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The Common Model of Cognition (CMC) has been proposed as a high level framework through which functional neuroimaging data can be predicted and interpreted. Previous work has found the CMC is capable of predicting brain activity across a variety of tasks, but it has not been tested on resting state data. This paper adapts a previously used method for comparing theoretical models of brain structure, Dynamic Causal Modeling, for the task-free environment of resting state, and compares the CMC against six alternate architectural frameworks while also separately modeling spontaneous low-frequency oscillations. For a large sample of subjects from the Human Connectome Project, the CMC provides the best account of resting state brain activity, suggesting the presence of a general purpose structure of connections in the brain that drives activity when at rest and when performing directed task behavior. At the same time, spontaneous brain activity was found to be present and significant across all frequencies and in all regions. Together, these results suggest that, at rest, spontaneous low-frequency oscillations interact with the general cognitive architecture for task-based activity. The possible functional implications of these findings are discussed.

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This article considers the evolution of brain architectures for predictive processing. We argue that brain mechanisms for predictive perception and action are not late evolutionary additions of advanced creatures like us. Rather, they emerged gradually from simpler predictive loops (e.g. autonomic and motor reflexes) that were a legacy from our earlier evolutionary ancestors-and were key to solving their fundamental problems of adaptive regulation. We characterize simpler-to-more-complex brains formally, in terms of generative models that include predictive loops of increasing hierarchical breadth and depth. These may start from a simple homeostatic motif and be elaborated during evolution in four main ways: these include the multimodal expansion of predictive control into an allostatic loop; its duplication to form multiple sensorimotor loops that expand an animal's behavioural repertoire; and the gradual endowment of generative models with hierarchical depth (to deal with aspects of the world that unfold at different spatial scales) and temporal depth (to select plans in a future-oriented manner). In turn, these elaborations underwrite the solution to biological regulation problems faced by increasingly sophisticated animals. Our proposal aligns neuroscientific theorising-about predictive processing-with evolutionary and comparative data on brain architectures in different animal species. This article is part of the theme issue 'Systems neuroscience through the lens of evolutionary theory'.

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The vastness of the design space that is created by the combination of numerous computational mechanisms, including machine learning, is an obstacle to creating artificial general intelligence (AGI). Brain-inspired AGI development; that is, the reduction of the design space to resemble a biological brain more closely, is a promising approach for solving this problem. However, it is difficult for an individual to design a software program that corresponds to the entire brain as the neuroscientific data that are required to understand the architecture of the brain are extensive and complicated. The whole-brain architecture approach divides the brain-inspired AGI development process into the task of designing the brain reference architecture (BRA), which provides the flow of information and a diagram of the corresponding components, and the task of developing each component using the BRA. This is known as BRA-driven development. Another difficulty lies in the extraction of the operating principles that are necessary for reproducing the cognitive-behavioral function of the brain from neuroscience data. Therefore, this study proposes structure-constrained interface decomposition (SCID), which is a hypothesis-building method for creating a hypothetical component diagram that is consistent with neuroscientific findings. The application of this approach has been initiated for constructing various regions of the brain. In the future, we will examine methods for evaluating the biological plausibility of brain-inspired software. This evaluation will also be used to prioritize different computational mechanisms, which should be integrated and associated with the same regions of the brain.

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The assessment of three-dimensional (3D) brain cytoarchitecture at a cellular resolution remains a great challenge in the field of neuroscience and constant development of imaging techniques has become crucial, particularly when it comes to offering direct and clear obtention of data from macro to nano scales. Magnetic resonance imaging (MRI) and electron or optical microscopy, although valuable, still face some issues such as the lack of contrast and extensive sample preparation protocols. In this context, x-ray microtomography (µCT) has become a promising non-destructive tool for imaging a broad range of samples, from dense materials to soft biological specimens. It is a new supplemental method to be explored for deciphering the cytoarchitecture and connectivity of the brain. This review aims to bring together published works using x-ray µCT in neurobiology in order to discuss the achievements made so far and the future of this technique for neuroscience.

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Brain size, brain architecture, and eye size vary extensively in vertebrates. However, the extent to which the evolution of these components is intricately connected remains unclear. Trinidadian killifish, Anablepsoides hartii, are found in sites that differ in the presence and absence of large predatory fish. Decreased rates of predation are associated with evolutionary shifts in brain size; males from sites without predators have evolved a relatively larger brain and eye size than males from sites with predators. Here, we evaluated the extent to which the evolution of brain size, brain structure, and eye size covary in male killifish. We utilized wild-caught and common garden-reared specimens to determine whether specific components of the brain have evolved in response to differences in predation and to determine if there is covariation between the evolution of brain size, brain structure, and eye size. We observed consistent shifts in brain architecture in second generation common garden reared, but not wild caught preserved fish. Male killifish from sites that lack predators exhibited a significantly larger telencephalon, optic tectum, cerebellum, and dorsal medulla when compared with fish from sites with predators. We also found positive connections between the evolution of brain structure and eye size but not between overall brain size and eye size. These results provide evidence for evolutionary covariation between the components of the brain and eye size. Such results suggest that selection, directly or indirectly, acts upon specific regions of the brain, rather than overall brain size, to enhance visual capabilities.

