1
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Ng C, Huang P, Cho Y, Lee P, Liu Y, Chang T. Frontoparietal and salience network synchronizations during nonsymbolic magnitude processing predict brain age and mathematical performance in youth. Hum Brain Mapp 2024; 45:e26777. [PMID: 39046114 PMCID: PMC11267564 DOI: 10.1002/hbm.26777] [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: 08/18/2023] [Revised: 06/03/2024] [Accepted: 06/19/2024] [Indexed: 07/25/2024] Open
Abstract
The development and refinement of functional brain circuits crucial to human cognition is a continuous process that spans from childhood to adulthood. Research increasingly focuses on mapping these evolving configurations, with the aim to identify markers for functional impairments and atypical development. Among human cognitive systems, nonsymbolic magnitude representations serve as a foundational building block for future success in mathematical learning and achievement for individuals. Using task-based frontoparietal (FPN) and salience network (SN) features during nonsymbolic magnitude processing alongside machine learning algorithms, we developed a framework to construct brain age prediction models for participants aged 7-30. Our study revealed differential developmental profiles in the synchronization within and between FPN and SN networks. Specifically, we observed a linear increase in FPN connectivity, concomitant with a decline in SN connectivity across the age span. A nonlinear U-shaped trajectory in the connectivity between the FPN and SN was discerned, revealing reduced FPN-SN synchronization among adolescents compared to both pediatric and adult cohorts. Leveraging the Gradient Boosting machine learning algorithm and nested fivefold stratified cross-validation with independent training datasets, we demonstrated that functional connectivity measures of the FPN and SN nodes predict chronological age, with a correlation coefficient of .727 and a mean absolute error of 2.944 between actual and predicted ages. Notably, connectivity within the FPN emerged as the most contributing feature for age prediction. Critically, a more matured brain age estimate is associated with better arithmetic performance. Our findings shed light on the intricate developmental changes occurring in the neural networks supporting magnitude representations. We emphasize brain age estimation as a potent tool for understanding cognitive development and its relationship to mathematical abilities across the critical developmental period of youth. PRACTITIONER POINTS: This study investigated the prolonged changes in the brain's architecture across childhood, adolescence, and adulthood, with a focus on task-state frontoparietal and salience networks. Distinct developmental pathways were identified: frontoparietal synchronization strengthens consistently throughout development, while salience network connectivity diminishes with age. Furthermore, adolescents show a unique dip in connectivity between these networks. Leveraging advanced machine learning methods, we accurately predicted individuals' ages based on these brain circuits, with a more mature estimated brain age correlating with better math skills.
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Affiliation(s)
- Chan‐Tat Ng
- Department of PsychologyNational Chengchi UniversityTaipeiTaiwan
| | - Po‐Hsien Huang
- Department of PsychologyNational Chengchi UniversityTaipeiTaiwan
- Research Center for Mind, Brain & LearningNational Chengchi UniversityTaipeiTaiwan
| | - Yi‐Cheng Cho
- Department of PsychologyNational Chengchi UniversityTaipeiTaiwan
| | - Pei‐Hong Lee
- Research Center for Mind, Brain & LearningNational Chengchi UniversityTaipeiTaiwan
| | - Yi‐Chang Liu
- Research Center for Mind, Brain & LearningNational Chengchi UniversityTaipeiTaiwan
| | - Ting‐Ting Chang
- Department of PsychologyNational Chengchi UniversityTaipeiTaiwan
- Research Center for Mind, Brain & LearningNational Chengchi UniversityTaipeiTaiwan
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2
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Marzi C, Giannelli M, Barucci A, Tessa C, Mascalchi M, Diciotti S. Efficacy of MRI data harmonization in the age of machine learning: a multicenter study across 36 datasets. Sci Data 2024; 11:115. [PMID: 38263181 PMCID: PMC10805868 DOI: 10.1038/s41597-023-02421-7] [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: 12/06/2022] [Accepted: 07/27/2023] [Indexed: 01/25/2024] Open
Abstract
Pooling publicly-available MRI data from multiple sites allows to assemble extensive groups of subjects, increase statistical power, and promote data reuse with machine learning techniques. The harmonization of multicenter data is necessary to reduce the confounding effect associated with non-biological sources of variability in the data. However, when applied to the entire dataset before machine learning, the harmonization leads to data leakage, because information outside the training set may affect model building, and potentially falsely overestimate performance. We propose a 1) measurement of the efficacy of data harmonization; 2) harmonizer transformer, i.e., an implementation of the ComBat harmonization allowing its encapsulation among the preprocessing steps of a machine learning pipeline, avoiding data leakage by design. We tested these tools using brain T1-weighted MRI data from 1740 healthy subjects acquired at 36 sites. After harmonization, the site effect was removed or reduced, and we showed the data leakage effect in predicting individual age from MRI data, highlighting that introducing the harmonizer transformer into a machine learning pipeline allows for avoiding data leakage by design.
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Affiliation(s)
- Chiara Marzi
- Department of Statistics, Computer Science and Applications "Giuseppe Parenti", University of Florence, 50134, Florence, Italy
- "Nello Carrara" Institute of Applied Physics (IFAC), National Research Council (CNR), 50019, Sesto Fiorentino, Florence, Italy
| | - Marco Giannelli
- Unit of Medical Physics, Pisa University Hospital "Azienda Ospedaliero-Universitaria Pisana", 56126, Pisa, Italy
| | - Andrea Barucci
- "Nello Carrara" Institute of Applied Physics (IFAC), National Research Council (CNR), 50019, Sesto Fiorentino, Florence, Italy
| | - Carlo Tessa
- Radiology Unit Apuane e Lunigiana, Azienda USL Toscana Nord Ovest, 54100, Massa, Italy
| | - Mario Mascalchi
- Department of Experimental and Clinical Biomedical Sciences "Mario Serio", University of Florence, 50139, Florence, Italy
- Division of Epidemiology and Clinical Governance, Institute for Study, Prevention and netwoRk in Oncology (ISPRO), 50139, Florence, Italy
| | - Stefano Diciotti
- Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi" - DEI, University of Bologna, 47522, Cesena, Italy.
- Alma Mater Research Institute for Human-Centered Artificial Intelligence, University of Bologna, 40121, Bologna, Italy.
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3
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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] [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.
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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.
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4
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Raud L, Sneve MH, Vidal-Piñeiro D, Sørensen Ø, Folvik L, Ness HT, Mowinckel AM, Grydeland H, Walhovd KB, Fjell AM. Hippocampal-cortical functional connectivity during memory encoding and retrieval. Neuroimage 2023; 279:120309. [PMID: 37544416 DOI: 10.1016/j.neuroimage.2023.120309] [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: 04/04/2023] [Revised: 07/16/2023] [Accepted: 08/04/2023] [Indexed: 08/08/2023] Open
Abstract
Memory encoding and retrieval are critical sub-processes of episodic memory. While the hippocampus is involved in both, less is known about its connectivity with the neocortex during memory processing in humans. This is partially due to variations in demands in common memory tasks, which inevitably recruit cognitive processes other than episodic memory. Conjunctive analysis of data from different tasks with the same core elements of encoding and retrieval can reduce the intrusion of patterns related to subsidiary perceptual and cognitive processing. Leveraging data from two large-scale functional resonance imaging studies with different episodic memory tasks (514 and 237 participants), we identified hippocampal-cortical networks active during memory tasks. Whole-brain functional connectivity maps were similar during resting state, encoding, and retrieval. Anterior and posterior hippocampus had distinct connectivity profiles, which were also stable across resting state and memory tasks. When contrasting encoding and retrieval connectivity, conjunctive encoding-related connectivity was sparse. During retrieval hippocampal connectivity was increased with areas known to be active during recollection, including medial prefrontal, inferior parietal, and parahippocampal cortices. This indicates that the stable functional connectivity of the hippocampus along its longitudinal axis is superposed by increased functional connectivity with the recollection network during retrieval, while auxiliary encoding connectivity likely reflects contextual factors.
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Affiliation(s)
- Liisa Raud
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, 0373 Oslo, Norway.
| | - Markus H Sneve
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, 0373 Oslo, Norway
| | - Didac Vidal-Piñeiro
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, 0373 Oslo, Norway
| | - Øystein Sørensen
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, 0373 Oslo, Norway
| | - Line Folvik
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, 0373 Oslo, Norway
| | - Hedda T Ness
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, 0373 Oslo, Norway
| | - Athanasia M Mowinckel
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, 0373 Oslo, Norway
| | - Håkon Grydeland
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, 0373 Oslo, Norway
| | - Kristine B Walhovd
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, 0373 Oslo, Norway; Department of Radiology and Nuclear Medicine, Oslo University Hospital, 0372 Oslo, Norway
| | - Anders M Fjell
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, 0373 Oslo, Norway; Department of Radiology and Nuclear Medicine, Oslo University Hospital, 0372 Oslo, Norway
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5
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Kumar S, Shovon AR, Deshpande G. The robustness of persistent homology of brain networks to data acquisition-related non-neural variability in resting state fMRI. Hum Brain Mapp 2023; 44:4637-4651. [PMID: 37449464 PMCID: PMC10400795 DOI: 10.1002/hbm.26403] [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: 07/18/2022] [Revised: 06/01/2023] [Accepted: 06/07/2023] [Indexed: 07/18/2023] Open
Abstract
There is increasing interest in investigating brain function based on functional connectivity networks (FCN) obtained from resting-state functional magnetic resonance imaging (fMRI). FCNs, typically obtained using measures of time series association such as Pearson's correlation, are sensitive to data acquisition parameters such as sampling period. This introduces non-neural variability in data pooled from different acquisition protocols and MRI scanners, negating the advantages of larger sample sizes in pooled data. To address this, we hypothesize that the topology or shape of brain networks must be preserved irrespective of how densely it is sampled, and metrics which capture this topology may be statistically similar across sampling periods, thereby alleviating this source of non-neural variability. Accordingly, we present an end-to-end pipeline that uses persistent homology (PH), a branch of topological data analysis, to demonstrate similarity across FCNs acquired at different temporal sampling periods. PH, as a technique, extracts topological features by capturing the network organization across all continuous threshold values, as opposed to graph theoretic methods, which fix a discrete network topology by thresholding the connectivity matrix. The extracted topological features are encoded in the form of persistent diagrams that can be compared against one another using the earth-moving metric, also popularly known as the Wasserstein distance. We extract topological features from three data cohorts, each acquired at different temporal sampling periods and demonstrate that these features are statistically the same, hence, empirically showing that PH may be robust to changes in data acquisition parameters such as sampling period.
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Affiliation(s)
- Sidharth Kumar
- Computer Science DepartmentUniversity of Alabama at BirminghamBirminghamAlabamaUSA
| | | | - Gopikrishna Deshpande
- Department of Electrical and Computer Engineering, AU MRI Research CenterAuburn UniversityAlabamaUSA
- Department of Psychological SciencesAuburn UniversityAuburnAlabamaUSA
- Alabama Advanced Imaging ConsortiumBirminghamAlabamaUSA
- Center for NeuroscienceAuburn UniversityAuburnAlabamaUSA
- School of PsychologyCapital Normal UniversityBeijingChina
- Key Laboratory for Learning and CognitionCapital Normal UniversityBeijingChina
- Department of PsychiatryNational Institute of Mental Health and NeurosciencesBangaloreIndia
- Centre for Brain ResearchIndian Institute of ScienceBangaloreIndia
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6
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Zhang R, Oliver LD, Voineskos AN, Park JY. RELIEF: A structured multivariate approach for removal of latent inter-scanner effects. IMAGING NEUROSCIENCE (CAMBRIDGE, MASS.) 2023; 1:1-16. [PMID: 37719839 PMCID: PMC10503485 DOI: 10.1162/imag_a_00011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 08/02/2023] [Indexed: 09/19/2023]
Abstract
Combining data collected from multiple study sites is becoming common and is advantageous to researchers to increase the generalizability and replicability of scientific discoveries. However, at the same time, unwanted inter-scanner biases are commonly observed across neuroimaging data collected from multiple study sites or scanners, rendering difficulties in integrating such data to obtain reliable findings. While several methods for handling such unwanted variations have been proposed, most of them use univariate approaches that could be too simple to capture all sources of scanner-specific variations. To address these challenges, we propose a novel multivariate harmonization method called RELIEF (REmoval of Latent Inter-scanner Effects through Factorization) for estimating and removing both explicit and latent scanner effects. Our method is the first approach to introduce the simultaneous dimension reduction and factorization of interlinked matrices to a data harmonization context, which provides a new direction in methodological research for correcting inter-scanner biases. Analyzing diffusion tensor imaging (DTI) data from the Social Processes Initiative in Neurobiology of the Schizophrenia (SPINS) study and conducting extensive simulation studies, we show that RELIEF outperforms existing harmonization methods in mitigating inter-scanner biases and retaining biological associations of interest to increase statistical power. RELIEF is publicly available as an R package.
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Affiliation(s)
- Rongqian Zhang
- Department of Statistical Sciences, University of Toronto, Toronto, Canada
| | | | - Aristotle N. Voineskos
- Centre for Addiction and Mental Health, Toronto, Canada
- Department of Psychiatry, University of Toronto, Toronto, Canada
| | - Jun Young Park
- Department of Statistical Sciences, University of Toronto, Toronto, Canada
- Department of Psychology, University of Toronto, Toronto, Canada
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7
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Kamarajan C, Pandey AK, Chorlian DB, Meyers JL, Kinreich S, Pandey G, Subbie-Saenz de Viteri S, Zhang J, Kuang W, Barr PB, Aliev F, Anokhin AP, Plawecki MH, Kuperman S, Almasy L, Merikangas A, Brislin SJ, Bauer L, Hesselbrock V, Chan G, Kramer J, Lai D, Hartz S, Bierut LJ, McCutcheon VV, Bucholz KK, Dick DM, Schuckit MA, Edenberg HJ, Porjesz B. Predicting Alcohol-Related Memory Problems in Older Adults: A Machine Learning Study with Multi-Domain Features. Behav Sci (Basel) 2023; 13:bs13050427. [PMID: 37232664 DOI: 10.3390/bs13050427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Revised: 05/08/2023] [Accepted: 05/11/2023] [Indexed: 05/27/2023] Open
Abstract
Memory problems are common among older adults with a history of alcohol use disorder (AUD). Employing a machine learning framework, the current study investigates the use of multi-domain features to classify individuals with and without alcohol-induced memory problems. A group of 94 individuals (ages 50-81 years) with alcohol-induced memory problems (the memory group) were compared with a matched control group who did not have memory problems. The random forests model identified specific features from each domain that contributed to the classification of the memory group vs. the control group (AUC = 88.29%). Specifically, individuals from the memory group manifested a predominant pattern of hyperconnectivity across the default mode network regions except for some connections involving the anterior cingulate cortex, which were predominantly hypoconnected. Other significant contributing features were: (i) polygenic risk scores for AUD, (ii) alcohol consumption and related health consequences during the past five years, such as health problems, past negative experiences, withdrawal symptoms, and the largest number of drinks in a day during the past twelve months, and (iii) elevated neuroticism and increased harm avoidance, and fewer positive "uplift" life events. At the neural systems level, hyperconnectivity across the default mode network regions, including the connections across the hippocampal hub regions, in individuals with memory problems may indicate dysregulation in neural information processing. Overall, the study outlines the importance of utilizing multidomain features, consisting of resting-state brain connectivity data collected ~18 years ago, together with personality, life experiences, polygenic risk, and alcohol consumption and related consequences, to predict the alcohol-related memory problems that arise in later life.
