Low-Dimensional Embedding of Functional Connectivity Graphs for Brain State Decoding

Abstract Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New Background Functional brain connectivity studies can provide important information about changes in brain-state dynamics during general anesthesia. In adults, γ-aminobutyric acid–mediated agents disrupt integration of information from local to the whole-brain scale. Beginning around 3 to 4 months postnatal age, γ-aminobutyric acid–mediated anesthetics such as sevoflurane generate α-electroencephalography oscillations. In previous studies of sevoflurane-anesthetized infants 0 to 3.9 months of age, α-oscillations were absent, and power spectra did not distinguish between anesthetized and emergence from anesthesia conditions. Few studies detailing functional connectivity during general anesthesia in infants exist. This study’s aim was to identify changes in functional connectivity of the infant brain during anesthesia. Methods A retrospective cohort study was performed using multichannel electroencephalograph recordings of 20 infants aged 0 to 3.9 months old who underwent sevoflurane anesthesia for elective surgery. Whole-brain functional connectivity was evaluated during maintenance of a surgical state of anesthesia and during emergence from anesthesia. Functional connectivity was represented as networks, and network efficiency indices (including complexity and modularity) were computed at the sensor and source levels. Results Sevoflurane decreased functional connectivity at the δ-frequency (1 to 4 Hz) in infants 0 to 3.9 months old when comparing anesthesia with emergence. At the sensor level, complexity decreased during anesthesia, showing less whole-brain integration with prominent alterations in the connectivity of frontal and parietal sensors (median difference, 0.0293; 95% CI, −0.0016 to 0.0397). At the source level, similar results were observed (median difference, 0.0201; 95% CI, −0.0025 to 0.0482) with prominent alterations in the connectivity between default-mode and frontoparietal regions. Anesthesia resulted in fragmented modules as modularity increased at the sensor (median difference, 0.0562; 95% CI, 0.0048 to 0.1298) and source (median difference, 0.0548; 95% CI, −0.0040 to 0.1074) levels. Conclusions Sevoflurane is associated with decreased capacity for efficient information transfer in the infant brain. Such findings strengthen the hypothesis that conscious processing relies on an efficient system of integrated information transfer across the whole brain.

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Manifold Learning of Dynamic Functional Connectivity Reliably Identifies Functionally Consistent Coupling Patterns in Human Brains

Brain Sciences ◽

10.3390/brainsci9110309 ◽

2019 ◽

Vol 9 (11) ◽

pp. 309

Author(s):

Yuyuan Yang ◽

Lubin Wang ◽

Yu Lei ◽

Yuyang Zhu ◽

Hui Shen

Keyword(s):

Functional Connectivity ◽

Sleep Deprivation ◽

Manifold Learning ◽

Temporal Dynamics ◽

Functional Coupling ◽

Dimensional Manifold ◽

Dynamic Functional Connectivity ◽

Local Linear Embedding ◽

Human Connectome Project ◽

Low Dimensional

Most previous work on dynamic functional connectivity (dFC) has focused on analyzing temporal traits of functional connectivity (similar coupling patterns at different timepoints), dividing them into functional connectivity states and detecting their between-group differences. However, the coherent functional connectivity of brain activity among the temporal dynamics of functional connectivity remains unknown. In the study, we applied manifold learning of local linear embedding to explore the consistent coupling patterns (CCPs) that reflect functionally homogeneous regions underlying dFC throughout the entire scanning period. By embedding the whole-brain functional connectivity in a low-dimensional manifold space based on the Human Connectome Project (HCP) resting-state data, we identified ten stable patterns of functional coupling across regions that underpin the temporal evolution of dFC. Moreover, some of these CCPs exhibited significant neurophysiological meaning. Furthermore, we apply this method to HCP rsfMR and tfMRI data as well as sleep-deprivation data and found that the topological organization of these low-dimensional structures has high potential for predicting sleep-deprivation states (classification accuracy of 92.3%) and task types (100% identification for all seven tasks).In summary, this work provides a methodology for distilling coherent low-dimensional functional connectivity structures in complex brain dynamics that play an important role in performing tasks or characterizing specific states of the brain.

