Species -shared and -unique gyral peaks on human and macaque brains.

Cortical folding is an important feature of primate brains that plays a crucial role in various cognitive and behavioral processes. Extensive research has revealed both similarities and differences in folding morphology and brain function among primates including macaque and human. The folding morphology is the basis of brain function, making cross-species studies on folding morphology important for understanding brain function and species evolution. However, prior studies on cross-species folding morphology mainly focused on partial regions of the cortex instead of the entire brain. Previously, our research defined a whole-brain landmark based on folding morphology: the gyral peak. It was found to exist stably across individuals and ages in both human and macaque brains. Shared and unique gyral peaks in human and macaque are identified in this study, and their similarities and differences in spatial distribution, anatomical morphology, and functional connectivity were also dicussed.

Species -Shared and -Unique Gyral Peaks on Human and Macaque Brains.

Cortical folding is an important feature of primate brains that plays a crucial role in various cognitive and behavioral processes. Extensive research has revealed both similarities and differences in folding morphology and brain function among primates including macaque and human. The folding morphology is the basis of brain function, making cross-species studies on folding morphology important for understanding brain function and species evolution. However, prior studies on cross-species folding morphology mainly focused on partial regions of the cortex instead of the entire brain. Previously, we defined a whole-brain landmark based on folding morphology: the gyral peak. It was found to exist stably across individuals and ages in both human and macaque brains. In this study, we identified shared and unique gyral peaks in human and macaque, and investigated the similarities and differences in the spatial distribution, anatomical morphology, and functional connectivity of them.

A hybrid spatiotemporal deep belief network and sparse representation-based framework reveals multilevel core functional components in decoding multitask fMRI signals.

Decoding human brain activity on various task-based functional brain imaging data is of great significance for uncovering the functioning mechanism of the human mind. Currently, most feature extraction model-based methods for brain state decoding are shallow machine learning models, which may struggle to capture complex and precise spatiotemporal patterns of brain activity from the highly noisy fMRI raw data. Moreover, although decoding models based on deep learning methods benefit from their multilayer structure that could extract spatiotemporal features at multiscale, the relatively large populations of fMRI datasets are indispensable, and the explainability of their results is elusive. To address the above problems, we proposed a computational framework based on hybrid spatiotemporal deep belief network and sparse representations to differentiate multitask fMRI (tfMRI) signals. Using a relatively small cohort of tfMRI data as a test bed, our framework can achieve an average classification accuracy of 97.86% and define the multilevel temporal and spatial patterns of multiple cognitive tasks. Intriguingly, our model can characterize the key components for differentiating the multitask fMRI signals. Overall, the proposed framework can identify the interpretable and discriminative fMRI composition patterns at multiple scales, offering an effective methodology for basic neuroscience and clinical research with relatively small cohorts.

Arousal modulates the amygdala-insula reciprocal connectivity during naturalistic emotional movie watching.

Emotional arousal is a complex state recruiting distributed cortical and subcortical structures, in which the amygdala and insula play an important role. Although previous neuroimaging studies have showed that the amygdala and insula manifest reciprocal connectivity, the effective connectivities and modulatory patterns on the amygdala-insula interactions underpinning arousal are still largely unknown. One of the reasons may be attributed to static and discrete laboratory brain imaging paradigms used in most existing studies. In this study, by integrating naturalistic-paradigm (i.e., movie watching) functional magnetic resonance imaging (fMRI) with a computational affective model that predicts dynamic arousal for the movie stimuli, we investigated the effective amygdala-insula interactions and the modulatory effect of the input arousal on the effective connections. Specifically, the predicted dynamic arousal of the movie served as regressors in general linear model (GLM) analysis and brain activations were identified accordingly. The regions of interest (i.e., the bilateral amygdala and insula) were localized according to the GLM activation map. The effective connectivity and modulatory effect were then inferred by using dynamic causal modeling (DCM). Our experimental results demonstrated that amygdala was the site of driving arousal input and arousal had a modulatory effect on the reciprocal connections between amygdala and insula. Our study provides novel evidence to the underlying neural mechanisms of arousal in a dynamical naturalistic setting.

