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What does daytime actigraphy reveal about an active and awake brain?

What does daytime actigraphy reveal about an active and awake brain?


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I have interest in the study of human motion ( Actigraphy), and have built a couple of smartphone apps using its principles. The apps look at gross motor activity of an individual. Up until now, most of my reading into the field has been focused on the study of sleep. With Actigraphy, there are clear ~90 minute activity patterns visible over the course of the night that roughly correlate with the 4 stages of sleep. There are algorithms by Cole and Sadeh which describe how to score sleep using activity counts.

  • Are there any uses of actigraphy for awake patients?
  • Are there any investigations of the correlation between the overall activity pattern/level and the brain wave frequency (alpha/beta/… )?
  • Are there any wake-related actigraphy algorithms?

Update thank you for answers and comments! Let me clarify the question. I'm interested in comparing a rather long history of events for the same individual wearing an actigraph.

For example we have a person who's both an athlete and also does meditation. I'm interested in using actigraphy to determine the length of the meditation sessions, along with any increase/decrease of overall activity pattern that may accompany meditation activity (ex: deeper concentration moves the brain to a different brainwave and relaxes the body, or nervousness and fidgeting rising, the person cannot get to the correct brainwave and cuts the session short).

Lets say the same person is a runner. Wearing an actigraph during sessions displays two important characteristics: When the person is and is not running. (going out to run is an action that requires conscious action. How long the person is running. If a person is feeling "negative", the person may not go out to run). If a person does go out to run, the running session may be cut short if a person is not in the right state of mind.

Combined with self-reported events (ex: running session started, meditation session started), such actigraphy data may be plotted day by day. I'm interested if there have been studies of such data.


It's obvious that people will move more when awake or doing exercise compared to being asleep or resting, however actigraphy provides a quantitative way to measure that. Therefore actigraphy is useful for studying sleep-wake cycles, activity-rest cycles and circadian rhythms. They have been shown to be reliable in determining when a subject is awake or asleep, so that's why they are generally used for that. Although there still needs to be more standardization and consistency of analysis and reporting in scientific literature to make comparison between studies more useful (Berger et al., 2008).

In order for actigraphy to be useful for understanding brain activity while awake, there needs to be a way to monitor brain activity. When exercise is involved methods such as fMRI, PET, TMS are not suitable since subjects need to stay still. EEG and EMG are really the only options. Winkler et al., 2003 has correlated short-term actigraphy with awake EEG patterns, and results suggest that they can be used to indicate CNS arousal. It probably doesn't affect the results, but you should note that the study was with psychiatric patients who were taking medication. It would have been more thorough to do this with normal population as a control to ensure generalizability. The software/algorithm they used was "Actiwatch Sleep Analysis 2001" (Cambridge Neurotechnology). Maybe you can look that up to find out more, although I'm not sure the algorithm would be published.

References

  • Berger, A.M., Wielgus, K.K., Young-McCaughan, S., Fischer, P., Farr, L. & Lee, K.A. (2008). Methodological challenges when using actigraphy in research. Journal of pain and symptom management, 36, 191-199.
  • Winkler, D., Pjrek, E., Pezawas, L., Presslich, O., Tauscher, J. & Kasper, S. (2003). Relationship between power spectra of the awake EEG and psychomotor activity patterns measured by short-term actigraphy. Neuropsychobiology, 48, 176-181.

Using activity monitors on awake patients is very domain specific. You need to equate them on some activity parameters and then look at how the individuals vary across some domain. For example, you could monitor athletes and correlate the amount of physical activity with performance. Or, you could monitor grade school children and look at something like childhood obesity.

So no, some general classifications based on Actigraph patterns are not done on awake individuals. Daily activities vary greatly across awake individuals while sleep has very defined patterns of behaviour.


Chapter 6 Consciousness (Fiest) questions

A. When the various sensory elements get integrated.
B.
When the mind is awake but not very aware.

B. driving.
C.
extremely drunk.

B.
When the body only responds to stimuli causing shock

A.
Intentionally repressed material that takes the form of unconscious

B.
Potentially accessible material currently unavailable to awareness

C.
Repressed unconscious material that cannot be consciously recollected

B.
The conscious experience of knowing something that can be brought into awareness

C.
The conscious experience of knowing something that cannot be brought into awareness

B.
The person cannot be in a minimally conscious state for prolonged periods of time.

C.
The person can experience vacillating consciousness.

B.
Individuals who are mindful are not aware of their own feelings in response.

A.
Consciousness focuses our attention on changes in stimulation.

B.
It is possible for us to be aware of all material at all times.

C.
All of us can do more than one thing at a time without compromising our performance on either task.

B.
The right-left orientation test

C.
The dichotic listening test

C.
The cocktail party effect

A.
Audience's sustained attention

B.
Audience's divided attention

C.
Audience's short attention span

A.
Inattentional blindness

B.
The cognitive load theory

C.
The global workspace model

B. Baddeley's theory
C.
The cognitive load theory

B.
When neurons from many distinct brain regions work together

C.
When neurons from many distinct brain regions work independently

B.
The ability to direct one's sense organs to form a complete perspective

C.
The ability to respond simultaneously to multiple task demands

A.
The ability to individually respond to specific auditory, visual, or tactile stimuli

B.
The ability to consistently maintain a behavioral response for continuous and repetitive activity

C.
The capacity to maintain quick and enhanced behavioral responses to involuntary stimuli

B.
Respond discretely to specific visual, auditory, or tactile stimuli

C.
Maintain attentional focus for an extended period of time

A.
less sustained attention.

B.
more sustained attention.

C.
less selective attention.

A.
There is absolutely no lost time, unlike while switching between complex tasks.

B.
The amount of lost time is high.

C.
There is an equal amount of lost time just as in switching between complex tasks.

B.
Increase in parietal lobe activity

C.
Decrease in the parietal lobe activity

A.
Chatting with someone in the vehicle

B.
Using the phone with a hands-free device

A.
52% of the population can switch from tasks without any performance decrements.

B.
67% of the cell phone users keep their cell phones next to their beds.

C.
4.4% of the population checks their phones even when they are not ringing.

C.
The administration of a psychoactive drug

B.
Elimination of obsolete neurons

C.
Increase in the activity of the parietal lobe

B.
Attention to the details of momentary experience

C.
Diverting selective attention toward thoughts

B. minimally conscious.
C.
low on self-consciousness.

A.
an increased EEG activity in the left frontal cortex.

B.
an increased EEG activity in the right frontal cortex.

C.
an increased EEG activity in the occipital lobe.

A.
Those who had meditated the shortest showed the greatest cortical thickness in certain areas.

B.
Those who had meditated the longest showed the least cortical thickness in certain areas.

C.
Those who had meditated the longest showed the greatest cortical thickness in certain areas.

A.
decrease in growth of brain tissues associated with emotional processing.

B.
increase in growth of brain tissues associated with spatial visualization.

C.
decrease in growth of brain tissues associated with spatial visualization.

B.
Variations in physiological processes that cycle longer than 48 hours

C.
Variations in physiological processes that cycle within approximately a 24-hour period


Human Brain Still Awake, Even During Deep Sleep

Sleep in humans is divided in two main phases: non-REM sleep, which occupies most of our early sleep night, and REM sleep, during which our dreams prevail. Non-REM sleep is usually considered as a compensatory &lsquoresting&rsquo state for the brain, following the intense waking brain activity. Indeed, previous brain imaging studies showed that the brain was less active during periods of non-REM sleep as compared to periods of wakefulness.

Although not rejecting this concept, researchers from the Cyclotron Research Centre of the University of Liège in Belgium and from the Department of Neurology of Liege University Hospital demonstrate that, even during its deepest stages (also called &lsquoslow-wave-sleep&rsquo), non-REM sleep should not be viewed as a stage of constant and continuous brain activity decrease, but is also characterized by transient and recurrent activity increases in specific brain areas.

In a study published recently in the American journal Proceedings of the National Academy of Sciences, the research team led by Dr Thanh Dang-Vu and Pr Pierre Maquet shows that brain activity during these sleep stages is actually profoundly influenced by spontaneous slow rhythms (also called &lsquoslow oscillations&rsquo) which organize neuronal functioning during non-REM sleep.

