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Dannhauer M, Gomez LJ, Robins PL, Wang D, Hasan NI, Thielscher A, Siebner HR, Fan Y, Deng ZD. Electric Field Modeling in Personalizing Transcranial Magnetic Stimulation Interventions. Biol Psychiatry 2024; 95:494-501. [PMID: 38061463 PMCID: PMC10922371 DOI: 10.1016/j.biopsych.2023.11.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 11/21/2023] [Accepted: 11/25/2023] [Indexed: 01/21/2024]
Abstract
The modeling of transcranial magnetic stimulation (TMS)-induced electric fields (E-fields) is a versatile technique for evaluating and refining brain targeting and dosing strategies, while also providing insights into dose-response relationships in the brain. This review outlines the methodologies employed to derive E-field estimations, covering TMS physics, modeling assumptions, and aspects of subject-specific head tissue and coil modeling. We also summarize various numerical methods for solving the E-field and their suitability for various applications. Modeling methodologies have been optimized to efficiently execute numerous TMS simulations across diverse scalp coil configurations, facilitating the identification of optimal setups or rapid cortical E-field visualization for specific brain targets. These brain targets are extrapolated from neurophysiological measurements and neuroimaging, enabling precise and individualized E-field dosing in experimental and clinical applications. This necessitates the quantification of E-field estimates using metrics that enable the comparison of brain target engagement, functional localization, and TMS intensity adjustments across subjects. The integration of E-field modeling with empirical data has the potential to uncover pivotal insights into the aspects of E-fields responsible for stimulating and modulating brain function and states, enhancing behavioral task performance, and impacting the clinical outcomes of personalized TMS interventions.
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Affiliation(s)
- Moritz Dannhauer
- Computational Neurostimulation Research Program, Noninvasive Neuromodulation Unit, Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, Bethesda, Maryland
| | - Luis J Gomez
- School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana
| | - Pei L Robins
- Computational Neurostimulation Research Program, Noninvasive Neuromodulation Unit, Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, Bethesda, Maryland
| | - Dezhi Wang
- School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana
| | - Nahian I Hasan
- School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana
| | - Axel Thielscher
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark; Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Hartwig R Siebner
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark; Department of Neurology, Copenhagen University Hospital Bispebjerg, Copenhagen, Denmark; Institute for Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Yong Fan
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Zhi-De Deng
- Computational Neurostimulation Research Program, Noninvasive Neuromodulation Unit, Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, Bethesda, Maryland.
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Lin C, Huang C, Chang W, Chang Y, Liu H, Ng S, Lin H, Lee TM, Lee S, Wu S. Predicting suicidality in late-life depression by 3D convolutional neural network and cross-sample entropy analysis of resting-state fMRI. Brain Behav 2024; 14:e3348. [PMID: 38376042 PMCID: PMC10790060 DOI: 10.1002/brb3.3348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 11/19/2023] [Accepted: 11/20/2023] [Indexed: 02/21/2024] Open
Abstract
BACKGROUND Predicting suicide is a pressing issue among older adults; however, predicting its risk is difficult. Capitalizing on the recent development of machine learning, considerable progress has been made in predicting complex behavior such as suicide. As depression remained the strongest risk for suicide, we aimed to apply deep learning algorithms to identify suicidality in a group with late-life depression (LLD). METHODS We enrolled 83 patients with LLD, 35 of which were non-suicidal and 48 were suicidal, including 26 with only suicidal ideation and 22 with past suicide attempts, for resting-state functional magnetic resonance imaging (MRI). Cross-sample entropy (CSE) analysis was conducted to examine the complexity of MRI signals among brain regions. Three-dimensional (3D) convolutional neural networks (CNNs) were used, and the classification accuracy in each brain region was averaged to predict suicidality after sixfold cross-validation. RESULTS We found brain regions with a mean accuracy above 75% to predict suicidality located mostly in default mode, fronto-parietal, and cingulo-opercular resting-state networks. The models with right amygdala and left caudate provided the most reliable accuracy in all cross-validation folds, indicating their neurobiological importance in late-life suicide. CONCLUSION Combining CSE analysis and the 3D CNN, several brain regions were found to be associated with suicidality.
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Affiliation(s)
- Chemin Lin
- Department of PsychiatryKeelung Chang Gung Memorial HospitalKeelungTaiwan
- College of MedicineChang Gung UniversityTaoyuanTaiwan
- Community Medicine Research CenterChang Gung Memorial HospitalKeelungTaiwan
| | - Chih‐Mao Huang
- Department of Biological Science and TechnologyNational Yang Ming Chiao Tung UniversityHsinchuTaiwan
| | - Wei Chang
- Department of Engineering and System ScienceNational Tsing Hua UniversityHsinchuTaiwan
| | - You‐Xun Chang
- Department of Engineering and System ScienceNational Tsing Hua UniversityHsinchuTaiwan
| | - Ho‐Ling Liu
- Community Medicine Research CenterChang Gung Memorial HospitalKeelungTaiwan
- Department of Imaging PhysicsUniversity of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Shu‐Hang Ng
- Department of Head and Neck Oncology GroupLinkou Chang Gung Memorial Hospital and Chang Gung UniversityTaoyuanTaiwan
- Department of Diagnostic RadiologyLinkou Chang Gung Memorial Hospital and Chang Gung UniversityTaoyuanTaiwan
| | - Huang‐Li Lin
- Department of PsychiatryLinkou Chang Gung Memorial HospitalTaoyuanTaiwan
| | - Tatia Mei‐Chun Lee
- Community Medicine Research CenterChang Gung Memorial HospitalKeelungTaiwan
- Laboratory of Neuropsychology and Human NeuroscienceThe University of Hong KongPok Fu LamHong Kong
- State Key Laboratory of Brain and Cognitive ScienceThe University of Hong KongPok Fu LamHong Kong
| | - Shwu‐Hua Lee
- Department of PsychiatryLinkou Chang Gung Memorial HospitalTaoyuanTaiwan
| | - Shun‐Chi Wu
- Department of Engineering and System ScienceNational Tsing Hua UniversityHsinchuTaiwan
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Vogt KM, Ibinson JW, Burlew AC, Smith CT, Aizenstein HJ, Fiez JA. Brain connectivity under light sedation with midazolam and ketamine during task performance and the periodic experience of pain: Examining concordance between different approaches for seed-based connectivity analysis. Brain Imaging Behav 2023; 17:519-529. [PMID: 37166623 PMCID: PMC10543548 DOI: 10.1007/s11682-023-00782-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/29/2023] [Indexed: 05/12/2023]
Abstract
This work focused on functional connectivity changes under midazolam and ketamine sedation during performance of a memory task, with the periodic experience of pain. To maximize ability to compare to previous and future work, we performed secondary region of interest (ROI)-to-ROI functional connectivity analyses on these data, using two granularities of scale for ROIs. These findings are compared to the results of a previous seed-to-voxel analysis methodology, employed in the primary analysis. Healthy adult volunteers participated in this randomized crossover 3 T functional MRI study under no drug, followed by subanesthetic doses of midazolam or ketamine achieving minimal sedation. Periodic painful stimulation was delivered while subjects repeatedly performed a memory-encoding task. Atlas-based and network-level ROIs were used from within Conn Toolbox (ver 18). Timing of experimental task events was regressed from the data to assess drug-induced changes in background connectivity, using ROI-to-ROI methodology. Compared to saline, ROI-to-ROI connectivity changes under ketamine did not survive correction for multiple comparisons, thus data presented is from 16 subjects in a paired analysis between saline and midazolam. In both ROI-to-ROI analyses, the predominant direction of change was towards increased connectivity under midazolam, compared to saline. These connectivity increases occurred between functionally-distinct brain areas, with a posterior-predominant spatial distribution that included many long-range connectivity changes. During performance of an experimental task that involved periodic painful stimulation, compared to saline, low-dose midazolam was associated with robust increases in functional connectivity. This finding was concordant across different seed-based analyses for midazolam, but not ketamine. The neuroimaging drug trial from which this data was drawn was pre-registered (NCT-02515890) prior to enrollment of the first subject.
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Affiliation(s)
- Keith M Vogt
- Department of Anesthesiology and Perioperative Medicine, School of Medicine, University of Pittsburgh, 3459 Fifth Avenue, UPMC Montefiore - Suite 467, Pittsburgh, PA, 15213, USA.
- Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA.
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA.
- Clinical and Translational Science Institute, University of Pittsburgh, Pittsburgh, PA, USA.
| | - James W Ibinson
- Department of Anesthesiology and Perioperative Medicine, School of Medicine, University of Pittsburgh, 3459 Fifth Avenue, UPMC Montefiore - Suite 467, Pittsburgh, PA, 15213, USA
- Department of Anesthesiology, Surgical Service Line, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA, USA
- Clinical and Translational Science Institute, University of Pittsburgh, Pittsburgh, PA, USA
| | - Alex C Burlew
- School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - C Tyler Smith
- Department of Anesthesiology and Perioperative Medicine, School of Medicine, University of Pittsburgh, 3459 Fifth Avenue, UPMC Montefiore - Suite 467, Pittsburgh, PA, 15213, USA
| | - Howard J Aizenstein
- Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Julie A Fiez
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Neuroscience, University of Pittsburgh, Pittsburgh, PA, USA
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Li MT, Sun JW, Zhan LL, Antwi CO, Lv YT, Jia XZ, Ren J. The effect of seed location on functional connectivity: evidence from an image-based meta-analysis. Front Neurosci 2023; 17:1120741. [PMID: 37325032 PMCID: PMC10264592 DOI: 10.3389/fnins.2023.1120741] [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: 12/10/2022] [Accepted: 05/15/2023] [Indexed: 06/17/2023] Open
Abstract
Introduction Default mode network (DMN) is the most involved network in the study of brain development and brain diseases. Resting-state functional connectivity (rsFC) is the most used method to study DMN, but different studies are inconsistent in the selection of seed. To evaluate the effect of different seed selection on rsFC, we conducted an image-based meta-analysis (IBMA). Methods We identified 59 coordinates of seed regions of interest (ROIs) within the default mode network (DMN) from 11 studies (retrieved from Web of Science and Pubmed) to calculate the functional connectivity; then, the uncorrected t maps were obtained from the statistical analyses. The IBMA was performed with the t maps. Results We demonstrate that the overlap of meta-analytic maps across different seeds' ROIs within DMN is relatively low, which cautions us to be cautious with seeds' selection. Discussion Future studies using the seed-based functional connectivity method should take the reproducibility of different seeds into account. The choice of seed may significantly affect the connectivity results.
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Affiliation(s)
- Meng-Ting Li
- School of Psychology, Zhejiang Normal University, Jinhua, China
| | - Jia-Wei Sun
- Department of Clinical Neuroscience, Division of Neuro, Karolinska Institutet, Stockholm, Sweden
| | - Lin-Lin Zhan
- School of Western Studies, Heilongjiang University, Harbin, China
| | | | - Ya-Ting Lv
- Center for Cognition and Brain Disorders, The Affiliated Hospital, Hangzhou Normal University, Hangzhou, China
| | - Xi-Ze Jia
- School of Psychology, Zhejiang Normal University, Jinhua, China
| | - Jun Ren
- School of Psychology, Zhejiang Normal University, Jinhua, China
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Montero-Hernandez S, Pollonini L, Park L, Martorella G, Miao H, Mathis KB, Ahn H. Self-administered transcranial direct current stimulation treatment of knee osteoarthritis alters pain-related fNIRS connectivity networks. NEUROPHOTONICS 2023; 10:015011. [PMID: 37006323 PMCID: PMC10063907 DOI: 10.1117/1.nph.10.1.015011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 03/07/2023] [Indexed: 06/19/2023]
Abstract
Significance Knee osteoarthritis (OA) is a disease that causes chronic pain in the elderly population. Currently, OA is mainly treated pharmacologically with analgesics, although research has shown that neuromodulation via transcranial direct current stimulation (tDCS) may be beneficial in reducing pain in clinical settings. However, no studies have reported the effects of home-based self-administered tDCS on functional brain networks in older adults with knee OA. Aim We used functional near-infrared spectroscopy (fNIRS) to investigate the functional connectivity effects of tDCS on underlying pain processing mechanisms at the central nervous level in older adults with knee OA. Approach Pain-related brain connectivity networks were extracted using fNIRS at baseline and for three consecutive weeks of treatment from 120 subjects randomly assigned to two groups undergoing active tDCS and sham tDCS. Results Our results showed that the tDCS intervention significantly modulated pain-related connectivity correlation only in the group receiving active treatment. We also found that only the active treatment group showed a significantly reduced number and strength of functional connections evoked during nociception in the prefrontal cortex, primary motor (M1), and primary somatosensory (S1) cortices. To our knowledge, this is the first study in which the effect of tDCS on pain-related connectivity networks is investigated using fNIRS. Conclusions fNIRS-based functional connectivity can be effectively used to investigate neural circuits of pain at the cortical level in association with nonpharmacological, self-administered tDCS treatment.
