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Del Mauro G, Wang Z. Associations of Brain Entropy Estimated by Resting State fMRI With Physiological Indices, Body Mass Index, and Cognition. J Magn Reson Imaging 2024; 59:1697-1707. [PMID: 37578314 PMCID: PMC10864678 DOI: 10.1002/jmri.28948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 07/27/2023] [Accepted: 07/28/2023] [Indexed: 08/15/2023] Open
Abstract
BACKGROUND In recent years, resting-state fMRI (rsfMRI)-based brain entropy (BEN) has gained increasing interest as a tool to characterize brain activity. While previous studies indicate that BEN is correlated with cognition, it remains unclear whether BEN is influenced by other factors that typically affect brain activity measured by fMRI. PURPOSE To investigate the relationship between BEN and physiological indices, including respiratory rate (RR), heart rate (HR), systolic blood pressure (s-BP), and body mass index (BMI), and to investigate whether and to what extent the relationship between BEN and cognition is influenced by physiological variables. STUDY TYPE Retrospective. SUBJECTS One thousand two hundred six healthy subjects (mean age: 28.83 ± 3.69 years; 550 male) with rsfMRI datasets selected from the Human Connectome Project (HCP). FIELD STRENGTH/SEQUENCE Multiband echo planar imaging (EPI) sequence at 3.0 Tesla. ASSESSMENT Neurocognitive, physical health (RR, HR, s-BP, BMI), and rsfMRI data were retrieved from the HCP datasets. Neurocognition was measured through the total cognition composite (TCC) score provided by HCP. BEN maps were calculated from rsfMRI data. STATISTICAL TESTS Multiple regression models, pheight-family wise error (FWE) < 0.05 and pcluster-FWE < 0.05 were considered statistically significant. RESULTS BEN was negatively associated with RR (T-thresholds ranging from 4.75 to 4.8; r-threshold = |0.15|) and positively associated with s-BP and BMI (T-thresholds ranging from 4.75 to 4.8; r-threshold = |0.15|) in areas overlapping with the default mode network. After controlling the physiological effects, BEN still showed regional associations with TCC, including negative associations (T-thresholds = 3.09; r-threshold = |0.1|) in the fronto-parietal cortex and positive associations (T-thresholds = 3.09; r-threshold = |0.1|) in the sensorimotor system (motor network and the limbic system). DATA CONCLUSIONS RR negatively affects rsfMRI-derived BEN, while s-BP and BMI positively affect BEN. The positive associations between BEN and cognition in the motor network and the limbic system might indicate a facilitation of information processing in the sensorimotor system. EVIDENCE LEVEL 3 TECHNICAL EFFICACY: Stage 3.
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Affiliation(s)
- Gianpaolo Del Mauro
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Ze Wang
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA
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Mekbib DB, Cai M, Wu D, Dai W, Liu X, Zhao L. Reproducibility and Sensitivity of Resting-State fMRI in Patients With Parkinson's Disease Using Cross Validation-Based Data Censoring. J Magn Reson Imaging 2024; 59:1630-1642. [PMID: 37584329 DOI: 10.1002/jmri.28958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 08/02/2023] [Accepted: 08/02/2023] [Indexed: 08/17/2023] Open
Abstract
BACKGROUND Uncontrollable body movements are typical symptoms of Parkinson's disease (PD), which results in inconsistent findings regarding resting-state functional connectivity (rsFC) networks, especially for group difference clusters. Systematically identifying the motion-associated data was highly demanded. PURPOSE To determine data censoring criteria using a quantitative cross validation-based data censoring (CVDC) method and to improve the detection of rsFC deficits in PD. STUDY TYPE Prospective. SUBJECTS Forty-one PD patients (68.63 ± 9.17 years, 44% female) and 20 healthy controls (66.83 ± 12.94 years, 55% female). FIELD STRENGTH/SEQUENCE 3-T, T1-weighted gradient echo and EPI sequences. ASSESSMENT Clusters with significant differences between groups were found in three visual networks, default network, and right sensorimotor network. Five-fold cross-validation tests were performed using multiple motion exclusion criteria, and the selected criteria were determined based on cluster sizes, significance values, and Dice coefficients among the cross-validation tests. As a reference method, whole brain rsFC comparisons between groups were analyzed using a FMRIB Software Library (FSL) pipeline with default settings. STATISTICAL TESTS Group difference clusters were calculated using nonparametric permutation statistics of FSL-randomize. The family-wise error was corrected. Demographic information was evaluated using independent sample t-tests and Pearson's Chi-squared tests. The level of statistical significance was set at P < 0.05. RESULTS With the FSL processing pipeline, the mean Dice coefficient of the network clusters was 0.411, indicating a low reproducibility. With the proposed CVDC method, motion exclusion criteria were determined as frame-wise displacement >0.55 mm. Group-difference clusters showed a mean P-value of 0.01 and a 72% higher mean Dice coefficient compared to the FSL pipeline. Furthermore, the CVDC method was capable of detecting subtle rsFC deficits in the medial sensorimotor network and auditory network that were unobservable using the conventional pipeline. DATA CONCLUSION The CVDC method may provide superior sensitivity and improved reproducibility for detecting rsFC deficits in PD. LEVEL OF EVIDENCE 1 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Destaw Bayabil Mekbib
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
- Department of Physics and Statistics, Addis Ababa Science and Technology University, Addis Ababa, Ethiopia
| | - Miao Cai
- Department of Neurology, Affiliated Zhejiang Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Dan Wu
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Weiying Dai
- Department of Computer Science, State University of New York at Binghamton, Binghamton, New York, USA
| | - Xiaoli Liu
- Department of Neurology, Affiliated Zhejiang Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Li Zhao
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
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Alemanno F, Fedeli D, Monti A, Houdayer E, Della Rosa PA, Zangrillo F, Emedoli D, Pelagallo E, Corbo M, Iannaccone S, Abutalebi J. Increased interhemispheric functional connectivity after right anodal tDCS in chronic non-fluent aphasia: preliminary findings. Front Neurosci 2024; 18:1346095. [PMID: 38406588 PMCID: PMC10884287 DOI: 10.3389/fnins.2024.1346095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 01/23/2024] [Indexed: 02/27/2024] Open
Abstract
Introduction Anodal transcranial Direct Current Stimulation (tDCS) is a non-invasive, low-cost and environment-friendly brain neuromodulation technique that increases cortical excitability. In post-stroke aphasia, the role of the right hemisphere in language recovery remains debated. In this preliminary study, we aimed to investigate the efficacy of excitatory tDCS on the right hemisphere in chronic aphasic patients. Methods We applied anodal tDCS to the right homologous region of Broca's area in four chronic aphasic patients while performing a one-month naming rehabilitation treatment. Longitudinal data on language assessment and naming performance were collected. Resting-state fMRI images were acquired before and after treatment to measure changes in functional connectivity. Results Results showed enhanced positive functional connectivity of the right Broca homologous with the left middle frontal and middle temporal gyri. Every patient showed improvements in language functions, but no major changes in naming performance. Conclusion These preliminary findings suggest that tDCS applied over the unaffected hemisphere may result in longitudinal inter-hemispheric functional neuroplastic changes that could specifically improve language recovery and could potentially be included in therapeutic neurorehabilitative plans.
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Affiliation(s)
- Federica Alemanno
- Neuropsychology Service, Department of Rehabilitation and Functional Recovery, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Davide Fedeli
- Centre for Neurolinguistics and Psycholinguistics, Scientific Institute San Raffaele, Vita-Salute San Raffaele University, Milan, Italy
- Department of Neuroradiology, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Alessia Monti
- Department of Neurorehabilitation Sciences, Casa di Cura Igea, Milan, Italy
| | - Elise Houdayer
- Neuropsychology Service, Department of Rehabilitation and Functional Recovery, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | | | - Federica Zangrillo
- Neuropsychology Service, Department of Rehabilitation and Functional Recovery, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Daniele Emedoli
- Neuropsychology Service, Department of Rehabilitation and Functional Recovery, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Elisabetta Pelagallo
- Neuropsychology Service, Department of Rehabilitation and Functional Recovery, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Massimo Corbo
- Department of Neurorehabilitation Sciences, Casa di Cura Igea, Milan, Italy
| | - Sandro Iannaccone
- Neuropsychology Service, Department of Rehabilitation and Functional Recovery, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Jubin Abutalebi
- Neuropsychology Service, Department of Rehabilitation and Functional Recovery, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
- Centre for Neurolinguistics and Psycholinguistics, Scientific Institute San Raffaele, Vita-Salute San Raffaele University, Milan, Italy
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Li Y, Yin Y, Yu Y, Hu X, Liu X, Wu S. The potential predictors for treatment-resistance depression after selective serotonin reuptake inhibitors therapy in Han Chinese: A resting-state functional magnetic resonance imaging study. Early Interv Psychiatry 2024. [PMID: 38320861 DOI: 10.1111/eip.13509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 11/26/2023] [Accepted: 01/24/2024] [Indexed: 02/08/2024]
Abstract
AIM Selective serotonin reuptake inhibitors (SSRIs) are among the most important antidepressants. However, there is limited research on predicting the occurrence of treatment-resistant depression (TRD) after 5 years. Examining the predictive effect of TRD occurrence using resting-state fMRI in patients initiating SSRIs treatment at the onset of major depressive disorder (MDD) could potentially enhance TRD management. METHODS A total of 60 first-episode drug-naive MDD patients who met the criteria, along with 41 healthy controls of Han Chinese ethnicity, were recruited. All MDD patients received SSRIs as the initial treatment for relieving depressive symptoms. Resting-state fMRI scans were conducted for all subjects. Follow-up assessments were conducted over a period of five years, during which MDD patients were categorized into treatment-resistant depression (TRD) and non-treatment-resistant depression (NRD) groups based on disease progression. Amplitude of low-frequency fluctuations (ALFF), fractional amplitude of low-frequency fluctuations (fALFF), and Regional Homogeneity (ReHo) values were calculated and compared among the three groups. Additionally, receiver operating characteristic (ROC) curves were employed to identify potential predictors. RESULTS After 5 years of follow-up, it was found that 43 MDD patients were classified as NRD, while 17 were classified as TRD. In comparison to TRD, NRD exhibited decreased ALFF in the left middle cingulum gyrus (MCG.L) and in the right middle frontal gyrus (MFG.R), as well as decreased ReHo in MCG.L. Furthermore, NRD showed increased fALFF in the left precuneus (PCUN.L). The area under the curve (AUC) values were as follows: 0.724 (MCG.L by ALFF), 0.732 (MFG.R), 0.767 (PCUN.L), 0.774 (MCG.L by ReHo), 0.878 (combined), 0.547 (HAMD), and 0.408 (HAMA) respectively. CONCLUSION The findings suggest that PCUN.L, MFG.R, MCG.L, and the combined measures may indicate the possibility of developing TRD after 5 years when SSRIs are used as the initial therapy for relieving depressive symptoms in MDD patients.
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Affiliation(s)
- Yi Li
- Department of Radiology, Zhejiang University School of Medicine Affiliated Mental Health Centre & Hangzhou Seventh People's Hospital, Hangzhou, China
| | - Yan Yin
- Department of Psychosomatic, Zhejiang University School of Medicine Affiliated Mental Health Centre & Hangzhou Seventh People's Hospital, Hangzhou, China
| | - Yingyi Yu
- Department of Radiology, Zhejiang University School of Medicine Affiliated Mental Health Centre & Hangzhou Seventh People's Hospital, Hangzhou, China
| | - Xiwen Hu
- The sixth ward of Psychiatry Department, Zhejiang University School of Medicine Affiliated Mental Health Centre & Hangzhou Seventh People's Hospital, Hangzhou, China
| | - XiaoYan Liu
- The fifth ward of Psychiatry Department, Zhejiang University School of Medicine Affiliated Mental Health Centre & Hangzhou Seventh People's Hospital, Hangzhou, China
| | - Sha Wu
- Department of intensive care unit, Zhejiang University School of Medicine Affiliated Mental Health Centre & Hangzhou Seventh People's Hospital, Hangzhou, China
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Tan JB, Müller EJ, Orlando IF, Taylor NL, Margulies DS, Szeto J, Lewis SJG, Shine JM, O'Callaghan C. Abnormal higher-order network interactions in Parkinson's disease visual hallucinations. Brain 2024; 147:458-471. [PMID: 37677056 DOI: 10.1093/brain/awad305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 07/14/2023] [Accepted: 08/11/2023] [Indexed: 09/09/2023] Open
Abstract
Visual hallucinations in Parkinson's disease can be viewed from a systems-level perspective, whereby dysfunctional communication between brain networks responsible for perception predisposes a person to hallucinate. To this end, abnormal functional interactions between higher-order and primary sensory networks have been implicated in the pathophysiology of visual hallucinations in Parkinson's disease, however the precise signatures remain to be determined. Dimensionality reduction techniques offer a novel means for simplifying the interpretation of multidimensional brain imaging data, identifying hierarchical patterns in the data that are driven by both within- and between-functional network changes. Here, we applied two complementary non-linear dimensionality reduction techniques-diffusion-map embedding and t-distributed stochastic neighbour embedding (t-SNE)-to resting state functional MRI data, in order to characterize the altered functional hierarchy associated with susceptibility to visual hallucinations. Our study involved 77 people with Parkinson's disease (31 with hallucinations; 46 without hallucinations) and 19 age-matched healthy control subjects. In patients with visual hallucinations, we found compression of the unimodal-heteromodal gradient consistent with increased functional integration between sensory and higher order networks. This was mirrored in a traditional functional connectivity analysis, which showed increased connectivity between the visual and default mode networks in the hallucinating group. Together, these results suggest a route by which higher-order regions may have excessive influence over earlier sensory processes, as proposed by theoretical models of hallucinations across disorders. By contrast, the t-SNE analysis identified distinct alterations in prefrontal regions, suggesting an additional layer of complexity in the functional brain network abnormalities implicated in hallucinations, which was not apparent in traditional functional connectivity analyses. Together, the results confirm abnormal brain organization associated with the hallucinating phenotype in Parkinson's disease and highlight the utility of applying convergent dimensionality reduction techniques to investigate complex clinical symptoms. In addition, the patterns we describe in Parkinson's disease converge with those seen in other conditions, suggesting that reduced hierarchical differentiation across sensory-perceptual systems may be a common transdiagnostic vulnerability in neuropsychiatric disorders with perceptual disturbances.
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Affiliation(s)
- Joshua B Tan
- Brain and Mind Centre, School of Medical Sciences, Faculty of Medicine and Health, University of Sydney, Sydney 2050, Australia
| | - Eli J Müller
- Brain and Mind Centre, School of Medical Sciences, Faculty of Medicine and Health, University of Sydney, Sydney 2050, Australia
- Centre for Complex Systems, School of Physics, University of Sydney, Sydney 2050, Australia
| | - Isabella F Orlando
- Brain and Mind Centre, School of Medical Sciences, Faculty of Medicine and Health, University of Sydney, Sydney 2050, Australia
| | - Natasha L Taylor
- Brain and Mind Centre, School of Medical Sciences, Faculty of Medicine and Health, University of Sydney, Sydney 2050, Australia
| | - Daniel S Margulies
- Integrative Neuroscience and Cognition Center, Center National de la Recherche Scientifique (CNRS) and Université de Paris, 75006 Paris, France
| | - Jennifer Szeto
- Brain and Mind Centre, School of Medical Sciences, Faculty of Medicine and Health, University of Sydney, Sydney 2050, Australia
| | - Simon J G Lewis
- Brain and Mind Centre, School of Medical Sciences, Faculty of Medicine and Health, University of Sydney, Sydney 2050, Australia
| | - James M Shine
- Brain and Mind Centre, School of Medical Sciences, Faculty of Medicine and Health, University of Sydney, Sydney 2050, Australia
- Centre for Complex Systems, School of Physics, University of Sydney, Sydney 2050, Australia
| | - Claire O'Callaghan
- Brain and Mind Centre, School of Medical Sciences, Faculty of Medicine and Health, University of Sydney, Sydney 2050, Australia
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Kulkarni AP, Hwang G, Cook CJ, Mohanty R, Guliani A, Nair VA, Bendlin BB, Meyerand E, Prabhakaran V. Genetic and environmental influence on resting state networks in young male and female adults: a cartographer mapping study. Hum Brain Mapp 2023; 44:5238-5293. [PMID: 36537283 PMCID: PMC10543121 DOI: 10.1002/hbm.25947] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 04/16/2022] [Accepted: 04/19/2022] [Indexed: 09/07/2023] Open
Abstract
We propose a unique, minimal assumption, approach based on variance analyses (compared with standard approaches) to investigate genetic influence on individual differences on the functional connectivity of the brain using 65 monozygotic and 65 dizygotic healthy young adult twin pairs' low-frequency oscillation resting state functional Magnetic Resonance Imaging (fMRI) data from the Human Connectome Project. Overall, we found high number of genetically-influenced functional (GIF) connections involving posterior to posterior brain regions (occipital/temporal/parietal) implicated in low-level processes such as vision, perception, motion, categorization, dorsal/ventral stream visuospatial, and long-term memory processes, as well as high number across midline brain regions (cingulate) implicated in attentional processes, and emotional responses to pain. We found low number of GIF connections involving anterior to anterior/posterior brain regions (frontofrontal > frontoparietal, frontotemporal, frontooccipital) implicated in high-level processes such as working memory, reasoning, emotional judgment, language, and action planning. We found very low number of GIF connections involving subcortical/noncortical networks such as basal ganglia, thalamus, brainstem, and cerebellum. In terms of sex-specific individual differences, individual differences in males were more genetically influenced while individual differences in females were more environmentally influenced in terms of the interplay of interactions of Task positive networks (brain regions involved in various task-oriented processes and attending to and interacting with environment), extended Default Mode Network (a central brain hub for various processes such as internal monitoring, rumination, and evaluation of self and others), primary sensorimotor systems (vision, audition, somatosensory, and motor systems), and subcortical/noncortical networks. There were >8.5-19.1 times more GIF connections in males than females. These preliminary (young adult cohort-specific) findings suggest that individual differences in the resting state brain may be more genetically influenced in males and more environmentally influenced in females; furthermore, standard approaches may suggest that it is more substantially nonadditive genetics, rather than additive genetics, which contribute to the differences in sex-specific individual differences based on this young adult (male and female) specific cohort. Finally, considering the preliminary cohort-specific results, based on standard approaches, environmental influences on individual differences may be substantially greater than that of genetics, for either sex, frontally and brain-wide. [Correction added on 10 May 2023, after first online publication: added: functional Magnetic Resonance Imaging. Added: individual differences in, twice. Added statement between furthermore … based on standard approaches.].