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Affiliation(s)
- Chella Kamarajan
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry and Behavioral Science, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Ashwini K Pandey
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry and Behavioral Science, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - David B Chorlian
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry and Behavioral Science, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Jacquelyn L Meyers
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry and Behavioral Science, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Sivan Kinreich
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry and Behavioral Science, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Gayathri Pandey
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry and Behavioral Science, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Stacey Subbie-Saenz de Viteri
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry and Behavioral Science, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Jian Zhang
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry and Behavioral Science, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Weipeng Kuang
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry and Behavioral Science, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Peter B Barr
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry and Behavioral Science, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Fazil Aliev
- Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ 08854, USA
| | - Andrey P Anokhin
- Department of Psychiatry, School of Medicine, Washington University, St. Louis, MO 63110, USA
| | | | - Samuel Kuperman
- Department of Psychiatry, University of Iowa, Iowa City, IA 52242, USA
| | - Laura Almasy
- The Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Alison Merikangas
- The Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sarah J Brislin
- Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ 08854, USA
| | - Lance Bauer
- Department of Psychiatry, University of Connecticut, Farmington, CT 06030, USA
| | - Victor Hesselbrock
- Department of Psychiatry, University of Connecticut, Farmington, CT 06030, USA
| | - Grace Chan
- Department of Psychiatry, University of Iowa, Iowa City, IA 52242, USA
- Department of Psychiatry, University of Connecticut, Farmington, CT 06030, USA
| | - John Kramer
- Department of Psychiatry, University of Iowa, Iowa City, IA 52242, USA
| | - Dongbing Lai
- Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Sarah Hartz
- Department of Psychiatry, School of Medicine, Washington University, St. Louis, MO 63110, USA
| | - Laura J Bierut
- Department of Psychiatry, School of Medicine, Washington University, St. Louis, MO 63110, USA
| | - Vivia V McCutcheon
- Department of Psychiatry, School of Medicine, Washington University, St. Louis, MO 63110, USA
| | - Kathleen K Bucholz
- Department of Psychiatry, School of Medicine, Washington University, St. Louis, MO 63110, USA
| | - Danielle M Dick
- Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ 08854, USA
| | - Marc A Schuckit
- Department of Psychiatry, University of California, San Diego, CA 92103, USA
| | | | - Bernice Porjesz
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry and Behavioral Science, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
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8
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Wang YW, Chen X, Yan CG. Comprehensive evaluation of harmonization on functional brain imaging for multisite data-fusion. Neuroimage 2023; 274:120089. [PMID: 37086875 DOI: 10.1016/j.neuroimage.2023.120089] [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: 09/19/2022] [Revised: 03/05/2023] [Accepted: 04/03/2023] [Indexed: 04/24/2023] Open
Abstract
To embrace big-data neuroimaging, harmonizing the site effect in resting-state functional magnetic resonance imaging (R-fMRI) data fusion is a fundamental challenge. A comprehensive evaluation of potentially effective harmonization strategies, particularly with specifically collected data, has been scarce, especially for R-fMRI metrics. Here, we comprehensively assess harmonization strategies from multiple perspectives, including tests on residual site effect, individual identification, test-retest reliability, and replicability of group-level statistical results, on widely used R-fMRI metrics across various datasets, including data obtained from participants with repetitive measures at different scanners. For individual identifiability (i.e., whether the same subject could be identified across R-fMRI data scanned across different sites), we found that, while most methods decreased site effects, the Subsampling Maximum-mean-distance based distribution shift correction Algorithm (SMA) and parametric unadjusted CovBat outperformed linear regression models, linear mixed models, ComBat series and invariant conditional variational auto-encoder in clustering accuracy. Test-retest reliability was better for SMA and parametric adjusted CovBat than unadjusted ComBat series and parametric unadjusted CovBat in the number of overlapped voxels. At the same time, SMA was superior to the latter in replicability in terms of the Dice coefficient and the scale of brain areas showing sex differences reproducibly observed across datasets. Furthermore, SMA better detected reproducible sex differences of ALFF under the site-sex confounded situation. Moreover, we designed experiments to identify the best target site features to optimize SMA identifiability, test-retest reliability, and stability. We noted both sample size and distribution of the target site matter and introduced a heuristic formula for selecting the target site. In addition to providing practical guidelines, this work can inform continuing improvements and innovations in harmonizing methodologies for big R-fMRI data.
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Affiliation(s)
- Yu-Wei Wang
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China; International Big-Data Center for Depression Research, Chinese Academy of Sciences, Beijing 100101, China
| | - Xiao Chen
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China; International Big-Data Center for Depression Research, Chinese Academy of Sciences, Beijing 100101, China; Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
| | - Chao-Gan Yan
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China; International Big-Data Center for Depression Research, Chinese Academy of Sciences, Beijing 100101, China; Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China..
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9
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Wang R, Bashyam V, Yang Z, Yu F, Tassopoulou V, Chintapalli SS, Skampardoni I, Sreepada LP, Sahoo D, Nikita K, Abdulkadir A, Wen J, Davatzikos C. Applications of generative adversarial networks in neuroimaging and clinical neuroscience. Neuroimage 2023; 269:119898. [PMID: 36702211 PMCID: PMC9992336 DOI: 10.1016/j.neuroimage.2023.119898] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 12/16/2022] [Accepted: 01/21/2023] [Indexed: 01/25/2023] Open
Abstract
Generative adversarial networks (GANs) are one powerful type of deep learning models that have been successfully utilized in numerous fields. They belong to the broader family of generative methods, which learn to generate realistic data with a probabilistic model by learning distributions from real samples. In the clinical context, GANs have shown enhanced capabilities in capturing spatially complex, nonlinear, and potentially subtle disease effects compared to traditional generative methods. This review critically appraises the existing literature on the applications of GANs in imaging studies of various neurological conditions, including Alzheimer's disease, brain tumors, brain aging, and multiple sclerosis. We provide an intuitive explanation of various GAN methods for each application and further discuss the main challenges, open questions, and promising future directions of leveraging GANs in neuroimaging. We aim to bridge the gap between advanced deep learning methods and neurology research by highlighting how GANs can be leveraged to support clinical decision making and contribute to a better understanding of the structural and functional patterns of brain diseases.
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Affiliation(s)
- Rongguang Wang
- Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA.
| | - Vishnu Bashyam
- Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
| | - Zhijian Yang
- Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
| | - Fanyang Yu
- Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
| | - Vasiliki Tassopoulou
- Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
| | - Sai Spandana Chintapalli
- Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
| | - Ioanna Skampardoni
- Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA; School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
| | - Lasya P Sreepada
- Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
| | - Dushyant Sahoo
- Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
| | - Konstantina Nikita
- School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
| | - Ahmed Abdulkadir
- Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA; Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Junhao Wen
- Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
| | - Christos Davatzikos
- Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania, Philadelphia, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA.
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10
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Helwegen K, Libedinsky I, van den Heuvel MP. Statistical power in network neuroscience. Trends Cogn Sci 2023; 27:282-301. [PMID: 36725422 DOI: 10.1016/j.tics.2022.12.011] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 12/14/2022] [Accepted: 12/15/2022] [Indexed: 01/31/2023]
Abstract
Network neuroscience has emerged as a leading method to study brain connectivity. The success of these investigations is dependent not only on approaches to accurately map connectivity but also on the ability to detect real effects in the data - that is, statistical power. We review the state of statistical power in the field and discuss sample size, effect size, measurement error, and network topology as key factors that influence the power of brain connectivity investigations. We use the term 'differential power' to describe how power can vary between nodes, edges, and graph metrics, leaving traces in both positive and negative connectome findings. We conclude with strategies for working with, rather than around, power in connectivity studies.
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Affiliation(s)
- Koen Helwegen
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Ilan Libedinsky
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Martijn P van den Heuvel
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands; Department of Child and Adolescent Psychiatry and Psychology, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
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11
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Schmidt SA, Shahsavarani S, Khan RA, Tai Y, Granato EC, Willson CM, Ramos P, Sherman P, Esquivel C, Sutton BP, Husain F. An examination of the reliability of seed-to-seed resting state functional connectivity in tinnitus patients. NEUROIMAGE: REPORTS 2023. [DOI: 10.1016/j.ynirp.2023.100158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/17/2023]
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12
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De Rosa AP, Esposito F, Valsasina P, d'Ambrosio A, Bisecco A, Rocca MA, Tommasin S, Marzi C, De Stefano N, Battaglini M, Pantano P, Cirillo M, Tedeschi G, Filippi M, Gallo A. Resting-state functional MRI in multicenter studies on multiple sclerosis: a report on raw data quality and functional connectivity features from the Italian Neuroimaging Network Initiative. J Neurol 2023; 270:1047-1066. [PMID: 36350401 PMCID: PMC9886598 DOI: 10.1007/s00415-022-11479-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 11/03/2022] [Accepted: 11/04/2022] [Indexed: 11/11/2022]
Abstract
The Italian Neuroimaging Network Initiative (INNI) is an expanding repository of brain MRI data from multiple sclerosis (MS) patients recruited at four Italian MRI research sites. We describe the raw data quality of resting-state functional MRI (RS-fMRI) time-series in INNI and the inter-site variability in functional connectivity (FC) features after unified automated data preprocessing. MRI datasets from 489 MS patients and 246 healthy control (HC) subjects were retrieved from the INNI database. Raw data quality metrics included temporal signal-to-noise ratio (tSNR), spatial smoothness (FWHM), framewise displacement (FD), and differential variation in signals (DVARS). Automated preprocessing integrated white-matter lesion segmentation (SAMSEG) into a standard fMRI pipeline (fMRIPrep). FC features were calculated on pre-processed data and harmonized between sites (Combat) prior to assessing general MS-related alterations. Across centers (both groups), median tSNR and FWHM ranged from 47 to 84 and from 2.0 to 2.5, and median FD and DVARS ranged from 0.08 to 0.24 and from 1.06 to 1.22. After preprocessing, only global FC-related features were significantly correlated with FD or DVARS. Across large-scale networks, age/sex/FD-adjusted and harmonized FC features exhibited both inter-site and site-specific inter-group effects. Significant general reductions were obtained for somatomotor and limbic networks in MS patients (vs. HC). The implemented procedures provide technical information on raw data quality and outcome of fully automated preprocessing that might serve as reference in future RS-fMRI studies within INNI. The unified pipeline introduced little bias across sites and appears suitable for multisite FC analyses on harmonized network estimates.
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Affiliation(s)
- Alessandro Pasquale De Rosa
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Piazza Luigi Miraglia, 2, 80138, Naples, Italy
| | - Fabrizio Esposito
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Piazza Luigi Miraglia, 2, 80138, Naples, Italy.
| | - Paola Valsasina
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
| | - Alessandro d'Ambrosio
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Piazza Luigi Miraglia, 2, 80138, Naples, Italy
| | - Alvino Bisecco
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Piazza Luigi Miraglia, 2, 80138, Naples, Italy
| | - Maria A Rocca
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
- Vita-Salute San Raffaele University, Via Olgettina 58, 20132, Milan, Italy
| | - Silvia Tommasin
- Department of Human Neurosciences, Sapienza University of Rome, Viale Dell'Università, 30, 00185, Rome, Italy
| | - Chiara Marzi
- Institute of Applied Physics "Nello Cararra" (IFAC), National Research Council (CNR), Via Madonna del Piano, 10, Sesto Fiorentino, 50019, Florence, Italy
| | - Nicola De Stefano
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Marco Battaglini
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Patrizia Pantano
- Department of Human Neurosciences, Sapienza University of Rome, Viale Dell'Università, 30, 00185, Rome, Italy
| | - Mario Cirillo
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Piazza Luigi Miraglia, 2, 80138, Naples, Italy
| | - Gioacchino Tedeschi
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Piazza Luigi Miraglia, 2, 80138, Naples, Italy
| | - Massimo Filippi
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
- Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
- Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
- Vita-Salute San Raffaele University, Via Olgettina 58, 20132, Milan, Italy
| | - Antonio Gallo
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Piazza Luigi Miraglia, 2, 80138, Naples, Italy
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13
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Roffet F, Delrieux C, Patow G. Assessing Multi-Site rs-fMRI-Based Connectomic Harmonization Using Information Theory. Brain Sci 2022; 12:brainsci12091219. [PMID: 36138956 PMCID: PMC9496818 DOI: 10.3390/brainsci12091219] [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: 07/14/2022] [Revised: 09/01/2022] [Accepted: 09/06/2022] [Indexed: 11/17/2022] Open
Abstract
Several harmonization techniques have recently been proposed for connectomics/networks derived from resting-state functional magnetic resonance imaging (rs-fMRI) acquired at multiple sites. These techniques have the objective of mitigating site-specific biases that complicate its subsequent analysis and, therefore, compromise the quality of the results when these images are analyzed together. Thus, harmonization is indispensable when large cohorts are required in which the data obtained must be independent of the particular condition of each resonator, its make and model, its calibration, and other features or artifacts that may affect the significance of the acquisition. To date, no assessment of the actual efficacy of these harmonization techniques has been proposed. In this work, we apply recently introduced Information Theory tools to analyze the effectiveness of these techniques, developing a methodology that allows us to compare different harmonization models. We demonstrate the usefulness of this methodology by applying it to some of the most widespread harmonization frameworks and datasets. As a result, we are able to show that some of these techniques are indeed ineffective since the acquisition site can still be determined from the fMRI data after the processing.
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Affiliation(s)
- Facundo Roffet
- Department of Electrical and Computer Engineering (DIEC), Universidad Nacional del Sur, Bahía Blanca AR-B8000, Argentina
| | - Claudio Delrieux
- Department of Electrical and Computer Engineering (DIEC), Universidad Nacional del Sur and National Council for Scientific and Technical Research (CONICET), Bahía Blanca AR-B8000, Argentina
| | - Gustavo Patow
- ViRVIG, University of Girona, 17003 Girona, Spain
- Correspondence:
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14
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Kamishikiryo T, Okada G, Itai E, Masuda Y, Yokoyama S, Takamura M, Fuchikami M, Yoshino A, Mawatari K, Numata S, Takahashi A, Ohmori T, Okamoto Y. Left DLPFC activity is associated with plasma kynurenine levels and can predict treatment response to escitalopram in major depressive disorder. Psychiatry Clin Neurosci 2022; 76:367-376. [PMID: 35543406 PMCID: PMC9544423 DOI: 10.1111/pcn.13373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Revised: 03/16/2022] [Accepted: 04/24/2022] [Indexed: 11/27/2022]
Abstract
AIM To establish treatment response biomarkers that reflect the pathophysiology of depression, it is important to use an integrated set of features. This study aimed to determine the relationship between regional brain activity at rest and blood metabolites related to treatment response to escitalopram to identify the characteristics of depression that respond to treatment. METHODS Blood metabolite levels and resting-state brain activity were measured in patients with moderate to severe depression (n = 65) before and after 6-8 weeks of treatment with escitalopram, and these were compared between Responders and Nonresponders to treatment. We then examined the relationship between blood metabolites and brain activity related to treatment responsiveness in patients and healthy controls (n = 36). RESULTS Thirty-two patients (49.2%) showed a clinical response (>50% reduction in the Hamilton Rating Scale for Depression score) and were classified as Responders, and the remaining 33 patients were classified as Nonresponders. The pretreatment fractional amplitude of low-frequency fluctuation (fALFF) value of the left dorsolateral prefrontal cortex (DLPFC) and plasma kynurenine levels were lower in Responders, and the rate of increase of both after treatment was correlated with an improvement in symptoms. Moreover, the fALFF value of the left DLPFC was significantly correlated with plasma kynurenine levels in pretreatment patients with depression and healthy controls. CONCLUSION Decreased resting-state regional activity of the left DLPFC and decreased plasma kynurenine levels may predict treatment response to escitalopram, suggesting that it may be involved in the pathophysiology of major depressive disorder in response to escitalopram treatment.
