1
|
Morawetz C, Basten U. Neural underpinnings of individual differences in emotion regulation: A systematic review. Neurosci Biobehav Rev 2024; 162:105727. [PMID: 38759742 DOI: 10.1016/j.neubiorev.2024.105727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Revised: 05/06/2024] [Accepted: 05/13/2024] [Indexed: 05/19/2024]
Abstract
This review synthesises individual differences in neural processes related to emotion regulation (ER). It comprises individual differences in self-reported and physiological regulation success, self-reported ER-related traits, and demographic variables, to assess their correlation with brain activation during ER tasks. Considering region-of-interest (ROI) and whole-brain analyses, the review incorporated data from 52 functional magnetic resonance imaging studies. Results can be summarized as follows: (1) Self-reported regulation success (assessed by emotional state ratings after regulation) and self-reported ER-related traits (assessed by questionnaires) correlated with brain activity in the lateral prefrontal cortex. (2) Amygdala activation correlated with ER-related traits only in ROI analyses, while it was associated with regulation success in whole-brain analyses. (3) For demographic and physiological measures, there was no systematic overlap in effects reported across studies. In showing that individual differences in regulation success and ER-related traits can be traced back to differences in the neural activity of brain regions associated with emotional reactivity (amygdala) and cognitive control (lateral prefrontal cortex), our findings can inform prospective personalised intervention models.
Collapse
Affiliation(s)
| | - Ulrike Basten
- Department of Psychology, RPTU Kaiserslautern-Landau, Germany
| |
Collapse
|
2
|
Sellers KK, Cohen JL, Khambhati AN, Fan JM, Lee AM, Chang EF, Krystal AD. Closed-loop neurostimulation for the treatment of psychiatric disorders. Neuropsychopharmacology 2024; 49:163-178. [PMID: 37369777 PMCID: PMC10700557 DOI: 10.1038/s41386-023-01631-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 05/31/2023] [Accepted: 06/02/2023] [Indexed: 06/29/2023]
Abstract
Despite increasing prevalence and huge personal and societal burden, psychiatric diseases still lack treatments which can control symptoms for a large fraction of patients. Increasing insight into the neurobiology underlying these diseases has demonstrated wide-ranging aberrant activity and functioning in multiple brain circuits and networks. Together with varied presentation and symptoms, this makes one-size-fits-all treatment a challenge. There has been a resurgence of interest in the use of neurostimulation as a treatment for psychiatric diseases. Initial studies using continuous open-loop stimulation, in which clinicians adjusted stimulation parameters during patient visits, showed promise but also mixed results. Given the periodic nature and fluctuations of symptoms often observed in psychiatric illnesses, the use of device-driven closed-loop stimulation may provide more effective therapy. The use of a biomarker, which is correlated with specific symptoms, to deliver stimulation only during symptomatic periods allows for the personalized therapy needed for such heterogeneous disorders. Here, we provide the reader with background motivating the use of closed-loop neurostimulation for the treatment of psychiatric disorders. We review foundational studies of open- and closed-loop neurostimulation for neuropsychiatric indications, focusing on deep brain stimulation, and discuss key considerations when designing and implementing closed-loop neurostimulation.
Collapse
Affiliation(s)
- Kristin K Sellers
- Department of Neurological Surgery, University of California, San Francisco, CA, USA
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Joshua L Cohen
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA, USA
| | - Ankit N Khambhati
- Department of Neurological Surgery, University of California, San Francisco, CA, USA
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Joline M Fan
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
- Department of Neurology, University of California, San Francisco, CA, USA
| | - A Moses Lee
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA, USA
| | - Edward F Chang
- Department of Neurological Surgery, University of California, San Francisco, CA, USA
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Andrew D Krystal
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA.
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA, USA.
