You now have one more good reason to learn that new skill. After you learn a new skill, you need to practice it. Teaching it to someone else requires you to explain the concept and correct any mistakes you make. For example, learn to swing a golf club, then teach the steps to a friend.

Do you want an easy way to increase your creative brain power? The answer may lie in turning on some music. According to a study , listening to happy tunes helps generate more innovative solutions compared to being in silence. Which means, cranking up some feel-good music can help boost your creative thinking and brain power.

And if you want to learn how to play music , now is a great time to start because your brain is capable of learning new skills at any point in your life. Instead, be willing to try new ways to do the same things. Choose a different route to get to work each week or try a different mode of transport, like biking or using public transport instead of driving.

Your brain can benefit from this simple change, and you might be surprised by how easy it is to change your thinking. Daily meditation can calm your body, slow your breathing, and reduce stress and anxiety.

A review of research has overwhelmingly proven the many cognitive benefits of being able to speak more than one language. According to numerous studies, bilingualism can contribute to better memory, improved visual-spatial skills, and higher levels of creativity.

Being fluent in more than one language may also help you switch more easily between different tasks, and delay the onset of age-related mental decline. According to researchers, you can boost your memory and improve other mental functions by becoming a student of a new language at any time in your life.

Plus, it can also help center you when life seems out of balance. Taking up a regular practice of tai chi can help reduce stress, enhance sleep quality, and improve memory.

A study found that long-term tai chi practice could induce structural changes in the brain, resulting in an increase in brain volume. Beginners do best by taking a class to learn the different movements. But once you know the basics, you can practice tai chi anywhere, anytime. The next time you interact with someone, take note of four things about them.

Maybe you observe the color of their shirt or pants. Are they wearing glasses? Do they have a hat on, and if so, what kind of hat? What color is their hair? Once you decide on four things to remember, make a mental note, and come back to it later in the day.

Write down what you remember about those four details. Focusing on your brain health is one of the best things you can do to improve your concentration, focus, memory, and mental agility, no matter what age you are.

Our experts continually monitor the health and wellness space, and we update our articles when new information becomes available. Practicing certain lifestyle habits may help boost your intelligence and stimulate your brain.

Research has shown that when done regularly, these…. Constantly dream of romance? Fixate on thoughts of your partner? Feel a need to always be in love? Learn why — and why this isn't an "addiction.

Toxic femininity, or behavior that aligns with patriarchal beliefs about what women should and shouldn't do, can affect your well-being. Here's how. A new study, released this week has found that death rates are increased for people with obesity who are also socially isolated and lonely.

A new study finds a type of psychedelic called ibogaine may help people with traumatic brain injury. In the study 30 male Special Operations Forces…. New research suggests that moderate-intensity aerobic exercise like swimming, cycling, jogging, and dancing may be more effective for reducing….

Finding a therapist that makes you feel comfortable is crucial. But that's not the only consideration. Here's what else to look for when starting a…. A Quiz for Teens Are You a Workaholic? How Well Do You Sleep?

Health Conditions Discover Plan Connect. Mental Well-Being. Medically reviewed by Timothy J. Legg, PhD, PsyD — By Sara Lindberg — Updated on February 17, Try puzzles Play cards Build vocabulary Dance Use your senses Learn a new skill Teach a skill Listen to music Try a new route Meditate Learn a new language Do tai chi Focus Bottom line Exercising the brain to improve memory, focus, or daily functionality is a top priority for many older adults.

Share on Pinterest. PEOPLE WITH LOWER IQs: Research suggests that cognition-enhancing drugs offer the greatest performance boost among individuals with low-to-average intelligence.

SENIORS: Some studies suggest that older adults may not derive much benefit from cognition-enhancing drugs. One study found that methylphenidate Ritalin , which boosts working memory and attention in young adults, had no effect on performance among healthy elderly volunteers who were asked to perform various cognitive tasks.

