Mapping cross-species connectome atlas of human and macaque striatum.

Cross-species connectome atlas (CCA) that can provide connectionally homogeneous and homologous brain nodes is essential and customized for cross-species neuroscience. However, existing CCAs were flawed in design and coarse-grained in results. In this study, a normative mapping framework of CCA was proposed and applied on human and macaque striatum. Specifically, all striatal voxels in the 2 species were mixed together and classified based on their represented and characterized feature of within-striatum resting-state functional connectivity, which was shared between the species. Six pairs of striatal parcels in these species were delineated in both hemispheres. Furthermore, this striatal parcellation was demonstrated by the best-matched whole-brain functional and structural connectivity between interspecies corresponding subregions. Besides, detailed interspecies differences in whole-brain multimodal connectivities and involved brain functions of these subregions were described to flesh out this CCA of striatum. In particular, this flexible and scalable mapping framework enables reliable construction of CCA of the whole brain, which would enable reliable findings in future cross-species research and advance our understandings into how the human brain works.

Striatonigrostriatal connectivity-based cross-species parcellation of human and macaque substantia nigra.

Anatomical and functional heterogeneous substantia nigra (SN) has been extensively studied in humans and animals like rhesus monkeys given its crucial role in modulating a broad range of behaviors. Increasingly important cross-species research of SN may require connectionally homogeneous and homologous subregions of SN as objective and stable starting points from which the evolutionary characteristics of brain could be inspected. However, existing atlases of SN were all inaccurate mappings as a cross-species connectome atlas due to inadequate homology constraint during their constructions, and arbitrary paired use of these atlases might cause unreliable findings. In this study, a reliable blind-source cross-species parcellation of SN was developed based on the following rationale: striatonigrostriatal circuits form major structure of nigral connectivity; different nigral components have unique striatonigrostriatal connectivity; and inter-species corresponding human and macaque nigral components have similar striatonigrostriatal connectivity. Specifically, all voxels in human and macaque SN were grouped together and then classified based on inter-species identically characterized striatonigrostriatal connectivity attributes. Our results delineated a pars compacta-pars reticulate-like parcellation and further demonstrated its reliability by illustrating best-matched whole-brain structural and functional connectivity profiles of inter-species corresponding nigral subregions. Detailed inter-species and inter-regional differences in multi-aspect connectivities of these nigral subregions were inspected. It is expected that this cross-species connectome atlas of SN can offer biologically reliable cornerstones and important information to facilitate future cross-species research.

Species and individual differences and connectional asymmetry of Broca's area in humans and macaques.

To reveal the connectional specialization of the Broca's area (or its homologue), voxel-wise inter-species and individual differences, and inter-hemispheric asymmetry were respectively inspected in humans and macaques at both whole-brain connectivity and single tract levels. It was discovered that the developed connectivity blueprint approach is able to localize connectionally comparable voxels between the two species in Broca's area, whereas the quantitative differences between blueprints of locationally or connectionally corresponding voxels enable us to generate inter-hemispheric, inter-subject, and inter-species connectional variabilities, respectively. More importantly, the inter-species and inter-subject variabilities exhibited positive correlation in both two primates, and relatively higher variabilities were detected in the anatomically defined pars triangularis. By contrast, negative relationship was identified between the inter-species variability and hemispheric asymmetry in human brain. In particular, relatively higher asymmetry was revealed in the anatomically defined pars opercularis. Therefore, our novel findings demonstrated that pars triangularis, as compared to pars opercularis, might be a more active area during primate evolution, in which the brain connectivity and possible functions of pars triangularis show relatively higher degree in species specialization, yet lower in hemispheric specialization. Meanwhile, brain connectivity and possible functions of pars opercularis manifested an opposite pattern. At the tract level, functional roles related to the ventral stream in speech comprehension were relatively conservative and bilaterally organized, while those related to the dorsal stream in speech production show relatively higher species and hemispheric specializations.

Rostro-caudal organization of the human posterior superior temporal sulcus revealed by connectivity profiles.

The posterior superior temporal sulcus (pSTS) plays an important role in biological motion perception but is also thought to be essential for speech and facial processing. However, although there are many previous investigations of distinct functional modules within the pSTS, the functional organization of the pSTS in its full functional heterogeneity has not yet been established. Here we applied a connectivity-based parcellation strategy to delineate the human pSTS subregions based on distinct anatomical connectivity profiles and divided it into rostral and caudal subregions using diffusion tensor imaging. Subsequent multimodal connection pattern analyses revealed distinct subregional connectivity profiles. From this we inferred that the two subregions are involved in distinct functional circuits, the language processing loop and the cognition attention network. These results indicate a convergent functional architecture of the pSTS that can be revealed based on different types of connectivity and is reflected in different functions and interactions. In addition, when the subregions were performing their processing in the different functional circuits, we found asymmetry in the bilateral pSTS. Our findings may improve the understanding of the functional organization of the pSTS and provide new insights into its interactions and integration of information at the subregional level.