| |
Collapse
|
3
|
Jia F, Chen X, Du X, Tang Z, Ma X, Ning T, Zou S, Zuo S, Li H, Cui S, Deng Z, Fu J, Fu X, Huang Y, Li X, Lian T, Liao Y, Liu L, Lu B, Wang Y, Wang Y, Wang Z, Ye G, Zhang X, Zhu H, Quan C, Sun H, Yan C, Liu Y. Aberrant degree centrality profiles during rumination in major depressive disorder. Hum Brain Mapp 2023; 44:6245-6257. [PMID: 37837649 PMCID: PMC10619375 DOI: 10.1002/hbm.26510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 09/14/2023] [Accepted: 09/25/2023] [Indexed: 10/16/2023] Open
Abstract
Rumination is closely linked to the onset and maintenance of major depressive disorder (MDD). Prior neuroimaging studies have identified the association between self-reported rumination trait and the functional coupling among a network of brain regions using resting-state functional magnetic resonance imaging (MRI). However, little is known about the underlying neural circuitry mechanism during active rumination in MDD. Degree centrality (DC) is a simple metric to denote network integration, which is critical for higher-order psychological processes such as rumination. During an MRI scan, individuals with MDD (N = 45) and healthy controls (HC, N = 46) completed a rumination state task. We examined the interaction effect between the group (MDD vs. HC) and condition (rumination vs. distraction) on vertex-wise DC. We further characterized the identified brain region's functional involvement with Neurosynth and BrainMap. Network-wise seed-based functional connectivity (FC) analysis was also conducted for the identified region of interest. Finally, exploratory correlation analysis was conducted between the identified region of interest's network FCs and self-reported in-scanner affect levels. We found that a left superior frontal gyrus (SFG) region, generally overlapped with the frontal eye field, showed a significant interaction effect. Further analysis revealed its involvement with executive functions. FCs between this region, the frontoparietal, and the dorsal attention network (DAN) also showed significant interaction effects. Furthermore, its FC to DAN during distraction showed a marginally significant negative association with in-scanner affect level at the baseline. Our results implicated an essential role of the left SFG in the rumination's underlying neural circuitry mechanism in MDD and provided novel evidence for the conceptualization of rumination in terms of impaired executive control.
Collapse
Affiliation(s)
- Feng‐Nan Jia
- Soochow UniversitySuzhouJiangsuChina
- Suzhou Guangji HospitalThe Affiliated Guangji Hospital of Soochow UniversitySuzhouJiangsuChina
| | - Xiao Chen
- CAS Key Laboratory of Behavioral ScienceInstitute of PsychologyBeijingChina
- Temerty Centre for Therapeutic Brain Intervention, Campbell Family Research InstituteCentre for Addiction and Mental HealthTorontoOntarioCanada
- Department of PsychologyUniversity of Chinese Academy of SciencesBeijingChina
- Magnetic Resonance Imaging Research CenterInstitute of Psychology, Chinese Academy of SciencesBeijingChina
- International Big‐Data Center for Depression ResearchChinese Academy of SciencesBeijingChina
| | - Xiang‐Dong Du
- Soochow UniversitySuzhouJiangsuChina
- Suzhou Guangji HospitalThe Affiliated Guangji Hospital of Soochow UniversitySuzhouJiangsuChina
| | - Zhen Tang
- Suzhou Guangji HospitalThe Affiliated Guangji Hospital of Soochow UniversitySuzhouJiangsuChina
| | - Xiao‐Yun Ma
- Suzhou Guangji HospitalThe Affiliated Guangji Hospital of Soochow UniversitySuzhouJiangsuChina
| | - Tian‐Tian Ning
- Suzhou Guangji HospitalThe Affiliated Guangji Hospital of Soochow UniversitySuzhouJiangsuChina
| | - Si‐Yun Zou
- Suzhou Guangji HospitalThe Affiliated Guangji Hospital of Soochow UniversitySuzhouJiangsuChina
| | - Shang‐Fu Zuo
- Boston University Chobanian & Avedisian School of MedicineBostonMassachusettsUSA
| | - Hui‐Xian Li
- The Third Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| | - Shi‐Xian Cui
- CAS Key Laboratory of Behavioral ScienceInstitute of PsychologyBeijingChina
- Department of PsychologyUniversity of Chinese Academy of SciencesBeijingChina
- Magnetic Resonance Imaging Research CenterInstitute of Psychology, Chinese Academy of SciencesBeijingChina
- International Big‐Data Center for Depression ResearchChinese Academy of SciencesBeijingChina
- Sino‐Danish CollegeUniversity of Chinese Academy of SciencesBeijingChina
- Sino‐Danish Center for Education and ResearchBeijingChina
| | - Zhao‐Yu Deng
