A system hierarchy for brain-inspired computing.

Neuromorphic computing draws inspiration from the brain to provide computing technology and architecture with the potential to drive the next wave of computer engineering1-13. Such brain-inspired computing also provides a promising platform for the development of artificial general intelligence14,15. However, unlike conventional computing systems, which have a well established computer hierarchy built around the concept of Turing completeness and the von Neumann architecture16-18, there is currently no generalized system hierarchy or understanding of completeness for brain-inspired computing. This affects the compatibility between software and hardware, impairing the programming flexibility and development productivity of brain-inspired computing. Here we propose 'neuromorphic completeness', which relaxes the requirement for hardware completeness, and a corresponding system hierarchy, which consists of a Turing-complete software-abstraction model and a versatile abstract neuromorphic architecture. Using this hierarchy, various programs can be described as uniform representations and transformed into the equivalent executable on any neuromorphic complete hardware-that is, it ensures programming-language portability, hardware completeness and compilation feasibility. We implement toolchain software to support the execution of different types of program on various typical hardware platforms, demonstrating the advantage of our system hierarchy, including a new system-design dimension introduced by the neuromorphic completeness. We expect that our study will enable efficient and compatible progress in all aspects of brain-inspired computing systems, facilitating the development of various applications, including artificial general intelligence.

Distinct Neuron Types Contribute to Hybrid Auditory Spatial Coding.

Neural decoding is a tool for understanding how activities from a population of neurons inside the brain relate to the outside world and for engineering applications such as brain-machine interfaces. However, neural decoding studies mainly focused on different decoding algorithms rather than different neuron types which could use different coding strategies. In this study, we used two-photon calcium imaging to assess three auditory spatial decoders (space map, opponent channel, and population pattern) in excitatory and inhibitory neurons in the dorsal inferior colliculus of male and female mice. Our findings revealed a clustering of excitatory neurons that prefer similar interaural level difference (ILD), the primary spatial cues in mice, while inhibitory neurons showed random local ILD organization. We found that inhibitory neurons displayed lower decoding variability under the opponent channel decoder, while excitatory neurons achieved higher decoding accuracy under the space map and population pattern decoders. Further analysis revealed that the inhibitory neurons' preference for ILD off the midline and the excitatory neurons' heterogeneous ILD tuning account for their decoding differences. Additionally, we discovered a sharper ILD tuning in the inhibitory neurons. Our computational model, linking this to increased presynaptic inhibitory inputs, was corroborated using monaural and binaural stimuli. Overall, this study provides experimental and computational insight into how excitatory and inhibitory neurons uniquely contribute to the coding of sound locations.

Towards artificial general intelligence with hybrid Tianjic chip architecture.

There are two general approaches to developing artificial general intelligence (AGI)1: computer-science-oriented and neuroscience-oriented. Because of the fundamental differences in their formulations and coding schemes, these two approaches rely on distinct and incompatible platforms2-8, retarding the development of AGI. A general platform that could support the prevailing computer-science-based artificial neural networks as well as neuroscience-inspired models and algorithms is highly desirable. Here we present the Tianjic chip, which integrates the two approaches to provide a hybrid, synergistic platform. The Tianjic chip adopts a many-core architecture, reconfigurable building blocks and a streamlined dataflow with hybrid coding schemes, and can not only accommodate computer-science-based machine-learning algorithms, but also easily implement brain-inspired circuits and several coding schemes. Using just one chip, we demonstrate the simultaneous processing of versatile algorithms and models in an unmanned bicycle system, realizing real-time object detection, tracking, voice control, obstacle avoidance and balance control. Our study is expected to stimulate AGI development by paving the way to more generalized hardware platforms.

Contrastive learning of shared spatiotemporal EEG representations across individuals for naturalistic neuroscience.

