1
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Luppi AI, Gellersen HM, Liu ZQ, Peattie ARD, Manktelow AE, Adapa R, Owen AM, Naci L, Menon DK, Dimitriadis SI, Stamatakis EA. Systematic evaluation of fMRI data-processing pipelines for consistent functional connectomics. Nat Commun 2024; 15:4745. [PMID: 38834553 PMCID: PMC11150439 DOI: 10.1038/s41467-024-48781-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 05/10/2024] [Indexed: 06/06/2024] Open
Abstract
Functional interactions between brain regions can be viewed as a network, enabling neuroscientists to investigate brain function through network science. Here, we systematically evaluate 768 data-processing pipelines for network reconstruction from resting-state functional MRI, evaluating the effect of brain parcellation, connectivity definition, and global signal regression. Our criteria seek pipelines that minimise motion confounds and spurious test-retest discrepancies of network topology, while being sensitive to both inter-subject differences and experimental effects of interest. We reveal vast and systematic variability across pipelines' suitability for functional connectomics. Inappropriate choice of data-processing pipeline can produce results that are not only misleading, but systematically so, with the majority of pipelines failing at least one criterion. However, a set of optimal pipelines consistently satisfy all criteria across different datasets, spanning minutes, weeks, and months. We provide a full breakdown of each pipeline's performance across criteria and datasets, to inform future best practices in functional connectomics.
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Affiliation(s)
- Andrea I Luppi
- Division of Anaesthesia, University of Cambridge, Cambridge, UK.
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
- St John's College, University of Cambridge, Cambridge, UK.
- Montreal Neurological Institute, McGill University, Montreal, Canada.
| | - Helena M Gellersen
- German Center for Neurodegenerative Diseases, Magdeburg, Germany
- Department of Psychology, University of Cambridge, Cambridge, UK
| | - Zhen-Qi Liu
- Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Alexander R D Peattie
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Anne E Manktelow
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Ram Adapa
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Adrian M Owen
- Department of Psychology, Western Institute for Neuroscience (WIN), Western University, London, ON, Canada
- Department of Physiology and Pharmacology, Western Institute for Neuroscience (WIN), Western University, London, ON, Canada
| | - Lorina Naci
- Trinity College Institute of Neuroscience, School of Psychology, Trinity College Dublin, Dublin, Ireland
| | - David K Menon
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
| | - Stavros I Dimitriadis
- Department of Clinical Psychology and Psychobiology, University of Barcelona, Barcelona, Spain
- Institut de Neurociències, University of Barcelona, Barcelona, Spain
- Neuroinformatics Group, Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff, Wales, UK
- Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, Wales, UK
- Neuroscience and Mental Health Research Institute, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, Wales, UK
- MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, Wales, UK
- Integrative Neuroimaging Lab, Thessaloniki, Greece
| | - Emmanuel A Stamatakis
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
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2
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Rai S, Graff K, Tansey R, Bray S. How do tasks impact the reliability of fMRI functional connectivity? Hum Brain Mapp 2024; 45:e26535. [PMID: 38348730 PMCID: PMC10884875 DOI: 10.1002/hbm.26535] [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: 06/21/2023] [Revised: 10/13/2023] [Accepted: 11/01/2023] [Indexed: 02/24/2024] Open
Abstract
While there is growing interest in the use of functional magnetic resonance imaging-functional connectivity (fMRI-FC) for biomarker research, low measurement reliability of conventional acquisitions may limit applications. Factors known to impact FC reliability include scan length, head motion, signal properties, such as temporal signal-to-noise ratio (tSNR), and the acquisition state or task. As tasks impact signal in a region-wise fashion, they likely impact FC reliability differently across the brain, making task an important decision in study design. Here, we use the densely sampled Midnight Scan Club (MSC) dataset, comprising 5 h of rest and 6 h of task fMRI data in 10 healthy adults, to investigate regional effects of tasks on FC reliability. We further considered how BOLD signal properties contributing to tSNR, that is, temporal mean signal (tMean) and temporal standard deviation (tSD), vary across the brain, associate with FC reliability, and are modulated by tasks. We found that, relative to rest, tasks enhanced FC reliability and increased tSD for specific task-engaged regions. However, FC signal variability and reliability is broadly dampened during tasks outside task-engaged regions. From our analyses, we observed signal variability was the strongest driver of FC reliability. Overall, our findings suggest that the choice of task can have an important impact on reliability and should be considered in relation to maximizing reliability in networks of interest as part of study design.
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Affiliation(s)
- Shefali Rai
- Child and Adolescent Imaging Research ProgramUniversity of CalgaryCalgaryAlbertaCanada
- Alberta Children's Hospital Research InstituteUniversity of CalgaryCalgaryAlbertaCanada
- Hotchkiss Brain InstituteUniversity of CalgaryCalgaryAlbertaCanada
- Department of NeuroscienceUniversity of CalgaryCalgaryAlbertaCanada
| | - Kirk Graff
- Child and Adolescent Imaging Research ProgramUniversity of CalgaryCalgaryAlbertaCanada
- Alberta Children's Hospital Research InstituteUniversity of CalgaryCalgaryAlbertaCanada
- Hotchkiss Brain InstituteUniversity of CalgaryCalgaryAlbertaCanada
- Department of NeuroscienceUniversity of CalgaryCalgaryAlbertaCanada
| | - Ryann Tansey
- Child and Adolescent Imaging Research ProgramUniversity of CalgaryCalgaryAlbertaCanada
- Alberta Children's Hospital Research InstituteUniversity of CalgaryCalgaryAlbertaCanada
- Hotchkiss Brain InstituteUniversity of CalgaryCalgaryAlbertaCanada
- Department of NeuroscienceUniversity of CalgaryCalgaryAlbertaCanada
| | - Signe Bray
- Child and Adolescent Imaging Research ProgramUniversity of CalgaryCalgaryAlbertaCanada
- Alberta Children's Hospital Research InstituteUniversity of CalgaryCalgaryAlbertaCanada
- Hotchkiss Brain InstituteUniversity of CalgaryCalgaryAlbertaCanada
- Department of RadiologyUniversity of CalgaryCalgaryAlbertaCanada
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3
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Zhang X, Cheng B, Yang X, Suo X, Pan N, Chen T, Wang S, Gong Q. Emotional intelligence mediates the protective role of the orbitofrontal cortex spontaneous activity measured by fALFF against depressive and anxious symptoms in late adolescence. Eur Child Adolesc Psychiatry 2023; 32:1957-1967. [PMID: 35737106 DOI: 10.1007/s00787-022-02020-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Accepted: 06/01/2022] [Indexed: 02/05/2023]
Abstract
As a stable personality construct, trait emotional intelligence (TEI) refers to a battery of perceived emotion-related skills that make individuals behave effectively to adapt to the environment and maintain well-being. Abundant evidence has consistently shown that TEI is important for the outcomes of many mental health issues, particularly depression and anxiety. However, the neural substrates involved in TEI and the underlying neurobehavioral mechanism of how TEI reduces depression and anxiety symptoms remain largely unknown. Herein, resting-state functional magnetic resonance imaging and a group of behavioral measures were applied to examine these questions among a large sample comprising 231 general adolescent students aged 16-20 years (52% female). Whole-brain correlation analysis and prediction analysis demonstrated that TEI was negatively linked with spontaneous activity (measured with the fractional amplitude of low-frequency fluctuations) in the bilateral medial orbitofrontal cortex (OFC), a critical site implicated in emotion-related processes. Furthermore, structural equation modeling analysis found that TEI mediated the link of OFC spontaneous activity to depressive and anxious symptoms. Collectively, the current findings present new evidence for the neurofunctional bases of TEI and suggest a potential "brain-personality-symptom" pathway for alleviating depressive and anxious symptoms among students in late adolescence.
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Affiliation(s)
- Xun Zhang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
| | - Bochao Cheng
- Department of Radiology, West China Second University Hospital of Sichuan University, Chengdu, China
| | - Xun Yang
- School of Public Affairs, Chongqing University, Chongqing, China
| | - Xueling Suo
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
| | - Nanfang Pan
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
| | - Taolin Chen
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
| | - Song Wang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China.
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China.
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China.
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China.
- Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, China.
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4
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Tozer DJ, Pflanz CP, Markus HS. Reproducibility of regional structural and functional MRI networks in cerebral small vessel disease compared to age matched and stroke-free controls. CEREBRAL CIRCULATION - COGNITION AND BEHAVIOR 2023; 4:100167. [PMID: 37397269 PMCID: PMC10313873 DOI: 10.1016/j.cccb.2023.100167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 05/11/2023] [Accepted: 05/12/2023] [Indexed: 07/04/2023]
Abstract
Abnormalities in structural and functional MRI connectivity measures have been reported in cerebral small vessel disease (SVD). Previous research has shown that whole-brain structural connectivity was highly reproducible in SVD patients, while whole-brain functional connectivity showed low reproducibility. It remains unclear whether the lower reproducibility of functional networks reported in SVD is due to selective disruption of reproducibility in specific networks or is generalised in patients with SVD. In this case-control study 15 SVD and 10 age-matched control participants were imaged twice with diffusion tensor imaging and resting state fMRI. Structural and functional connectivity matrices were constructed from this data and the default mode, fronto-parietal, limbic, salience, somatomotor and visual networks were extracted and the average connectivity between connections calculated and used to determine their reproducibility. Regional structural networks were more reproducible than functional networks, all structural networks showed ICC values ≥0.64 (except the salience network in SVD). The functional networks showed greater reproducibility in the controls compared to SVD with ICC values >0.7 for control participants and <=0.5 for the SVD group. The default mode network showed the greatest reproducibility for both control and SVD groups. Reproducibility of functional networks was affected by disease status with lower reproducibility in SVD compared with controls.
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Affiliation(s)
- Daniel J. Tozer
- Corresponding author at: University of Cambridge, Department of Clinical Neurosciences, Neurology Unit, R3, Box 83, Cambridge Biomedical Campus, Cambridge CB2 0QQ, United Kingdom.
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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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6
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Dimitriadis SI. Assessing the Repeatability of Multi-Frequency Multi-Layer Brain Network Topologies Across Alternative Researcher's Choice Paths. Neuroinformatics 2023; 21:71-88. [PMID: 36372844 DOI: 10.1007/s12021-022-09610-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/05/2022] [Indexed: 11/15/2022]
Abstract
There is a growing interest in the neuroscience community on the advantages of multilayer functional brain networks. Researchers usually treated different frequencies separately at distinct functional brain networks. However, there is strong evidence that these networks share complementary information while their interdependencies could reveal novel findings. For this purpose, neuroscientists adopt multilayer networks, which can be described mathematically as an extension of trivial single-layer networks. Multilayer networks have become popular in neuroscience due to their advantage to integrate different sources of information. Here, Ι will focus on the multi-frequency multilayer functional connectivity analysis on resting-state fMRI (rs-fMRI) recordings. However, constructing a multilayer network depends on selecting multiple pre-processing steps that can affect the final network topology. Here, I analyzed the rs-fMRI dataset from a single human performing scanning over a period of 18 months (84 scans in total), and the rs-fMRI dataset containing 25 subjects with 3 repeat scans. I focused on assessing the reproducibility of multi-frequency multilayer topologies exploring the effect of two filtering methods for extracting frequencies from BOLD activity, three connectivity estimators, with or without a topological filtering scheme, and two spatial scales. Finally, I untangled specific combinations of researchers' choices that yield consistently brain networks with repeatable topologies, giving me the chance to recommend best practices over consistent topologies.
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Affiliation(s)
- Stavros I Dimitriadis
- Department of Clinical Psychology and Psychobiology, Faculty of Psychology, University of Barcelona, Passeig de la Vall d'Hebron, 171, 08035, Barcelona, Spain.
- Institut de Neurociències, University of Barcelona, Campus Mundet, Edifici de PonentPasseig de la Vall d'Hebron, 171, 08035, Barcelona, Spain.
- Integrative Neuroimaging Lab, 55133, Thessaloniki, Greece.
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Wales, CF24 4HQ, Cardiff, UK.
- Neuroinformatics Group, School of Psychology, College of Biomedical and Life Sciences, Cardiff University Brain Research Imaging Centre (CUBRIC), CF24 4HQ, Cardiff, Wales, UK.
- Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, CF24 4HQ, Wales, UK.
- Neuroscience and Mental Health Research Institute, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, CF24 4HQ, Cardiff, Wales, UK.
- MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, CF24 4HQ, Wales, UK.
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7
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Dong M, Zhang P, Chai W, Zhang X, Chen BT, Wang H, Wu J, Chen C, Niu Y, Liang J, Shi G, Jin C. Early stage of radiological expertise modulates resting-state local coherence in the inferior temporal lobe. PSYCHORADIOLOGY 2022; 2:199-206. [PMID: 38665273 PMCID: PMC10917200 DOI: 10.1093/psyrad/kkac024] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Revised: 12/06/2022] [Accepted: 12/08/2022] [Indexed: 04/28/2024]
Abstract
Background The visual system and its inherent functions undergo experience-dependent changes through the lifespan, enabling acquisition of new skills. Previous fMRI studies using tasks reported increased specialization in a number of cortical regions subserving visual expertise. Although ample studies focused on representation of long-term visual expertise in the brain, i.e. in terms of year, monthly-based early-stage representation of visual expertise remains unstudied. Given that spontaneous neuronal oscillations actively encode previous experience, we propose brain representations in the resting state is fundamentally important. Objective The current study aimed to investigate how monthly-based early-stage visual expertise are represented in the resting state using the expertise model of radiologists. Methods In particular, we investigated the altered local clustering pattern of spontaneous brain activity using regional homogeneity (ReHo). A cohort group of radiology interns (n = 22) after one-month training in X-ray department and matched laypersons (n = 22) were recruited after rigorous behavioral assessment. Results The results showed higher ReHo in the right hippocampus (HIP) and the right ventral anterior temporal lobe (vATL) (corrected by Alphasim correction, P < 0.05). Moreover, ReHo in the right HIP correlated with the number of cases reviewed during intern radiologists' training (corrected by Alphasim correction, P < 0.05). Conclusions In sum, our results demonstrated that the early stage of visual expertise is more concerned with stabilizing visual feature and domain-specific knowledge into long-term memory. The results provided novel evidence regarding how early-stage visual expertise is represented in the resting brain, which help further elaborate how human visual expertise is acquired. We propose that our current study may provide novel ideas for developing new training protocols in medical schools.