- CAS Key Laboratory of Behavioral ScienceInstitute of PsychologyBeijingChina
- Department of PsychologyUniversity of Chinese Academy of SciencesBeijingChina
- Magnetic Resonance Imaging Research CenterInstitute of Psychology, Chinese Academy of SciencesBeijingChina
- International Big‐Data Center for Depression ResearchChinese Academy of SciencesBeijingChina
| | - Jia‐Lin Fu
- Suzhou Guangji HospitalThe Affiliated Guangji Hospital of Soochow UniversitySuzhouJiangsuChina
| | - Xiao‐Qian Fu
- Suzhou Guangji HospitalThe Affiliated Guangji Hospital of Soochow UniversitySuzhouJiangsuChina
| | - Yue‐Xiang Huang
- Suzhou Guangji HospitalThe Affiliated Guangji Hospital of Soochow UniversitySuzhouJiangsuChina
| | - Xue‐Ying Li
- CAS Key Laboratory of Behavioral ScienceInstitute of PsychologyBeijingChina
- Department of PsychologyUniversity of Chinese Academy of SciencesBeijingChina
- Magnetic Resonance Imaging Research CenterInstitute of Psychology, Chinese Academy of SciencesBeijingChina
- International Big‐Data Center for Depression ResearchChinese Academy of SciencesBeijingChina
| | - Tao Lian
- CAS Key Laboratory of Behavioral ScienceInstitute of PsychologyBeijingChina
- Department of PsychologyUniversity of Chinese Academy of SciencesBeijingChina
- Magnetic Resonance Imaging Research CenterInstitute of Psychology, Chinese Academy of SciencesBeijingChina
- International Big‐Data Center for Depression ResearchChinese Academy of SciencesBeijingChina
| | - Yi‐Fan Liao
- CAS Key Laboratory of Behavioral ScienceInstitute of PsychologyBeijingChina
- Department of PsychologyUniversity of Chinese Academy of SciencesBeijingChina
- Magnetic Resonance Imaging Research CenterInstitute of Psychology, Chinese Academy of SciencesBeijingChina
- International Big‐Data Center for Depression ResearchChinese Academy of SciencesBeijingChina
| | - Li‐Li Liu
- Suzhou Guangji HospitalThe Affiliated Guangji Hospital of Soochow UniversitySuzhouJiangsuChina
| | - Bin Lu
- CAS Key Laboratory of Behavioral ScienceInstitute of PsychologyBeijingChina
- Department of PsychologyUniversity of Chinese Academy of SciencesBeijingChina
- Magnetic Resonance Imaging Research CenterInstitute of Psychology, Chinese Academy of SciencesBeijingChina
- International Big‐Data Center for Depression ResearchChinese Academy of SciencesBeijingChina
| | - Yan Wang
- Suzhou Guangji HospitalThe Affiliated Guangji Hospital of Soochow UniversitySuzhouJiangsuChina
| | - Yu‐Wei Wang
- CAS Key Laboratory of Behavioral ScienceInstitute of PsychologyBeijingChina
- Department of PsychologyUniversity of Chinese Academy of SciencesBeijingChina
- Magnetic Resonance Imaging Research CenterInstitute of Psychology, Chinese Academy of SciencesBeijingChina
- International Big‐Data Center for Depression ResearchChinese Academy of SciencesBeijingChina
| | - Zi‐Han Wang
- CAS Key Laboratory of Behavioral ScienceInstitute of PsychologyBeijingChina
- Department of PsychologyUniversity of Chinese Academy of SciencesBeijingChina
- Magnetic Resonance Imaging Research CenterInstitute of Psychology, Chinese Academy of SciencesBeijingChina
- International Big‐Data Center for Depression ResearchChinese Academy of SciencesBeijingChina
| | - Gang Ye
- Suzhou Guangji HospitalThe Affiliated Guangji Hospital of Soochow UniversitySuzhouJiangsuChina
| | - Xin‐Zhu Zhang
- Suzhou Guangji HospitalThe Affiliated Guangji Hospital of Soochow UniversitySuzhouJiangsuChina
| | - Hong‐Liang Zhu
- Suzhou Guangji HospitalThe Affiliated Guangji Hospital of Soochow UniversitySuzhouJiangsuChina
| | - Chuan‐Sheng Quan
- Department of PsychologyZhangjiagang Fourth People's HospitalZhangjiagangJiangsuChina
| | - Hong‐Yan Sun
- Department of RadiologySuzhou Psychiatric Hospital, The Affiliated Guangji Hospital of Soochow UniversitySuzhouJiangsuChina
| | - Chao‐Gan Yan
- CAS Key Laboratory of Behavioral ScienceInstitute of PsychologyBeijingChina
- Department of PsychologyUniversity of Chinese Academy of SciencesBeijingChina
- Magnetic Resonance Imaging Research CenterInstitute of Psychology, Chinese Academy of SciencesBeijingChina
- International Big‐Data Center for Depression ResearchChinese Academy of SciencesBeijingChina
- Sino‐Danish CollegeUniversity of Chinese Academy of SciencesBeijingChina
- Sino‐Danish Center for Education and ResearchBeijingChina
| | - Yan‐Song Liu
- Soochow UniversitySuzhouJiangsuChina
- Suzhou Guangji HospitalThe Affiliated Guangji Hospital of Soochow UniversitySuzhouJiangsuChina
| |
Collapse
|
4
|