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Affiliation(s)
- Toshiharu Kamishikiryo
- Department of Psychiatry and Neurosciences, Graduate School of Biomedical Sciences, Hiroshima University, Hiroshima, Japan
| | - Go Okada
- Department of Psychiatry and Neurosciences, Graduate School of Biomedical Sciences, Hiroshima University, Hiroshima, Japan
| | - Eri Itai
- Department of Psychiatry and Neurosciences, Graduate School of Biomedical Sciences, Hiroshima University, Hiroshima, Japan
| | - Yoshikazu Masuda
- Department of Psychiatry and Neurosciences, Graduate School of Biomedical Sciences, Hiroshima University, Hiroshima, Japan
| | - Satoshi Yokoyama
- Department of Psychiatry and Neurosciences, Graduate School of Biomedical Sciences, Hiroshima University, Hiroshima, Japan
| | - Masahiro Takamura
- Department of Neurology, Faculty of Medicine, Shimane University, Izumo-shi, Japan
| | - Manabu Fuchikami
- Department of Psychiatry and Neurosciences, Graduate School of Biomedical Sciences, Hiroshima University, Hiroshima, Japan
| | - Atsuo Yoshino
- Department of Psychiatry and Neurosciences, Graduate School of Biomedical Sciences, Hiroshima University, Hiroshima, Japan
| | - Kazuaki Mawatari
- Department of Preventive Environment and Nutrition, Institute of Biomedical Sciences, Tokushima University, Tokushima, Japan
| | - Shusuke Numata
- Department of Psychiatry, Institute of Biomedical Science, Tokushima University Graduate School, Tokushima, Japan
| | - Akira Takahashi
- Department of Preventive Environment and Nutrition, Institute of Biomedical Sciences, Tokushima University, Tokushima, Japan
| | - Tetsuro Ohmori
- Department of Psychiatry, Institute of Biomedical Science, Tokushima University Graduate School, Tokushima, Japan
| | - Yasumasa Okamoto
- Department of Psychiatry and Neurosciences, Graduate School of Biomedical Sciences, Hiroshima University, Hiroshima, Japan
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15
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Chen AA, Srinivasan D, Pomponio R, Fan Y, Nasrallah IM, Resnick SM, Beason-Held LL, Davatzikos C, Satterthwaite TD, Bassett DS, Shinohara RT, Shou H. Harmonizing functional connectivity reduces scanner effects in community detection. Neuroimage 2022; 256:119198. [PMID: 35421567 PMCID: PMC9202339 DOI: 10.1016/j.neuroimage.2022.119198] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 04/06/2022] [Accepted: 04/07/2022] [Indexed: 12/12/2022] Open
Abstract
Community detection on graphs constructed from functional magnetic resonance imaging (fMRI) data has led to important insights into brain functional organization. Large studies of brain community structure often include images acquired on multiple scanners across different studies. Differences in scanner can introduce variability into the downstream results, and these differences are often referred to as scanner effects. Such effects have been previously shown to significantly impact common network metrics. In this study, we identify scanner effects in data-driven community detection results and related network metrics. We assess a commonly employed harmonization method and propose new methodology for harmonizing functional connectivity that leverage existing knowledge about network structure as well as patterns of covariance in the data. Finally, we demonstrate that our new methods reduce scanner effects in community structure and network metrics. Our results highlight scanner effects in studies of brain functional organization and provide additional tools to address these unwanted effects. These findings and methods can be incorporated into future functional connectivity studies, potentially preventing spurious findings and improving reliability of results.
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Affiliation(s)
- Andrew A Chen
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA.
| | - Dhivya Srinivasan
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Raymond Pomponio
- Department of Biostatistics, Colorado School of Public Health, Aurora, CO 80045, USA
| | - Yong Fan
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ilya M Nasrallah
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD 21224, USA
| | - Lori L Beason-Held
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD 21224, USA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Theodore D Satterthwaite
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn Lifespan Informatics & Neuroimaging Center, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Dani S Bassett
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Electrical & Systems Engineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Physics & Astronomy, College of Arts & Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Nuerology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Santa Fe Institute, 1399 Hyde Park Rd, Santa Fe, NM 87501, USA
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Haochang Shou
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA
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16
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Hao Z, Shi Y, Huang L, Sun J, Li M, Gao Y, Li J, Wang Q, Zhan L, Ding Q, Jia X, Li H. The Atypical Effective Connectivity of Right Temporoparietal Junction in Autism Spectrum Disorder: A Multi-Site Study. Front Neurosci 2022; 16:927556. [PMID: 35924226 PMCID: PMC9340667 DOI: 10.3389/fnins.2022.927556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Accepted: 06/21/2022] [Indexed: 11/13/2022] Open
Abstract
Social function impairment is the core deficit of autism spectrum disorder (ASD). Although many studies have investigated ASD through a variety of neuroimaging tools, its brain mechanism of social function remains unclear due to its complex and heterogeneous symptoms. The present study aimed to use resting-state functional magnetic imaging data to explore effective connectivity between the right temporoparietal junction (RTPJ), one of the key brain regions associated with social impairment of individuals with ASD, and the whole brain to further deepen our understanding of the neuropathological mechanism of ASD. This study involved 1,454 participants from 23 sites from the Autism Brain Imaging Data Exchange (ABIDE) public dataset, which included 618 individuals with ASD and 836 with typical development (TD). First, a voxel-wise Granger causality analysis (GCA) was conducted with the RTPJ selected as the region of interest (ROI) to investigate the differences in effective connectivity between the ASD and TD groups in every site. Next, to obtain further accurate and representative results, an image-based meta-analysis was implemented to further analyze the GCA results of each site. Our results demonstrated abnormal causal connectivity between the RTPJ and the widely distributed brain regions and that the connectivity has been associated with social impairment in individuals with ASD. The current study could help to further elucidate the pathological mechanisms of ASD and provides a new perspective for future research.
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Affiliation(s)
- Zeqi Hao
- School of Teacher Education, Zhejiang Normal University, Jinhua, China
- Key Laboratory of Intelligent Education Technology and Application, Zhejiang Normal University, Jinhua, China
| | - Yuyu Shi
- School of Teacher Education, Zhejiang Normal University, Jinhua, China
- Key Laboratory of Intelligent Education Technology and Application, Zhejiang Normal University, Jinhua, China
| | - Lina Huang
- Department of Radiology, Changshu No. 2 People's Hospital, The Affiliated Changshu Hospital of Xuzhou Medical University, Changshu, China
| | - Jiawei Sun
- School of Information and Electronics Technology, Jiamusi University, Jiamusi, China
| | - Mengting Li
- School of Teacher Education, Zhejiang Normal University, Jinhua, China
- Key Laboratory of Intelligent Education Technology and Application, Zhejiang Normal University, Jinhua, China
| | - Yanyan Gao
- School of Teacher Education, Zhejiang Normal University, Jinhua, China
- Key Laboratory of Intelligent Education Technology and Application, Zhejiang Normal University, Jinhua, China
| | - Jing Li
- School of Teacher Education, Zhejiang Normal University, Jinhua, China
- Key Laboratory of Intelligent Education Technology and Application, Zhejiang Normal University, Jinhua, China
| | - Qianqian Wang
- School of Teacher Education, Zhejiang Normal University, Jinhua, China
- Key Laboratory of Intelligent Education Technology and Application, Zhejiang Normal University, Jinhua, China
| | - Linlin Zhan
- School of Western Languages, Heilongjiang University, Harbin, China
| | - Qingguo Ding
- Department of Radiology, Changshu No. 2 People's Hospital, The Affiliated Changshu Hospital of Xuzhou Medical University, Changshu, China
- Qingguo Ding
| | - Xize Jia
- School of Teacher Education, Zhejiang Normal University, Jinhua, China
- Key Laboratory of Intelligent Education Technology and Application, Zhejiang Normal University, Jinhua, China
- Xize Jia
| | - Huayun Li
- School of Teacher Education, Zhejiang Normal University, Jinhua, China
- Key Laboratory of Intelligent Education Technology and Application, Zhejiang Normal University, Jinhua, China
- *Correspondence: Huayun Li
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17
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Weber S, Heim S, Richiardi J, Van De Ville D, Serranová T, Jech R, Marapin RS, Tijssen MAJ, Aybek S. Multi-centre classification of functional neurological disorders based on resting-state functional connectivity. Neuroimage Clin 2022; 35:103090. [PMID: 35752061 PMCID: PMC9240866 DOI: 10.1016/j.nicl.2022.103090] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 05/28/2022] [Accepted: 06/16/2022] [Indexed: 11/28/2022]
Abstract
Using machine learning on multi-centre data, FND patients were successfully classified with an accuracy of 72%. The angular- and supramarginal gyri, cingular- and insular cortex, and the hippocampus were the most discriminant regions. To provide diagnostic utility, future studies must include patients with similar symptoms but different diagnoses.
Background Patients suffering from functional neurological disorder (FND) experience disabling neurological symptoms not caused by an underlying classical neurological disease (such as stroke or multiple sclerosis). The diagnosis is made based on reliable positive clinical signs, but clinicians often require additional time- and cost consuming medical tests and examinations. Resting-state functional connectivity (RS FC) showed its potential as an imaging-based adjunctive biomarker to help distinguish patients from healthy controls and could represent a “rule-in” procedure to assist in the diagnostic process. However, the use of RS FC depends on its applicability in a multi-centre setting, which is particularly susceptible to inter-scanner variability. The aim of this study was to test the robustness of a classification approach based on RS FC in a multi-centre setting. Methods This study aimed to distinguish 86 FND patients from 86 healthy controls acquired in four different centres using a multivariate machine learning approach based on whole-brain resting-state functional connectivity. First, previously published results were replicated in each centre individually (intra-centre cross-validation) and its robustness across inter-scanner variability was assessed by pooling all the data (pooled cross-validation). Second, we evaluated the generalizability of the method by using data from each centre once as a test set, and the data from the remaining centres as a training set (inter-centre cross-validation). Results FND patients were successfully distinguished from healthy controls in the replication step (accuracy of 74%) as well as in each individual additional centre (accuracies of 73%, 71% and 70%). The pooled cross validation confirmed that the classifier was robust with an accuracy of 72%. The results survived post-hoc adjustment for anxiety, depression, psychotropic medication intake, and symptom severity. The most discriminant features involved the angular- and supramarginal gyri, sensorimotor cortex, cingular- and insular cortex, and hippocampal regions. The inter-centre validation step did not exceed chance level (accuracy below 50%). Conclusions The results demonstrate the applicability of RS FC to correctly distinguish FND patients from healthy controls in different centres and its robustness against inter-scanner variability. In order to generalize its use across different centres and aim for clinical application, future studies should work towards optimization of acquisition parameters and include neurological and psychiatric control groups presenting with similar symptoms.
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Affiliation(s)
- Samantha Weber
- Psychosomatic Medicine, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Switzerland
| | - Salome Heim
- Psychosomatic Medicine, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Switzerland
| | - Jonas Richiardi
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Dimitri Van De Ville
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Department of Radiology and Medical Informatics, Geneva University Hospitals, Geneva, Switzerland
| | - Tereza Serranová
- Centre for Interventional Therapy of Movement Disorders, Department of Neurology, Charles University, 1(st) Faculty of Medicine and General University Hospital in Prague, Czech Republic
| | - Robert Jech
- Centre for Interventional Therapy of Movement Disorders, Department of Neurology, Charles University, 1(st) Faculty of Medicine and General University Hospital in Prague, Czech Republic; Department of Neurology, Charles University and General University Hospital in Prague, Prague, Czech Republic
| | - Ramesh S Marapin
- Department of Neurology, University Medical Center Groningen, Hanzeplein 1, 9713 GZ Groningen, the Netherlands; UMCG Expertise Center Movement Disorders Groningen, University Medical Center Groningen (UMCG), Groningen, the Netherlands
| | - Marina A J Tijssen
- Department of Neurology, University Medical Center Groningen, Hanzeplein 1, 9713 GZ Groningen, the Netherlands; UMCG Expertise Center Movement Disorders Groningen, University Medical Center Groningen (UMCG), Groningen, the Netherlands
| | - Selma Aybek
- Psychosomatic Medicine, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Switzerland.
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18
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Tonic pain alters functional connectivity of the descending pain modulatory network involving amygdala, periaqueductal gray, parabrachial nucleus and anterior cingulate cortex. Neuroimage 2022; 256:119278. [PMID: 35523367 PMCID: PMC9250649 DOI: 10.1016/j.neuroimage.2022.119278] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 04/07/2022] [Accepted: 05/02/2022] [Indexed: 11/21/2022] Open
Abstract
INTRODUCTION Resting state functional connectivity (FC) is widely used to assess functional brain alterations in patients with chronic pain. However, reports of FC accompanying tonic pain in pain-free persons are rare. A network we term the Descending Pain Modulatory Network (DPMN) is implicated in healthy and pathologic pain modulation. Here, we evaluate the effect of tonic pain on FC of specific nodes of this network: anterior cingulate cortex (ACC), amygdala (AMYG), periaqueductal gray (PAG), and parabrachial nuclei (PBN). METHODS In 50 pain-free participants (30F), we induced tonic pain using a capsaicin-heat pain model. functional MRI measured resting BOLD signal during pain-free rest with a 32°C thermode and then tonic pain where participants experienced a previously warm temperature combined with capsaicin. We evaluated FC from ACC, AMYG, PAG, and PBN with correlation of self-report pain intensity during both states. We hypothesized tonic pain would diminish FC dyads within the DPMN. RESULTS Of all hypothesized FC dyads, only PAG and subgenual ACC was weakly altered during pain (F=3.34; p=0.074; pain-free>pain d=0.25). After pain induction sACC-PAG FC became positively correlated with pain intensity (R=0.38; t=2.81; p=0.007). Right PBN-PAG FC during pain-free rest positively correlated with subsequently experienced pain (R=0.44; t=3.43; p=0.001). During pain, this connection's FC was diminished (paired t=-3.17; p=0.0026). In whole-brain analyses, during pain-free rest, FC between left AMYG and right superior parietal lobule and caudate nucleus were positively correlated with subsequent pain. During pain, FC between left AMYG and right inferior temporal gyrus negatively correlated with pain. Subsequent pain positively correlated with right AMYG FC with right claustrum; right primary visual cortex and right temporo-occipitoparietal junction Conclusion: We demonstrate sACC-PAG tonic pain FC positively correlates with experienced pain and resting right PBN-PAG FC correlates with subsequent pain and is diminished during tonic pain. Finally, we reveal PAG- and right AMYG-anchored networks which correlate with subsequently experienced pain intensity. Our findings suggest specific connectivity patterns within the DPMN at rest are associated with subsequently experienced pain and modulated by tonic pain. These nodes and their functional modulation may reveal new therapeutic targets for neuromodulation or biomarkers to guide interventions.
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Differentiating Individuals with and without Alcohol Use Disorder Using Resting-State fMRI Functional Connectivity of Reward Network, Neuropsychological Performance, and Impulsivity Measures. Behav Sci (Basel) 2022; 12:bs12050128. [PMID: 35621425 PMCID: PMC9137599 DOI: 10.3390/bs12050128] [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: 03/15/2022] [Revised: 04/11/2022] [Accepted: 04/27/2022] [Indexed: 11/17/2022] Open
Abstract
Individuals with alcohol use disorder (AUD) may manifest an array of neural and behavioral abnormalities, including altered brain networks, impaired neurocognitive functioning, and heightened impulsivity. Using multidomain measures, the current study aimed to identify specific features that can differentiate individuals with AUD from healthy controls (CTL), utilizing a random forests (RF) classification model. Features included fMRI-based resting-state functional connectivity (rsFC) across the reward network, neuropsychological task performance, and behavioral impulsivity scores, collected from thirty abstinent adult males with prior history of AUD and thirty CTL individuals without a history of AUD. It was found that the RF model achieved a classification accuracy of 86.67% (AUC = 93%) and identified key features of FC and impulsivity that significantly contributed to classifying AUD from CTL individuals. Impulsivity scores were the topmost predictors, followed by twelve rsFC features involving seventeen key reward regions in the brain, such as the ventral tegmental area, nucleus accumbens, anterior insula, anterior cingulate cortex, and other cortical and subcortical structures. Individuals with AUD manifested significant differences in impulsivity and alterations in functional connectivity relative to controls. Specifically, AUD showed heightened impulsivity and hypoconnectivity in nine connections across 13 regions and hyperconnectivity in three connections involving six regions. Relative to controls, visuo-spatial short-term working memory was also found to be impaired in AUD. In conclusion, specific multidomain features of brain connectivity, impulsivity, and neuropsychological performance can be used in a machine learning framework to effectively classify AUD individuals from healthy controls.
