NeuroImage
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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.
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It has traditionally been held that the hippocampus is not part of the neural substrate of working memory (WM), and that WM is preserved in Temporal Lobe Epilepsy (TLE). Recent imaging and neuropsychological data suggest this view may need revision. The aim of this study was to investigate the neural correlates of WM in TLE using functional MRI (fMRI). ⋯ Our results suggest that WM is impaired in unilateral HS and the underlying neural correlates of WM are disrupted. Our findings suggest that hippocampal activity is progressively suppressed as the WM load increases, with maintenance of good performance. Implications for understanding the role of the hippocampus in WM are discussed.
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Reactivation of visual cortex during memory retrieval: content specificity and emotional modulation.
Studies on memory retrieval suggest a reactivation of cortical regions engaged during encoding, such that visual or auditory areas reactivate for visual or auditory memories. The content specificity and any emotion dependency of such reactivations are still unclear. Because distinct visual areas are specialized in processing distinct stimulus categories, we tested for face and word specific reactivations during a memory task using functional magnetic resonance imaging (fMRI). ⋯ Reactivation tended to be larger in both the face- and word-responsive regions when the associated scene was negative as compared to neutral. However, relative to neutral context, the recall of faces and words paired with a negative context produced smaller activations in brain regions associated with social and semantic processing, respectively, as well as poorer memory performance overall. Taken together, these results support the idea of cortical memory reactivations, even at a content-specific level, and further suggest that emotional context may produce opposite effects on reactivations in early sensory areas and more elaborate processing in higher-level cortical areas.
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Brain functional states are established by functional connectivities between brain regions. In experienced meditators (13 Tibetan Buddhists, 15 QiGong, 14 Sahaja Yoga, 14 Ananda Marga Yoga, 15 Zen), 19-channel EEG was recorded before, during and after that meditation exercise which their respective tradition regards as route to the most desirable meditative state. The head surface EEG data were recomputed (sLORETA) into 19 cortical regional source model time series. ⋯ The topography of the functional connectivities was examined via PCA-based computation of principal connectivities. When going into and out of meditation, significantly different connectivities revealed clearly different topographies in the delta frequency band and minor differences in the beta-2 band. The globally reduced functional interdependence between brain regions in meditation suggests that interaction between the self process functions is minimized, and that constraints on the self process by other processes are minimized, thereby leading to the subjective experience of non-involvement, detachment and letting go, as well as of all-oneness and dissolution of ego borders during meditation.