biology 152 independent research project

7/27/2019 Biology 152 Independent Research Project

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Abstract : Little is known about exactly why and how brain waveforms and their associated

amplitudes change and reflect sleep homeostasis. Sleep is a topic that applies to everyone and

knowing how sleep varies between healthy individuals and individuals with psychiatric disorders

may be useful in discovering possible treatments. Slow-wave activity, a measure of sleep

homeostasis that increases after waking and decreases after sleep, has been hypothesized to vary

between individuals with major depression and healthy controls. Specifically, a greater overnight

decrease in N1 amplitude in controls versus depressed individuals may reflect an abnormal

homeostatic sleep regulation. Brain wave activity in eleven subjects with major depression and

eleven gender and age-matched controls were measured before and after sleep using high-density

electroencephalography (hdEEG), auditory evoked potentials (AEPs), and spontaneous waking

data. T -tests were done to look for statistical significance (significance was set at p


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Introduction : Six main stages occur during sleep: wakefulness, REM, or rapid eye movement

sleep, stage 1, stage 2, stage 3, and stage 4 (Steiger and Kimura, 2010). The last four stages

combined represent NREM, or non-rapid eye movement sleep (Aldrich, 1999). In healthy

subjects, the REM and NREM sleep stages alternate in a cyclic manner throughout an extended

sleep period (Figure 1). While in REM sleep, an electroencephalogram (EEG), or the measure of

the brains electrical activity, is faster, and rapid eye and skeletal muscle movements occur

frequently. During stage 1, a transition from drowsiness to light sleep occurs, and eye and muscle

activity slows. The following stages reflect a deeper, more subconscious sleep. In stage 2, eye

movements stop and brain waves become slower. In addition, spindles and K-complexes,different types of waveforms, occur during stage 2, whereas stages 3 and 4 are characterized by

slow waves. These last two stages combined are also referred to as the N3 stage (Steiger and

Kimura, 2010). Slow-wave sleep, or SWS, is the specific stage that is occurring during N3.

Slow-wave activity, or SWA, is a measure of EEG power across time, which is derived from the

amplitude of the waveforms. It analyzes the specific amplitude patterns that occur during SWS.

SWS is characterized by waveforms of higher amplitudes when compared to the waveforms of

proceeding stages (Figure 2). Steiger and Kimura (2010) stated that the first REM period occurs

a mean of 90 minutes after the first NREM period, and it is between these two stages that the

most SWA occurs. Length of the REM periods also increases throughout the night, meaning that

the most SWS occurs in the first few cycles of sleep. Variations in these SWS waveforms are

thought to reflect an abnormal homeostatic process in depressed subjects (Tononi and Cirelli,

2006).

With the synaptic homeostasis hypothesis, Tononi and Cirelli (2006) postulated that sleep

plays a critical role in regulating synaptic weight in the brain; this is tightly linked to sleep

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homeostasis. This hypothesis emphasizes that wakefulness involves synaptic potentiation, which

is homeostatically regulated via synaptic downscaling during SWS, and that SWA is

representative of such synaptic downscaling, which indirectly benefits brain function and

learning. Tononi and Cirelli (2006) also hypothesized that plastic processes during wakefulness

result in a net increase in synaptic weight or synaptic strength. Synaptic weight occurs between

two neurons and increases when a large signal from input neurons causes a large signal output,

meaning that the brain is more active. As the period of wakefulness increases, an increase in

energy costs, space costs, and saturation in the brain occurs due to continuous active learning.

Tononi and Cirelli (2006) express that the purpose of sleep is to downscale this synaptic strengthto a baseline that is energetically sustainable, preserves gray matter for waking tasks, and

prepares for alertness and efficient learning and memory. Therefore, sleep is necessary for

plasticity, or the ability of the brain to take in information and react efficiently based on

environmental changes during wakefulness. Slow waves increase as sleep begins and decrease as

the following period of wakefulness approaches and sleep ends, showing a possible correlation

between SWS and sleep homeostasis as a means of helping to relieve the need for sleep.