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Affiliation(s)
| | - Luca Pollonini
- University of Houston, Department of Engineering Technology, Houston, Texas, United States
- University of Houston, Department of Electrical and Computer Engineering, Houston, Texas, United States
- University of Houston, Department of Biomedical Engineering, Houston, Texas, United States
- Basque Center on Cognition, Brain and Language, San Sebastian, Spain
| | - Lindsey Park
- Florida State University, College of Nursing, Tallahassee, Florida, United States
| | - Geraldine Martorella
- Florida State University, College of Nursing, Tallahassee, Florida, United States
| | - Hongyu Miao
- Florida State University, College of Nursing, Tallahassee, Florida, United States
| | - Kenneth B. Mathis
- The University of Texas Health Science Center at Houston, McGovern Medical School, Department of Orthopedic Surgery, Houston, Texas, United States
| | - Hyochol Ahn
- Florida State University, College of Nursing, Tallahassee, Florida, United States
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6
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Adolescents with a concussion have altered brain network functional connectivity one month following injury when compared to adolescents with orthopedic injuries. Neuroimage Clin 2022; 36:103211. [PMID: 36182818 PMCID: PMC9668608 DOI: 10.1016/j.nicl.2022.103211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 09/15/2022] [Accepted: 09/26/2022] [Indexed: 12/14/2022]
Abstract
Concussion is a mild traumatic brain injury (mTBI) with increasing prevalence among children and adolescents. Functional connectivity (FC) within and between the default mode network (DMN), central executive network (CEN) and salience network (SN) has been shown to be altered post-concussion. Few studies have investigated connectivity within and between these 3 networks following a pediatric concussion. The present study explored whether within and between-network FC differs between a pediatric concussion and orthopedic injury (OI) group aged 10-18. Participants underwent a resting-state functional magnetic resonance imaging (rs-fMRI) scan at 4 weeks post-injury. One-way ANCOVA analyses were conducted between groups with the seed-based FC of the 3 networks. A total of 55 concussion and 27 OI participants were included in the analyses. Increased within-network FC of the CEN and decreased between-network FC of the DMN-CEN was found in the concussion group when compared to the OI group. Secondary analyses using spherical SN regions of interest revealed increased within-network FC of the SN and increased between-network FC of the DMN-SN and CEN-SN in the concussion group when compared to the OI group. This study identified differential connectivity patterns following a pediatric concussion as compared to an OI 4 weeks post-injury. These differences indicate potential adaptive brain mechanisms that may provide insight into recovery trajectories and appropriate timing of treatment within the first month following a concussion.
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Pilmeyer J, Huijbers W, Lamerichs R, Jansen JFA, Breeuwer M, Zinger S. Functional MRI in major depressive disorder: A review of findings, limitations, and future prospects. J Neuroimaging 2022; 32:582-595. [PMID: 35598083 PMCID: PMC9540243 DOI: 10.1111/jon.13011] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 05/04/2022] [Accepted: 05/04/2022] [Indexed: 02/02/2023] Open
Abstract
Objective diagnosis and prognosis in major depressive disorder (MDD) remains a challenge due to the absence of biomarkers based on physiological parameters or medical tests. Numerous studies have been conducted to identify functional magnetic resonance imaging‐based biomarkers of depression that either objectively differentiate patients with depression from healthy subjects, predict personalized treatment outcome, or characterize biological subtypes of depression. While there are some findings of consistent functional biomarkers, there is still lack of robust data acquisition and analysis methodology. According to current findings, primarily, the anterior cingulate cortex, prefrontal cortex, and default mode network play a crucial role in MDD. Yet, there are also less consistent results and the involvement of other regions or networks remains ambiguous. We further discuss image acquisition, processing, and analysis limitations that might underlie these inconsistencies. Finally, the current review aims to address and discuss possible remedies and future opportunities that could improve the search for consistent functional imaging biomarkers of depression. Novel acquisition techniques, such as multiband and multiecho imaging, and neural network‐based cleaning approaches can enhance the signal quality in limbic and frontal regions. More comprehensive analyses, such as directed or dynamic functional features or the identification of biological depression subtypes, can improve objective diagnosis or treatment outcome prediction and mitigate the heterogeneity of MDD. Overall, these improvements in functional MRI imaging techniques, processing, and analysis could advance the search for biomarkers and ultimately aid patients with MDD and their treatment course.
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Affiliation(s)
- Jesper Pilmeyer
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.,Department of Research and Development, Epilepsy Centre Kempenhaeghe, Heeze, The Netherlands
| | - Willem Huijbers
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.,Philips Research, Eindhoven, The Netherlands
| | - Rolf Lamerichs
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.,Department of Research and Development, Epilepsy Centre Kempenhaeghe, Heeze, The Netherlands.,Philips Research, Eindhoven, The Netherlands
| | - Jacobus F A Jansen
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.,Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre, Maastricht, The Netherlands.,School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Marcel Breeuwer
- Philips Healthcare, Best, The Netherlands.,Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Svitlana Zinger
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.,Department of Research and Development, Epilepsy Centre Kempenhaeghe, Heeze, The Netherlands
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Medina JP, Nigri A, Stanziano M, D’Incerti L, Sattin D, Ferraro S, Rossi Sebastiano D, Pinardi C, Marotta G, Leonardi M, Bruzzone MG, Rosazza C. Resting-State fMRI in Chronic Patients with Disorders of Consciousness: The Role of Lower-Order Networks for Clinical Assessment. Brain Sci 2022; 12:brainsci12030355. [PMID: 35326311 PMCID: PMC8946756 DOI: 10.3390/brainsci12030355] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 02/24/2022] [Accepted: 02/28/2022] [Indexed: 01/27/2023] Open
Abstract
Resting-state fMRI (rs-fMRI) is a widely used technique to investigate the residual brain functions of patients with Disorders of Consciousness (DoC). Nonetheless, it is unclear how the networks that are more associated with primary functions, such as the sensory–motor, medial/lateral visual and auditory networks, contribute to clinical assessment. In this study, we examined the rs-fMRI lower-order networks alongside their structural MRI data to clarify the corresponding association with clinical assessment. We studied 109 chronic patients with DoC and emerged from DoC with structural MRI and rs-fMRI: 65 in vegetative state/unresponsive wakefulness state (VS/UWS), 34 in minimally conscious state (MCS) and 10 with severe disability. rs-fMRI data were analyzed with independent component analyses and seed-based analyses, in relation to structural MRI and clinical data. The results showed that VS/UWS had fewer networks than MCS patients and the rs-fMRI activity in each network was decreased. Visual networks were correlated to the clinical status, and in cases where no clinical response occurred, rs-fMRI indicated distinctive networks conveying information in a similar way to other techniques. The information provided by single networks was limited, whereas the four networks together yielded better classification results, particularly when the model included rs-fMRI and structural MRI data (AUC = 0.80). Both quantitative and qualitative rs-fMRI analyses yielded converging results; vascular etiology might confound the results, and disease duration generally reduced the number of networks observed. The lower-order rs-fMRI networks could be used clinically to support and corroborate visual function assessments in DoC.
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Affiliation(s)
- Jean Paul Medina
- Neuroradiology Unit, Diagnostic and Technology Department, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy; (J.P.M.); (M.S.); (L.D.); (S.F.); (C.P.); (M.G.B.)
| | - Anna Nigri
- Neuroradiology Unit, Diagnostic and Technology Department, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy; (J.P.M.); (M.S.); (L.D.); (S.F.); (C.P.); (M.G.B.)
- Correspondence: (A.N.); (C.R.)
| | - Mario Stanziano
- Neuroradiology Unit, Diagnostic and Technology Department, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy; (J.P.M.); (M.S.); (L.D.); (S.F.); (C.P.); (M.G.B.)
- Neurosciences Department “Rita Levi Montalcini”, University of Turin, 10126 Turin, Italy
| | - Ludovico D’Incerti
- Neuroradiology Unit, Diagnostic and Technology Department, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy; (J.P.M.); (M.S.); (L.D.); (S.F.); (C.P.); (M.G.B.)
- Neuroradiology Unit, Children’s Hospital A. Meyer—University of Florence, 50139 Florence, Italy
| | - Davide Sattin
- IRCCS Istituti Clinici Scientifici Maugeri di Milano, 20138 Milan, Italy;
| | - Stefania Ferraro
- Neuroradiology Unit, Diagnostic and Technology Department, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy; (J.P.M.); (M.S.); (L.D.); (S.F.); (C.P.); (M.G.B.)
- MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Davide Rossi Sebastiano
- Epileptology Unit, Department of Neurophysiology and Diagnostic, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy;
| | - Chiara Pinardi
- Neuroradiology Unit, Diagnostic and Technology Department, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy; (J.P.M.); (M.S.); (L.D.); (S.F.); (C.P.); (M.G.B.)
- Medical Physics Unit, Asst Nord Milano, Sesto San Giovanni, 20099 Milan, Italy
| | - Giorgio Marotta
- Department of Nuclear Medicine, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, 20122 Milan, Italy;
| | - Matilde Leonardi
- Neurology, Public Health, Disability Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy;
| | - Maria Grazia Bruzzone
- Neuroradiology Unit, Diagnostic and Technology Department, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy; (J.P.M.); (M.S.); (L.D.); (S.F.); (C.P.); (M.G.B.)
| | - Cristina Rosazza
- Neuroradiology Unit, Diagnostic and Technology Department, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy; (J.P.M.); (M.S.); (L.D.); (S.F.); (C.P.); (M.G.B.)
- Department of Humanistic Studies, University of Urbino Carlo Bo, 61029 Urbino, Italy
- Correspondence: (A.N.); (C.R.)
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Wu C, Ferreira F, Fox M, Harel N, Hattangadi-Gluth J, Horn A, Jbabdi S, Kahan J, Oswal A, Sheth SA, Tie Y, Vakharia V, Zrinzo L, Akram H. Clinical applications of magnetic resonance imaging based functional and structural connectivity. Neuroimage 2021; 244:118649. [PMID: 34648960 DOI: 10.1016/j.neuroimage.2021.118649] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 09/24/2021] [Accepted: 10/10/2021] [Indexed: 12/23/2022] Open
Abstract
Advances in computational neuroimaging techniques have expanded the armamentarium of imaging tools available for clinical applications in clinical neuroscience. Non-invasive, in vivo brain MRI structural and functional network mapping has been used to identify therapeutic targets, define eloquent brain regions to preserve, and gain insight into pathological processes and treatments as well as prognostic biomarkers. These tools have the real potential to inform patient-specific treatment strategies. Nevertheless, a realistic appraisal of clinical utility is needed that balances the growing excitement and interest in the field with important limitations associated with these techniques. Quality of the raw data, minutiae of the processing methodology, and the statistical models applied can all impact on the results and their interpretation. A lack of standardization in data acquisition and processing has also resulted in issues with reproducibility. This limitation has had a direct impact on the reliability of these tools and ultimately, confidence in their clinical use. Advances in MRI technology and computational power as well as automation and standardization of processing methods, including machine learning approaches, may help address some of these issues and make these tools more reliable in clinical use. In this review, we will highlight the current clinical uses of MRI connectomics in the diagnosis and treatment of neurological disorders; balancing emerging applications and technologies with limitations of connectivity analytic approaches to present an encompassing and appropriate perspective.
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Affiliation(s)
- Chengyuan Wu
- Department of Neurological Surgery, Vickie and Jack Farber Institute for Neuroscience, Thomas Jefferson University, 909 Walnut Street, Third Floor, Philadelphia, PA 19107, USA; Jefferson Integrated Magnetic Resonance Imaging Center, Department of Radiology, Thomas Jefferson University, 909 Walnut Street, First Floor, Philadelphia, PA 19107, USA.
| | - Francisca Ferreira
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, 33 Queen Square, London WC1N 3BG, UK; Unit of Functional Neurosurgery, UCL Queen Square Institute of Neurology, 33 Queen Square, London WC1N 3BG, UK.
| | - Michael Fox
- Center for Brain Circuit Therapeutics, Departments of Neurology, Psychiatry, Radiology, and Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 60 Fenwood Road, Boston, MA 02115, USA.
| | - Noam Harel
- Center for Magnetic Resonance Research, University of Minnesota, 2021 Sixth Street S.E., Minneapolis, MN 55455, USA.
| | - Jona Hattangadi-Gluth
- Department of Radiation Medicine and Applied Sciences, Center for Precision Radiation Medicine, University of California, San Diego, 3855 Health Sciences Drive, La Jolla, CA 92037, USA.
| | - Andreas Horn
- Neurology Department, Movement Disorders and Neuromodulation Section, Charité - University Medicine Berlin, Charitéplatz 1, D-10117, Berlin, Germany.
| | - Saad Jbabdi
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DU, UK.
| | - Joshua Kahan
- Department of Neurology, Weill Cornell Medicine, 525 East 68th Street, New York, NY, 10065, USA.
| | - Ashwini Oswal
- Medical Research Council Brain Network Dynamics Unit, University of Oxford, Mansfield Rd, Oxford OX1 3TH, UK.
| | - Sameer A Sheth
- Department of Neurosurgery, Baylor College of Medicine, 7200 Cambridge, Ninth Floor, Houston, TX 77030, USA.
| | - Yanmei Tie
- Center for Brain Circuit Therapeutics, Departments of Neurology, Psychiatry, Radiology, and Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 60 Fenwood Road, Boston, MA 02115, USA; Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 60 Fenwood Road, Boston, MA 02115, USA.
| | - Vejay Vakharia
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, 33 Queen Square, London WC1N 3BG, UK.
| | - Ludvic Zrinzo
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, 33 Queen Square, London WC1N 3BG, UK; Unit of Functional Neurosurgery, UCL Queen Square Institute of Neurology, 33 Queen Square, London WC1N 3BG, UK.
| | - Harith Akram
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, 33 Queen Square, London WC1N 3BG, UK; Unit of Functional Neurosurgery, UCL Queen Square Institute of Neurology, 33 Queen Square, London WC1N 3BG, UK.
