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Aims: Schizophrenia (SZ) is a brain disorder characterized by psychotic symptoms and cognitive dysfunction. Recently, irregularities in sharp‐wave ripples (SPW‐Rs) have been reported in SZ. As SPW‐Rs play a critical role in memory, their irregularities can cause psychotic symptoms and cognitive dysfunction in patients with SZ. In this study, we investigated the SPW‐Rs in human SZ. Methods: We measured whole‐brain activity using magnetoencephalography (MEG) in patients with SZ (n = 20) and sex‐ and age‐matched healthy participants (n = 20) during open‐eye rest. We identified SPW‐Rs and analyzed their occurrence and time‐frequency traits. Furthermore, we developed a novel multivariate analysis method, termed "ripple‐gedMEG" to extract the global features of SPW‐Rs. We also examined the association between SPW‐Rs and brain state transitions. The outcomes of these analyses were modeled to predict the positive and negative syndrome scale (PANSS) scores of SZ. Results: We found that SPW‐Rs in the SZ (1) occurred more frequently, (2) the delay of the coupling phase (3) appeared in different brain areas, (4) consisted of a less organized spatiotemporal pattern, and (5) were less involved in brain state transitions. Finally, some of the neural features associated with the SPW‐Rs were found to be PANSS‐positive, a pathological indicator of SZ. These results suggest that widespread but disorganized SPW‐Rs underlies the symptoms of SZ. Conclusion: We identified irregularities in SPW‐Rs in SZ and confirmed that their alternations were strongly associated with SZ neuropathology. These results suggest a new direction for human SZ research. [ABSTRACT FROM AUTHOR]

Background: Alzheimer's disease (AD) is a common neurodegenerative dementia, characterized by abnormal dynamic functional connectivity (DFC). Traditional DFC analysis, assuming linear brain dynamics, may neglect the complexity of the brain's nonlinear interactions. Energy landscape analysis offers a holistic, nonlinear perspective to investigate brain network attractor dynamics, which was applied to resting-state fMRI data for AD in this study. Methods: This study utilized resting-state fMRI data from 60 individuals, comparing 30 Alzheimer's patients with 30 controls, from the Alzheimer's Disease Neuroimaging Initiative. Energy landscape analysis was applied to the data to characterize the aberrant brain network dynamics of AD patients. Results: The AD group stayed in the co-activation state for less time than the healthy control (HC) group, and a positive correlation was identified between the transition frequency of the co-activation state and behavior performance. Furthermore, the AD group showed a higher occurrence frequency and transition frequency of the cognitive control state and sensory integration state than the HC group. The transition between the two states was positively correlated with behavior performance. Conclusion: The results suggest that the co-activation state could be important to cognitive processing and that the AD group possibly raised cognitive ability by increasing the occurrence and transition between the impaired cognitive control and sensory integration states. [ABSTRACT FROM AUTHOR]

Aim: While conservatism bias refers to the human need for more evidence for decision‐making than rational thinking expects, the jumping to conclusions (JTC) bias refers to the need for less evidence among individuals with schizophrenia/delusion compared to healthy people. Although the hippocampus‐midbrain‐striatal aberrant salience system and the salience, default mode (DMN), and frontoparietal networks ("triple networks") are implicated in delusion/schizophrenia pathophysiology, the associations between conservatism/JTC and these systems/networks are unclear. Methods: Thirty‐seven patients with schizophrenia and 33 healthy controls performed the beads task, with large and small numbers of bead draws to decision (DTD) indicating conservatism and JTC, respectively. We performed independent component analysis (ICA) of resting functional magnetic resonance imaging (fMRI) data. For systems/networks above, we investigated interactions between diagnosis and DTD, and main effects of DTD. We similarly applied ICA to structural and diffusion MRI to explore the associations between DTD and gray/white matter. Results: We identified a significant main effect of DTD with functional connectivity between the striatum and DMN, which was negatively correlated with delusion severity in patients, indicating that the greater the anti‐correlation between these networks, the stronger the JTC and delusion. We further observed the main effects of DTD on a gray matter network resembling the DMN, and a white matter network connecting the functional and gray matter networks (all P < 0.05, family‐wise error [FWE] correction). Function and gray/white matter showed no significant interactions. Conclusion: Our results support the novel association of conservatism and JTC biases with aberrant salience and default brain mode. [ABSTRACT FROM AUTHOR]

