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The trait and state negative affect can be separately predicted by stable and variable resting-state functional connectivity

Published online by Cambridge University Press:  13 July 2020

Yu Li ,
Kaixiang Zhuang ,
Zili Yi ,
Dongtao Wei ,
Jiangzhou Sun  and
Jiang Qiu Open the ORCID record for Jiang Qiu [Opens in a new window]
Yu Li
Affiliation:
Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China Department of Psychology, Southwest University, Chongqing, China
Kaixiang Zhuang
Affiliation:
Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China Department of Psychology, Southwest University, Chongqing, China
Zili Yi
Affiliation:
Beibei Mental Health Center, Chongqing400715, China
Dongtao Wei
Affiliation:
Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China Department of Psychology, Southwest University, Chongqing, China
Jiangzhou Sun
Affiliation:
Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China Department of Psychology, Southwest University, Chongqing, China
Jiang Qiu*
Affiliation:
Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China Department of Psychology, Southwest University, Chongqing, China Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality at Beijing Normal University
*
Author for correspondence: Jiang Qiu, E-mail: [email protected]
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Abstract

Background

Many emotional experiences such as anxiety and depression are influenced by negative affect (NA). NA has both trait and state features, which play different roles in physiological and mental health. Attending to NA common to various emotional experiences and their trait-state features might help deepen the understanding of the shared foundation of related emotional disorders.

Methods

The principal component of five measures was calculated to indicate individuals' NA level. Applying the connectivity-based correlation analysis, we first identified resting-state functional connectives (FCs) relating to NA in sample 1 (n = 367), which were validated through an independent sample (n = 232; sample 2). Next, based on the variability of FCs across large timescale, we further divided the NA-related FCs into high- and low-variability groups. Finally, FCs in different variability groups were separately applied to predict individuals' neuroticism level (which is assumed to be the core trait-related factor underlying NA), and the change of NA level (which represents the state-related fluctuation of NA).

Results

The low-variability FCs were primarily within the default mode network (DMN) and between the DMN and dorsal attention network/sensory system and significantly predicted trait rather than state NA. The high-variability FCs were primarily between the DMN and ventral attention network, the fronto-parietal network and DMN/sensory system, and significantly predicted the change of NA level.

Conclusions

The trait and state NA can be separately predicted by stable and variable spontaneous FCs with different attentional processes and emotion regulatory mechanisms, which could deepen our understanding of NA.

Keywords

Negative affectresting-state functional connectivitystate-traitvariability
Type
Original Article
Information
Psychological Medicine , Volume 52 , Issue 5 , April 2022 , pp. 813 - 823
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Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

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Footnotes

*

These authors contributed equally to this work.

