Aerobic glycolysis (AG; i.e., nonoxidative metabolism of glucose despite the presence of abundant oxygen) accounts for 10%–12% of glucose used by the adult human brain. AG varies regionally in the ...resting state. Brain AG may support synaptic growth and remodeling; however, data supporting this hypothesis are sparse. Here, we report on investigations on the role of AG in the human brain. Meta-analysis of prior brain glucose and oxygen metabolism studies demonstrates that AG increases during childhood, precisely when synaptic growth rates are highest. In resting adult humans, AG correlates with the persistence of gene expression typical of infancy (transcriptional neoteny). In brain regions with the highest AG, we find increased gene expression related to synapse formation and growth. In contrast, regions high in oxidative glucose metabolism express genes related to mitochondria and synaptic transmission. Our results suggest that brain AG supports developmental processes, particularly those required for synapse formation and growth.
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•Brain aerobic glycolysis regionally relates to synaptic growth gene expression•Oxidative glycolysis instead relates to mitochondria and synaptic transmission•In humans, whole-brain aerobic glycolysis peaks during childhood•Neotenous regions of the adult brain maintain relatively high aerobic glycolysis
The human brain, especially that of children, has a high basal metabolic rate. Aerobic glycolysis (AG; nonoxidative metabolism of glucose despite abundant oxygen) accounts for 10%–12% of brain glucose consumption in adults. Here, Goyal et al. report that, in resting adults, AG correlates with the persistence of gene expression typical of infancy (transcriptional neoteny) and supports developmental processes required for synapse formation and neurite growth.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Resting-state functional connectivity is a powerful tool for studying human functional brain networks. Temporal fluctuations in functional connectivity, i.e., dynamic functional connectivity (dFC), ...are thought to reflect dynamic changes in brain organization and non-stationary switching of discrete brain states. However, recent studies have suggested that dFC might be attributed to sampling variability of static FC. Despite this controversy, a detailed exposition of stationarity and statistical testing of dFC is lacking in the literature. This article seeks an in-depth exploration of these statistical issues at a level appealing to both neuroscientists and statisticians.
We first review the statistical notion of stationarity, emphasizing its reliance on ensemble statistics. In contrast, all FC measures depend on sample statistics. An important consequence is that the space of stationary signals is much broader than expected, e.g., encompassing hidden markov models (HMM) widely used to extract discrete brain states. In other words, stationarity does not imply the absence of brain states. We then expound the assumptions underlying the statistical testing of dFC. It turns out that the two popular frameworks - phase randomization (PR) and autoregressive randomization (ARR) - generate stationary, linear, Gaussian null data. Therefore, statistical rejection can be due to non-stationarity, nonlinearity and/or non-Gaussianity. For example, the null hypothesis can be rejected for the stationary HMM due to nonlinearity and non-Gaussianity. Finally, we show that a common form of ARR (bivariate ARR) is susceptible to false positives compared with PR and an adapted version of ARR (multivariate ARR).
Application of PR and multivariate ARR to Human Connectome Project data suggests that the stationary, linear, Gaussian null hypothesis cannot be rejected for most participants. However, failure to reject the null hypothesis does not imply that static FC can fully explain dFC. We find that first order AR models explain temporal FC fluctuations significantly better than static FC models. Since first order AR models encode both static FC and one-lag FC, this suggests the presence of dynamical information beyond static FC. Furthermore, even in subjects where the null hypothesis was rejected, AR models explain temporal FC fluctuations significantly better than a popular HMM, suggesting the lack of discrete states (as measured by resting-state fMRI). Overall, our results suggest that AR models are not only useful as a means for generating null data, but may be a powerful tool for exploring the dynamical properties of resting-state fMRI. Finally, we discuss how apparent contradictions in the growing dFC literature might be reconciled.
