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Yan H, Shan X, Li H, Liu F, Xie G, Li P, Guo W. Cerebellar functional connectivity and its associated genes: A longitudinal study in drug-naive patients with obsessive-compulsive disorder. J Psychiatr Res 2024; 177:378-391. [PMID: 39083996 DOI: 10.1016/j.jpsychires.2024.07.040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Revised: 07/19/2024] [Accepted: 07/27/2024] [Indexed: 08/02/2024]
Abstract
The role of cerebellar-cerebral functional connectivity (CC-FC) in obsessive-compulsive disorder (OCD), its trajectory post-pharmacotherapy, and its potential as a prognostic biomarker and genetic mechanism remain uncertain. To address these gaps, this study included 37 drug-naive OCD patients and 37 healthy controls (HCs). Participants underwent baseline functional magnetic resonance imaging (fMRI), followed by four weeks of paroxetine treatment for patients with OCD, and another fMRI scan post-treatment. We examined seed-based CC-FC differences between the patients and HCs, and pre- and post-treatment patients. Support vector regression (SVR) based on CC-FC was performed to predict treatment response. Correlation analysis explored associations between CC-FC and clinical features, as well as gene profiles. Compared to HCs, drug-naive OCD patients exhibited reduced CC-FC in executive, affective-limbic, and sensorimotor networks, with specific genetic profiles associated with altered CC-FC. Gene enrichment analyses highlighted the involvement of these genes in various biological processes, molecular functions, and pathways. Post-treatment, the patients showed partial clinical improvement and partial restoration of the previously decreased CC-FC. Abnormal CC-FC at baseline correlated negatively with compulsions severity and social functional impairment, while changes in CC-FC correlated with cognitive function changes post-treatment. CC-FC emerged as a potential predictor of symptom severity in patients following paroxetine treatment. This longitudinal resting-state fMRI study underscores the crucial role of CC-FC in the neuropsychological mechanisms of OCD and its pharmacological treatment. Transcriptome-neuroimaging spatial correlation analyses provide insight into the neurobiological mechanisms underlying OCD pathology. Furthermore, SVR analyses hold promise for advancing precision medicine approaches in treating patients with OCD.
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Affiliation(s)
- Haohao Yan
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, 410011, Hunan, China
| | - Xiaoxiao Shan
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, 410011, Hunan, China
| | - Huabing Li
- Department of Radiology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, 410011, China
| | - Feng Liu
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Guojun Xie
- Department of Psychiatry, The Third People's Hospital of Foshan, Foshan, 528000, Guangdong, China
| | - Ping Li
- Department of Psychiatry, Qiqihar Medical University, Qiqihar, Heilongjiang, 161006, China
| | - Wenbin Guo
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, 410011, Hunan, China.
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2
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Jing H, Zhang C, Yan H, Li X, Liang J, Liang W, Ou Y, Wu W, Guo H, Deng W, Xie G, Guo W. Deviant spontaneous neural activity as a potential early-response predictor for therapeutic interventions in patients with schizophrenia. Front Neurosci 2023; 17:1243168. [PMID: 37727324 PMCID: PMC10505796 DOI: 10.3389/fnins.2023.1243168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 08/18/2023] [Indexed: 09/21/2023] Open
Abstract
Objective Previous studies have established significant differences in the neuroimaging characteristics between healthy controls (HCs) and patients with schizophrenia (SCZ). However, the relationship between homotopic connectivity and clinical features in patients with SCZ is not yet fully understood. Furthermore, there are currently no established neuroimaging biomarkers available for the diagnosis of SCZ or for predicting early treatment response. The aim of this study is to investigate the association between regional homogeneity and specific clinical features in SCZ patients. Methods We conducted a longitudinal investigation involving 56 patients with SCZ and 51 HCs. The SCZ patients underwent a 3-month antipsychotic treatment. Resting-state functional magnetic resonance imaging (fMRI), regional homogeneity (ReHo), support vector machine (SVM), and support vector regression (SVR) were used for data acquisition and analysis. Results In comparison to HCs, individuals with SCZ demonstrated reduced ReHo values in the right postcentral/precentral gyrus, left postcentral/inferior parietal gyrus, left middle/inferior occipital gyrus, and right middle temporal/inferior occipital gyrus, and increased ReHo values in the right putamen. It is noteworthy that there was decreased ReHo values in the right inferior parietal gyrus after treatment compared to baseline data. Conclusion The observed decrease in ReHo values in the sensorimotor network and increase in ReHo values in the right putamen may represent distinctive neurobiological characteristics of patients with SCZ, as well as a potential neuroimaging biomarker for distinguishing between patients with SCZ and HCs. Furthermore, ReHo values in the sensorimotor network and right putamen may serve as predictive indicators for early treatment response in patients with SCZ.
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Affiliation(s)
- Huan Jing
- Department of Psychiatry, The Third People's Hospital of Foshan, Foshan, Guangdong, China
| | - Chunguo Zhang
- Department of Psychiatry, The Third People's Hospital of Foshan, Foshan, Guangdong, China
| | - Haohao Yan
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Xiaoling Li
- Department of Psychiatry, The Third People's Hospital of Foshan, Foshan, Guangdong, China
| | - Jiaquan Liang
- Department of Psychiatry, The Third People's Hospital of Foshan, Foshan, Guangdong, China
| | - Wenting Liang
- Department of Psychiatry, The Third People's Hospital of Foshan, Foshan, Guangdong, China
| | - Yangpan Ou
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Weibin Wu
- Department of Psychiatry, The Third People's Hospital of Foshan, Foshan, Guangdong, China
| | - Huagui Guo
- Department of Psychiatry, The Third People's Hospital of Foshan, Foshan, Guangdong, China
| | - Wen Deng
- Department of Psychiatry, The Third People's Hospital of Foshan, Foshan, Guangdong, China
| | - Guojun Xie
- Department of Psychiatry, The Third People's Hospital of Foshan, Foshan, Guangdong, China
| | - Wenbin Guo
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
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Ghiles CW, Clark MD, Kuzminski SJ, Fraser MA, Petrella JR, Guskiewicz KM. Changes in resting state networks in high school football athletes across a single season. Br J Radiol 2023; 96:20220359. [PMID: 36607807 PMCID: PMC10078860 DOI: 10.1259/bjr.20220359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
OBJECTIVE The aim of this pilot cohort study was to examine changes in the organization of resting-state brain networks in high school football athletes and its relationship to exposure to on-field head impacts over the course of a single season. METHODS Seventeen male high school football players underwent functional magnetic resonance imaging and computerized neurocognitive testing (CNS Vital Signs) before the start of contact practices and again after the conclusion of the season. The players were equipped with helmet accelerometer systems (Head Impact Telemetry System) to record head impacts in practices and games. Graph theory analysis was applied to study intranetwork local efficiency and strength of connectivity within six anatomically defined brain networks. RESULTS We observed a significant decrease in the local efficiency (-24.9 ± 51.4%, r = 0.7, p < 0.01) and strength (-14.5 ± 26.8%, r = 0.5, p < 0.01) of functional connectivity within the frontal lobe resting-state network and strength within the parietal lobe resting-state network (-7.5 ± 17.3%, r = 0.1, p < 0.01), as well as a concomitant increase in the local efficiency (+55.0 +/- 59.8%, r = 0.5, p < 0.01) and strength (+47.4 +/- 47.3%, r = 0.5, p < 0.01) within the mediotemporal networks. These alterations in network organization were associated with changes in performance on verbal memory (p < 0.05) and executive function (p < 0.05). We did not observe a significant relationship between the frequency or cumulative magnitude of impacts sustained during the season and neurocognitive or imaging outcomes (p > 0.05). CONCLUSION Our findings suggest the efficiency and strength of resting-state networks are altered across a season of high school football, but the association of exposure levels to subconcussive impacts is unclear. ADVANCES IN KNOWLEDGE The efficiency of resting-state networks is dynamic in high school football athletes; such changes may be related to impacts sustained during the season, though further study is needed.
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Affiliation(s)
- Connor W Ghiles
- Wake Forest University School of Medicine, Winston-Salem, United States
| | - Michael D Clark
- Department of Exercise and Sport Science, University of North Carolina, Chapel Hill, United States
| | | | - Melissa A Fraser
- Department of Exercise and Sport Science, University of North Carolina, Chapel Hill, United States
| | | | - Kevin M Guskiewicz
- Department of Exercise and Sport Science, University of North Carolina, Chapel Hill, United States
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4
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Wen X, Yang M, Hsu L, Zhang D. Test-retest reliability of modular-relevant analysis in brain functional network. Front Neurosci 2022; 16:1000863. [PMID: 36570835 PMCID: PMC9770801 DOI: 10.3389/fnins.2022.1000863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 11/22/2022] [Indexed: 12/13/2022] Open
Abstract
Introduction The human brain could be modeled as a complex network via functional magnetic resonance imaging (fMRI), and the architecture of these brain functional networks can be studied from multiple spatial scales with different graph theory tools. Detecting modules is an important mesoscale network measuring approach that has provided crucial insights for uncovering how brain organizes itself among different functional subsystems. Despite its successful application in a wide range of brain network studies, the lack of comprehensive reliability assessment prevents its potential extension to clinical trials. Methods To fill this gap, this paper, using resting-state test-retest fMRI data, systematically explored the reliabilities of five popular network metrics derived from modular structure. Considering the repeatability of network partition depends heavily on network size and module detection algorithm, we constructed three types of brain functional networks for each subject by using a set of coarse-to-fine brain atlases and adopted four methods for single-subject module detection and twelve methods for group-level module detection. Results The results reported moderate-to-good reliability in modularity, intra- and inter-modular functional connectivities, within-modular degree and participation coefficient at both individual and group levels, indicating modular-relevant network metrics can provide robust evaluation results. Further analysis identified the significant influence of module detection algorithm and node definition approach on reliabilities of network partitions and its derived network analysis results. Discussion This paper provides important guidance for choosing reliable modular-relevant network metrics and analysis strategies in future studies.
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Affiliation(s)
- Xuyun Wen
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China
- MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing, Jiangsu, China
| | - Mengting Yang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China
- MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing, Jiangsu, China
| | - Liming Hsu
- Center for Animal MRI, University of North Carolina, Chapel Hill, Chapel Hill, NC, United States
| | - Daoqiang Zhang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China
- MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing, Jiangsu, China
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5
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Casas-Roma J, Martinez-Heras E, Solé-Ribalta A, Solana E, Lopez-Soley E, Vivó F, Diaz-Hurtado M, Alba-Arbalat S, Sepulveda M, Blanco Y, Saiz A, Borge-Holthoefer J, Llufriu S, Prados F. Applying multilayer analysis to morphological, structural, and functional brain networks to identify relevant dysfunction patterns. Netw Neurosci 2022; 6:916-933. [PMID: 36605412 PMCID: PMC9810367 DOI: 10.1162/netn_a_00258] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 06/07/2022] [Indexed: 01/09/2023] Open
Abstract
In recent years, research on network analysis applied to MRI data has advanced significantly. However, the majority of the studies are limited to single networks obtained from resting-state fMRI, diffusion MRI, or gray matter probability maps derived from T1 images. Although a limited number of previous studies have combined two of these networks, none have introduced a framework to combine morphological, structural, and functional brain connectivity networks. The aim of this study was to combine the morphological, structural, and functional information, thus defining a new multilayer network perspective. This has proved advantageous when jointly analyzing multiple types of relational data from the same objects simultaneously using graph- mining techniques. The main contribution of this research is the design, development, and validation of a framework that merges these three layers of information into one multilayer network that links and relates the integrity of white matter connections with gray matter probability maps and resting-state fMRI. To validate our framework, several metrics from graph theory are expanded and adapted to our specific domain characteristics. This proof of concept was applied to a cohort of people with multiple sclerosis, and results show that several brain regions with a synchronized connectivity deterioration could be identified.
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Affiliation(s)
- Jordi Casas-Roma
- e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain,* Corresponding Author:
| | - Eloy Martinez-Heras
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clínic de Barcelona, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain
| | | | - Elisabeth Solana
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clínic de Barcelona, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain
| | - Elisabet Lopez-Soley
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clínic de Barcelona, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain
| | - Francesc Vivó
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clínic de Barcelona, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain
| | | | - Salut Alba-Arbalat
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clínic de Barcelona, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain
| | - Maria Sepulveda
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clínic de Barcelona, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain
| | - Yolanda Blanco
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clínic de Barcelona, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain
| | - Albert Saiz
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clínic de Barcelona, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain
| | | | - Sara Llufriu
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clínic de Barcelona, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain
| | - Ferran Prados
- e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain,Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom,Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
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6
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Sen S, Khalsa NN, Tong N, Ovadia-Caro S, Wang X, Bi Y, Striem-Amit E. The Role of Visual Experience in Individual Differences of Brain Connectivity. J Neurosci 2022; 42:5070-5084. [PMID: 35589393 PMCID: PMC9233442 DOI: 10.1523/jneurosci.1700-21.2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 04/05/2022] [Accepted: 04/09/2022] [Indexed: 11/21/2022] Open
Abstract
Visual cortex organization is highly consistent across individuals. But to what degree does this consistency depend on life experience, in particular sensory experience? In this study, we asked whether visual cortex reorganization in congenital blindness results in connectivity patterns that are particularly variable across individuals, focusing on resting-state functional connectivity (RSFC) patterns from the primary visual cortex. We show that the absence of shared visual experience results in more variable RSFC patterns across blind individuals than sighted controls. Increased variability is specifically found in areas that show a group difference between the blind and sighted in their RSFC. These findings reveal a relationship between brain plasticity and individual variability; reorganization manifests variably across individuals. We further investigated the different patterns of reorganization in the blind, showing that the connectivity to frontal regions, proposed to have a role in the reorganization of the visual cortex of the blind toward higher cognitive roles, is highly variable. Further, we link some of the variability in visual-to-frontal connectivity to another environmental factor-duration of formal education. Together, these findings show a role of postnatal sensory and socioeconomic experience in imposing consistency on brain organization. By revealing the idiosyncratic nature of neural reorganization, these findings highlight the importance of considering individual differences in fitting sensory aids and restoration approaches for vision loss.SIGNIFICANCE STATEMENT The typical visual system is highly consistent across individuals. What are the origins of this consistency? Comparing the consistency of visual cortex connectivity between people born blind and sighted people, we showed that blindness results in higher variability, suggesting a key impact of postnatal individual experience on brain organization. Further, connectivity patterns that changed following blindness were particularly variable, resulting in diverse patterns of brain reorganization. Individual differences in reorganization were also directly affected by nonvisual experiences in the blind (years of formal education). Together, these findings show a role of sensory and socioeconomic experiences in creating individual differences in brain organization and endorse the use of individual profiles for rehabilitation and restoration of vision loss.