Multimodal connectivity-based parcellation reveals a shell-core dichotomy of the human nucleus accumbens.

The subdifferentiation of the nucleus accumbens (NAc) has been extensively studied using neuroanatomy and histochemistry, yielding a well-accepted dichotomic shell/core architecture that reflects dissociable roles, such as in reward and aversion, respectively. However, in vivo parcellation of these structures in humans has been rare, potentially impairing future research into the structural and functional characteristics and alterations of putative NAc subregions. Here, we used three complementary parcellation schemes based on tractography, task-independent functional connectivity, and task-dependent co-activation to investigate the regional differentiation within the NAc. We found that a 2-cluster solution with shell-like and core-like subdivisions provided the best description of the data and was consistent with the earlier anatomical shell/core architecture. The consensus clusters from this optimal solution, which was based on the three schemes, were used as the final parcels for the subsequent connection analyses. The resulting connectivity patterns presented inter-hemispheric symmetry, convergence and divergence across the modalities, and, most importantly, clearly distinct patterns between the two subregions. This convergent connectivity patterns also confirmed the connections in animal models, supporting views that the two subregions could have antagonistic roles in some circumstances. Finally, the identified parcels should be helpful in further neuroimaging studies of the NAc. Hum Brain Mapp 38:3878-3898, 2017. © 2017 Wiley Periodicals, Inc.

Individual-specific functional connectivity improves prediction of Alzheimer's disease's symptoms in elderly people regardless of APOE ε4 genotype.

To date, reliable biomarkers remain unclear that could link functional connectivity to patients' symptoms for detecting and predicting the process from normal aging to Alzheimer's disease (AD) in elderly people with specific genotypes. To address this, individual-specific functional connectivity is constructed for elderly participants with/without APOE ε4 allele. Then, we utilize recursive feature selection-based machine learning to reveal individual brain-behavior relationships and to predict the symptom transition in different genotypes. Our findings reveal that compared with conventional atlas-based functional connectivity, individual-specific functional connectivity exhibits higher classification and prediction performance from normal aging to AD in both APOE ε4 groups, while no significant performance is detected when the data of two genotyping groups are combined. Furthermore, individual-specific between-network connectivity constitutes a major contributor to assessing cognitive symptoms. This study highlights the essential role of individual variation in cortical functional anatomy and the integration of brain and behavior in predicting individualized symptoms.

A Macaque Brain Extraction Model Based on U-Net Combined with Residual Structure.

Accurately extracting brain tissue is a critical and primary step in brain neuroimaging research. Due to the differences in brain size and structure between humans and nonhuman primates, the performance of the existing tools for brain tissue extraction, working on macaque brain MRI, is constrained. A new transfer learning training strategy was utilized to address the limitations, such as insufficient training data and unsatisfactory model generalization ability, when deep neural networks processing the limited samples of macaque magnetic resonance imaging(MRI). First, the project combines two human brain MRI data modes to pre-train the neural network, in order to achieve faster training and more accurate brain extraction. Then, a residual network structure in the U-Net model was added, in order to propose a ResTLU-Net model that aims to improve the generalization ability of multiple research sites data. The results demonstrated that the ResTLU-Net, combined with the proposed transfer learning strategy, achieved comparable accuracy for the macaque brain MRI extraction tasks on different macaque brain MRI volumes that were produced by various medical centers. The mean Dice of the ResTLU-Net was 95.81% (no need for denoise and recorrect), and the method required only approximately 30-60 s for one extraction task on an NVIDIA 1660S GPU.

Fine-Grained Topography and Modularity of the Macaque Frontal Pole Cortex Revealed by Anatomical Connectivity Profiles.

The frontal pole cortex (FPC) plays key roles in various higher-order functions and is highly developed in non-human primates. An essential missing piece of information is the detailed anatomical connections for finer parcellation of the macaque FPC than provided by the previous tracer results. This is important for understanding the functional architecture of the cerebral cortex. Here, combining cross-validation and principal component analysis, we formed a tractography-based parcellation scheme that applied a machine learning algorithm to divide the macaque FPC (2 males and 6 females) into eight subareas using high-resolution diffusion magnetic resonance imaging with the 9.4T Bruker system, and then revealed their subregional connections. Furthermore, we applied improved hierarchical clustering to the obtained parcels to probe the modular structure of the subregions, and found that the dorsolateral FPC, which contains an extension to the medial FPC, was mainly connected to regions of the default-mode network. The ventral FPC was mainly involved in the social-interaction network and the dorsal FPC in the metacognitive network. These results enhance our understanding of the anatomy and circuitry of the macaque brain, and contribute to FPC-related clinical research.

Adjustment of Synchronization Stability of Dynamic Brain-Networks Based on Feature Fusion.