Mapping the time-varying functional brain networks in response to naturalistic movie stimuli.

One of human brain's remarkable traits lies in its capacity to dynamically coordinate the activities of multiple brain regions or networks, adapting to an externally changing environment. Studying the dynamic functional brain networks (DFNs) and their role in perception, assessment, and action can significantly advance our comprehension of how the brain responds to patterns of sensory input. Movies provide a valuable tool for studying DFNs, as they offer a naturalistic paradigm that can evoke complex cognitive and emotional experiences through rich multimodal and dynamic stimuli. However, most previous research on DFNs have predominantly concentrated on the resting-state paradigm, investigating the topological structure of temporal dynamic brain networks generated via chosen templates. The dynamic spatial configurations of the functional networks elicited by naturalistic stimuli demand further exploration. In this study, we employed an unsupervised dictionary learning and sparse coding method combing with a sliding window strategy to map and quantify the dynamic spatial patterns of functional brain networks (FBNs) present in naturalistic functional magnetic resonance imaging (NfMRI) data, and further evaluated whether the temporal dynamics of distinct FBNs are aligned to the sensory, cognitive, and affective processes involved in the subjective perception of the movie. The results revealed that movie viewing can evoke complex FBNs, and these FBNs were time-varying with the movie storylines and were correlated with the movie annotations and the subjective ratings of viewing experience. The reliability of DFNs was also validated by assessing the Intra-class coefficient (ICC) among two scanning sessions under the same naturalistic paradigm with a three-month interval. Our findings offer novel insight into comprehending the dynamic properties of FBNs in response to naturalistic stimuli, which could potentially deepen our understanding of the neural mechanisms underlying the brain's dynamic changes during the processing of visual and auditory stimuli.

Hierarchical Individual Naturalistic Functional Brain Networks with Group Consistency uncovered by a Two-Stage NAS-Volumetric Sparse DBN Framework.

The functional magnetic resonance imaging under naturalistic paradigm (NfMRI) showed great advantages in identifying complex and interactive functional brain networks due to its dynamics and multimodal information. In recent years, various deep learning models, such as deep convolutional autoencoder (DCAE), deep belief network (DBN) and volumetric sparse deep belief network (vsDBN), can obtain hierarchical functional brain networks (FBN) and temporal features from fMRI data. Among them, the vsDBN model revealed a good capability in identifying hierarchical FBNs by modelling fMRI volume images. However, due to the high dimensionality of fMRI volumes and the diverse training parameters of deep learning methods, especially the network architecture that is the most critical parameter for uncovering the hierarchical organization of human brain function, researchers still face challenges in designing an appropriate deep learning framework with automatic network architecture optimization to model volumetric NfMRI. In addition, most of the existing deep learning models ignore the group-wise consistency and inter-subject variation properties embedded in NfMRI volumes. To solve these problems, we proposed a two-stage neural architecture search and vs DBN model (two-stage NAS-vsDBN model) to identify the hierarchical human brain spatio-temporal features possessing both group-consistency and individual-uniqueness under naturalistic condition. Moreover, our model defined reliable network structure for modelling volumetric NfMRI data via NAS framework, and the group-level and individual-level FBNs and associated temporal features exhibited great consistency. In general, our method well identified the hierarchical temporal and spatial features of the brain function and revealed the crucial properties of neural processes under natural viewing condition.Significance StatementIn this paper, we proposed and applied a novel analytical strategy - a two-stage NAS-vsDBN model to identify both group-level and individual-level spatio-temporal features at multi-scales from volumetric NfMRI data. The proposed PSO-based NAS framework can find optimal neural structure for both group-wise and individual-level vs-DBN models. Furthermore, with well-established correspondence between two stages of vsDBN models, our model can effectively detect group-level FBNs that reveal the consistency in neural processes across subjects and individual-level FBNs that maintain the subject specific variability, verifying the inherent property of brain function under naturalistic condition.

Multitask fMRI Data Classification via Group-Wise Hybrid Temporal and Spatial Sparse Representations.

