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Open datasets and code for multi-scale relations on structure, function and neuro-genetics in the human brain.

ABSTRACT: The human brain is an extremely complex network of structural and functional connections that operate at multiple spatial and temporal scales. Investigating the relationship between these multi-scale connections is critical to advancing our comprehension of brain function and disorders. However, accurately predicting structural connectivity from its functional counterpart remains a challenging pursuit. One of the major impediments is the lack of public repositories that integrate structural and functional networks at diverse resolutions, in conjunction with modular transcriptomic profiles, which are essential for comprehensive biological interpretation. To mitigate this limitation, our contribution encompasses the provision of an open-access dataset consisting of derivative matrices of functional and structural connectivity across multiple scales, accompanied by code that facilitates the investigation of their interrelations. We also provide additional resources focused on neuro-genetic associations of module-level network metrics, which present promising opportunities to further advance research in the field of network neuroscience, particularly concerning brain disorders.

SUBMITTER: Jimenez-Marin A

PROVIDER: S-EPMC10904384 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature

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Open datasets and code for multi-scale relations on structure, function and neuro-genetics in the human brain.

Jimenez-Marin Antonio A Diez Ibai I Erramuzpe Asier A Stramaglia Sebastiano S Bonifazi Paolo P Cortes Jesus M JM

Scientific data 20240229 1

The human brain is an extremely complex network of structural and functional connections that operate at multiple spatial and temporal scales. Investigating the relationship between these multi-scale connections is critical to advancing our comprehension of brain function and disorders. However, accurately predicting structural connectivity from its functional counterpart remains a challenging pursuit. One of the major impediments is the lack of public repositories that integrate structural and ...[more]

PMID: 38424112

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Project description:ObjectiveA general psychopathology (p) factor captures shared variation across mental disorders. Structural neural alterations have been associated with the p factor concurrently, but less is known about whether these alterations relate to within-person change in the p factor over time, especially during preadolescence, a period of neurodevelopmental changes.MethodThis study examined whether baseline brain structure was prospectively related to the trajectory of the p factor and specific forms of psychopathology over 2 years in 9,220 preadolescents (aged 9-10 at baseline) from the Adolescent Brain Cognitive Development Study (ABCD). Longitudinal multilevel models were conducted to determine whether baseline brain structure (volume, surface area, thickness) was associated with between-person differences and within-person change in the p factor (from a higher-order confirmatory factor model) and internalizing, externalizing, neurodevelopmental, somatization, and detachment factor scores (from a correlated factors model) over 3 study waves.ResultsSmaller global volume and surface area, but not thickness, were associated with higher between-person levels of the p factor scores, which persisted over time. None of the brain structure measures were related to within-person change in the p factor scores. Lower baseline cortical thickness was associated with steeper decreases in internalizing psychopathology, which was driven by lower thickness within sensorimotor and temporal regions.ConclusionThese novel results identify specific brain structure features that might contribute to transdiagnostic psychopathology development in preadolescence. Children with smaller total brain volume and surface area may be vulnerable to persistent general psychopathology during preadolescence. Cortical thinning reflective of pruning and myelination in sensorimotor and temporal brain regions specifically may protect against increases in internalizing, but not general psychopathology, during preadolescence.

Project description:BackgroundEntity relation extraction technology can be used to extract entities and relations from medical literature, and automatically establish professional mapping knowledge domains. The classical text classification model, convolutional neural networks for sentence classification (TEXTCNN), has been shown to have good classification performance, but also has a long-distance dependency problem, which is a common problem of convolutional neural networks (CNNs). Recurrent neural networks (RNN) address the long-distance dependency problem but cannot capture text features at a specific scale in the text.MethodsTo solve these problems, this study sought to establish a model with a multi-scale convolutional recurrent neural network for Sentence Classification (TEXTCRNN) to address the deficiencies in the 2 neural network structures. In entity relation extraction, the entity pair is generally composed of a subject and an object, but as the subject in the entity pair of medical literature is always omitted, it is difficult to use this coding method to obtain general entity position information. Thus, we proposed a new coding method to obtain entity position information to re-establish the relationship between subject and object and complete the entity relation extraction.ResultsBy comparing the benchmark neural network model and 2 typical multi-scale TEXTCRNN models, the TEXTCRNN [bidirectional long- and short-term memory (BiLSTM)] and TEXTCRNN [double-layer stacking gated recurrent unit (GRU)], the results showed that the multi-scale CRNN model had the best F1 value performance, and the TEXTCRNN (double-layer stacking GRU) was more capable of entity relation classification when the same entity word did not belong to the same entity relation.ConclusionsThe experimental results of the entity relation extraction from Pharmacopoeia of the People's Republic of China-Guidelines for Clinical Drug Use-Volume of Chemical Drugs and Biological Products showed that entity relation extraction could effectively proceed using the new labeling method. Additionally, compared to typical neural network models, including the TEXTCNN, GRU, and BiLSTM, the multi-scale convolutional recurrent neural network structure had advantages across several evaluation indicators.

Dataset Information

Open datasets and code for multi-scale relations on structure, function and neuro-genetics in the human brain.

Publications

Open datasets and code for multi-scale relations on structure, function and neuro-genetics in the human brain.

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