People have been searching for ways to boost their brainpower perhaps for all of history. In the past century scientific efforts have revealed a few promising chemicals, but only modafinil has passed rigorous tests of cognitive enhancement. CAFFEINE: One of the oldest and most popular stimulants.

It can enhance alertness and attention; however, effects are short-lived, and tolerance builds up quickly.

NICOTINE: Also a stimulant, used for hundreds of years for a range of medicinal purposes. It is very addictive and has many dangerous side effects. Benzedrine was the first drug to treat hyperactivity in children.

Amphetamine can enhance attention and memory by increasing levels of norepinephrine and dopamine in the brain, but the compound can be addictive and comes with a range of side effects, including hyperactivity, loss of appetite, disturbed sleep, even psychosis.

It became popular for ADHD in the s. As with amphetamine, it can improve memory and focus for those with ADHD, but it is also used off-label as a study and work aid. Some individuals build up a tolerance to Ritalin over time. It has been shown in some studies to enhance memory and attention in healthy individuals.

MODAFINIL: Originally used to treat narcolepsy. It can also enhance cognitive function, especially when completing difficult tasks. Experts are not quite sure how it works or what long-term effects would look like.

March 1, 4 min read. Credit: Andrew Bret Wallis Getty Images. March Issue.

A recent ad for one tDCS device urges you to "elevate your performance. Brain stimulation therapies aim to activate or inhibit parts of the brain. tDCS has been around for years, but its popularity has spiked over the last decade. tDCS devices use headgear that may look like a swim cap or headband to position electrodes against the scalp.

When a power source is switched on, the electrodes deliver low levels of electrical current to the brain. A typical session lasts 20 to 30 minutes and may be repeated over days or weeks.

The brain normally functions by sending and receiving tiny electrical signals between nerve cells. Stimulating specific regions of the brain with low levels of electricity might improve focus or memory, mood, or even dementia, according to tDCS advocates. The jury is still out.

Research suggests that tDCS holds promise for certain conditions, but techniques tested through research may differ from devices sold commercially for at-home use.

However, a case can be made for their differentiation even though they may share some properties under varying physiological and behavioral conditions. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This is a preview of subscription content, log in via an institution. Parasuraman R, Davies DR, eds : Varieties of Attention. New York: Academic Press. Google Scholar. Posner MI, Marin OSM, eds : Mechanisms of Attention.

Attention and Performance XI. Hillsdale, NJ: Erlbaum. Hillyard SA : Electrophysiology of human selective attention. Trend Neurosci 8: — Article Google Scholar. Lindsley DB : Neural mechanisms of arousal, attention and information processing.

In: Neuropsychology After Lashley , Orbach J, ed. Mountcastle MV, Motter BC, Steinmetz MA, Duffy CJ : Looking and seeing: The visual functions of the parietal lobe.

In: Dynamic Aspects of Neocortical Function , Edelman G, Gall, E, Cowan, M eds. New York: Wiley. Pribram KH, McGuinness D : Arousal, activation, and effort in the control of attention. Psychol Rev — Skinner JE, Yingling CD : Central gating mechanisms that regulate event-related potentials and behavior.

In: Attention , Volunwry Contraction and Event-Related Potentials. Vol 1, Desmedt J, ed. Insomnia illustrates our lack of knowledge concerning the functional significance of these criteria as there is often a remarkable dissociation between the subjective experience of sleep disruptions and the associated EEG parameters Drummond et al.

Several studies have been carried out combining EEG and imaging methods in order to explore regional specific brain activity during sleep Buchsbaum et al. Almost all of these studies used positron emission tomography PET or single photon emission computed tomography SPECT with the exception of Løvblad et al.

who used functional MRI fMRI. The findings obtained from these studies suggest a global decrease in cerebral and thalamic activity during non-rapid eye movement NREM sleep, which is in accordance with the slow potential activity measured with EEG.

More specifically, light sleep seems to be characterized by decreased activity in the frontal and parietal cortices, and in the thalamus. As compared with wakefulness the decline of activity is continued during slow-wave sleep SWS with an additional decrease in activity within the basal ganglia.