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20
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Broeders TA, Douw L, Eijlers AJ, Dekker I, Uitdehaag BM, Barkhof F, Hulst HE, Vinkers CH, Geurts JJ, Schoonheim MM. A more unstable resting-state functional network in cognitively declining multiple sclerosis. Brain Commun 2022; 4:fcac095. [PMID: 35620116 PMCID: PMC9128379 DOI: 10.1093/braincomms/fcac095] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Revised: 02/14/2022] [Accepted: 04/11/2022] [Indexed: 11/24/2022] Open
Abstract
Cognitive impairment is common in people with multiple sclerosis and strongly
affects their daily functioning. Reports have linked disturbed cognitive
functioning in multiple sclerosis to changes in the organization of the
functional network. In a healthy brain, communication between brain regions and
which network a region belongs to is continuously and dynamically adapted to
enable adequate cognitive function. However, this dynamic network adaptation has
not been investigated in multiple sclerosis, and longitudinal network data
remain particularly rare. Therefore, the aim of this study was to longitudinally
identify patterns of dynamic network reconfigurations that are related to the
worsening of cognitive decline in multiple sclerosis. Resting-state functional
MRI and cognitive scores (expanded Brief Repeatable Battery of
Neuropsychological tests) were acquired in 230 patients with multiple sclerosis
and 59 matched healthy controls, at baseline (mean disease duration: 15 years)
and at 5-year follow-up. A sliding-window approach was used for functional MRI
analyses, where brain regions were dynamically assigned to one of seven
literature-based subnetworks. Dynamic reconfigurations of subnetworks were
characterized using measures of promiscuity (number of subnetworks switched to),
flexibility (number of switches), cohesion (mutual switches) and disjointedness
(independent switches). Cross-sectional differences between cognitive groups and
longitudinal changes were assessed, as well as relations with structural damage
and performance on specific cognitive domains. At baseline, 23% of
patients were cognitively impaired (≥2/7 domains
Z < −2) and 18% were mildly
impaired (≥2/7 domains
Z < −1.5). Longitudinally,
28% of patients declined over time (0.25 yearly change on ≥2/7
domains based on reliable change index). Cognitively impaired patients displayed
more dynamic network reconfigurations across the whole brain compared with
cognitively preserved patients and controls, i.e. showing higher promiscuity
(P = 0.047), flexibility
(P = 0.008) and cohesion
(P = 0.008). Over time, cognitively
declining patients showed a further increase in cohesion
(P = 0.004), which was not seen in stable
patients (P = 0.544). More cohesion was
related to more severe structural damage (average
r = 0.166,
P = 0.015) and worse verbal memory
(r = −0.156,
P = 0.022), information processing speed
(r = −0.202,
P = 0.003) and working memory
(r = −0.163,
P = 0.017). Cognitively impaired multiple
sclerosis patients exhibited a more unstable network reconfiguration compared to
preserved patients, i.e. brain regions switched between subnetworks more often,
which was related to structural damage. This shift to more unstable network
reconfigurations was also demonstrated longitudinally in patients that showed
cognitive decline only. These results indicate the potential relevance of a
progressive destabilization of network topology for understanding cognitive
decline in multiple sclerosis.
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Affiliation(s)
- Tommy A.A. Broeders
- Departments of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Linda Douw
- Departments of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Anand J.C. Eijlers
- Departments of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Iris Dekker
- Departments of Neurology, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Bernard M.J. Uitdehaag
- Departments of Neurology, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Frederik Barkhof
- Departments of Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Queen Square Institute of Neurology and Centre for Medical Image Computing, University College London, UK
| | - Hanneke E. Hulst
- Departments of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Christiaan H. Vinkers
- Departments of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Departments of Psychiatry, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Jeroen J.G. Geurts
- Departments of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Menno M. Schoonheim
- Departments of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
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21
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Jin Z, Huyang S, Jiang L, Yan Y, Xu M, Wang J, Li Q, Wu D. Increased Resting-State Interhemispheric Functional Connectivity of Posterior Superior Temporal Gyrus and Posterior Cingulate Cortex in Congenital Amusia. Front Neurosci 2021; 15:653325. [PMID: 33994929 PMCID: PMC8120159 DOI: 10.3389/fnins.2021.653325] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 04/06/2021] [Indexed: 11/26/2022] Open
Abstract
Interhemispheric connectivity of the two cerebral hemispheres is crucial for a broad repertoire of cognitive functions including music and language. Congenital amusia has been reported as a neurodevelopment disorder characterized by impaired music perception and production. However, little is known about the characteristics of the interhemispheric functional connectivity (FC) in amusia. In the present study, we used a newly developed voxel-mirrored homotopic connectivity (VMHC) method to investigate the interhemispheric FC of the whole brain in amusia at resting-state. Thirty amusics and 29 matched participants underwent a resting-state functional magnetic resonance imaging (fMRI) scanning. An automated VMHC approach was used to analyze the fMRI data. Compared to the control group, amusics showed increased VMHC within the posterior part of the default mode network (DMN) mainly in the posterior superior temporal gyrus (pSTG) and posterior cingulate cortex (PCC). Correlation analyses revealed negative correlations between the VMHC value in pSTG/PCC and the music perception ability among amusics. Further ROC analyses showed that the VMHC value of pSTG/PCC showed a good sensibility/specificity to differentiate the amusics from the controls. These findings provide a new perspective for understanding the neural basis of congenital amusia and imply the immature state of DMN may be a credible neural marker of amusia.
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Affiliation(s)
- Zhishuai Jin
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Sizhu Huyang
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Lichen Jiang
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Yajun Yan
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Ming Xu
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Jinyu Wang
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Qixiong Li
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Daxing Wu
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, China.,Medical Psychological Institute, Central South University, Changsha, China.,National Clinical Research Center for Mental Disorders, Changsha, China
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22
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Cohen AL, Ferguson MA, Fox MD. Lesion network mapping predicts post-stroke behavioural deficits and improves localization. Brain 2021; 144:e35. [PMID: 33899085 DOI: 10.1093/brain/awab002] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
Affiliation(s)
- Alexander L Cohen
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.,Center for Brain Circuit Therapeutics, Department of Neurology, Psychiatry, and Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Michael A Ferguson
- Center for Brain Circuit Therapeutics, Department of Neurology, Psychiatry, and Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Michael D Fox
- Center for Brain Circuit Therapeutics, Department of Neurology, Psychiatry, and Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Athinoula A. Martinos Centre for Biomedical Imaging, Department of Neurology and Radiology, Massachusetts General Hospital, Charlestown, MA, USA.,Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
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23
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Wang X, Wang Q, Zhang P, Qian S, Liu S, Liu DQ. Reducing Inter-Site Variability for Fluctuation Amplitude Metrics in Multisite Resting State BOLD-fMRI Data. Neuroinformatics 2021; 19:23-38. [PMID: 32285299 DOI: 10.1007/s12021-020-09463-x] [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] [Indexed: 11/24/2022]
Abstract
It has been reported that resting state fluctuation amplitude (RSFA) exhibits extremely large inter-site variability, which limits its application in multisite studies. Although global normalization (GN) based approaches are efficient in reducing the site effects, they may cause spurious results. In this study, our purpose was to find alternative strategies to minimize the substantial site effects for RSFA, without the risk of introducing artificial findings. We firstly modified the ALFF algorithm so that it is conceptually validated and insensitive to data length, then found that (a) global mean amplitude of low-frequency fluctuation (ALFF) covaried only with BOLD signal intensity, while global mean fractional ALFF (fALFF) was significantly correlated with TRs across different sites; (b) The inter-site variations in raw RSFA values were significant across the entire brain and exhibited similar trends between gray matter and white matter; (c) For ALFF, signal intensity rescaling could dramatically reduce inter-site variability by several orders, but could not fully removed the globally distributed inter-site variability. For fALFF, the global site effects could be completely removed by TR controlling; (d) Meanwhile, the magnitude of the inter-site variability of fALFF could also be reduced to an acceptable level, as indicated by the detection power of fALFF in multisite data quite close to that in monosite data. Thus our findings suggest GN based harmonization methods could be replaced with only controlling for confounding factors including signal scaling, TR and full-band power.
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Affiliation(s)
- Xinbo Wang
- Research Center of Brain and Cognitive Neuroscience, Liaoning Normal University, Dalian, 116029, China
| | - Qing Wang
- Research Center of Brain and Cognitive Neuroscience, Liaoning Normal University, Dalian, 116029, China
| | - Peiwen Zhang
- Research Center of Brain and Cognitive Neuroscience, Liaoning Normal University, Dalian, 116029, China
| | - Shufang Qian
- Research Center of Brain and Cognitive Neuroscience, Liaoning Normal University, Dalian, 116029, China
| | - Shiyu Liu
- Research Center of Brain and Cognitive Neuroscience, Liaoning Normal University, Dalian, 116029, China
| | - Dong-Qiang Liu
- Research Center of Brain and Cognitive Neuroscience, Liaoning Normal University, Dalian, 116029, China.
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24
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Haas SS, Antonucci LA, Wenzel J, Ruef A, Biagianti B, Paolini M, Rauchmann BS, Weiske J, Kambeitz J, Borgwardt S, Brambilla P, Meisenzahl E, Salokangas RKR, Upthegrove R, Wood SJ, Koutsouleris N, Kambeitz-Ilankovic L. A multivariate neuromonitoring approach to neuroplasticity-based computerized cognitive training in recent onset psychosis. Neuropsychopharmacology 2021; 46:828-835. [PMID: 33027802 PMCID: PMC8027389 DOI: 10.1038/s41386-020-00877-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Revised: 09/11/2020] [Accepted: 09/15/2020] [Indexed: 02/07/2023]
Abstract
Two decades of studies suggest that computerized cognitive training (CCT) has an effect on cognitive improvement and the restoration of brain activity. Nevertheless, individual response to CCT remains heterogenous, and the predictive potential of neuroimaging in gauging response to CCT remains unknown. We employed multivariate pattern analysis (MVPA) on whole-brain resting-state functional connectivity (rsFC) to (neuro)monitor clinical outcome defined as psychosis-likeness change after 10-hours of CCT in recent onset psychosis (ROP) patients. Additionally, we investigated if sensory processing (SP) change during CCT is associated with individual psychosis-likeness change and cognitive gains after CCT. 26 ROP patients were divided into maintainers and improvers based on their SP change during CCT. A support vector machine (SVM) classifier separating 56 healthy controls (HC) from 35 ROP patients using rsFC (balanced accuracy of 65.5%, P < 0.01) was built in an independent sample to create a naturalistic model representing the HC-ROP hyperplane. This model was out-of-sample cross-validated in the ROP patients from the CCT trial to assess associations between rsFC pattern change, cognitive gains and SP during CCT. Patients with intact SP threshold at baseline showed improved attention despite psychosis status on the SVM hyperplane at follow-up (p < 0.05). Contrarily, the attentional gains occurred in the ROP patients who showed impaired SP at baseline only if rsfMRI diagnosis status shifted to the healthy-like side of the SVM continuum. Our results reveal the utility of MVPA for elucidating treatment response neuromarkers based on rsFC-SP change and pave the road to more personalized interventions.
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Affiliation(s)
- Shalaila S. Haas
- grid.59734.3c0000 0001 0670 2351Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Linda A. Antonucci
- grid.5252.00000 0004 1936 973XDepartment of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany ,grid.7644.10000 0001 0120 3326Department of Education, Psychology, Communication – University of Bari “Aldo Moro”, Bari, Italy
| | - Julian Wenzel
- grid.6190.e0000 0000 8580 3777University of Cologne, Faculty of Medicine and University Hospital of Cologne, Cologne, Germany
| | - Anne Ruef
- grid.5252.00000 0004 1936 973XDepartment of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany
| | - Bruno Biagianti
- grid.438587.50000 0004 0450 1574Department of R&D, Posit Science Corporation, San Francisco, CA USA ,grid.4708.b0000 0004 1757 2822Department of Pathophysiology and Transplantation, Faculty of Medicine and Surgery, University of Milan, Milan, Italy
| | - Marco Paolini
- Department of Radiology, University Hospital, Ludwig-Maximilian University, Munich, Germany
| | - Boris-Stephan Rauchmann
- grid.5252.00000 0004 1936 973XDepartment of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany ,Department of Radiology, University Hospital, Ludwig-Maximilian University, Munich, Germany
| | - Johanna Weiske
- grid.5252.00000 0004 1936 973XDepartment of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany
| | - Joseph Kambeitz
- grid.6190.e0000 0000 8580 3777University of Cologne, Faculty of Medicine and University Hospital of Cologne, Cologne, Germany
| | - Stefan Borgwardt
- grid.4562.50000 0001 0057 2672Translational Psychiatry Unit (TPU), Department of Psychiatry and Psychotherapy, University of Luebeck, Luebeck, Germany
| | - Paolo Brambilla
- grid.414818.00000 0004 1757 8749Department of Neuroscience and Mental Health, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy ,grid.4708.b0000 0004 1757 2822Department of Pathophysiology and Mental Health, University of Milan, Milan, Italy
| | - Eva Meisenzahl
- grid.411327.20000 0001 2176 9917Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich-Heine University, Düsseldorf, Germany
| | - Raimo K. R. Salokangas
- grid.1374.10000 0001 2097 1371Department of Psychiatry, University of Turku, Turku, Finland
| | - Rachel Upthegrove
- grid.6572.60000 0004 1936 7486School of Psychology, University of Birmingham, Birmingham, United Kingdom ,grid.6572.60000 0004 1936 7486Institute of Mental Health, University of Birmingham, Birmingham, United Kingdom
| | - Stephen J. Wood
- grid.6572.60000 0004 1936 7486School of Psychology, University of Birmingham, Birmingham, United Kingdom ,grid.488501.0Orygen, the National Centre of Excellence for Youth Mental Health, Melbourne, VIC Australia ,grid.1008.90000 0001 2179 088XCentre for Youth Mental Health, University of Melbourne, Melbourne, VIC Australia
| | - Nikolaos Koutsouleris
- grid.5252.00000 0004 1936 973XDepartment of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany
| | - Lana Kambeitz-Ilankovic
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany. .,University of Cologne, Faculty of Medicine and University Hospital of Cologne, Cologne, Germany.
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25
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Regonia PR, Takamura M, Nakano T, Ichikawa N, Fermin A, Okada G, Okamoto Y, Yamawaki S, Ikeda K, Yoshimoto J. Modeling Heterogeneous Brain Dynamics of Depression and Melancholia Using Energy Landscape Analysis. Front Psychiatry 2021; 12:780997. [PMID: 34899435 PMCID: PMC8656401 DOI: 10.3389/fpsyt.2021.780997] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 11/01/2021] [Indexed: 11/13/2022] Open
Abstract
Our current understanding of melancholic depression is shaped by its position in the depression spectrum. The lack of consensus on how it should be treated-whether as a subtype of depression, or as a distinct disorder altogethe-interferes with the recovery of suffering patients. In this study, we analyzed brain state energy landscape models of melancholic depression, in contrast to healthy and non-melancholic energy landscapes. Our analyses showed significant group differences on basin energy, basin frequency, and transition dynamics in several functional brain networks such as basal ganglia, dorsal default mode, and left executive control networks. Furthermore, we found evidences suggesting the connection between energy landscape characteristics (basin characteristics) and depressive symptom scores (BDI-II and SHAPS). These results indicate that melancholic depression is distinguishable from its non-melancholic counterpart, not only in terms of depression severity, but also in brain dynamics.