Neural representations induced by naturalistic stimuli offer insights into how humans respond to stimuli in daily life. Understanding neural mechanisms underlying naturalistic stimuli processing hinges on the precise identification and extraction of the shared neural patterns that are consistently present across individuals. Targeting the Electroencephalogram (EEG) technique, known for its rich spatial and temporal information, this study presents a framework for Contrastive Learning of Shared SpatioTemporal EEG Representations across individuals (CL-SSTER). CL-SSTER utilizes contrastive learning to maximize the similarity of EEG representations across individuals for identical stimuli, contrasting with those for varied stimuli. The network employs spatial and temporal convolutions to simultaneously learn the spatial and temporal patterns inherent in EEG. The versatility of CL-SSTER was demonstrated on three EEG datasets, including a synthetic dataset, a natural speech comprehension EEG dataset, and an emotional video watching EEG dataset. CL-SSTER attained the highest inter-subject correlation (ISC) values compared to the state-of-the-art ISC methods. The latent representations generated by CL-SSTER exhibited reliable spatiotemporal EEG patterns, which can be explained by properties of the naturalistic stimuli. CL-SSTER serves as an interpretable and scalable framework for the identification of inter-subject shared neural representations in naturalistic neuroscience.

Electrophysiological representations of multivariate human emotion experience.

ABSTRACTDespite the fact that human daily emotions are co-occurring by nature, most neuroscience studies have primarily adopted a univariate approach to identify the neural representation of emotion (emotion experience within a single emotion category) without adequate consideration of the co-occurrence of different emotions (emotion experience across different emotion categories simultaneously). To investigate the neural representations of multivariate emotion experience, this study employed the inter-situation representational similarity analysis (RSA) method. Researchers used an EEG dataset of 78 participants who watched 28 video clips and rated their experience on eight emotion categories. The EEG-based electrophysiological representation was extracted as the power spectral density (PSD) feature per channel in the five frequency bands. The inter-situation RSA method revealed significant correlations between the multivariate emotion experience ratings and PSD features in the Alpha and Beta bands, primarily over the frontal and parietal-occipital brain regions. The study found the identified EEG representations to be reliable with sufficient situations and participants. Moreover, through a series of ablation analyses, the inter-situation RSA further demonstrated the stability and specificity of the EEG representations for multivariate emotion experience. These findings highlight the importance of adopting a multivariate perspective for a comprehensive understanding of the neural representation of human emotion experience.

An effective self-supervised framework for learning expressive molecular global representations to drug discovery.

How to produce expressive molecular representations is a fundamental challenge in artificial intelligence-driven drug discovery. Graph neural network (GNN) has emerged as a powerful technique for modeling molecular data. However, previous supervised approaches usually suffer from the scarcity of labeled data and poor generalization capability. Here, we propose a novel molecular pre-training graph-based deep learning framework, named MPG, that learns molecular representations from large-scale unlabeled molecules. In MPG, we proposed a powerful GNN for modelling molecular graph named MolGNet, and designed an effective self-supervised strategy for pre-training the model at both the node and graph-level. After pre-training on 11 million unlabeled molecules, we revealed that MolGNet can capture valuable chemical insights to produce interpretable representation. The pre-trained MolGNet can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of drug discovery tasks, including molecular properties prediction, drug-drug interaction and drug-target interaction, on 14 benchmark datasets. The pre-trained MolGNet in MPG has the potential to become an advanced molecular encoder in the drug discovery pipeline.

Riboexp: an interpretable reinforcement learning framework for ribosome density modeling.

Translation elongation is a crucial phase during protein biosynthesis. In this study, we develop a novel deep reinforcement learning-based framework, named Riboexp, to model the determinants of the uneven distribution of ribosomes on mRNA transcripts during translation elongation. In particular, our model employs a policy network to perform a context-dependent feature selection in the setting of ribosome density prediction. Our extensive tests demonstrated that Riboexp can significantly outperform the state-of-the-art methods in predicting ribosome density by up to 5.9% in terms of per-gene Pearson correlation coefficient on the datasets from three species. In addition, Riboexp can indicate more informative sequence features for the prediction task than other commonly used attribution methods in deep learning. In-depth analyses also revealed the meaningful biological insights generated by the Riboexp framework. Moreover, the application of Riboexp in codon optimization resulted in an increase of protein production by around 31% over the previous state-of-the-art method that models ribosome density. These results have established Riboexp as a powerful and useful computational tool in the studies of translation dynamics and protein synthesis. Availability: The data and code of this study are available on GitHub: https://github.com/Liuxg16/Riboexp. Contact:zengjy321@tsinghua.edu.cn; songsen@tsinghua.edu.cn.