References

Anand, A., Li, Y., Wang, Y., Wu, J., Gao, S., Bukhari, L., … Lowe, M. J. (2005). Activity and connectivity of brain mood regulating circuit in depression: A functional magnetic resonance study. Biological Psychiatry, 57, 1079–1088.CrossRefGoogle ScholarPubMed
Andrews, G. (1996). Comorbidity in neurotic disorders: The similarities are more important than the differences. In Rapee, R. M. (Ed.), Current Controversies in the Anxiety Disorders (pp. 3–20). New York, NY: Guilforg Press.Google Scholar
Anticevic, A., Cole, M. W., Murray, J. D., Corlett, P. R., Wang, X.-J., & Krystal, J. H. (2012). The role of default network deactivation in cognition and disease. Trends in Cognitive Sciences, 16, 584–592.CrossRefGoogle ScholarPubMed
Ashburner, J., & Friston, K. J. (1999). Nonlinear spatial normalization using basis functions. Human Brain Mapping, 7, 254–266.3.0.CO;2-G>CrossRefGoogle ScholarPubMed
Baldassarre, A., Lewis, C. M., Committeri, G., Snyder, A. Z., Romani, G. L., & Corbetta, M. (2012). Individual variability in functional connectivity predicts performance of a perceptual task. Proceedings of the National Academy of Sciences, 109, 3516–3521.CrossRefGoogle ScholarPubMed
Bassett, D. S., Wymbs, N. F., Porter, M. A., Mucha, P. J., Carlson, J. M., & Grafton, S. T. (2011). Dynamic reconfiguration of human brain networks during learning. Proceedings of the National Academy of Sciences, 108, 7641–7646.CrossRefGoogle ScholarPubMed
Beck, A. T., Steer, R. A., Ball, R., & Ranieri, W. F. (1996). Comparison of Beck Depression Inventories-IA and-II in psychiatric outpatients. Journal of Personality Assessment, 67, 588–597.CrossRefGoogle ScholarPubMed
Berkowitz, L. (1994). Is something missing? Some observations prompted by the cognitive-neoassociationist view of anger and emotional aggression. In Huesmann, L. R. (Ed.), Aggressive behavior : Current perspectives (pp. 35–57). New York, NY: Plenum Press.CrossRefGoogle Scholar
Betzel, R. F., Satterthwaite, T. D., Gold, J. I., & Bassett, D. S. (2017). Positive affect, surprise, and fatigue are correlates of network flexibility. Scientific Reports, 7, 1–10.CrossRefGoogle ScholarPubMed
Buckner, R. L., Andrews-Hanna, J. R., & Schacter, D. L. (2008). The brain's default network: anatomy, function, and relevance to disease. Annals of the New York Academy of Sciences, 1124, 1–38.CrossRefGoogle ScholarPubMed
Christoff, K., Gordon, A. M., Smallwood, J., Smith, R., & Schooler, J. W. (2009). Experience sampling during fMRI reveals default network and executive system contributions to mind wandering. Proceedings of the National Academy of Sciences of the United States of America, 106, 8719–8724.CrossRefGoogle ScholarPubMed
Clark, L. A., & Watson, D. (1991). Tripartite model of anxiety and depression: Psychometric evidence and taxonomic implications. Journal of Abnormal Psychology, 100, 316–336.CrossRefGoogle ScholarPubMed
Cole, M. W., Bassett, D. S., Power, J. D., Braver, T. S., & Petersen, S. E. (2014). Intrinsic and task-evoked network architectures of the human brain. Neuron, 83, 238–251.CrossRefGoogle ScholarPubMed
Cole, M. W., Ito, T., Bassett, D. S., & Schultz, D. H. (2016). Activity flow over resting-state networks shapes cognitive task activations. Nature Neuroscience, 19, 1718.CrossRefGoogle ScholarPubMed
Cole, M. W., Yarkoni, T., Repovš, G., Anticevic, A., & Braver, T. S. (2012). Global connectivity of prefrontal cortex predicts cognitive control and intelligence. Journal of Neuroscience, 32, 8988–8999.CrossRefGoogle ScholarPubMed
Corbetta, M., Patel, G., & Shulman, G. L. (2008). The reorienting system of the human brain: From environment to theory of mind. Neuron, 58, 306–324.CrossRefGoogle ScholarPubMed
