Connectome-scale functional intrinsic connectivity networks in macaques

The hierarchical organization of the precuneus captured by functional gradients

The precuneus shows considerable heterogeneity in multiple dimensions including anatomy, function, and involvement in brain disorders. Leveraging the state-of-the-art functional gradient approach, we aimed to investigate the hierarchical organization of the precuneus, which may hold promise for a unified understanding of precuneus heterogeneity. Resting-state functional MRI data from 793 healthy individuals were used to discover and validate functional gradients of the precuneus, which were calculated based on the voxel-wise precuneus-to-cerebrum functional connectivity patterns. Then, we further explored the potential relationships of the precuneus functional gradients with cortical morphology, intrinsic geometry, canonical functional networks, and behavioral domains. We found that the precuneus principal and secondary gradients showed dorsoanterior-ventral and ventroposterior-dorsal organizations, respectively. Concurrently, the principal gradient was associated with cortical morphology, and both the principal and secondary gradients showed geometric distance dependence. Importantly, precuneus functional subdivisions corresponding to canonical functional networks (behavioral domains) were distributed along both gradients in a hierarchical manner, i.e., from the sensorimotor network (somatic movement and sensation) at one extreme to the default mode network (abstract cognitive functions) at the other extreme for the principal gradient and from the visual network (vision) at one end to the dorsal attention network (top-down control of attention) at the other end for the secondary gradient. These findings suggest that the precuneus functional gradients may provide mechanistic insights into the multifaceted nature of precuneus heterogeneity.

Control energy assessment of spatial interactions among macro‐scale brain networks

Many recent studies have revealed that spatial interactions of functional brain networks derived from fMRI data can well model functional connectomes of the human brain. However, it has been rarely explored what the energy consumption characteristics are for such spatial interactions of macro‐scale functional networks, which remains crucial for the understanding of brain organization, behavior, and dynamics. To explore this unanswered question, this article presents a novel framework for quantitative assessment of energy consumptions of macro‐scale functional brain network's spatial interactions via two main effective computational methodologies. First, we designed a novel scheme combining dictionary learning and hierarchical clustering to derive macro‐scale consistent brain network templates that can be used to define a common reference space for brain network interactions and energy assessments. Second, the control energy consumption for driving the brain networks during their spatial interactions is computed from the viewpoint of the linear network control theory. Especially, the energetically favorable brain networks were identified and their energy characteristics were comprehensively analyzed. Experimental results on the Human Connectome Project (HCP) task‐based fMRI (tfMRI) data showed that the proposed methods can reveal meaningful, diverse energy consumption patterns of macro‐scale network interactions. In particular, those networks present remarkable differences in energy consumption. The energetically least favorable brain networks are stable and consistent across HCP tasks such as motor, language, social, and working memory tasks. In general, our framework provides a new perspective to characterize human brain functional connectomes by quantitative assessment for the energy consumption of spatial interactions of macro‐scale brain networks.

Development of functional connectivity within and among the resting-state networks in anesthetized rhesus monkeys

OBJECTIVE

The age-related changes in the resting-state networks (RSNs) exhibited temporally specific patterns in humans, and humans and rhesus monkeys have similar RSNs. We hypothesized that the RSNs in rhesus monkeys experienced similar developmental patterns as humans.

METHODS

We acquired resting-state fMRI data from 62 rhesus monkeys, which were divided into childhood, adolescence, and early adulthood groups. Group independent component analysis (ICA) was used to identify monkey RSNs. We detected the between-group differences in the RSNs and static, dynamic, and effective functional network connections (FNCs) using one-way variance analysis (ANOVA) and post-hoc analysis.

RESULTS

Eight rhesus RSNs were identified, including cerebellum (CN), left and right lateral visual (LVN and RVN), posterior default mode (pDMN), visuospatial (VSN), frontal (FN), salience (SN), and sensorimotor networks (SMN). In internal connections, the CN, SN, FN, and SMN mainly matured in early adulthood. The static FNCs associated with FN, SN, pDMN primarily experienced fast descending slow ascending type (U-shaped) developmental patterns for maturation, and the dynamic FNCs related to pDMN (RVN, CN, and SMN) and SMN (CN) were mature in early adulthood. The effective FNC results showed that the pDMN and VSN (stimulated), SN (inhibited), and FN (first inhibited then stimulated) chiefly matured in early adulthood.

CONCLUSION

We identified eight monkey RSNs, which exhibited similar development patterns as humans. All the RSNs and FNCs in monkeys were not widely changed but fine-tuned. Our study clarified that the progressive synchronization, exploration, and regulation of cognitive RSNs within the pDMN, FN, SN, and VSN denoted potential maturation of the RSNs throughout development. We confirmed the development patterns of RSNs and FNCs would support the use of monkeys as a best animal model for human brain function.