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Affiliation(s)
- Paul Rossener Regonia
- Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Japan.,Department of Computer Science, College of Engineering, University of the Philippines Diliman, Quezon City, Philippines
| | - Masahiro Takamura
- Center for Brain, Mind and KANSEI Research Sciences, Hiroshima University, Hiroshima, Japan.,Department of Neurology, Faculty of Medicine, Shimane University, Izumo, Japan
| | - Takashi Nakano
- Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Japan.,School of Medicine, Fujita Health University, Toyoake, Japan
| | - Naho Ichikawa
- Center for Brain, Mind and KANSEI Research Sciences, Hiroshima University, Hiroshima, Japan
| | - Alan Fermin
- Center for Brain, Mind and KANSEI Research Sciences, Hiroshima University, Hiroshima, Japan
| | - Go Okada
- Department of Psychiatry and Neurosciences, Hiroshima University, Hiroshima, Japan
| | - Yasumasa Okamoto
- Center for Brain, Mind and KANSEI Research Sciences, Hiroshima University, Hiroshima, Japan.,Department of Psychiatry and Neurosciences, Hiroshima University, Hiroshima, Japan
| | - Shigeto Yamawaki
- Center for Brain, Mind and KANSEI Research Sciences, Hiroshima University, Hiroshima, Japan
| | - Kazushi Ikeda
- Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Japan
| | - Junichiro Yoshimoto
- Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Japan
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26
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Cohen AL, Fox MD. Reply: The influence of sample size and arbitrary statistical thresholds in lesion-network mapping. Brain 2020; 143:e41. [PMID: 32365379 DOI: 10.1093/brain/awaa095] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Affiliation(s)
- Alexander L Cohen
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.,Berenson-Allen Center for Non-Invasive Brain Stimulation and Division of Cognitive Neurology, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.,Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Michael D Fox
- Berenson-Allen Center for Non-Invasive Brain Stimulation and Division of Cognitive Neurology, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.,Athinoula A. Martinos Centre for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA.,Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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27
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Adam KCS, Vogel EK, Awh E. Multivariate analysis reveals a generalizable human electrophysiological signature of working memory load. Psychophysiology 2020; 57:e13691. [PMID: 33040349 DOI: 10.1111/psyp.13691] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Revised: 08/30/2020] [Accepted: 09/01/2020] [Indexed: 11/30/2022]
Abstract
Working memory (WM) is an online memory system that is critical for holding information in a rapidly accessible state during ongoing cognitive processing. Thus, there is strong value in methods that provide a temporally resolved index of WM load. While univariate EEG signals have been identified that vary with WM load, recent advances in multivariate analytic approaches suggest that there may be rich sources of information that do not generate reliable univariate signatures. Here, using data from four published studies (n = 286 and >250,000 trials), we demonstrate that multivariate analysis of EEG voltage topography provides a sensitive index of the number of items stored in WM that generalizes to novel human observers. Moreover, multivariate load detection ("mvLoad") can provide robust information at the single-trial level, exceeding the sensitivity of extant univariate approaches. We show that this method tracks WM load in a manner that is (1) independent of the spatial position of the memoranda, (2) precise enough to differentiate item-by-item increments in the number of stored items, (3) generalizable across distinct tasks and stimulus displays, and (4) correlated with individual differences in WM behavior. Thus, this approach provides a powerful complement to univariate analytic approaches, enabling temporally resolved tracking of online memory storage in humans.
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Affiliation(s)
- Kirsten C S Adam
- Department of Psychology, University of California San Diego, La Jolla, CA, USA.,Institute for Neural Computation, University of California San Diego, La Jolla, CA, USA
| | - Edward K Vogel
- Grossman Institute for Neuroscience, Quantitative Biology, and Human Behavior, University of Chicago, Chicago, IL, USA.,Department of Psychology, University of Chicago, Chicago, IL, USA.,Institute for Mind and Biology, University of Chicago, Chicago, IL, USA
| | - Edward Awh
- Grossman Institute for Neuroscience, Quantitative Biology, and Human Behavior, University of Chicago, Chicago, IL, USA.,Department of Psychology, University of Chicago, Chicago, IL, USA.,Institute for Mind and Biology, University of Chicago, Chicago, IL, USA
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28
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Alahmadi AAS. Effects of different smoothing on global and regional resting functional connectivity. Neuroradiology 2020; 63:99-109. [PMID: 32840683 DOI: 10.1007/s00234-020-02523-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Accepted: 08/13/2020] [Indexed: 01/25/2023]
Abstract
PURPOSE Spatial smoothing is an essential pre-processing step in the process of analysing functional magnetic resonance imaging (fMRI) data, both during an experimental task or during resting-state fMRI (rsfMRI). The main benefit of this spatial smoothing step is to artificially increase the signal-to-noise ratio of the fMRI signal. Previous fMRI studies have investigated the impact of spatial smoothing on task fMRI data, while rsfMRI studies usually apply the same analytical process used for the task data. However, this study investigates changes in different rsfMRI analyses, such as ROI-to-ROI, seed-to-voxels and ICA analyses. METHODS Nineteen healthy volunteers were scanned using rsfMRI with three applied smoothing kernels: 0 mm, 4 mm and 8 mm. Appropriate statistical comparisons were made. RESULTS The findings showed that spatial smoothing has a greater effect on rsfMRI data when analysed using seed-to-voxel-based analysis. The effect was less pronounced when analysing data using ROI-ROI or ICA analyses. The results demonstrated that even when analysing the data without the application of spatial smoothing, the results were significant compared with data analysed using a typical smoothing kernel. However, data analysed with lower-smoothing kernels produced greater negative correlations, particularly with the ICA analysis. CONCLUSION The results suggest that a medium smoothing kernel (around 4 mm) may be preferable, as it is comparable with the 8 mm kernel in all of the analyses performed. It is also recommended that the researchers consider analysing the data using two different smoothing kernels, as this will help to confirm the significance of the results and avoid overestimating the findings.
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Affiliation(s)
- Adnan A S Alahmadi
- Department of Radiologic Sciences, College of Applied Medical Science, King Abdulaziz University (KAU), Jeddah, Saudi Arabia.
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29
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DuPre E, Hanke M, Poline JB. Nature abhors a paywall: How open science can realize the potential of naturalistic stimuli. Neuroimage 2020; 216:116330. [PMID: 31704292 PMCID: PMC7198323 DOI: 10.1016/j.neuroimage.2019.116330] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Revised: 10/31/2019] [Accepted: 10/31/2019] [Indexed: 11/26/2022] Open
Abstract
Naturalistic stimuli show significant potential to inform behavioral, cognitive, and clinical neuroscience. To date, this impact is still limited by the relative inaccessibility of both generated neuroimaging data as well as the supporting naturalistic stimuli. In this perspective, we highlight currently available naturalistic datasets and technical solutions such as DataLad that continue to advance our ability to share this data. We also review scientific and sociological challenges in selecting naturalistic stimuli for reproducible research. Overall, we encourage researchers to share their naturalistic datasets to the full extent possible under local copyright law.
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Affiliation(s)
| | - Michael Hanke
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
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30
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Jin D, Zhou B, Han Y, Ren J, Han T, Liu B, Lu J, Song C, Wang P, Wang D, Xu J, Yang Z, Yao H, Yu C, Zhao K, Wintermark M, Zuo N, Zhang X, Zhou Y, Zhang X, Jiang T, Wang Q, Liu Y. Generalizable, Reproducible, and Neuroscientifically Interpretable Imaging Biomarkers for Alzheimer's Disease. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2020; 7:2000675. [PMID: 32714766 PMCID: PMC7375255 DOI: 10.1002/advs.202000675] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/22/2020] [Revised: 05/01/2020] [Indexed: 06/01/2023]
Abstract
Precision medicine for Alzheimer's disease (AD) necessitates the development of personalized, reproducible, and neuroscientifically interpretable biomarkers, yet despite remarkable advances, few such biomarkers are available. Also, a comprehensive evaluation of the neurobiological basis and generalizability of the end-to-end machine learning system should be given the highest priority. For this reason, a deep learning model (3D attention network, 3DAN) that can simultaneously capture candidate imaging biomarkers with an attention mechanism module and advance the diagnosis of AD based on structural magnetic resonance imaging is proposed. The generalizability and reproducibility are evaluated using cross-validation on in-house, multicenter (n = 716), and public (n = 1116) databases with an accuracy up to 92%. Significant associations between the classification output and clinical characteristics of AD and mild cognitive impairment (MCI, a middle stage of dementia) groups provide solid neurobiological support for the 3DAN model. The effectiveness of the 3DAN model is further validated by its good performance in predicting the MCI subjects who progress to AD with an accuracy of 72%. Collectively, the findings highlight the potential for structural brain imaging to provide a generalizable, and neuroscientifically interpretable imaging biomarker that can support clinicians in the early diagnosis of AD.
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Affiliation(s)
- Dan Jin
- Brainnetome Center & National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of SciencesBeijing100190China
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijing100049China
| | - Bo Zhou
- Department of Neurologythe Second Medical CentreNational Clinical Research Centre for Geriatric DiseasesChinese PLA General HospitalBeijing100853China
| | - Ying Han
- Department of NeurologyXuanwu Hospital of Capital Medical UniversityBeijing100053China
| | - Jiaji Ren
- Brainnetome Center & National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of SciencesBeijing100190China
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijing100049China
| | - Tong Han
- Department of RadiologyTianjin Huanhu HospitalTianjin300350China
| | - Bing Liu
- Brainnetome Center & National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of SciencesBeijing100190China
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijing100049China
- Center for Excellence in Brain Science and Intelligence TechnologyInstitute of AutomationChinese Academy of SciencesBeijing100190China
| | - Jie Lu
- Department of RadiologyXuanwu Hospital of Capital Medical UniversityBeijing100053China
| | - Chengyuan Song
- Department of NeurologyQilu Hospital of Shandong UniversityJinan250012China
| | - Pan Wang
- Department of NeurologyTianjin Huanhu HospitalTianjin UniversityTianjin300350China
| | - Dawei Wang
- Department of RadiologyQilu Hospital of Shandong UniversityJinan250012China
| | - Jian Xu
- State Key Laboratory of Management and Control for Complex SystemsInstitute of AutomationChinese Academy of SciencesBeijing100190China
| | - Zhengyi Yang
- Brainnetome Center & National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of SciencesBeijing100190China
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijing100049China
| | - Hongxiang Yao
- Department of Radiologythe Second Medical CentreNational Clinical Research Centre for Geriatric DiseasesChinese PLA General HospitalBeijing100853China
| | - Chunshui Yu
- Department of RadiologyTianjin Medical University General HospitalTianjin300052China
| | - Kun Zhao
- Beihang UniversityBeijing100191China
| | - Max Wintermark
- Department of RadiologyStanford UniversityStanfordCA94305USA
| | - Nianming Zuo
- Brainnetome Center & National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of SciencesBeijing100190China
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijing100049China
| | - Xinqing Zhang
- Department of NeurologyXuanwu Hospital of Capital Medical UniversityBeijing100053China
| | - Yuying Zhou
- Department of NeurologyTianjin Huanhu HospitalTianjin UniversityTianjin300350China
| | - Xi Zhang
- Department of Neurologythe Second Medical CentreNational Clinical Research Centre for Geriatric DiseasesChinese PLA General HospitalBeijing100853China
| | - Tianzi Jiang
- Brainnetome Center & National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of SciencesBeijing100190China
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijing100049China
- Center for Excellence in Brain Science and Intelligence TechnologyInstitute of AutomationChinese Academy of SciencesBeijing100190China
| | - Qing Wang
- Department of RadiologyQilu Hospital of Shandong UniversityJinan250012China
| | - Yong Liu
- Brainnetome Center & National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of SciencesBeijing100190China
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijing100049China
- Center for Excellence in Brain Science and Intelligence TechnologyInstitute of AutomationChinese Academy of SciencesBeijing100190China
- Pazhou LabGuangzhou510330China
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31
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Triana AM, Glerean E, Saramäki J, Korhonen O. Effects of spatial smoothing on group-level differences in functional brain networks. Netw Neurosci 2020; 4:556-574. [PMID: 32885115 PMCID: PMC7462426 DOI: 10.1162/netn_a_00132] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Accepted: 02/20/2020] [Indexed: 12/19/2022] Open
Abstract
Brain connectivity with functional magnetic resonance imaging (fMRI) is a popular approach for detecting differences between healthy and clinical populations. Before creating a functional brain network, the fMRI time series must undergo several preprocessing steps to control for artifacts and to improve data quality. However, preprocessing may affect the results in an undesirable way. Spatial smoothing, for example, is known to alter functional network structure. Yet, its effects on group-level network differences remain unknown. Here, we investigate the effects of spatial smoothing on the difference between patients and controls for two clinical conditions: autism spectrum disorder and bipolar disorder, considering fMRI data smoothed with Gaussian kernels (0–32 mm). We find that smoothing affects network differences between groups. For weighted networks, incrementing the smoothing kernel makes networks more different. For thresholded networks, larger smoothing kernels lead to more similar networks, although this depends on the network density. Smoothing also alters the effect sizes of the individual link differences. This is independent of the region of interest (ROI) size, but varies with link length. The effects of spatial smoothing are diverse, nontrivial, and difficult to predict. This has important consequences: The choice of smoothing kernel affects the observed network differences. Spatial smoothing is a preprocessing tool commonly applied to reduce the amount of noise in functional magnetic resonance imaging (fMRI) data. However, smoothing is known to affect the outcomes of functional brain network analysis at the level of individual subjects in undesired ways. Here, we investigate how spatial smoothing affects the observed differences in brain network structure between subject groups. Using fMRI data from two clinical populations and healthy controls, we show that the between-group differences in network structure depend on the amount of spatial smoothing applied during preprocessing in a nontrivial way. The optimal level of spatial smoothing is difficult to define and probably depends on a set of analysis parameters. Therefore, we recommend applying spatial smoothing only after careful consideration.
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Affiliation(s)
- Ana María Triana
- Department of Computer Science, School of Science, Aalto University, Espoo, Finland
| | - Enrico Glerean
- Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, Espoo, Finland
| | - Jari Saramäki
- Department of Computer Science, School of Science, Aalto University, Espoo, Finland
| | - Onerva Korhonen
- Université de Lille, CNRS, UMR 9193 - SCALab - Sciences Cognitives et Sciences Affectives, Lille, France
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32
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Yang G, Bozek J, Han M, Gao J. Constructing and evaluating a cortical surface atlas and analyzing cortical sex differences in young Chinese adults. Hum Brain Mapp 2020; 41:2495-2513. [PMID: 32141680 PMCID: PMC7267952 DOI: 10.1002/hbm.24960] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Revised: 01/29/2020] [Accepted: 02/11/2020] [Indexed: 12/17/2022] Open
Abstract
Cortical surface templates are an important standardized coordinate frame for cortical structure and function analysis in magnetic resonance (MR) imaging studies. The widely used adult cortical surface templates (e.g., fsaverage, Conte69, and the HCP-MMP atlas) are based on the Caucasian population. Neuroanatomical differences related to environmental and genetic factors between Chinese and Caucasian populations make these templates unideal for analysis of the cortex in the Chinese population. We used a multimodal surface matching algorithm in an iterative procedure to create Chinese (sCN200) and Caucasian (sUS200) cortical surface atlases based on 200 demographically matched high-quality T1- and T2-weighted (T1w and T2w, respectively) MR images from the Chinese Human Connectome Project (CHCP) and the Human Connectome Project (HCP), respectively. Templates for anatomical cortical surfaces (white matter, pial, midthickness) and cortical feature maps of sulcal depth, curvature, thickness, T1w/T2w myelin, and cortical labels were generated. Using independent subsets from the CHCP and the HCP, we quantified the accuracy of cortical registration when using population-matched and mismatched atlases. The performance of the cortical registration and accuracy of curvature alignment when using population-matched atlases was significantly improved, thereby demonstrating the importance of using the sCN200 cortical surface atlas for Chinese adult population studies. Finally, we analyzed female and male cortical differences within the Chinese and Caucasian populations. We identified significant between-sex differences in cortical curvature, sulcal depth, thickness, and T1w/T2w myelin maps in the frontal, temporal, parietal, occipital, and insular lobes as well as the cingulate cortices.