Costa, P. Jr., & McCrae, R. (1992). Professional manual: Revised NEO personality inventory (NEO-PI-R) and NEO five-factor inventory (NEO-FFI) professional manual. Odessa, FL: Psychological Assessment Resources: Odessa.Google Scholar
Cui, H., Zhang, J., Liu, Y., Li, Q., Li, H., Zhang, L., … Li, J. (2016). Differential alterations of resting-state functional connectivity in generalized anxiety disorder and panic disorder. Human Brain Mapping, 37, 1459–1473.CrossRefGoogle ScholarPubMed
Davidson, R. J. (2000). Affective style, psychopathology, and resilience: Brain mechanisms and plasticity. American Psychologist, 55, 1196–1214.CrossRefGoogle ScholarPubMed
Davidson, R. J. (2002). Anxiety and affective style: Role of prefrontal cortex and amygdala. Biological Psychiatry, 51, 68–80.CrossRefGoogle ScholarPubMed
De Havas, J. A., Parimal, S., Soon, C. S., & Chee, M. W. (2012). Sleep deprivation reduces default mode network connectivity and anti-correlation during rest and task performance. Neuroimage, 59, 1745–1751.CrossRefGoogle ScholarPubMed
Disner, S. G., Marquardt, C. A., Mueller, B. A., Burton, P. C., & Sponheim, S. R. (2018). Spontaneous neural activity differences in posttraumatic stress disorder: A quantitative resting-state meta-analysis and fMRI validation. Human Brain Mapping, 39, 837–850.CrossRefGoogle ScholarPubMed
Doré, B., Scholz, C., Baek, E., Garcia, J., O'Donnell, M., Bassett, D., … Falk, E. (2019). Brain activity tracks population information sharing by capturing consensus judgments of value. Cerebral Cortex, 29, 3102–3110.CrossRefGoogle ScholarPubMed
Douw, L., Wakeman, D. G., Tanaka, N., Liu, H., & Stufflebeam, S. M. (2016). State-dependent variability of dynamic functional connectivity between frontoparietal and default networks relates to cognitive flexibility. Neuroscience, 339, 12–21.CrossRefGoogle ScholarPubMed
Drevets, W. C., Price, J. L., & Furey, M. L. (2008). Brain structural and functional abnormalities in mood disorders: Implications for neurocircuitry models of depression. Brain Structure and Function, 213, 93–118.CrossRefGoogle ScholarPubMed
Etkin, A., Egner, T., & Kalisch, R. (2011). Emotional processing in anterior cingulate and medial prefrontal cortex. Trends in Cognitive Sciences, 15, 85–93.CrossRefGoogle ScholarPubMed
Etkin, A., & Wager, T. D. (2007). Functional neuroimaging of anxiety: A meta-analysis of emotional processing in PTSD, social anxiety disorder, and specific phobia. American Journal of Psychiatry, 164, 1476–1488.CrossRefGoogle ScholarPubMed
Finn, E. S., Shen, X., Scheinost, D., Rosenberg, M. D., Huang, J., Chun, M. M., … Constable, R. T. (2015). Functional connectome fingerprinting: Identifying individuals using patterns of brain connectivity. Nature Neuroscience, 18, 1664.CrossRefGoogle ScholarPubMed
Fox, M. D., Corbetta, M., Snyder, A. Z., Vincent, J. L., & Raichle, M. E. (2006). Spontaneous neuronal activity distinguishes human dorsal and ventral attention systems. Proceedings of the National Academy of Sciences of the United States of America, 103, 10046–10051.CrossRefGoogle ScholarPubMed
Fox, K. C., Spreng, R. N., Ellamil, M., Andrews-Hanna, J. R., & Christoff, K. (2015). The wandering brain: Meta-analysis of functional neuroimaging studies of mind-wandering and related spontaneous thought processes. Neuroimage, 111, 611–621.CrossRefGoogle ScholarPubMed
Friston, K. J., Williams, S., Howard, R., Frackowiak, R. S., & Turner, R. (1996). Movement-related effects in fMRI time-series. Magnetic Resonance in Medicine, 35, 346–355.CrossRefGoogle ScholarPubMed
Gordon, E. M., Laumann, T. O., Adeyemo, B., Huckins, J. F., Kelley, W. M., & Petersen, S. E. (2016). Generation and evaluation of a cortical area parcellation from resting-state correlations. Cerebral Cortex, 26, 288–303.CrossRefGoogle ScholarPubMed