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10
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Zhang J, Li Z, Li Z, Li J, Hu Q, Xu J, Yu H. Progress of Acupuncture Therapy in Diseases Based on Magnetic Resonance Image Studies: A Literature Review. Front Hum Neurosci 2021; 15:694919. [PMID: 34489662 PMCID: PMC8417610 DOI: 10.3389/fnhum.2021.694919] [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] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Accepted: 07/21/2021] [Indexed: 01/18/2023] Open
Abstract
The neural mechanisms of acupuncture are not well-understood. Over the past decades, an increasing number of studies have used MRI to investigate the response of the brain to acupuncture. The current review aims to provide an update on acupuncture therapy in disease. The PubMed, Embase, Web of Science, and Cochrane Library databases were searched from inception to January 31, 2021. Article selection and data extraction were conducted by two review authors. A total of 107 publications about MRI in acupuncture were included, the collective findings of which were as follows: (1) stroke and GB34 (Yanglingquan) are the most studied disease and acupoint. Related studies suggested that the mechanism of acupuncture treatment for stroke may associate with structural and functional plasticity, left and right hemispheres balance, and activation of brain areas related to movement and cognition. GB34 is mainly used in stroke and Parkinson's disease, which mainly activates brain response in the premotor cortex, the supplementary motor area, and the supramarginal gyrus; (2) resting-state functional MRI (rs-fMRI) and functional connectivity (FC) analysis are the most frequently used approaches; (3) estimates of efficacy and brain response to acupuncture depend on the type of sham acupuncture (SA) used for comparison. Brain processing after acupuncture differs between patients and health controls (HC) and occurs mainly in disorder-related areas. Factors that influence the effect of acupuncture include depth of needling, number and locations of acupoints, and deqi and expectation effect, each contributing to the brain response. While studies using MRI have increased understanding of the mechanism underlying the effects of acupuncture, there is scope for development in this field. Due to the small sample sizes, heterogeneous study designs, and analytical methods, the results were inconsistent. Further studies with larger sample sizes, careful experimental design, multimodal neuroimaging techniques, and standardized methods should be conducted to better explain the efficacy and specificity of acupuncture, and to prepare for accurate efficacy prediction in the future.
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Affiliation(s)
- Jinhuan Zhang
- Department of Acupuncture, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, China
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Zihan Li
- Department of Acupuncture, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, China
| | - Zhixian Li
- Department of Acupuncture, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, China
| | - Jiaying Li
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Qingmao Hu
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Chinese Academy of Sciences (CAS) Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Jinping Xu
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Haibo Yu
- Department of Acupuncture, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, China
- Shenzhen Traditional Chinese Medicine Hospital, Shenzhen, China
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11
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Ayyash S, Davis AD, Alders GL, MacQueen G, Strother SC, Hassel S, Zamyadi M, Arnott SR, Harris JK, Lam RW, Milev R, Müller DJ, Kennedy SH, Rotzinger S, Frey BN, Minuzzi L, Hall GB. Exploring brain connectivity changes in major depressive disorder using functional-structural data fusion: A CAN-BIND-1 study. Hum Brain Mapp 2021; 42:4940-4957. [PMID: 34296501 PMCID: PMC8449113 DOI: 10.1002/hbm.25590] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 06/14/2021] [Accepted: 07/01/2021] [Indexed: 01/23/2023] Open
Abstract
There is a growing interest in examining the wealth of data generated by fusing functional and structural imaging information sources. These approaches may have clinical utility in identifying disruptions in the brain networks that underlie major depressive disorder (MDD). We combined an existing software toolbox with a mathematically dense statistical method to produce a novel processing pipeline for the fast and easy implementation of data fusion analysis (FATCAT‐awFC). The novel FATCAT‐awFC pipeline was then utilized to identify connectivity (conventional functional, conventional structural and anatomically weighted functional connectivy) changes in MDD patients compared to healthy comparison participants (HC). Data were acquired from the Canadian Biomarker Integration Network for Depression (CAN‐BIND‐1) study. Large‐scale resting‐state networks were assessed. We found statistically significant anatomically‐weighted functional connectivity (awFC) group differences in the default mode network and the ventral attention network, with a modest effect size (d < 0.4). Functional and structural connectivity seemed to overlap in significance between one region‐pair within the default mode network. By combining structural and functional data, awFC served to heighten or reduce the magnitude of connectivity differences in various regions distinguishing MDD from HC. This method can help us more fully understand the interconnected nature of structural and functional connectivity as it relates to depression.
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Affiliation(s)
- Sondos Ayyash
- School of Biomedical Engineering, McMaster University, Hamilton, Ontario, Canada.,Department of Psychology Neuroscience & Behaviour, McMaster University, Hamilton, Ontario, Canada
| | - Andrew D Davis
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario, Canada.,Rotman Research Institute, Baycrest, Toronto, Ontario, Canada
| | - Gésine L Alders
- Neuroscience Graduate Program, McMaster University, Hamilton, Ontario, Canada
| | - Glenda MacQueen
- Mathison Centre for Mental Health Research and Education, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.,Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Stephen C Strother
- Rotman Research Institute, Baycrest, Toronto, Ontario, Canada.,Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Ontario, Canada
| | - Stefanie Hassel
- Mathison Centre for Mental Health Research and Education, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.,Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Mojdeh Zamyadi
- Rotman Research Institute, Baycrest, Toronto, Ontario, Canada
| | | | - Jacqueline K Harris
- Department of Computer Science, University of Alberta, Edmonton, Alberta, Canada
| | - Raymond W Lam
- Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada
| | - Roumen Milev
- Departments of Psychiatry and Psychology, Queen's University, Providence Care Hospital, Kingston, Ontario, Canada
| | - Daniel J Müller
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada.,Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Sidney H Kennedy
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada.,Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada.,Centre for Mental Health, University Health Network, Toronto, Ontario, Canada.,Krembil Research Institute, University Health Network, Toronto, Ontario, Canada.,Centre for Depression and Suicide Studies, and Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ontario, Canada
| | - Susan Rotzinger
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada.,Krembil Research Institute, University Health Network, Toronto, Ontario, Canada.,Centre for Depression and Suicide Studies, and Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ontario, Canada
| | - Benicio N Frey
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario, Canada.,Neuroscience Graduate Program, McMaster University, Hamilton, Ontario, Canada.,Mood Disorders Treatment and Research Centre and Women's Health Concerns Clinic, St. Joseph's Healthcare, Hamilton, Ontario, Canada
| | - Luciano Minuzzi
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario, Canada.,Neuroscience Graduate Program, McMaster University, Hamilton, Ontario, Canada.,Mood Disorders Treatment and Research Centre and Women's Health Concerns Clinic, St. Joseph's Healthcare, Hamilton, Ontario, Canada
| | - Geoffrey B Hall
- School of Biomedical Engineering, McMaster University, Hamilton, Ontario, Canada.,Department of Psychology Neuroscience & Behaviour, McMaster University, Hamilton, Ontario, Canada.,Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario, Canada.,Neuroscience Graduate Program, McMaster University, Hamilton, Ontario, Canada
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12
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Provenzi L, Lindstedt J, De Coen K, Gasparini L, Peruzzo D, Grumi S, Arrigoni F, Ahlqvist-Björkroth S. The Paternal Brain in Action: A Review of Human Fathers' fMRI Brain Responses to Child-Related Stimuli. Brain Sci 2021; 11:brainsci11060816. [PMID: 34202946 PMCID: PMC8233834 DOI: 10.3390/brainsci11060816] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 06/11/2021] [Accepted: 06/17/2021] [Indexed: 11/25/2022] Open
Abstract
As fathers are increasingly involved in childcare, understanding the neurological underpinnings of fathering has become a key research issue in developmental psychobiology research. This systematic review specifically focused on (1) highlighting methodological issues of paternal brain research using functional magnetic resonance imaging (fMRI) and (2) summarizing findings related to paternal brain responses to auditory and visual infant stimuli. Sixteen papers were included from 157 retrieved records. Sample characteristics (e.g., fathers’ and infant’s age, number of kids, and time spent caregiving), neuroimaging information (e.g., technique, task, stimuli, and processing), and main findings were synthesized by two independent authors. Most of the reviewed works used different stimuli and tasks to test fathers’ responses to child visual and/or auditory stimuli. Pre-processing and first-level analyses were performed with standard pipelines. Greater heterogeneity emerged in second-level analyses. Three main cortical networks (mentalization, embodied simulation, and emotion regulation) and a subcortical network emerged linked with fathers’ responses to infants’ stimuli, but additional areas (e.g., frontal gyrus, posterior cingulate cortex) were also responsive to infants’ visual or auditory stimuli. This review suggests that a distributed and complex brain network may be involved in facilitating fathers’ sensitivity and responses to infant-related stimuli. Nonetheless, specific methodological caveats, the exploratory nature of large parts of the literature to date, and the presence of heterogeneous tasks and measures also demonstrate that systematic improvements in study designs are needed to further advance the field.
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Affiliation(s)
- Livio Provenzi
- Child Psychiatry and Neurology Unit, IRCCS Mondino Foundation, 27100 Pavia, Italy;
- Correspondence: ; Tel.: +39-0382-380287
| | - Johanna Lindstedt
- Department of Psychology and Speech-Language Pathology, University of Turku, 20500 Turku, Finland; (J.L.); (S.A.-B.)
| | - Kris De Coen
- Neonatal Intensive Care Department, University Hospital of Ghent, 9000 Ghent, Belgium;
| | - Linda Gasparini
- Department of Brain and Behavioral Sciences, Università di Pavia, 27100 Pavia, Italy;
| | - Denis Peruzzo
- Neuroimaging Lab, Scientific Institute IRCCS E. Medea, 23842 Bosisio Parini, Italy; (D.P.); (F.A.)
| | - Serena Grumi
- Child Psychiatry and Neurology Unit, IRCCS Mondino Foundation, 27100 Pavia, Italy;
| | - Filippo Arrigoni
- Neuroimaging Lab, Scientific Institute IRCCS E. Medea, 23842 Bosisio Parini, Italy; (D.P.); (F.A.)
| | - Sari Ahlqvist-Björkroth
- Department of Psychology and Speech-Language Pathology, University of Turku, 20500 Turku, Finland; (J.L.); (S.A.-B.)
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13
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The Time Varying Networks of the Interoceptive Attention and Rest. eNeuro 2021; 8:ENEURO.0341-20.2021. [PMID: 33975858 PMCID: PMC8174797 DOI: 10.1523/eneuro.0341-20.2021] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Revised: 03/09/2021] [Accepted: 04/27/2021] [Indexed: 11/21/2022] Open
Abstract
Focused attention to spontaneous sensations is a dynamic process that demands interoceptive abilities. Failure to control it has been linked to neuropsychiatric disorders like illness-anxiety disorder. Regulatory strategies, such as focused attention meditation (FAM), may enhance the ability to control focused attention particularly to body sensations, which can be reflected on functional neuroanatomy. The functional connectivity (FC) related to focused attention has been described, however, the dynamic brain organization associated to this process and the differences to the resting state remains to be studied. To quantify the cerebral dynamic counterpart of focused attention to interoception, we examined fifteen experienced meditators while performing a 20-min attentional task to spontaneous sensations. Subjects underwent three scanning sessions obtaining a resting-state scan before and after the task. Sliding window dynamic FC (DFC) and k-means clustering identified five recurrent FC patterns along the dorsal attention network (DAN), default mode network (DMN), and frontoparietal network (FPN). Subjects remained longer in a low connectivity brain pattern during the resting conditions. By contrast, subjects spent a higher proportion of time in complex patterns during the task than rest. Moreover, a carry-over effect in FC was observed following the interoceptive task performance, suggestive of an active role in the learning process linked to cognitive training. Our results suggest that focused attention to interoceptive processes, demands a dynamic brain organization with specific features that distinguishes it from the resting condition. This approach may provide new insights characterizing the neural basis of the focused attention, an essential component for human adaptability.