TrimNet: learning molecular representation from triplet messages for biomedicine.

MOTIVATION: Computational methods accelerate drug discovery and play an important role in biomedicine, such as molecular property prediction and compound-protein interaction (CPI) identification. A key challenge is to learn useful molecular representation. In the early years, molecular properties are mainly calculated by quantum mechanics or predicted by traditional machine learning methods, which requires expert knowledge and is often labor-intensive. Nowadays, graph neural networks have received significant attention because of the powerful ability to learn representation from graph data. Nevertheless, current graph-based methods have some limitations that need to be addressed, such as large-scale parameters and insufficient bond information extraction. RESULTS: In this study, we proposed a graph-based approach and employed a novel triplet message mechanism to learn molecular representation efficiently, named triplet message networks (TrimNet). We show that TrimNet can accurately complete multiple molecular representation learning tasks with significant parameter reduction, including the quantum properties, bioactivity, physiology and CPI prediction. In the experiments, TrimNet outperforms the previous state-of-the-art method by a significant margin on various datasets. Besides the few parameters and high prediction accuracy, TrimNet could focus on the atoms essential to the target properties, providing a clear interpretation of the prediction tasks. These advantages have established TrimNet as a powerful and useful computational tool in solving the challenging problem of molecular representation learning. AVAILABILITY: The quantum and drug datasets are available on the website of MoleculeNet: http://moleculenet.ai. The source code is available in GitHub: https://github.com/yvquanli/trimnet. CONTACT: xjyao@lzu.edu.cn, songsen@tsinghua.edu.cn.

Deep geometric representations for modeling effects of mutations on protein-protein binding affinity.

Modeling the impact of amino acid mutations on protein-protein interaction plays a crucial role in protein engineering and drug design. In this study, we develop GeoPPI, a novel structure-based deep-learning framework to predict the change of binding affinity upon mutations. Based on the three-dimensional structure of a protein, GeoPPI first learns a geometric representation that encodes topology features of the protein structure via a self-supervised learning scheme. These representations are then used as features for training gradient-boosting trees to predict the changes of protein-protein binding affinity upon mutations. We find that GeoPPI is able to learn meaningful features that characterize interactions between atoms in protein structures. In addition, through extensive experiments, we show that GeoPPI achieves new state-of-the-art performance in predicting the binding affinity changes upon both single- and multi-point mutations on six benchmark datasets. Moreover, we show that GeoPPI can accurately estimate the difference of binding affinities between a few recently identified SARS-CoV-2 antibodies and the receptor-binding domain (RBD) of the S protein. These results demonstrate the potential of GeoPPI as a powerful and useful computational tool in protein design and engineering. Our code and datasets are available at: https://github.com/Liuxg16/GeoPPI.

Site-specific proteolytic cleavage prevents ubiquitination and degradation of human REV3L, the catalytic subunit of DNA polymerase ζ.