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Whole brain neuroanatomy using tera-voxel light-microscopic data sets is of much current interest. A fundamental problem in this field is the mapping of individual brain data sets to a reference space. Previous work has not rigorously quantified in-vivo to ex-vivo distortions in brain geometry from tissue processing. Further, existing approaches focus on registering unimodal volumetric data; however, given the increasing interest in the marmoset model for neuroscience research and the importance of addressing individual brain architecture variations, new algorithms are necessary to cross-register multimodal data sets including MRIs and multiple histological series. Here we present a computational approach for same-subject multimodal MRI-guided reconstruction of a series of consecutive histological sections, jointly with diffeomorphic mapping to a reference atlas. We quantify the scale change during different stages of brain histological processing using the Jacobian determinant of the diffeomorphic transformations involved. By mapping the final image stacks to the ex-vivo post-fixation MRI, we show that (a) tape-transfer assisted histological sections can be reassembled accurately into 3D volumes with a local scale change of 2.0 ± 0.4% per axis dimension; in contrast, (b) tissue perfusion/fixation as assessed by mapping the in-vivo MRIs to the ex-vivo post fixation MRIs shows a larger median absolute scale change of 6.9 ± 2.1% per axis dimension. This is the first systematic quantification of local metric distortions associated with whole-brain histological processing, and we expect that the results will generalize to other species. These local scale changes will be important for computing local properties to create reference brain maps.

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BACKGROUND: Major depressive disorder (MDD) treatment is characterized by low remission rate and often involves weeks to months of treatment. Identification of pretreatment biomarkers of response may play a critical role in novel drug development, in enhanced prognostic predictions, and perhaps in providing more personalized medicine. Using a network restricted strength predictive modeling (NRS-PM) approach, the goal of the current study was to identify pretreatment functional connectome fingerprints (CFPs) that (1) predict symptom improvement regardless of treatment modality and (2) predict treatment specific improvement. METHODS: Functional magnetic resonance imaging and behavioral data from unmedicated patients with MDD (n = 200) were investigated. Participants were randomized to daily treatment of sertraline or placebo for 8 weeks. NRS-PM with 1000 iterations of 10 cross-validation were implemented to identify brain connectivity signatures that predict percent improvement in depression severity at week-8. RESULTS: The study identified a pretreatment CFP that significantly predicts symptom improvement independent of treatment modality but failed to identify a treatment specific CFP. Regardless of treatment modality, improved antidepressant response was predicted by high pretreatment connectivity between modules in the default mode network and the rest of the brain, but low external connectivity in the executive network. Moreover, high pretreatment internal nodal connectivity in the bilateral caudate predicted better response. CONCLUSIONS: The identified CFP may contribute to drug development and ultimately to enhanced prognostic predictions. However, the results do not assist with providing personalized medicine, as pretreatment functional connectivity failed to predict treatment specific response.

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A key component of current theories of brain structure and function is the layer-specific origin of structural connections of the cerebral cortex. This fundamental connectional feature pertains to different mammalian cortices, and recent neuroimaging advancements have started to pave the way for its function-based mapping in humans. Here, we propose a framework that systematically explains the characteristic layer-specific origin of structural connections and its graded variation across the cortical sheet and across mammalian species. The framework unifies seemingly dispersed observations on multiple levels of cortical organization, including the cellular, connectional, and functional level. Moreover, the framework allows the prediction of the layer-specific origin of connections in a spectrum of mammals, from rodents to humans.

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Since the publication of the primate brain volumetric dataset of Stephan and colleagues in the early 1980s, no major new comparative datasets covering multiple brain regions and a large number of primate species have become available. However, technological and other advances in the last two decades, particularly magnetic resonance imaging (MRI) and the creation of institutions devoted to the collection and preservation of rare brain specimens, provide opportunities to rectify this situation. Here, we present a new dataset including brain region volumetric measurements of 39 species, including 20 species not previously available in the literature, with measurements of 16 brain areas. These volumes were extracted from MRI of 46 brains of 38 species from the Netherlands Institute of Neuroscience Primate Brain Bank, scanned at high resolution with a 9.4-T scanner, plus a further 7 donated MRI of 4 primate species. Partial measurements were made on an additional 8 brains of 5 species. We make the dataset and MRI scans available online in the hope that they will be of value to researchers conducting comparative studies of primate evolution.

