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Lechner S, Northoff G. Abnormal resting-state EEG phase dynamics distinguishes major depressive disorder and bipolar disorder. J Affect Disord 2024; 359:269-276. [PMID: 38795776 DOI: 10.1016/j.jad.2024.05.095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 04/22/2024] [Accepted: 05/18/2024] [Indexed: 05/28/2024]
Abstract
Changes in EEG have been reported in both major depressive disorder (MDD) and bipolar disorder (BD). Specifically, power changes in EEG alpha and theta frequency bands during rest and task are known in both disorders. This leaves open whether there are changes in yet another component of the electrophysiological EEG signal, namely phase-related processes that may allow for distinguishing MDD and BD. For that purpose, we investigate EEG-based spontaneous phase in the resting state of MDD, BD and healthy controls. Our main findings show: (i) decreased spontaneous phase variability in frontal theta of both MDD and BD compared to HC; (ii) decreased spontaneous phase variability in central-parietal alpha in MDD compared to both BD and HC; (iii) increased delays or lags of alpha phase cycles in MDD (but not in BD), which (iv) correlate with the decreased phase variability in MDD. Together, we show similar (decreased frontal theta variability) and distinct (decreased central-parietal alpha variability with increased lags or delays) findings in the spontaneous phase dynamics of MDD and BD. This suggests potential relevance of theta and alpha phase dynamics in distinguishing MDD and BD in clinical differential-diagnosis.
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Affiliation(s)
- Stephan Lechner
- The Royal's Institute of Mental Health Research, Brain and Mind Research Institute, University of Ottawa, Ottawa, ON K1Z 7K4, Canada; Research Group Neuroinformatics, Faculty of Computer Science, University of Vienna, 1010 Vienna, Austria; Vienna Doctoral School Cognition, Behavior and Neuroscience, University of Vienna, 1030 Vienna, Austria.
| | - Georg Northoff
- The Royal's Institute of Mental Health Research, Brain and Mind Research Institute, University of Ottawa, Ottawa, ON K1Z 7K4, Canada
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2
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Veréb D, Szabó N, Kincses B, Szücs-Bencze L, Faragó P, Csomós M, Antal S, Kocsis K, Tuka B, Kincses ZT. Imbalanced temporal states of cortical blood-oxygen-level-dependent signal variability during rest in episodic migraine. J Headache Pain 2024; 25:114. [PMID: 39014299 PMCID: PMC11251240 DOI: 10.1186/s10194-024-01824-0] [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: 05/12/2024] [Accepted: 07/10/2024] [Indexed: 07/18/2024] Open
Abstract
BACKGROUND Migraine has been associated with functional brain changes including altered connectivity and activity both during and between headache attacks. Recent studies established that the variability of the blood-oxygen-level-dependent (BOLD) signal is an important attribute of brain activity, which has so far been understudied in migraine. In this study, we investigate how time-varying measures of BOLD variability change interictally in episodic migraine patients. METHODS Two independent resting state functional MRI datasets acquired on 3T (discovery cohort) and 1.5T MRI scanners (replication cohort) including 99 episodic migraine patients (n3T = 42, n1.5T=57) and 78 healthy controls (n3T = 46, n1.5T=32) were analyzed in this cross-sectional study. A framework using time-varying measures of BOLD variability was applied to derive BOLD variability states. Descriptors of BOLD variability states such as dwell time and fractional occupancy were calculated, then compared between migraine patients and healthy controls using Mann-Whitney U-tests. Spearman's rank correlation was calculated to test associations with clinical parameters. RESULTS Resting-state activity was characterized by states of high and low BOLD signal variability. Migraine patients in the discovery cohort spent more time in the low variability state (mean dwell time: p = 0.014, median dwell time: p = 0.022, maximum dwell time: p = 0.013, fractional occupancy: p = 0.013) and less time in the high variability state (mean dwell time: p = 0.021, median dwell time: p = 0.021, maximum dwell time: p = 0.025, fractional occupancy: p = 0.013). Higher uptime of the low variability state was associated with greater disability as measured by MIDAS scores (maximum dwell time: R = 0.45, p = 0.007; fractional occupancy: R = 0.36, p = 0.035). Similar results were observed in the replication cohort. CONCLUSION Episodic migraine patients spend more time in a state of low BOLD variability during rest in headache-free periods, which is associated with greater disability. BOLD variability states show potential as a replicable functional imaging marker in episodic migraine.
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Affiliation(s)
- Dániel Veréb
- Department of Radiology, Albert Szent-Györgyi Health Centre, University of Szeged, Semmelweis u. 6, Szeged, 6725, Hungary.
| | - Nikoletta Szabó
- Department of Neurology, Albert Szent-Györgyi Health Centre, University of Szeged, Szeged, Hungary
| | - Bálint Kincses
- Institute for Diagnostic and Interventional Radiology and Neuroradiology, University Medicine Essen, Essen, Germany
- Department of Neurology, Center for Translational Neuro- and Behavioural Sciences, University Medicine Essen, Essen, Germany
| | - Laura Szücs-Bencze
- Department of Neurology, Albert Szent-Györgyi Health Centre, University of Szeged, Szeged, Hungary
| | - Péter Faragó
- Department of Neurology, Albert Szent-Györgyi Health Centre, University of Szeged, Szeged, Hungary
| | - Máté Csomós
- Department of Radiology, Albert Szent-Györgyi Health Centre, University of Szeged, Semmelweis u. 6, Szeged, 6725, Hungary
| | - Szabolcs Antal
- Department of Radiology, Albert Szent-Györgyi Health Centre, University of Szeged, Semmelweis u. 6, Szeged, 6725, Hungary
| | - Krisztián Kocsis
- Department of Radiology, Albert Szent-Györgyi Health Centre, University of Szeged, Semmelweis u. 6, Szeged, 6725, Hungary
| | - Bernadett Tuka
- Department of Radiology, Albert Szent-Györgyi Health Centre, University of Szeged, Semmelweis u. 6, Szeged, 6725, Hungary
| | - Zsigmond Tamás Kincses
- Department of Radiology, Albert Szent-Györgyi Health Centre, University of Szeged, Semmelweis u. 6, Szeged, 6725, Hungary
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3
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Wehrheim MH, Faskowitz J, Schubert A, Fiebach CJ. Reliability of variability and complexity measures for task and task-free BOLD fMRI. Hum Brain Mapp 2024; 45:e26778. [PMID: 38980175 PMCID: PMC11232465 DOI: 10.1002/hbm.26778] [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: 12/21/2023] [Revised: 05/06/2024] [Accepted: 06/24/2024] [Indexed: 07/10/2024] Open
Abstract
Brain activity continuously fluctuates over time, even if the brain is in controlled (e.g., experimentally induced) states. Recent years have seen an increasing interest in understanding the complexity of these temporal variations, for example with respect to developmental changes in brain function or between-person differences in healthy and clinical populations. However, the psychometric reliability of brain signal variability and complexity measures-which is an important precondition for robust individual differences as well as longitudinal research-is not yet sufficiently studied. We examined reliability (split-half correlations) and test-retest correlations for task-free (resting-state) BOLD fMRI as well as split-half correlations for seven functional task data sets from the Human Connectome Project to evaluate their reliability. We observed good to excellent split-half reliability for temporal variability measures derived from rest and task fMRI activation time series (standard deviation, mean absolute successive difference, mean squared successive difference), and moderate test-retest correlations for the same variability measures under rest conditions. Brain signal complexity estimates (several entropy and dimensionality measures) showed moderate to good reliabilities under both, rest and task activation conditions. We calculated the same measures also for time-resolved (dynamic) functional connectivity time series and observed moderate to good reliabilities for variability measures, but poor reliabilities for complexity measures derived from functional connectivity time series. Global (i.e., mean across cortical regions) measures tended to show higher reliability than region-specific variability or complexity estimates. Larger subcortical regions showed similar reliability as cortical regions, but small regions showed lower reliability, especially for complexity measures. Lastly, we also show that reliability scores are only minorly dependent on differences in scan length and replicate our results across different parcellation and denoising strategies. These results suggest that the variability and complexity of BOLD activation time series are robust measures well-suited for individual differences research. Temporal variability of global functional connectivity over time provides an important novel approach to robustly quantifying the dynamics of brain function. PRACTITIONER POINTS: Variability and complexity measures of BOLD activation show good split-half reliability and moderate test-retest reliability. Measures of variability of global functional connectivity over time can robustly quantify neural dynamics. Length of fMRI data has only a minor effect on reliability.