Similarly, Bersagliere and Achermann (2010) analyzed slow oscillations of waveforms in

NREM sleep and found evidence that SWS is correlated to sleep homeostasis and the restorative

nature of sleep. They found that slow oscillations of less than 1 Hz in EEG scalp readings occur

due to membrane fluctuations of cortical neurons that alternate between depolarized up states

and hyperpolarized down states. They took EEG recordings for baseline and recovery sleep after

a total sleep deprivation (40 hours of wakefulness) in 8 male subjects. While this study is limited

to only a few same-sex participants, it did show that the increased pressure or need for sleep

causes longer duration of up states and a higher frequency of slow wave oscillations after long

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waking periods. Thus, the restorative nature of sleep can be seen in SWS, for it is evidenced that

frequencies and amplitudes increase in response to being awake for a longer period of time.

A previous study done in my lab (Hulse et al., in press), the Center for Sleep Medicine

and Sleep Research, hypothesized that SWA in individuals free of psychiatric disorders, or

healthy individuals, should increase after waking and decrease after sleep. SWA is considered

a measure of sleep homeostasis and is thought to reflect cortical strength as well. After

performing the study using AEPs (see below), it was concluded that the pre- to post-sleep decline

in amplitude reflects a homeostatic process of sleep, whereby synaptic strength is downscaled. In

other words, sleep returns brain activity back to a baseline, or reduces it to allow for efficientresponses to waking-related tasks. While this may be true for healthy individuals, a lower change

in amplitude in depressed individuals may reflect an abnormal sleep homeostasis process.

Auditory evoked potentials (AEPs), or small electrical voltage potentials recorded in

conjunction with auditory stimulation, are effective in creating measurable waveforms collected

by EEG scalp recordings. Computer-generated stimuli are often used in AEPs and data is

recorded pre- and post-sleep. Dorokhov and Verbitskaya (2005) recorded AEP data at eight

different points during SWS in 26 healthy subjects. This study resulted in individual variability

of the AEP waveforms, yet the resulting graphs still followed the expected pattern, whereby

amplitudes will decrease after sleep (Figure 3). Because these sleep measures of brain activity

reflect sleep homeostasis, it is of interest to repeat these measures in waking, as well, to see if

similar trends are formed. Both changes in SWS found in EEG recordings of AEPs and waking

AEPs may emphasize that sleep homeostasis is altered in depression.

There are some common clinical signals of major depressive disorder, including fatigue,

feelings of worthlessness, impaired concentration, indecisiveness, restlessness, thoughts of

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suicide, and weight loss or weight gain (Gopal et al., 2004). One of the most common symptoms,

however, is sleep disturbance, particularly in terms of SWS (Steiger and Kimura, 2010).

According to Steiger and Kimura (2010), around 80% of depressed individuals claim that they

have insomnia. Disturbed sleep continuity and lower SWS and SWA in depressed patients has

also been observed in previous studies. Roth et al. (1981) collected AEP data in controls,

individuals with schizophrenia, and individuals with depression. The schizophrenia group

showed reduced N1 amplitude and P2 latency to frequent tones and reduced P3 and slow-wave

amplitudes to non-target, infrequent tones when compared to controls. Depressed individuals

were intermediate between the controls and schizophrenic subjects, and their P2 amplitude alsodecreased. Similarly, another study found a positive correlation between the N1 amplitude slope

and the degree of depression symptoms (Linka et al., 2009); however, they correlated

amplitude/intensity slope with the level of depression. Our study chose to correlate amplitude

with subjects that have similar degrees of major depression because we want to investigate

whether a decrease in both N1 and P2 amplitudes occurs. Khanna et al. (1989) found no

difference in latency, but the depressed group had significantly greater N1 and P2 amplitudes

before sleep than the control group. Similarly, Vandoolaeghe et al. (1997) showed that subjects

with major depression were found to have a higher P3 latency and P2 amplitude than normal

subjects. We expect to replicate these baseline findings with our pre-sleep recordings, and take

post-sleep recordings to provide data that will allow us to investigate whether both amplitudes

change.

