Generally, a lower heart rate at rest implies more efficient heart function and better cardiovascular fitness. For example, a well-trained athlete might have a normal resting heart rate closer to 40 beats per minute.
Anticorrelated relationships in spontaneous signal fluctuation have been previously observed in resting-state functional magnetic resonance imaging (fMRI). In particular, it was proposed that there exists two systems in the brain that are intrinsically organized into anticorrelated networks, the default mode network, which usually exhibits task-related deactivations, and the task-positive network, which usually exhibits task-related activations during tasks that demands external attention. However, it is currently under debate whether the anticorrelations observed in resting state fMRI were valid or were instead artificially introduced by global signal regression, a common preprocessing technique to remove physiological and other noise in resting-state fMRI signal. We examined positive and negative correlations in resting-state connectivity using two different preprocessing methods: a component base noise reduction method (CompCor, Behzadi et al., 2007), in which principal components from noise regions-of-interest were removed, and the global signal regression method. Robust anticorrelations between a default mode network seed region in the medial prefrontal cortex and regions of the task-positive network were observed under both methods. Specificity of the anticorrelations was similar between the two methods. Specificity and sensitivity for positive correlations were higher under CompCor compared to the global regression method. Our results suggest that anticorrelations observed in resting-state connectivity are not an artifact introduced by global signal regression and might have biological origins, and that the CompCor method can be used to examine valid anticorrelations during rest.
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Microglia are the primary immune cells in the brain. Under physiological conditions, they typically stay in a "resting" state, with ramified processes continuously extending to and retracting from surrounding neural tissues. Whether and how such highly dynamic resting microglia functionally interact with surrounding neurons are still unclear. Using in vivo time-lapse imaging of both microglial morphology and neuronal activity in the optic tectum of larval zebrafish, we found that neuronal activity steers resting microglial processes and facilitates their contact with highly active neurons. This process requires the activation of pannexin-1 hemichannels on neurons. Reciprocally, such resting microglia-neuron contact reduces both spontaneous and visually evoked activities of contacted neurons. Our findings reveal an instructive role for neuronal activity in resting microglial motility and suggest the function for microglia in homeostatic regulation of neuronal activity in the healthy brain.
The human insula is hidden in the depth of the cerebral hemisphere by the overlying frontal and temporal opercula, and consists of three cytoarchitectonically distinct regions: the anterior agranular area, posterior granular area, and the transitional dysgranular zone; each has distinct histochemical staining patterns and specific connectivity. Even though there are several studies reporting the functional connectivity of the insula with the cingulated cortex, its relationships with other brain areas remain elusive in humans. Therefore, we decided to use resting state functional connectivity to elucidate in details its connectivity, in terms of cortical and subcortical areas, and also of lateralization. We investigated correlations in BOLD fluctuations between specific regions of interest of the insula and other brain areas of right-handed healthy volunteers, on both sides of the brain. Our findings document two major complementary networks involving the ventral-anterior and dorsal-posterior insula: one network links the anterior insula to the middle and inferior temporal cortex and anterior cingulate cortex, and is primarily related to limbic regions which play a role in emotional aspects; the second links the middle-posterior insula to premotor, sensorimotor, supplementary motor and middle-posterior cingulate cortices, indicating a role for the insula in sensorimotor integration. The clear bipartition of the insula was confirmed by negative correlation analysis. Correlation maps are partially lateralized: the salience network, related to the ventral anterior insula, displays stronger connections with the anterior cingulate cortex on the right side, and with the frontal cortex on the left side; the posterior network has stronger connections with the superior temporal cortex and the occipital cortex on the right side. These results are in agreement with connectivity studies in primates, and support the use of resting state functional analysis to investigate connectivity in the living human brain.
Your grandmother may have referred to your heart as "your ticker," but that nickname has proved to be a misnomer. A healthy heart doesn't beat with the regularity of clockwork. It speeds up and slows down to accommodate your changing need for oxygen as your activities vary throughout the day. What is a "normal" heart rate varies from person to person. However, an unusually high resting heart rate or low maximum heart rate may signify an increased risk of heart attack and death.
