1
|
Liu C, Jing J, Jiang J, Wen W, Zhu W, Li Z, Pan Y, Cai X, Liu H, Zhou Y, Meng X, Zhang J, Wang Y, Li H, Jiang Y, Zheng H, Wang S, Niu H, Kochan N, Brodaty H, Wei T, Sachdev P, Liu T, Wang Y. Relationships between brain structure-function coupling in normal aging and cognition: A cross-ethnicity population-based study. Neuroimage 2024; 299:120847. [PMID: 39265959 DOI: 10.1016/j.neuroimage.2024.120847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Revised: 08/19/2024] [Accepted: 09/09/2024] [Indexed: 09/14/2024] Open
Abstract
Increased efforts in neuroscience seek to understand how macro-anatomical and physiological connectomes cooperatively work to generate cognitive behaviors. However, the structure-function coupling characteristics in normal aging individuals remain unclear. Here, we developed an index, the Coupling in Brain Structural connectome and Functional connectome (C-BSF) index, to quantify regional structure-function coupling in a large community-based cohort. C-BSF used diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (fMRI) data from the Polyvascular Evaluation for Cognitive Impairment and Vascular Events study (PRECISE) cohort (2007 individuals, age: 61.15 ± 6.49 years) and the Sydney Memory and Ageing Study (MAS) cohort (254 individuals, age: 83.45 ± 4.33 years). We observed that structure-function coupling was the strongest in the visual network and the weakest in the ventral attention network. We also observed that the weaker structure-function coupling was associated with increased age and worse cognitive level of the participant. Meanwhile, the structure-function coupling in the visual network was associated with the visuospatial performance and partially mediated the connections between age and the visuospatial function. This work contributes to our understanding of the underlying brain mechanisms by which aging affects cognition and also help establish early diagnosis and treatment approaches for neurological diseases in the elderly.
Collapse
Affiliation(s)
- Chang Liu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Jing Jing
- China National Clinical Research Center for Neurological Diseases, Beijing, China; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
| | - Jiyang Jiang
- Neuropsychiatric Institute, Prince of Wales Hospital, Sydney, NSW, Australia
| | - Wei Wen
- Neuropsychiatric Institute, Prince of Wales Hospital, Sydney, NSW, Australia; Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical Medicine, UNSW Medicine, Sydney NSW 2052, Australia
| | - Wanlin Zhu
- China National Clinical Research Center for Neurological Diseases, Beijing, China; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Zixiao Li
- China National Clinical Research Center for Neurological Diseases, Beijing, China; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yuesong Pan
- China National Clinical Research Center for Neurological Diseases, Beijing, China; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xueli Cai
- Department of Neurology, Lishui Hospital, Zhejiang University School of Medicine, Lishui, Zhejiang, China
| | - Hao Liu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Yijun Zhou
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Xia Meng
- China National Clinical Research Center for Neurological Diseases, Beijing, China; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jicong Zhang
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Yilong Wang
- China National Clinical Research Center for Neurological Diseases, Beijing, China; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Hao Li
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Yong Jiang
- China National Clinical Research Center for Neurological Diseases, Beijing, China; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Huaguang Zheng
- China National Clinical Research Center for Neurological Diseases, Beijing, China; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Suying Wang
- Cerebrovascular Research Lab, Lishui Hospital, Zhejiang University School of Medicine, Lishui, Zhejiang, China
| | - Haijun Niu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Nicole Kochan
- Neuropsychiatric Institute, Prince of Wales Hospital, Sydney, NSW, Australia; Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical Medicine, UNSW Medicine, Sydney NSW 2052, Australia
| | - Henry Brodaty
- Neuropsychiatric Institute, Prince of Wales Hospital, Sydney, NSW, Australia; Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical Medicine, UNSW Medicine, Sydney NSW 2052, Australia
| | - Tiemin Wei
- Department of Cardiology, Lishui Hospital, Zhejiang University School of Medicine, Lishui, Zhejiang, China
| | - Perminder Sachdev
- Neuropsychiatric Institute, Prince of Wales Hospital, Sydney, NSW, Australia; Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical Medicine, UNSW Medicine, Sydney NSW 2052, Australia
| | - Tao Liu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China.
| | - Yongjun Wang
- China National Clinical Research Center for Neurological Diseases, Beijing, China; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
| |
Collapse
|
2
|
Wu Y, Vasung L, Calixto C, Gholipour A, Karimi D. Characterizing normal perinatal development of the human brain structural connectivity. Hum Brain Mapp 2024; 45:e26784. [PMID: 39031955 PMCID: PMC11259574 DOI: 10.1002/hbm.26784] [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] [Received: 08/02/2023] [Revised: 06/17/2024] [Accepted: 07/01/2024] [Indexed: 07/22/2024] Open
Abstract
Early brain development is characterized by the formation of a highly organized structural connectome, which underlies brain's cognitive abilities and influences its response to diseases and environmental factors. Hence, quantitative assessment of structural connectivity in the perinatal stage is useful for studying normal and abnormal neurodevelopment. However, estimation of the connectome from diffusion MRI data involves complex computations. For the perinatal period, these computations are further challenged by the rapid brain development, inherently low signal quality, imaging difficulties, and high inter-subject variability. These factors make it difficult to chart the normal development of the structural connectome. As a result, there is a lack of reliable normative baselines of structural connectivity metrics at this critical stage in brain development. In this study, we developed a computational method based on spatio-temporal averaging in the image space for determining such baselines. We used this method to analyze the structural connectivity between 33 and 44 postmenstrual weeks using data from 166 subjects. Our results unveiled clear and strong trends in the development of structural connectivity in the perinatal stage. We observed increases in measures of network integration and segregation, and widespread strengthening of the connections within and across brain lobes and hemispheres. We also observed asymmetry patterns that were consistent between different connection weighting approaches. Connection weighting based on fractional anisotropy and neurite density produced the most consistent results. Our proposed method also showed considerable agreement with an alternative technique based on connectome averaging. The new computational method and results of this study can be useful for assessing normal and abnormal development of the structural connectome early in life.
Collapse
Affiliation(s)
- Yihan Wu
- Computational Radiology Laboratory (CRL), Department of RadiologyBoston Children's Hospital, and Harvard Medical SchoolBostonMassachusettsUSA
| | - Lana Vasung
- Department of Pediatrics at Boston Children's HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Camilo Calixto
- Computational Radiology Laboratory (CRL), Department of RadiologyBoston Children's Hospital, and Harvard Medical SchoolBostonMassachusettsUSA
| | - Ali Gholipour
- Computational Radiology Laboratory (CRL), Department of RadiologyBoston Children's Hospital, and Harvard Medical SchoolBostonMassachusettsUSA
| | - Davood Karimi
- Computational Radiology Laboratory (CRL), Department of RadiologyBoston Children's Hospital, and Harvard Medical SchoolBostonMassachusettsUSA
| |
Collapse
|
3
|
Luppi AI, Gellersen HM, Liu ZQ, Peattie ARD, Manktelow AE, Adapa R, Owen AM, Naci L, Menon DK, Dimitriadis SI, Stamatakis EA. Systematic evaluation of fMRI data-processing pipelines for consistent functional connectomics. Nat Commun 2024; 15:4745. [PMID: 38834553 PMCID: PMC11150439 DOI: 10.1038/s41467-024-48781-5] [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] [Received: 10/04/2022] [Accepted: 05/10/2024] [Indexed: 06/06/2024] Open
Abstract
Functional interactions between brain regions can be viewed as a network, enabling neuroscientists to investigate brain function through network science. Here, we systematically evaluate 768 data-processing pipelines for network reconstruction from resting-state functional MRI, evaluating the effect of brain parcellation, connectivity definition, and global signal regression. Our criteria seek pipelines that minimise motion confounds and spurious test-retest discrepancies of network topology, while being sensitive to both inter-subject differences and experimental effects of interest. We reveal vast and systematic variability across pipelines' suitability for functional connectomics. Inappropriate choice of data-processing pipeline can produce results that are not only misleading, but systematically so, with the majority of pipelines failing at least one criterion. However, a set of optimal pipelines consistently satisfy all criteria across different datasets, spanning minutes, weeks, and months. We provide a full breakdown of each pipeline's performance across criteria and datasets, to inform future best practices in functional connectomics.
Collapse
Affiliation(s)
- Andrea I Luppi
- Division of Anaesthesia, University of Cambridge, Cambridge, UK.
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
- St John's College, University of Cambridge, Cambridge, UK.
- Montreal Neurological Institute, McGill University, Montreal, Canada.
| | - Helena M Gellersen
- German Center for Neurodegenerative Diseases, Magdeburg, Germany
- Department of Psychology, University of Cambridge, Cambridge, UK
| | - Zhen-Qi Liu
- Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Alexander R D Peattie
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Anne E Manktelow
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Ram Adapa
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Adrian M Owen
- Department of Psychology, Western Institute for Neuroscience (WIN), Western University, London, ON, Canada
- Department of Physiology and Pharmacology, Western Institute for Neuroscience (WIN), Western University, London, ON, Canada
| | - Lorina Naci
- Trinity College Institute of Neuroscience, School of Psychology, Trinity College Dublin, Dublin, Ireland
| | - David K Menon
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
| | - Stavros I Dimitriadis
- Department of Clinical Psychology and Psychobiology, University of Barcelona, Barcelona, Spain
- Institut de Neurociències, University of Barcelona, Barcelona, Spain
- Neuroinformatics Group, Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff, Wales, UK
- Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, Wales, UK
- Neuroscience and Mental Health Research Institute, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, Wales, UK
- MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, Wales, UK
- Integrative Neuroimaging Lab, Thessaloniki, Greece
| | - Emmanuel A Stamatakis
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| |
Collapse
|
4
|
Huang H, Chen C, Rong B, Zhou Y, Yuan W, Peng Y, Liu Z, Wang G, Wang H. Distinct resting-state functional connectivity of the anterior cingulate cortex subregions in first-episode schizophrenia. Brain Imaging Behav 2024; 18:675-685. [PMID: 38349504 DOI: 10.1007/s11682-024-00863-0] [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] [Accepted: 02/06/2024] [Indexed: 07/04/2024]
Abstract
The anterior cingulate cortex (ACC) is a heterogeneous region of the brain's limbic system that regulates cognitive and emotional processing, and is frequently implicated in schizophrenia. This study aims to characterize resting-state functional connectivity (rsFC) profiles of three subregions of ACC in patients with first-episode schizophrenia and healthy controls. Resting-state functional magnetic resonance imaging (rs-fMRI) scans were collected from 60 first-episode schizophrenia (FES) patients and 60 healthy controls (HC), and the subgenual ACC (sgACC), pregenual ACC (pgACC), and dorsal ACC (dACC) were selected as seed regions from the newest automated anatomical labeling atlas 3 (AAL3). Seed-based rsFC maps for each ACC subregion were generated and compared between the two groups. The results revealed that compared to the HC group, the FES group showed higher rsFC between the pgACC and bilateral lateral orbitofrontal cortex (lOFC), and lower rsFC between the dACC and right posterior OFC (pOFC), the medial prefrontal gyrus (MPFC), and the precuneus cortex (PCu). These findings point to a selective functional dysconnectivity of pgACC and dACC in schizophrenia and provide more accurate information about the functional role of the ACC in this disorder.
Collapse
Affiliation(s)
- Huan Huang
- Department of Psychiatry, Renmin Hospital of Wuhan University, 238 Jiefang Road, Wuhan, 430060, China
| | - Cheng Chen
- Department of Psychiatry, Renmin Hospital of Wuhan University, 238 Jiefang Road, Wuhan, 430060, China
| | - Bei Rong
- Department of Psychiatry, Renmin Hospital of Wuhan University, 238 Jiefang Road, Wuhan, 430060, China
| | - Yuan Zhou
- Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Wei Yuan
- Department of Psychiatry, Yidu People's Hospital, Yidu, 443300, China
| | - Yunlong Peng
- Department of Psychiatry, Yidu People's Hospital, Yidu, 443300, China
| | - Zhongchun Liu
- Department of Psychiatry, Renmin Hospital of Wuhan University, 238 Jiefang Road, Wuhan, 430060, China
| | - Gaohua Wang
- Department of Psychiatry, Renmin Hospital of Wuhan University, 238 Jiefang Road, Wuhan, 430060, China
- Hubei Institute of Neurology and Psychiatry Research, Wuhan, 430060, China
| | - Huiling Wang
- Department of Psychiatry, Renmin Hospital of Wuhan University, 238 Jiefang Road, Wuhan, 430060, China.
- Hubei Provincial Key Laboratory of Developmentally Originated Disease, Wuhan, 430071, China.
| |
Collapse
|
5
|
Jamison KW, Gu Z, Wang Q, Sabuncu MR, Kuceyeski A. Release the Krakencoder: A unified brain connectome translation and fusion tool. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.12.589274. [PMID: 38659856 PMCID: PMC11042193 DOI: 10.1101/2024.04.12.589274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Brain connectivity can be estimated in many ways, depending on modality and processing strategy. Here we present the Krakencoder, a joint connectome mapping tool that simultaneously, bidirectionally translates between structural (SC) and functional connectivity (FC), and across different atlases and processing choices via a common latent representation. These mappings demonstrate unprecedented accuracy and individual-level identifiability; the mapping between SC and FC has identifiability 42-54% higher than existing models. The Krakencoder combines all connectome flavors via a shared low-dimensional latent space. This "fusion" representation i) better reflects familial relatedness, ii) preserves age- and sex-relevant information and iii) enhances cognition-relevant information. The Krakencoder can be applied without retraining to new, out-of-age-distribution data while still preserving inter-individual differences in the connectome predictions and familial relationships in the latent representations. The Krakencoder is a significant leap forward in capturing the relationship between multi-modal brain connectomes in an individualized, behaviorally- and demographically-relevant way.
Collapse
Affiliation(s)
- Keith W Jamison
- Department of Computational Biology, Cornell University, Ithaca, NY, USA
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Zijin Gu
- School of Electrical and Computer Engineering, Cornell University and Cornell Tech, New York, NY, USA
| | - Qinxin Wang
- Department of Biomedical Engineering, Tsinghua University, Beijing, 100084, China
| | - Mert R Sabuncu
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
- School of Electrical and Computer Engineering, Cornell University and Cornell Tech, New York, NY, USA
| | - Amy Kuceyeski
- Department of Computational Biology, Cornell University, Ithaca, NY, USA
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| |
Collapse
|
6
|
Kopetzky SJ, Li Y, Kaiser M, Butz-Ostendorf M. Predictability of intelligence and age from structural connectomes. PLoS One 2024; 19:e0301599. [PMID: 38557681 PMCID: PMC10984540 DOI: 10.1371/journal.pone.0301599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 03/19/2024] [Indexed: 04/04/2024] Open
Abstract
In this study, structural images of 1048 healthy subjects from the Human Connectome Project Young Adult study and 94 from ADNI-3 study were processed by an in-house tractography pipeline and analyzed together with pre-processed data of the same subjects from braingraph.org. Whole brain structural connectome features were used to build a simple correlation-based regression machine learning model to predict intelligence and age of healthy subjects. Our results showed that different forms of intelligence as well as age are predictable to a certain degree from diffusion tensor imaging detecting anatomical fiber tracts in the living human brain. Though we did not identify significant differences in the prediction capability for the investigated features depending on the imaging feature extraction method, we did find that crystallized intelligence was consistently better predictable than fluid intelligence from structural connectivity data through all datasets. Our findings suggest a practical and scalable processing and analysis framework to explore broader research topics employing brain MR imaging.
Collapse
Affiliation(s)
- Sebastian J. Kopetzky
- Labvantage—Biomax GmbH, Planegg, Germany
- School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Yong Li
- Labvantage—Biomax GmbH, Planegg, Germany
| | - Marcus Kaiser
- Precision Imaging Beacon, School of Medicine, University of Nottingham, Nottingham, United Kingdom
- Department of Functional Neurosurgery, Rui Jin Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Markus Butz-Ostendorf
- Labvantage—Biomax GmbH, Planegg, Germany
- Laboratory for Parallel Programming, Department of Computer Science, Technical University of Darmstadt, Darmstadt, Germany
| | | |
Collapse
|
7
|
Liu S, Ye H, Yang B, Li M, Cao F. A joint parcellation and boundary network with multi-rate-shared dilated graph attention for cortical surface parcellation. Med Biol Eng Comput 2024; 62:537-549. [PMID: 37945794 DOI: 10.1007/s11517-023-02942-8] [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] [Received: 05/18/2023] [Accepted: 09/27/2023] [Indexed: 11/12/2023]
Abstract
Cortical surface parcellation aims to segment the surface into anatomically and functionally significant regions, which are crucial for diagnosing and treating numerous neurological diseases. However, existing methods generally ignore the difficulty in learning labeling patterns of boundaries, hindering the performance of parcellation. To this end, this paper proposes a joint parcellation and boundary network (JPBNet) to promote the effectiveness of cortical surface parcellation. Its core is developing a multi-rate-shared dilated graph attention (MDGA) module and incorporating boundary learning into the parcellation process. The former, in particular, constructs a dilated graph attention strategy, extending the dilated convolution from regular data to irregular graph data. We fuse it with different dilated rates to extract context information in various scales by devising a shared graph attention layer. After that, a boundary enhancement module and a parcellation enhancement module based on graph attention mechanisms are built in each layer, forcing MDGA to capture informative and valuable features for boundary detection and parcellation tasks. Integrating MDGA, the boundary enhancement module, and the parcellation enhancement module at each layer to supervise boundary and parcellation information, an effective JPBNet is formed by stacking multiple layers. Experiments on the public dataset reveal that the proposed method outperforms comparison methods and performs well on boundaries for cortical surface parcellation.