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Detecting Brain State Changes via Fiber-Centered Functional Connectivity Analysis

Neuroinformatics ◽

10.1007/s12021-012-9157-y ◽

2012 ◽

Vol 11 (2) ◽

pp. 193-210 ◽

Cited By ~ 20

Author(s):

Xiang Li ◽

Chulwoo Lim ◽

Kaiming Li ◽

Lei Guo ◽

Tianming Liu

Keyword(s):

Functional Connectivity ◽

Brain State ◽

Connectivity Analysis ◽

Functional Connectivity Analysis ◽

State Changes

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Psilocybin modulation of dynamic functional connectivity is associated with plasma psilocin and subjective effects

10.1101/2021.12.17.21267992 ◽

2021 ◽

Author(s):

Anders S Olsen ◽

Anders Lykkebo-Valloee ◽

Brice Ozenne ◽

Martin K Madsen ◽

Dea Siggaard Stenbaek ◽

...

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Dwell Time ◽

Brain State ◽

Healthy Individuals ◽

Acute Effects ◽

Dynamic Functional Connectivity ◽

Negatively Associated ◽

State Dynamic ◽

Brain States

Background: Psilocin, the neuroactive metabolite of psilocybin, is a serotonergic psychedelic that induces an acute altered state of consciousness, evokes lasting changes in mood and personality in healthy individuals, and has potential as an antidepressant treatment. Examining the acute effects of psilocin on resting-state dynamic functional connectivity implicates network-level connectivity motifs that may underlie acute and lasting behavioral and clinical effects. Aim: Evaluate the association between resting-state dynamic functional connectivity (dFC) characteristics and plasma psilocin level (PPL) and subjective drug intensity (SDI) before and right after intake of a psychedelic dose of psilocybin in healthy humans. Methods: Fifteen healthy individuals completed the study. Before and at multiple time points after psilocybin intake, we acquired 10-minute resting-state blood-oxygen-level-dependent functional magnetic resonance imaging scans. Leading Eigenvector Dynamics Analysis (LEiDA) and diametrical clustering were applied to estimate discrete, sequentially active brain states. We evaluated associations between the fractional occurrence of brain states during a scan session and PPL and SDI using linear mixed-effects models. We examined associations between brain state dwell time and PPL and SDI using frailty Cox proportional hazards survival analysis. Results: Fractional occurrences for two brain states characterized by lateral frontoparietal and medial fronto-parietal-cingulate coherence were statistically significantly negatively associated with PPL and SDI. Dwell time for these brain states was negatively associated with SDI and, to a lesser extent, PPL. Conversely, fractional occurrence and dwell time of a fully connected brain state was positively associated with PPL and SDI. Conclusion: Our findings suggest that the acute perceptual psychedelic effects induced by psilocybin may stem from drug-level associated decreases in the occurrence and duration of lateral and medial frontoparietal connectivity motifs in exchange for increases in a uniform connectivity structure. We apply and argue for a modified approach to modeling eigenvectors produced by LEiDA that more fully acknowledges their underlying structure. Together these findings contribute to a more comprehensive neurobiological framework underlying acute effects of serotonergic psychedelics.

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Time-varying Dynamic Network Model For Dynamic Resting State Functional Connectivity in fMRI and MEG imaging

10.1101/2021.04.01.438060 ◽

2021 ◽

Author(s):

Fei Jiang ◽

Huaqing Jin ◽

Yijing Bao ◽

Xihe Xie ◽

Jennifer Cummings ◽

...

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Dynamic Network ◽

Dynamic State ◽

State Transitions ◽

Dimensional Manifold ◽

Time Varying ◽

Resting State Functional Connectivity ◽

Brain Signals ◽

Low Dimensional

Dynamic resting state functional connectivity (RSFC) characterizes fluctuations that occurs over time in functional brain networks. Existing methods to extract dynamic RSFCs, such as sliding-window and clustering methods, have various limitations due to their inherent non-adaptive nature and high-dimensionality including an inability to reconstruct brain signals, insufficiency of data for reliable estimation, insensitivity to rapid changes in dynamics, and a lack of generalizability across multi- modal functional imaging datasets. To overcome these deficiencies, we develop a novel and unifying time-varying dynamic network (TVDN) framework for examining dynamic resting state functional connectivity. TVDN includes a generative model that describes the relation between low-dimensional dynamic RSFC and the brain signals, and an inference algorithm that automatically and adaptively learns to detect dynamic state transitions in data and a low-dimensional manifold of dynamic RSFC. TVDN is generalizable to handle multimodal functional neuroimaging data (fMRI and MEG/EEG). The resulting estimated low-dimensional dynamic RSFCs manifold directly links to the frequency content of brain signals. Hence we can evaluate TVDN performance by examining whether learnt features can reconstruct observed brain signals. We conduct comprehensive simulations to evaluate TVDN under hypothetical settings. We then demonstrate the application of TVDN with real fMRI and MEG data, and compare the results with existing benchmarks. Results demonstrate that TVDN is able to correctly capture the dynamics of brain activity and more robustly detect brain state switching both in resting state fMRI and MEG data.