REV3L, the catalytic subunit of DNA polymerase ζ (Pol ζ), is indispensable for translesion DNA synthesis, which protects cells from deleterious DNA lesions resulting from various intrinsic and environmental sources. However, REV3L lacks a proofreading exonuclease activity and consequently bypasses DNA lesions at the expense of increased mutations, which poses a severe threat to genome stability. Here we report a site-specific proteolytic event of human REV3L. We show that REV3L is cleaved by a threonine aspartase, Taspase1 (TASP1), to generate an N-terminal 70-kDa fragment (N70) and a polypeptide carrying the C-terminal polymerase catalytic domain in human cells. Strikingly, such a post-translational cleavage event plays a vital role in controlling REV3L stability by preventing ubiquitination and proteasome-mediated degradation of REV3L. Indicative of the biological importance of the above REV3L post-translational processing, cellular responses to UV and cisplatin-induced DNA lesions are markedly impaired in human HCT116 cell derivatives bearing defined point mutations in the endogenous REV3L gene that compromise REV3L cleavage. These findings establish a new paradigm in modulating the abundance of REV3L through site-specific proteolysis in human cells.

The duration of withdrawal affects the muscarinic signaling in the nucleus accumbens after chronic morphine exposure in neonatal rats.

The infants experience withdrawal from opiates, and time-dependent adaptations in neuronal activity of nucleus accumbens (NAc) may be crucial for this process. A key adaptation is an increased release of acetylcholine. The present study investigates muscarinic acetylcholine receptors (mAChRs) functions in the NAc at short-term (SWT) and long-term (LWT) withdrawal time following chronic morphine exposure in neonatal rats. The inhibitory role of presynaptic mAChRs activation in spontaneous excitatory postsynaptic currents (sEPSCs) in medium spiny neurons was decreased at LWT but not at SWT. Whereas, the excitatory role of post/extrasynaptic mAChRs activation in membrane currents was reduced at LWT but enhanced at SWT. Furthermore, the inhibitory effect of acute morphine on post/extrasynaptic mAChRs-mediated inward currents was enhanced at SWT but not at LWT. These results suggest that withdrawal from morphine leads to downregulation of presynaptic and post/extrasynaptic mAChRs functions in the NAc, which may coregulate the development of withdrawal in neonates.NEW & NOTEWORTHY We investigated for the first time how the duration of withdrawal affects mAChRs functions in the nucleus accumbens in neonatal rats. Compared with short-term withdrawal time, rats showed downregulation of presynaptic and post/extrasynaptic mAChRs functions during long-term withdrawal time. Our finding introduces a new possible correlation between the mAChRs dysfunction in the nucleus accumbens and the development of withdrawal in neonates.

Neuronal Organization in the Inferior Colliculus Revisited with Cell-Type-Dependent Monosynaptic Tracing.

The inferior colliculus (IC) is a critical integration center in the auditory pathway. However, because the inputs to the IC have typically been studied by the use of conventional anterograde and retrograde tracers, the neuronal organization and cell-type-specific connections in the IC are poorly understood. Here, we used monosynaptic rabies tracing and in situ hybridization combined with excitatory and inhibitory Cre transgenic mouse lines of both sexes to characterize the brainwide and cell-type-specific inputs to specific neuron types within the lemniscal IC core and nonlemniscal IC shell. We observed that both excitatory and inhibitory neurons of the IC shell predominantly received ascending inputs rather than descending or core inputs. Correlation and clustering analyses revealed two groups of excitatory neurons in the shell: one received inputs from a combination of ascending nuclei, and the other received inputs from a combination of descending nuclei, neuromodulatory nuclei, and the contralateral IC. In contrast, inhibitory neurons in the core received inputs from the same combination of all nuclei. After normalizing the extrinsic inputs, we found that core inhibitory neurons received a higher proportion of inhibitory inputs from the ventral nucleus of the lateral lemniscus than excitatory neurons. Furthermore, the inhibitory neurons preferentially received inhibitory inputs from the contralateral IC shell. Because IC inhibitory neurons innervate the thalamus and contralateral IC, the inhibitory inputs we uncovered here suggest two long-range disinhibitory circuits. In summary, we found: (1) dominant ascending inputs to the shell, (2) two subpopulations of shell excitatory neurons, and (3) two disinhibitory circuits.SIGNIFICANCE STATEMENT Sound undergoes extensive processing in the brainstem. The inferior colliculus (IC) core is classically viewed as the integration center for ascending auditory information, whereas the IC shell integrates descending feedback information. Here, we demonstrate that ascending inputs predominated in the IC shell but appeared to be separated from the descending inputs. The presence of inhibitory projection neurons is a unique feature of the auditory ascending pathways, but the connections of these neurons are poorly understood. Interestingly, we also found that inhibitory neurons in the IC core and shell preferentially received inhibitory inputs from ascending nuclei and contralateral IC, respectively. Therefore, our results suggest a bipartite domain in the IC shell and disinhibitory circuits in the IC.