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Affiliation(s)
- Arman P. Kulkarni
- Department of Biomedical EngineeringUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Gyujoon Hwang
- Department of Medical PhysicsUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Cole J. Cook
- Department of Medical PhysicsUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Rosaleena Mohanty
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and SocietyKarolinska InstitutetStockholmSweden
| | - Akhil Guliani
- Department of Computer ScienceUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Veena A. Nair
- Department of RadiologyUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Barbara B. Bendlin
- Department of MedicineUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Elizabeth Meyerand
- Department of Biomedical EngineeringUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
- Department of Medical PhysicsUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Vivek Prabhakaran
- Department of Medical PhysicsUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
- Department of Computer ScienceUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
- Department of MedicineUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
- Department of NeurologyUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
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Karavallil Achuthan S, Stavrinos D, Holm HB, Anteraper SA, Kana RK. Alterations of Functional Connectivity in Autism and Attention-Deficit/Hyperactivity Disorder Revealed by Multi-Voxel Pattern Analysis. Brain Connect 2023; 13:528-540. [PMID: 37522594 DOI: 10.1089/brain.2023.0006] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/01/2023] Open
Abstract
Background: Autism and attention-deficit/hyperactivity disorder (ADHD) are comorbid neurodevelopmental disorders that share common and distinct neurobiological mechanisms, with disrupted brain connectivity patterns being a hallmark feature of both conditions. It is challenging to gain a mechanistic understanding of the underlying disorder, because brain connectivity changes in autism and ADHD are heterogeneous. Objectives: The present resting state functional MRI (rs-fMRI) study focuses on investigating the shared and distinct resting state-fMRI connectivity (rsFC) patterns in autistic and ADHD adults using multi-voxel pattern analysis (MVPA). By identifying spatial patterns of fMRI activity across a given time course, MVPA is an innovative and powerful method for generating seed regions of interest (ROIs) without a priori hypotheses. Methods: We performed a data-driven, whole-brain, connectome-wide MVPA on rs-fMRI data collected from 15 autistic, 19 ADHD, and 15 neurotypical (NT) young adults. Results: MVPA identified cerebellar vermis 9, precuneus, and the right cerebellum VI for autistic versus NT, right inferior frontal gyrus and vermis 9 for ADHD versus NT, and right dorsolateral prefrontal cortex for autistic versus ADHD as significant clusters. Post hoc seed-to-voxel analyses using these clusters as seed ROIs were performed for further characterization of group differences. The cerebellum VI, vermis, and precuneus in autistic adults, and the vermis and frontal regions in ADHD showed different connectivity patterns in comparison with NT. Conclusions: The study characterizes the rsFC profile of cerebellum with key cortical areas in autism and ADHD, and it emphasizes the importance of studying the role of the functional connectivity of the cerebellum in neurodevelopmental disorders.
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Affiliation(s)
- Smitha Karavallil Achuthan
- Department of Psychology & The Center for Innovative Research in Autism, University of Alabama, Tuscaloosa, Alabama, USA
| | - Despina Stavrinos
- Department of Psychology, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Haley B Holm
- Children's Hospital of Atlanta, Atlanta, Georgia, USA
| | - Sheeba Arnold Anteraper
- Stephens Family Clinical Research Institute, Carle Illinois Advanced Imaging Center, Urbana, Illinois, USA
| | - Rajesh K Kana
- Department of Psychology & The Center for Innovative Research in Autism, University of Alabama, Tuscaloosa, Alabama, USA
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Zhang SP, Mao B, Zhou T, Su CW, Li C, Jiang J, An S, Yao N, Li Y, Huang ZG. Frequency dependent whole-brain coactivation patterns analysis in Alzheimer's disease. Front Neurosci 2023; 17:1198839. [PMID: 37946728 PMCID: PMC10631782 DOI: 10.3389/fnins.2023.1198839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Accepted: 09/21/2023] [Indexed: 11/12/2023] Open
Abstract
Background The brain in resting state has complex dynamic properties and shows frequency dependent characteristics. The frequency-dependent whole-brain dynamic changes of resting state across the scans have been ignored in Alzheimer's disease (AD). Objective Coactivation pattern (CAP) analysis can identify different brain states. This paper aimed to investigate the dynamic characteristics of frequency dependent whole-brain CAPs in AD. Methods We utilized a multiband CAP approach to model the state space and study brain dynamics in both AD and NC. The correlation between the dynamic characteristics and the subjects' clinical index was further analyzed. Results The results showed similar CAP patterns at different frequency bands, but the occurrence of patterns was different. In addition, CAPs associated with the default mode network (DMN) and the ventral/dorsal visual network (dorsal/ventral VN) were altered significantly between the AD and NC groups. This study also found the correlation between the altered dynamic characteristics of frequency dependent CAPs and the patients' clinical Mini-Mental State Examination assessment scale scores. Conclusion This study revealed that while similar CAP spatial patterns appear in different frequency bands, their dynamic characteristics in subbands vary. In addition, delineating subbands was more helpful in distinguishing AD from NC in terms of CAP.
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Affiliation(s)
- Si-Ping Zhang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, The Key Laboratory of Neuro-Informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi, China
- Research Center for Brain-Inspired Intelligence, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Bi Mao
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, The Key Laboratory of Neuro-Informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi, China
- Research Center for Brain-Inspired Intelligence, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Tianlin Zhou
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, The Key Laboratory of Neuro-Informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi, China
- Research Center for Brain-Inspired Intelligence, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Chun-Wang Su
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, The Key Laboratory of Neuro-Informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi, China
- Research Center for Brain-Inspired Intelligence, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Chenxi Li
- Department of Military Medical Psychology, Air Force Medical University, Xi’an, Shaanxi, China
| | - Junjie Jiang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, The Key Laboratory of Neuro-Informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi, China
- Research Center for Brain-Inspired Intelligence, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Simeng An
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, The Key Laboratory of Neuro-Informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi, China
- Research Center for Brain-Inspired Intelligence, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Nan Yao
- Research Center for Brain-Inspired Intelligence, Xi'an Jiaotong University, Xi'an, Shaanxi, China
- Department of Applied Physics, Xi'an University of Technology, Xi'an, China
| | - Youjun Li
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, The Key Laboratory of Neuro-Informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi, China
- Research Center for Brain-Inspired Intelligence, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Zi-Gang Huang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, The Key Laboratory of Neuro-Informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi, China
- Research Center for Brain-Inspired Intelligence, Xi'an Jiaotong University, Xi'an, Shaanxi, China
- The State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
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Sakthivel R, Criado-Marrero M, Barroso D, Braga IM, Bolen M, Rubinovich U, Hery GP, Grudny MM, Koren J, Prokop S, Febo M, Abisambra JF. Fixed Time-Point Analysis Reveals Repetitive Mild Traumatic Brain Injury Effects on Resting State Functional Magnetic Resonance Imaging Connectivity and Neuro-Spatial Protein Profiles. J Neurotrauma 2023; 40:2037-2049. [PMID: 37051703 PMCID: PMC10541943 DOI: 10.1089/neu.2022.0464] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/14/2023] Open
Abstract
Repetitive mild traumatic brain injuries (rmTBIs) are serious trauma events responsible for the development of numerous neurodegenerative disorders. A major challenge in developing diagnostics and treatments for the consequences of rmTBI is the fundamental knowledge gaps of the molecular mechanisms responsible for neurodegeneration. It is both critical and urgent to understand the neuropathological and functional consequences of rmTBI to develop effective therapeutic strategies. Using the Closed-Head Impact Model of Engineered Rotational Acceleration, or CHIMERA, we measured neural changes following injury, including brain volume, diffusion tensor imaging, and resting-state functional magnetic resonance imaging coupled with graph theory and functional connectivity analyses. We determined the effect of rmTBI on markers of gliosis and used NanoString-GeoMx to add a digital-spatial protein profiling analysis of neurodegenerative disease-associated proteins in gray and white matter regions. Our analyses revealed aberrant connectivity changes in the thalamus, independent of microstructural damage or neuroinflammation. We also identified distinct changes in the levels of proteins linked to various neurodegenerative processes including total and phospho-tau species and cell proliferation markers. Together, our data show that rmTBI significantly alters brain functional connectivity and causes distinct protein changes in morphologically intact brain areas.
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Affiliation(s)
- Ravi Sakthivel
- Center for Translational Research in Neurodegenerative Disease (CTRND), University of Florida, Gainesville, Florida, USA
- Department of Neuroscience, University of Florida, Gainesville, Florida, USA
- McKnight Brain Institute, University of Florida, Gainesville, Florida, USA
| | - Marangelie Criado-Marrero
- Center for Translational Research in Neurodegenerative Disease (CTRND), University of Florida, Gainesville, Florida, USA
- Department of Neuroscience, University of Florida, Gainesville, Florida, USA
- McKnight Brain Institute, University of Florida, Gainesville, Florida, USA
| | - Daylin Barroso
- Center for Translational Research in Neurodegenerative Disease (CTRND), University of Florida, Gainesville, Florida, USA
- Department of Neuroscience, University of Florida, Gainesville, Florida, USA
- McKnight Brain Institute, University of Florida, Gainesville, Florida, USA
| | - Isadora M. Braga
- McKnight Brain Institute, University of Florida, Gainesville, Florida, USA
| | - Mackenzie Bolen
- Department of Neuroscience, University of Florida, Gainesville, Florida, USA
- McKnight Brain Institute, University of Florida, Gainesville, Florida, USA
| | - Uriel Rubinovich
- Center for Translational Research in Neurodegenerative Disease (CTRND), University of Florida, Gainesville, Florida, USA
- McKnight Brain Institute, University of Florida, Gainesville, Florida, USA
| | - Gabriela P. Hery
- Center for Translational Research in Neurodegenerative Disease (CTRND), University of Florida, Gainesville, Florida, USA
- Department of Pathology, University of Florida, Gainesville, Florida, USA
| | - Matteo M. Grudny
- Department of Psychiatry, University of Florida, Gainesville, Florida, USA
| | - John Koren
- Center for Translational Research in Neurodegenerative Disease (CTRND), University of Florida, Gainesville, Florida, USA
- Department of Neuroscience, University of Florida, Gainesville, Florida, USA
- McKnight Brain Institute, University of Florida, Gainesville, Florida, USA
| | - Stefan Prokop
- Center for Translational Research in Neurodegenerative Disease (CTRND), University of Florida, Gainesville, Florida, USA
- McKnight Brain Institute, University of Florida, Gainesville, Florida, USA
- Department of Pathology, University of Florida, Gainesville, Florida, USA
- Fixel Institute for Neurological Diseases, University of Florida, Gainesville, Florida, USA
| | - Marcelo Febo
- McKnight Brain Institute, University of Florida, Gainesville, Florida, USA
- Department of Psychiatry, University of Florida, Gainesville, Florida, USA
| | - Jose Francisco Abisambra
- Center for Translational Research in Neurodegenerative Disease (CTRND), University of Florida, Gainesville, Florida, USA
- Department of Neuroscience, University of Florida, Gainesville, Florida, USA
- McKnight Brain Institute, University of Florida, Gainesville, Florida, USA
- Brain Injury Rehabilitation and Neuroresilience (BRAIN) Center, University of Florida, Gainesville, Florida, USA
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Cook KM, De Asis-Cruz J, Basu SK, Andescavage N, Murnick J, Spoehr E, du Plessis AJ, Limperopoulos C. Ex-utero third trimester developmental changes in functional brain network organization in infants born very and extremely preterm. Front Neurosci 2023; 17:1214080. [PMID: 37719160 PMCID: PMC10502339 DOI: 10.3389/fnins.2023.1214080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 08/22/2023] [Indexed: 09/19/2023] Open
Abstract
Introduction The latter half of gestation is a period of rapid brain development, including the formation of fundamental functional brain network architecture. Unlike in-utero fetuses, infants born very and extremely preterm undergo these critical maturational changes in the extrauterine environment, with growing evidence suggesting this may result in altered brain networks. To date, however, the development of functional brain architecture has been unexplored. Methods From a prospective cohort of preterm infants, graph parameters were calculated for fMRI scans acquired prior to reaching term equivalent age. Eight graph properties were calculated, Clustering Coefficient (C), Characteristic Path Length (L), Modularity (Q), Local Efficiency (LE), Global Efficiency (GE), Normalized Clustering (λ), Normalized Path Length (γ), and Small-Worldness (σ). Properties were first compared to values generated from random and lattice networks and cost efficiency was evaluated. Subsequently, linear mixed effect models were used to assess relationship with postmenstrual age and infant sex. Results A total of 111 fMRI scans were acquired from 85 preterm infants born at a mean GA 28.93 ± 2.8. Infants displayed robust small world properties as well as both locally and globally efficient networks. Regression models found that GE increased while L, Q, λ, γ, and σ decreased with increasing postmenstrual age following multiple comparison correction (r2Adj range 0.143-0.401, p < 0048), with C and LE exhibited trending increases with age. Discussion This is the first direct investigation on the extra-uterine formation of functional brain architecture in preterm infants. Importantly, our results suggest that changes in functional architecture with increasing age exhibit a different trajectory relative to in utero fetus. Instead, they exhibit developmental changes more similar to the early postnatal period in term born infants.
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Affiliation(s)
- Kevin M. Cook
- Developing Brain Institute, Children’s National Hospital, Washington, DC, United States
| | | | - Sudeepta K. Basu
- Developing Brain Institute, Children’s National Hospital, Washington, DC, United States
| | - Nickie Andescavage
- Developing Brain Institute, Children’s National Hospital, Washington, DC, United States
| | - Jonathan Murnick
- Department of Diagnostic Imaging & Radiology, Children’s National Health System, Children’s National Hospital, Washington, DC, United States
| | - Emma Spoehr
- Developing Brain Institute, Children’s National Hospital, Washington, DC, United States
| | - Adré J. du Plessis
- Prenatal Pediatrics Institute, Children’s National Hospital, Washington, DC, United States
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11
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Hidalgo-Lopez E, Noachtar I, Pletzer B. Hormonal contraceptive exposure relates to changes in resting state functional connectivity of anterior cingulate cortex and amygdala. Front Endocrinol (Lausanne) 2023; 14:1131995. [PMID: 37522123 PMCID: PMC10374315 DOI: 10.3389/fendo.2023.1131995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Accepted: 06/09/2023] [Indexed: 08/01/2023] Open
Abstract
Introduction Hormonal contraceptives (HCs), nowadays one of the most used contraceptive methods, downregulate endogenous ovarian hormones, which have multiple plastic effects in the adult brain. HCs usually contain a synthetic estrogen, ethinyl-estradiol, and a synthetic progestin, which can be classified as androgenic or anti-androgenic, depending on their interaction with androgen receptors. Both the anterior cingulate cortex (ACC) and the amygdala express steroid receptors and have shown differential functionality depending on the hormonal status of the participant and the use of HC. In this work, we investigated for the first time the relationship between ACC and amygdala resting state functional connectivity (rs-FC) and HC use duration, while controlling for progestin androgenicity. Methods A total of 231 healthy young women participated in five different magnetic resonance imaging studies and were included in the final analysis. The relation between HC use duration and (i) gray matter volume, (ii) fractional amplitude of low-frequency fluctuations, and (iii) seed-based connectivity during resting state in the amygdalae and ACC was investigated in this large sample of women. Results In general, rs-FC of the amygdalae with frontal areas, and between the ACC and temporoparietal areas, decreased the longer the HC exposure and independently of the progestin's androgenicity. The type of HC's progestin did show a differential effect in the gray matter volume of left ACC and the connectivity between bilateral ACC and the right inferior frontal gyrus.
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Affiliation(s)
- Esmeralda Hidalgo-Lopez
- Centre for Cognitive Neuroscience, University of Salzburg, Salzburg, Austria
- Department of Psychology, University of Salzburg, Salzburg, Austria
| | - Isabel Noachtar
- Centre for Cognitive Neuroscience, University of Salzburg, Salzburg, Austria
- Department of Psychology, University of Salzburg, Salzburg, Austria
| | - Belinda Pletzer
- Centre for Cognitive Neuroscience, University of Salzburg, Salzburg, Austria
- Department of Psychology, University of Salzburg, Salzburg, Austria
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12
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Upton S, Brown AA, Golzy M, Garland EL, Froeliger B. Right inferior frontal gyrus theta-burst stimulation reduces smoking behaviors and strengthens fronto-striatal-limbic resting-state functional connectivity: a randomized crossover trial. Front Psychiatry 2023; 14:1166912. [PMID: 37457779 PMCID: PMC10338839 DOI: 10.3389/fpsyt.2023.1166912] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 05/22/2023] [Indexed: 07/18/2023] Open
Abstract
Introduction Functional and anatomical irregularities in the right inferior frontal gyrus (rIFG), a ventrolateral prefrontal region that mediates top-down inhibitory control over prepotent behavioral responding, are implicated in the ongoing maintenance of nicotine dependence (ND). However, there is little research on the effects of neuromodulation of the rIFG on smoking behavior, inhibitory control, and resting-state functional connectivity (rsFC) among individuals with ND. Methods In this double-blind, crossover, theta-burst stimulation (TBS) study, adults with ND (N = 31; female: n = 15) completed a baseline session and were then randomized to two counterbalanced sessions of functionally neuronavigated TBS to the rIFG: continuous TBS (cTBS) on 1 day and intermittent TBS (iTBS) on another. Differences in cigarette cravings, smoking, and fronto-striatal-limbic rsFC were assessed. Results Relative to baseline, cTBS significantly reduced appetitive and withdrawal cravings immediately after treatment. The effects of cTBS on withdrawal craving persisted for 24 h, as well as produced a reduction in smoking. Furthermore, cTBS significantly strengthened rsFC between the rIFG pars opercularis and subcallosal cingulate (fronto-striatal circuit), and between the rIFG pars opercularis and the right posterior parahippocampal gyrus (fronto-limbic circuit). At post-24 h, cTBS-induced increase in fronto-striatal rsFC was significantly associated with less appetitive craving, while the increase in fronto-limbic rsFC was significantly associated with less withdrawal craving and smoking. Discussion These findings warrant further investigation into the potential value of rIFG cTBS to attenuate smoking behavior among individuals with ND.