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Affiliation(s)
- Sriparna Sen
- Department of Neuroscience, Georgetown University Medical Center, Washington, DC 20057
| | - Nanak Nihal Khalsa
- Department of Neuroscience, Georgetown University Medical Center, Washington, DC 20057
| | - Ningcong Tong
- Department of Psychology, Harvard University, Cambridge, MA 02138
| | - Smadar Ovadia-Caro
- Department of Cognitive Sciences, University of Haifa, Haifa 3498838, Israel
| | - Xiaoying Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Yanchao Bi
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
- Chinese Institute for Brain Research, Beijing 102206, China
| | - Ella Striem-Amit
- Department of Neuroscience, Georgetown University Medical Center, Washington, DC 20057
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7
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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: 6] [Impact Index Per Article: 3.0] [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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8
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Comparison of test–retest reliability of BOLD and pCASL fMRI in a two-center study. BMC Med Imaging 2022; 22:62. [PMID: 35366813 PMCID: PMC8977011 DOI: 10.1186/s12880-022-00791-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Accepted: 03/21/2022] [Indexed: 11/17/2022] Open
Abstract
Background The establishment of test–retest reliability and reproducibility (TRR) is an important part of validating any research tool, including functional magnetic resonance imaging (fMRI). The primary objective of this study is to investigate the reliability of pseudo-Continuous Arterial Spin Labeling (pCASL) and Blood Oxygen Level Dependent (BOLD) fMRI data acquired across two different scanners in a sample of healthy adults. While single site/single scanner studies have shown acceptable repeatability, TRR of both in a practical multisite study occurring in two facilities spread out across the country with weeks to months between scans is critically needed. Methods Ten subjects were imaged with similar 3 T MRI scanners at the University of Pittsburgh and Massachusetts General Hospital. Finger-tapping and Resting-state data were acquired for both techniques. Analysis of the resting state data for functional connectivity was performed with the Functional Connectivity Toolbox, while analysis of the finger tapping data was accomplished with FSL. pCASL Blood flow data was generated using AST Toolbox. Activated areas and networks were identified via pre-defined atlases and dual-regression techniques. Analysis for TRR was conducted by comparing pCASL and BOLD images in terms of Intraclass correlation coefficients, Dice Similarity Coefficients, and repeated measures ANOVA. Results Both BOLD and pCASL scans showed strong activation and correlation between the two locations for the finger tapping tasks. Functional connectivity analyses identified elements of the default mode network in all resting scans at both locations. Multivariate repeated measures ANOVA showed significant variability between subjects, but no significant variability for location. Global CBF was very similar between the two scanning locations, and repeated measures ANOVA showed no significant differences between the two scanning locations. Conclusions The results of this study show that when similar scanner hardware and software is coupled with identical data analysis protocols, consistent and reproducible functional brain images can be acquired across sites. The variability seen in the activation maps is greater for pCASL versus BOLD images, as expected, however groups maps are remarkably similar despite the low number of subjects. This demonstrates that multi-site fMRI studies of task-based and resting state brain activity is feasible.
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9
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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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10
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Individual differences in (dis)honesty are represented in the brain's functional connectivity at rest. Neuroimage 2021; 246:118761. [PMID: 34861396 DOI: 10.1016/j.neuroimage.2021.118761] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 11/23/2021] [Accepted: 11/23/2021] [Indexed: 01/22/2023] Open
Abstract
Measurement of the determinants of socially undesirable behaviors, such as dishonesty, are complicated and obscured by social desirability biases. To circumvent these biases, we used connectome-based predictive modeling (CPM) on resting state functional connectivity patterns in combination with a novel task which inconspicuously measures voluntary cheating to gain access to the neurocognitive determinants of (dis)honesty. Specifically, we investigated whether task-independent neural patterns within the brain at rest could be used to predict a propensity for (dis)honest behavior. Our analyses revealed that functional connectivity, especially between brain networks linked to self-referential thinking (vmPFC, temporal poles, and PCC) and reward processing (caudate nucleus), reliably correlates, in an independent sample, with participants' propensity to cheat. Participants who cheated the most also scored highest on several self-report measures of impulsivity which underscores the generalizability of our results. Notably, when comparing neural and self-report measures, the neural measures were found to be more important in predicting cheating propensity.
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11
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Ionescu TM, Amend M, Hafiz R, Biswal BB, Wehrl HF, Herfert K, Pichler BJ. Elucidating the complementarity of resting-state networks derived from dynamic [ 18F]FDG and hemodynamic fluctuations using simultaneous small-animal PET/MRI. Neuroimage 2021; 236:118045. [PMID: 33848625 PMCID: PMC8339191 DOI: 10.1016/j.neuroimage.2021.118045] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 03/15/2021] [Accepted: 04/04/2021] [Indexed: 12/02/2022] Open
Abstract
Functional connectivity (FC) and resting-state network (RSN) analyses using functional magnetic resonance imaging (fMRI) have evolved into a growing field of research and have provided useful biomarkers for the assessment of brain function in neurological disorders. However, the underlying mechanisms of the blood oxygen level-dependant (BOLD) signal are not fully resolved due to its inherent complexity. In contrast, [18F]fluorodeoxyglucose positron emission tomography ([18F]FDG-PET) has been shown to provide a more direct measure of local synaptic activity and may have additional value for the readout and interpretation of brain connectivity. We performed an RSN analysis from simultaneously acquired PET/fMRI data on a single-subject level to directly compare fMRI and [18F]FDG-PET-derived networks during the resting state. Simultaneous [18F]FDG-PET/fMRI scans were performed in 30 rats. Pairwise correlation analysis, as well as independent component analysis (ICA), were used to compare the readouts of both methods. We identified three RSNs with a high degree of similarity between PET and fMRI-derived readouts: the default-mode-like network (DMN), the basal ganglia network and the cerebellar-midbrain network. Overall, [18F]FDG connectivity indicated increased integration between different, often distant, brain areas compared to the results indicated by the more segregated fMRI-derived FC. Additionally, several networks exclusive to either modality were observed using ICA. These networks included mainly bilateral cortical networks of a limited spatial extent for fMRI and more spatially widespread networks for [18F]FDG-PET, often involving several subcortical areas. This is the first study using simultaneous PET/fMRI to report RSNs subject-wise from dynamic [18F]FDG tracer delivery and BOLD fluctuations with both independent component analysis (ICA) and pairwise correlation analysis in small animals. Our findings support previous studies, which show a close link between local synaptic glucose consumption and BOLD-fMRI-derived FC. However, several brain regions were exclusively attributed to either [18F]FDG or BOLD-derived networks underlining the complementarity of this hybrid imaging approach, which may contribute to the understanding of brain functional organization and could be of interest for future clinical applications.
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Affiliation(s)
- Tudor M Ionescu
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, Eberhard Karls University Tuebingen, Tuebingen, Germany
| | - Mario Amend
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, Eberhard Karls University Tuebingen, Tuebingen, Germany
| | - Rakibul Hafiz
- Department of Biomedical Engineering, New Jersey Institute of Technology, University Heights, Newark, NJ, United States
| | - Bharat B Biswal
- Department of Biomedical Engineering, New Jersey Institute of Technology, University Heights, Newark, NJ, United States
| | - Hans F Wehrl
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, Eberhard Karls University Tuebingen, Tuebingen, Germany
| | - Kristina Herfert
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, Eberhard Karls University Tuebingen, Tuebingen, Germany
| | - Bernd J Pichler
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, Eberhard Karls University Tuebingen, Tuebingen, Germany.
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12
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Engelhardt M, Komnenić D, Roth F, Kawelke L, Finke C, Picht T. No Impact of Functional Connectivity of the Motor System on the Resting Motor Threshold: A Replication Study. Front Neurosci 2021; 15:627445. [PMID: 33867916 PMCID: PMC8044353 DOI: 10.3389/fnins.2021.627445] [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: 11/09/2020] [Accepted: 02/26/2021] [Indexed: 11/29/2022] Open
Abstract
The physiological mechanisms of corticospinal excitability and factors influencing its measurement with transcranial magnetic stimulation are still poorly understood. A recent study reported an impact of functional connectivity (FC) between the primary motor cortex (M1) and the dorsal premotor cortex (PMd) on the resting motor threshold (RMT) of the dominant hemisphere. We aimed to replicate these findings in a larger sample of 38 healthy right-handed subjects with data from both hemispheres. Resting-state FC was assessed between the M1 and five a priori defined motor-relevant regions on each hemisphere as well as interhemispherically between both primary motor cortices. Following the procedure by the original authors, we included age, cortical gray matter volume, and coil-to-cortex distance (CCD) as further predictors in the analysis. We report replication models for the dominant hemisphere as well as an extension to data from both hemispheres and support the results with Bayes factors. FC between the M1 and the PMd did not explain the variability in the RMT, and we obtained moderate evidence for the absence of this effect. In contrast, CCD could be confirmed as an important predictor with strong evidence. These findings contradict the previously proposed effect, thus questioning the notion of the PMd playing a major role in modifying corticospinal excitability.
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Affiliation(s)
- Melina Engelhardt
- Charité – Universitätsmedizin Berlin, Department of Neurosurgery, Berlin, Germany
- Charité – Universitätsmedizin Berlin, Einstein Center for Neurosciences, Berlin, Germany
| | - Darko Komnenić
- Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Fabia Roth
- Charité – Universitätsmedizin Berlin, Department of Neurosurgery, Berlin, Germany
| | - Leona Kawelke
- Charité – Universitätsmedizin Berlin, Department of Neurosurgery, Berlin, Germany
| | - Carsten Finke
- Charité – Universitätsmedizin Berlin, Einstein Center for Neurosciences, Berlin, Germany
- Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
- Charité – Universitätsmedizin Berlin, Department of Neurology, Berlin, Germany
| | - Thomas Picht
- Charité – Universitätsmedizin Berlin, Department of Neurosurgery, Berlin, Germany
- Charité – Universitätsmedizin Berlin, Einstein Center for Neurosciences, Berlin, Germany
- Cluster of Excellence Matters of Activity, Image Space Material, Humboldt-Universität zu Berlin, Berlin, Germany
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13
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McCusker MC, Lew BJ, Wilson TW. Three-Year Reliability of MEG Visual and Somatosensory Responses. Cereb Cortex 2021; 31:2534-2548. [PMID: 33341876 DOI: 10.1093/cercor/bhaa372] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 11/12/2020] [Accepted: 11/12/2020] [Indexed: 12/14/2022] Open
Abstract
A major goal of many translational neuroimaging studies is the identification of biomarkers of disease. However, a prerequisite for any such biomarker is robust reliability, which for magnetoencephalography (MEG) and many other imaging modalities has not been established. In this study, we examined the reliability of visual (Experiment 1) and somatosensory gating (Experiment 2) responses in 19 healthy adults who repeated these experiments for three visits spaced 18 months apart. Visual oscillatory and somatosensory oscillatory and evoked responses were imaged, and intraclass correlation coefficients (ICC) were computed to examine the long-term reliability of these responses. In Experiment 1, ICCs showed good reliability for visual theta and alpha responses in occipital cortices, but poor reliability for gamma responses. In Experiment 2, the time series of somatosensory gamma and evoked responses in the contralateral somatosensory cortex showed good reliability. Finally, analyses of spontaneous baseline activity indicated excellent reliability for occipital alpha, moderate reliability for occipital theta, and poor reliability for visual/somatosensory gamma activity. Overall, MEG responses to visual and somatosensory stimuli show a high degree of reliability across 3 years and therefore may be stable indicators of sensory processing long term and thereby of potential interest as biomarkers of disease.
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Affiliation(s)
- Marie C McCusker
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, 68010, USA
| | - Brandon J Lew
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, 68010, USA.,College of Medicine, University of Nebraska Medical Center, Omaha, NE, 68198, USA
| | - Tony W Wilson
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, 68010, USA.,College of Medicine, University of Nebraska Medical Center, Omaha, NE, 68198, USA
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14
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Haas SS, Antonucci LA, Wenzel J, Ruef A, Biagianti B, Paolini M, Rauchmann BS, Weiske J, Kambeitz J, Borgwardt S, Brambilla P, Meisenzahl E, Salokangas RKR, Upthegrove R, Wood SJ, Koutsouleris N, Kambeitz-Ilankovic L. A multivariate neuromonitoring approach to neuroplasticity-based computerized cognitive training in recent onset psychosis. Neuropsychopharmacology 2021; 46:828-835. [PMID: 33027802 PMCID: PMC8027389 DOI: 10.1038/s41386-020-00877-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Revised: 09/11/2020] [Accepted: 09/15/2020] [Indexed: 02/07/2023]
Abstract
Two decades of studies suggest that computerized cognitive training (CCT) has an effect on cognitive improvement and the restoration of brain activity. Nevertheless, individual response to CCT remains heterogenous, and the predictive potential of neuroimaging in gauging response to CCT remains unknown. We employed multivariate pattern analysis (MVPA) on whole-brain resting-state functional connectivity (rsFC) to (neuro)monitor clinical outcome defined as psychosis-likeness change after 10-hours of CCT in recent onset psychosis (ROP) patients. Additionally, we investigated if sensory processing (SP) change during CCT is associated with individual psychosis-likeness change and cognitive gains after CCT. 26 ROP patients were divided into maintainers and improvers based on their SP change during CCT. A support vector machine (SVM) classifier separating 56 healthy controls (HC) from 35 ROP patients using rsFC (balanced accuracy of 65.5%, P < 0.01) was built in an independent sample to create a naturalistic model representing the HC-ROP hyperplane. This model was out-of-sample cross-validated in the ROP patients from the CCT trial to assess associations between rsFC pattern change, cognitive gains and SP during CCT. Patients with intact SP threshold at baseline showed improved attention despite psychosis status on the SVM hyperplane at follow-up (p < 0.05). Contrarily, the attentional gains occurred in the ROP patients who showed impaired SP at baseline only if rsfMRI diagnosis status shifted to the healthy-like side of the SVM continuum. Our results reveal the utility of MVPA for elucidating treatment response neuromarkers based on rsFC-SP change and pave the road to more personalized interventions.
