhanced interdisciplinary collaborations, as illustrated new care models combining psychological physical therapies creation multidisciplinary society supporting knowledge dissemination field. Within this context, article summarizes output first International Neuroimaging Workgroup meeting, held virtually, June 17th, 2020 appraise state research field catalyze large-scale collaborations. We briefly summarize neural circuit FND, then detail approaches used date within core content areas: cohort characterization; control group considerations; task-based functional neuroimaging; resting-state networks; structural biomarkers symptom severity risk illness; predictors treatment response prognosis. Lastly, we outline neuroimaging-focused agenda elucidate aid development novel biologically psychologically-informed treatments.

Language: Английский

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

Nature Methods, Journal Year: 2024, Volume and Issue: 21(5), P. 809 - 813

Published: April 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.

Language: Английский

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Practical recommendations to conduct a neuroimaging meta‐analysis for neuropsychiatric disorders

Human Brain Mapping, Journal Year: 2019, Volume and Issue: 40(17), P. 5142 - 5154

Published: Aug. 4, 2019

Over the past decades, neuroimaging has become widely used to investigate structural and functional brain abnormality in neuropsychiatric disorders. The results of individual studies, however, are frequently inconsistent due small heterogeneous samples, analytical flexibility, publication bias toward positive findings. To consolidate emergent findings clinically useful insight, meta-analyses have been developed integrate studies identify areas that consistently involved pathophysiology particular However, it should be considered could also divergent heterogeneity search strategy, selection criteria, imaging modalities, behavioral tasks, number experiments, data organization methods, statistical analysis with different multiple comparison thresholds. Following an introduction problem concepts quantitative summaries findings, we propose practical recommendations for clinicians researchers conducting transparent methodologically sound meta-analyses. This help convergent regional

Language: Английский

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Subcortical shape alterations in major depressive disorder: Findings from the ENIGMA major depressive disorder working group

Human Brain Mapping, Journal Year: 2020, Volume and Issue: 43(1), P. 341 - 351

Published: March 21, 2020

Alterations in regional subcortical brain volumes have been investigated as part of the efforts an international consortium, ENIGMA, to identify reliable neural correlates major depressive disorder (MDD). Given that structures are comprised distinct subfields, we sought build significantly from prior work by precisely mapping localized MDD-related differences regions using shape analysis. In this meta-analysis ENIGMA-MDD working group, compared 1,781 patients with MDD and 2,953 healthy controls (CTL) on individual measures metrics (thickness surface area) seven bilateral structures: nucleus accumbens, amygdala, caudate, hippocampus, pallidum, putamen, thalamus. Harmonized data processing statistical analyses were conducted locally at each site, findings aggregated meta-analysis. Relative CTL, adolescent-onset (≤ 21 years) had lower thickness area subiculum, cornu ammonis (CA) 1 hippocampus basolateral amygdala (Cohen's d = -0.164 -0.180). first-episode MDD, recurrent CA1 -0.173 -0.184). Our results suggest previously reported MDD-associated volumetric may be specific subfields these shown sensitive effects stress, important implications for treatments based targets key clinical features.

Language: Английский

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Linking Brain Structure, Activity, and Cognitive Function through Computation

eNeuro, Journal Year: 2022, Volume and Issue: 9(2), P. ENEURO.0316 - 21.2022

Published: Feb. 25, 2022

Understanding the human brain is a "Grand Challenge" for 21st century research. Computational approaches enable large and complex datasets to be addressed efficiently, supported by artificial neural networks, modeling simulation. Dynamic generative multiscale models, which investigation of causation across scales are guided principles theories function, instrumental linking structure function. An example resource enabling such an integrated approach neuroscientific discovery BigBrain, spatially anchors tissue models data different ensures that data, making bridge both basic neuroscience medicine. Research at intersection neuroscience, computing robotics has potential advance neuro-inspired technologies taking advantage growing body insights into perception, plasticity learning. To render tools methods, theories, concepts interoperable, Human Brain Project (HBP) launched EBRAINS, digital research infrastructure, brings together transdisciplinary community researchers united quest understand brain, with fascinating perspectives societal benefits.