By using functional magnetic resonance imaging (fMRI) combined with electroencephalography (EEG), researchers have evidenced that these slow oscillations are associated with brain activity increases during non-REM sleep (see image, side panels), therefore discarding the concept of brain &lsquoquiescence&rsquo that prevailed for a long time in the characterization of non-REM sleep. Besides, these activity increases are located in specific brain areas, including the inferior frontal gyrus, the parahippocampal gyrus, the precuneus and the posterior cingulate cortex, as well as the brainstem and cerebellum (see image, central panels).

These results improve our understanding of non-REM sleep mechanisms. On the one hand, they demonstrate that non-REM sleep is a stage of brain activation organized by the slow oscillations. On the other hand, they allow the identification of brain areas potentially involved in the generation of these oscillations, which are a hallmark of brain functioning during non-REM sleep. Moreover, by showing the activation of areas involved in the processing of memory traces such as para-hippocampal areas, the study might point to the potential functions of sleep, in particular the increasingly well-defined role of sleep in memory consolidation. Finally, the activation of areas such as the brainstem, usually associated with arousal and waking, might reveal these oscillations of non-REM sleep as &lsquomicro-wake&rsquo periods allowing the brain to fulfil crucial and active functions, even during the deepest stages of sleep.

This research was supported by the National Fund for Scientific Research (Belgium), the University of Liège and the Queen Elisabeth Medical Foundation.

Story Source:

Materials provided by University of Liège. Note: Content may be edited for style and length.


Games and Apps to Keep Your Brain Active and Sharp during the Pandemic

In the hour of pandemic and during a stay at home, it could be hard for anyone to spend time. With monotonous types of activities day to day, one is stuck into boredom. Thinking the way out is impossible now because of a long stay at home.

During stressful times, we all found a solution to keep ourselves engaged and active. Those who were fond of reading books ordered series of new books available on Amazon. While others having an interest in painting and artwork locked themselves up to create beautiful pieces including sculptures and handmade items.

However, most people are not interested in anything else other than hooking themselves to television or their mobile screen. When the lockdown was imposed in most of the states, most of the people headed towards cable providers to get a new internet and cable connection. From streaming favorite services like Netflix and watching channels like FXX and CNN, Optimum, Suddenlink, and Spectrum packages were quite helpful in keeping people busy without losing their savings.

You might have watched all the seasons on Netflix, played games with your friends, and browsed how-to websites. It is time to give your brain some activity so that when things go normal, you might not find any trouble activating it. Today, we are going to reveal some of the best apps and games to keep your mind sharp and active through the pandemic. Stick with us and let’s get started.


Brain may flush out toxins during sleep

NIH-funded study suggests sleep clears brain of damaging molecules associated with neurodegeneration.

Scientists watched dye flow through the brain of a sleeping mouse. Nedergaard Lab, University of Rochester Medical Center.

A good night’s rest may literally clear the mind. Using mice, researchers showed for the first time that the space between brain cells may increase during sleep, allowing the brain to flush out toxins that build up during waking hours. These results suggest a new role for sleep in health and disease. The study was funded by the National Institute of Neurological Disorders and Stroke (NINDS), part of the NIH.

“Sleep changes the cellular structure of the brain. It appears to be a completely different state,” said Maiken Nedergaard, M.D., D.M.Sc., co-director of the Center for Translational Neuromedicine at the University of Rochester Medical Center in New York, and a leader of the study.

For centuries, scientists and philosophers have wondered why people sleep and how it affects the brain. Only recently have scientists shown that sleep is important for storing memories. In this study, Dr. Nedergaard and her colleagues unexpectedly found that sleep may be also be the period when the brain cleanses itself of toxic molecules.

Their results, published in Science, show that during sleep a plumbing system called the glymphatic system may open, letting fluid flow rapidly through the brain. Dr. Nedergaard’s lab recently discovered the glymphatic system helps control the flow of cerebrospinal fluid (CSF), a clear liquid surrounding the brain and spinal cord.

“It’s as if Dr. Nedergaard and her colleagues have uncovered a network of hidden caves and these exciting results highlight the potential importance of the network in normal brain function,” said Roderick Corriveau, Ph.D., a program director at NINDS.

Initially the researchers studied the system by injecting dye into the CSF of mice and watching it flow through their brains while simultaneously monitoring electrical brain activity. The dye flowed rapidly when the mice were unconscious, either asleep or anesthetized. In contrast, the dye barely flowed when the same mice were awake.

“We were surprised by how little flow there was into the brain when the mice were awake,” said Dr. Nedergaard. “It suggested that the space between brain cells changed greatly between conscious and unconscious states.”

To test this idea, the researchers inserted electrodes into the brain to directly measure the space between brain cells. They found that the space inside the brains increased by 60 percent when the mice were asleep or anesthetized.

“These are some dramatic changes in extracellular space,” said Charles Nicholson, Ph.D., a professor at New York University’s Langone Medical Center and an expert in measuring the dynamics of brain fluid flow and how it influences nerve cell communication.

Certain brain cells, called glia, control flow through the glymphatic system by shrinking or swelling. Noradrenaline is an arousing hormone that is also known to control cell volume. Similar to using anesthesia, treating awake mice with drugs that block noradrenaline induced unconsciousness and increased brain fluid flow and the space between cells, further supporting the link between the glymphatic system and consciousness.

Previous studies suggest that toxic molecules involved in neurodegenerative disorders accumulate in the space between brain cells. In this study, the researchers tested whether the glymphatic system controls this by injecting mice with labeled beta-amyloid, a protein associated with Alzheimer’s disease, and measuring how long it lasted in their brains when they were asleep or awake. Beta-amyloid disappeared faster in mice brains when the mice were asleep, suggesting sleep normally clears toxic molecules from the brain.

“These results may have broad implications for multiple neurological disorders,” said Jim Koenig, Ph.D., a program director at NINDS. “This means the cells regulating the glymphatic system may be new targets for treating a range of disorders.”

The results may also highlight the importance of sleep.

“We need sleep. It cleans up the brain,” said Dr. Nedergaard.

This work was supported by grants from the NINDS (NS078167, NS07830, NS028642).

For more information about neurological disorders and the latest neuroscience research: http://www.ninds.nih.gov

NINDS is the nation’s leading funder of research on the brain and nervous system. The NINDS mission is to reduce the burden of neurological disease – a burden borne by every age group, by every segment of society, by people all over the world.

About the National Institutes of Health (NIH): NIH, the nation's medical research agency, includes 27 Institutes and Centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and is investigating the causes, treatments, and cures for both common and rare diseases. For more information about NIH and its programs, visit www.nih.gov.

NIH&hellipTurning Discovery Into Health ®

Reference

Xie et al “Sleep initiated fluid flux drives metabolite clearance from the adult brain.” Science, October 18, 2013. DOI: 10.1126/science.1241224


Tips for Getting a Good Night's Sleep

Getting enough sleep is good for your health. Here are a few tips to improve your sleep:

Set a schedule &ndash go to bed and wake up at the same time each day.

Exercise 20 to 30 minutes a day but no later than a few hours before going to bed.

Avoid caffeine and nicotine late in the day and alcoholic drinks before bed.

Relax before bed &ndash try a warm bath, reading, or another relaxing routine.

Create a room for sleep &ndash avoid bright lights and loud sounds, keep the room at a comfortable temperature, and don&rsquot watch TV or have a computer in your bedroom.

Don&rsquot lie in bed awake. If you can&rsquot get to sleep, do something else, like reading or listening to music, until you feel tired.

See a doctor if you have a problem sleeping or if you feel unusually tired during the day. Most sleep disorders can be treated effectively.


A new paper suggest travel faster than the speed of light might be possible given the creation of a new way of looking at propelling a vehicle.

When considering the advancement of life changing technology, does our current economic model speed up or suppress the collaboration, creation and advancement of ideas?

Take a moment and breathe. Place your hand over your chest area, near your heart. Breathe slowly into the area for about a minute, focusing on a sense of ease entering your mind and body. Click here to learn why we suggest this.

In Star Trek Gene Roddenberry imagined it possible to set a ship into ‘warp drive’ and travel at speeds 6000+ times the speed of light, moving from one galaxy to a distant one very quickly. Imagine having that type of technology here on earth?! It has been said before that if we can think it, we can create it. Well, maybe that’s sometimes true.