Evolutionary analysis of mitochondrially encoded proteins of toad-headed lizards, Phrynocephalus, along an altitudinal gradient.

BACKGROUND: Animals living at high altitude must adapt to environments with hypoxia and low temperatures, but relatively little is known about underlying genetic changes. Toad-headed lizards of the genus Phrynocephalus cover a broad altitudinal gradient of over 4000 m and are useful models for studies of such adaptive responses. In one of the first studies to have considered selection on mitochondrial protein-coding regions in an ectothermic group distributed over such a wide range of environments, we analysed nineteen complete mitochondrial genomes from all Chinese Phrynocephalus (including eight genomes sequenced for the first time). Initial analyses used site and branch-site model (program: PAML) approaches to examine nonsynonymous: synonymous substitution rates across the mtDNA tree. RESULTS: Ten positively selected sites were discovered, nine of which corresponded to subunits ND2, ND3, ND4, ND5, and ND6 within the respiratory chain enzyme mitochondrial Complex I (NADH Coenzyme Q oxidoreductase). Four of these sites showed evidence of general long-term selection across the group while the remainder showed evidence of episodic selection across different branches of the tree. Some of these branches corresponded to increases in altitude and/or latitude. Analyses of physicochemical changes in protein structures revealed that residue changes at sites that were under selection corresponded to major functional differences. Analyses of coevolution point to coevolution of selected sites within the ND4 subunit, with key sites associated with proton translocation across the mitochondrial membrane. CONCLUSIONS: Our results identify mitochondrial Complex I as a target for environment-mediated selection in this group of lizards, a complex that frequently appears to be under selection in other organisms. This makes these lizards good candidates for more detailed future studies of molecular evolution.

Highly Compact Artificial Memristive Neuron with Low Energy Consumption.

Neuromorphic systems aim to implement large-scale artificial neural network on hardware to ultimately realize human-level intelligence. The recent development of nonsilicon nanodevices has opened the huge potential of full memristive neural networks (FMNN), consisting of memristive neurons and synapses, for neuromorphic applications. Unlike the widely reported memristive synapses, the development of artificial neurons on memristive devices has less progress. Sophisticated neural dynamics is the major obstacle behind the lagging. Here a rich dynamics-driven artificial neuron is demonstrated, which successfully emulates partial essential neural features of neural processing, including leaky integration, automatic threshold-driven fire, and self-recovery, in a unified manner. The realization of bioplausible artificial neurons on a single device with ultralow power consumption paves the way for constructing energy-efficient large-scale FMNN and may boost the development of neuromorphic systems with high density, low power, and fast speed.

Neural Coding of Appetitive Food Experiences in the Amygdala.

Real-life experiences involve the consumption of various foods, yet it is unclear how the brain distinguishes and categorizes such food experiences. Despite the crucial roles of the basolateral amygdala (BLA) in appetitive behavior and emotion, how BLA pyramidal cells and interneurons encode food experiences has not yet been well characterized. Here we employ large-scale tetrode recording techniques to investigate the coding properties of pyramidal neurons vs. fast-spiking interneurons in the BLA as mice freely consumed a variety of foods, such as biscuits, rice, milk and water. We found that putative pyramidal cells conformed to the power-of-two-based permutation logic, as postulated by the Theory of Connectivity, to generate specific-to-general neural clique-coding patterns. Many pyramidal cells exhibited firing increases specific to a given food type, while some other pyramidal cells increased firings to various combinations of multiple foods. In contrast, fast-spiking interneurons can increase or decrease firings to given food types, and were more broadly tuned to various food experiences. We further show that a subset of pyramidal cells exhibited rapid desensitization to repeated eating of the same food, correlated with rapid behavioral habituation. Finally, we provide the intuitive visualization of BLA ensemble activation patterns using the dimensionality-reduction classification method to decode real-time appetitive stimulus identity on a moment-to-moment, single trial basis. Elucidation of the neural coding patterns in the BLA provides a key insight into how the brain's emotion and memory circuits performs the computational operation of pattern discrimination and categorization of natural food experiences.

