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The detection of amyloid-β (Aβ) deposition in the brain provides crucial evidence in the clinical diagnosis of Alzheimer’s disease (AD). However, the efficiency of the current PET-based brain Aβ examination suffers from both coarse, visual inspection-based bi-class stratification and high scanning cost and risks. In this work, we explored the feasibility of using non-invasive functional magnetic resonance imaging (fMRI) to predict Aβ-PET phenotypes in the AD continuum with graph learning on brain networks. First, three whole-brain Aβ-PET phenotypes were identified through clustering and their association with clinical phenotypes were investigated. Second, both conventional and high-order functional connectivity (FC) networks were constructed using resting-state fMRI and the network topological architectures were learned with graph convolutional networks (GCNs) to predict such Aβ-PET phenotypes. The experiment of Aβ-PET phenotype prediction on 258 samples from the AD continuum showed that our algorithm achieved a high fMRI-to-PET prediction accuracy (78.8%). The results demonstrated the existence of distinguishable brain Aβ deposition phenotypes in the AD continuum and the feasibility of using artificial intelligence and non-invasive brain imaging technique to approximate PET-based evaluations. It can be a promising technique for high-throughput screening of AD with less costs and restrictions.

The entropy profiles of cortical activity have become novel perspectives to investigate individual differences in behavior. However, previous studies have neglected foundational aspects of individual entropy profiles, that is, the test–retest reliability, the predictive power for cognitive ability in out-of-sample data, and the underlying neuroanatomical basis. We explored these issues in a large young healthy adult dataset (Human Connectome Project, N = 998). We showed the whole cortical entropy profile from resting-state functional magnetic resonance imaging is a robust personalized measure, while subsystem profiles exhibited heterogeneous reliabilities. The limbic network exhibited lowest reliability. We tested the out-of-sample predictive power for general and specific cognitive abilities based on reliable cortical entropy profiles. The default mode and visual networks are most crucial when predicting general cognitive ability. We investigated the anatomical features underlying cross-region and cross-individual variations in cortical entropy profiles. Cortical thickness and structural connectivity explained spatial variations in the group-averaged entropy profile. Cortical folding and myelination in the attention and frontoparietal networks determined predominantly individual cortical entropy profile. This study lays foundations for brain-entropy-based studies on individual differences to understand cognitive ability and related pathologies. These findings broaden our understanding of the associations between neural structures, functional dynamics, and cognitive ability.

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 STATEMENTIntrinsic 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.

The brain requires diverse segregated and integrated processing to perform normal functions in terms of anatomical structure and self-organized dynamics with critical features, but the fundamental relationships between the complex structural connectome, critical state, and functional diversity remain unknown. Herein, we extend the eigenmode analysis to investigate the joint contribution of hierarchical modular structural organization and critical state to brain functional diversity. We show that the structural modes inherent to the hierarchical modular structural connectome allow a nested functional segregation and integration across multiple spatiotemporal scales. The real brain hierarchical modular organization provides large structural capacity for diverse functional interactions, which are generated by sequentially activating and recruiting the hierarchical connectome modes, and the critical state can best explore the capacity to maximize the functional diversity. Our results reveal structural and dynamical mechanisms that jointly support a balanced segregated and integrated brain processing with diverse functional interactions, and they also shed light on dysfunctional segregation and integration in neurodegenerative diseases and neuropsychiatric disorders.

Reading is a complex task involving different brain areas. As a crystallized ability, reading is also known to have effects on brain structure and function development. However, there are still open questions about what are the elements of the reading networks and how structural and functional brain measures shape the reading ability. The present study used a data-driven approach to investigate whether reading-related brain structural measures of cortical thickness, myelination, sulcus depth and structural connectivity and functional connectivity from the whole brain can predict individual differences in reading skills. It used different brain measures and performance scores from the Oral Reading Recognition Test (ORRT) measuring reading ability from 998 participants. We revealed reading-related brain areas and connections, and evaluated how well area and connection measures predict reading performance. Interestingly, the combination of all brain measures obtained the best predictions. We further grouped reading-related areas into positive and negative networks, each with four different levels (Core Regions, Extended-Regions 1, 2, 3), representing different correlation levels with the reading scores, and the non-correlated Region irrelevant to reading ability. The Core Regions are composed of areas that are most strongly correlated with reading performance. Insular and frontal opercular cortex, lateral temporal cortex, and early auditory cortex occupy the positive Core Region, while inferior temporal and motor cortex occupy the negative Core Region. Aside from those areas, the present study also found more reading-related areas including visual and language-related areas. In addition, connections predicting reading scores are denser inside the reading-related networks than outside. Together, the present study reveals extended reading networks of the brain and provides an extended data-driven analytical framework to study interpretable brain-behavior relationships, which are transferable also to studying other abilities.

Brain signaling occurs across a wide range of spatial and temporal scales, and analysis of brain signal variability and synchrony has attracted recent attention as markers of intelligence, cognitive states, and brain disorders. However, current technologies to measure brain signals in humans have limited resolutions either in space or in time and cannot fully capture spatiotemporal variability, leaving it untested whether temporal variability and spatiotemporal synchrony are valid and reliable proxy of spatiotemporal variability in vivo. Here we used optical voltage imaging in mice under anesthesia and wakefulness to monitor cortical voltage activity at both high spatial and temporal resolutions to investigate functional connectivity (FC, a measure of spatiotemporal synchronization), Multi-Scale Entropy (MSE, a measure of temporal variability), and their relationships to Regional Entropy (RE, a measure of spatiotemporal variability). We observed that across cortical space, MSE pattern can largely explain RE pattern at small and large temporal scales with high positive and negative correlation respectively, while FC pattern strongly negatively associated with RE pattern. The time course of FC and small scale MSE tightly followed that of RE, while large scale MSE was more loosely coupled to RE. fMRI and EEG data simulated by reducing spatiotemporal resolution of the voltage imaging data or considering hemodynamics yielded MSE and FC measures that still contained information about RE based on the high resolution voltage imaging data. This suggested that MSE and FC could still be effective measures to capture spatiotemporal variability under limitation of imaging modalities applicable to human subjects. Our results support the notion that FC and MSE are effective biomarkers for brain states, and provide a promising viewpoint to unify these two principal domains in human brain data analysis.

Background Multi-Scale Entropy (MSE) is a widely used marker of Brain Signal Complexity (BSC) at multiple temporal scales. Methodological improvement There is no systematic research addressing the psychometric quality and reliability of MSE. It is unknown how recording conditions of EEG signals affect individual differences in MSE. These gaps can be addressed by means of Structural Equation Modeling (SEM). Results Based on a large sample of 210 young adults, we estimated measurement models for MSE derived from multiple epochs of EEG signal measured during resting state conditions with closed and open eyes, and during a visual task with multiple experimental manipulations. Factor reliability estimates, quantified by the McDonald’s ω coefficient, are high at lower and acceptable at higher time scales. Above individual differences in signal entropy observed across all recording conditions, persons specifically differ with respect to their BSC in open eyes resting state condition as compared with closed eyes state, and in task processing state MSE as compared with resting state. Comparison with existing methods By means of SEM, we decomposed individual differences in BSC into different factors depending on the recording condition of EEG signals. This goes beyond existing methods that aim at estimating average MSE differences across recording conditions, but do not address whether individual differences are additionally affected by the type of EEG recording condition. Conclusion Eyes closed and open and task conditions strongly influence individual differences in MSE. We provide recommendations for future studies aiming to address BSC using MSE as a neural marker of cognitive abilities.