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Affiliation(s)
- Minghao Dong
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi’an City, Shaanxi 710071, China
- Xian Key Laboratory of Intelligent Sensing and Regulation of tran-Scale Life Information, Xi’an City, Shaanxi 710071, China
- Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, Xi’an City, Shaanxi 710071, China
| | - Peiming Zhang
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi’an City, Shaanxi 710071, China
| | - Weilu Chai
- Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, Xi’an City, Shaanxi 710071, China
| | - Xiaoyan Zhang
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi’an City, Shaanxi 710071, China
| | - Bihong T Chen
- City of Hope Medical Center, Duarte City, California 91010, USA
| | - Hongmei Wang
- Department of Medical Imaging, First Affiliated Hospital of Medical College, Xi’an Jiaotong University, Xi’an City, Shaanxi 710000, China
| | - Jia Wu
- School of Foreign Languages, Northwestern Polytechnical University, Xi'an City, Shaanxi 710071, China
| | - Chao Chen
- PLA Funding Payment Center, Beijing 100000, China
| | - Yi Niu
- Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, Xi’an City, Shaanxi 710071, China
| | - Jimin Liang
- School of Electronics and Engineering, Xidian University, Xi'an City, Shaanxi 710071, China
| | - Guangming Shi
- Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, Xi’an City, Shaanxi 710071, China
| | - Chenwang Jin
- Department of Medical Imaging, First Affiliated Hospital of Medical College, Xi’an Jiaotong University, Xi’an City, Shaanxi 710000, China
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The Location Reliability of the Resting-State fMRI FC of Emotional Regions Towards rTMS Therapy. Neuroinformatics 2022; 20:1055-1064. [PMID: 35608748 DOI: 10.1007/s12021-022-09585-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/02/2022] [Indexed: 12/31/2022]
Abstract
Resting-state magnetic resonance imaging (RS-fMRI) studies indicated that the repetitive transcranial magnetic stimulation (rTMS) exerts antidepression effect through the functional connectivity (FC) of the DLPFC with the subgenual anterior cingulate cortex (sgACC), pregneual ACC (pgACC), or nucleus accumbens (NAc). It is proposed that the FC-guided individualized precise stimulation on the DLPFC would be more effective. The current study systematically investigated the reliability of the RS-fMRI FC location as well as the FC strength with multiple potential factors. We aimed to provide a stable stimulation target for future FC-guided TMS therapy for affective related disorders. Twenty-one subjects under RS-fMRI conditions with the first two times (V1, V2) scanned on a GE 3 T scanner and the third visit (V3) on a Siemens 3 T scanner. Then the FC strength and location reliability were assessed by using intra-class correlation (ICC) and intra-individual distance, respectively. The factors included deep seed ROIs (midline (mid-) sgACC, left pgACC, mid-pgACC, and left NAc), eyes closed (EC) vs eyes open (EO), frequency bands, FC algorithm (Pearson vs Spearman), scanning length (half a session vs whole session), and location method (FC peak vs center of gravity (COG)). The reliability of the voxel-wise FC strength was low to moderate. The intra-individual distances of the COG were 3.8-7.3 mm across all factors, much smaller than that of FC peak (approximately 30 mm). The COG of seed-based FC might be a potential rTMS stimulation target. Anyway, all potential stimulation targets should be tested in future rTMS treatment studies.
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9
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Li T, Pei Z, Zhu Z, Wu X, Feng C. Intrinsic brain activity patterns across large-scale networks predict reciprocity propensity. Hum Brain Mapp 2022; 43:5616-5629. [PMID: 36054523 PMCID: PMC9704792 DOI: 10.1002/hbm.26038] [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: 03/09/2022] [Revised: 06/06/2022] [Accepted: 07/25/2022] [Indexed: 01/15/2023] Open
Abstract
Reciprocity is prevalent across human societies, but individuals are heterogeneous regarding their reciprocity propensity. Although a large body of task-based brain imaging measures has shed light on the neural underpinnings of reciprocity at group level, the neural basis underlying the individual differences in reciprocity propensity remains largely unclear. Here, we combined brain imaging and machine learning techniques to individually predict reciprocity propensity from resting-state brain activity measured by fractional amplitude of low-frequency fluctuation. The brain regions contributing to the prediction were then analyzed for functional connectivity and decoding analyses, allowing for a data-driven quantitative inference on psychophysiological functions. Our results indicated that patterns of resting-state brain activity across multiple brain systems were capable of predicting individual reciprocity propensity, with the contributing regions distributed across the salience (e.g., ventrolateral prefrontal cortex), fronto-parietal (e.g., dorsolateral prefrontal cortex), default mode (e.g., ventromedial prefrontal cortex), and sensorimotor (e.g., supplementary motor area) networks. Those contributing brain networks are implicated in emotion and cognitive control, mentalizing, and motor-based processes, respectively. Collectively, these findings provide novel evidence on the neural signatures underlying the individual differences in reciprocity, and lend support the assertion that reciprocity emerges from interactions among regions embodied in multiple large-scale brain networks.
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Affiliation(s)
- Ting Li
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University)Ministry of EducationGuangzhouChina,School of Psychology, Institute of Brain Research and Rehabilitation (IBRR), Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive ScienceSouth China Normal UniversityGuangzhouChina,Institute of Brain and Psychological SciencesSichuan Normal UniversityChengduChina
| | - Zhaodi Pei
- School of Artificial IntelligenceBeijing Normal UniversityBeijingChina,Engineering Research Center of Intelligent Technology and Educational Application of Ministry of EducationBeijing Normal UniversityBeijingChina
| | - Zhiyuan Zhu
- School of Artificial IntelligenceBeijing Normal UniversityBeijingChina,Engineering Research Center of Intelligent Technology and Educational Application of Ministry of EducationBeijing Normal UniversityBeijingChina
| | - Xia Wu
- School of Artificial IntelligenceBeijing Normal UniversityBeijingChina,Engineering Research Center of Intelligent Technology and Educational Application of Ministry of EducationBeijing Normal UniversityBeijingChina
| | - Chunliang Feng
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University)Ministry of EducationGuangzhouChina,School of Psychology, Institute of Brain Research and Rehabilitation (IBRR), Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive ScienceSouth China Normal UniversityGuangzhouChina
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10
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Vedaei F, Alizadeh M, Romo V, Mohamed FB, Wu C. The effect of general anesthesia on the test–retest reliability of resting-state fMRI metrics and optimization of scan length. Front Neurosci 2022; 16:937172. [PMID: 36051647 PMCID: PMC9425911 DOI: 10.3389/fnins.2022.937172] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 07/27/2022] [Indexed: 01/01/2023] Open
Abstract
Resting-state functional magnetic resonance imaging (rs-fMRI) has been known as a powerful tool in neuroscience. However, exploring the test–retest reliability of the metrics derived from the rs-fMRI BOLD signal is essential, particularly in the studies of patients with neurological disorders. Here, two factors, namely, the effect of anesthesia and scan length, have been estimated on the reliability of rs-fMRI measurements. A total of nine patients with drug-resistant epilepsy (DRE) requiring interstitial thermal therapy (LITT) were scanned in two states. The first scan was performed in an awake state before surgery on the same patient. The second scan was performed 2 weeks later under general anesthesia necessary for LITT surgery. At each state, two rs-fMRI sessions were obtained that each one lasted 15 min, and the effect of scan length was evaluated. Voxel-wise rs-fMRI metrics, including the amplitude of low-frequency fluctuation (ALFF), the fractional amplitude of low-frequency fluctuation (fALFF), functional connectivity (FC), and regional homogeneity (ReHo), were measured. Intraclass correlation coefficient (ICC) was calculated to estimate the reliability of the measurements in two states of awake and under anesthesia. Overall, it appeared that the reliability of rs-fMRI metrics improved under anesthesia. From the 15-min data, we found mean ICC values in awake state including 0.81, 0.51, 0.65, and 0.84 for ALFF, fALFF, FC, and ReHo, respectively, as well as 0.80, 0.59, 0.83, and 0.88 for ALFF, fALFF, FC, and ReHo, respectively, under anesthesia. Additionally, our findings revealed that reliability increases as the function of scan length. We showed that the optimized scan length to achieve less variability of rs-fMRI measurements was 3.1–7.5 min shorter in an anesthetized, compared to a wakeful state.
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Affiliation(s)
- Faezeh Vedaei
- Department of Radiology, Jefferson Integrated Magnetic Resonance Imaging Center, Thomas Jefferson University, Philadelphia, PA, United States
- *Correspondence: Faezeh Vedaei
| | - Mahdi Alizadeh
- Department of Radiology, Jefferson Integrated Magnetic Resonance Imaging Center, Thomas Jefferson University, Philadelphia, PA, United States
- Department of Neurological Surgery, Vickie and Jack Farber Institute for Neuroscience, Thomas Jefferson University, Philadelphia, PA, United States
| | - Victor Romo
- Department of Anesthesiology, Thomas Jefferson University, Philadelphia, PA, United States
| | - Feroze B. Mohamed
- Department of Radiology, Jefferson Integrated Magnetic Resonance Imaging Center, Thomas Jefferson University, Philadelphia, PA, United States
| | - Chengyuan Wu
- Department of Radiology, Jefferson Integrated Magnetic Resonance Imaging Center, Thomas Jefferson University, Philadelphia, PA, United States
- Department of Neurological Surgery, Vickie and Jack Farber Institute for Neuroscience, Thomas Jefferson University, Philadelphia, PA, United States
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11
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A longitudinal resource for studying connectome development and its psychiatric associations during childhood. Sci Data 2022; 9:300. [PMID: 35701428 PMCID: PMC9197863 DOI: 10.1038/s41597-022-01329-y] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 04/20/2022] [Indexed: 12/14/2022] Open
Abstract
Most psychiatric disorders are chronic, associated with high levels of disability and distress, and present during pediatric development. Scientific innovation increasingly allows researchers to probe brain-behavior relationships in the developing human. As a result, ambitions to (1) establish normative pediatric brain development trajectories akin to growth curves, (2) characterize reliable metrics for distinguishing illness, and (3) develop clinically useful tools to assist in the diagnosis and management of mental health and learning disorders have gained significant momentum. To this end, the NKI-Rockland Sample initiative was created to probe lifespan development as a large-scale multimodal dataset. The NKI-Rockland Sample Longitudinal Discovery of Brain Development Trajectories substudy (N = 369) is a 24- to 30-month multi-cohort longitudinal pediatric investigation (ages 6.0-17.0 at enrollment) carried out in a community-ascertained sample. Data include psychiatric diagnostic, medical, behavioral, and cognitive phenotyping, as well as multimodal brain imaging (resting fMRI, diffusion MRI, morphometric MRI, arterial spin labeling), genetics, and actigraphy. Herein, we present the rationale, design, and implementation of the Longitudinal Discovery of Brain Development Trajectories protocol.
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12
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Heilicher M, Crombie KM, Cisler JM. Test-retest reliability of fMRI during an emotion processing task: Investigating the impact of analytical approaches on ICC values. FRONTIERS IN NEUROIMAGING 2022; 1:859792. [PMID: 35782991 PMCID: PMC9245148 DOI: 10.3389/fnimg.2022.859792] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Test-retest reliability of fMRI is often assessed using the intraclass correlation coefficient (ICC), a numerical representation of reliability. Reports of low reliability at the individual level may be attributed to analytical approaches and inherent bias/error in the measures used to calculate ICC. It is unclear whether low reliability at the individual level is related to methodological decisions or if fMRI is inherently unreliable. The purpose of this study was to investigate methodological considerations when calculating ICC to improve understanding of fMRI reliability. fMRI data were collected from adolescent females (N=23) at pre- and post-cognitive behavioral therapy. Participants completed an emotion processing task during fMRI. We calculated ICC values using contrasts and β coefficients separately from voxelwise and network (ICA) analyses of the task-based fMRI data. For both voxelwise analysis and ICA, ICC values were higher when calculated using β coefficients. This work provides support for the use of β coefficients over contrasts when assessing reliability of fMRI, and the use of contrasts may underlie low reliability estimates reported in the existing literature. Continued research in this area is warranted to establish fMRI as a reliable measure to draw conclusions and utilize fMRI in clinical settings.
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Affiliation(s)
- Mickela Heilicher
- Mental Health and Incarceration Laboratory, University of
Wisconsin-Madison, School of Medicine and Public Health, Psychiatry Department,
Madison, WI, USA
| | - Kevin M. Crombie
- Neurocircuitry of Trauma and PTSD Laboratory, The
University of Texas at Austin, Dell Medical School, Department of Psychiatry and
Behavioral Sciences, Austin, TX, USA
| | - Josh M. Cisler
- Neurocircuitry of Trauma and PTSD Laboratory, The
University of Texas at Austin, Dell Medical School, Department of Psychiatry and
Behavioral Sciences, Austin, TX, USA
- Institute for Early Life Adversity Research, The University
of Texas at Austin, Dell Medical School, Department of Psychiatry and Behavioral
Sciences, Austin, TX, USA
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13
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Zhang T, Dong M, Wang H, Jia R, Li F, Ni X, Jin C. Visual expertise modulates baseline brain activity: a preliminary resting-state fMRI study using expertise model of radiologists. BMC Neurosci 2022; 23:24. [PMID: 35413843 PMCID: PMC9003981 DOI: 10.1186/s12868-022-00707-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 03/25/2022] [Indexed: 01/27/2023] Open
Abstract
Background visual expertise and experience modulate evoked brain activity in response to training-related stimuli. However, few studies have considered how the visual experience is represented in the resting state brain activity. This study tried to investigate the way visual experience, i.e., visual recognition expertise, modulates baseline brain neuronal activity in the resting state using the model of radiologists. Methods The amplitude of low-frequency (< 0.08 Hz) fluctuation (ALFF) was used as the metric of baseline brain activity and a visual expertise model of radiologists to investigated this question. The visual recognition skill enables them to accurately identify pathological information in medical images. After the behavior measurement, a cohort group of radiology interns (n = 22) and a group of matched layperson (n = 22) were selected for inclusion in the study. The resting state functional magnetic resonance imaging (fMRI) scans were performed for all of the subjects. Results Higher ALFF in the right fusiform gyrus and the left orbitofrontal cortex were observed, and the ALFF in the fusiform gyrus was correlated with the intern radiologists’ behavioral expertise(all results corrected for multiple comparisons). Conclusions Visual experience modulates the baseline brain activity in both high-level visual cortex and high-order cognitive cortex, indicating the engagement of both top-down and bottom-up facilitation. We provide a novel perspective to how visual experience modulated cortical brain activity by introducing the resting state changes. Also, we propose that our current study may provide novel ideas for the development of new training protocols in medical school.
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Affiliation(s)
- Ting Zhang
- School of Humanities and Social Science, Xi'an Jiaotong University, Xi'an, China.,College of Tourism & Landscape Architecture, Guilin University of Technology, Guilin, China
| | - Minghao Dong
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, 710071, Shaanxi, China.,Xi'an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information, School of Life Science and Technology, Xidian University, Xi'an, 710126, Shaanxi, China
| | - Hongmei Wang
- Department of Medical Imaging, First Affiliated Hospital of Medical College, Xi'an Jiaotong University, Xi'an, China
| | - Rui Jia
- College Students Mental Health Education Center, Xi'an University of Technology, Xi'an, China
| | - Fu Li
- Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, Xi'an, China
| | - Xiaoli Ni
- School of Humanities and Social Science, Xi'an Jiaotong University, Xi'an, China.
| | - Chenwang Jin
- Department of Medical Imaging, First Affiliated Hospital of Medical College, Xi'an Jiaotong University, Xi'an, China.
