Fractal-Based Analysis of fMRI BOLD Signal During Naturalistic Viewing Conditions

Background: Temporal fractals are characterized by prominent scale-invariance and self-similarity across time scales. Monofractal analysis quantifies this scaling behavior in a single parameter, the Hurst exponent (H). Higher H reflects greater correlation in the signal structure, which is taken as being more fractal. Previous fMRI studies have observed lower H during conventional tasks relative to resting state conditions, and shown that H is negatively correlated with task difficulty and novelty. To date, no study has investigated the fractal dynamics of BOLD signal during naturalistic conditions.Methods: We performed fractal analysis on Human Connectome Project 7T fMRI data (n = 72, 41 females, mean age 29.46 ± 3.76 years) to compare H across movie-watching and rest.Results: In contrast to previous work using conventional tasks, we found higher H values for movie relative to rest (mean difference = 0.014; p = 5.279 × 10−7; 95% CI [0.009, 0.019]). H was significantly higher in movie than rest in the visual, somatomotor and dorsal attention networks, but was significantly lower during movie in the frontoparietal and default networks. We found no cross-condition differences in test-retest reliability of H. Finally, we found that H of movie-derived stimulus properties (e.g., luminance changes) were fractal whereas H of head motion estimates were non-fractal.Conclusions: Overall, our findings suggest that movie-watching induces fractal signal dynamics. In line with recent work characterizing connectivity-based brain state dynamics during movie-watching, we speculate that these fractal dynamics reflect the configuring and reconfiguring of brain states that occurs during naturalistic processing, and are markedly different than dynamics observed during conventional tasks.

Individual coactivation patterns improve the subject identification and their behavior association

Brain states can be characterized by recurring coactivation patterns (CAPs). Traditional CAP analysis is performed at the group-level, while the human brain is individualized and the functional connectome has shown the uniqueness as fingerprint. Whether stable individual CAPs could be obtained from a single fMRI scan and could individual CAPs improve the identification is unclear. An open dataset, the midnight scan club was used in this study to answer these questions. Four CAP states were identified at three distinct levels (group-, subject- and scan-level) separately, and the CAPs were then reconstructed for each scan. Identification rate and differential identifiability were used to evaluate the identification performance. Our results demonstrated that the individual CAPs were unstable when using a single scan. By maintaining high intra-subject similarity and inter-subject differences, subject-level CAPs achieved the best identification performance. Brain regions that contributed to the identifiability were mainly located in higher-order networks (e.g., frontal-parietal network). Besides, head motion reduced the intra-subject similarity, while its impact on identification rate was non-significant. Finally, a pipeline was developed to depict brain-behavior associations in dataset with few samples but dense sampling, and individualized CAP dynamics showed an above-chance level correlation with IQ.

The emergence of molecular systems neuroscience

Systems neuroscience is focused on how ensemble properties in the brain, such as the activity of neuronal circuits, gives rise to internal brain states and behavior. Many of the studies in this field have traditionally involved electrophysiological recordings and computational approaches that attempt to decode how the brain transforms inputs into functional outputs. More recently, systems neuroscience has received an infusion of approaches and techniques that allow the manipulation (e.g., optogenetics, chemogenetics) and imaging (e.g., two-photon imaging, head mounted fluorescent microscopes) of neurons, neurocircuits, their inputs and outputs. Here, we will review novel approaches that allow the manipulation and imaging of specific molecular mechanisms in specific cells (not just neurons), cell ensembles and brain regions. These molecular approaches, with the specificity and temporal resolution appropriate for systems studies, promise to infuse the field with novel ideas, emphases and directions, and are motivating the emergence of a molecularly oriented systems neuroscience, a new discipline that studies how the spatial and temporal patterns of molecular systems modulate circuits and brain networks, and consequently shape the properties of brain states and behavior.

Artifacts in EEG-Based BCI Therapies: Friend or Foe?

EEG-based brain–computer interfaces (BCI) have promising therapeutic potential beyond traditional neurofeedback training, such as enabling personalized and optimized virtual reality (VR) neurorehabilitation paradigms where the timing and parameters of the visual experience is synchronized with specific brain states. While BCI algorithms are often designed to focus on whichever portion of a signal is most informative, in these brain-state-synchronized applications, it is of critical importance that the resulting decoder is sensitive to physiological brain activity representative of various mental states, and not to artifacts, such as those arising from naturalistic movements. In this study, we compare the relative classification accuracy with which different motor tasks can be decoded from both extracted brain activity and artifacts contained in the EEG signal. EEG data were collected from 17 chronic stroke patients while performing six different head, hand, and arm movements in a realistic VR-based neurorehabilitation paradigm. Results show that the artifactual component of the EEG signal is significantly more informative than brain activity with respect to classification accuracy. This finding is consistent across different feature extraction methods and classification pipelines. While informative brain signals can be recovered with suitable cleaning procedures, we recommend that features should not be designed solely to maximize classification accuracy, as this could select for remaining artifactual components. We also propose the use of machine learning approaches that are interpretable to verify that classification is driven by physiological brain states. In summary, whereas informative artifacts are a helpful friend in BCI-based communication applications, they can be a problematic foe in the estimation of physiological brain states.