He, T., Kong, R., Holmes, A. J., Nguyen, M., Sabuncu, M. R., Eickhoff, S. B., … Yeo, B. T. T. (2020). Deep neural networks and kernel regression achieve comparable accuracies for functional connectivity prediction of behavior and demographics. NeuroImage, 206, 116276.CrossRefGoogle ScholarPubMed
Henriques, G. (2011). A new unified theory of psychology. Springer Science & Business Media.CrossRefGoogle Scholar
Huang, S., Li, Y., Zhang, W., Zhang, B., Liu, X., Mo, L., & Chen, Q. (2015). Multisensory competition is modulated by sensory pathway interactions with fronto-sensorimotor and default-mode network regions. The Journal of Neuroscience, 35, 9064–9077.CrossRefGoogle ScholarPubMed
Jeronimus, B., Kotov, R., Riese, H., & Ormel, J. (2016). Neuroticism's prospective association with mental disorders halves after adjustment for baseline symptoms and psychiatric history, but the adjusted association hardly decays with time: A meta-analysis on 59 longitudinal/prospective studies with 443 313 participants. Psychological Medicine, 46, 2883–2906.CrossRefGoogle ScholarPubMed
Jeronimus, B. F., Ormel, J., Aleman, A., Penninx, B. W., & Riese, H. (2013). Negative and positive life events are associated with small but lasting change in neuroticism. Psychological Medicine, 43, 2403–2415.CrossRefGoogle ScholarPubMed
Jeronimus, B. F., Riese, H., Sanderman, R., & Ormel, J. (2014). Mutual reinforcement between neuroticism and life experiences: A five-wave, 16-year study to test reciprocal causation. Journal of Personality and Social Psychology, 107, 751.CrossRefGoogle ScholarPubMed
Kaiser, R. H., Andrews-Hanna, J. R., Spielberg, J. M., Warren, S. L., Sutton, B. P., Miller, G. A., … Banich, M. T. (2015b). Distracted and down: Neural mechanisms of affective interference in subclinical depression. Social Cognitive and Affective Neuroscience, 10, 654–663.CrossRefGoogle Scholar
Kaiser, R., Andrews-Hanna, J., Wager, T., & Pizzagalli, D. (2015a). Large-Scale network dysfunction in major depressive disorder: A meta-analysis of resting-state functional connectivity. Jama Psychiatry, 72, 603–611.CrossRefGoogle Scholar
Kelly, A. C., Uddin, L. Q., Biswal, B. B., Castellanos, F. X., & Milham, M. P. (2008). Competition between functional brain networks mediates behavioral variability. Neuroimage, 39, 527–537.CrossRefGoogle ScholarPubMed
Koenigs, M., & Grafman, J. (2009). The functional neuroanatomy of depression: Distinct roles for ventromedial and dorsolateral prefrontal cortex. Behavioural Brain Research, 201, 239–243.CrossRefGoogle ScholarPubMed
Kong, F., Zhao, J., & You, X. (2012). Trait emotional intelligence and mental distress: The mediating role of positive and negative affect. International Journal of Psychology, 47, 460–466.CrossRefGoogle ScholarPubMed
Kong, R., Li, J., Orban, C., Sabuncu, M. R., Liu, H., Schaefer, A., … Yeo, B. T. T. (2018). Spatial topography of individual-specific cortical networks predicts human cognition, personality, and emotion. Cerebral Cortex, 29, 2533–2551.CrossRefGoogle Scholar
Kucyi, A., Esterman, M., Riley, C. S., & Valera, E. M. (2016). Spontaneous default network activity reflects behavioral variability independent of mind-wandering. Proceedings of the National Academy of Sciences, 113, 13899–13904.CrossRefGoogle ScholarPubMed
Laumann, T. O., Gordon, E. M., Adeyemo, B., Snyder, A. Z., Joo, S. J., Chen, M.-Y., … Dosenbach, N. U. (2015). Functional system and areal organization of a highly sampled individual human brain. Neuron, 87, 657–670.CrossRefGoogle ScholarPubMed
Li, J., Bolt, T., Bzdok, D., Nomi, J. S., Yeo, B. T., Spreng, R. N., & Uddin, L. Q. (2019a). Topography and behavioral relevance of the global signal in the human brain. Scientific Reports, 9, 1–10.Google Scholar
Li, J., Kong, R., Liegeois, R., Orban, C., Tan, Y., Sun, N., … Yeo, B. T. T. (2019b). Global signal regression strengthens association between resting-state functional connectivity and behavior. NeuroImage, 196, 126–141.CrossRefGoogle Scholar
