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In memory, our continuous experiences are broken up into discrete events. Boundaries between events are known to influence the temporal organization of memory. However, how and through which mechanism event boundaries shape temporal order memory (TOM) remains unknown. Across four experiments, we show that event boundaries exert a dual role: improving TOM for items within an event and impairing TOM for items across events. Decreasing event length in a list enhances TOM, but only for items at earlier local event positions, an effect we term the local primacy effect. A computational model, in which items are associated to a temporal context signal that drifts over time but resets at boundaries captures all behavioural results. Our findings provide a unified algorithmic mechanism for understanding how and why event boundaries affect TOM, reconciling a long-standing paradox of why both contextual similarity and dissimilarity promote TOM. Our memory is temporally organized, but our internal clock can be distorted. The authors demonstrate how environmental changes (termed event boundaries) affect memory for event order, and provide a computational model to explain these effects.

Understanding the evolutionarily conserved feature of functional laterality in the habenula has been attracting attention due to its potential role in human cognition and neuropsychiatric disorders. Deciphering the structure of the human habenula remains to be challenging, which resulted in inconsistent findings for brain disorders. Here, we present a large‐scale meta‐analysis of the left–right differences in the habenular volume in the human brain to provide a clearer picture of the habenular asymmetry. We searched PubMed, Web of Science, and Google Scholar for articles that reported volume data of the bilateral habenula in the human brain, and assessed the left–right differences. We also assessed the potential effects of several moderating variables including the mean age of the participants, magnetic field strengths of the scanners and different disorders by using meta‐regression and subgroup analysis. In total 52 datasets (N = 1427) were identified and showed significant heterogeneity in the left–right differences and the unilateral volume per se. Moderator analyses suggested that such heterogeneity was mainly due to different MRI scanners and segmentation approaches used. While inversed asymmetry patterns were suggested in patients with depression (leftward) and schizophrenia (rightward), no significant disorder‐related differences relative to healthy controls were found in either the left–right asymmetry or the unilateral volume. This study provides useful data for future studies of brain imaging and methodological developments related to precision habenula measurements, and also helps to further understand potential roles of the habenula in various disorders. We present a large‐scale meta‐analysis of the habenula volume and its asymmetry in humans. Results revealed significant heterogeneity and important methodological factors. No significant disorder‐related differences in the habenula measures were found.

Lateralization is a fundamental characteristic of many behaviors and the organization of the brain, and atypical lateralization has been suggested to be linked to various brain-related disorders such as autism and schizophrenia. Right-handedness is one of the most prominent markers of human behavioural lateralization, yet its neurobiological basis remains to be determined. Here, we present a large-scale analysis of handedness, as measured by self-reported direction of hand preference, and its variability related to brain structural and functional organization in the UK Biobank (N = 36,024). A multivariate machine learning approach with multi-modalities of brain imaging data was adopted, to reveal how well brain imaging features could predict individual's handedness (i.e., right-handedness vs. non-right-handedness) and further identify the top brain signatures that contributed to the prediction. Overall, the results showed a good prediction performance, with an area under the receiver operating characteristic curve (AUROC) score of up to 0.72, driven largely by resting-state functional measures. Virtual lesion analysis and large-scale decoding analysis suggested that the brain networks with the highest importance in the prediction showed functional relevance to hand movement and several higher-level cognitive functions including language, arithmetic, and social interaction. Genetic analyses of contributions of common DNA polymorphisms to the imaging-derived handedness prediction score showed a significant heritability (h2=7.55%, p <0.001) that was similar to and slightly higher than that for the behavioural measure itself (h2=6.74%, p <0.001). The genetic correlation between the two was high (rg=0.71), suggesting that the imaging-derived score could be used as a surrogate in genetic studies where the behavioural measure is not available. This large-scale study using multimodal brain imaging and multivariate machine learning has shed new light on the neural correlates of human handedness.

Representing brain morphology as a network has the advantage that the regional morphology of 'isolated' structures can be described statistically based on graph theory. However, very few studies have investigated brain morphology from the holistic perspective of complex networks, particularly in individual brains. We proposed a new network framework for individual brain morphology. Technically, in the new network, nodes are defined as regions based on a brain atlas, and edges are estimated using our newly-developed inter-regional relation measure based on regional morphological distributions. This implementation allows nodes in the brain network to be functionally/anatomically homogeneous but different with respect to shape and size. We first demonstrated the new network framework in a healthy sample. Thereafter, we studied the graph-theoretical properties of the networks obtained and compared the results with previous morphological, anatomical, and functional networks. The robustness of the method was assessed via measurement of the reliability of the network metrics using a test-retest dataset. Finally, to illustrate potential applications, the networks were used to measure age-related changes in commonly used network metrics. Results suggest that the proposed method could provide a concise description of brain organization at a network level and be used to investigate interindividual variability in brain morphology from the perspective of complex networks. Furthermore, the method could open a new window into modeling the complexly distributed brain and facilitate the emerging field of human connectomics.

Magnetic resonance imaging (MRI) provides valuable data for understanding the human mind and brain disorders, but in-scanner head motion introduces systematic and spurious biases. For example, differences in MRI measures (e.g., network strength, white matter integrity) between patient and control groups may be due to the differences in their head motion. To determine whether head motion is an important variable in itself, or just simply a confounding variable, we explored individual differences in psychological traits that may predispose some people to move more than others during an MRI scan. In the first two studies, we demonstrated in both children (N  =  245) and adults (N  =  581) that head motion, estimated from resting-state functional MRI and diffusion tensor imaging, was reliably correlated with impulsivity scores. Further, the difference in head motion between children with attention deficit hyperactivity disorder (ADHD) and typically developing children was largely due to differences in impulsivity. Finally, in the third study, we confirmed the observation that the regression approach, which aims to deal with motion issues by regressing out motion in the group analysis, would underestimate the effect of interest. Taken together, the present findings provide empirical evidence that links in-scanner head motion to psychological traits.

