1
|
Huang W, Dong X, Zhao T, Kucikova L, Fu A, Shu N. DCP: A pipeline toolbox for diffusion connectome. Hum Brain Mapp 2024; 45:e26626. [PMID: 38375916 PMCID: PMC10877999 DOI: 10.1002/hbm.26626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 12/29/2023] [Accepted: 02/02/2024] [Indexed: 02/21/2024] Open
Abstract
The brain structural network derived from diffusion magnetic resonance imaging (dMRI) reflects the white matter connections between brain regions, which can quantitatively describe the anatomical connection pattern of the entire brain. The development of structural brain connectome leads to the emergence of a large number of dMRI processing packages and network analysis toolboxes. However, the fully automated network analysis based on dMRI data remains challenging. In this study, we developed a cross-platform MATLAB toolbox named "Diffusion Connectome Pipeline" (DCP) for automatically constructing brain structural networks and calculating topological attributes of the networks. The toolbox integrates a few developed packages, including FSL, Diffusion Toolkit, SPM, Camino, MRtrix3, and MRIcron. It can process raw dMRI data collected from any number of participants, and it is also compatible with preprocessed files from public datasets such as HCP and UK Biobank. Moreover, a friendly graphical user interface allows users to configure their processing pipeline without any programming. To prove the capacity and validity of the DCP, two tests were conducted with using DCP. The results showed that DCP can reproduce the findings in our previous studies. However, there are some limitations of DCP, such as relying on MATLAB and being unable to fixel-based metrics weighted network. Despite these limitations, overall, the DCP software provides a standardized, fully automated computational workflow for white matter network construction and analysis, which is beneficial for advancing future human brain connectomics application research.
Collapse
Affiliation(s)
- Weijie Huang
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingPR China
- School of Systems Science, Beijing Normal UniversityBeijingPR China
- Department of NeuroscienceSheffield Institute for Translational Neuroscience, Medical School and Insigneo Institute for in Silico Medicine, University of SheffieldSheffieldUK
| | - Xinyi Dong
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingPR China
| | - Tengda Zhao
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingPR China
| | - Ludmila Kucikova
- Department of NeuroscienceSheffield Institute for Translational Neuroscience, Medical School and Insigneo Institute for in Silico Medicine, University of SheffieldSheffieldUK
| | - Anguo Fu
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingPR China
| | - Ni Shu
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingPR China
| |
Collapse
|
2
|
Kucikova L, Kalabizadeh H, Motsi KG, Rashid S, O'Brien JT, Taylor JP, Su L. A systematic literature review of fMRI and EEG resting-state functional connectivity in Dementia with Lewy Bodies: Underlying mechanisms, clinical manifestation, and methodological considerations. Ageing Res Rev 2024; 93:102159. [PMID: 38056505 DOI: 10.1016/j.arr.2023.102159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 08/14/2023] [Accepted: 12/01/2023] [Indexed: 12/08/2023]
Abstract
Previous studies suggest that there may be important links between functional connectivity, disease mechanisms underpinning the Dementia with Lewy Body (DLB) and the key clinical symptoms, but the exact relationship remains unclear. We performed a systematic literature review to address this gap by summarising the research findings while critically considering the impact of methodological differences on findings. The main methodological choices of fMRI articles included data-driven, seed-based or regions of interest approaches, or their combinations. Most studies focused on examining large-scale resting-state networks, which revealed a consistent decrease in connectivity and some associations with non-cognitive symptoms. Although the inter-network connectivity showed mixed results, the main finding is consistent with theories positing disconnection between visual and attentional areas of the brain implicated in the aetiology of psychotic symptoms in the DLB. The primary methodological choice of EEG studies was implementing the phase lag index and using graph theory. The EEG studies revealed a consistent decrease in connectivity on alpha and beta frequency bands. While the overall trend of findings showed decreased connectivity, more subtle changes in the directionality of connectivity were observed when using a hypothesis-driven approach. Problems with cognition were also linked with greater functional connectivity disturbances. In summary, connectivity measures can capture brain disturbances in the DLB and remain crucial in uncovering the causal relationship between the networks' disorganisation and underlying mechanisms resulting in psychotic, motor, and cognitive symptoms of the DLB.