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Affiliation(s)
- Maren H. Wehrheim
- Department of PsychologyGoethe University FrankfurtFrankfurtGermany
- Department of Computer Science and MathematicsGoethe University FrankfurtFrankfurtGermany
- Frankfurt Institute for Advanced Studies (FIAS)FrankfurtGermany
| | - Joshua Faskowitz
- Department of Psychological and Brain SciencesIndiana UniversityBloomingtonUSA
| | - Anna‐Lena Schubert
- Department of PsychologyJohannes Gutenberg‐Universität MainzMainzGermany
| | - Christian J. Fiebach
- Department of PsychologyGoethe University FrankfurtFrankfurtGermany
- Brain Imaging CenterGoethe University FrankfurtFrankfurtGermany
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4
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Garke MÅ, Hentati Isacsson N, Kolbeinsson Ö, Hesser H, Månsson KNT. Improvements in emotion regulation during cognitive behavior therapy predict subsequent social anxiety reductions. Cogn Behav Ther 2024:1-18. [PMID: 38985458 DOI: 10.1080/16506073.2024.2373784] [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: 02/02/2023] [Accepted: 06/17/2024] [Indexed: 07/11/2024]
Abstract
Individuals with social anxiety disorder (SAD) experience overall emotion regulation difficulties, but less is known about the long-term role of such difficulties in cognitive behavior therapy (CBT) for SAD. Forty-six patients with SAD receiving internet-delivered CBT, and matched healthy controls (HCs; n = 39), self-reported the Difficulties in Emotion Regulation Scale (DERS), Liebowitz Social Anxiety Scale (LSAS-SR), and participated in anticipatory speech anxiety behavioral experiments. Patients were measured at seven time points before, during and after CBT over a total period of 28 months, and HCs at two timepoints. Disaggregated growth curve models with a total of 263 observations were used, as well as intra-class correlation coefficients and regression models. Patients' LSAS-SR and DERS ratings were reliable (ICC = .83 and .75 respectively), and patients, relative to controls, showed larger difficulties in emotion regulation at pre-treatment (p < .001). During CBT, within-individual improvements in emotion regulation significantly predicted later LSAS-SR reductions (p = .041, pseudo-R2 = 43%). Changes in emotion regulation may thus be important to monitor on an individual level and may be used to improve outcomes in future developments of internet-delivered CBT.
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Affiliation(s)
- Maria Å Garke
- Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, & Stockholm Health Care Services, Stockholm 171 77, Sweden
| | - Nils Hentati Isacsson
- Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, & Stockholm Health Care Services, Stockholm 171 77, Sweden
| | - Örn Kolbeinsson
- Department of Behavioural Sciences and Learning, Linköping University, Linkoping 581 83, Sweden
| | - Hugo Hesser
- Department of Behavioural Sciences and Learning, Linköping University, Linkoping 581 83, Sweden
- School of Law, Psychology and Social Work, Örebro University, Orebro 701 82, Sweden
| | - Kristoffer N T Månsson
- Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, & Stockholm Health Care Services, Stockholm 171 77, Sweden
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5
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Yang C, Biswal B, Cui Q, Jing X, Ao Y, Wang Y. Frequency-dependent alterations of global signal topography in patients with major depressive disorder. Psychol Med 2024; 54:2152-2161. [PMID: 38362834 DOI: 10.1017/s0033291724000254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/17/2024]
Abstract
BACKGROUND Major depressive disorder (MDD) is associated not only with disorders in multiple brain networks but also with frequency-specific brain activities. The abnormality of spatiotemporal networks in patients with MDD remains largely unclear. METHODS We investigated the alterations of the global spatiotemporal network in MDD patients using a large-sample multicenter resting-state functional magnetic resonance imaging dataset. The spatiotemporal characteristics were measured by the variability of global signal (GS) and its correlation with local signals (GSCORR) at multiple frequency bands. The association between these indicators and clinical scores was further assessed. RESULTS The GS fluctuations were reduced in patients with MDD across the full frequency range (0-0.1852 Hz). The GSCORR was also reduced in the MDD group, especially in the relatively higher frequency range (0.0728-0.1852 Hz). Interestingly, these indicators showed positive correlations with depressive scores in the MDD group and relative negative correlations in the control group. CONCLUSION The GS and its spatiotemporal effects on local signals were weakened in patients with MDD, which may impair inter-regional synchronization and related functions. Patients with severe depression may use the compensatory mechanism to make up for the functional impairments.
