BOLD signal variability and complexity in children and adolescents with and without autism spectrum disorder

Easson, Amanda; McIntosh, Anthony R.

doi:10.1016/j.dcn.2019.100630

Cited by 46 publications

(51 citation statements)

References 79 publications

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“…Our findings may be in apparent contrast to the prevailing view that brain signal variability facilitates neural flexibility by allowing fluid transitions between brain states via a stochastic resonance effect (Deco et al, 2009;Ghosh et al, 2008). However, while it appears to be beneficial to task performance, heightened neural variability was also shown to correlate with worse clinical symptoms in various conditions (Easson and McIntosh, 2019;Martino et al, 2016;Nomi et al, 2018). As neural variability is thought to increase the sensitivity to incoming stimuli, it is possible that heightened neural variability might lead to over-reactivity of specific neural circuits, as a maladaptive strategy to prepare for potentially relevant events, which instead supports the maintenance of emotion dysregulation in affective disorders.…”

Section: Discussioncontrasting

confidence: 96%

Fronto-limbic neural variability as a transdiagnostic correlate of emotion dysregulation

Kebets

Favre

Houenou

et al. 2020

Preprint

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BackgroundEmotion dysregulation is central to the development and maintenance of psychopathology, and is common across many psychiatric disorders. Neurobiological models of emotion dysregulation involve the fronto-limbic brain network, including in particular the amygdala and prefrontal cortex (PFC). Neural variability has recently been suggested as an index of cognitive flexibility. We hypothesized that within-subject neural variability in the fronto-limbic network would be related to inter-individual variation in emotion dysregulation in the context of low affective control.MethodsIn a multi-site cohort (N = 166, 93 females) of healthy individuals and individuals with emotional dysregulation (attention deficit/hyperactivity disorder (ADHD), bipolar disorder (BD), and borderline personality disorder (BPD)), we applied partial least squares (PLS), a multivariate data-driven technique, to derive latent components yielding maximal covariance between blood-oxygen level-dependent (BOLD) signal variability at rest and emotion dysregulation, as expressed by affective lability, depression and mania scores.ResultsPLS revealed one significant latent component (r = 0.62, p = 0.001), whereby greater emotion dysregulation was associated with increased neural variability in the amygdala, hippocampus, ventromedial, dorsomedial and dorsolateral PFC, insula and motor cortex, and decreased neural variability in occipital regions. This spatial pattern bears a striking resemblance to the fronto-limbic network, which is thought to subserve emotion regulation, and is impaired in individuals with ADHD, BD, and BPD.ConclusionsOur work supports emotion dysregulation as a transdiagnostic dimension with neurobiological underpinnings that transcend diagnostic boundaries, and adds evidence to neural variability being a relevant proxy of neural efficiency.

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Section: Discussioncontrasting

confidence: 96%

Fronto-limbic neural variability as a transdiagnostic correlate of emotion dysregulation

Kebets

Favre

Houenou

et al. 2020

Preprint

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“…Here, it can be seen that the relative error was well below the selected threshold of 0.1, and potential combinations with a relative error of less than 0.05 were present, which is the best value for the efficiency metric reported in [20]. The relative error was small compared to some recent studies [13,21], which may be due to the high quality of data used, high temporal resolution, and long rfMRI data sets.…”

Section: Discussionmentioning

confidence: 52%

“…where µ SampEn and σ SampEn are the mean and standard deviation of the SampEn of CSF signals, respectively. The acceptable MSE parameter space of BOLD signals was determined by using a threshold value of 0.1 for RE [13,21] and excluding parameter combinations that failed to calculate SampEn for a subject using CSF signals.…”

Section: Optimal Parameter Selection For Calculating Sampen Of Bold Smentioning

confidence: 99%

“…The variability and complexity of the brain's neural signals are two quantitative measures that can be used to assess the health of a brain [13]. These measures indicate the ability of the brain to react to uncertainty and adapt to and function in a dynamic environment.…”