On the contrary, REM sleep is accompanied by increased metabolism within the pons, the limbic system and the occipital cortex secondary visual cortex and diminished metabolism within parietal and prefrontal regions. Both REM and NREM sleep show diminished brain activity in prefrontal and parietal regions compared with wakefulness.

The findings for several other brain regions, as for cerebellar and medial temporal areas, hypothalamic nuclei and paralimbic regions such as the cingulate cortex are still not conclusive. In general, PET studies show a relatively low spatial as well as temporal resolution compared with fMRI, which can overcome these limitations allowing for a more precise detection of brain activity during sleep.

We recently reported sleep stage specific blood oxygenation level dependent BOLD signal decreases associated with transient increases in EEG hyperpolarization upon acoustic stimulation Czisch et al. However, processing of sensory information interferes with a putatively dynamic pattern of brain activity during the sleep—wake cycle.

Periods free of stimulation would be needed to allow for mapping of brain activations associated with sleep stages. In the present study, we therefore compared the baseline activity across sleep stages by relating local BOLD signal changes to the sleep stage classifications derived from simultaneous polysomnographic recordings without applying external stimulation.

Furthermore, as only fMRI allows for a comparison of activity across the brain, and identification of brain regions which alter their activity synchronously with other areas, we demonstrate sleep specific interaction of the hypothalamus, which is known to be of central importance for the regulation of the sleep—wake cycle, with other brain regions by calculating functional connectivity on the basis of the hypothalamic time curves.

Fourteen young healthy paid volunteers gave written informed consent according to the institutional guidelines before participating in this study, which was approved by the local Ethical Committee.

The subjects had no history of neurological and psychiatric disorders, or substance abuse, and had no sleep disturbances or recent time zone shifts. Five subjects were deaf, and the initial idea that deaf persons might be less influenced by the scanner noise and thus be better able to fall asleep has proven wrong as all of our subjects reported raised sensitivity towards the gradient switches resulting in vibrations during data acquisition.

We included these subjects in the analysis since their sleep stage scores did not differ from the hearing subjects see Results.

In total, data from five subjects had to be discarded because they were not able to fall asleep in the uncomfortable and noisy laboratory environment two subjects , because of movement artefacts, because they immediately fell asleep and thus did not provide baseline data for the awake state or because of technical issues not related to the subject.

Data from 9 out of 14 subjects—four of them being deaf—were finally included in the analysis. Ages ranged from All subjects underwent a habituation fMRI session. Prior to the second session, which took place within a week, subjects underwent total sleep deprivation for 1 day, i.

Sleep deprivation was controlled using a wrist actigraph. MRI experiments started between 10 and 11 p. All subjects wore ear muffs as well as a headphone-like ear protection, and each subject's head was carefully immobilized with a vacuum cushion to minimize movement artefacts.

The scanning room was completely darkened during the experiment. The scans were repeated up to three times, if possible. The session was stopped when the volunteer was either completely awake or started to feel uncomfortable, with a typical session lasting between 1 and 3 h.

Imaging was performed on a 1. Repetition time TR was 10 s, flip angle 90° and echo time TE 60 ms. The volume acquired covered 20 slices.

The first 5 of the acquired images were excluded from further analysis to avoid non steady-state effects due to T1 saturation. Scanning time therefore was 32 min 30 s.

We had to choose a TR of 10 s because of technical constraints. Image processing was carried out using statistical parametric mapping SPM99 and statistical analysis with SPM2 were used Friston et al. After defining the anterior and posterior commissural line all volumes were realigned to the first volume.

Datasets with more than 2 mm motion in any direction were excluded from further analysis. The mean image built on the basis of all realigned volumes was spatially normalized into standard stereotactic space using an EPI template SPM99 standard template from the Montreal Neurological Institute.

We estimated the Talairach coordinates from the subsequently derived SPM maps with a non-linear transform of MNI to Talairach different linear transforms to different brain regions Brett et al. Next, we estimated global effects from the images using a voxel-level linear model of the global signal Macey et al.