Liu, H., Kaneko, Y., Ouyang, X., Li, L., Hao, Y., Chen, E. Y., … Liu, Z. (2012). Schizophrenic patients and their unaffected siblings share increased resting-state connectivity in the task-negative network but not its anticorrelated task-positive network. Schizophrenia Bulletin, 38, 285–294.CrossRefGoogle Scholar
Liu, T. T., Nalci, A., & Falahpour, M. (2017b). The global signal in fMRI: Nuisance or information? Neuroimage, 150, 213–229.CrossRefGoogle Scholar
Liu, F., Wang, Y., Li, M., Wang, W., Li, R., Zhang, Z., … Chen, H. (2017a). Dynamic functional network connectivity in idiopathic generalized epilepsy with generalized tonic–clonic seizure. Human Brain Mapping, 38, 957–973.CrossRefGoogle Scholar
Liu, W., Wei, D., Chen, Q., Yang, W., Meng, J., Wu, G., … Qiu, J. (2017c). Longitudinal test-retest neuroimaging data from healthy young adults in southwest China. Scientific Data, 4, 170017.CrossRefGoogle Scholar
Lois, G., & Wessa, M. (2016). Differential association of default mode network connectivity and rumination in healthy individuals and remitted MDD patients. Social Cognitive and Affective Neuroscience, 11, 1792–1801.CrossRefGoogle ScholarPubMed
Magioncalda, P., Martino, M., Conio, B., Escelsior, A., Piaggio, N., Presta, A., … Vassallo, L. (2015). Functional connectivity and neuronal variability of resting state activity in bipolar disorder – reduction and decoupling in anterior cortical midline structures. Human Brain Mapping, 36, 666–682.CrossRefGoogle ScholarPubMed
Mantini, D., Corbetta, M., Romani, G. L., Orban, G. A., & Vanduffel, W. (2013). Evolutionarily novel functional networks in the human brain? Journal of Neuroscience, 33, 3259–3275.CrossRefGoogle ScholarPubMed
Markon, K. E., Krueger, R. F., & Watson, D. (2005). Delineating the structure of normal and abnormal personality: An integrative hierarchical approach. Journal of Personality and Social Psychology, 88, 139.CrossRefGoogle ScholarPubMed
Martino, M., Magioncalda, P., Huang, Z., Conio, B., Piaggio, N., Duncan, N. W., … Wolff, A. (2016). Contrasting variability patterns in the default mode and sensorimotor networks balance in bipolar depression and mania. Proceedings of the National Academy of Sciences, 113, 4824–4829.CrossRefGoogle ScholarPubMed
Mehra, R., & Sah, R. (2002). Mood fluctuations, projection bias, and volatility of equity prices. Journal of Economic Dynamics and Control, 26, 869–887.CrossRefGoogle Scholar
Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J.-P. (2010). Community structure in time-dependent, multiscale, and multiplex networks. Science, 328, 876–878.CrossRefGoogle ScholarPubMed
Northoff, G. (2016). Spatiotemporal psychopathology I: No rest for the brain's resting state activity in depression? Spatiotemporal psychopathology of depressive symptoms. Journal of Affective Disorders, 190, 854–866.CrossRefGoogle ScholarPubMed
Northoff, G., & Bermpohl, F. (2004). Cortical midline structures and the self. Trends in Cognitive Sciences, 8, 102–107.CrossRefGoogle ScholarPubMed
Northoff, G., Heinzel, A., De Greck, M., Bermpohl, F., Dobrowolny, H., & Panksepp, J. (2006). Self-referential processing in our brain—a meta-analysis of imaging studies on the self. Neuroimage, 31, 440–457.CrossRefGoogle ScholarPubMed
Pessoa, L. (2008). On the relationship between emotion and cognition. Nature Reviews Neuroscience, 9, 148.CrossRefGoogle ScholarPubMed
Pessoa, L. (2017). A network model of the emotional brain. Trends in Cognitive Sciences, 21, 357–371.CrossRefGoogle ScholarPubMed
Pizzagalli, D. A. (2011). Frontocingulate dysfunction in depression: Toward biomarkers of treatment response. Neuropsychopharmacology, 36, 183–206.CrossRefGoogle ScholarPubMed