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14
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Sbaihat H, Rajkumar R, Ramkiran S, Assi AAN, Felder J, Shah NJ, Veselinović T, Neuner I. Test-retest stability of spontaneous brain activity and functional connectivity in the core resting-state networks assessed with ultrahigh field 7-Tesla resting-state functional magnetic resonance imaging. Hum Brain Mapp 2022; 43:2026-2040. [PMID: 35044722 PMCID: PMC8933332 DOI: 10.1002/hbm.25771] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 11/26/2021] [Accepted: 12/14/2021] [Indexed: 12/12/2022] Open
Abstract
The growing demand for precise and reliable biomarkers in psychiatry is fueling research interest in the hope that identifying quantifiable indicators will improve diagnoses and treatment planning across a range of mental health conditions. The individual properties of brain networks at rest have been highlighted as a possible source for such biomarkers, with the added advantage that they are relatively straightforward to obtain. However, an important prerequisite for their consideration is their reproducibility. While the reliability of resting‐state (RS) measurements has often been studied at standard field strengths, they have rarely been investigated using ultrahigh‐field (UHF) magnetic resonance imaging (MRI) systems. We investigated the intersession stability of four functional MRI RS parameters—amplitude of low‐frequency fluctuations (ALFF) and fractional ALFF (fALFF; representing the spontaneous brain activity), regional homogeneity (ReHo; measure of local connectivity), and degree centrality (DC; measure of long‐range connectivity)—in three RS networks, previously shown to play an important role in several psychiatric diseases—the default mode network (DMN), the central executive network (CEN), and the salience network (SN). Our investigation at individual subject space revealed a strong stability for ALFF, ReHo, and DC in all three networks, and a moderate level of stability in fALFF. Furthermore, the internetwork connectivity between each network pair was strongly stable between CEN/SN and moderately stable between DMN/SN and DMN/SN. The high degree of reliability and reproducibility in capturing the properties of the three major RS networks by means of UHF‐MRI points to its applicability as a potentially useful tool in the search for disease‐relevant biomarkers.
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Affiliation(s)
- Hasan Sbaihat
- Institute of Neuroscience and Medicine, INM-4, Jülich, Germany.,Department of Medical Imaging, Arab-American University Palestine (AAUP), Jenin, Palestine.,Department of Psychiatry, Psychotherapy, and Psychosomatics, RWTH Aachen University, Aachen, Germany
| | - Ravichandran Rajkumar
- Institute of Neuroscience and Medicine, INM-4, Jülich, Germany.,Department of Psychiatry, Psychotherapy, and Psychosomatics, RWTH Aachen University, Aachen, Germany.,JARA-BRAIN-Translational Medicine, Aachen, Germany
| | - Shukti Ramkiran
- Institute of Neuroscience and Medicine, INM-4, Jülich, Germany.,Department of Psychiatry, Psychotherapy, and Psychosomatics, RWTH Aachen University, Aachen, Germany.,JARA-BRAIN-Translational Medicine, Aachen, Germany
| | - Abed Al-Nasser Assi
- Department of Medical Imaging, Arab-American University Palestine (AAUP), Jenin, Palestine
| | - Jörg Felder
- Institute of Neuroscience and Medicine, INM-4, Jülich, Germany.,Department of Medical Imaging, Arab-American University Palestine (AAUP), Jenin, Palestine
| | - Nadim Jon Shah
- Institute of Neuroscience and Medicine, INM-4, Jülich, Germany.,JARA-BRAIN-Translational Medicine, Aachen, Germany.,Department of Neurology, RWTH Aachen University, Aachen, Germany.,Institute of Neuroscience and Medicine, INM-11, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Tanja Veselinović
- Department of Psychiatry, Psychotherapy, and Psychosomatics, RWTH Aachen University, Aachen, Germany
| | - Irene Neuner
- Institute of Neuroscience and Medicine, INM-4, Jülich, Germany.,Department of Psychiatry, Psychotherapy, and Psychosomatics, RWTH Aachen University, Aachen, Germany.,JARA-BRAIN-Translational Medicine, Aachen, Germany
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15
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Peng J, Debnath M, Biswas AK. Efficacy of novel Summation-based Synergetic Artificial Neural Network in ADHD diagnosis. MACHINE LEARNING WITH APPLICATIONS 2021. [DOI: 10.1016/j.mlwa.2021.100120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
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16
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Johnson CA, Garnett EO, Chow HM, Spray GJ, Zhu DC, Chang SE. Developmental Factors That Predict Head Movement During Resting-State Functional Magnetic Resonance Imaging in 3-7-Year-Old Stuttering and Non-stuttering Children. Front Neurosci 2021; 15:753010. [PMID: 34803590 PMCID: PMC8595248 DOI: 10.3389/fnins.2021.753010] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Accepted: 10/14/2021] [Indexed: 11/30/2022] Open
Abstract
Early childhood marks a period of dynamic neurocognitive development. Preschool-age coincides with the onset of many childhood disorders and is a developmental period that is frequently studied to determine markers of neurodevelopmental disorders. Magnetic resonance imaging (MRI) is often used to explore typical brain development and the neural bases of neurodevelopmental disorders. However, acquiring high-quality MRI data in young children is challenging. The enclosed space and loud sounds can trigger unease and cause excessive head movement. A better understanding of potential factors that predict successful MRI acquisition would increase chances of collecting useable data in children with and without neurodevelopmental disorders. We investigated whether age, sex, stuttering status, and childhood temperament as measured using the Child Behavioral Questionnaire, could predict movement extent during resting-state functional MRI (rs-fMRI) in 76 children aged 3–7 years, including 42 children who stutter (CWS). We found that age, sex, and temperament factors could predict motion during rs-fMRI scans. The CWS were not found to differ significantly from controls in temperament or head movement during scanning. Sex and age were significant predictors of movement. However, age was no longer a significant predictor when temperament, specifically effortful control, was considered. Controlling for age, boys with higher effortful control scores moved less during rs-fMRI procedures. Additionally, boys who showed higher negative affectivity showed a trend for greater movement. Considering temperament factors in addition to age and sex may help predict the success of acquiring useable rs-fMRI (and likely general brain MRI) data in young children in MR neuroimaging.
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Affiliation(s)
- Chelsea A Johnson
- Department of Communicative Sciences and Disorders, Michigan State University, East Lansing, MI, United States
| | - Emily O Garnett
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, United States
| | - Ho Ming Chow
- Department of Communication Sciences and Disorders, University of Delaware, Newark, DE, United States
| | - Gregory J Spray
- Department of Communicative Sciences and Disorders, Michigan State University, East Lansing, MI, United States
| | - David C Zhu
- Department of Radiology and Cognitive Imaging Research Center, Michigan State University, East Lansing, MI, United States
| | - Soo-Eun Chang
- Department of Communicative Sciences and Disorders, Michigan State University, East Lansing, MI, United States.,Department of Psychiatry, University of Michigan, Ann Arbor, MI, United States
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17
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Zhang S, Spoletini LJ, Gold BP, Morgan VL, Rogers BP, Chang C. Interindividual Signatures of fMRI Temporal Fluctuations. Cereb Cortex 2021; 31:4450-4463. [PMID: 33903915 PMCID: PMC8408464 DOI: 10.1093/cercor/bhab099] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 02/28/2021] [Accepted: 03/26/2021] [Indexed: 11/13/2022] Open
Abstract
The complexity and variability of human brain activity, such as quantified from Functional Magnetic Resonance Imaging (fMRI) time series, have been widely studied as potential markers of healthy and pathological states. However, the extent to which fMRI temporal features exhibit stable markers of inter-individual differences in brain function across healthy young adults is currently an open question. In this study, we draw upon two widely used time-series measures-a nonlinear complexity measure (sample entropy; SampEn) and a spectral measure of low-frequency content (fALFF)-to capture dynamic properties of resting-state fMRI in a large sample of young adults from the Human Connectome Project. We observe that these two measures are closely related, and that both generate reproducible patterns across brain regions over four different fMRI runs, with intra-class correlations of up to 0.8. Moreover, we find that both metrics can uniquely differentiate subjects with high identification rates (ca. 89%). Canonical correlation analysis revealed a significant relationship between multivariate brain temporal features and behavioral measures. Overall, these findings suggest that regional profiles of fMRI temporal characteristics may provide stable markers of individual differences, and motivate future studies to further probe relationships between fMRI time series metrics and behavior.
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Affiliation(s)
- Shengchao Zhang
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37212, USA
| | - Liam J Spoletini
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37212, USA
| | - Benjamin P Gold
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37212, USA
- Vanderbilt University, Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37212, USA
| | - Victoria L Morgan
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37212, USA
- Vanderbilt University, Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37212, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37212, USA
| | - Baxter P Rogers
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37212, USA
- Vanderbilt University, Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37212, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37212, USA
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN 37212, USA
| | - Catie Chang
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37212, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37212, USA
- Vanderbilt University, Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37212, USA
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18
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Wang Y, Hinds W, Duarte CS, Lee S, Monk C, Wall M, Canino G, Milani ACC, Jackowski A, Mamin MG, Foerster BU, Gingrich J, Weissman MM, Peterson BS, Semanek D, Perez EA, Labat E, Torres IB, Da Silva I, Parente C, Abdala N, Posner J. Intra-session test-retest reliability of functional connectivity in infants. Neuroimage 2021; 239:118284. [PMID: 34147630 PMCID: PMC8335644 DOI: 10.1016/j.neuroimage.2021.118284] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2020] [Revised: 06/11/2021] [Accepted: 06/16/2021] [Indexed: 12/01/2022] Open
Abstract
Resting functional MRI studies of the infant brain are increasingly becoming an important tool in developmental neuroscience. Whereas the test-retest reliability of functional connectivity (FC) measures derived from resting fMRI data have been characterized in the adult and child brain, similar assessments have not been conducted in infants. In this study, we examined the intra-session test-retest reliability of FC measures from 119 infant brain MRI scans from four neurodevelopmental studies. We investigated edge-level and subject-level reliability within one MRI session (between and within runs) measured by the Intraclass correlation coefficient (ICC). First, using an atlas-based approach, we examined whole-brain connectivity as well as connectivity within two common resting fMRI networks - the default mode network (DMN) and the sensorimotor network (SMN). Second, we examined the influence of run duration, study site, and scanning manufacturer (e.g., Philips and General Electric) on ICCs. Lastly, we tested spatial similarity using the Jaccard Index from networks derived from independent component analysis (ICA). Consistent with resting fMRI studies from adults, our findings indicated poor edge-level reliability (ICC = 0.14-0.18), but moderate-to-good subject-level intra-session reliability for whole-brain, DMN, and SMN connectivity (ICC = 0.40-0.78). We also found significant effects of run duration, site, and scanning manufacturer on reliability estimates. Some ICA-derived networks showed strong spatial reproducibility (e.g., DMN, SMN, and Visual Network), and were labelled based on their spatial similarity to analogous networks measured in adults. These networks were reproducibly found across different study sites. However, other ICA-networks (e.g. Executive Control Network) did not show strong spatial reproducibility, suggesting that the reliability and/or maturational course of functional connectivity may vary by network. In sum, our findings suggest that developmental scientists may be on safe ground examining the functional organization of some major neural networks (e.g. DMN and SMN), but judicious interpretation of functional connectivity is essential to its ongoing success.
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Affiliation(s)
- Yun Wang
- Department of Psychiatry, Columbia University Medical Center, New York, NY, USA
| | - Walter Hinds
- Department of Psychiatry, Columbia University Medical Center, New York, NY, USA
| | - Cristiane S Duarte
- Department of Psychiatry, Columbia University Medical Center, New York, NY, USA
| | - Seonjoo Lee
- Department of Psychiatry, Columbia University Medical Center, New York, NY, USA; Mental Health Data Science, New York State Psychiatric Institute, New York, NY, USA
| | - Catherine Monk
- Department of Obstetrics and Gynecology, New York State Psychiatric Institute, New York, NY, USA
| | - Melanie Wall
- Department of Psychiatry, Columbia University Medical Center, New York, NY, USA; Mental Health Data Science, New York State Psychiatric Institute, New York, NY, USA
| | - Glorisa Canino
- School of Medicine, Medical Science Campus, University of Puerto Rico, San Juan, PR, USA
| | | | - Andrea Jackowski
- Interdisciplinary Lab for Clinical Neurosciences, Federal University of Sao Paulo, Sao Paulo, Brazil
| | | | - Bernd U Foerster
- Department of Psychiatry, Federal University of Sao Paulo, Sao Paulo, Brazil
| | - Jay Gingrich
- Department of Psychiatry, Columbia University Medical Center, New York, NY, USA
| | - Myrna M Weissman
- Department of Psychiatry, Columbia University Medical Center, New York, NY, USA; Department of Obstetrics and Gynecology, New York State Psychiatric Institute, New York, NY, USA
| | - Bradley S Peterson
- Institute for the Developing Mind, The Saban Research Institute, Children's Hospital Los Angeles, CA, USA
| | - David Semanek
- Department of Psychiatry, Columbia University Medical Center, New York, NY, USA
| | - Edna Acosta Perez
- School of Medicine, Medical Science Campus, University of Puerto Rico, San Juan, PR, USA; Graduate School of Public Health, Medical Science Campus, University of Puerto Rico, San Juan, PR, USA
| | - Eduardo Labat
- School of Medicine, Medical Science Campus, University of Puerto Rico, San Juan, PR, USA
| | - Ioannisely Berrios Torres
- Behavioral Science Research Insitute, Academic Deanship, Medical Science Campus, University of Puerto Rico, San Juan, PR, USA
| | - Ivaldo Da Silva
- Department of Gynecology, Federal University of Sao Paulo, Sao Paulo, Brazil
| | - Camila Parente
- Department of Gynecology, Federal University of Sao Paulo, Sao Paulo, Brazil
| | - Nitamar Abdala
- Department of Diagnostic Radiology, Federal University of Sao Paulo, Sao Paulo, Brazil
| | - Jonathan Posner
- Department of Psychiatry, Columbia University Medical Center, New York, NY, USA.
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19
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Ma Y, MacDonald A. "Impact of ICA Dimensionality on the Test-Retest Reliability of Resting-State Functional Connectivity. Brain Connect 2021; 11:875-886. [PMID: 33926215 DOI: 10.1089/brain.2020.0970] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
As resting-state functional connectivity (rsFC) research moves toward the study of individual differences, test-retest reliability is increasingly important to understand. Previous literature supports the test-retest reliability of rsFC derived with independent component analysis (ICA) and dual regression, yet the impact of dimensionality (i.e., the number of components to extract from group-ICA) remained obscure in the current context of large-scale datasets. To provide principled guidelines on this issue, ICA at dimensionalities varying from 25 to 350 was applied to the cortical surface with resting-state functional magnetic resonance imaging data from 1003 participants in the Human Connectome Project. The reliability of two rsFC measures: (within-component) coherence and (between-component) connectivity was estimated. Reliability and its change with dimensionality varied by network: the cognitive (frontoparietal, cingulo-opercular, dorsal attention, and default) networks were measured with the highest reliability which improved with increased dimensionality until at least 150; the visual and somatomotor networks were measured with lower reliability which benefited mildly from increased dimensionality; the temporal pole/orbitofrontal cortex (TP/OFC) network was measured with the lowest reliability. Overall, ICA reliability was optimized at dimensionalities of 150 or above. Compared with two popular binary, non-overlapping cortical atlases, ICA and dual regression resulted in higher reliability for the cognitive networks, lower reliability for the somatomotor network, and similar reliability for the visual and TP/OFC networks. These findings highlight analytical decisions that maximize the reliability of rsFC measures and how they depend on one's networks of interest.