Effects that match the global signal are removed from the voxel's time course based on the assumption that the global signal is replicated in the same pattern throughout the brain with varying magnitudes. The data were then smoothed using a full-width at half maximum isotropic Gaussian kernel of 8 mm.

Data analysis was performed by modelling the different conditions sleep stages as stimulus functions with the movement parameters as regressors of no interest within the context of the general linear model.

Applying a multi-level approach we accounted for intra-subject variance in a fixed effects analysis, and for between-subject variance in a random effects analysis subject by response interaction.

As we had nine subjects left for the random effects analysis we chose a less conservative threshold for an alpha level of uncorrected 0. An extent threshold of 25 voxels was chosen in order to not include clusters which are considerably smaller than the estimated resolution after the image post-processing steps.

Although the thresholded results for some contrasts i. Wakefulness versus Sleep, Wakefulness more than S1, Wakefulness more than S2, Awake more than SWS and Hypothalamic connectivity survived P values corrected for whole brain volume, we present all results equally thresholded to standardize comparisons.

From the resulting SPM maps of the condition effect, after smoothing the data with 4 mm full-width at half maximum to enhance sensitivity for subcortical structures, we chose a region of interest in the hypothalamus according to the definitions of the Talairach coordinate system 5 × 3 × 10 mm Lancaster et al.

Within these ROIs we extracted the averaged hypothalamic time series and modelled them as regressors of interest in a second SPM analysis to determine functional connectivity between the awake and sleeping conditions.

The localization of the results is presented according to the Talairach Daemon Lancaster et al. Polysomnographic recordings were performed using a MR-compatible EEG system Schwarzer, Munich, Germany according to the international ten-twenty electrode system with eight channels F3, F4, C3, C4, P3, P4, O1 and O2 versus common average reference , an electrooculogram, a chin electromyogram and a three-lead electrocardiogram.

The sampling rate was Hz, and band width was set to 0. All necessary precautions were taken to guarantee the safe recording of the electrophysiological signals during image acquisition, and careful electrode placement could sufficiently suppress cardioballistic artefacts in any of the recording channels.

EEG post-processing using a Fourier filtering algorithm Hoffmann et al. Briefly, the algorithm compares the power spectrum observed during MR imaging and the combined spectrum of three referential 10 s epochs representing artefact-free EEG of different vigilance states during the same session.

All frequency bands resolution 0. This correction results in sufficient suppression of scanner-induced artefacts with only a minor reduction of the original frequencies while phases are correctly retained.

Since the frequency components of the gradient-induced artefacts solely depend on the timing of the imaging experiment and because their amplitude is much larger than subject-specific influences, the correction algorithm removes identical frequency components in all subjects and conditions.

The complete recording including fMRI periods was then evaluated off-line Czisch et al. According to Rechtschaffen and Kales' sleep stage criteria all subjects reached sleep stage 2 S2 , seven subjects sleep stage 3 S3 and four subjects sleep stage 4 S4 within the scanning duration of 32 min 30 s.

Altogether the subjects spent min awake, Duration of NREM sleep stages of nine subjects in minutes of 32 min 30 s which was the duration of one scan. The values denote the duration when applying the Rechtschaffen and Kales criteria.

All fMRI results reported relate to random effects analysis. In several brain regions activity was reduced during all NREM sleep stages.

First, most of these deactivated areas are located in the frontal lobes [inferior frontal gyrus IFG , middle frontal gyrus MFG , superior frontal gyrus SFG , medial frontal gyrus MedFG , precentral gyrus and paracentral lobule] with a predominance in the right cerebrum the laterality index for the number of activated voxels is Secondly, regions of the limbic lobe such as the anterior cingulate cortex ACC and PCG were also less activated during NREM sleep.

Furthermore, the anterior nucleus of the thalamus and the body of the caudate nucleus showed reduced activity, again with a predominance in the right hemisphere. Thirdly, temporal [superior temporal gyrus STG ], parietal [post-central gyrus, inferior parietal lobule IPL ], occipital cuneus, precuneus as well as insular activation [restricted to the right anterior Brodmann area BA 13] diminished during all NREM sleep stages.

