Front Hum Neurosci
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Resting state functional connectivity analysis is a widely used method for mapping intrinsic functional organization of the brain. Global signal regression (GSR) is commonly employed for removing systemic global variance from resting state BOLD-fMRI data; however, recent studies have demonstrated that GSR may introduce spurious negative correlations within and between functional networks, calling into question the meaning of anticorrelations reported between some networks. In the present study, we propose that global signal from resting state fMRI is composed primarily of systemic low frequency oscillations (sLFOs) that propagate with cerebral blood circulation throughout the brain. ⋯ We test our hypothesis on two functional systems that are suggested to be intrinsically organized into anticorrelated networks: the default mode network (DMN) and task positive network (TPN). We evaluate the efficacy of dGSR and compare its performance with the conventional "static" global regression (sGSR) method in terms of (i) explaining systemic variance in the data and (ii) enhancing specificity and sensitivity of functional connectivity measures. dGSR increases the amount of BOLD signal variance being modeled and removed relative to sGSR while reducing spurious negative correlations introduced in reference regions by sGSR, and attenuating inflated positive connectivity measures. We conclude that incorporating time delay information for sLFOs into global noise removal strategies is of crucial importance for optimal noise removal from resting state functional connectivity maps.
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Transcranial direct current stimulation (tDCS) is a representative non-invasive brain stimulation method (NIBS). tDCS increases cortical excitability not only in healthy individuals, but also in stroke patients where it contributes to motor function improvement. Recently, two additional types of transcranial electrical stimulation (tES) methods have been introduced that may also prove beneficial for stimulating cortical excitability; these are transcranial random noise stimulation (tRNS) and transcranial alternating current stimulation (tACS). However, comparison of tDCS with tRNS and tACS, in terms of efficacy in cortical excitability alteration, has not been reported thus far. ⋯ Compared with sham measurements, significant increases in MEPs were also observed with tRNS and tACS (p < 0.05), but not with tDCS. In addition, a significant correlation of the mean stimulation effect was observed between tRNS and tACS (p = 0.019, r = 0.598). tRNS induced a significant increase in MEP compared with the Pre or Sham at all time points. tRNS resulted in the largest significant increase in MEPs. These findings suggest that tRNS is the most effective tES method and should be considered as part of a treatment plan for improving motor function in stroke patients.
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Functional disconnectivity during the resting state has been observed in mild traumatic brain injury (mTBI) patients during the acute stage. However, it remains largely unknown whether the abnormalities are related to specific frequency bands of the low-frequency oscillations (LFO). Here, we used the amplitude of low-frequency fluctuations (ALFF) to examine the amplitudes of LFO in different frequency bands (slow-5: 0.01-0.027 Hz; slow-4: 0.027-0.073 Hz; and typical: 0.01-0.08 Hz) in patients with acute mTBI. ⋯ We concluded that the abnormality of spontaneous brain activity in acute mTBI patients existed in the frontal lobe as well as in distributed brain regions associated with integrative, sensory, and emotional roles, and the abnormal spontaneous neuronal activity in different brain regions could be better detected by the slow-4 band. These findings might contribute to a better understanding of local neural psychopathology of acute mTBI. Future studies should take the frequency bands into account when measuring intrinsic brain activity of mTBI patients.
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Functional imaging studies in human reliably identify a trio of scene-selective regions, one on each of the lateral [occipital place area (OPA)], ventral [parahippocampal place area (PPA)], and medial [retrosplenial complex (RSC)] cortical surfaces. Recently, we demonstrated differential retinotopic biases for the contralateral lower and upper visual fields within OPA and PPA, respectively. Here, using functional magnetic resonance imaging, we combine detailed mapping of both population receptive fields (pRF) and category-selectivity, with independently acquired resting-state functional connectivity analyses, to examine scene and retinotopic processing within medial parietal cortex. ⋯ Consistent with prior research, we also observed differential functional connectivity in medial parietal cortex for anterior over posterior PPA, as well as a region on the lateral surface, the caudal inferior parietal lobule (cIPL). However, the differential connectivity in medial parietal cortex was found principally anterior of MPA. We suggest that there is posterior-anterior gradient within medial parietal cortex, with posterior regions in the POS showing retinotopically based scene-selectivity and more anterior regions showing connectivity that may be more reflective of abstract, navigationally pertinent and possibly mnemonic representations.
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We often estimate an unknown value based on available relevant information, a process known as cognitive estimation. In this study, we assess the cognitive and neuroanatomic basis for quantitative estimation by examining deficits in patients with focal neurodegenerative disease in frontal and parietal cortex. Executive function and number knowledge are key components in cognitive estimation. ⋯ MCI were not impaired on BCET relative to controls. Regression analyses related BCET performance to gray matter atrophy in right lateral prefrontal and orbital frontal cortices in bvFTD, and to atrophy in right inferior parietal cortex, right insula, and fusiform cortices in CBS/PCA. These results are consistent with the hypothesis that a frontal-parietal network plays a crucial role in cognitive estimation.