Language: Английский

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Virtual histology of multi-modal magnetic resonance imaging of cerebral cortex in young men

NeuroImage, Journal Year: 2020, Volume and Issue: 218, P. 116968 - 116968

Published: May 22, 2020

Neurobiology underlying inter-regional variations - across the human cerebral cortex in measures derived with multi-modal magnetic resonance imaging (MRI) is poorly understood. Here, we characterize a large number of such measures, including T1 and T2 relaxation times, myelin water fraction (MWF), T1w/T2w ratio, mean diffusivity (MD), fractional anisotropy (FA), magnetization transfer ratio (MTR) cortical thickness. We then employ virtual-histology approach relate these profiles to those cell-specific gene expression. Virtual histology revealed that most MRI-derived T1, time, MWF, MTR, FA thickness, are associated expression genes specific CA1 pyramidal cells; enriched biological processes related dendritic arborisation. In addition, MWF oligodendrocyte-specific gene-expression profiles, supporting their use as sensitive intra-cortical myelin. contributes more variance than oligodendrocyte profile, suggesting greater sensitivity These MRI associations may help provide framework for determining which sequences acquire studies neurobiological hypotheses.

Language: Английский

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Artificial intelligence for classification of temporal lobe epilepsy with ROI-level MRI data: A worldwide ENIGMA-Epilepsy study

NeuroImage Clinical, Journal Year: 2021, Volume and Issue: 31, P. 102765 - 102765

Published: Jan. 1, 2021

Artificial intelligence has recently gained popularity across different medical fields to aid in the detection of diseases based on pathology samples or imaging findings. Brain magnetic resonance (MRI) is a key assessment tool for patients with temporal lobe epilepsy (TLE). The role machine learning and artificial increase brain abnormalities TLE remains inconclusive. We used support vector (SV) deep (DL) models region interest (ROI-based) structural (n = 336) diffusion 863) MRI data from ("lesional") without ("non-lesional") radiographic features suggestive underlying hippocampal sclerosis multinational (multi-center) ENIGMA-Epilepsy consortium. Our showed that identify performed better similar (68-75%) compared lateralize side (56-73%, except structural-based) opposite pattern seen (67-75% diagnose vs. 83% lateralize). In other aspects, diffusion-based classification accuracies. were more accurate (68-76%) than stratified non-lesional (53-62%). Overall, SV DL similarly several instances which mildly outperformed DL. discuss relative performance these ROI-level implications future applications care.

Language: Английский

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Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research

Experimental Neurology, Journal Year: 2021, Volume and Issue: 339, P. 113608 - 113608

Published: Jan. 26, 2021

Language: Английский

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Mapping neurotransmitter systems to the structural and functional organization of the human neocortex

Research Square (Research Square), Journal Year: 2022, Volume and Issue: unknown

Published: Jan. 13, 2022

Abstract Neurotransmitter receptors support the propagation of signals in human brain. How receptor systems are situated within macroscale neuroanatomy and how they shape emergent function remains poorly understood, there exists no comprehensive atlas receptors. Here we collate positron emission tomography scans >1,200 healthy individuals to construct a whole-brain 3-D normative 18 transporters across 9 different neurotransmitter systems. We find that profiles align with structural connectivity mediate function, including neurophysiological oscillatory dynamics resting state hemodynamic functional connectivity. Using Neurosynth cognitive atlas, uncover topographic gradient overlapping distributions separates extrinsic intrinsic psychological processes. Finally, both expected novel associations between cortical thinning patterns 13 disorders. replicate all findings an independently collected autoradiography dataset. This work demonstrates chemoarchitecture shapes brain structure providing new direction for studying multi-scale organization.

Language: Английский

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