•Space of stationary models bigger than expected; includes hidden Markov model (HMM).•Phase & autoregressive randomizations test for stationarity, linearity, Gaussianity.•Resting-state fMRI is mostly stationary, linear, and Gaussian.•1st order autoregressive (AR) model encodes static & one-lag FC.•1st order AR model explains sliding window correlations very well, better than HMM.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
1 Departments of Radiology, 2 Neurology, 3 Anatomy and Neurobiology, and 4 Biomedical Engineering, Washington University in St. Louis, St. Louis, Missouri
Submitted 17 July 2008;
accepted in final ...form 23 March 2009
Resting state studies of spontaneous fluctuations in the functional MRI (fMRI) blood oxygen level dependent (BOLD) signal have shown great promise in mapping the brain's intrinsic, large-scale functional architecture. An important data preprocessing step used to enhance the quality of these observations has been removal of spontaneous BOLD fluctuations common to the whole brain (the so-called global signal). One reproducible consequence of global signal removal has been the finding that spontaneous BOLD fluctuations in the default mode network and an extended dorsal attention system are consistently anticorrelated, a relationship that these two systems exhibit during task performance. The dependence of these resting-state anticorrelations on global signal removal has raised important questions regarding the nature of the global signal, the validity of global signal removal, and the appropriate interpretation of observed anticorrelated brain networks. In this study, we investigate several properties of the global signal and find that it is, indeed, global, not residing preferentially in systems exhibiting anticorrelations. We detail the influence of global signal removal on resting state correlation maps both mathematically and empirically, showing an enhancement in detection of system-specific correlations and improvement in the correspondence between resting-state correlations and anatomy. Finally, we show that several characteristics of anticorrelated networks including their spatial distribution, cross-subject consistency, presence with modified whole brain masks, and existence before global regression are not attributable to global signal removal and therefore suggest a biological basis.
Address for reprint requests and other correspondence: (E-mail: foxmdphd{at}gmail.com )
Scale-free dynamics, with a power spectrum following P ∝
f
−β, are an intrinsic feature of many complex processes in nature. In neural systems, scale-free activity is often neglected in ...electrophysiological research. Here, we investigate scale-free dynamics in human brain and show that it contains extensive nested frequencies, with the phase of lower frequencies modulating the amplitude of higher frequencies in an upward progression across the frequency spectrum. The functional significance of scale-free brain activity is indicated by task performance modulation and regional variation, with β being larger in default network and visual cortex and smaller in hippocampus and cerebellum. The precise patterns of nested frequencies in the brain differ from other scale-free dynamics in nature, such as earth seismic waves and stock market fluctuations, suggesting system-specific generative mechanisms. Our findings reveal robust temporal structures and behavioral significance of scale-free brain activity and should motivate future study on its physiological mechanisms and cognitive implications.
► A rich, robust temporal structure is present within scale-free brain activity ► The power-law exponent of scale-free brain activity is modulated by task performance ► The power-law exponent of scale-free brain activity varies across brain regions ► The temporal structures of scale-free dynamics are different in different systems
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
1 Mallinckrodt Institute of Radiology, 2 Departments of Neurology, 3 Anatomy and Neurobiology, and 4 Biomedical Engineering, Washington University School of Medicine, St. Louis, Missouri; 5 ...Department of Psychology and Center for Brain Science, Harvard University; Cambridge, Massachusetts; 6 Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts; and 7 Howard Hughes Medical Institute, Chevy Chase, Maryland
Submitted 11 March 2008;
accepted in final form 10 September 2008
Two functionally distinct, and potentially competing, brain networks have been recently identified that can be broadly distinguished by their contrasting roles in attention to the external world versus internally directed mentation involving long-term memory. At the core of these two networks are the dorsal attention system and the hippocampal-cortical memory system, a component of the brain's default network. Here spontaneous blood-oxygenation-level-dependent (BOLD) signal correlations were used in three separate functional magnetic resonance imaging data sets ( n = 105) to define a third system, the frontoparietal control system, which is spatially interposed between these two previously defined systems. The frontoparietal control system includes many regions identified as supporting cognitive control and decision-making processes including lateral prefrontal cortex, anterior cingulate cortex, and inferior parietal lobule. Detailed analysis of frontal and parietal cortex, including use of high-resolution data, revealed clear evidence for contiguous but distinct regions: in general, the regions associated with the frontoparietal control system are situated between components of the dorsal attention and hippocampal-cortical memory systems. The frontoparietal control system is therefore anatomically positioned to integrate information from these two opposing brain systems.