What Happened:

The question of whether travel faster than the speed of light is possible was again approached in a new research paper written by an American physicist Erik Lentz. In the paper Lentz proposed a new theory for how faster-than-light travel could be possible. Given their models, Lentz and his team feel that travel to distant stars and planets could be possible in the near future, perhaps with proper research and development they could have something working in as little as 10 years.

The question of whether this is possible does not challenge our current understanding of physics that Albert Einstein’s theory of relativity sets forth that it is not possible to travel faster than light.

Instead of focusing on our current understanding of matter, Lentz’s new paper puts greater importance on a possible engineering solution as opposed to the theoretical physics. The new paper was published in Classical and Quantum Gravity.

The paper proposes a plan to travel faster than light by creating a series of ‘solitons’ to provide the basis for propulsion. A soliton is a compact wave that keeps its speed and shape while moving with little loss of energy.

Interestingly, this technology would allow travel at ANY speed. This brings me back to an article I wrote yesterday discussing the incoming reality within collective consciousness that UFOs and Extraterrestrials are real. In that article I state that the question of ‘how are they getting here’ is of importance as it could give humanity access to technology that would completely change the way we live on this planet.

[The method] “uses the very structure of space and time arranged in a soliton to provide a solution to faster-than-light travel,” From the press release.

Imagine this, the nearest star beyond our solar system is called Proxima Centauri. We know it to be about 4.25 light years away. (A light year is the distance it takes light to travel in one year.)

Lentz stated that using our current rocket fuel methods fo travel, it would take about 50,000 to 70,000 years to reach Proxima Centauri. If we were to upgrade to nuclear propulsion technology, it would take about 100 years. But if we employed a light speed warp drive, it would take only four years and three months.

This would mean that the average person would be able to travel to distant interstellar planets and complete the trip in a current human lifetime. Think of the vacations!

According to Lentz there are some barriers to making this all work, but they aren’t impossible to surpass. For the tech to work, it would require lowering the energy needed down to the level of modern nuclear power reactors. That is if we don’t take into consideration energy technologies that are currently suppressed. Lentz also stated that what would be needed is a way to develop and speed up the solitons (waves.)

“This work has moved the problem of faster-than-light travel one step away from theoretical research in fundamental physics and closer to engineering,”

Why Its Matters:

Humans are curious beings who seem to gain a great deal from expanding our curiosity beyond everyday plights of a system and way of life that doesn’t necessarily inspire the deepest use of our creativity. Perhaps a knowing that we can indeed go elsewhere without primitive technology would shift the way we see our role on this earth and how we choose to fight over what we believe are limited resources.

Then again, perhaps if humans carry their current story of separation and competition to other worlds, we’ll produce the same mess there. I guess the question is, would the possibility of being able to leave this earth and go almost anywhere change the underlying nature of how we choose to set up our cultural beliefs and narratives of what it means to be human?

It’s my feeling that humanity does not lack the solutions to live in a thriving world, we lack the worldview and state of being. Both of which we could change with a little effort.

The Takeaway:

When I hear research like this I am fascinated. Then again I also sometimes wonder if all scientists around the world saw the technology I have seen first hand, that completely changes the way we perceive energy generation today, would the way we look at creating technology that requires energy change entirely? Yes, of course it would.

In my mind and heart I see a world of true collaboration and curiosity. One where we aren’t competing to see who’s the greatest scientist with the best copy written tech, but a world where we transparently share what is out there to advance the entire human race. No powerful interests suppressing technology because it’s too threatening to an economy, but instead true open advancement where we can solve problems incredibly fast.

Can you imagine this world?

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Early birds vs. night owls: How one has an advantage at work, according to science

You probably know whether you're a morning or night person. Now science has some good news for the early risers: Apparently the early bird really does get the worm.

There are fundamental differences in brain function between night owls and early birds, and night owls may have impaired function during regular work-day hours, according to a new study published Thursday in the academic journal "Sleep."

Researchers at the University of Birmingham looked at the brain function (among other things) of 38 people who were categorized as either night owls, who had an average bedtime of 2:30 a.m. and a wake-up time of 10:00 a.m., or morning larks, who had average bedtime of 11 p.m. and wake time of 6:30 a.m.

Participants underwent MRI scans, were asked to complete a series of tasks and participated in testing sessions at different times during the day between 8 a.m. and 8 p.m., while also being asked to report their level of sleepiness.

Overall, researchers found that night owls had lower resting brain connectivity in ways that are associated with poorer attention, slower reactions and increased sleepiness throughout the hours of a typical work day.

Meanwhile, brain connectivity in the regions of the brain that can predict better performance and lower sleepiness were significantly higher in larks at all times, "suggesting that the resting state brain connectivity of night owls is impaired throughout the whole day." (The "resting state" of the brain, Live Science notes, means not doing a particular task and letting the mind wander.)

"A huge number of people struggle to deliver their best performance during work or school hours they are not naturally suited to," says the study's lead researcher, Dr. Elise Facer-Childs, of the University of Birmingham's Centre for Human Brain Health. "There is a critical need to increase our understanding of these issues in order to minimize health risks in society, as well as maximize productivity."

And whether you're a morning or night person might be dictated by your genes. A separate study published in January looked at the genomes of almost 700,000 people, using data from 23andMe and the U.K. Biobank. It found that there are hundreds of genes that are associated with whether you are a night owl or an early bird. Regions of the genome that the study found to be relevant to whether you're a morning or night person included genes involved in metabolism, the biological clock and genes that function in the retina.

"All times of day are not created equal," Pink previously told CNBC Make It. "Our performance varies considerably over the course of the day, and what task to do at a certain time really depends on the nature of the task. If we look at the evidence, we can be doing the right work, at the right time."

According to Pink, for larks, the morning is the best time to do analytical work that requires focus, and more administrative or routine work should be done later in the day. The reverse is true for night owls.


Milk chocolate vs. Dark chocolate for Health Benefits

The key to benefiting from the brain and mood-boosting effects of chocolate is to choose a good dark chocolate or cocoa powder and to consume moderate amounts regularly. Different cocoa products and chocolates can have greatly differing amounts of flavanols and methylxanthines depending on the manufacturing process and cocoa content.

Dark chocolate with a high percentage of cocoa—70 percent or more—has a lot of these beneficial phytochemicals. On the other hand, chocolate that contains little cocoa has much less therapeutic value. You’ll learn more about exactly how to choose the right chocolate and how much to consume in Surprising Cocoa Benefits Include Heart Health and Prediabetes Improvement, along with how chocolate benefits many aspects of heart and blood vessel health.


Methods

Participants and recruitment

Participants from the campus of Leiden University were recruited by using advertisements on a closed online platform and department-wide emails. Candidates with known neurological and psychiatric diagnosis based on self-reports were excluded from recruitment. Due to technical limitations, those users with an Android operating smartphone were invited to participate and under the condition that the phone remains strictly un-shared during the study period. A total of 88 right-handed participants were recruited (44 females, 16–45 years of age, mean age 23). The experimental procedures used here were approved by the Ethical Committee at the Institute of Psychology at Leiden University. All the participants provided written and informed consent and were compensated for their time using a cash reward or course credits. The weight with one layer of clothing, height and the year/month of birth was collected from each participant. As a part of a larger study, the volunteers also consecutively participated in a range of laboratory assessments beyond the scope of this report.

Actigraphy measurement

The gross movements (three-axis accelerometer), the ambient light and near-body temperature were measured using GENEACTIV watches (Activinsights, Cambridgeshire, UK). Participants were instructed to wear the watch on both wrists for a minimum of 2 weeks, and the data from the left wrist are primarily presented here. Four participants were unable to follow the instruction due to reported discomfort and were eliminated from the study. Participants who only intermittently removed the watches during part of the observation period were not eliminated, as these periods could be accounted for using the near-body temperature measurements. The watches were set to acquire the data at 50 Hz and the data were recovered after 14 days of acquisition or earlier, only to be reset for continued use if the subjects were willing to participate for an additional week.