During S1 deactivations in thalamic and cingulate structures were most prominent. Specifically, we found mostly bilaterally less activity during S1 in limbic structures PCG, dorsal part of the cingulate gyrus, thalamus and caudate nucleus , the frontal lobes MedFG, MFG, precentral gyrus and SFG , the occipital lobes precuneus, cuneus and lingual gyrus and the insula, and less pronounced activity in the IPL and temporal lobes Table 2 and Fig.

Transverse slice view: The coloured areas represent BOLD-related activations and are superimposed onto a T1-weighted MRI of a male individual as supplied with SPM2. Blue denotes less activation during the sleep stages and red more activation during sleep stages. Location of activated voxels in stereotactic space MNI: x y z for the comparison of Awake versus Sleep stage 1 S1.

More precisely, deactivations were related to regions in the temporal lobes [STG, middle temporal gyrus MTG ] with a dominance of the right hemisphere, the right parietal lobe IPL , the limbic lobe cingulate gyrus, thalamus and hypothalamus , the frontal lobes MedFG, SFG and right IFG and the insula in the right hemisphere Table 3 and Fig.

Location of activated voxels in stereotactic space MNI: x y z for the comparison of Awake versus Sleep stage 2 S2.

During SWS deactivations were most prominent in frontal areas MFG, SFG, MedFG, IFG and precentral gyrus , the cingulate cortex, in several regions of the association cortices insular, temporal, parietal and occipital and in subcortical structures such as the thalamus and hypothalamus.

Compared with wakefulness we found small occipital precuneus and temporal regions to be more active but the number of activated voxels was considerably smaller than that of the opposite contrast Table 2 and Fig.

During S2 there was more activity only in a small region of left IFG Table 3 and Fig. SWS showed more activity in temporal, parahippocampal and cerebellar regions. More precisely, activations were related to the STG, the parahippocampal gyrus, the cerebellum declive, culmen , and the small regions of the IFG, MFG, inferior temporal gyrus and fusiform gyrus Table 4 and Fig.

Remarkably, there were considerably less voxels more active during sleep, e. S1 showed more BOLD-related activity than S2 in the middle and MedFG, supramarginal gyrus, superior temporal gyri, cingulate cortex mainly median , supplementary motor area and paracentral lobule.

In contrast, S2 showed more activity in the cerebellum, the parahippocampal gyrus and the hippocampus. Compared with SWS S1 showed more activation in the anterior cingulate gyrus. Similar to S2, SWS showed more activation compared with S1 in the hippocampus, the parahippocampal gyrus and the cerebellum.

S2 showed more activity in the anterior cingulate gyrus when compared with SWS. SWS compared with S2 showed more activity in the middle occipital gyrus, cerebellum, parahippocampal gyrus, hippocampus, pre- and post-central gyrus, inferior temporal gyrus, angular gyrus, precuneus and right insula.

The hypothalamic region was less active throughout all NREM sleep stages including S1 as compared with wakefulness. Calculating the functional connectivity of this region to any other brain area revealed several regions for the first sleep cycle periods: cingulate gyrus anterior, median, posterior , caudate nucleus, frontal gyri middle, inferior, superior, precentral , hippocampus, parahippocampal gyrus, thalamus pulvinar , IPL, angular gyrus, temporal gyri middle, inferior and brainstem pons, midbrain.

In contrast, hypothalamic activity of only the awake period close to sleep onset revealed no connected regions Table 5 and Fig. First row: Contrasting the BOLD response of resting awake with NREM sleep S1 to SWS with less smoothed data 4 mm FWHM to enhance sensitivity for subcortical regions.

We extracted the averaged hypothalamic time series arrow and modelled them as regressors of interest in a second SPM analysis to determine functional connectivity between the awake and sleeping conditions. The grey coloured regions Table 5 within the glass brains denote functional connectivity of hypothalamic activity during the resting awake state shortly before sleep onset and during NREM sleep when hypothalamic activity was decreased compared with the awake state.