Task-based functional magnetic resonance imaging (tfMRI) has been widely used to induce functional brain activities corresponding to various cognitive tasks. A relatively under-explored question is whether there exist fundamental differences in fMRI signal composition patterns that can effectively classify the task states of tfMRI data, furthermore, whether there exist key functional components in characterizing the diverse tfMRI signals. Recently, fMRI signal composition patterns of multiple tasks have been investigated via deep learning models, where relatively large populations of fMRI datasets are indispensable and the neurologic meaning of their results is elusive. Thus, the major challenges arise from the high dimensionality, low signal-to-noise ratio, interindividual variability, a small sample size of fMRI data, and the explainability of classification results. To address the above challenges, we proposed a computational framework based on group-wise hybrid temporal and spatial sparse representations (HTSSR) to identify and differentiate multitask fMRI signal composition patterns. Using relatively small cohorts of Human Connectome Project (HCP) tfMRI data as test-bed, the experimental results demonstrated that the multitask of fMRI data can be successfully classified with an average accuracy of 96.67%, where the key components in differentiating the multitask can be characterized, suggesting the effectiveness and explainability of the proposed method. Moreover, both task-related components and resting-state networks (RSNs) can be reliably detected. Therefore, our study proposed a novel framework that identifies the interpretable and discriminative fMRI composition patterns and can be potentially applied for controlling fMRI data quality and inferring biomarkers in brain disorders with small sample neuroimaging datasets.

Hierarchical Spatio-Temporal Modeling of Naturalistic Functional Magnetic Resonance Imaging Signals via Two-Stage Deep Belief Network With Neural Architecture Search.

Naturalistic functional magnetic resonance imaging (NfMRI) has become an effective tool to study brain functional activities in real-life context, which reduces the anxiety or boredom due to difficult or repetitive tasks and avoids the problem of unreliable collection of brain activity caused by the subjects' microsleeps during resting state. Recent studies have made efforts on characterizing the brain's hierarchical organizations from fMRI data by various deep learning models. However, most of those models have ignored the properties of group-wise consistency and inter-subject difference in brain function under naturalistic paradigm. Another critical issue is how to determine the optimal neural architecture of deep learning models, as manual design of neural architecture is time-consuming and less reliable. To tackle these problems, we proposed a two-stage deep belief network (DBN) with neural architecture search (NAS) combined framework (two-stage NAS-DBN) to model both the group-consistent and individual-specific naturalistic functional brain networks (FBNs), which reflected the hierarchical organization of brain function and the nature of brain functional activities under naturalistic paradigm. Moreover, the test-retest reliability and spatial overlap rate of the FBNs identified by our model reveal better performance than that of widely used traditional methods. In general, our model provides a promising method for characterizing hierarchical spatiotemporal features under the natural paradigm.

Intracranial-EEG evidence for medial temporal pole driving amygdala activity induced by multi-modal emotional stimuli.

The temporal pole (TP) is an associative cortical region required for complex cognitive functions such as social and emotional cognition. However, mapping the TP with functional magnetic resonance imaging is technically challenging and thus understanding its interaction with other key emotional circuitry, such as the amygdala, remains elusive. We exploited the unique advantages of stereo-electroencephalography (sEEG) to assess the responses of the TP and the amygdala during the perception of emotionally salient stimuli of pictures, music and movies. These stimuli consistently elicited high gamma responses (70-140 Hz) in both the TP and the amygdala, accompanied by functional connectivity in the low frequency range (2-12 Hz). Computational analyses suggested that the TP drove this effect in the theta frequency range, modulated by the emotional valence of the stimuli. Notably, cross-frequency analysis indicated the phase of theta oscillations in the TP modulated the amplitude of high gamma activity in the amygdala. These results were reproducible across three types of sensory inputs including naturalistic stimuli. Our results suggest that multimodal emotional stimuli induce a hierarchical influence of the TP over the amygdala.

A connectivity-based parcellation improved functional representation of the human cerebellum.