One simple thing people can do is to check their resting heart rate. It's a fairly easy to do and having the information can help down the road. It's a good idea to take your pulse occasionally to get a sense of what's normal for you and to identify unusual changes in rate or regularity that may warrant medical attention.
Vigorous exercise is the best way to both lower your resting heart rate and increase your maximum heart rate and aerobic capacity. Because it's impossible to maintain a maximum heart rate for more than a few minutes, physiologists have advised setting a percentage of your maximum heart rate as a target during exercise. If you're starting an exercise program, you may want to set your target rate at 50% of maximum and gradually increase the intensity of your workout until you reach 70% to 80%.
The best time to get your resting heart rate is first thing in the morning, even before you get out of bed. To gauge your maximum heart rate, take your pulse immediately after exercising as vigorously as possible.
Functional MRI (fMRI) can be applied to study the functional connectivity of the human brain. It has been suggested that fluctuations in the blood oxygenation level-dependent (BOLD) signal during rest reflect the neuronal baseline activity of the brain, representing the state of the human brain in the absence of goal-directed neuronal action and external input, and that these slow fluctuations correspond to functionally relevant resting-state networks. Several studies on resting fMRI have been conducted, reporting an apparent similarity between the identified patterns. The spatial consistency of these resting patterns, however, has not yet been evaluated and quantified. In this study, we apply a data analysis approach called tensor probabilistic independent component analysis to resting-state fMRI data to find coherencies that are consistent across subjects and sessions. We characterize and quantify the consistency of these effects by using a bootstrapping approach, and we estimate the BOLD amplitude modulation as well as the voxel-wise cross-subject variation. The analysis found 10 patterns with potential functional relevance, consisting of regions known to be involved in motor function, visual processing, executive functioning, auditory processing, memory, and the so-called default-mode network, each with BOLD signal changes up to 3%. In general, areas with a high mean percentage BOLD signal are consistent and show the least variation around the mean. These findings show that the baseline activity of the brain is consistent across subjects exhibiting significant temporal dynamics, with percentage BOLD signal change comparable with the signal changes found in task-related experiments.
A predictive equation for resting energy expenditure (REE) was derived from data from 498 healthy subjects, including females (n = 247) and males (n = 251), aged 19-78 y (45 +/- 14 y, mean +/- SD). Normal-weight (n = 264) and obese (n = 234) individuals were studied and REE was measured by indirect calorimetry. Multiple-regression analyses were employed to drive relationships between REE and weight, height, and age for both men and women (R2 = 0.71): REE = 9.99 x weight + 6.25 x height - 4.92 x age + 166 x sex (males, 1; females, 0) - 161. Simplification of this formula and separation by sex did not affect its predictive value: REE (males) = 10 x weight (kg) + 6.25 x height (cm) - 5 x age (y) + 5; REE (females) = 10 x weight (kg) + 6.25 x height (cm) - 5 x age (y) - 161. The inclusion of relative body weight and body-weight distribution did not significantly improve the predictive value of these equations. The Harris-Benedict Equations derived in 1919 overestimated measured REE by 5% (p less than 0.01). Fat-free mass (FFM) was the best single predictor of REE (R2 = 0.64): REE = 19.7 x FFM + 413. Weight also was closely correlated with REE (R2 = 0.56): REE = 15.1 x weight + 371.
Emotion-related disturbances, such as depression and anxiety, have been linked to relative right-sided resting frontal electroencephalograph (EEG) asymmetry among adults and infants of afflicted mothers. However, a somewhat inconsistent pattern of findings has emerged. A meta-analysis was undertaken to (a) evaluate the magnitude of effects across EEG studies of resting frontal asymmetry and depression, anxiety, and comorbid depression and anxiety and (b) determine whether certain moderator variables could help reconcile inconsistent findings. Moderate effects of similar magnitude were obtained for the depression and anxiety studies, whereas a smaller effect emerged for comorbid studies. Three moderating variables predicted effect sizes: (a) Shorter EEG recording periods were associated with larger effects among adults, (b) different operationalizations of depression yielded effects of marginally different magnitudes, and (c) younger infant samples showed larger effects than older ones. The current data support a link between resting frontal EEG asymmetry and depression and anxiety and provide a partial account of inconsistent findings across studies. 2ff7e9595c
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