Section: Introductionmentioning

confidence: 99%

“…On each box, the central mark shows the median, bottom and top edges show the 25th and 75th percentiles, respectively, and the whiskers extend to the most extreme data points that are not considered as outliers, which are defined as 1.5 times the interquartile range away from the top or bottom of the box. Nodes belonging to the RSNs are shown in parentheses-auditory (1-3), basal ganglia(4-7), dorsal default mode network(8)(9)(10)(11)(12)(13)(14)(15)(16)(17), primary visual(18)(19), language (20-26), left executive control network(27)(28)(29)(30)(31)(32), sensorimotor(33)(34)(35)(36)(37)(38), posterior salience(39)(40)(41)(42)(43)(44)(45)(46)(47)(48)(49)(50), precuneus(51)(52)(53)(54), higher visual (55-56), right executive control network (57-62), anterior salience (63-69), ventral default mode network (70-79), visuospatial (80-90).…”

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confidence: 99%

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A Study of Brain Neuronal and Functional Complexities Estimated Using Multiscale Entropy in Healthy Young Adults

Menon

Krishnamurthy

2019

Entropy

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Brain complexity estimated using sample entropy and multiscale entropy (MSE) has recently gained much attention to compare brain function between diseased or neurologically impaired groups and healthy control groups. Using resting-state functional magnetic resonance imaging (rfMRI) blood oxygen-level dependent (BOLD) signals in a large cohort (n = 967) of healthy young adults, the present study maps neuronal and functional complexities estimated by using MSE of BOLD signals and BOLD phase coherence connectivity, respectively, at various levels of the brain’s organization. The functional complexity explores patterns in a higher dimension than neuronal complexity and may better discern changes in brain functioning. The leave-one-subject-out cross-validation method is used to predict fluid intelligence using neuronal and functional complexity MSE values as features. While a wide range of scales was selected with neuronal complexity, only the first three scales were selected with functional complexity. Fewer scales are advantageous as they preclude the need for long BOLD signals to calculate good estimates of MSE. The presented results corroborate with previous findings and provide a baseline for other studies exploring the use of MSE to examine changes in brain function related to aging, diseases, and clinical disorders.

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Aberrant functional brain network dynamics in patients with functional constipation

Yin

et al. 2021

Human Brain Mapping

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The aberrant static functional connectivity of brain network has been widely investigated in patients with functional constipation (FCon). However, the dynamics of brain functional connectivity in FCon patients remained unknown. This study aimed to detect the brain dynamics of functional connectivity states and network topological organizations of FCon patients and investigate the correlations of the aberrant brain dynamics with symptom severity. Eighty-three FCon patients and 80 healthy subjects (HS) were included in data analysis. The spatial group independent component analysis, sliding-window approach, k-means clustering, and graph-theoretic analysis were applied to investigate the dynamic temporal properties and coupling patterns of functional connectivity states, as well as the time-variation of network topological organizations in FCon patients. Four reoccurring functional connectivity states were identified in k-means clustering analysis. Compared to HS, FCon patients manifested the lower occurrence rate and mean dwell time in the state with a complex connection between default mode network and cognitive control network, as well as the aberrant anterior insula-cortical coupling patterns in this state, which were significantly correlated with the symptom severity. The graph-theoretic analysis demonstrated that FCon patients had higher sample entropy at the nodal efficiency of anterior insula than HS. The current findings provided dynamic perspectives for Tao Yin, Zhaoxuan He, and Peihong Ma contributed equally to this study and are also the co-first authors.

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BOLD signal variability and complexity in children and adolescents with and without autism spectrum disorder

Cited by 46 publications

References 79 publications

Fronto-limbic neural variability as a transdiagnostic correlate of emotion dysregulation

Fronto-limbic neural variability as a transdiagnostic correlate of emotion dysregulation

A Study of Brain Neuronal and Functional Complexities Estimated Using Multiscale Entropy in Healthy Young Adults

Aberrant functional brain network dynamics in patients with functional constipation

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