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Affiliation(s)
- Yizhou Ma
- University of Minnesota Twin Cities, 5635, Psychology, Minneapolis, Minnesota, United States;
| | - Angus MacDonald
- University of Minnesota Twin Cities, 5635, Psychology, N219 Elliot Hall 75 E. River Rd., Minneapolis, Minnesota, United States, 55455.,N219 Elliot Hall 75 E. River Rd.Minneapolis, Minnesota, United States, 55455;
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20
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Functional connectivity patterns predict naturalistic viewing versus rest across development. Neuroimage 2021; 229:117630. [PMID: 33401011 DOI: 10.1016/j.neuroimage.2020.117630] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2019] [Revised: 10/16/2020] [Accepted: 12/07/2020] [Indexed: 11/22/2022] Open
Abstract
Cognitive states, such as rest and task engagement, share an 'intrinsic' functional network organization that is subject to minimal variation over time and yields stable signatures within an individual. Importantly, there are also transient state-specific functional connectivity (FC) patterns that vary across neural states. Here, we examine functional brain organization differences that underlie distinct states in a cross-sectional developmental sample. We compare FC fMRI data acquired during naturalistic viewing (i.e., movie-watching) and resting-state paradigms in a large cohort of 157 children and young adults aged 6-20. Naturalistic paradigms are commonly implemented in pediatric research because they maintain the child's attention and contribute to reduced head motion. It remains unknown, however, to what extent the brain-wide functional network organization is comparable during movie-watching and rest across development. Here, we identify a widespread FC pattern that predicts whether individuals are watching a movie or resting. Specifically, we develop a model for prediction of multilevel neural effects (termed PrimeNet), which can with high reliability distinguish between movie-watching and rest irrespective of age and that generalizes across movies. In turn, we characterize FC patterns in the most predictive functional networks for movie-watching versus rest and show that these patterns can indeed vary as a function of development. Collectively, these effects highlight a 'core' FC pattern that is robustly associated with naturalistic viewing, which also exhibits change across age. These results, focused here on naturalistic viewing, provide a roadmap for quantifying state-specific functional neural organization across development, which may reveal key variation in neurodevelopmental trajectories associated with behavioral phenotypes.
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21
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Lanka P, Rangaprakash D, Dretsch MN, Katz JS, Denney TS, Deshpande G. Supervised machine learning for diagnostic classification from large-scale neuroimaging datasets. Brain Imaging Behav 2020; 14:2378-2416. [PMID: 31691160 PMCID: PMC7198352 DOI: 10.1007/s11682-019-00191-8] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
There are growing concerns about the generalizability of machine learning classifiers in neuroimaging. In order to evaluate this aspect across relatively large heterogeneous populations, we investigated four disorders: Autism spectrum disorder (N = 988), Attention deficit hyperactivity disorder (N = 930), Post-traumatic stress disorder (N = 87) and Alzheimer's disease (N = 132). We applied 18 different machine learning classifiers (based on diverse principles) wherein the training/validation and the hold-out test data belonged to samples with the same diagnosis but differing in either the age range or the acquisition site. Our results indicate that overfitting can be a huge problem in heterogeneous datasets, especially with fewer samples, leading to inflated measures of accuracy that fail to generalize well to the general clinical population. Further, different classifiers tended to perform well on different datasets. In order to address this, we propose a consensus-classifier by combining the predictive power of all 18 classifiers. The consensus-classifier was less sensitive to unmatched training/validation and holdout test data. Finally, we combined feature importance scores obtained from all classifiers to infer the discriminative ability of connectivity features. The functional connectivity patterns thus identified were robust to the classification algorithm used, age and acquisition site differences, and had diagnostic predictive ability in addition to univariate statistically significant group differences between the groups. A MATLAB toolbox called Machine Learning in NeuroImaging (MALINI), which implements all the 18 different classifiers along with the consensus classifier is available from Lanka et al. (2019) The toolbox can also be found at the following URL: https://github.com/pradlanka/malini .
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Affiliation(s)
- Pradyumna Lanka
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, 560 Devall Dr., Suite 266D, Auburn, AL, 36849, USA
- Department of Psychological Sciences, University of California Merced, Merced, CA, USA
| | - D Rangaprakash
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, 560 Devall Dr., Suite 266D, Auburn, AL, 36849, USA
- Departments of Radiology and Biomedical Engineering, Northwestern University, Chicago, IL, USA
| | - Michael N Dretsch
- U.S. Army Aeromedical Research Laboratory, Fort Rucker, AL, USA
- US Army Medical Research Directorate-West, Walter Reed Army Institute for Research, Joint Base Lewis-McCord, WA, USA
- Department of Psychology, Auburn University, Auburn, AL, USA
| | - Jeffrey S Katz
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, 560 Devall Dr., Suite 266D, Auburn, AL, 36849, USA
- Department of Psychology, Auburn University, Auburn, AL, USA
- Alabama Advanced Imaging Consortium, Birmingham, AL, USA
- Center for Neuroscience, Auburn University, Auburn, AL, USA
| | - Thomas S Denney
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, 560 Devall Dr., Suite 266D, Auburn, AL, 36849, USA
- Department of Psychology, Auburn University, Auburn, AL, USA
- Alabama Advanced Imaging Consortium, Birmingham, AL, USA
- Center for Neuroscience, Auburn University, Auburn, AL, USA
| | - Gopikrishna Deshpande
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, 560 Devall Dr., Suite 266D, Auburn, AL, 36849, USA.
- Department of Psychology, Auburn University, Auburn, AL, USA.
- Alabama Advanced Imaging Consortium, Birmingham, AL, USA.
- Center for Neuroscience, Auburn University, Auburn, AL, USA.
- Center for Health Ecology and Equity Research, Auburn University, Auburn, AL, USA.
- Department of Psychiatry, National Institute of Mental and Neurosciences, Bangalore, India.
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22
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Vanderwal T, Eilbott J, Kelly C, Frew SR, Woodward TS, Milham MP, Castellanos FX. Stability and similarity of the pediatric connectome as developmental measures. Neuroimage 2020; 226:117537. [PMID: 33186720 DOI: 10.1016/j.neuroimage.2020.117537] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2019] [Revised: 11/02/2020] [Accepted: 11/05/2020] [Indexed: 01/04/2023] Open
Abstract
Patterns of functional connectivity are unique at the individual level, enabling test-retest matching algorithms to identify a subject from among a group using only their functional connectome. Recent findings show that accuracies of these algorithms in children increase with age. Relatedly, the persistence of functional connectivity (FC) patterns across tasks and rest also increases with age. This study investigated the hypothesis that within-subject stability and between-subject similarity of the whole-brain pediatric connectome are developmentally relevant outcomes. Using data from 210 help-seeking children and adolescents, ages 6-21 years (Healthy Brain Network Biobank), we computed whole-brain FC matrices for each participant during two different movies (MovieDM and MovieTP) and two runs of task-free rest (all from a single scan session) and fed these matrices to a test-retest matching algorithm. We replicated the finding that matching accuracies for children and youth (ages 6-21 years) are low (18-44%), and that cross-state and cross-movie accuracies were the lowest. Results also showed that parcellation resolution and the number of volumes used in each matrix affect fingerprinting accuracies. Next, we calculated three measures of whole-connectome stability for each subject: cross-rest (Rest1-Rest2), cross-state (MovieDM-Rest1), and cross-movie (MovieDM-MovieTP), and three measures of within-state between-subject connectome similarity for Rest1, MovieDM, and MovieTP. We show that stability and similarity were correlated, but that these measures were not related to age. A principal component analysis of these measures yielded two components that we used to test for brain-behavior correlations with IQ, general psychopathology, and social skills measures (n = 119). The first component was significantly correlated with the social skills measure (r=-0.26, p = 0.005). Post hoc correlations showed that the social skills measure correlated with both cross-rest stability (r=-0.29, p = 0.001) and with connectome similarity during MovieDM (r=-0.28, p = 0.002). These findings suggest that the stability and similarity of the whole-brain connectome relate to the development of social skills. We infer that the maturation of the functional connectome simultaneously achieves patterns of FC that are distinct at the individual subject level, that are shared across individuals, and that are persistent across states and across runs-features which presumably combine to optimize neural processing during development. Future longitudinal work could reveal the developmental trajectories of stability and similarity of the connectome.
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Affiliation(s)
- Tamara Vanderwal
- Department of Psychiatry, University of British Columbia, 2255 Wesbrook Mall, Vancouver V6T 2A1, BC, Canada; BC Children's Hospital Research Institute, Vancouver, BC, Canada.
| | - Jeffrey Eilbott
- BC Children's Hospital Research Institute, Vancouver, BC, Canada
| | - Clare Kelly
- Department of Psychology, Trinity College Dublin, Dublin, Ireland; Department of Psychiatry at the School of Medicine, Trinity College Dublin, Dublin, Ireland; Trinity College Institute of Neuroscience, Trinity College, Dublin, Ireland
| | - Simon R Frew
- BC Children's Hospital Research Institute, Vancouver, BC, Canada
| | - Todd S Woodward
- Department of Psychiatry, University of British Columbia, 2255 Wesbrook Mall, Vancouver V6T 2A1, BC, Canada; Behavioral & Cognitive Neuroscience Institute, Vancouver, BC, Canada
| | - Michael P Milham
- Center for the Developing Brain, Child Mind Institute, New York, NY, USA; Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, New Orangeburg, NY, USA
| | - F Xavier Castellanos
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, New Orangeburg, NY, USA; Department of Child and Adolescent Psychiatry, NYU Grossman School of Medicine, New York, NY, USA
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23
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Gur RC, Butler ER, Moore TM, Rosen AFG, Ruparel K, Satterthwaite TD, Roalf DR, Gennatas ED, Bilker WB, Shinohara RT, Port A, Elliott MA, Verma R, Davatzikos C, Wolf DH, Detre JA, Gur RE. Structural and Functional Brain Parameters Related to Cognitive Performance Across Development: Replication and Extension of the Parieto-Frontal Integration Theory in a Single Sample. Cereb Cortex 2020; 31:1444-1463. [PMID: 33119049 DOI: 10.1093/cercor/bhaa282] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2019] [Revised: 07/16/2020] [Accepted: 08/24/2020] [Indexed: 02/06/2023] Open
Abstract
The parieto-frontal integration theory (PFIT) identified a fronto-parietal network of regions where individual differences in brain parameters most strongly relate to cognitive performance. PFIT was supported and extended in adult samples, but not in youths or within single-scanner well-powered multimodal studies. We performed multimodal neuroimaging in 1601 youths age 8-22 on the same 3-Tesla scanner with contemporaneous neurocognitive assessment, measuring volume, gray matter density (GMD), mean diffusivity (MD), cerebral blood flow (CBF), resting-state functional magnetic resonance imaging measures of the amplitude of low frequency fluctuations (ALFFs) and regional homogeneity (ReHo), and activation to a working memory and a social cognition task. Across age and sex groups, better performance was associated with higher volumes, greater GMD, lower MD, lower CBF, higher ALFF and ReHo, and greater activation for the working memory task in PFIT regions. However, additional cortical, striatal, limbic, and cerebellar regions showed comparable effects, hence PFIT needs expansion into an extended PFIT (ExtPFIT) network incorporating nodes that support motivation and affect. Associations of brain parameters became stronger with advancing age group from childhood to adolescence to young adulthood, effects occurring earlier in females. This ExtPFIT network is developmentally fine-tuned, optimizing abundance and integrity of neural tissue while maintaining a low resting energy state.
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Affiliation(s)
- Ruben C Gur
- Brain Behavior Laboratory and the Neurodevelopment and Psychosis Section, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.,Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.,Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Ellyn R Butler
- Brain Behavior Laboratory and the Neurodevelopment and Psychosis Section, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Tyler M Moore
- Brain Behavior Laboratory and the Neurodevelopment and Psychosis Section, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Adon F G Rosen
- Brain Behavior Laboratory and the Neurodevelopment and Psychosis Section, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Kosha Ruparel
- Brain Behavior Laboratory and the Neurodevelopment and Psychosis Section, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Theodore D Satterthwaite
- Brain Behavior Laboratory and the Neurodevelopment and Psychosis Section, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - David R Roalf
- Brain Behavior Laboratory and the Neurodevelopment and Psychosis Section, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Efstathios D Gennatas
- Brain Behavior Laboratory and the Neurodevelopment and Psychosis Section, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Warren B Bilker
- Brain Behavior Laboratory and the Neurodevelopment and Psychosis Section, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.,Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Russell T Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Allison Port
- Brain Behavior Laboratory and the Neurodevelopment and Psychosis Section, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Mark A Elliott
- Brain Behavior Laboratory and the Neurodevelopment and Psychosis Section, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.,Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Ragini Verma
- Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Christos Davatzikos
- Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Daniel H Wolf
- Brain Behavior Laboratory and the Neurodevelopment and Psychosis Section, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - John A Detre
- Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Raquel E Gur
- Brain Behavior Laboratory and the Neurodevelopment and Psychosis Section, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.,Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.,Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
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24
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Drobinin V, Van Gestel H, Helmick CA, Schmidt MH, Bowen CV, Uher R. Reliability of multimodal MRI brain measures in youth at risk for mental illness. Brain Behav 2020; 10:e01609. [PMID: 32304355 PMCID: PMC7303399 DOI: 10.1002/brb3.1609] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/08/2019] [Revised: 03/01/2020] [Accepted: 03/03/2020] [Indexed: 12/14/2022] Open
Abstract
INTRODUCTION A new generation of large-scale studies is using neuroimaging to investigate adolescent brain development across health and disease. However, imaging artifacts such as head motion remain a challenge and may be exacerbated in pediatric clinical samples. In this study, we assessed the scan-rescan reliability of multimodal MRI in a sample of youth enriched for risk of mental illness. METHODS We obtained repeated MRI scans, an average of 2.7 ± 1.4 weeks apart, from 50 youth (mean age 14.7 years, SD = 4.4). Half of the sample (52%) had a diagnosis of an anxiety disorder; 22% had attention-deficit/hyperactivity disorder (ADHD). We quantified reliability with the test-retest intraclass correlation coefficient (ICC). RESULTS Gray matter measurements were highly reliable with mean ICCs as follows: cortical volume (ICC = 0.90), cortical surface area (ICC = 0.89), cortical thickness (ICC = 0.82), and local gyrification index (ICC = 0.85). White matter volume reliability was excellent (ICC = 0.98). Diffusion tensor imaging (DTI) components were also highly reliable. Fractional anisotropy was most consistently measured (ICC = 0.88), followed by radial diffusivity (ICC = 0.84), mean diffusivity (ICC = 0.81), and axial diffusivity (ICC = 0.78). We also observed regional variability in reconstruction, with some brain structures less reliably reconstructed than others. CONCLUSIONS Overall, we showed that developmental MRI measures are highly reliable, even in youth at risk for mental illness and those already affected by anxiety and neurodevelopmental disorders. Yet, caution is warranted if patterns of results cluster within regions of lower reliability.
