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Liu RP, Lin GL, Ma LL, Huang SS, Yuan CX, Zhu SG, Sheng ML, Zou M, Zhu JH, Zhang X, Wang JY. Changes of brain structure and structural covariance networks in Parkinson's disease associated cognitive impairment. Front Aging Neurosci 2024; 16:1449276. [PMID: 39391587 PMCID: PMC11464354 DOI: 10.3389/fnagi.2024.1449276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2024] [Accepted: 09/11/2024] [Indexed: 10/12/2024] Open
Abstract
Background Cognitive impairment (CI) is common in Parkinson's disease (PD). Multiple brain regions and their interactions are involved in PD associated CI. Magnetic resonance imaging (MRI) technology is a non-invasive method in investigating brain structure and inter-regional connections. In this study, by comparing cortical thickness, subcortical volume, and brain network topology properties in PD patients with and without CI, we aimed to understand the changes of brain structure and structural covariance network properties in PD associated CI. Methods A total of 18 PD patients with CI and 33 PD patients without CI were recruited. Movement Disorder Society Unified Parkinson's Disease Rating Scale, Hoehn and Yahr stage, Mini Mental State Examination Scale, Non-motor Symptom Rating Scale, Hamilton Anxiety Scale, and Hamilton Depression Scale were assessed. All participants underwent structural 3T MRI. Cortical thickness, subcortical volume, global and nodal network topology properties were measured. Results Compared with PD patients without CI, the volumes of white matter, thalamus and hippocampus were lower in PD patients with CI. And decreased whole-brain local efficiency is associated with CI in PD patients. While the cortical thickness and nodal network topology properties were comparable between PD patients with and without CI. Conclusion Our findings support the alterations of brain structure and disruption of structural covariance network are involved in PD associated CI, providing a new insight into the association between graph properties and PD associated CI.
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Affiliation(s)
- Rong-Pei Liu
- Department of Neurology, Institute of Geriatric Neurology, The Second Affiliated Hospital and Yuying Children’s Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Guo-Liang Lin
- Department of Neurology, Institute of Geriatric Neurology, The Second Affiliated Hospital and Yuying Children’s Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Lu-Lu Ma
- Department of Neurology, Institute of Geriatric Neurology, The Second Affiliated Hospital and Yuying Children’s Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Shi-Shi Huang
- Department of Neurology, Institute of Geriatric Neurology, The Second Affiliated Hospital and Yuying Children’s Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Cheng-Xiang Yuan
- Department of Neurology, Institute of Geriatric Neurology, The Second Affiliated Hospital and Yuying Children’s Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Shi-Guo Zhu
- Department of Neurology, Institute of Geriatric Neurology, The Second Affiliated Hospital and Yuying Children’s Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Mei-Ling Sheng
- Department of Neurology, Institute of Geriatric Neurology, The Second Affiliated Hospital and Yuying Children’s Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Ming Zou
- Department of Neurology, Institute of Geriatric Neurology, The Second Affiliated Hospital and Yuying Children’s Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Jian-Hong Zhu
- Department of Preventive Medicine, Institute of Nutrition and Diseases, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Xiong Zhang
- Department of Neurology, Institute of Geriatric Neurology, The Second Affiliated Hospital and Yuying Children’s Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Jian-Yong Wang
- Department of Neurology, Institute of Geriatric Neurology, The Second Affiliated Hospital and Yuying Children’s Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China
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Huang S, Vigotsky AD, Apkarian AV, Huang L. Body mass index associated with respiration predicts motion in resting-state functional magnetic resonance imaging scans. Hum Brain Mapp 2024; 45:e70015. [PMID: 39225333 PMCID: PMC11369907 DOI: 10.1002/hbm.70015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 07/01/2024] [Accepted: 08/20/2024] [Indexed: 09/04/2024] Open
Abstract
Decreasing body mass index (BMI) reduces head motion in resting-state fMRI (rs-fMRI) data. Yet, the mechanism by which BMI affects head motion remains poorly understood. Understanding how BMI interacts with respiration to affect head motion can improve head motion reduction strategies. A total of 254 patients with back pain were included in this study, each of whom had two visits (interval time = 13.85 ± 7.81 weeks) during which two consecutive re-fMRI scans were obtained. We investigated the relationships between head motion and demographic and pain-related characteristics-head motion was reliable across scans and correlated with age, pain intensity, and BMI. Multiple linear regression models determined that BMI was the main determinant in predicting head motion. BMI was also associated with two features derived from respiration signal. Anterior-posterior and superior-inferior motion dominated both overall motion magnitude and the coupling between motion and respiration. BMI interacted with respiration to influence motion only in the pitch dimension. These findings indicate that BMI should be a critical parameter in both study designs and analyses of fMRI data.