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Affiliation(s)
- Shalaila S. Haas
- grid.59734.3c0000 0001 0670 2351Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Linda A. Antonucci
- grid.5252.00000 0004 1936 973XDepartment of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany ,grid.7644.10000 0001 0120 3326Department of Education, Psychology, Communication – University of Bari “Aldo Moro”, Bari, Italy
| | - Julian Wenzel
- grid.6190.e0000 0000 8580 3777University of Cologne, Faculty of Medicine and University Hospital of Cologne, Cologne, Germany
| | - Anne Ruef
- grid.5252.00000 0004 1936 973XDepartment of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany
| | - Bruno Biagianti
- grid.438587.50000 0004 0450 1574Department of R&D, Posit Science Corporation, San Francisco, CA USA ,grid.4708.b0000 0004 1757 2822Department of Pathophysiology and Transplantation, Faculty of Medicine and Surgery, University of Milan, Milan, Italy
| | - Marco Paolini
- Department of Radiology, University Hospital, Ludwig-Maximilian University, Munich, Germany
| | - Boris-Stephan Rauchmann
- grid.5252.00000 0004 1936 973XDepartment of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany ,Department of Radiology, University Hospital, Ludwig-Maximilian University, Munich, Germany
| | - Johanna Weiske
- grid.5252.00000 0004 1936 973XDepartment of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany
| | - Joseph Kambeitz
- grid.6190.e0000 0000 8580 3777University of Cologne, Faculty of Medicine and University Hospital of Cologne, Cologne, Germany
| | - Stefan Borgwardt
- grid.4562.50000 0001 0057 2672Translational Psychiatry Unit (TPU), Department of Psychiatry and Psychotherapy, University of Luebeck, Luebeck, Germany
| | - Paolo Brambilla
- grid.414818.00000 0004 1757 8749Department of Neuroscience and Mental Health, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy ,grid.4708.b0000 0004 1757 2822Department of Pathophysiology and Mental Health, University of Milan, Milan, Italy
| | - Eva Meisenzahl
- grid.411327.20000 0001 2176 9917Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich-Heine University, Düsseldorf, Germany
| | - Raimo K. R. Salokangas
- grid.1374.10000 0001 2097 1371Department of Psychiatry, University of Turku, Turku, Finland
| | - Rachel Upthegrove
- grid.6572.60000 0004 1936 7486School of Psychology, University of Birmingham, Birmingham, United Kingdom ,grid.6572.60000 0004 1936 7486Institute of Mental Health, University of Birmingham, Birmingham, United Kingdom
| | - Stephen J. Wood
- grid.6572.60000 0004 1936 7486School of Psychology, University of Birmingham, Birmingham, United Kingdom ,grid.488501.0Orygen, the National Centre of Excellence for Youth Mental Health, Melbourne, VIC Australia ,grid.1008.90000 0001 2179 088XCentre for Youth Mental Health, University of Melbourne, Melbourne, VIC Australia
| | - Nikolaos Koutsouleris
- grid.5252.00000 0004 1936 973XDepartment of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany
| | - Lana Kambeitz-Ilankovic
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany. .,University of Cologne, Faculty of Medicine and University Hospital of Cologne, Cologne, Germany.
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15
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Hwang SN. Editorial for "Decoupling of Gray and White Matter Functional Networks in Medication-Naive Patients With Major Depressive Disorder". J Magn Reson Imaging 2020; 53:753-754. [PMID: 33331026 DOI: 10.1002/jmri.27467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2020] [Accepted: 11/30/2020] [Indexed: 11/06/2022] Open
Affiliation(s)
- Scott N Hwang
- Department of Diagnostic Imaging, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
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16
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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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17
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Hyperactive sensorimotor cortex during voice perception in spasmodic dysphonia. Sci Rep 2020; 10:17298. [PMID: 33057071 PMCID: PMC7566443 DOI: 10.1038/s41598-020-73450-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2019] [Accepted: 09/17/2020] [Indexed: 11/30/2022] Open
Abstract
Spasmodic dysphonia (SD) is characterized by an involuntary laryngeal muscle spasm during vocalization. Previous studies measured brain activation during voice production and suggested that SD arises from abnormal sensorimotor integration involving the sensorimotor cortex. However, it remains unclear whether this abnormal sensorimotor activation merely reflects neural activation produced by abnormal vocalization. To identify the specific neural correlates of SD, we used a sound discrimination task without overt vocalization to compare neural activation between 11 patients with SD and healthy participants. Participants underwent functional MRI during a two-alternative judgment task for auditory stimuli, which could be modal or falsetto voice. Since vocalization in falsetto is intact in SD, we predicted that neural activation during speech perception would differ between the two groups only for modal voice and not for falsetto voice. Group-by-stimulus interaction was observed in the left sensorimotor cortex and thalamus, suggesting that voice perception activates different neural systems between the two groups. Moreover, the sensorimotor signals positively correlated with disease severity of SD, and classified the two groups with 73% accuracy in linear discriminant analysis. Thus, the sensorimotor cortex and thalamus play a central role in SD pathophysiology and sensorimotor signals can be a new biomarker for SD diagnosis.
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18
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Bernard F, Lemee JM, Mazerand E, Leiber LM, Menei P, Ter Minassian A. The ventral attention network: the mirror of the language network in the right brain hemisphere. J Anat 2020; 237:632-642. [PMID: 32579719 DOI: 10.1111/joa.13223] [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: 09/03/2019] [Revised: 04/28/2020] [Accepted: 05/04/2020] [Indexed: 12/29/2022] Open
Abstract
Resting-state functional MRI (RfMRI) analyses have identified two anatomically separable fronto-parietal attention networks in the human brain: a bilateral dorsal attention network and a right-lateralised ventral attention network (VAN). The VAN has been implicated in visuospatial cognition and, thus, potentially in the unilateral spatial neglect associated with right hemisphere lesions. Its parietal, frontal and temporal endpoints are thought to be structurally supported by undefined white matter tracts. We investigated the white matter tract connecting the VAN. We used three approaches to study the structural anatomy of the VAN: (a) independent component analysis on RfMRI (50 subjects), defining the endpoints of the VAN, (b) tractography in the same 50 healthy volunteers, with regions of interest defined by the MNI coordinates of cortical areas involved in the VAN used in a seed-based approach and (c) dissection, by Klingler's method, of 20 right hemispheres, for ex vivo studies of the fibre tracts connecting VAN endpoints. The VAN includes the temporoparietal junction and the ventral frontal cortex. The endpoints of the superior longitudinal fasciculus in its third portion (SLF III) and the arcuate fasciculus (AF) overlap with the VAN endpoints. The SLF III connects the supramarginal gyrus to the ventral portion of the precentral gyrus and the pars opercularis. The AF connects the middle and inferior temporal gyrus and the middle and inferior frontal gyrus. We reconstructed the structural connectivity of the VAN and considered it in the context if the pathophysiology of unilateral neglect and right hemisphere awake brain surgery.
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Affiliation(s)
- Florian Bernard
- Laboratory of Anatomy, Faculté de Médecine, Angers, France.,Department of Neurosurgery, Angers Teaching Hospital, Angers, France.,UMR 1232 INSERM/CNRS and EA7315 Team, CRCINA, Angers, France
| | - Jean-Michel Lemee
- Department of Neurosurgery, Angers Teaching Hospital, Angers, France.,UMR 1232 INSERM/CNRS and EA7315 Team, CRCINA, Angers, France
| | - Edouard Mazerand
- Department of Neurosurgery, Angers Teaching Hospital, Angers, France
| | | | - Philippe Menei
- Department of Neurosurgery, Angers Teaching Hospital, Angers, France.,UMR 1232 INSERM/CNRS and EA7315 Team, CRCINA, Angers, France
| | - Aram Ter Minassian
- Department of Reanimation, Angers Teaching Hospital, Angers, France.,EA7315 Team, INSERM 1066, Angers, France
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19
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Yang J, Gohel S, Vachha B. Current methods and new directions in resting state fMRI. Clin Imaging 2020; 65:47-53. [PMID: 32353718 DOI: 10.1016/j.clinimag.2020.04.004] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Revised: 03/24/2020] [Accepted: 04/08/2020] [Indexed: 12/12/2022]
Abstract
Resting state functional connectivity magnetic resonance imaging (rsfcMRI) has become a key component of investigations of neurocognitive and psychiatric behaviors. Over the past two decades, several methods and paradigms have been adopted to utilize and interpret data from resting-state fluctuations in the brain. These findings have increased our understanding of changes in many disease states. As the amount of resting state data available for research increases with big datasets and data-sharing projects, it is important to review the established traditional analysis methods and recognize areas where research methodology can be adapted to better accommodate the scale and complexity of rsfcMRI analysis. In this paper, we review established methods of analysis as well as areas that have been receiving increasing attention such as dynamic rsfcMRI, independent vector analysis, multiband rsfcMRI and network of networks.
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Affiliation(s)
- Jackie Yang
- NYU Grossman School of Medicine, 550 1(st) Avenue, New York, NY 10016, USA
| | - Suril Gohel
- Department of Health Informatics, Rutgers University School of Health Professions, 65 Bergen Street, Newark, NJ 07107, USA
| | - Behroze Vachha
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065, USA.
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20
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Warren SM, Chou YH, Steklis HD. Potential for Resting-State fMRI of the Amygdala in Elucidating Neural Mechanisms of Adaptive Self-Regulatory Strategies: A Systematic Review. Brain Connect 2020; 10:3-17. [PMID: 31950847 DOI: 10.1089/brain.2019.0700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Evolutionary-developmental theories consider the evolved mechanisms underlying adaptive behavioral strategies shaped in response to early environmental cues. Identifying neural mechanisms mediating processes of conditional adaptation in humans is an active area of research. Resting-state functional magnetic resonance imaging (RS-fMRI) captures functional connectivity theorized to represent the underlying functional architecture of the brain. This allows for investigating how underlying functional brain connections are related to early experiences during development, as well as current traits and behaviors. This review explores the potential of RS-fMRI of the amygdala (AMY) for advancing research on the neural mechanisms underlying adaptive strategies developed in early adverse environments. RS-fMRI studies of early life stress (ELS) and AMY functional connectivity within the frame of evolutionary theories are reviewed, specifically regarding the development of self-regulatory strategies. The potential of RS-fMRI for investigating the effects of ELS on developmental trajectories of self-regulation is discussed.
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Affiliation(s)
- Shannon M Warren
- Norton School of Family & Consumer Sciences, The University of Arizona, Tucson, Arizona
| | - Ying-Hui Chou
- Department of Psychology, Graduate Interdisciplinary Program in Cognitive Science, Arizona Center on Aging, BIO5 Institute, Evelyn F. McKnight Brain Institute, The University of Arizona, Tucson, Arizona
| | - Horst Dieter Steklis
- School of Animal and Comparative Biomedical Sciences, The University of Arizona, Tucson, Arizona
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21
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Argaman Y, Kisler LB, Granovsky Y, Coghill RC, Sprecher E, Manor D, Weissman-Fogel I. The Endogenous Analgesia Signature in the Resting Brain of Healthy Adults and Migraineurs. THE JOURNAL OF PAIN 2020; 21:905-918. [PMID: 31904502 DOI: 10.1016/j.jpain.2019.12.006] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Accepted: 12/19/2019] [Indexed: 12/15/2022]
Abstract
Altered pain modulation and resting state functional connectivity (rsFC) were found to be related to migraine pathology and clinical manifestation. We examined how pain modulation psychophysical measures are related to resting-state networks and rsFC between bottom-up and top-down pain modulation areas. Thirty-two episodic migraineurs and 23 age-matched healthy individuals underwent temporal summation of pain (TSOP) and conditioned pain modulation (CPM) tests, followed by a resting-state imaging scan. No differences in temporal summation of pain and CPM were found between groups. However, in healthy individuals, more efficient CPM was correlated with 1) stronger rsFCs of the posterior cingulate cortex, with the ventromedial prefrontal cortex and with the pregenual anterior cingulate cortex; 2) weaker rsFC of the anterior insula with the angular gyrus. Conversely, in migraineurs, the association between CPM and rsFC was altered. Our results suggest that the functional connectivity within the default mode network (DMN) components and the functional coupling between the DMN and pain inhibitory brain areas is linked with pain inhibition efficiency. In migraineurs, this interplay is changed, yet enables normal pain inhibition. Our findings shed light on potential functional adaptation of the DMN and its role in pain inhibition in health and migraine. PERSPECTIVE: This article establishes evidence for the relationship between the resting-state brain and individual responses in psychophysical pain modulation tests, in both migraine and healthy individuals. The results emphasize the significant role of the default mode network in maintaining pain inhibition efficiency in health and in the presence of chronic pain.
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Affiliation(s)
- Yuval Argaman
- Laboratory of Clinical Neurophysiology, Technion Faculty of Medicine, Haifa, Israel
| | - Lee B Kisler
- Laboratory of Clinical Neurophysiology, Technion Faculty of Medicine, Haifa, Israel
| | - Yelena Granovsky
- Laboratory of Clinical Neurophysiology, Technion Faculty of Medicine, Haifa, Israel; Department of Neurology, Rambam Health Care Campus, Haifa, Israel
| | - Robert C Coghill
- Department of Anesthesiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | - Elliot Sprecher
- Department of Neurology, Rambam Health Care Campus, Haifa, Israel
| | - David Manor
- MRI Unit, Rambam Health Care Campus, Haifa, Israel; Faculty of Natural Sciences, University of Haifa, Haifa, Israel
| | - Irit Weissman-Fogel
- Faculty of Social Welfare and Health Sciences, University of Haifa, Haifa, Israel.