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Affiliation(s)
- Spencer Upton
- Department of Psychological Sciences, University of Missouri, Columbia, MO, United States
| | - Alexander A. Brown
- Department of Psychological Sciences, University of Missouri, Columbia, MO, United States
- Department of Psychiatry, School of Medicine, University of Missouri, Columbia, MO, United States
- Cognitive Neuroscience Systems Core Facility, University of Missouri, Columbia, MO, United States
| | - Mojgan Golzy
- Biostatistics Unit, Department of Family and Community Medicine, School of Medicine, University of Missouri, Columbia, MO, United States
| | - Eric L. Garland
- Center on Mindfulness and Integrative Health Intervention Development, College of Social Work, University of Utah, Salt Lake City, UT, United States
| | - Brett Froeliger
- Department of Psychological Sciences, University of Missouri, Columbia, MO, United States
- Department of Psychiatry, School of Medicine, University of Missouri, Columbia, MO, United States
- Cognitive Neuroscience Systems Core Facility, University of Missouri, Columbia, MO, United States
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13
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Genç E, Metzen D, Fraenz C, Schlüter C, Voelkle MC, Arning L, Streit F, Nguyen HP, Güntürkün O, Ocklenburg S, Kumsta R. Structural architecture and brain network efficiency link polygenic scores to intelligence. Hum Brain Mapp 2023; 44:3359-3376. [PMID: 37013679 PMCID: PMC10171514 DOI: 10.1002/hbm.26286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 02/15/2023] [Accepted: 03/01/2023] [Indexed: 04/05/2023] Open
Abstract
Intelligence is highly heritable. Genome-wide association studies (GWAS) have shown that thousands of alleles contribute to variation in intelligence with small effect sizes. Polygenic scores (PGS), which combine these effects into one genetic summary measure, are increasingly used to investigate polygenic effects in independent samples. Whereas PGS explain a considerable amount of variance in intelligence, it is largely unknown how brain structure and function mediate this relationship. Here, we show that individuals with higher PGS for educational attainment and intelligence had higher scores on cognitive tests, larger surface area, and more efficient fiber connectivity derived by graph theory. Fiber network efficiency as well as the surface of brain areas partly located in parieto-frontal regions were found to mediate the relationship between PGS and cognitive performance. These findings are a crucial step forward in decoding the neurogenetic underpinnings of intelligence, as they identify specific regional networks that link polygenic predisposition to intelligence.
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Affiliation(s)
- Erhan Genç
- Department of Psychology and Neuroscience, Leibniz Research Centre for Working Environment and Human Factors (IfADo), Dortmund, Germany
| | - Dorothea Metzen
- Biopsychology, Institute for Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Bochum, Germany
| | - Christoph Fraenz
- Department of Psychology and Neuroscience, Leibniz Research Centre for Working Environment and Human Factors (IfADo), Dortmund, Germany
| | - Caroline Schlüter
- Biopsychology, Institute for Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Bochum, Germany
| | - Manuel C Voelkle
- Psychological Research Methods Department of Psychology, Humboldt University, Berlin, Germany
| | - Larissa Arning
- Department of Human Genetics, Faculty of Medicine, Ruhr University Bochum, Bochum, Germany
| | - Fabian Streit
- Department Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Huu Phuc Nguyen
- Department of Human Genetics, Faculty of Medicine, Ruhr University Bochum, Bochum, Germany
| | - Onur Güntürkün
- Biopsychology, Institute for Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Bochum, Germany
| | - Sebastian Ocklenburg
- Biopsychology, Institute for Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Bochum, Germany
- Department of Psychology, Medical School Hamburg, Hamburg, Germany
- ICAN Institute for Cognitive and Affective Neuroscience, Medical School Hamburg, Hamburg, Germany
| | - Robert Kumsta
- Genetic Psychology, Faculty of Psychology, Ruhr University Bochum, Bochum, Germany
- Department of Behavioural and Cognitive Sciences, Laboratory for Stress and Gene-Environment Interplay, University of Luxembourg, Esch-sur-Alzette, Luxembourg
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14
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Ramkiran S, Veselinović T, Dammers J, Gaebler AJ, Rajkumar R, Shah NJ, Neuner I. How brain networks tic: Predicting tic severity through rs-fMRI dynamics in Tourette syndrome. Hum Brain Mapp 2023. [PMID: 37232486 DOI: 10.1002/hbm.26341] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 04/28/2023] [Accepted: 05/02/2023] [Indexed: 05/27/2023] Open
Abstract
Tourette syndrome (TS) is a neuropsychiatric disorder characterized by motor and phonic tics, which several different theories, such as basal ganglia-thalamo-cortical loop dysfunction and amygdala hypersensitivity, have sought to explain. Previous research has shown dynamic changes in the brain prior to tic onset leading to tics, and this study aims to investigate the contribution of network dynamics to them. For this, we have employed three methods of functional connectivity to resting-state fMRI data - namely the static, the sliding window dynamic and the ICA based estimated dynamic; followed by an examination of the static and dynamic network topological properties. A leave-one-out (LOO-) validated regression model with LASSO regularization was used to identify the key predictors. The relevant predictors pointed to dysfunction of the primary motor cortex, the prefrontal-basal ganglia loop and amygdala-mediated visual social processing network. This is in line with a recently proposed social decision-making dysfunction hypothesis, opening new horizons in understanding tic pathophysiology.
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Affiliation(s)
- Shukti Ramkiran
- Institute of Neuroscience and Medicine 4 (INM-4), Forschungszentrum Juelich, Juelich, Germany
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany
- JARA - BRAIN - Translational Medicine, Aachen, Germany
| | - Tanja Veselinović
- Institute of Neuroscience and Medicine 4 (INM-4), Forschungszentrum Juelich, Juelich, Germany
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany
| | - Jürgen Dammers
- Institute of Neuroscience and Medicine 4 (INM-4), Forschungszentrum Juelich, Juelich, Germany
| | - Arnim Johannes Gaebler
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany
- JARA - BRAIN - Translational Medicine, Aachen, Germany
- Institute of Physiology, Faculty of Medicine, RWTH Aachen University, Aachen, Germany
| | - Ravichandran Rajkumar
- Institute of Neuroscience and Medicine 4 (INM-4), Forschungszentrum Juelich, Juelich, Germany
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany
- JARA - BRAIN - Translational Medicine, Aachen, Germany
| | - N Jon Shah
- Institute of Neuroscience and Medicine 4 (INM-4), Forschungszentrum Juelich, Juelich, Germany
- JARA - BRAIN - Translational Medicine, Aachen, Germany
- Department of Neurology, Faculty of Medicine, RWTH Aachen University, Aachen, Germany
- Institute of Neuroscience and Medicine 11 (INM-11), JARA, Forschungszentrum Juelich, Juelich, Germany
| | - Irene Neuner
- Institute of Neuroscience and Medicine 4 (INM-4), Forschungszentrum Juelich, Juelich, Germany
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany
- JARA - BRAIN - Translational Medicine, Aachen, Germany
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15
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Wu X, Ma L, Yin Q, Liu M, Wu K, Wang D. The impact of wearing a KN95 face mask on human brain function: evidence from resting state functional magnetic resonance imaging. Front Neurol 2023; 14:1102335. [PMID: 37273685 PMCID: PMC10237040 DOI: 10.3389/fneur.2023.1102335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 05/09/2023] [Indexed: 06/06/2023] Open
Abstract
Background Face masks are widely used in daily life because of the COVID-19 pandemic. The objective of this study was to explore the impact of wearing face masks on brain functions by using resting-state functional MRI (RS-fMRI). Methods Scanning data from 15 healthy subjects (46.20 ± 6.67 years) were collected in this study. Each subject underwent RS-fMRI scans under two comparative conditions, wearing a KN95 mask and natural breathing (no mask). The amplitude of low frequency fluctuation (ALFF) and functional connectivity under the two conditions were analyzed and then compared using the paired t-test. Results Compared with those of the no-mask condition, the ALFF activities when wearing masks were increased significantly in the right middle frontal gyrus, bilateral precuneus, right superior marginal gyrus, left inferior parietal gyrus, and left supplementary motor area and decreased significantly in the anterior cingulate gyrus, right fusiform gyrus, left superior temporal gyrus, bilateral lingual gyrus, and bilateral calcarine cortex (p < 0.05). Taking the posterior cingulate cortex area as a seed point, the correlations with the occipital cortex, prefrontal lobe, and motor sensory cortex were sensitive to wearing masks compared with not wearing masks (p < 0.05). Taking the medial prefrontal cortex region as a seed point, the functional connectivity with the bilateral temporal lobe, bilateral motor sensory cortex, and occipital lobe was influenced by wearing a KN95 mask (p < 0.05). Conclusion This study demonstrated that wearing a KN95 face mask can cause short-term changes in human resting brain function. Both local neural activities and functional connectivity in brain regions were sensitive to mask wearing. However, the neural mechanism causing these changes and its impact on cognitive function still need further investigation.
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Affiliation(s)
- Xiaomeng Wu
- Philips (China) Investment Co., Ltd, Shanghai, China
| | - Lifei Ma
- Philips (China) Investment Co., Ltd, Shanghai, China
| | - Qiufeng Yin
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ming Liu
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Kyle Wu
- Philips (China) Investment Co., Ltd, Shanghai, China
| | - Dengbin Wang
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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16
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Chen Y, Li CSR. Overnight Abstinence, Ventrostriatal-Insular Connectivity, and Tridimensional Personality Traits in Cigarette Smokers. J Integr Neurosci 2023; 22:66. [PMID: 37258442 DOI: 10.31083/j.jin2203066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2022] [Revised: 02/08/2023] [Accepted: 02/15/2023] [Indexed: 06/02/2023] Open
Abstract
BACKGROUND Personality traits contribute to the risks of smoking. The striatum has been implicated in nicotine addiction and nicotine deprivation is associated with alterations in resting state functional connectivity (rsFC) of the ventral (VS) and dorsal (DS) striatum. However, it remains unclear how striatal rsFC may change following overnight abstinence or how these shorter-term changes in inter-regional connectivity relate to personality traits. METHODS In the current study, 28 smokers completed assessments with Fagerström Test of Nicotine Dependence, Tridimensional Personality Questionnaire (TPQ), as well as resting state functional magnetic resonance imaging (fMRI) scans during satiety and after overnight abstinence. We processed imaging data with published routines and evaluated the results with a corrected threshold. RESULTS Smokers showed increases in the VS-insula rsFC but no significant changes in the DS rsFC after overnight abstinence as compared to satiety. The difference in the VS-insula rsFC (abstinence minus satiety) was negatively correlated with harm avoidance. CONCLUSIONS These findings highlighted striatal connectivity correlates of very short-term abstinence from smoking and how the VS-insula rsFC may vary with individual personality traits, interlinking neural markers and personality risk factors of cigarette smoking at the earliest stage of abstinence.
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Affiliation(s)
- Yu Chen
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06520, USA
| | - Chiang-Shan R Li
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06520, USA
- Department of Neuroscience, Yale University School of Medicine, New Haven, CT 06520, USA
- Inter-Department Neuroscience Program, Yale University, New Haven, CT 06520, USA
- Wu Tsai Institute, Yale University, New Haven, CT 06520, USA
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17
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Zarghami TS, Zeidman P, Razi A, Bahrami F, Hossein‐Zadeh G. Dysconnection and cognition in schizophrenia: A spectral dynamic causal modeling study. Hum Brain Mapp 2023; 44:2873-2896. [PMID: 36852654 PMCID: PMC10089110 DOI: 10.1002/hbm.26251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 01/28/2023] [Accepted: 02/13/2023] [Indexed: 03/01/2023] Open
Abstract
Schizophrenia (SZ) is a severe mental disorder characterized by failure of functional integration (aka dysconnection) across the brain. Recent functional connectivity (FC) studies have adopted functional parcellations to define subnetworks of large-scale networks, and to characterize the (dys)connection between them, in normal and clinical populations. While FC examines statistical dependencies between observations, model-based effective connectivity (EC) can disclose the causal influences that underwrite the observed dependencies. In this study, we investigated resting state EC within seven large-scale networks, in 66 SZ and 74 healthy subjects from a public dataset. The results showed that a remarkable 33% of the effective connections (among subnetworks) of the cognitive control network had been pathologically modulated in SZ. Further dysconnection was identified within the visual, default mode and sensorimotor networks of SZ subjects, with 24%, 20%, and 11% aberrant couplings. Overall, the proportion of discriminative connections was remarkably larger in EC (24%) than FC (1%) analysis. Subsequently, to study the neural correlates of impaired cognition in SZ, we conducted a canonical correlation analysis between the EC parameters and the cognitive scores of the patients. As such, the self-inhibitions of supplementary motor area and paracentral lobule (in the sensorimotor network) and the excitatory connection from parahippocampal gyrus to inferior temporal gyrus (in the cognitive control network) were significantly correlated with the social cognition, reasoning/problem solving and working memory capabilities of the patients. Future research can investigate the potential of whole-brain EC as a biomarker for diagnosis of brain disorders and for neuroimaging-based cognitive assessment.
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Affiliation(s)
- Tahereh S. Zarghami
- Bio‐Electric Department, School of Electrical and Computer Engineering, College of EngineeringUniversity of TeranTehranIran
- Human Motor Control and Computational Neuroscience Laboratory, School of Electrical and Computer Engineering, College of EngineeringUniversity of TehranTehranIran
| | - Peter Zeidman
- The Wellcome Centre for Human NeuroimagingUniversity College LondonLondonUK
| | - Adeel Razi
- The Wellcome Centre for Human NeuroimagingUniversity College LondonLondonUK
- Turner Institute for Brain and Mental HealthMonash UniversityClaytonVictoriaAustralia
- Monash Biomedical ImagingMonash UniversityClaytonVictoriaAustralia
- CIFAR Azrieli Global Scholars Program, CIFARTorontoCanada
| | - Fariba Bahrami
- Bio‐Electric Department, School of Electrical and Computer Engineering, College of EngineeringUniversity of TeranTehranIran
- Human Motor Control and Computational Neuroscience Laboratory, School of Electrical and Computer Engineering, College of EngineeringUniversity of TehranTehranIran
| | - Gholam‐Ali Hossein‐Zadeh
- Bio‐Electric Department, School of Electrical and Computer Engineering, College of EngineeringUniversity of TeranTehranIran
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Zhu X, Lazarov A, Dolan S, Bar-Haim Y, Dillon DG, Pizzagalli DA, Schneier F. Resting state connectivity predictors of symptom change during gaze-contingent music reward therapy of social anxiety disorder. Psychol Med 2023; 53:3115-3123. [PMID: 35314008 PMCID: PMC9612546 DOI: 10.1017/s0033291721005171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Revised: 11/10/2021] [Accepted: 11/29/2021] [Indexed: 11/07/2022]
Abstract
BACKGROUND Social anxiety disorder (SAD) is common, first-line treatments are often only partially effective, and reliable predictors of treatment response are lacking. Here, we assessed resting state functional connectivity (rsFC) at pre-treatment and during early treatment as a potential predictor of response to a novel attention bias modification procedure, gaze-contingent music reward therapy (GC-MRT). METHODS Thirty-two adults with SAD were treated with GC-MRT. rsFC was assessed with multi-voxel pattern analysis of fMRI at pre-treatment and after 2-3 weeks. For comparison, 20 healthy control (HC) participants without treatment were assessed twice for rsFC over the same time period. All SAD participants underwent clinical evaluation at pre-treatment, early-treatment (week 2-3), and post-treatment. RESULTS SAD and depressive symptoms improved significantly from pre-treatment to post-treatment. After 2-3 weeks of treatment, decreased connectivity between the executive control network (ECN) and salience network (SN), and increased connectivity within the ECN predicted improvement in SAD and depressive symptoms at week 8. Increased connectivity between the ECN and default mode network (DMN) predicted greater improvement in SAD but not depressive symptoms at week 8. Connectivity within the DMN decreased significantly after 2-3 weeks of treatment in the SAD group, while no changes were found in HC over the same time interval. CONCLUSION We identified early changes in rsFC during a course of GC-MRT for SAD that predicted symptom change. Connectivity changes within the ECN, ECN-DMN, and ECN-SN may be related to mechanisms underlying the clinical effects of GC-MRT and warrant further study in controlled trials.