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Affiliation(s)
- Chengxiao Yang
- Institute of Brain and Psychological Sciences, Sichuan Normal University, Chengdu 610066, China
| | - Bharat Biswal
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Qian Cui
- School of Public Affairs and Administration, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Xiujuan Jing
- Institute of Brain and Psychological Sciences, Sichuan Normal University, Chengdu 610066, China
| | - Yujia Ao
- Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada
| | - Yifeng Wang
- Institute of Brain and Psychological Sciences, Sichuan Normal University, Chengdu 610066, China
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Murray CH, Frohlich J, Haggarty CJ, Tare I, Lee R, de Wit H. Neural complexity is increased after low doses of LSD, but not moderate to high doses of oral THC or methamphetamine. Neuropsychopharmacology 2024; 49:1120-1128. [PMID: 38287172 PMCID: PMC11109226 DOI: 10.1038/s41386-024-01809-2] [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: 12/10/2023] [Revised: 01/07/2024] [Accepted: 01/17/2024] [Indexed: 01/31/2024]
Abstract
Neural complexity correlates with one's level of consciousness. During coma, anesthesia, and sleep, complexity is reduced. During altered states, including after lysergic acid diethylamide (LSD), complexity is increased. In the present analysis, we examined whether low doses of LSD (13 and 26 µg) were sufficient to increase neural complexity in the absence of altered states of consciousness. In addition, neural complexity was assessed after doses of two other drugs that significantly altered consciousness and mood: delta-9-tetrahydrocannabinol (THC; 7.5 and 15 mg) and methamphetamine (MA; 10 and 20 mg). In three separate studies (N = 73; 21, LSD; 23, THC; 29, MA), healthy volunteers received placebo or drug in a within-subjects design over three laboratory visits. During anticipated peak drug effects, resting state electroencephalography (EEG) recorded Limpel-Ziv complexity and spectral power. LSD, but not THC or MA, dose-dependently increased neural complexity. LSD also reduced delta and theta power. THC reduced, and MA increased, alpha power, primarily in frontal regions. Neural complexity was not associated with any subjective drug effect; however, LSD-induced reductions in delta and theta were associated with elation, and THC-induced reductions in alpha were associated with altered states. These data inform relationships between neural complexity, spectral power, and subjective states, demonstrating that increased neural complexity is not necessary or sufficient for altered states of consciousness. Future studies should address whether greater complexity after low doses of LSD is related to cognitive, behavioral, or therapeutic outcomes, and further examine the role of alpha desynchronization in mediating altered states of consciousness.
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Affiliation(s)
- Conor H Murray
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, 5841 S Maryland Ave, Chicago, IL, 60637, USA.
- Department of Psychiatry and Biobehavioral Sciences, University of Los Angeles, California, 760 Westwood Plaza, Los Angeles, CA, 90024, USA.
| | - Joel Frohlich
- Institute for Neuromodulation and Neurotechnology, University of Tübingen, Otfried-Müller-Straße 45, 72076, Tübingen, Germany
- Institute for Advanced Consciousness Studies, Santa Monica, California; 2811 Wilshire Blvd # 510, Santa Monica, CA, 90403, USA
| | - Connor J Haggarty
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, 5841 S Maryland Ave, Chicago, IL, 60637, USA
| | - Ilaria Tare
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, 5841 S Maryland Ave, Chicago, IL, 60637, USA
| | - Royce Lee
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, 5841 S Maryland Ave, Chicago, IL, 60637, USA
| | - Harriet de Wit
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, 5841 S Maryland Ave, Chicago, IL, 60637, USA
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7
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Page CE, Epperson CN, Novick AM, Duffy KA, Thompson SM. Beyond the serotonin deficit hypothesis: communicating a neuroplasticity framework of major depressive disorder. Mol Psychiatry 2024:10.1038/s41380-024-02625-2. [PMID: 38816586 DOI: 10.1038/s41380-024-02625-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 05/15/2024] [Accepted: 05/21/2024] [Indexed: 06/01/2024]
Abstract
The serotonin deficit hypothesis explanation for major depressive disorder (MDD) has persisted among clinicians and the general public alike despite insufficient supporting evidence. To combat rising mental health crises and eroding public trust in science and medicine, researchers and clinicians must be able to communicate to patients and the public an updated framework of MDD: one that is (1) accessible to a general audience, (2) accurately integrates current evidence about the efficacy of conventional serotonergic antidepressants with broader and deeper understandings of pathophysiology and treatment, and (3) capable of accommodating new evidence. In this article, we summarize a framework for the pathophysiology and treatment of MDD that is informed by clinical and preclinical research in psychiatry and neuroscience. First, we discuss how MDD can be understood as inflexibility in cognitive and emotional brain circuits that involves a persistent negativity bias. Second, we discuss how effective treatments for MDD enhance mechanisms of neuroplasticity-including via serotonergic interventions-to restore synaptic, network, and behavioral function in ways that facilitate adaptive cognitive and emotional processing. These treatments include typical monoaminergic antidepressants, novel antidepressants like ketamine and psychedelics, and psychotherapy and neuromodulation techniques. At the end of the article, we discuss this framework from the perspective of effective science communication and provide useful language and metaphors for researchers, clinicians, and other professionals discussing MDD with a general or patient audience.
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Affiliation(s)
- Chloe E Page
- Department of Psychiatry, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - C Neill Epperson
- Department of Psychiatry, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
- Department of Family Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
- Helen and Arthur E. Johnson Depression Center, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Andrew M Novick
- Department of Psychiatry, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Korrina A Duffy
- Department of Psychiatry, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Scott M Thompson
- Department of Psychiatry, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
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8
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Yu Y, Oh Y, Kounios J, Beeman M. Electroencephalography Spectral-power Volatility Predicts Problem-solving Outcomes. J Cogn Neurosci 2024; 36:901-915. [PMID: 38437171 DOI: 10.1162/jocn_a_02136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2024]
Abstract
Temporal variability is a fundamental property of brain processes and is functionally important to human cognition. This study examined how fluctuations in neural oscillatory activity are related to problem-solving performance as one example of how temporal variability affects high-level cognition. We used volatility to assess step-by-step fluctuations of EEG spectral power while individuals attempted to solve word-association puzzles. Inspired by recent results with hidden-state modeling, we tested the hypothesis that spectral-power volatility is directly associated with problem-solving outcomes. As predicted, volatility was lower during trials solved with insight compared with those solved analytically. Moreover, volatility during prestimulus preparation for problem-solving predicted solving outcomes, including solving success and solving time. These novel findings were replicated in a separate data set from an anagram-solving task, suggesting that less-rapid transitions between neural oscillatory synchronization and desynchronization predict better solving performance and are conducive to solving with insight for these types of problems. Thus, volatility can be a valuable index of cognition-related brain dynamics.
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Affiliation(s)
- Yuhua Yu
- Northwestern University, Evanston, IL
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9
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Casartelli L, Maronati C, Cavallo A. From neural noise to co-adaptability: Rethinking the multifaceted architecture of motor variability. Phys Life Rev 2023; 47:245-263. [PMID: 37976727 DOI: 10.1016/j.plrev.2023.10.036] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 10/27/2023] [Indexed: 11/19/2023]
Abstract
In the last decade, the source and the functional meaning of motor variability have attracted considerable attention in behavioral and brain sciences. This construct classically combined different levels of description, variable internal robustness or coherence, and multifaceted operational meanings. We provide here a comprehensive review of the literature with the primary aim of building a precise lexicon that goes beyond the generic and monolithic use of motor variability. In the pars destruens of the work, we model three domains of motor variability related to peculiar computational elements that influence fluctuations in motor outputs. Each domain is in turn characterized by multiple sub-domains. We begin with the domains of noise and differentiation. However, the main contribution of our model concerns the domain of adaptability, which refers to variation within the same exact motor representation. In particular, we use the terms learning and (social)fitting to specify the portions of motor variability that depend on our propensity to learn and on our largely constitutive propensity to be influenced by external factors. A particular focus is on motor variability in the context of the sub-domain named co-adaptability. Further groundbreaking challenges arise in the modeling of motor variability. Therefore, in a separate pars construens, we attempt to characterize these challenges, addressing both theoretical and experimental aspects as well as potential clinical implications for neurorehabilitation. All in all, our work suggests that motor variability is neither simply detrimental nor beneficial, and that studying its fluctuations can provide meaningful insights for future research.