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Affiliation(s)
- Shishi Huang
- Department of NeurologyThe Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical UniversityWenzhouChina
| | - Andrew D. Vigotsky
- Department of Biomedical EngineeringNorthwestern UniversityEvanstonIllinoisUSA
- Department of StatisticsNorthwestern UniversityEvanstonIllinoisUSA
| | - Apkar Vania Apkarian
- Department of Neuroscience, Feinberg School of MedicineNorthwestern UniversityChicagoIllinoisUSA
- Center for Translational Pain Research, Feinberg School of MedicineNorthwestern UniversityChicagoIllinoisUSA
| | - Lejian Huang
- Department of Neuroscience, Feinberg School of MedicineNorthwestern UniversityChicagoIllinoisUSA
- Center for Translational Pain Research, Feinberg School of MedicineNorthwestern UniversityChicagoIllinoisUSA
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Huang Q, Jiang X, Jin Y, Wu B, Vigotsky AD, Fan L, Gu P, Tu W, Huang L, Jiang S. Immersive virtual reality-based rehabilitation for subacute stroke: a randomized controlled trial. J Neurol 2024; 271:1256-1266. [PMID: 37947856 PMCID: PMC10896795 DOI: 10.1007/s00415-023-12060-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 10/11/2023] [Accepted: 10/12/2023] [Indexed: 11/12/2023]
Abstract
OBJECTIVE Few effective treatments improve upper extremity (UE) function after stroke. Immersive virtual reality (imVR) is a novel and promising strategy for stroke UE recovery. We assessed the extent to which imVR-based UE rehabilitation can augment conventional treatment and explored changes in brain functional connectivity (FC) that were related to the rehabilitation. METHODS An assessor-blinded, parallel-group randomized controlled trial was performed with 40 subjects randomly assigned to either imVR or Control group (1:1 allocation), each receiving rehabilitation 5 times per week for 3 weeks. Subjects in the imVR received both imVR and conventional rehabilitation, while those in the Control received conventional rehabilitation only. Our primary and secondary outcomes were the Fugl-Meyer assessment's upper extremity subscale (FMA-UE) and the Barthel Index (BI), respectively. Both intention-to-treat (ITT) and per-protocol (PP) analyses were performed to assess the effectiveness of the trial. For both the FMA-UE/BI, a one-way analysis of covariance (ANCOVA) model was used, with the FMA-UE/BI at post-intervention or at follow-up, respectively, as the dependent variable, the two groups as the independent variable, baseline FMA-UE/BI, age, sex, site, time since onset, hypertension and diabetes as covariates. RESULTS Both ITT and PP analyses demonstrated the effectiveness of imVR-based rehabilitation. The FMA-UE score was greater in the imVR compared with the Control at the post-intervention (mean difference: 9.1 (95% CI 1.6, 16.6); P = 0.019) and follow-up (mean difference:11.5 (95% CI 1.9, 21.0); P = 0.020). The results were consistent for BI scores. Moreover, brain FC analysis found that the motor function improvements were associated with a change in degree in ipsilesional premotor cortex and ipsilesional dorsolateral prefrontal cortex immediately following the intervention and in ipsilesional visual region and ipsilesional middle frontal gyrus after the 12-week follow-up. CONCLUSIONS ImVR-based rehabilitation is an effective tool that can improve the recovery of UE functional capabilities of subacute stroke patients when added to standard care. These improvements were associated with distinctive brain changes at two post-stroke timepoints. The study results will benefit future patients with stroke and provide evidence for a promising new method of stroke rehabilitation. TRIAL REGISTRATION ClinicalTrials.gov identifier: NCT03086889.
