1
|
Gracia-Tabuenca Z, Díaz-Patiño JC, Arelio-Ríos I, Moreno-García MB, Barrios FA, Alcauter S. Development of the Functional Connectome Topology in Adolescence: Evidence from Topological Data Analysis. eNeuro 2023; 10:ENEURO.0296-21.2022. [PMID: 36717266 PMCID: PMC9933932 DOI: 10.1523/eneuro.0296-21.2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Revised: 11/09/2022] [Accepted: 12/01/2022] [Indexed: 01/31/2023] Open
Abstract
Adolescence is a crucial developmental period in terms of behavior and mental health. Therefore, understanding how the brain develops during this stage is a fundamental challenge for neuroscience. Recent studies have modeled the brain as a network or connectome, mainly applying measures from graph theory, showing a change in its functional organization, such as an increase in its segregation and integration. Topological Data Analysis (TDA) complements such modeling by extracting high-dimensional features across the whole range of connectivity values instead of exploring a fixed set of connections. This study inquires into the developmental trajectories of such properties using a longitudinal sample of typically developing human participants (N = 98; 53/45 female/male; 6.7-18.1 years), applying TDA to their functional connectomes. In addition, we explore the effect of puberty on individual developmental trajectories. Results showed that the adolescent brain has a more distributed topology structure compared with random networks but is more densely connected at the local level. Furthermore, developmental effects showed nonlinear trajectories for the topology of the whole brain and fronto-parietal networks, with an inflection point and increasing trajectories after puberty onset. These results add to the insights into the development of the functional organization of the adolescent brain.
Collapse
Affiliation(s)
- Zeus Gracia-Tabuenca
- Instituto de Neurobiología, Universidad Nacional Autónoma de México, Querétaro 76230, México
| | - Juan Carlos Díaz-Patiño
- Instituto de Neurobiología, Universidad Nacional Autónoma de México, Querétaro 76230, México
- Centro de Investigación Científica y de Educación Superior de Ensenada, Ensenada 22860, México
| | - Isaac Arelio-Ríos
- Instituto de Neurobiología, Universidad Nacional Autónoma de México, Querétaro 76230, México
| | | | - Fernando A Barrios
- Instituto de Neurobiología, Universidad Nacional Autónoma de México, Querétaro 76230, México
| | - Sarael Alcauter
- Instituto de Neurobiología, Universidad Nacional Autónoma de México, Querétaro 76230, México
| |
Collapse
|
2
|
Zhang J, He X, Qing L, Xu Y, Liu Y, Chen H. Multi-scale discriminative regions analysis in FDG-PET imaging for early diagnosis of Alzheimer's disease. J Neural Eng 2022; 19. [PMID: 35882218 DOI: 10.1088/1741-2552/ac8450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 07/26/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Alzheimer's disease (AD) is a degenerative brain disorder, one of the main causes of death in elderly people, so early diagnosis of AD is vital to prompt access to medication and medical care. Fluorodeoxyglucose positron emission tomography (FDG-PET) proves to be effective to help understand neurological changes via measuring glucose uptake. Our aim is to explore information-rich regions of FDG-PET imaging, which enhance the accuracy and interpretability of AD-related diagnosis. APPROACH We develop a novel method for early diagnosis of AD based on multi-scale discriminative regions in FDG-PET imaging, which considers the diagnosis interpretability. Specifically, a multi-scale region localization (MSRL) module is discussed to automatically identify disease-related discriminative regions in full-volume FDG-PET images in an unsupervised manner, upon which a confidence score is designed to evaluate the prioritization of regions according to the density distribution of anomalies. Then, the proposed multi-scale region classification (MSRC) module adaptively fuses multi-scale region representations and makes decision fusion, which not only reduces useless information but also offers complementary information. Most of previous methods concentrate on discriminating AD from cognitively normal (CN), while mild cognitive impairment (MCI), a transitional state, facilitates early diagnosis. Therefore, our method is further applied to multiple AD-related diagnosis tasks, not limited to AD vs. CN. MAIN RESULTS Experimental results on the ADNI dataset show that the proposed method achieves superior performance over state-of-the-art FDG-PET-based approaches. Besides, some cerebral cortices highlighted by extracted regions cohere with medical research, further demonstrating the superiority. SIGNIFICANCE This work offers an effective method to achieve AD diagnosis and detect disease-affected regions in FDG-PET imaging. Our results could be beneficial for providing an additional opinion on the clinical diagnosis.
Collapse
Affiliation(s)
- Jin Zhang
- Sichuan University, College of Electronics and Information Engineering, Sichuan University, Chengdu, Sichuan, China, Chengdu, Sichuan, 610065, CHINA
| | - Xiaohai He
- Sichuan University, College of Electronics and Information Engineering, Sichuan University, Chengdu, Sichuan, China, Chengdu, Sichuan, 610065, CHINA
| | - Linbo Qing
- Sichuan University, College of Electronics and Information Engineering, Sichuan University, Chengdu, Sichuan, China, Chengdu, Sichuan, 610065, CHINA
| | - Yining Xu
- Sichuan University, College of Electronics and Information Engineering, Sichuan University, Chengdu, Sichuan, China, Chengdu, Sichuan, 610065, CHINA
| | - Yan Liu
- Chengdu Third People's Hospital, Department of Neurology, The Affiliated Hospital of Southwest Jiaotong University, The Third People's Hospital of Chengdu, Chengdu, Sichuan, China, Chengdu, Sichuan, 610014, CHINA
| | - Honggang Chen
- Sichuan University, College of Electronics and Information Engineering, Sichuan University, Chengdu, Sichuan, China, Chengdu, Sichuan, 610065, CHINA
| |
Collapse
|