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Affiliation(s)
- Luca Casartelli
- Theoretical and Cognitive Neuroscience Unit, Scientific Institute IRCCS E. MEDEA, Italy
| | - Camilla Maronati
- Move'n'Brains Lab, Department of Psychology, Università degli Studi di Torino, Italy
| | - Andrea Cavallo
- Move'n'Brains Lab, Department of Psychology, Università degli Studi di Torino, Italy; C'MoN Unit, Fondazione Istituto Italiano di Tecnologia, Genova, Italy.
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Wei W, Deng L, Qiao C, Yin Y, Zhang Y, Li X, Yu H, Jian L, Li M, Guo W, Wang Q, Deng W, Ma X, Zhao L, Sham PC, Palaniyappan L, Li T. Neural variability in three major psychiatric disorders. Mol Psychiatry 2023; 28:5217-5227. [PMID: 37443193 DOI: 10.1038/s41380-023-02164-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Revised: 06/16/2023] [Accepted: 06/23/2023] [Indexed: 07/15/2023]
Abstract
Across the major psychiatric disorders (MPDs), a shared disruption in brain physiology is suspected. Here we investigate the neural variability at rest, a well-established behavior-relevant marker of brain function, and probe its basis in gene expression and neurotransmitter receptor profiles across the MPDs. We recruited 219 healthy controls and 279 patients with schizophrenia, major depressive disorder, or bipolar disorders (manic or depressive state). The standard deviation of blood oxygenation level-dependent signal (SDBOLD) obtained from resting-state fMRI was used to characterize neural variability. Transdiagnostic disruptions in SDBOLD patterns and their relationships with clinical symptoms and cognitive functions were tested by partial least-squares correlation. Moving beyond the clinical sample, spatial correlations between the observed patterns of SDBOLD disruption and postmortem gene expressions, Neurosynth meta-analytic cognitive functions, and neurotransmitter receptor profiles were estimated. Two transdiagnostic patterns of disrupted SDBOLD were discovered. Pattern 1 is exhibited in all diagnostic groups and is most pronounced in schizophrenia, characterized by higher SDBOLD in the language/auditory networks but lower SDBOLD in the default mode/sensorimotor networks. In comparison, pattern 2 is only exhibited in unipolar and bipolar depression, characterized by higher SDBOLD in the default mode/salience networks but lower SDBOLD in the sensorimotor network. The expression of pattern 1 related to the severity of clinical symptoms and cognitive deficits across MPDs. The two disrupted patterns had distinct spatial correlations with gene expressions (e.g., neuronal projections/cellular processes), meta-analytic cognitive functions (e.g., language/memory), and neurotransmitter receptor expression profiles (e.g., D2/serotonin/opioid receptors). In conclusion, neural variability is a potential transdiagnostic biomarker of MPDs with a substantial amount of its spatial distribution explained by gene expressions and neurotransmitter receptor profiles. The pathophysiology of MPDs can be traced through the measures of neural variability at rest, with varying clinical-cognitive profiles arising from differential spatial patterns of aberrant variability.
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Affiliation(s)
- Wei Wei
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital and School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310013, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou, 311121, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou, 310058, China
| | - Lihong Deng
- Psychiatric Laboratory and Mental Health Center, The State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Chunxia Qiao
- Psychiatric Laboratory and Mental Health Center, The State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Yubing Yin
- Psychiatric Laboratory and Mental Health Center, The State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Yamin Zhang
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital and School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310013, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou, 311121, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou, 310058, China
| | - Xiaojing Li
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital and School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310013, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou, 311121, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou, 310058, China
| | - Hua Yu
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital and School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310013, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou, 311121, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou, 310058, China
| | - Lingqi Jian
- Psychiatric Laboratory and Mental Health Center, The State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Mingli Li
- Psychiatric Laboratory and Mental Health Center, The State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Wanjun Guo
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital and School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310013, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou, 311121, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou, 310058, China
| | - Qiang Wang
- Psychiatric Laboratory and Mental Health Center, The State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Wei Deng
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital and School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310013, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou, 311121, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou, 310058, China
| | - Xiaohong Ma
- Psychiatric Laboratory and Mental Health Center, The State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Liansheng Zhao
- Psychiatric Laboratory and Mental Health Center, The State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Pak C Sham
- Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- Centre for PanorOmic Sciences, The University of Hong Kong, Hong Kong SAR, China
- State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong SAR, China
| | - Lena Palaniyappan
- Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada.
- Robarts Research Institute, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada.
| | - Tao Li
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital and School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310013, China.
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou, 311121, China.
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou, 310058, China.
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11
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Lindahl J, Asp M, Ståhl D, Tjernberg J, Eklund M, Björkstrand J, van Westen D, Jensen J, Månsson K, Tornberg Å, Svensson M, Deierborg T, Ventorp F, Lindqvist D. Add-on pramipexole for anhedonic depression: study protocol for a randomised controlled trial and open-label follow-up in Lund, Sweden. BMJ Open 2023; 13:e076900. [PMID: 38035737 PMCID: PMC10689415 DOI: 10.1136/bmjopen-2023-076900] [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: 06/20/2023] [Accepted: 11/03/2023] [Indexed: 12/02/2023] Open
Abstract
INTRODUCTION Many depressed patients do not achieve remission with available treatments. Anhedonia is a common residual symptom associated with treatment resistance as well as low function and quality of life. There are currently no specific and effective treatments for anhedonia. Some trials have shown that dopamine agonist pramipexole is efficacious for treating depression, but more data is needed before it could become ready for clinical prime time. Given its mechanism of action, pramipexole might be a useful treatment for a depression subtype characterised by significant anhedonia and lack of motivation-symptoms associated with dopaminergic hypofunction. We recently showed, in an open-label pilot study, that add-on pramipexole is a feasible treatment for depression with significant anhedonia, and that pramipexole increases reward-related activity in the ventral striatum. We will now confirm or refute these preliminary results in a randomised controlled trial (RCT) and an open-label follow-up study. METHODS AND ANALYSIS Eighty patients with major depression (bipolar or unipolar) or dysthymia and significant anhedonia according to the Snaith Hamilton Pleasure Scale (SHAPS) are randomised to either add-on pramipexole or placebo for 9 weeks. Change in anhedonia symptoms per the SHAPS is the primary outcome, and secondary outcomes include change in core depressive symptoms, apathy, sleep problems, life quality, anxiety and side effects. Accelerometers are used to assess treatment-associated changes in physical activity and sleep patterns. Blood and brain biomarkers are investigated as treatment predictors and to establish target engagement. After the RCT phase, patients continue with open-label treatment in a 6-month follow-up study aiming to assess long-term efficacy and tolerability of pramipexole. ETHICS AND DISSEMINATION The study has been approved by the Swedish Ethical Review Authority and the Swedish Medical Products Agency. The study is externally monitored according to Good Clinical Practice guidelines. Results will be disseminated via conference presentations and peer-reviewed publications. TRIAL REGISTRATION NUMBER NCT05355337 and NCT05825235.