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Affiliation(s)
- Guoyuan Yang
- Beijing City Key Lab for Medical Physics and EngineeringInstitute of Heavy Ion Physics, School of Physics, Peking UniversityBeijingChina
- Center for MRI Research, Academy for Advanced Interdisciplinary StudiesPeking UniversityBeijingChina
- McGovern Institute for Brain Research, Peking UniversityBeijingChina
| | - Jelena Bozek
- Faculty of Electrical Engineering and ComputingUniversity of ZagrebZagrebCroatia
| | - Meizhen Han
- Beijing City Key Lab for Medical Physics and EngineeringInstitute of Heavy Ion Physics, School of Physics, Peking UniversityBeijingChina
- Center for MRI Research, Academy for Advanced Interdisciplinary StudiesPeking UniversityBeijingChina
- McGovern Institute for Brain Research, Peking UniversityBeijingChina
| | - Jia‐Hong Gao
- Beijing City Key Lab for Medical Physics and EngineeringInstitute of Heavy Ion Physics, School of Physics, Peking UniversityBeijingChina
- Center for MRI Research, Academy for Advanced Interdisciplinary StudiesPeking UniversityBeijingChina
- McGovern Institute for Brain Research, Peking UniversityBeijingChina
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33
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Graph Fourier transform of fMRI temporal signals based on an averaged structural connectome for the classification of neuroimaging. Artif Intell Med 2020; 106:101870. [DOI: 10.1016/j.artmed.2020.101870] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2019] [Revised: 03/02/2020] [Accepted: 05/02/2020] [Indexed: 11/17/2022]
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34
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Cao H, McEwen SC, Forsyth JK, Gee DG, Bearden CE, Addington J, Goodyear B, Cadenhead KS, Mirzakhanian H, Cornblatt BA, Carrión RE, Mathalon DH, McGlashan TH, Perkins DO, Belger A, Seidman LJ, Thermenos H, Tsuang MT, van Erp TGM, Walker EF, Hamann S, Anticevic A, Woods SW, Cannon TD. Toward Leveraging Human Connectomic Data in Large Consortia: Generalizability of fMRI-Based Brain Graphs Across Sites, Sessions, and Paradigms. Cereb Cortex 2020. [PMID: 29522112 DOI: 10.1093/cercor/bhy032] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
While graph theoretical modeling has dramatically advanced our understanding of complex brain systems, the feasibility of aggregating connectomic data in large imaging consortia remains unclear. Here, using a battery of cognitive, emotional and resting fMRI paradigms, we investigated the generalizability of functional connectomic measures across sites and sessions. Our results revealed overall fair to excellent reliability for a majority of measures during both rest and tasks, in particular for those quantifying connectivity strength, network segregation and network integration. Processing schemes such as node definition and global signal regression (GSR) significantly affected resulting reliability, with higher reliability detected for the Power atlas (vs. AAL atlas) and data without GSR. While network diagnostics for default-mode and sensori-motor systems were consistently reliable independently of paradigm, those for higher-order cognitive systems were reliable predominantly when challenged by task. In addition, based on our present sample and after accounting for observed reliability, satisfactory statistical power can be achieved in multisite research with sample size of approximately 250 when the effect size is moderate or larger. Our findings provide empirical evidence for the generalizability of brain functional graphs in large consortia, and encourage the aggregation of connectomic measures using multisite and multisession data.
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Affiliation(s)
- Hengyi Cao
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Sarah C McEwen
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, USA
| | - Jennifer K Forsyth
- Department of Psychology, University of California Los Angeles, Los Angeles, CA, USA
| | - Dylan G Gee
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Carrie E Bearden
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, USA
| | - Jean Addington
- Department of Psychiatry, University of Calgary, Calgary, Canada
| | - Bradley Goodyear
- Departments of Radiology, Clinical Neuroscience and Psychiatry, University of Calgary, Calgary, Canada
| | - Kristin S Cadenhead
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
| | - Heline Mirzakhanian
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
| | - Barbara A Cornblatt
- Department of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, NY, USA
| | - Ricardo E Carrión
- Department of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, NY, USA
| | - Daniel H Mathalon
- Department of Psychiatry, University of California San Francisco, San Francisco, CA, USA
| | | | - Diana O Perkins
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
| | - Aysenil Belger
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
| | - Larry J Seidman
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Heidi Thermenos
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Ming T Tsuang
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
| | - Theo G M van Erp
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA
| | - Elaine F Walker
- Department of Psychology, Emory University, Atlanta, GA, USA
| | - Stephan Hamann
- Department of Psychology, Emory University, Atlanta, GA, USA
| | - Alan Anticevic
- Department of Psychiatry, Yale University, New Haven, CT, USA
| | - Scott W Woods
- Department of Psychiatry, Yale University, New Haven, CT, USA
| | - Tyrone D Cannon
- Department of Psychology, Yale University, New Haven, CT, USA.,Department of Psychiatry, Yale University, New Haven, CT, USA
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35
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Kamarajan C, Ardekani BA, Pandey AK, Chorlian DB, Kinreich S, Pandey G, Meyers JL, Zhang J, Kuang W, Stimus AT, Porjesz B. Random Forest Classification of Alcohol Use Disorder Using EEG Source Functional Connectivity, Neuropsychological Functioning, and Impulsivity Measures. Behav Sci (Basel) 2020; 10:bs10030062. [PMID: 32121585 PMCID: PMC7139327 DOI: 10.3390/bs10030062] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Revised: 02/26/2020] [Accepted: 02/28/2020] [Indexed: 12/16/2022] Open
Abstract
: Individuals with alcohol use disorder (AUD) manifest a variety of impairments that can be attributed to alterations in specific brain networks. The current study aims to identify features of EEG-based functional connectivity, neuropsychological performance, and impulsivity that can classify individuals with AUD (N = 30) from unaffected controls (CTL, N = 30) using random forest classification. The features included were: (i) EEG source functional connectivity (FC) of the default mode network (DMN) derived using eLORETA algorithm, (ii) neuropsychological scores from the Tower of London test (TOLT) and the visual span test (VST), and (iii) impulsivity factors from the Barratt impulsiveness scale (BIS). The random forest model achieved a classification accuracy of 80% and identified 29 FC connections (among 66 connections per frequency band), 3 neuropsychological variables from VST (total number of correctly performed trials in forward and backward sequences and average time for correct trials in forward sequence) and all four impulsivity scores (motor, non-planning, attentional, and total) as significantly contributing to classifying individuals as either AUD or CTL. Although there was a significant age difference between the groups, most of the top variables that contributed to the classification were not significantly correlated with age. The AUD group showed a predominant pattern of hyperconnectivity among 25 of 29 significant connections, indicating aberrant network functioning during resting state suggestive of neural hyperexcitability and impulsivity. Further, parahippocampal hyperconnectivity with other DMN regions was identified as a major hub region dysregulated in AUD (13 connections overall), possibly due to neural damage from chronic drinking, which may give rise to cognitive impairments, including memory deficits and blackouts. Furthermore, hypoconnectivity observed in four connections (prefrontal nodes connecting posterior right-hemispheric regions) may indicate a weaker or fractured prefrontal connectivity with other regions, which may be related to impaired higher cognitive functions. The AUD group also showed poorer memory performance on the VST task and increased impulsivity in all factors compared to controls. Features from all three domains had significant associations with one another. These results indicate that dysregulated neural connectivity across the DMN regions, especially relating to hyperconnected parahippocampal hub as well as hypoconnected prefrontal hub, may potentially represent neurophysiological biomarkers of AUD, while poor visual memory performance and heightened impulsivity may serve as cognitive-behavioral indices of AUD.
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Affiliation(s)
- Chella Kamarajan
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA; (A.K.P.); (D.B.C.); (S.K.); (G.P.); (J.L.M.); (J.Z.); (W.K.); (A.T.S.); (B.P.)
- Correspondence: ; Tel.: +1-718-270-2913
| | - Babak A. Ardekani
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY 10962, USA;
- Department of Psychiatry, NYU School of Medicine, New York, NY 10016, USA
| | - Ashwini K. Pandey
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA; (A.K.P.); (D.B.C.); (S.K.); (G.P.); (J.L.M.); (J.Z.); (W.K.); (A.T.S.); (B.P.)
| | - David B. Chorlian
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA; (A.K.P.); (D.B.C.); (S.K.); (G.P.); (J.L.M.); (J.Z.); (W.K.); (A.T.S.); (B.P.)
| | - Sivan Kinreich
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA; (A.K.P.); (D.B.C.); (S.K.); (G.P.); (J.L.M.); (J.Z.); (W.K.); (A.T.S.); (B.P.)
| | - Gayathri Pandey
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA; (A.K.P.); (D.B.C.); (S.K.); (G.P.); (J.L.M.); (J.Z.); (W.K.); (A.T.S.); (B.P.)
| | - Jacquelyn L. Meyers
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA; (A.K.P.); (D.B.C.); (S.K.); (G.P.); (J.L.M.); (J.Z.); (W.K.); (A.T.S.); (B.P.)
| | - Jian Zhang
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA; (A.K.P.); (D.B.C.); (S.K.); (G.P.); (J.L.M.); (J.Z.); (W.K.); (A.T.S.); (B.P.)
| | - Weipeng Kuang
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA; (A.K.P.); (D.B.C.); (S.K.); (G.P.); (J.L.M.); (J.Z.); (W.K.); (A.T.S.); (B.P.)
| | - Arthur T. Stimus
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA; (A.K.P.); (D.B.C.); (S.K.); (G.P.); (J.L.M.); (J.Z.); (W.K.); (A.T.S.); (B.P.)
| | - Bernice Porjesz
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA; (A.K.P.); (D.B.C.); (S.K.); (G.P.); (J.L.M.); (J.Z.); (W.K.); (A.T.S.); (B.P.)
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36
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Kamarajan C, Ardekani BA, Pandey AK, Kinreich S, Pandey G, Chorlian DB, Meyers JL, Zhang J, Bermudez E, Stimus AT, Porjesz B. Random Forest Classification of Alcohol Use Disorder Using fMRI Functional Connectivity, Neuropsychological Functioning, and Impulsivity Measures. Brain Sci 2020; 10:brainsci10020115. [PMID: 32093319 PMCID: PMC7071377 DOI: 10.3390/brainsci10020115] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Revised: 02/12/2020] [Accepted: 02/18/2020] [Indexed: 12/22/2022] Open
Abstract
Individuals with alcohol use disorder (AUD) are known to manifest a variety of neurocognitive impairments that can be attributed to alterations in specific brain networks. The current study aims to identify specific features of brain connectivity, neuropsychological performance, and impulsivity traits that can classify adult males with AUD (n = 30) from healthy controls (CTL, n = 30) using the Random Forest (RF) classification method. The predictor variables were: (i) fMRI-based within-network functional connectivity (FC) of the Default Mode Network (DMN), (ii) neuropsychological scores from the Tower of London Test (TOLT), and the Visual Span Test (VST), and (iii) impulsivity factors from the Barratt Impulsiveness Scale (BIS). The RF model, with a classification accuracy of 76.67%, identified fourteen DMN connections, two neuropsychological variables (memory span and total correct scores of the forward condition of the VST), and all impulsivity factors as significantly important for classifying participants into either the AUD or CTL group. Specifically, the AUD group manifested hyperconnectivity across the bilateral anterior cingulate cortex and the prefrontal cortex as well as between the bilateral posterior cingulate cortex and the left inferior parietal lobule, while showing hypoconnectivity in long-range anterior-posterior and interhemispheric long-range connections. Individuals with AUD also showed poorer memory performance and increased impulsivity compared to CTL individuals. Furthermore, there were significant associations among FC, impulsivity, neuropsychological performance, and AUD status. These results confirm the previous findings that alterations in specific brain networks coupled with poor neuropsychological functioning and heightened impulsivity may characterize individuals with AUD, who can be efficiently identified using classification algorithms such as Random Forest.
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Affiliation(s)
- Chella Kamarajan
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA; (A.K.P.); (S.K.); (G.P.); (D.B.C.); (J.L.M.); (J.Z.); (A.T.S.); (B.P.)
- Correspondence: ; Tel.: +1-718-270-2913
| | - Babak A. Ardekani
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY 10962, USA;
- Department of Psychiatry, NYU School of Medicine, New York, NY 10016, USA;
| | - Ashwini K. Pandey
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA; (A.K.P.); (S.K.); (G.P.); (D.B.C.); (J.L.M.); (J.Z.); (A.T.S.); (B.P.)
| | - Sivan Kinreich
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA; (A.K.P.); (S.K.); (G.P.); (D.B.C.); (J.L.M.); (J.Z.); (A.T.S.); (B.P.)
| | - Gayathri Pandey
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA; (A.K.P.); (S.K.); (G.P.); (D.B.C.); (J.L.M.); (J.Z.); (A.T.S.); (B.P.)
| | - David B. Chorlian
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA; (A.K.P.); (S.K.); (G.P.); (D.B.C.); (J.L.M.); (J.Z.); (A.T.S.); (B.P.)
| | - Jacquelyn L. Meyers
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA; (A.K.P.); (S.K.); (G.P.); (D.B.C.); (J.L.M.); (J.Z.); (A.T.S.); (B.P.)
| | - Jian Zhang
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA; (A.K.P.); (S.K.); (G.P.); (D.B.C.); (J.L.M.); (J.Z.); (A.T.S.); (B.P.)
| | - Elaine Bermudez
- Department of Psychiatry, NYU School of Medicine, New York, NY 10016, USA;
| | - Arthur T. Stimus
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA; (A.K.P.); (S.K.); (G.P.); (D.B.C.); (J.L.M.); (J.Z.); (A.T.S.); (B.P.)
| | - Bernice Porjesz
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA; (A.K.P.); (S.K.); (G.P.); (D.B.C.); (J.L.M.); (J.Z.); (A.T.S.); (B.P.)
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37
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He Y, Byrge L, Kennedy DP. Nonreplication of functional connectivity differences in autism spectrum disorder across multiple sites and denoising strategies. Hum Brain Mapp 2020; 41:1334-1350. [PMID: 31916675 PMCID: PMC7268009 DOI: 10.1002/hbm.24879] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Revised: 10/25/2019] [Accepted: 11/19/2019] [Indexed: 12/24/2022] Open
Abstract
A rapidly growing number of studies on autism spectrum disorder (ASD) have used resting‐state fMRI to identify alterations of functional connectivity, with the hope of identifying clinical biomarkers or underlying neural mechanisms. However, results have been largely inconsistent across studies, and there remains a pressing need to determine the primary factors influencing replicability. Here, we used resting‐state fMRI data from the Autism Brain Imaging Data Exchange to investigate two potential factors: denoising strategy and data site (which differ in terms of sample, data acquisition, etc.). We examined the similarity of both group‐averaged functional connectomes and group‐level differences (ASD vs. control) across 33 denoising pipelines and four independently‐acquired datasets. The group‐averaged connectomes were highly consistent across pipelines (r = 0.92 ± 0.06) and sites (r = 0.88 ± 0.02). However, the group differences, while still consistent within site across pipelines (r = 0.76 ± 0.12), were highly inconsistent across sites regardless of choice of denoising strategies (r = 0.07 ± 0.04), suggesting lack of replication may be strongly influenced by site and/or cohort differences. Across‐site similarity remained low even when considering the data at a large‐scale network level or when considering only the most significant edges. We further show through an extensive literature survey that the parameters chosen in the current study (i.e., sample size, age range, preprocessing methods) are quite representative of the published literature. These results highlight the importance of examining replicability in future studies of ASD, and, more generally, call for extra caution when interpreting alterations in functional connectivity across groups of individuals.