Collapse
Affiliation(s)
- Siqi Liu
- College of Sciences, China Jiliang University, Hangzhou, 310018, China
| | - Hailiang Ye
- College of Sciences, China Jiliang University, Hangzhou, 310018, China.
| | - Bing Yang
- College of Sciences, China Jiliang University, Hangzhou, 310018, China
| | - Ming Li
- Key Laboratory of Intelligent Education Technology and Application, Zhejiang Normal University, Jinhua, 321004, China
| | - Feilong Cao
- College of Sciences, China Jiliang University, Hangzhou, 310018, China.
| |
Collapse
|
8
|
Lizarraga A, Ripp I, Sala A, Shi K, Düring M, Koch K, Yakushev I. Similarity between structural and proxy estimates of brain connectivity. J Cereb Blood Flow Metab 2024; 44:284-295. [PMID: 37773727 PMCID: PMC10993877 DOI: 10.1177/0271678x231204769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Revised: 08/01/2023] [Accepted: 08/18/2023] [Indexed: 10/01/2023]
Abstract
Functional magnetic resonance and diffusion weighted imaging have so far made a major contribution to delineation of the brain connectome at the macroscale. While functional connectivity (FC) was shown to be related to structural connectivity (SC) to a certain degree, their spatial overlap is unknown. Even less clear are relations of SC with estimates of connectivity from inter-subject covariance of regional F18-fluorodeoxyglucose uptake (FDGcov) and grey matter volume (GMVcov). Here, we asked to what extent SC underlies three proxy estimates of brain connectivity: FC, FDGcov and GMVcov. Simultaneous PET/MR acquisitions were performed in 56 healthy middle-aged individuals. Similarity between four networks was assessed using Spearman correlation and convergence ratio (CR), a measure of spatial overlap. Spearman correlation coefficient was 0.27 for SC-FC, 0.40 for SC-FDGcov, and 0.15 for SC-GMVcov. Mean CRs were 51% for SC-FC, 48% for SC-FDGcov, and 37% for SC-GMVcov. These results proved to be reproducible and robust against image processing steps. In sum, we found a relevant similarity of SC with FC and FDGcov, while GMVcov consistently showed the weakest similarity. These findings indicate that white matter tracts underlie FDGcov to a similar degree as FC, supporting FDGcov as estimate of functional brain connectivity.
Collapse
Affiliation(s)
- Aldana Lizarraga
- Department of Nuclear Medicine, School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Isabelle Ripp
- Department of Nuclear Medicine, School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Arianna Sala
- Department of Nuclear Medicine, School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
- Coma Science Group, GIGA Consciousness, University of Liege; Centre du Cerveau2, University Hospital of Liege, Avenue de L'Hôpital 1, Liege, Belgium
| | - Kuangyu Shi
- Department of Nuclear Medicine, University Hospital Bern, Bern, Switzerland
| | - Marco Düring
- Medical Image Analysis Center (MIAC AG) and Qbig, Department of Biomedical Engineering, University of Basel, Basel, Switzerland
| | - Kathrin Koch
- Department of Neuroradiology, School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Igor Yakushev
- Department of Nuclear Medicine, School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| |
Collapse
|
9
|
Wang M, Zhu L, Li X, Pan Y, Li L. Dynamic functional connectivity analysis with temporal convolutional network for attention deficit/hyperactivity disorder identification. Front Neurosci 2023; 17:1322967. [PMID: 38148943 PMCID: PMC10750397 DOI: 10.3389/fnins.2023.1322967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 11/24/2023] [Indexed: 12/28/2023] Open
Abstract
Introduction Dynamic functional connectivity (dFC), which can capture the abnormality of brain activity over time in resting-state functional magnetic resonance imaging (rs-fMRI) data, has a natural advantage in revealing the abnormal mechanism of brain activity in patients with Attention Deficit/Hyperactivity Disorder (ADHD). Several deep learning methods have been proposed to learn dynamic changes from rs-fMRI for FC analysis, and achieved superior performance than those using static FC. However, most existing methods only consider dependencies of two adjacent timestamps, which is limited when the change is related to the course of many timestamps. Methods In this paper, we propose a novel Temporal Dependence neural Network (TDNet) for FC representation learning and temporal-dependence relationship tracking from rs-fMRI time series for automated ADHD identification. Specifically, we first partition rs-fMRI time series into a sequence of consecutive and non-overlapping segments. For each segment, we design an FC generation module to learn more discriminative representations to construct dynamic FCs. Then, we employ the Temporal Convolutional Network (TCN) to efficiently capture long-range temporal patterns with dilated convolutions, followed by three fully connected layers for disease prediction. Results As the results, we found that considering the dynamic characteristics of rs-fMRI time series data is beneficial to obtain better diagnostic performance. In addition, dynamic FC networks generated in a data-driven manner are more informative than those constructed by Pearson correlation coefficients. Discussion We validate the effectiveness of the proposed approach through extensive experiments on the public ADHD-200 database, and the results demonstrate the superiority of the proposed model over state-of-the-art methods in ADHD identification.
Collapse
Affiliation(s)
- Mingliang Wang
- School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, China
- Nanjing Xinda Institute of Safety and Emergency Management, Nanjing, China
- MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Lingyao Zhu
- School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, China
| | - Xizhi Li
- School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, China
| | - Yong Pan
- School of Accounting, Nanjing University of Finance and Economics, Nanjing, China
| | - Long Li
- Taian Tumor Prevention and Treatment Hospital, Taian, China
| |
Collapse
|
10
|
Griffa A, Mach M, Dedelley J, Gutierrez-Barragan D, Gozzi A, Allali G, Grandjean J, Van De Ville D, Amico E. Evidence for increased parallel information transmission in human brain networks compared to macaques and male mice. Nat Commun 2023; 14:8216. [PMID: 38081838 PMCID: PMC10713651 DOI: 10.1038/s41467-023-43971-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 11/24/2023] [Indexed: 12/18/2023] Open
Abstract
Brain communication, defined as information transmission through white-matter connections, is at the foundation of the brain's computational capacities that subtend almost all aspects of behavior: from sensory perception shared across mammalian species, to complex cognitive functions in humans. How did communication strategies in macroscale brain networks adapt across evolution to accomplish increasingly complex functions? By applying a graph- and information-theory approach to assess information-related pathways in male mouse, macaque and human brains, we show a brain communication gap between selective information transmission in non-human mammals, where brain regions share information through single polysynaptic pathways, and parallel information transmission in humans, where regions share information through multiple parallel pathways. In humans, parallel transmission acts as a major connector between unimodal and transmodal systems. The layout of information-related pathways is unique to individuals across different mammalian species, pointing at the individual-level specificity of information routing architecture. Our work provides evidence that different communication patterns are tied to the evolution of mammalian brain networks.
Collapse
Affiliation(s)
- Alessandra Griffa
- Leenaards Memory Center, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
- Medical Image Processing Laboratory, Neuro-X Institute, École Polytechnique Fédérale De Lausanne (EPFL), Geneva, Switzerland.
- Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland.
| | - Mathieu Mach
- Medical Image Processing Laboratory, Neuro-X Institute, École Polytechnique Fédérale De Lausanne (EPFL), Geneva, Switzerland
| | - Julien Dedelley
- Medical Image Processing Laboratory, Neuro-X Institute, École Polytechnique Fédérale De Lausanne (EPFL), Geneva, Switzerland
| | - Daniel Gutierrez-Barragan
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive systems, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Alessandro Gozzi
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive systems, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Gilles Allali
- Leenaards Memory Center, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Joanes Grandjean
- Department of Medical Imaging, Radboud University Medical Center, 6525 GA, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, 6525 EN, Nijmegen, The Netherlands
| | - Dimitri Van De Ville
- Medical Image Processing Laboratory, Neuro-X Institute, École Polytechnique Fédérale De Lausanne (EPFL), Geneva, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - Enrico Amico
- Medical Image Processing Laboratory, Neuro-X Institute, École Polytechnique Fédérale De Lausanne (EPFL), Geneva, Switzerland.
- Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland.
| |
Collapse
|
11
|
Hong Y, Cornea E, Girault JB, Bagonis M, Foster M, Kim SH, Prieto JC, Chen H, Gao W, Styner MA, Gilmore JH. Structural and functional connectome relationships in early childhood. Dev Cogn Neurosci 2023; 64:101314. [PMID: 37898019 PMCID: PMC10630618 DOI: 10.1016/j.dcn.2023.101314] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 09/27/2023] [Accepted: 10/12/2023] [Indexed: 10/30/2023] Open
Abstract
There is strong evidence that the functional connectome is highly related to the white matter connectome in older children and adults, though little is known about structure-function relationships in early childhood. We investigated the development of cortical structure-function coupling in children longitudinally scanned at 1, 2, 4, and 6 years of age (N = 360) and in a comparison sample of adults (N = 89). We also applied a novel graph convolutional neural network-based deep learning model with a new loss function to better capture inter-subject heterogeneity and predict an individual's functional connectivity from the corresponding structural connectivity. We found regional patterns of structure-function coupling in early childhood that were consistent with adult patterns. In addition, our deep learning model improved the prediction of individual functional connectivity from its structural counterpart compared to existing models.
Collapse
Affiliation(s)
- Yoonmi Hong
- Department of Psychiatry, University of North Carolina at Chapel Hill, United States of America.
| | - Emil Cornea
- Department of Psychiatry, University of North Carolina at Chapel Hill, United States of America
| | - Jessica B Girault
- Department of Psychiatry, University of North Carolina at Chapel Hill, United States of America; Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, United States of America
| | - Maria Bagonis
- Department of Psychiatry, University of North Carolina at Chapel Hill, United States of America
| | - Mark Foster
- Department of Psychiatry, University of North Carolina at Chapel Hill, United States of America
| | - Sun Hyung Kim
- Department of Psychiatry, University of North Carolina at Chapel Hill, United States of America
| | - Juan Carlos Prieto
- Department of Psychiatry, University of North Carolina at Chapel Hill, United States of America
| | - Haitao Chen
- Biomedical Imaging Research Institute (BIRI), Department of Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, United States of America
| | - Wei Gao
- Biomedical Imaging Research Institute (BIRI), Department of Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, United States of America
| | - Martin A Styner
- Department of Psychiatry, University of North Carolina at Chapel Hill, United States of America; Department of Computer Science, University of North Carolina at Chapel Hill, United States of America
| | - John H Gilmore
- Department of Psychiatry, University of North Carolina at Chapel Hill, United States of America
| |
Collapse
|
12
|
Artiles O, Al Masry Z, Saeed F. Confounding Effects on the Performance of Machine Learning Analysis of Static Functional Connectivity Computed from rs-fMRI Multi-site Data. Neuroinformatics 2023; 21:651-668. [PMID: 37581850 DOI: 10.1007/s12021-023-09639-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/16/2023] [Indexed: 08/16/2023]
Abstract
Resting-state functional magnetic resonance imaging (rs-fMRI) is a non-invasive imaging technique widely used in neuroscience to understand the functional connectivity of the human brain. While rs-fMRI multi-site data can help to understand the inner working of the brain, the data acquisition and processing of this data has many challenges. One of the challenges is the variability of the data associated with different acquisitions sites, and different MRI machines vendors. Other factors such as population heterogeneity among different sites, with variables such as age and gender of the subjects, must also be considered. Given that most of the machine-learning models are developed using these rs-fMRI multi-site data sets, the intrinsic confounding effects can adversely affect the generalizability and reliability of these computational methods, as well as the imposition of upper limits on the classification scores. This work aims to identify the phenotypic and imaging variables producing the confounding effects, as well as to control these effects. Our goal is to maximize the classification scores obtained from the machine learning analysis of the Autism Brain Imaging Data Exchange (ABIDE) rs-fMRI multi-site data. To achieve this goal, we propose novel methods of stratification to produce homogeneous sub-samples of the 17 ABIDE sites, as well as the generation of new features from the static functional connectivity values, using multiple linear regression models, ComBat harmonization models, and normalization methods. The main results obtained with our statistical models and methods are an accuracy of 76.4%, sensitivity of 82.9%, and specificity of 77.0%, which are 8.8%, 20.5%, and 7.5% above the baseline classification scores obtained from the machine learning analysis of the static functional connectivity computed from the ABIDE rs-fMRI multi-site data.
Collapse
Affiliation(s)
- Oswaldo Artiles
- Knight Foundation School of Computing and Information Sciences, Florida International University, 11200 SW 8th Street CASE 354, Miami, Florida, 33199, USA
| | - Zeina Al Masry
- SUPMICROTECH, CNRS, institut FEMTO-ST, 24 rue Alain Savary, Besançon, F-25000, France
| | - Fahad Saeed
- Knight Foundation School of Computing and Information Sciences, Florida International University, 11200 SW 8th Street CASE 354, Miami, Florida, 33199, USA.
| |
Collapse
|
13
|
Messé A, Hollensteiner KJ, Delettre C, Dell-Brown LA, Pieper F, Nentwig LJ, Galindo-Leon EE, Larrat B, Mériaux S, Mangin JF, Reillo I, de Juan Romero C, Borrell V, Engler G, Toro R, Engel AK, Hilgetag CC. Structural basis of envelope and phase intrinsic coupling modes in the cerebral cortex. Neuroimage 2023; 276:120212. [PMID: 37269959 PMCID: PMC10300241 DOI: 10.1016/j.neuroimage.2023.120212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 05/09/2023] [Accepted: 05/30/2023] [Indexed: 06/05/2023] Open
Abstract
Intrinsic coupling modes (ICMs) can be observed in ongoing brain activity at multiple spatial and temporal scales. Two families of ICMs can be distinguished: phase and envelope ICMs. The principles that shape these ICMs remain partly elusive, in particular their relation to the underlying brain structure. Here we explored structure-function relationships in the ferret brain between ICMs quantified from ongoing brain activity recorded with chronically implanted micro-ECoG arrays and structural connectivity (SC) obtained from high-resolution diffusion MRI tractography. Large-scale computational models were used to explore the ability to predict both types of ICMs. Importantly, all investigations were conducted with ICM measures that are sensitive or insensitive to volume conduction effects. The results show that both types of ICMs are significantly related to SC, except for phase ICMs when using measures removing zero-lag coupling. The correlation between SC and ICMs increases with increasing frequency which is accompanied by reduced delays. Computational models produced results that were highly dependent on the specific parameter settings. The most consistent predictions were derived from measures solely based on SC. Overall, the results demonstrate that patterns of cortical functional coupling as reflected in both phase and envelope ICMs are both related, albeit to different degrees, to the underlying structural connectivity in the cerebral cortex.
