Comparing univalent and bivalent brain functional connectivity measures using machine learning

Objectives: Delayed neurocognitive recovery (DNR) seriously affects the post-operative recovery of elderly surgical patients, but there is still a lack of effective methods to recognize high-risk patients with DNR. This study proposed a machine learning method based on a multi-order brain functional connectivity (FC) network to recognize DNR.Method: Seventy-four patients who completed assessments were included in this study, in which 16/74 (21.6%) had DNR following surgery. Based on resting-state functional magnetic resonance imaging (rs-fMRI), we first constructed low-order FC networks of 90 brain regions by calculating the correlation of brain region signal changing in the time dimension. Then, we established high-order FC networks by calculating correlations among each pair of brain regions. Afterward, we built sparse representation-based machine learning model to recognize DNR on the extracted multi-order FC network features. Finally, an independent testing was conducted to validate the established recognition model.Results: Three hundred ninety features of FC networks were finally extracted to identify DNR. After performing the independent-sample T test between these features and the categories, 15 features showed statistical differences (P < 0.05) and 3 features had significant statistical differences (P < 0.01). By comparing DNR and non-DNR patients’ brain region connection matrices, it is found that there are more connections among brain regions in DNR patients than in non-DNR patients. For the machine learning recognition model based on multi-feature combination, the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity of the classifier reached 95.61, 92.00, 66.67, and 100.00%, respectively.Conclusion: This study not only reveals the significance of preoperative rs-fMRI in recognizing post-operative DNR in elderly patients but also establishes a promising machine learning method to recognize DNR.

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Visual network alterations in brain functional connectivity in chronic low back pain: A resting state functional connectivity and machine learning study

NeuroImage Clinical ◽

10.1016/j.nicl.2019.101775 ◽

2019 ◽

Vol 22 ◽

pp. 101775 ◽

Cited By ~ 16

Author(s):

Wei Shen ◽

Yiheng Tu ◽

Randy L. Gollub ◽

Ana Ortiz ◽

Vitaly Napadow ◽

...

Keyword(s):

Machine Learning ◽

Low Back Pain ◽

Back Pain ◽

Functional Connectivity ◽

Resting State ◽

Learning Study ◽

Low Back ◽

Resting State Functional Connectivity ◽

Brain Functional Connectivity ◽

Visual Network

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T253. A Machine Learning Approach to Predict the Changes of Brain Functional Connectivity in Autism Spectrum Disorder From the Gene Expression Data

Biological Psychiatry ◽

10.1016/j.biopsych.2018.02.590 ◽

2018 ◽

Vol 83 (9) ◽

pp. S227-S228 ◽

Cited By ~ 1

Author(s):

Sanjeevani Choudhery ◽

Chuan Huang ◽

Daifeng Wang

Keyword(s):

Gene Expression ◽

Machine Learning ◽

Autism Spectrum Disorder ◽

Functional Connectivity ◽

Gene Expression Data ◽

Autism Spectrum ◽

Learning Approach ◽

Expression Data ◽

Machine Learning Approach ◽

Brain Functional Connectivity

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Localized prediction of glutamate from whole-brain functional connectivity of the pregenual anterior cingulate cortex

10.1101/2020.02.26.966259 ◽

2020 ◽

Author(s):

Louise Martens ◽

Nils B. Kroemer ◽

Vanessa Teckentrup ◽

Lejla Colic ◽

Nicola Palomero-Gallagher ◽

...

Keyword(s):

Machine Learning ◽

Functional Connectivity ◽

Anterior Cingulate Cortex ◽

Cingulate Cortex ◽

Anterior Cingulate ◽

Resonance Spectroscopy ◽

Learning Approaches ◽

Least Squares Regression ◽

Brain Functional Connectivity ◽

Spatial Specificity

AbstractLocal measures of neurotransmitters provide crucial insights into neurobiological changes underlying altered functional connectivity in psychiatric disorders. However, non-invasive neuroimaging techniques such as magnetic resonance spectroscopy (MRS) may cover anatomically and functionally distinct areas, such as p32 and p24 of the pregenual anterior cingulate cortex (pgACC). Here, we aimed to overcome this low spatial specificity of MRS by predicting local glutamate and GABA based on functional characteristics and neuroanatomy, using complementary machine learning approaches. Functional connectivity profiles of pgACC area p32 predicted pgACC glutamate better than chance (R2 = .324) and explained more variance compared to area p24 using both elastic net and partial least squares regression. In contrast, GABA could not be robustly predicted. To summarize, machine learning helps exploit the high resolution of fMRI to improve the interpretation of local neurometabolism. Our augmented multimodal imaging analysis can deliver novel insights into neurobiology by using complementary information.

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