Address for reprint requests and other correspondence: J. L. Vincent, Harvard University, 33 Kirkland St., WJH 274, Cambridge, MA 02138 (E-mail: vincent{at}nmr.mgh.harvard.edu )
The interplay between hemodynamic-based markers of cortical activity (e.g. fMRI and optical intrinsic signal imaging), which are an indirect and relatively slow report of neural activity, and ...underlying synaptic electrical and metabolic activity through neurovascular coupling is a topic of ongoing research and debate. As application of resting state functional connectivity measures is extended further into topics such as brain development, aging and disease, the importance of understanding the fundamental physiological basis for functional connectivity will grow. Here we extend functional connectivity analysis from hemodynamic- to calcium-based imaging. Transgenic mice (n = 7) expressing a fluorescent calcium indicator (GCaMP6) driven by the Thy1 promoter in glutamatergic neurons were imaged transcranially in both anesthetized (using ketamine/xylazine) and awake states. Sequential LED illumination (λ = 454, 523, 595, 640nm) enabled concurrent imaging of both GCaMP6 fluorescence emission (corrected for hemoglobin absorption) and hemodynamics. Functional connectivity network maps were constructed for infraslow (0.009-0.08Hz), intermediate (0.08-0.4Hz), and high (0.4-4.0Hz) frequency bands. At infraslow and intermediate frequencies, commonly used in BOLD fMRI and fcOIS studies of functional connectivity and implicated in neurovascular coupling mechanisms, GCaMP6 and HbO2 functional connectivity structures were in high agreement, both qualitatively and also quantitatively through a measure of spatial similarity. The spontaneous dynamics of both contrasts had the highest correlation when the GCaMP6 signal was delayed with a ~0.6-1.5s temporal offset. Within the higher-frequency delta band, sensitive to slow wave sleep oscillations in non-REM sleep and anesthesia, we evaluate the speed with which the connectivity analysis stabilized and found that the functional connectivity maps captured putative network structure within time window lengths as short as 30 seconds. Homotopic GCaMP6 functional connectivity maps at 0.4-4.0Hz in the anesthetized states show a striking correlated and anti-correlated structure along the anterior to posterior axis. This structure is potentially explained in part by observed propagation of delta-band activity from frontal somatomotor regions to visuoparietal areas. During awake imaging, this spatio-temporal quality is altered, and a more complex and detailed functional connectivity structure is observed. The combined calcium/hemoglobin imaging technique described here will enable the dissociation of changes in ionic and hemodynamic functional structure and neurovascular coupling and provide a framework for subsequent studies of neurological disease such as stroke.
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Resting state functional connectivity is defined in terms of temporal correlations between physiologic signals, most commonly studied using functional magnetic resonance imaging. Major features of ...functional connectivity correspond to structural (axonal) connectivity. However, this relation is not one-to-one. Interhemispheric functional connectivity in relation to the corpus callosum presents a case in point. Specifically, several reports have documented nearly intact interhemispheric functional connectivity in individuals in whom the corpus callosum (the major commissure between the hemispheres) never develops. To investigate this question, we assessed functional connectivity before and after surgical section of the corpus callosum in 22 patients with medically refractory epilepsy. Section of the corpus callosum markedly reduced interhemispheric functional connectivity. This effect was more profound in multimodal associative areas in the frontal and parietal lobe than primary regions of sensorimotor and visual function. Moreover, no evidence of recovery was observed in a limited sample in which multiyear, longitudinal follow-up was obtained. Comparison of partial vs. complete callosotomy revealed several effects implying the existence of polysynaptic functional connectivity between remote brain regions. Thus, our results demonstrate that callosal as well as extracallosal anatomical connections play a role in the maintenance of interhemispheric functional connectivity.