Uehara K, Yasuhara M, Koguchi J, Oku T, Shiotani S, Morise M, Furuya S. Brain network flexibility as a predictor of skilled musical performance. Cereb Cortex 2023; 33:10492-10503. [PMID: 37566918 DOI: 10.1093/cercor/bhad298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 07/25/2023] [Accepted: 07/26/2023] [Indexed: 08/13/2023] Open
Abstract
Interactions between the body and the environment are dynamically modulated by upcoming sensory information and motor execution. To adapt to this behavioral state-shift, brain activity must also be flexible and possess a large repertoire of brain networks so as to switch them flexibly. Recently, flexible internal brain communications, i.e. brain network flexibility, have come to be recognized as playing a vital role in integrating various sensorimotor information. Therefore, brain network flexibility is one of the key factors that define sensorimotor skill. However, little is known about how flexible communications within the brain characterize the interindividual variation of sensorimotor skill and trial-by-trial variability within individuals. To address this, we recruited skilled musical performers and used a novel approach that combined multichannel-scalp electroencephalography, behavioral measurements of musical performance, and mathematical approaches to extract brain network flexibility. We found that brain network flexibility immediately before initiating the musical performance predicted interindividual differences in the precision of tone timbre when required for feedback control, but not for feedforward control. Furthermore, brain network flexibility in broad cortical regions predicted skilled musical performance. Our results provide novel evidence that brain network flexibility plays an important role in building skilled sensorimotor performance.
Collapse
Affiliation(s)
- Kazumasa Uehara
- Neural Information Dynamics Laboratory, Department of Computer Science and Engineering, Toyohashi University of Technology, Toyohashi, Japan
- Sony Computer Science Laboratories Inc, Tokyo 1410022, Japan
| | - Masaki Yasuhara
- Sony Computer Science Laboratories Inc, Tokyo 1410022, Japan
- Neural Engineering Laboratory, Department of Science of Technology Innovation, Nagaoka University of Technology, Nagaoka, Japan
| | - Junya Koguchi
- Sony Computer Science Laboratories Inc, Tokyo 1410022, Japan
- Graduate School of Advanced Mathematical Sciences, Meiji University, Tokyo, Japan
| | | | | | - Masanori Morise
- Sony Computer Science Laboratories Inc, Tokyo 1410022, Japan
- School of Interdisciplinary Mathematical Sciences, Meiji University, Tokyo, Japan
| | - Shinichi Furuya
- Sony Computer Science Laboratories Inc, Tokyo 1410022, Japan
- NeuroPiano Institute, Kyoto 6008086, Japan
| |
Collapse
|
5
|
Mwilambwe-Tshilobo L, Setton R, Bzdok D, Turner GR, Spreng RN. Age differences in functional brain networks associated with loneliness and empathy. Netw Neurosci 2023; 7:496-521. [PMID: 37397888 PMCID: PMC10312262 DOI: 10.1162/netn_a_00293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 11/18/2022] [Indexed: 03/14/2024] Open
Abstract
Loneliness is associated with differences in resting-state functional connectivity (RSFC) within and between large-scale networks in early- and middle-aged adult cohorts. However, age-related changes in associations between sociality and brain function into late adulthood are not well understood. Here, we examined age differences in the association between two dimensions of sociality-loneliness and empathic responding-and RSFC of the cerebral cortex. Self-report measures of loneliness and empathy were inversely related across the entire sample of younger (mean age = 22.6y, n = 128) and older (mean age = 69.0y, n = 92) adults. Using multivariate analyses of multi-echo fMRI RSFC, we identified distinct functional connectivity patterns for individual and age group differences associated with loneliness and empathic responding. Loneliness in young and empathy in both age groups was related to greater visual network integration with association networks (e.g., default, fronto-parietal control). In contrast, loneliness was positively related to within- and between-network integration of association networks for older adults. These results extend our previous findings in early- and middle-aged cohorts, demonstrating that brain systems associated with loneliness, as well as empathy, differ in older age. Further, the findings suggest that these two aspects of social experience engage different neurocognitive processes across human life-span development.