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Affiliation(s)
- Xi Zhu
- Department of Psychiatry, Columbia University Irving Medical Center, New York, USA
- New York State Psychiatric Institute, New York, USA
| | - Amit Lazarov
- Department of Psychiatry, Columbia University Irving Medical Center, New York, USA
- School of Psychological Sciences, Tel-Aviv University, Tel-Aviv, Israel
| | - Sarah Dolan
- New York State Psychiatric Institute, New York, USA
| | - Yair Bar-Haim
- School of Psychological Sciences, Tel-Aviv University, Tel-Aviv, Israel
| | - Daniel G Dillon
- Department of Psychiatry, McLean Hospital/Harvard Medical School, Belmont, MA, USA
| | - Diego A Pizzagalli
- Department of Psychiatry, McLean Hospital/Harvard Medical School, Belmont, MA, USA
| | - Franklin Schneier
- Department of Psychiatry, Columbia University Irving Medical Center, New York, USA
- New York State Psychiatric Institute, New York, USA
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19
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Alahmadi AAS. The Cerebellum's Orchestra: Understanding the Functional Connectivity of Its Lobes and Deep Nuclei in Coordination and Integration of Brain Networks. Tomography 2023; 9:883-893. [PMID: 37104143 PMCID: PMC10142847 DOI: 10.3390/tomography9020072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 04/18/2023] [Accepted: 04/19/2023] [Indexed: 04/28/2023] Open
Abstract
The cerebellum, a crucial brain region, significantly contributes to various brain functions. Although it occupies a small portion of the brain, it houses nearly half of the neurons in the nervous system. Previously thought to be solely involved in motor activities, the cerebellum has since been found to play a role in cognitive, sensory, and associative functions. To further elucidate the intricate neurophysiological characteristics of the cerebellum, we investigated the functional connectivity of cerebellar lobules and deep nuclei with 8 major functional brain networks in 198 healthy subjects. Our findings revealed both similarities and differences in the functional connectivity of key cerebellar lobules and nuclei. Despite robust functional connectivity among these lobules, our results demonstrated that they exhibit heterogeneous functional integration with different functional networks. For instance, lobules 4, 5, 6, and 8 were linked to sensorimotor networks, while lobules 1, 2, and 7 were associated with higher-order, non-motor, and complex functional networks. Notably, our study uncovered a lack of functional connectivity in lobule 3, strong connections between lobules 4 and 5 with the default mode networks, and connections between lobules 6 and 8 with the salience, dorsal attention, and visual networks. Additionally, we found that cerebellar nuclei, particularly the dentate cerebellar nuclei, were connected to sensorimotor, salience, language, and default-mode networks. This study provides valuable insights into the diverse functional roles of the cerebellum in cognitive processing.
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Affiliation(s)
- Adnan A S Alahmadi
- Department of Radiologic Sciences, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
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20
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Abrol A, Fu Z, Du Y, Wilson TW, Wang Y, Stephen JM, Calhoun VD. Developmental and aging resting functional magnetic resonance imaging brain state adaptations in adolescents and adults: A large N (>47K) study. Hum Brain Mapp 2023; 44:2158-2175. [PMID: 36629328 PMCID: PMC10028673 DOI: 10.1002/hbm.26200] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 12/02/2022] [Accepted: 12/28/2022] [Indexed: 01/12/2023] Open
Abstract
The brain's functional architecture and organization undergo continual development and modification throughout adolescence. While it is well known that multiple factors govern brain maturation, the constantly evolving patterns of time-resolved functional connectivity are still unclear and understudied. We systematically evaluated over 47,000 youth and adult brains to bridge this gap, highlighting replicable time-resolved developmental and aging functional brain patterns. The largest difference between the two life stages was captured in a brain state that indicated coherent strengthening and modularization of functional coupling within the auditory, visual, and motor subdomains, supplemented by anticorrelation with other subdomains in adults. This distinctive pattern, which we replicated in independent data, was consistently less modular or absent in children and presented a negative association with age in adults, thus indicating an overall inverted U-shaped trajectory. This indicates greater synchrony, strengthening, modularization, and integration of the brain's functional connections beyond adolescence, and gradual decline of this pattern during the healthy aging process. We also found evidence that the developmental changes may also bring along a departure from the canonical static functional connectivity pattern in favor of more efficient and modularized utilization of the vast brain interconnections. State-based statistical summary measures presented robust and significant group differences that also showed significant age-related associations. The findings reported in this article support the idea of gradual developmental and aging brain state adaptation processes in different phases of life and warrant future research via lifespan studies to further authenticate the projected time-resolved brain state trajectories.
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Affiliation(s)
- Anees Abrol
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
| | - Zening Fu
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
| | - Yuhui Du
- School of Computer & Information TechnologyShanxi UniversityTaiyuanChina
| | - Tony W. Wilson
- Boys Town National Research HospitalInstitute for Human NeuroscienceBoys TownNebraskaUSA
| | - Yu‐Ping Wang
- Department of Biomedical EngineeringTulane UniversityNew OrleansLouisianaUSA
- Department of Global Biostatistics and Data ScienceTulane UniversityNew OrleansLouisianaUSA
| | | | - Vince D. Calhoun
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
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21
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Zito GA, Hartmann A, Béranger B, Weber S, Aybek S, Faouzi J, Roze E, Vidailhet M, Worbe Y. Multivariate classification provides a neural signature of Tourette disorder. Psychol Med 2023; 53:2361-2369. [PMID: 35135638 DOI: 10.1017/s0033291721004232] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
BACKGROUND Tourette disorder (TD), hallmarks of which are motor and vocal tics, has been related to functional abnormalities in large-scale brain networks. Using a fully data driven approach in a prospective, case-control study, we tested the hypothesis that functional connectivity of these networks carries a neural signature of TD. Our aim was to investigate (i) the brain networks that distinguish adult patients with TD from controls, and (ii) the effects of antipsychotic medication on these networks. METHODS Using a multivariate analysis based on support vector machine (SVM), we developed a predictive model of resting state functional connectivity in 48 patients and 51 controls, and identified brain networks that were most affected by disease and pharmacological treatments. We also performed standard univariate analyses to identify differences in specific connections across groups. RESULTS SVM was able to identify TD with 67% accuracy (p = 0.004), based on the connectivity in widespread networks involving the striatum, fronto-parietal cortical areas and the cerebellum. Medicated and unmedicated patients were discriminated with 69% accuracy (p = 0.019), based on the connectivity among striatum, insular and cerebellar networks. Univariate approaches revealed differences in functional connectivity within the striatum in patients v. controls, and between the caudate and insular cortex in medicated v. unmedicated TD. CONCLUSIONS SVM was able to identify a neuronal network that distinguishes patients with TD from control, as well as medicated and unmedicated patients with TD, holding a promise to identify imaging-based biomarkers of TD for clinical use and evaluation of the effects of treatment.
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Affiliation(s)
- Giuseppe A Zito
- Sorbonne University, Inserm U1127, CNRS UMR7225, UM75, Paris Brain Institute, Movement Investigation and Therapeutics Team, Paris, France
- Support Centre for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse, Bern CH-3010, Switzerland
| | - Andreas Hartmann
- Sorbonne University, Inserm U1127, CNRS UMR7225, UM75, Paris Brain Institute, Movement Investigation and Therapeutics Team, Paris, France
- National Reference Center for Tourette Syndrome, Assistance Publique-Hôpitaux de Paris, Groupe Hospitalier Pitié-Salpêtrière, Paris, France
| | - Benoît Béranger
- Center for NeuroImaging Research (CENIR), Paris Brain Institute, Sorbonne University, UPMC Univ Paris 06, Inserm U1127, CNRS UMR, 7225, Paris, France
| | - Samantha Weber
- Psychosomatics Unit of the Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse, Bern CH-3010, Switzerland
| | - Selma Aybek
- Psychosomatics Unit of the Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse, Bern CH-3010, Switzerland
| | - Johann Faouzi
- Sorbonne University, Inserm U1127, CNRS UMR7225, UM75, ICM, Inria Paris, Aramis project-team, Paris, France
| | - Emmanuel Roze
- Sorbonne University, Inserm U1127, CNRS UMR7225, UM75, Paris Brain Institute, Movement Investigation and Therapeutics Team, Paris, France
| | - Marie Vidailhet
- Sorbonne University, Inserm U1127, CNRS UMR7225, UM75, Paris Brain Institute, Movement Investigation and Therapeutics Team, Paris, France
| | - Yulia Worbe
- Sorbonne University, Inserm U1127, CNRS UMR7225, UM75, Paris Brain Institute, Movement Investigation and Therapeutics Team, Paris, France
- National Reference Center for Tourette Syndrome, Assistance Publique-Hôpitaux de Paris, Groupe Hospitalier Pitié-Salpêtrière, Paris, France
- Department of Neurophysiology, Saint-Antoine Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
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22
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Bianco MG, Duggento A, Nigro S, Conti A, Toschi N, Passamonti L. Heritability of human "directed" functional connectome. Brain Behav 2023; 13:e2839. [PMID: 36989125 PMCID: PMC10175995 DOI: 10.1002/brb3.2839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 10/03/2022] [Accepted: 11/15/2022] [Indexed: 03/30/2023] Open
Abstract
INTRODUCTION The functional connectivity patterns in the brain are highly heritable; however, it is unclear how genetic factors influence the directionality of such "information flows." Studying the "directionality" of the brain functional connectivity and assessing how heritability modulates it can improve our understanding of the human connectome. METHODS Here, we investigated the heritability of "directed" functional connections using a state-space formulation of Granger causality (GC), in conjunction with blind deconvolution methods accounting for local variability in the hemodynamic response function. Such GC implementation is ideal to explore the directionality of functional interactions across a large number of networks. Resting-state functional magnetic resonance imaging data were drawn from the Human Connectome Project (total n = 898 participants). To add robustness to our findings, the dataset was randomly split into a "discovery" and a "replication" sample (each with n = 449 participants). The two cohorts were carefully matched in terms of demographic variables and other confounding factors (e.g., education). The effect of shared environment was also modeled. RESULTS The parieto- and prefronto-cerebellar, parieto-prefrontal, and posterior-cingulate to hippocampus connections showed the highest and most replicable heritability effects with little influence by shared environment. In contrast, shared environmental factors significantly affected the visuo-parietal and sensory-motor directed connectivity. CONCLUSION We suggest a robust role of heritability in influencing the directed connectivity of some cortico-subcortical circuits implicated in cognition. Further studies, for example using task-based fMRI and GC, are warranted to confirm the asymmetric effects of genetic factors on the functional connectivity within cognitive networks and their role in supporting executive functions and learning.
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Affiliation(s)
- Maria Giovanna Bianco
- Neuroscience Research Center, Department of Medical and Surgical Sciences, "Magna Graecia" University of Catanzaro, Italy
| | - Andrea Duggento
- Department of Biomedicine and Prevention, University "Tor Vergata", Rome, Italy
| | - Salvatore Nigro
- Institute of Nanotechnology (NANOTEC), National Research Council, Lecce, Italy
- Center for Neurodegenerative Diseases and the Aging Brain, Department of Clinical Research in Neurology, University of Bari 'Aldo Moro, "Pia Fondazione Cardinale G. Panico", Tricase, Italy
| | - Allegra Conti
- Department of Biomedicine and Prevention, University "Tor Vergata", Rome, Italy
| | - Nicola Toschi
- Department of Biomedicine and Prevention, University "Tor Vergata", Rome, Italy
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital & Harvard Medical School, Charlestown, Boston, MA, 02129, USA
| | - Luca Passamonti
- Institute of Bioimaging and Molecular Physiology, National Research Council, Milan, Italy
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
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23
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Williams B, Hedger N, McNabb CB, Rossetti GMK, Christakou A. Inter-rater reliability of functional MRI data quality control assessments: A standardised protocol and practical guide using pyfMRIqc. Front Neurosci 2023; 17:1070413. [PMID: 36816136 PMCID: PMC9936142 DOI: 10.3389/fnins.2023.1070413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 01/11/2023] [Indexed: 02/05/2023] Open
Abstract
Quality control is a critical step in the processing and analysis of functional magnetic resonance imaging data. Its purpose is to remove problematic data that could otherwise lead to downstream errors in the analysis and reporting of results. The manual inspection of data can be a laborious and error-prone process that is susceptible to human error. The development of automated tools aims to mitigate these issues. One such tool is pyfMRIqc, which we previously developed as a user-friendly method for assessing data quality. Yet, these methods still generate output that requires subjective interpretations about whether the quality of a given dataset meets an acceptable standard for further analysis. Here we present a quality control protocol using pyfMRIqc and assess the inter-rater reliability of four independent raters using this protocol for data from the fMRI Open QC project (https://osf.io/qaesm/). Data were classified by raters as either "include," "uncertain," or "exclude." There was moderate to substantial agreement between raters for "include" and "exclude," but little to no agreement for "uncertain." In most cases only a single rater used the "uncertain" classification for a given participant's data, with the remaining raters showing agreement for "include"/"exclude" decisions in all but one case. We suggest several approaches to increase rater agreement and reduce disagreement for "uncertain" cases, aiding classification consistency.
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Affiliation(s)
- Brendan Williams
- Centre for Integrative Neuroscience and Neurodynamics, University of Reading, Reading, United Kingdom,School of Psychology and Clinical Language Sciences, University of Reading, Reading, United Kingdom,*Correspondence: Brendan Williams,
| | - Nicholas Hedger
- Centre for Integrative Neuroscience and Neurodynamics, University of Reading, Reading, United Kingdom,School of Psychology and Clinical Language Sciences, University of Reading, Reading, United Kingdom
| | - Carolyn B. McNabb
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Cardiff, United Kingdom
| | - Gabriella M. K. Rossetti
- Centre for Integrative Neuroscience and Neurodynamics, University of Reading, Reading, United Kingdom,School of Psychology and Clinical Language Sciences, University of Reading, Reading, United Kingdom
| | - Anastasia Christakou
- Centre for Integrative Neuroscience and Neurodynamics, University of Reading, Reading, United Kingdom,School of Psychology and Clinical Language Sciences, University of Reading, Reading, United Kingdom
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24
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Waugh RE, Parker JA, Hallett M, Horovitz SG. Classification of Functional Movement Disorders with Resting-State Functional Magnetic Resonance Imaging. Brain Connect 2023; 13:4-14. [PMID: 35570651 PMCID: PMC9942186 DOI: 10.1089/brain.2022.0001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Introduction: Functional movement disorder (FMD) is a type of functional neurological disorder characterized by abnormal movements that patients do not perceive as self-generated. Prior imaging studies show a complex pattern of altered activity, linking regions of the brain involved in emotional responses, motor control, and agency. This study aimed to better characterize these relationships by building a classifier using a support vector machine to accurately distinguish between 61 FMD patients and 59 healthy controls using features derived from resting-state functional magnetic resonance imaging. Materials and Methods: First, we selected 66 seed regions based on prior related studies, then we calculated the full correlation matrix between them before performing recursive feature elimination to winnow the feature set to the most predictive features and building the classifier. Results: We identified 29 features of interest that were highly predictive of the FMD condition, classifying patients and controls with 80% accuracy. Several key features included regions in the right sensorimotor cortex, left dorsolateral prefrontal cortex, left cerebellum, and left posterior insula. Conclusions: The features selected by the model highlight the importance of the interconnected relationship between areas associated with emotion, reward, and sensorimotor integration, potentially mediating communication between regions associated with motor function, attention, and executive function. Exploratory machine learning was able to identify this distinctive abnormal pattern, suggesting that alterations in functional linkages between these regions may be a consistent feature of the condition in many FMD patients. Clinical-Trials.gov ID: NCT00500994 Impact statement Our research presents novel results that further elucidate the pathophysiology of functional movement disorder (FMD) with a machine learning model that classifies FMD and healthy controls correctly 80% of the time. Herein, we demonstrate how known differences in resting-state functional magnetic resonance imaging connectivity in FMD patients can be leveraged to better understand the complex pattern of neural changes in these patients. Knowing that there are measurable predictable differences in brain activity in patients with FMD may help both clinicians and patients conceptualize and better understand the illness at the point of diagnosis and during treatment. Our methods demonstrate how an effective combination of machine learning and qualitative approaches to analyzing functional brain connectivity can enhance our understanding of abnormal patterns of brain activity in FMD patients.
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Affiliation(s)
- Rebecca E. Waugh
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland, USA
| | - Jacob A. Parker
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland, USA
| | - Mark Hallett
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland, USA
| | - Silvina G. Horovitz
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland, USA
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25
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Gao Y, Lawless RD, Li M, Zhao Y, Schilling KG, Xu L, Shafer AT, Beason-Held LL, Resnick SM, Rogers BP, Ding Z, Anderson AW, Landman BA, Gore JC. Automatic Preprocessing Pipeline for White Matter Functional Analyses of Large-Scale Databases. Proc SPIE Int Soc Opt Eng 2023; 12464:124640U. [PMID: 37600506 PMCID: PMC10437151 DOI: 10.1117/12.2653132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/22/2023]
Abstract
Recently, increasing evidence suggests that fMRI signals in white matter (WM), conventionally ignored as nuisance, are robustly detectable using appropriate processing methods and are related to neural activity, while changes in WM with aging and degeneration are also well documented. These findings suggest variations in patterns of BOLD signals in WM should be investigated. However, existing fMRI analysis tools, which were designed for processing gray matter signals, are not well suited for large-scale processing of WM signals in fMRI data. We developed an automatic pipeline for high-performance preprocessing of fMRI images with emphasis on quantifying changes in BOLD signals in WM in an aging population. At the image processing level, the pipeline integrated existing software modules with fine parameter tunings and modifications to better extract weaker WM signals. The preprocessing results primarily included whole-brain time-courses, functional connectivity, maps and tissue masks in a common space. At the job execution level, this pipeline exploited a local XNAT to store datasets and results, while using DAX tool to automatic distribute batch jobs that run on high-performance computing clusters. Through the pipeline, 5,034 fMRI/T1 scans were preprocessed. The intraclass correlation coefficient (ICC) of test-retest experiment based on the preprocessed data is 0.52 - 0.86 (N=1000), indicating a high reliability of our pipeline, comparable to previously reported ICC in gray matter experiments. This preprocessing pipeline highly facilitates our future analyses on WM functional alterations in aging and may be of benefit to a larger community interested in WM fMRI studies.