Analysis of brain waves enables variations in depression among patients to be measured

and analyzed. Psychiatrists have been interested in sleep EEG since the 1970s after it was

determined that a shortened REM latency indicated depression and that antidepressants also

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suppressed REM sleep. Waveforms of SWS in depressed individuals may show patterns that will

help predict symptoms of depression and provide insight into effective methods of treatment

(Linka et al., 2009).

Studying how certain drugs and treatments affect SWS reflects the extent to which sleep

homeostasis can be changed. Walsh et al. (2010) studied 58 healthy adults, where 28 participants

received a placebo and 30 were given Sodium Oxybate, which was thought to reduce the

behavioral and physiological effects of sleep loss. Each individual had two baseline nights, two

sleep deprivation nights, followed by a 3-hour daytime sleep, and a recovery night. During the

day, the Sodium Oxybate group had more SWS or SWA, showing the brains need for sleep.However, after deprivation and a recovery sleep, this group had less SWS and SWA than the

placebo group, showing that Sodium Oxybate caused a reduced response to sleep deprivation.

Further investigation of change in SWS waveforms in depressed individuals both with and

without treatment will increase understanding of depression. Research allowing for comparisons

of depressed individuals with controls will also provide insight into treatment and increase the

general understanding of sleep homeostasis.

The purpose of my study at the Center for Sleep Medicine and Sleep Research is to

provide further evidence that sleep entails a homeostatic process that causes changes in waking

brain activity, as measured by AEP amplitude, and to expand on these ideas by comparing slow-

wave sleep in healthy individuals to depressed individuals to examine whether the nature of sleep

homeostasis differs. This may ultimately add to existing knowledge by providing possible insight

on the nature of sleep and its regulatory processes. The primary purpose of this study is to

determine the pre- and post-sleep waking AEP amplitudes to see if our findings agree with the

previous studies and reflects the idea of sleep homeostasis and that it differs among depressed

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individuals. Our hypothesis is that slow-wave activity will increase after waking and decrease

after sleep, characterized by a greater decline in amplitude overnight among controls when

compared with depressed individuals. In addition, larger N1 and P2 waking amplitudes of

depressed individuals may suggest "abnormal" homeostatic sleep regulation.

Methods: There were 22 subjects used in this study. Eleven healthy subjects (three males and

eight females), absent of psychiatric illness and in good physical and mental condition,

participated and were used as controls. Average subject age was 21 years (range=19-25 years;

Table 1). Eleven depressed subjects also participated, including three depressed males and eightdepressed females with an average age of 22 years (range=18-26 years; Table 1). These subjects

were clinically diagnosed via structured clinical interviews for DSM-IV Axis 1 disorders

(SCID). The DSM-IV (Diagnostic and Statistical Manual of Mental Disorders, fourth edition) is

the standard classification of mental disorders used by medical professionals in the United States.

The study area was in a psychiatry and basic neuroscience research lab, the Center for Sleep

Medicine and Sleep Research (Madison, WI). In general, pre-sleep data acted as a control to be

compared to post-sleep data, and data from healthy subjects acted as a control against depressed

subject data.

Subjects had one adaptation night to acclimate to conditions as well as one experimental

night in the lab. On the experimental night, data was collected with a high-density EEG (hdEEG;

256-channel HydroCel Sensor Net) while subjects performed an auditory oddball task. HdEEG

was used due to its strong temporal resolution and spatial resolution over the scalp. After the

task, patients slept in comfortable rooms, were woken the next morning, and re-performed this

task again as new recordings were collected.