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Affiliation(s)
- Qianqian Huang
- Department of Rehabilitation Medicine, Rehabilitation Medicine Center, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, Zhejiang, China
- Integrative and Optimized Medicine Research Center, China-USA Institute for Acupuncture and Rehabilitation, Wenzhou Medical University, Wenzhou, 325027, Zhejiang, China
| | - Xixi Jiang
- Department of Rehabilitation Medicine, Rehabilitation Medicine Center, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, Zhejiang, China
- Integrative and Optimized Medicine Research Center, China-USA Institute for Acupuncture and Rehabilitation, Wenzhou Medical University, Wenzhou, 325027, Zhejiang, China
| | - Yun Jin
- Department of Rehabilitation Medicine, Rehabilitation Medicine Center, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, Zhejiang, China
- Integrative and Optimized Medicine Research Center, China-USA Institute for Acupuncture and Rehabilitation, Wenzhou Medical University, Wenzhou, 325027, Zhejiang, China
| | - Bo Wu
- Department of Information, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, Zhejiang, China
| | - Andrew D Vigotsky
- Departments of Biomedical Engineering and Statistics, Northwestern University, Evanston, IL, 60208, USA
| | - Linyu Fan
- Department of Radiology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, Zhejiang, China
| | - Pengpeng Gu
- Department of Rehabilitation Medicine, Rehabilitation Medicine Center, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, Zhejiang, China
- Integrative and Optimized Medicine Research Center, China-USA Institute for Acupuncture and Rehabilitation, Wenzhou Medical University, Wenzhou, 325027, Zhejiang, China
| | - Wenzhan Tu
- Department of Rehabilitation Medicine, Rehabilitation Medicine Center, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, Zhejiang, China
- Integrative and Optimized Medicine Research Center, China-USA Institute for Acupuncture and Rehabilitation, Wenzhou Medical University, Wenzhou, 325027, Zhejiang, China
| | - Lejian Huang
- Department of Neuroscience, Feinberg School of Medicine, Northwestern University, Chicago, IL, 60611, USA.
| | - Songhe Jiang
- Department of Rehabilitation Medicine, Rehabilitation Medicine Center, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, Zhejiang, China.
- Integrative and Optimized Medicine Research Center, China-USA Institute for Acupuncture and Rehabilitation, Wenzhou Medical University, Wenzhou, 325027, Zhejiang, China.
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Branco P, Bosak N, Bielefeld J, Cong O, Granovsky Y, Kahn I, Yarnitsky D, Apkarian AV. Structural brain connectivity predicts early acute pain after mild traumatic brain injury. Pain 2023; 164:1312-1320. [PMID: 36355048 DOI: 10.1097/j.pain.0000000000002818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 10/20/2022] [Indexed: 11/12/2022]
Abstract
ABSTRACT Mild traumatic brain injury (mTBI), is a leading cause of disability worldwide, with acute pain manifesting as one of its most debilitating symptoms. Understanding acute postinjury pain is important because it is a strong predictor of long-term outcomes. In this study, we imaged the brains of 157 patients with mTBI, following a motorized vehicle collision. We extracted white matter structural connectivity networks and used a machine learning approach to predict acute pain. Stronger white matter tracts within the sensorimotor, thalamiccortical, and default-mode systems predicted 20% of the variance in pain severity within 72 hours of the injury. This result generalized in 2 independent groups: 39 mTBI patients and 13 mTBI patients without whiplash symptoms. White matter measures collected at 6 months after the collision still predicted mTBI pain at that timepoint (n = 36). These white matter connections were associated with 2 nociceptive psychophysical outcomes tested at a remote body site-namely, conditioned pain modulation and magnitude of suprathreshold pain-and with pain sensitivity questionnaire scores. Our findings demonstrate a stable white matter network, the properties of which determine an important amount of pain experienced after acute injury, pinpointing a circuitry engaged in the transformation and amplification of nociceptive inputs to pain perception.
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Affiliation(s)
- Paulo Branco
- Department of Neuroscience, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
- Center for Translational Pain Research, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Noam Bosak
- Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel
- Department of Neurology, Rambam Health Care Campus, Haifa, Israel
| | - Jannis Bielefeld
- Department of Neuroscience, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
- Center for Translational Pain Research, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Olivia Cong
- Department of Neuroscience, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
- Center for Translational Pain Research, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Yelena Granovsky
- Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel
- Department of Neurology, Rambam Health Care Campus, Haifa, Israel
| | - Itamar Kahn
- Department of Neuroscience and Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, United States
| | - David Yarnitsky
- Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel
- Department of Neurology, Rambam Health Care Campus, Haifa, Israel
| | - A Vania Apkarian
- Department of Neuroscience, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
- Center for Translational Pain Research, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
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5
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Drossel G, Brucar LR, Rawls E, Hendrickson TJ, Zilverstand A. Subtypes in addiction and their neurobehavioral profiles across three functional domains. Transl Psychiatry 2023; 13:127. [PMID: 37072391 PMCID: PMC10113211 DOI: 10.1038/s41398-023-02426-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 03/28/2023] [Accepted: 03/31/2023] [Indexed: 04/20/2023] Open
Abstract
