Computing personalized brain functional networks from fMRI using self-supervised deep learning

Li, Hongming; Dhivya, S.; Zhuo, Chuanjun; Cui, Zaixu; Gur, Raquel E.; Gur, Ruben; Oathes, Desmond J.; Davatzikos, Christos; Satterthwaite, Theodore D.; Fan, Yong

doi:10.1016/j.media.2023.102756

Cited by 18 publications

(24 citation statements)

References 69 publications

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“…Tractography projection [35] Only used for dMRI data, relying on reference atlas to offer seed brain regions for tractography between cortical and subcortical areas Figure 1: tractography projection Decomposition [36,37] Mostly used for fMRI data, utilizing a group of subjects to build the grouplevel reference components and then projecting it onto individual subjects Figure 1: decomposition Exemplar-based clustering [38,39] Mostly used for fMRI data, utilizing a group of subjects to build the group-level exemplar map and then performing af nity propagation clustering for individuals Boundary iterative adjustment [40,41] Not limited to data modalities, utilizing a group of subjects to build a reference probability atlas and then iteratively adjusting the region boundary in individuals until convergence Probabilistic modeling [42,43] Mostly used for fMRI data, utilizing a group of subjects to optimize inter-subject, intra-subject, and inter-region variability and to build the individual atlas Deep learning [44,45] Not limited to data modalities, utilizing a group of subjects to train an individualized brain mapping model and then predicting the parcellation pattern for individuals points simultaneously. Thus, the parcellation granularity of the region growing method is determined by the number of seed points.…”

Section: Group Prior-guided Individualized Brain Mappingmentioning

confidence: 99%

“…[37] More recently, an autoencoder model has been employed to extract group-level components in a non-linear manner, providing an individualized brain atlas with exible parcellation granularities. [45] Exemplar-based clustering builds upon traditional individual clustering while incorporating prior information from reference atlases [Figure 1, Table 1]. Researchers have used unsupervised learning-based individual clustering methods to construct an individualized brain atlas based on data from a speci c subject; however, this approach may produce an individualized brain atlas that is sensitive to initial centroids and cluster numbers.…”

Section: Group Prior-guided Individualized Brain Mappingmentioning

confidence: 99%

“…[19] In addition to the research described above, recent studies have used decomposition or deep learning methods [Figure 2] to obtain an individualized functional network-for instance, using non-negative matrix decomposition to divide the brain network into meaningful functional networks at the individual level [37] or computing the individualized functional networks utilizing a deep learning method. [45] In addition to simulating electric elds, various methods employ the use of BOLD signal [74] and EEG signal [75] to gauge the effects of TMS on cortical tissue. These methods primarily consider that TMS stimulation not only directly affects the stimulated area but also ripples through the entire brain network.…”

Section: Tms Targeting Based On Brain Networkmentioning

confidence: 99%

See 2 more Smart Citations

Individualized brain mapping for navigated neuromodulation

Gao,

Wu,

Cheng

et al. 2024

Chinese Medical Journal

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The brain is a complex organ that requires precise mapping to understand its structure and function. Brain atlases provide a powerful tool for studying brain circuits, discovering biological markers for early diagnosis, and developing personalized treatments for neuropsychiatric disorders. Neuromodulation techniques, such as transcranial magnetic stimulation and deep brain stimulation, have revolutionized clinical therapies for neuropsychiatric disorders. However, the lack of fine-scale brain atlases limits the precision and effectiveness of these techniques. Advances in neuroimaging and machine learning techniques have led to the emergence of stereotactic-assisted neurosurgery and navigation systems. Still, the individual variability among patients and the diversity of brain diseases make it necessary to develop personalized solutions. The article provides an overview of recent advances in individualized brain mapping and navigated neuromodulation and discusses the methodological profiles, advantages, disadvantages, and future trends of these techniques. The article concludes by posing open questions about the future development of individualized brain mapping and navigated neuromodulation.