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14
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Lee J, Ko W, Kang E, Suk HI. A unified framework for personalized regions selection and functional relation modeling for early MCI identification. Neuroimage 2021; 236:118048. [PMID: 33878379 DOI: 10.1016/j.neuroimage.2021.118048] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Accepted: 04/02/2021] [Indexed: 12/21/2022] Open
Abstract
Resting-state functional magnetic resonance imaging (rs-fMRI) has been widely adopted to investigate functional abnormalities in brain diseases. Rs-fMRI data is unsupervised in nature because the psychological and neurological labels are coarse-grained, and no accurate region-wise label is provided along with the complex co-activities of multiple regions. To the best of our knowledge, most studies regarding univariate group analysis or multivariate pattern recognition for brain disease identification have focused on discovering functional characteristics shared across subjects; however, they have paid less attention to individual properties of neural activities that result from different symptoms or degrees of abnormality. In this work, we propose a novel framework that can identify subjects with early-stage mild cognitive impairment (eMCI) and consider individual variability by learning functional relations from automatically selected regions of interest (ROIs) for each subject concurrently. In particular, we devise a deep neural network composed of a temporal embedding module, an ROI selection module, and a disease-identification module. Notably, the ROI selection module is equipped with a reinforcement learning mechanism so it adaptively selects ROIs to facilitate the learning of discriminative feature representations from a temporally embedded blood-oxygen-level-dependent signals. Furthermore, our method allows us to capture the functional relations of a subject-specific ROI subset through the use of a graph-based neural network. Our method considers individual characteristics for diagnosis, as opposed to most conventional methods that identify the same biomarkers across subjects within a group. Based on the ADNI cohort, we validate the effectiveness of our method by presenting the superior performance of our network in eMCI identification. Furthermore, we provide insightful neuroscientific interpretations by analyzing the regions selected for the eMCI classification.
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Affiliation(s)
- Jiyeon Lee
- Department of Brain and Cognitive Engineering, Korea University, Republic of Korea
| | - Wonjun Ko
- Department of Brain and Cognitive Engineering, Korea University, Republic of Korea
| | - Eunsong Kang
- Department of Brain and Cognitive Engineering, Korea University, Republic of Korea
| | - Heung-Il Suk
- Department of Brain and Cognitive Engineering, Korea University, Republic of Korea; Department of Artificial Intelligence, Korea University, Republic of Korea.
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15
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Hsieh YT, Wu R, Tseng HH, Wei SY, Huang MC, Chang HH, Yang YK, Chen PS. Childhood neglect is associated with corticostriatal circuit dysfunction in bipolar disorder adults. Psychiatry Res 2021; 295:113550. [PMID: 33223273 DOI: 10.1016/j.psychres.2020.113550] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Accepted: 10/31/2020] [Indexed: 12/11/2022]
Abstract
Bipolar disorder (BD) is characterized with cognitive impairment, which may be mediated by corticostriatal dysfunction. Here we examined whether history of childhood trauma, a risk factor for BD, was linked to corticostriatal dysfunction in BD patients. Furthermore, the possible associations between childhood trauma and cognitive impairment were examined. Thirty-eight BD participants who met the DSM-IV diagnostic criteria were enrolled. Childhood trauma was identified via the Childhood Trauma Questionnaire (CTQ). Participants completed the Wisconsin Card-Sorting Test (WCST). Resting-state functional magnetic resonance imaging (rsfMRI) was performed in participants using a 3T scanner. Bilateral caudate to whole-brain functional connectivity (FC) were analyzed, and childhood trauma was entered as a regressor of interest when controlling for age. Results showed the level of physical neglect was negatively correlated with left-caudate-seed FC to the frontoparietal network, including the right supramarginal gyrus, left inferior parietal lobule, right middle frontal gyrus, and right superior parietal lobule. The level of physical neglect was also negatively correlated with WCST performance. And the left-caudate-seed FCs to the frontoparietal network were positively correlated with WCST performance. Unequivocally, the specific impacts of physical neglect on brain connectivity and executive function in the BD population merit further investigation.
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Affiliation(s)
- Yi-Ting Hsieh
- Department of Psychiatry, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Rebecca Wu
- Department of Medicine, Division of Neurology, University of Toronto, Toronto, Canada
| | - Huai-Hsuan Tseng
- Department of Psychiatry, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan; Institute of Behavioral Medicine, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Shyh-Yuh Wei
- Department of Psychiatry, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Ming-Chyi Huang
- Department of Psychiatry, Taipei City Hospital, Songde Branch, Taipei, Taiwan
| | - Hui Hua Chang
- Institute of Clinical Pharmacy and Pharmaceutical Sciences, National Cheng Kung University, Tainan, Taiwan; School of Pharmacy, College of Medicine, National Cheng Kung University, Tainan, Taiwan; Department of Pharmacy, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan; Department of Pharmacy, National Cheng Kung University Hospital, Dou-Liou Branch, Yunlin, Taiwan
| | - Yen Kuang Yang
- Department of Psychiatry, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan; Institute of Behavioral Medicine, College of Medicine, National Cheng Kung University, Tainan, Taiwan; Department of Psychiatry, Tainan Hospital, Ministry of Health and Welfare, Tainan, Taiwan
| | - Po See Chen
- Department of Psychiatry, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan; Institute of Behavioral Medicine, College of Medicine, National Cheng Kung University, Tainan, Taiwan; Department of Psychiatry, National Cheng Kung University Hospital, Dou-Liou Branch, Yunlin, Taiwan.
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16
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Pamplona GSP, Vieira BH, Scharnowski F, Salmon CEG. Personode: A Toolbox for ICA Map Classification and Individualized ROI Definition. Neuroinformatics 2020; 18:339-349. [PMID: 31900722 DOI: 10.1007/s12021-019-09449-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
Canonical resting state networks (RSNs) can be obtained through independent component analysis (ICA). RSNs are reproducible across subjects but also present inter-individual differences, which can be used to individualize regions-of-interest (ROI) definition, thus making fMRI analyses more accurate. Unfortunately, no automatic tool for defining subject-specific ROIs exists, making the classification of ICAs as representatives of RSN time-consuming and largely dependent on visual inspection. Here, we present Personode, a user-friendly and open source MATLAB-based toolbox that semi-automatically performs the classification of RSN and allows for defining subject- and group-specific ROIs. To validate the applicability of our new approach and to assess potential improvements compared to previous approaches, we applied Personode to both task-related activation and resting-state data. Our analyses show that for task-related activation analyses, subject-specific spherical ROIs defined with Personode produced higher activity contrasts compared to ROIs derived from single-study and meta-analytic coordinates. We also show that subject-specific irregular ROIs defined with Personode improved ROI-to-ROI functional connectivity analyses.Hence, Personode might be a useful toolbox for ICA map classification into RSNs and group- as well as subject-specific ROI definitions, leading to improved analyses of task-related activation and functional connectivity.
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Affiliation(s)
- Gustavo S P Pamplona
- Sensory-Motor Laboratory (SeMoLa), Jules-Gonin Eye Hospital/Fondation Asile des Aveugles, Department of Ophthalmology/University of Lausanne, Avenue de France 15, 1004, Lausanne, Switzerland. .,Department of Psychiatry, Psychotherapy, and Psychosomatics, Psychiatric Hospital, University of Zürich, Lenggstr. 31, 8032, Zürich, Switzerland. .,Inbrain Lab, Department of Physics, University of São Paulo, Av. Bandeirantes 3900, Ribeirão Preto, 14040-900, Brazil.
| | - Bruno H Vieira
- Inbrain Lab, Department of Physics, University of São Paulo, Av. Bandeirantes 3900, Ribeirão Preto, 14040-900, Brazil
| | - Frank Scharnowski
- Department of Psychiatry, Psychotherapy, and Psychosomatics, Psychiatric Hospital, University of Zürich, Lenggstr. 31, 8032, Zürich, Switzerland.,Neuroscience Center Zürich, University of Zürich and Swiss Federal Institute of Technology, Winterthurerstr. 190, 8057, Zürich, Switzerland.,Zürich Center for Integrative Human Physiology (ZIHP), University of Zürich, Winterthurerstr. 190, 8057, Zürich, Switzerland.,Department of Basic Psychological Research and Research Methods, Faculty of Psychology, University of Vienna, Liebiggasse 5, 1010, Vienna, Austria
| | - Carlos E G Salmon
- Inbrain Lab, Department of Physics, University of São Paulo, Av. Bandeirantes 3900, Ribeirão Preto, 14040-900, Brazil
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17
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Pamplona GS, Heldner J, Langner R, Koush Y, Michels L, Ionta S, Scharnowski F, Salmon CE. Network-based fMRI-neurofeedback training of sustained attention. Neuroimage 2020; 221:117194. [DOI: 10.1016/j.neuroimage.2020.117194] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2020] [Revised: 07/07/2020] [Accepted: 07/20/2020] [Indexed: 11/29/2022] Open
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18
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Ross MC, Cisler JM. Altered large-scale functional brain organization in posttraumatic stress disorder: A comprehensive review of univariate and network-level neurocircuitry models of PTSD. Neuroimage Clin 2020; 27:102319. [PMID: 32622316 PMCID: PMC7334481 DOI: 10.1016/j.nicl.2020.102319] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Revised: 06/15/2020] [Accepted: 06/17/2020] [Indexed: 12/31/2022]
Abstract
Classical neural circuitry models of posttraumatic stress disorder (PTSD) are largely derived from univariate activation studies and implicate the fronto-limbic circuit as a main neural correlate of PTSD symptoms. Though well-supported by human neuroimaging literature, these models are limited in their ability to explain the widely distributed neural and behavioral deficits in PTSD. Emerging interest in the application of large-scale network methods to functional neuroimaging provides a new opportunity to overcome such limitations and conceptualize the neural circuitry of PTSD in the context of network patterns. This review aims to evaluate both the classical neural circuitry model and a new, network-based model of PTSD neural circuitry using a breadth of functional brain organization research in subjects with PTSD. Taken together, this literature suggests global patterns of reduced functional connectivity (FC) in PTSD groups as well as altered FC targets that reside disproportionately in canonical functional networks, especially the default mode network. This provides evidence for an integrative model that includes elements of both the classical models and network-based models to characterize the neural circuitry of PTSD.
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Affiliation(s)
- Marisa C Ross
- Neuroscience and Training Program, University of Wisconsin-Madison, United States; Neuroscience and Public Policy Program, University of Wisconsin-Madison, United States.
| | - Josh M Cisler
- Neuroscience and Training Program, University of Wisconsin-Madison, United States; Department of Psychiatry, University of Wisconsin School of Medicine and Public Health, United States
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19
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An analytical workflow for seed-based correlation and independent component analysis in interventional resting-state fMRI studies. Neurosci Res 2020; 165:26-37. [PMID: 32464181 DOI: 10.1016/j.neures.2020.05.006] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2020] [Revised: 05/08/2020] [Accepted: 05/18/2020] [Indexed: 12/12/2022]
Abstract
Resting-state functional MRI (rs-fMRI) is a task-free method of detecting spatially distinct brain regions with correlated activity, which form organised networks known as resting-state networks (RSNs). The two most widely used methods for analysing RSN connectivity are seed-based correlation analysis (SCA) and independent component analysis (ICA) but there is no established workflow of the optimal combination of analytical steps and how to execute them. Rodent rs-fMRI data from our previous longitudinal brain stimulation studies were used to investigate these two methods using FSL. Specifically, we examined: (1) RSN identification and group comparisons in ICA, (2) ICA-based denoising compared to nuisance signal regression in SCA, and (3) seed selection in SCA. In ICA, using a baseline-only template resulted in greater functional connectivity within RSNs and more sensitive detection of group differences than when an average pre/post stimulation template was used. In SCA, the use of an ICA-based denoising method in the preprocessing of rs-fMRI data and the use of seeds from individual functional connectivity maps in running group comparisons increased the sensitivity of detecting group differences by preventing the reduction in signals of interest. Accordingly, when analysing animal and human rs-fMRI data, we infer that the use of baseline-only templates in ICA and ICA-based denoising and individualised seeds in SCA will improve the reliability of results and comparability across rs-fMRI studies.
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20
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Pan F, Xu Y, Zhou W, Chen J, Wei N, Lu S, Shang D, Wang J, Huang M. Disrupted intrinsic functional connectivity of the cognitive control network underlies disease severity and executive dysfunction in first-episode, treatment-naive adolescent depression. J Affect Disord 2020; 264:455-463. [PMID: 31780136 DOI: 10.1016/j.jad.2019.11.076] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/14/2019] [Revised: 10/01/2019] [Accepted: 11/12/2019] [Indexed: 12/12/2022]
Abstract
BACKGROUND Previous neuroimaging studies have showed that imbalanced functional integration of distributed large-scale brain networks is associated with pathophysiological characteristics of major depressive disorder (MDD). However, the association between network integrative disturbances and clinical features and cognitive functions remains largely unclear in adolescent MDD. This study investigated the neural correlates of abnormal functional connectivity networks with clinical and cognitive characteristics in adolescent MDD. METHODS Twenty-eight first-episode, treatment-naive adolescents with MDD and 24 well-matched healthy controls (HCs) underwent functional magnetic resonance imaging (fMRI) and a battery of cognitive tests. A seed-based functional connectivity (FC) approach was used to depict connectivity patterns of the cognitive control network (CCN), affective network (AN) and default mode network (DMN), whose between-group differences were correlated with clinical variables and cognitive functions in the patients. RESULTS Compared with the HCs, the MDD patients exhibited impaired executive functions. The FC analysis revealed lower CCN FC with the temporal, parietal and frontal regions and the limbic system, higher AN FC with the temporal and occipital regions and the cerebellum, and lower DMN FC with the cerebellum and insula. Interestingly, the decreased CCN FC was related to disease severity (with the inferior frontal gyrus) and executive dysfunctions (with the middle cingulate gyrus and supramarginal gyrus) in the patients. LIMITATIONS The main limitations were the relatively small sample size and suboptimal imaging parameters. CONCLUSION Functional alteration of CCN during the developmentally sensitive period may be important in the neurobiology of adolescent MDD.