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22
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Dinis Fernandes C, Varsou O, Stringer M, Macleod MJ, Schwarzbauer C. Scanning Conditions in Functional Connectivity Magnetic Resonance Imaging: How to Standardise Resting-State for Optimal Data Acquisition and Visualisation? ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2020; 1235:35-52. [PMID: 32488635 DOI: 10.1007/978-3-030-37639-0_3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Functional connectivity magnetic resonance imaging (fcMRI), performed during resting wakefulness without tasks or stimulation, is a non-invasive technique to assess and visualise functional brain networks in vivo. Acquisition of resting-state imaging data has become increasingly common in longitudinal studies to investigate brain health and disease. However, the scanning protocols vary considerably across different institutions creating challenges for comparability especially for the interpretation of findings in patient cohorts and establishment of diagnostic or prognostic imaging biomarkers. The aim of this chapter is to discuss the effect of two experimental conditions (i.e. a low cognitive demand paradigm and a pure resting-state fcMRI) on the reproducibility of brain networks between a baseline and a follow-up session, 30 (±5) days later, acquired from 12 right-handed volunteers (29 ± 5 yrs). A novel method was developed and used for a direct statistical comparison of the test-retest reliability using 28 well-established functional brain networks. Overall, both scanning conditions produced good levels of test-retest reliability. While the pure resting-state condition showed higher test-retest reliability for 18 of the 28 analysed networks, the low cognitive demand paradigm produced higher test-retest reliability for 8 of the 28 brain networks (i.e. visual, sensorimotor and frontal areas); in 2 of the 28 brain networks no significant changes could be detected. These results are relevant to planning of longitudinal studies, as higher test-retest reliability generally increases statistical power. This work also makes an important contribution to neuroimaging where optimising fcMRI experimental scanning conditions, and hence data visualisation of brain function, remains an on-going topic of interest. In this chapter, we provide a full methodological explanation of the two paradigms and our analysis so that readers can apply them to their own scanning protocols.
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Affiliation(s)
| | - Ourania Varsou
- School of Life Sciences, Anatomy Facility, University of Glasgow, Glasgow, Scotland, UK
| | - Michael Stringer
- Edinburgh Imaging, University of Edinburgh, Edinburgh, Scotland, UK
| | - Mary Joan Macleod
- The Institute of Medical Sciences, King's College, University of Aberdeen, Aberdeen, Scotland, UK
| | - Christian Schwarzbauer
- Faculty of Applied Sciences & Mechatronics, Munich University of Applied Sciences, Munich, Germany
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23
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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: 295] [Impact Index Per Article: 59.0] [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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24
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Resting state coupling between the amygdala and ventromedial prefrontal cortex is related to household income in childhood and indexes future psychological vulnerability to stress. Dev Psychopathol 2019; 31:1053-1066. [PMID: 31084654 DOI: 10.1017/s0954579419000592] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
While child poverty is a significant risk factor for poor mental health, the developmental pathways involved with these associations are poorly understood. To advance knowledge about these important linkages, the present study examined the developmental sequelae of childhood exposure to poverty in a multiyear longitudinal study. Here, we focused on exposure to poverty, neurobiological circuitry connected to emotion dysregulation, later exposure to stressful life events, and symptoms of psychopathology. We grounded our work in a biopsychosocial perspective, with a specific interest in "stress sensitization" and emotion dysregulation. Motivated by past work, we first tested whether exposure to poverty was related to changes in the resting-state coupling between two brain structures centrally involved with emotion processing and regulation (the amygdala and the ventromedial prefrontal cortex; vmPFC). As predicted, we found lower household income at age 10 was related to lower resting-state coupling between these areas at age 15. We then tested if variations in amygdala-vmPFC connectivity interacted with more contemporaneous stressors to predict challenges with mental health at age 16. In line with past reports showing risk for poor mental health is greatest in those exposed to early and then later, more contemporaneous stress, we predicted and found that lower vmPFC-amygdala coupling in the context of greater contemporaneous stress was related to higher levels of internalizing and externalizing symptoms. We believe these important interactions between neurobiology and life history are an additional vantage point for understanding risk and resiliency, and suggest avenues for prediction of psychopathology related to early life challenge.
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25
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O'Connor EE, Zeffiro TA. Why is Clinical fMRI in a Resting State? Front Neurol 2019; 10:420. [PMID: 31068901 PMCID: PMC6491723 DOI: 10.3389/fneur.2019.00420] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2019] [Accepted: 04/05/2019] [Indexed: 12/28/2022] Open
Abstract
While resting state fMRI (rs-fMRI) has gained widespread application in neuroimaging clinical research, its penetration into clinical medicine has been more limited. We surveyed a neuroradiology professional group to ascertain their experience with rs-fMRI, identify perceived barriers to using rs-fMRI clinically and elicit suggestions about ways to facilitate its use in clinical practice. The electronic survey also collected information about demographics and work environment using Likert scales. We found that 90% of the respondents had adequate equipment to conduct rs-fMRI and 82% found rs-fMRI data easy to collect. Fifty-nine percent have used rs-fMRI in their past research and 72% reported plans to use rs-fMRI for research in the next year. Nevertheless, only 40% plan to use rs-fMRI in clinical practice in the next year and 82% agreed that their clinical fMRI use is largely confined to pre-surgical planning applications. To explore the reasons for the persistent low utilization of rs-fMRI in clinical applications, we identified barriers to clinical rs-fMRI use related to the availability of robust denoising procedures, single-subject analysis techniques, demonstration of functional connectivity map reliability, regulatory clearance, reimbursement, and neuroradiologist training opportunities. In conclusion, while rs-fMRI use in clinical neuroradiology practice is limited, enthusiasm appears to be quite high and there are several possible avenues in which further research and development may facilitate its penetration into clinical practice.
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Affiliation(s)
- Erin E O'Connor
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland Medical Center, Baltimore, MD, United States
| | - Thomas A Zeffiro
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland Medical Center, Baltimore, MD, United States
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26
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Fede SJ, Grodin EN, Dean SF, Diazgranados N, Momenan R. Resting state connectivity best predicts alcohol use severity in moderate to heavy alcohol users. Neuroimage Clin 2019; 22:101782. [PMID: 30921611 PMCID: PMC6438989 DOI: 10.1016/j.nicl.2019.101782] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2018] [Revised: 02/07/2019] [Accepted: 03/14/2019] [Indexed: 01/12/2023]
Abstract
BACKGROUND In the United States, 13% of adults are estimated to have alcohol use disorder (AUD). Most studies examining the neurobiology of AUD treat individuals with this disorder as a homogeneous group; however, the theories of the neurocircuitry of AUD call for a quantitative and dimensional approach. Previous imaging studies find differences in brain structure, function, and resting-state connectivity in AUD, but few use a multimodal approach to understand the association between severity of alcohol use and the brain differences. METHODS Adults (ages 22-60) with problem drinking patterns (n = 59) completed a behavioral and neuroimaging protocol at the National Institutes of Health. Alcohol severity was quantified with the Alcohol Use Disorders Identification Test (AUDIT). In a 3 T MRI scanner, participants underwent a structural MRI as well as resting-state, monetary incentive delay, and face matching fMRI scans. Machine learning was applied and trained using the neural data from MRI scanning. The model was tested for generalizability in a validation sample (n = 24). RESULTS The resting state-connectivity features model best predicted AUD severity in the naïve sample, compared to task fMRI, structural MRI, combined MRI features, or demographic features. Network connectivity features between salience network, default mode network, executive control network, and sensory networks explained 33% of the variance associated with AUDIT in this model. CONCLUSIONS These findings indicate that the neural effects of AUD vary according to severity. Our results emphasize the utility of resting state fMRI as a neuroimaging biomarker for quantitative clinical evaluation of AUD.
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Affiliation(s)
- Samantha J Fede
- Clinical NeuroImaging Research Core, National Institute of Alcohol Abuse and Alcoholism, National Institutes of Health, 10 Center Drive, Bethesda, MD 20814, MSC 1108, United States.
| | - Erica N Grodin
- Clinical NeuroImaging Research Core, National Institute of Alcohol Abuse and Alcoholism, National Institutes of Health, 10 Center Drive, Bethesda, MD 20814, MSC 1108, United States
| | - Sarah F Dean
- Clinical NeuroImaging Research Core, National Institute of Alcohol Abuse and Alcoholism, National Institutes of Health, 10 Center Drive, Bethesda, MD 20814, MSC 1108, United States
| | - Nancy Diazgranados
- Office of Clinical Director, National Institute of Alcohol Abuse and Alcoholism, National Institutes of Health, 10 Center Drive, Bethesda, MD 20814, MSC 1108, United States
| | - Reza Momenan
- Clinical NeuroImaging Research Core, National Institute of Alcohol Abuse and Alcoholism, National Institutes of Health, 10 Center Drive, Bethesda, MD 20814, MSC 1108, United States.
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27
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Postema MC, De Marco M, Colato E, Venneri A. A study of within-subject reliability of the brain's default-mode network. MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE 2019; 32:391-405. [PMID: 30730023 PMCID: PMC6525123 DOI: 10.1007/s10334-018-00732-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/07/2018] [Revised: 12/19/2018] [Accepted: 12/21/2018] [Indexed: 12/12/2022]
Abstract
Objective Resting-state functional magnetic resonance imaging (fMRI) is promising for Alzheimer’s disease (AD). This study aimed to examine short-term reliability of the default-mode network (DMN), one of the main haemodynamic patterns of the brain. Materials and methods Using a 1.5 T Philips Achieva scanner, two consecutive resting-state fMRI runs were acquired on 69 healthy adults, 62 patients with mild cognitive impairment (MCI) due to AD, and 28 patients with AD dementia. The anterior and posterior DMN and, as control, the visual-processing network (VPN) were computed using two different methodologies: connectivity of predetermined seeds (theory-driven) and dual regression (data-driven). Divergence and convergence in network strength and topography were calculated with paired t tests, global correlation coefficients, voxel-based correlation maps, and indices of reliability. Results No topographical differences were found in any of the networks. High correlations and reliability were found in the posterior DMN of healthy adults and MCI patients. Lower reliability was found in the anterior DMN and in the VPN, and in the posterior DMN of dementia patients. Discussion Strength and topography of the posterior DMN appear relatively stable and reliable over a short-term period of acquisition but with some degree of variability across clinical samples.
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Affiliation(s)
- Merel Charlotte Postema
- Department of Neuroscience, University of Sheffield, Royal Hallamshire Hospital, Beech Hill Road, N Floor, Room N133, Sheffield, S10 2RX, UK.,Faculty of Earth and Life Sciences, VU University Amsterdam, Amsterdam, The Netherlands.,Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
| | - Matteo De Marco
- Department of Neuroscience, University of Sheffield, Royal Hallamshire Hospital, Beech Hill Road, N Floor, Room N133, Sheffield, S10 2RX, UK.
| | - Elisa Colato
- Department of Neuroscience, University of Sheffield, Royal Hallamshire Hospital, Beech Hill Road, N Floor, Room N133, Sheffield, S10 2RX, UK
| | - Annalena Venneri
- Department of Neuroscience, University of Sheffield, Royal Hallamshire Hospital, Beech Hill Road, N Floor, Room N133, Sheffield, S10 2RX, UK
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28
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Xie H, Calhoun VD, Gonzalez-Castillo J, Damaraju E, Miller R, Bandettini PA, Mitra S. Whole-brain connectivity dynamics reflect both task-specific and individual-specific modulation: A multitask study. Neuroimage 2018; 180:495-504. [PMID: 28549798 PMCID: PMC5700856 DOI: 10.1016/j.neuroimage.2017.05.050] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2017] [Revised: 05/18/2017] [Accepted: 05/22/2017] [Indexed: 10/19/2022] Open
Abstract
Functional connectivity (FC) has been widely used to study the functional organization of temporally correlated and spatially distributed brain regions. Recent studies of FC dynamics, quantified by windowed correlations, provide new insights to analyze dynamic, context-dependent reconfiguration of brain networks. A set of reoccurring whole-brain connectivity patterns at rest, referred to as FC states, have been identified, hypothetically reflecting underlying cognitive processes or mental states. We posit that the mean FC information for a given subject represents a significant contribution to the group-level FC dynamics. We show that the subject-specific FC profile, termed as FC individuality, can be removed to increase sensitivity to cognitively relevant FC states. To assess the impact of the FC individuality and task-specific FC modulation on the group-level FC dynamics analysis, we generate and analyze group studies of four subjects engaging in four cognitive conditions (rest, simple math, two-back memory, and visual attention task). We also propose a model to quantitatively evaluate the effect of two factors, namely, subject-specific and task-specific modulation on FC dynamics. We show that FC individuality is a predominant factor in group-level FC variability, and the embedded cognitively relevant FC states are clearly visible after removing the individual's connectivity profile. Our results challenge the current understanding of FC states and emphasize the importance of individual heterogeneity in connectivity dynamics analysis.