Multiple myeloma (MM) is a cancer of plasma cells. Normal (NL) cells are considered to pass through a precancerous state, such as monoclonal gammopathy of undetermined significance (MGUS), before transitioning to MM. In the present study, we acquired Raman spectra at three stages—834 NL, 711 MGUS, and 970 MM spectra—and applied the dynamical network biomarker (DNB) theory to these spectra. The DNB analysis identified MGUS as the unstable pre-disease state of MM and extracted Raman shifts at 1149 and 1527–1530 cm − 1 as DNB variables. The distribution of DNB scores for each patient showed a significant difference between the mean values for MGUS and MM patients. Furthermore, an energy landscape (EL) analysis showed that the NL and MM stages were likely to become stable states. Raman spectroscopy, the DNB theory, and, complementarily, the EL analysis will be applicable to the identification of the pre-disease state in clinical samples. [ABSTRACT FROM AUTHOR]

Multiple myeloma (MM) is a cancer of plasma cells. Normal (NL) cells are considered to pass through a precancerous state, such as monoclonal gammopathy of undetermined significance (MGUS), before transitioning to MM. In the present study, we acquired Raman spectra at three stages—834 NL, 711 MGUS, and 970 MM spectra—and applied the dynamical network biomarker (DNB) theory to these spectra. The DNB analysis identified MGUS as the unstable pre-disease state of MM and extracted Raman shifts at 1149 and 1527–1530 cm−1 as DNB variables. The distribution of DNB scores for each patient showed a significant difference between the mean values for MGUS and MM patients. Furthermore, an energy landscape (EL) analysis showed that the NL and MM stages were likely to become stable states. Raman spectroscopy, the DNB theory, and, complementarily, the EL analysis will be applicable to the identification of the pre-disease state in clinical samples.

Achievement of task performance is required to maintain a constant level of attention. Attentional level fluctuates over the course of daily activities. However, brain dynamics leading to attentional fluctuation are still unknown. We investigated the underlying mechanisms of sustained attention using functional magnetic resonance imaging (fMRI). Participants were scanned with fMRI while performing an auditory, gradual-onset, continuous performance task (gradCPT). In this task, narrations gradually changed from one to the next. Participants pressed a button for frequent Go trials (i.e., male voices) as quickly as possible and withheld responses to infrequent No-go trials (i.e., female voices). Event-related analysis revealed that frontal and temporal areas, including the auditory cortex, were activated during successful and unsuccessful inhibition of predominant responses. Reaction-time (RT) variability throughout the auditory gradCPT was positively correlated with signal changes in regions of the dorsal attention network: superior frontal gyrus and superior parietal lobule. Energy landscape analysis showed that task-related activations could be clustered into different attractors: regions of the dorsal attention network and default mode network. The number of alternations between RT-stable and erratic periods increased with an increase in transitions between attractors in the brain. Therefore, we conclude that dynamic transitions between brain states are closely linked to auditory attentional fluctuations. [ABSTRACT FROM AUTHOR]

Our current understanding of melancholic depression is shaped by its position in the depression spectrum. The lack of consensus on how it should be treated—whether as a subtype of depression, or as a distinct disorder altogethe—interferes with the recovery of suffering patients. In this study, we analyzed brain state energy landscape models of melancholic depression, in contrast to healthy and non-melancholic energy landscapes. Our analyses showed significant group differences on basin energy, basin frequency, and transition dynamics in several functional brain networks such as basal ganglia, dorsal default mode, and left executive control networks. Furthermore, we found evidences suggesting the connection between energy landscape characteristics (basin characteristics) and depressive symptom scores (BDI-II and SHAPS). These results indicate that melancholic depression is distinguishable from its non-melancholic counterpart, not only in terms of depression severity, but also in brain dynamics.