The cerebellum is traditionally well known for its role in motor learning and coordination. Recently, it is recognized that the function of the cerebellum is highly diverse and extends to non-motor domains, such as working memory, emotion and language. The diversity of the cerebellum can be appreciated by examining its extensive connectivity to the cerebral regions selective for both motor and cognitive functions. Importantly, the pattern of cerebro-cerebellar connectivity is specific and distinct to different cerebellar subregions. Therefore, to understand the cerebellum and the various functions it involves, it is essential to identify and differentiate its subdivisions. However, most studies are still referring the cerebellum as one brain structure or by its gross anatomical subdivisions, which does not necessarily reflect the functional mapping of the cerebellum. We here employed a data-driven method to generate a functional connectivity-based parcellation of the cerebellum. Our results demonstrated that functional connectivity-based atlas is superior to existing atlases in regards to cluster homogeneity, accuracy of functional connectivity representation and individual identification. Furthermore, our functional atlas improves statistical results of task fMRI analyses, as compared to the standard voxel-based approach and existing atlases. Our detailed functional parcellation provides a valuable tool for elucidating the functional diversity and connectivity of the cerebellum as well as its network relationships with the whole brain.

Effective connectivity of the anterior hippocampus predicts recollection confidence during natural memory retrieval.

Human interactions with the world are influenced by memories of recent events. This effect, often triggered by perceptual cues, occurs naturally and without conscious effort. However, the neuroscience of involuntary memory in a dynamic milieu has received much less attention than the mechanisms of voluntary retrieval with deliberate purpose. Here, we investigate the neural processes driven by naturalistic cues that relate to, and presumably trigger the retrieval of recent experiences. Viewing the continuation of recently viewed clips evokes greater bilateral activation in anterior hippocampus, precuneus and angular gyrus than naïve clips. While these regions manifest reciprocal connectivity, continued viewing specifically modulates the effective connectivity from the anterior hippocampus to the precuneus. The strength of this modulation predicts participants' confidence in later voluntary recall of news details. Our study reveals network mechanisms of dynamic, involuntary memory retrieval and its relevance to metacognition in a rich context resembling everyday life.

Screening and Function Analysis of MicroRNAs Involved in Exercise Preconditioning-Attenuating Pathological Cardiac Hypertrophy.

Exercise preconditioning (EP) attenuates pathological cardiac hypertrophy by increasing the functional capacity of the cardiovascular system; however, the underlying molecular mechanisms remain unclear. MicroRNAs (miRNAs) play important roles in various physiological and pathological processes by regulating the expression of the targeted gene. In this study, we aimed to screen the miRNAs involved in EP-attenuating pathological cardiac hypertrophy. The histological and echocardiographic parameters assessment showed that pathological cardiac hypertrophy induced by transverse aortic constriction (TAC) was significantly alleviated in EP treated rats. The left ventricular tissues (n = 3) from Sham, TAC and EP + TAC groups were subjected to small RNA deep sequencing. A total of 570 known mature miRNAs and 530 putative novel miRNAs were detected. DEGseq analysis showed that there were 37 and 88 differentially expressed miRNAs in the comparisons of TAC versus Sham and EP + TAC versus TAC, respectively. Among them, EP treatment could relieve the expression changes of 32 miRNAs, which were supposed to be involved in EP-attenuating pathological cardiac hypertrophy. After miRNAs target genes prediction by miRDB algorithm, pathway analysis showed that the most frequently represented pathways were involved in Calcium signaling pathway and MAPK signaling pathway. The results would provide valuable clues to finding therapeutic targets for the treatment of pathological cardiac hypertrophy.

Neural Correlates of Temporal Complexity and Synchrony during Audiovisual Correspondence Detection.