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BFBNIB, NMLJ, NUK, PNG, SAZU, UL, UM, UPUK
Human functional MRI (fMRI) research primarily focuses on analyzing data averaged across groups, which limits the detail, specificity, and clinical utility of fMRI resting-state functional ...connectivity (RSFC) and task-activation maps. To push our understanding of functional brain organization to the level of individual humans, we assembled a novel MRI dataset containing 5 hr of RSFC data, 6 hr of task fMRI, multiple structural MRIs, and neuropsychological tests from each of ten adults. Using these data, we generated ten high-fidelity, individual-specific functional connectomes. This individual-connectome approach revealed several new types of spatial and organizational variability in brain networks, including unique network features and topologies that corresponded with structural and task-derived brain features. We are releasing this highly sampled, individual-focused dataset as a resource for neuroscientists, and we propose precision individual connectomics as a model for future work examining the organization of healthy and diseased individual human brains.
•Individual brain organization is qualitatively different from group-average estimates•Individualized measures of brain function become reliable with large amounts of data•Individuals exhibit distinct brain network topography and topology•We release highly sampled, multi-modal fMRI data on ten subjects as a NeuroResource
Gordon et al. demonstrate advantages of conducting whole-brain fMRI research in individual humans using large amounts of per-individual data, which greatly increases reliability and specificity. This work illustrates new approaches for fMRI-based neuroscience that allow detailed characterization of individual brain organization.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Functional neuroimaging (e.g., with fMRI) has been difficult to perform in mice, making it challenging to translate between human fMRI studies and molecular and genetic mechanisms. A method to easily ...perform large-scale functional neuroimaging in mice would enable the discovery of functional correlates of genetic manipulations and bridge with mouse models of disease. To satisfy this need, we combined resting-state functional connectivity mapping with optical intrinsic signal imaging (fcOIS). We demonstrate functional connectivity in mice through highly detailed fcOIS mapping of resting-state networks across most of the cerebral cortex. Synthesis of multiple network connectivity patterns through iterative parcellation and clustering provides a comprehensive map of the functional neuroarchitecture and demonstrates identification of the major functional regions of the mouse cerebral cortex. The method relies on simple and relatively inexpensive camera-based equipment, does not require exogenous contrast agents and involves only reflection of the scalp (the skull remains intact) making it minimally invasive. In principle, fcOIS allows new paradigms linking human neuroscience with the power of molecular/genetic manipulations in mouse models.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Stroke disrupts the brain’s vascular supply, not only within but also outside areas of infarction. We investigated temporal delays (lag) in resting state functional magnetic resonance imaging signals ...in 130 stroke patients scanned two weeks, three months and 12 months post stroke onset. Thirty controls were scanned twice at an interval of three months. Hemodynamic lag was determined using cross-correlation with the global gray matter signal. Behavioral performance in multiple domains was assessed in all patients. Regional cerebral blood flow and carotid patency were assessed in subsets of the cohort using arterial spin labeling and carotid Doppler ultrasonography. Significant hemodynamic lag was observed in 30% of stroke patients sub-acutely. Approximately 10% of patients showed lag at one-year post-stroke. Hemodynamic lag corresponded to gross aberrancy in functional connectivity measures, performance deficits in multiple domains and local and global perfusion deficits. Correcting for lag partially normalized abnormalities in measured functional connectivity. Yet post-stroke FC–behavior relationships in the motor and attention systems persisted even after hemodynamic delays were corrected. Resting state fMRI can reliably identify areas of hemodynamic delay following stroke. Our data reveal that hemodynamic delay is common sub-acutely, alters functional connectivity, and may be of clinical importance.
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NUK, OILJ, SAZU, UKNU, UL, UM, UPUK
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