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Affiliation(s)
- Yurui Gao
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Richard D Lawless
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Muwei Li
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yu Zhao
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Kurt G Schilling
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lyuan Xu
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Andrea T Shafer
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Lori L Beason-Held
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Baxter P Rogers
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Zhaohua Ding
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Adam W Anderson
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Bennett A Landman
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
- Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - John C Gore
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
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26
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Zhang D, Fu Q, Xue C, Xiao C, Sun Y, Liu W, Hu X. Characterization of Hemodynamic Alteration in Parkinson's Disease and Effect on Resting-State Connectivity. Neuroscience 2023:S0306-4522(23)00006-4. [PMID: 36642395 DOI: 10.1016/j.neuroscience.2023.01.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 12/30/2022] [Accepted: 01/03/2023] [Indexed: 01/14/2023]
Abstract
Functional magnetic resonance imaging (fMRI) is a convolution of latent neural activity and the hemodynamic response function (HRF). According to prior studies, the neurodegenerative process in idiopathic Parkinson's Disease (PD) interacts significantly with neuromuscular abnormalities. Although these underlying neuromuscular changes might influence the temporal characteristics of HRF and fMRI signals, relatively few studies have explored this possibility. We hypothesized that such alterations would engender changes in estimated functional connectivity (FC) in fMRI space compared to latent neural space. To test these theories, we calculated voxel-level HRFs by deconvolving resting-state fMRI data from PD patients (n = 61) and healthy controls (HC) (n = 47). Significant group differences in HRF (P < 0.05, Gaussian random field-corrected) were observed in several regions previously associated with PD. Subsequently, we focused on putamen-seed-based FC differences between the PD and HC groups using fMRI and latent neural signals. The results suggested that neglecting HRF variability may cultivate false-positive and false-negative FC group differences. Furthermore, HRF was related to dopamine receptor type 2 (DRD2) gene expression (P < 0.001, t = -7.06, false discover rate-corrected). Taken together, these findings reveal HRF variation and its possible underlying molecular mechanism in PD, and suggest that deconvolution could reduce the impact of HRF variation on FC group differences.
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Affiliation(s)
- Da Zhang
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Qianyi Fu
- International Laboratory for Children's Medical Imaging Research, School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, Jiangsu, China
| | - Chen Xue
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Chaoyong Xiao
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China; Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Yu Sun
- International Laboratory for Children's Medical Imaging Research, School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, Jiangsu, China; Research Centre for University of Birmingham and Southeast University, Southeast University, Nanjing, Jiangsu, China
| | - Weiguo Liu
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.
| | - Xiao Hu
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China; Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, Jiangsu, China.
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27
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Penalba-Sánchez L, Oliveira-Silva P, Sumich AL, Cifre I. Increased functional connectivity patterns in mild Alzheimer's disease: A rsfMRI study. Front Aging Neurosci 2023; 14:1037347. [PMID: 36698861 PMCID: PMC9869068 DOI: 10.3389/fnagi.2022.1037347] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 12/08/2022] [Indexed: 01/12/2023] Open
Abstract
Background Alzheimer's disease (AD) is the most common age-related neurodegenerative disorder. In view of our rapidly aging population, there is an urgent need to identify Alzheimer's disease (AD) at an early stage. A potential way to do so is by assessing the functional connectivity (FC), i.e., the statistical dependency between two or more brain regions, through novel analysis techniques. Methods In the present study, we assessed the static and dynamic FC using different approaches. A resting state (rs)fMRI dataset from the Alzheimer's disease neuroimaging initiative (ADNI) was used (n = 128). The blood-oxygen-level-dependent (BOLD) signals from 116 regions of 4 groups of participants, i.e., healthy controls (HC; n = 35), early mild cognitive impairment (EMCI; n = 29), late mild cognitive impairment (LMCI; n = 30), and Alzheimer's disease (AD; n = 34) were extracted and analyzed. FC and dynamic FC were extracted using Pearson's correlation, sliding-windows correlation analysis (SWA), and the point process analysis (PPA). Additionally, graph theory measures to explore network segregation and integration were computed. Results Our results showed a longer characteristic path length and a decreased degree of EMCI in comparison to the other groups. Additionally, an increased FC in several regions in LMCI and AD in contrast to HC and EMCI was detected. These results suggest a maladaptive short-term mechanism to maintain cognition. Conclusion The increased pattern of FC in several regions in LMCI and AD is observable in all the analyses; however, the PPA enabled us to reduce the computational demands and offered new specific dynamic FC findings.
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Affiliation(s)
- Lucía Penalba-Sánchez
- Facultat de Psicologia, Ciències de l’educació i de l’Esport, Blanquerna, Universitat Ramon Llull, Barcelona, Spain,Human Neurobehavioral Laboratory (HNL), Research Centre for Human Development (CEDH), Faculdade de Educação e Psicologia, Universidade Católica Portuguesa, Porto, Portugal,NTU Psychology, School of Social Sciences, Nottingham Trent University, Nottingham, United Kingdom,*Correspondence: Lucía Penalba-Sánchez,
| | - Patrícia Oliveira-Silva
- Human Neurobehavioral Laboratory (HNL), Research Centre for Human Development (CEDH), Faculdade de Educação e Psicologia, Universidade Católica Portuguesa, Porto, Portugal
| | - Alexander Luke Sumich
- NTU Psychology, School of Social Sciences, Nottingham Trent University, Nottingham, United Kingdom
| | - Ignacio Cifre
- Facultat de Psicologia, Ciències de l’educació i de l’Esport, Blanquerna, Universitat Ramon Llull, Barcelona, Spain
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Banerjee A, Kamboj P, Wyckoff SN, Sussman BL, Gupta SKS, Boerwinkle VL. Automated seizure onset zone locator from resting-state functional MRI in drug-resistant epilepsy. Front Neuroimaging 2023; 1:1007668. [PMID: 37555141 PMCID: PMC10406253 DOI: 10.3389/fnimg.2022.1007668] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Accepted: 10/24/2022] [Indexed: 08/10/2023]
Abstract
OBJECTIVE Accurate localization of a seizure onset zone (SOZ) from independent components (IC) of resting-state functional magnetic resonance imaging (rs-fMRI) improves surgical outcomes in children with drug-resistant epilepsy (DRE). Automated IC sorting has limited success in identifying SOZ localizing ICs in adult normal rs-fMRI or uncategorized epilepsy. Children face unique challenges due to the developing brain and its associated surgical risks. This study proposes a novel SOZ localization algorithm (EPIK) for children with DRE. METHODS EPIK is developed in a phased approach, where fMRI noise-related biomarkers are used through high-fidelity image processing techniques to eliminate noise ICs. Then, the SOZ markers are used through a maximum likelihood-based classifier to determine SOZ localizing ICs. The performance of EPIK was evaluated on a unique pediatric DRE dataset (n = 52). A total of 24 children underwent surgical resection or ablation of an rs-fMRI identified SOZ, concurrently evaluated with an EEG and anatomical MRI. Two state-of-art techniques were used for comparison: (a) least squares support-vector machine and (b) convolutional neural networks. The performance was benchmarked against expert IC sorting and Engel outcomes for surgical SOZ resection or ablation. The analysis was stratified across age and sex. RESULTS EPIK outperformed state-of-art techniques for SOZ localizing IC identification with a mean accuracy of 84.7% (4% higher), a precision of 74.1% (22% higher), a specificity of 81.9% (3.2% higher), and a sensitivity of 88.6% (16.5% higher). EPIK showed consistent performance across age and sex with the best performance in those < 5 years of age. It helped achieve a ~5-fold reduction in the number of ICs to be potentially analyzed during pre-surgical screening. SIGNIFICANCE Automated SOZ localization from rs-fMRI, validated against surgical outcomes, indicates the potential for clinical feasibility. It eliminates the need for expert sorting, outperforms prior automated methods, and is consistent across age and sex.
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Affiliation(s)
- Ayan Banerjee
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, United States
| | - Payal Kamboj
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, United States
| | - Sarah N. Wyckoff
- Division of Neuroscience, Barrow Neurological Institute at Phoenix Children's Hospital, Phoenix, AZ, United States
| | - Bethany L. Sussman
- Division of Neuroscience, Barrow Neurological Institute at Phoenix Children's Hospital, Phoenix, AZ, United States
| | - Sandeep K. S. Gupta
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, United States
| | - Varina L. Boerwinkle
- Division of Child Neurology, University of North Carolina Department of Neurology, Chapel Hill, NC, United States
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Dobbertin M, Blair KS, Carollo E, Blair JR, Dominguez A, Bajaj S. Neuroimaging alterations of the suicidal brain and its relevance to practice: an updated review of MRI studies. Front Psychiatry 2023; 14:1083244. [PMID: 37181903 PMCID: PMC10174251 DOI: 10.3389/fpsyt.2023.1083244] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 04/04/2023] [Indexed: 05/16/2023] Open
Abstract
Suicide is a leading cause of death in the United States. Historically, scientific inquiry has focused on psychological theory. However, more recent studies have started to shed light on complex biosignatures using MRI techniques, including task-based and resting-state functional MRI, brain morphometry, and diffusion tensor imaging. Here, we review recent research across these modalities, with a focus on participants with depression and Suicidal Thoughts and Behavior (STB). A PubMed search identified 149 articles specific to our population of study, and this was further refined to rule out more diffuse pathologies such as psychotic disorders and organic brain injury and illness. This left 69 articles which are reviewed in the current study. The collated articles reviewed point to a complex impairment showing atypical functional activation in areas associated with perception of reward, social/affective stimuli, top-down control, and reward-based learning. This is broadly supported by the atypical morphometric and diffusion-weighted alterations and, most significantly, in the network-based resting-state functional connectivity data that extrapolates network functions from well validated psychological paradigms using functional MRI analysis. We see an emerging picture of cognitive dysfunction evident in task-based and resting state fMRI and network neuroscience studies, likely preceded by structural changes best demonstrated in morphometric and diffusion-weighted studies. We propose a clinically-oriented chronology of the diathesis-stress model of suicide and link other areas of research that may be useful to the practicing clinician, while helping to advance the translational study of the neurobiology of suicide.
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Affiliation(s)
- Matthew Dobbertin
- Multimodal Clinical Neuroimaging Laboratory (MCNL), Center for Neurobehavioral Research, Boys Town National Research Hospital, Boys Town, NE, United States
- Child and Adolescent Psychiatric Inpatient Center, Boys Town National Research Hospital, Boys Town, NE, United States
- *Correspondence: Matthew Dobbertin,
| | - Karina S. Blair
- Program for Trauma and Anxiety in Children (PTAC), Center for Neurobehavioral Research, Boys Town National Research Hospital, Boys Town, NE, United States
| | - Erin Carollo
- Stritch School of Medicine, Loyola University Chicago, Chicago, IL, United States
| | - James R. Blair
- Child and Adolescent Mental Health Centre, Mental Health Services, Copenhagen, Denmark
| | - Ahria Dominguez
- Multimodal Clinical Neuroimaging Laboratory (MCNL), Center for Neurobehavioral Research, Boys Town National Research Hospital, Boys Town, NE, United States
| | - Sahil Bajaj
- Multimodal Clinical Neuroimaging Laboratory (MCNL), Center for Neurobehavioral Research, Boys Town National Research Hospital, Boys Town, NE, United States
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Liddell BJ, Das P, Malhi GS, Nickerson A, Felmingham KL, Askovic M, Aroche J, Coello M, Cheung J, Den M, Outhred T, Bryant RA. Refugee visa insecurity disrupts the brain's default mode network. Eur J Psychotraumatol 2023; 14:2213595. [PMID: 37289090 PMCID: PMC10251781 DOI: 10.1080/20008066.2023.2213595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 04/17/2023] [Indexed: 06/09/2023] Open
Abstract
BACKGROUND Research has largely focused on the psychological consequences of refugee trauma exposure, but refugees living with visa insecurity face an uncertain future that also adversely affects psychological functioning and self-determination. OBJECTIVE This study aimed to examine how refugee visa insecurity affects the functional brain. METHOD We measured resting state brain activity via fMRI in 47 refugees with insecure visas (i.e. temporary visa status) and 52 refugees with secure visas (i.e. permanent visa status) residing in Australia, matched on key demographic, trauma exposure and psychopathology. Data analysis comprised independent components analysis to identify active networks and dynamic functional causal modelling tested visa security group differences in network connectivity. RESULTS We found that visa insecurity specifically affected sub-systems within the default mode network (DMN) - an intrinsic network subserving self-referential processes and mental simulations about the future. The insecure visa group showed less spectral power in the low frequency band in the anterior ventromedial DMN, and reduced activity in the posterior frontal DMN, compared to the secure visa group. Using functional dynamic causal modelling, we observed positive coupling between the anterior and posterior midline DMN hubs in the secure visa group, while the insecure visa group displayed negative coupling that correlated with self-reported fear of future deportation. CONCLUSIONS Living with visa-related uncertainty appears to undermine synchrony between anterior-posterior midline components of the DMN responsible for governing the construction of the self and making mental representations of the future. This could represent a neural signature of refugee visa insecurity, which is marked by a perception of living in limbo and a truncated sense of the future.
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Affiliation(s)
| | - Pritha Das
- Department of Psychiatry, Faculty of Medicine and Health, Northern Clinical School, The University of Sydney, Sydney, Australia
- Academic Department of Psychiatry, Royal North Shore Hospital, St Leonards, Australia
- CADE Clinic, Royal North Shore Hospital, St Leonards, Australia
| | - Gin S. Malhi
- Department of Psychiatry, Faculty of Medicine and Health, Northern Clinical School, The University of Sydney, Sydney, Australia
- Academic Department of Psychiatry, Royal North Shore Hospital, St Leonards, Australia
- CADE Clinic, Royal North Shore Hospital, St Leonards, Australia
| | | | - Kim L. Felmingham
- School of Psychological Sciences, University of Melbourne, Melbourne, Australia
| | - Mirjana Askovic
- NSW Service for the Treatment and Rehabilitation of Torture and Trauma Survivors (STARTTS), Sydney, Australia
| | - Jorge Aroche
- NSW Service for the Treatment and Rehabilitation of Torture and Trauma Survivors (STARTTS), Sydney, Australia
| | - Mariano Coello
- NSW Service for the Treatment and Rehabilitation of Torture and Trauma Survivors (STARTTS), Sydney, Australia
| | | | - Miriam Den
- School of Psychology, UNSW Sydney, Sydney, Australia
| | - Tim Outhred
- Department of Psychiatry, Faculty of Medicine and Health, Northern Clinical School, The University of Sydney, Sydney, Australia
- Academic Department of Psychiatry, Royal North Shore Hospital, St Leonards, Australia
- CADE Clinic, Royal North Shore Hospital, St Leonards, Australia
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31
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Radstake WE, Jillings S, Laureys S, Demertzi A, Sunaert S, Van Ombergen A, Wuyts FL. Neuroplasticity in F16 fighter jet pilots. Front Physiol 2023; 14:1082166. [PMID: 36875024 PMCID: PMC9974643 DOI: 10.3389/fphys.2023.1082166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 01/09/2023] [Indexed: 02/17/2023] Open
Abstract
Exposure to altered g-levels causes unusual sensorimotor demands that must be dealt with by the brain. This study aimed to investigate whether fighter pilots, who are exposed to frequent g-level transitions and high g-levels, show differential functional characteristics compared to matched controls, indicative of neuroplasticity. We acquired resting-state functional magnetic resonance imaging data to assess brain functional connectivity (FC) changes with increasing flight experience in pilots and to assess differences in FC between pilots and controls. We performed whole-brain exploratory and region-of-interest (ROI) analyses, with the right parietal operculum 2 (OP2) and the right angular gyrus (AG) as ROIs. Our results show positive correlations with flight experience in the left inferior and right middle frontal gyri, and in the right temporal pole. Negative correlations were observed in primary sensorimotor regions. We found decreased whole-brain functional connectivity of the left inferior frontal gyrus in fighter pilots compared to controls and this cluster showed decreased functional connectivity with the medial superior frontal gyrus. Functional connectivity increased between the right parietal operculum 2 and the left visual cortex, and between the right and left angular gyrus in pilots compared to controls. These findings suggest altered motor, vestibular, and multisensory processing in the brains of fighter pilots, possibly reflecting coping strategies to altered sensorimotor demands during flight. Altered functional connectivity in frontal areas may reflect adaptive cognitive strategies to cope with challenging conditions during flight. These findings provide novel insights into brain functional characteristics of fighter pilots, which may be of interest to humans traveling to space.
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Affiliation(s)
| | - Steven Jillings
- Laboratory for Equilibrium Investigations and Aerospace, University of Antwerp, Antwerp, Belgium
| | - Steven Laureys
- Coma Science Group, GIGA Consciousness, GIGA Institute, University and University Hospital of Liège, Liège, Belgium
| | - Athena Demertzi
- Physiology of Cognition Lab, GIGA-CRC In Vivo Imaging, University of Liège, Liège, Belgium.,Psychology & Neuroscience of Cognition, University of Liège, Liège, Belgium
| | - Stefan Sunaert
- Translational MRI, Department of Imaging and Pathology, KU Leuven and University Hospital of Leuven, Leuven, Belgium
| | - Angelique Van Ombergen
- Laboratory for Equilibrium Investigations and Aerospace, University of Antwerp, Antwerp, Belgium.,Department of Translational Neurosciences-ENT, University of Antwerp, Antwerp, Belgium
| | - Floris L Wuyts
- Laboratory for Equilibrium Investigations and Aerospace, University of Antwerp, Antwerp, Belgium
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Dong M, Zhang P, Chai W, Zhang X, Chen BT, Wang H, Wu J, Chen C, Niu Y, Liang J, Shi G, Jin C. Early stage of radiological expertise modulates resting-state local coherence in the inferior temporal lobe. Psychoradiology 2022; 2:199-206. [PMID: 38665273 PMCID: PMC10917200 DOI: 10.1093/psyrad/kkac024] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Revised: 12/06/2022] [Accepted: 12/08/2022] [Indexed: 04/28/2024]
Abstract
Background The visual system and its inherent functions undergo experience-dependent changes through the lifespan, enabling acquisition of new skills. Previous fMRI studies using tasks reported increased specialization in a number of cortical regions subserving visual expertise. Although ample studies focused on representation of long-term visual expertise in the brain, i.e. in terms of year, monthly-based early-stage representation of visual expertise remains unstudied. Given that spontaneous neuronal oscillations actively encode previous experience, we propose brain representations in the resting state is fundamentally important. Objective The current study aimed to investigate how monthly-based early-stage visual expertise are represented in the resting state using the expertise model of radiologists. Methods In particular, we investigated the altered local clustering pattern of spontaneous brain activity using regional homogeneity (ReHo). A cohort group of radiology interns (n = 22) after one-month training in X-ray department and matched laypersons (n = 22) were recruited after rigorous behavioral assessment. Results The results showed higher ReHo in the right hippocampus (HIP) and the right ventral anterior temporal lobe (vATL) (corrected by Alphasim correction, P < 0.05). Moreover, ReHo in the right HIP correlated with the number of cases reviewed during intern radiologists' training (corrected by Alphasim correction, P < 0.05). Conclusions In sum, our results demonstrated that the early stage of visual expertise is more concerned with stabilizing visual feature and domain-specific knowledge into long-term memory. The results provided novel evidence regarding how early-stage visual expertise is represented in the resting brain, which help further elaborate how human visual expertise is acquired. We propose that our current study may provide novel ideas for developing new training protocols in medical schools.