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Affiliation(s)
- Vladislav Drobinin
- Department of Medical Neuroscience, Dalhousie University, Halifax, NS, Canada.,Nova Scotia Health Authority, Halifax, NS, Canada
| | | | - Carl A Helmick
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
| | - Matthias H Schmidt
- Department of Diagnostic Radiology, Dalhousie University, Halifax, NS, Canada
| | - Chris V Bowen
- Department of Diagnostic Radiology, Dalhousie University, Halifax, NS, Canada
| | - Rudolf Uher
- Department of Medical Neuroscience, Dalhousie University, Halifax, NS, Canada.,Nova Scotia Health Authority, Halifax, NS, Canada.,Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
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25
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Topological Data Analysis Reveals Robust Alterations in the Whole-Brain and Frontal Lobe Functional Connectomes in Attention-Deficit/Hyperactivity Disorder. eNeuro 2020; 7:ENEURO.0543-19.2020. [PMID: 32317343 PMCID: PMC7221355 DOI: 10.1523/eneuro.0543-19.2020] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Revised: 03/04/2020] [Accepted: 04/02/2020] [Indexed: 11/21/2022] Open
Abstract
Attention-deficit/hyperactivity disorder (ADHD) is a developmental disorder characterized by difficulty to control the own behavior. Neuroimaging studies have related ADHD with the interplay of fronto-parietal attention systems with the default mode network (DMN; Castellanos and Aoki, 2016). However, some results have been inconsistent, potentially due to methodological differences in the analytical strategies when defining the brain functional network, i.e., the functional connectivity threshold and/or the brain parcellation scheme. Here, we make use of topological data analysis (TDA) to explore the brain connectome as a function of the filtration value (i.e., the connectivity threshold), instead of using a static connectivity threshold. Specifically, we characterized the transition from all nodes being isolated to being connected into a single component as a function of the filtration value. We explored the utility of such a method to identify differences between 81 children with ADHD (45 male, age: 7.26–17.61 years old) and 96 typically developing children (TDC; 59 male, age: 7.17–17.96 years old), using a public dataset of resting state (rs)fMRI in human subjects. Results were highly congruent when using four different brain segmentations (atlases), and exhibited significant differences for the brain topology of children with ADHD, both at the whole-brain network and the functional subnetwork levels, particularly involving the frontal lobe and the DMN. Therefore, this is a solid approach that complements connectomics-related methods and may contribute to identify the neurophysio-pathology of ADHD.
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26
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Xu M, Reiss PT, Cribben I. Generalized reliability based on distances. Biometrics 2020; 77:258-270. [PMID: 32339252 PMCID: PMC7984087 DOI: 10.1111/biom.13287] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2019] [Revised: 04/07/2020] [Accepted: 04/10/2020] [Indexed: 12/30/2022]
Abstract
The intraclass correlation coefficient (ICC) is a classical index of measurement reliability. With the advent of new and complex types of data for which the ICC is not defined, there is a need for new ways to assess reliability. To meet this need, we propose a new distance-based ICC (dbICC), defined in terms of arbitrary distances among observations. We introduce a bias correction to improve the coverage of bootstrap confidence intervals for the dbICC, and demonstrate its efficacy via simulation. We illustrate the proposed method by analyzing the test-retest reliability of brain connectivity matrices derived from a set of repeated functional magnetic resonance imaging scans. The Spearman-Brown formula, which shows how more intensive measurement increases reliability, is extended to encompass the dbICC.
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Affiliation(s)
- Meng Xu
- Department of Statistics, University of Haifa, Haifa, Israel
| | - Philip T Reiss
- Department of Statistics, University of Haifa, Haifa, Israel
| | - Ivor Cribben
- Department of Accounting, Operations, and Information Systems, University of Alberta School of Business, Edmonton, Canada
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27
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Li X, Pan Y, Fang Z, Lei H, Zhang X, Shi H, Ma N, Raine P, Wetherill R, Kim JJ, Wan Y, Rao H. Test-retest reliability of brain responses to risk-taking during the balloon analogue risk task. Neuroimage 2019; 209:116495. [PMID: 31887425 PMCID: PMC7061333 DOI: 10.1016/j.neuroimage.2019.116495] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Revised: 12/20/2019] [Accepted: 12/23/2019] [Indexed: 12/24/2022] Open
Abstract
The Balloon Analogue Risk Task (BART) provides a reliable and ecologically valid model for the assessment of individual risk-taking propensity and is frequently used in neuroimaging and developmental research. Although the test-retest reliability of risk-taking behavior during the BART is well established, the reliability of brain activation patterns in response to risk-taking during the BART remains elusive. In this study, we used functional magnetic resonance imaging (fMRI) and evaluated the test-retest reliability of brain responses in 34 healthy adults during a modified BART by calculating the intraclass correlation coefficients (ICC) and Dice’s similarity coefficients (DSC). Analyses revealed that risk-induced brain activation patterns showed good test-retest reliability (median ICC = 0.62) and moderate to high spatial consistency, while brain activation patterns associated with win or loss outcomes only had poor to fair reliability (median ICC = 0.33 for win and 0.42 for loss). These findings have important implications for future utility of the BART in fMRI to examine brain responses to risk-taking and decision-making.
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Affiliation(s)
- Xiong Li
- School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, China
| | - Yu Pan
- School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, China; Key Laboratory of Applied Brain and Cognitive Sciences, School of Business and Management, Shanghai International Studies University, Shanghai, China
| | - Zhuo Fang
- Key Laboratory of Applied Brain and Cognitive Sciences, School of Business and Management, Shanghai International Studies University, Shanghai, China; Center for Functional Neuroimaging, Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Hui Lei
- Center for Functional Neuroimaging, Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Xiaocui Zhang
- Center for Functional Neuroimaging, Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Hui Shi
- Center for Functional Neuroimaging, Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Ning Ma
- Center for Functional Neuroimaging, Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Philip Raine
- Center for Functional Neuroimaging, Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Reagan Wetherill
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Junghoon J Kim
- Department of Molecular, Cellular, and Biomedical Sciences, CUNY School of Medicine, The City College of New York, New York, NY, USA
| | - Yan Wan
- School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, China
| | - Hengyi Rao
- Key Laboratory of Applied Brain and Cognitive Sciences, School of Business and Management, Shanghai International Studies University, Shanghai, China; Center for Functional Neuroimaging, Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
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28
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Noble S, Scheinost D, Constable RT. A decade of test-retest reliability of functional connectivity: A systematic review and meta-analysis. Neuroimage 2019; 203:116157. [PMID: 31494250 PMCID: PMC6907736 DOI: 10.1016/j.neuroimage.2019.116157] [Citation(s) in RCA: 283] [Impact Index Per Article: 56.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2018] [Revised: 08/30/2019] [Accepted: 09/02/2019] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND Once considered mere noise, fMRI-based functional connectivity has become a major neuroscience tool in part due to early studies demonstrating its reliability. These fundamental studies revealed only the tip of the iceberg; over the past decade, many test-retest reliability studies have continued to add nuance to our understanding of this complex topic. A summary of these diverse and at times contradictory perspectives is needed. OBJECTIVES We aimed to summarize the existing knowledge regarding test-retest reliability of functional connectivity at the most basic unit of analysis: the individual edge level. This entailed (1) a meta-analytic estimate of reliability and (2) a review of factors influencing reliability. METHODS A search of Scopus was conducted to identify studies that estimated edge-level test-retest reliability. To facilitate comparisons across studies, eligibility was restricted to studies measuring reliability via the intraclass correlation coefficient (ICC). The meta-analysis included a random effects pooled estimate of mean edge-level ICC, with studies nested within datasets. The review included a narrative summary of factors influencing edge-level ICC. RESULTS From an initial pool of 212 studies, 44 studies were identified for the qualitative review and 25 studies for quantitative meta-analysis. On average, individual edges exhibited a "poor" ICC of 0.29 (95% CI = 0.23 to 0.36). The most reliable measurements tended to involve: (1) stronger, within-network, cortical edges, (2) eyes open, awake, and active recordings, (3) more within-subject data, (4) shorter test-retest intervals, (5) no artifact correction (likely due in part to reliable artifact), and (6) full correlation-based connectivity with shrinkage. CONCLUSION This study represents the first meta-analysis and systematic review investigating test-retest reliability of edge-level functional connectivity. Key findings suggest there is room for improvement, but care should be taken to avoid promoting reliability at the expense of validity. By pooling existing knowledge regarding this key facet of accuracy, this study supports broader efforts to improve inferences in the field.
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Affiliation(s)
- Stephanie Noble
- Interdepartmental Neuroscience Program, Yale University, USA.
| | - Dustin Scheinost
- Interdepartmental Neuroscience Program, Yale University, USA; Department of Radiology and Biomedical Imaging, Yale School of Medicine, USA; Department of Statistics and Data Science, Yale University, USA; Child Study Center, Yale School of Medicine, USA
| | - R Todd Constable
- Interdepartmental Neuroscience Program, Yale University, USA; Department of Radiology and Biomedical Imaging, Yale School of Medicine, USA; Department of Neurosurgery, Yale School of Medicine, USA
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29
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Kuo PC, Tseng YL, Zilles K, Suen S, Eickhoff SB, Lee JD, Cheng PE, Liou M. Brain dynamics and connectivity networks under natural auditory stimulation. Neuroimage 2019; 202:116042. [PMID: 31344485 DOI: 10.1016/j.neuroimage.2019.116042] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2019] [Revised: 07/17/2019] [Accepted: 07/20/2019] [Indexed: 02/03/2023] Open
Abstract
The analysis of functional magnetic resonance imaging (fMRI) data is challenging when subjects are under exposure to natural sensory stimulation. In this study, a two-stage approach was developed to enable the identification of connectivity networks involved in the processing of information in the brain under natural sensory stimulation. In the first stage, the degree of concordance between the results of inter-subject and intra-subject correlation analyses is assessed statistically. The microstructurally (i.e., cytoarchitectonically) defined brain areas are designated either as concordant in which the results of both correlation analyses are in agreement, or as discordant in which one analysis method shows a higher proportion of supra-threshold voxels than does the other. In the second stage, connectivity networks are identified using the time courses of supra-threshold voxels in brain areas contingent upon the classifications derived in the first stage. In an empirical study, fMRI data were collected from 40 young adults (19 males, average age 22.76 ± 3.25), who underwent auditory stimulation involving sound clips of human voices and animal vocalizations under two operational conditions (i.e., eyes-closed and eyes-open). The operational conditions were designed to assess confounding effects due to auditory instructions or visual perception. The proposed two-stage analysis demonstrated that stress modulation (affective) and language networks in the limbic and cortical structures were respectively engaged during sound stimulation, and presented considerable variability among subjects. The network involved in regulating visuomotor control was sensitive to the eyes-open instruction, and presented only small variations among subjects. A high degree of concordance was observed between the two analyses in the primary auditory cortex which was highly sensitive to the pitch of sound clips. Our results have indicated that brain areas can be identified as concordant or discordant based on the two correlation analyses. This may further facilitate the search for connectivity networks involved in the processing of information under natural sensory stimulation.
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Affiliation(s)
- Po-Chih Kuo
- Institute of Statistical Science, Academia Sinica, Taipei, Taiwan
| | - Yi-Li Tseng
- Department of Electrical Engineering, Fu Jen Catholic University, New Taipei City, Taiwan
| | - Karl Zilles
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
| | - Summit Suen
- Institute of Statistical Science, Academia Sinica, Taipei, Taiwan
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine (INM-7), Research Centre Jülich, Jülich, Germany
| | - Juin-Der Lee
- Graduate Institute of Business Administration, National Chengchi University, Taipei, Taiwan
| | - Philip E Cheng
- Institute of Statistical Science, Academia Sinica, Taipei, Taiwan
| | - Michelle Liou
- Institute of Statistical Science, Academia Sinica, Taipei, Taiwan.
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30
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Kaiser RH, Treadway MT, Wooten DW, Kumar P, Goer F, Murray L, Beltzer M, Pechtel P, Whitton A, Cohen AL, Alpert NM, El Fakhri G, Normandin MD, Pizzagalli DA. Frontostriatal and Dopamine Markers of Individual Differences in Reinforcement Learning: A Multi-modal Investigation. Cereb Cortex 2019; 28:4281-4290. [PMID: 29121332 DOI: 10.1093/cercor/bhx281] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2017] [Indexed: 01/07/2023] Open
Abstract
Prior studies have shown that dopamine (DA) functioning in frontostriatal circuits supports reinforcement learning (RL), as phasic DA activity in ventral striatum signals unexpected reward and may drive coordinated activity of striatal and orbitofrontal regions that support updating of action plans. However, the nature of DA functioning in RL is complex, in particular regarding the role of DA clearance in RL behavior. Here, in a multi-modal neuroimaging study with healthy adults, we took an individual differences approach to the examination of RL behavior and DA clearance mechanisms in frontostriatal learning networks. We predicted that better RL would be associated with decreased striatal DA transporter (DAT) availability and increased intrinsic functional connectivity among DA-rich frontostriatal regions. In support of these predictions, individual differences in RL behavior were related to DAT binding potential in ventral striatum and resting-state functional connectivity between ventral striatum and orbitofrontal cortex. Critically, DAT binding potential had an indirect effect on reinforcement learning behavior through frontostriatal connectivity, suggesting potential causal relationships across levels of neurocognitive functioning. These data suggest that individual differences in DA clearance and frontostriatal coordination may serve as markers for RL, and suggest directions for research on psychopathologies characterized by altered RL.