Biclustering algorithm is an effective tool for processing gene expression datasets. There are two kinds of data matrices, binary data and non-binary data, which are processed by biclustering method. A binary matrix is usually converted from pre-processed gene expression data, which can effectively reduce the interference from noise and abnormal data, and is then processed using a biclustering algorithm. However, biclustering algorithms of dealing with binary data have a poor balance between running time and performance. In this paper, we propose a new biclustering algorithm called the Adjacency Difference Matrix Binary Biclustering algorithm (AMBB) for dealing with binary data to address the drawback. The AMBB algorithm constructs the adjacency matrix based on the adjacency difference values, and the submatrix obtained by continuously updating the adjacency difference matrix is called a bicluster. The adjacency matrix allows for clustering of gene that undergo similar reactions under different conditions into clusters, which is important for subsequent genes analysis. Meanwhile, experiments on synthetic and real datasets visually demonstrate that the AMBB algorithm has high practicability.

Most people have a right-ear advantage for the perception of spoken syllables, consistent with left hemisphere dominance for speech processing. However, there is considerable variation, with some people showing left-ear advantage. The extent to which this variation is reflected in brain structure remains unclear. We tested for relations between hemispheric asymmetries of auditory processing and of grey matter in 281 adults, using dichotic listening and voxel-based morphometry. This was the largest study of this issue to date. Per-voxel asymmetry indexes were derived for each participant following registration of brain magnetic resonance images to a template that was symmetrized. The asymmetry index derived from dichotic listening was related to grey matter asymmetry in clusters of voxels corresponding to the amygdala and cerebellum lobule VI. There was also a smaller, non-significant cluster in the posterior superior temporal gyrus, a region of auditory cortex. These findings contribute to the mapping of asymmetrical structure–function links in the human brain and suggest that subcortical structures should be investigated in relation to hemispheric dominance for speech processing, in addition to auditory cortex.

Public speaking serves as a powerful tool for informing, inspiring, persuading, motivating, or entertaining an audience. While some speeches effectively engage audience and disseminate knowledge, others fail to resonate. This dataset presents functional magnetic resonance imaging (fMRI) data from 31 participants (14 females; age: 22.29 ± 2.84 years) who viewed two informative speeches with varying effectiveness, selected from YiXi talks (similar to TED Talks), and matched in length and topic. A total of 22 participants (10 females; age: 22.64 ± 2.77 years) who completed the full task were included in the validation analyses. A comprehensive validation process, involving behavioral data analysis and head motion assessment, confirmed the quality of the fMRI dataset. While previous analyses have used inter-subject correlation to examine neural synchronization during the reception of informative public speaking, this dataset can be utilized for a variety of analyses to further elucidate the neural mechanisms underlying audience engagement and effective communication.

Background The identification of cancer types is of great significance for early diagnosis and clinical treatment of cancer. Clustering cancer samples is an important means to identify cancer types, which has been paid much attention in the field of bioinformatics. The purpose of cancer clustering is to find expression patterns of different cancer types, so that the samples with similar expression patterns can be gathered into the same type. In order to improve the accuracy and reliability of cancer clustering, many clustering methods begin to focus on the integration analysis of cancer multi-omics data. Obviously, the methods based on multi-omics data have more advantages than those using single omics data. However, the high heterogeneity and noise of cancer multi-omics data pose a great challenge to the multi-omics analysis method. Results In this study, in order to extract more complementary information from cancer multi-omics data for cancer clustering, we propose a low-rank subspace clustering method called multi-view manifold regularized compact low-rank representation (MmCLRR). In MmCLRR, each omics data are regarded as a view, and it learns a consistent subspace representation by imposing a consistence constraint on the low-rank affinity matrix of each view to balance the agreement between different views. Moreover, the manifold regularization and concept factorization are introduced into our method. Relying on the concept factorization, the dictionary can be updated in the learning, which greatly improves the subspace learning ability of low-rank representation. We adopt linearized alternating direction method with adaptive penalty to solve the optimization problem of MmCLRR method. Conclusions Finally, we apply MmCLRR into the clustering of cancer samples based on multi-omics data, and the clustering results show that our method outperforms the existing multi-view methods.

Spatial navigation is a crucial ability for living. Previous studies have shown that males are better at navigation than females, but little is known about the neural basis underlying the sex differences. In this study, we investigated whether cortical scene processing in three well-established scene-selective regions was sexually different, by examining sex differences in scene selectivity and its behavioral relevance to navigation. To do this, we used functional magnetic resonance imaging (fMRI) to scan the parahippocampal place area (PPA), retrosplenial complex (RSC), and occipital place area (OPA) in a large cohort of healthy young adults viewing navigationally relevant scenes (N = 202), and correlated their neural selectivity to scenes with their self-reported navigational ability. Behaviorally, we replicated the previous finding that males were better at navigation than females. Neurally, we found that the scene selectivity in the bilateral PPA, not in the RSC or OPA, was significantly higher in males than females. Such differences could not be explained by confounding factors including brain size and fMRI data quality. Importantly, males, not females, with stronger scene selectivity in the left PPA possessed better navigational ability. This brain-behavior association could not be accounted for by non-navigational abilities (i.e., intelligence and mental rotation ability). Overall, our study provides novel empirical evidence demonstrating sex differences in the brain activity, inviting further studies on sex differences in the neural network for spatial navigation. •Males reported better navigational ability than females.•Males showed stronger cortical scene selectivity in the bilateral PPA.•Higher selectivity in the left PPA was related to better navigational ability in males.•Females and males possessed different neural correlates of navigation.