NeuroImage
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The abilities to empathize and to systemize, two fundamental dimensions of cognitive style, are characterized by apparent individual differences. These abilities are typically measured using an empathizing quotient (EQ) and a systemizing quotient (SQ) questionnaire, respectively. The purpose of this study was to reveal any correlations between EQ and SQ scores and regional gray matter volumes in healthy children by applying voxel-based morphometry to magnetic resonance images. ⋯ The EQ-C scores showed significant positive correlations with the regional gray matter volumes of the left fronto-opercular and superior temporal cortices, including the precentral gyrus, the inferior frontal gyrus, the superior temporal gyrus, and the insula, which are functionally related to empathic processing. Additionally, SQ-C scores showed a significant negative correlation with the regional gray matter volume of the left posterior parietal cortex, which is functionally involved in selective attention processing. Our findings suggest that individual differences in cognitive style pertaining to empathizing or systemizing abilities could be explained by differences in the volume of brain structures that are functionally relevant to empathizing and systemizing.
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During the maintenance period of propofol-induced general anesthesia, specific changes in spontaneous EEG rhythms can be observed. These comprise increased delta and theta power and the emergence of alpha oscillations over frontal regions. In this study we use a meanfield model of the thalamo-cortical system to reproduce these changes and to elucidate the underlying mechanisms. ⋯ Specifically, while observed increases in delta and alpha power are reflections of amplified resonances in the respective frequency bands, increases in theta power are caused indirectly by spectral power leakage from delta and alpha bands. The model suggests that these changes are brought about through increased inhibition within local cortical interneuron circuits. These results are encouraging and motivate more extensive use of neural meanfield models in elucidating the physiological mechanisms underlying the effects of pharmacological agents on macroscopic brain dynamics.
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Neuroimaging research suggests that the resting cerebral physiology is characterized by complex patterns of neuronal activity in widely distributed functional networks. As studied using functional magnetic resonance imaging (fMRI) of the blood-oxygenation-level dependent (BOLD) signal, the resting brain activity is associated with slowly fluctuating hemodynamic signals (~10s). More recently, multimodal functional imaging studies involving simultaneous acquisition of BOLD-fMRI and electroencephalography (EEG) data have suggested that the relatively slow hemodynamic fluctuations of some resting state networks (RSNs) evinced in the BOLD data are related to much faster (~100 ms) transient brain states reflected in EEG signals, that are referred to as "microstates". ⋯ We found that each RSN was characterized further by a specific electrophysiological signature involving from one to a combination of several microstates. Moreover, by comparing the time course of EEG microstates to that of the whole-brain BOLD signal, on a multi-subject group level, we unraveled for the first time a set of microstate-associated networks that correspond to a range of previously described RSNs, including visual, sensorimotor, auditory, attention, frontal, visceromotor and default mode networks. These results extend our understanding of the electrophysiological signature of BOLD RSNs and demonstrate the intrinsic connection between the fast neuronal activity and slow hemodynamic fluctuations.
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Confounding noise in BOLD fMRI data arises primarily from fluctuations in blood flow and oxygenation due to cardiac and respiratory effects, spontaneous low frequency oscillations (LFO) in arterial pressure, and non-task related neural activity. Cardiac noise is particularly problematic, as the low sampling frequency of BOLD fMRI ensures that these effects are aliased in recorded data. Various methods have been proposed to estimate the noise signal through measurement and transformation of the cardiac and respiratory waveforms (e.g. ⋯ By comparison, the nine RETROICOR+RVT regressors together explain a median of 6.8% of the variance in the BOLD data. Detection of resting state networks was enhanced with NIRS denoising, and there were no appreciable differences in the bias of the different techniques. Physiological noise regressors generated using Regressor Interpolation at Progressive Time Delays (RIPTiDe) offer an effective method for efficiently removing hemodynamic noise from BOLD data.
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Even more than in cognitive research applications, moving fMRI to the clinic and the drug development process requires the generation of stable and reliable signal changes. The performance characteristics of the fMRI paradigm constrain experimental power and may require different study designs (e.g., crossover vs. parallel groups), yet fMRI reliability characteristics can be strongly dependent on the nature of the fMRI task. The present study investigated both within-subject and group-level reliability of a combined three-task fMRI battery targeting three systems of wide applicability in clinical and cognitive neuroscience: an emotional (face matching), a motivational (monetary reward anticipation) and a cognitive (n-back working memory) task. ⋯ In conclusion, all three tasks are well suited to between-subject designs, including imaging genetics. When specific recommendations are followed, the n-back and reward task are also suited for within-subject designs, including pharmaco-fMRI. The present study provides task-specific fMRI reliability performance measures that will inform the optimal use, powering and design of fMRI studies using comparable tasks.