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Affiliation(s)
- Hua Xie
- Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, USA; Section on Functional Imaging Methods, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA.
| | - Vince D Calhoun
- The Mind Research Network, Albuquerque, NM, USA; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA
| | - Javier Gonzalez-Castillo
- Section on Functional Imaging Methods, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Eswar Damaraju
- The Mind Research Network, Albuquerque, NM, USA; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA
| | | | - Peter A Bandettini
- Section on Functional Imaging Methods, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA; Functional MRI Facility, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Sunanda Mitra
- Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, USA
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29
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Hernandez-Castillo CR, Diedrichsen J, Aguilar-Castañeda E, Iglesias M. Decoupling between the hand territory and the default mode network after bilateral arm transplantation: four-year follow-up case study. Brain Imaging Behav 2018; 12:296-302. [PMID: 28185062 DOI: 10.1007/s11682-017-9683-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Several studies have suggested both a local and network reorganization of the sensorimotor system following amputation. Transplantation of a new limb results in a new shifting of cortical activity in the local territory of the transplanted limb. However, there is a lack of information about the reversibility of the abnormalities at the network level. The objective of this study was to characterize the functional connectivity changes between the cortical territory of the new hand and two intrinsic network of interest: the sensorimotor network (SMN) and the default mode network (DMN) of one patient whom received bilateral forearm transplants. Using resting-state fMRI these two networks were identified across four different time points, starting four months after the transplantation surgery and during three consecutive years while the patient underwent physical rehabilitation. The topology of the SMN was disrupted at the first acquisition and over the years returned to its canonical pattern. Analysis of the DMN showed the normal topology with no significant changes across acquisitions. Functional connectivity between the missing hand's cortical territory and the SMN increased over time. Accordingly, functional connectivity between the missing hand's cortical territory and the DMN became anticorrelated over time. Our results suggest that after transplantation a new reorganization occurs at the network level, supporting the idea that extreme behavioral changes can affect not only the local rewiring but also the intrinsic network organization in neurologically healthy subjects. Overall this study provides new insight on the complex dynamics of brain organization.
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Affiliation(s)
- Carlos R Hernandez-Castillo
- CONACYT - Instituto de Neuroetologia, Universidad Veracruzana, Av. Luis Cartelazo Ayala s/n, Col. Industrial Ánimas, Xalapa, Veracruz, Mexico. .,The Brain and Mind Institute, Western University, London, Canada.
| | - Jörn Diedrichsen
- The Brain and Mind Institute, Western University, London, Canada
| | - Erika Aguilar-Castañeda
- Instituto Nacional de Neurología y Neurocirugía "Manuel Velasco Suarez", Ciudad de México, Mexico
| | - Martin Iglesias
- Instituto Nacional de Ciencias Médicas y Nutrición "Salvador Zubirán", Ciudad de México, Mexico
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30
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Associations of functional connectivity and walking performance in multiple sclerosis. Neuropsychologia 2018; 117:8-12. [DOI: 10.1016/j.neuropsychologia.2018.05.007] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2018] [Revised: 05/03/2018] [Accepted: 05/07/2018] [Indexed: 11/22/2022]
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31
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Conwell K, von Reutern B, Richter N, Kukolja J, Fink GR, Onur OA. Test-retest variability of resting-state networks in healthy aging and prodromal Alzheimer's disease. NEUROIMAGE-CLINICAL 2018; 19:948-962. [PMID: 30003032 PMCID: PMC6039839 DOI: 10.1016/j.nicl.2018.06.016] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/20/2018] [Revised: 06/11/2018] [Accepted: 06/13/2018] [Indexed: 12/03/2022]
Abstract
In recent years, changes in resting-state networks (RSN), identified by functional magnetic resonance imaging (fMRI), have gained increasing attention as potential biomarkers and trackers of neurological disorders such as Alzheimer's disease (AD). Intersession reliability of RSN is fundamental to this approach. In this study, we investigated the test-retest reliability of three memory related RSN (i.e., the default mode, salience, and executive control network) in 15 young, 15 healthy seniors (HS), and 15 subjects affected by mild cognitive impairment (MCI) with positive biomarkers suggestive of incipient AD (6 females each). FMRI was conducted on three separate occasions. Independent Component Analysis decomposed the resting-state data into RSNs. Comparisons of variation in functional connectivity between groups were made applying different thresholds in an explorative approach. Intersession test-retest reliability was evaluated by intraclass correlation coefficient (ICC) comparisons. To assess the effect of gray matter volume loss, motion, cerebrospinal fluid based biomarkers and the time gap between sessions on intersession variation, the former four were correlated separately with the latter. Data showed that i) young subjects ICCs (relative to HS/MCI-subjects) had higher intersession reliability, ii) stringent statistical thresholds need to be applied to prevent false-positives, iii) both HS and MCI-subjects (relative to young) showed significantly more clusters of intersession variation in all three RSN, iv) while intersession variation was highly correlated with head motion, it was also correlated with biomarkers (especially phospho-tau), the time gap between sessions and local GMV. Results indicate that time gaps between sessions should be kept constant and that head motion must be taken into account when using RSN to assess aging and neurodegeneration. In patients with prodromal AD, re-test reliability may be increased by accouting for overall disease burden by including biomarkers of neuronal injury (especially phospho-tau) in statistical analyses. Local atrophy however, does not seem to play a major role in regards to reliability, but should be used as covariate depending on the research question. Intersession reliability of resting state networks is highest in young subjects. Test-Retest Variability increases with aging and in MCI. Motion and csf-biomarkers correlate with increased variability. Motion and biomarkers should be included as confounders in the statistical models. Stringent statistical thresholds should be applied to prevent type I-errors.
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Affiliation(s)
- K Conwell
- Department of Neurology, University Hospital of Cologne, Cologne 50937, Germany; Department of General, Abdominal, Endocrine and Minimally Invasive Surgery, Academic Hospital Bogenhausen, 81925 Munich, Germany
| | - B von Reutern
- Department of Neurology, University Hospital of Cologne, Cologne 50937, Germany; Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre, Jülich 52428, Germany
| | - N Richter
- Department of Neurology, University Hospital of Cologne, Cologne 50937, Germany; Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre, Jülich 52428, Germany
| | - J Kukolja
- Department of Neurology, University Hospital of Cologne, Cologne 50937, Germany; Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre, Jülich 52428, Germany
| | - G R Fink
- Department of Neurology, University Hospital of Cologne, Cologne 50937, Germany; Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre, Jülich 52428, Germany
| | - O A Onur
- Department of Neurology, University Hospital of Cologne, Cologne 50937, Germany; Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre, Jülich 52428, Germany.
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32
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Alarcón G, Pfeifer JH, Fair DA, Nagel BJ. Adolescent Gender Differences in Cognitive Control Performance and Functional Connectivity Between Default Mode and Fronto-Parietal Networks Within a Self-Referential Context. Front Behav Neurosci 2018; 12:73. [PMID: 29740292 PMCID: PMC5924772 DOI: 10.3389/fnbeh.2018.00073] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2018] [Accepted: 04/03/2018] [Indexed: 11/13/2022] Open
Abstract
Ineffective reduction of functional connectivity between the default mode network (DMN) and frontoparietal network (FPN) during cognitive control can interfere with performance in healthy individuals—a phenomenon present in psychiatric disorders, such as depression. Here, this mechanism is studied in healthy adolescents by examining gender differences in task-regressed functional connectivity using functional magnetic resonance imaging (MRI) and a novel task designed to place the DMN—supporting self-referential processing (SRP)—and FPN—supporting cognitive control—into conflict. Compared to boys, girls showed stronger functional connectivity between DMN and FPN during cognitive control in an SRP context (n = 40; boys = 20), a context that also elicited more errors of omission in girls. The gender difference in errors of omission was mediated by higher self-reported co-rumination—the extensive and repetitive discussion of problems and focus on negative feelings with a same-gender peer—by girls, compared to boys. These findings indicate that placing internal and external attentional demands in conflict lead to persistent functional connectivity between FPN and DMN in girls, but not boys; however, deficits in performance during this context were explained by co-rumination, such that youth with higher co-rumination displayed the largest performance deficits. Previous research shows that co-rumination predicts depressive symptoms during adolescence; thus, gender differences in the mechanisms involved with transitioning from internal to external processing may be relevant for understanding heightened vulnerability for depression in adolescent girls.
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Affiliation(s)
- Gabriela Alarcón
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, United States
| | - Jennifer H Pfeifer
- Department of Psychology, University of Oregon, Eugene, OR, United States
| | - Damien A Fair
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR, United States.,Department of Psychiatry, Oregon Health & Science University, Portland, OR, United States.,Advanced Imaging Research Center, Oregon Health & Science University, Portland, OR, United States
| | - Bonnie J Nagel
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR, United States.,Department of Psychiatry, Oregon Health & Science University, Portland, OR, United States
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33
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Yoris A, Abrevaya S, Esteves S, Salamone P, Lori N, Martorell M, Legaz A, Alifano F, Petroni A, Sánchez R, Sedeño L, García AM, Ibáñez A. Multilevel convergence of interoceptive impairments in hypertension: New evidence of disrupted body-brain interactions. Hum Brain Mapp 2018; 39:1563-1581. [PMID: 29271093 PMCID: PMC6866355 DOI: 10.1002/hbm.23933] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2017] [Accepted: 12/12/2017] [Indexed: 12/18/2022] Open
Abstract
Interoception, the sensing of visceral body signals, involves an interplay between neural and autonomic mechanisms. Clinical studies into this domain have focused on patients with neurological and psychiatric disorders, showing that damage to relevant brain mechanisms can variously alter interoceptive functions. However, the association between peripheral cardiac-system alterations and neurocognitive markers of interoception remains poorly understood. To bridge this gap, we examined multidimensional neural markers of interoception in patients with early stage of hypertensive disease (HTD) and healthy controls. Strategically, we recruited only HTD patients without cognitive impairment (as shown by neuropsychological tests), brain atrophy (as assessed with voxel-based morphometry), or white matter abnormalities (as evidenced by diffusion tensor imaging analysis). Interoceptive domains were assessed through (a) a behavioral heartbeat detection task; (b) measures of the heart-evoked potential (HEP), an electrophysiological cortical signature of attention to cardiac signals; and (c) neuroimaging recordings (MRI and fMRI) to evaluate anatomical and functional connectivity properties of key interoceptive regions (namely, the insula and the anterior cingulate cortex). Relative to controls, patients exhibited poorer interoceptive performance and reduced HEP modulations, alongside an abnormal association between interoceptive performance and both the volume and functional connectivity of the above regions. Such results suggest that peripheral cardiac-system impairments can be associated with abnormal behavioral and neurocognitive signatures of interoception. More generally, our findings indicate that interoceptive processes entail bidirectional influences between the cardiovascular and the central nervous systems.
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Affiliation(s)
- Adrián Yoris
- Laboratory of Experimental Psychology and Neuroscience (LPEN)Institute of Cognitive and Translational Neuroscience (INCYT), INECO Foundation, Favaloro UniversityBuenos AiresArgentina
- National Scientific and Technical Research Council (CONICET)Buenos AiresArgentina
| | - Sofía Abrevaya
- Laboratory of Experimental Psychology and Neuroscience (LPEN)Institute of Cognitive and Translational Neuroscience (INCYT), INECO Foundation, Favaloro UniversityBuenos AiresArgentina
- National Scientific and Technical Research Council (CONICET)Buenos AiresArgentina
| | - Sol Esteves
- Laboratory of Experimental Psychology and Neuroscience (LPEN)Institute of Cognitive and Translational Neuroscience (INCYT), INECO Foundation, Favaloro UniversityBuenos AiresArgentina
| | - Paula Salamone
- Laboratory of Experimental Psychology and Neuroscience (LPEN)Institute of Cognitive and Translational Neuroscience (INCYT), INECO Foundation, Favaloro UniversityBuenos AiresArgentina
- National Scientific and Technical Research Council (CONICET)Buenos AiresArgentina
| | - Nicolás Lori
- Laboratory of Neuroimaging and Neuroscience (LANEN)INECO Neurosciences Oroño, Institute of Cognitive and Translational Neuroscience (INCYT), INECO Foundation, Favaloro UniversityRosarioArgentina
- Diagnóstico Médico Oroño, Grupo OroñoRosarioArgentina
- ICVS/3Bs & Centre AlgoritmiUniversity of MinhoBragaPortugal
| | - Miguel Martorell
- Laboratory of Experimental Psychology and Neuroscience (LPEN)Institute of Cognitive and Translational Neuroscience (INCYT), INECO Foundation, Favaloro UniversityBuenos AiresArgentina
| | - Agustina Legaz
- Laboratory of Experimental Psychology and Neuroscience (LPEN)Institute of Cognitive and Translational Neuroscience (INCYT), INECO Foundation, Favaloro UniversityBuenos AiresArgentina
| | - Florencia Alifano
- Laboratory of Experimental Psychology and Neuroscience (LPEN)Institute of Cognitive and Translational Neuroscience (INCYT), INECO Foundation, Favaloro UniversityBuenos AiresArgentina
| | - Agustín Petroni
- Laboratory of Experimental Psychology and Neuroscience (LPEN)Institute of Cognitive and Translational Neuroscience (INCYT), INECO Foundation, Favaloro UniversityBuenos AiresArgentina
- National Scientific and Technical Research Council (CONICET)Buenos AiresArgentina
- Instituto de Ingeniería BiomédicaFacultad de Ingeniería, Universidad de Buenos AiresArgentina
- Deptartamento de ComputaciónUniversidad de Buenos AiresArgentina
| | - Ramiro Sánchez
- Metabolic and Arterial Hypertension UnitFavaloro Foundation HospitalBuenos AiresArgentina
| | - Lucas Sedeño
- Laboratory of Experimental Psychology and Neuroscience (LPEN)Institute of Cognitive and Translational Neuroscience (INCYT), INECO Foundation, Favaloro UniversityBuenos AiresArgentina
- National Scientific and Technical Research Council (CONICET)Buenos AiresArgentina
| | - Adolfo M. García
- Laboratory of Experimental Psychology and Neuroscience (LPEN)Institute of Cognitive and Translational Neuroscience (INCYT), INECO Foundation, Favaloro UniversityBuenos AiresArgentina
- National Scientific and Technical Research Council (CONICET)Buenos AiresArgentina
- Faculty of EducationNational University of Cuyo (UNCuyo)MendozaArgentina
| | - Agustín Ibáñez
- Laboratory of Experimental Psychology and Neuroscience (LPEN)Institute of Cognitive and Translational Neuroscience (INCYT), INECO Foundation, Favaloro UniversityBuenos AiresArgentina
- National Scientific and Technical Research Council (CONICET)Buenos AiresArgentina
- Universidad Autónoma del CaribeBarranquillaColombia
- Center for Social and Cognitive Neuroscience (CSCN), School of PsychologyUniversidad Adolfo IbañezSantiagoChile
- Centre of Excellence in Cognition and its DisordersAustralian Research Council (ACR)SydneyAustralia
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34
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Temporal reliability of ultra-high field resting-state MRI for single-subject sensorimotor and language mapping. Neuroimage 2018; 168:499-508. [DOI: 10.1016/j.neuroimage.2016.11.029] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2016] [Revised: 10/29/2016] [Accepted: 11/12/2016] [Indexed: 11/19/2022] Open
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35
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Inter-vender and test-retest reliabilities of resting-state functional magnetic resonance imaging: Implications for multi-center imaging studies. Magn Reson Imaging 2017; 44:125-130. [DOI: 10.1016/j.mri.2017.09.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2016] [Revised: 04/19/2017] [Accepted: 09/01/2017] [Indexed: 01/26/2023]
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36
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Connectome-based predictive modeling of attention: Comparing different functional connectivity features and prediction methods across datasets. Neuroimage 2017; 167:11-22. [PMID: 29122720 DOI: 10.1016/j.neuroimage.2017.11.010] [Citation(s) in RCA: 107] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2017] [Revised: 11/02/2017] [Accepted: 11/04/2017] [Indexed: 12/17/2022] Open
Abstract
Connectome-based predictive modeling (CPM; Finn et al., 2015; Shen et al., 2017) was recently developed to predict individual differences in traits and behaviors, including fluid intelligence (Finn et al., 2015) and sustained attention (Rosenberg et al., 2016a), from functional brain connectivity (FC) measured with fMRI. Here, using the CPM framework, we compared the predictive power of three different measures of FC (Pearson's correlation, accordance, and discordance) and two different prediction algorithms (linear and partial least square [PLS] regression) for attention function. Accordance and discordance are recently proposed FC measures that respectively track in-phase synchronization and out-of-phase anti-correlation (Meskaldji et al., 2015). We defined connectome-based models using task-based or resting-state FC data, and tested the effects of (1) functional connectivity measure and (2) feature-selection/prediction algorithm on individualized attention predictions. Models were internally validated in a training dataset using leave-one-subject-out cross-validation, and externally validated with three independent datasets. The training dataset included fMRI data collected while participants performed a sustained attention task and rested (N = 25; Rosenberg et al., 2016a). The validation datasets included: 1) data collected during performance of a stop-signal task and at rest (N = 83, including 19 participants who were administered methylphenidate prior to scanning; Farr et al., 2014a; Rosenberg et al., 2016b), 2) data collected during Attention Network Task performance and rest (N = 41, Rosenberg et al., in press), and 3) resting-state data and ADHD symptom severity from the ADHD-200 Consortium (N = 113; Rosenberg et al., 2016a). Models defined using all combinations of functional connectivity measure (Pearson's correlation, accordance, and discordance) and prediction algorithm (linear and PLS regression) predicted attentional abilities, with correlations between predicted and observed measures of attention as high as 0.9 for internal validation, and 0.6 for external validation (all p's < 0.05). Models trained on task data outperformed models trained on rest data. Pearson's correlation and accordance features generally showed a small numerical advantage over discordance features, while PLS regression models were usually better than linear regression models. Overall, in addition to correlation features combined with linear models (Rosenberg et al., 2016a), it is useful to consider accordance features and PLS regression for CPM.