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Affiliation(s)
- Minghao Dong
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi’an City, Shaanxi 710071, China
- Xian Key Laboratory of Intelligent Sensing and Regulation of tran-Scale Life Information, Xi’an City, Shaanxi 710071, China
- Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, Xi’an City, Shaanxi 710071, China
| | - Peiming Zhang
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi’an City, Shaanxi 710071, China
| | - Weilu Chai
- Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, Xi’an City, Shaanxi 710071, China
| | - Xiaoyan Zhang
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi’an City, Shaanxi 710071, China
| | - Bihong T Chen
- City of Hope Medical Center, Duarte City, California 91010, USA
| | - Hongmei Wang
- Department of Medical Imaging, First Affiliated Hospital of Medical College, Xi’an Jiaotong University, Xi’an City, Shaanxi 710000, China
| | - Jia Wu
- School of Foreign Languages, Northwestern Polytechnical University, Xi'an City, Shaanxi 710071, China
| | - Chao Chen
- PLA Funding Payment Center, Beijing 100000, China
| | - Yi Niu
- Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, Xi’an City, Shaanxi 710071, China
| | - Jimin Liang
- School of Electronics and Engineering, Xidian University, Xi'an City, Shaanxi 710071, China
| | - Guangming Shi
- Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, Xi’an City, Shaanxi 710071, China
| | - Chenwang Jin
- Department of Medical Imaging, First Affiliated Hospital of Medical College, Xi’an Jiaotong University, Xi’an City, Shaanxi 710000, China
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Yao S, Kendrick KM. Reduced homotopic interhemispheric connectivity in psychiatric disorders: evidence for both transdiagnostic and disorder specific features. Psychoradiology 2022; 2:129-145. [PMID: 38665271 PMCID: PMC11003433 DOI: 10.1093/psyrad/kkac016] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 10/24/2022] [Accepted: 10/28/2022] [Indexed: 04/28/2024]
Abstract
There is considerable interest in the significance of structural and functional connections between the two brain hemispheres in terms of both normal function and in relation to psychiatric disorders. In recent years, many studies have used voxel mirrored homotopic connectivity analysis of resting state data to investigate the importance of connectivity between homotopic regions in the brain hemispheres in a range of neuropsychiatric disorders. The current review summarizes findings from these voxel mirrored homotopic connectivity studies in individuals with autism spectrum disorder, addiction, attention deficit hyperactivity disorder, anxiety and depression disorders, and schizophrenia, as well as disorders such as Alzheimer's disease, mild cognitive impairment, epilepsy, and insomnia. Overall, other than attention deficit hyperactivity disorder, studies across psychiatric disorders report decreased homotopic resting state functional connectivity in the default mode, attention, salience, sensorimotor, social cognition, visual recognition, primary visual processing, and reward networks, which are often associated with symptom severity and/or illness onset/duration. Decreased homotopic resting state functional connectivity may therefore represent a transdiagnostic marker for general psychopathology. In terms of disorder specificity, the extensive decreases in homotopic resting state functional connectivity in autism differ markedly from attention deficit hyperactivity disorder, despite both occurring during early childhood and showing extensive co-morbidity. A pattern of more posterior than anterior regions showing reductions in schizophrenia is also distinctive. Going forward, more studies are needed to elucidate the functions of these homotopic functional connections in both health and disorder and focusing on associations with general psychopathology, and not only on disorder specific symptoms.
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Affiliation(s)
- Shuxia Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Keith M Kendrick
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China
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Chaudhary S, Zhornitsky S, Roy A, Summers C, Ahles T, Li CR, Chao HH. The effects of androgen deprivation on working memory and quality of life in prostate cancer patients: The roles of hypothalamic connectivity. Cancer Med 2022; 11:3425-3436. [PMID: 35315585 PMCID: PMC9487881 DOI: 10.1002/cam4.4704] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 03/08/2022] [Accepted: 03/11/2022] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND Androgen deprivation therapy (ADT) has been associated with adverse effects on the brain. ADT alters testosterone levels via its action on the hypothalamus-pituitary-gonadal axis and may influence hypothalamic functions. Given the wide regional connectivity of the hypothalamus and its role in regulating cognition and behavior, we assessed the effects of ADT on hypothalamic resting state functional connectivity (rsFC) and their cognitive and clinical correlates. METHODS In a prospective observational study, 22 men with nonmetastatic prostate cancer receiving ADT and 28 patients not receiving ADT (controls), matched in age, years of education, and Montreal Cognitive Assessment score, participated in N-back task and quality of life (QoL) assessments and brain imaging at baseline and at 6 months. Imaging data were processed with published routines and the results of a group by time flexible factorial analysis were evaluated at a corrected threshold. RESULTS ADT and control groups did not differ in N-back performance or QoL across time points. Relative to controls, patients receiving ADT showed significantly higher hypothalamus-right mid-cingulate cortex (MCC) and precentral gyrus (PCG) rsFC during follow-up versus baseline. Further, the changes in MCC and PCG rsFC were correlated positively with the change in QoL score and 0-back correct response rate, respectively, in patients with undergoing ADT. CONCLUSION Six-month ADT affects hypothalamic functional connectivity with brain regions critical to cognitive motor and affective functions. Elevated hypothalamic MCC and PCG connectivity likely serve to functionally compensate for the effects of ADT and sustain attention and overall QoL. The longer-term effects of ADT remain to be investigated.
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Affiliation(s)
- Shefali Chaudhary
- Department of PsychiatryYale University School of MedicineNew HavenConnecticutUSA
| | - Simon Zhornitsky
- Department of PsychiatryYale University School of MedicineNew HavenConnecticutUSA
| | - Alicia Roy
- VA Connecticut Healthcare SystemWest HavenConnecticutUSA
| | | | - Tim Ahles
- Department of Psychiatry and Behavioral SciencesMemorial Sloan Kettering Cancer CenterNew YorkNew YorkUSA
| | - Chiang‐Shan R. Li
- Departments of Psychiatry and Neuroscience, Interdepartmental Neuroscience ProgramYale University School of Medicine, Wu Tsai Institute, Yale UniversityNew HavenConnecticutUSA
| | - Herta H. Chao
- VA Connecticut Healthcare SystemWest HavenConnecticutUSA
- Department of Medicine & Yale Comprehensive Cancer CenterYale University School of MedicineNew HavenCTUSA
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Su J, Zhang X, Zhang Z, Wang H, Wu J, Shi G, Jin C, Dong M. Real-World Visual Experience Alters Baseline Brain Activity in the Resting State: A Longitudinal Study Using Expertise Model of Radiologists. Front Neurosci 2022; 16:904623. [PMID: 35712457 PMCID: PMC9195622 DOI: 10.3389/fnins.2022.904623] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 04/25/2022] [Indexed: 12/20/2022] Open
Abstract
Visual experience modulates the intensity of evoked brain activity in response to training-related stimuli. Spontaneous fluctuations in the restful brain actively encode previous learning experience. However, few studies have considered how real-world visual experience alters the level of baseline brain activity in the resting state. This study aimed to investigate how short-term real-world visual experience modulates baseline neuronal activity in the resting state using the amplitude of low-frequency (<0.08 Hz) fluctuation (ALFF) and a visual expertise model of radiologists, who possess fine-level visual discrimination skill of homogeneous stimuli. In detail, a group of intern radiologists (n = 32) were recruited. The resting-state fMRI data and the behavioral data regarding their level of visual expertise in radiology and face recognition were collected before and after 1 month of training in the X-ray department in a local hospital. A machine learning analytical method, i.e., support vector machine, was used to identify subtle changes in the level of baseline brain activity. Our method led to a superb classification accuracy of 86.7% between conditions. The brain regions with highest discriminative power were the bilateral cingulate gyrus, the left superior frontal gyrus, the bilateral precentral gyrus, the bilateral superior parietal lobule, and the bilateral precuneus. To the best of our knowledge, this study is the first to investigate baseline neurodynamic alterations in response to real-world visual experience using longitudinal experimental design. These results suggest that real-world visual experience alters the resting-state brain representation in multidimensional neurobehavioral components, which are closely interrelated with high-order cognitive and low-order visual factors, i.e., attention control, working memory, memory, and visual processing. We propose that our findings are likely to help foster new insights into the neural mechanisms of visual expertise.
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Affiliation(s)
- Jiaxi Su
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Sciences and Technology, Xidian University, Xi'an, China.,Xi'an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information, School of Life Sciences and Technology, Xidian University, Xi'an, China
| | - Xiaoyan Zhang
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Sciences and Technology, Xidian University, Xi'an, China.,Xi'an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information, School of Life Sciences and Technology, Xidian University, Xi'an, China
| | - Ziyuan Zhang
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Sciences and Technology, Xidian University, Xi'an, China.,Xi'an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information, School of Life Sciences and Technology, Xidian University, Xi'an, China
| | - Hongmei Wang
- Department of Medical Imaging, First Affiliated Hospital of Medical College, Xi'an Jiaotong University, Xi'an, China
| | - Jia Wu
- School of Foreign Languages, Northwestern Polytechnical University, Xi'an, China
| | - Guangming Shi
- Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, Xi'an, China
| | - Chenwang Jin
- Department of Medical Imaging, First Affiliated Hospital of Medical College, Xi'an Jiaotong University, Xi'an, China
| | - Minghao Dong
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Sciences and Technology, Xidian University, Xi'an, China.,Xi'an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information, School of Life Sciences and Technology, Xidian University, Xi'an, China.,Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, Xi'an, China
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Schneider SC, Archila-Meléndez ME, Göttler J, Kaczmarz S, Zott B, Priller J, Kallmayer M, Zimmer C, Sorg C, Preibisch C. Resting-state BOLD functional connectivity depends on the heterogeneity of capillary transit times in the human brain A combined lesion and simulation study about the influence of blood flow response timing. Neuroimage 2022; 255:119208. [PMID: 35427773 DOI: 10.1016/j.neuroimage.2022.119208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 02/23/2022] [Accepted: 04/11/2022] [Indexed: 11/25/2022] Open
Abstract
Functional connectivity (FC) derived from blood oxygenation level dependent (BOLD) functional magnetic resonance imaging at rest (rs-fMRI), is commonly interpreted as indicator of neuronal connectivity. In a number of brain disorders, however, metabolic, vascular, and hemodynamic impairments can be expected to alter BOLD-FC independently from neuronal activity. By means of a neurovascular coupling (NVC) model of BOLD-FC, we recently demonstrated that aberrant timing of cerebral blood flow (CBF) responses may influence BOLD-FC. In the current work, we support and extend this finding by empirically linking BOLD-FC with capillary transit time heterogeneity (CTH), which we consider as an indicator of delayed and broadened CBF responses. We assessed 28 asymptomatic patients with unilateral high-grade internal carotid artery stenosis (ICAS) as a hemodynamic lesion model with largely preserved neurocognitive functioning and 27 age-matched healthy controls. For each participant, we obtained rs-fMRI, arterial spin labeling, and dynamic susceptibility contrast MRI to study the dependence of left-right homotopic BOLD-FC on local perfusion parameters. Additionally, we investigated the dependency of BOLD-FC on CBF response timing by detailed simulations. Homotopic BOLD-FC was negatively associated with increasing CTH differences between homotopic brain areas. This relation was more pronounced in asymptomatic ICAS patients even after controlling for baseline CBF and relative cerebral blood volume influences. These findings match simulation results that predict an influence of delayed and broadened CBF responses on BOLD-FC. Results demonstrate that increasing CTH differences between homotopic brain areas lead to BOLD-FC reductions. Simulations suggest that CTH increases correspond to broadened and delayed CBF responses to fluctuations in ongoing neuronal activity.
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Affiliation(s)
- Sebastian C Schneider
- Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Department of Diagnostic and Interventional Neuroradiology, Ismaningerstr. 22, 81675, Munich, Germany; Technical University of Munich, School of Medicine, Klinikum rechts der Isar, TUM Neuroimaging Center, Ismaningerstr. 22, 81675, Munich, Germany
| | - Mario E Archila-Meléndez
- Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Department of Diagnostic and Interventional Neuroradiology, Ismaningerstr. 22, 81675, Munich, Germany; Technical University of Munich, School of Medicine, Klinikum rechts der Isar, TUM Neuroimaging Center, Ismaningerstr. 22, 81675, Munich, Germany
| | - Jens Göttler
- Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Department of Diagnostic and Interventional Neuroradiology, Ismaningerstr. 22, 81675, Munich, Germany; Technical University of Munich, School of Medicine, Klinikum rechts der Isar, TUM Neuroimaging Center, Ismaningerstr. 22, 81675, Munich, Germany
| | - Stephan Kaczmarz
- Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Department of Diagnostic and Interventional Neuroradiology, Ismaningerstr. 22, 81675, Munich, Germany; Technical University of Munich, School of Medicine, Klinikum rechts der Isar, TUM Neuroimaging Center, Ismaningerstr. 22, 81675, Munich, Germany; Philips GmbH Market DACH, Hamburg, Germany
| | - Benedikt Zott
- Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Department of Diagnostic and Interventional Neuroradiology, Ismaningerstr. 22, 81675, Munich, Germany; Technical University of Munich, School of Medicine, Klinikum rechts der Isar, TUM Neuroimaging Center, Ismaningerstr. 22, 81675, Munich, Germany
| | - Josef Priller
- Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Department of Psychiatry, Ismaningerstr. 22, 81675, Munich, Munich, Germany
| | - Michael Kallmayer
- Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Department of Vascular and Endovascular Surgery, Ismaningerstr. 22, 81675, Munich, Munich, Germany
| | - Claus Zimmer
- Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Department of Diagnostic and Interventional Neuroradiology, Ismaningerstr. 22, 81675, Munich, Germany
| | - Christian Sorg
- Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Department of Diagnostic and Interventional Neuroradiology, Ismaningerstr. 22, 81675, Munich, Germany; Technical University of Munich, School of Medicine, Klinikum rechts der Isar, TUM Neuroimaging Center, Ismaningerstr. 22, 81675, Munich, Germany
| | - Christine Preibisch
- Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Department of Diagnostic and Interventional Neuroradiology, Ismaningerstr. 22, 81675, Munich, Germany; Technical University of Munich, School of Medicine, Klinikum rechts der Isar, TUM Neuroimaging Center, Ismaningerstr. 22, 81675, Munich, Germany; Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Department of Neurology, Ismaningerstr. 22, 81675, Munich, Munich, Germany.
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Shin W, Koenig KA, Lowe M. A comprehensive investigation of physiologic noise modeling in resting state fMRI; time shifted cardiac noise in EPI and its removal without external physiologic signal measures. Neuroimage 2022;:119136. [PMID: 35346840 DOI: 10.1016/j.neuroimage.2022.119136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 02/18/2022] [Accepted: 03/22/2022] [Indexed: 11/23/2022] Open
Abstract
Hemodynamic cardiac and respiratory-cycle fluctuations are a source of unwanted non-neuronal signal components, often called physiologic noise, in resting state (rs-) fMRI studies. Here, we use image-based retrospective correction of physiological motion (RETROICOR) with externally measured physiologic signals to investigate cardiac and respiratory hemodynamic phase functions reflected in rs-fMRI data. We find that the cardiac phase function is time shifted locally, while the respiratory phase function is described as single, fixed phase form across the brain. In light of these findings, we propose an update to Physiologic EStimation by Temporal ICA (PESTICA), our publically available software package that estimates physiologic signals when external physiologic measures are not available. This update incorporates: 1) auto-selection of slicewise physiologic regressors and generation of physiologic fixed phase regressors with total slices/TR sampling rate, 2) Fourier series expansion of the cardiac fixed phase regressor to account for time delayed cardiac noise 3) removal of cardiac and respiratory noise in imaging data. We compare the efficacy of the updated method to RETROICOR.
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Hirjak D, Schmitgen MM, Werler F, Wittemann M, Kubera KM, Wolf ND, Sambataro F, Calhoun VD, Reith W, Wolf RC. Multimodal MRI data fusion reveals distinct structural, functional and neurochemical correlates of heavy cannabis use. Addict Biol 2022; 27:e13113. [PMID: 34808703 DOI: 10.1111/adb.13113] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 09/24/2021] [Accepted: 10/29/2021] [Indexed: 12/19/2022]
Abstract
Heavy cannabis use (HCU) is frequently associated with a plethora of cognitive, psychopathological and sensorimotor phenomena. Although HCU is frequent, specific patterns of abnormal brain structure and function underlying HCU in individuals presenting without cannabis-use disorder or other current and life-time major mental disorders are unclear at present. This multimodal magnetic resonance imaging (MRI) study examined resting-state functional MRI (rs-fMRI) and structural MRI (sMRI) data from 24 persons with HCU and 16 controls. Parallel independent component analysis (p-ICA) was used to examine covarying components among grey matter volume (GMV) maps computed from sMRI and intrinsic neural activity (INA), as derived from amplitude of low-frequency fluctuations (ALFF) maps computed from rs-fMRI data. Further, we used JuSpace toolbox for cross-modal correlations between MRI-based modalities with nuclear imaging derived estimates, to examine specific neurotransmitter system changes underlying HCU. We identified two transmodal components, which significantly differed between the HCU and controls (GMV: p = 0.01, ALFF p = 0.03, respectively). The GMV component comprised predominantly cerebello-temporo-thalamic regions, whereas the INA component included fronto-parietal regions. Across HCU, loading parameters of both components were significantly associated with distinct HCU behavior. Finally, significant associations between GMV and the serotonergic system as well as between INA and the serotonergic, dopaminergic and μ-opioid receptor system were detected. This study provides novel multimodal neuromechanistic insights into HCU suggesting co-altered structure/function-interactions in neural systems subserving cognitive and sensorimotor functions.