Tappigraphy measurement and on-phone sleep diary

The touchscreen interactions were quantified using the TapCounter App (QuantActions Ltd. Lausanne, Switzerland). 17 The App was installed by each user from the Google Playstore (Google, Mountain View, USA). The App is designed to gather the precise timestamps of all touchscreen interactions and operates in the background. Only those touchscreen interactions which occurred during the ‘unlocked’ state of the screen were considered here. Each user was provided with a unique user code—and when entered into the App, the data were streamed to the cloud along with the unique code for further processing. All data were encrypted during transmissions. Users were instructed to note the bed, sleep, wake-up and out-of-bed times every day during the actigraphy measurements on a ‘notes’ feature built into the TapCounter (sleep and wake-up reports are were here). The nights after which the participants failed to report these times were eliminated from the analysis pertaining to sleep diaries versus tappigraphy, and sleep diaries versus actigraphy. The app failed to operate in five participants due to missing device permissions.

Participant instructions

Participants were not explicitly made aware that any analysis linking actigraphy, sleep diaries and tappigraphy prior to the de-briefing at the end of the observation period. Through the observation period, the participants were under the impression that the sleep-related variables from the watches and diaries would be linked to the laboratory measures obtained towards a broad study on sensorimotor properties. Towards actigraphy, the participants were instructed to wear the watches throughout the observation period (24 h/day). No instruction was given as to where to place the smartphone (as in next to the bed or not) in the same period. Towards the sleep diaries, the participants were instructed to use the ‘notes’ feature on the TapCounter App whenever convenient, but in the morning period to report on the previous night’s sleep using a single entry per day. The timestamps of these notes were further recorded to screen for any consistent patterns in the note-taking behaviour or to address whether participants adhered to the instructions.

Actigraphy algorithm

The accelerations gathered along the three axes by the actigraphy watches were combined using the sum of squares and low-pass filtered at 2 Hz. To estimate the putative sleep and wake times, we employed the standard Cole–Kripke algorithm on the filtered data with slight modifications. 3 A key part of this algorithm—the minute-by-minute categorisation of the data into rest-active states based on the weighted sum of the current minute with that of the surrounding minutes—was extracted to study the physical activity state during smartphone usage. The algorithm was implemented on MATLAB (MathWorks, Natick, USA) and used pre-existing codes. 18 We modified the codes such that the automatic scoring of sleep and wake by the Cole–Kripke algorithm was further checked by the near-body temperature and ambient light measurements. Firstly, any putative sleep period where the median temperature dropped below 25 °C was ignored— removing instances where the user removed the watch from the body. Secondly, any putative sleep period where the median ambient light levels failed to drop below 25 lux was ignored and thus restricting the analysis to nighttime sleep and ignoring day-time naps. Thirdly, the putative sleep times had to contain a 10% (36 min) overlap with the 6 -h low-activity period determined using a 24 -h sinewave fit (Casey Cox’s cosinor function implemented in MATLAB). 19 This final step is commonly substituted using sleep diaries, but our approach avoided mixing the subjective diary entries with the objective measurements to determine sleep durations—ensuring the estimated durations are entirely objective.

Tappigraphy algorithm

The raw touchscreen timestamps were parsed into MATLAB using the parser extractTaps (QuantActions Ltd. Lausanne, Switzerland). The touchscreen timestamps were then processed using a separate algorithm (getresttimesphone, implemented in MALTAB) designed to gather the gaps in smartphone use at the circadian rest phase (i.e., at the putative night). To elaborate on this algorithm, first, the phone data were reduced to binary states in 60-s bins (1 as active and 0 as rest). The activity was further processed using a cut-off (5% in an hour threshold) such that the brief periods of activity surrounded by inactivity were labelled as rest. Next, we extracted all of the continuous gaps in smartphone activity, such that the gap in usage was greater than an arbitrarily set 2 h threshold. In a parallel set of computations, we obtained a 24 -h sinewave fit on the time series of smartphone interactions using the Cosinor analysis (Casey Cox’s cosinor function implemented in MATLAB). 19 This sinewave fit was then used to determine the 6-h long periods with the least activity in the tapping data in 24 -h windows. The two parallel streams were combined to select those activity gaps which had a minimum of arbitrarily set 10% overlap (36 min) with the 6 h period, and these gaps were labelled as ‘sleep’.

Statistical analysis

Simple linear regressions (using the bi-square fitting method, implemented using the fitlm function in MATLAB) were employed for all of the analysis, except that for the analysis including demographic information where multiple regression was used. The simple pairwise regression was the method of choice where the relationship between a traditional parameter—from actigraphy or sleep diary—was compared against the output of tappigraphy. 20 These paired comparisons were conducted on concatenated data with the resolution of each night and with non-concatenated data with the resolution of each individual. The concatenation for bed times, wake-up times and sleep duration, from one subject say S1 with another S2 say for an estimate e: Sc = S1 ei ∈ <1. m>S2 ei ∈ <1. n>, where m and n are the number of nights recorded for S1 and S2, respectively, and Sc is the concatenated data. The subsequent subject’s data, say S3 was concatenated to Sc and so on. For bed times and wake-up times, to enable the correlations of values from a 24 h clock in a linear space a simple transformation for the sleep onset values under 10 am, such that 01 h in past midnight was considered as 25 h. Non-concatenated data were used to study inter-individual differences and given the importance of central tendency estimate of sleep (median sleep duration, and the corresponding coefficient of variation, CoV), we used tappigraphy versus actigraphy paired comparisons. 21 Pairwise regression was also used to address the dependency of measurement error in tappigraphy (evaluated against actigraphy) versus smartphone usage. The simple regression method was also used to address how much phone use is needed to obtain useful tappigraphy-based estimates (therefore serving to establish an inclusion criterion in future studies based on phone usage alone).

Multiple regression was used in the explorative analysis of how the demographic information was related to the key sleep metrics of sleep median and CoV. The purpose of this analysis was to illustrate the nature of the relationships that may be discovered when using tappigraphy or actigraphy independently. Four different regression models were tested with the following dependent variables: actigraphy-based median sleep duration and CoV, and tappigraphy-based median sleep duration and CoV. Towards all of these models, the following explanatory variables were used: phone usage (measured as the number of touches per day), age, gender (dummy variable), height and weight. Each model was used to test five hypotheses simultaneously, and the corresponding t test α (set at 0.05) was Bonferroni corrected for this multiple comparison (the values which were >α post correction, but <0.05 are still noted in the results and indicated as such due to the exploratory nature of this analysis). In these multiple regressions, the two subjects with ages higher than 35 were excluded as outliers (>5 STD from the mean age). The subject elimination is detailed in the Supplementary Table, and the statistics in the results section is reported with the corresponding degrees of freedom. When MATLAB estimated p-value was at 0, then p < 0.0001 is used to describe the results.

Paired t test was used to compare the median sleep durations (from each individual) obtained using actigraphy versus tappigraphy in the sampled population (α = 0.05). In a separate analysis, one sample t tests of mean probability of phone interactions in 3 -min bin versus 0 were conducted to establish the dynamics of interrupted nights (α = 0.05, Bonferroni corrected).

Reporting summary

Further information on experimental design is available in the Nature Research Reporting Summary linked to this article.


The Benefits of Daydreaming

Does your mind wander? During a class or meeting, do you find yourself staring out the window and thinking about what you’ll do tomorrow or next week? As a child, were you constantly reminded by teachers to stop daydreaming?

Related Content

Well, psychological research is beginning to reveal that daydreaming is a strong indicator of an active and well-equipped brain. Tell that to your third-grade teacher.

A new study, published in Psychological Science by researchers from the University of Wisconsin and the Max Planck Institute for Human Cognitive and Brain Science, suggests that a wandering mind correlates with higher degrees of what is referred to as working memory. Cognitive scientists define this type of memory as the brain’s ability to retain and recall information in the face of distractions.

For example, imagine that, when leaving a friend ‘s house, you promise to call when you get home safely. On the way, you stop to buy gas and a few groceries, and then drive by a car accident and get out to see if anyone needs help. Finally, when you get to your house, you remember to call your friend. The ability to do this depends on the brain’s working memory system.

In the study, the researchers sought to examine the relationship between people’s working memory capacity and their tendency to daydream. To accomplish this, they first asked participants to do one of two extremely easy tasks that might prompt them to daydream—either press a button in response to a letter appearing on a screen or tap their finger in time with their own breath—and periodically checked in to see if the subjects were paying attention or not. Then they measured each participant’s working memory by testing their ability to remember a series of letters interspersed with a set of easy math questions.