Plitt, M., Barnes, K. A., & Martin, A. (2015). Functional connectivity classification of autism identifies highly predictive brain features but falls short of biomarker standards. NeuroImage: Clinical, 7, 359–366.CrossRefGoogle ScholarPubMed
Polk, D. E., Cohen, S., Doyle, W. J., Skoner, D. P., & Kirschbaum, C. (2005). State and trait affect as predictors of salivary cortisol in healthy adults. Psychoneuroendocrinology, 30, 261–272.CrossRefGoogle ScholarPubMed
Poole, V. N., Robinson, M. E., Singleton, O., DeGutis, J., Milberg, W. P., McGlinchey, R. E., … Esterman, M. (2016). Intrinsic functional connectivity predicts individual differences in distractibility. Neuropsychologia, 86, 176–182.CrossRefGoogle ScholarPubMed
Power, J. D., Cohen, A. L., Nelson, S. M., Wig, G. S., Barnes, K. A., Church, J. A., … Schlaggar, B. L. (2011). Functional network organization of the human brain. Neuron, 72, 665–678.CrossRefGoogle ScholarPubMed
Power, J. D., Mitra, A., Laumann, T. O., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2014). Methods to detect, characterize, and remove motion artifact in resting state fMRI. NeuroImage, 84, 320–341.CrossRefGoogle ScholarPubMed
Power, J. D., Plitt, M., Laumann, T. O., & Martin, A. (2017). Sources and implications of whole-brain fMRI signals in humans. 146, 609–625.Google ScholarPubMed
Raichle, M. E. (2015). The restless brain: How intrinsic activity organizes brain function. Philosophical Transactions of the Royal Society B: Biological Sciences, 370, 20140172.CrossRefGoogle ScholarPubMed
Reddy, P. G., Mattar, M. G., Murphy, A. C., Wymbs, N. F., Grafton, S. T., Satterthwaite, T. D., & Bassett, D. S. (2018). Brain state flexibility accompanies motor-skill acquisition. NeuroImage, 171, 135–147.CrossRefGoogle ScholarPubMed
Russell, J. A. (1980). A circumplex model of affect. Journal of Personality and Social Psychology, 39, 1161.CrossRefGoogle Scholar
Rutherford, E., MacLeod, C., & Campbell, L. (2004). BRIEF REPORT negative selectivity effects and emotional selectivity effects in anxiety: Differential attentional correlates of state and trait variables. Cognition and Emotion, 18, 711–720.CrossRefGoogle Scholar
Savitz, J., & Drevets, W. C. (2009). Bipolar and major depressive disorder: Neuroimaging the developmental-degenerative divide. Neuroscience & Biobehavioral Reviews, 33, 699–771.CrossRefGoogle ScholarPubMed
Scalabrini, A., Huang, Z., Mucci, C., Perrucci, M. G., Ferretti, A., Fossati, A., … Ebisch, S. J. (2017). How spontaneous brain activity and narcissistic features shape social interaction. Scientific Reports, 7, 9986.CrossRefGoogle ScholarPubMed
Scalabrini, A., Mucci, C., & Northoff, G. (2018). Is our self related to personality? A neuropsychodynamic model. Frontiers in Human Neuroscience, 12, 346.CrossRefGoogle ScholarPubMed
Sheline, Y. I., Barch, D. M., Price, J. L., Rundle, M. M., Vaishnavi, S. N., Snyder, A. Z., … Raichle, M. E. (2009). The default mode network and self-referential processes in depression. Proceedings of the National Academy of Sciences of the United States of America, 106, 1942–1947.CrossRefGoogle ScholarPubMed
Shim, G., Oh, J. S., Jung, W. H., Jang, J. H., Choi, C.-H., Kim, E., … Kwon, J. S. (2010). Altered resting-state connectivity in subjects at ultra-high risk for psychosis: An fMRI study. Behavioral and Brain Functions, 6, 58.CrossRefGoogle Scholar
Spreng, R. N. (2012). The fallacy of a “task-negative” network. Frontiers in Psychology, 3, 145.CrossRefGoogle ScholarPubMed
Sun, J., Liu, Z., Rolls, E. T., Chen, Q., Yao, Y., Yang, W., … Feng, J. (2019). Verbal creativity correlates with the temporal variability of brain networks during the resting state. Cerebral Cortex, 29, 1047–1058.CrossRefGoogle ScholarPubMed
Tackett, J. L., Waldman, I. D., Van Hulle, C. A., & Lahey, B. B. (2011). Shared genetic influences on negative emotionality and major depression/conduct disorder comorbidity. Journal of the American Academy of Child & Adolescent Psychiatry, 50, 818–827.CrossRefGoogle ScholarPubMed
