Predicting brain structural network using functional connectivity

Zhang, Lu; Wang, Li; Zhu, Dajiang; Disease, Alzheimer’s

doi:10.1016/j.media.2022.102463

Cited by 33 publications

(10 citation statements)

References 54 publications

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“…Functional connectivity (FC), on the other hand, is commonly inferred through the correlation of nodal activities based on blood oxygenation level-dependent (BOLD) functional MRI (fMRI) or coherence analysis of electro- or magnetoencephalogram (EEG/MEG) signals acquired during task performance or in a resting state. In their work, Zhang et al ., 2022 provided a comprehensive summary of commonly used measurements for representing pairwise relationships between two fMRI signals. These measurements encompass correlation, partial correlation, covariance, and include both weighted and binary edges.…”

Section: Brain Connectomementioning

confidence: 99%

Exploring Alzheimer's disease: a comprehensive brain connectome-based survey

Zhang,

Qu,

et al. 2024

Psychoradiology

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Dementia is an escalating global health challenge, with Alzheimer's disease (AD) at its forefront. Substantial evidence highlights the accumulation of AD-related pathological proteins in specific brain regions and their subsequent dissemination throughout the broader area along the brain network, leading to disruptions in both individual brain regions and their interconnections. Although a comprehensive understanding of the neurodegeneration-brain network link is lacking, it's undeniable that brain networks play a pivotal role in the development and progression of AD. To thoroughly elucidate the intricate network of elements and connections constituting the human brain, the concept of the brain connectome was introduced. Research based on the connectome holds immense potential for revealing the mechanisms underlying disease development, and it has become a prominent topic that has attracted the attention of numerous researchers. In this review, we aim to systematically summarize studies on brain networks within the context of AD, critically analyze the strengths and weaknesses of existing methodologies, and offer novel perspectives and insights, intending to serve as inspiration for future research.

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Section: Brain Connectomementioning

confidence: 99%

Exploring Alzheimer's disease: a comprehensive brain connectome-based survey

Zhang,

Qu,

et al. 2024

Psychoradiology

Self Cite

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“…Using a multi-layer perceptron, Sarwar and colleagues (19) achieved a correlation between empirical and predicted functional connectivity of r = 0.9 for group-averaged networks and r = 0.55 ± 0.1 for individual networks. More recently, studies have sought to predict brain function from structural connectomes and vice versa using Reinmann Networks (21), Graph Convolutional Networks (20) and GANs (22). These studies report prediction accuracies comparable to Sarwar and colleagues.…”

Section: Discussionmentioning

confidence: 99%

“…Supervised learning approaches have been proven instrumental in overcoming the limitations of mechanistic models (19), (20), (21), (22). These approaches utilize a large data set of empirical structural and functional connectome pairs to learn the unidirectional mapping between them.…”

Section: Introductionmentioning

confidence: 99%

Inferring the Joint Distribution of Structural and Functional Connectivity in the Human Brain using UNIT-DDPM

Canamedi

2024

Preprint

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The structural wiring of the brain is expected to produce a repertoire of functional networks, across time, context, individuals and vice versa. Therefore, a method to infer the joint distribution of structural and functional connectomes would be of immense value. However, existing approaches only provide deterministic snapshots of the structure-function relationship. Here we use an unpaired image translation method, UNIT-DDPM, that infers a joint distribution of structural and functional connectomes. Our approach allows estimates of variability of function for a given structure and vice versa. Furthermore, we found a significant improvement in prediction accuracy among individual brain networks, implicating a tighter coupling of structure and function than previously understood. Also, our approach has the ad-vantage of not relying on paired samples for training. This novel approach provides a means for identifying regions of consistent structure-function coupling.

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“…This study developed hybrid S-F markers, considering the linear S-F association. However, recent studies demonstrated the potential of non-linear models like deep neural networks to reveal more complex S-F relationships (95,96). Moreover, there are a number of functional (e.g., amplitude of low-frequency fluctuations (ALFF)) and structural (e.g., structural connectivity) features that were not considered in our study.…”

Section: Limitations and Future Workmentioning

confidence: 99%

Assessment of resting state structural-functional relationships in perisylvian region during the early weeks after birth

Namiranian,

Moghaddam,

Khadem

et al. 2023

Preprint

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This study investigates the structural-functional (S-F) relationships of perisylvian region in the first weeks after birth during rest. Previous joint S-F studies of the perisylvian development were mainly conducted on individual structural and functional features and outperformed non-joint studies. By using a weighted combination of features, a joint S-F study can advance understanding of the perisylvian development in neonates, thereby offering valuable markers for predicting neurodevelopmental disorders. To this purpose, we used 390 structural and 89 functional features for each region of interest (ROI) in perisylvian. Structural features include morphological and myelination features of cortical regions, and functional features include functional connectivity (FC) between each ROI and other brain regions. Subsequently, the S-F hybrid marker for each ROI is produced using the sparse canonical correlation analysis (SCCA). In addition, the significance and consistency of the contribution of each structural and functional feature in the hybrid marker are examined using statistical tests. The findings indicated the correlation between the FC of each ROI with other brain regions, and the myelination of that ROI or other brain regions, while no correlation was observed between the FC and the morphological features of the cortical regions. Furthermore, in the hybrid markers, auditory regions show greater contributions of FC and myelination compared to higher-order regions such as Broca area, suggesting their early development in infants. Additionally, regions such as insula and cingulate gyrus have not only FC but also jointly S-F relationships with most of the perisylvian sub-regions. Therefore, the hybrid markers outperform individual joint markers by introducing additional S-F relationships for characterizing the normal development of the perisylvian region in neonates.

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Predicting brain structural network using functional connectivity

Cited by 33 publications

References 54 publications

Exploring Alzheimer's disease: a comprehensive brain connectome-based survey

Exploring Alzheimer's disease: a comprehensive brain connectome-based survey

Inferring the Joint Distribution of Structural and Functional Connectivity in the Human Brain using UNIT-DDPM

Assessment of resting state structural-functional relationships in perisylvian region during the early weeks after birth

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