Location of activated voxels in stereotactic space MNI: x y z for hypothalamic connectivity. We simultaneously measured spontaneous EEG and fMRI during the night's first sleep cycles and report here, for the first time, NREM sleep stage and regional specific alterations of the BOLD response.

When comparing light sleep S1 and S2 with wakefulness brain activity was considerably decreased in all lobes of the cerebrum, the cingulate cortex, the insula and the thalamus, whereas it was further reduced during SWS in the ACC, left insula and hippocampal regions.

Our results are in overall agreement with earlier data from other laboratories employing different methodologies: these studies used PET to relate measurements of either cerebral blood flow or cerebral glucose utilization with light sleep Kajimura et al.

All studies found less blood flow or metabolism in the thalamus and in the cortex with varying locations during sleep, but the different intrinsic temporal resolution and baseline references hamper an easy comparison. To sum up our sleep stage specific findings we propose a topography of reduced activity from S1 to SWS Fig.

In comparison with S2 and SWS there is less activation related to S1 within the PCG BA 23 , the cuneus and precuneus, and the thalamic nuclei. Brain regions deactivated during S2 are the MedFG BA 6 , the right ventral part of the IPL BA 40 , the STG, the right insula BA 13 and the right IFG BA In addition, there are several regions less activated during SWS: the frontal gyri BA 6, 8, 9 and 45 , the precentral gyrus, bilaterally the dorsal IPL BA 40 , the cingulate gyri BA 23, 24, 32 , the left insula BA 13 , the caudate body, the hippocampus and the parahippocampal gyri BA Following these results we suggest a network of cortical brain regions relevant for falling asleep.

When falling asleep S1 occipital, cingulate, posterior cingulate, thalamic and hypothalamic regions reduce their activity. Subsequently, when sleep gets synchronized S2 most of these regions remain less activated with the exception of the thalamic nuclei and occipital regions.

But in addition the activity of the right insula anterior and posterior as well as frontal, temporal and parietal regions is reduced. Finally, when reaching SWS the activity of the ACC, left insula and hippocampal regions is decreased. A schematic illustration of brain regions with decreased activity during different stages of NREM sleep.

The model is a summary derived from our results and we expect that the precise anatomical localizations may vary depending on pre-sleep activity. Regions of the cingulate cortex and hypothalamic regions alter their activity in all NREM sleep stages box with dashed line. As sleep deepens different brain regions get involved indicating that NREM sleep is associated with dynamic regional brain processes with sleep stage specific activation and deactivation patterns as indicated.

It has been proposed that at any time some neuronal groups are in a so-called disjunctive state, i. in a sleeping-like state due to a temporary disjunction at the local neuronal level between input and output of a neuronal group Krueger et al.

If a sufficient number of neuronal groups are in the disjunctive state, the discontinuation of the perception of wakefulness occurs. As this is usually linked to S2 we suggest that the network for a disjunctive state might comprise thalamic and hypothalamic regions, the cingulate cortex, the right insula, the IPL, STG, and IFG as well as MFG.

Additionally, our model is contradictory to that of Kajimura et al. They suggested three groups of brain structures each representing one type of deactivation during the progression of NREM sleep. Group one comprises brain regions deactivated during sleep irrespective of sleep stages cerebellum, putamen, ACC, IFG, MFG and IPL.

Group two and three refer to brain structures specific for light sleep pons, thalamus and deep sleep midbrain, caudate nucleus, vermis. Our observations differ concerning the role of the cerebellum, the insula, the thalamic regions and the ACC Fig.

The differences between PET and fMRI results can be explained by higher time resolution of fMRI, reflecting more dynamic changes. Thalamic neurons change their firing mode from tonic to phasic, and as sleep deepens dorsal thalamic neurons get more hyperpolarized because of prolonged burst firing patterns of GABAergic reticular neurons Steriade, b.

