State-Dependent tACS Effects Reveal the Potential Causal Role of Prestimulus Alpha Traveling Waves in Visual Contrast Detection.

The intricate relationship between prestimulus alpha oscillations and visual contrast detection variability has been the focus of numerous studies. However, the causal impact of prestimulus alpha traveling waves on visual contrast detection remains largely unexplored. In our research, we sought to discern the causal link between prestimulus alpha traveling waves and visual contrast detection across different levels of mental fatigue. Using electroencephalography alongside a visual detection task with 30 healthy adults (13 females; 17 males), we identified a robust negative correlation between prestimulus alpha forward traveling waves (FTWs) and visual contrast threshold (VCT). Inspired by this correlation, we utilized 45/-45° phase-shifted transcranial alternating current stimulation (tACS) in a sham-controlled, double-blind, within-subject experiment with 33 healthy adults (23 females; 10 males) to directly modulate these alpha traveling waves. After the application of 45° phase-shifted tACS, we observed a substantial decrease in FTW and an increase in backward traveling waves, along with a concurrent increase in VCT, compared with the sham condition. These changes were particularly pronounced under a low fatigue state. The findings of state-dependent tACS effects reveal the potential causal role of prestimulus alpha traveling waves in visual contrast detection. Moreover, our study highlights the potential of 45/-45° phase-shifted tACS in cognitive modulation and therapeutic applications.

Acute stress promotes brain network integration and reduces state transition variability.

Despite the prevalence of stress, how brains reconfigure their multilevel, hierarchical functional organization in response to acute stress remains unclear. We examined changes in brain networks after social stress using whole-brain resting-state functional MRI (fMRI) by extending our recently published nested-spectral partition method, which quantified the functional balance between network segregation and integration. Acute stress was found to shift the brain into a more integrated and less segregated state, especially in frontal-temporal regions. Stress also stabilized brain states by reducing the variability of dynamic transition between segregated and integrated states. Transition frequency was associated with the change of cortisol, and transition variability was correlated with cognitive control. Our results show that brain networks tend to be more integrated and less variable after acute stress, possibly to enable efficient coping.

Enhanced intersubject similarity in functional connectivity by long-term abacus training.

The individual difference of intrinsic functional connectivity is increasingly acknowledged to be biologically informative and behaviorally relevant. However, such valuable information is still discounted as a stochastic variation in previous studies of cognitive training. Here, we explored the plasticity of intersubject similarity in functional connectivity (ISFC), induced by long-term abacus-based mental calculation (AMC) training. Using a longitudinal dataset (AMC: n = 40, 5-year training; Control: n = 43), we found robust training effect of enhanced ISFC, after accounting for the factor of development. Notably, the enhancement focused on selective subsets of FCs, or the "critical FCs," which predominantly impacted the default-mode and visual networks. Using a cross-sectional dataset with a larger sample (AMC: n = 93, 1/3/5-year training; Control: n = 110), we observed that the "critical FCs" and its intersubject similarity could predict mental calculation ability and its intersubject similarity, respectively, in the AMC group. However, such predictions cannot be generalized to the control group, suggesting that long-term training may be a prerequisite for establishing such brain-behavior relationships. Jointly, our findings implicated that the enhanced ISFC with profound impact on the default-mode network could be a plastic change that is associated with behavioral gains of training.

Segregation, integration, and balance of large-scale resting brain networks configure different cognitive abilities.

Diverse cognitive processes set different demands on locally segregated and globally integrated brain activity. However, it remains an open question how resting brains configure their functional organization to balance the demands on network segregation and integration to best serve cognition. Here we use an eigenmode-based approach to identify hierarchical modules in functional brain networks and quantify the functional balance between network segregation and integration. In a large sample of healthy young adults (n = 991), we combine the whole-brain resting state functional magnetic resonance imaging (fMRI) data with a mean-filed model on the structural network derived from diffusion tensor imaging and demonstrate that resting brain networks are on average close to a balanced state. This state allows for a balanced time dwelling at segregated and integrated configurations and highly flexible switching between them. Furthermore, we employ structural equation modeling to estimate general and domain-specific cognitive phenotypes from nine tasks and demonstrate that network segregation, integration, and their balance in resting brains predict individual differences in diverse cognitive phenotypes. More specifically, stronger integration is associated with better general cognitive ability, stronger segregation fosters crystallized intelligence and processing speed, and an individual's tendency toward balance supports better memory. Our findings provide a comprehensive and deep understanding of the brain's functioning principles in supporting diverse functional demands and cognitive abilities and advance modern network neuroscience theories of human cognition.

