More Like this

1. Differential effects of propofol and ketamine on critical brain dynamics

   https://doi.org/10.1371/journal.pcbi.1008418  

   Varley, Thomas F.; Sporns, Olaf; Puce, Aina; Beggs, John (December 2020, PLOS Computational Biology)

   Jbabdi, Saad (Ed.)

   Whether the brain operates at a critical “tipping” point is a long standing scientific question, with evidence from both cellular and systems-scale studies suggesting that the brain does sit in, or near, a critical regime. Neuroimaging studies of humans in altered states of consciousness have prompted the suggestion that maintenance of critical dynamics is necessary for the emergence of consciousness and complex cognition, and that reduced or disorganized consciousness may be associated with deviations from criticality. Unfortunately, many of the cellular-level studies reporting signs of criticality were performed in non-conscious systems (in vitro neuronal cultures) or unconscious animals (e.g. anaesthetized rats). Here we attempted to address this knowledge gap by exploring critical brain dynamics in invasive ECoG recordings from multiple sessions with a single macaque as the animal transitioned from consciousness to unconsciousness under different anaesthetics (ketamine and propofol). We use a previously-validated test of criticality: avalanche dynamics to assess the differences in brain dynamics between normal consciousness and both drug-states. Propofol and ketamine were selected due to their differential effects on consciousness (ketamine, but not propofol, is known to induce an unusual state known as “dissociative anaesthesia”). Our analyses indicate that propofol dramatically restricted the size and duration of avalanches, while ketamine allowed for more awake-like dynamics to persist. In addition, propofol, but not ketamine, triggered a large reduction in the complexity of brain dynamics. All states, however, showed some signs of persistent criticality when testing for exponent relations and universal shape-collapse. Further, maintenance of critical brain dynamics may be important for regulation and control of conscious awareness. 

   more » « less
2. Propofol selectively modulates functional connectivity signatures of sustained attention during rest and narrative listening

   https://doi.org/10.1093/cercor/bhac020  

   Chamberlain, Taylor A; Rosenberg, Monica D (March 2022, Cerebral Cortex)

   Abstract Sustained attention is a critical cognitive function reflected in an individual’s whole-brain pattern of functional magnetic resonance imaging functional connectivity. However, sustained attention is not a purely static trait. Rather, attention waxes and wanes over time. Do functional brain networks that underlie individual differences in sustained attention also underlie changes in attentional state? To investigate, we replicate the finding that a validated connectome-based model of individual differences in sustained attention tracks pharmacologically induced changes in attentional state. Specifically, preregistered analyses revealed that participants exhibited functional connectivity signatures of stronger attention when awake than when under deep sedation with the anesthetic agent propofol. Furthermore, this effect was relatively selective to the predefined sustained attention networks: propofol administration modulated strength of the sustained attention networks more than it modulated strength of canonical resting-state networks and a network defined to predict fluid intelligence, and the functional connections most affected by propofol sedation overlapped with the sustained attention networks. Thus, propofol modulates functional connectivity signatures of sustained attention within individuals. More broadly, these findings underscore the utility of pharmacological intervention in testing both the generalizability and specificity of network-based models of cognitive function. 

   more » « less
3. State-transition dynamics of resting-state functional magnetic resonance imaging data: model comparison and test-to-retest analysis

   https://doi.org/10.1186/s12868-024-00854-3  

   Islam, Saiful; Khanra, Pitambar; Nakuci, Johan; Muldoon, Sarah F; Watanabe, Takamitsu; Masuda, Naoki (December 2024, BMC Neuroscience)

   Abstract Electroencephalogram (EEG) microstate analysis entails finding dynamics of quasi-stable and generally recurrent discrete states in multichannel EEG time series data and relating properties of the estimated state-transition dynamics to observables such as cognition and behavior. While microstate analysis has been widely employed to analyze EEG data, its use remains less prevalent in functional magnetic resonance imaging (fMRI) data, largely due to the slower timescale of such data. In the present study, we extend various data clustering methods used in EEG microstate analysis to resting-state fMRI data from healthy humans to extract their state-transition dynamics. We show that the quality of clustering is on par with that for various microstate analyses of EEG data. We then develop a method for examining test–retest reliability of the discrete-state transition dynamics between fMRI sessions and show that the within-participant test–retest reliability is higher than between-participant test–retest reliability for different indices of state-transition dynamics, different networks, and different data sets. This result suggests that state-transition dynamics analysis of fMRI data could discriminate between different individuals and is a promising tool for performing fingerprinting analysis of individuals. 

   more » « less
4. Mechanistic basis of propofol-induced disruption of kinesin processivity

   https://doi.org/10.1073/pnas.2023659118  

   Dutta, Mandira; Gilbert, Susan P.; Onuchic, José N.; Jana, Biman (January 2021, Proceedings of the National Academy of Sciences)

   Propofol is a widely used general anesthetic to induce and maintain anesthesia, and its effects are thought to occur through impact on the ligand-gated channels including the GABA_Areceptor. Propofol also interacts with a large number of proteins including molecular motors and inhibits kinesin processivity, resulting in significant decrease in the run length for conventional kinesin-1 and kinesin-2. However, the molecular mechanism by which propofol achieves this outcome is not known. The structural transition in the kinesin neck-linker region is crucial for its processivity. In this study, we analyzed the effect of propofol and its fluorine derivative (fropofol) on the transition in the neck-linker region of kinesin. Propofol binds at two crucial surfaces in the leading head: one at the microtubule-binding interface and the other in the neck-linker region. We observed in both the cases the order–disorder transition of the neck-linker was disrupted and kinesin lost its signal for forward movement. In contrast, there was not an effect on the neck-linker transition with propofol binding at the trailing head. Free-energy calculations show that propofol at the microtubule-binding surface significantly reduces the microtubule-binding affinity of the kinesin head. While propofol makes pi–pi stacking and H-bond interactions with the propofol binding cavity, fropofol is unable to make a suitable interaction at this binding surface. Therefore, the binding affinity of fropofol is much lower compared to propofol. Hence, this study provides a mechanism by which propofol disrupts kinesin processivity and identifies transitions in the ATPase stepping cycle likely affected. 

   more » « less
5. Bayesian mixed-effect higher-order hidden Markov models with applications to predictive healthcare using electronic health records

   https://doi.org/10.1080/24725854.2024.2302368  

   Liao, Ying; Xiang, Yisha; Zhao, Zhigen; Ai, Di (February 2024, IISE Transactions)

   Yu_Ding_Georgia_Institute_of_Technology (Ed.)

   The disease progression dynamics observed in electronic health records often reflect patients’ health condition evolution, holding the promise of enabling the development of clinical predictive models. These dynamics, however, generally display significant variability among patients, due to some critical factors (e.g., gender and age) and patient-level heterogeneity. Moreover, future health state may not only depend on the current state, but also more distant history states due to the complicated disease progression. To capture this complex transition behavior and address mixed effects in clinical prediction problems, we propose a novel and flexible Bayesian Mixed-Effect Higher-Order Hidden Markov Model (MHOHMM), and develop a classifier based on MHOHMMs. A range of MHOHMMs are designed to capture different data structures and the optimal one is identified by using the k-fold cross-validation approach. An effective two-stage Markov chain Monte Carlo (MCMC) sampling algorithm is designed for model inference. A simulation study is conducted to evaluate the performance of the proposed sampling algorithm and the MHOHMM-based classification method. The practical utility of the proposed framework is demonstrated by a case study on the acute hypotensive episode prediction for intensive care unit patients. Our results show that the MHOHMM-based framework provides good prediction performance. 

   more » « less