A Single, Continuously Applied Control Policy for Modeling Reaching Movements with and without Perturbation.

It has been debated whether kinematic features, such as the number of peaks or decomposed submovements in a velocity profile, indicate the number of discrete motor impulses or result from a continuous control process. The debate is particularly relevant for tasks involving target perturbation, which can alter movement kinematics. To simulate such tasks, finite-horizon models require two preset movement durations to compute two control policies before and after the perturbation. Another model employs infinite- and finite-horizon formulations to determine, respectively, movement durations and control policies, which are updated every time step. We adopted an infinite-horizon optimal feedback control model that, unlike previous approaches, does not preset movement durations or use multiple control policies. It contains both control-dependent and independent noises in system dynamics, state-dependent and independent noises in sensory feedbacks, and different delays and noise levels for visual and proprioceptive feedbacks. We analytically derived an optimal solution that can be applied continuously to move an effector toward a target regardless of whether, when, or where the target jumps. This single policy produces different numbers of peaks and "submovements" in velocity profiles for different conditions and trials. Movements that are slower or perturbed later appear to have more submovements. The model is also consistent with the observation that subjects can perform the perturbation task even without detecting the target jump or seeing their hands during reaching. Finally, because the model incorporates Weber's law via a state representation relative to the target, it explains why initial and terminal visual feedback are, respectively, less and more effective in improving end-point accuracy. Our work suggests that the number of peaks or submovements in a velocity profile does not necessarily reflect the number of motor impulses and that the difference between initial and terminal feedback does not necessarily imply a transition between open- and closed-loop strategies.

Decreased between-hemisphere connectivity strength and network efficiency in geriatric depression.

White matter (WM) lesions have been recognized as a key etiological factor in geriatric depression. However, little is known about the topological pattern changes of WM in geriatric depression in the remitted state (RGD) and its relationship to depressive episodes. To address these questions, we acquired diffusion tensor images in 24 RGD and 24 healthy participants. Among them, 10 patients and 19 healthy controls completed a 1-year follow up. Between-hemisphere connectivity and graph theoretical methods were used to analyze the data. We found significantly reduced WM connectivity between the left and right hemisphere in the RGD group compared with the control group. Those with multiple depression episodes had greater reduction in between-hemisphere connectivity strength than those with fewer episodes. In addition, the RGD group had a reduced global clustering coefficient, global efficiency, and network strength, and an increased shortest path length compared with the controls. A lower clustering coefficient was correlated with poorer memory function. The reduction of nodal clustering coefficient, global efficiency, and network strength in several regions were associated with slower information processing speed. At 1-year follow up, the network properties in the RGD subjects were significantly changed suggesting instability of WM network properties of depressed patients. Together, our study provides direct evidence of reduced between-hemisphere WM connectivity with greater depressive episodes, and of alterations of network properties with cognitive dysfunction in geriatric depression. Hum Brain Mapp 38:53-67, 2017. © 2016 Wiley Periodicals, Inc.

Increased axonal bouton dynamics in the aging mouse cortex.