We often perceive real-life objects as multisensory cues through space and time. A key challenge for audiovisual integration is to match neural signals that not only originate from different sensory modalities but also that typically reach the observer at slightly different times. In humans, complex, unpredictable audiovisual streams lead to higher levels of perceptual coherence than predictable, rhythmic streams. In addition, perceptual coherence for complex signals seems less affected by increased asynchrony between visual and auditory modalities than for simple signals. Here, we used functional magnetic resonance imaging to determine the human neural correlates of audiovisual signals with different levels of temporal complexity and synchrony. Our study demonstrated that greater perceptual asynchrony and lower signal complexity impaired performance in an audiovisual coherence-matching task. Differences in asynchrony and complexity were also underpinned by a partially different set of brain regions. In particular, our results suggest that, while regions in the dorsolateral prefrontal cortex (DLPFC) were modulated by differences in memory load due to stimulus asynchrony, areas traditionally thought to be involved in speech production and recognition, such as the inferior frontal and superior temporal cortex, were modulated by the temporal complexity of the audiovisual signals. Our results, therefore, indicate specific processing roles for different subregions of the fronto-temporal cortex during audiovisual coherence detection.

Sparse coding reveals greater functional connectivity in female brains during naturalistic emotional experience.

Functional neuroimaging is widely used to examine changes in brain function associated with age, gender or neuropsychiatric conditions. FMRI (functional magnetic resonance imaging) studies employ either laboratory-designed tasks that engage the brain with abstracted and repeated stimuli, or resting state paradigms with little behavioral constraint. Recently, novel neuroimaging paradigms using naturalistic stimuli are gaining increasing attraction, as they offer an ecologically-valid condition to approximate brain function in real life. Wider application of naturalistic paradigms in exploring individual differences in brain function, however, awaits further advances in statistical methods for modeling dynamic and complex dataset. Here, we developed a novel data-driven strategy that employs group sparse representation to assess gender differences in brain responses during naturalistic emotional experience. Comparing to independent component analysis (ICA), sparse coding algorithm considers the intrinsic sparsity of neural coding and thus could be more suitable in modeling dynamic whole-brain fMRI signals. An online dictionary learning and sparse coding algorithm was applied to the aggregated fMRI signals from both groups, which was subsequently factorized into a common time series signal dictionary matrix and the associated weight coefficient matrix. Our results demonstrate that group sparse representation can effectively identify gender differences in functional brain network during natural viewing, with improved sensitivity and reliability over ICA-based method. Group sparse representation hence offers a superior data-driven strategy for examining brain function during naturalistic conditions, with great potential for clinical application in neuropsychiatric disorders.

Inter-subject Functional Correlation Reveal a Hierarchical Organization of Extrinsic and Intrinsic Systems in the Brain.

The brain is constantly monitoring and integrating both cues from the external world and signals generated intrinsically. These extrinsically and intrinsically-driven neural processes are thought to engage anatomically distinct regions, which are thought to constitute the extrinsic and intrinsic systems of the brain. While the specialization of extrinsic and intrinsic system is evident in primary and secondary sensory cortices, a systematic mapping of the whole brain remains elusive. Here, we characterized the extrinsic and intrinsic functional activities in the brain during naturalistic movie-viewing. Using a novel inter-subject functional correlation (ISFC) analysis, we found that the strength of ISFC shifts along the hierarchical organization of the brain. Primary sensory cortices appear to have strong inter-subject functional correlation, consistent with their role in processing exogenous information, while heteromodal regions that attend to endogenous processes have low inter-subject functional correlation. Those brain systems with higher intrinsic tendency show greater inter-individual variability, likely reflecting the aspects of brain connectivity architecture unique to individuals. Our study presents a novel framework for dissecting extrinsically- and intrinsically-driven processes, as well as examining individual differences in brain function during naturalistic stimulation.

Elucidating functional differences between cortical gyri and sulci via sparse representation HCP grayordinate fMRI data.

The highly convoluted cerebral cortex is characterized by two different topographic structures: convex gyri and concave sulci. Increasing studies have demonstrated that cortical gyri and sulci exhibit different structural connectivity patterns. Inspired by the intrinsic structural differences between gyri and sulci, in this paper, we present a data-driven framework based on sparse representation of fMRI data for functional network inferences, then examine the interactions within and across gyral and sulcal functional networks and finally elucidate possible functional differences using graph theory based properties. We apply the proposed framework to the high-resolution Human Connectome Project (HCP) grayordinate fMRI data. Extensive experimental results on both resting state fMRI data and task-based fMRI data consistently suggested that gyri are more functionally integrated, while sulci are more functionally segregated in the organizational architecture of cerebral cortex, offering novel understanding of the byzantine cerebral cortex.

