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Orlichenko A, Qu G, Zhang G, Patel B, Wilson TW, Stephen JM, Calhoun VD, Wang YP. Latent Similarity Identifies Important Functional Connections for Phenotype Prediction. IEEE Trans Biomed Eng 2023; 70:1979-1989. [PMID: 37015625 PMCID: PMC10284019 DOI: 10.1109/tbme.2022.3232964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
OBJECTIVE Endophenotypes such as brain age and fluid intelligence are important biomarkers of disease status. However, brain imaging studies to identify these biomarkers often encounter limited numbers of subjects but high dimensional imaging features, hindering reproducibility. Therefore, we develop an interpretable, multivariate classification/regression algorithm, called Latent Similarity (LatSim), suitable for small sample size but high feature dimension datasets. METHODS LatSim combines metric learning with a kernel similarity function and softmax aggregation to identify task-related similarities between subjects. Inter-subject similarity is utilized to improve performance on three prediction tasks using multi-paradigm fMRI data. A greedy selection algorithm, made possible by LatSim's computational efficiency, is developed as an interpretability method. RESULTS LatSim achieved significantly higher predictive accuracy at small sample sizes on the Philadelphia Neurodevelopmental Cohort (PNC) dataset. Connections identified by LatSim gave superior discriminative power compared to those identified by other methods. We identified 4 functional brain networks enriched in connections for predicting brain age, sex, and intelligence. CONCLUSION We find that most information for a predictive task comes from only a few (1-5) connections. Additionally, we find that the default mode network is over-represented in the top connections of all predictive tasks. SIGNIFICANCE We propose a novel prediction algorithm for small sample, high feature dimension datasets and use it to identify connections in task fMRI data. Our work can lead to new insights in both algorithm design and neuroscience research.
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Yang X, Zhou X, Xin F, Becker B, Linden D, Hernaus D. Age-dependent changes in the dynamic functional organization of the brain at rest: a cross-cultural replication approach. Cereb Cortex 2023; 33:6394-6406. [PMID: 36642496 PMCID: PMC10183740 DOI: 10.1093/cercor/bhac512] [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: 08/25/2022] [Revised: 12/02/2022] [Accepted: 12/03/2022] [Indexed: 01/17/2023] Open
Abstract
Age-associated changes in brain function play an important role in the development of neurodegenerative diseases. Although previous work has examined age-related changes in static functional connectivity, accumulating evidence suggests that advancing age is especially associated with alterations in the dynamic interactions and transitions between different brain states, which hitherto have received less attention. Conclusions of previous studies in this domain are moreover limited by suboptimal replicability of resting-state functional magnetic resonance imaging (fMRI) and culturally homogenous cohorts. Here, we investigate the robustness of age-associated changes in dynamic functional connectivity (dFC) by capitalizing on the availability of fMRI cohorts from two cultures (Western European and Chinese). In both the LEMON (Western European) and SALD (Chinese) cohorts, we consistently identify two distinct states: a more frequent segregated within-network connectivity state (state I) and a less frequent integrated between-network connectivity state (state II). Moreover, in both these cohorts, older (55-80 years) compared to younger participants (20-35 years) exhibited lower occurrence of and spent less time in state I. Older participants also tended to exhibit more transitions between networks and greater variance in global efficiency. Overall, our cross-cultural replication of age-associated changes in dFC metrics implies that advancing age is robustly associated with a reorganization of dynamic brain activation that favors the use of less functionally specific networks.