Background: Alzheimer's disease (AD) is a common neurodegenerative dementia, characterized by abnormal dynamic functional connectivity (DFC). Traditional DFC analysis, assuming linear brain dynamics, may neglect the complexity of the brain's nonlinear interactions. Energy landscape analysis offers a holistic, nonlinear perspective to investigate brain network attractor dynamics, which was applied to resting-state fMRI data for AD in this study., Methods: This study utilized resting-state fMRI data from 60 individuals, comparing 30 Alzheimer's patients with 30 controls, from the Alzheimer's Disease Neuroimaging Initiative. Energy landscape analysis was applied to the data to characterize the aberrant brain network dynamics of AD patients., Results: The AD group stayed in the co-activation state for less time than the healthy control (HC) group, and a positive correlation was identified between the transition frequency of the co-activation state and behavior performance. Furthermore, the AD group showed a higher occurrence frequency and transition frequency of the cognitive control state and sensory integration state than the HC group. The transition between the two states was positively correlated with behavior performance., Conclusion: The results suggest that the co-activation state could be important to cognitive processing and that the AD group possibly raised cognitive ability by increasing the occurrence and transition between the impaired cognitive control and sensory integration states., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Xing, Guo and Long.)

The prefrontal cortex (PFC) is thought to orchestrate cognitive dynamics. However, in tests of bistable visual perception, no direct evidence supporting such presumable causal roles of the PFC has been reported except for a recent work. Here, using a novel brain-state-dependent neural stimulation system, we identified causal effects on percept dynamics in three PFC activities—right frontal eye fields, dorsolateral PFC (DLPFC), and inferior frontal cortex (IFC). The causality is behaviourally detectable only when we track brain state dynamics and modulate the PFC activity in brain-state-/state-history-dependent manners. The behavioural effects are underpinned by transient neural changes in the brain state dynamics, and such neural effects are quantitatively explainable by structural transformations of the hypothetical energy landscapes. Moreover, these findings indicate distinct functions of the three PFC areas: in particular, the DLPFC enhances the integration of two PFC-active brain states, whereas IFC promotes the functional segregation between them. This work resolves the controversy over the PFC roles in spontaneous perceptual switching and underlines brain state dynamics in fine investigations of brain-behaviour causality.

Our knowledge of ecological stability is built on assumptions of scale. These assumptions limit our ability to reach a generalizable and mechanistic understanding of stability under global environmental change. Moving towards a multiscale approach—across space, time and environment—will allow us to better understand the intrinsic (e.g., demographic) and extrinsic (environmental) drivers of ecological stability. In this perspective, we review multiple sources of variation responsible for shaping ecological dynamics, and how scale affects our observation of these dynamics through its confounding effect on drivers of variation in ecosystems. We discuss the effect of temporal scale when combining empirical dynamic modeling with high‐resolution population time series to consider the time‐varying nature of multispecies interaction networks, highlighting interspecific interactions as an intrinsic driver of community dynamics. Next, we examine energy landscape analysis as a method for inferring stability and transience during community assembly and its interaction with spatial scale, emphasizing the intrinsic role of compositional variability in assembly dynamics. We then examine population dynamics at species' range margins and show how considering the interaction between spatial and temporal environmental heterogeneity, an extrinsic driver of population dynamics, can facilitate a nuanced understanding of population expansions, range shifts, and species invasions. Finally, we discuss broadly how the sources of intrinsic and extrinsic variation interact with each other and with spatiotemporal scale to shape ecological dynamics. Better recognition of the scale‐dependent nature of the relationship between drivers of variation and ecological dynamics will be invaluable to illuminate the dynamics influencing ecological stability across scales. [ABSTRACT FROM AUTHOR]