Association of aerobic glycolysis with the structural connectome reveals a benefit-risk balancing mechanism in the human brain.

Aerobic glycolysis (AG), that is, the nonoxidative metabolism of glucose, contributes significantly to anabolic pathways, rapid energy generation, task-induced activity, and neuroprotection; yet high AG is also associated with pathological hallmarks such as amyloid-ß deposition. An important yet unresolved question is whether and how the metabolic benefits and risks of brain AG is structurally shaped by connectome wiring. Using positron emission tomography and magnetic resonance imaging techniques as well as computational models, we investigate the relationship between brain AG and the macroscopic connectome. Specifically, we propose a weighted regional distance-dependent model to estimate the total axonal projection length of a brain node. This model has been validated in a macaque connectome derived from tract-tracing data and shows a high correspondence between experimental and estimated axonal lengths. When applying this model to the human connectome, we find significant associations between the estimated total axonal projection length and AG across brain nodes, with higher levels primarily located in the default-mode and prefrontal regions. Moreover, brain AG significantly mediates the relationship between the structural and functional connectomes. Using a wiring optimization model, we find that the estimated total axonal projection length in these high-AG regions exhibits a high extent of wiring optimization. If these high-AG regions are randomly rewired, their total axonal length and vulnerability risk would substantially increase. Together, our results suggest that high-AG regions have expensive but still optimized wiring cost to fulfill metabolic requirements and simultaneously reduce vulnerability risk, thus revealing a benefit-risk balancing mechanism in the human brain.

Cognitive abilities are associated with specific conjunctions of structural and functional neural subnetworks.

Cognitive neuroscience assumes that different mental abilities correspond to at least partly separable brain subnetworks and strives to understand their relationships. However, single-task approaches typically revealed multiple brain subnetworks to be involved in performance. Here, we chose a bottom-up approach of investigating the association between structural and functional brain subnetworks, on the one hand, and domain-specific cognitive abilities, on the other. Structural network was identified using machine-learning graph neural network by clustering anatomical brain properties measured in 838 individuals enroled in the WU-Minn Young Adult Human Connectome Project. Functional network was adapted from seven Resting State Networks (7-RSN). We then analyzed the results of 15 cognitive tasks and estimated five latent abilities: fluid reasoning (Gf), crystallized intelligence (Gc), memory (Mem), executive functions (EF), and processing speed (Gs). In a final step we determined linear associations between these independently identified ability and brain entities. We found no one-to-one mapping between latent abilities and brain subnetworks. Analyses revealed that abilities are associated with properties of particular combinations of brain subnetworks. While some abilities are more strongly associated to within-subnetwork connections, others are related with connections between multiple subnetworks. Importantly, domain-specific abilities commonly rely on node(s) as hub(s) to connect with other subnetworks. To test the robustness of our findings, we ran the analyses through several defensible analytical decisions. Together, the present findings allow a novel perspective on the distinct nature of domain-specific cognitive abilities building upon unique combinations of associated brain subnetworks.

Resting-state brain signal complexity discriminates young healthy APOE e4 carriers from non-e4 carriers.

It is well established that the e4 allele of the APOE gene is associated with impaired brain functionality and cognitive decline in humans at elder age. However, it is controversial whether and how the APOE e4 allele is associated with superior brain function among young healthy individuals, thus indicates a case of antagonistic pleiotropy of APOE e4 allele. Signal complexity is a critical aspect of brain activity that has been associated with brain function. In this study, the multiscale entropy (MSE) of resting-state EEG signals among a sample of young healthy adults (N = 260) as an indicator of brain signal complexity was investigated. It was of interest whether MSE differs across APOE genotype groups while age and education level were controlled for and whether the APOE genotype effect on MSE interacts with MSE time scale, as well as EEG recording condition. Results of linear mixed models indicate overall larger MSE in APOE e4 carriers. This genotype-dependent difference is larger at high as compared with low time scales. The interaction effect between APOE genotype and recording condition indicates increased between-state MSE change in young healthy APOE e4 carriers as compared with non-carriers. Because higher complexity is commonly taken to be associated with better cognitive functioning, the present results complement previous findings and therefore point to a pleiotropic spectrum of the APOE gene polymorphism.

Criticality enhances the multilevel reliability of stimulus responses in cortical neural networks.

