The characteristic patterns of individual brain susceptibility networks underlie Alzheimer’s disease and white matter hyperintensity-related cognitive impairment
Abstract Excessive iron accumulation in the brain cortex increases the risk of cognitive deterioration. However, interregional relationships (defined as susceptibility connectivity) of local brain iron have not been explored, which could provide new insights into the underlying mechanisms of cogniti...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Publishing Group
2024-04-01
|
| Series: | Translational Psychiatry |
| Online Access: | https://doi.org/10.1038/s41398-024-02861-8 |
| _version_ | 1797219543143153664 |
|---|---|
| author | Haifeng Chen Jingxian Xu Weikai Li Zheqi Hu Zhihong Ke Ruomeng Qin Yun Xu |
| author_facet | Haifeng Chen Jingxian Xu Weikai Li Zheqi Hu Zhihong Ke Ruomeng Qin Yun Xu |
| author_sort | Haifeng Chen |
| collection | DOAJ |
| description | Abstract Excessive iron accumulation in the brain cortex increases the risk of cognitive deterioration. However, interregional relationships (defined as susceptibility connectivity) of local brain iron have not been explored, which could provide new insights into the underlying mechanisms of cognitive decline. Seventy-six healthy controls (HC), 58 participants with mild cognitive impairment due to probable Alzheimer’s disease (MCI-AD) and 66 participants with white matter hyperintensity (WMH) were included. We proposed a novel approach to construct a brain susceptibility network by using Kullback‒Leibler divergence similarity estimation from quantitative susceptibility mapping and further evaluated its topological organization. Moreover, sparse logistic regression (SLR) was applied to classify MCI-AD from HC and WMH with normal cognition (WMH-NC) from WMH with MCI (WMH-MCI).The altered susceptibility connectivity in the MCI-AD patients indicated that relatively more connectivity was involved in the default mode network (DMN)-related and visual network (VN)-related connectivity, while more altered DMN-related and subcortical network (SN)-related connectivity was found in the WMH-MCI patients. For the HC vs. MCI-AD classification, the features selected by the SLR were primarily distributed throughout the DMN-related and VN-related connectivity (accuracy = 76.12%). For the WMH-NC vs. WMH-MCI classification, the features with high appearance frequency were involved in SN-related and DMN-related connectivity (accuracy = 84.85%). The shared and specific patterns of the susceptibility network identified in both MCI-AD and WMH-MCI may provide a potential diagnostic biomarker for cognitive impairment, which could enhance the understanding of the relationships between brain iron burden and cognitive decline from a network perspective. |
| first_indexed | 2024-04-24T12:35:19Z |
| format | Article |
| id | doaj.art-9a265f9d687c4d3b9f623f9446b8f31c |
| institution | Directory Open Access Journal |
| issn | 2158-3188 |
| language | English |
| last_indexed | 2024-04-24T12:35:19Z |
| publishDate | 2024-04-01 |
| publisher | Nature Publishing Group |
| record_format | Article |
| series | Translational Psychiatry |
| spelling | doaj.art-9a265f9d687c4d3b9f623f9446b8f31c2024-04-07T11:31:49ZengNature Publishing GroupTranslational Psychiatry2158-31882024-04-0114111310.1038/s41398-024-02861-8The characteristic patterns of individual brain susceptibility networks underlie Alzheimer’s disease and white matter hyperintensity-related cognitive impairmentHaifeng Chen0Jingxian Xu1Weikai Li2Zheqi Hu3Zhihong Ke4Ruomeng Qin5Yun Xu6Nanjing Drum Tower Hospital Clinical College of Traditional Chinese and Western Medicine, Nanjing University of Chinese MedicineDepartment of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing UniversitySchool of Mathematics and Statistics, Chongqing Jiaotong UniversityDepartment of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing UniversityDepartment of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing UniversityDepartment of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing UniversityDepartment of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing UniversityAbstract Excessive iron accumulation in the brain cortex increases the risk of cognitive deterioration. However, interregional relationships (defined as susceptibility connectivity) of local brain iron have not been explored, which could provide new insights into the underlying mechanisms of cognitive decline. Seventy-six healthy controls (HC), 58 participants with mild cognitive impairment due to probable Alzheimer’s disease (MCI-AD) and 66 participants with white matter hyperintensity (WMH) were included. We proposed a novel approach to construct a brain susceptibility network by using Kullback‒Leibler divergence similarity estimation from quantitative