Collapse
Affiliation(s)
- Laetitia Mwilambwe-Tshilobo
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Roni Setton
- Department of Psychology, Harvard University, Boston, MA, USA
| | - Danilo Bzdok
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- Department of Biomedical Engineering, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, McGill University, Montreal, QC, Canada
- School of Computer Science, McGill University, Montreal, QC, Canada
- Mila–Quebec Artificial Intelligence Institute, Montreal, QC, Canada
| | - Gary R. Turner
- Department of Psychology, York University, Toronto, ON, Canada
| | - R. Nathan Spreng
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, McGill University, Montreal, QC, Canada
- Departments of Psychiatry and Psychology, McGill University, Montreal, QC, Canada
- Douglas Mental Health University Institute, Verdun, QC, Canada
| |
Collapse
|
6
|
Liu Y, Kang XG, Chen BB, Song CG, Liu Y, Hao JM, Yuan F, Jiang W. Detecting residual brain networks in disorders of consciousness: a resting-state fNIRS study. Brain Res 2022; 1798:148162. [DOI: 10.1016/j.brainres.2022.148162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 10/22/2022] [Accepted: 11/08/2022] [Indexed: 11/12/2022]
|
7
|
Castilla D, Navarro-Haro MV, Suso-Ribera C, Díaz-García A, Zaragoza I, García-Palacios A. Ecological momentary intervention to enhance emotion regulation in healthcare workers via smartphone: a randomized controlled trial protocol. BMC Psychiatry 2022; 22:164. [PMID: 35248015 PMCID: PMC8897724 DOI: 10.1186/s12888-022-03800-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 02/19/2022] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND CUIDA-TE is an APP that offers transdiagnostic cognitive behavioral therapy focused on enhancing emotion regulation. As a novelty, it incorporates ecological momentary interventions (EMI), which can provide psychological support in real time, when suffering arises. The main goal of the study is to evaluate the efficacy of CUIDA-TE to improve emotion regulation in healthcare workers, a population that has been particularly emotionally impacted by the COVID-19 pandemic. METHODS In this three-arm, randomized controlled trial (RCT) the study sample will be composed of a minimum of 174 healthcare workers. They will be randomly assigned to a 2-month EMI group (CUIDA-TE APP, n ≥ 58), a 2-month ecological momentary assessment (EMA) only group (MONITOR EMOCIONAL APP, n ≥ 58), or a wait-list control group (no daily monitoring nor intervention, n ≥ 58). CUIDA-TE will provide EMI if EMA reveals emotional problems, poor sleep quality/quantity, burnout, stress, or low perceived self-efficacy when regulating emotions. Depression will be the primary outcome. Secondary outcomes will include emotion regulation, quality of life, and resilience. Treatment acceptance and usability will also be measured. Primary and secondary outcomes will be obtained at pre- and post-intervention measurements, and at the 3-month follow-up for all groups. DISCUSSION To our knowledge, this is the first RCT that evaluates the efficacy of an APP-based EMI to improve emotion regulation skills in healthcare workers. This type of intervention might ultimately help disseminate treatments and reach a larger number of individuals than traditional face-to-face individual therapies. TRIAL REGISTRATION ClinicalTrial.gov : NCT04958941 Registered 7 Jun 2021. STUDY STATUS Participant recruitment has not started.
Collapse
Affiliation(s)
- Diana Castilla
- grid.5338.d0000 0001 2173 938XDepartment of Personality, Evaluation and Psychological Treatment, University of Valencia, Avenida Blasco Ibáñez, 21, 46010 Valencia, Spain ,grid.413448.e0000 0000 9314 1427CIBER of Physiopathology of Obesity and Nutrition (CIBEROBN), ISCIII CB06/03/0052, Instituto Salud Carlos III, 28029 Madrid, Spain
| | - María Vicenta Navarro-Haro
- Department of Psychology and Sociology, University of Zaragoza, Calle Atarazana, 4, 44003 C/ Ciudad Escolar, s/n, 44001, Teruel, Spain. .,Instituto de Investigación Sanitaria Aragón, Avenida de San Juan Bosco, 13, 50009, Zaragoza, Spain.