Surprisingly, there was a correlation between mind wandering during the first task and high scores on the working memory test. The participants who more frequently daydreamed were actually better at remembering the series of letters when distracted by the math problems compared to those whose minds were less prone to wandering.

Why might this be the case? “What this study seems to suggest is that, when circumstances for the task aren’t very difficult, people who have additional working memory resources deploy them to think about things other than what they’re doing,” said Jonathan Smallwood in a press release. In other words, daydreamers’ minds wander because they have too much extra capacity to merely concentrate on the task at hand.

These results, the researchers believe, point to the fact that the mental processes underlying daydreaming may be quite similar to those of the brain’s working memory system. Previously, working memory had been correlated with measures of intelligence, such as IQ score. But this study shows how working memory is also closely tied to our tendency to think beyond our immediate surroundings at any given time. “Our results suggest that the sorts of planning that people do quite often in daily life—when they’re on the bus, when they’re cycling to work, when they’re in the shower—are probably supported by working memory,” Smallwood said. “Their brains are trying to allocate resources to the most pressing problems.”

The researchers stress that those with higher working memory capacities—and thus those who are naturally most prone to daydreaming—still have the ability to train themselves to focus their attention on what’s in front of them, when necessary. “Mind wandering isn’t free—it takes resources,” Smallwood said. “But you get to decide how you want to use your resources. If your priority is to keep attention on task, you can use working memory to do that, too.”

About Joseph Stromberg

Joseph Stromberg was previously a digital reporter for Smithsonian.


Milk chocolate vs. Dark chocolate for Health Benefits

The key to benefiting from the brain and mood-boosting effects of chocolate is to choose a good dark chocolate or cocoa powder and to consume moderate amounts regularly. Different cocoa products and chocolates can have greatly differing amounts of flavanols and methylxanthines depending on the manufacturing process and cocoa content.

Dark chocolate with a high percentage of cocoa—70 percent or more—has a lot of these beneficial phytochemicals. On the other hand, chocolate that contains little cocoa has much less therapeutic value. You’ll learn more about exactly how to choose the right chocolate and how much to consume in Surprising Cocoa Benefits Include Heart Health and Prediabetes Improvement, along with how chocolate benefits many aspects of heart and blood vessel health.


Human Brain Still Awake, Even During Deep Sleep

Sleep in humans is divided in two main phases: non-REM sleep, which occupies most of our early sleep night, and REM sleep, during which our dreams prevail. Non-REM sleep is usually considered as a compensatory &lsquoresting&rsquo state for the brain, following the intense waking brain activity. Indeed, previous brain imaging studies showed that the brain was less active during periods of non-REM sleep as compared to periods of wakefulness.

Although not rejecting this concept, researchers from the Cyclotron Research Centre of the University of Liège in Belgium and from the Department of Neurology of Liege University Hospital demonstrate that, even during its deepest stages (also called &lsquoslow-wave-sleep&rsquo), non-REM sleep should not be viewed as a stage of constant and continuous brain activity decrease, but is also characterized by transient and recurrent activity increases in specific brain areas.

In a study published recently in the American journal Proceedings of the National Academy of Sciences, the research team led by Dr Thanh Dang-Vu and Pr Pierre Maquet shows that brain activity during these sleep stages is actually profoundly influenced by spontaneous slow rhythms (also called &lsquoslow oscillations&rsquo) which organize neuronal functioning during non-REM sleep.

By using functional magnetic resonance imaging (fMRI) combined with electroencephalography (EEG), researchers have evidenced that these slow oscillations are associated with brain activity increases during non-REM sleep (see image, side panels), therefore discarding the concept of brain &lsquoquiescence&rsquo that prevailed for a long time in the characterization of non-REM sleep. Besides, these activity increases are located in specific brain areas, including the inferior frontal gyrus, the parahippocampal gyrus, the precuneus and the posterior cingulate cortex, as well as the brainstem and cerebellum (see image, central panels).

These results improve our understanding of non-REM sleep mechanisms. On the one hand, they demonstrate that non-REM sleep is a stage of brain activation organized by the slow oscillations. On the other hand, they allow the identification of brain areas potentially involved in the generation of these oscillations, which are a hallmark of brain functioning during non-REM sleep. Moreover, by showing the activation of areas involved in the processing of memory traces such as para-hippocampal areas, the study might point to the potential functions of sleep, in particular the increasingly well-defined role of sleep in memory consolidation. Finally, the activation of areas such as the brainstem, usually associated with arousal and waking, might reveal these oscillations of non-REM sleep as &lsquomicro-wake&rsquo periods allowing the brain to fulfil crucial and active functions, even during the deepest stages of sleep.

This research was supported by the National Fund for Scientific Research (Belgium), the University of Liège and the Queen Elisabeth Medical Foundation.

Story Source:

Materials provided by University of Liège. Note: Content may be edited for style and length.


Games and Apps to Keep Your Brain Active and Sharp during the Pandemic

In the hour of pandemic and during a stay at home, it could be hard for anyone to spend time. With monotonous types of activities day to day, one is stuck into boredom. Thinking the way out is impossible now because of a long stay at home.

During stressful times, we all found a solution to keep ourselves engaged and active. Those who were fond of reading books ordered series of new books available on Amazon. While others having an interest in painting and artwork locked themselves up to create beautiful pieces including sculptures and handmade items.

However, most people are not interested in anything else other than hooking themselves to television or their mobile screen. When the lockdown was imposed in most of the states, most of the people headed towards cable providers to get a new internet and cable connection. From streaming favorite services like Netflix and watching channels like FXX and CNN, Optimum, Suddenlink, and Spectrum packages were quite helpful in keeping people busy without losing their savings.

You might have watched all the seasons on Netflix, played games with your friends, and browsed how-to websites. It is time to give your brain some activity so that when things go normal, you might not find any trouble activating it. Today, we are going to reveal some of the best apps and games to keep your mind sharp and active through the pandemic. Stick with us and let’s get started.


A new paper suggest travel faster than the speed of light might be possible given the creation of a new way of looking at propelling a vehicle.

When considering the advancement of life changing technology, does our current economic model speed up or suppress the collaboration, creation and advancement of ideas?

Take a moment and breathe. Place your hand over your chest area, near your heart. Breathe slowly into the area for about a minute, focusing on a sense of ease entering your mind and body. Click here to learn why we suggest this.

In Star Trek Gene Roddenberry imagined it possible to set a ship into ‘warp drive’ and travel at speeds 6000+ times the speed of light, moving from one galaxy to a distant one very quickly. Imagine having that type of technology here on earth?! It has been said before that if we can think it, we can create it. Well, maybe that’s sometimes true.

What Happened:

The question of whether travel faster than the speed of light is possible was again approached in a new research paper written by an American physicist Erik Lentz. In the paper Lentz proposed a new theory for how faster-than-light travel could be possible. Given their models, Lentz and his team feel that travel to distant stars and planets could be possible in the near future, perhaps with proper research and development they could have something working in as little as 10 years.

The question of whether this is possible does not challenge our current understanding of physics that Albert Einstein’s theory of relativity sets forth that it is not possible to travel faster than light.

Instead of focusing on our current understanding of matter, Lentz’s new paper puts greater importance on a possible engineering solution as opposed to the theoretical physics. The new paper was published in Classical and Quantum Gravity.

The paper proposes a plan to travel faster than light by creating a series of ‘solitons’ to provide the basis for propulsion. A soliton is a compact wave that keeps its speed and shape while moving with little loss of energy.

Interestingly, this technology would allow travel at ANY speed. This brings me back to an article I wrote yesterday discussing the incoming reality within collective consciousness that UFOs and Extraterrestrials are real. In that article I state that the question of ‘how are they getting here’ is of importance as it could give humanity access to technology that would completely change the way we live on this planet.

[The method] “uses the very structure of space and time arranged in a soliton to provide a solution to faster-than-light travel,” From the press release.

Imagine this, the nearest star beyond our solar system is called Proxima Centauri. We know it to be about 4.25 light years away. (A light year is the distance it takes light to travel in one year.)