Rates of return to use in addiction treatment remain high. We argue that the development of improved treatment options will require advanced understanding of individual heterogeneity in Substance Use Disorders (SUDs). We hypothesized that considerable individual differences exist in the three functional domains underlying addiction-approach-related behavior, executive function, and negative emotionality. We included N = 593 participants from the enhanced Nathan Kline Institute-Rockland Sample community sample (ages 18-59, 67% female) that included N = 420 Controls and N = 173 with past SUDs [54% female; N = 75 Alcohol Use Disorder (AUD) only, N = 30 Cannabis Use Disorder (CUD) only, and N = 68 Multiple SUDs]. To test our a priori hypothesis that distinct neuro-behavioral subtypes exist within individuals with past SUDs, we conducted a latent profile analysis with all available phenotypic data as input (74 subscales from 18 measures), and then characterized resting-state brain function for each discovered subtype. Three subtypes with distinct neurobehavioral profiles were recovered (p < 0.05, Cohen's D: 0.4-2.8): a "Reward type" with higher approach-related behavior (N = 69); a "Cognitive type" with lower executive function (N = 70); and a "Relief type" with high negative emotionality (N = 34). For those in the Reward type, substance use mapped onto resting-state connectivity in the Value/Reward, Ventral-Frontoparietal and Salience networks; for the Cognitive type in the Auditory, Parietal Association, Frontoparietal and Salience networks; and for the Relief type in the Parietal Association, Higher Visual and Salience networks (pFDR < 0.05). Subtypes were equally distributed amongst individuals with different primary SUDs (χ2 = 4.71, p = 0.32) and gender (χ2 = 3.44, p = 0.18). Results support functionally derived subtypes, demonstrating considerable individual heterogeneity in the multi-dimensional impairments in addiction. This confirms the need for mechanism-based subtyping to inform the development of personalized addiction medicine approaches.
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Affiliation(s)
- Gunner Drossel
- Graduate Program in Neuroscience, University of Minnesota, Minneapolis, MN, USA
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
| | - Leyla R Brucar
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
| | - Eric Rawls
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
| | - Timothy J Hendrickson
- University of Minnesota Informatics Institute, University of Minnesota, Minneapolis, MN, USA
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Anna Zilverstand
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA.
- Medical Discovery Team on Addiction, University of Minnesota, Minneapolis, MN, USA.
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Abstract
Pain is an unpleasant sensory and emotional experience. Understanding the neural mechanisms of acute and chronic pain and the brain changes affecting pain factors is important for finding pain treatment methods. The emergence and progress of non-invasive neuroimaging technology can help us better understand pain at the neural level. Recent developments in identifying brain-based biomarkers of pain through advances in advanced imaging can provide some foundations for predicting and detecting pain. For example, a neurologic pain signature (involving brain regions that receive nociceptive afferents) and a stimulus intensity-independent pain signature (involving brain regions that do not show increased activity in proportion to noxious stimulus intensity) were developed based on multivariate modeling to identify processes related to the pain experience. However, an accurate and comprehensive review of common neuroimaging techniques for evaluating pain is lacking. This paper reviews the mechanism, clinical application, reliability, strengths, and limitations of common neuroimaging techniques for assessing pain to promote our further understanding of pain.
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Affiliation(s)
- Jing Luo
- Department of Sport Rehabilitation, Xian Physical Education University, Xian, China
- Department of Sport Rehabilitation, Shanghai University of Sport, Shanghai, China
| | - Hui-Qi Zhu
- Department of Sport Rehabilitation, Shanghai University of Sport, Shanghai, China
- Department of Sport Rehabilitation, Shenyang Sport University, Shenyang, China
| | - Bo Gou
- Department of Sport Rehabilitation, Xian Physical Education University, Xian, China.
| | - Xue-Qiang Wang
- Department of Sport Rehabilitation, Shanghai University of Sport, Shanghai, China.
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7
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Huang Q, Lin D, Huang S, Cao Y, Jin Y, Wu B, Fan L, Tu W, Huang L, Jiang S. Brain Functional Topology Alteration in Right Lateral Occipital Cortex Is Associated With Upper Extremity Motor Recovery. Front Neurol 2022; 13:780966. [PMID: 35309550 PMCID: PMC8927543 DOI: 10.3389/fneur.2022.780966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 01/17/2022] [Indexed: 12/02/2022] Open
Abstract
Stroke is a chief cause of sudden brain damage that severely disrupts the whole-brain network. However, the potential mechanisms of motor recovery after stroke are uncertain and the prognosis of poststroke upper extremity recovery is still a challenge. This study investigated the global and local topological properties of the brain functional connectome in patients with subacute ischemic stroke and their associations with the clinical measurements. A total of 57 patients, consisting of 29 left-sided and 28 right-sided stroke patients, and 32 age- and gender-matched healthy controls (HCs) were recruited to undergo a resting-state functional magnetic resonance imaging (rs-fMRI) study; patients were also clinically evaluated with the Upper Extremity Fugl-Meyer Assessment (FMA_UE). The assessment was repeated at 15 weeks to assess upper extremity functional recovery for the patient remaining in the study (12 left- 20 right-sided stroke patients). Global graph topological disruption indices of stroke patients were significantly decreased compared with HCs but these indices were not significantly associated with FMA_UE. In addition, local brain network structure of stroke patients was altered, and the altered regions were dependent on the stroke site. Significant associations between local degree and motor performance and its recovery were observed in the right lateral occipital cortex (R LOC) in the right-sided stroke patients. Our findings suggested that brain functional topologies alterations in R LOC are promising as prognostic biomarkers for right-sided subacute stroke. This cortical area might be a potential target to be further validated for non-invasive brain stimulation treatment to improve poststroke upper extremity recovery.