| | - Carlos Suso-Ribera
- grid.413448.e0000 0000 9314 1427CIBER of Physiopathology of Obesity and Nutrition (CIBEROBN), ISCIII CB06/03/0052, Instituto Salud Carlos III, 28029 Madrid, Spain ,grid.9612.c0000 0001 1957 9153Department of Basic Psychology, Clinical Psychology and Psychobiology, Universitat Jaume I, Avenida de Vicent Sos Baynat, s/n, 12071 Castellón de la Plana, Spain
| | - Amanda Díaz-García
- grid.11205.370000 0001 2152 8769Department of Psychology and Sociology, University of Zaragoza, Calle Atarazana, 4, 44003 C/ Ciudad Escolar, s/n, 44001 Teruel, Spain
| | - Irene Zaragoza
- grid.413448.e0000 0000 9314 1427CIBER of Physiopathology of Obesity and Nutrition (CIBEROBN), ISCIII CB06/03/0052, Instituto Salud Carlos III, 28029 Madrid, Spain
| | - Azucena García-Palacios
- grid.413448.e0000 0000 9314 1427CIBER of Physiopathology of Obesity and Nutrition (CIBEROBN), ISCIII CB06/03/0052, Instituto Salud Carlos III, 28029 Madrid, Spain ,grid.9612.c0000 0001 1957 9153Department of Basic Psychology, Clinical Psychology and Psychobiology, Universitat Jaume I, Avenida de Vicent Sos Baynat, s/n, 12071 Castellón de la Plana, Spain
| |
Collapse
|
8
|
Martin CG, He BJ, Chang C. State-related neural influences on fMRI connectivity estimation. Neuroimage 2021; 244:118590. [PMID: 34560268 PMCID: PMC8815005 DOI: 10.1016/j.neuroimage.2021.118590] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 09/11/2021] [Accepted: 09/16/2021] [Indexed: 12/01/2022] Open
Abstract
The spatiotemporal structure of functional magnetic resonance imaging (fMRI) signals has provided a valuable window into the network underpinnings of human brain function and dysfunction. Although some cross-regional temporal correlation patterns (functional connectivity; FC) exhibit a high degree of stability across individuals and species, there is growing acknowledgment that measures of FC can exhibit marked changes over a range of temporal scales. Further, FC can covary with experimental task demands and ongoing neural processes linked to arousal, consciousness and perception, cognitive and affective state, and brain-body interactions. The increased recognition that such interrelated neural processes modulate FC measurements has raised both challenges and new opportunities in using FC to investigate brain function. Here, we review recent advances in the quantification of neural effects that shape fMRI FC and discuss the broad implications of these findings in the design and analysis of fMRI studies. We also discuss how a more complete understanding of the neural factors that shape FC measurements can resolve apparent inconsistencies in the literature and lead to more interpretable conclusions from fMRI studies.
Collapse
Affiliation(s)
- Caroline G Martin
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Biyu J He
- Neuroscience Institute, New York University School of Medicine, New York, NY 10016, USA; Departments of Neurology, Neuroscience & Physiology, and Radiology, New York University School of Medicine, New York, NY 10016, USA
| | - Catie Chang
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA.
| |
Collapse
|
9
|
Jo Y, Faskowitz J, Esfahlani FZ, Sporns O, Betzel RF. Subject identification using edge-centric functional connectivity. Neuroimage 2021; 238:118204. [PMID: 34087363 DOI: 10.1016/j.neuroimage.2021.118204] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Revised: 05/23/2021] [Accepted: 05/24/2021] [Indexed: 12/11/2022] Open
Abstract
Group-level studies do not capture individual differences in network organization, an important prerequisite for understanding neural substrates shaping behavior and for developing interventions in clinical conditions. Recent studies have employed 'fingerprinting' analyses on functional connectivity to identify subjects' idiosyncratic features. Here, we develop a complementary approach based on an edge-centric model of functional connectivity, which focuses on the co-fluctuations of edges. We first show whole-brain edge functional connectivity (eFC) to be a robust substrate that improves identifiability over nodal FC (nFC) across different datasets and parcellations. Next, we characterize subjects' identifiability at different spatial scales, from single nodes to the level of functional systems and clusters using k-means clustering. Across spatial scales, we find that heteromodal brain regions exhibit consistently greater identifiability than unimodal, sensorimotor, and limbic regions. Lastly, we show that identifiability can be further improved by reconstructing eFC using specific subsets of its principal components. In summary, our results highlight the utility of the edge-centric network model for capturing meaningful subject-specific features and sets the stage for future investigations into individual differences using edge-centric models.