Lentz stated that using our current rocket fuel methods fo travel, it would take about 50,000 to 70,000 years to reach Proxima Centauri. If we were to upgrade to nuclear propulsion technology, it would take about 100 years. But if we employed a light speed warp drive, it would take only four years and three months.

This would mean that the average person would be able to travel to distant interstellar planets and complete the trip in a current human lifetime. Think of the vacations!

According to Lentz there are some barriers to making this all work, but they aren’t impossible to surpass. For the tech to work, it would require lowering the energy needed down to the level of modern nuclear power reactors. That is if we don’t take into consideration energy technologies that are currently suppressed. Lentz also stated that what would be needed is a way to develop and speed up the solitons (waves.)

“This work has moved the problem of faster-than-light travel one step away from theoretical research in fundamental physics and closer to engineering,”

Why Its Matters:

Humans are curious beings who seem to gain a great deal from expanding our curiosity beyond everyday plights of a system and way of life that doesn’t necessarily inspire the deepest use of our creativity. Perhaps a knowing that we can indeed go elsewhere without primitive technology would shift the way we see our role on this earth and how we choose to fight over what we believe are limited resources.

Then again, perhaps if humans carry their current story of separation and competition to other worlds, we’ll produce the same mess there. I guess the question is, would the possibility of being able to leave this earth and go almost anywhere change the underlying nature of how we choose to set up our cultural beliefs and narratives of what it means to be human?

It’s my feeling that humanity does not lack the solutions to live in a thriving world, we lack the worldview and state of being. Both of which we could change with a little effort.

The Takeaway:

When I hear research like this I am fascinated. Then again I also sometimes wonder if all scientists around the world saw the technology I have seen first hand, that completely changes the way we perceive energy generation today, would the way we look at creating technology that requires energy change entirely? Yes, of course it would.

In my mind and heart I see a world of true collaboration and curiosity. One where we aren’t competing to see who’s the greatest scientist with the best copy written tech, but a world where we transparently share what is out there to advance the entire human race. No powerful interests suppressing technology because it’s too threatening to an economy, but instead true open advancement where we can solve problems incredibly fast.

Can you imagine this world?

Dive Deeper

Click below to watch a sneak peek of our brand new course!

Our new course is called 'Overcoming Bias & Improving Critical Thinking.' This 5 week course is instructed by Dr. Madhava Setty & Joe Martino

If you have been wanting to build your self awareness, improve your.critical thinking, become more heart centered and be more aware of bias, this is the perfect course!

Discover


Brain may flush out toxins during sleep

NIH-funded study suggests sleep clears brain of damaging molecules associated with neurodegeneration.

Scientists watched dye flow through the brain of a sleeping mouse. Nedergaard Lab, University of Rochester Medical Center.

A good night’s rest may literally clear the mind. Using mice, researchers showed for the first time that the space between brain cells may increase during sleep, allowing the brain to flush out toxins that build up during waking hours. These results suggest a new role for sleep in health and disease. The study was funded by the National Institute of Neurological Disorders and Stroke (NINDS), part of the NIH.

“Sleep changes the cellular structure of the brain. It appears to be a completely different state,” said Maiken Nedergaard, M.D., D.M.Sc., co-director of the Center for Translational Neuromedicine at the University of Rochester Medical Center in New York, and a leader of the study.

For centuries, scientists and philosophers have wondered why people sleep and how it affects the brain. Only recently have scientists shown that sleep is important for storing memories. In this study, Dr. Nedergaard and her colleagues unexpectedly found that sleep may be also be the period when the brain cleanses itself of toxic molecules.

Their results, published in Science, show that during sleep a plumbing system called the glymphatic system may open, letting fluid flow rapidly through the brain. Dr. Nedergaard’s lab recently discovered the glymphatic system helps control the flow of cerebrospinal fluid (CSF), a clear liquid surrounding the brain and spinal cord.

“It’s as if Dr. Nedergaard and her colleagues have uncovered a network of hidden caves and these exciting results highlight the potential importance of the network in normal brain function,” said Roderick Corriveau, Ph.D., a program director at NINDS.

Initially the researchers studied the system by injecting dye into the CSF of mice and watching it flow through their brains while simultaneously monitoring electrical brain activity. The dye flowed rapidly when the mice were unconscious, either asleep or anesthetized. In contrast, the dye barely flowed when the same mice were awake.

“We were surprised by how little flow there was into the brain when the mice were awake,” said Dr. Nedergaard. “It suggested that the space between brain cells changed greatly between conscious and unconscious states.”

To test this idea, the researchers inserted electrodes into the brain to directly measure the space between brain cells. They found that the space inside the brains increased by 60 percent when the mice were asleep or anesthetized.

“These are some dramatic changes in extracellular space,” said Charles Nicholson, Ph.D., a professor at New York University’s Langone Medical Center and an expert in measuring the dynamics of brain fluid flow and how it influences nerve cell communication.

Certain brain cells, called glia, control flow through the glymphatic system by shrinking or swelling. Noradrenaline is an arousing hormone that is also known to control cell volume. Similar to using anesthesia, treating awake mice with drugs that block noradrenaline induced unconsciousness and increased brain fluid flow and the space between cells, further supporting the link between the glymphatic system and consciousness.

Previous studies suggest that toxic molecules involved in neurodegenerative disorders accumulate in the space between brain cells. In this study, the researchers tested whether the glymphatic system controls this by injecting mice with labeled beta-amyloid, a protein associated with Alzheimer’s disease, and measuring how long it lasted in their brains when they were asleep or awake. Beta-amyloid disappeared faster in mice brains when the mice were asleep, suggesting sleep normally clears toxic molecules from the brain.

“These results may have broad implications for multiple neurological disorders,” said Jim Koenig, Ph.D., a program director at NINDS. “This means the cells regulating the glymphatic system may be new targets for treating a range of disorders.”

The results may also highlight the importance of sleep.

“We need sleep. It cleans up the brain,” said Dr. Nedergaard.

This work was supported by grants from the NINDS (NS078167, NS07830, NS028642).

For more information about neurological disorders and the latest neuroscience research: http://www.ninds.nih.gov

NINDS is the nation’s leading funder of research on the brain and nervous system. The NINDS mission is to reduce the burden of neurological disease – a burden borne by every age group, by every segment of society, by people all over the world.

About the National Institutes of Health (NIH): NIH, the nation's medical research agency, includes 27 Institutes and Centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and is investigating the causes, treatments, and cures for both common and rare diseases. For more information about NIH and its programs, visit www.nih.gov.

NIH&hellipTurning Discovery Into Health ®

Reference

Xie et al “Sleep initiated fluid flux drives metabolite clearance from the adult brain.” Science, October 18, 2013. DOI: 10.1126/science.1241224


Tips for Getting a Good Night's Sleep

Getting enough sleep is good for your health. Here are a few tips to improve your sleep:

Set a schedule &ndash go to bed and wake up at the same time each day.

Exercise 20 to 30 minutes a day but no later than a few hours before going to bed.

Avoid caffeine and nicotine late in the day and alcoholic drinks before bed.

Relax before bed &ndash try a warm bath, reading, or another relaxing routine.

Create a room for sleep &ndash avoid bright lights and loud sounds, keep the room at a comfortable temperature, and don&rsquot watch TV or have a computer in your bedroom.

Don&rsquot lie in bed awake. If you can&rsquot get to sleep, do something else, like reading or listening to music, until you feel tired.

See a doctor if you have a problem sleeping or if you feel unusually tired during the day. Most sleep disorders can be treated effectively.


Early birds vs. night owls: How one has an advantage at work, according to science

You probably know whether you're a morning or night person. Now science has some good news for the early risers: Apparently the early bird really does get the worm.

There are fundamental differences in brain function between night owls and early birds, and night owls may have impaired function during regular work-day hours, according to a new study published Thursday in the academic journal "Sleep."

Researchers at the University of Birmingham looked at the brain function (among other things) of 38 people who were categorized as either night owls, who had an average bedtime of 2:30 a.m. and a wake-up time of 10:00 a.m., or morning larks, who had average bedtime of 11 p.m. and wake time of 6:30 a.m.

Participants underwent MRI scans, were asked to complete a series of tasks and participated in testing sessions at different times during the day between 8 a.m. and 8 p.m., while also being asked to report their level of sleepiness.