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Affiliation(s)
- Roselinde H Kaiser
- Department of Psychiatry, Harvard Medical School, and Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, USA.,Department of Psychology, University of California Los Angeles, CA, USA
| | - Michael T Treadway
- Department of Psychiatry, Harvard Medical School, and Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, USA.,Department of Psychology, Emory University, Atlanta, GA, USA
| | - Dustin W Wooten
- Department of Radiology, Gordon Center for Medical Imaging, Nuclear Medicine and Molecular Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Poornima Kumar
- Department of Psychiatry, Harvard Medical School, and Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, USA
| | - Franziska Goer
- Department of Psychiatry, Harvard Medical School, and Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, USA
| | - Laura Murray
- Department of Psychiatry, Harvard Medical School, and Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, USA
| | - Miranda Beltzer
- Department of Psychiatry, Harvard Medical School, and Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, USA
| | - Pia Pechtel
- Department of Psychiatry, Harvard Medical School, and Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, USA
| | - Alexis Whitton
- Department of Psychiatry, Harvard Medical School, and Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, USA
| | - Andrew L Cohen
- Department of Psychiatry, Harvard Medical School, and Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, USA
| | | | | | | | - Diego A Pizzagalli
- Department of Psychiatry, Harvard Medical School, and Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, USA
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31
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Wang S, Zhao Y, Zhang L, Wang X, Wang X, Cheng B, Luo K, Gong Q. Stress and the brain: Perceived stress mediates the impact of the superior frontal gyrus spontaneous activity on depressive symptoms in late adolescence. Hum Brain Mapp 2019; 40:4982-4993. [PMID: 31397949 DOI: 10.1002/hbm.24752] [Citation(s) in RCA: 72] [Impact Index Per Article: 14.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2018] [Revised: 03/11/2019] [Accepted: 07/25/2019] [Indexed: 02/05/2023] Open
Abstract
Identifying factors for the prediction of depression is a long-standing research topic in psychiatry and psychology. Perceived stress, which reflects the tendency to appraise one's life situations as stressful and overwhelming, has emerged as a stable predictor for depressive symptoms. However, the neurobiological bases of perceived stress and how perceived stress influences depressive symptoms in the healthy brain remain largely unknown. Here, we investigated these issues in 217 healthy adolescents by estimating the fractional amplitude of low-frequency fluctuations (fALFFs) via resting-state functional magnetic resonance imaging. A whole-brain correlation analysis showed that higher levels of perceived stress were associated with greater fALFF in the left superior frontal gyrus (SFG), which is a core brain region for cognitive control and emotion regulation-related processes. Mediation analysis further indicated that perceived stress mediated the link between the fALFF in the left SFG and depressive symptoms. Importantly, our results remained significant even when excluding the influences of head motion, anxiety, SFG gray matter structure, and school environment. Altogether, our findings suggested that the fALFF in the left SFG is a neurofunctional marker of perceived stress in adolescents and revealed a potential indirect effect of perceived stress on the association between the SFG spontaneous activity and depressive symptoms.
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Affiliation(s)
- Song Wang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China.,Department of Psychoradiology, Chengdu Mental Health Center, Chengdu, China.,Psychoradiology Research Unit of Chinese Academy of Medical Sciences (2018RU011), West China Hospital of Sichuan University, Chengdu, China
| | - Yajun Zhao
- School of Sociology and Psychology, Southwest Minzu University, Chengdu, China
| | - Lei Zhang
- Psychoradiology Research Unit of Chinese Academy of Medical Sciences (2018RU011), West China Hospital of Sichuan University, Chengdu, China
| | - Xu Wang
- School of Life Sciences, Beijing University of Chinese Medicine, Beijing, China
| | - Xiuli Wang
- Department of Psychoradiology, Chengdu Mental Health Center, Chengdu, China
| | - Bochao Cheng
- Department of Radiology, West China Second University Hospital of Sichuan University, Chengdu, China
| | - Kui Luo
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China.,Department of Psychoradiology, Chengdu Mental Health Center, Chengdu, China.,Psychoradiology Research Unit of Chinese Academy of Medical Sciences (2018RU011), West China Hospital of Sichuan University, Chengdu, China
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32
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Zhao N, Yuan LX, Jia XZ, Zhou XF, Deng XP, He HJ, Zhong J, Wang J, Zang YF. Intra- and Inter-Scanner Reliability of Voxel-Wise Whole-Brain Analytic Metrics for Resting State fMRI. Front Neuroinform 2018; 12:54. [PMID: 30186131 PMCID: PMC6110941 DOI: 10.3389/fninf.2018.00054] [Citation(s) in RCA: 58] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2018] [Accepted: 08/03/2018] [Indexed: 01/01/2023] Open
Abstract
As the multi-center studies with resting-state functional magnetic resonance imaging (RS-fMRI) have been more and more applied to neuropsychiatric studies, both intra- and inter-scanner reliability of RS-fMRI are becoming increasingly important. The amplitude of low frequency fluctuation (ALFF), regional homogeneity (ReHo), and degree centrality (DC) are 3 main RS-fMRI metrics in a way of voxel-wise whole-brain (VWWB) analysis. Although the intra-scanner reliability (i.e., test-retest reliability) of these metrics has been widely investigated, few studies has investigated their inter-scanner reliability. In the current study, 21 healthy young subjects were enrolled and scanned with blood oxygenation level dependent (BOLD) RS-fMRI in 3 visits (V1 - V3), with V1 and V2 scanned on a GE MR750 scanner and V3 on a Siemens Prisma. RS-fMRI data were collected under two conditions, eyes open (EO) and eyes closed (EC), each lasting 8 minutes. We firstly evaluated the intra- and inter-scanner reliability of ALFF, ReHo, and DC. Secondly, we measured systematic difference between two scanning visits of the same scanner as well as between two scanners. Thirdly, to account for the potential difference of intra- and inter-scanner local magnetic field inhomogeneity, we measured the difference of relative BOLD signal intensity to the mean BOLD signal intensity of the whole brain between each pair of visits. Last, we used percent amplitude of fluctuation (PerAF) to correct the difference induced by relative BOLD signal intensity. The inter-scanner reliability was much worse than intra-scanner reliability; Among the VWWB metrics, DC showed the worst (both for intra-scanner and inter-scanner comparisons). PerAF showed similar intra-scanner reliability with ALFF and the best reliability among all the 4 metrics. PerAF reduced the influence of BOLD signal intensity and hence increase the inter-scanner reliability of ALFF. For multi-center studies, inter-scanner reliability should be taken into account.
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Affiliation(s)
- Na Zhao
- Center for Cognition and Brain Disorders, Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou, China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, China
| | - Li-Xia Yuan
- Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, China
| | - Xi-Ze Jia
- Center for Cognition and Brain Disorders, Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou, China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, China
| | - Xu-Feng Zhou
- Center for Cognition and Brain Disorders, Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou, China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, China
| | - Xin-Ping Deng
- Center for Cognition and Brain Disorders, Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou, China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, China
| | - Hong-Jian He
- Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, China
| | - Jianhui Zhong
- Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, China
| | - Jue Wang
- Center for Cognition and Brain Disorders, Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou, China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, China
| | - Yu-Feng Zang
- Center for Cognition and Brain Disorders, Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou, China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, China
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Bazelmans T, Jones EJH, Ghods S, Corrigan S, Toth K, Charman T, Webb SJ. Heart rate mean and variability as a biomarker for phenotypic variation in preschoolers with autism spectrum disorder. Autism Res 2018; 12:39-52. [PMID: 30114343 DOI: 10.1002/aur.1982] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2018] [Revised: 06/06/2018] [Accepted: 06/10/2018] [Indexed: 02/03/2023]
Abstract
Interest in autonomic arousal in autism spectrum disorder (ASD) is increasing; however, reliability of these measures in ASD is unknown, and previously reported associations with social and cognitive abilities are inconsistent. This study assesses heart rate (HR) and HR variability (HRV) in preschoolers with ASD or typical development (TD) while they passively watched naturalistic videos. Measurement reliability, group differences, and the relationship with social and cognitive abilities were evaluated. Seventy one ASD and 66 TD children (2-4 years) provided cardiac data from two sessions. Test-retest intraclass correlations of HR and HRV over a 3-week period were moderate to good in both groups. Groups did not differ in mean level of HR or HRV. Intra-individual variability of HR between video segments within a session was higher in the ASD group, but intraclass correlations of this metric were low. Higher HR related to better language skills in TD children, but not after accounting for age and nonverbal ability. Higher HRV related to better expressive and receptive language in ASD children after controlling for age and nonverbal ability. HR/HRV were not related to social or executive functioning skills and did not explain any additional variance in abilities at a 12-month follow-up visit. In summary, variation in language abilities is associated with HR in the TD group and HRV in the ASD group. While preliminary, these results are promising for consideration of autonomic control as a biomarker for individual differences in ASD and may help us understand the mechanisms that contribute to communication skills. Autism Research 2019, 12: 39-52. © 2018 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Cardiac activity, such as heart rate and heart rate variability, is linked to a wide range of psychological functions. This study shows that there is an association between heart rate and heart rate variability and language skills in young children with autism spectrum disorder (ASD). These results may help us understand what underlies individual differences in developmental abilities in young children with ASD.
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Affiliation(s)
- Tessel Bazelmans
- Psychology Department, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Emily J H Jones
- Centre for Brain and Cognitive Development, Birkbeck, University of London, London, UK.,Center for Child Health, Behavior & Development, Seattle Children's Research Institute, Seattle, Washington
| | - Sheila Ghods
- Center for Child Health, Behavior & Development, Seattle Children's Research Institute, Seattle, Washington.,Department of Psychiatry, University of California San Francisco, San Francisco, California
| | - Sarah Corrigan
- Center for Child Health, Behavior & Development, Seattle Children's Research Institute, Seattle, Washington
| | - Karen Toth
- Center for Child Health, Behavior & Development, Seattle Children's Research Institute, Seattle, Washington
| | - Tony Charman
- Psychology Department, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Sara J Webb
- Center for Child Health, Behavior & Development, Seattle Children's Research Institute, Seattle, Washington.,Department of Psychiatry & Behavioral Sciences, University of Washington, Seattle, Washington
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34
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Noble S, Spann MN, Tokoglu F, Shen X, Constable RT, Scheinost D. Influences on the Test-Retest Reliability of Functional Connectivity MRI and its Relationship with Behavioral Utility. Cereb Cortex 2018; 27:5415-5429. [PMID: 28968754 PMCID: PMC6248395 DOI: 10.1093/cercor/bhx230] [Citation(s) in RCA: 219] [Impact Index Per Article: 36.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2017] [Accepted: 08/23/2017] [Indexed: 12/15/2022] Open
Abstract
Best practices are currently being developed for the acquisition and processing of
resting-state magnetic resonance imaging data used to estimate brain functional
organization—or “functional connectivity.” Standards have been proposed based on
test–retest reliability, but open questions remain. These include how amount of data per
subject influences whole-brain reliability, the influence of increasing runs versus
sessions, the spatial distribution of reliability, the reliability of multivariate
methods, and, crucially, how reliability maps onto prediction of behavior. We collected a
dataset of 12 extensively sampled individuals (144 min data each across 2 identically
configured scanners) to assess test–retest reliability of whole-brain connectivity within
the generalizability theory framework. We used Human Connectome Project data to replicate
these analyses and relate reliability to behavioral prediction. Overall, the historical
5-min scan produced poor reliability averaged across connections. Increasing the number of
sessions was more beneficial than increasing runs. Reliability was lowest for subcortical
connections and highest for within-network cortical connections. Multivariate reliability
was greater than univariate. Finally, reliability could not be used to improve prediction;
these findings are among the first to underscore this distinction for functional
connectivity. A comprehensive understanding of test–retest reliability, including its
limitations, supports the development of best practices in the field.
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Affiliation(s)
- Stephanie Noble
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT 06520, USA
| | - Marisa N Spann
- Department of Psychiatry, College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA
| | - Fuyuze Tokoglu
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, USA
| | - Xilin Shen
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, USA
| | - R Todd Constable
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT 06520, USA.,Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, USA.,Department of Neurosurgery, Yale School of Medicine, New Haven, CT 06520, USA
| | - Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, USA
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35
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King JL, Fearnbach SN, Ramakrishnapillai S, Shankpal P, Geiselman PJ, Martin CK, Murray KB, Hicks JL, McClernon FJ, Apolzan JW, Carmichael OT. Perceptual Characterization of the Macronutrient Picture System (MaPS) for Food Image fMRI. Front Psychol 2018; 9:17. [PMID: 29434559 PMCID: PMC5790788 DOI: 10.3389/fpsyg.2018.00017] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2017] [Accepted: 01/08/2018] [Indexed: 01/28/2023] Open
Abstract
Food image fMRI paradigms are used widely for investigating the neural basis of ingestive behavior. However, these paradigms have not been validated in terms of ingestive behavior constructs, engagement of food-relevant neural systems, or test-retest reliability, making the generalizability of study findings unclear. Therefore, we validated the Macronutrient Picture System (MaPS) (McClernon et al., 2013), which includes food images from the six categories represented in the Geiselman Food Preference Questionnaire (FPQ) (Geiselman et al., 1998). Twenty-five healthy young adults (n = 21 female, mean age = 20.6 ± 1.1 years, mean BMI = 22.1 ± 1.9 kg/m2) rated the MaPS images in terms of visual interest, appetitive quality, nutrition, emotional valence, liking, and frequency of consumption, and completed the FPQ. In a second study, 12 individuals (n=8 female, mean age = 25.0 ± 6.5 years, mean BMI = 28.2 ± 8.7 kg/m2) viewed MaPS and control images (vegetables and non-food) during two separate 3T BOLD fMRI scans after fasting overnight. Intuitively, high fat/high sugar (HF/HS) and high fat/high complex carbohydrate (HF/HCCHO) images achieved higher liking and appetitive ratings, and lower nutrition ratings, than low fat/low complex carbohydrate/high protein (LF/LCHO/HP) images on average. Within each food category, FPQ scores correlated strongly with MaPS image liking ratings (p < 0.001). Brain activation differences between viewing images of HF/HS and vegetables, and between HF/HCCHO and vegetables, were seen in several reward-related brain regions (e.g., putamen, insula, and medial frontal gyrus). Intra-individual, inter-scan agreement in a summary measure of brain activation differences in seven reward network regions of interest was high (ICC = 0.61), and was even higher when two distinct sets of food images with matching visual ratings were shown in the two scans (ICC = 0.74). These results suggest that the MaPS provides valid representation of food categories and reliably activates food-reward-relevant neural systems.