Collapse
Affiliation(s)
- Youngheun Jo
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA; Cognitive Science Program, Indiana University, Bloomington, IN 47405, USA
| | - Joshua Faskowitz
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA; Cognitive Science Program, Indiana University, Bloomington, IN 47405, USA
| | - Farnaz Zamani Esfahlani
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA; Cognitive Science Program, Indiana University, Bloomington, IN 47405, USA; Program in Neuroscience, Indiana University, Bloomington, IN 47405, USA; Network Science Institute, Indiana University, Bloomington, IN 47405, USA
| | - Richard F Betzel
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA; Cognitive Science Program, Indiana University, Bloomington, IN 47405, USA; Program in Neuroscience, Indiana University, Bloomington, IN 47405, USA; Network Science Institute, Indiana University, Bloomington, IN 47405, USA.
| |
Collapse
|
10
|
Fedota JR, Ross TJ, Castillo J, McKenna MR, Matous AL, Salmeron BJ, Menon V, Stein EA. Time-Varying Functional Connectivity Decreases as a Function of Acute Nicotine Abstinence. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2021; 6:459-469. [PMID: 33436331 PMCID: PMC8035238 DOI: 10.1016/j.bpsc.2020.10.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Revised: 09/15/2020] [Accepted: 10/03/2020] [Indexed: 01/08/2023]
Abstract
BACKGROUND The nicotine withdrawal syndrome (NWS) includes affective and cognitive disruptions whose incidence and severity vary across time during acute abstinence. However, most network-level neuroimaging uses static measures of resting-state functional connectivity and assumes time-invariance and is thus unable to capture dynamic brain-behavior relationships. Recent advances in resting-state functional connectivity signal processing allow characterization of time-varying functional connectivity (TVFC), which characterizes network communication between networks that reconfigure over the course of data collection. Therefore, TVFC may more fully describe network dysfunction related to the NWS. METHODS To isolate alterations in the frequency and diversity of communication across network boundaries during acute nicotine abstinence, we scanned 25 cigarette smokers in the nicotine-sated and abstinent states and applied a previously validated method to characterize TVFC at a network and a nodal level within the brain. RESULTS During abstinence, we found brain-wide decreases in the frequency of interactions between network nodes in different modular communities (i.e., temporal flexibility). In addition, within a subset of the networks examined, the variability of these interactions across community boundaries (i.e., spatiotemporal diversity) also decreased. Finally, within 2 of these networks, the decrease in spatiotemporal diversity was significantly related to NWS clinical symptoms. CONCLUSIONS Using multiple measures of TVFC in a within-subjects design, we characterized a novel set of changes in network communication and linked these changes to specific behavioral symptoms of the NWS. These reductions in TVFC provide a meso-scale network description of the relative inflexibility of specific large-scale brain networks during acute abstinence.
Collapse
Affiliation(s)
- John R Fedota
- Neuroimaging Research Branch, National Institute on Drug Abuse-Intramural Research Program, National Institutes of Health, Baltimore, Maryland.
| | - Thomas J Ross
- Neuroimaging Research Branch, National Institute on Drug Abuse-Intramural Research Program, National Institutes of Health, Baltimore, Maryland
| | - Juan Castillo
- Neuroimaging Research Branch, National Institute on Drug Abuse-Intramural Research Program, National Institutes of Health, Baltimore, Maryland
| | - Michael R McKenna
- Neuroimaging Research Branch, National Institute on Drug Abuse-Intramural Research Program, National Institutes of Health, Baltimore, Maryland; Department of Psychology, Ohio State University, Columbus, Ohio
| | - Allison L Matous
- Neuroimaging Research Branch, National Institute on Drug Abuse-Intramural Research Program, National Institutes of Health, Baltimore, Maryland; Geisel School of Medicine at Dartmouth College, Hanover, New Hampshire
| | - Betty Jo Salmeron
- Neuroimaging Research Branch, National Institute on Drug Abuse-Intramural Research Program, National Institutes of Health, Baltimore, Maryland
| | - Vinod Menon
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California; Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, California; Stanford Neuroscience Institute, Stanford, California
| | - Elliot A Stein
- Neuroimaging Research Branch, National Institute on Drug Abuse-Intramural Research Program, National Institutes of Health, Baltimore, Maryland.