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Affiliation(s)
- Jesper Lindahl
- Unit for Biological and Precision Psychiatry, Department of Clinical Sciences Lund, Lund University, Lund, Sweden
- Office for Psychiatry and Habilitation, Psychiatric Clinic Lund, Region Skåne, Lund, Sweden
| | - Marie Asp
- Unit for Biological and Precision Psychiatry, Department of Clinical Sciences Lund, Lund University, Lund, Sweden
- Office for Psychiatry and Habilitation, Psychiatric Clinic Lund, Region Skåne, Lund, Sweden
| | - Darya Ståhl
- Unit for Biological and Precision Psychiatry, Department of Clinical Sciences Lund, Lund University, Lund, Sweden
| | - Johanna Tjernberg
- Unit for Biological and Precision Psychiatry, Department of Clinical Sciences Lund, Lund University, Lund, Sweden
- Office for Psychiatry and Habilitation, Psychiatry Research Skåne, Region Skåne, Lund, Sweden
| | - Moa Eklund
- Unit for Biological and Precision Psychiatry, Department of Clinical Sciences Lund, Lund University, Lund, Sweden
- Office for Psychiatry and Habilitation, Psychiatric Clinic Lund, Region Skåne, Lund, Sweden
| | | | - Danielle van Westen
- Diagnostic Radiology, Department of Clinical Sciences Lund, Lund University, Lund, Sweden
- Image and Function, Skåne University Hospital, Lund, Sweden
| | - Jimmy Jensen
- Department of Psychology, Kristianstad University, Kristianstad, Sweden
| | - Kristoffer Månsson
- Center for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Åsa Tornberg
- Department of Health Sciences, Lund University, Lund, Sweden
| | - Martina Svensson
- Experimental Neuroinflammation Laboratory, Department of Experimental Medical Science, Lund University, Lund, Sweden
| | - Tomas Deierborg
- Experimental Neuroinflammation Laboratory, Department of Experimental Medical Science, Lund University, Lund, Sweden
| | - Filip Ventorp
- Unit for Biological and Precision Psychiatry, Department of Clinical Sciences Lund, Lund University, Lund, Sweden
- Office for Psychiatry and Habilitation, Psychiatric Clinic Lund, Region Skåne, Lund, Sweden
| | - Daniel Lindqvist
- Unit for Biological and Precision Psychiatry, Department of Clinical Sciences Lund, Lund University, Lund, Sweden
- Office for Psychiatry and Habilitation, Psychiatry Research Skåne, Region Skåne, Lund, Sweden
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12
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Steinberg SN, King TZ. Within-Individual BOLD Signal Variability and its Implications for Task-Based Cognition: A Systematic Review. Neuropsychol Rev 2023:10.1007/s11065-023-09619-x. [PMID: 37889371 DOI: 10.1007/s11065-023-09619-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 09/08/2023] [Indexed: 10/28/2023]
Abstract
Within-individual blood oxygen level-dependent (BOLD) signal variability, intrinsic moment-to-moment signal fluctuations within a single individual in specific voxels across a given time course, is a relatively new metric recognized in the neuroimaging literature. Within-individual BOLD signal variability has been postulated to provide information beyond that provided by mean-based analysis. Synthesis of the literature using within-individual BOLD signal variability methodology to examine various cognitive domains is needed to understand how intrinsic signal fluctuations contribute to optimal performance. This systematic review summarizes and integrates this literature to assess task-based cognitive performance in healthy groups and few clinical groups. Included papers were published through October 17, 2022. Searches were conducted on PubMed and APA PsycInfo. Studies eligible for inclusion used within-individual BOLD signal variability methodology to examine BOLD signal fluctuations during task-based functional magnetic resonance imaging (fMRI) and/or examined relationships between task-based BOLD signal variability and out-of-scanner behavioral measure performance, were in English, and were empirical research studies. Data from each of the included 19 studies were extracted and study quality was systematically assessed. Results suggest that variability patterns for different cognitive domains across the lifespan (ages 7-85) may depend on task demands, measures, variability quantification method used, and age. As neuroimaging methods explore individual-level contributions to cognition, within-individual BOLD signal variability may be a meaningful metric that can inform understanding of neurocognitive performance. Further research in understudied domains/populations, and with consistent quantification methods/cognitive measures, will help conceptualize how intrinsic BOLD variability impacts cognitive abilities in healthy and clinical groups.
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Affiliation(s)
- Stephanie N Steinberg
- Department of Psychology, Georgia State University, Urban Life Building, 11th Floor, 140 Decatur St, Atlanta, GA, 30303, USA
| | - Tricia Z King
- Department of Psychology, Georgia State University, Urban Life Building, 11th Floor, 140 Decatur St, Atlanta, GA, 30303, USA.
- Neuroscience Institute, Georgia State University, Atlanta, GA, 30302, USA.
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13
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Baracchini G, Zhou Y, da Silva Castanheira J, Hansen JY, Rieck J, Turner GR, Grady CL, Misic B, Nomi J, Uddin LQ, Spreng RN. The biological role of local and global fMRI BOLD signal variability in human brain organization. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.22.563476. [PMID: 37961684 PMCID: PMC10634715 DOI: 10.1101/2023.10.22.563476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Variability drives the organization and behavior of complex systems, including the human brain. Understanding the variability of brain signals is thus necessary to broaden our window into brain function and behavior. Few empirical investigations of macroscale brain signal variability have yet been undertaken, given the difficulty in separating biological sources of variance from artefactual noise. Here, we characterize the temporal variability of the most predominant macroscale brain signal, the fMRI BOLD signal, and systematically investigate its statistical, topographical and neurobiological properties. We contrast fMRI acquisition protocols, and integrate across histology, microstructure, transcriptomics, neurotransmitter receptor and metabolic data, fMRI static connectivity, and empirical and simulated magnetoencephalography data. We show that BOLD signal variability represents a spatially heterogeneous, central property of multi-scale multi-modal brain organization, distinct from noise. Our work establishes the biological relevance of BOLD signal variability and provides a lens on brain stochasticity across spatial and temporal scales.
