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Reductions in social anxiety symptoms and task-related brain signal variability as a predictor of treatment outcome. A) Change in the primary social anxiety outcome LSAS-SR from screening, baseline 1, baseline 2 to post-treatment. The solid line represents the median cubic spline. B) Task-based SDBOLD predicted treatment change score is strongly related to empirical change scores. C) Task-based SDBOLD spatial pattern reflecting treatment outcome. Blue regions=lower SDBOLD associated with better treatment outcome; yellow/red regions: higher SDBOLD associated with better treatment outcome. X Y Z below the brains represent MNI coordinates. Further, unthresholded statistical maps are available at NeuroVault.org (https://identifiers.org/neurovault.collection:9030). Abbreviations: LSAS-SR, Liebowitz social anxiety scale, self-report version; SDBOLD, standard deviation of BOLD; BOLD, Blood-oxygen-level-dependent imaging;
Source publication
BACKGROUND: Biomarkers of psychiatric treatment response remain elusive. Functional magnetic resonance imaging (fMRI) has shown promise, but low reliability has limited the utility of typical fMRI measures (e.g., average brain signal) as harbingers of treatment success. Notably, although historically considered a source of “noise,” temporal brain s...
Citations
... Conversely, amygdala activation that returns more quickly to baseline is associated with positive affect and greater psychological wellbeing. Similarly, high moment-to-moment variability in neuronal activity in regions of the cortex while viewing both positive and negative stimuli predicts greater efficacy of cognitive behavior therapy (CBT) among those with social anxiety [56]. These observations suggest that neural variability allows the brain to be flexible in the face of changing circumstances [57], akin to how higher heart rate variability (HRV) indicates adaptive stress and arousal responses and the ability to transition between parasympathetic and sympathetic nervous system activation as necessary [58]. ...
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.
... Alpha and its related phase are key for processing and gating of external inputs (Klimesch, 2012). Incoming inputs are mediated by alpha phase cycles, including their variability: the more variable the alpha phase cycle, the better the external input can be processed (Garrett et al., 2011;Grady and Garrett, 2014;Månsson et al., 2022;Waschke et al., 2021). In contrast, lower alpha phase variability may result in decreased capacity for input processing of external stimuli from the environment (Klimesch et al., 2011); that is compatible with the observation of social and environmental withdrawal of depressed patients (Derntl et al., 2011;Girard et al., 2014). ...
... SD is primarily used to normalize (as a denominator) signal variance across conditions or participants. However, it has also been associated, as a dependent variable, with cognition at both intra-and inter-individual levels (Månsson et al., 2022;Kumral et al., 2020;Alavash et al., 2018;Grady & Garrett, 2018;Garrett et al., 2015;Garrett, Kovacevic, Mcintosh, & Grady, 2011;Zou et al., 2008). ...
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.
... We employed the GS sd and GS power to measure variabilities in the GS (Tolkunov, Rubin, & Mujica-Parodi, 2010). The BSV has been demonstrated to have a close relationship with brain functions and psychiatric disorders (Garrett et al., 2013;Li et al., 2019;Månsson et al., 2022). The GS variability, as an overall response of BSV, has been demonstrated to be negatively correlated to alertness or arousal (Falahpour, Wong, & Liu, 2016;Wong, Olafsson, Tal, & Liu, 2013;Zhang & Northoff, 2022). ...
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.
... 34 Månsson et al reported good test-retest reliability for BOLD signal variability, and that it predicted anxiety disordered patients' response to psychological treatment. 35 Here, we will use a similar suppression-repetition task using visual stimulation (eg, emotional faces), and test both neural variability as a pretreatment predictor, and sensitive marker of psychiatric treatment response in depression. ...
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 .
... Thus far, BOLD signal variability has been studied in relation to behavior, cognition, development, and clinical status [14][15][16][17][18][19][20] . A robust characterization of its neurobiological features, however, is lacking. ...
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.
... The degree to which a neural signal varies over time is often quantified by the standard deviation (SD or, equivalently, the variance) or by entropy 6 . SD is not onlfy widely used to normalize (as a denominator) signal variance across conditions or participants, it has also been associated, as a dependent variable, with cognition at both intra-and inter-individual levels [7][8][9][10][11][12][13] . A less-used metric for temporal variability is volatility, computed by the standard deviation of the step-by-step changes in the signal 1 (see spreadsheet example in Online Supplement). ...
... Moment") or with conscious, deliberate, stepwise analysis. In general, as people solve problems, a relatively high level of alpha power (8)(9)(10)(11)(12) has been observed during the pre-stimulus period compared to the solving period 55,56 . As people approach a solution, alpha power trends downward 56,57 . ...