Collapse
Affiliation(s)
- Arnaud Messé
- Institute of Computational Neuroscience, Hamburg Center of Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg University, Martinistraße 52, Hamburg 20246, Germany.
| | - Karl J Hollensteiner
- Department of Neurophysiology and Pathophysiology, Hamburg Center of Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg University, Martinistraße 52, Hamburg 20246, Germany
| | - Céline Delettre
- Institute of Computational Neuroscience, Hamburg Center of Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg University, Martinistraße 52, Hamburg 20246, Germany; Unité de Neuroanatomie Appliquée et Théorique, Unité de Génétique Humaine et Fonctions Cognitives, Institut Pasteur, Université Paris Cité, 25-28 rue du Dr Roux, Paris 75015, France
| | - Leigh-Anne Dell-Brown
- Institute of Computational Neuroscience, Hamburg Center of Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg University, Martinistraße 52, Hamburg 20246, Germany
| | - Florian Pieper
- Department of Neurophysiology and Pathophysiology, Hamburg Center of Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg University, Martinistraße 52, Hamburg 20246, Germany
| | - Lena J Nentwig
- Institute of Computational Neuroscience, Hamburg Center of Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg University, Martinistraße 52, Hamburg 20246, Germany
| | - Edgar E Galindo-Leon
- Department of Neurophysiology and Pathophysiology, Hamburg Center of Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg University, Martinistraße 52, Hamburg 20246, Germany
| | - Benoît Larrat
- NeuroSpin, CEA, Paris-Saclay University, Centre d'études de Saclay, Bâtiment 145, Gif-sur-Yvette 91191, France
| | - Sébastien Mériaux
- NeuroSpin, CEA, Paris-Saclay University, Centre d'études de Saclay, Bâtiment 145, Gif-sur-Yvette 91191, France
| | - Jean-François Mangin
- NeuroSpin, CEA, Paris-Saclay University, Centre d'études de Saclay, Bâtiment 145, Gif-sur-Yvette 91191, France
| | - Isabel Reillo
- Developmental Neurobiology Unit, Instituto de Neurociencias, Consejo Superior de Investigaciones Científicas - Universidad Miguel Hernández, Sant Joan d'Alacant, Av. Santiago Ramón y Cajal s/n, Sant Joan d'Alacant 03550, Spain
| | - Camino de Juan Romero
- Developmental Neurobiology Unit, Instituto de Neurociencias, Consejo Superior de Investigaciones Científicas - Universidad Miguel Hernández, Sant Joan d'Alacant, Av. Santiago Ramón y Cajal s/n, Sant Joan d'Alacant 03550, Spain
| | - Víctor Borrell
- Developmental Neurobiology Unit, Instituto de Neurociencias, Consejo Superior de Investigaciones Científicas - Universidad Miguel Hernández, Sant Joan d'Alacant, Av. Santiago Ramón y Cajal s/n, Sant Joan d'Alacant 03550, Spain
| | - Gerhard Engler
- Department of Neurophysiology and Pathophysiology, Hamburg Center of Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg University, Martinistraße 52, Hamburg 20246, Germany
| | - Roberto Toro
- Unité de Neuroanatomie Appliquée et Théorique, Unité de Génétique Humaine et Fonctions Cognitives, Institut Pasteur, Université Paris Cité, 25-28 rue du Dr Roux, Paris 75015, France; Center for Research and Interdisciplinarity, Paris Descartes University, 24, rue du Faubourg Saint Jacques, Paris 75014, France
| | - Andreas K Engel
- Department of Neurophysiology and Pathophysiology, Hamburg Center of Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg University, Martinistraße 52, Hamburg 20246, Germany
| | - Claus C Hilgetag
- Institute of Computational Neuroscience, Hamburg Center of Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg University, Martinistraße 52, Hamburg 20246, Germany; Department of Health Sciences, Boston University, 635 Commonwealth Avenue, Boston, Massachusetts 02215, USA
| |
Collapse
|
14
|
Liu R, Li M, Dunson DB. PPA: Principal parcellation analysis for brain connectomes and multiple traits. Neuroimage 2023; 276:120214. [PMID: 37286151 DOI: 10.1016/j.neuroimage.2023.120214] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 05/31/2023] [Indexed: 06/09/2023] Open
Abstract
Our understanding of the structure of the brain and its relationships with human traits is largely determined by how we represent the structural connectome. Standard practice divides the brain into regions of interest (ROIs) and represents the connectome as an adjacency matrix having cells measuring connectivity between pairs of ROIs. Statistical analyses are then heavily driven by the (largely arbitrary) choice of ROIs. In this article, we propose a human trait prediction framework utilizing a tractography-based representation of the brain connectome, which clusters fiber endpoints to define a data-driven white matter parcellation targeted to explain variation among individuals and predict human traits. This leads to Principal Parcellation Analysis (PPA), representing individual brain connectomes by compositional vectors building on a basis system of fiber bundles that captures the connectivity at the population level. PPA eliminates the need to choose atlases and ROIs a priori, and provides a simpler, vector-valued representation that facilitates easier statistical analysis compared to the complex graph structures encountered in classical connectome analyses. We illustrate the proposed approach through applications to data from the Human Connectome Project (HCP) and show that PPA connectomes improve power in predicting human traits over state-of-the-art methods based on classical connectomes, while dramatically improving parsimony and maintaining interpretability. Our PPA package is publicly available on GitHub, and can be implemented routinely for diffusion image data.
Collapse
Affiliation(s)
- Rongjie Liu
- Department of Statistics, Florida State University, Tallahassee, FL, USA
| | - Meng Li
- Department of Statistics, Rice University, Houston, TX, USA.
| | - David B Dunson
- Department of Statistical Science, Duke University, Durham, NC, USA
| |
Collapse
|
15
|
Lin J, Zhang L, Guo R, Jiao S, Song X, Feng S, Wang K, Li M, Luo Y, Han Z. The influence of visual deprivation on the development of the thalamocortical network: Evidence from congenitally blind children and adults. Neuroimage 2022; 264:119722. [PMID: 36323383 DOI: 10.1016/j.neuroimage.2022.119722] [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: 03/24/2022] [Revised: 10/23/2022] [Accepted: 10/29/2022] [Indexed: 11/06/2022] Open
Abstract
The thalamus is heavily involved in relaying sensory signals to the cerebral cortex. A relevant issue is how the deprivation of congenital visual sensory information modulates the development of the thalamocortical network. The answer is unclear because previous studies on this topic did not investigate network development, structure-function combinations, and cognition-related behaviors in the same study. To overcome these limitations, we recruited 30 congenitally blind subjects (8 children, 22 adults) and 31 sighted subjects (10 children, 21 adults), and conducted multiple analyses [i.e., gray matter volume (GMV) analysis using the voxel-based morphometry (VBM) method, resting-state functional connectivity (FC), and brain-behavior correlation]. We found that congenital blindness elicited significant changes in the development of GMV in visual and somatosensory thalamic regions. Blindness also resulted in significant changes in the development of FC between somatosensory thalamic regions and visual cortical regions as well as advanced information processing regions. Moreover, the somatosensory thalamic regions and their FCs with visual cortical regions were reorganized to process high-level tactile language information in blind individuals. These findings provide a refined understanding of the neuroanatomical and functional plasticity of the thalamocortical network.
Collapse
Affiliation(s)
- Junfeng Lin
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Linjun Zhang
- School of Chinese as a Second Language, Peking University, Beijing 100091, China
| | - Runhua Guo
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Saiyi Jiao
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Xiaomeng Song
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Suting Feng
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Ke Wang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Mingyang Li
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou 310027, China
| | - Yudan Luo
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Zaizhu Han
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China.
| |
Collapse
|
16
|
Zhao LM, Chen X, Zhang YM, Qu ML, Selvarajah D, Tesfaye S, Yang FX, Ou CY, Liao WH, Wu J. Changed cerebral function and morphology serve as neuroimaging evidence for subclinical type 2 diabetic polyneuropathy. Front Endocrinol (Lausanne) 2022; 13:1069437. [PMID: 36506054 PMCID: PMC9729333 DOI: 10.3389/fendo.2022.1069437] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 11/08/2022] [Indexed: 11/25/2022] Open
Abstract
Introduction Central and peripheral nervous systems are all involved in type 2 diabetic polyneuropathy mechanisms, but such subclinical changes and associations remain unknown. This study aims to explore subclinical changes of the central and peripheral and unveil their association. Methods A total of 55 type-2 diabetes patients consisting of symptomatic (n = 23), subclinical (n = 12), and no polyneuropathy (n = 20) were enrolled in this study. Cerebral morphology, function, peripheral electrophysiology, and clinical information were collected and assessed using ANOVA and post-hoc analysis. Gaussian random field correction was used for multiple comparison corrections. Pearson/Spearman correlation analysis was used to evaluate the association of the cerebral with the peripheral. Results When comparing the subclinical group with no polyneuropathy groups, no statistical differences were shown in peripheral evaluations except amplitudes of tibial nerves. At the same time, functional connectivity from the orbitofrontal to bilateral postcentral and middle temporal cortex increased significantly. Gray matter volume of orbitofrontal and its functional connectivity show a transient elevation in the subclinical group compared with the symptomatic group. Besides, gray matter volume in the orbitofrontal cortex negatively correlated with the Neuropathy Symptom Score (r = -0.5871, p < 0.001), Neuropathy Disability Score (r = -0.3682, p = 0.009), and Douleur Neuropathique en 4 questions (r = -0.4403, p = 0.003), and also found correlated positively with bilateral peroneal amplitude (r > 0.4, p < 0.05) and conduction velocities of the right sensory sural nerve(r = 0.3181, p = 0.03). Similarly, functional connectivity from the orbitofrontal to the postcentral cortex was positively associated with cold detection threshold (r = 0.3842, p = 0.03) and negatively associated with Neuropathy Symptom Score (r = -0.3460, p = 0.01). Discussion Function and morphology of brain changes in subclinical type 2 diabetic polyneuropathy might serve as an earlier biomarker. Novel insights from subclinical stage to investigate the mechanism of type 2 diabetic polyneuropathy are warranted.
Collapse
Affiliation(s)
- Lin-Mei Zhao
- Department of Endocrinology, Xiangya Hospital, Central South University, Changsha, China
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
- Department of Radiology and Radiological Sciences, Johns Hopkins Hospital, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Xin Chen
- Department of Endocrinology, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
- Hunan Engineering Research Center for Obesity and its Metabolic Complications, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - You-Ming Zhang
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Min-Li Qu
- Department of Endocrinology, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
- Hunan Engineering Research Center for Obesity and its Metabolic Complications, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Dinesh Selvarajah
- Diabetes Research Unit, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom
| | - Solomon Tesfaye
- Diabetes Research Unit, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom
| | - Fang-Xue Yang
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Chu-Ying Ou
- Department of Endocrinology, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
- Hunan Engineering Research Center for Obesity and its Metabolic Complications, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Wei-Hua Liao
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
- National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Xiangya Hospital, Central South University, Changsha, China
| | - Jing Wu
- Department of Endocrinology, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
- Hunan Engineering Research Center for Obesity and its Metabolic Complications, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Xiangya Hospital, Central South University, Changsha, China
| |
Collapse
|
17
|
Zhang YS, Takahashi DY, El Hady A, Liao DA, Ghazanfar AA. Active neural coordination of motor behaviors with internal states. Proc Natl Acad Sci U S A 2022; 119:e2201194119. [PMID: 36122243 PMCID: PMC9522379 DOI: 10.1073/pnas.2201194119] [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] [Received: 01/21/2022] [Accepted: 08/16/2022] [Indexed: 11/18/2022] Open
Abstract
The brain continuously coordinates skeletomuscular movements with internal physiological states like arousal, but how is this coordination achieved? One possibility is that the brain simply reacts to changes in external and/or internal signals. Another possibility is that it is actively coordinating both external and internal activities. We used functional ultrasound imaging to capture a large medial section of the brain, including multiple cortical and subcortical areas, in marmoset monkeys while monitoring their spontaneous movements and cardiac activity. By analyzing the causal ordering of these different time series, we found that information flowing from the brain to movements and heart-rate fluctuations were significantly greater than in the opposite direction. The brain areas involved in this external versus internal coordination were spatially distinct, but also extensively interconnected. Temporally, the brain alternated between network states for this regulation. These findings suggest that the brain's dynamics actively and efficiently coordinate motor behavior with internal physiology.
Collapse
Affiliation(s)
- Yisi S. Zhang
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544
| | - Daniel Y. Takahashi
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544
- Brain Institute, Federal University of Rio Grande do Norte, Natal 59076-550, Brazil
| | - Ahmed El Hady
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544
- Center for Advanced Study of Collective Behavior, University of Konstanz, Konstanz 78464, Germany
- Department of Collective Behavior, Max Planck Institute of Animal Behavior, Konstanz 78464, Germany
| | - Diana A. Liao
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544
| | - Asif A. Ghazanfar
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544
- Department of Psychology, Princeton University, Princeton, NJ 08544
| |
Collapse
|
18
|
Zarkali A, Luppi AI, Stamatakis EA, Reeves S, McColgan P, Leyland LA, Lees AJ, Weil RS. Changes in dynamic transitions between integrated and segregated states underlie visual hallucinations in Parkinson's disease. Commun Biol 2022; 5:928. [PMID: 36075964 PMCID: PMC9458713 DOI: 10.1038/s42003-022-03903-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 08/25/2022] [Indexed: 11/09/2022] Open
Abstract
Hallucinations are a core feature of psychosis and common in Parkinson's. Their transient, unexpected nature suggests a change in dynamic brain states, but underlying causes are unknown. Here, we examine temporal dynamics and underlying structural connectivity in Parkinson's-hallucinations using a combination of functional and structural MRI, network control theory, neurotransmitter density and genetic analyses. We show that Parkinson's-hallucinators spent more time in a predominantly Segregated functional state with fewer between-state transitions. The transition from integrated-to-segregated state had lower energy cost in Parkinson's-hallucinators; and was therefore potentially preferable. The regional energy needed for this transition was correlated with regional neurotransmitter density and gene expression for serotoninergic, GABAergic, noradrenergic and cholinergic, but not dopaminergic, receptors. We show how the combination of neurochemistry and brain structure jointly shape functional brain dynamics leading to hallucinations and highlight potential therapeutic targets by linking these changes to neurotransmitter systems involved in early sensory and complex visual processing.
Collapse
Affiliation(s)
- Angeliki Zarkali
- Dementia Research Centre, University College London, 8-11 Queen Square, London, WC1N 3AR, UK.
| | - Andrea I Luppi
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, CB2 0QQ, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Emmanuel A Stamatakis
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, CB2 0QQ, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Suzanne Reeves
- Division of Psychiatry, University College London, 149 Tottenham Court Rd, London, W1T 7BN, UK
| | - Peter McColgan
- Huntington's Disease Centre, University College London, Russell Square House, London, WC1B 5EH, UK
| | - Louise-Ann Leyland
- Dementia Research Centre, University College London, 8-11 Queen Square, London, WC1N 3AR, UK
| | - Andrew J Lees
- Reta Lila Weston Institute of Neurological Studies, University College London, 1 Wakefield Street, London, WC1N 1PJ, UK
| | - Rimona S Weil
- Dementia Research Centre, University College London, 8-11 Queen Square, London, WC1N 3AR, UK
- Wellcome Centre for Human Neuroimaging, University College London, 12 Queen Square, London, WC1N 3AR, UK
- Movement Disorders Consortium, University College London, London, WC1N 3BG, UK
| |
Collapse
|
19
|
Reliability and subject specificity of personalized whole-brain dynamical models. Neuroimage 2022; 257:119321. [PMID: 35580807 DOI: 10.1016/j.neuroimage.2022.119321] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 05/06/2022] [Accepted: 05/12/2022] [Indexed: 11/23/2022] Open
Abstract
Dynamical whole-brain models were developed to link structural (SC) and functional connectivity (FC) together into one framework. Nowadays, they are used to investigate the dynamical regimes of the brain and how these relate to behavioral, clinical and demographic traits. However, there is no comprehensive investigation on how reliable and subject specific the modeling results are given the variability of the empirical FC. In this study, we show that the parameters of these models can be fitted with a "poor" to "good" reliability depending on the exact implementation of the modeling paradigm. We find, as a general rule of thumb, that enhanced model personalization leads to increasingly reliable model parameters. In addition, we observe no clear effect of the model complexity evaluated by separately sampling results for linear, phase oscillator and neural mass network models. In fact, the most complex neural mass model often yields modeling results with "poor" reliability comparable to the simple linear model, but demonstrates an enhanced subject specificity of the model similarity maps. Subsequently, we show that the FC simulated by these models can outperform the empirical FC in terms of both reliability and subject specificity. For the structure-function relationship, simulated FC of individual subjects may be identified from the correlations with the empirical SC with an accuracy up to 70%, but not vice versa for non-linear models. We sample all our findings for 8 distinct brain parcellations and 6 modeling conditions and show that the parcellation-induced effect is much more pronounced for the modeling results than for the empirical data. In sum, this study provides an exploratory account on the reliability and subject specificity of dynamical whole-brain models and may be relevant for their further development and application. In particular, our findings suggest that the application of the dynamical whole-brain modeling should be tightly connected with an estimate of the reliability of the results.
Collapse
|
20
|
Litwińczuk MC, Trujillo-Barreto N, Muhlert N, Cloutman L, Woollams A. Combination of structural and functional connectivity explains unique variation in specific domains of cognitive function. Neuroimage 2022; 262:119531. [PMID: 35931312 DOI: 10.1016/j.neuroimage.2022.119531] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 07/20/2022] [Accepted: 08/01/2022] [Indexed: 11/29/2022] Open
Abstract
The relationship between structural and functional brain networks has been characterised as complex: the two networks mirror each other and show mutual influence but they also diverge in their organisation. This work explored whether a combination of structural and functional connectivity can improve the fit of regression models of cognitive performance. Principal Component Analysis (PCA) was first applied to cognitive data from the Human Connectome Project to identify latent cognitive components: Executive Function, Self-regulation, Language, Encoding and Sequence Processing. A Principal Component Regression approach with embedded Step-Wise Regression (SWR-PCR) was then used to fit regression models of each cognitive domain based on structural (SC), functional (FC) or combined structural-functional (CC) connectivity. Executive Function was best explained by the CC model. Self-regulation was equally well explained by SC and FC. Language was equally well explained by CC and FC models. Encoding and Sequence Processing were best explained by SC. Evaluation of out-of-sample models' skill via cross-validation showed that SC, FC and CC produced generalisable models of Language performance. SC models performed most effectively at predicting Language performance in unseen sample. Executive Function was most effectively predicted by SC models, followed only by CC models. Self-regulation was only effectively predicted by CC models and Sequence Processing was only effectively predicted by FC models. The present study demonstrates that integrating structural and functional connectivity can help explaining cognitive performance, but that the added explanatory value (in sample) may be domain-specific and can come at the expense of reduced generalisation performance (out-of-sample).