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Auditory evoked potentials (AEPs) were recorded using a standard oddball paradigm (as

described in Hulse et al., in press). Tones with 50 ms duration and 800 Hz or 560 Hz (target

tone) frequencies were presented at an unchanging, normal volume into headphones as the

subjects sat with their eyes open looking at a point 90 cm in front of them. A total of 120 stimuli

were presented and subjects had to count the number of target tones heard. These tones occurred

randomly throughout the recording. Ninety-six standard tones, or trials, were used to allow for a

high signal-noise ratio when averaging data together.

Using NetStation 4.4 software (Electrical Geodesics, Inc.), AEP recordings were first-

order high-pass filtered (Kaiser type FIR, 0.1 Hz), band-pass filtered (Kaiser type FIR, 1-15 Hz),and segmented (100ms pre- to 500ms post-tone). Artifact rejection was performed to exclude

channels and segments with unusable data with such things as eye movements (peak-to-peak

amplitude >150 V), eye blinks, and bad channels (if >20% of recording bad). Each channel

was referenced to linked mastoid electrodes, and bad channels were re-interpolated using a

spherical spline computation. Segments were then averaged and the baseline was corrected (-

100ms to 0ms).

The AEP recordings of each subject pre- and post-sleep were collected. This data was run

through the scripts we created with the functions listed above to get an average of the corrected

segments for the 96 trials. To analyze this data, we examined if there was a decline in the

amplitude of N1 and P2 components of the AEP readings after sleep for both the control and

depressed groups. Overnight change in amplitude was determined by taking the average

amplitude for each group before and after sleep, calculated by looking at the peaks. We defined a

range that would include peaks for all eleven subjects. For N1 peaks, the range was between

60ms and 140 ms after the stimulus, and for P2 peaks, the range was set between 160ms and

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p=0.829; Table 5).

Figure 4 displays the overnight change in AEP as the raw amplitude calculated from the

group averages of the 11 subjects analyzed. It shows data collected at a fronto-central channel.

This figure shows that the controls had a greater decline in N1 amplitude overnight when

compared to the depressed. In addition, as anticipated, our depressed group had a larger N1

amplitude than the gender and age-matched controls. The controls also had a higher P2

amplitude than the depressed group for both the pre- and post-sleep data even though no

significant decline was present.

There was also no significant change in N1 latency for the control group ( t (10)=0.53, p=0.605; Table 6) and the depressed group ( t (10)=0.63, p=0.541; Table 6). No significant change

was found for P2 latency as well for both the control group ( t (10)=1.34, p=0.209; Table 6) and

the depressed group ( t (10)=1.19, p=0.261; Table 6). Similarly, there was no significant

difference for overnight change in N1 amplitude ( t (10)=0.25, p=0.808; Table 6) and overnight

change in P2 amplitude ( t (10)=0.41, p=0.685; Table 6).

Discussion: The fact that our depressed group had a larger N1 amplitude than the gender and

age-matched controls agreed with previously published results (Khanna et al., 1989). A greater

decrease in N1 amplitude in control individuals versus depressed individuals also matches our

hypothesis that depressed individuals may have an abnormal process of sleep homeostasis.

Because sleep homeostasis is characterized by an increase in slow-wave activity after waking

and a decrease in slow-wave activity after a nights sleep, we expected to see a decline in N1 and

P2 amplitude when comparing our pre- and post-sleep data. The decrease in amplitude for the

control group in our study matches this idea, and the fact that we found no significant decrease in

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N1 amplitude for the depressed group reflects that an irregular sleep homeostatic regulation may

be occurring. As described in one study (Hulse et al., in print), the pre- to post-sleep decline in

N1 amplitude may reflect a homeostatic reduction of synaptic weight or synaptic strength,

showing that sleep brings the brain back to baseline to prepare for efficient memory and learning

during the following waking period. Depressed individuals, however, do not properly return to

baseline, as evidenced by their insignificant decrease in amplitude. They do not undergo the

same homeostatic process as normal, healthy controls, which supports our hypothesis. Further

research should be done to investigate whether this abnormal homeostasis may be correlated

with specific depressive symptoms.The P2 amplitude had a smaller, though not significant, decrease among the depressed

group when compared to the controls, and the control subjects had larger pre-sleep P2

amplitudes than the depressed subjects, which is consistent with what was previously reported