| |
Collapse
|
11
|
Acute aerobic exercise enhances cortical connectivity between structures involved in shaping mood and improves self-reported mood: An EEG effective-connectivity study in young male adults. Int J Psychophysiol 2021; 162:22-33. [PMID: 33508334 DOI: 10.1016/j.ijpsycho.2021.01.016] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Revised: 01/07/2021] [Accepted: 01/22/2021] [Indexed: 02/06/2023]
Abstract
There seems to be a general consensus among researchers that acute aerobic exercise (exercise hereafter) improves mood, but the neural mechanisms which drive these effects are far from being clear. The current study investigated the cortical connectivity patterns that underlie changes in mood after exercise. Twenty male adults underwent three different experimental protocols that were carefully controlled in terms of underlying metabolism and were administered in a randomized order: moderate-intensity continuous exercise, high-intensity interval exercise, and seated rest condition. Before and after each experimental protocol, we collected data on the participants' mood using the UMACL questionnaire and recorded their resting-state EEG. We focused on the effective connectivity patterns exerted by the dorso-lateral prefrontal cortex (dlPFC) over the temporal region (TMP), as these are important cortical structures involved in shaping mood. The cortical connectivity patterns in the resting-state EEG were evaluated using the directed transfer function (DTF), which is an autoregressive effective connectivity method. The results showed that both moderate-intensity exercise and high-intensity interval exercise improved participants' self-reported mood. Crucially, this improvement was accompanied by stronger influences of dlPFC over TMP. The observed changes in the effective connectivity patterns between dlPFC and TMP might help to better understand the effects of exercise on mood.
Collapse
|
12
|
Kirschner M, Shafiei G, Markello RD, Makowski C, Talpalaru A, Hodzic-Santor B, Devenyi GA, Paquola C, Bernhardt BC, Lepage M, Chakravarty MM, Dagher A, Mišić B. Latent Clinical-Anatomical Dimensions of Schizophrenia. Schizophr Bull 2020; 46:1426-1438. [PMID: 32744604 PMCID: PMC8496914 DOI: 10.1093/schbul/sbaa097] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Widespread structural brain abnormalities have been consistently reported in schizophrenia, but their relation to the heterogeneous clinical manifestations remains unknown. In particular, it is unclear whether anatomical abnormalities in discrete regions give rise to discrete symptoms or whether distributed abnormalities give rise to the broad clinical profile associated with schizophrenia. Here, we apply a multivariate data-driven approach to investigate covariance patterns between multiple-symptom domains and distributed brain abnormalities in schizophrenia. Structural magnetic resonance imaging and clinical data were derived from one discovery sample (133 patients and 113 controls) and one independent validation sample (108 patients and 69 controls). Disease-related voxel-wise brain abnormalities were estimated using deformation-based morphometry. Partial least-squares analysis was used to comprehensively map clinical, neuropsychological, and demographic data onto distributed deformation in a single multivariate model. The analysis identified 3 latent clinical-anatomical dimensions that collectively accounted for 55% of the covariance between clinical data and brain deformation. The first latent clinical-anatomical dimension was replicated in an independent sample, encompassing cognitive impairments, negative symptom severity, and brain abnormalities within the default mode and visual networks. This cognitive-negative dimension was associated with low socioeconomic status and was represented across multiple races. Altogether, we identified a continuous cognitive-negative dimension of schizophrenia, centered on 2 intrinsic networks. By simultaneously taking into account both clinical manifestations and neuroanatomical abnormalities, the present results open new avenues for multi-omic stratification and biotyping of individuals with schizophrenia.
Collapse
Affiliation(s)
- Matthias Kirschner
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zurich, Zurich, Switzerland,McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montreal, Canada
| | - Golia Shafiei
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montreal, Canada
| | - Ross D Markello
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montreal, Canada
| | - Carolina Makowski
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montreal, Canada
| | - Alexandra Talpalaru
- Cerebral Imaging Center, Douglas Mental Health University Institute, McGill University, Montréal, Canada,Department of Biological and Biomedical Engineering, McGill University, Montréal, Canada
| | - Benazir Hodzic-Santor
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montreal, Canada
| | - Gabriel A Devenyi
- Cerebral Imaging Center, Douglas Mental Health University Institute, McGill University, Montréal, Canada,Department of Psychiatry, McGill University, Montréal, Canada
| | - Casey Paquola
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montreal, Canada
| | - Boris C Bernhardt
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montreal, Canada
| | - Martin Lepage
- Cerebral Imaging Center, Douglas Mental Health University Institute, McGill University, Montréal, Canada,Department of Psychiatry, McGill University, Montréal, Canada
| | - M Mallar Chakravarty
- Cerebral Imaging Center, Douglas Mental Health University Institute, McGill University, Montréal, Canada,Department of Biological and Biomedical Engineering, McGill University, Montréal, Canada,Department of Psychiatry, McGill University, Montréal, Canada
| | - Alain Dagher
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montreal, Canada
| | - Bratislav Mišić
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montreal, Canada,To whom correspondence should be addressed; tel: 514-398-1857, fax: 514-398-1857, e-mail:
| |
Collapse
|