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Affiliation(s)
- Fen Pan
- Department of Psychiatry, First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China; The Key Laboratory of Mental Disorder's Management of Zhejiang Province, Hangzhou, China
| | - Yi Xu
- Department of Psychiatry, First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China; The Key Laboratory of Mental Disorder's Management of Zhejiang Province, Hangzhou, China
| | - Weihua Zhou
- Department of Psychiatry, First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China; The Key Laboratory of Mental Disorder's Management of Zhejiang Province, Hangzhou, China
| | - Jinkai Chen
- Department of Psychiatry, First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China; The Key Laboratory of Mental Disorder's Management of Zhejiang Province, Hangzhou, China
| | - Ning Wei
- Department of Psychiatry, First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China; The Key Laboratory of Mental Disorder's Management of Zhejiang Province, Hangzhou, China
| | - Shaojia Lu
- Department of Psychiatry, First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China; The Key Laboratory of Mental Disorder's Management of Zhejiang Province, Hangzhou, China
| | - Desheng Shang
- Department of Radiology, First Affiliated Hospital, College of Medicine, Zhejiang University, The Key Laboratory of Mental Disorder's Management of Zhejiang Province, Hangzhou, China
| | - Jinhui Wang
- Institute for Brain Research and Rehabilitation, Guangdong Key Laboratory of Mental Health and Cognitive Science, Center for Studies of Psychological Application, South China Normal University, Guangzhou, China.
| | - Manli Huang
- Department of Psychiatry, First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China; The Key Laboratory of Mental Disorder's Management of Zhejiang Province, Hangzhou, China.
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21
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Kambeitz-Ilankovic L, Wenzel J, Haas SS, Ruef A, Antonucci LA, Sanfelici R, Paolini M, Koutsouleris N, Biagianti B. Modeling Social Sensory Processing During Social Computerized Cognitive Training for Psychosis Spectrum: The Resting-State Approach. Front Psychiatry 2020; 11:554475. [PMID: 33329091 PMCID: PMC7716799 DOI: 10.3389/fpsyt.2020.554475] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Accepted: 09/15/2020] [Indexed: 11/13/2022] Open
Abstract
Background: Greater impairments in early sensory processing predict response to auditory computerized cognitive training (CCT) in patients with recent-onset psychosis (ROP). Little is known about neuroimaging predictors of response to social CCT, an experimental treatment that was recently shown to induce cognitive improvements in patients with psychosis. Here, we investigated whether ROP patients show interindividual differences in sensory processing change and whether different patterns of SPC are (1) related to the differential response to treatment, as indexed by gains in social cognitive neuropsychological tests and (2) associated with unique resting-state functional connectivity (rsFC). Methods: Twenty-six ROP patients completed 10 h of CCT over the period of 4-6 weeks. Subject-specific improvement in one CCT exercise targeting early sensory processing-a speeded facial Emotion Matching Task (EMT)-was studied as potential proxy for target engagement. Based on the median split of SPC from the EMT, two patient groups were created. Resting-state activity was collected at baseline, and bold time series were extracted from two major default mode network (DMN) hubs: left medial prefrontal cortex (mPFC) and left posterior cingulate cortex (PCC). Seed rsFC analysis was performed using standardized Pearson correlation matrices, generated between the average time course for each seed and each voxel in the brain. Results: Based on SPC, we distinguished improvers-i.e., participants who showed impaired performance at baseline and reached the EMT psychophysical threshold during CCT-from maintainers-i.e., those who showed intact EMT performance at baseline and sustained the EMT psychophysical threshold throughout CCT. Compared to maintainers, improvers showed an increase of rsFC at rest between PCC and left superior and medial frontal regions and the cerebellum. Compared to improvers, maintainers showed increased rsFC at baseline between PCC and superior temporal and insular regions bilaterally. Conclusions: In ROP patients with an increase of connectivity at rest in the default mode network, social CCT is still able to induce sensory processing changes that however do not translate into social cognitive gains. Future studies should investigate if impairments in short-term synaptic plasticity are responsible for this lack of response and can be remediated by pharmacological augmentation during CCT.
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Affiliation(s)
- Lana Kambeitz-Ilankovic
- Department of Psychiatry, Faculty of Medicine and University Hospital of Cologne, University of Cologne, Cologne, Germany.,Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany
| | - Julian Wenzel
- Department of Psychiatry, Faculty of Medicine and University Hospital of Cologne, University of Cologne, Cologne, Germany.,Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany
| | - Shalaila S Haas
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Anne Ruef
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany
| | - Linda A Antonucci
- Department of Psychiatry, Faculty of Medicine and University Hospital of Cologne, University of Cologne, Cologne, Germany.,Department of Education, Psychology, Communication, University of Bari "Aldo Moro", Bari, Italy
| | - Rachele Sanfelici
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany.,Max Planck School of Cognition, Leipzig, Germany
| | - Marco Paolini
- Department of Radiology, University Hospital, Ludwig-Maximilian-University, Munich, Germany
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany
| | - Bruno Biagianti
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy.,Department of R&D, Posit Science Corporation, San Francisco, CA, United States
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22
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Pakravan M, Shamsollahi MB. Spatial and temporal joint, partially-joint and individual sources in independent component analysis: Application to social brain fMRI dataset. J Neurosci Methods 2020; 329:108453. [PMID: 31644994 DOI: 10.1016/j.jneumeth.2019.108453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2019] [Revised: 09/25/2019] [Accepted: 09/30/2019] [Indexed: 11/16/2022]
Abstract
absectionBackground Three types of sources can be considered in the analysis of multi-subject datasets: (i) joint sources which are common among all subjects, (ii) partially-joint sources which are common only among a subset of subjects, and (iii) individual sources which belong to each subject and represent the specific conditions of that subject. Extracting spatial and temporal joint, partially-joint, and individual sources of multi-subject datasets is of significant importance to analyze common and cross information of multiple subjects. NEW METHOD We present a new framework to extract these three types of spatial and temporal sources in multi-subject functional magnetic resonance imaging (fMRI) datasets. In this framework, temporal and spatial independent component analysis are utilized, and a weighted sum of higher-order cumulants is maximized. RESULTS We evaluate the presented algorithm by analyzing simulated data and one real multi-subject fMRI dataset. Our results on the real dataset are consistent with the existing meta-analysis studies. We show that spatial and temporal jointness of extracted joint and partially-joint sources in the theory of mind regions of brain increase with the age of subjects. COMPARISON WITH EXISTING METHOD In Richardson et al. (2018), predefined regions of interest (ROI) have been used to analyze the real dataset, whereas our unified algorithm simultaneously extracts activated and uncorrelated ROIs, and determines their spatial and temporal jointness without additional computations. CONCLUSIONS Extracting temporal and spatial joint and partially-joint sources in a unified algorithm improves the accuracy of joint analysis of the multi-subject fMRI dataset.
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Affiliation(s)
- Mansooreh Pakravan
- Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran.
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23
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Hsu AL, Chen HSM, Hou P, Wu CW, Johnson JM, Noll KR, Prabhu SS, Ferguson SD, Kumar VA, Schomer DF, Chen JH, Liu HL. Presurgical resting-state functional MRI language mapping with seed selection guided by regional homogeneity. Magn Reson Med 2019; 84:375-383. [PMID: 31793025 DOI: 10.1002/mrm.28107] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2019] [Revised: 10/24/2019] [Accepted: 11/14/2019] [Indexed: 01/09/2023]
Abstract
PURPOSE Resting-state functional MRI (rs-FMRI) has shown potential for presurgical mapping of eloquent cortex when a patient's performance on task-based FMRI is compromised. The seed-based analysis is a practical approach for detecting rs-FMRI functional networks; however, seed localization remains challenging for presurgical language mapping. Therefore, we proposed a data-driven approach to guide seed localization for presurgical rs-FMRI language mapping. METHODS Twenty-six patients with brain tumors located in left perisylvian regions had undergone task-based FMRI and rs-FMRI before tumor resection. For the seed-based rs-FMRI language mapping, a seeding approach that integrates regional homogeneity and meta-analysis maps (RH+MA) was proposed to guide the seed localization. Canonical and task-based seeding approaches were used for comparison. The performance of the 3 seeding approaches was evaluated by calculating the Dice coefficients between each rs-FMRI language mapping result and the result from task-based FMRI. RESULTS With the RH+MA approach, selecting among the top 6 seed candidates resulted in the highest Dice coefficient for 81% of patients (21 of 26) and the top 9 seed candidates for 92% of patients (24 of 26). The RH+MA approach yielded rs-FMRI language mapping results that were in greater agreement with the results of task-based FMRI, with significantly higher Dice coefficients (P < .05) than that of canonical and task-based approaches within putative language regions. CONCLUSION The proposed RH+MA approach outperformed the canonical and task-based seed localization for rs-FMRI language mapping. The results suggest that RH+MA is a robust and feasible method for seed-based functional connectivity mapping in clinical practice.
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Affiliation(s)
- Ai-Ling Hsu
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas.,Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan
| | - Henry Szu-Meng Chen
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Ping Hou
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Changwei W Wu
- Graduate Institute of Mind, Brain and Consciousness, Taipei Medical University, Taipei, Taiwan.,Brain and Consciousness Research Center, Shuang Ho Hospital, New Taipei, Taiwan
| | - Jason M Johnson
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Kyle R Noll
- Section of Neuropsychology, Department of Neuro-Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Sujit S Prabhu
- Department of Neurosurgery, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Sherise D Ferguson
- Department of Neurosurgery, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Vinodh A Kumar
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Donald F Schomer
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Jyh-Horng Chen
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan
| | - Ho-Ling Liu
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
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24
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Trait and state patterns of basolateral amygdala connectivity at rest are related to endogenous testosterone and aggression in healthy young women. Brain Imaging Behav 2019; 13:564-576. [PMID: 29744800 DOI: 10.1007/s11682-018-9884-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
The steroid hormone testosterone (T) has been suggested to influence reactive aggression upon its action on the basolateral amygdala (BLA), a key brain region for threat detection. However, it is unclear whether T modulates resting-state functional connectivity (rsFC) of the BLA, and whether this predicts subsequent aggressive behavior. Aggressive interactions themselves, which often induce changes in T concentrations, could further alter BLA rsFC, but this too remains untested. Here we investigated the effect of endogenous T on rsFC of the BLA at baseline as well as after an aggressive encounter, and whether this was related to behavioral aggression in healthy young women (n = 39). Pre-scan T was negatively correlated with basal rsFC between BLA and left superior temporal gyrus (STG; p < .001, p < .05 Family-Wise Error [FWE] cluster-level corrected), which in turn was associated with increased aggression (r = .37, p = .020). BLA-STG coupling at rest might thus underlie hostile readiness in low-T women. In addition, connectivity between the BLA and the right superior parietal lobule (SPL), a brain region involved in higher-order perceptual processes, was reduced in aggressive participants (p < .001, p < .05 FWE cluster-level corrected). On the other hand, post-task increases in rsFC between BLA and medial orbitofrontal cortex (mOFC) were linked to reduced aggression (r = -.36, p = .023), consistent with the established notion that the mOFC regulates amygdala activity in order to curb aggressive impulses. Finally, competition-induced changes in T were associated with increased coupling between the BLA and the right lateral OFC (p < .001, p < .05 FWE cluster-level corrected), but this effect was unrelated to aggression. We thus identified connectivity patterns that prospectively predict aggression in women, and showed how aggressive interactions in turn impact these neural systems.