The resulting rhythmic activity then spreads into the cortex Steriade, , where additional inherent slow rhythmic activity is generated during NREM sleep. We noticed no significant differential decrease in thalamic activation with increasing sleep depth.

On the other hand, cortical deactivation increased upon sleep deepening. This highlights the autonomous contribution of purely cortical-generated delta waves within the cortex, leading to a BOLD signal decrease due to neuronal hyperpolarization, as discussed in detail previously Czisch et al.

There is some debate about whether the prefrontal cortex is of particular importance in sleep Muzur et al. Our results show a predominantly fronto-central down-regulation of neuronal activity, but association cortices in the brain also reduce their activity incrementally from wakefulness to S2, which is further enhanced for most of them in SWS Fig.

Several studies compared SWS with an alert awake state using either [ 18 F]fluorodeoxyglucose-PET Buchsbaum et al. During SWS we noted a decrease in activity in several cortical areas such as regions of the frontal cortex, the IPL, the cingulate gyrus, the right STG, the precuneus, the cuneus as well as regions of the basal ganglia.

During SWS oscillations the cortex shows periodical and rich spontaneous activity which presumably represents the preserved capacity to process internally generated signals Steriade, a. Therefore, although large areas of the cerebral cortex show less BOLD activity during sleep, one may not conclude that the processing of information is fully cut-off, as the BOLD contrast between wakefulness and sleep does not allow for an absolute measure of neuronal activity.

In agreement with findings concerning insular activity Critchley et al. The activity of the posterior cingulate gyrus was also reduced during all sleep stages again more pronounced during S1 , as most parts of the cingulate cortex were.

In the hypotheses of a default mode of brain function Raichle et al. resting state, of brain function with greater activity during resting states than during cognitive tasks.

The activation of the areas is explained by continuously higher levels of alertness in expectation of environmental stimuli that are only reduced when focusing attention within the context of a specific task. We demonstrate a general reduction in activity reflecting fading alertness, with the PCC already dampened in S1, while ventral anterior cingulate cortex activity was only reduced in SWS, revealing a sleep stage specific grading in loss of alertness.

Finally, we observed brain regions albeit small clusters that showed increased BOLD-related activity during SWS when compared with wakefulness, mainly in the IFG, temporal areas, the parahippocampal gyrus and the cerebellum.

It should be considered that the pattern of neuronal activation and deactivation during NREM sleep is influenced by pre-sleep activity patterns of the brain, thereby adding between-subject variance in our study. Therefore, one should be cautious about proposing an active role of the observed small brain regions with increased activity during sleep.

Nevertheless, in a so-called transfer model it is assumed that memory consolidation depends on hippocampally initiated reinstantiation during SWS of distributed cortical activity patterns. These patterns characterize previous active behavioural states Kali and Dayan, Whether the parahippocampal activation in our study during SWS is related to memory consolidation processes or not has to be addressed in future studies with experimentally controlled pre-sleep learning periods.

The hypothalamus is of central importance for sleep regulation involved in a reciprocal network of wake-promoting nuclei in the brainstem as well as the lateral hypothalamus itself and sleep-promoting neurons inside the hypothalamic VLPO. Neuronal firing has been shown to be reduced during NREM sleep in the tuberomammillary nucleus, locus coeruleus, pons and dorsal raphe as a consequence of the increased activity of the inhibitory VLPO and the innervation of the activating lateral hypothalamus Saper et al.

In our study, due to limited spatial resolution of the data, we cannot differentiate hypothalamic subregions, or other structures like areas in the midbrain or pons important for sleep—wake regulation, serving these functions.

Although we measured a relatively diffuse hypothalamic region we were able to identify a temporally correlated network throughout the brain Table 5 and Fig. We observed a decreased activity of hypothalamic regions throughout the NREM sleep stages in accordance with a dominant contribution to the BOLD signal arising from the reduced activity of wake-promoting neurons.

It was only during NREM sleep stages, rather than wakefulness, that some pronounced synchronous BOLD activity changes that correlated with the hypothalamus occurred: limbic structures, regions of the frontal and parietal cortex, the basal forebrain and the brainstem showed a similar time course of activation as hypothalamic regions did.

