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      Parallel distributed networks resolved at high resolution reveal close juxtaposition of distinct regions

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          Abstract

          Examination of large-scale distributed networks within the individual reveals details of cortical network organization that are absent in group-averaged studies. One recent discovery is that a distributed transmodal network, often referred to as the “default network,” comprises two closely interdigitated networks, only one of which is coupled to posterior parahippocampal cortex. Not all studies of individuals have identified the same networks, and questions remain about the degree to which the two networks are separate, particularly within regions hypothesized to be interconnected hubs. In this study we replicate the observation of network separation across analytical (seed-based connectivity and parcellation) and data projection (volume and surface) methods in two individuals each scanned 31 times. Additionally, three individuals were examined with high-resolution (7T; 1.35 mm) functional magnetic resonance imaging to gain further insight into the anatomical details. The two networks were identified with separate regions localized to adjacent portions of the cortical ribbon, sometimes inside the same sulcus. Midline regions previously implicated as hubs revealed near complete spatial separation of the two networks, displaying a complex spatial topography in the posterior cingulate and precuneus. The network coupled to parahippocampal cortex also revealed a separate region directly within the hippocampus, at or near the subiculum. These collective results support that the default network is composed of at least two spatially juxtaposed networks. Fine spatial details and juxtapositions of the two networks can be identified within individuals at high resolution, providing insight into the network organization of association cortex and placing further constraints on interpretation of group-averaged neuroimaging data.

          NEW & NOTEWORTHY Recent evidence has emerged that canonical large-scale networks such as the “default network” fractionate into parallel distributed networks when defined within individuals. This research uses high-resolution imaging to show that the networks possess juxtapositions sometimes evident inside the same sulcus and within regions that have been previously hypothesized to be network hubs. Distinct circumscribed regions of one network were also resolved in the hippocampal formation, at or near the parahippocampal cortex and subiculum.

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          Situating the default-mode network along a principal gradient of macroscale cortical organization.

          Understanding how the structure of cognition arises from the topographical organization of the cortex is a primary goal in neuroscience. Previous work has described local functional gradients extending from perceptual and motor regions to cortical areas representing more abstract functions, but an overarching framework for the association between structure and function is still lacking. Here, we show that the principal gradient revealed by the decomposition of connectivity data in humans and the macaque monkey is anchored by, at one end, regions serving primary sensory/motor functions and at the other end, transmodal regions that, in humans, are known as the default-mode network (DMN). These DMN regions exhibit the greatest geodesic distance along the cortical surface-and are precisely equidistant-from primary sensory/motor morphological landmarks. The principal gradient also provides an organizing spatial framework for multiple large-scale networks and characterizes a spectrum from unimodal to heteromodal activity in a functional metaanalysis. Together, these observations provide a characterization of the topographical organization of cortex and indicate that the role of the DMN in cognition might arise from its position at one extreme of a hierarchy, allowing it to process transmodal information that is unrelated to immediate sensory input.
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            Intrinsic functional connectivity as a tool for human connectomics: theory, properties, and optimization.

            Resting state functional connectivity MRI (fcMRI) is widely used to investigate brain networks that exhibit correlated fluctuations. While fcMRI does not provide direct measurement of anatomic connectivity, accumulating evidence suggests it is sufficiently constrained by anatomy to allow the architecture of distinct brain systems to be characterized. fcMRI is particularly useful for characterizing large-scale systems that span distributed areas (e.g., polysynaptic cortical pathways, cerebro-cerebellar circuits, cortical-thalamic circuits) and has complementary strengths when contrasted with the other major tool available for human connectomics-high angular resolution diffusion imaging (HARDI). We review what is known about fcMRI and then explore fcMRI data reliability, effects of preprocessing, analysis procedures, and effects of different acquisition parameters across six studies (n = 98) to provide recommendations for optimization. Run length (2-12 min), run structure (1 12-min run or 2 6-min runs), temporal resolution (2.5 or 5.0 s), spatial resolution (2 or 3 mm), and the task (fixation, eyes closed rest, eyes open rest, continuous word-classification) were varied. Results revealed moderate to high test-retest reliability. Run structure, temporal resolution, and spatial resolution minimally influenced fcMRI results while fixation and eyes open rest yielded stronger correlations as contrasted to other task conditions. Commonly used preprocessing steps involving regression of nuisance signals minimized nonspecific (noise) correlations including those associated with respiration. The most surprising finding was that estimates of correlation strengths stabilized with acquisition times as brief as 5 min. The brevity and robustness of fcMRI positions it as a powerful tool for large-scale explorations of genetic influences on brain architecture. We conclude by discussing the strengths and limitations of fcMRI and how it can be combined with HARDI techniques to support the emerging field of human connectomics.
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              Tree of Life Reveals Clock-Like Speciation and Diversification