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Affiliation(s)
- Jill L King
- Pennington Biomedical Research Center, Baton Rouge, LA, United States.,Department of Psychology, Louisiana State University, Baton Rouge, LA, United States
| | | | | | - Preetham Shankpal
- Department of Electronics and Communication Engineering, MS Ramaiah University of Applied Sciences, Bangalore, India
| | - Paula J Geiselman
- Pennington Biomedical Research Center, Baton Rouge, LA, United States.,Department of Psychology, Louisiana State University, Baton Rouge, LA, United States
| | - Corby K Martin
- Pennington Biomedical Research Center, Baton Rouge, LA, United States
| | - Kori B Murray
- Pennington Biomedical Research Center, Baton Rouge, LA, United States
| | - Jason L Hicks
- Department of Psychology, Louisiana State University, Baton Rouge, LA, United States
| | - F Joseph McClernon
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, United States
| | - John W Apolzan
- Pennington Biomedical Research Center, Baton Rouge, LA, United States
| | - Owen T Carmichael
- Pennington Biomedical Research Center, Baton Rouge, LA, United States
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36
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Ordaz SJ, LeMoult J, Colich NL, Prasad G, Pollak M, Popolizio M, Price A, Greicius M, Gotlib IH. Ruminative brooding is associated with salience network coherence in early pubertal youth. Soc Cogn Affect Neurosci 2017; 12:298-310. [PMID: 27633394 PMCID: PMC5390708 DOI: 10.1093/scan/nsw133] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2016] [Accepted: 09/06/2016] [Indexed: 12/12/2022] Open
Abstract
Rumination, and particularly ruminative brooding, perpetuates dysphoric mood states and contributes to the emergence of depression. Studies of adults and older adolescents have characterized the association between rumination and intrinsic functional connectivity within default mode (DMN), salience (SN) and executive control (ECN) networks; we know little, however, about the brain network basis of rumination during early puberty, a sensitive period for network reorganization. 112 early puberty boys and girls completed resting-state scans, the Ruminative Response Scale, and the Youth Self-Report questionnaire. Using independent components analysis and dual regression, we quantified coherence for each individual in networks of interest (SN, ECN, DMN) and in non-relevant networks (motor, visual) in which we predicted no correlations with behavioral measures. Boys and girls did not differ in levels of rumination or internalizing symptoms, or in coherence for any network. The relation between SN network coherence and rumination; however, and specifically ruminative brooding, was moderated by sex: greater SN coherence was associated with higher levels of brooding in girls but not in boys. Further, in girls, brooding mediated the relation between SN coherence and internalizing symptoms. These results point to coherence within the SN as a potential neurodevelopmental marker of risk for depression in early pubertal girls.
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Affiliation(s)
- Sarah J Ordaz
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Joelle LeMoult
- Department of Psychology, Stanford University, Stanford, CA, USA
| | - Natalie L Colich
- Department of Psychology, Stanford University, Stanford, CA, USA
| | - Gautam Prasad
- Imaging Genetics Center & Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Madeline Pollak
- Department of Psychology, Stanford University, Stanford, CA, USA
| | - Morgan Popolizio
- Department of Psychology, Stanford University, Stanford, CA, USA
| | - Alexandra Price
- Department of Psychology, Stanford University, Stanford, CA, USA
| | - Michael Greicius
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Ian H Gotlib
- Department of Psychology, Stanford University, Stanford, CA, USA
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37
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Zhang H, Chen X, Zhang Y, Shen D. Test-Retest Reliability of "High-Order" Functional Connectivity in Young Healthy Adults. Front Neurosci 2017; 11:439. [PMID: 28824362 PMCID: PMC5539178 DOI: 10.3389/fnins.2017.00439] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2017] [Accepted: 07/18/2017] [Indexed: 12/16/2022] Open
Abstract
Functional connectivity (FC) has become a leading method for resting-state functional magnetic resonance imaging (rs-fMRI) analysis. However, the majority of the previous studies utilized pairwise, temporal synchronization-based FC. Recently, high-order FC (HOFC) methods were proposed with the idea of computing "correlation of correlations" to capture high-level, more complex associations among the brain regions. There are two types of HOFC. The first type is topographical profile similarity-based HOFC (tHOFC) and its variant, associated HOFC (aHOFC), for capturing different levels of HOFC. Instead of measuring the similarity of the original rs-fMRI signals with the traditional FC (low-order FC, or LOFC), tHOFC measures the similarity of LOFC profiles (i.e., a set of LOFC values between a region and all other regions) between each pair of brain regions. The second type is dynamics-based HOFC (dHOFC) which defines the quadruple relationship among every four brain regions by first calculating two pairwise dynamic LOFC "time series" and then measuring their temporal synchronization (i.e., temporal correlation of the LOFC fluctuations, not the BOLD fluctuations). Applications have shown the superiority of HOFC in both disease biomarker detection and individualized diagnosis than LOFC. However, no study has been carried out for the assessment of test-retest reliability of different HOFC metrics. In this paper, we systematically evaluate the reliability of the two types of HOFC methods using test-retest rs-fMRI data from 25 (12 females, age 24.48 ± 2.55 years) young healthy adults with seven repeated scans (with interval = 3-8 days). We found that all HOFC metrics have satisfactory reliability, specifically (1) fair-to-good for tHOFC and aHOFC, and (2) fair-to-moderate for dHOFC with relatively strong connectivity strength. We further give an in-depth analysis of the biological meanings of each HOFC metric and highlight their differences compared to the LOFC from the aspects of cross-level information exchanges, within-/between-network connectivity, and modulatory connectivity. In addition, how the dynamic analysis parameter (i.e., sliding window length) affects dHOFC reliability is also investigated. Our study reveals unique functional associations characterized by the HOFC metrics. Guidance and recommendations for future applications and clinical research using HOFC are provided. This study has made a further step toward unveiling more complex human brain connectome.
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Affiliation(s)
- Han Zhang
- Department of Radiology and Brain Research Imaging Center, University of North Carolina at Chapel HillChapel Hill, NC, United States
| | - Xiaobo Chen
- Department of Radiology and Brain Research Imaging Center, University of North Carolina at Chapel HillChapel Hill, NC, United States
| | - Yu Zhang
- Department of Radiology and Brain Research Imaging Center, University of North Carolina at Chapel HillChapel Hill, NC, United States
| | - Dinggang Shen
- Department of Radiology and Brain Research Imaging Center, University of North Carolina at Chapel HillChapel Hill, NC, United States
- Department of Brain and Cognitive Engineering, Korea UniversitySeoul, South Korea
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38
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Chen X, Zhang H, Zhang L, Shen C, Lee SW, Shen D. Extraction of dynamic functional connectivity from brain grey matter and white matter for MCI classification. Hum Brain Mapp 2017; 38:5019-5034. [PMID: 28665045 DOI: 10.1002/hbm.23711] [Citation(s) in RCA: 95] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2016] [Revised: 05/11/2017] [Accepted: 06/16/2017] [Indexed: 12/11/2022] Open
Abstract
Brain functional connectivity (FC) extracted from resting-state fMRI (RS-fMRI) has become a popular approach for diagnosing various neurodegenerative diseases, including Alzheimer's disease (AD) and its prodromal stage, mild cognitive impairment (MCI). Current studies mainly construct the FC networks between grey matter (GM) regions of the brain based on temporal co-variations of the blood oxygenation level-dependent (BOLD) signals, which reflects the synchronized neural activities. However, it was rarely investigated whether the FC detected within the white matter (WM) could provide useful information for diagnosis. Motivated by the recently proposed functional correlation tensors (FCT) computed from RS-fMRI and used to characterize the structured pattern of local FC in the WM, we propose in this article a novel MCI classification method based on the information conveyed by both the FC between the GM regions and that within the WM regions. Specifically, in the WM, the tensor-based metrics (e.g., fractional anisotropy [FA], similar to the metric calculated based on diffusion tensor imaging [DTI]) are first calculated based on the FCT and then summarized along each of the major WM fiber tracts connecting each pair of the brain GM regions. This could capture the functional information in the WM, in a similar network structure as the FC network constructed for the GM, based only on the same RS-fMRI data. Moreover, a sliding window approach is further used to partition the voxel-wise BOLD signal into multiple short overlapping segments. Then, both the FC and FCT between each pair of the brain regions can be calculated based on the BOLD signal segments in the GM and WM, respectively. In such a way, our method can generate dynamic FC and dynamic FCT to better capture functional information in both GM and WM and further integrate them together by using our developed feature extraction, selection, and ensemble learning algorithms. The experimental results verify that the dynamic FCT can provide valuable functional information in the WM; by combining it with the dynamic FC in the GM, the diagnosis accuracy for MCI subjects can be significantly improved even using RS-fMRI data alone. Hum Brain Mapp 38:5019-5034, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Xiaobo Chen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Han Zhang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Lichi Zhang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Celina Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Seong-Whan Lee
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.,Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
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39
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Töger J, Sorensen T, Somandepalli K, Toutios A, Lingala SG, Narayanan S, Nayak K. Test-retest repeatability of human speech biomarkers from static and real-time dynamic magnetic resonance imaging. THE JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA 2017; 141:3323. [PMID: 28599561 PMCID: PMC5436977 DOI: 10.1121/1.4983081] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
Static anatomical and real-time dynamic magnetic resonance imaging (RT-MRI) of the upper airway is a valuable method for studying speech production in research and clinical settings. The test-retest repeatability of quantitative imaging biomarkers is an important parameter, since it limits the effect sizes and intragroup differences that can be studied. Therefore, this study aims to present a framework for determining the test-retest repeatability of quantitative speech biomarkers from static MRI and RT-MRI, and apply the framework to healthy volunteers. Subjects (n = 8, 4 females, 4 males) are imaged in two scans on the same day, including static images and dynamic RT-MRI of speech tasks. The inter-study agreement is quantified using intraclass correlation coefficient (ICC) and mean within-subject standard deviation (σe). Inter-study agreement is strong to very strong for static measures (ICC: min/median/max 0.71/0.89/0.98, σe: 0.90/2.20/6.72 mm), poor to strong for dynamic RT-MRI measures of articulator motion range (ICC: 0.26/0.75/0.90, σe: 1.6/2.5/3.6 mm), and poor to very strong for velocities (ICC: 0.21/0.56/0.93, σe: 2.2/4.4/16.7 cm/s). In conclusion, this study characterizes repeatability of static and dynamic MRI-derived speech biomarkers using state-of-the-art imaging. The introduced framework can be used to guide future development of speech biomarkers. Test-retest MRI data are provided free for research use.
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Affiliation(s)
- Johannes Töger
- Ming Hsieh Department of Electrical Engineering, University of Southern California, 3740 McClintock Avenue, EEB 400, Los Angeles, California 90089-2560, USA
| | - Tanner Sorensen
- Ming Hsieh Department of Electrical Engineering, University of Southern California, 3740 McClintock Avenue, EEB 400, Los Angeles, California 90089-2560, USA
| | - Krishna Somandepalli
- Ming Hsieh Department of Electrical Engineering, University of Southern California, 3740 McClintock Avenue, EEB 400, Los Angeles, California 90089-2560, USA
| | - Asterios Toutios
- Ming Hsieh Department of Electrical Engineering, University of Southern California, 3740 McClintock Avenue, EEB 400, Los Angeles, California 90089-2560, USA
| | - Sajan Goud Lingala
- Ming Hsieh Department of Electrical Engineering, University of Southern California, 3740 McClintock Avenue, EEB 400, Los Angeles, California 90089-2560, USA
| | - Shrikanth Narayanan
- Ming Hsieh Department of Electrical Engineering, University of Southern California, 3740 McClintock Avenue, EEB 400, Los Angeles, California 90089-2560, USA
| | - Krishna Nayak
- Ming Hsieh Department of Electrical Engineering, University of Southern California, 3740 McClintock Avenue, EEB 400, Los Angeles, California 90089-2560, USA
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Algarin C, Karunakaran KD, Reyes S, Morales C, Lozoff B, Peirano P, Biswal B. Differences on Brain Connectivity in Adulthood Are Present in Subjects with Iron Deficiency Anemia in Infancy. Front Aging Neurosci 2017; 9:54. [PMID: 28326037 PMCID: PMC5339238 DOI: 10.3389/fnagi.2017.00054] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2016] [Accepted: 02/23/2017] [Indexed: 11/13/2022] Open
Abstract
Iron deficiency continues to be the most prevalent micronutrient deficit worldwide. Since iron is involved in several processes including myelination, dopamine neurotransmission and neuronal metabolism, the presence of iron deficiency anemia (IDA) in infancy relates to long-lasting neurofunctional effects. There is scarce data regarding whether these effects would extend to former iron deficient anemic human adults. Resting state functional magnetic resonance imaging (fMRI) is a novel technique to explore patterns of functional connectivity. Default Mode Network (DMN), one of the resting state networks, is deeply involved in memory, social cognition and self-referential processes. The four core regions consistently identified in the DMN are the medial prefrontal cortex, posterior cingulate/retrosplenial cortex and left and right inferior parietal cortex. Therefore to investigate the DMN in former iron deficient anemic adults is a particularly useful approach to elucidate de long term effects on functional brain. We conducted this research to explore the connection between IDA in infancy and altered patterns of resting state brain functional networks in young adults. Resting-state fMRI studies were performed to 31 participants that belong to a follow-up study since infancy. Of them, 14 participants were former iron deficient anemic in infancy and 17 were controls, with mean age of 21.5 years (±1.5) and 54.8% were males. Resting-state fMRI protocol was used and the data was analyzed using the seed based connectivity statistical analysis to assess the DMN. We found that compared to controls, former iron deficient anemic subjects showed posterior DMN decreased connectivity to the left posterior cingulate cortex (PCC), whereas they exhibited increased anterior DMN connectivity to the right PCC. Differences between groups were also apparent in the left medial frontal gyrus, with former iron deficient anemic participants having increased connectivity with areas included in DMN and dorsal attention networks. These preliminary results suggest different patterns of functional connectivity between former iron deficient anemic and control young adults. Indeed, IDA in infancy, a common nutritional problem among human infants, may turn out to be important for understanding the mechanisms of cognitive alterations, common in adulthood.
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Affiliation(s)
- Cecilia Algarin
- Sleep and Neurofunctional Laboratory, Institute of Nutrition and Food Technology, University of Chile Santiago, Chile
| | | | - Sussanne Reyes
- Sleep and Neurofunctional Laboratory, Institute of Nutrition and Food Technology, University of Chile Santiago, Chile
| | - Cristian Morales
- Sleep and Neurofunctional Laboratory, Institute of Nutrition and Food Technology, University of Chile Santiago, Chile
| | - Betsy Lozoff
- Center for Human Growth and Development, University of Michigan Ann Arbor, MI, USA
| | - Patricio Peirano
- Sleep and Neurofunctional Laboratory, Institute of Nutrition and Food Technology, University of Chile Santiago, Chile
| | - Bharat Biswal
- Department of Biomedical Engineering, New Jersey Institute of Technology, University Heights Newark, NJ, USA
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Li F, Zheng Y, Smith SD, Shic F, Moore CC, Zheng X, Qi Y, Liu Z, Leckman JF. A preliminary study of movement intensity during a Go/No-Go task and its association with ADHD outcomes and symptom severity. Child Adolesc Psychiatry Ment Health 2016; 10:47. [PMID: 27999615 PMCID: PMC5153899 DOI: 10.1186/s13034-016-0135-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2016] [Accepted: 11/23/2016] [Indexed: 12/22/2022] Open
Abstract
OBJECTIVE At present, there are no well-validated biomarkers for attention-deficit/hyperactivity disorder (ADHD). The present study used an infrared motion tracking system to monitor and record the movement intensity of children and to determine its diagnostic precision for ADHD and its possible associations with ratings of ADHD symptom severity. METHODS A Microsoft motion sensing camera recorded the movement of children during a modified Go/No-Go Task. Movement intensity measures extracted from these data included a composite measure of total movement intensity (TMI measure) and a movement intensity distribution (MID measure) measure across 15 frequency bands (FB measures). In phase 1 of the study, 30 children diagnosed with ADHD or at subthreshold for ADHD and 30 matched healthy controls were compared to determine if measures of movement intensity successfully distinguished children with ADHD from healthy control children. In phase 2, associations between measures of movement intensity and clinician-rated ADHD symptom severity (Clinical Global Impression Scale [CGI] and the ADHD-Rating Scale IV [ADHD-RS]) were examined in a subset of children with ADHD (n = 14) from the phase I sample. RESULTS Both measures of movement intensity were able to distinguish children with ADHD from healthy controls. However, only the measures linked to the 15 pre-determined 1 Hz frequency bands were significantly correlated with both the CGI scores and ADHD-RS total scores. CONCLUSIONS Preliminary findings suggest that measures of movement intensity, particularly measures linked to the 10-11 and 12-13 Hz frequency bands, have the potential to become valid biomarkers for ADHD.