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Affiliation(s)
- Dusan Hirjak
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim Heidelberg University Mannheim Germany
| | - Mike M. Schmitgen
- Department of General Psychiatry at the Center for Psychosocial Medicine Heidelberg University Mannheim Germany
| | - Florian Werler
- Department of General Psychiatry at the Center for Psychosocial Medicine Heidelberg University Mannheim Germany
| | - Miriam Wittemann
- Department of Psychiatry and Psychotherapy Saarland University Saarbrücken Germany
| | - Katharina M. Kubera
- Department of General Psychiatry at the Center for Psychosocial Medicine Heidelberg University Mannheim Germany
| | - Nadine D. Wolf
- Department of General Psychiatry at the Center for Psychosocial Medicine Heidelberg University Mannheim Germany
| | - Fabio Sambataro
- Department of Neurosciences, Padua Neuroscience Center University of Padua Padua Italy
| | - Vince D. Calhoun
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology Emory University Atlanta Georgia USA
| | - Wolfgang Reith
- Department of Neuroradiology Saarland University Saarbrücken Germany
| | - Robert Christian Wolf
- Department of General Psychiatry at the Center for Psychosocial Medicine Heidelberg University Mannheim Germany
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Thome J, Steinbach R, Grosskreutz J, Durstewitz D, Koppe G. Classification of amyotrophic lateral sclerosis by brain volume, connectivity, and network dynamics. Hum Brain Mapp 2022; 43:681-699. [PMID: 34655259 PMCID: PMC8720197 DOI: 10.1002/hbm.25679] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 09/27/2021] [Indexed: 12/19/2022] Open
Abstract
Emerging studies corroborate the importance of neuroimaging biomarkers and machine learning to improve diagnostic classification of amyotrophic lateral sclerosis (ALS). While most studies focus on structural data, recent studies assessing functional connectivity between brain regions by linear methods highlight the role of brain function. These studies have yet to be combined with brain structure and nonlinear functional features. We investigate the role of linear and nonlinear functional brain features, and the benefit of combining brain structure and function for ALS classification. ALS patients (N = 97) and healthy controls (N = 59) underwent structural and functional resting state magnetic resonance imaging. Based on key hubs of resting state networks, we defined three feature sets comprising brain volume, resting state functional connectivity (rsFC), as well as (nonlinear) resting state dynamics assessed via recurrent neural networks. Unimodal and multimodal random forest classifiers were built to classify ALS. Out-of-sample prediction errors were assessed via five-fold cross-validation. Unimodal classifiers achieved a classification accuracy of 56.35-61.66%. Multimodal classifiers outperformed unimodal classifiers achieving accuracies of 62.85-66.82%. Evaluating the ranking of individual features' importance scores across all classifiers revealed that rsFC features were most dominant in classification. While univariate analyses revealed reduced rsFC in ALS patients, functional features more generally indicated deficits in information integration across resting state brain networks in ALS. The present work undermines that combining brain structure and function provides an additional benefit to diagnostic classification, as indicated by multimodal classifiers, while emphasizing the importance of capturing both linear and nonlinear functional brain properties to identify discriminative biomarkers of ALS.
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Affiliation(s)
- Janine Thome
- Department of Theoretical Neuroscience, Central Institute of Mental Health Mannheim, Medical Faculty MannheimHeidelberg UniversityGermany
- Clinic for Psychiatry and Psychotherapy, Central Institute of Mental Health Mannheim, Medical Faculty MannheimHeidelberg UniversityGermany
| | - Robert Steinbach
- Hans Berger Department of NeurologyJena University HospitalJenaGermany
| | - Julian Grosskreutz
- Precision Neurology, Department of NeurologyUniversity of LuebeckLuebeckGermany
| | - Daniel Durstewitz
- Department of Theoretical Neuroscience, Central Institute of Mental Health Mannheim, Medical Faculty MannheimHeidelberg UniversityGermany
| | - Georgia Koppe
- Department of Theoretical Neuroscience, Central Institute of Mental Health Mannheim, Medical Faculty MannheimHeidelberg UniversityGermany
- Clinic for Psychiatry and Psychotherapy, Central Institute of Mental Health Mannheim, Medical Faculty MannheimHeidelberg UniversityGermany
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Smirnov AS, Melnikova-Pitskhelauri TV, Sharaev MG, Yarkin VE, Turkin AM, Afandiev RM, Khasieva LM, Bernshtein AV, Pitskhelauri DI, Pronin IN. [Comparison of resting state and task-based functional MRI in preoperative mapping in patients with brain gliomas]. Zh Vopr Neirokhir Im N N Burdenko 2022; 86:33-40. [PMID: 35942835 DOI: 10.17116/neiro20228604133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
OBJECTIVE To analyze and compare the results of cerebral cortex mapping with task-based (tb-fMRI) and resting-state functional MRI in patients with glioma of eloquent cortical areas. MATERIAL AND METHODS There were 55 patients (24 men and 31 women aged 24 - 74 years, median 39) with glial tumors. In 26 patients, the tumor was located in motor areas. Twenty-nine patients had lesions of Broca and Wernicke's areas. All patients underwent preoperative tb-fMRI and rs-fMRI. Then, resection of tumor was carried out in all cases. RESULTS Comparison of fMRI and rs-fMRI activation maps was assessed by calculating the Dice coefficient for inclusive speech and motor cortex masks and exclusive masks without brainstem, cerebellum, subcortical nuclei. Inclusive Dice coefficient for motor cortex ranged from 0.11 to 0.50, for speech cortex - from 0.006 to 0.240 (p<0.05). In case of exclusive masks, this value ranged from 0.15 to 0.55 for motor cortex and from 0.004 to 0.205 for speech cortex (p<0.05). CONCLUSION When comparing the results of cortical mapping in patients with glial tumors, the use of hemispheric exclusive and inclusive masks did not significantly increase activation maps matching. Probably, low degree of correspondence was associated with different genesis of activations, as well as with high variability of speech cortex.
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Affiliation(s)
- A S Smirnov
- Burdenko Neurosurgery Center, Moscow, Russia
| | | | - M G Sharaev
- Skolkovo Institute of Science and Technology, Moscow, Russia
| | - V E Yarkin
- Skolkovo Institute of Science and Technology, Moscow, Russia
| | - A M Turkin
- Burdenko Neurosurgery Center, Moscow, Russia
| | | | - L M Khasieva
- Sechenov First Moscow State Medical University, Moscow, Russia
| | - A V Bernshtein
- Skolkovo Institute of Science and Technology, Moscow, Russia
| | | | - I N Pronin
- Burdenko Neurosurgery Center, Moscow, Russia
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Guo L, Zhang Y, Liu Q, Guo K, Wang Z. Multi-band network fusion for Alzheimer's disease identification with functional MRI. Front Psychiatry 2022; 13:1070198. [PMID: 36590604 PMCID: PMC9798220 DOI: 10.3389/fpsyt.2022.1070198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 11/28/2022] [Indexed: 12/23/2022] Open
Abstract
INTRODUCTION The analysis of functional brain networks (FBNs) has become a promising and powerful tool for auxiliary diagnosis of brain diseases, such as Alzheimer's disease (AD) and its prodromal stage. Previous studies usually estimate FBNs using full band Blood Oxygen Level Dependent (BOLD) signal. However, a single band is not sufficient to capture the diagnostic and prognostic information contained in multiple frequency bands. METHOD To address this issue, we propose a novel multi-band network fusion framework (MBNF) to combine the various information (e.g., the diversification of structural features) of multi-band FBNs. We first decompose the BOLD signal adaptively into two frequency bands named high-frequency band and low-frequency band by the ensemble empirical mode decomposition (EEMD). Then the similarity network fusion (SNF) is performed to blend two networks constructed by two frequency bands together into a multi-band fusion network. In addition, we extract the features of the fused network towards a better classification performance. RESULT To verify the validity of the scheme, we conduct our MBNF method on the public ADNI database for identifying subjects with AD/MCI from normal controls. DISCUSSION Experimental results demonstrate that the proposed scheme extracts rich multi-band network features and biomarker information, and also achieves better classification accuracy.
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Affiliation(s)
- Lingyun Guo
- School of Computer Science and Technology, Hainan University, Haikou, China
| | - Yangyang Zhang
- School of Computer Science and Technology, Hainan University, Haikou, China
| | - Qinghua Liu
- School of Computer Science and Technology, Hainan University, Haikou, China
| | - Kaiyu Guo
- School of Computer Science and Technology, Hainan University, Haikou, China
| | - Zhengxia Wang
- School of Computer Science and Technology, Hainan University, Haikou, China
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Pronin IN, Sharaev MG, Melnikova-Pitskhelauri TV, Smirnov AS, Bernshtein AV, Yarkin VE, Zhukov VY, Buklina SB, Pogosbekyan EL, Afandiev RM, Turkin AM, Ogurtsova AA, Kulikov AS, Pitskhelauri DI. [Machine learning for resting state fMRI-based preoperative mapping: comparison with task-based fMRI and direct cortical stimulation]. Zh Vopr Neirokhir Im N N Burdenko 2022; 86:25-32. [PMID: 35942834 DOI: 10.17116/neiro20228604125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
OBJECTIVE To develop a system for preoperative prediction of individual activations of motor and speech areas in patients with brain gliomas using resting state fMRI (rsfMRI), task-based fMRI (tb-fMRI), direct cortical stimulation and machine learning methods. MATERIAL AND METHODS Thirty-three patients with gliomas (19 females and 14 males aged 19 - 540) underwent DCS-assisted resection of tumor (19 ones with lesion of motor zones and 14 patients with lesions of speech areas). Awake craniotomy was performed in 14 cases. Preoperative mapping was performed according to special MRI protocol (T1, tb-fMRI, rs-fMRI). UNLABELLED Machine learning system was built on open source data from The Human Connectome Project. MR data of 200 healthy subjects from this database were used for system pre-training. Further, this system was trained on the data of our patients with gliomas. RESULTS In DCS, we obtained 332 stimulations including 173 with positive response. According to comparison of functional activations between rs-fMRI and tb-fMRI, there were more positive DCS responses predicted by rs-fMRI (132 vs 112). Non-response stimulation sites (negative) prevailed in tb-fMRI activations (69 vs 44). CONCLUSION The developed method with machine learning based on resting state fMRI showed greater sensitivity compared to classical task-based fMRI after verification with DCS: 0.72 versus 0.66 (p<0.05) for identifying the speech zones and 0.79 versus 0.62 (p<0.05) for motor areas.
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Affiliation(s)
- I N Pronin
- Burdenko Neurosurgical Center, Moscow, Russia
| | - M G Sharaev
- Skolkovo Institute of Science and Technology, Moscow, Russia
| | | | - A S Smirnov
- Burdenko Neurosurgical Center, Moscow, Russia
| | - A V Bernshtein
- Skolkovo Institute of Science and Technology, Moscow, Russia
| | - V E Yarkin
- Skolkovo Institute of Science and Technology, Moscow, Russia
| | - V Yu Zhukov
- Burdenko Neurosurgical Center, Moscow, Russia
| | - S B Buklina
- Burdenko Neurosurgical Center, Moscow, Russia
| | | | | | - A M Turkin
- Burdenko Neurosurgical Center, Moscow, Russia
| | | | - A S Kulikov
- Burdenko Neurosurgical Center, Moscow, Russia
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Dalle Molle R, de Mendonça Filho EJ, Minuzzi L, Machado TD, Reis RS, Rodrigues DM, Mucellini AB, Franco AR, Buchweitz A, Toazza R, Bortoluzzi A, Salum GA, Boscenco S, Meaney MJ, Levitan RD, Manfro GG, Silveira PP. Thrifty-Eating Behavior Phenotype at the Food Court - Programming Goes Beyond Food Preferences. Front Endocrinol (Lausanne) 2022; 13:882532. [PMID: 35677721 PMCID: PMC9168906 DOI: 10.3389/fendo.2022.882532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 04/14/2022] [Indexed: 11/18/2022] Open
Abstract
INTRODUCTION Prenatal growth impairment leads to higher preference for palatable foods in comparison to normal prenatal growth subjects, which can contribute to increased body fat mass and a higher risk for developing chronic diseases in small-for-gestational-age (SGA) individuals throughout life. This study aimed to investigate the effect of SGA on feeding behavior in children and adolescents, as well as resting-state connectivity between areas related to reward, self-control, and value determination, such as orbitofrontal cortex (OFC), dorsolateral prefrontal cortex (DL-PFC), amygdala and dorsal striatum (DS). METHODS Caregivers and their offspring were recruited from two independent cohorts in Brazil (PROTAIA) and Canada (MAVAN). Both cohorts included anthropometric measurements, food choice tasks, and resting-state functional magnetic resonance imaging (fMRI) data. RESULTS In the Brazilian sample (17 ± 0.28 years, n=70), 21.4% of adolescents were classified as SGA. They exhibited lower monetary-related expenditure to buy a snack compared to controls in the food choice test. Decreased functional connectivity (n=40) between left OFC and left DL-PFC; and between right OFC and: left amygdala, right DS, and left DS were observed in the Brazilian SGA participants. Canadian SGA participants (14.9%) had non-significant differences in comparison with controls in a food choice task at 4 years old ( ± 0.01, n=315). At a follow-up brain scan visit (10.21 ± 0.140 years, n=49), SGA participants (28.6%) exhibited higher connectivity between the left OFC and left DL-PFC, also higher connectivity between the left OFC and right DL-PFC. We did not observe significant anthropometric neither nutrients' intake differences between groups in both samples. CONCLUSIONS Resting-state fMRI results showed that SGA individuals had altered connectivity between areas involved in encoding the subjective value for available goods and decision-making in both samples, which can pose them in disadvantage when facing food options daily. Over the years, the cumulative exposure to particular food cues together with the altered behavior towards food, such as food purchasing, as seen in the adolescent cohort, can play a role in the long-term risk for developing chronic non-communicable diseases.
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Affiliation(s)
- Roberta Dalle Molle
- Programa de Pós-Graduação em Ciências da Nutrição, Universidade Federal de Ciências da Saúde de Porto Alegre, Porto Alegre, Brazil
- Ludmer Centre for Neuroinformatics and Mental Health, Douglas Hospital Research Center, Montreal, QC, Canada
- Programa de Pós-Graduação em Saúde da Criança e do Adolescente, Faculdade de Medicina, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Euclides José de Mendonça Filho
- Ludmer Centre for Neuroinformatics and Mental Health, Douglas Hospital Research Center, Montreal, QC, Canada
- Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - Luciano Minuzzi
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada
| | - Tania Diniz Machado
- Programa de Pós-Graduação em Saúde da Criança e do Adolescente, Faculdade de Medicina, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Roberta Sena Reis
- Programa de Pós-Graduação em Saúde da Criança e do Adolescente, Faculdade de Medicina, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
- Faculdade de Nutrição, Universidade Federal de Goiás, Goiânia, Brazil
| | - Danitsa Marcos Rodrigues
- Programa de Pós-Graduação em Neurociências, Instituto de Ciências Básicas da Saúde, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Amanda Brondani Mucellini
- Programa de Pós-Graduação em Ciências Médicas: Psiquiatria, Faculdade de Medicina, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Alexandre Rosa Franco
- Instituto do Cérebro (InsCer), Pontifícia Universidade Católica do Rio Grande do Sul (PUCRS), Porto Alegre, Brazil
- Faculdade de Medicina, Programa de Pós-Graduação em Medicina e Ciências da Saúde, Pontifícia Universidade Católica do Rio Grande do Sul (PUCRS), Porto Alegre, Brazil
- Faculdade de Engenharia, Programa de Pós-Graduação em Engenharia Elétrica, PUCRS, Porto Alegre, Brazil
| | - Augusto Buchweitz
- Instituto do Cérebro (InsCer), Pontifícia Universidade Católica do Rio Grande do Sul (PUCRS), Porto Alegre, Brazil
- Faculdade de Medicina, Programa de Pós-Graduação em Medicina e Ciências da Saúde, Pontifícia Universidade Católica do Rio Grande do Sul (PUCRS), Porto Alegre, Brazil
- Faculdade de Letras, Programa de Pós-Graduação em Letras, Linguística, Pontifícia Universidade Católica do Rio Grande do Sul (PUCRS), Porto Alegre, Brazil
| | - Rudineia Toazza
- Programa de Pós-Graduação em Neurociências, Instituto de Ciências Básicas da Saúde, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Andressa Bortoluzzi
- Programa de Pós-Graduação em Neurociências, Instituto de Ciências Básicas da Saúde, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Giovanni Abrahão Salum
- Programa de Pós-Graduação em Ciências Médicas: Psiquiatria, Faculdade de Medicina, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Sonia Boscenco
- Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - Michael J. Meaney
- Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research (A∗STAR), Singapore, Singapore
| | - Robert D. Levitan
- Department of Psychiatry, University of Toronto and Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Gisele Gus Manfro
- Programa de Pós-Graduação em Neurociências, Instituto de Ciências Básicas da Saúde, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
- Programa de Pós-Graduação em Ciências Médicas: Psiquiatria, Faculdade de Medicina, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Patricia Pelufo Silveira
- Ludmer Centre for Neuroinformatics and Mental Health, Douglas Hospital Research Center, Montreal, QC, Canada
- Department of Psychiatry, McGill University, Montreal, QC, Canada
- *Correspondence: Patricia Pelufo Silveira,
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Caulfield KA, Brown JC. The Problem and Potential of TMS' Infinite Parameter Space: A Targeted Review and Road Map Forward. Front Psychiatry 2022; 13:867091. [PMID: 35619619 PMCID: PMC9127062 DOI: 10.3389/fpsyt.2022.867091] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 04/21/2022] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND Repetitive transcranial magnetic stimulation (rTMS) is a non-invasive, effective, and FDA-approved brain stimulation method. However, rTMS parameter selection remains largely unexplored, with great potential for optimization. In this review, we highlight key studies underlying next generation rTMS therapies, particularly focusing on: (1) rTMS Parameters, (2) rTMS Target Engagement, (3) rTMS Interactions with Endogenous Brain Activity, and (4) Heritable Predisposition to Brain Stimulation Treatments. METHODS We performed a targeted review of pre-clinical and clinical rTMS studies. RESULTS Current evidence suggests that rTMS pattern, intensity, frequency, train duration, intertrain interval, intersession interval, pulse and session number, pulse width, and pulse shape can alter motor excitability, long term potentiation (LTP)-like facilitation, and clinical antidepressant response. Additionally, an emerging theme is how endogenous brain state impacts rTMS response. Researchers have used resting state functional magnetic resonance imaging (rsfMRI) analyses to identify personalized rTMS targets. Electroencephalography (EEG) may measure endogenous alpha rhythms that preferentially respond to personalized stimulation frequencies, or in closed-loop EEG, may be synchronized with endogenous oscillations and even phase to optimize response. Lastly, neuroimaging and genotyping have identified individual predispositions that may underlie rTMS efficacy. CONCLUSIONS We envision next generation rTMS will be delivered using optimized stimulation parameters to rsfMRI-determined targets at intensities determined by energy delivered to the cortex, and frequency personalized and synchronized to endogenous alpha-rhythms. Further research is needed to define the dose-response curve of each parameter on plasticity and clinical response at the group level, to determine how these parameters interact, and to ultimately personalize these parameters.