Overall, researchers found that night owls had lower resting brain connectivity in ways that are associated with poorer attention, slower reactions and increased sleepiness throughout the hours of a typical work day.

Meanwhile, brain connectivity in the regions of the brain that can predict better performance and lower sleepiness were significantly higher in larks at all times, "suggesting that the resting state brain connectivity of night owls is impaired throughout the whole day." (The "resting state" of the brain, Live Science notes, means not doing a particular task and letting the mind wander.)

"A huge number of people struggle to deliver their best performance during work or school hours they are not naturally suited to," says the study's lead researcher, Dr. Elise Facer-Childs, of the University of Birmingham's Centre for Human Brain Health. "There is a critical need to increase our understanding of these issues in order to minimize health risks in society, as well as maximize productivity."

And whether you're a morning or night person might be dictated by your genes. A separate study published in January looked at the genomes of almost 700,000 people, using data from 23andMe and the U.K. Biobank. It found that there are hundreds of genes that are associated with whether you are a night owl or an early bird. Regions of the genome that the study found to be relevant to whether you're a morning or night person included genes involved in metabolism, the biological clock and genes that function in the retina.

"All times of day are not created equal," Pink previously told CNBC Make It. "Our performance varies considerably over the course of the day, and what task to do at a certain time really depends on the nature of the task. If we look at the evidence, we can be doing the right work, at the right time."

According to Pink, for larks, the morning is the best time to do analytical work that requires focus, and more administrative or routine work should be done later in the day. The reverse is true for night owls.


The Benefits of Daydreaming

Does your mind wander? During a class or meeting, do you find yourself staring out the window and thinking about what you’ll do tomorrow or next week? As a child, were you constantly reminded by teachers to stop daydreaming?

Related Content

Well, psychological research is beginning to reveal that daydreaming is a strong indicator of an active and well-equipped brain. Tell that to your third-grade teacher.

A new study, published in Psychological Science by researchers from the University of Wisconsin and the Max Planck Institute for Human Cognitive and Brain Science, suggests that a wandering mind correlates with higher degrees of what is referred to as working memory. Cognitive scientists define this type of memory as the brain’s ability to retain and recall information in the face of distractions.

For example, imagine that, when leaving a friend ‘s house, you promise to call when you get home safely. On the way, you stop to buy gas and a few groceries, and then drive by a car accident and get out to see if anyone needs help. Finally, when you get to your house, you remember to call your friend. The ability to do this depends on the brain’s working memory system.

In the study, the researchers sought to examine the relationship between people’s working memory capacity and their tendency to daydream. To accomplish this, they first asked participants to do one of two extremely easy tasks that might prompt them to daydream—either press a button in response to a letter appearing on a screen or tap their finger in time with their own breath—and periodically checked in to see if the subjects were paying attention or not. Then they measured each participant’s working memory by testing their ability to remember a series of letters interspersed with a set of easy math questions.

Surprisingly, there was a correlation between mind wandering during the first task and high scores on the working memory test. The participants who more frequently daydreamed were actually better at remembering the series of letters when distracted by the math problems compared to those whose minds were less prone to wandering.

Why might this be the case? “What this study seems to suggest is that, when circumstances for the task aren’t very difficult, people who have additional working memory resources deploy them to think about things other than what they’re doing,” said Jonathan Smallwood in a press release. In other words, daydreamers’ minds wander because they have too much extra capacity to merely concentrate on the task at hand.

These results, the researchers believe, point to the fact that the mental processes underlying daydreaming may be quite similar to those of the brain’s working memory system. Previously, working memory had been correlated with measures of intelligence, such as IQ score. But this study shows how working memory is also closely tied to our tendency to think beyond our immediate surroundings at any given time. “Our results suggest that the sorts of planning that people do quite often in daily life—when they’re on the bus, when they’re cycling to work, when they’re in the shower—are probably supported by working memory,” Smallwood said. “Their brains are trying to allocate resources to the most pressing problems.”

The researchers stress that those with higher working memory capacities—and thus those who are naturally most prone to daydreaming—still have the ability to train themselves to focus their attention on what’s in front of them, when necessary. “Mind wandering isn’t free—it takes resources,” Smallwood said. “But you get to decide how you want to use your resources. If your priority is to keep attention on task, you can use working memory to do that, too.”

About Joseph Stromberg

Joseph Stromberg was previously a digital reporter for Smithsonian.


Methods

Participants and recruitment

Participants from the campus of Leiden University were recruited by using advertisements on a closed online platform and department-wide emails. Candidates with known neurological and psychiatric diagnosis based on self-reports were excluded from recruitment. Due to technical limitations, those users with an Android operating smartphone were invited to participate and under the condition that the phone remains strictly un-shared during the study period. A total of 88 right-handed participants were recruited (44 females, 16–45 years of age, mean age 23). The experimental procedures used here were approved by the Ethical Committee at the Institute of Psychology at Leiden University. All the participants provided written and informed consent and were compensated for their time using a cash reward or course credits. The weight with one layer of clothing, height and the year/month of birth was collected from each participant. As a part of a larger study, the volunteers also consecutively participated in a range of laboratory assessments beyond the scope of this report.

Actigraphy measurement

The gross movements (three-axis accelerometer), the ambient light and near-body temperature were measured using GENEACTIV watches (Activinsights, Cambridgeshire, UK). Participants were instructed to wear the watch on both wrists for a minimum of 2 weeks, and the data from the left wrist are primarily presented here. Four participants were unable to follow the instruction due to reported discomfort and were eliminated from the study. Participants who only intermittently removed the watches during part of the observation period were not eliminated, as these periods could be accounted for using the near-body temperature measurements. The watches were set to acquire the data at 50 Hz and the data were recovered after 14 days of acquisition or earlier, only to be reset for continued use if the subjects were willing to participate for an additional week.

Tappigraphy measurement and on-phone sleep diary

The touchscreen interactions were quantified using the TapCounter App (QuantActions Ltd. Lausanne, Switzerland). 17 The App was installed by each user from the Google Playstore (Google, Mountain View, USA). The App is designed to gather the precise timestamps of all touchscreen interactions and operates in the background. Only those touchscreen interactions which occurred during the ‘unlocked’ state of the screen were considered here. Each user was provided with a unique user code—and when entered into the App, the data were streamed to the cloud along with the unique code for further processing. All data were encrypted during transmissions. Users were instructed to note the bed, sleep, wake-up and out-of-bed times every day during the actigraphy measurements on a ‘notes’ feature built into the TapCounter (sleep and wake-up reports are were here). The nights after which the participants failed to report these times were eliminated from the analysis pertaining to sleep diaries versus tappigraphy, and sleep diaries versus actigraphy. The app failed to operate in five participants due to missing device permissions.

Participant instructions

Participants were not explicitly made aware that any analysis linking actigraphy, sleep diaries and tappigraphy prior to the de-briefing at the end of the observation period. Through the observation period, the participants were under the impression that the sleep-related variables from the watches and diaries would be linked to the laboratory measures obtained towards a broad study on sensorimotor properties. Towards actigraphy, the participants were instructed to wear the watches throughout the observation period (24 h/day). No instruction was given as to where to place the smartphone (as in next to the bed or not) in the same period. Towards the sleep diaries, the participants were instructed to use the ‘notes’ feature on the TapCounter App whenever convenient, but in the morning period to report on the previous night’s sleep using a single entry per day. The timestamps of these notes were further recorded to screen for any consistent patterns in the note-taking behaviour or to address whether participants adhered to the instructions.

Actigraphy algorithm

The accelerations gathered along the three axes by the actigraphy watches were combined using the sum of squares and low-pass filtered at 2 Hz. To estimate the putative sleep and wake times, we employed the standard Cole–Kripke algorithm on the filtered data with slight modifications. 3 A key part of this algorithm—the minute-by-minute categorisation of the data into rest-active states based on the weighted sum of the current minute with that of the surrounding minutes—was extracted to study the physical activity state during smartphone usage. The algorithm was implemented on MATLAB (MathWorks, Natick, USA) and used pre-existing codes. 18 We modified the codes such that the automatic scoring of sleep and wake by the Cole–Kripke algorithm was further checked by the near-body temperature and ambient light measurements. Firstly, any putative sleep period where the median temperature dropped below 25 °C was ignored— removing instances where the user removed the watch from the body. Secondly, any putative sleep period where the median ambient light levels failed to drop below 25 lux was ignored and thus restricting the analysis to nighttime sleep and ignoring day-time naps. Thirdly, the putative sleep times had to contain a 10% (36 min) overlap with the 6 -h low-activity period determined using a 24 -h sinewave fit (Casey Cox’s cosinor function implemented in MATLAB). 19 This final step is commonly substituted using sleep diaries, but our approach avoided mixing the subjective diary entries with the objective measurements to determine sleep durations—ensuring the estimated durations are entirely objective.