Evolutional Neural Architecture Search for Optimization of Spatiotemporal Brain Network Decomposition

Using deep neural networks (DNNs) to explore spatial patterns and temporal dynamics of human brain activities has been an important yet challenging problem because the artificial neural networks are hard to be designed manually. There have been several promising deep learning methods, e.g., deep belief network (DBN), convolutional neural network (CNN), and deep sparse recurrent auto-encoder (DSRAE), that can decompose neuroscientific and meaningful spatiotemporal patterns from 4D functional Magnetic Resonance Imaging (fMRI) data. However, those previous studies still depend on hand-crafted neural network architectures and hyperparameters, which are not optimal in various senses. In this paper, we employ the evolutionary algorithms (EA) to optimize the deep neural architecture of DSRAE by minimizing the expected loss of initialized models, named eNAS-DSRAE (evolutionary Neural Architecture Search on Deep Sparse Recurrent Auto-Encoder). Also, validation experiments are designed and performed on the publicly available human connectome project (HCP) 900 datasets, and the results achieved by the optimized eNAS-DSRAE suggested that our framework can successfully identify the spatiotemporal features and perform better than the hand-crafted neural network models. To our best knowledge, the proposed eNAS-DSRAE is not only among the earliest NAS models that can extract connectome-scale meaningful spatiotemporal brain networks from 4D fMRI data, but also is an effective framework to optimize the RNN-based models.

Functional Parcellation of Human Brain Precuneus Using Density-Based Clustering

The human precuneus is involved in many high-level cognitive functions, which strongly suggests the existence of biologically meaningful subdivisions. However, the functional parcellation of the precuneus needs much to be investigated. In this study, we developed an eigen clustering (EIC) approach for the parcellation using precuneus-cortical functional connectivity from fMRI data of the Human Connectome Project. The EIC approach is robust to noise and can automatically determine the cluster number. It is consistently demonstrated that the human precuneus can be subdivided into six symmetrical and connected parcels. The anterior and posterior precuneus participate in sensorimotor and visual functions, respectively. The central precuneus with four subregions indicates a media role in the interaction of the default mode, dorsal attention, and frontoparietal control networks. The EIC-based functional parcellation is free of the spatial distance constraint and is more functionally coherent than parcellation using typical clustering algorithms. The precuneus subregions had high accordance with cortical morphology and revealed good functional segregation and integration characteristics in functional task-evoked activations. This study may shed new light on the human precuneus function at a delicate level and offer an alternative scheme for human brain parcellation.

Differentiable neural architecture search for optimal spatial/temporal brain function network decomposition

It has been a key topic to decompose the brain's spatial/temporal function networks from 4D functional magnetic resonance imaging (fMRI) data. With the advantages of robust and meaningful brain pattern extraction, deep neural networks have been shown to be more powerful and flexible in fMRI data modeling than other traditional methods. However, the challenge of designing neural network architecture for high-dimensional and complex fMRI data has also been realized recently. In this paper, we propose a new spatial/temporal differentiable neural architecture search algorithm (ST-DARTS) for optimal brain network decomposition. The core idea of ST-DARTS is to optimize the inner cell structure of the vanilla recurrent neural network (RNN) in order to effectively decompose spatial/temporal brain function networks from fMRI data. Based on the evaluations on all seven fMRI tasks in human connectome project (HCP) dataset, the ST-DARTS model is shown to perform promisingly, both spatially (i.e., it can recognize the most stimuli-correlated spatial brain network activation that is very similar to the benchmark) and temporally (i.e., its temporal activity is highly positively correlated with the task-design). To further improve the efficiency of ST-DARTS model, we introduce a flexible early-stopping mechanism, named as ST-DARTS+, which further improves experimental results significantly. To our best knowledge, the proposed ST-DARTS and ST-DARTS+ models are among the early efforts in optimally decomposing spatial/temporal brain function networks from fMRI data with neural architecture search strategy and they demonstrate great promise.

Developmental outcomes of early adverse care on amygdala functional connectivity in nonhuman primates

Despite the strong link between childhood maltreatment and psychopathology, the underlying neurodevelopmental mechanisms are poorly understood and difficult to disentangle from heritable and prenatal factors. This study used a translational macaque model of infant maltreatment in which the adverse experience occurs in the first months of life, during intense maturation of amygdala circuits important for stress and emotional regulation. Thus, we examined the developmental impact of maltreatment on amygdala functional connectivity (FC) longitudinally, from infancy through the juvenile period. Using resting state functional magnetic resonance imaging (MRI) we performed amygdala-prefrontal cortex (PFC) region-of-interest and exploratory whole-brain amygdala FC analyses. The latter showed (a) developmental increases in amygdala FC with many regions, likely supporting increased processing of socioemotional-relevant stimuli with age; and (b) maltreatment effects on amygdala coupling with arousal and stress brain regions (locus coeruleus, laterodorsal tegmental area) that emerged with age. Maltreated juveniles showed weaker FC than controls, which was negatively associated with infant hair cortisol concentrations. Findings from the region-of-interest analysis also showed weaker amygdala FC with PFC regions in maltreated animals than controls since infancy, whereas bilateral amygdala FC was stronger in maltreated animals. These effects on amygdala FC development may underlie the poor behavioral outcomes associated with this adverse experience.