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25
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Hamidian S, Vachha B, Jenabi M, Karimi S, Young RJ, Holodny AI, Peck KK. Resting-State Functional Magnetic Resonance Imaging and Probabilistic Diffusion Tensor Imaging Demonstrate That the Greatest Functional and Structural Connectivity in the Hand Motor Homunculus Occurs in the Area of the Thumb. Brain Connect 2019; 8:371-379. [PMID: 29987948 DOI: 10.1089/brain.2018.0589] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
The primary hand motor region is classically believed to be in the "hand knob" area in the precentral gyrus (PCG). However, hand motor task-based activation is often localized outside this area. The purpose of this study is to investigate the structural and functional connectivity driven by different seed locations corresponding to the little, index, and thumb in the PCG using probabilistic diffusion tractography (PDT) and resting-state functional magnetic resonance imaging (rfMRI). Twelve healthy subjects had three regions of interest (ROIs) placed in the left PCG: lateral to the hand knob (thumb area), within the hand knob (index finger area), and medial to the hand knob (little finger area). Connectivity maps were generated using PDT and rfMRI. Individual and group level analyses were performed. Results show that the greatest hand motor connectivity between both hemispheres was obtained using the ROI positioned just lateral to the hand knob in the PCG (the thumb area). The number of connected voxels in the PCG between the two hemispheres was greatest in the lateral-most ROI (the thumb area): 279 compared with 13 for the medial-most ROI and 9 for the central hand knob ROI. Similarly, the highest white matter connectivity between the two hemispheres resulted from the ROI placed in the lateral portion of PCG (p < 0.003). The maximal functional and structural connectivity of the hand motor area between hemispheres occurs in the thumb area, located laterally at the "hand knob." Thus, this location appears maximal for rfMRI and PDT seeding of the motor area.
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Affiliation(s)
- Shaminta Hamidian
- 1 Department of Radiology, Memorial Sloan-Kettering Cancer Center , New York, New York
| | - Behroze Vachha
- 1 Department of Radiology, Memorial Sloan-Kettering Cancer Center , New York, New York
| | - Mehrnaz Jenabi
- 1 Department of Radiology, Memorial Sloan-Kettering Cancer Center , New York, New York
| | - Sasan Karimi
- 1 Department of Radiology, Memorial Sloan-Kettering Cancer Center , New York, New York
| | - Robert J Young
- 1 Department of Radiology, Memorial Sloan-Kettering Cancer Center , New York, New York
| | - Andrei I Holodny
- 1 Department of Radiology, Memorial Sloan-Kettering Cancer Center , New York, New York
| | - Kyung K Peck
- 1 Department of Radiology, Memorial Sloan-Kettering Cancer Center , New York, New York.,2 Department of Medical Physics, Memorial Sloan-Kettering Cancer Center , New York, New York
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26
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Lee YB, Yoo K, Roh JH, Moon WJ, Jeong Y. Brain-State Extraction Algorithm Based on the State Transition (BEST): A Dynamic Functional Brain Network Analysis in fMRI Study. Brain Topogr 2019; 32:897-913. [DOI: 10.1007/s10548-019-00719-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2018] [Accepted: 05/28/2019] [Indexed: 12/23/2022]
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27
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Yoo K, Rosenberg MD, Noble S, Scheinost D, Constable RT, Chun MM. Multivariate approaches improve the reliability and validity of functional connectivity and prediction of individual behaviors. Neuroimage 2019; 197:212-223. [PMID: 31039408 DOI: 10.1016/j.neuroimage.2019.04.060] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2018] [Revised: 04/17/2019] [Accepted: 04/23/2019] [Indexed: 10/26/2022] Open
Abstract
Brain functional connectivity features can predict cognition and behavior at the level of the individual. Most studies measure univariate signals, correlating timecourses from the average of constituent voxels in each node. While straightforward, this approach overlooks the spatial patterns of voxel-wise signals within individual nodes. Given that multivariate spatial activity patterns across voxels can improve fMRI measures of mental representations, here we asked whether using voxel-wise timecourses can better characterize region-by-region interactions relative to univariate approaches. Using two fMRI datasets, the Human Connectome Project sample and a local test-retest sample, we measured multivariate functional connectivity with multivariate distance correlation and univariate connectivity with Pearson's correlation. We compared multivariate and univariate connectivity estimates, demonstrating that relative to univariate estimates, multivariate estimates exhibited higher reliability at both the edge-level and connectome-level, stronger prediction of individual differences, and greater sensitivity to brain states within individuals. Our findings suggest that multivariate estimates reliably provide more powerful information about an individual's functional brain organization and its relation to cognitive skills.
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Affiliation(s)
| | | | - Stephanie Noble
- Interdepartmental Neuroscience Program, Yale University, USA
| | - Dustin Scheinost
- Department of Radiology and Biomedical Imaging, 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
| | - Marvin M Chun
- Department of Psychology, Yale University, USA; Interdepartmental Neuroscience Program, Yale University, USA; Department of Neuroscience, Yale School of Medicine, New Haven, CT, 06520, USA
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28
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Falco D, Chowdury A, Rosenberg DR, Diwadkar VA, Bressler SL. From nodes to networks: How methods for defining nodes influence inferences regarding network interactions. Hum Brain Mapp 2019; 40:1458-1469. [PMID: 30536968 DOI: 10.1002/hbm.24459] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2018] [Revised: 09/13/2018] [Accepted: 10/22/2018] [Indexed: 11/06/2022] Open
Abstract
Functional connectivity (FC) analysis of fMRI data typically rests on prior identification of network nodes from activation profiles. We compared Activation Likelihood Estimate (ALE) and the Experimentally Derived Estimate (EDE) approaches to network node identification and functional inference for both verbal and visual forms of working memory. ALE arrives at canonical activation maxima that are assumed to reliably represent peaks of brain activity underlying a psychological process (e.g., working memory). By comparison, EDEs of activation maxima are typically derived from individual participant data, and are thus sensitive to individual participant activation profiles. Here, nodes were localized by both ALE and EDE methods for each participant, and subsequently extracted time series were compared using connectivity analysis. Two sets of significance tests were performed: (1) correlations computed between nodal time series of each method were compared, and (2) correlations computed between network edges (functional connections) of each network node pair were compared. Large proportions of edge correlations significantly differed between methods. ALE effectively summarizes working memory network node locations across studies and subjects, but the sensitivity to individual functional loci suggest that EDE methods provide individualized estimates of network connectivity. We suggest that a hybrid method incorporating both ALE and EDE is optimal for network inference.
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Affiliation(s)
- Dimitri Falco
- Center for Complex Systems and Brain Sciences, Florida Atlantic University, Boca Raton, Florida
| | - Asadur Chowdury
- Department of Psychiatry and Behavioral Neuroscience, Wayne State University School of Medicine, Detroit, Michigan
| | - David R Rosenberg
- Department of Psychiatry and Behavioral Neuroscience, Wayne State University School of Medicine, Detroit, Michigan
| | - Vaibhav A Diwadkar
- Department of Psychiatry and Behavioral Neuroscience, Wayne State University School of Medicine, Detroit, Michigan
| | - Steven L Bressler
- Center for Complex Systems and Brain Sciences, Florida Atlantic University, Boca Raton, Florida.,Department of Psychology, Florida Atlantic University, Boca Raton, Florida
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29
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Amygdala Functional Connectivity During Self-Face Processing in Depressed Adolescents With Recent Suicide Attempt. J Am Acad Child Adolesc Psychiatry 2019; 58:221-231. [PMID: 30738549 PMCID: PMC6492541 DOI: 10.1016/j.jaac.2018.06.036] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/01/2018] [Revised: 06/24/2018] [Accepted: 07/09/2018] [Indexed: 12/30/2022]
Abstract
OBJECTIVE Suicide is the second leading cause of death among adolescents; however, objective biomarkers of suicide risk are lacking. Aberrant self-face amygdala activity is associated with suicide ideation, and its connectivity with neural regions that enable self-processing (eg medial prefrontal cortex) may be a suicide risk factor. METHOD Adolescents (aged 11-17 years; N = 120) were sorted into four groups: healthy controls (HC), depressed individuals with low suicide ideation (LS), depressed individuals with high suicide ideation (HS), and depressed suicide attempters (SA). Youth completed an emotional (Happy, Sad, Neutral) self-face recognition task in the scanner. Bilateral amygdala task-dependent functional connectivity was determined with psychophysiological interaction analysis. Connectivity was compared across groups and within Self versus Other faces across emotions and hemispheres. Voxelwise results were thresholded (p < .005, uncorrected) and corrected for multiple comparisons (p < .05, familywise error). RESULTS Both HS and SA displayed greater amygdala connectivity with the dorsolateral prefrontal cortex, dorsomedial prefrontal cortex, and precuneus, compared to LS, who, in turn, showed greater connectivity than HC. Greater left amygdala-rostral anterior cingulate cortex (rACC) connectivity was observed in SA compared to all other groups, whereas right amygdala-rACC connectivity was greater in HS versus LS and HC. CONCLUSION Greater connectivity between amygdala and other regions implicated in self-face processing differentiated suicide ideation and suicide attempt groups. A dose-dependent response showed that greater rACC-left amygdala connectivity during self-face processing was associated with a recent suicide attempt, but that a greater rACC-right amygdala connectivity was associated with suicide ideation.
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30
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Mash LE, Linke AC, Olson LA, Fishman I, Liu TT, Müller RA. Transient states of network connectivity are atypical in autism: A dynamic functional connectivity study. Hum Brain Mapp 2019; 40:2377-2389. [PMID: 30681228 DOI: 10.1002/hbm.24529] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2018] [Accepted: 01/09/2019] [Indexed: 01/17/2023] Open
Abstract
There is ample evidence of atypical functional connectivity (FC) in autism spectrum disorders (ASDs). However, transient relationships between neural networks cannot be captured by conventional static FC analyses. Dynamic FC (dFC) approaches have been used to identify repeating, transient connectivity patterns ("states"), revealing spatiotemporal network properties not observable in static FC. Recent studies have found atypical dFC in ASDs, but questions remain about the nature of group differences in transient connectivity, and the degree to which states persist or change over time. This study aimed to: (a) describe and relate static and dynamic FC in typical development and ASDs, (b) describe group differences in transient states and compare them with static FC patterns, and (c) examine temporal stability and flexibility between identified states. Resting-state functional magnetic resonance imaging (fMRI) data were collected from 62 ASD and 57 typically developing (TD) children and adolescents. Whole-brain, data-driven regions of interest were derived from group independent component analysis. Sliding window analysis and k-means clustering were used to explore dFC and identify transient states. Across all regions, static overconnnectivity and increased variability over time in ASDs predominated. Furthermore, significant patterns of group differences emerged in two transient states that were not observed in the static FC matrix, with group differences in one state primarily involving sensory and motor networks, and in the other involving higher-order cognition networks. Default mode network segregation was significantly reduced in ASDs in both states. Results highlight that dynamic approaches may reveal more nuanced transient patterns of atypical FC in ASDs.
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Affiliation(s)
- Lisa E Mash
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California.,Joint Doctoral Program in Clinical Psychology, San Diego State University/University of California San Diego, San Diego, California
| | - Annika C Linke
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California
| | - Lindsay A Olson
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California.,Joint Doctoral Program in Clinical Psychology, San Diego State University/University of California San Diego, San Diego, California
| | - Inna Fishman
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California
| | - Thomas T Liu
- Center for Functional MRI, Department of Radiology, University of California San Diego, San Diego, California
| | - Ralph-Axel Müller
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California.,Joint Doctoral Program in Clinical Psychology, San Diego State University/University of California San Diego, San Diego, California
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31
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A principled multivariate intersubject analysis of generalized partial directed coherence with Dirichlet regression: Application to healthy aging in areas exhibiting cortical thinning. J Neurosci Methods 2019; 311:243-252. [DOI: 10.1016/j.jneumeth.2018.10.033] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2018] [Revised: 10/24/2018] [Accepted: 10/24/2018] [Indexed: 01/01/2023]
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32
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Wu L, Caprihan A, Bustillo J, Mayer A, Calhoun V. An approach to directly link ICA and seed-based functional connectivity: Application to schizophrenia. Neuroimage 2018; 179:448-470. [PMID: 29894827 PMCID: PMC6072460 DOI: 10.1016/j.neuroimage.2018.06.024] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2017] [Revised: 06/05/2018] [Accepted: 06/07/2018] [Indexed: 12/13/2022] Open
Abstract
Independent component analysis (ICA) and seed-based analyses are widely used techniques for studying intrinsic neuronal activity in task-based or resting scans. In this work, we show there is a direct link between the two, and show that there are some important differences between the two approaches in terms of what information they capture. We developed an enhanced connectivity-matrix independent component analysis (cmICA) for calculating whole brain voxel maps of functional connectivity, which reduces the computational complexity of voxel-based connectivity analysis on performing many temporal correlations. We also show there is a mathematical equivalency between parcellations on voxel-to-voxel functional connectivity and simplified cmICA. Next, we used this cost-efficient data-driven method to examine the resting state fMRI connectivity in schizophrenia patients (SZ) and healthy controls (HC) on a whole brain scale and further quantified the relationship between brain functional connectivity and cognitive performances measured by the Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) battery. Current results suggest that SZ exhibit a wide-range abnormality, primarily a decrease, in functional connectivity both between networks and within different network hubs. Specific functional connectivity decreases were associated with MATRICS performance deficits. In addition, we found that resting state functional connectivity decreases was extensively associated with aging regardless of groups. In contrast, there was no relationship between positive and negative symptoms in the patients and functional connectivity. In sum, we have developed a novel mathematical relationship between ICA and seed-based connectivity that reduces computational complexity, which has broad applicability, and showed a specific application of this approach to characterize connectivity changes associated with cognitive scores in SZ.