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Dynamic Functional Connectivity Signifies the Joint Impact of Dance Intervention and Cognitive Reserve

Frontiers in Aging Neuroscience ◽

10.3389/fnagi.2021.724094 ◽

2021 ◽

Vol 13 ◽

Author(s):

Kristína Mitterová ◽

Martin Lamoš ◽

Radek Mareček ◽

Monika Pupíková ◽

Patrik Šimko ◽

...

Keyword(s):

Information Processing ◽

Functional Connectivity ◽

Dwell Time ◽

Cognitive Reserve ◽

Cognitive Outcomes ◽

Elderly Subjects ◽

Control Group ◽

Brain State ◽

Resting State Functional Connectivity ◽

Relevant Effect

Research on dance interventions (DIs) in the elderly has shown promising benefits to physical and cognitive outcomes. The effect of DIs on resting-state functional connectivity (rs-FC) varies, which is possibly due to individual variability. In this study, we assessed the moderation effects of residual cognitive reserve (CR) on DI-induced changes in dynamic rs-FC and their association on cognitive outcomes. Dynamic rs-FC (rs-dFC) and cognitive functions were evaluated in non-demented elderly subjects before and after a 6-month DI (n = 36) and a control group, referred to as the life-as-usual (LAU) group (n = 32). Using linear mixed models and moderation, we examined the interaction effect of DIs and CR on changes in the dwell time and coverage of rs-dFC. Cognitive reserve was calculated as the residual difference between the observed memory performance and the performance predicted by brain state. Partial correlations accounting for CR evaluated the unique association between changes in rs-dFC and cognition in the DI group. In subjects with lower residual CR, we observed DI-induced increases in dwell time [t(58) = –2.14, p = 0.036] and coverage [t(58) = –2.22, p = 0.030] of a rs-dFC state, which was implicated in bottom-up information processing. Increased dwell time was also correlated with a DI-induced improvement in Symbol Search (r = 0.42, p = 0.02). In subjects with higher residual CR, we observed a DI-induced increase in coverage [t(58) = 2.11, p = 0.039] of another rs-dFC state, which was implicated in top-down information processing. The study showed that DIs have a differential and behaviorally relevant effect on dynamic rs-dFC, but these benefits depend on the current CR level.

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Imaging the spontaneous flow of thought: Distinct periods of cognition contribute to dynamic functional connectivity during rest

10.1101/527804 ◽

2019 ◽

Author(s):

Javier Gonzalez-Castillo ◽

César Caballero-Gaudes ◽

Natasha Topolski ◽

Daniel A. Handwerker ◽

Francisco Pereira ◽

...

Keyword(s):

Functional Connectivity ◽

Task Engagement ◽

Head Motion ◽

Clinical Value ◽

Dynamic Functional Connectivity ◽

Brain Maintenance ◽

And Performance ◽

Low Dimensional ◽

Brain Functional Connectivity ◽

And Task

AbstractBrain functional connectivity (FC) changes have been measured across seconds using fMRI. This is true for both rest and task scenarios. Moreover, it is well accepted that task engagement alters FC, and that dynamic estimates of FC during and before task events can help predict their nature and performance. Yet, when it comes to dynamic FC (dFC) during rest, there is no consensus about its origin or significance. Some argue that rest dFC reflects fluctuations in on-going cognition, or is a manifestation of intrinsic brain maintenance mechanisms, which could have predictive clinical value. Conversely, others have concluded that rest dFC is mostly the result of sampling variability, head motion or fluctuating sleep states. Here, we present novel analyses suggesting that rest dFC is influenced by short periods of distinct mental processing, and that the cognitive nature of such mental processes can be inferred blindly from the data. As such, several different behaviorally relevant whole-brain FC configurations may occur during a single rest scan even when subjects were continuously awake and displayed minimal motion. In addition, using low dimensional embeddings as visualization aids, we show how FC states—commonly used to summarize and interpret resting dFC—can accurately and robustly reveal periods of externally imposed tasks; however, they may be less effective in capturing periods of distinct cognition during rest.