Test-retest reliability of functional connectivity networks during naturalistic fMRI paradigms.

Functional connectivity analysis has become a powerful tool for probing the human brain function and its breakdown in neuropsychiatry disorders. So far, most studies adopted resting-state paradigm to examine functional connectivity networks in the brain, thanks to its low demand and high tolerance that are essential for clinical studies. However, the test-retest reliability of resting-state connectivity measures is moderate, potentially due to its low behavioral constraint. On the other hand, naturalistic neuroimaging paradigms, an emerging approach for cognitive neuroscience with high ecological validity, could potentially improve the reliability of functional connectivity measures. To test this hypothesis, we characterized the test-retest reliability of functional connectivity measures during a natural viewing condition, and benchmarked it against resting-state connectivity measures acquired within the same functional magnetic resonance imaging (fMRI) session. We found that the reliability of connectivity and graph theoretical measures of brain networks is significantly improved during natural viewing conditions over resting-state conditions, with an average increase of almost 50% across various connectivity measures. Not only sensory networks for audio-visual processing become more reliable, higher order brain networks, such as default mode and attention networks, but also appear to show higher reliability during natural viewing. Our results support the use of natural viewing paradigms in estimating functional connectivity of brain networks, and have important implications for clinical application of fMRI. Hum Brain Mapp 38:2226-2241, 2017. © 2017 Wiley Periodicals, Inc.

Assessing the effects of cocaine dependence and pathological gambling using group-wise sparse representation of natural stimulus FMRI data.

Assessing functional brain activation patterns in neuropsychiatric disorders such as cocaine dependence (CD) or pathological gambling (PG) under naturalistic stimuli has received rising interest in recent years. In this paper, we propose and apply a novel group-wise sparse representation framework to assess differences in neural responses to naturalistic stimuli across multiple groups of participants (healthy control, cocaine dependence, pathological gambling). Specifically, natural stimulus fMRI (N-fMRI) signals from all three groups of subjects are aggregated into a big data matrix, which is then decomposed into a common signal basis dictionary and associated weight coefficient matrices via an effective online dictionary learning and sparse coding method. The coefficient matrices associated with each common dictionary atom are statistically assessed for each group separately. With the inter-group comparisons based on the group-wise correspondence established by the common dictionary, our experimental results demonstrated that the group-wise sparse coding and representation strategy can effectively and specifically detect brain networks/regions affected by different pathological conditions of the brain under naturalistic stimuli.

Photo inactivation of virus particles in microfluidic capillary systems.

It has long been established that UVC light is a very effective method for inactivating pathogens in a fluid, yet the application of UVC irradiation to modern biotechnological processes is limited by the intrinsic short penetration distance of UVC light in optically dense protein solutions. This experimental and numerical study establishes that irradiating a fluid flowing continuously in a microfluidic capillary system, in which the diameter of the capillary is tuned to the depth of penetration of UVC light, uniquely treats the whole volume of the fluid to UVC light, resulting in fast and effective inactivation of pathogens, with particular focus to virus particles. This was demonstrated by inactivating human herpes simplex virus type-1 (HSV-1, a large enveloped virus) on a dense 10% fetal calf serum solution in a range of fluoropolymer capillary systems, including a 0.75 mm and 1.50 mm internal diameter capillaries and a high-throughput MicroCapillary Film with mean hydraulic diameter of 206 µm. Up to 99.96% of HSV-1 virus particles were effectively inactivated with a mean exposure time of up to 10 s, with undetectable collateral damage to solution proteins. The kinetics of virus inactivation matched well the results from a new mathematical model that considers the parabolic flow profile in the capillaries, and showed the methodology is fully predictable and scalable and avoids both the side effect of UVC light to proteins and the dilution of the fluid in current tubular UVC inactivation systems. This is expected to speed up the industrial adoption of non-invasive UVC virus inactivation in clinical biotechnology and biomanufacturing of therapeutic molecules. Biotechnol. Bioeng. 2016;113: 1481-1492. © 2015 Wiley Periodicals, Inc.