susceptibility mapping and further evaluated its topological organization. Moreover, sparse logistic regression (SLR) was applied to classify MCI-AD from HC and WMH with normal cognition (WMH-NC) from WMH with MCI (WMH-MCI).The altered susceptibility connectivity in the MCI-AD patients indicated that relatively more connectivity was involved in the default mode network (DMN)-related and visual network (VN)-related connectivity, while more altered DMN-related and subcortical network (SN)-related connectivity was found in the WMH-MCI patients. For the HC vs. MCI-AD classification, the features selected by the SLR were primarily distributed throughout the DMN-related and VN-related connectivity (accuracy = 76.12%). For the WMH-NC vs. WMH-MCI classification, the features with high appearance frequency were involved in SN-related and DMN-related connectivity (accuracy = 84.85%). The shared and specific patterns of the susceptibility network identified in both MCI-AD and WMH-MCI may provide a potential diagnostic biomarker for cognitive impairment, which could enhance the understanding of the relationships between brain iron burden and cognitive decline from a network perspective.https://doi.org/10.1038/s41398-024-02861-8 |
| spellingShingle | Haifeng Chen Jingxian Xu Weikai Li Zheqi Hu Zhihong Ke Ruomeng Qin Yun Xu The characteristic patterns of individual brain susceptibility networks underlie Alzheimer’s disease and white matter hyperintensity-related cognitive impairment Translational Psychiatry |
| title | The characteristic patterns of individual brain susceptibility networks underlie Alzheimer’s disease and white matter hyperintensity-related cognitive impairment |
| title_full | The characteristic patterns of individual brain susceptibility networks underlie Alzheimer’s disease and white matter hyperintensity-related cognitive impairment |
| title_fullStr | The characteristic patterns of individual brain susceptibility networks underlie Alzheimer’s disease and white matter hyperintensity-related cognitive impairment |
| title_full_unstemmed | The characteristic patterns of individual brain susceptibility networks underlie Alzheimer’s disease and white matter hyperintensity-related cognitive impairment |
| title_short | The characteristic patterns of individual brain susceptibility networks underlie Alzheimer’s disease and white matter hyperintensity-related cognitive impairment |
| title_sort | characteristic patterns of individual brain susceptibility networks underlie alzheimer s disease and white matter hyperintensity related cognitive impairment |
| url | https://doi.org/10.1038/s41398-024-02861-8 |
| work_keys_str_mv | AT haifengchen thecharacteristicpatternsofindividualbrainsusceptibilitynetworksunderliealzheimersdiseaseandwhitematterhyperintensityrelatedcognitiveimpairment AT jingxianxu thecharacteristicpatternsofindividualbrainsusceptibilitynetworksunderliealzheimersdiseaseandwhitematterhyperintensityrelatedcognitiveimpairment AT weikaili thecharacteristicpatternsofindividualbrainsusceptibilitynetworksunderliealzheimersdiseaseandwhitematterhyperintensityrelatedcognitiveimpairment AT zheqihu thecharacteristicpatternsofindividualbrainsusceptibilitynetworksunderliealzheimersdiseaseandwhitematterhyperintensityrelatedcognitiveimpairment AT zhihongke thecharacteristicpatternsofindividualbrainsusceptibilitynetworksunderliealzheimersdiseaseandwhitematterhyperintensityrelatedcognitiveimpairment AT ruomengqin thecharacteristicpatternsofindividualbrainsusceptibilitynetworksunderliealzheimersdiseaseandwhitematterhyperintensityrelatedcognitiveimpairment AT yunxu thecharacteristicpatternsofindividualbrainsusceptibilitynetworksunderliealzheimersdiseaseandwhitematterhyperintensityrelatedcognitiveimpairment AT haifengchen characteristicpatternsofindividualbrainsusceptibilitynetworksunderliealzheimersdiseaseandwhitematterhyperintensityrelatedcognitiveimpairment AT jingxianxu characteristicpatternsofindividualbrainsusceptibilitynetworksunderliealzheimersdiseaseandwhitematterhyperintensityrelatedcognitiveimpairment AT weikaili characteristicpatternsofindividualbrainsusceptibilitynetworksunderliealzheimersdiseaseandwhitematterhyperintensityrelatedcognitiveimpairment AT zheqihu characteristicpatternsofindividualbrainsusceptibilitynetworksunderliealzheimersdiseaseandwhitematterhyperintensityrelatedcognitiveimpairment AT zhihongke characteristicpatternsofindividualbrainsusceptibilitynetworksunderliealzheimersdiseaseandwhitematterhyperintensityrelatedcognitiveimpairment AT ruomengqin characteristicpatternsofindividualbrainsusceptibilitynetworksunderliealzheimersdiseaseandwhitematterhyperintensityrelatedcognitiveimpairment AT yunxu characteristicpatternsofindividualbrainsusceptibilitynetworksunderliealzheimersdiseaseandwhitematterhyperintensityrelatedcognitiveimpairment |