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Section: Group Prior-guided Individualized Brain Mappingmentioning

confidence: 99%

Section: Group Prior-guided Individualized Brain Mappingmentioning

confidence: 99%

Section: Tms Targeting Based On Brain Networkmentioning

confidence: 99%

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Individualized brain mapping for navigated neuromodulation

Gao,

Wu,

Cheng

et al. 2024

Chinese Medical Journal

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“…Generally, current approaches to individualized brain parcellation can be broadly categorized into three classes: group atlas registration (GAreg; Evans et al, 2012), unsupervised learning-based parcellation (Eickhoff et al, 2018), and prior-guided parcellation (PG-par;Cui et al, 2020;Kong et al, 2019;Li et al, 2023;Ma et al, 2022;Salehi et al, 2018;Wang et al, 2015). GA-reg methods assume that different In this study, we introduce a prior-guided individualized thalamic parcellation method.…”

Section: Introductionmentioning

confidence: 99%

Prior‐guided individualized thalamic parcellation based on local diffusion characteristics

Gao,

Wu,

Wang

et al. 2024

Human Brain Mapping

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Comprising numerous subnuclei, the thalamus intricately interconnects the cortex and subcortex, orchestrating various facets of brain functions. Extracting personalized parcellation patterns for these subnuclei is crucial, as different thalamic nuclei play varying roles in cognition and serve as therapeutic targets for neuromodulation. However, accurately delineating the thalamic nuclei boundary at the individual level is challenging due to intersubject variability. In this study, we proposed a prior‐guided parcellation (PG‐par) method to achieve robust individualized thalamic parcellation based on a central‐boundary prior. We first constructed probabilistic atlas of thalamic nuclei using high‐quality diffusion MRI datasets based on the local diffusion characteristics. Subsequently, high‐probability voxels in the probabilistic atlas were utilized as prior guidance to train unique multiple classification models for each subject based on a multilayer perceptron. Finally, we employed the trained model to predict the parcellation labels for thalamic voxels and construct individualized thalamic parcellation. Through a test–retest assessment, the proposed prior‐guided individualized thalamic parcellation exhibited excellent reproducibility and the capacity to detect individual variability. Compared with group atlas registration and individual clustering parcellation, the proposed PG‐par demonstrated superior parcellation performance under different scanning protocols and clinic settings. Furthermore, the prior‐guided individualized parcellation exhibited better correspondence with the histological staining atlas. The proposed prior‐guided individualized thalamic parcellation method contributes to the personalized modeling of brain parcellation.

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“…Although this approach can incorporate similarity of views into the learning algorithm, it cannot reveal the shared structure across views. This may be important in certain applications such as network neuroscience, where both the subject level connectomes as well as a group connectome that summarizes what is common across subjects for a given task are essential to identify individual variation [30].…”

Section: Introductionmentioning

confidence: 99%

Multiview Graph Learning for single-cell RNA sequencing data

Karaaslanli

Saha

Aviyente

et al. 2021

Preprint

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Characterizing the underlying topology of gene regulatory networks is one of the fundamental problems of systems biology. Ongoing developments in high throughput sequencing technologies has made it possible to capture the expression of thousands of genes at the single cell resolution. However, inherent cellular heterogeneity and high sparsity of the single cell datasets render void the application of regular Gaussian assumptions for constructing gene regulatory networks. Additionally, most algorithms aimed at single cell gene regulatory network reconstruction, estimate a single network ignoring group-level (cell-type) information present within the datasets. To better characterize single cell gene regulatory networks under different but related conditions we propose the joint estimation of multiple networks using multiview graph learning (mvGL). The proposed method is developed based on recent works in graph signal processing (GSP) for graph learning, where graph signals are assumed to be smooth over the unknown graph structure. Graphs corresponding to the different datasets are regularized to be similar to each other through a learned consensus graph. We further kernelize mvGL with the kernel selected to suit the structure of single cell data. An efficient algorithm based on prox-linear block coordinate descent is used to optimize mvGL. We study the performance of mvGL using synthetic data generated with a diverse set of parameters. We further show that mvGL successfully identifies well-established regulators in a mouse embryonic stem cell differentiation study and a cancer clinical study of medulloblastoma.

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Computing personalized brain functional networks from fMRI using self-supervised deep learning

Cited by 18 publications

References 69 publications

Individualized brain mapping for navigated neuromodulation

Individualized brain mapping for navigated neuromodulation

Prior‐guided individualized thalamic parcellation based on local diffusion characteristics

Multiview Graph Learning for single-cell RNA sequencing data

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