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Affiliation(s)
- Ye He
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana
| | - Lisa Byrge
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana
| | - Daniel P Kennedy
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana.,Cognitive Science Program, Indiana University, Bloomington, Indiana.,Program in Neuroscience, Indiana University, Bloomington, Indiana
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38
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Cai X, Xie D, Madsen KH, Wang Y, Bögemann SA, Cheung EFC, Møller A, Chan RCK. Generalizability of machine learning for classification of schizophrenia based on resting-state functional MRI data. Hum Brain Mapp 2020; 41:172-184. [PMID: 31571320 PMCID: PMC7268030 DOI: 10.1002/hbm.24797] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2019] [Revised: 08/19/2019] [Accepted: 09/04/2019] [Indexed: 12/11/2022] Open
Abstract
Machine learning has increasingly been applied to classification of schizophrenia in neuroimaging research. However, direct replication studies and studies seeking to investigate generalizability are scarce. To address these issues, we assessed within-site and between-site generalizability of a machine learning classification framework which achieved excellent performance in a previous study using two independent resting-state functional magnetic resonance imaging data sets collected from different sites and scanners. We established within-site generalizability of the classification framework in the main data set using cross-validation. Then, we trained a model in the main data set and investigated between-site generalization in the validated data set using external validation. Finally, recognizing the poor between-site generalization performance, we updated the unsupervised algorithm to investigate if transfer learning using additional unlabeled data were able to improve between-site classification performance. Cross-validation showed that the published classification procedure achieved an accuracy of 0.73 using majority voting across all selected components. External validation found a classification accuracy of 0.55 (not significant) and 0.70 (significant) using the direct and transfer learning procedures, respectively. The failure of direct generalization from one site to another demonstrates the limitation of within-site cross-validation and points toward the need to incorporate efforts to facilitate application of machine learning across multiple data sets. The improvement in performance with transfer learning highlights the importance of taking into account the properties of data when constructing predictive models across samples and sites. Our findings suggest that machine learning classification result based on a single study should be interpreted cautiously.
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Affiliation(s)
- Xin‐Lu Cai
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental HealthInstitute of PsychologyBeijingChina
- Sino‐Danish College, University of Chinese Academy of SciencesBeijingChina
- Sino‐Danish Center for Education and ResearchBeijingChina
| | - Dong‐Jie Xie
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental HealthInstitute of PsychologyBeijingChina
- Hangzhou College of Preschool Teacher EducationZhejiang Normal UniversityHangzhouChina
| | - Kristoffer H. Madsen
- Sino‐Danish Center for Education and ResearchBeijingChina
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and ResearchCopenhagen University Hospital HvidovreCopenhagenDenmark
- Department of Applied Mathematics and Computer ScienceTechnical University of DenmarkKongens LyngbyDenmark
| | - Yong‐Ming Wang
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental HealthInstitute of PsychologyBeijingChina
- Sino‐Danish College, University of Chinese Academy of SciencesBeijingChina
- Sino‐Danish Center for Education and ResearchBeijingChina
| | - Sophie Alida Bögemann
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental HealthInstitute of PsychologyBeijingChina
- Sino‐Danish College, University of Chinese Academy of SciencesBeijingChina
- Sino‐Danish Center for Education and ResearchBeijingChina
| | | | - Arne Møller
- Sino‐Danish Center for Education and ResearchBeijingChina
- Department of Nuclear Medicine and PET CentreAarhus University HospitalAarhusDenmark
| | - Raymond C. K. Chan
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental HealthInstitute of PsychologyBeijingChina
- Sino‐Danish College, University of Chinese Academy of SciencesBeijingChina
- Sino‐Danish Center for Education and ResearchBeijingChina
- Department of PsychologyUniversity of Chinese Academy of SciencesBeijingChina
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39
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Khosla M, Jamison K, Ngo GH, Kuceyeski A, Sabuncu MR. Machine learning in resting-state fMRI analysis. Magn Reson Imaging 2019; 64:101-121. [PMID: 31173849 PMCID: PMC6875692 DOI: 10.1016/j.mri.2019.05.031] [Citation(s) in RCA: 81] [Impact Index Per Article: 16.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2018] [Revised: 05/20/2019] [Accepted: 05/21/2019] [Indexed: 12/13/2022]
Abstract
Machine learning techniques have gained prominence for the analysis of resting-state functional Magnetic Resonance Imaging (rs-fMRI) data. Here, we present an overview of various unsupervised and supervised machine learning applications to rs-fMRI. We offer a methodical taxonomy of machine learning methods in resting-state fMRI. We identify three major divisions of unsupervised learning methods with regard to their applications to rs-fMRI, based on whether they discover principal modes of variation across space, time or population. Next, we survey the algorithms and rs-fMRI feature representations that have driven the success of supervised subject-level predictions. The goal is to provide a high-level overview of the burgeoning field of rs-fMRI from the perspective of machine learning applications.
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Affiliation(s)
- Meenakshi Khosla
- School of Electrical and Computer Engineering, Cornell University, United States of America
| | - Keith Jamison
- Radiology, Weill Cornell Medical College, United States of America
| | - Gia H Ngo
- School of Electrical and Computer Engineering, Cornell University, United States of America
| | - Amy Kuceyeski
- Radiology, Weill Cornell Medical College, United States of America; Brain and Mind Research Institute, Weill Cornell Medical College, United States of America
| | - Mert R Sabuncu
- School of Electrical and Computer Engineering, Cornell University, United States of America; Nancy E. & Peter C. Meinig School of Biomedical Engineering, Cornell University, United States of America.
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40
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Badhwar A, Collin-Verreault Y, Orban P, Urchs S, Chouinard I, Vogel J, Potvin O, Duchesne S, Bellec P. Multivariate consistency of resting-state fMRI connectivity maps acquired on a single individual over 2.5 years, 13 sites and 3 vendors. Neuroimage 2019; 205:116210. [PMID: 31593793 DOI: 10.1016/j.neuroimage.2019.116210] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2018] [Revised: 09/15/2019] [Accepted: 09/17/2019] [Indexed: 11/26/2022] Open
Abstract
Studies using resting-state functional magnetic resonance imaging (rsfMRI) are increasingly collecting data at multiple sites in order to speed up recruitment or increase sample size. The main objective of this study was to assess the long-term consistency of rsfMRI connectivity maps derived at multiple sites and vendors using the Canadian Dementia Imaging Protocol (CDIP, www.cdip-pcid.ca). Nine to 10 min of functional BOLD images were acquired from an adult cognitively healthy volunteer scanned repeatedly at 13 Canadian sites on three scanner makes (General Electric, Philips and Siemens) over the course of 2.5 years. The consistency (spatial Pearson's correlation) of rsfMRI connectivity maps for seven canonical networks ranged from 0.3 to 0.8, with a negligible effect of time, but significant site and vendor effects. We noted systematic differences in data quality (i.e. head motion, number of useable time frames, temporal signal-to-noise ratio) across vendors, which may also confound some of these results, and could not be disentangled in this sample. We also pooled the long-term longitudinal data with a single-site, short-term (1 month) data sample acquired on 26 subjects (10 scans per subject), called HNU1. Using randomly selected pairs of scans from each subject, we quantified the ability of a data-driven unsupervised cluster analysis to match two scans of the same subjects. In this "fingerprinting" experiment, we found that scans from the Canadian subject (Csub) could be matched with high accuracy intra-site (>95% for some networks), but that the accuracy decreased substantially for scans drawn from different sites and vendors, even falling outside of the range of accuracies observed in HNU1. Overall, our results demonstrate good multivariate stability of rsfMRI measures over several years, but substantial impact of scanning site and vendors. How detrimental these effects are will depend on the application, yet our results demonstrate that new methods for harmonizing multisite analysis represent an important area for future work.
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Affiliation(s)
- AmanPreet Badhwar
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal (CRIUGM), Montréal, Canada; Université de Montréal, Montréal, Canada.
| | - Yannik Collin-Verreault
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal (CRIUGM), Montréal, Canada
| | - Pierre Orban
- Centre de Recherche de l'Institut Universitaire en Santé Mentale de Montréal, Montréal, Canada; Université de Montréal, Montréal, Canada
| | - Sebastian Urchs
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal (CRIUGM), Montréal, Canada; McGill University, Montréal, Canada
| | | | | | - Olivier Potvin
- Centre CERVO, Quebec City Mental Health Institute, Quebec, Canada
| | - Simon Duchesne
- Centre CERVO, Quebec City Mental Health Institute, Quebec, Canada; Department of Radiology, Faculty of Medicine, Université Laval, Quebec, Canada
| | - Pierre Bellec
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal (CRIUGM), Montréal, Canada; Université de Montréal, Montréal, Canada
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41
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Karrer TM, Bassett DS, Derntl B, Gruber O, Aleman A, Jardri R, Laird AR, Fox PT, Eickhoff SB, Grisel O, Varoquaux G, Thirion B, Bzdok D. Brain-based ranking of cognitive domains to predict schizophrenia. Hum Brain Mapp 2019; 40:4487-4507. [PMID: 31313451 DOI: 10.1002/hbm.24716] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2019] [Revised: 06/10/2019] [Accepted: 06/26/2019] [Indexed: 12/22/2022] Open
Abstract
Schizophrenia is a devastating brain disorder that disturbs sensory perception, motor action, and abstract thought. Its clinical phenotype implies dysfunction of various mental domains, which has motivated a series of theories regarding the underlying pathophysiology. Aiming at a predictive benchmark of a catalog of cognitive functions, we developed a data-driven machine-learning strategy and provide a proof of principle in a multisite clinical dataset (n = 324). Existing neuroscientific knowledge on diverse cognitive domains was first condensed into neurotopographical maps. We then examined how the ensuing meta-analytic cognitive priors can distinguish patients and controls using brain morphology and intrinsic functional connectivity. Some affected cognitive domains supported well-studied directions of research on auditory evaluation and social cognition. However, rarely suspected cognitive domains also emerged as disease relevant, including self-oriented processing of bodily sensations in gustation and pain. Such algorithmic charting of the cognitive landscape can be used to make targeted recommendations for future mental health research.
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Affiliation(s)
- Teresa M Karrer
- Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen, Aachen, Germany
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania.,Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, Pennsylvania.,Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania.,Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Birgit Derntl
- Translational Brain Medicine, Jülich Aachen Research Alliance (JARA), Aachen, Germany.,Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
| | - Oliver Gruber
- Department of Psychiatry, University of Heidelberg, Heidelberg, Germany
| | - André Aleman
- BCN Neuroimaging Center, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Renaud Jardri
- Division of Psychiatry, University of Lille, CNRS UMR 9193, SCALab and CHU Lille, Fontan Hospital, Lille, France
| | - Angela R Laird
- Department of Physics, Florida International University, Miami, Florida
| | - Peter T Fox
- Research Imaging Institute, University of Texas Health Science Center, San Antonio, Texas.,South Texas Veterans Health Care System, San Antonio, Texas.,State Key Laboratory for Brain and Cognitive Sciences, University of Hong Kong, Hong Kong, China
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Heinrich-Heine University, Düsseldorf, Germany.,Institute of Neuroscience and Medicine (INM-7), Research Centre Jülich, Jülich, Germany
| | - Olivier Grisel
- Parietal Team, INRIA Saclay/NeuroSpin, Palaiseau, France
| | - Gaël Varoquaux
- Parietal Team, INRIA Saclay/NeuroSpin, Palaiseau, France
| | | | - Danilo Bzdok
- Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen, Aachen, Germany.,Translational Brain Medicine, Jülich Aachen Research Alliance (JARA), Aachen, Germany.,Parietal Team, INRIA Saclay/NeuroSpin, Palaiseau, France
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42
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Yamashita A, Yahata N, Itahashi T, Lisi G, Yamada T, Ichikawa N, Takamura M, Yoshihara Y, Kunimatsu A, Okada N, Yamagata H, Matsuo K, Hashimoto R, Okada G, Sakai Y, Morimoto J, Narumoto J, Shimada Y, Kasai K, Kato N, Takahashi H, Okamoto Y, Tanaka SC, Kawato M, Yamashita O, Imamizu H. Harmonization of resting-state functional MRI data across multiple imaging sites via the separation of site differences into sampling bias and measurement bias. PLoS Biol 2019; 17:e3000042. [PMID: 30998673 PMCID: PMC6472734 DOI: 10.1371/journal.pbio.3000042] [Citation(s) in RCA: 85] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2018] [Accepted: 03/14/2019] [Indexed: 01/07/2023] Open
Abstract
When collecting large amounts of neuroimaging data associated with psychiatric disorders, images must be acquired from multiple sites because of the limited capacity of a single site. However, site differences represent a barrier when acquiring multisite neuroimaging data. We utilized a traveling-subject dataset in conjunction with a multisite, multidisorder dataset to demonstrate that site differences are composed of biological sampling bias and engineering measurement bias. The effects on resting-state functional MRI connectivity based on pairwise correlations because of both bias types were greater than or equal to psychiatric disorder differences. Furthermore, our findings indicated that each site can sample only from a subpopulation of participants. This result suggests that it is essential to collect large amounts of neuroimaging data from as many sites as possible to appropriately estimate the distribution of the grand population. Finally, we developed a novel harmonization method that removed only the measurement bias by using a traveling-subject dataset and achieved the reduction of the measurement bias by 29% and improvement of the signal-to-noise ratios by 40%. Our results provide fundamental knowledge regarding site effects, which is important for future research using multisite, multidisorder resting-state functional MRI data.