This pattern of connectivity resembles the pathway of the ascending arousal system which sends projections from the brainstem and posterior hypothalamus throughout the forebrain Saper et al.

Moderate-to-high doses of caffeine administrated 1 h before and during exercise have been known to increase endurance athletic performance. In contrast, recent evidence has shown an ergogenic effect of low and extremely low doses of caffeine taken late during a period of prolonged exercise Hogervorst et al.

Furthermore, low doses of caffeine do not affect peripheral whole-body responses to exercise and are associated with few, if any, side effects; Spriet suggested that low doses of caffeine ingestion improve exercise performance In this study, we observed that ingestion of low-dose caffeine had greater effect on cognition and brain activation than had moderate and high doses, which means that low doses of caffeine have greater effect on stimulating the CNS.

The present study maintained a few limitations. We used G-power to estimate the sample size, and the numbers of subjects in this study met the minimum sample size requirements. However, more samples are needed in the future research so that the research results can be further verified and repeated.

In the double-blind designed study, it is best to ask subjects which dose they think they ingested in each trail after completion of all groups and to outline why they identified which trial as which. However, in the present study, we did not note the responses of the subjects, so we could not assess the efficacy of blinding.

Although four conditions in the present study are difficultly for participants to identify, we should value the assessment of blinding in future studies.

Moreover, only Stroop task was used to measure executive function. There are other cognitive tasks on executive function, such as n-back and switching task. Therefore, more tasks are need to measure to ensure effects of various doses of caffeine ingestion on executive function in the future.

These results demonstrate that ingestion of low-dose caffeine has greater effects on cognition and brain activation than moderate and high doses of caffeine, suggesting that low-dose caffeine may be a selective supplement in enhancing executive function and prefrontal activities.

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation, to any qualified researcher. The study followed the ethical guidelines of the Declaration of Helsinki and was approved by the local Ethics Committee at the Shanghai University in Sport, Shanghai, China No.

XZ and YD conceived and supervised the study and designed the experiments. BZ and YL carried out the experiments. YL and XW analyzed the data. BZ wrote the manuscript. All authors contributed to the article and approved the submitted version. This work was supported by the National Natural Science Foundation of China The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Niioka, T. It can enhance alertness and attention; however, effects are short-lived, and tolerance builds up quickly. NICOTINE: Also a stimulant, used for hundreds of years for a range of medicinal purposes. It is very addictive and has many dangerous side effects. Benzedrine was the first drug to treat hyperactivity in children.

Amphetamine can enhance attention and memory by increasing levels of norepinephrine and dopamine in the brain, but the compound can be addictive and comes with a range of side effects, including hyperactivity, loss of appetite, disturbed sleep, even psychosis.

It became popular for ADHD in the s. As with amphetamine, it can improve memory and focus for those with ADHD, but it is also used off-label as a study and work aid. Some individuals build up a tolerance to Ritalin over time.

It has been shown in some studies to enhance memory and attention in healthy individuals. MODAFINIL: Originally used to treat narcolepsy. It can also enhance cognitive function, especially when completing difficult tasks. Experts are not quite sure how it works or what long-term effects would look like.

March 1, 4 min read. Credit: Andrew Bret Wallis Getty Images. But the use of scarf is always there, as well as the other blue things that you identified, but what the RAS does is then bring to light that we're looking for. So as leaders, we can talk about the word "hope" for example.

We "hope" this will get done. But when you use the word "hope" there's a lot of doubt floating around in there. You could change that to "want" - I "want" this to be done, I "want" to complete this, but again, the door is open for that doubt.

You know, will you or will you not get there. But when you use the term consciously - use the term "intend" - what it does is that it takes away the doubt and it kind of kicks that RAS into high gear.

It helps you to start filtering out those pieces that can help you with that intention and so the conscious mind brings that into your subconscious mind and so you start to see and hear and understand the things that will help you get to what you're intending.