Hierarchical Organization of Functional Brain Networks Revealed by Hybrid Spatiotemporal Deep Learning

Hierarchical organization of brain function has been an established concept in the neuroscience field for a long time, however, it has been rarely demonstrated how such hierarchical macroscale functional networks are actually organized in the human brain. In this study, to answer this question, we propose a novel methodology to provide an evidence of hierarchical organization of functional brain networks. This article introduces the hybrid spatiotemporal deep learning (HSDL), by jointly using deep belief networks (DBNs) and deep least absolute shrinkage and selection operator (LASSO) to reveal the temporal hierarchical features and spatial hierarchical maps of brain networks based on the Human Connectome Project 900 functional magnetic resonance imaging (fMRI) data sets. Briefly, the key idea of HSDL is to extract the weights between two adjacent layers of DBNs, which are then treated as the hierarchical dictionaries for deep LASSO to identify the corresponding hierarchical spatial maps. Our results demonstrate that both spatial and temporal aspects of dozens of functional networks exhibit multiscale properties that can be well characterized and interpreted based on existing computational tools and neuroscience knowledge. Our proposed novel hybrid deep model is used to provide the first insightful opportunity to reveal the potential hierarchical organization of time series and functional brain networks, using task-based fMRI signals of human brain.

Identifying Brain Networks at Multiple Time Scales via Deep Recurrent Neural Network

For decades, task functional magnetic resonance imaging has been a powerful noninvasive tool to explore the organizational architecture of human brain function. Researchers have developed a variety of brain network analysis methods for task fMRI data, including the general linear model, independent component analysis, and sparse representation methods. However, these shallow models are limited in faithful reconstruction and modeling of the hierarchical and temporal structures of brain networks, as demonstrated in more and more studies. Recently, recurrent neural networks (RNNs) exhibit great ability of modeling hierarchical and temporal dependence features in the machine learning field, which might be suitable for task fMRI data modeling. To explore such possible advantages of RNNs for task fMRI data, we propose a novel framework of a deep recurrent neural network (DRNN) to model the functional brain networks from task fMRI data. Experimental results on the motor task fMRI data of Human Connectome Project 900 subjects release demonstrated that the proposed DRNN can not only faithfully reconstruct functional brain networks, but also identify more meaningful brain networks with multiple time scales which are overlooked by traditional shallow models. In general, this work provides an effective and powerful approach to identifying functional brain networks at multiple time scales from task fMRI data.

A macaque connectome for large-scale network simulations in TheVirtualBrain

Models of large-scale brain networks that are informed by the underlying anatomical connectivity contribute to our understanding of the mapping between the structure of the brain and its dynamical function. Connectome-based modelling is a promising approach to a more comprehensive understanding of brain function across spatial and temporal scales, but it must be constrained by multi-scale empirical data from animal models. Here we describe the construction of a macaque (Macaca mulatta and Macaca fascicularis) connectome for whole-cortex simulations in TheVirtualBrain, an open-source simulation platform. We take advantage of available axonal tract-tracing datasets and enhance the existing connectome data using diffusion-based tractography in macaques. We illustrate the utility of the connectome as an extension of TheVirtualBrain by simulating resting-state BOLD-fMRI data and fitting it to empirical resting-state data.

Discovering hierarchical common brain networks via multimodal deep belief network

Studying a common architecture reflecting both brain's structural and functional organizations across individuals and populations in a hierarchical way has been of significant interest in the brain mapping field. Recently, deep learning models exhibited ability in extracting meaningful hierarchical structures from brain imaging data, e.g., fMRI and DTI. However, deep learning models have been rarely used to explore the relation between brain structure and function yet. In this paper, we proposed a novel multimodal deep believe network (DBN) model to discover and quantitatively represent the hierarchical organizations of common and consistent brain networks from both fMRI and DTI data. A prominent characteristic of DBN is that it is capable of extracting meaningful features from complex neuroimaging data with a hierarchical manner. With our proposed DBN model, three hierarchical layers with hundreds of common and consistent brain networks across individual brains are successfully constructed through learning a large dimension of representative features from fMRI/DTI data.