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Affiliation(s)
- Ayumu Yamashita
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan
- * E-mail: (HI); (OY); or (AY)
| | - Noriaki Yahata
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Department of Molecular Imaging and Theranostics, National Institute of Radiological Sciences, National Institutes for Quantum and Radiological Science and Technology, Chiba, Japan
| | - Takashi Itahashi
- Medical Institute of Developmental Disabilities Research, Showa University, Tokyo, Japan
| | - Giuseppe Lisi
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan
| | - Takashi Yamada
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan
- Medical Institute of Developmental Disabilities Research, Showa University, Tokyo, Japan
| | - Naho Ichikawa
- Department of Psychiatry and Neurosciences, Hiroshima University, Hiroshima, Japan
| | - Masahiro Takamura
- Department of Psychiatry and Neurosciences, Hiroshima University, Hiroshima, Japan
| | - Yujiro Yoshihara
- Department of Psychiatry, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Akira Kunimatsu
- Department of Radiology, IMSUT Hospital, Institute of Medical Science, The University of Tokyo, Tokyo, Japan
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Naohiro Okada
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- The International Research Center for Neurointelligence (WPI-IRCN) at the University of Tokyo Institutes for Advanced Study (UTIAS), Tokyo, Japan
| | - Hirotaka Yamagata
- Division of Neuropsychiatry, Department of Neuroscience, Yamaguchi University Graduate School of Medicine, Yamaguchi, Japan
| | - Koji Matsuo
- Division of Neuropsychiatry, Department of Neuroscience, Yamaguchi University Graduate School of Medicine, Yamaguchi, Japan
- Department of Psychiatry, Faculty of Medicine, Saitama Medical University, Saitama, Japan
| | - Ryuichiro Hashimoto
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan
- Medical Institute of Developmental Disabilities Research, Showa University, Tokyo, Japan
- Department of Language Sciences, Tokyo Metropolitan University, Tokyo, Japan
| | - Go Okada
- Department of Psychiatry and Neurosciences, Hiroshima University, Hiroshima, Japan
| | - Yuki Sakai
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan
- Department of Psychiatry, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Jun Morimoto
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan
| | - Jin Narumoto
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan
- Department of Psychiatry, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Yasuhiro Shimada
- Brain Activity Imaging Center, ATR-Promotions Inc., Kyoto, Japan
| | - Kiyoto Kasai
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- The International Research Center for Neurointelligence (WPI-IRCN) at the University of Tokyo Institutes for Advanced Study (UTIAS), Tokyo, Japan
| | - Nobumasa Kato
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan
- Medical Institute of Developmental Disabilities Research, Showa University, Tokyo, Japan
| | - Hidehiko Takahashi
- Department of Psychiatry, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Yasumasa Okamoto
- Department of Psychiatry and Neurosciences, Hiroshima University, Hiroshima, Japan
| | - Saori C. Tanaka
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan
| | - Mitsuo Kawato
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan
- Center for Advanced Intelligence Project, RIKEN, Tokyo, Japan
| | - Okito Yamashita
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan
- Center for Advanced Intelligence Project, RIKEN, Tokyo, Japan
- * E-mail: (HI); (OY); or (AY)
| | - Hiroshi Imamizu
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan
- Department of Psychology, Graduate School of Humanities and Sociology, The University of Tokyo, Tokyo, Japan
- * E-mail: (HI); (OY); or (AY)
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43
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Daniels JK, Schulz A, Schellong J, Han P, Rottstädt F, Diers K, Weidner K, Croy I. Gray Matter Alterations Associated With Dissociation in Female Survivors of Childhood Trauma. Front Psychol 2019; 10:738. [PMID: 31024390 PMCID: PMC6460891 DOI: 10.3389/fpsyg.2019.00738] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2018] [Accepted: 03/16/2019] [Indexed: 12/31/2022] Open
Abstract
Objective Across various axis-1 disorders, the severity of dissociative symptoms is significantly related to a history of childhood traumatization. Thus, the question arises if coping with childhood trauma leads to neural adaptations that enhance the frequency of dissociative processing during adulthood. The aim of the two reported studies therefore was to identify and replicate gray matter alterations associated with dissociation. Methods and Results In a first study, whole-brain MRI data were acquired for 22 female in-patients with trauma-spectrum disorders and a history of severe childhood trauma. Voxel-based morphometry (VBM) was carried out to test for significant correlations between dissociation (depersonalization/derealization) severity and gray matter volume. Dissociation severity was positively associated with volume in the left angular gyrus. This result was diagnosis-invariant. The replication study involved 26 female in-patients with trauma-spectrum disorders and 25 healthy controls. No significant association between dissociation severity and brain volume in a left angular gyrus region of interest located at the peak identified in study 1 was identified and no significant group difference in this region could be established. Conclusion The angular gyrus has previously been implicated in the processing of agency and vestibular integration as well as dissociative processing. The current attempt at a direct replication of brain volume alterations however, failed. The data thus only partially support the notion that dissociative processing is associated trans-diagnostically with structural brain alterations in the left angular gyrus and independent replication in a larger patient sample is essential.
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Affiliation(s)
- Judith K Daniels
- Division of Clinical Psychology and Experimental Psychopathology, Department of Psychology, University of Groningen, Groningen, Netherlands.,Psychologische Hochschule Berlin, Berlin, Germany
| | - Anna Schulz
- Abteilung für Psychotherapie und Psychosomatik, Medizinische Fakultät Dresden, Technische Universität Dresden, Dresden, Germany
| | - Julia Schellong
- Abteilung für Psychotherapie und Psychosomatik, Medizinische Fakultät Dresden, Technische Universität Dresden, Dresden, Germany
| | - Pengfei Han
- Abteilung für Psychologie, Technische Universität Dresden, Dresden, Germany
| | - Fabian Rottstädt
- Abteilung für Psychotherapie und Psychosomatik, Medizinische Fakultät Dresden, Technische Universität Dresden, Dresden, Germany
| | - Kersten Diers
- Abteilung für Hals-Nasen-Ohren-Heilkunde, Medizinische Fakultät Dresden, Technische Universität Dresden, Dresden, Germany
| | - Kerstin Weidner
- Abteilung für Psychotherapie und Psychosomatik, Medizinische Fakultät Dresden, Technische Universität Dresden, Dresden, Germany
| | - Ilona Croy
- Abteilung für Psychotherapie und Psychosomatik, Medizinische Fakultät Dresden, Technische Universität Dresden, Dresden, Germany
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44
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Easson AK, McIntosh AR. BOLD signal variability and complexity in children and adolescents with and without autism spectrum disorder. Dev Cogn Neurosci 2019; 36:100630. [PMID: 30878549 PMCID: PMC6969202 DOI: 10.1016/j.dcn.2019.100630] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2018] [Revised: 02/02/2019] [Accepted: 03/04/2019] [Indexed: 11/29/2022] Open
Abstract
Resting-state BOLD signal variability and complexity were examined. No significant group differences were observed in youth with and without autism. A continuum of brain-behavior relationships was observed across diagnostic groups. Positive correlations were found between brain measures, age and global efficiency. Negative correlations were found between the brain measures and behavioral severity.
Variability of neural signaling is an important index of healthy brain functioning, as is signal complexity, which relates to information processing capacity. Alterations in variability and complexity may underlie certain brain dysfunctions. Here, resting-state fMRI was used to examine variability and complexity in children and adolescents with and without autism spectrum disorder (ASD). Variability was measured using the mean square successive difference (MSSD) of the time series, and complexity was assessed using sample entropy. A categorical approach was implemented to determine if the brain measures differed between diagnostic groups (ASD and controls). A dimensional approach was used to examine the continuum of relationships between each brain measure and behavioral severity, age, IQ, and the global efficiency (GE) of each participant’s structural connectome, which reflects the structural capacity for information processing. Using the categorical approach, no significant group differences were found for neither MSSD nor entropy. The dimensional approach revealed significant positive correlations between each brain measure, GE, and age. Negative correlations were observed between each brain measure and the severity of ASD behaviors across all participants. These results reveal the nature of variability and complexity of BOLD signals in children and adolescents with and without ASD.
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Affiliation(s)
- Amanda K Easson
- Rotman Research Institute, Baycrest Hospital, 3560 Bathurst Street, Toronto, ON, M6A 2E1, Canada; Department of Psychology, University of Toronto, 100 St. George Street, Toronto, ON, M5S 3G3, Canada.
| | - Anthony R McIntosh
- Rotman Research Institute, Baycrest Hospital, 3560 Bathurst Street, Toronto, ON, M6A 2E1, Canada; Department of Psychology, University of Toronto, 100 St. George Street, Toronto, ON, M5S 3G3, Canada.
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45
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Ten simple rules for predictive modeling of individual differences in neuroimaging. Neuroimage 2019; 193:35-45. [PMID: 30831310 PMCID: PMC6521850 DOI: 10.1016/j.neuroimage.2019.02.057] [Citation(s) in RCA: 211] [Impact Index Per Article: 42.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2018] [Revised: 01/28/2019] [Accepted: 02/21/2019] [Indexed: 11/24/2022] Open
Abstract
Establishing brain-behavior associations that map brain organization to phenotypic measures and generalize to novel individuals remains a challenge in neuroimaging. Predictive modeling approaches that define and validate models with independent datasets offer a solution to this problem. While these methods can detect novel and generalizable brain-behavior associations, they can be daunting, which has limited their use by the wider connectivity community. Here, we offer practical advice and examples based on functional magnetic resonance imaging (fMRI) functional connectivity data for implementing these approaches. We hope these ten rules will increase the use of predictive models with neuroimaging data.
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Proudfoot M, Bede P, Turner MR. Imaging Cerebral Activity in Amyotrophic Lateral Sclerosis. Front Neurol 2019; 9:1148. [PMID: 30671016 PMCID: PMC6332509 DOI: 10.3389/fneur.2018.01148] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2018] [Accepted: 12/11/2018] [Indexed: 01/30/2023] Open
Abstract
Advances in neuroimaging, complementing histopathological insights, have established a multi-system involvement of cerebral networks beyond the traditional neuromuscular pathological view of amyotrophic lateral sclerosis (ALS). The development of effective disease-modifying therapy remains a priority and this will be facilitated by improved biomarkers of motor system integrity against which to assess the efficacy of candidate drugs. Functional MRI (FMRI) is an established measure of both cerebral activity and connectivity, but there is an increasing recognition of neuronal oscillations in facilitating long-distance communication across the cortical surface. Such dynamic synchronization vastly expands the connectivity foundations defined by traditional neuronal architecture. This review considers the unique pathogenic insights afforded by the capture of cerebral disease activity in ALS using FMRI and encephalography.
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Affiliation(s)
- Malcolm Proudfoot
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Peter Bede
- Computational Neuroimaging Group, Academic Unit of Neurology, Trinity College Dublin, Dublin, Ireland
| | - Martin R Turner
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom.,Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
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Pei S, Guan J, Zhou S. Classifying early and late mild cognitive impairment stages of Alzheimer's disease by fusing default mode networks extracted with multiple seeds. BMC Bioinformatics 2018; 19:523. [PMID: 30598074 PMCID: PMC6311889 DOI: 10.1186/s12859-018-2528-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND The default mode network (DMN) in resting state has been increasingly used in disease diagnosis since it was found in 2001. Prior work has mainly focused on extracting a single DMN with various techniques. However, by using seeding-based analysis with more than one desirable seed, we can obtain multiple DMNs, which are likely to have complementary information, and thus are more promising for disease diagnosis. In the study, we used 18 early mild cognitive impairment (EMCI) participants and 18 late mild cognitive impairment (LMCI) participants of Alzheimer's disease (AD). First, we used seeding-based analysis with four seeds to extract four DMNs for each subject. Then, we conducted fusion analysis for all different combinations of the four DMNs. Finally, we carried out nonlinear support vector machine classification based on the mixing coefficients from the fusion analysis. RESULTS We found that (1) the four DMNs corresponding to the four different seeds indeed capture different functional regions of each subject; (2) Maps of the four DMNs in the most different joint source from fusion analysis are centered at the regions of the corresponding seeds; (3) Classification results reveal the effectiveness of using multiple seeds to extract DMNs. When using a single seed, the regions of posterior cingulate cortex (PCC) extractions of EMCI and LMCI show the largest difference. For multiple-seed cases, the regions of PCC extraction and right lateral parietal cortex (RLP) extraction provide complementary information for each other in fusion, which improves the classification accuracy. Furthermore, the regions of left lateral parietal cortex (LLP) extraction and RLP extraction also have complementary effect in fusion. In summary, AD diagnosis can be improved by exploiting complementary information of DMNs extracted with multiple seeds. CONCLUSIONS In this study, we applied fusion analysis to the DMNs extracted by using different seeds for exploiting the complementary information hidden among the separately extracted DMNs, and the results supported our expectation that using the complementary information can improve classification accuracy.
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Affiliation(s)
- Shengbing Pei
- Department of Computer Science and Technology, Tongji University, 4800 Cao An Road, Shanghai, 201800, China
| | - Jihong Guan
- Department of Computer Science and Technology, Tongji University, 4800 Cao An Road, Shanghai, 201800, China.
| | - Shuigeng Zhou
- Shanghai Key Lab of Intelligent Information Processing, and School of Computer Science, Fudan University, 220 Handan Road, Shanghai, 200433, China
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Food addiction linked to changes in ventral striatum functional connectivity between fasting and satiety. Appetite 2018; 133:18-23. [PMID: 30312737 DOI: 10.1016/j.appet.2018.10.009] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2018] [Revised: 10/08/2018] [Accepted: 10/08/2018] [Indexed: 01/07/2023]
Abstract
INTRODUCTION The concept of "food addiction" (FA) has gained popularity in view of clinical and neurobiological overlaps between excessive food intake and addictive disorders. However, no studies have examined the link between FA and striatocortical circuits involved in addictive disorders, or the influence of homeostatic status, which regulates the drive to eat, on these systems. This study aims to investigate changes in striatal functional connectivity between fasted and fed conditions among adults ranging in body mass index (BMI) and FA symptoms. METHODS Thirty adults were recruited from the general community and completed self-reported surveys including demographics, FA symptoms using the Yale Food Addiction Scale, as well as height and weight measures, used to determine BMI. Participants completed two 3-T MRI scans, one in a fasted state and one in a fed state. We conducted seed-based analyses to examine between-session ("fasted > fed") change in resting-state functional connectivity of the ventral and dorsal striatum, and its association with FA scores (controlling for BMI). RESULTS Higher symptoms of FA correlated with greater changes in ventral caudate-hippocampus connectivity between fasted and fed conditions. FA symptoms did not correlate with connectivity in the dorsal caudate circuit. Post-hoc analyses revealed that participants with higher symptoms of FA had ventral caudate-hippocampus hyperconnectivity in the fasted scan only, as well as a significant reduction of this connectivity between the fasted and fed scans. CONCLUSIONS Heightened connectivity in the ventral striatum during a fasted state, which has been linked to reward prediction signals, underpins symptoms of FA. In contrast, connectivity in the dorsal striatum or "habit" system is unrelated to homeostatic status and FA symptoms, and is thus less relevant for subclinical manifestations of FA.
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Varoquaux G. Cross-validation failure: Small sample sizes lead to large error bars. Neuroimage 2018; 180:68-77. [DOI: 10.1016/j.neuroimage.2017.06.061] [Citation(s) in RCA: 211] [Impact Index Per Article: 35.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2017] [Revised: 06/19/2017] [Accepted: 06/22/2017] [Indexed: 10/19/2022] Open
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50
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Dimitriadis SI, Antonakakis M, Simos P, Fletcher JM, Papanicolaou AC. Data-Driven Topological Filtering Based on Orthogonal Minimal Spanning Trees: Application to Multigroup Magnetoencephalography Resting-State Connectivity. Brain Connect 2018; 7:661-670. [PMID: 28891322 DOI: 10.1089/brain.2017.0512] [Citation(s) in RCA: 51] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
Abstract
In the present study, a novel data-driven topological filtering technique is introduced to derive the backbone of functional brain networks relying on orthogonal minimal spanning trees (OMSTs). The method aims to identify the essential functional connections to ensure optimal information flow via the objective criterion of global efficiency minus the cost of surviving connections. The OMST technique was applied to multichannel, resting-state neuromagnetic recordings from four groups of participants: healthy adults (n = 50), adults who have suffered mild traumatic brain injury (n = 30), typically developing children (n = 27), and reading-disabled children (n = 25). Weighted interactions between network nodes (sensors) were computed using an integrated approach of dominant intrinsic coupling modes based on two alternative metrics (symbolic mutual information and phase lag index), resulting in excellent discrimination of individual cases according to their group membership. Classification results using OMST-derived functional networks were clearly superior to results using either relative power spectrum features or functional networks derived through the conventional minimal spanning tree algorithm.
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Affiliation(s)
- Stavros I Dimitriadis
- 1 Division of Psychological Medicine and Clinical Neurosciences, Cardiff University School of Medicine, Cardiff, United Kingdom .,2 Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom .,3 School of Psychology, Cardiff University, Cardiff, United Kingdom .,4 Neuroinformatics Group, Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom .,5 MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University School of Medicine , Cardiff, United Kingdom
| | - Marios Antonakakis
- 6 Institute of Biomagnetism and Biosignal Analysis, Westfalian Wilhelms-University Muenster, Muenster, Germany
| | - Panagiotis Simos
- 7 School of Medicine, University of Crete, Crete, Greece .,8 Institute of Computer Science, Foundation for Research and Technology, Crete, Greece
| | - Jack M Fletcher
- 9 Department of Psychology, University of Houston, Houston, Texas
| | - Andrew C Papanicolaou
- 10 Department of Pediatrics, University of Tennessee Health Science Center, Memphis, Tennessee.,11 Neuroscience Institute, Le Bonheur Children s Hospital, Memphis, Tennessee
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