              Genomic data are rapidly resolving the tree of living species calibrated to time, the timetree of life, which will provide a framework for research in diverse fields of science. Previous analyses of taxonomically restricted timetrees have found a decline in the rate of diversification in many groups of organisms, often attributed to ecological interactions among species. Here, we have synthesized a global timetree of life from 2,274 studies representing 50,632 species and examined the pattern and rate of diversification as well as the timing of speciation. We found that species diversity has been mostly expanding overall and in many smaller groups of species, and that the rate of diversification in eukaryotes has been mostly constant. We also identified, and avoided, potential biases that may have influenced previous analyses of diversification including low levels of taxon sampling, small clade size, and the inclusion of stem branches in clade analyses. We found consistency in time-to-speciation among plants and animals, ∼2 My, as measured by intervals of crown and stem species times. Together, this clock-like change at different levels suggests that speciation and diversification are processes dominated by random events and that adaptive change is largely a separate process.
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                Author and article information

                Journal
                J Neurophysiol
                J. Neurophysiol
                jn
                J Neurophysiol
                JN
                Journal of Neurophysiology
                American Physiological Society (Bethesda, MD )
                0022-3077
                1522-1598
                1 April 2019
                20 February 2019
                20 February 2019
                : 121
                : 4
                : 1513-1534
                Affiliations
                [1] 1Department of Psychology, Center for Brain Science, Harvard University , Cambridge, Massachusetts
                [2] 2The Computational, Cognitive & Clinical Neuroimaging Laboratory, Hammersmith Hospital Campus, Imperial College London , London, United Kingdom
                [3] 3Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
                [4] 4Department of Radiology, Harvard Medical School , Boston, Massachusetts
                [5] 5Division of Health Sciences and Technology, Massachusetts Institute of Technology , Cambridge, Massachusetts
                [6] 6Department of Psychiatry, Massachusetts General Hospital, Charlestown, Massachusetts
                Author notes
                Address for reprint requests and other correspondence: R. Braga, Center for Brain Science, 52 Oxford St., Harvard University, Cambridge, MA 02138 (e-mail: rbraga@ 123456fas.harvard.edu ).
                Article
                JN-00808-2018 JN-00808-2018
                10.1152/jn.00808.2018
                6485740
                30785825
                9a80918e-3cf7-48c6-ae88-3b358949eb97
                Copyright © 2019 the American Physiological Society

                Licensed under Creative Commons Attribution CC-BY 4.0: © the American Physiological Society.

                History
                : 3 December 2018
                : 15 February 2019
                : 15 February 2019
                Funding
                Funded by: Wellcome Trust (Wellcome) 10.13039/100004440
                Award ID: 103980/Z/14/Z
                Funded by: HHS | National Institutes of Health (NIH) 10.13039/100000002
                Award ID: K99MH117226
                Award ID: K23MH099413
                Award ID: P41EB015896
                Award ID: S10OD020039
                Award ID: S10RR019371
                Award ID: P50MH106435
                Categories
                Research Article
                Neural Circuits

                Neurology
                association cortex,default network,hippocampus,subiculum,uk biobank
                Neurology
                association cortex, default network, hippocampus, subiculum, uk biobank

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