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Affiliation(s)
- Xi Yang
- Department of Psychiatry & Neuropsychology, School for Mental Health and NeuroScience MHeNS, Maastricht University, Minderbroedersberg 4-6, 6211 LK Maastricht, The Netherlands
| | - Xinqi Zhou
- Institute of Brain and Psychological Sciences, Sichuan Normal University, 610066 Chengdu, Sichuan, China
| | - Fei Xin
- School of Psychology, Shenzhen University, 518060 Shenzhen, Guangdong, China
| | - Benjamin Becker
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Xiyuan Ave, West Hi-Tech Zone, 611731 Chengdu, Sichuan, China
| | - David Linden
- Department of Psychiatry & Neuropsychology, School for Mental Health and NeuroScience MHeNS, Maastricht University, Minderbroedersberg 4-6, 6211 LK Maastricht, The Netherlands
| | - Dennis Hernaus
- Department of Psychiatry & Neuropsychology, School for Mental Health and NeuroScience MHeNS, Maastricht University, Minderbroedersberg 4-6, 6211 LK Maastricht, The Netherlands
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Liu N, Jia G, Li H, Zhang S, Wang Y, Niu H, Liu L, Qian Q. The potential shared brain functional alterations between adults with ADHD and children with ADHD co-occurred with disruptive behaviors. Child Adolesc Psychiatry Ment Health 2022; 16:54. [PMID: 35761295 PMCID: PMC9238266 DOI: 10.1186/s13034-022-00486-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 06/08/2022] [Indexed: 11/20/2022] Open
Abstract
BACKGROUND Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder. Many previous studies have shown that the comorbid status of disruptive behaviour disorders (DBD) was a predictor for ADHD persistence into adulthood. However, the brain mechanisms underlying such a relationship remain unclear. Thus, we aim to investigate whether the brain functional alteration in adults with ADHD could also be detected in children with ADHD co-occurring with disruptive behaviours from both quantitative and categorical dimensions. METHODS A total of 172 children with ADHD (cADHD), 98 adults with ADHD (aADHD), 77 healthy control children (cHC) and 40 healthy control adults (aHC) were recruited. The whole-brain spontaneous fluctuations in brain activity of each participant were recorded using functional near-infrared spectroscopy (fNIRS), and the functional connectivities (FCs) were calculated. We first compared the FC differences between aADHD and aHC. Then, for the regions with significantly abnormal FCs in aADHD, we further compared these features between cADHD and cHC. In addition, the correlation between these FCs and the conduct disorder (CD)/oppositional defiant disorder (ODD) symptoms were analysed in cADHD. Moreover, to render the results readily interpretable, we compared the FC differences among ADHDCD-, subthreshold ADHDCD+ and cHC groups, and among ADHDODD-, ADHDODD+ and cHC groups. Finally, we repeated the above analysis after controlling for other comorbidities and core symptoms to diminish the potential confounding effects. RESULTS We found that compared with aHC, aADHD showed significantly increased FCs in the VN, DMN, SMN, and DAN. The aforementioned abnormal FCs were also detected in cADHD, however, in an opposite orientation. Notably, these abnormal FCs were positively correlated with CD symptoms. Finally, the subthreshold ADHDCD+ group even exhibited a tendency of adult-like increased FCs compared with the cHC. The results held after controlling for other comorbidities and core symptoms. CONCLUSION This study provides functional neuroimaging evidence that CD might be a risk factor for ADHD persistence into adulthood. Our work highlights the importance of differentiating ADHDCD+ from ADHD and inspiring further understanding of brain development in ADHD.
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Affiliation(s)
- Ningning Liu
- grid.11135.370000 0001 2256 9319Peking University Sixth Hospital/Institute of Mental Health, Beijing, 100191 China ,grid.459847.30000 0004 1798 0615NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191 China
| | - Gaoding Jia
- grid.20513.350000 0004 1789 9964State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875 China
| | - Haimei Li
- grid.11135.370000 0001 2256 9319Peking University Sixth Hospital/Institute of Mental Health, Beijing, 100191 China ,grid.459847.30000 0004 1798 0615NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191 China
| | - Shiyu Zhang
- grid.11135.370000 0001 2256 9319Peking University Sixth Hospital/Institute of Mental Health, Beijing, 100191 China ,grid.459847.30000 0004 1798 0615NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191 China
| | - Yufeng Wang
- grid.11135.370000 0001 2256 9319Peking University Sixth Hospital/Institute of Mental Health, Beijing, 100191 China ,grid.459847.30000 0004 1798 0615NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191 China
| | - Haijing Niu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China.
| | - Lu Liu
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, 100191, China. .,NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China.
| | - Qiujin Qian
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, 100191, China. .,NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China.
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