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Exploring brain effective connectivity of early MCI with GRU_GC model on resting-state fMRI.

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Title: Exploring brain effective connectivity of early MCI with GRU_GC model on resting-state fMRI.
Authors: Wang, Lei (AUTHOR), Zeng, Weiming (AUTHOR), Zhao, Le (AUTHOR), Shi, Yuhu (AUTHOR)
Source: Applied Neuropsychology: Adult. Jan/Feb2026, Vol. 33 Issue 1, p295-306. 12p.
Subjects: Mild cognitive impairment, Functional magnetic resonance imaging, Functional connectivity, Granger causality test, Brain abnormalities, Diagnosis, Artificial neural networks
Abstract: Background: Investigating the functional interactions between different brain regions and revealing the transmission of information by computing brain connectivity have great potential and significance in the diagnosis of early Mild Cognitive Impairment (EMCI). Methods: The Granger causality with Gate Recurrent Unit (GRU_GC) model is a suitable method that allows the detection of a nonlinear causal relationship and solves the limitation of fixed time lag, which cannot be detected by the classical Granger method. The model can transmit time series signals with any transmission delay length, and the time series can be screened and learned through the gate model. Results: The classification experiment of 89 EMCI and 73 neurologically healthy controls (HC) shows that the accuracy reached 87.88%. Compared with multivariate variables GC (MVGC) and Long Short-Term Memory-based GC (LSTM_GC), the GRU_GC significantly improved the estimation of brain connectivity communication. Constructing a difference network to explore the brain effective connectivity between EMCI and HC. Conclusions: The GRU_GC can discover the abnormal brain regions, including the parahippocampal gyrus, the posterior cingulate gyrus. The method can be used in clinical applications as an effective brain connectivity analysis tool and provides auxiliary means for the medical diagnosis of EMCI. [ABSTRACT FROM AUTHOR]
Copyright of Applied Neuropsychology: Adult is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Label: Title
  Group: Ti
  Data: Exploring brain effective connectivity of early MCI with GRU_GC model on resting-state fMRI.
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  Data: <searchLink fieldCode="AR" term="%22Wang%2C+Lei%22">Wang, Lei</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zeng%2C+Weiming%22">Zeng, Weiming</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhao%2C+Le%22">Zhao, Le</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Shi%2C+Yuhu%22">Shi, Yuhu</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Applied+Neuropsychology%3A+Adult%22">Applied Neuropsychology: Adult</searchLink>. Jan/Feb2026, Vol. 33 Issue 1, p295-306. 12p.
– Name: Subject
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  Data: <searchLink fieldCode="DE" term="%22Mild+cognitive+impairment%22">Mild cognitive impairment</searchLink><br /><searchLink fieldCode="DE" term="%22Functional+magnetic+resonance+imaging%22">Functional magnetic resonance imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Functional+connectivity%22">Functional connectivity</searchLink><br /><searchLink fieldCode="DE" term="%22Granger+causality+test%22">Granger causality test</searchLink><br /><searchLink fieldCode="DE" term="%22Brain+abnormalities%22">Brain abnormalities</searchLink><br /><searchLink fieldCode="DE" term="%22Diagnosis%22">Diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Background: Investigating the functional interactions between different brain regions and revealing the transmission of information by computing brain connectivity have great potential and significance in the diagnosis of early Mild Cognitive Impairment (EMCI). Methods: The Granger causality with Gate Recurrent Unit (GRU_GC) model is a suitable method that allows the detection of a nonlinear causal relationship and solves the limitation of fixed time lag, which cannot be detected by the classical Granger method. The model can transmit time series signals with any transmission delay length, and the time series can be screened and learned through the gate model. Results: The classification experiment of 89 EMCI and 73 neurologically healthy controls (HC) shows that the accuracy reached 87.88%. Compared with multivariate variables GC (MVGC) and Long Short-Term Memory-based GC (LSTM_GC), the GRU_GC significantly improved the estimation of brain connectivity communication. Constructing a difference network to explore the brain effective connectivity between EMCI and HC. Conclusions: The GRU_GC can discover the abnormal brain regions, including the parahippocampal gyrus, the posterior cingulate gyrus. The method can be used in clinical applications as an effective brain connectivity analysis tool and provides auxiliary means for the medical diagnosis of EMCI. [ABSTRACT FROM AUTHOR]
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  Label:
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  Data: <i>Copyright of Applied Neuropsychology: Adult is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1080/23279095.2024.2330100
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        Text: English
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      – SubjectFull: Functional connectivity
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      – SubjectFull: Artificial neural networks
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              Text: Jan/Feb2026
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              Y: 2026
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