(Roth et al., 1981). This study, however, found no significant difference in N1 amplitude, which

differs with our results. This may be due to the fact that only waking data was collected over

one-hour intervals. While our study did compare waking data as well, we compared wakefulness

pre- and post-sleep, which is a much more effective way to see amplitude change in correlation

with sleep homeostasis. Their study also performed one-intensity, three-intensity, two-tone, and

three-tone AEPs, all at different frequencies, whereas our AEP tones were done with 50 ms

duration and 800 Hz or 560 Hz (target tone) frequencies. We did not perform multiple AEP tests

at multiple frequencies, which may explain why we obtained different results. The same

reasoning may explain why Vandoolaeghe et al. (1997) also found that P3 latency and P2

amplitude was higher in depressed individuals, which does not support our results.

Possible sources of error in our study include the possibility of defective EEG scalp

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recordings. If gel was not properly placed on each of the electrodes, then the electrodes cannot

properly attach to the scalp and readings at these defective electrodes may be non-existent or

inaccurate. Similarly, eye blinks and muscle movements also create artifacts in the data, which

need to be accounted for. To compensate for this, we ran the data through our script, which

corrected for any channels we deemed bad, or that were characterized by extremely high

frequencies, amplitudes, or strange patterns that greatly differed from data for the rest of the

channels. A lot of this process was up to our discretion, however, because we created the scripts

ourselves and set the parameters for which channels were deemed good and which were

deemed bad by outlining which specific frequencies could be accepted. Therefore, humanerror, or setting parameters that may exclude some good channels, may have thrown off our

averages and, ultimately, our waveform amplitudes, which we based our comparisons on.

Further research could be done to examine the effects that sleep homeostasis and a

change in amplitude have on serotonin uptake in the brain. Serotonin regulates mood, appetite,

sleep, muscle contraction, memory, and learning, and low serotonin level is a factor in

depression and anxiety (Gopal et al., 2004). People with depression have serotonin receptor sites,

but the amount of serotonin available to stimulate adjacent neurons is inadequate. Selective

serotonin re-uptake inhibitors (SSRIs) are often given to depressed individuals, and Gopal (2004)

hypothesized that these SSRIs are responsible for positive changes in abnormal auditory

behavior. Chen et al. (2002) found that the serotonergic system is correlated with the N1 and P2

components of AEPs. The N1 and P2 components are generated in the auditory cortex of the

brain, which is also responsible for a high rate of serotonin synthesis. The serotonin transporter is

the major site of serotonin re-uptake back into the pre-synaptic neuron. The serotonin transporter

gene-linked polymorphic region (5-HTTLPR) is related to N1 and P2 components. This study

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found that a shorter P2 latency was found in patients with the s/s genotype versus the l allele

carriers. Thus, 5-HTTLPR polymorphism and AEP P2 latency are correlated, therefore further

AEP studies relating change in amplitude between control and depression groups may also

provide insight into serotonin re-uptake between these two groups if genotype of the individuals

is considered as well. Future studies could address their hypothesis that lower central

serotonergic neurotransmission leads to higher intensity dependence for AEP N1 and P2

components, and information from these studies could be used to better understand effectiveness

of SSRIs.

While our study concentrated mainly on amplitude change pre- to post-sleep, further studies could address changes in latency, or the time it takes to respond to a stimulus. Any

observed change in latency could then be observed to determine whether a correlation also exists

between change in N1 and P2 amplitude and a change in N1 and P2 latency between controls

and depressed individuals. A third variable group, subjects with schizophrenia, has been used in

other studies to allow for further comparison against depressed and control groups (Medved et

al., 2001). Comparing their results, in which they found AEP amplitudes were smaller in

schizophrenics when compared to depressives, with what we found in our study, an abnormal

sleep homeostasis may also be present in subjects with schizophrenia, and further research

should be done to investigate this.

