Computational models and machine-learning methods are increasingly valuable tools to shed light on the dynamics that govern information processing in the nervous system, as well as their disruption in pathological conditions. A variety of techniques has been used to understand how networks of neurons in the brain encode, elaborate and transmit information about the external world, and how this information influences decision-making and behavior. Structural and functional abnormalities in the above-mentioned networks can lead to a wide range of brain disorders. Recent advances in brain simulation and machine-learning techniques, together with progress in the neuroimaging field, have been essential for bridging the different spatial scales in the brain and uncovering the processes underlying cognitive, motor and behavioral impairment in neurodevelopmental and neurodegenerative disorders.
Understanding the computational mechanisms of neural processing and functional connectivity interrelationships is critical to uncovering patterns of clinical conditions, ultimately leading to superior diagnosis and treatment. However, providing effective diagnostic explanations has proven difficult for several reasons, including:
(1) the examination of neural disorders at different microscopic and macroscopic levels – neurotransmitters, single neurons or receptors, local networks of neurons, and large-scale brain networks, though applications of multiscale approaches are still limited;
(2) the limited interpretability of machine learning tools, which makes their application in clinical and translational settings unfeasible despite their effectiveness in the analysis of large-scale multimodal data; and
(3) a lack of non-invasive biomarkers of neural disorders, which are interpretable, validated on humans, and deployable on a large scale.
This Research Topic will include contributions from different sub-fields of computational neuroscience, aiming at overcoming the above-mentioned challenges.
The articles in this Research Topic will highlight both the recent advances and future challenges of brain modeling and computational methods to provide relevant insight into clinical and diagnostic questions on neurodevelopment and neurodegeneration.
Topics of interest include but are not limited to:
- Interpretable machine learning techniques for neuroimaging data analysis;
- Multi-scale modeling of neurodevelopmental disorders and neurodegeneration;
- Non-invasive interpretable biomarkers of neural-circuit parameters;
- Stochastic modeling of cascades of events leading to neurodegeneration;
- Computational analysis of cognitive impairment and motor abnormalities in neurodevelopmental disorders and neurodegeneration;
- Data integration and quantitative techniques to make large-scale clinical data more tractable;
- Quantification of disease impact on quality of life;
- Genetic factors and gene networks in development and aging;
- Impact of early vascular impairments on neurodegenerative diseases;
- Functional connectivity network disruptions in different life stages.
Computational models and machine-learning methods are increasingly valuable tools to shed light on the dynamics that govern information processing in the nervous system, as well as their disruption in pathological conditions. A variety of techniques has been used to understand how networks of neurons in the brain encode, elaborate and transmit information about the external world, and how this information influences decision-making and behavior. Structural and functional abnormalities in the above-mentioned networks can lead to a wide range of brain disorders. Recent advances in brain simulation and machine-learning techniques, together with progress in the neuroimaging field, have been essential for bridging the different spatial scales in the brain and uncovering the processes underlying cognitive, motor and behavioral impairment in neurodevelopmental and neurodegenerative disorders.
Understanding the computational mechanisms of neural processing and functional connectivity interrelationships is critical to uncovering patterns of clinical conditions, ultimately leading to superior diagnosis and treatment. However, providing effective diagnostic explanations has proven difficult for several reasons, including:
(1) the examination of neural disorders at different microscopic and macroscopic levels – neurotransmitters, single neurons or receptors, local networks of neurons, and large-scale brain networks, though applications of multiscale approaches are still limited;
(2) the limited interpretability of machine learning tools, which makes their application in clinical and translational settings unfeasible despite their effectiveness in the analysis of large-scale multimodal data; and
(3) a lack of non-invasive biomarkers of neural disorders, which are interpretable, validated on humans, and deployable on a large scale.
This Research Topic will include contributions from different sub-fields of computational neuroscience, aiming at overcoming the above-mentioned challenges.
The articles in this Research Topic will highlight both the recent advances and future challenges of brain modeling and computational methods to provide relevant insight into clinical and diagnostic questions on neurodevelopment and neurodegeneration.
Topics of interest include but are not limited to:
- Interpretable machine learning techniques for neuroimaging data analysis;
- Multi-scale modeling of neurodevelopmental disorders and neurodegeneration;
- Non-invasive interpretable biomarkers of neural-circuit parameters;
- Stochastic modeling of cascades of events leading to neurodegeneration;
- Computational analysis of cognitive impairment and motor abnormalities in neurodevelopmental disorders and neurodegeneration;
- Data integration and quantitative techniques to make large-scale clinical data more tractable;
- Quantification of disease impact on quality of life;
- Genetic factors and gene networks in development and aging;
- Impact of early vascular impairments on neurodegenerative diseases;
- Functional connectivity network disruptions in different life stages.