Collapse
Affiliation(s)
| | | | - Nils Muhlert
- Division of Neuroscience and Experimental Psychology, University of Manchester, UK
| | - Lauren Cloutman
- Division of Neuroscience and Experimental Psychology, University of Manchester, UK
| | - Anna Woollams
- Division of Neuroscience and Experimental Psychology, University of Manchester, UK
| |
Collapse
|
21
|
Baldi S, Michielse S, Vriend C, van den Heuvel MP, van den Heuvel OA, Schruers KRJ, Goossens L. Abnormal white-matter rich-club organization in obsessive-compulsive disorder. Hum Brain Mapp 2022; 43:4699-4709. [PMID: 35735129 PMCID: PMC9491289 DOI: 10.1002/hbm.25984] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 05/24/2022] [Accepted: 06/03/2022] [Indexed: 11/09/2022] Open
Abstract
Rich‐club organization is key to efficient global neuronal signaling and integration of information. Alterations interfere with higher‐order cognitive processes, and are common to several psychiatric and neurological conditions. A few studies examining the structural connectome in obsessive–compulsive disorder (OCD) suggest lower efficiency of information transfer across the brain. However, it remains unclear whether this is due to alterations in rich‐club organization. In the current study, the structural connectome of 28 unmedicated OCD patients, 8 of their unaffected siblings and 28 healthy controls was reconstructed by means of diffusion‐weighted imaging and probabilistic tractography. Topological and weighted measures of rich‐club organization and connectivity were computed, alongside global and nodal measures of network integration and segregation. The relationship between clinical scores and network properties was explored. Compared to healthy controls, OCD patients displayed significantly lower topological and weighted rich‐club organization, allocating a smaller fraction of all connection weights to the rich‐club core. Global clustering coefficient, local efficiency, and clustering of nonrich club nodes were significantly higher in OCD patients. Significant three‐group differences emerged, with siblings displaying highest and lowest values in different measures. No significant correlation with any clinical score was found. Our results suggest weaker structural connectivity between rich‐club nodes in OCD patients, possibly resulting in lower network integration in favor of higher network segregation. We highlight the need of looking at network‐based alterations in brain organization and function when investigating the neurobiological basis of this disorder, and stimulate further research into potential familial protective factors against the development of OCD.
Collapse
Affiliation(s)
- Samantha Baldi
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Stijn Michielse
- Department of Neurosurgery, School for Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Chris Vriend
- Department of Anatomy and Neurosciences, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, Netherlands.,Department of Psychiatry, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, Netherlands
| | - Martijn P van den Heuvel
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Odile A van den Heuvel
- Department of Anatomy and Neurosciences, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, Netherlands.,Department of Psychiatry, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, Netherlands
| | - Koen R J Schruers
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Liesbet Goossens
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands
| |
Collapse
|
22
|
A Riemannian approach to predicting brain function from the structural connectome. Neuroimage 2022; 257:119299. [PMID: 35636736 DOI: 10.1016/j.neuroimage.2022.119299] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 03/29/2022] [Accepted: 05/09/2022] [Indexed: 11/21/2022] Open
Abstract
Ongoing brain function is largely determined by the underlying wiring of the brain, but the specific rules governing this relationship remain unknown. Emerging literature has suggested that functional interactions between brain regions emerge from the structural connections through mono- as well as polysynaptic mechanisms. Here, we propose a novel approach based on diffusion maps and Riemannian optimization to emulate this dynamic mechanism in the form of random walks on the structural connectome and predict functional interactions as a weighted combination of these random walks. Our proposed approach was evaluated in two different cohorts of healthy adults (Human Connectome Project, HCP; Microstructure-Informed Connectomics, MICs). Our approach outperformed existing approaches and showed that performance plateaus approximately around the third random walk. At macroscale, we found that the largest number of walks was required in nodes of the default mode and frontoparietal networks, underscoring an increasing relevance of polysynaptic communication mechanisms in transmodal cortical networks compared to primary and unimodal systems.
Collapse
|
23
|
Cabral J, Jirsa V, Popovych OV, Torcini A, Yanchuk S. Editorial: From Structure to Function in Neuronal Networks: Effects of Adaptation, Time-Delays, and Noise. Front Syst Neurosci 2022; 16:871165. [PMID: 35528150 PMCID: PMC9069101 DOI: 10.3389/fnsys.2022.871165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 02/28/2022] [Indexed: 12/03/2022] Open
Affiliation(s)
- Joana Cabral
- Life and Health Sciences Research Institute, School of Medicine, University of Minho, Braga, Portugal
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, United Kingdom
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Viktor Jirsa
- Aix Marseille University, INSERM, INS, Institut de Neurosciences des Systèmes, Marseille, France
| | - Oleksandr V. Popovych
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM7), Research Centre Jülich, Jülich, Germany
| | - Alessandro Torcini
- CY Cergy Paris Université, Laboratoire de Physique Théorique et Modélisation, CNRS, UMR 8089, Cergy, France
| | - Serhiy Yanchuk
- Research Department Complexity Science, Potsdam Institute for Climate Impact Research, Potsdam, Germany
- Department of Mathematics, Faculty of Mathematics and Natural Sciences, Humboldt-Universität zu Berlin, Berlin, Germany
- *Correspondence: Serhiy Yanchuk
| |
Collapse
|
24
|
Kim WSH, Luciw NJ, Atwi S, Shirzadi Z, Dolui S, Detre JA, Nasrallah IM, Swardfager W, Bryan RN, Launer LJ, MacIntosh BJ. Associations of white matter hyperintensities with networks of gray matter blood flow and volume in midlife adults: A coronary artery risk development in young adults magnetic resonance imaging substudy. Hum Brain Mapp 2022; 43:3680-3693. [PMID: 35429100 PMCID: PMC9294299 DOI: 10.1002/hbm.25876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 04/01/2022] [Accepted: 04/04/2022] [Indexed: 12/02/2022] Open
Abstract
White matter hyperintensities (WMHs) are emblematic of cerebral small vessel disease, yet effects on the brain have not been well characterized at midlife. Here, we investigated whether WMH volume is associated with brain network alterations in midlife adults. Two hundred and fifty‐four participants from the Coronary Artery Risk Development in Young Adults study were selected and stratified by WMH burden into Lo‐WMH (mean age = 50 ± 3.5 years) and Hi‐WMH (mean age = 51 ± 3.7 years) groups of equal size. We constructed group‐level covariance networks based on cerebral blood flow (CBF) and gray matter volume (GMV) maps across 74 gray matter regions. Through consensus clustering, we found that both CBF and GMV covariance networks partitioned into modules that were largely consistent between groups. Next, CBF and GMV covariance network topologies were compared between Lo‐ and Hi‐WMH groups at global (clustering coefficient, characteristic path length, global efficiency) and regional (degree, betweenness centrality, local efficiency) levels. At the global level, there were no between‐group differences in either CBF or GMV covariance networks. In contrast, we found between‐group differences in the regional degree, betweenness centrality, and local efficiency of several brain regions in both CBF and GMV covariance networks. Overall, CBF and GMV covariance analyses provide evidence that WMH‐related network alterations are present at midlife.
Collapse
Affiliation(s)
- William S. H. Kim
- Department of Medical Biophysics University of Toronto Toronto Ontario Canada
- Hurvitz Brain Sciences Program Sunnybrook Research Institute Toronto Ontario Canada
| | - Nicholas J. Luciw
- Department of Medical Biophysics University of Toronto Toronto Ontario Canada
- Hurvitz Brain Sciences Program Sunnybrook Research Institute Toronto Ontario Canada
| | - Sarah Atwi
- Department of Medical Biophysics University of Toronto Toronto Ontario Canada
- Hurvitz Brain Sciences Program Sunnybrook Research Institute Toronto Ontario Canada
| | - Zahra Shirzadi
- Department of Medical Biophysics University of Toronto Toronto Ontario Canada
- Hurvitz Brain Sciences Program Sunnybrook Research Institute Toronto Ontario Canada
| | - Sudipto Dolui
- Center for Functional Neuroimaging University of Pennsylvania Philadelphia Pennsylvania USA
- Department of Neurology University of Pennsylvania Philadelphia Pennsylvania USA
- Department of Radiology University of Pennsylvania Philadelphia Pennsylvania USA
| | - John A. Detre
- Center for Functional Neuroimaging University of Pennsylvania Philadelphia Pennsylvania USA
- Department of Neurology University of Pennsylvania Philadelphia Pennsylvania USA
- Department of Radiology University of Pennsylvania Philadelphia Pennsylvania USA
| | - Ilya M. Nasrallah
- Department of Radiology University of Pennsylvania Philadelphia Pennsylvania USA
| | - Walter Swardfager
- Hurvitz Brain Sciences Program Sunnybrook Research Institute Toronto Ontario Canada
- Canadian Partnership for Stroke Recovery Sunnybrook Research Institute Toronto Ontario Canada
- Department of Pharmacology and Toxicology University of Toronto Toronto Ontario Canada
- Toronto Rehabilitation Institute, University Health Network Toronto Ontario Canada
- Dr. Sandra Black Centre for Brain Resilience & Recovery Sunnybrook Research Institute Toronto Ontario Canada
| | - Robert Nick Bryan
- Department of Diagnostic Medicine University of Texas Austin Texas USA
| | - Lenore J. Launer
- Laboratory of Epidemiology and Population Science National Institute on Aging Bethesda Maryland USA
| | - Bradley J. MacIntosh
- Department of Medical Biophysics University of Toronto Toronto Ontario Canada
- Hurvitz Brain Sciences Program Sunnybrook Research Institute Toronto Ontario Canada
- Canadian Partnership for Stroke Recovery Sunnybrook Research Institute Toronto Ontario Canada
- Dr. Sandra Black Centre for Brain Resilience & Recovery Sunnybrook Research Institute Toronto Ontario Canada
| |
Collapse
|
25
|
Zhang F, Daducci A, He Y, Schiavi S, Seguin C, Smith RE, Yeh CH, Zhao T, O'Donnell LJ. Quantitative mapping of the brain's structural connectivity using diffusion MRI tractography: A review. Neuroimage 2022; 249:118870. [PMID: 34979249 PMCID: PMC9257891 DOI: 10.1016/j.neuroimage.2021.118870] [Citation(s) in RCA: 99] [Impact Index Per Article: 49.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 12/03/2021] [Accepted: 12/31/2021] [Indexed: 12/13/2022] Open
Abstract
Diffusion magnetic resonance imaging (dMRI) tractography is an advanced imaging technique that enables in vivo reconstruction of the brain's white matter connections at macro scale. It provides an important tool for quantitative mapping of the brain's structural connectivity using measures of connectivity or tissue microstructure. Over the last two decades, the study of brain connectivity using dMRI tractography has played a prominent role in the neuroimaging research landscape. In this paper, we provide a high-level overview of how tractography is used to enable quantitative analysis of the brain's structural connectivity in health and disease. We focus on two types of quantitative analyses of tractography, including: 1) tract-specific analysis that refers to research that is typically hypothesis-driven and studies particular anatomical fiber tracts, and 2) connectome-based analysis that refers to research that is more data-driven and generally studies the structural connectivity of the entire brain. We first provide a review of methodology involved in three main processing steps that are common across most approaches for quantitative analysis of tractography, including methods for tractography correction, segmentation and quantification. For each step, we aim to describe methodological choices, their popularity, and potential pros and cons. We then review studies that have used quantitative tractography approaches to study the brain's white matter, focusing on applications in neurodevelopment, aging, neurological disorders, mental disorders, and neurosurgery. We conclude that, while there have been considerable advancements in methodological technologies and breadth of applications, there nevertheless remains no consensus about the "best" methodology in quantitative analysis of tractography, and researchers should remain cautious when interpreting results in research and clinical applications.
Collapse
Affiliation(s)
- Fan Zhang
- Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
| | | | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; Chinese Institute for Brain Research, Beijing, China
| | - Simona Schiavi
- Department of Computer Science, University of Verona, Verona, Italy
| | - Caio Seguin
- Melbourne Neuropsychiatry Centre, University of Melbourne and Melbourne Health, Melbourne, Australia; The University of Sydney, School of Biomedical Engineering, Sydney, Australia
| | - Robert E Smith
- The Florey Institute of Neuroscience and Mental Health, Melbourne, Australia; Florey Department of Neuroscience and Mental Health, The University of Melbourne, Melbourne, Australia
| | - Chun-Hung Yeh
- Institute for Radiological Research, Chang Gung University, Taoyuan, Taiwan; Department of Psychiatry, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
| | - Tengda Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | | |
Collapse
|
26
|
Cao M, Vogrin SJ, Peterson ADH, Woods W, Cook MJ, Plummer C. Dynamical Network Models From EEG and MEG for Epilepsy Surgery—A Quantitative Approach. Front Neurol 2022; 13:837893. [PMID: 35422755 PMCID: PMC9001937 DOI: 10.3389/fneur.2022.837893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 03/01/2022] [Indexed: 11/16/2022] Open
Abstract
There is an urgent need for more informative quantitative techniques that non-invasively and objectively assess strategies for epilepsy surgery. Invasive intracranial electroencephalography (iEEG) remains the clinical gold standard to investigate the nature of the epileptogenic zone (EZ) before surgical resection. However, there are major limitations of iEEG, such as the limited spatial sampling and the degree of subjectivity inherent in the analysis and clinical interpretation of iEEG data. Recent advances in network analysis and dynamical network modeling provide a novel aspect toward a more objective assessment of the EZ. The advantage of such approaches is that they are data-driven and require less or no human input. Multiple studies have demonstrated success using these approaches when applied to iEEG data in characterizing the EZ and predicting surgical outcomes. However, the limitations of iEEG recordings equally apply to these studies—limited spatial sampling and the implicit assumption that iEEG electrodes, whether strip, grid, depth or stereo EEG (sEEG) arrays, are placed in the correct location. Therefore, it is of interest to clinicians and scientists to see whether the same analysis and modeling techniques can be applied to whole-brain, non-invasive neuroimaging data (from MRI-based techniques) and neurophysiological data (from MEG and scalp EEG recordings), thus removing the limitation of spatial sampling, while safely and objectively characterizing the EZ. This review aims to summarize current state of the art non-invasive methods that inform epilepsy surgery using network analysis and dynamical network models. We also present perspectives on future directions and clinical applications of these promising approaches.
Collapse
Affiliation(s)
- Miao Cao
- Center for MRI Research, Peking University, Beijing, China
- Department of Medicine, The University of Melbourne, Melbourne, VIC, Australia
- Centre for Clinical Neurosciences and Neurological Research, St Vincent's Hospital Melbourne, Melbourne, VIC, Australia
| | - Simon J. Vogrin
- Department of Medicine, The University of Melbourne, Melbourne, VIC, Australia
- Centre for Clinical Neurosciences and Neurological Research, St Vincent's Hospital Melbourne, Melbourne, VIC, Australia
- School of Health Sciences, Swinburne University of Technology, Melbourne, VIC, Australia
| | - Andre D. H. Peterson
- Department of Medicine, The University of Melbourne, Melbourne, VIC, Australia
- Centre for Clinical Neurosciences and Neurological Research, St Vincent's Hospital Melbourne, Melbourne, VIC, Australia
| | - William Woods
- School of Health Sciences, Swinburne University of Technology, Melbourne, VIC, Australia
| | - Mark J. Cook
- Department of Medicine, The University of Melbourne, Melbourne, VIC, Australia
- Centre for Clinical Neurosciences and Neurological Research, St Vincent's Hospital Melbourne, Melbourne, VIC, Australia
| | - Chris Plummer
- Department of Medicine, The University of Melbourne, Melbourne, VIC, Australia
- Centre for Clinical Neurosciences and Neurological Research, St Vincent's Hospital Melbourne, Melbourne, VIC, Australia
- School of Health Sciences, Swinburne University of Technology, Melbourne, VIC, Australia
- *Correspondence: Chris Plummer
| |
Collapse
|
27
|
Manuello J, Mancuso L, Liloia D, Cauda F, Duca S, Costa T. A co-alteration parceling of the cingulate cortex. Brain Struct Funct 2022; 227:1803-1816. [PMID: 35238998 PMCID: PMC9098570 DOI: 10.1007/s00429-022-02473-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 02/14/2022] [Indexed: 11/24/2022]
Abstract
The cingulate cortex is known to be a complex structure, involved in several cognitive and emotional functions, as well as being altered by a variety of brain disorders. This heterogeneity is reflected in the multiple parceling models proposed in the literature. At the present, sub-regions of the cingulate cortex had been identified taking into account functional and structural connectivity, as well as cytological and electrochemical properties. In the present work, we propose an innovative node-wise parceling approach based on meta-analytic Bayesian co-alteration. To this aim, 193 case-control voxel-based morphometry experiments were analyzed, and the Patel's κ index was used to assess probability of morphometric co-alteration between nodes placed in the cingulate cortex and in the rest of the brain. Hierarchical clustering was then applied to identify nodes in the cingulate cortex exhibiting a similar pattern of whole-brain co-alteration. The obtained dendrogram highlighted a robust fronto-parietal cluster compatible with the default mode network, and being supported by the interplay between the retrosplenial cortex and the anterior and posterior cingulate cortex, rarely described in the literature. This ensemble was further confirmed by the analysis of functional patterns. Leveraging on co-alteration to investigate cortical organization could, therefore, allow to combine multimodal information, resolving conflicting results sometimes coming from the separate use of singular modalities. Crucially, this provides a valuable way to understand the pathological brain using data driven, whole-brain informed and context-specific evidence in a way not yet explored in the field.