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Affiliation(s)
- Giulia Baracchini
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Yigu Zhou
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Jason da Silva Castanheira
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Justine Y. Hansen
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | | | - Gary R. Turner
- Department of Psychology, York University, Toronto, ON, Canada
| | - Cheryl L. Grady
- Rotman Research Institute at Baycrest, and Department of Psychiatry and Psychology, University of Toronto, Toronto, ON, Canada
| | - Bratislav Misic
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Jason Nomi
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, USA
| | - Lucina Q. Uddin
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, USA
| | - R. Nathan Spreng
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
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14
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Grady CL, Rieck JR, Baracchini G, DeSouza B. Relation of resting brain signal variability to cognitive and socioemotional measures in an adult lifespan sample. Soc Cogn Affect Neurosci 2023; 18:nsad044. [PMID: 37698268 PMCID: PMC10508322 DOI: 10.1093/scan/nsad044] [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: 08/25/2022] [Revised: 07/09/2023] [Accepted: 09/06/2023] [Indexed: 09/13/2023] Open
Abstract
Temporal variability of the fMRI-derived blood-oxygen-level-dependent (BOLD) signal during cognitive tasks shows important associations with individual differences in age and performance. Less is known about relations between spontaneous BOLD variability measured at rest and relatively stable cognitive measures, such as IQ or socioemotional function. Here, we examined associations among resting BOLD variability, cognitive/socioemotional scores from the NIH Toolbox and optimal time of day for alertness (chronotype) in a sample of 157 adults from 20 to 86 years of age. To investigate individual differences in these associations independently of age, we regressed age out from both behavioral and BOLD variability scores. We hypothesized that greater BOLD variability would be related to higher fluid cognition scores, more positive scores on socioemotional scales and a morningness chronotype. Consistent with this idea, we found positive correlations between resting BOLD variability, positive socioemotional scores (e.g. self-efficacy) and morning chronotype, as well as negative correlations between variability and negative emotional scores (e.g. loneliness). Unexpectedly, we found negative correlations between BOLD variability and fluid cognition. These results suggest that greater resting brain signal variability facilitates optimal socioemotional function and characterizes those with morning-type circadian rhythms, but individuals with greater fluid cognition may be more likely to show less temporal variability in spontaneous measures of BOLD activity.
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Affiliation(s)
- Cheryl L Grady
- Rotman Research Institute at Baycrest, Toronto, Ontario M6A 2E1, Canada
- Departments of Psychiatry and Psychology, University of Toronto, Toronto, Ontario M5S 3G3, Canada
| | - Jenny R Rieck
- Rotman Research Institute at Baycrest, Toronto, Ontario M6A 2E1, Canada
| | - Giulia Baracchini
- Department of Neurology and Neurosurgery, Montréal Neurological Institute, McGill University, Montréal, Quebec H3A 0G4, Canada
| | - Brennan DeSouza
- Rotman Research Institute at Baycrest, Toronto, Ontario M6A 2E1, Canada
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15
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Haines N, Sullivan-Toole H, Olino T. From Classical Methods to Generative Models: Tackling the Unreliability of Neuroscientific Measures in Mental Health Research. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023; 8:822-831. [PMID: 36997406 PMCID: PMC10333448 DOI: 10.1016/j.bpsc.2023.01.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 12/28/2022] [Accepted: 01/03/2023] [Indexed: 01/13/2023]
Abstract
Advances in computational statistics and corresponding shifts in funding initiatives over the past few decades have led to a proliferation of neuroscientific measures being developed in the context of mental health research. Although such measures have undoubtedly deepened our understanding of neural mechanisms underlying cognitive, affective, and behavioral processes associated with various mental health conditions, the clinical utility of such measures remains underwhelming. Recent commentaries point toward the poor reliability of neuroscientific measures to partially explain this lack of clinical translation. Here, we provide a concise theoretical overview of how unreliability impedes clinical translation of neuroscientific measures; discuss how various modeling principles, including those from hierarchical and structural equation modeling frameworks, can help to improve reliability; and demonstrate how to combine principles of hierarchical and structural modeling within the generative modeling framework to achieve more reliable, generalizable measures of brain-behavior relationships for use in mental health research.
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Affiliation(s)
- Nathaniel Haines
- Department of Data Science, Bayesian Beginnings LLC, Columbus, Ohio.
| | | | - Thomas Olino
- Department of Psychology, Temple University, Philadelphia, Pennsylvania
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16
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Wang B, Chen Y, Chen K, Lu H, Zhang Z. From local properties to brain-wide organization: A review of intraregional temporal features in functional magnetic resonance imaging data. Hum Brain Mapp 2023; 44:3926-3938. [PMID: 37086446 DOI: 10.1002/hbm.26302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 03/15/2023] [Accepted: 03/21/2023] [Indexed: 04/24/2023] Open
Abstract
Based on the fluctuations ensembled over neighbouring neurons, blood oxygen level-dependent (BOLD) signal is a mesoscale measurement of brain signals. Intraregional temporal features (IRTFs) of BOLD signal, extracted from regional neural activities, are utilized to investigate how the brain functions in local brain areas. This literature highlights four types of IRTFs and their representative calculations including variability in the temporal domain, variability in the frequency domain, entropy, and intrinsic neural timescales, which are tightly related to cognitions. In the brain-wide spatial organization, these brain features generally organized into two spatial hierarchies, reflecting structural constraints of regional dynamics and hierarchical functional processing workflow in brain. Meanwhile, the spatial organization gives rise to the link between neuronal properties and cognitive performance. Disrupted or unbalanced spatial conditions of IRTFs emerge with suboptimal cognitive states, which improved our understanding of the aging process and/or neuropathology of brain disease. This review concludes that IRTFs are important properties of the brain functional system and IRTFs should be considered in a brain-wide manner.
