Computational Psychiatry and Computational Neurology: Seeking for Mechanistic Modeling in Cognitive Impairment and Dementia

Kucikova, Ludmila; Danso, Samuel; Jia, Lina; Su, Li

doi:10.3389/fncom.2022.865805

Cited by 3 publications

(1 citation statement)

References 36 publications

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“…Alternatively, a combination of modalities or implementing less-utilised modalities (e.g., MEG) may improve our understanding of the contribution of resting-state connectivity changes in the DLB to the overall mechanisms of observable clinical symptoms, as well as providing solutions to overcome the issues arising from unstandardised analytical pipelines. Moreover, neuroimaging data can be used as real-life parameters in mechanistic models of neurodegenerative disorders as proposed in our recent work (Kucikova et al, 2022).…”

Section: Limitations and Future Directionsmentioning

confidence: 99%

A systematic literature review of fMRI and EEG resting-state functional connectivity in Dementia with Lewy Bodies: Underlying mechanisms, clinical manifestation, and methodological considerations

Kucikova,

Kalabizadeh,

Motsi

et al. 2024

Ageing Research Reviews

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Section: Limitations and Future Directionsmentioning

confidence: 99%

A systematic literature review of fMRI and EEG resting-state functional connectivity in Dementia with Lewy Bodies: Underlying mechanisms, clinical manifestation, and methodological considerations

Kucikova,

Kalabizadeh,

Motsi

et al. 2024

Ageing Research Reviews

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Artificial intelligence ecosystem for computational psychiatry: Ideas to practice

Liu¹,

Ji²,

Wang³

et al. 2023

World J Meta-Anal

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Convolutional Neural Network Model to Predict Outdoor Comfort UTCI Microclimate Map

Zhong

2022

Atmosphere

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Although research on applying machine learning to the performance of the built environment has been advancing considerably, outdoor environment prediction models still need to be more accurate. In this study, I investigated hybrid-driven methods for developing environmental performance prediction models and studied how machine learning algorithms may interpret spatial information in the context of an environmental performance simulation challenge. The simulation of the Universal Thermal Climate Index (UTCI) for outdoor applications served as an example. Specifically, I designed two different network structures, each with six neural network models. These neural network models were built with various numbers of layers, convolutional kernel sizes, and convolutional kernel layers. As shown by these models’ training results, I investigated the effect of model parameter settings on performance. In addition, I conducted interpretable analysis through the visual observation of hidden internal layers. The use of multilayer and small convolutional kernels, as well as an increase in the amount of training data, may be the reason neural network prediction performance was improved. From the perspective of interpretability analysis, the convolutional layer can more accurately analyze building space problems, and full connection layers focus more on the regression between the spatial features and performance results. This “space analysis → data regression” network structure can be expanded to wind environment forecasting or heat environment in the future.

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Computational Psychiatry and Computational Neurology: Seeking for Mechanistic Modeling in Cognitive Impairment and Dementia

Cited by 3 publications

References 36 publications

A systematic literature review of fMRI and EEG resting-state functional connectivity in Dementia with Lewy Bodies: Underlying mechanisms, clinical manifestation, and methodological considerations

A systematic literature review of fMRI and EEG resting-state functional connectivity in Dementia with Lewy Bodies: Underlying mechanisms, clinical manifestation, and methodological considerations

Artificial intelligence ecosystem for computational psychiatry: Ideas to practice

Convolutional Neural Network Model to Predict Outdoor Comfort UTCI Microclimate Map

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