Collapse
Affiliation(s)
- Jordi Manuello
- GCS fMRI, Koelliker Hospital and University of Turin, Turin, Italy.,FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy
| | - Lorenzo Mancuso
- FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy
| | - Donato Liloia
- GCS fMRI, Koelliker Hospital and University of Turin, Turin, Italy. .,FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy.
| | - Franco Cauda
- GCS fMRI, Koelliker Hospital and University of Turin, Turin, Italy.,FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy.,Neuroscience Institute of Turin, Turin, Italy
| | - Sergio Duca
- GCS fMRI, Koelliker Hospital and University of Turin, Turin, Italy.,FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy
| | - Tommaso Costa
- GCS fMRI, Koelliker Hospital and University of Turin, Turin, Italy.,FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy
| |
Collapse
|
28
|
Bottino F, Lucignani M, Pasquini L, Mastrogiovanni M, Gazzellini S, Ritrovato M, Longo D, Figà-Talamanca L, Rossi Espagnet MC, Napolitano A. Spatial Stability of Functional Networks: A Measure to Assess the Robustness of Graph-Theoretical Metrics to Spatial Errors Related to Brain Parcellation. Front Neurosci 2022; 15:736524. [PMID: 35250432 PMCID: PMC8894326 DOI: 10.3389/fnins.2021.736524] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Accepted: 12/28/2021] [Indexed: 12/12/2022] Open
Abstract
There is growing interest in studying human brain connectivity and in modelling the brain functional structure as a network. Brain network creation requires parcellation of the cerebral cortex to define nodes. Parcellation might be affected by possible errors due to inter- and intra-subject variability as a consequence of brain structural and physiological characteristics and shape variations related to ageing and diseases, acquisition noise, and misregistration. These errors could induce a knock-on effect on network measure variability. The aim of this study was to investigate spatial stability, a measure of functional connectivity variations induced by parcellation errors. We simulated parcellation variability with random small spatial changes and evaluated its effects on twenty-seven graph-theoretical measures. The study included subjects from three public online datasets. Two brain parcellations were performed using FreeSurfer with geometric atlases. Starting from these, 100 new parcellations were created by increasing the area of 30% of parcels, reducing the area of neighbour parcels, with a rearrangement of vertices. fMRI data were filtered with linear regression, CompCor, and motion correction. Adjacency matrices were constructed with 0.1, 0.2, 0.3, and 0.4 thresholds. Differences in spatial stability between datasets, atlases, and threshold were evaluated. The higher spatial stability resulted for Characteristic-path-length, Density, Transitivity, and Closeness-centrality, and the lower spatial stability resulted for Bonacich and Katz. Multivariate analysis showed a significant effect of atlas, datasets, and thresholds. Katz and Bonacich centrality, which was subject to larger variations, can be considered an unconventional graph measure, poorly implemented in the clinical field and not yet investigated for reliability assessment. Spatial stability (SS) is affected by threshold, and it decreases with increasing threshold for several measures. Moreover, SS seems to depend on atlas choice and scanning parameters. Our study highlights the importance of paying close attention to possible parcellation-related spatial errors, which may affect the reliability of functional connectivity measures.
Collapse
Affiliation(s)
- Francesca Bottino
- Medical Physics Department, Bambino Gesù Children’s Hospital IRCCS, Rome, Italy
| | - Martina Lucignani
- Medical Physics Department, Bambino Gesù Children’s Hospital IRCCS, Rome, Italy
| | - Luca Pasquini
- Neuroradiology Unit, NESMOS Department, Sant’Andrea Hospital, La Sapienza University, Rome, Italy
- Neuroradiology Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | | | - Simone Gazzellini
- Neuroscience and Neurorehabilitation Department, Bambino Gesù Children’s Hospital – IRCCS, Rome, Italy
| | - Matteo Ritrovato
- Health Technology and Safety Research Unit, Bambino Gesù Children’s Hospital – IRCCS, Rome, Italy
| | - Daniela Longo
- Neuroradiology Unit, Imaging Department, Bambino Gesù Children’s Hospital, IRCCS, Rome, Italy
| | - Lorenzo Figà-Talamanca
- Neuroradiology Unit, Imaging Department, Bambino Gesù Children’s Hospital, IRCCS, Rome, Italy
| | - Maria Camilla Rossi Espagnet
- Neuroradiology Unit, Imaging Department, Bambino Gesù Children’s Hospital, IRCCS, Rome, Italy
- NESMOS, Neuroradiology Department, S. Andrea Hospital Sapienza Rome University, Rome, Italy
| | - Antonio Napolitano
- Medical Physics Department, Bambino Gesù Children’s Hospital IRCCS, Rome, Italy
- *Correspondence: Antonio Napolitano,
| |
Collapse
|
29
|
An interdisciplinary computational model for predicting traumatic brain injury: Linking biomechanics and functional neural networks. Neuroimage 2022; 251:119002. [PMID: 35176490 DOI: 10.1016/j.neuroimage.2022.119002] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Revised: 01/19/2022] [Accepted: 02/12/2022] [Indexed: 11/22/2022] Open
Abstract
The brain is a complex network consisting of neuron cell bodies in the gray matter and their axonal projections, forming the white matter tracts. These neurons are supported by an equally complex vascular network as well as glial cells. Traumatic brain injury (TBI) can lead to the disruption of the structural and functional brain networks due to disruption of both neuronal cell bodies in the gray matter as well as their projections and supporting cells. To explore how an impact can alter the function of brain networks, we integrated a finite element (FE) brain mechanics model with linked models of brain dynamics (Kuramoto oscillator) and vascular perfusion (Balloon-Windkessel) in this study. We used empirical resting-state functional magnetic resonance imaging (MRI) data to optimize the fit of our brain dynamics and perfusion models to clinical data. Results from the FE model were used to mimic injury in these optimized brain dynamics models: injury to the nodes (gray matter) led to a decrease in the nodal oscillation frequency, while damage to the edges (axonal connections/white matter) progressively decreased coupling among connected nodes. A total of 53 cases, including 33 non-injurious and 20 concussive head impacts experienced by professional American football players were simulated using this integrated model. We examined the correlation of injury outcomes with global measures of structural connectivity, neural dynamics, and functional connectivity of the brain networks when using different lesion methods. Results show that injurious head impacts cause significant alterations in global network topology regardless of lesion methods. Changes between the disrupted and healthy functional connectivity (measured by Pearson correlation) consistently correlated well with injury outcomes (AUC≥0.75), although the predictive performance is not significantly different (p>0.05) to that of traditional kinematic measures (angular acceleration). Intriguingly, our lesion model for gray matter damage predicted increases in global efficiency and clustering coefficient with increases in injury risk, while disrupting axonal connections led to lower network efficiency and clustering. When both injury mechanisms were combined into a single injury prediction model, the injury prediction performance depended on the thresholds used to determine neurodegeneration and mechanical tolerance for axonal injury. Together, these results point towards complex effects of mechanical trauma to the brain and provide a new framework for understanding brain injury at a causal mechanistic level and developing more effective diagnostic methods and therapeutic interventions.
Collapse
|
30
|
Faskowitz J, Betzel RF, Sporns O. Edges in brain networks: Contributions to models of structure and function. Netw Neurosci 2022; 6:1-28. [PMID: 35350585 PMCID: PMC8942607 DOI: 10.1162/netn_a_00204] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Accepted: 08/02/2021] [Indexed: 11/16/2022] Open
Abstract
Network models describe the brain as sets of nodes and edges that represent its distributed organization. So far, most discoveries in network neuroscience have prioritized insights that highlight distinct groupings and specialized functional contributions of network nodes. Importantly, these functional contributions are determined and expressed by the web of their interrelationships, formed by network edges. Here, we underscore the important contributions made by brain network edges for understanding distributed brain organization. Different types of edges represent different types of relationships, including connectivity and similarity among nodes. Adopting a specific definition of edges can fundamentally alter how we analyze and interpret a brain network. Furthermore, edges can associate into collectives and higher order arrangements, describe time series, and form edge communities that provide insights into brain network topology complementary to the traditional node-centric perspective. Focusing on the edges, and the higher order or dynamic information they can provide, discloses previously underappreciated aspects of structural and functional network organization.
Collapse
Affiliation(s)
- Joshua Faskowitz
- Program in Neuroscience, Indiana University, Bloomington, IN, USA
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Richard F. Betzel
- Program in Neuroscience, Indiana University, Bloomington, IN, USA
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
- Indiana University Network Science Institute, Indiana University, Bloomington, IN, USA
- Cognitive Science Program, Indiana University, Bloomington, IN, USA
| | - Olaf Sporns
- Program in Neuroscience, Indiana University, Bloomington, IN, USA
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
- Indiana University Network Science Institute, Indiana University, Bloomington, IN, USA
- Cognitive Science Program, Indiana University, Bloomington, IN, USA
| |
Collapse
|
31
|
Li X, Tan J, Wang P, Liu H, Li Z, Wang W. Anatomically constrained squeeze-and-excitation graph attention network for cortical surface parcellation. Comput Biol Med 2022; 140:105113. [PMID: 34891094 DOI: 10.1016/j.compbiomed.2021.105113] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Revised: 12/01/2021] [Accepted: 12/02/2021] [Indexed: 12/15/2022]
Abstract
In order to understand the organizational structures of healthy cerebral cortex and the abnormalities in neurological and psychiatric diseases, it is significant to parcellate the cortical surface. The cortical surface, however, is a highly folded complex geometric structure which challenges automatic cortical surface parcellation. Nowadays, the parcellation methods of cerebral cortex are mostly based on geometric simplification, i.e., iteratively inflating and mapping the cortical surface to a spherical surface for processing, which is time-consuming and cannot make full use of the intrinsic structural information of the original cortical surface. In this study, we proposed an anatomically constrained squeeze-and-excitation graph attention network (ASEGAT) for an end-to-end brain cortical surface parcellation on the original cortical surface manifold. The ASEGAT is formed by two graph attention modules and a squeeze-and-excitation module that incorporate self-attention and head attention for rendering features of each node. Furthermore, we designed an anatomic constraint loss to introduce the anatomical priori of regional adjacency relationships, which could improve the consistency of region labeling. We evaluated our model on a public dataset of 100 manually labeled brain surfaces. Compared with several advanced methods, the results showed that our proposed approach achieved state-of-the-art performance, obtaining an accuracy of 90.65% and a dice score of 89.00%.
Collapse
Affiliation(s)
- Xinwei Li
- School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, Chongqing, China.
| | - Jia Tan
- School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, Chongqing, China
| | - Panyu Wang
- School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Hong Liu
- School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, Chongqing, China
| | - Zhangyong Li
- School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, Chongqing, China
| | - Wei Wang
- School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, Chongqing, China.
| |
Collapse
|
32
|
Zugman A, Harrewijn A, Cardinale EM, Zwiebel H, Freitag GF, Werwath KE, Bas‐Hoogendam JM, Groenewold NA, Aghajani M, Hilbert K, Cardoner N, Porta‐Casteràs D, Gosnell S, Salas R, Blair KS, Blair JR, Hammoud MZ, Milad M, Burkhouse K, Phan KL, Schroeder HK, Strawn JR, Beesdo‐Baum K, Thomopoulos SI, Grabe HJ, Van der Auwera S, Wittfeld K, Nielsen JA, Buckner R, Smoller JW, Mwangi B, Soares JC, Wu M, Zunta‐Soares GB, Jackowski AP, Pan PM, Salum GA, Assaf M, Diefenbach GJ, Brambilla P, Maggioni E, Hofmann D, Straube T, Andreescu C, Berta R, Tamburo E, Price R, Manfro GG, Critchley HD, Makovac E, Mancini M, Meeten F, Ottaviani C, Agosta F, Canu E, Cividini C, Filippi M, Kostić M, Munjiza A, Filippi CA, Leibenluft E, Alberton BAV, Balderston NL, Ernst M, Grillon C, Mujica‐Parodi LR, van Nieuwenhuizen H, Fonzo GA, Paulus MP, Stein MB, Gur RE, Gur RC, Kaczkurkin AN, Larsen B, Satterthwaite TD, Harper J, Myers M, Perino MT, Yu Q, Sylvester CM, Veltman DJ, Lueken U, Van der Wee NJA, Stein DJ, Jahanshad N, Thompson PM, Pine DS, Winkler AM. Mega-analysis methods in ENIGMA: The experience of the generalized anxiety disorder working group. Hum Brain Mapp 2022; 43:255-277. [PMID: 32596977 PMCID: PMC8675407 DOI: 10.1002/hbm.25096] [Citation(s) in RCA: 45] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Revised: 05/26/2020] [Accepted: 05/31/2020] [Indexed: 12/15/2022] Open
Abstract
The ENIGMA group on Generalized Anxiety Disorder (ENIGMA-Anxiety/GAD) is part of a broader effort to investigate anxiety disorders using imaging and genetic data across multiple sites worldwide. The group is actively conducting a mega-analysis of a large number of brain structural scans. In this process, the group was confronted with many methodological challenges related to study planning and implementation, between-country transfer of subject-level data, quality control of a considerable amount of imaging data, and choices related to statistical methods and efficient use of resources. This report summarizes the background information and rationale for the various methodological decisions, as well as the approach taken to implement them. The goal is to document the approach and help guide other research groups working with large brain imaging data sets as they develop their own analytic pipelines for mega-analyses.