Aging is a major risk factor for many neurological diseases and is associated with mild cognitive decline. Previous studies suggest that aging is accompanied by reduced synapse number and synaptic plasticity in specific brain regions. However, most studies, to date, used either postmortem or ex vivo preparations and lacked key in vivo evidence. Thus, whether neuronal arbors and synaptic structures remain dynamic in the intact aged brain and whether specific synaptic deficits arise during aging remains unknown. Here we used in vivo two-photon imaging and a unique analysis method to rigorously measure and track the size and location of axonal boutons in aged mice. Unexpectedly, the aged cortex shows circuit-specific increased rates of axonal bouton formation, elimination, and destabilization. Compared with the young adult brain, large (i.e., strong) boutons show 10-fold higher rates of destabilization and 20-fold higher turnover in the aged cortex. Size fluctuations of persistent boutons, believed to encode long-term memories, also are larger in the aged brain, whereas bouton size and density are not affected. Our data uncover a striking and unexpected increase in axonal bouton dynamics in the aged cortex. The increased turnover and destabilization rates of large boutons indicate that learning and memory deficits in the aged brain arise not through an inability to form new synapses but rather through decreased synaptic tenacity. Overall our study suggests that increased synaptic structural dynamics in specific cortical circuits may be a mechanism for age-related cognitive decline.

Resolving conflict in eutherian mammal phylogeny using phylogenomics and the multispecies coalescent model.

The reconstruction of the Tree of Life has relied almost entirely on concatenation methods, which do not accommodate gene tree heterogeneity, a property that simulations and theory have identified as a likely cause of incongruent phylogenies. However, this incongruence has not yet been demonstrated in empirical studies. Several key relationships among eutherian mammals remain controversial and conflicting among previous studies, including the root of eutherian tree and the relationships within Euarchontoglires and Laurasiatheria. Both bayesian and maximum-likelihood analysis of genome-wide data of 447 nuclear genes from 37 species show that concatenation methods indeed yield strong incongruence in the phylogeny of eutherian mammals, as revealed by subsampling analyses of loci and taxa, which produced strongly conflicting topologies. In contrast, the coalescent methods, which accommodate gene tree heterogeneity, yield a phylogeny that is robust to variable gene and taxon sampling and is congruent with geographic data. The data also demonstrate that incomplete lineage sorting, a major source of gene tree heterogeneity, is relevant to deep-level phylogenies, such as those among eutherian mammals. Our results firmly place the eutherian root between Atlantogenata and Boreoeutheria and support ungulate polyphyly and a sister-group relationship between Scandentia and Primates. This study demonstrates that the incongruence introduced by concatenation methods is a major cause of long-standing uncertainty in the phylogeny of eutherian mammals, and the same may apply to other clades. Our analyses suggest that such incongruence can be resolved using phylogenomic data and coalescent methods that deal explicitly with gene tree heterogeneity.

[The impact of mood on the intrinsic functional connectivity].

Although a great number of studies have investigated the changes of resting-state functional connectivity (rsFC) in patients with mental disorders, such as depression and schizophrenia etc, little is known how stable the changes are, and whether temporal sad or happy mood can modulate the intrinsic rsFC. In our experiments, happy and sad video clips were used to induce temporally happy and sad mood states in 20 healthy young adults. We collected functional magnetic resonance imaging (fMRI) data while participants were watching happy or sad video clips, which were administrated in two consecutive days. Seed-based functional connectivity analyses were conducted using the anterior cingulate cortex (ACC), dorsolateral prefrontal cortex (DLPFC), and amygdala as seeds to investigate neural network related to executive function, attention, and emotion. We also investigated the association of the rsFC changes with emotional arousability level to understand individual differences. There is significantly stronger functional connectivity between the left DLPFC and posterior cingulate cortex (PCC) under sad mood than that under happy mood. The increased connectivity strength was positively correlated with subjects' emotional arousability. The increased positive correlation between the left DLPFC and PCC under sad relative to happy mood might reflect an increased processing of negative emotion-relevant stimuli. The easier one was induced by strong negative emotion (higher emotional arousability), the greater the left DLPFC-PCC connectivity was indicated, the greater the instability of the intrinsic rsFC was shown.