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Affiliation(s)
- Lei Wu
- The Mind Research Network, Albuquerque, NM, 87106, USA; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, 87131, USA.
| | | | - Juan Bustillo
- Department of Psychiatry, University of New Mexico, Albuquerque, NM, 87131, USA
| | - Andrew Mayer
- The Mind Research Network, Albuquerque, NM, 87106, USA
| | - Vince Calhoun
- The Mind Research Network, Albuquerque, NM, 87106, USA; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, 87131, USA; Department of Psychiatry, University of New Mexico, Albuquerque, NM, 87131, USA
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Kottaram A, Johnston L, Ganella E, Pantelis C, Kotagiri R, Zalesky A. Spatio-temporal dynamics of resting-state brain networks improve single-subject prediction of schizophrenia diagnosis. Hum Brain Mapp 2018; 39:3663-3681. [PMID: 29749660 PMCID: PMC6866493 DOI: 10.1002/hbm.24202] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2017] [Revised: 04/18/2018] [Accepted: 04/19/2018] [Indexed: 02/01/2023] Open
Abstract
Correlation in functional MRI activity between spatially separated brain regions can fluctuate dynamically when an individual is at rest. These dynamics are typically characterized temporally by measuring fluctuations in functional connectivity between brain regions that remain fixed in space over time. Here, dynamics in functional connectivity were characterized in both time and space. Temporal dynamics were mapped with sliding-window correlation, while spatial dynamics were characterized by enabling network regions to vary in size (shrink/grow) over time according to the functional connectivity profile of their constituent voxels. These temporal and spatial dynamics were evaluated as biomarkers to distinguish schizophrenia patients from controls, and compared to current biomarkers based on static measures of resting-state functional connectivity. Support vector machine classifiers were trained using: (a) static, (b) dynamic in time, (c) dynamic in space, and (d) dynamic in time and space characterizations of functional connectivity within canonical resting-state brain networks. Classifiers trained on functional connectivity dynamics mapped over both space and time predicted diagnostic status with accuracy exceeding 91%, whereas utilizing only spatial or temporal dynamics alone yielded lower classification accuracies. Static measures of functional connectivity yielded the lowest accuracy (79.5%). Compared to healthy comparison individuals, schizophrenia patients generally exhibited functional connectivity that was reduced in strength and more variable. Robustness was established with replication in an independent dataset. The utility of biomarkers based on temporal and spatial functional connectivity dynamics suggests that resting-state dynamics are not trivially attributable to sampling variability and head motion.
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Affiliation(s)
- Akhil Kottaram
- Department of Biomedical Engineering, The University of Melbourne, Victoria, 3010, Australia
| | - Leigh Johnston
- Department of Biomedical Engineering, The University of Melbourne, Victoria, 3010, Australia
- Department of Electrical and Electronic Engineering, The University of Melbourne, Victoria, 3010, Australia
- Florey Institute for Neurosciences and Mental health, Parkville, Victoria, 3052, Australia
| | - Eleni Ganella
- Melbourne Neuropsychiatry Centre, The University of Melbourne, Victoria, 3010, Australia
- Cooperative Research Centre for Mental Health, Carlton, Victoria, 3053, Australia
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, The University of Melbourne, Victoria, 3010, Australia
- Department of Psychiatry, The University of Melbourne, Victoria, 3010, Australia
- Florey Institute for Neurosciences and Mental health, Parkville, Victoria, 3052, Australia
- North Western Mental Health, Melbourne Health, Parkville, Victoria, Australia
- Centre for Neural Engineering, Department of Electrical and Electronic Engineering, The University of Melbourne, Victoria, 3053, Australia
- Cooperative Research Centre for Mental Health, Carlton, Victoria, 3053, Australia
| | - Ramamohanarao Kotagiri
- Department of Computing and Information Systems, The University of Melbourne, Victoria, 3010, Australia
| | - Andrew Zalesky
- Department of Biomedical Engineering, The University of Melbourne, Victoria, 3010, Australia
- Melbourne Neuropsychiatry Centre, The University of Melbourne, Victoria, 3010, Australia
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Exploring collective experience in watching dance through intersubject correlation and functional connectivity of fMRI brain activity. PROGRESS IN BRAIN RESEARCH 2018; 237:373-397. [PMID: 29779744 DOI: 10.1016/bs.pbr.2018.03.016] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
How the brain contends with naturalistic viewing conditions when it must cope with concurrent streams of diverse sensory inputs and internally generated thoughts is still largely an open question. In this study, we used fMRI to record brain activity while a group of 18 participants watched an edited dance duet accompanied by a soundtrack. After scanning, participants performed a short behavioral task to identify neural correlates of dance segments that could later be recalled. Intersubject correlation (ISC) analysis was used to identify the brain regions correlated among observers, and the results of this ISC map were used to define a set of regions for subsequent analysis of functional connectivity. The resulting network was found to be composed of eight subnetworks and the significance of these subnetworks is discussed. While most subnetworks could be explained by sensory and motor processes, two subnetworks appeared related more to complex cognition. These results inform our understanding of the neural basis of common experience in watching dance and open new directions for the study of complex cognition.
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35
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Kaboodvand N, Bäckman L, Nyberg L, Salami A. The retrosplenial cortex: A memory gateway between the cortical default mode network and the medial temporal lobe. Hum Brain Mapp 2018; 39:2020-2034. [PMID: 29363256 DOI: 10.1002/hbm.23983] [Citation(s) in RCA: 61] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2017] [Revised: 12/11/2017] [Accepted: 01/16/2018] [Indexed: 11/05/2022] Open
Abstract
The default mode network (DMN) involves interacting cortical areas, including the posterior cingulate cortex (PCC) and the retrosplenial cortex (RSC), and subcortical areas, including the medial temporal lobe (MTL). The degree of functional connectivity (FC) within the DMN, particularly between MTL and medial-parietal subsystems, relates to episodic memory (EM) processes. However, past resting-state studies investigating the link between posterior DMN-MTL FC and EM performance yielded inconsistent results, possibly reflecting heterogeneity in the degree of connectivity between MTL and specific cortical DMN regions. Animal work suggests that RSC has structural connections to both cortical DMN regions and MTL, and may thus serve as an intermediate layer that facilitates information transfer between cortical and subcortical DMNs. We studied 180 healthy old adults (aged 64-68 years), who underwent comprehensive assessment of EM, along with resting-state fMRI. We found greater FC between MTL and RSC than between MTL and the other cortical DMN regions (e.g., PCC), with the only significant association with EM observed for MTL-RSC FC. Mediational analysis showed that MTL-cortical DMN connectivity increased with RSC as a mediator. Further analysis using a graph-theoretical approach on DMN nodes revealed the highest betweenness centrality for RSC, confirming that a high proportion of short paths among DMN regions pass through RSC. Importantly, the degree of RSC mediation was associated with EM performance, suggesting that individuals with greater mediation have an EM advantage. These findings suggest that RSC forms a critical gateway between MTL and cortical DMN to support EM in older adults.
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Affiliation(s)
- Neda Kaboodvand
- Aging Research Center, Karolinska Institutet, Stockholm, Sweden.,Umeå Center for Functional Brain Imaging (UFBI), Umeå University, Umeå, Sweden
| | - Lars Bäckman
- Aging Research Center, Karolinska Institutet, Stockholm, Sweden
| | - Lars Nyberg
- Umeå Center for Functional Brain Imaging (UFBI), Umeå University, Umeå, Sweden.,Department of Radiation Sciences, Umeå University, Umeå, Sweden.,Department of Integrative Medical Biology, Umeå University, Umeå, Sweden
| | - Alireza Salami
- Aging Research Center, Karolinska Institutet, Stockholm, Sweden.,Umeå Center for Functional Brain Imaging (UFBI), Umeå University, Umeå, Sweden
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36
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Sohn WS, Lee TY, Kwak S, Yoon YB, Kwon JS. Higher extrinsic and lower intrinsic connectivity in resting state networks for professional Baduk (Go) players. Brain Behav 2017; 7:e00853. [PMID: 29299380 PMCID: PMC5745240 DOI: 10.1002/brb3.853] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/19/2017] [Revised: 09/07/2017] [Accepted: 09/11/2017] [Indexed: 12/22/2022] Open
Abstract
INTRODUCTION Dedication and training to a profession results in a certain level of expertise. This expertise, like any other skill obtained in our lifetime, is encoded in the brain and may be reflected in our brain's connectome. This property can be observed by mapping resting state connectivity. In this study, we examine the differences in resting state functional connectivity in four major networks between professional "Baduk" (Go) players and normal subjects. METHODS Resting state fMRI scans were acquired for professional "Baduk" (Go) players and normal controls. Major resting state networks were identified using independent component analysis and compared between the two groups. Networks which were compared include the default mode network, the left and right fronto-parietal network, and the salience network. RESULTS We found that normal subjects showed increased connectivity within certain areas of each target network. Professional players, however, showed higher connectivity to regions outside the traditional regions of each given network. Close examination of these regions revealed that regions shown to have higher connectivity in professional players have been revealed to be relevant in expertise for board games. CONCLUSION The findings in this study suggest that continuous training results in greater integration between regions and networks, which are necessary for high-level performance. The differences observed in our study between normal controls and professional players also shed light on the difference in brain connectivity which can arise through lifestyle and specialization in a specific field.
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Affiliation(s)
- William S Sohn
- Institute of Human Behavioral Medicine SNU-MRC Seoul Korea
| | - Tae Young Lee
- Department of Psychiatry Seoul National University College of Medicine Seoul Korea
| | - Seoyeon Kwak
- Department of Brain and Cognitive Sciences Seoul National University Seoul Korea
| | - Youngwoo Bryan Yoon
- Institute of Human Behavioral Medicine SNU-MRC Seoul Korea.,Department of Brain and Cognitive Sciences Seoul National University Seoul Korea
| | - Jun Soo Kwon
- Institute of Human Behavioral Medicine SNU-MRC Seoul Korea.,Department of Psychiatry Seoul National University College of Medicine Seoul Korea.,Department of Brain and Cognitive Sciences Seoul National University Seoul Korea
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37
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Du Y, Fryer SL, Fu Z, Lin D, Sui J, Chen J, Damaraju E, Mennigen E, Stuart B, Loewy RL, Mathalon DH, Calhoun VD. Dynamic functional connectivity impairments in early schizophrenia and clinical high-risk for psychosis. Neuroimage 2017; 180:632-645. [PMID: 29038030 DOI: 10.1016/j.neuroimage.2017.10.022] [Citation(s) in RCA: 84] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2017] [Revised: 09/29/2017] [Accepted: 10/11/2017] [Indexed: 01/14/2023] Open
Abstract
Individuals at clinical high-risk (CHR) for psychosis are characterized by attenuated psychotic symptoms. Only a minority of CHR individuals convert to full-blown psychosis. Therefore, there is a strong interest in identifying neurobiological abnormalities underlying the psychosis risk syndrome. Dynamic functional connectivity (DFC) captures time-varying connectivity over short time scales, and has the potential to reveal complex brain functional organization. Based on resting-state functional magnetic resonance imaging (fMRI) data from 70 healthy controls (HCs), 53 CHR individuals, and 58 early illness schizophrenia (ESZ) patients, we applied a novel group information guided ICA (GIG-ICA) to estimate inherent connectivity states from DFC, and then investigated group differences. We found that ESZ patients showed more aberrant connectivities and greater alterations than CHR individuals. Results also suggested that disease-related connectivity states occurred in CHR and ESZ groups. Regarding the dominant state with the highest contribution to dynamic connectivity, ESZ patients exhibited greater impairments than CHR individuals primarily in the cerebellum, frontal cortex, thalamus and temporal cortex, while CHR and ESZ populations shared common aberrances mainly in the supplementary motor area, parahippocampal gyrus and postcentral cortex. CHR-specific changes were also found in the connections between the superior frontal gyrus and calcarine cortex in the dominant state. Our findings suggest that CHR individuals generally show an intermediate functional connectivity pattern between HCs and SZ patients but also have unique connectivity alterations.