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In the brain mapping field, there have been significant interests in representation of structural/functional profiles to establish structural/functional landmark correspondences across individuals and populations. For example, from the structural perspective, our previous studies have identified hundreds of consistent DICCCOL (dense individualized and common connectivity-based cortical landmarks) landmarks across individuals and populations, each of which possess consistent DTI-derived fiber connection patterns. From the functional perspective, a large collection of well-characterized HAFNI (holistic atlases of functional networks and interactions) networks based on sparse representation of whole-brain fMRI signals have been identified in our prior studies. However, due to the remarkable variability of structural and functional architectures in the human brain, it is challenging for earlier studies to jointly represent the connectome-scale structural and functional profiles for establishing a common cortical architecture which can comprehensively encode both structural and functional characteristics across individuals. To address this challenge, we propose an effective computational framework to jointly represent the structural and functional profiles for identification of consistent and common cortical landmarks with both structural and functional correspondences across different brains based on DTI and fMRI data. Experimental results demonstrate that 55 structurally and functionally common cortical landmarks can be successfully identified.

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Penaeus vannamei (Dendrobranchiata, Decapoda) is best known as the "Pacific White Shrimp" and is currently the most important crustacean in commercial aquaculture worldwide. Although the neuroanatomy of crustaceans has been well examined in representatives of reptant decapods ("ground-dwelling decapods"), there are only a few studies focusing on shrimps and prawns. In order to obtain insights into the architecture of the brain of P. vannamei, we use neuroanatomical methods including X-ray micro-computed tomography, 3D reconstruction and immunohistochemical staining combined with confocal laser-scanning microscopy and serial sectioning. The brain of P. vannamei exhibits all the prominent neuropils and tracts that characterize the ground pattern of decapod crustaceans. However, the size proportion of some neuropils is salient. The large lateral protocerebrum that comprises the visual neuropils as well as the hemiellipsoid body and medulla terminalis is remarkable. This observation corresponds with the large size of the compound eyes of these animals. In contrast, the remaining median part of the brain is relatively small. It is dominated by the paired antenna 2 neuropils, while the deutocerebral chemosensory lobes play a minor role. Our findings suggest that visual input from the compound eyes and mechanosensory input from the second pair of antennae are major sensory modalities, which this brain processes.

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The great promise of comparative neuroscience is to understand why brains differ by investigating the relations between variations in the organization of different brains, their evolutionary history, and their current ecological niche. For this approach to be successful, the organization of different brains needs to be quantifiable. Here, we present an approach to formally comparing the connectivity of different cortical areas across different brains. We exploit the fact that cortical regions can be characterized by the unique pattern of connectivity, the so-called connectivity fingerprint. By comparing connectivity fingerprints between cortical areas in the human and non-human primate brain we can identify between-species homologs, but also illustrate that is driving differences between species. We illustrate the approach by comparing the organization of the frontal cortex between humans and macaques, showing general similarities combined with some differences in the lateral frontal pole.

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An industry standard 8'' silicon-on-insulator wafer based ultra-thin (1 µm), ultra-light-weight, fully flexible and remarkably transparent state-of-the-art non-planar three dimensional (3D) FinFET is shown. Introduced by Intel Corporation in 2011 as the most advanced transistor architecture, it reveals sub-20 nm features and the highest performance ever reported for a flexible transistor.

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Both resting state fMRI (R-fMRI) and task-based fMRI (T-fMRI) have been widely used to study the functional activities of the human brain during task-free and task-performance periods, respectively. However, due to the difficulty in strictly controlling the participating subject's mental status and their cognitive behaviors during R-fMRI/T-fMRI scans, it has been challenging to ascertain whether or not an R-fMRI/T-fMRI scan truly reflects the participant's functional brain states during task-free/task-performance periods. This paper presents a novel computational approach to characterizing and differentiating the brain's functional status into task-free or task-performance states, by which the functional brain activities can be effectively understood and differentiated. Briefly, the brain's functional state is represented by a whole-brain quasi-stable connectome pattern (WQCP) of R-fMRI or T-fMRI data based on 358 consistent cortical landmarks across individuals, and then an effective sparse representation method was applied to learn the atomic connectome patterns (ACPs) of both task-free and task-performance states. Experimental results demonstrated that the learned ACPs for R-fMRI and T-fMRI datasets are substantially different, as expected. A certain portion of ACPs from R-fMRI and T-fMRI data were overlapped, suggesting some subjects with overlapping ACPs were not in the expected task-free/task-performance brain states. Besides, potential outliers in the T-fMRI dataset were further investigated via functional activation detections in different groups, and our results revealed unexpected task-performances of some subjects. This work offers novel insights into the functional architectures of the brain.

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