Collapse
Affiliation(s)
- André Zugman
- National Institute of Mental Health (NIMH), National Institutes of Health (NIH)BethesdaMarylandUSA
| | - Anita Harrewijn
- National Institute of Mental Health (NIMH), National Institutes of Health (NIH)BethesdaMarylandUSA
| | - Elise M. Cardinale
- National Institute of Mental Health (NIMH), National Institutes of Health (NIH)BethesdaMarylandUSA
| | - Hannah Zwiebel
- National Institute of Mental Health (NIMH), National Institutes of Health (NIH)BethesdaMarylandUSA
| | - Gabrielle F. Freitag
- National Institute of Mental Health (NIMH), National Institutes of Health (NIH)BethesdaMarylandUSA
| | - Katy E. Werwath
- National Institute of Mental Health (NIMH), National Institutes of Health (NIH)BethesdaMarylandUSA
| | - Janna M. Bas‐Hoogendam
- Leiden University Medical Center, Department of PsychiatryLeidenThe Netherlands
- Leiden Institute for Brain and Cognition (LIBC)LeidenThe Netherlands
- Leiden University, Institute of Psychology, Developmental and Educational PsychologyLeidenThe Netherlands
| | - Nynke A. Groenewold
- Department of Psychiatry & Neuroscience InstituteUniversity of Cape TownCape TownSouth Africa
| | - Moji Aghajani
- Department. of PsychiatryAmsterdam UMC/VUMCAmsterdamThe Netherlands
- GGZ InGeestDepartment of Research & InnovationAmsterdamThe Netherlands
| | - Kevin Hilbert
- Department of PsychologyHumboldt‐Universität zu BerlinBerlinGermany
| | - Narcis Cardoner
- Department of Mental HealthUniversity Hospital Parc Taulí‐I3PTBarcelonaSpain
- Department of Psychiatry and Forensic MedicineUniversitat Autònoma de BarcelonaBarcelonaSpain
- Centro de Investigación Biomédica en Red de Salud MentalCarlos III Health InstituteMadridSpain
| | - Daniel Porta‐Casteràs
- Department of Mental HealthUniversity Hospital Parc Taulí‐I3PTBarcelonaSpain
- Department of Psychiatry and Forensic MedicineUniversitat Autònoma de BarcelonaBarcelonaSpain
- Centro de Investigación Biomédica en Red de Salud MentalCarlos III Health InstituteMadridSpain
| | - Savannah Gosnell
- Menninger Department of Psychiatry and Behavioral SciencesBaylor College of MedicineHoustonTexasUSA
| | - Ramiro Salas
- Menninger Department of Psychiatry and Behavioral SciencesBaylor College of MedicineHoustonTexasUSA
| | - Karina S. Blair
- Center for Neurobehavioral ResearchBoys Town National Research HospitalBoys TownNebraskaUSA
| | - James R. Blair
- Center for Neurobehavioral ResearchBoys Town National Research HospitalBoys TownNebraskaUSA
| | - Mira Z. Hammoud
- Department of PsychiatryNew York UniversityNew YorkNew YorkUSA
| | - Mohammed Milad
- Department of PsychiatryNew York UniversityNew YorkNew YorkUSA
| | - Katie Burkhouse
- Department of PsychiatryUniversity of Illinois at ChicagoChicagoIllinoisUSA
| | - K. Luan Phan
- Department of Psychiatry and Behavioral HealthThe Ohio State UniversityColumbusOhioUSA
| | - Heidi K. Schroeder
- Department of Psychiatry & Behavioral NeuroscienceUniversity of CincinnatiCincinnatiOhioUSA
| | - Jeffrey R. Strawn
- Department of Psychiatry & Behavioral NeuroscienceUniversity of CincinnatiCincinnatiOhioUSA
| | - Katja Beesdo‐Baum
- Behavioral EpidemiologyInstitute of Clinical Psychology and Psychotherapy, Technische Universität DresdenDresdenGermany
| | - Sophia I. Thomopoulos
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern CaliforniaMarina del ReyCaliforniaUSA
| | - Hans J. Grabe
- Department of Psychiatry and PsychotherapyUniversity Medicine GreifswaldGreifswaldGermany
- German Center for Neurodegenerative Diseases (DZNE)Site Rostock/GreifswaldGreifswaldGermany
| | - Sandra Van der Auwera
- Department of Psychiatry and PsychotherapyUniversity Medicine GreifswaldGreifswaldGermany
- German Center for Neurodegenerative Diseases (DZNE)Site Rostock/GreifswaldGreifswaldGermany
| | - Katharina Wittfeld
- Department of Psychiatry and PsychotherapyUniversity Medicine GreifswaldGreifswaldGermany
- German Center for Neurodegenerative Diseases (DZNE)Site Rostock/GreifswaldGreifswaldGermany
| | - Jared A. Nielsen
- Department of PsychologyHarvard UniversityCambridgeMassachusettsUSA
- Center for Brain ScienceHarvard UniversityCambridgeMassachusettsUSA
| | - Randy Buckner
- Department of PsychologyHarvard UniversityCambridgeMassachusettsUSA
- Center for Brain ScienceHarvard UniversityCambridgeMassachusettsUSA
- Department of PsychiatryMassachusetts General HospitalBostonMassachusettsUSA
| | - Jordan W. Smoller
- Department of PsychiatryMassachusetts General HospitalBostonMassachusettsUSA
| | - Benson Mwangi
- Center Of Excellence On Mood Disorders, Department of Psychiatry and Behavioral SciencesThe University of Texas Health Science Center at HoustonHoustonTexasUSA
| | - Jair C. Soares
- Center Of Excellence On Mood Disorders, Department of Psychiatry and Behavioral SciencesThe University of Texas Health Science Center at HoustonHoustonTexasUSA
| | - Mon‐Ju Wu
- Center Of Excellence On Mood Disorders, Department of Psychiatry and Behavioral SciencesThe University of Texas Health Science Center at HoustonHoustonTexasUSA
| | - Giovana B. Zunta‐Soares
- Center Of Excellence On Mood Disorders, Department of Psychiatry and Behavioral SciencesThe University of Texas Health Science Center at HoustonHoustonTexasUSA
| | - Andrea P. Jackowski
- LiNC, Department of PsychiatryFederal University of São PauloSão PauloSão PauloBrazil
| | - Pedro M. Pan
- LiNC, Department of PsychiatryFederal University of São PauloSão PauloSão PauloBrazil
| | - Giovanni A. Salum
- Section on Negative Affect and Social Processes, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do SulPorto AlegreRio Grande do SulBrazil
| | - Michal Assaf
- Olin Neuropsychiatry Research CenterInstitute of Living, Hartford HospitalHartfordConnecticutUSA
- Department of PsychiatryYale School of MedicineNew HavenConnecticutUSA
| | - Gretchen J. Diefenbach
- Anxiety Disorders CenterInstitute of Living, Hartford HospitalHartfordConnecticutUSA
- Yale School of MedicineNew HavenConnecticutUSA
| | - Paolo Brambilla
- Department of Neurosciences and Mental HealthFondazione IRCCS Ca' Granda Ospedale Maggiore PoliclinicoMilanItaly
| | - Eleonora Maggioni
- Department of Neurosciences and Mental HealthFondazione IRCCS Ca' Granda Ospedale Maggiore PoliclinicoMilanItaly
| | - David Hofmann
- Institute of Medical Psychology and Systems Neuroscience, University of MuensterMuensterGermany
| | - Thomas Straube
- Institute of Medical Psychology and Systems Neuroscience, University of MuensterMuensterGermany
| | - Carmen Andreescu
- Department of PsychiatryUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Rachel Berta
- Department of PsychiatryUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Erica Tamburo
- Department of PsychiatryUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Rebecca Price
- Department of Psychiatry & PsychologyUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Gisele G. Manfro
- Anxiety Disorder ProgramHospital de Clínicas de Porto AlegrePorto AlegreRio Grande do SulBrazil
- Department of PsychiatryFederal University of Rio Grande do SulPorto AlegreRio Grande do SulBrazil
| | - Hugo D. Critchley
- Department of NeuroscienceBrighton and Sussex Medical School, University of SussexBrightonUK
| | - Elena Makovac
- Centre for Neuroimaging ScienceKings College LondonLondonUK
| | - Matteo Mancini
- Department of NeuroscienceBrighton and Sussex Medical School, University of SussexBrightonUK
| | | | | | - Federica Agosta
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of NeuroscienceIRCCS San Raffaele Scientific InstituteMilanItaly
- Vita‐Salute San Raffaele UniversityMilanItaly
| | - Elisa Canu
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of NeuroscienceIRCCS San Raffaele Scientific InstituteMilanItaly
| | - Camilla Cividini
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of NeuroscienceIRCCS San Raffaele Scientific InstituteMilanItaly
| | - Massimo Filippi
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of NeuroscienceIRCCS San Raffaele Scientific InstituteMilanItaly
- Vita‐Salute San Raffaele UniversityMilanItaly
- Neurology and Neurophysiology UnitIRCCS San Raffaele Scientific InstituteMilanItaly
| | - Milutin Kostić
- Institute of Mental Health, University of BelgradeBelgradeSerbia
- Department of Psychiatry, School of MedicineUniversity of BelgradeBelgradeSerbia
| | - Ana Munjiza
- Institute of Mental Health, University of BelgradeBelgradeSerbia
| | - Courtney A. Filippi
- National Institute of Mental Health (NIMH), National Institutes of Health (NIH)BethesdaMarylandUSA
| | - Ellen Leibenluft
- National Institute of Mental Health (NIMH), National Institutes of Health (NIH)BethesdaMarylandUSA
| | - Bianca A. V. Alberton
- Graduate Program in Electrical and Computer Engineering, Universidade Tecnológica Federal do ParanáCuritibaPuerto RicoBrazil
| | - Nicholas L. Balderston
- Center for Neuromodulation in Depression and StressUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Monique Ernst
- National Institute of Mental Health (NIMH), National Institutes of Health (NIH)BethesdaMarylandUSA
| | - Christian Grillon
- National Institute of Mental Health (NIMH), National Institutes of Health (NIH)BethesdaMarylandUSA
| | | | | | - Gregory A. Fonzo
- Department of PsychiatryThe University of Texas at Austin Dell Medical SchoolAustinTexasUSA
| | | | - Murray B. Stein
- Department of Psychiatry & Family Medicine and Public HealthUniversity of CaliforniaLa JollaCaliforniaUSA
| | - Raquel E. Gur
- Department of PsychiatryUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Ruben C. Gur
- Department of PsychiatryUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | | | - Bart Larsen
- Department of PsychiatryUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | | | - Jennifer Harper
- Department of PsychiatryWashington UniversitySt. LouisMissouriUSA
| | - Michael Myers
- Department of PsychiatryWashington UniversitySt. LouisMissouriUSA
| | | | - Qiongru Yu
- Department of PsychiatryWashington UniversitySt. LouisMissouriUSA
| | | | - Dick J. Veltman
- Department. of PsychiatryAmsterdam UMC/VUMCAmsterdamThe Netherlands
| | - Ulrike Lueken
- Department of PsychologyHumboldt‐Universität zu BerlinBerlinGermany
| | - Nic J. A. Van der Wee
- Leiden University Medical Center, Department of PsychiatryLeidenThe Netherlands
- Leiden Institute for Brain and Cognition (LIBC)LeidenThe Netherlands
| | - Dan J. Stein
- Department of Psychiatry & Neuroscience InstituteUniversity of Cape TownCape TownSouth Africa
- SAMRC Unite on Risk & Resilience in Mental Disorders, Department of Psychiatry & Neuroscience InstituteUniversity of Cape TownCape TownSouth Africa
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern CaliforniaMarina del ReyCaliforniaUSA
| | - Paul M. Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern CaliforniaMarina del ReyCaliforniaUSA
| | - Daniel S. Pine
- National Institute of Mental Health (NIMH), National Institutes of Health (NIH)BethesdaMarylandUSA
| | - Anderson M. Winkler
- National Institute of Mental Health (NIMH), National Institutes of Health (NIH)BethesdaMarylandUSA
| |
Collapse
|
33
|
Luo W, Constable RT. Inside information: Systematic within-node functional connectivity changes observed across tasks or groups. Neuroimage 2021; 247:118792. [PMID: 34896289 PMCID: PMC8840325 DOI: 10.1016/j.neuroimage.2021.118792] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Revised: 10/16/2021] [Accepted: 12/07/2021] [Indexed: 11/23/2022] Open
Abstract
Mapping the human connectome and understanding its relationship to brain function holds tremendous clinical potential. The connectome has two fundamental components: the nodes and the sconnections between them. While much attention has been given to deriving atlases and measuring the connections between nodes, there have been no studies examining the networks within nodes. Here we demonstrate that each node contains significant connectivity information, that varies systematically across task-induced states and subjects, such that measures based on these variations can be used to classify tasks and identify subjects. The results are not specific for any particular atlas but hold across different atlas resolutions. To date, studies examining changes in connectivity have focused on edge changes and assumed there is no useful information within nodes. Our findings illustrate that for typical atlases, within-node changes can be significant and may account for a substantial fraction of the variance currently attributed to edge changes .
Collapse
Affiliation(s)
- Wenjing Luo
- Department of Biomedical Engineering, Yale University School of Medicine USA
| | - R Todd Constable
- Department of Biomedical Engineering, Yale University School of Medicine USA; Radiology and Biomedical Imaging, Yale University School of Medicine USA; Interdepartmental Neuroscience Program, Yale University School of Medicine USA.
| |
Collapse
|
34
|
Parcellation-Free prediction of task fMRI activations from dMRI tractography. Med Image Anal 2021; 76:102317. [PMID: 34871930 DOI: 10.1016/j.media.2021.102317] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Revised: 11/19/2021] [Accepted: 11/25/2021] [Indexed: 11/22/2022]
Abstract
The relationship between brain structure and function plays a crucial role in cognitive and clinical neuroscience. We present a supervised machine learning based approach that captures this relationship by predicting the spatial extent of activations that are observed with task based functional Magnetic Resonance Imaging (fMRI) from the local white matter connectivity, as reflected in diffusion MRI (dMRI) tractography. In particular, we explore three different feature representations of local connectivity patterns that do not require a pre-defined parcellation of cortical and subcortical structures. Instead, they employ cluster-based Bag of Features, Gaussian Mixture Models, and Fisher vectors. We demonstrate that our framework can be used to test the statistical significance of structure-function relationships, compare it to parcellation-based and group-average benchmarks, and propose an algorithm for visualizing our chosen feature representations that permits a neuroanatomical interpretation of our results.
Collapse
|
35
|
Markello RD, Arnatkeviciute A, Poline JB, Fulcher BD, Fornito A, Misic B. Standardizing workflows in imaging transcriptomics with the abagen toolbox. eLife 2021; 10:e72129. [PMID: 34783653 PMCID: PMC8660024 DOI: 10.7554/elife.72129] [Citation(s) in RCA: 130] [Impact Index Per Article: 43.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 11/15/2021] [Indexed: 12/12/2022] Open
Abstract
Gene expression fundamentally shapes the structural and functional architecture of the human brain. Open-access transcriptomic datasets like the Allen Human Brain Atlas provide an unprecedented ability to examine these mechanisms in vivo; however, a lack of standardization across research groups has given rise to myriad processing pipelines for using these data. Here, we develop the abagen toolbox, an open-access software package for working with transcriptomic data, and use it to examine how methodological variability influences the outcomes of research using the Allen Human Brain Atlas. Applying three prototypical analyses to the outputs of 750,000 unique processing pipelines, we find that choice of pipeline has a large impact on research findings, with parameters commonly varied in the literature influencing correlations between derived gene expression and other imaging phenotypes by as much as ρ ≥ 1.0. Our results further reveal an ordering of parameter importance, with processing steps that influence gene normalization yielding the greatest impact on downstream statistical inferences and conclusions. The presented work and the development of the abagen toolbox lay the foundation for more standardized and systematic research in imaging transcriptomics, and will help to advance future understanding of the influence of gene expression in the human brain.
Collapse
Affiliation(s)
- Ross D Markello
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill UniversityMontrealCanada
| | - Aurina Arnatkeviciute
- School of Psychological Sciences & Monash Biomedical Imaging, Monash UniversityClaytonAustralia
| | - Jean-Baptiste Poline
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill UniversityMontrealCanada
| | - Ben D Fulcher
- School of Physics, University of SydneySydneyAustralia
| | - Alex Fornito
- School of Psychological Sciences & Monash Biomedical Imaging, Monash UniversityClaytonAustralia
| | - Bratislav Misic
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill UniversityMontrealCanada
| |
Collapse
|
36
|
Domhof JWM, Jung K, Eickhoff SB, Popovych OV. Parcellation-induced variation of empirical and simulated brain connectomes at group and subject levels. Netw Neurosci 2021; 5:798-830. [PMID: 34746628 PMCID: PMC8567834 DOI: 10.1162/netn_a_00202] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 05/27/2021] [Indexed: 11/13/2022] Open
Abstract
Recent developments of whole-brain models have demonstrated their potential when investigating resting-state brain activity. However, it has not been systematically investigated how alternating derivations of the empirical structural and functional connectivity, serving as the model input, from MRI data influence modeling results. Here, we study the influence from one major element: the brain parcellation scheme that reduces the dimensionality of brain networks by grouping thousands of voxels into a few hundred brain regions. We show graph-theoretical statistics derived from the empirical data and modeling results exhibiting a high heterogeneity across parcellations. Furthermore, the network properties of empirical brain connectomes explain the lion’s share of the variance in the modeling results with respect to the parcellation variation. Such a clear-cut relationship is not observed at the subject-resolved level per parcellation. Finally, the graph-theoretical statistics of the simulated connectome correlate with those of the empirical functional connectivity across parcellations. However, this relation is not one-to-one, and its precision can vary between models. Our results imply that network properties of both empirical connectomes can explain the goodness-of-fit of whole-brain models to empirical data at a global group level but not at a single-subject level, which provides further insights into the personalization of whole-brain models. The structural and functional connectivities of the brain, which reflect the anatomical connections of axonal bundles and the amount of coactivation between brain regions, respectively, only weakly correlate with each other. In order to enhance and investigate this relationship, large-scale whole-brain dynamical models were involved in this branch of research. However, how the definitions of the brain regions parcellated according to a so-called brain atlas influence these models has so far not been systematically assessed. In this article, we show that this influence can be large, and link group-averaged, atlas-induced deviations to network properties extracted from both types of connectivity. Additionally, we demonstrate that the same association does not apply to subject-specific variations. These results may contribute to the further personalization of the whole-brain models.
Collapse
Affiliation(s)
- Justin W M Domhof
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Kyesam Jung
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Oleksandr V Popovych
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| |
Collapse
|
37
|
Kobeleva X, López-González A, Kringelbach ML, Deco G. Revealing the Relevant Spatiotemporal Scale Underlying Whole-Brain Dynamics. Front Neurosci 2021; 15:715861. [PMID: 34744605 PMCID: PMC8569182 DOI: 10.3389/fnins.2021.715861] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 09/23/2021] [Indexed: 12/02/2022] Open
Abstract
The brain rapidly processes and adapts to new information by dynamically transitioning between whole-brain functional networks. In this whole-brain modeling study we investigate the relevance of spatiotemporal scale in whole-brain functional networks. This is achieved through estimating brain parcellations at different spatial scales (100-900 regions) and time series at different temporal scales (from milliseconds to seconds) generated by a whole-brain model fitted to fMRI data. We quantify the richness of the dynamic repertoire at each spatiotemporal scale by computing the entropy of transitions between whole-brain functional networks. The results show that the optimal relevant spatial scale is around 300 regions and a temporal scale of around 150 ms. Overall, this study provides much needed evidence for the relevant spatiotemporal scales and recommendations for analyses of brain dynamics.
