Structural networks analysis for depression combined with graph theory and the properties of fiber tracts via diffusion tensor imaging

Zheng, Kaizhong; Wang, Huaning; Li, Jiaming; Yan, Baoyu; Liu, Jian; Xi, Yi‐Bin; Zhang, Xi; Yin, Hong; Tan, Qihua; Lu, Hongbing; Li, Baojuan

doi:10.1016/j.neulet.2018.11.025

Cited by 14 publications

(8 citation statements)

References 47 publications

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“…Our results in both the caudate-rMFG and hippocampus-PCC tracts are consistent with such pattern though not significant in PMC. On the other hand, reduced fiber numbers of the caudate-rMFG tract in our study might reflect loss of axons or myelination [53], indicating white matter tract degeneration [54]. Regarding functional changes, our results in the caudate-rMFG tract were also consistent with an inverted U-shape nonlinear pattern though not significant in SMC, probably due to a small sample size or early in the disease progression (short EYO) in SMC.…”

Section: Discussionsupporting

confidence: 58%

“…For the effect of gene mutation on the structure and function of the representative neural circuits, our results indicate that APP mutation has early deleterious effect on the structural integrity of frontostriatal neural circuits, while PS1 mutation has little or even compensatory effect on it. The detrimental effects of APP mutation on frontostriatal pathway include loss of axons or myelination, reduction in white matter integrity and disruption of white matter microstructures as measured by fiber numbers [53,54], FA [59], and MD [52], respectively. In addition, AxD and RD, the principal direction and perpendicular direction of the diffusion ellipsoid, have been shown to assess axonal integrity and myelin integrity, respectively [60].…”

Section: Discussionmentioning

confidence: 99%

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Effects of gene mutation and disease progression on representative neural circuits in familial Alzheimer’s disease

et al. 2020

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Background: Although structural and functional changes of the striatum and hippocampus are present in familial Alzheimer's disease, little is known about the effects of specific gene mutation or disease progression on their related neural circuits. This study was to evaluate the effects of known pathogenic gene mutation and disease progression on the striatum-and hippocampus-related neural circuits, including frontostriatal and hippocampusposterior cingulate cortex (PCC) pathways. Methods: A total of 102 healthy mutation non-carriers, 40 presymptomatic mutation carriers (PMC), and 30 symptomatic mutation carriers (SMC) of amyloid precursor protein (APP), presenilin 1 (PS1), or presenilin 2 gene, with T1 structural MRI, diffusion tensor imaging, and resting-state functional MRI were included. Representative neural circuits and their key nodes were obtained, including bilateral caudate-rostral middle frontal gyrus (rMFG), putamen-rMFG, and hippocampus-PCC. Volumes, diffusion indices, and functional connectivity of circuits were compared between groups and correlated with neuropsychological and clinical measures. Results: In PMC, APP gene mutation carriers showed impaired diffusion indices of caudate-rMFG and putamen-rMFG circuits; PS1 gene mutation carriers showed increased fiber numbers of putamen-rMFG circuit. SMC showed increased diffusivity of the left hippocampus-PCC circuit and volume reduction of all regions as compared with PMC. Imaging measures especially axial diffusivity of the representative circuits were correlated with neuropsychological measures. Conclusions: APP and PS1 gene mutations affect frontostriatal circuits in a different manner in familial Alzheimer's disease; disease progression primarily affects the structure of hippocampus-PCC circuit. The structural connectivity of both frontostriatal and hippocampus-PCC circuits is associated with general cognitive function. Such findings may provide further information about the imaging biomarkers for early identification and prognosis of familial Alzheimer's disease, and pave the way for early diagnosis, gene-or circuit-targeted treatment, and even prevention.

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Section: Discussionsupporting

confidence: 58%

Section: Discussionmentioning

confidence: 99%

Effects of gene mutation and disease progression on representative neural circuits in familial Alzheimer’s disease

et al. 2020

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“…To reduce dimensionality, global graph‐theoretic metrics are commonly derived from connectomes and analysed as coarse representations of the participants' brain networks (de Lange et al, 2019 ; Sun et al, 2017 ; Suo et al, 2018 ). A popular alternative technique is subnetwork analysis, where brain regions in the connectome are limited to those previously shown as relevant to the clinical traits or conditions of interest (Chen et al, 2016 ; Zheng et al, 2019 ). It is also common to employ feature selection to reduce dimensionality (Shen et al, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

Predicting sex, age, general cognition and mental health with machine learning on brain structural connectomes

Yeung

Stolicyn

Buchanan

et al. 2022

Human Brain Mapping

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There is an increasing expectation that advanced, computationally expensive machine learning (ML) techniques, when applied to large population‐wide neuroimaging datasets, will help to uncover key differences in the human brain in health and disease. We take a comprehensive approach to explore how multiple aspects of brain structural connectivity can predict sex, age, general cognitive function and general psychopathology, testing different ML algorithms from deep learning (DL) model (BrainNetCNN) to classical ML methods. We modelled N = 8183 structural connectomes from UK Biobank using six different structural network weightings obtained from diffusion MRI. Streamline count generally provided the highest prediction accuracies in all prediction tasks. DL did not improve on prediction accuracies from simpler linear models. Further, high correlations between gradient attribution coefficients from DL and model coefficients from linear models suggested the models ranked the importance of features in similar ways, which indirectly suggested the similarity in models' strategies for making predictive decision to some extent. This highlights that model complexity is unlikely to improve detection of associations between structural connectomes and complex phenotypes with the current sample size.

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“…To reduce dimensionality, global graph-theoretic metrics are commonly derived from connectomes and analysed as coarse representations of the participants’ brain networks (de Lange et al, 2019; Sun et al, 2017; Suo et al, 2018). A popular alternative technique is subnetwork analysis, where brain regions in the connectome are limited to those previously shown as relevant to the clinical traits or conditions of interest (Chen et al, 2016; Zheng et al, 2019). It is also common to employ feature selection to reduce dimensionality (Shen et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Predicting sex, age, general cognition and mental health with machine learning on brain structural connectomes

Yeung

Stolicyn

Buchanan

et al. 2022

Preprint

View full text Add to dashboard Cite

There is increasing expectation that advanced, computationally expensive machine learning techniques, when applied to large population-wide neuroimaging datasets, will help to uncover key differences in the human brain in health and disease. We take a comprehensive approach to explore how multiple aspects of brain structural connectivity can predict sex, age, general cognitive function and general psychopathology, testing different machine learning algorithms from deep learning model (BrainNetCNN) to classical machine learning methods. We modelled N = 8, 183 structural connectomes from UK Biobank using six different structural network weightings obtained from diffusion MRI. Streamline count generally provided highest prediction accuracies in all prediction tasks. Deep learning did not improve on prediction accuracies from simpler linear models. Further, high correlations between model coefficients from deep learning and linear models suggested similar decision mechanisms were used. This highlights that model complexity is unlikely to improve detection of associations between structural connectomes and complex phenotypes.

show abstract

Structural networks analysis for depression combined with graph theory and the properties of fiber tracts via diffusion tensor imaging

Cited by 14 publications

References 47 publications

Effects of gene mutation and disease progression on representative neural circuits in familial Alzheimer’s disease

Effects of gene mutation and disease progression on representative neural circuits in familial Alzheimer’s disease

Predicting sex, age, general cognition and mental health with machine learning on brain structural connectomes

Predicting sex, age, general cognition and mental health with machine learning on brain structural connectomes

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