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Affiliation(s)
- Yuhui Du
- The Mind Research Network, Albuquerque, NM, USA; School of Computer & Information Technology, Shanxi University, Taiyuan, China.
| | - Susanna L Fryer
- Department of Psychiatry, University of California San Francisco, San Francisco, CA, USA; The Mental Health Service, San Francisco VA Healthcare System, San Francisco, CA, USA
| | - Zening Fu
- The Mind Research Network, Albuquerque, NM, USA
| | | | - Jing Sui
- The Mind Research Network, Albuquerque, NM, USA; Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Jiayu Chen
- The Mind Research Network, Albuquerque, NM, USA
| | | | - Eva Mennigen
- The Mind Research Network, Albuquerque, NM, USA; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA
| | - Barbara Stuart
- Department of Psychiatry, University of California San Francisco, San Francisco, CA, USA
| | - Rachel L Loewy
- Department of Psychiatry, University of California San Francisco, San Francisco, CA, USA
| | - Daniel H Mathalon
- Department of Psychiatry, University of California San Francisco, San Francisco, CA, USA; The Mental Health Service, San Francisco VA Healthcare System, San Francisco, CA, USA
| | - Vince D Calhoun
- The Mind Research Network, Albuquerque, NM, USA; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA
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38
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Hiwa S, Miki M, Hiroyasu T. Validity of decision mode analysis on an ROI determination problem in multichannel fNIRS data. ARTIFICIAL LIFE AND ROBOTICS 2017. [DOI: 10.1007/s10015-017-0362-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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39
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Geerligs L, Tsvetanov KA, Cam-Can, Henson RN. Challenges in measuring individual differences in functional connectivity using fMRI: The case of healthy aging. Hum Brain Mapp 2017; 38:4125-4156. [PMID: 28544076 PMCID: PMC5518296 DOI: 10.1002/hbm.23653] [Citation(s) in RCA: 111] [Impact Index Per Article: 15.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2016] [Revised: 05/08/2017] [Accepted: 05/08/2017] [Indexed: 12/11/2022] Open
Abstract
Many studies report individual differences in functional connectivity, such as those related to age. However, estimates of connectivity from fMRI are confounded by other factors, such as vascular health, head motion and changes in the location of functional regions. Here, we investigate the impact of these confounds, and pre‐processing strategies that can mitigate them, using data from the Cambridge Centre for Ageing & Neuroscience (http://www.cam-can.com). This dataset contained two sessions of resting‐state fMRI from 214 adults aged 18–88. Functional connectivity between all regions was strongly related to vascular health, most likely reflecting respiratory and cardiac signals. These variations in mean connectivity limit the validity of between‐participant comparisons of connectivity estimates, and were best mitigated by regression of mean connectivity over participants. We also showed that high‐pass filtering, instead of band‐pass filtering, produced stronger and more reliable age‐effects. Head motion was correlated with gray‐matter volume in selected brain regions, and with various cognitive measures, suggesting that it has a biological (trait) component, and warning against regressing out motion over participants. Finally, we showed that the location of functional regions was more variable in older adults, which was alleviated by smoothing the data, or using a multivariate measure of connectivity. These results demonstrate that analysis choices have a dramatic impact on connectivity differences between individuals, ultimately affecting the associations found between connectivity and cognition. It is important that fMRI connectivity studies address these issues, and we suggest a number of ways to optimize analysis choices. Hum Brain Mapp 38:4125–4156, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Linda Geerligs
- MRC Cognition and Brain Sciences Unit, Cambridge, United Kingdom.,Cambridge Centre for Ageing and Neuroscience (Cam-CAN), University of Cambridge and MRC Cognition and Brain Sciences Unit, Cambridge, United Kingdom.,Donders Institute for Brain, Cognition and Behaviour, Radboud University, the Netherlands
| | - Kamen A Tsvetanov
- Cambridge Centre for Ageing and Neuroscience (Cam-CAN), University of Cambridge and MRC Cognition and Brain Sciences Unit, Cambridge, United Kingdom.,Centre for Speech, Language and the Brain, Department of Psychology, University of Cambridge, Cambridge, United Kingdom.,Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Cam-Can
- Cambridge Centre for Ageing and Neuroscience (Cam-CAN), University of Cambridge and MRC Cognition and Brain Sciences Unit, Cambridge, United Kingdom
| | - Richard N Henson
- MRC Cognition and Brain Sciences Unit, Cambridge, United Kingdom.,Cambridge Centre for Ageing and Neuroscience (Cam-CAN), University of Cambridge and MRC Cognition and Brain Sciences Unit, Cambridge, United Kingdom
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40
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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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41
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Klaassens BL, van Gerven JMA, van der Grond J, de Vos F, Möller C, Rombouts SARB. Diminished Posterior Precuneus Connectivity with the Default Mode Network Differentiates Normal Aging from Alzheimer's Disease. Front Aging Neurosci 2017; 9:97. [PMID: 28469571 PMCID: PMC5395570 DOI: 10.3389/fnagi.2017.00097] [Citation(s) in RCA: 52] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2016] [Accepted: 03/28/2017] [Indexed: 12/14/2022] Open
Abstract
Both normal aging and Alzheimer's disease (AD) have been associated with a reduction in functional brain connectivity. It is unknown how connectivity patterns due to aging and AD compare. Here, we investigate functional brain connectivity in 12 young adults (mean age 22.8 ± 2.8), 12 older adults (mean age 73.1 ± 5.2) and 12 AD patients (mean age 74.0 ± 5.2; mean MMSE 22.3 ± 2.5). Participants were scanned during 6 different sessions with resting state functional magnetic resonance imaging (RS-fMRI), resulting in 72 scans per group. Voxelwise connectivity with 10 functional networks was compared between groups (p < 0.05, corrected). Normal aging was characterized by widespread decreases in connectivity with multiple brain networks, whereas AD only affected connectivity between the default mode network (DMN) and precuneus. The preponderance of effects was associated with regional gray matter volume. Our findings indicate that aging has a major effect on functional brain interactions throughout the entire brain, whereas AD is distinguished by additional diminished posterior DMN-precuneus coherence.
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Affiliation(s)
- Bernadet L Klaassens
- Institute of Psychology, Leiden UniversityLeiden, Netherlands.,Department of Radiology, Leiden University Medical CenterLeiden, Netherlands.,Leiden Institute for Brain and Cognition, Leiden UniversityLeiden, Netherlands.,Centre for Human Drug ResearchLeiden, Netherlands
| | | | | | - Frank de Vos
- Institute of Psychology, Leiden UniversityLeiden, Netherlands.,Department of Radiology, Leiden University Medical CenterLeiden, Netherlands.,Leiden Institute for Brain and Cognition, Leiden UniversityLeiden, Netherlands
| | - Christiane Möller
- Institute of Psychology, Leiden UniversityLeiden, Netherlands.,Department of Radiology, Leiden University Medical CenterLeiden, Netherlands.,Leiden Institute for Brain and Cognition, Leiden UniversityLeiden, Netherlands
| | - Serge A R B Rombouts
- Institute of Psychology, Leiden UniversityLeiden, Netherlands.,Department of Radiology, Leiden University Medical CenterLeiden, Netherlands.,Leiden Institute for Brain and Cognition, Leiden UniversityLeiden, Netherlands
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42
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Nie L, Matthews PM, Guo Y. Inferring Individual-Level Variations in the Functional Parcellation of the Cerebral Cortex. IEEE Trans Biomed Eng 2016; 63:2505-2517. [PMID: 27875122 DOI: 10.1109/tbme.2016.2571221] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
OBJECTIVE Functional parcellation of the cerebral cortex is variable across different subjects or between cognitive states. Ignoring individual-or state-dependent variations in the functional parcellation may lead to inaccurate representations of individual functional connectivity, limiting the precision of interpretations of differences in individual connectivity profiles. However, it is difficult to infer the individual-level variations due to the relatively low robustness of methods for parcellation of individual subjects. METHODS We propose a method called "joint K-means" to robustly parcellate the cerebral cortex using functional magnetic resonance imaging (fMRI) data for contrasts between two states or subjects that intended to characterize variance in individual functional parcellations. The key idea of the proposed method is to jointly infer parcellations in contrasted datasets by iterative descent, while constraining the similarity of the two pathways in searches for local minima to reduce spurious variations. RESULTS Parcellations of resting-state fMRI datasets from the Human Connectome Project show that the similarity of parcellations for an individual subject studied on two sessions is greater than that between different subjects. Differences in parcellations between subjects are nonuniformly distributed across the cerebral cortex, with clusters of higher variance in the prefrontal, lateral temporal, and occipito-parietal cortices. This pattern is reproducible across sessions, between groups, and using different numbers of parcels. CONCLUSION The individual-level variations inferred by the proposed method are plausible and consistent with the previously reported functional connectivity variability. SIGNIFICANCE The proposed method is a promising tool for investigating relationships between the cerebral functional organization and behavioral differences.
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Effective Connectivity of Cortical Sensorimotor Networks During Finger Movement Tasks: A Simultaneous fNIRS, fMRI, EEG Study. Brain Topogr 2016; 29:645-60. [DOI: 10.1007/s10548-016-0507-1] [Citation(s) in RCA: 52] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2015] [Accepted: 07/11/2016] [Indexed: 10/21/2022]
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44
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O'Halloran R, Kopell BH, Sprooten E, Goodman WK, Frangou S. Multimodal Neuroimaging-Informed Clinical Applications in Neuropsychiatric Disorders. Front Psychiatry 2016; 7:63. [PMID: 27148092 PMCID: PMC4835492 DOI: 10.3389/fpsyt.2016.00063] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/22/2016] [Accepted: 03/29/2016] [Indexed: 01/10/2023] Open
Abstract
Recent advances in neuroimaging data acquisition and analysis hold the promise to enhance the ability to make diagnostic and prognostic predictions and perform treatment planning in neuropsychiatric disorders. Prior research using a variety of types of neuroimaging techniques has confirmed that neuropsychiatric disorders are associated with dysfunction in anatomical and functional brain circuits. We first discuss current challenges associated with the identification of reliable neuroimaging markers for diagnosis and prognosis in mood disorders and for neurosurgical treatment planning for deep brain stimulation (DBS). We then present data on the use of neuroimaging for the diagnosis and prognosis of mood disorders and for DBS treatment planning. We demonstrate how multivariate analyses of functional activation and connectivity parameters can be used to differentiate patients with bipolar disorder from those with major depressive disorder and non-affective psychosis. We also present data on connectivity parameters that mediate acute treatment response in affective and non-affective psychosis. We then focus on precision mapping of functional connectivity in native space. We describe the benefits of integrating anatomical fiber reconstruction with brain functional parameters and cortical surface measures to derive anatomically informed connectivity metrics within the morphological context of each individual brain. We discuss how this approach may be particularly promising in psychiatry, given the clinical and etiological heterogeneity of the disorders, and particularly in treatment response prediction and planning. Precision mapping of connectivity is essential for DBS. In DBS, treatment electrodes are inserted into positions near key gray matter nodes within the circuits considered relevant to disease expression. However, targeting white matter tracts that underpin connectivity within these circuits may increase treatment efficacy and tolerability therefore relevant for effective treatment. We demonstrate how this approach can be validated in the treatment of Parkinson's disease by identifying connectivity patterns that can be used as biomarkers for treatment planning and thus refine the traditional approach of DBS planning that uses only gray matter landmarks. Finally, we describe how this approach could be used in planning DBS treatment of psychiatric disorders.
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Affiliation(s)
- Rafael O'Halloran
- Brain Imaging Center, Translational and Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai , New York, NY , USA
| | - Brian H Kopell
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Emma Sprooten
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai , New York, NY , USA
| | - Wayne K Goodman
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sophia Frangou
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai , New York, NY , USA
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45
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Wang D, Buckner RL, Fox MD, Holt DJ, Holmes AJ, Stoecklein S, Langs G, Pan R, Qian T, Li K, Baker JT, Stufflebeam SM, Wang K, Wang X, Hong B, Liu H. Parcellating cortical functional networks in individuals. Nat Neurosci 2015; 18:1853-60. [PMID: 26551545 PMCID: PMC4661084 DOI: 10.1038/nn.4164] [Citation(s) in RCA: 315] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2015] [Accepted: 10/14/2015] [Indexed: 12/19/2022]
Abstract
The capacity to identify the unique functional architecture of an individual's brain is a crucial step toward personalized medicine and understanding the neural basis of variation in human cognition and behavior. Here we developed a cortical parcellation approach to accurately map functional organization at the individual level using resting-state functional magnetic resonance imaging (fMRI). A population-based functional atlas and a map of inter-individual variability were employed to guide the iterative search for functional networks in individual subjects. Functional networks mapped by this approach were highly reproducible within subjects and effectively captured the variability across subjects, including individual differences in brain lateralization. The algorithm performed well across different subject populations and data types, including task fMRI data. The approach was then validated by invasive cortical stimulation mapping in surgical patients, suggesting potential for use in clinical applications.
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Affiliation(s)
- Danhong Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Randy L. Buckner
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Michael D. Fox
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
- Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Daphne J. Holt
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Avram J. Holmes
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Sophia Stoecklein
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
- Ludwig Maximilians University Munich, Institute of Clinical Radiology, Munich, Germany
| | - Georg Langs
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Austria
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Ruiqi Pan
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Tianyi Qian
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
- Siemens Healthcare, MR Collaboration NE Asia, Beijing, China
| | - Kuncheng Li
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Justin T. Baker
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Psychotic Disorders Division, McLean Hospital, Belmont, MA, USA
| | - Steven M. Stufflebeam
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
- Harvard-MIT Health Sciences and Technology, Institute for Medical Engineering and Science, Cambridge, MA, USA
| | - Kai Wang
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Xiaomin Wang
- Beijing Institute for Brain Disorders, Capital Medical University, Beijing, China
| | - Bo Hong
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Hesheng Liu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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