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Affiliation(s)
- Bolong Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- BABRI Centre, Beijing Normal University, Beijing, China
| | - Yaojing Chen
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- BABRI Centre, Beijing Normal University, Beijing, China
| | - Kewei Chen
- Banner Alzheimer's Institute, Phoenix, Arizona, USA
| | - Hui Lu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- BABRI Centre, Beijing Normal University, Beijing, China
| | - Zhanjun Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- BABRI Centre, Beijing Normal University, Beijing, China
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17
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Lindqvist D, Furmark T, Lavebratt C, Ohlsson L, Månsson KNT. Plasma circulating cell-free mitochondrial DNA in social anxiety disorder. Psychoneuroendocrinology 2023; 148:106001. [PMID: 36508952 DOI: 10.1016/j.psyneuen.2022.106001] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 12/05/2022] [Accepted: 12/05/2022] [Indexed: 12/12/2022]
Abstract
OBJECTIVE To investigate plasma levels of circulating cell-free mitochondrial DNA (ccf-mtDNA) in patients with social anxiety disorder (SAD) and healthy controls (HC). METHODS In this study, 88 participants (46 patients with SAD and 42 HCs) were enrolled and both ccf-mtDNA and peripheral blood mononuclear cells (PBMC) mtDNA copy number (mtDNA-cn) were measured at up to three times per individual (9-11 weeks apart). SAD patients also received cognitive behavioral therapy (CBT) between the second and third time-point. RESULTS SAD patients had significantly lower ccf-mtDNA compared to HCs at all time points, but ccf-mtDNA did not change significantly after CBT, and was not associated with severity of anxiety symptoms. Plasma ccf-mtDNA did not significantly correlate with PBMC mtDNA-cn in patients. CONCLUSION This is the first report of lower ccf-mtDNA in patients with an anxiety disorder. Our findings could reflect a more chronic illness course in SAD patients with prolonged periods of psychological stress leading to decreased levels of ccf-mtDNA, but future longitudinal studies are needed to confirm or refute this hypothesis.
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Affiliation(s)
- Daniel Lindqvist
- Department of Clinical Sciences Lund, Psychiatry, Faculty of Medicine, Lund University, Lund, Sweden; Office for Psychiatry and Habilitation, Psychiatry Research Skåne, Lund, Sweden.
| | - Tomas Furmark
- Department of Psychology, Uppsala University, Uppsala, Sweden
| | - Catharina Lavebratt
- Department of Molecular Medicine and Surgery, Karolinska Institutet, and Center for Molecular Medicine, Karolinska University Hospital, Stockholm, Sweden
| | - Lars Ohlsson
- Department of Biomedical Science, Faculty of Health and Society, Malmö University, Malmö, Sweden
| | - Kristoffer N T Månsson
- Center for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
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18
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Petrican R, Paine AL, Escott-Price V, Shelton KH. Overlapping brain correlates of superior cognition among children at genetic risk for Alzheimer's disease and/or major depressive disorder. Sci Rep 2023; 13:984. [PMID: 36653486 PMCID: PMC9849214 DOI: 10.1038/s41598-023-28057-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 01/12/2023] [Indexed: 01/19/2023] Open
Abstract
Early life adversity (ELA) tends to accelerate neurobiological ageing, which, in turn, is thought to heighten vulnerability to both major depressive disorder (MDD) and Alzheimer's disease (AD). The two conditions are putatively related, with MDD representing either a risk factor or early symptom of AD. Given the substantial environmental susceptibility of both disorders, timely identification of their neurocognitive markers could facilitate interventions to prevent clinical onset. To this end, we analysed multimodal data from the Adolescent Brain and Cognitive Development study (ages 9-10 years). To disentangle genetic from correlated genetic-environmental influences, while also probing gene-adversity interactions, we compared adoptees, a group generally exposed to substantial ELA, with children raised by their biological families via genetic risk scores (GRS) from genome-wide association studies. AD and MDD GRSs predicted overlapping and widespread neurodevelopmental alterations associated with superior fluid cognition. Specifically, among adoptees only, greater AD GRS were related to accelerated structural maturation (i.e., cortical thinning) and higher MDD GRS were linked to delayed functional neurodevelopment, as reflected in compensatory brain activation on an inhibitory control task. Our study identifies compensatory mechanisms linked to MDD risk and highlights the potential cognitive benefits of accelerated maturation linked to AD vulnerability in late childhood.
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Affiliation(s)
- Raluca Petrican
- Institute of Population Health, Department of Psychology, University of Liverpool, Bedford Street South, Liverpool, L69 7ZA, UK.
| | - Amy L Paine
- School of Psychology, Cardiff University, 70 Park Place, Cardiff, CF10 3AT, UK
| | - Valentina Escott-Price
- Division of Neuroscience and Mental Health, School of Medicine, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK
| | - Katherine H Shelton
- School of Psychology, Cardiff University, 70 Park Place, Cardiff, CF10 3AT, UK
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19
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Philippi CL, Leutzinger K, Pessin S, Cassani A, Mikel O, Walsh EC, Hoks RM, Birn RM, Abercrombie HC. Neural signal variability relates to maladaptive rumination in depression. J Psychiatr Res 2022; 156:570-578. [PMID: 36368247 PMCID: PMC9817305 DOI: 10.1016/j.jpsychires.2022.10.070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 10/28/2022] [Accepted: 10/31/2022] [Indexed: 11/05/2022]
Abstract
Rumination is a common feature of depression and predicts the onset and maintenance of depressive episodes. Maladaptive and adaptive subtypes of rumination contribute to distinct outcomes, with brooding worsening negative mood and reflection related to fewer depression symptoms in healthy populations. Neuroimaging studies have implicated several cortical midline and lateral prefrontal brain regions in rumination. Recent research indicates that blood oxygen level-dependent (BOLD) signal variability may be a novel predictor of cognitive flexibility. However, no prior studies have investigated whether brooding and reflection are associated with distinct patterns of BOLD signal variability in depression. We collected resting-state fMRI data for 79 women with different depression histories: no history, past history, and current depression. We examined differences in BOLD signal variability (BOLDSD) related to rumination subtypes for the following regions of interest previously implicated in rumination: amygdala, medial prefrontal, anterior cingulate, posterior cingulate, and dorsolateral prefrontal cortices (dlPFC). Rumination subtype was associated with BOLDSD in the dlPFC, with greater levels of brooding associated with lower BOLDSD in the dlPFC, even after controlling for depression severity. Depression history was related to BOLDSD in the dlPFC, with reduced BOLDSD in those with current depression versus no history of depression. These findings provide a novel demonstration of the neural circuitry associated with maladaptive rumination in depression and implicate decreased prefrontal neural signal variability in the pathophysiology of depression.
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Affiliation(s)
- Carissa L Philippi
- Department of Psychological Sciences, University of Missouri-St. Louis, 1 University Blvd., St. Louis, Missouri, 63121, USA.