Collapse
Affiliation(s)
- Ludmila Kucikova
- Neuroscience Institute, University of Sheffield, Sheffield, United Kingdom; Insigneo Institute for in Silico Medicine, University of Sheffield, Sheffield, United Kingdom
| | - Hoda Kalabizadeh
- Oxford Machine Learning in NeuroImaging Lab, OMNI, Department of Computer Science, University of Oxford, Oxford, United Kingdom
| | | | - Sidrah Rashid
- Academic Unit of Medical Education, University of Sheffield, Sheffield, United Kingdom
| | - John T O'Brien
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
| | - John-Paul Taylor
- Institute of Neuroscience, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, United Kingdom
| | - Li Su
- Neuroscience Institute, University of Sheffield, Sheffield, United Kingdom; Insigneo Institute for in Silico Medicine, University of Sheffield, Sheffield, United Kingdom; Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom.
| |
Collapse
|
3
|
Kucikova L, Zeng J, Muñoz-Neira C, Muniz-Terrera G, Huang W, Gregory S, Ritchie C, O'Brien J, Su L. Genetic risk factors of Alzheimer's Disease disrupt resting-state functional connectivity in cognitively intact young individuals. J Neurol 2023; 270:4949-4958. [PMID: 37358635 PMCID: PMC10511575 DOI: 10.1007/s00415-023-11809-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 06/05/2023] [Accepted: 06/06/2023] [Indexed: 06/27/2023]
Abstract
BACKGROUND Past evidence shows that changes in functional brain connectivity in multiple resting-state networks occur in cognitively healthy individuals who have non-modifiable risk factors for Alzheimer's Disease. Here, we aimed to investigate how those changes differ in early adulthood and how they might relate to cognition. METHODS We investigated the effects of genetic risk factors of AD, namely APOEe4 and MAPTA alleles, on resting-state functional connectivity in a cohort of 129 cognitively intact young adults (aged 17-22 years). We used Independent Component Analysis to identify networks of interest, and Gaussian Random Field Theory to compare connectivity between groups. Seed-based analysis was used to quantify inter-regional connectivity strength from the clusters that exhibited significant between-group differences. To investigate the relationship with cognition, we correlated the connectivity and the performance on the Stroop task. RESULTS The analysis revealed a decrease in functional connectivity in the Default Mode Network (DMN) in both APOEe4 carriers and MAPTA carriers in comparison with non-carriers. APOEe4 carriers showed decreased connectivity in the right angular gyrus (size = 246, p-FDR = 0.0079), which was correlated with poorer performance on the Stroop task. MAPTA carriers showed decreased connectivity in the left middle temporal gyrus (size = 546, p-FDR = 0.0001). In addition, we found that only MAPTA carriers had a decreased connectivity between the DMN and multiple other brain regions. CONCLUSIONS Our findings indicate that APOEe4 and MAPTA alleles modulate brain functional connectivity in the brain regions within the DMN in cognitively intact young adults. APOEe4 carriers also showed a link between connectivity and cognition.
Collapse
Affiliation(s)
- Ludmila Kucikova
- Department of Neuroscience, Faculty of Medicine, Dentistry and Heath, Sheffield Institute for Translational Neuroscience, University of Sheffield, 385a Glossop Road, Sheffield, S10 2HQ, SY, UK
- Insigneo Institute for in Silico Medicine, University of Sheffield, Sheffield, UK
| | - Jianmin Zeng
- Sino-Britain Centre for Cognition and Ageing Research, Faculty of Psychology, Southwest University, Chongqing, China.
| | - Carlos Muñoz-Neira
- Department of Neuroscience, Faculty of Medicine, Dentistry and Heath, Sheffield Institute for Translational Neuroscience, University of Sheffield, 385a Glossop Road, Sheffield, S10 2HQ, SY, UK
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Graciela Muniz-Terrera
- Edinburgh Dementia Prevention, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Ohio University Heritage College of Osteopathic Medicine, Ohio University, Athens, OH, USA
| | - Weijie Huang
- Department of Neuroscience, Faculty of Medicine, Dentistry and Heath, Sheffield Institute for Translational Neuroscience, University of Sheffield, 385a Glossop Road, Sheffield, S10 2HQ, SY, UK
- Insigneo Institute for in Silico Medicine, University of Sheffield, Sheffield, UK
- School of Systems Science, Beijing Normal University, Beijing, China
| | - Sarah Gregory
- Edinburgh Dementia Prevention, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Craig Ritchie
- Edinburgh Dementia Prevention, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Scottish Brain Sciences, Edinburgh, UK
| | - John O'Brien
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Li Su
- Department of Neuroscience, Faculty of Medicine, Dentistry and Heath, Sheffield Institute for Translational Neuroscience, University of Sheffield, 385a Glossop Road, Sheffield, S10 2HQ, SY, UK.