Tavor, I., Jones, O. P., Mars, R., Smith, S., Behrens, T., & Jbabdi, S. (2016). Task-free MRI predicts individual differences in brain activity during task performance. Science, 352, 216–220.CrossRefGoogle ScholarPubMed
Tepper, B. J., Duffy, M. K., Henle, C. A., & Lambert, L. S. (2006). Procedural injustice, victim precipitation, and abusive supervision. Personnel Psychology, 59, 101–123.CrossRefGoogle Scholar
Vartanian, O. (2009). Variable attention facilitates creative problem solving. Psychology of Aesthetics, Creativity, and the Arts, 3, 57.CrossRefGoogle Scholar
Vincent, J. L., Kahn, I., Snyder, A. Z., Raichle, M. E., & Buckner, R. L. (2008). Evidence for a frontoparietal control system revealed by intrinsic functional connectivity. Journal of Neurophysiology, 100, 3328–3342.CrossRefGoogle ScholarPubMed
Viola, B., Galina, S., Johan, V. D. M., Micha?, B., J?rg, F., Linda, LA,M,S-RC, … Tobias, N. (2018). Exposure to attachment narratives dynamically modulates cortical arousal during the resting state in the listener. Brain & Behavior, e01007.Google Scholar
Watson, D., & Clark, L. A. (1984). Negative affectivity: The disposition to experience aversive emotional states. Psychological Bulletin, 96, 465.CrossRefGoogle ScholarPubMed
Watson, D., Clark, L. A., & Tellegen, A. (1988). Development and validation of brief measures of positive and negative affect: The PANAS scales. Journal of Personality and Social Psychology, 54, 1063.CrossRefGoogle ScholarPubMed
Watson, D., & Walker, L. M. (1996). The long-term stability and predictive validity of trait measures of affect. Journal of Personality and Social Psychology, 70, 567–577.CrossRefGoogle ScholarPubMed
Whitton, A. E., Deccy, S., Ironside, M. L., Kumar, P., Beltzer, M., & Pizzagalli, D. A. (2018). Electroencephalography source functional connectivity reveals abnormal high-frequency communication among large-scale functional networks in depression. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 3, 50–58.Google ScholarPubMed
Yang, Z., Gu, S., Honnorat, N., Linn, K. A., Shinohara, R. T., Aselcioglu, I., … Satterthwaite, T. D. (2018). Network changes associated with transdiagnostic depressive symptom improvement following cognitive behavioral therapy in MDD and PTSD. Molecular Psychiatry, 23, 2314–2323.CrossRefGoogle ScholarPubMed
Yang, T., & Huang, H. (2003). An epidemiological study on stress among urban residents in social transition period. Zhonghua liu xing bing xue za zhi= Zhonghua liuxingbingxue zazhi, 24, 760–764.Google ScholarPubMed
Yeo, B. T., Tandi, J., & Chee, M. W. (2015). Functional connectivity during rested wakefulness predicts vulnerability to sleep deprivation. Neuroimage, 111, 147–158.CrossRefGoogle ScholarPubMed
Yu, Q., Erhardt, E. B., Sui, J., Du, Y., He, H., Hjelm, D., … Pearlson, G. (2015). Assessing dynamic brain graphs of time-varying connectivity in fMRI data: Application to healthy controls and patients with schizophrenia. Neuroimage, 107, 345–355.CrossRefGoogle ScholarPubMed
Zanto, T. P., & Gazzaley, A. (2013). Fronto-parietal network: Flexible hub of cognitive control. Trends in Cognitive Sciences, 17, 602–603.CrossRefGoogle ScholarPubMed
Zelenski, J. M., & Larsen, R. J. (2000). The distribution of basic emotions in everyday life: A state and trait perspective from experience sampling data. Journal of Research in Personality, 34, 178–197.CrossRefGoogle Scholar
Zhang, J., Cheng, W., Liu, Z., Zhang, K., Lei, X., Yao, Y., … Lu, G. (2016). Neural, electrophysiological and anatomical basis of brain-network variability and its characteristic changes in mental disorders. Brain, 139, 2307–2321.CrossRefGoogle ScholarPubMed
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