| | - Katie Leutzinger
- Department of Psychological Sciences, University of Missouri-St. Louis, 1 University Blvd., St. Louis, Missouri, 63121, USA
| | - Sally Pessin
- Department of Psychological Sciences, University of Missouri-St. Louis, 1 University Blvd., St. Louis, Missouri, 63121, USA
| | - Alexis Cassani
- Department of Psychological Sciences, University of Missouri-St. Louis, 1 University Blvd., St. Louis, Missouri, 63121, USA
| | - Olivia Mikel
- Department of Psychological Sciences, University of Missouri-St. Louis, 1 University Blvd., St. Louis, Missouri, 63121, USA
| | - Erin C Walsh
- Department of Psychiatry, University of North Carolina at Chapel Hill School of Medicine, CB# 7167, Chapel Hill, NC, 27599, USA
| | - Roxanne M Hoks
- Center for Healthy Minds, University of Wisconsin-Madison, 625 W. Washington Ave., Madison, WI, 53703, USA
| | - Rasmus M Birn
- Department of Psychiatry, University of Wisconsin-Madison, University of Wisconsin-Madison, 6001 Research Park Blvd., Madison, WI, 53719, USA
| | - Heather C Abercrombie
- Center for Healthy Minds, University of Wisconsin-Madison, 625 W. Washington Ave., Madison, WI, 53703, USA
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20
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Klingelhöfer-Jens M, Ehlers MR, Kuhn M, Keyaniyan V, Lonsdorf TB. Robust group- but limited individual-level (longitudinal) reliability and insights into cross-phases response prediction of conditioned fear. eLife 2022; 11:e78717. [PMID: 36098500 PMCID: PMC9691022 DOI: 10.7554/elife.78717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 09/12/2022] [Indexed: 11/13/2022] Open
Abstract
Here, we follow the call to target measurement reliability as a key prerequisite for individual-level predictions in translational neuroscience by investigating (1) longitudinal reliability at the individual and (2) group level, (3) internal consistency and (4) response predictability across experimental phases. One hundred and twenty individuals performed a fear conditioning paradigm twice 6 months apart. Analyses of skin conductance responses, fear ratings and blood oxygen level dependent functional magnetic resonance imaging (BOLD fMRI) with different data transformations and included numbers of trials were conducted. While longitudinal reliability was rather limited at the individual level, it was comparatively higher for acquisition but not extinction at the group level. Internal consistency was satisfactory. Higher responding in preceding phases predicted higher responding in subsequent experimental phases at a weak to moderate level depending on data specifications. In sum, the results suggest that while individual-level predictions are meaningful for (very) short time frames, they also call for more attention to measurement properties in the field.
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Affiliation(s)
- Maren Klingelhöfer-Jens
- Institute for Systems Neuroscience, University Medical Center Hamburg-EppendorfHamburgGermany
| | - Mana R Ehlers
- Institute for Systems Neuroscience, University Medical Center Hamburg-EppendorfHamburgGermany
| | - Manuel Kuhn
- Institute for Systems Neuroscience, University Medical Center Hamburg-EppendorfHamburgGermany
- Department of Psychiatry, Harvard Medical School, and Center for Depression, Anxiety and Stress Research, McLean HospitalBelmontUnited States
| | - Vincent Keyaniyan
- Institute for Systems Neuroscience, University Medical Center Hamburg-EppendorfHamburgGermany
| | - Tina B Lonsdorf
- Institute for Systems Neuroscience, University Medical Center Hamburg-EppendorfHamburgGermany
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21
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Pessin S, Walsh EC, Hoks RM, Birn RM, Abercrombie HC, Philippi CL. Resting-state neural signal variability in women with depressive disorders. Behav Brain Res 2022; 433:113999. [PMID: 35811000 PMCID: PMC9559753 DOI: 10.1016/j.bbr.2022.113999] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 06/15/2022] [Accepted: 07/05/2022] [Indexed: 11/21/2022]
Abstract
Aberrant activity and connectivity in default mode (DMN), frontoparietal (FPN), and salience (SN) network regions is well-documented in depression. Recent neuroimaging research suggests that altered variability in the blood oxygen level-dependent (BOLD) signal may disrupt normal network integration and be an important novel predictor of psychopathology. However, no studies have yet determined the relationship between resting-state BOLD signal variability and depressive disorders nor applied BOLD signal variability features to the classification of depression history using machine learning (ML). We collected resting-state fMRI data for 79 women with different depression histories: no history, past history, and current depressive disorder. We tested voxelwise differences in BOLD signal variability related to depression group and severity. We also investigated whether BOLD signal variability of DMN, FPN, and SN regions could predict depression history group using a supervised random forest ML model. Results indicated that individuals with any history of depression had significantly decreased BOLD signal variability in the left and right cerebellum and right parietal cortex (pFWE <0.05). Furthermore, greater depression severity was also associated with reduced BOLD signal variability in the cerebellum. A random forest model classified participant depression history with 74% accuracy, with the ventral anterior cingulate cortex of the DMN as the most important variable in the model. These findings provide novel support for resting-state BOLD signal variability as a marker of neural dysfunction in depression and implicate decreased neural signal variability in the pathophysiology of depression.
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Affiliation(s)
- Sally Pessin
- Department of Psychological Sciences, University of Missouri-St. Louis, 1 University Blvd., St. Louis, MO 63121, USA
| | - Erin C Walsh
- Department of Psychiatry, University of North Carolina at Chapel Hill School of Medicine, CB# 7167, Chapel Hill, NC 27599, USA
| | - Roxanne M Hoks
- Center for Healthy Minds, University of Wisconsin-Madison, 625W. Washington Ave., Madison, WI 53703, USA
| | - Rasmus M Birn
- Department of Psychiatry, University of Wisconsin-Madison, 6001 Research Park Blvd., Madison, WI 53719, USA
| | - Heather C Abercrombie
- Center for Healthy Minds, University of Wisconsin-Madison, 625W. Washington Ave., Madison, WI 53703, USA
| | - Carissa L Philippi
- Department of Psychological Sciences, University of Missouri-St. Louis, 1 University Blvd., St. Louis, MO 63121, USA.
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Baumel WT, Lu L, Huang X, Drysdale AT, Sweeny JA, Gong Q, Sylvester CM, Strawn JR. Neurocircuitry of Treatment in Anxiety Disorders. Biomark Neuropsychiatry 2022; 6. [PMID: 35756886 PMCID: PMC9222661 DOI: 10.1016/j.bionps.2022.100052] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
Background: Methods: Results: Conclusions:
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Affiliation(s)
- W. Tommy Baumel
- Department of Psychiatry & Behavioral Neuroscience, College of Medicine, University of Cincinnati, Cincinnati, OH, USA
- Correspondence to: University of Cincinnati College of Medicine, 3230 Eden Avenue, Cincinnati, OH 45267, USA. (W.T. Baumel)
| | - Lu Lu
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
- Psychoradiology Research Unit of Chinese Academy of Medical Sciences, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Xiaoqi Huang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
- Psychoradiology Research Unit of Chinese Academy of Medical Sciences, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Andrew T. Drysdale
- Department of Psychiatry, School of Medicine, Washington University in St. Louis, St Louis, MO, USA
| | - John A. Sweeny
- Department of Psychiatry & Behavioral Neuroscience, College of Medicine, University of Cincinnati, Cincinnati, OH, USA
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
- Psychoradiology Research Unit of Chinese Academy of Medical Sciences, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Chad M. Sylvester
- Department of Psychiatry, School of Medicine, Washington University in St. Louis, St Louis, MO, USA
| | - Jeffrey R. Strawn
- Department of Psychiatry & Behavioral Neuroscience, College of Medicine, University of Cincinnati, Cincinnati, OH, USA
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