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Golestani AM, Kwinta JB, Khatamian YB, Chen JJ. The Effect of Low-Frequency Physiological Correction on the Reproducibility and Specificity of Resting-State fMRI Metrics: Functional Connectivity, ALFF, and ReHo. Front Neurosci 2017; 11:546. [PMID: 29051724 PMCID: PMC5633680 DOI: 10.3389/fnins.2017.00546] [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: 01/22/2017] [Accepted: 09/19/2017] [Indexed: 01/08/2023] Open
Abstract
The resting-state fMRI (rs-fMRI) signal is affected by a variety of low-frequency physiological phenomena, including variations in cardiac-rate (CRV), respiratory-volume (RVT), and end-tidal CO2 (PETCO2). While these effects have become better understood in recent years, the impact that their correction has on the quality of rs-fMRI measurements has yet to be clarified. The objective of this paper is to investigate the effect of correcting for CRV, RVT and PETCO2 on the rs-fMRI measurements. Nine healthy subjects underwent a test-retest rs-fMRI acquisition using repetition times (TRs) of 2 s (long-TR) and 0.323 s (short-TR), and the data were processed using eight different physiological correction strategies. Subsequently, regional homogeneity (ReHo), amplitude of low-frequency fluctuation (ALFF), and resting-state connectivity of the motor and default-mode networks are calculated for each strategy. Reproducibility is calculated using intra-class correlation and the Dice Coefficient, while the accuracy of functional-connectivity measures is assessed through network separability, sensitivity and specificity. We found that: (1) the reproducibility of the rs-fMRI measures improved significantly after correction for PETCO2; (2) separability of functional networks increased after PETCO2 correction but was not affected by RVT and CRV correction; (3) the effect of physiological correction does not depend on the data sampling-rate; (4) the effect of physiological processes and correction strategies is network-specific. Our findings highlight limitations in our understanding of rs-fMRI quality measures, and underscore the importance of using multiple quality measures to determine the optimal physiological correction strategy.
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Affiliation(s)
- Ali M Golestani
- Rotman Research Institute at Baycrest Centre, University of Toronto, Toronto, ON, Canada
| | - Jonathan B Kwinta
- Rotman Research Institute at Baycrest Centre, University of Toronto, Toronto, ON, Canada.,Department of Medical Biophysics, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Yasha B Khatamian
- Rotman Research Institute at Baycrest Centre, University of Toronto, Toronto, ON, Canada
| | - J Jean Chen
- Rotman Research Institute at Baycrest Centre, University of Toronto, Toronto, ON, Canada.,Department of Medical Biophysics, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
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38
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The vestibulocochlear bases for wartime posttraumatic stress disorder manifestations. Med Hypotheses 2017; 106:44-56. [DOI: 10.1016/j.mehy.2017.06.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2016] [Accepted: 06/28/2017] [Indexed: 11/23/2022]
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39
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Sohn WS, Lee TY, Yoo K, Kim M, Yun JY, Hur JW, Yoon YB, Seo SW, Na DL, Jeong Y, Kwon JS. Node Identification Using Inter-Regional Correlation Analysis for Mapping Detailed Connections in Resting State Networks. Front Neurosci 2017; 11:238. [PMID: 28507502 PMCID: PMC5410606 DOI: 10.3389/fnins.2017.00238] [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: 12/14/2016] [Accepted: 04/11/2017] [Indexed: 11/13/2022] Open
Abstract
Brain function is often characterized by the connections and interactions between highly interconnected brain regions. Pathological disruptions in these networks often result in brain dysfunction, which manifests as brain disease. Typical analysis investigates disruptions in network connectivity based correlations between large brain regions. To obtain a more detailed description of disruptions in network connectivity, we propose a new method where functional nodes are identified in each region based on their maximum connectivity to another brain region in a given network. Since this method provides a unique approach to identifying functionally relevant nodes in a given network, we can provide a more detailed map of brain connectivity and determine new measures of network connectivity. We applied this method to resting state fMRI of Alzheimer's disease patients to validate our method and found decreased connectivity within the default mode network. In addition, new measure of network connectivity revealed a more detailed description of how the network connections deteriorate with disease progression. This suggests that analysis using key relative network hub regions based on regional correlation can be used to detect detailed changes in resting state network connectivity.
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Affiliation(s)
- William S Sohn
- Institute of Human Behavioral Medicine, Medical Research Center, Seoul National UniversitySeoul, South Korea
| | - Tae Young Lee
- Department of Psychiatry, Seoul National University College of MedicineSeoul, South Korea
| | - Kwangsun Yoo
- Department of Bio and Brain Engineering, KAISTDaejeon, South Korea
| | - Minah Kim
- Department of Psychiatry, Seoul National University College of MedicineSeoul, South Korea
| | - Je-Yeon Yun
- Department of Psychiatry, Seoul National University College of MedicineSeoul, South Korea
| | - Ji-Won Hur
- Department of Psychology, Chung-Ang UniversitySeoul, South Korea
| | - Youngwoo Bryan Yoon
- Department of Brain and Cognitive Sciences, Seoul National UniversitySeoul, South Korea
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sunkyunkwan UniversitySeoul, South Korea.,Neuroscience Center, Samsung Medical CenterSeoul, South Korea
| | - Duk L Na
- Department of Neurology, Samsung Medical Center, Sunkyunkwan UniversitySeoul, South Korea.,Neuroscience Center, Samsung Medical CenterSeoul, South Korea
| | - Yong Jeong
- Department of Bio and Brain Engineering, KAISTDaejeon, South Korea
| | - Jun Soo Kwon
- Institute of Human Behavioral Medicine, Medical Research Center, Seoul National UniversitySeoul, South Korea.,Department of Psychiatry, Seoul National University College of MedicineSeoul, South Korea.,Department of Brain and Cognitive Sciences, Seoul National UniversitySeoul, South Korea
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40
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Teipel SJ, Wohlert A, Metzger C, Grimmer T, Sorg C, Ewers M, Meisenzahl E, Klöppel S, Borchardt V, Grothe MJ, Walter M, Dyrba M. Multicenter stability of resting state fMRI in the detection of Alzheimer's disease and amnestic MCI. NEUROIMAGE-CLINICAL 2017; 14:183-194. [PMID: 28180077 PMCID: PMC5279697 DOI: 10.1016/j.nicl.2017.01.018] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2016] [Revised: 11/30/2016] [Accepted: 01/17/2017] [Indexed: 12/26/2022]
Abstract
Background In monocentric studies, patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD) dementia exhibited alterations of functional cortical connectivity in resting-state functional MRI (rs-fMRI) analyses. Multicenter studies provide access to large sample sizes, but rs-fMRI may be particularly sensitive to multiscanner effects. Methods We used data from five centers of the “German resting-state initiative for diagnostic biomarkers” (psymri.org), comprising 367 cases, including AD patients, MCI patients and healthy older controls, to assess the influence of the distributed acquisition on the group effects. We calculated accuracy of group discrimination based on whole brain functional connectivity of the posterior cingulate cortex (PCC) using pooled samples as well as second-level analyses across site-specific group contrast maps. Results We found decreased functional connectivity in AD patients vs. controls, including clusters in the precuneus, inferior parietal cortex, lateral temporal cortex and medial prefrontal cortex. MCI subjects showed spatially similar, but less pronounced, differences in PCC connectivity when compared to controls. Group discrimination accuracy for AD vs. controls (MCI vs. controls) in the test data was below 76% (72%) based on the pooled analysis, and even lower based on the second level analysis stratified according to scanner. Only a subset of quality measures was useful to detect relevant scanner effects. Conclusions Multicenter rs-fMRI analysis needs to employ strict quality measures, including visual inspection of all the data, to avoid seriously confounded group effects. While pending further confirmation in biomarker stratified samples, these findings suggest that multicenter acquisition limits the use of rs-fMRI in AD and MCI diagnosis. Diagnostic accuracy of multicenter rs-fMRI in AD and MCI Quality metrics for multicenter rs-fMRI that should be used Quality metrics for multicenter rs-fMRI that should not be used Multicenter rs-fMRI will have limited diagnostic use in clinical routine diagnosis
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Affiliation(s)
- Stefan J Teipel
- Department of Psychosomatic Medicine, University of Rostock, Rostock, Germany; DZNE, German Center for Neurodegenerative Diseases, Rostock, Germany
| | - Alexandra Wohlert
- Department of Psychosomatic Medicine, University of Rostock, Rostock, Germany
| | - Coraline Metzger
- Institute of Cognitive Neurology and Dementia Research (IKND), Department of Psychiatry and Psychotherapy, Otto von Guericke University, Germany and German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Timo Grimmer
- Department of Psychiatry and Psychotherapy, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Christian Sorg
- Department of Neuroradiology of Klinikum rechts der Isar, Technische Universität München, Department of Psychiatry of Klinikum rechts der Isar, TUM-Neuroimaging Center, Einsteinstr. 1, 81675 Munich, Germany
| | - Michael Ewers
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Munich, Germany
| | - Eva Meisenzahl
- Department of Psychiatry, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Munich, Germany
| | - Stefan Klöppel
- Department of Psychiatry and Psychotherapy, Section of Gerontopsychiatry and Neuropsychology, Faculty of Medicine, University of Freiburg, Germany; University Hospital of Old Age Psychiatry, Bern, Switzerland
| | - Viola Borchardt
- Leibniz Institute for Neurobiology, Magdeburg, Germany; Department of Psychiatry, University Tübingen, Germany
| | - Michel J Grothe
- DZNE, German Center for Neurodegenerative Diseases, Rostock, Germany
| | - Martin Walter
- Leibniz Institute for Neurobiology, Magdeburg, Germany; Department of Psychiatry, University Tübingen, Germany
| | - Martin Dyrba
- DZNE, German Center for Neurodegenerative Diseases, Rostock, Germany
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41
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Chou YH, Sundman M, Whitson HE, Gaur P, Chu ML, Weingarten CP, Madden DJ, Wang L, Kirste I, Joliot M, Diaz MT, Li YJ, Song AW, Chen NK. Maintenance and Representation of Mind Wandering during Resting-State fMRI. Sci Rep 2017; 7:40722. [PMID: 28079189 PMCID: PMC5227708 DOI: 10.1038/srep40722] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2016] [Accepted: 12/09/2016] [Indexed: 11/09/2022] Open
Abstract
Major advances in resting-state functional magnetic resonance imaging (fMRI) techniques in the last two decades have provided a tool to better understand the functional organization of the brain both in health and illness. Despite such developments, characterizing regulation and cerebral representation of mind wandering, which occurs unavoidably during resting-state fMRI scans and may induce variability of the acquired data, remains a work in progress. Here, we demonstrate that a decrease or decoupling in functional connectivity involving the caudate nucleus, insula, medial prefrontal cortex and other domain-specific regions was associated with more sustained mind wandering in particular thought domains during resting-state fMRI. Importantly, our findings suggest that temporal and between-subject variations in functional connectivity of above-mentioned regions might be linked with the continuity of mind wandering. Our study not only provides a preliminary framework for characterizing the maintenance and cerebral representation of different types of mind wandering, but also highlights the importance of taking mind wandering into consideration when studying brain organization with resting-state fMRI in the future.