When the brain is active, the neural activities of different regions are integrated on various spatial and temporal scales; this is termed the synchronization phenomenon in neurobiological theory. This synchronicity is also the main underlying mechanism for information integration and processing in the brain. Clinical medicine has found that some of the neurological diseases that are difficult to cure have deficiencies or abnormalities in the whole or local integration processes of the brain. By studying the synchronization capabilities of the brain-network, we can intensively describe and characterize both the state of the interactions between brain regions and their differences between people with a mental illness and a set of controls by measuring the rapid changes in brain activity in patients with psychiatric disorders and the strength and integrity of their entire brain network. This is significant for the study of mental illness. Because static brain network connection methods are unable to assess the dynamic interactions within the brain, we introduced the concepts of dynamics and variability in a constructed EEG brain functional network based on dynamic connections, and used it to analyze the variability in the time characteristics of the EEG functional network. We used the spectral features of the brain network to extract its synchronization features and used the synchronization features to describe the process of change and the differences in the brain network's synchronization ability between a group of patients and healthy controls during a working memory task. We propose a method based on the fusion of traditional features and spectral features to achieve an adjustment of the patient's brain network synchronization ability, so that its synchronization ability becomes consistent with that of healthy controls, theoretically achieving the purpose of the treatment of the diseases. Studying the stability of brain network synchronization can provide new insights into the pathogenic mechanism and cure of mental diseases and has a wide range of potential applications.

Interspecies Differences in the Connectivity of Ventral Striatal Components Between Humans and Macaques.

Although the evolutionarily conserved functions of the ventral striatal components have been used as a priori knowledge for further study, whether these functions are conserved between species remains unclear. In particular, whether macroscopic connectivity supports this given the disproportionate volumetric differences between species in the brain regions that project to the ventral striatum, including the prefrontal and limbic areas, has not been established In this study, the human and macaque striatum was first tractographically parcellated to define the ventral striatum and its two subregions, the nucleus accumbens (Acb)-like and the neurochemically unique domains of the Acb and putamen (NUDAPs)-like divisions. Our results revealed a similar topographical distribution of the connectivity-based ventral striatal components in the two primate brains. Successively, a set of targets was extracted to construct a connectivity fingerprint to characterize these parcellation results, enabling cross-species comparisons. Our results indicated that the connectivity fingerprints of the ventral striatum-like divisions were dissimilar in the two species. We localized this difference to specific targets to analyze possible interspecies functional modifications. Our results also revealed interspecies-convergent connectivity ratio fingerprints of the target group to these two ventral striatum-like subregions. This convergence may suggest synchronous connectional changes of these ventral striatal components during primate evolution.

Fine-Grained Parcellation of the Macaque Nucleus Accumbens by High-Resolution Diffusion Tensor Tractography.

Limited in part by the spatial resolution of typical in vivo magnetic resonance imaging (MRI) data, recent neuroimaging studies have only identified a connectivity-based shell-core-like partitioning of the nucleus accumbens (Acb) in humans. This has hindered the process of making a more refined description of the Acb using non-invasive neuroimaging technologies and approaches. In this study, high-resolution ex vivo macaque brain diffusion MRI data were acquired to investigate the tractography-based parcellation of the Acb. Our results identified a shell-core-like partitioning in macaques that is similar to that in humans as well as an alternative solution that subdivided the Acb into four parcels, the medial shell, the lateral shell, the ventral core, and the dorsal core. Furthermore, we characterized the specific anatomical and functional connectivity profiles of these Acb subregions and generalized their specialized functions to establish a fine-grained macaque Acb brainnetome atlas. This atlas should be helpful in neuroimaging, stereotactic surgery, and comparative neuroimaging studies to reveal the neurophysiological substrates of various diseases and cognitive functions associated with the Acb.

Brain functional network connectivity based on a visual task: visual information processing-related brain regions are significantly activated in the task state.

It is not clear whether the method used in functional brain-network related research can be applied to explore the feature binding mechanism of visual perception. In this study, we investigated feature binding of color and shape in visual perception. Functional magnetic resonance imaging data were collected from 38 healthy volunteers at rest and while performing a visual perception task to construct brain networks active during resting and task states. Results showed that brain regions involved in visual information processing were obviously activated during the task. The components were partitioned using a greedy algorithm, indicating the visual network existed during the resting state. Z-values in the vision-related brain regions were calculated, confirming the dynamic balance of the brain network. Connectivity between brain regions was determined, and the result showed that occipital and lingual gyri were stable brain regions in the visual system network, the parietal lobe played a very important role in the binding process of color features and shape features, and the fusiform and inferior temporal gyri were crucial for processing color and shape information. Experimental findings indicate that understanding visual feature binding and cognitive processes will help establish computational models of vision, improve image recognition technology, and provide a new theoretical mechanism for feature binding in visual perception.