Tappigraphy algorithm

The raw touchscreen timestamps were parsed into MATLAB using the parser extractTaps (QuantActions Ltd. Lausanne, Switzerland). The touchscreen timestamps were then processed using a separate algorithm (getresttimesphone, implemented in MALTAB) designed to gather the gaps in smartphone use at the circadian rest phase (i.e., at the putative night). To elaborate on this algorithm, first, the phone data were reduced to binary states in 60-s bins (1 as active and 0 as rest). The activity was further processed using a cut-off (5% in an hour threshold) such that the brief periods of activity surrounded by inactivity were labelled as rest. Next, we extracted all of the continuous gaps in smartphone activity, such that the gap in usage was greater than an arbitrarily set 2 h threshold. In a parallel set of computations, we obtained a 24 -h sinewave fit on the time series of smartphone interactions using the Cosinor analysis (Casey Cox’s cosinor function implemented in MATLAB). 19 This sinewave fit was then used to determine the 6-h long periods with the least activity in the tapping data in 24 -h windows. The two parallel streams were combined to select those activity gaps which had a minimum of arbitrarily set 10% overlap (36 min) with the 6 h period, and these gaps were labelled as ‘sleep’.

Statistical analysis

Simple linear regressions (using the bi-square fitting method, implemented using the fitlm function in MATLAB) were employed for all of the analysis, except that for the analysis including demographic information where multiple regression was used. The simple pairwise regression was the method of choice where the relationship between a traditional parameter—from actigraphy or sleep diary—was compared against the output of tappigraphy. 20 These paired comparisons were conducted on concatenated data with the resolution of each night and with non-concatenated data with the resolution of each individual. The concatenation for bed times, wake-up times and sleep duration, from one subject say S1 with another S2 say for an estimate e: Sc = S1 ei ∈ <1. m>S2 ei ∈ <1. n>, where m and n are the number of nights recorded for S1 and S2, respectively, and Sc is the concatenated data. The subsequent subject’s data, say S3 was concatenated to Sc and so on. For bed times and wake-up times, to enable the correlations of values from a 24 h clock in a linear space a simple transformation for the sleep onset values under 10 am, such that 01 h in past midnight was considered as 25 h. Non-concatenated data were used to study inter-individual differences and given the importance of central tendency estimate of sleep (median sleep duration, and the corresponding coefficient of variation, CoV), we used tappigraphy versus actigraphy paired comparisons. 21 Pairwise regression was also used to address the dependency of measurement error in tappigraphy (evaluated against actigraphy) versus smartphone usage. The simple regression method was also used to address how much phone use is needed to obtain useful tappigraphy-based estimates (therefore serving to establish an inclusion criterion in future studies based on phone usage alone).

Multiple regression was used in the explorative analysis of how the demographic information was related to the key sleep metrics of sleep median and CoV. The purpose of this analysis was to illustrate the nature of the relationships that may be discovered when using tappigraphy or actigraphy independently. Four different regression models were tested with the following dependent variables: actigraphy-based median sleep duration and CoV, and tappigraphy-based median sleep duration and CoV. Towards all of these models, the following explanatory variables were used: phone usage (measured as the number of touches per day), age, gender (dummy variable), height and weight. Each model was used to test five hypotheses simultaneously, and the corresponding t test α (set at 0.05) was Bonferroni corrected for this multiple comparison (the values which were >α post correction, but <0.05 are still noted in the results and indicated as such due to the exploratory nature of this analysis). In these multiple regressions, the two subjects with ages higher than 35 were excluded as outliers (>5 STD from the mean age). The subject elimination is detailed in the Supplementary Table, and the statistics in the results section is reported with the corresponding degrees of freedom. When MATLAB estimated p-value was at 0, then p < 0.0001 is used to describe the results.

Paired t test was used to compare the median sleep durations (from each individual) obtained using actigraphy versus tappigraphy in the sampled population (α = 0.05). In a separate analysis, one sample t tests of mean probability of phone interactions in 3 -min bin versus 0 were conducted to establish the dynamics of interrupted nights (α = 0.05, Bonferroni corrected).

Reporting summary

Further information on experimental design is available in the Nature Research Reporting Summary linked to this article.


Chapter 6 Consciousness (Fiest) questions

A. When the various sensory elements get integrated.
B.
When the mind is awake but not very aware.

B. driving.
C.
extremely drunk.

B.
When the body only responds to stimuli causing shock

A.
Intentionally repressed material that takes the form of unconscious

B.
Potentially accessible material currently unavailable to awareness

C.
Repressed unconscious material that cannot be consciously recollected

B.
The conscious experience of knowing something that can be brought into awareness

C.
The conscious experience of knowing something that cannot be brought into awareness

B.
The person cannot be in a minimally conscious state for prolonged periods of time.

C.
The person can experience vacillating consciousness.

B.
Individuals who are mindful are not aware of their own feelings in response.

A.
Consciousness focuses our attention on changes in stimulation.

B.
It is possible for us to be aware of all material at all times.

C.
All of us can do more than one thing at a time without compromising our performance on either task.

B.
The right-left orientation test

C.
The dichotic listening test

C.
The cocktail party effect

A.
Audience's sustained attention

B.
Audience's divided attention

C.
Audience's short attention span

A.
Inattentional blindness

B.
The cognitive load theory

C.
The global workspace model

B. Baddeley's theory
C.
The cognitive load theory

B.
When neurons from many distinct brain regions work together

C.
When neurons from many distinct brain regions work independently

B.
The ability to direct one's sense organs to form a complete perspective

C.
The ability to respond simultaneously to multiple task demands

A.
The ability to individually respond to specific auditory, visual, or tactile stimuli

B.
The ability to consistently maintain a behavioral response for continuous and repetitive activity

C.
The capacity to maintain quick and enhanced behavioral responses to involuntary stimuli

B.
Respond discretely to specific visual, auditory, or tactile stimuli

C.
Maintain attentional focus for an extended period of time

A.
less sustained attention.

B.
more sustained attention.

C.
less selective attention.

A.
There is absolutely no lost time, unlike while switching between complex tasks.

B.
The amount of lost time is high.

C.
There is an equal amount of lost time just as in switching between complex tasks.

B.
Increase in parietal lobe activity

C.
Decrease in the parietal lobe activity

A.
Chatting with someone in the vehicle

B.
Using the phone with a hands-free device

A.
52% of the population can switch from tasks without any performance decrements.

B.
67% of the cell phone users keep their cell phones next to their beds.

C.
4.4% of the population checks their phones even when they are not ringing.

C.
The administration of a psychoactive drug

B.
Elimination of obsolete neurons

C.
Increase in the activity of the parietal lobe

B.
Attention to the details of momentary experience

C.
Diverting selective attention toward thoughts

B. minimally conscious.
C.
low on self-consciousness.

A.
an increased EEG activity in the left frontal cortex.

B.
an increased EEG activity in the right frontal cortex.

C.
an increased EEG activity in the occipital lobe.

A.
Those who had meditated the shortest showed the greatest cortical thickness in certain areas.

B.
Those who had meditated the longest showed the least cortical thickness in certain areas.

C.
Those who had meditated the longest showed the greatest cortical thickness in certain areas.

A.
decrease in growth of brain tissues associated with emotional processing.

B.
increase in growth of brain tissues associated with spatial visualization.

C.
decrease in growth of brain tissues associated with spatial visualization.

B.
Variations in physiological processes that cycle longer than 48 hours

C.
Variations in physiological processes that cycle within approximately a 24-hour period


Watch the video: Υποφέρεις από ροχαλητό ή αϋπνίες; Πώς να ξεπεράσεις αυτές τις διαταραχές ύπνου Follow 191117 (June 2022).


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