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Affiliation(s)
- Ying-Hui Chou
- Department of Psychology, University of Arizona, Tucson, AZ, USA.,Cognitive Science Program, University of Arizona, Tucson, AZ, USA.,Arizona Center on Aging, University of Arizona, Tucson, AZ, USA
| | - Mark Sundman
- Department of Psychology, University of Arizona, Tucson, AZ, USA
| | - Heather E Whitson
- Department of Medicine and Ophthalmology, Duke University Medical Center, Durham, NC, USA.,Geriatrics Research Education and Clinical Center, Durham Veterans Administration Hospital, Durham, NC, USA
| | - Pooja Gaur
- Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
| | - Mei-Lan Chu
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC, USA
| | - Carol P Weingarten
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, USA
| | - David J Madden
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC, USA.,Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, USA
| | - Lihong Wang
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC, USA.,Department of Psychiatry, University of Connecticut Health Center, Farmington, CT, USA
| | - Imke Kirste
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC, USA
| | - Marc Joliot
- Neuroimaging Group (GIN), UMR5293, CEA CNRS Université de Bordeaux, Bordeaux, CEDEX, France
| | - Michele T Diaz
- Department of Psychology, Penn State University, University Park, PA, USA
| | - Yi-Ju Li
- Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC, USA
| | - Allen W Song
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC, USA.,Department of Radiology, Duke University Medical Center, Durham, NC, USA
| | - Nan-Kuei Chen
- Arizona Center on Aging, University of Arizona, Tucson, AZ, USA.,Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC, USA.,Department of Radiology, Duke University Medical Center, Durham, NC, USA.,Department of Biomedical Engineering, University of Arizona, Tucson, AZ, USA.,Department of Medical Imaging, University of Arizona, Tucson, AZ, USA
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42
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Teipel SJ, Grothe MJ, Metzger CD, Grimmer T, Sorg C, Ewers M, Franzmeier N, Meisenzahl E, Klöppel S, Borchardt V, Walter M, Dyrba M. Robust Detection of Impaired Resting State Functional Connectivity Networks in Alzheimer's Disease Using Elastic Net Regularized Regression. Front Aging Neurosci 2017; 8:318. [PMID: 28101051 PMCID: PMC5209379 DOI: 10.3389/fnagi.2016.00318] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2016] [Accepted: 12/09/2016] [Indexed: 11/25/2022] Open
Abstract
The large number of multicollinear regional features that are provided by resting state (rs) fMRI data requires robust feature selection to uncover consistent networks of functional disconnection in Alzheimer's disease (AD). Here, we compared elastic net regularized and classical stepwise logistic regression in respect to consistency of feature selection and diagnostic accuracy using rs-fMRI data from four centers of the “German resting-state initiative for diagnostic biomarkers” (psymri.org), comprising 53 AD patients and 118 age and sex matched healthy controls. Using all possible pairs of correlations between the time series of rs-fMRI signal from 84 functionally defined brain regions as the initial set of predictor variables, we calculated accuracy of group discrimination and consistency of feature selection with bootstrap cross-validation. Mean areas under the receiver operating characteristic curves as measure of diagnostic accuracy were 0.70 in unregularized and 0.80 in regularized regression. Elastic net regression was insensitive to scanner effects and recovered a consistent network of functional connectivity decline in AD that encompassed parts of the dorsal default mode as well as brain regions involved in attention, executive control, and language processing. Stepwise logistic regression found no consistent network of AD related functional connectivity decline. Regularized regression has high potential to increase diagnostic accuracy and consistency of feature selection from multicollinear functional neuroimaging data in AD. Our findings suggest an extended network of functional alterations in AD, but the diagnostic accuracy of rs-fMRI in this multicenter setting did not reach the benchmark defined for a useful biomarker of AD.
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Affiliation(s)
- Stefan J Teipel
- Department of Psychosomatic Medicine, University of RostockRostock, Germany; German Center for Neurodegenerative Diseases, Site Rostock/GreifswaldRostock, Germany
| | - Michel J Grothe
- German Center for Neurodegenerative Diseases, Site Rostock/Greifswald Rostock, Germany
| | - Coraline D Metzger
- Institute of Cognitive Neurology and Dementia Research and Department of Psychiatry and Psychotherapy, Otto von Guericke UniversityMagdeburg, Germany; German Center for Neurodegenerative Diseases, Site MagdeburgMagdeburg, Germany
| | - Timo Grimmer
- Department of Psychiatry and Psychotherapy, Klinikum rechts der Isar, Technische Universität München Munich, Germany
| | - Christian Sorg
- Department of Neuroradiology of Klinikum rechts der Isar, Technische Universität MünchenMunich, Germany; Department of Psychiatry of Klinikum rechts der Isar, Technische Universität MünchenMunich, Germany; TUM-Neuroimaging Center, Technische Universität MünchenMunich, Germany
| | - Michael Ewers
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität Munich, Germany
| | - Nicolai Franzmeier
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität Munich, Germany
| | - Eva Meisenzahl
- Department of Psychiatry, Klinikum der Universität München, Ludwig-Maximilians-Universität Munich, Germany
| | - Stefan Klöppel
- Department of Psychiatry and Psychotherapy, Section of Gerontopsychiatry and Neuropsychology, Faculty of Medicine, University of FreiburgFreiburg, Germany; University Hospital of Old Age PsychiatryBern, Switzerland
| | | | - Martin Walter
- Leibniz Institute for NeurobiologyMagdeburg, Germany; Department of Psychiatry, University of TübingenTübingen, Germany
| | - Martin Dyrba
- German Center for Neurodegenerative Diseases, Site Rostock/Greifswald Rostock, Germany
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43
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The contributions of resting state and task-based functional connectivity studies to our understanding of adolescent brain network maturation. Neurosci Biobehav Rev 2016; 70:13-32. [DOI: 10.1016/j.neubiorev.2016.07.027] [Citation(s) in RCA: 71] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2016] [Revised: 07/21/2016] [Accepted: 07/24/2016] [Indexed: 12/18/2022]
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44
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Cao Z, Lin PY, Shen ZW, Wu RH, Xiao YY. Alterations in brain metabolism and function following administration of low-dose codeine phosphate: 1H-magnetic resonance spectroscopy and resting-state functional magnetic resonance imaging studies. Exp Ther Med 2016; 12:619-626. [PMID: 27446252 PMCID: PMC4950574 DOI: 10.3892/etm.2016.3358] [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: 02/24/2015] [Accepted: 04/06/2016] [Indexed: 02/05/2023] Open
Abstract
The aim of the present study was to identify alterations in brain function following administration of a single, low-dose of codeine phosphate in healthy volunteers using resting-state functional magnetic resonance imaging (fMRI). In addition, the metabolic changes in the two sides of the frontal lobe were identified using 1H-magnetic resonance spectroscopy (1H-MRS). A total of 20 right-handed healthy participants (10 males, 10 females) were evaluated, and a Signa HDx 1.5T MRI scanner was used for data acquisition. An echo planar imaging sequence was used for resting-state fMRI, whereas a point resolved spectroscopy sequence was used for 1H-MRS. Regional Saturation Technique, Data Processing Assistant for Resting-State fMRI, and Statistical Parameter Mapping 8 were used to analyze the fMRI data. The 1H-MRS data were analyzed using LCModel software. At 1 h after oral administration of codeine phosphate (1.0 mg/kg), the amplitude of low-frequency fluctuation (ALFF) and regional homogeneity were altered in different brain areas. The choline content was significantly increased in the right and left frontal lobes following codeine phosphate administration (P=0.02 and P=0.03, respectively), whereas the inositol content was significantly decreased in the left frontal lobe (P=0.02). There was no change in the glutamic acid content in the frontal lobes. In conclusion, the functions of different brain regions can be affected by a single, low-dose administration of codeine phosphate. The alterations in metabolite content in the two frontal lobes may be associated with changes in brain function, whereas the ALFF in the globus pallidus may have an effect on codeine phosphate addiction. Finally, glutamic acid may be useful in the estimation of codeine dependence.
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Affiliation(s)
- Zhen Cao
- Department of Medical Imaging, The Second Affiliated Hospital, Shantou University Medical College, Shantou, Guangdong 515041, P.R. China
| | - Pei-Yin Lin
- Department of Medical Imaging, The First Affiliated Hospital, Shantou University Medical College, Shantou, Guangdong 515041, P.R. China
| | - Zhi-Wei Shen
- Department of Medical Imaging, The Second Affiliated Hospital, Shantou University Medical College, Shantou, Guangdong 515041, P.R. China
| | - Ren-Hua Wu
- Department of Medical Imaging, The Second Affiliated Hospital, Shantou University Medical College, Shantou, Guangdong 515041, P.R. China
- Correspondence to: Dr Ye-Yu Xiao or Dr Ren-Hua Wu, Department of Medical Imaging, The Second Affiliated Hospital, Shantou University Medical College, 69 DongXia North Road, Shantou, Guangdong 515041, P.R. China, E-mail: , E-mail:
| | - Ye-Yu Xiao
- Department of Medical Imaging, The Second Affiliated Hospital, Shantou University Medical College, Shantou, Guangdong 515041, P.R. China
- Correspondence to: Dr Ye-Yu Xiao or Dr Ren-Hua Wu, Department of Medical Imaging, The Second Affiliated Hospital, Shantou University Medical College, 69 DongXia North Road, Shantou, Guangdong 515041, P.R. China, E-mail: , E-mail:
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45
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Floris DL, Barber AD, Nebel MB, Martinelli M, Lai MC, Crocetti D, Baron-Cohen S, Suckling J, Pekar JJ, Mostofsky SH. Atypical lateralization of motor circuit functional connectivity in children with autism is associated with motor deficits. Mol Autism 2016; 7:35. [PMID: 27429731 PMCID: PMC4946094 DOI: 10.1186/s13229-016-0096-6] [Citation(s) in RCA: 88] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2015] [Accepted: 06/28/2016] [Indexed: 12/27/2022] Open
Abstract
Background Atypical lateralization of language-related functions has been repeatedly found in individuals with autism spectrum conditions (ASC). Few studies have, however, investigated deviations from typically occurring asymmetry of other lateralized cognitive and behavioural domains. Motor deficits are among the earliest and most prominent symptoms in individuals with ASC and precede core social and communicative symptoms. Methods Here, we investigate whether motor circuit connectivity is (1) atypically lateralized in children with ASC and (2) whether this relates to core autistic symptoms and motor performance. Participants comprised 44 right-handed high-functioning children with autism (36 males, 8 females) and 80 typically developing control children (58 males, 22 females) matched on age, sex and performance IQ. We examined lateralization of functional motor circuit connectivity based on homotopic seeds derived from peak activations during a finger tapping paradigm. Motor performance was assessed using the Physical and Neurological Examination for Subtle Signs (PANESS). Results Children with ASC showed rightward lateralization in mean motor circuit connectivity compared to typically developing children, and this was associated with poorer performance on all three PANESS measures. Conclusions Our findings reveal that atypical lateralization in ASC is not restricted to language functions but is also present in circuits subserving motor functions and may underlie motor deficits in children with ASC. Future studies should investigate whether this is an age-invariant finding extending to adolescents and adults and whether these asymmetries relate to atypical lateralization in the language domain. Electronic supplementary material The online version of this article (doi:10.1186/s13229-016-0096-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Dorothea L Floris
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK ; Department of Child and Adolescent Psychiatry, the Child Study Center, New York University Langone Medical Center, New York, NY USA
| | - Anita D Barber
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD USA ; Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD USA ; Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD USA
| | - Mary Beth Nebel
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD USA ; Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD USA ; Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD USA
| | - Mary Martinelli
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD USA ; Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD USA ; Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD USA
| | - Meng-Chuan Lai
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK ; Child, Youth and Family Services, Centre for Addiction and Mental Health and Department of Psychiatry, University of Toronto, Toronto, Canada ; Department of Psychiatry, College of Medicine, National Taiwan University Hospital, Taipei City, Taiwan
| | - Deana Crocetti
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD USA ; Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD USA ; Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD USA
| | - Simon Baron-Cohen
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK ; Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK ; National Institute of Health Research, Cambridge Biomedical Research Centre, Cambridge, UK ; Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK
| | - John Suckling
- Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK ; National Institute of Health Research, Cambridge Biomedical Research Centre, Cambridge, UK ; Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK ; Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - James J Pekar
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, USA ; Department of Radiology, Johns Hopkins School of Medicine, Baltimore, USA
| | - Stewart H Mostofsky
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD USA ; Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD USA ; Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD USA
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46
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Vassal M, Charroud C, Deverdun J, Le Bars E, Molino F, Bonnetblanc F, Boyer A, Dutta A, Herbet G, Moritz-Gasser S, Bonafé A, Duffau H, de Champfleur NM. Recovery of functional connectivity of the sensorimotor network after surgery for diffuse low-grade gliomas involving the supplementary motor area. J Neurosurg 2016; 126:1181-1190. [PMID: 27315027 DOI: 10.3171/2016.4.jns152484] [Citation(s) in RCA: 78] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
OBJECTIVE The supplementary motor area (SMA) syndrome is a well-studied lesional model of brain plasticity involving the sensorimotor network. Patients with diffuse low-grade gliomas in the SMA may exhibit this syndrome after resective surgery. They experience a temporary loss of motor function, which completely resolves within 3 months. The authors used functional MRI (fMRI) resting state analysis of the sensorimotor network to investigate large-scale brain plasticity between the immediate postoperative period and 3 months' follow-up. METHODS Resting state fMRI was performed preoperatively, during the immediate postoperative period, and 3 months postoperatively in 6 patients with diffuse low-grade gliomas who underwent partial surgical excision of the SMA. Correlation analysis within the sensorimotor network was carried out on those 3 time points to study modifications of its functional connectivity. RESULTS The results showed a large-scale reorganization of the sensorimotor network. Interhemispheric connectivity was decreased in the postoperative period, and increased again during the recovery process. Connectivity between the lesion side motor area and the contralateral SMA rose to higher values than in the preoperative period. Intrahemispheric connectivity was decreased during the immediate postoperative period and had returned to preoperative values at 3 months after surgery. CONCLUSIONS These results confirm the findings reported in the existing literature on the plasticity of the SMA, showing large-scale modifications of the sensorimotor network, at both inter- and intrahemispheric levels. They suggest that interhemispheric connectivity might be a correlate of SMA syndrome recovery.