Cortical neural networks exhibit high internal variability in spontaneous dynamic activities and they can robustly and reliably respond to external stimuli with multilevel features-from microscopic irregular spiking of neurons to macroscopic oscillatory local field potential. A comprehensive study integrating these multilevel features in spontaneous and stimulus-evoked dynamics with seemingly distinct mechanisms is still lacking. Here, we study the stimulus-response dynamics of biologically plausible excitation-inhibition (E-I) balanced networks. We confirm that networks around critical synchronous transition states can maintain strong internal variability but are sensitive to external stimuli. In this dynamical region, applying a stimulus to the network can reduce the trial-to-trial variability and shift the network oscillatory frequency while preserving the dynamical criticality. These multilevel features widely observed in different experiments cannot simultaneously occur in non-critical dynamical states. Furthermore, the dynamical mechanisms underlying these multilevel features are revealed using a semi-analytical mean-field theory that derives the macroscopic network field equations from the microscopic neuronal networks, enabling the analysis by nonlinear dynamics theory and linear noise approximation. The generic dynamical principle revealed here contributes to a more integrative understanding of neural systems and brain functions and incorporates multimodal and multilevel experimental observations. The E-I balanced neural network in combination with the effective mean-field theory can serve as a mechanistic modeling framework to study the multilevel neural dynamics underlying neural information and cognitive processes.

Understanding the computation of time using neural network models.

To maximize future rewards in this ever-changing world, animals must be able to discover the temporal structure of stimuli and then anticipate or act correctly at the right time. How do animals perceive, maintain, and use time intervals ranging from hundreds of milliseconds to multiseconds in working memory? How is temporal information processed concurrently with spatial information and decision making? Why are there strong neuronal temporal signals in tasks in which temporal information is not required? A systematic understanding of the underlying neural mechanisms is still lacking. Here, we addressed these problems using supervised training of recurrent neural network models. We revealed that neural networks perceive elapsed time through state evolution along stereotypical trajectory, maintain time intervals in working memory in the monotonic increase or decrease of the firing rates of interval-tuned neurons, and compare or produce time intervals by scaling state evolution speed. Temporal and nontemporal information is coded in subspaces orthogonal with each other, and the state trajectories with time at different nontemporal information are quasiparallel and isomorphic. Such coding geometry facilitates the decoding generalizability of temporal and nontemporal information across each other. The network structure exhibits multiple feedforward sequences that mutually excite or inhibit depending on whether their preferences of nontemporal information are similar or not. We identified four factors that facilitate strong temporal signals in nontiming tasks, including the anticipation of coming events. Our work discloses fundamental computational principles of temporal processing, and it is supported by and gives predictions to a number of experimental phenomena.

Cortex-Wide Dynamics of Intrinsic Electrical Activities: Propagating Waves and Their Interactions.

Cortical circuits generate patterned activities that reflect intrinsic brain dynamics that lay the foundation for any, including stimuli-evoked, cognition and behavior. However, the spatiotemporal organization properties and principles of this intrinsic activity have only been partially elucidated because of previous poor resolution of experimental data and limited analysis methods. Here we investigated continuous wave patterns in the 0.5-4 Hz (delta band) frequency range on data from high-spatiotemporal resolution optical voltage imaging of the upper cortical layers in anesthetized mice. Waves of population activities propagate in heterogeneous directions to coordinate neuronal activities between different brain regions. The complex wave patterns show characteristics of both stereotypy and variety. The location and type of wave patterns determine the dynamical evolution when different waves interact with each other. Local wave patterns of source, sink, or saddle emerge at preferred spatial locations. Specifically, "source" patterns are predominantly found in cortical regions with low multimodal hierarchy such as the primary somatosensory cortex. Our findings reveal principles that govern the spatiotemporal dynamics of spontaneous cortical activities and associate them with the structural architecture across the cortex.SIGNIFICANCE STATEMENT Intrinsic brain activities, as opposed to external stimulus-evoked responses, have increasingly gained attention, but it remains unclear how these intrinsic activities are spatiotemporally organized at the cortex-wide scale. By taking advantage of the high spatiotemporal resolution of optical voltage imaging, we identified five wave pattern types, and revealed the organization properties of different wave patterns and the dynamical mechanisms when they interact with each other. Moreover, we found a relationship between the emergence probability of local wave patterns and the multimodal structure hierarchy across cortical areas. Our findings reveal the principles of spatiotemporal wave dynamics of spontaneous activities and associate them with the underlying hierarchical architecture across the cortex.