- Insigneo Institute for in Silico Medicine, University of Sheffield, Sheffield, UK.
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK.
| |
Collapse
|
4
|
Kucikova L, Danso S, Jia L, Su L. Computational Psychiatry and Computational Neurology: Seeking for Mechanistic Modeling in Cognitive Impairment and Dementia. Front Comput Neurosci 2022; 16:865805. [PMID: 35645752 PMCID: PMC9130488 DOI: 10.3389/fncom.2022.865805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Accepted: 04/25/2022] [Indexed: 12/02/2022] Open
Affiliation(s)
- Ludmila Kucikova
- Department of Neuroscience, Sheffield Institute for Translational Neuroscience, Insigneo Institute for in silico Medicine, University of Sheffield, Sheffield, United Kingdom
| | - Samuel Danso
- Edinburgh Dementia Prevention and Centre for Clinical Brain Sciences, Edinburgh Medical School, University of Edinburgh, Edinburgh, United Kingdom
| | - Lina Jia
- Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Li Su
- Department of Neuroscience, Sheffield Institute for Translational Neuroscience, Insigneo Institute for in silico Medicine, University of Sheffield, Sheffield, United Kingdom
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
- *Correspondence: Li Su
| |
Collapse
|
5
|
Kucikova L, Goerdten J, Dounavi ME, Mak E, Su L, Waldman AD, Danso S, Muniz-Terrera G, Ritchie CW. Resting-state brain connectivity in healthy young and middle-aged adults at risk of progressive Alzheimer's disease. Neurosci Biobehav Rev 2021; 129:142-153. [PMID: 34310975 DOI: 10.1016/j.neubiorev.2021.07.024] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 05/18/2021] [Accepted: 07/21/2021] [Indexed: 11/15/2022]
Abstract
Functional brain connectivity of the resting-state networks has gained recent attention as a possible biomarker of Alzheimer's Disease (AD). In this paper, we review the literature of functional connectivity differences in young adults and middle-aged cognitively intact individuals with non-modifiable risk factors of AD (n = 17). We focus on three main intrinsic resting-state networks: The Default Mode network, Executive network, and the Salience network. Overall, the evidence from the literature indicated early vulnerability of functional connectivity across different at-risk groups, particularly in the Default Mode Network. While there was little consensus on the interpretation on directionality, the topography of the findings showed frequent overlap across studies, especially in regions that are characteristic of AD (i.e., precuneus, posterior cingulate cortex, and medial prefrontal cortex areas). We conclude that while resting-state functional connectivity markers have great potential to identify at-risk individuals, implementing more data-driven approaches, further longitudinal and cross-validation studies, and the analysis of greater sample sizes are likely to be necessary to fully establish the effectivity and utility of resting-state network-based analyses.
Collapse
Affiliation(s)
- Ludmila Kucikova
- Edinburgh Dementia Prevention and Centre for Clinical Brain Sciences, Edinburgh Medical School, University of Edinburgh, Edinburgh, United Kingdom.
| | - Jantje Goerdten
- Department of Epidemiological Methods and Etiological Research, Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany
| | - Maria-Eleni Dounavi
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Elijah Mak
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Li Su
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Adam D Waldman
- Edinburgh Dementia Prevention and Centre for Clinical Brain Sciences, Edinburgh Medical School, University of Edinburgh, Edinburgh, United Kingdom
| | - Samuel Danso
- Edinburgh Dementia Prevention and Centre for Clinical Brain Sciences, Edinburgh Medical School, University of Edinburgh, Edinburgh, United Kingdom
| | - Graciela Muniz-Terrera
- Edinburgh Dementia Prevention and Centre for Clinical Brain Sciences, Edinburgh Medical School, University of Edinburgh, Edinburgh, United Kingdom
| | - Craig W Ritchie
- Edinburgh Dementia Prevention and Centre for Clinical Brain Sciences, Edinburgh Medical School, University of Edinburgh, Edinburgh, United Kingdom
| |
Collapse
|