Collapse
Affiliation(s)
- Xenia Kobeleva
- Department of Neurology, University of Bonn, Bonn, Germany
- Computational Neuroscience Group, Department of Information and Communication Technologies, Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain
- German Center for Neurodegenerative Diseases (DZNE) Bonn, Bonn, Germany
| | - Ane López-González
- Computational Neuroscience Group, Department of Information and Communication Technologies, Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain
| | - Morten L. Kringelbach
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
- Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, United Kingdom
- Department of Clinical Medicine, Center for Music in the Brain, Aarhus University, Aarhus, Denmark
| | - Gustavo Deco
- Computational Neuroscience Group, Department of Information and Communication Technologies, Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain
- Institució Catalana de la Recerca i Estudis Avançats (ICREA), Barcelona, Spain
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- School of Psychological Sciences, Monash University, Clayton, VIC, Australia
| |
Collapse
|
38
|
Neudorf J, Kress S, Borowsky R. Structure can predict function in the human brain: a graph neural network deep learning model of functional connectivity and centrality based on structural connectivity. Brain Struct Funct 2021; 227:331-343. [PMID: 34633514 PMCID: PMC8741721 DOI: 10.1007/s00429-021-02403-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Accepted: 09/30/2021] [Indexed: 02/07/2023]
Abstract
Although functional connectivity and associated graph theory measures (e.g., centrality; how centrally important to the network a region is) are widely used in brain research, the full extent to which these functional measures are related to the underlying structural connectivity is not yet fully understood. Graph neural network deep learning methods have not yet been applied for this purpose, and offer an ideal model architecture for working with connectivity data given their ability to capture and maintain inherent network structure. Here, we applied this model to predict functional connectivity from structural connectivity in a sample of 998 participants from the Human Connectome Project. Our results showed that the graph neural network accounted for 89% of the variance in mean functional connectivity, 56% of the variance in individual-level functional connectivity, 99% of the variance in mean functional centrality, and 81% of the variance in individual-level functional centrality. These results represent an important finding that functional centrality can be robustly predicted from structural connectivity. Regions of particular importance to the model's performance as determined through lesioning are discussed, whereby regions with higher centrality have a higher impact on model performance. Future research on models of patient, demographic, or behavioural data can also benefit from this graph neural network method as it is ideally-suited for depicting connectivity and centrality in brain networks. These results have set a new benchmark for prediction of functional connectivity from structural connectivity, and models like this may ultimately lead to a way to predict functional connectivity in individuals who are unable to do fMRI tasks (e.g., non-responsive patients).
Collapse
Affiliation(s)
- Josh Neudorf
- Cognitive Neuroscience Lab, Department of Psychology, University of Saskatchewan, Saskatoon, SK, Canada
| | - Shaylyn Kress
- Cognitive Neuroscience Lab, Department of Psychology, University of Saskatchewan, Saskatoon, SK, Canada
| | - Ron Borowsky
- Cognitive Neuroscience Lab, Department of Psychology, University of Saskatchewan, Saskatoon, SK, Canada.
| |
Collapse
|
39
|
Contribution of animal models toward understanding resting state functional connectivity. Neuroimage 2021; 245:118630. [PMID: 34644593 DOI: 10.1016/j.neuroimage.2021.118630] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 08/06/2021] [Accepted: 09/29/2021] [Indexed: 12/27/2022] Open
Abstract
Functional connectivity, which reflects the spatial and temporal organization of intrinsic activity throughout the brain, is one of the most studied measures in human neuroimaging research. The noninvasive acquisition of resting state functional magnetic resonance imaging (rs-fMRI) allows the characterization of features designated as functional networks, functional connectivity gradients, and time-varying activity patterns that provide insight into the intrinsic functional organization of the brain and potential alterations related to brain dysfunction. Functional connectivity, hence, captures dimensions of the brain's activity that have enormous potential for both clinical and preclinical research. However, the mechanisms underlying functional connectivity have yet to be fully characterized, hindering interpretation of rs-fMRI studies. As in other branches of neuroscience, the identification of the neurophysiological processes that contribute to functional connectivity largely depends on research conducted on laboratory animals, which provide a platform where specific, multi-dimensional investigations that involve invasive measurements can be carried out. These highly controlled experiments facilitate the interpretation of the temporal correlations observed across the brain. Indeed, information obtained from animal experimentation to date is the basis for our current understanding of the underlying basis for functional brain connectivity. This review presents a compendium of some of the most critical advances in the field based on the efforts made by the animal neuroimaging community.
Collapse
|
40
|
Bonzanni M, Kaplan DL. On the prediction of neuronal microscale topology descriptors based on mesoscale recordings. Eur J Neurosci 2021; 54:6147-6167. [PMID: 34365680 DOI: 10.1111/ejn.15417] [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: 01/24/2021] [Revised: 07/30/2021] [Accepted: 08/01/2021] [Indexed: 11/28/2022]
Abstract
The brain possesses structural and functional hierarchical architectures organized over multiple scales. Considering that functional recordings commonly focused on a single spatial level, and because multiple scales interact with one another, we explored the behaviour of in silico neuronal networks across different scales. We established ad hoc relations of several topological descriptors (average clustering coefficient, average path length, small-world propensity, modularity, network degree, synchronizability and fraction of long-term connections) between different scales upon application and empirical validation of a Euclidian renormalization approach. We tested a simple network (distance-dependent model) as well as an artificial cortical network (Vertex; undirected and directed networks) finding the same qualitative power law relations of the parameters across levels: their quantitative nature is model dependent. Those findings were then organized in a workflow that can be used to predict, with approximation, microscale topologies from mesoscale recordings. The present manuscript not only presents a theoretical framework for the renormalization of biological neuronal network and their study across scales in light of the spatial features of the recording method but also proposes an applicable workflow to compare real functional networks across scales.
Collapse
Affiliation(s)
- Mattia Bonzanni
- Department of Biomedical Engineering, Tufts University, Medford, Massachusetts, USA.,Department of Neuroscience, Tufts University School of Medicine, Boston, Massachusetts, USA
| | - David L Kaplan
- Department of Biomedical Engineering, Tufts University, Medford, Massachusetts, USA
| |
Collapse
|
41
|
Luciw NJ, Toma S, Goldstein BI, MacIntosh BJ. Correspondence between patterns of cerebral blood flow and structure in adolescents with and without bipolar disorder. J Cereb Blood Flow Metab 2021; 41:1988-1999. [PMID: 33487070 PMCID: PMC8323335 DOI: 10.1177/0271678x21989246] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/23/2020] [Revised: 12/06/2020] [Accepted: 12/23/2020] [Indexed: 11/16/2022]
Abstract
Adolescence is a period of rapid development of the brain's inherent functional and structural networks; however, little is known about the region-to-region organization of adolescent cerebral blood flow (CBF) or its relationship to neuroanatomy. Here, we investigate both the regional covariation of CBF MRI and the covariation of structural MRI, in adolescents with and without bipolar disorder. Bipolar disorder is a disease with increased onset during adolescence, putative vascular underpinnings, and evidence of anomalous CBF and brain structure. In both groups, through hierarchical clustering, we found CBF covariance was principally described by clusters of regions circumscribed to the left hemisphere, right hemisphere, and the inferior brain; these clusters were spatially reminiscent of cerebral vascular territories. CBF covariance was associated with structural covariance in both the healthy group (n = 56; r = 0.20, p < 0.0001) and in the bipolar disorder group (n = 68; r = 0.36, p < 0.0001), and this CBF-structure correspondence was higher in bipolar disorder (p = 0.0028). There was lower CBF covariance in bipolar disorder compared to controls between the left angular gyrus and pre- and post-central gyri. Altogether, CBF covariance revealed distinct brain organization, had modest correspondence to structural covariance, and revealed evidence of differences in bipolar disorder.
Collapse
Affiliation(s)
- Nicholas J Luciw
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
- Hurvitz Brain Sciences, Sunnybrook Research Institute, University of Toronto, Toronto, Canada
| | - Simina Toma
- Department of Psychiatry, Sunnybrook Health Sciences Centre, Toronto, Canada
| | - Benjamin I Goldstein
- Hurvitz Brain Sciences, Sunnybrook Research Institute, University of Toronto, Toronto, Canada
- Department of Psychiatry, Sunnybrook Health Sciences Centre, Toronto, Canada
- Centre for Youth Bipolar Disorder, Centre for Addiction and Mental Health, Toronto, Canada
- Departments of Pharmacology and Psychiatry, University of Toronto, Toronto, Canada
| | - Bradley J MacIntosh
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
- Hurvitz Brain Sciences, Sunnybrook Research Institute, University of Toronto, Toronto, Canada
- Heart and Stroke Foundation, Canadian Partnership for Stroke Recovery, Sunnybrook Research Institute, University of Toronto, Toronto, Canada
| |
Collapse
|
42
|
Tait L, Lopes MA, Stothart G, Baker J, Kazanina N, Zhang J, Goodfellow M. A large-scale brain network mechanism for increased seizure propensity in Alzheimer's disease. PLoS Comput Biol 2021; 17:e1009252. [PMID: 34379638 PMCID: PMC8382184 DOI: 10.1371/journal.pcbi.1009252] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 08/23/2021] [Accepted: 07/06/2021] [Indexed: 11/19/2022] Open
Abstract
People with Alzheimer's disease (AD) are 6-10 times more likely to develop seizures than the healthy aging population. Leading hypotheses largely consider hyperexcitability of local cortical tissue as primarily responsible for increased seizure prevalence in AD. However, in the general population of people with epilepsy, large-scale brain network organization additionally plays a role in determining seizure likelihood and phenotype. Here, we propose that alterations to large-scale brain network organization seen in AD may contribute to increased seizure likelihood. To test this hypothesis, we combine computational modelling with electrophysiological data using an approach that has proved informative in clinical epilepsy cohorts without AD. EEG was recorded from 21 people with probable AD and 26 healthy controls. At the time of EEG acquisition, all participants were free from seizures. Whole brain functional connectivity derived from source-reconstructed EEG recordings was used to build subject-specific brain network models of seizure transitions. As cortical tissue excitability was increased in the simulations, AD simulations were more likely to transition into seizures than simulations from healthy controls, suggesting an increased group-level probability of developing seizures at a future time for AD participants. We subsequently used the model to assess seizure propensity of different regions across the cortex. We found the most important regions for seizure generation were those typically burdened by amyloid-beta at the early stages of AD, as previously reported by in-vivo and post-mortem staging of amyloid plaques. Analysis of these spatial distributions also give potential insight into mechanisms of increased susceptibility to generalized (as opposed to focal) seizures in AD vs controls. This research suggests avenues for future studies testing patients with seizures, e.g. co-morbid AD/epilepsy patients, and comparisons with PET and MRI scans to relate regional seizure propensity with AD pathologies.
Collapse
Affiliation(s)
- Luke Tait
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom
| | - Marinho A. Lopes
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom
| | - George Stothart
- Department of Psychology, University of Bath, Bath, United Kingdom
| | - John Baker
- Dementia Research Centre, Queen Square Institute of Neurology, UCL, London, United Kingdom
| | - Nina Kazanina
- School of Psychological Science, University of Bristol, Bristol, United Kingdom
| | - Jiaxiang Zhang
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom
| | - Marc Goodfellow
- Living Systems Institute, University of Exeter, Exeter, United Kingdom
| |
Collapse
|
43
|
Cole M, Murray K, St‐Onge E, Risk B, Zhong J, Schifitto G, Descoteaux M, Zhang Z. Surface-Based Connectivity Integration: An atlas-free approach to jointly study functional and structural connectivity. Hum Brain Mapp 2021; 42:3481-3499. [PMID: 33956380 PMCID: PMC8249904 DOI: 10.1002/hbm.25447] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Revised: 03/03/2021] [Accepted: 04/06/2021] [Indexed: 01/29/2023] Open
Abstract
There has been increasing interest in jointly studying structural connectivity (SC) and functional connectivity (FC) derived from diffusion and functional MRI. Previous connectome integration studies almost exclusively required predefined atlases. However, there are many potential atlases to choose from and this choice heavily affects all subsequent analyses. To avoid such an arbitrary choice, we propose a novel atlas-free approach, named Surface-Based Connectivity Integration (SBCI), to more accurately study the relationships between SC and FC throughout the intra-cortical gray matter. SBCI represents both SC and FC in a continuous manner on the white surface, avoiding the need for prespecified atlases. The continuous SC is represented as a probability density function and is smoothed for better facilitation of its integration with FC. To infer the relationship between SC and FC, three novel sets of SC-FC coupling (SFC) measures are derived. Using data from the Human Connectome Project, we introduce the high-quality SFC measures produced by SBCI and demonstrate the use of these measures to study sex differences in a cohort of young adults. Compared with atlas-based methods, this atlas-free framework produces more reproducible SFC features and shows greater predictive power in distinguishing biological sex. This opens promising new directions for all connectomics studies.
Collapse
Affiliation(s)
- Martin Cole
- Department of Biostatistics and Computational BiologyUniversity of RochesterRochesterNew YorkUSA
| | - Kyle Murray
- Department of Physics and AstronomyUniversity of RochesterRochesterNew YorkUSA
| | - Etienne St‐Onge
- Sherbrooke Connectivity Imaging Laboratory (SCIL)Université de SherbrookeQuébecCanada
| | - Benjamin Risk
- Department of Biostatistics and BioinformaticsEmory UniversityAtlantaGeorgiaUSA
| | - Jianhui Zhong
- Department of Physics and AstronomyUniversity of RochesterRochesterNew YorkUSA
- Department of Imaging SciencesUniversity of RochesterRochesterNew YorkUSA
| | - Giovanni Schifitto
- Department of Imaging SciencesUniversity of RochesterRochesterNew YorkUSA
- Department of NeurologyUniversity of RochesterRochesterNew YorkUSA
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Laboratory (SCIL)Université de SherbrookeQuébecCanada
| | - Zhengwu Zhang
- Department of Statistics and Operations ResearchUniversity of North Carolina at Chapel HillNorth CarolinaUSA
| |
Collapse
|
44
|
Chiêm B, Crevecoeur F, Delvenne JC. Structure-informed functional connectivity driven by identifiable and state-specific control regions. Netw Neurosci 2021; 5:591-613. [PMID: 34189379 PMCID: PMC8233121 DOI: 10.1162/netn_a_00192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Accepted: 03/17/2021] [Indexed: 11/19/2022] Open
Abstract
Describing how the brain anatomical wiring contributes to the emergence of coordinated neural activity underlying complex behavior remains challenging. Indeed, patterns of remote coactivations that adjust with the ongoing task-demand do not systematically match direct, static anatomical links. Here, we propose that observed coactivation patterns, known as functional connectivity (FC), can be explained by a controllable linear diffusion dynamics defined on the brain architecture. Our model, termed structure-informed FC, is based on the hypothesis that different sets of brain regions controlling the information flow on the anatomical wiring produce state-specific functional patterns. We thus introduce a principled framework for the identification of potential control centers in the brain. We find that well-defined, sparse, and robust sets of control regions, partially overlapping across several tasks and resting state, produce FC patterns comparable to empirical ones. Our findings suggest that controllability is a fundamental feature allowing the brain to reach different states. Understanding how brain anatomy promotes particular patterns of coactivations among neural regions is a key challenge in neuroscience. This challenge can be addressed using network science and systems theory. Here, we propose that coactivations result from the diffusion of information through the network of anatomical links connecting brain regions, with certain regions controlling the dynamics. We translate this hypothesis into a model called structure-informed functional connectivity, and we introduce a framework for identifying control regions based on empirical data. We find that our model produces coactivation patterns comparable to empirical ones, and that distinct sets of control regions are associated with different functional states. These findings suggest that controllability is an important feature allowing the brain to reach different states.