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Affiliation(s)
- Matthieu Vassal
- Departments of 1 Neurosurgery and.,Neuroradiology.,Institut d'Imagerie Fonctionnelle Humaine, and.,Institut des Neurosciences de Montpellier, INSERM U1051, Centre Hospitalier Régional Universitaire de Montpellier; and
| | - Céline Charroud
- Neuroradiology.,Institut d'Imagerie Fonctionnelle Humaine, and
| | - Jérémy Deverdun
- Neuroradiology.,Institut d'Imagerie Fonctionnelle Humaine, and.,Institut des Neurosciences de Montpellier, INSERM U1051, Centre Hospitalier Régional Universitaire de Montpellier; and.,Institut de Génomique Fonctionnelle, UMR 5203-INSERM U661.,Laboratoire Charles Coulomb, CNRS UMR 5221, and
| | - Emmanuelle Le Bars
- Neuroradiology.,Institut d'Imagerie Fonctionnelle Humaine, and.,Laboratoire Charles Coulomb, CNRS UMR 5221, and
| | - François Molino
- Institut de Génomique Fonctionnelle, UMR 5203-INSERM U661.,Laboratoire Charles Coulomb, CNRS UMR 5221, and
| | - Francois Bonnetblanc
- Laboratoire d'Informatique, de Robotique et de Microélectronique de Montpellier, CNRS UMR5506, Université de Montpellier, Montpellier, France
| | - Anthony Boyer
- Laboratoire d'Informatique, de Robotique et de Microélectronique de Montpellier, CNRS UMR5506, Université de Montpellier, Montpellier, France
| | - Anirban Dutta
- Laboratoire d'Informatique, de Robotique et de Microélectronique de Montpellier, CNRS UMR5506, Université de Montpellier, Montpellier, France
| | - Guillaume Herbet
- Departments of 1 Neurosurgery and.,Institut des Neurosciences de Montpellier, INSERM U1051, Centre Hospitalier Régional Universitaire de Montpellier; and
| | - Sylvie Moritz-Gasser
- Departments of 1 Neurosurgery and.,Institut des Neurosciences de Montpellier, INSERM U1051, Centre Hospitalier Régional Universitaire de Montpellier; and
| | - Alain Bonafé
- Neuroradiology.,Institut d'Imagerie Fonctionnelle Humaine, and.,Institut des Neurosciences de Montpellier, INSERM U1051, Centre Hospitalier Régional Universitaire de Montpellier; and
| | - Hugues Duffau
- Departments of 1 Neurosurgery and.,Institut des Neurosciences de Montpellier, INSERM U1051, Centre Hospitalier Régional Universitaire de Montpellier; and
| | - Nicolas Menjot de Champfleur
- Neuroradiology.,Institut d'Imagerie Fonctionnelle Humaine, and.,Institut des Neurosciences de Montpellier, INSERM U1051, Centre Hospitalier Régional Universitaire de Montpellier; and.,Laboratoire Charles Coulomb, CNRS UMR 5221, and
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47
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Lv D, Lin W, Xue Z, Pu W, Yang Q, Huang X, Zhou L, Yang L, Liu Z. Decreased functional connectivity in the language regions in bipolar patients during depressive episodes but not remission. J Affect Disord 2016; 197:116-124. [PMID: 26991366 DOI: 10.1016/j.jad.2016.03.026] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/15/2015] [Accepted: 03/07/2016] [Indexed: 12/11/2022]
Abstract
BACKGROUND Retardation of thought is a crucial clinical feature in patients with bipolar depression, characterized by dysfunctional semantic processing and language communication. However, the underlying neuropathological mechanisms remain largely unknown. The objective of this study was to evaluate the disruption in resting-state functional connectivity in 90 different brain regions during the depressive episodes of bipolar disorder and during disease remission. METHODS Applying the whole brain and language regions of interest methods to the resting-state functional magnetic resonance imaging data, we explored the discrepancies in 90 brain regions' functional connectivity in 42 patients with bipolar disorder - 23 experiencing a depressive episode and 19 in remission - and 28 healthy controls matched for gender, age, and education. RESULTS Bipolar depressive patients had significantly reduced connectivity strength in the language regions relative to healthy controls. Specifically, the affected regions included the left triangular part of the inferior frontal gyrus, left opercular part of the inferior frontal gyrus, left middle temporal gyrus, and left angular gyrus. However, no significant differences in these regions were observed between bipolar patients in remission and healthy controls. Furthermore, the decreased connectivity strength between the left middle temporal gyrus and right lingual gyrus showed significant positive correlation with the scores on the Hamilton Depression Rating Scale. LIMITATIONS Bipolar depressive patients received treatment of benzodiazepines, which may confound the findings. CONCLUSIONS Our results illustrated that connectivity disturbances in the language regions may change depending on the disease phase of bipolar disorder.
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Affiliation(s)
- Dongsheng Lv
- Institute of Mental Health, Second Xiangya Hospital of Central South University, Changsha, China; Institute of Mental Health, Inner Mongolia Autonomous Region, Hohhot, China
| | - Wuhong Lin
- School of Mathematics and Computational Science, Sun Yat-sen University, Guangzhou, China
| | - Zhimin Xue
- Institute of Mental Health, Second Xiangya Hospital of Central South University, Changsha, China
| | - Weidan Pu
- Medical Psychological Institute, Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Qing Yang
- Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Xiaojun Huang
- Institute of Mental Health, Second Xiangya Hospital of Central South University, Changsha, China
| | - Li Zhou
- Institute of Mental Health, Second Xiangya Hospital of Central South University, Changsha, China
| | - Lihua Yang
- School of Mathematics and Computational Science, Sun Yat-sen University, Guangzhou, China; Key Laboratory of Computational Science, Sun Yat-sen University, Guangzhou, China.
| | - Zhening Liu
- Institute of Mental Health, Second Xiangya Hospital of Central South University, Changsha, China; State Key Laboratories of Medical Genetics, Central South University, Changsha, China.
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48
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Quantitative mapping of cerebrovascular reactivity using resting-state BOLD fMRI: Validation in healthy adults. Neuroimage 2016; 138:147-163. [PMID: 27177763 DOI: 10.1016/j.neuroimage.2016.05.025] [Citation(s) in RCA: 59] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2016] [Revised: 05/04/2016] [Accepted: 05/07/2016] [Indexed: 11/23/2022] Open
Abstract
In conventional neuroimaging, cerebrovascular reactivity (CVR) is quantified primarily using the blood-oxygenation level-dependent (BOLD) functional MRI (fMRI) signal, specifically, as the BOLD response to intravascular carbon dioxide (CO2) modulations, in units of [%ΔBOLD/mmHg]. While this method has achieved wide appeal and clinical translation, the tolerability of CO2-related tasks amongst patients and the elderly remains a challenge in more routine and large-scale applications. In this work, we propose an improved method to quantify CVR by exploiting intrinsic fluctuations in CO2 and corresponding changes in the resting-state BOLD signal (rs-qCVR). Our rs-qCVR approach requires simultaneous monitoring of PETCO2, cardiac pulsation and respiratory volume. In 16 healthy adults, we compare our quantitative CVR estimation technique to the prospective CO2-targeting based CVR quantification approach (qCVR, the "standard"). We also compare our rs-CVR to non-quantitative alternatives including the resting-state fluctuation amplitude (RSFA), amplitude of low-frequency fluctuation (ALFF) and global-signal regression. When all subjects were pooled, only RSFA and ALFF were significantly associated with qCVR. However, for characterizing regional CVR variations within each subject, only the PETCO2-based rs-qCVR measure is strongly associated with standard qCVR in 100% of the subjects (p≤0.1). In contrast, for the more qualitative CVR measures, significant within-subject association with qCVR was only achieved in 50-70% of the subjects. Our work establishes the feasibility of extracting quantitative CVR maps using rs-fMRI, opening the possibility of mapping functional connectivity and qCVR simultaneously.
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49
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Airan RD, Vogelstein JT, Pillai JJ, Caffo B, Pekar JJ, Sair HI. Factors affecting characterization and localization of interindividual differences in functional connectivity using MRI. Hum Brain Mapp 2016; 37:1986-97. [PMID: 27012314 DOI: 10.1002/hbm.23150] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2015] [Revised: 02/08/2016] [Accepted: 02/10/2016] [Indexed: 01/22/2023] Open
Abstract
Much recent attention has been paid to quantifying anatomic and functional neuroimaging on the individual subject level. For optimal individual subject characterization, specific acquisition and analysis features need to be identified that maximize interindividual variability while concomitantly minimizing intra-subject variability. We delineate the effect of various acquisition parameters (length of acquisition, sampling frequency) and analysis methods (time course extraction, region of interest parcellation, and thresholding of connectivity-derived network graphs) on characterizing individual subject differentiation. We utilize a non-parametric statistical metric that quantifies the degree to which a parameter set allows this individual subject differentiation by both maximizing interindividual variance and minimizing intra-individual variance. We apply this metric to analysis of four publicly available test-retest resting-state fMRI (rs-fMRI) data sets. We find that for the question of maximizing individual differentiation, (i) for increasing sampling, there is a relative tradeoff between increased sampling frequency and increased acquisition time; (ii) for the sizes of the interrogated data sets, only 3-4 min of acquisition time was sufficient to maximally differentiate each subject with an algorithm that utilized no a priori information regarding subject identification; and (iii) brain regions that most contribute to this individual subject characterization lie in the default mode, attention, and executive control networks. These findings may guide optimal rs-fMRI experiment design and may elucidate the neural bases for subject-to-subject differences. Hum Brain Mapp 37:1986-1997, 2016. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Raag D Airan
- Russell H. Morgan Department of Radiology and Radiological Science, School of Medicine, Johns Hopkins Medical Institutions, Baltimore, Maryland
| | - Joshua T Vogelstein
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland.,Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland
| | - Jay J Pillai
- Russell H. Morgan Department of Radiology and Radiological Science, School of Medicine, Johns Hopkins Medical Institutions, Baltimore, Maryland
| | - Brian Caffo
- Department of Biostatistics, Johns Hopkins University, Baltimore, Maryland
| | - James J Pekar
- Russell H. Morgan Department of Radiology and Radiological Science, School of Medicine, Johns Hopkins Medical Institutions, Baltimore, Maryland.,F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland
| | - Haris I Sair
- Russell H. Morgan Department of Radiology and Radiological Science, School of Medicine, Johns Hopkins Medical Institutions, Baltimore, Maryland
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50
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Pinter D, Beckmann C, Koini M, Pirker E, Filippini N, Pichler A, Fuchs S, Fazekas F, Enzinger C. Reproducibility of Resting State Connectivity in Patients with Stable Multiple Sclerosis. PLoS One 2016; 11:e0152158. [PMID: 27007237 PMCID: PMC4805264 DOI: 10.1371/journal.pone.0152158] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2015] [Accepted: 03/09/2016] [Indexed: 01/05/2023] Open
Abstract
Given increasing efforts to use resting-state fMRI (rfMRI) as a biomarker of disease progression in multiple sclerosis (MS) we here explored the reproducibility of longitudinal rfMRI over three months in patients with clinically and radiologically stable MS. To pursue this aim, two approaches were applied in nine rfMRI networks: First, the intraclass correlation coefficient (ICC 3,1) was assessed for the mean functional connectivity maps across the entire network and a region of interest (ROI). Second, the ratio of overlap between Z-thresholded connectivity maps for each network was assessed. We quantified between-session functional reproducibility of rfMRI for 20 patients with stable MS and 14 healthy controls (HC). Nine rfMRI networks (RSNs) were examined at baseline and after 3 months of follow-up: three visual RSNs, the default-mode network, sensorimotor-, auditory-, executive control, and the left and right fronto-parietal RSN. ROI analyses were constrained to thresholded overlap masks for each individual (Z>0) at baseline and follow-up.In both stable MS and HC mean functional connectivity across the entire network did not reach acceptable ICCs for several networks (ICC<0.40) but we found a high reproducibility of ROI ICCs and of the ratio of overlap. ROI ICCs of all nine networks were between 0.98 and 0.99 for HC and ranged from 0.88 to 0.99 in patients with MS, respectively. The ratio of overlap for all networks was similar for both groups, ranging from 0.60 to 0.75.Our findings attest to a high reproducibility of rfMRI networks not only in HC but also in patients with stable MS when applying ROI analysis. This supports the utility of rfMRI to monitor functional changes related to disease progression or therapeutic interventions in MS.
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Affiliation(s)
- Daniela Pinter
- Department of Neurology, Medical University of Graz, Graz, Austria
- * E-mail:
| | - Christian Beckmann
- Donders Institute, Cognitive Neuroscience Department and Centre for Cognitive Neuroimaging, Radboud University Nijmegen, Nijmegen, The Netherlands
| | - Marisa Koini
- Department of Neurology, Medical University of Graz, Graz, Austria
| | - Eva Pirker
- Department of Neurology, Medical University of Graz, Graz, Austria
| | - Nicola Filippini
- The Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | | | - Siegrid Fuchs
- Department of Neurology, Medical University of Graz, Graz, Austria
| | - Franz Fazekas
- Department of Neurology, Medical University of Graz, Graz, Austria
| | - Christian Enzinger
- Department of Neurology, Medical University of Graz, Graz, Austria
- Division of Neuroradiology, Department of Radiology, Medical University of Graz, Graz, Austria
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