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Contains fulltext : 239571.pdf (Publisher’s version ) (Open Access), Social interaction is thought to provide a selection pressure for human intelligence, yet little is known about its neurobiological basis and evolution throughout the primate lineage. Recent advances in neuroimaging have enabled whole brain investigation of brain structure, function, and connectivity in humans and non-human primates (NHPs), leading to a nascent field of comparative connectomics. However, linking social behavior to brain organization across the primates remains challenging. Here, we review the current understanding of the macroscale neural mechanisms of social behaviors from the viewpoint of system neuroscience. We first demonstrate an association between the number of cortical neurons and the size of social groups across primates, suggesting a link between neural information-processing capacity and social capabilities. Moreover, by capitalizing on recent advances in species-harmonized functional MRI, we demonstrate that portions of the mirror neuron system and default-mode networks, which are thought to be important for representation of the other's actions and sense of self, respectively, exhibit similarities in functional organization in macaque monkeys and humans, suggesting possible homologies. With respect to these two networks, we describe recent developments in the neurobiology of social perception, joint attention, personality and social complexity. Together, the Human Connectome Project (HCP)-style comparative neuroimaging, hyperscanning, behavioral, and other multi-modal investigations are expected to yield important insights into the evolutionary foundations of human social behavior.

Contains fulltext : 205926.pdf (Publisher’s version ) (Closed access), There is evidence for enlargement of association cortex in humans compared to other primate species. Expansion of temporal association cortex appears to have displaced extrastriate cortex posteriorly and inferiorly in humans compared to macaques. However, the details of the organization of these recently expanded areas are still being uncovered. Here, we used diffusion tractography to examine the organization of extrastriate and temporal association cortex in chimpanzees, humans, and macaques. Our goal was to characterize the organization of visual and auditory association areas with respect to their corresponding primary areas (primary visual cortex and auditory core) in humans and chimpanzees. We report three results: (1) Humans, chimpanzees, and macaques show expected retinotopic organization of primary visual cortex (V1) connectivity to V2 and to areas immediately anterior to V2; (2) In contrast to macaques, chimpanzee and human V1 shows apparent connectivity with lateral, inferior, and anterior temporal regions, beyond the retinotopically organized extrastriate areas; (3) Also in contrast to macaques, chimpanzee and human auditory core shows apparent connectivity with temporal association areas, with some important differences between humans and chimpanzees. Diffusion tractography reconstructs diffusion patterns that reflect white matter organization, but does not definitively represent direct anatomical connectivity. Therefore, it is important to recognize that our findings are suggestive of species differences in long-distance white matter organization rather than demonstrations of direct connections. Our data support the conclusion that expansion of temporal association cortex, and the resulting posterior displacement of extrastriate cortex, occurred in the human lineage after its separation from the chimpanzee lineage. It is possible, however, that some expansion of the temporal lobe occurred prior to the separation of humans and chimpanzees, reflected in t

Contains fulltext : 203458.pdf (publisher's version ) (Open Access), Understanding the phylogeny of the human brain requires an appreciation of brain organization of our closest animal relatives. Neuroimaging tools such as magnetic resonance imaging (MRI) allow us to study whole-brain organization in species which can otherwise not be studied. Here, we used diffusion MRI to reconstruct the connections of the cortical hemispheres of the chimpanzee. This allowed us to perform an exploratory analysis of the grey matter structures of the chimpanzee cerebral cortex and their underlying white matter connectivity profiles. We identified a number of networks that strongly resemble those found in other primates, including the corticospinal system, limbic connections through the cingulum bundle and fornix, and occipital–temporal and temporal–frontal systems. Notably, chimpanzee temporal cortex showed a strong resemblance to that of the human brain, providing some insight into the specialization of the two species’ shared lineage.

Contains fulltext : 190146.pdf (publisher's version ) (Open Access)

Contains fulltext : 175042.pdf (Publisher’s version ) (Open Access), We present a practical "how-to" guide to help determine whether single-subject fMRI independent components (ICs) characterise structured noise or not. Manual identification of signal and noise after ICA decomposition is required for efficient data denoising: to train supervised algorithms, to check the results of unsupervised ones or to manually clean the data. In this paper we describe the main spatial and temporal features of ICs and provide general guidelines on how to evaluate these. Examples of signal and noise components are provided from a wide range of datasets (3T data, including examples from the UK Biobank and the Human Connectome Project, and 7T data), together with practical guidelines for their identification. Finally, we discuss how the data quality, data type and preprocessing can influence the characteristics of the ICs and present examples of particularly challenging datasets.