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Affiliation(s)
- Fenghua Li
- Key Lab of Mental Health, Institute of Psychology, Chinese Academy of Sciences, 218 South Block, #16 Lincui Road, Chaoyang District, Beijing, 100101 People’s Republic of China ,University of Chinese Academy of Sciences, Beijing, People’s Republic of China
| | - Yi Zheng
- Beijing Institute for Brain Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, People’s Republic of China
| | - Stephanie D. Smith
- Child Study Center, Yale University School of Medicine, I-265 SHM, 230 South Frontage Road, New Haven, CT 06520-7900 USA ,Department of Psychology, University of Southern Mississippi, Hattiesburg, MS USA
| | - Frederick Shic
- Center for Child Health, Behavior and Development, Seattle Children’s Research Institute, 2001 8th Ave #400, Seattle, WA 98121 USA
| | - Christina C. Moore
- Child Study Center, Yale University School of Medicine, I-265 SHM, 230 South Frontage Road, New Haven, CT 06520-7900 USA ,Department of Psychology, University of Delaware, Newark, DE USA
| | - Xixi Zheng
- Chinese Academy of Medical Sciences, Peking Union Medical College Hospital, Peking Union Medical College, Beijing, People’s Republic of China
| | - Yanjie Qi
- Beijing Institute for Brain Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, People’s Republic of China
| | - Zhengkui Liu
- Key Lab of Mental Health, Institute of Psychology, Chinese Academy of Sciences, 218 South Block, #16 Lincui Road, Chaoyang District, Beijing, 100101 People’s Republic of China
| | - James F. Leckman
- Child Study Center, Yale University School of Medicine, I-265 SHM, 230 South Frontage Road, New Haven, CT 06520-7900 USA
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Noble S, Scheinost D, Finn ES, Shen X, Papademetris X, McEwen SC, Bearden CE, Addington J, Goodyear B, Cadenhead KS, Mirzakhanian H, Cornblatt BA, Olvet DM, Mathalon DH, McGlashan TH, Perkins DO, Belger A, Seidman LJ, Thermenos H, Tsuang MT, van Erp TGM, Walker EF, Hamann S, Woods SW, Cannon TD, Constable RT. Multisite reliability of MR-based functional connectivity. Neuroimage 2016; 146:959-970. [PMID: 27746386 DOI: 10.1016/j.neuroimage.2016.10.020] [Citation(s) in RCA: 110] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2016] [Revised: 10/10/2016] [Accepted: 10/12/2016] [Indexed: 11/26/2022] Open
Abstract
Recent years have witnessed an increasing number of multisite MRI functional connectivity (fcMRI) studies. While multisite studies provide an efficient way to accelerate data collection and increase sample sizes, especially for rare clinical populations, any effects of site or MRI scanner could ultimately limit power and weaken results. Little data exists on the stability of functional connectivity measurements across sites and sessions. In this study, we assess the influence of site and session on resting state functional connectivity measurements in a healthy cohort of traveling subjects (8 subjects scanned twice at each of 8 sites) scanned as part of the North American Prodrome Longitudinal Study (NAPLS). Reliability was investigated in three types of connectivity analyses: (1) seed-based connectivity with posterior cingulate cortex (PCC), right motor cortex (RMC), and left thalamus (LT) as seeds; (2) the intrinsic connectivity distribution (ICD), a voxel-wise connectivity measure; and (3) matrix connectivity, a whole-brain, atlas-based approach to assessing connectivity between nodes. Contributions to variability in connectivity due to subject, site, and day-of-scan were quantified and used to assess between-session (test-retest) reliability in accordance with Generalizability Theory. Overall, no major site, scanner manufacturer, or day-of-scan effects were found for the univariate connectivity analyses; instead, subject effects dominated relative to the other measured factors. However, summaries of voxel-wise connectivity were found to be sensitive to site and scanner manufacturer effects. For all connectivity measures, although subject variance was three times the site variance, the residual represented 60-80% of the variance, indicating that connectivity differed greatly from scan to scan independent of any of the measured factors (i.e., subject, site, and day-of-scan). Thus, for a single 5min scan, reliability across connectivity measures was poor (ICC=0.07-0.17), but increased with increasing scan duration (ICC=0.21-0.36 at 25min). The limited effects of site and scanner manufacturer support the use of multisite studies, such as NAPLS, as a viable means of collecting data on rare populations and increasing power in univariate functional connectivity studies. However, the results indicate that aggregation of fcMRI data across longer scan durations is necessary to increase the reliability of connectivity estimates at the single-subject level.
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Affiliation(s)
- Stephanie Noble
- Yale University, Interdepartmental Neuroscience Program, New Haven, CT, USA.
| | - Dustin Scheinost
- Yale University, Department of Radiology and Biomedical Imaging, New Haven, CT, USA
| | - Emily S Finn
- Yale University, Interdepartmental Neuroscience Program, New Haven, CT, USA
| | - Xilin Shen
- Yale University, Department of Radiology and Biomedical Imaging, New Haven, CT, USA
| | - Xenophon Papademetris
- Yale University, Department of Radiology and Biomedical Imaging, New Haven, CT, USA; Yale University, Department of Biomedical Engineering, New Haven, CT, USA
| | - Sarah C McEwen
- University of California, Los Angeles, Departments of Psychology and Psychiatry, Los Angeles, CA, USA
| | - Carrie E Bearden
- University of California, Los Angeles, Departments of Psychology and Psychiatry, Los Angeles, CA, USA
| | - Jean Addington
- University of Calgary, Department of Psychiatry, Calgary, Alberta, Canada
| | - Bradley Goodyear
- University of Calgary, Departments of Radiology, Clinical Neurosciences and Psychiatry, Calgary, Alberta, Canada
| | - Kristin S Cadenhead
- University of California, San Diego, Department of Psychiatry, La Jolla, CA, USA
| | - Heline Mirzakhanian
- University of California, San Diego, Department of Psychiatry, La Jolla, CA, USA
| | - Barbara A Cornblatt
- Zucker Hillside Hospital, Department of Psychiatry Research, Glen Oaks, NY, USA
| | - Doreen M Olvet
- Zucker Hillside Hospital, Department of Psychiatry Research, Glen Oaks, NY, USA
| | - Daniel H Mathalon
- University of California, San Francisco, Department of Psychiatry, San Francisco, CA, USA
| | | | - Diana O Perkins
- Yale University, Department of Psychiatry, New Haven, CT, USA
| | - Aysenil Belger
- University of North Carolina, Chapel Hill, Department of Psychiatry, Chapel Hill, NC, USA
| | - Larry J Seidman
- Beth Israel Deaconess Medical Center, Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Heidi Thermenos
- Beth Israel Deaconess Medical Center, Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Ming T Tsuang
- University of California, San Diego, Department of Psychiatry, La Jolla, CA, USA
| | - Theo G M van Erp
- University of California, Irvine, Department of Psychiatry and Human Behavior, Irvine, CA, USA
| | - Elaine F Walker
- Emory University, Department of Psychology, Atlanta, GA, USA
| | - Stephan Hamann
- Emory University, Department of Psychology, Atlanta, GA, USA
| | - Scott W Woods
- Yale University, Department of Psychiatry, New Haven, CT, USA
| | - Tyrone D Cannon
- Yale University, Departments of Psychology and Psychiatry, New Haven, CT, USA
| | - R Todd Constable
- Yale University, Department of Radiology and Biomedical Imaging, New Haven, CT, USA
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Castellanos FX, Aoki Y. Intrinsic Functional Connectivity in Attention-Deficit/Hyperactivity Disorder: A Science in Development. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2016; 1:253-261. [PMID: 27713929 DOI: 10.1016/j.bpsc.2016.03.004] [Citation(s) in RCA: 84] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Functional magnetic resonance imaging (fMRI) without an explicit task, i.e., resting state fMRI, of individuals with Attention-Deficit/Hyperactivity Disorder (ADHD) is growing rapidly. Early studies were unaware of the vulnerability of this method to even minor degrees of head motion, a major concern in the field. Recent efforts are implementing various strategies to address this source of artifact along with a growing set of analytical tools. Availability of the ADHD-200 Consortium dataset, a large-scale multi-site repository, is facilitating increasingly sophisticated approaches. In parallel, investigators are beginning to explicitly test the replicability of published findings. In this narrative review, we sketch out broad, overarching hypotheses being entertained while noting methodological uncertainties. Current hypotheses implicate the interplay of default, cognitive control (frontoparietal) and attention (dorsal, ventral, salience) networks in ADHD; functional connectivities of reward-related and amygdala-related circuits are also supported as substrates for dimensional aspects of ADHD. Before these can be further specified and definitively tested, we assert the field must take on the challenge of mapping the "topography" of the analytical space, i.e., determining the sensitivities of results to variations in acquisition, analysis, demographic and phenotypic parameters. Doing so with openly available datasets will provide the needed foundation for delineating typical and atypical developmental trajectories of brain structure and function in neurodevelopmental disorders including ADHD when applied to large-scale multi-site prospective longitudinal studies such as the forthcoming Adolescent Brain Cognitive Development study.
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Affiliation(s)
- F Xavier Castellanos
- The Child Study Center at NYU Langone Medical Center, New York, NY; Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY
| | - Yuta Aoki
- The Child Study Center at NYU Langone Medical Center, New York, NY
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Chen B, Xu T, Zhou C, Wang L, Yang N, Wang Z, Dong HM, Yang Z, Zang YF, Zuo XN, Weng XC. Individual Variability and Test-Retest Reliability Revealed by Ten Repeated Resting-State Brain Scans over One Month. PLoS One 2015; 10:e0144963. [PMID: 26714192 PMCID: PMC4694646 DOI: 10.1371/journal.pone.0144963] [Citation(s) in RCA: 90] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2015] [Accepted: 11/27/2015] [Indexed: 11/18/2022] Open
Abstract
Individual differences in mind and behavior are believed to reflect the functional variability of the human brain. Due to the lack of a large-scale longitudinal dataset, the full landscape of variability within and between individual functional connectomes is largely unknown. We collected 300 resting-state functional magnetic resonance imaging (rfMRI) datasets from 30 healthy participants who were scanned every three days for one month. With these data, both intra- and inter-individual variability of six common rfMRI metrics, as well as their test-retest reliability, were estimated across multiple spatial scales. Global metrics were more dynamic than local regional metrics. Cognitive components involving working memory, inhibition, attention, language and related neural networks exhibited high intra-individual variability. In contrast, inter-individual variability demonstrated a more complex picture across the multiple scales of metrics. Limbic, default, frontoparietal and visual networks and their related cognitive components were more differentiable than somatomotor and attention networks across the participants. Analyzing both intra- and inter-individual variability revealed a set of high-resolution maps on test-retest reliability of the multi-scale connectomic metrics. These findings represent the first collection of individual differences in multi-scale and multi-metric characterization of the human functional connectomes in-vivo, serving as normal references for the field to guide the use of common functional metrics in rfMRI-based applications.
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Affiliation(s)
- Bing Chen
- Fujian Provincial Key Lab of the Brain-like Intelligent systems, Xiamen University School of Information Science and Engineering, Xiamen, Fujian 361005, China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, Zhejiang 311121, China
| | - Ting Xu
- Key Laboratory of Behavioural Sciences and Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
| | - Changle Zhou
- Fujian Provincial Key Lab of the Brain-like Intelligent systems, Xiamen University School of Information Science and Engineering, Xiamen, Fujian 361005, China
| | - Luoyu Wang
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, Zhejiang 311121, China
| | - Ning Yang
- Key Laboratory of Behavioural Sciences and Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Laboratory for Functional Connectome and Development, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
| | - Ze Wang
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, Zhejiang 311121, China
| | - Hao-Ming Dong
- Key Laboratory of Behavioural Sciences and Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Laboratory for Functional Connectome and Development, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
| | - Zhi Yang
- Key Laboratory of Behavioural Sciences and Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
- Laboratory for Functional Connectome and Development, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
| | - Yu-Feng Zang
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, Zhejiang 311121, China
| | - Xi-Nian Zuo
- Key Laboratory of Behavioural Sciences and Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
- Laboratory for Functional Connectome and Development, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
- Faculty of Psychology, Southwest University, Beibei, Chongqing 400715, China
- Department of Psychology, School of Education Science, Guangxi Teachers Education University, Nanning, Guangxi 530001, China
| | - Xu-Chu Weng
- Fujian Provincial Key Lab of the Brain-like Intelligent systems, Xiamen University School of Information Science and Engineering, Xiamen, Fujian 361005, China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, Zhejiang 311121, China
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Attention-Deficit/Hyperactivity Disorder: Focus upon Aberrant N-Methyl-D-Aspartate Receptors Systems. Curr Top Behav Neurosci 2015; 29:295-311. [PMID: 26718589 DOI: 10.1007/7854_2015_415] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Attention-deficit/hyperactivity disorder (ADHD) pathophysiology persists in an obscure manner with complex interactions between symptoms, staging, interventions, genes, and environments. Only on the basis of increasing incidence of the disorder, the need for understanding is greater than ever. The notion of an imbalance between central inhibitory/excitatory neurotransmitters is considered to exert an essential role. In this chapter, we first review how the default mode network functions and dysfunction in individuals diagnosed with ADHD. We also present and briefly review some of the animal models used to examine the neurobiological aspects of ADHD. There is much evidence indicating that compounds/interventions that antagonize/block glutamic acid receptors and/or block the glutamate signal during the "brain growth spurt" or in the adult animal may induce functional and biomarker deficits. Additionally, we present evidence suggesting that animals treated with glutamate blockers at the period of the "brain growth spurt" fail to perform the exploratory activity, observed invariably with control mice, that is associated with introduction to a novel environment (the test cages). Later, when the control animals show less locomotor and rearing activity, i.e., interest in the test cages, the MK-801, ketamine and ethanol treated mice showed successively greater levels of locomotion and rearing (interest), i.e., they fail to "habituate" effectively, implying a cognitive dysfunction. These disturbances of glutamate signaling during a critical period of brain development may contribute to the ADHD pathophysiology. As a final addition, we have briefly identified new research venues in the interaction between ADHD, molecular studies, and personality research.
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