Temporal variability is a fundamental property of brain processes and functionally important to human cognition. This study examined how fluctuations in neural oscillatory activity are related to problem-solving performance. We used volatility to assess the 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 to those solved analytically. Moreover, volatility during pre-stimulus preparation for solving predicted general problem-solving outcomes, including solving-success and solving-time. These novel findings were replicated in a separate dataset 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. Thus, volatility can be a valuable index to characterize brain dynamics relevant to cognition.
... In terms of variability measures, both major depressive disorder and generalized anxiety disorder are accompanied by greater variability of dynamic ALFF in both cortical and subcortical regions, compared to healthy controls (Cui et al., 2020;Zheng et al., 2021). Similarly, individuals with depression or borderline personality disorder who show emotional lability also have greater resting BOLD-SD in the amygdala and ventromedial prefrontal cortex (PFC) than controls (Kebets et al., 2021), and resting BOLD-SD obtained prior to treatment (Mansson et al., 2022) can predict good treatment outcome in patients with social anxiety disorder. Although these data from patient groups would suggest that greater BOLD variability is associated with poorer socioemotional function, it remains unclear how BOLD-SD might relate to measures of socioemotional function in healthy adults. ...
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 20-86 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.
... To our knowledge, two studies conducted individual CBT outcome prediction using task-based fMRI in patients with panic disorder [12,13], four in patients with social anxiety disorder [14][15][16], and one in a mixed sample of patients with panic disorder or generalized anxiety disorder [17] (see [8] for a recent review). However, no study had a sample with N > 60. ...
... Graph-theoretical measures derived from functional connectivity, reported to have overall good reproducibility [22], have also been used in recent years for fine-tuned investigation of functional network dysfunctions in anxiety disorders [23,24]. Additionally, BOLD signal variability measures have recently been reported as promising individual-level predictors for therapeutic outcomes in anxiety disorders [14,16]. ...
... Performance. Overall, our findings challenge the existing literature reporting above-chance predictive accuracies for machine-learning psychotherapy outcome prediction using neuroimaging data in patients with anxiety disorders [12][13][14][15][16][17]46]. However, they echo a more recent body of methodological work underlining that, despite initial promise in the field, prediction accuracies for patient classification based on medical imaging features appear to be decreasing as sample sizes increase, perhaps reflecting unwitting biases in performance evaluation, overhyping, and cross-validation error bars in the neuroimaging literature [19,[47][48][49]. ...
Machine-learning prediction studies have shown potential to inform treatment stratification, but recent efforts to predict psychotherapy outcomes with clinical routine data have only resulted in moderate prediction accuracies. Neuroimaging data showed promise to predict treatment outcome, but previous prediction attempts have been exploratory and reported small clinical sample sizes. Herein, we aimed to examine the incremental predictive value of neuroimaging data in contrast to clinical and demographic data alone (for which results were previously published), using a two-level multimodal ensemble machine-learning strategy. We used pretreatment structural and task-based fMRI data to predict virtual reality exposure therapy outcome in a bicentric sample of N = 190 patients with spider phobia. First, eight 1st-level random forest classifications were conducted using separate data modalities (clinical questionnaire scores and sociodemographic data, cortical thickness and gray matter volumes, functional activation, connectivity, connectivity-derived graph metrics, and BOLD signal variance). Then, the resulting predictions were used to train a 2nd-level classifier that produced a final prediction. No 1st-level or 2nd-level classifier performed above chance level except BOLD signal variance, which showed potential as a contributor to higher-level prediction from multiple regions across the brain (1st-level balanced accuracy = 0.63 ). Overall, neuroimaging data did not provide any incremental accuracy for treatment outcome prediction in patients with spider phobia with respect to clinical and sociodemographic data alone. Thus, we advise caution in the interpretation of prediction performances from small-scale, single-site patient samples. Larger multimodal datasets are needed to further investigate individual-level neuroimaging predictors of therapy response in anxiety disorders.
... Our finding suggests that D2 and 5HT2A-related distributed neural variability pattern could be a potential transdiagnostic pathophysiology for MPDs. Considering activation of dopamine receptors modulates SD BOLD [85], and baseline SD BOLD predicts improved behavior of SCZ patients [86], future studies may investigate if SD BOLD can predict the prognosis of antipsychotic treatment. In contrast to pattern 1, pattern 2 relates to several neurotransmitter receptors, including 5HT1A, 5HT2A, 5HT6, CB1, D1, M1, mGluR5, and MOR receptors. ...
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.