Why Can the Brain (and Not a Computer) Make Sense of the Liar Paradox?

Ordinary computing machines prohibit self-reference because it leads to logical inconsistencies and undecidability. In contrast, the human mind can understand self-referential statements without necessitating physically impossible brain states. Why can the brain make sense of self-reference? Here, we address this question by defining the Strange Loop Model, which features causal feedback between two brain modules, and circumvents the paradoxes of self-reference and negation by unfolding the inconsistency in time. We also argue that the metastable dynamics of the brain inhibit and terminate unhalting inferences. Finally, we show that the representation of logical inconsistencies in the Strange Loop Model leads to causal incongruence between brain subsystems in Integrated Information Theory.

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

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.

What lies underneath: Precise classification of brain states using time-dependent topological structure of dynamics

The self-organising global dynamics underlying brain states emerge from complex recursive nonlinear interactions between interconnected brain regions. Until now, all efforts of capturing the causal mechanistic generating principles have proven elusive, since they have been unable to describe the non-stationarity of brain dynamics, i.e. time-dependent changes. Here, we present a novel framework able to characterise brain states with high specificity, precisely by modelling the time-dependent dynamics. Through describing the topological structure of the brain at each moment in time (its ‘information structure’), we are able to classify different brain states by using the statistics across time of these exact ‘information structures’ hitherto hidden in the neuroimaging dynamics. Proving the strong potential of this framework, we were able to classify the neuroimaging data from two classes of comatose patients (minimally conscious state and unresponsive wakefulness syndrome) compared with healthy controls with very high precision.

Activity and Coupling to Hippocampal Oscillations of Median Raphe GABAergic Cells in Awake Mice

Ascending serotonergic/glutamatergic projection from the median raphe region (MRR) to the hippocampal formation regulates both encoding and consolidation of memory and the oscillations associated with them. The firing of various types of MRR neurons exhibits rhythmic modulation coupled to hippocampal oscillatory activity. A possible intermediary between rhythm-generating forebrain regions and entrained ascending modulation may be the GABAergic circuit in the MRR, known to be targeted by a diverse array of top-down inputs. However, the activity of inhibitory MRR neurons in an awake animal is still largely unexplored. In this study, we utilized whole cell patch-clamp, single cell, and multichannel extracellular recordings of GABAergic and non-GABAergic MRR neurons in awake, head-fixed mice. First, we have demonstrated that glutamatergic and serotonergic neurons receive both transient, phasic, and sustained tonic inhibition. Then, we observed substantial heterogeneity of GABAergic firing patterns but a marked modulation of activity by brain states and fine timescale coupling of spiking to theta and ripple oscillations. We also uncovered a correlation between the preferred theta phase and the direction of activity change during ripples, suggesting the segregation of inhibitory neurons into functional groups. Finally, we could detect complementary alteration of non-GABAergic neurons’ ripple-coupled activity. Our findings support the assumption that the local inhibitory circuit in the MRR may synchronize ascending serotonergic/glutamatergic modulation with hippocampal activity on a subsecond timescale.

What lies underneath: Precise classification of brain states using time-dependent topological structure of dynamics

The self-organising global dynamics underlying brain states emerge from complex recursive nonlinear interactions between interconnected brain regions. Until now, all efforts of capturing the causal mechanistic generating principles have proven elusive, since they have been unable to describe the non-stationarity of brain dynamics, i.e. time-dependent changes. Here, we present a novel framework able to characterise brain states with high specificity, precisely by modelling the time-dependent dynamics. Through describing the topological structure of the brain at each moment in time (its ‘information structure’), we are able to classify different brain states by using the statistics across time of these exact ‘information structures’ hitherto hidden in the neuroimaging dynamics. Proving the strong potential of this framework, we were able to classify the neuroimaging data from two classes of comatose patients (minimally conscious state and unresponsive wakefulness syndrome) compared with healthy controls with very high precision.

What lies underneath: Precise classification of brain states using time-dependent topological structure of dynamics

The self-organising global dynamics underlying brain states emerge from complex recursive nonlinear interactions between interconnected brain regions. Until now, all efforts of capturing the causal mechanistic generating principles have proven elusive, since they have been unable to describe the non-stationarity of brain dynamics, i.e. time-dependent changes. Here, we present a novel framework able to characterise brain states with high specificity, precisely by modelling the time-dependent dynamics. Through describing the topological structure of the brain at each moment in time (its ‘information structure’), we are able to classify different brain states by using the statistics across time of these exact ‘information structures’ hitherto hidden in the neuroimaging dynamics. Proving the strong potential of this framework, we were able to classify the neuroimaging data from two classes of comatose patients (minimally conscious state and unresponsive wakefulness syndrome) compared with healthy controls with very high precision.