NeuroImage
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Studies employing functional connectivity-type analyses have established that spontaneous fluctuations in functional magnetic resonance imaging (fMRI) signals are organized within large-scale brain networks. Meanwhile, fMRI signals have been shown to exhibit 1/f-type power spectra - a hallmark of scale-free dynamics. We studied the interplay between functional connectivity and scale-free dynamics in fMRI signals, utilizing the fractal connectivity framework - a multivariate extension of the univariate fractional Gaussian noise model, which relies on a wavelet formulation for robust parameter estimation. ⋯ Third, in addition to a decrease of the Hurst exponent and inter-regional correlations, task performance modified cross-temporal dynamics, inducing a larger contribution of the highest frequencies within the scale-free range to global correlation. Lastly, we found that across individuals, a weaker task modulation of the frequency contribution to inter-regional connectivity was associated with better task performance manifesting as shorter and less variable reaction times. These findings bring together two related fields that have hitherto been studied separately - resting-state networks and scale-free dynamics, and show that scale-free dynamics of human brain activity manifest in cross-regional interactions as well.
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Hippocampal activity is characterized by the coordinated firing of a subset of neurons. Such neuronal ensembles can either be driven by external stimuli to form new memory traces or be reactivated by intrinsic mechanisms to reactivate and consolidate old memories. Hippocampal network oscillations orchestrate this coherent activity. ⋯ Interestingly, cells were more tightly clustered in large ensembles than in smaller groups. Together, our data show that spatiotemporal activity patterns of hippocampal neuronal ensembles can be reliably detected with deconvolution-based imaging techniques in mouse hippocampal slices. The here presented techniques are fully applicable to similar studies of distributed optical measurements of neuronal activity (in vivo), where signal-to-noise ratio is critical.
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Cerebral dysfunction occurring in mental disorders can show metabolic disturbances which are limited to circumscribed brain areas. Auditory hallucinations have been shown to be related to defined cortical areas linked to specific language functions. Here, we investigated if the study of metabolic changes in auditory hallucinations requires a functional rather than an anatomical definition of their location and size to allow a reliable investigation by magnetic resonance spectroscopy (MRS). ⋯ This study supports the neurodegenerative hypothesis of schizophrenia only in a frontal region whereas the results obtained from temporal regions are in contrast to the majority of previous studies. Future research should test the hypothesis raised by this study that a functional definition of language regions is needed if neurochemical imbalances are expected to be restricted to functional foci.
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Brain function critically relies on the supply with energy substrates (oxygen and glucose) via blood flow. Alterations in energy demand as during neuronal activation induce dynamic changes in substrate fluxes and blood flow. To study the complex system that regulates cerebral metabolism requires the combination of methods for the simultaneous assessment of multiple parameters. ⋯ In the vicinity of the ischemic territory, we observed PIDs that were characterized by reduced CMRO2 and increased oxygen extraction fraction (OEF), indicating a limitation of oxygen supply. Simultaneously measured PET showed an increased (18)F-FDG uptake in these regions. Our combined system proved to be a novel tool for the simultaneous study of dynamic spatiotemporal alterations of cortical blood flow, oxygen metabolism and glucose consumption under normal and pathologic conditions.
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Although functional near-infrared spectroscopy (fNIRS) has an advantage of simultaneously measuring changes in oxy- and deoxy-hemoglobin concentrations (Δ[HbO] and Δ[HbR]), only few analysis approaches exploit this advantage. As an extension of our recently proposed method (task-related component analysis, TRCA), this study proposes a new analysis method that extracts task-related oxygenation and cerebral blood volume (CBV) changes. In the original formulation of TRCA, task-relatedness of a signal is defined as consistent appearance of a same waveform in every task block, thereby constructing task-related components by maximizing inter-block covariance. ⋯ The proposed method (collectively called TRCA(±)) was formulated as a matrix eigenvalue problem, which can be solved efficiently with standard numerical methods, and was tested with a synthetic data generated by a balloon model, successfully recovering oxygenation and CBV components. fNIRS data from sensorimotor areas in a finger-tapping task and from prefrontal lobe in a working-memory (WM) task were then analyzed. For both tasks, the time courses and the spatial maps for oxygenation and CBV changes were found to differ consistently, providing certain constraints in the parameters of balloon models. In summary, TRCA can estimate task-related oxygenation and CBV changes simultaneously, thereby extending the applicability of fNIRS.