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Global field synchronization reveals rapid eye movement sleep as most synchronized brain state in the human EEG

Royal Society Open Science ◽

10.1098/rsos.160201 ◽

2016 ◽

Vol 3 (10) ◽

pp. 160201 ◽

Cited By ~ 13

Author(s):

Peter Achermann ◽

Thomas Rusterholz ◽

Roland Dürr ◽

Thomas König ◽

Leila Tarokh

Keyword(s):

Functional Connectivity ◽

Sleep Deprivation ◽

Eye Movement ◽

Rem Sleep ◽

Nrem Sleep ◽

Brain Regions ◽

Global Field ◽

Brain State ◽

Rapid Eye Movement ◽

Eyes Closed

Sleep is characterized by a loss of consciousness, which has been attributed to a breakdown of functional connectivity between brain regions. Global field synchronization (GFS) can estimate functional connectivity of brain processes. GFS is a frequency-dependent measure of global synchronicity of multi-channel EEG data. Our aim was to explore and extend the hypothesis of disconnection during sleep by comparing GFS spectra of different vigilance states. The analysis was performed on eight healthy adult male subjects. EEG was recorded during a baseline night, a recovery night after 40 h of sustained wakefulness and at 3 h intervals during the 40 h of wakefulness. Compared to non-rapid eye movement (NREM) sleep, REM sleep showed larger GFS values in all frequencies except in the spindle and theta bands, where NREM sleep showed a peak in GFS. Sleep deprivation did not affect GFS spectra in REM and NREM sleep. Waking GFS values were lower compared with REM and NREM sleep except for the alpha band. Waking alpha GFS decreased following sleep deprivation in the eyes closed condition only. Our surprising finding of higher synchrony during REM sleep challenges the view of REM sleep as a desynchronized brain state and may provide insight into the function of REM sleep.

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Latent brain state dynamics distinguish behavioral variability, impaired decision-making, and inattention

Molecular Psychiatry ◽

10.1038/s41380-021-01022-3 ◽

2021 ◽

Author(s):

Weidong Cai ◽

Stacie L. Warren ◽

Katherine Duberg ◽

Bruce Pennington ◽

Stephen P. Hinshaw ◽

...

Keyword(s):

Decision Making ◽

Functional Connectivity ◽

Learning Algorithm ◽

Response Variability ◽

Brain State ◽

Behavioral Variability ◽

Individual Response ◽

Decision Threshold ◽

Default Mode ◽

Evidence Accumulation

AbstractChildren with Attention Deficit Hyperactivity Disorder (ADHD) have prominent deficits in sustained attention that manifest as elevated intra-individual response variability and poor decision-making. Influential neurocognitive models have linked attentional fluctuations to aberrant brain dynamics, but these models have not been tested with computationally rigorous procedures. Here we use a Research Domain Criteria approach, drift-diffusion modeling of behavior, and a novel Bayesian Switching Dynamic System unsupervised learning algorithm, with ultrafast temporal resolution (490 ms) whole-brain task-fMRI data, to investigate latent brain state dynamics of salience, frontoparietal, and default mode networks and their relation to response variability, latent decision-making processes, and inattention. Our analyses revealed that occurrence of a task-optimal latent brain state predicted decreased intra-individual response variability and increased evidence accumulation related to decision-making. In contrast, occurrence and dwell time of a non-optimal latent brain state predicted inattention symptoms and furthermore, in a categorical analysis, distinguished children with ADHD from controls. Importantly, functional connectivity between salience and frontoparietal networks predicted rate of evidence accumulation to a decision threshold, whereas functional connectivity between salience and default mode networks predicted inattention. Taken together, our computational modeling reveals dissociable latent brain state features underlying response variability, impaired decision-making, and inattentional symptoms common to ADHD. Our findings provide novel insights into the neurobiology of attention deficits in children.

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