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Affiliation(s)
- Kevin A Caulfield
- Department of Psychiatry, Medical University of South Carolina, Charleston, SC, United States
| | - Joshua C Brown
- Departments of Psychiatry and Neurology, Brown University Medical School, Providence, RI, United States
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45
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Dupont G, van Rooij D, Buitelaar JK, Reif A, Grimm O. Sex-related differences in adult attention-deficit hyperactivity disorder patients - An analysis of external globus pallidus functional connectivity in resting-state functional MRI. Front Psychiatry 2022; 13:962911. [PMID: 36117656 PMCID: PMC9478108 DOI: 10.3389/fpsyt.2022.962911] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 08/05/2022] [Indexed: 11/16/2022] Open
Abstract
In the last two decades, there has been a growing body of research that identified sex-related differences in attention-deficit hyperactivity disorder (ADHD). Our objective was to quantify whether these sex differences are based on altered functional brain connectivity profiles. In addition, we investigated whether the presence of comorbid disorders, including depression, substance use disorder (SUD) and overweight, influenced these sex differences. A seed-based connectivity analysis of the external globus pallidus (GPe), an important inhibitory relay hub of the fronto-thalamo-striatal-loop, was performed. In a first step, we searched for sex-related differences in ADHD patients (N = 137) and separately in healthy controls (HC) (N = 45), after that, we compared an equal group of HC and ADHD patients to compare sex-related differences in ADHD patients and HC. In a second step, we studied whether the neural basis of comorbidity patterns is different between male and female patients. We observed that male ADHD patients demonstrated a decrease in functional connectivity (FC) from the GPe to the left middle temporal gyrus compared to female ADHD patients. Moreover, within the full ADHD group (N = 137), there was a lower FC in male patients from GPe to the right frontal pole/middle frontal gyrus compared to female patients. Male ADHD patients with depression demonstrated decreased FC from the GPe to parts of the occipital cortex compared to female ADHD patients with depression. No such effect was demonstrated for overweight or SUD. The current study reveals different FC profiles in males and females with ADHD, which are centered around altered connectivity with the GPe. An improved understanding of sex-differences in ADHD, and the role of comorbid disorders, therein can result in improved diagnostic and therapeutic opportunities for ADHD patients.
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Affiliation(s)
- Gabriele Dupont
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital, Goethe University Frankfurt, Frankfurt, Germany
| | - Daan van Rooij
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition, and Behavior, Donders Center for Cognitive Neuroimaging, Radboud University Medical Center, Nijmegen, Netherlands
| | - Jan K Buitelaar
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition, and Behavior, Donders Center for Cognitive Neuroimaging, Radboud University Medical Center, Nijmegen, Netherlands
| | - Andreas Reif
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital, Goethe University Frankfurt, Frankfurt, Germany
| | - Oliver Grimm
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital, Goethe University Frankfurt, Frankfurt, Germany
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46
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Li Q, Zhu W, Wen X, Zang Z, Da Y, Lu J. Different sensorimotor mechanism in fast and slow progression amyotrophic lateral sclerosis. Hum Brain Mapp 2021; 43:1710-1719. [PMID: 34931392 PMCID: PMC8886636 DOI: 10.1002/hbm.25752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Revised: 11/22/2021] [Accepted: 12/01/2021] [Indexed: 11/12/2022] Open
Abstract
The huge heterogeneity of the disease progression rate may cause inconsistent findings between local activity and functional connectivity of the primary sensorimotor area (PSMA) in amyotrophic lateral sclerosis (ALS). For illustration of this hypothesis, resting-state fMRI (RS-fMRI) data were collected and analyzed on 38 "definite" or "probable" ALS patients (19 fast and 19 slow, cut off median = 0.41) and 37 matched healthy controls. Amplitude of low frequency fluctuations (ALFFs) and functional connectivity strength (FCS) were analyzed within the PSMA. There was a decreased ALFF (pFDR <.05) and FCS (p = .022) in all ALS patients. The two metrics shared about 50% of variance (R = .7) and both showed significant positive correlation with ALS Functional Rating Scale-Revised (ALSFRS-R) in the fast (p values <.034) but not in the slow progression groups. Interestingly, when regressing out the ALFF, the PSMA network FCS, especially the inter-hemisphere FCS, showed negative correlation with the ALSFRS-R score in the slow (R = -.54, p = .026) but not the fast progression group. In summary, the current results suggest that RS-fMRI local activity and network functional connectivity accounts for the severity differently in the slow and fast progression ALS patients.
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Affiliation(s)
- Qianwen Li
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China.,Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, China
| | - Wenjia Zhu
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Xinmei Wen
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Zhenxiang Zang
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China.,Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, China
| | - Yuwei Da
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Jie Lu
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China.,Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, China
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Herrington JD, Hartung EA, Laney NC, Hooper SR, Furth SL. Decreased Neural Connectivity in the Default Mode Network Among Youth and Young Adults With Chronic Kidney Disease. Semin Nephrol 2021; 41:455-461. [PMID: 34916007 DOI: 10.1016/j.semnephrol.2021.09.008] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
An increasing amount of literature has indicated that chronic kidney disease (CKD) is associated with cognitive deficits that increase with worsening disease severity. Although abnormalities in brain structure have been widely documented, few studies to date have examined the functioning of brain areas associated with the specific cognitive domains affected by CKD (namely, attention and executive functions). Furthermore, few studies have examined functional connectivity among CKD youth who are relatively early in the course of the disease. The present study used functional magnetic resonance imaging to examine the resting state connectivity in 67 youth with CKD (mean age, 17 y) and 58 age-matched healthy controls. Using seed-based multiple regression, decreased connectivity was observed within the anterior cingulate portion of the default mode network. In addition, decreased connectivity within the dorsolateral prefrontal cortex, paracingulate gyrus, and frontal pole were correlated significantly with disease severity. These data indicate that connectivity deficits in circuits implementing attentional processes may represent an early marker for cognitive decline in CKD.
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Affiliation(s)
- John D Herrington
- Center for Autism Research, Children's Hospital of Philadelphia, Philadelphia, PA; Department of Child Psychiatry and Behavioral Science, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.
| | - Erum A Hartung
- Division of Nephrology, Children's Hospital of Philadelphia, Philadelphia, PA; Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Nina C Laney
- Division of Nephrology, Children's Hospital of Philadelphia, Philadelphia, PA
| | - Stephen R Hooper
- Department of Allied Health Sciences, School of Medicine, University of North Carolina School-Chapel Hill, Chapel Hill, NC
| | - Susan L Furth
- Division of Nephrology, Children's Hospital of Philadelphia, Philadelphia, PA; Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA; Department of Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
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48
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Vaisvilaite L, Hushagen V, Grønli J, Specht K. Time-of-Day Effects in Resting-State Functional Magnetic Resonance Imaging: Changes in Effective Connectivity and Blood Oxygenation Level Dependent Signal. Brain Connect 2021; 12:515-523. [PMID: 34636252 PMCID: PMC9419957 DOI: 10.1089/brain.2021.0129] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Introduction: In the light of the ongoing replication crisis in the field of neuroimaging, it is necessary to assess the possible exogenous and endogenous factors that may affect functional magnetic resonance imaging (fMRI). The current project investigated time-of-day effects in the spontaneous fluctuations (<0.1 Hz) of the blood oxygenation level dependent (BOLD) signal. Method: Using data from the human connectome project release S1200, cross-spectral density dynamic causal modeling (DCM) was used to analyze time-dependent effects on the hemodynamic response and effective connectivity parameters. The DCM analysis covered three networks, namely the default mode network, the central executive network, and the saliency network. Hierarchical group-parametric empirical Bayes (PEB) was used to test varying design-matrices against the time-of-day model. Results: Hierarchical group-PEB found no support for changes in effective connectivity, whereas the hemodynamic parameters exhibited a significant time-of-day dependent effect, indicating a diurnal vascular effect that might affect the measured BOLD signal in the absence of any diurnal variations of the underlying neuronal activations and effective connectivity. Conclusion: We conclude that these findings urge the need to account for the time of data acquisition in future MRI studies and suggest that time-of-day dependent metabolic variations contribute to reduced reliability in resting-state fMRI studies. Impact statement The results from this study suggest that the circadian mechanism influences the blood oxygenation level dependent signal in resting-state functional magnetic resonance imaging (fMRI). The current study urges to record and report the time of fMRI scan acquisition in future research, as it may increase the replicability of findings. Both exploratory and clinical studies would benefit by incorporating this small change in fMRI protocol, which to date has been often overlooked.
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Affiliation(s)
- Liucija Vaisvilaite
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway.,Mohn Medical and Imaging Visualization Centre, Haukeland University Hospital, Bergen, Norway.,The publication in the preprint server is available at https://www.biorxiv.org/content/10.1101/2020.08.20.258517v2
| | - Vetle Hushagen
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway.,Mohn Medical and Imaging Visualization Centre, Haukeland University Hospital, Bergen, Norway.,The publication in the preprint server is available at https://www.biorxiv.org/content/10.1101/2020.08.20.258517v2
| | - Janne Grønli
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway.,The publication in the preprint server is available at https://www.biorxiv.org/content/10.1101/2020.08.20.258517v2
| | - Karsten Specht
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway.,Mohn Medical and Imaging Visualization Centre, Haukeland University Hospital, Bergen, Norway.,Department of Radiology, Haukeland University Hospital, Bergen, Norway.,Department of Education, UiT/The Arctic University of Norway, Tromsø, Norway.,The publication in the preprint server is available at https://www.biorxiv.org/content/10.1101/2020.08.20.258517v2
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Park HJ, Eo J, Pae C, Son J, Park SM, Kang J. State-Dependent Effective Connectivity in Resting-State fMRI. Front Neural Circuits 2021; 15:719364. [PMID: 34776875 PMCID: PMC8579116 DOI: 10.3389/fncir.2021.719364] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 09/22/2021] [Indexed: 01/02/2023] Open
Abstract
The human brain at rest exhibits intrinsic dynamics transitioning among the multiple metastable states of the inter-regional functional connectivity. Accordingly, the demand for exploring the state-specific functional connectivity increases for a deeper understanding of mental diseases. Functional connectivity, however, lacks information about the directed causal influences among the brain regions, called effective connectivity. This study presents the dynamic causal modeling (DCM) framework to explore the state-dependent effective connectivity using spectral DCM for the resting-state functional MRI (rsfMRI). We established the sequence of brain states using the hidden Markov model with the multivariate autoregressive coefficients of rsfMRI, summarizing the functional connectivity. We decomposed the state-dependent effective connectivity using a parametric empirical Bayes scheme that models the effective connectivity of consecutive windows with the time course of the discrete states as regressors. We showed the plausibility of the state-dependent effective connectivity analysis in a simulation setting. To test the clinical applicability, we applied the proposed method to characterize the state- and subtype-dependent effective connectivity of the default mode network in children with combined-type attention deficit hyperactivity disorder (ADHD-C) compared with age-matched, typically developed children (TDC). All 88 children were subtyped according to the occupation times (i.e., dwell times) of the three dominant functional connectivity states, independently of clinical diagnosis. The state-dependent effective connectivity differences between ADHD-C and TDC according to the subtypes and those between the subtypes of ADHD-C were expressed mainly in self-inhibition, magnifying the importance of excitation inhibition balance in the subtyping. These findings provide a clear motivation for decomposing the state-dependent dynamic effective connectivity and state-dependent analysis of the directed coupling in exploring mental diseases.
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Affiliation(s)
- Hae-Jeong Park
- Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul, South Korea.,Department of Psychiatry, Yonsei University College of Medicine, Seoul, South Korea.,Brain Korea 21 Project, Graduate School of Medical Science, Yonsei University College of Medicine, Seoul, South Korea.,Center for Systems and Translational Brain Science, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, South Korea.,Department of Cognitive Science, Yonsei University, Seoul, South Korea
| | - Jinseok Eo
- Brain Korea 21 Project, Graduate School of Medical Science, Yonsei University College of Medicine, Seoul, South Korea.,Center for Systems and Translational Brain Science, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, South Korea
| | - Chongwon Pae
- Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul, South Korea.,Department of Psychiatry, Yonsei University College of Medicine, Seoul, South Korea.,Brain Korea 21 Project, Graduate School of Medical Science, Yonsei University College of Medicine, Seoul, South Korea.,Center for Systems and Translational Brain Science, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, South Korea
| | - Junho Son
- Brain Korea 21 Project, Graduate School of Medical Science, Yonsei University College of Medicine, Seoul, South Korea.,Center for Systems and Translational Brain Science, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, South Korea
| | - Sung Min Park
- Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul, South Korea.,Department of Psychiatry, Yonsei University College of Medicine, Seoul, South Korea
| | - Jiyoung Kang
- Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul, South Korea.,Department of Psychiatry, Yonsei University College of Medicine, Seoul, South Korea.,Brain Korea 21 Project, Graduate School of Medical Science, Yonsei University College of Medicine, Seoul, South Korea.,Center for Systems and Translational Brain Science, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, South Korea
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50
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Balazova Z, Marecek R, Novakova L, Nemcova-Elfmarkova N, Kropacova S, Brabenec L, Grmela R, Vaculíková P, Svobodova L, Rektorova I. Dance Intervention Impact on Brain Plasticity: A Randomized 6-Month fMRI Study in Non-expert Older Adults. Front Aging Neurosci 2021; 13:724064. [PMID: 34776925 PMCID: PMC8579817 DOI: 10.3389/fnagi.2021.724064] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Accepted: 09/28/2021] [Indexed: 12/12/2022] Open
Abstract
Background: Dance is a complex activity combining physical exercise with cognitive, social, and artistic stimulation. Objectives: We aimed to assess the effects of dance intervention (DI) on intra and inter-network resting-state functional connectivity (rs-FC) and its association to cognitive changes in a group of non-demented elderly participants. Methods: Participants were randomly assigned into two groups: DI and life as usual (LAU). Six-month-long DI consisted of supervised 60 min lessons three times per week. Resting-state fMRI data were processed using independent component analysis to evaluate the intra and inter-network connectivity of large-scale brain networks. Interaction between group (DI, LAU) and visit (baseline, follow-up) was assessed using ANOVA, and DI-induced changes in rs-FC were correlated with cognitive outcomes. Results: Data were analyzed in 68 participants (DI; n = 36 and LAU; n = 32). A significant behavioral effect was found in the attention domain, with Z scores increasing in the DI group and decreasing in the LAU group (p = 0.017). The DI as compared to LAU led to a significant rs-FC increase of the default mode network (DMN) and specific inter-network pairings, including insulo-opercular and right frontoparietal/frontoparietal control networks (p = 0.019 and p = 0.023), visual and language/DMN networks (p = 0.012 and p = 0.015), and cerebellar and visual/language networks (p = 0.015 and p = 0.003). The crosstalk of the insulo-opercular and right frontoparietal networks were associated with attention/executive domain Z-scores (R = 0.401, p = 0.015, and R = 0.412, p = 0.012). Conclusion: The DI led to intervention-specific complex brain plasticity changes that were of cognitive relevance.
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Affiliation(s)
- Zuzana Balazova
- Brain and Mind Research, Central European Institute of Technology, Masaryk University, Brno, Czechia.,Faculty of Medicine, Masaryk University, Brno, Czechia
| | - Radek Marecek
- Brain and Mind Research, Central European Institute of Technology, Masaryk University, Brno, Czechia.,First Department of Neurology, Faculty of Medicine, St. Anne's University Hospital, Masaryk University, Brno, Czechia
| | - L'ubomíra Novakova
- Brain and Mind Research, Central European Institute of Technology, Masaryk University, Brno, Czechia
| | - Nela Nemcova-Elfmarkova
- Brain and Mind Research, Central European Institute of Technology, Masaryk University, Brno, Czechia
| | - Sylvie Kropacova
- Brain and Mind Research, Central European Institute of Technology, Masaryk University, Brno, Czechia
| | - Luboš Brabenec
- Brain and Mind Research, Central European Institute of Technology, Masaryk University, Brno, Czechia
| | - Roman Grmela
- Department of Health Promotion, Faculty of Sports Studies, Masaryk University, Brno, Czechia
| | - Pavlína Vaculíková
- Department of Gymnastics and Combatives, Faculty of Sports Studies, Masaryk University, Brno, Czechia
| | - Lenka Svobodova
- Department of Gymnastics and Combatives, Faculty of Sports Studies, Masaryk University, Brno, Czechia
| | - Irena Rektorova
- Brain and Mind Research, Central European Institute of Technology, Masaryk University, Brno, Czechia.,First Department of Neurology, Faculty of Medicine, St. Anne's University Hospital, Masaryk University, Brno, Czechia
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