Collapse
Affiliation(s)
- Benjamin Chiêm
- Institute of Communication Technologies, Electronics, and Applied Mathematics, Department of Mathematical Engineering, Université Catholique de Louvain, Louvain-la-Neuve, Belgium
| | - Frédéric Crevecoeur
- Institute of Communication Technologies, Electronics, and Applied Mathematics, Department of Mathematical Engineering, Université Catholique de Louvain, Louvain-la-Neuve, Belgium
| | - Jean-Charles Delvenne
- Institute of Communication Technologies, Electronics, and Applied Mathematics, Department of Mathematical Engineering, Université Catholique de Louvain, Louvain-la-Neuve, Belgium
| |
Collapse
|
45
|
Albers KJ, Ambrosen KS, Liptrot MG, Dyrby TB, Schmidt MN, Mørup M. Using connectomics for predictive assessment of brain parcellations. Neuroimage 2021; 238:118170. [PMID: 34087365 DOI: 10.1016/j.neuroimage.2021.118170] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 04/19/2021] [Accepted: 05/10/2021] [Indexed: 12/29/2022] Open
Abstract
The organization of the human brain remains elusive, yet is of great importance to the mechanisms of integrative brain function. At the macroscale, its structural and functional interpretation is conventionally assessed at the level of cortical units. However, the definition and validation of such cortical parcellations are problematic due to the absence of a true gold standard. We propose a framework for quantitative evaluation of brain parcellations via statistical prediction of connectomics data. Specifically, we evaluate the extent in which the network representation at the level of cortical units (defined as parcels) accounts for high-resolution brain connectivity. Herein, we assess the pertinence and comparative ranking of ten existing parcellation atlases to account for functional (FC) and structural connectivity (SC) data based on data from the Human Connectome Project (HCP), and compare them to data-driven as well as spatially-homogeneous geometric parcellations including geodesic parcellations with similar size distributions as the atlases. We find substantial discrepancy in parcellation structures that well characterize FC and SC and differences in what well represents an individual's functional connectome when compared against the FC structure that is preserved across individuals. Surprisingly, simple spatial homogenous parcellations generally provide good representations of both FC and SC, but are inferior when their within-parcellation distribution of individual parcel sizes is matched to that of a valid atlas. This suggests that the choice of fine grained and coarse representations used by existing atlases are important. However, we find that resolution is more critical than the exact border location of parcels.
Collapse
Affiliation(s)
- Kristoffer J Albers
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Richard Petersens Plads, Building 324, DK-2800 Kgs. Lyngby, Denmark
| | - Karen S Ambrosen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Richard Petersens Plads, Building 324, DK-2800 Kgs. Lyngby, Denmark; Danish Research Centre for Magnetic Resonance,Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Copenhagen, Denmark
| | - Matthew G Liptrot
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Richard Petersens Plads, Building 324, DK-2800 Kgs. Lyngby, Denmark
| | - Tim B Dyrby
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Richard Petersens Plads, Building 324, DK-2800 Kgs. Lyngby, Denmark; Danish Research Centre for Magnetic Resonance,Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Copenhagen, Denmark
| | - Mikkel N Schmidt
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Richard Petersens Plads, Building 324, DK-2800 Kgs. Lyngby, Denmark
| | - Morten Mørup
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Richard Petersens Plads, Building 324, DK-2800 Kgs. Lyngby, Denmark.
| |
Collapse
|
46
|
Popovych OV, Jung K, Manos T, Diaz-Pier S, Hoffstaedter F, Schreiber J, Yeo BTT, Eickhoff SB. Inter-subject and inter-parcellation variability of resting-state whole-brain dynamical modeling. Neuroimage 2021; 236:118201. [PMID: 34033913 PMCID: PMC8271096 DOI: 10.1016/j.neuroimage.2021.118201] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 05/21/2021] [Accepted: 05/22/2021] [Indexed: 11/05/2022] Open
Abstract
Modern approaches to investigate complex brain dynamics suggest to represent the brain as a functional network of brain regions defined by a brain atlas, while edges represent the structural or functional connectivity among them. This approach is also utilized for mathematical modeling of the resting-state brain dynamics, where the applied brain parcellation plays an essential role in deriving the model network and governing the modeling results. There is however no consensus and empirical evidence on how a given brain atlas affects the model outcome, and the choice of parcellation is still rather arbitrary. Accordingly, we explore the impact of brain parcellation on inter-subject and inter-parcellation variability of model fitting to empirical data. Our objective is to provide a comprehensive empirical evidence of potential influences of parcellation choice on resting-state whole-brain dynamical modeling. We show that brain atlases strongly influence the quality of model validation and propose several variables calculated from empirical data to account for the observed variability. A few classes of such data variables can be distinguished depending on their inter-subject and inter-parcellation explanatory power.
Collapse
Affiliation(s)
- Oleksandr V Popovych
- Institute of Neuroscience and Medicine (INM-7), Research Centre Juelich, Juelich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine, University Duesseldorf, Duesseldorf, Germany.
| | - Kyesam Jung
- Institute of Neuroscience and Medicine (INM-7), Research Centre Juelich, Juelich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine, University Duesseldorf, Duesseldorf, Germany
| | - Thanos Manos
- Institute of Neuroscience and Medicine (INM-7), Research Centre Juelich, Juelich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine, University Duesseldorf, Duesseldorf, Germany; Laboratoire de Physique Théorique et Modélisation, CY Cergy Paris Université, CNRS, UMR 8089, Cergy-Pontoise cedex 95302, France
| | - Sandra Diaz-Pier
- Institute for Advanced Simulation, Juelich Supercomputing Centre (JSC), Research Centre Juelich, Juelich, Germany
| | - Felix Hoffstaedter
- Institute of Neuroscience and Medicine (INM-7), Research Centre Juelich, Juelich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine, University Duesseldorf, Duesseldorf, Germany
| | - Jan Schreiber
- Institute of Neuroscience and Medicine (INM-1), Research Centre Juelich, Juelich, Germany
| | - B T Thomas Yeo
- Centre for Sleep and Cognition, Centre for Translational MR Research & N.1 Institute for Health, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Integrative Sciences and Engineering Programme (ISEP), Singapore
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine (INM-7), Research Centre Juelich, Juelich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine, University Duesseldorf, Duesseldorf, Germany
| |
Collapse
|
47
|
Buchanan CR, Muñoz Maniega S, Valdés Hernández MC, Ballerini L, Barclay G, Taylor AM, Russ TC, Tucker-Drob EM, Wardlaw JM, Deary IJ, Bastin ME, Cox SR. Comparison of structural MRI brain measures between 1.5 and 3 T: Data from the Lothian Birth Cohort 1936. Hum Brain Mapp 2021; 42:3905-3921. [PMID: 34008899 PMCID: PMC8288101 DOI: 10.1002/hbm.25473] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 04/26/2021] [Accepted: 04/29/2021] [Indexed: 11/16/2022] Open
Abstract
Multi‐scanner MRI studies are reliant on understanding the apparent differences in imaging measures between different scanners. We provide a comprehensive analysis of T1‐weighted and diffusion MRI (dMRI) structural brain measures between a 1.5 T GE Signa Horizon HDx and a 3 T Siemens Magnetom Prisma using 91 community‐dwelling older participants (aged 82 years). Although we found considerable differences in absolute measurements (global tissue volumes were measured as ~6–11% higher and fractional anisotropy [FA] was 33% higher at 3 T than at 1.5 T), between‐scanner consistency was good to excellent for global volumetric and dMRI measures (intraclass correlation coefficient [ICC] range: .612–.993) and fair to good for 68 cortical regions (FreeSurfer) and cortical surface measures (mean ICC: .504–.763). Between‐scanner consistency was fair for dMRI measures of 12 major white matter tracts (mean ICC: .475–.564), and the general factors of these tracts provided excellent consistency (ICC ≥ .769). Whole‐brain structural networks provided good to excellent consistency for global metrics (ICC ≥ .612). Although consistency was poor for individual network connections (mean ICCs: .275−.280), this was driven by a large difference in network sparsity (.599 vs. .334), and consistency was improved when comparing only the connections present in every participant (mean ICCs: .533–.647). Regression‐based k‐fold cross‐validation showed that, particularly for global volumes, between‐scanner differences could be largely eliminated (R2 range .615–.991). We conclude that low granularity measures of brain structure can be reliably matched between the scanners tested, but caution is warranted when combining high granularity information from different scanners.
Collapse
Affiliation(s)
- Colin R Buchanan
- Lothian Birth Cohorts Group, The University of Edinburgh, Edinburgh, UK.,Department of Psychology, The University of Edinburgh, Edinburgh, UK.,Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK
| | - Susana Muñoz Maniega
- Lothian Birth Cohorts Group, The University of Edinburgh, Edinburgh, UK.,Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK.,Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK
| | - Maria C Valdés Hernández
- Lothian Birth Cohorts Group, The University of Edinburgh, Edinburgh, UK.,Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK.,Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK
| | - Lucia Ballerini
- Lothian Birth Cohorts Group, The University of Edinburgh, Edinburgh, UK.,Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK.,Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK
| | - Gayle Barclay
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK
| | - Adele M Taylor
- Lothian Birth Cohorts Group, The University of Edinburgh, Edinburgh, UK.,Department of Psychology, The University of Edinburgh, Edinburgh, UK
| | - Tom C Russ
- Lothian Birth Cohorts Group, The University of Edinburgh, Edinburgh, UK.,Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK.,Alzheimer Scotland Dementia Research Centre, The University of Edinburgh, Edinburgh, UK
| | | | - Joanna M Wardlaw
- Lothian Birth Cohorts Group, The University of Edinburgh, Edinburgh, UK.,Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK.,Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK
| | - Ian J Deary
- Lothian Birth Cohorts Group, The University of Edinburgh, Edinburgh, UK.,Department of Psychology, The University of Edinburgh, Edinburgh, UK
| | - Mark E Bastin
- Lothian Birth Cohorts Group, The University of Edinburgh, Edinburgh, UK.,Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK.,Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK
| | - Simon R Cox
- Lothian Birth Cohorts Group, The University of Edinburgh, Edinburgh, UK.,Department of Psychology, The University of Edinburgh, Edinburgh, UK.,Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK
| |
Collapse
|
48
|
Markello RD, Misic B. Comparing spatial null models for brain maps. Neuroimage 2021; 236:118052. [PMID: 33857618 DOI: 10.1016/j.neuroimage.2021.118052] [Citation(s) in RCA: 122] [Impact Index Per Article: 40.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Revised: 04/02/2021] [Accepted: 04/05/2021] [Indexed: 01/31/2023] Open
Abstract
Technological and data sharing advances have led to a proliferation of high-resolution structural and functional maps of the brain. Modern neuroimaging research increasingly depends on identifying correspondences between the topographies of these maps; however, most standard methods for statistical inference fail to account for their spatial properties. Recently, multiple methods have been developed to generate null distributions that preserve the spatial autocorrelation of brain maps and yield more accurate statistical estimates. Here, we comprehensively assess the performance of ten published null frameworks in statistical analyses of neuroimaging data. To test the efficacy of these frameworks in situations with a known ground truth, we first apply them to a series of controlled simulations and examine the impact of data resolution and spatial autocorrelation on their family-wise error rates. Next, we use each framework with two empirical neuroimaging datasets, investigating their performance when testing (1) the correspondence between brain maps (e.g., correlating two activation maps) and (2) the spatial distribution of a feature within a partition (e.g., quantifying the specificity of an activation map within an intrinsic functional network). Finally, we investigate how differences in the implementation of these null models may impact their performance. In agreement with previous reports, we find that naive null models that do not preserve spatial autocorrelation consistently yield elevated false positive rates and unrealistically liberal statistical estimates. While spatially-constrained null models yielded more realistic, conservative estimates, even these frameworks suffer from inflated false positive rates and variable performance across analyses. Throughout our results, we observe minimal impact of parcellation and resolution on null model performance. Altogether, our findings highlight the need for continued development of statistically-rigorous methods for comparing brain maps. The present report provides a harmonised framework for benchmarking and comparing future advancements.
Collapse
Affiliation(s)
- Ross D Markello
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada.
| | - Bratislav Misic
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada.
| |
Collapse
|
49
|
Dhamala E, Jamison KW, Jaywant A, Dennis S, Kuceyeski A. Distinct functional and structural connections predict crystallised and fluid cognition in healthy adults. Hum Brain Mapp 2021; 42:3102-3118. [PMID: 33830577 PMCID: PMC8193532 DOI: 10.1002/hbm.25420] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 03/03/2021] [Accepted: 03/10/2021] [Indexed: 12/24/2022] Open
Abstract
White matter pathways between neurons facilitate neuronal coactivation patterns in the brain. Insight into how these structural and functional connections underlie complex cognitive functions provides an important foundation with which to delineate disease‐related changes in cognitive functioning. Here, we integrate neuroimaging, connectomics, and machine learning approaches to explore how functional and structural brain connectivity relate to cognition. Specifically, we evaluate the extent to which functional and structural connectivity predict individual crystallised and fluid cognitive abilities in 415 unrelated healthy young adults (202 females) from the Human Connectome Project. We report three main findings. First, we demonstrate functional connectivity is more predictive of cognitive scores than structural connectivity, and, furthermore, integrating the two modalities does not increase explained variance. Second, we show the quality of cognitive prediction from connectome measures is influenced by the choice of grey matter parcellation, and, possibly, how that parcellation is derived. Third, we find that distinct functional and structural connections predict crystallised and fluid abilities. Taken together, our results suggest that functional and structural connectivity have unique relationships with crystallised and fluid cognition and, furthermore, studying both modalities provides a more comprehensive insight into the neural correlates of cognition.
Collapse
Affiliation(s)
- Elvisha Dhamala
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA.,Brain and Mind Research Institute, Weill Cornell Medicine, New York, New York, USA
| | - Keith W Jamison
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Abhishek Jaywant
- Department of Psychiatry, Weill Cornell Medicine, New York, New York, USA.,Department of Rehabilitation Medicine, Weill Cornell Medicine, New York, New York, USA.,NewYork-Presbyterian Hospital/Weill Cornell Medical Center, New York, New York, USA
| | - Sarah Dennis
- Sarah Lawrence College, Bronxville, New York, USA
| | - Amy Kuceyeski
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA.,Brain and Mind Research Institute, Weill Cornell Medicine, New York, New York, USA
| |
Collapse
|
50
|
Shi TC, Pagliaccio D, Cyr M, Simpson HB, Marsh R. Network-based functional connectivity predicts response to exposure therapy in unmedicated adults with obsessive-compulsive disorder. Neuropsychopharmacology 2021; 46:1035-1044. [PMID: 33446895 PMCID: PMC8115173 DOI: 10.1038/s41386-020-00929-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Revised: 10/30/2020] [Accepted: 11/20/2020] [Indexed: 02/07/2023]
Abstract
Obsessive-compulsive disorder (OCD) is associated with alterations in cortico-striato-thalamo-cortical brain networks, but some resting-state functional magnetic resonance imaging studies report more diffuse alterations in brain connectivity. Few studies have assessed functional connectivity within or between networks across the whole brain in unmedicated OCD patients or how patterns of connectivity predict response to exposure and ritual prevention (EX/RP) therapy, a first-line treatment for OCD. Herein, multiband resting-state functional MRI scans were collected from unmedicated, adult patients with OCD (n = 41) and healthy participants (n = 36); OCD patients were then offered twice weekly EX/RP (17 sessions). A whole-brain-network-based statistic approach was used to identify group differences in resting-state connectivity. We detected altered pre-treatment functional connectivity between task-positive regions in the temporal gyri (middle and superior) and regions of the cingulo-opercular and default networks in individuals with OCD. Signal extraction was performed using a reconstruction independent components analysis and isolated two independent subcomponents (IC1 and IC2) within this altered connectivity. In the OCD group, linear mixed-effects models tested whether IC1 or IC2 values predicted the slope of change in Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) scores across EX/RP treatment. Lower (more different from controls) IC2 score significantly predicted greater symptom reduction with EX/RP (Bonferroni-corrected p = 0.002). Collectively, these findings suggest that an altered balance between task-positive and task-negative regions centered around temporal gyri may contribute to difficulty controlling intrusive thoughts or urges to perform ritualistic behaviors.
Collapse
Affiliation(s)
- Tracey C. Shi
- grid.413734.60000 0000 8499 1112Department of Psychiatry, New York State Psychiatric Institute, 1051 Riverside Drive, Unit 74, New York, NY 10032 USA ,grid.21729.3f0000000419368729Department of Psychiatry, Columbia University Irving Medical Center, 1051 Riverside Drive, Unit 74, New York, NY 10032 USA
| | - David Pagliaccio
- grid.413734.60000 0000 8499 1112Department of Psychiatry, New York State Psychiatric Institute, 1051 Riverside Drive, Unit 74, New York, NY 10032 USA
| | - Marilyn Cyr
- grid.413734.60000 0000 8499 1112Department of Psychiatry, New York State Psychiatric Institute, 1051 Riverside Drive, Unit 74, New York, NY 10032 USA
| | - H. Blair Simpson
- grid.413734.60000 0000 8499 1112Department of Psychiatry, New York State Psychiatric Institute, 1051 Riverside Drive, Unit 74, New York, NY 10032 USA ,grid.21729.3f0000000419368729Department of Psychiatry, Columbia University Irving Medical Center, 1051 Riverside Drive, Unit 74, New York, NY 10032 USA
| | - Rachel Marsh
- grid.413734.60000 0000 8499 1112Department of Psychiatry, New York State Psychiatric Institute, 1051 Riverside Drive, Unit 74, New York, NY 10032 USA ,grid.21729.3f0000000419368729Department of Psychiatry, Columbia University Irving Medical Center, 1051 Riverside Drive, Unit 74, New York, NY 10032 USA
| |
Collapse
|