Item does not contain fulltext, Understanding the amazingly complex human cerebral cortex requires a map (or parcellation) of its major subdivisions, known as cortical areas. Making an accurate areal map has been a century-old objective in neuroscience. Using multi-modal magnetic resonance images from the Human Connectome Project (HCP) and an objective semi-automated neuroanatomical approach, we delineated 180 areas per hemisphere bounded by sharp changes in cortical architecture, function, connectivity, and/or topography in a precisely aligned group average of 210 healthy young adults. We characterized 97 new areas and 83 areas previously reported using post-mortem microscopy or other specialized study-specific approaches. To enable automated delineation and identification of these areas in new HCP subjects and in future studies, we trained a machine-learning classifier to recognize the multi-modal 'fingerprint' of each cortical area. This classifier detected the presence of 96.6% of the cortical areas in new subjects, replicated the group parcellation, and could correctly locate areas in individuals with atypical parcellations. The freely available parcellation and classifier will enable substantially improved neuroanatomical precision for studies of the structural and functional organization of human cerebral cortex and its variation across individuals and in development, aging, and disease.

Contains fulltext : 135316.pdf (Publisher’s version ) (Open Access), It is well established that it is possible to observe spontaneous, highly structured, fluctuations in human brain activity from functional magnetic resonance imaging (fMRI) when the subject is 'at rest'. However, characterising this activity in an interpretable manner is still a very open problem. In this paper, we introduce a method for identifying modes of coherent activity from resting state fMRI (rfMRI) data. Our model characterises a mode as the outer product of a spatial map and a time course, constrained by the nature of both the between-subject variation and the effect of the haemodynamic response function. This is presented as a probabilistic generative model within a variational framework that allows Bayesian inference, even on voxelwise rfMRI data. Furthermore, using this approach it becomes possible to infer distinct extended modes that are correlated with each other in space and time, a property which we believe is neuroscientifically desirable. We assess the performance of our model on both simulated data and high quality rfMRI data from the Human Connectome Project, and contrast its properties with those of both spatial and temporal independent component analysis (ICA). We show that our method is able to stably infer sets of modes with complex spatio-temporal interactions and spatial differences between subjects.

Item does not contain fulltext, Many sources of fluctuation contribute to the fMRI signal, and this makes identifying the effects that are truly related to the underlying neuronal activity difficult. Independent component analysis (ICA) - one of the most widely used techniques for the exploratory analysis of fMRI data - has shown to be a powerful technique in identifying various sources of neuronally-related and artefactual fluctuation in fMRI data (both with the application of external stimuli and with the subject "at rest"). ICA decomposes fMRI data into patterns of activity (a set of spatial maps and their corresponding time series) that are statistically independent and add linearly to explain voxel-wise time series. Given the set of ICA components, if the components representing "signal" (brain activity) can be distinguished form the "noise" components (effects of motion, non-neuronal physiology, scanner artefacts and other nuisance sources), the latter can then be removed from the data, providing an effective cleanup of structured noise. Manual classification of components is labour intensive and requires expertise; hence, a fully automatic noise detection algorithm that can reliably detect various types of noise sources (in both task and resting fMRI) is desirable. In this paper, we introduce FIX ("FMRIB's ICA-based X-noiseifier"), which provides an automatic solution for denoising fMRI data via accurate classification of ICA components. For each ICA component FIX generates a large number of distinct spatial and temporal features, each describing a different aspect of the data (e.g., what proportion of temporal fluctuations are at high frequencies). The set of features is then fed into a multi-level classifier (built around several different classifiers). Once trained through the hand-classification of a sufficient number of training datasets, the classifier can then automatically classify new datasets. The noise components can then be subtracted from (or regressed out of) the original data

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Item does not contain fulltext, The Human Connectome Project (HCP) is an ambitious 5-year effort to characterize brain connectivity and function and their variability in healthy adults. This review summarizes the data acquisition plans being implemented by a consortium of HCP investigators who will study a population of 1200 subjects (twins and their non-twin siblings) using multiple imaging modalities along with extensive behavioral and genetic data. The imaging modalities will include diffusion imaging (dMRI), resting-state fMRI (R-fMRI), task-evoked fMRI (T-fMRI), T1- and T2-weighted MRI for structural and myelin mapping, plus combined magnetoencephalography and electroencephalography (MEG/EEG). Given the importance of obtaining the best possible data quality, we discuss the efforts underway during the first two years of the grant (Phase I) to refine and optimize many aspects of HCP data acquisition, including a new 7T scanner, a customized 3T scanner, and improved MR pulse sequences.