Previous diagnostic systems precluded the co-existence of autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) in one person; but, after many clinical reports, the diagnostic criteria were updated to allow their co-occurrence. Despite such a clinical change, the neurobiological bases underpinning the comorbidity remain poorly understood, and whether the ASD+ADHD condition is a simple overlap of the two disorders is unknown. Here, to answer this question, we compared the brain dynamics of high-functioning ASD+ADHD children with age-/sex-/IQ-matched pure ASD, pure ADHD, and typically developing (TD) children. Regarding autistic traits, the socio-communicational symptom of the ASD+ADHD children was explained by the same overstable brain dynamics as seen in pure ASD. In contrast, their ADHD-like traits were grounded on a unique neural mechanism that was unseen in pure ADHD: the core symptoms of pure ADHD were associated with the overly flexible whole-brain dynamics that were triggered by the unstable activity of the dorsal-attention network and the left parietal cortex; by contrast, the ADHD-like cognitive instability of the ASD+ADHD condition was correlated with the atypically frequent neural transition along a specific brain state pathway, which was induced by the atypically unstable activity of the frontoparietal control network and the left prefrontal cortex. These observations need to be validated in future studies using more direct and comprehensive behavioral indices, but the current findings suggest that the ASD+ADHD comorbidity is not a mere overlap of the two disorders. Particularly, its ADHD-like traits could represent a unique condition that would need a specific diagnosis and bespoke treatments., (Copyright © 2023 Watanabe and Watanabe.)

The analysis of energy landscapes plays an important role in mathematical modelling, simulation and optimisation. Among the main features of interest are the number and distribution of local minima within the energy landscape. Granier and Kallel proposed in 2002 a new sampling procedure for estimating the number of local minima. In the present paper, we focus on improved heuristic implementations of the general framework devised by Granier and Kallel with regard to run-time behaviour and accuracy of predictions. The new heuristic method is demonstrated for the case of partial energy landscapes induced by RNA secondary structures. While the computation of minimum free energy RNA secondary structures has been studied for a long time, the analysis of folding landscapes has gained momentum over the past years in the context of co-transcriptional folding and deeper insights into cell processes. The new approach has been applied to ten RNA instances of length between 99 nt and 504 nt and their respective partial energy landscapes defined by secondary structures within an energy offset Δ E above the minimum free energy conformation. The number of local minima within the partial energy landscapes ranges from 1440 to 3441. Our heuristic method produces for the best approximations on average a deviation below 3.0% from the true number of local minima. [ABSTRACT FROM AUTHOR]

Our current understanding of melancholic depression is shaped by its position in the depression spectrum. The lack of consensus on how it should be treated-whether as a subtype of depression, or as a distinct disorder altogethe-interferes with the recovery of suffering patients. In this study, we analyzed brain state energy landscape models of melancholic depression, in contrast to healthy and non-melancholic energy landscapes. Our analyses showed significant group differences on basin energy, basin frequency, and transition dynamics in several functional brain networks such as basal ganglia, dorsal default mode, and left executive control networks. Furthermore, we found evidences suggesting the connection between energy landscape characteristics (basin characteristics) and depressive symptom scores (BDI-II and SHAPS). These results indicate that melancholic depression is distinguishable from its non-melancholic counterpart, not only in terms of depression severity, but also in brain dynamics., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Regonia, Takamura, Nakano, Ichikawa, Fermin, Okada, Okamoto, Yamawaki, Ikeda and Yoshimoto.)

The prefrontal cortex (PFC) is thought to orchestrate cognitive dynamics. However, in tests of bistable visual perception, no direct evidence supporting such presumable causal roles of the PFC has been reported except for a recent work. Here, using a novel brain-state-dependent neural stimulation system, we identified causal effects on percept dynamics in three PFC activities-right frontal eye fields, dorsolateral PFC (DLPFC), and inferior frontal cortex (IFC). The causality is behaviourally detectable only when we track brain state dynamics and modulate the PFC activity in brain-state-/state-history-dependent manners. The behavioural effects are underpinned by transient neural changes in the brain state dynamics, and such neural effects are quantitatively explainable by structural transformations of the hypothetical energy landscapes. Moreover, these findings indicate distinct functions of the three PFC areas: in particular, the DLPFC enhances the integration of two PFC-active brain states, whereas IFC promotes the functional segregation between them. This work resolves the controversy over the PFC roles in spontaneous perceptual switching and underlines brain state dynamics in fine investigations of brain-behaviour causality., Competing Interests: TW No competing interests declared, (© 2021, Watanabe.)