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Affiliation(s)
- Qianqian Huang
- Rehabilitation Medicine Center, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
- Intelligent Rehabilitation Research Center, China-USA Institute for Acupuncture and Rehabilitation, Wenzhou Medical University, Wenzhou, China
| | - Dinghong Lin
- Rehabilitation Medicine Center, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
- Intelligent Rehabilitation Research Center, China-USA Institute for Acupuncture and Rehabilitation, Wenzhou Medical University, Wenzhou, China
| | - Shishi Huang
- Department of Neurology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yungang Cao
- Department of Neurology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yun Jin
- Rehabilitation Medicine Center, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
- Intelligent Rehabilitation Research Center, China-USA Institute for Acupuncture and Rehabilitation, Wenzhou Medical University, Wenzhou, China
| | - Bo Wu
- Department of Information, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Linyu Fan
- Department of Radiology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Wenzhan Tu
- Rehabilitation Medicine Center, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
- Intelligent Rehabilitation Research Center, China-USA Institute for Acupuncture and Rehabilitation, Wenzhou Medical University, Wenzhou, China
| | - Lejian Huang
- Department of Neuroscience, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
- *Correspondence: Lejian Huang
| | - Songhe Jiang
- Rehabilitation Medicine Center, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
- Intelligent Rehabilitation Research Center, China-USA Institute for Acupuncture and Rehabilitation, Wenzhou Medical University, Wenzhou, China
- Songhe Jiang
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8
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Yang L, Wu B, Fan L, Huang S, Vigotsky AD, Baliki MN, Yan Z, Apkarian AV, Huang L. Dissimilarity of functional connectivity uncovers the influence of participant's motion in functional magnetic resonance imaging studies. Hum Brain Mapp 2020; 42:713-723. [PMID: 33079467 PMCID: PMC7814752 DOI: 10.1002/hbm.25255] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Revised: 10/05/2020] [Accepted: 10/09/2020] [Indexed: 01/07/2023] Open
Abstract
Head motion is a major confounding factor impairing the quality of functional magnetic resonance imaging (fMRI) data. In particular, head motion can reduce analytical efficiency, and its effects are still present even after preprocessing. To examine the validity of motion removal and to evaluate the remaining effects of motion on the quality of the preprocessed fMRI data, a new metric of group quality control (QC), dissimilarity of functional connectivity, is introduced. Here, we investigate the association between head motion, represented by mean framewise displacement, and dissimilarity of functional connectivity by applying four preprocessing methods in two independent resting‐state fMRI datasets: one consisting of healthy participants (N = 167) scanned in a 3T GE‐Discovery 750 with longer TR (2.5 s), and the other of chronic back pain patients (N = 143) in a 3T Siemens Magnetom Prisma scanner with shorter TR (0.555 s). We found that dissimilarity of functional connectivity uncovers the influence of participant's motion, and this relationship is independent of population, scanner, and preprocessing method. The association between motion and dissimilarity of functional connectivity, and how the removal of high‐motion participants affects this association, is a new strategy for group‐level QC following preprocessing.
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Affiliation(s)
- Lili Yang
- Department of Radiology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Bo Wu
- Department of Information, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Linyu Fan
- Department of Radiology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Shishi Huang
- Department of Neurology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Andrew D Vigotsky
- Departments of Biomedical Engineering and Statistics, Northwestern University, Evanston, Illinois, USA
| | - Marwan N Baliki
- Shirley Ryan AbilityLab, Chicago, Illinois, USA.,Department of Physical Management and Rehabilitation, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Zhihan Yan
- Department of Radiology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - A Vania Apkarian
- Department of Physiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA.,Center for Translational Pain Research, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Lejian Huang
- Department of Physiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA.,Center for Translational Pain Research, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
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