New insights into atypical Alzheimer's disease in the era of biomarkers

The Lancet Neurology, Journal Year: 2021, Volume and Issue: 20(3), P. 222 - 234

Published: Feb. 18, 2021

Language: Английский

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Common genetic variation influencing human white matter microstructure

Science, Journal Year: 2021, Volume and Issue: 372(6548)

Published: June 17, 2021

Connecting the dots on white matter The of brain, which is composed axonal tracts connecting different brain regions, plays key roles in both normal function and a variety neurological disorders. Zhao et al. combined detailed magnetic resonance imaging–based assessment structures with genetic data nearly 44,000 individuals (see Perspective by Filley). On basis this comprehensive analysis, authors identified structural abnormalities associated psychiatric disorders, as well some nondisease traits, thus creating valuable resource providing insights into underlying neurobiology. Science , abf3736, issue p. eabf3736 ; see also abj1881, 1265

Language: Английский

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Graph Neural Networks in Network Neuroscience

IEEE Transactions on Pattern Analysis and Machine Intelligence, Journal Year: 2022, Volume and Issue: 45(5), P. 5833 - 5848

Published: Sept. 26, 2022

Noninvasive medical neuroimaging has yielded many discoveries about the brain connectivity. Several substantial techniques mapping morphological, structural and functional connectivities were developed to create a comprehensive road map of neuronal activities in human -namely graph. Relying on its non-euclidean data type, graph neural network (GNN) provides clever way learning deep structure it is rapidly becoming state-of-the-art leading enhanced performance various neuroscience tasks. Here we review current GNN-based methods, highlighting ways that they have been used several applications related graphs such as missing synthesis disease classification. We conclude by charting path toward better application GNN models field for neurological disorder diagnosis population integration. The list papers cited our work available at https://github.com/basiralab/GNNs-in-Network-Neuroscience.

Language: Английский

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Toward a neurocircuit-based taxonomy to guide treatment of obsessive–compulsive disorder

Molecular Psychiatry, Journal Year: 2021, Volume and Issue: 26(9), P. 4583 - 4604

Published: Jan. 7, 2021

Language: Английский

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Toward a connectivity gradient-based framework for reproducible biomarker discovery

NeuroImage, Journal Year: 2020, Volume and Issue: 223, P. 117322 - 117322

Published: Sept. 1, 2020

Despite myriad demonstrations of feasibility, the high dimensionality fMRI data remains a critical barrier to its utility for reproducible biomarker discovery. Recent efforts address this challenge have capitalized on reduction techniques applied resting-state fMRI, identifying principal components intrinsic connectivity which describe smooth transitions across different cortical systems, so called "connectivity gradients". These gradients recapitulate neurocognitively meaningful organizational principles that are present in both human and primate brains, also appear differ among individuals clinical populations. Here, we provide assessment suitability Using Human Connectome Project (discovery subsample=209; two replication subsamples= 209 × 2) Midnight scan club (n = 9), tested following key traits - reliability, reproducibility predictive validity functional gradients. In doing so, systematically assessed effects three analytical settings, including i) algorithms (i.e., linear vs. non-linear methods), ii) input types raw time series, [un-]thresholded connectivity), iii) amount (resting-state time-series lengths). We found subsamples is generally higher those explaining more variances whole-brain data, as well having reliability. Notably, (principal component analysis our study), conservatively thresholded (e.g., 95-97%) longer (at least ≥20mins) was be preferential conditions obtain Those with reliability were able predict unseen phenotypic scores accuracy, highlighting prerequisite validity. Importantly, prediction accuracy exceeded observed traditional edge-based measures, suggesting added value low-dimensional multivariate gradient approach. Finally, work highlights importance benefits exploring parameter space new imaging methods before widespread deployment.

Language: Английский

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Brain network dynamics during working memory are modulated by dopamine and diminished in schizophrenia

Nature Communications, Journal Year: 2021, Volume and Issue: 12(1)

Published: June 9, 2021

Abstract Dynamical brain state transitions are critical for flexible working memory but the network mechanisms incompletely understood. Here, we show that performance entails brain-wide switching between activity states using a combination of functional magnetic resonance imaging in healthy controls and individuals with schizophrenia, pharmacological fMRI, genetic analyses control theory. The stability relates to dopamine D1 receptor gene expression while influenced by D2 modulation. Individuals schizophrenia altered properties, including more diverse energy landscape decreased representations. Our results demonstrate relevance signaling steering whole-brain dynamics during link these processes pathophysiology.

Language: Английский

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Brain network communication: concepts, models and applications

Nature reviews. Neuroscience, Journal Year: 2023, Volume and Issue: 24(9), P. 557 - 574

Published: July 12, 2023

Language: Английский

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Inflammation-Related Functional and Structural Dysconnectivity as a Pathway to Psychopathology

Biological Psychiatry, Journal Year: 2022, Volume and Issue: 93(5), P. 405 - 418

Published: Nov. 9, 2022

Language: Английский

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Local molecular and global connectomic contributions to cross-disorder cortical abnormalities

Nature Communications, Journal Year: 2022, Volume and Issue: 13(1)

Published: Aug. 10, 2022

Abstract Numerous brain disorders demonstrate structural abnormalities, which are thought to arise from molecular perturbations or connectome miswiring. The unique and shared contributions of these connectomic vulnerabilities remain unknown, has yet be studied in a single multi-disorder framework. Using MRI morphometry the ENIGMA consortium, we construct maps cortical abnormalities for thirteen neurodevelopmental, neurological, psychiatric N = 21,000 participants 26,000 controls, collected using harmonised processing protocol. We systematically compare multiple micro-architectural measures, including gene expression, neurotransmitter density, metabolism, myelination (molecular vulnerability), as well global measures number connections, centrality, connection diversity (connectomic vulnerability). find relationship between vulnerability white-matter architecture that drives disorder profiles. Local attributes, particularly receptor profiles, constitute best predictors both disorder-specific morphology cross-disorder similarity. Finally, consistently subtended by small subset network epicentres bilateral sensory-motor, inferior temporal lobe, precuneus, superior parietal cortex. Collectively, our results highlight how local attributes connectivity jointly shape abnormalities.

Language: Английский

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Brain connectomics: time for a molecular imaging perspective?

Trends in Cognitive Sciences, Journal Year: 2023, Volume and Issue: 27(4), P. 353 - 366

Published: Jan. 6, 2023

Language: Английский

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