Different methods of analyzing data could also be used to further examine our results as

well as results from future studies. Figure 5 shows an example of overnight change in AEP

expressed as the global mean field power (GMFP), which places both negative and positive

amplitudes on the same positive scale. This graph considers all 185 channels used to collect the

EEG scalp readings, and our data collected from our eleven depressed and eleven control

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subjects could have been plotted in this way to observe amplitude difference on a positive scale.

In addition, a more complicated, yet informative spectral comparison could be done in the future,

which would show whether or not there was a significant difference in amplitude for every half

second of brain wave data collected.

While it appears evident that a greater decrease in amplitude pre- to post-sleep in control

individuals versus depressed individuals is evidence of abnormal sleep homeostasis, further

studies could examine latency, variations of affects on medicated and non-medicated depressed

subjects, effectiveness of treatments, correlation with serotonin re-uptake, and other similar

psychiatric disorders to learn more about this. Though our hypothesis matched our results, it isstill unclear as to exactly why depressed individuals have an abnormal system of homeostatic

sleep regulation and much more research is still needed to provide insight into this topic.

References:

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Bersagliere A and Achermann P 2010. Slow oscillations in human non-rapid eye movementsleep electroencephalogram: effects of increased sleep pressure. Journal of Sleep Research. 19:228-237.

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Tables and Figures:

Table 1: Subject Data for Study - Healthy, control subjects are matched with a depressedsubject for comparison. They are matched for sex and age, within a 3-year range. Mean age andstandard deviation is calculated for both the depressed and control groups.

Motor Control Night

Depressed Age* SexControlMatch Age* Sex

B120NW 24 F C114EM 22 FB130AU 21 M C100JM 21 MB135LK 21 F C78CP 21 FB142SJ 22 F C106AA 22 FB155DK 26 F C107MF 23 FB157KK 26 F C85TR 25 FB158OT 19 F C89AM 19 FB159DW 21 M C86EL 19 MB162TF 22 F C74MD 21 FB165LWB 21 M C97SL 19 MB167KD 18 F C73EM 20 F

N= 11 8 F N= 11 8 FM= 22 M= 21

STDEV= 2.547726259 STDEV= 1.868397466

Table 2: N1 Amplitude of Control Subjects- This table displays the calculated N1 amplitude pre- and post-sleep for each of the eleven control subjects. The overnight change in amplitude isalso calculated, as well as the mean, standard deviation, and p and t values. Both of these valuesshow that there is a significant difference between pre- and post-sleep N1 amplitude. Orange

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values represent points that would have been outliers if we had used 2 standard deviation.

Subject PreSleep_Con PostSleep_Con Overnight Change_Con (-)C114EM -16.43457413 -10.02989483 -6.404679298C100JM -11.88212967 -8.879209518 -3.002920151C78CP -4.73160553 -1.156788111 -3.574817419C106AA -9.46758461 -7.941483974 -1.526100636C107MF -3.716108799 -5.928866386 2.212757587C85TR -5.385984898 -3.774027824 -1.611957073C89AM -13.53852654 -9.479260445 -4.05926609C86EL -10.00778484 -9.008646965 -0.999137878C74MD -7.791980267 -4.991571426 -2.80040884C97SL -19.18057442 -6.929605007 -12.25096941C73EM -9.643204689 -12.29873371 2.655529022

M= -10.16182349 -7.310735291 -2.851088199STDEV= 4.850575252 3.170647034 4.070393113

2.5 STDEVhi 1.964614641 0.615882294 7.3248945852.5 STDEVlo -22.28826162 -15.23735288 -13.02707098

t tests Pre vs. Post_Con p = 0.042546426

t (10) = 2.32

Table 3: N1 Amplitude of Depressed Subjects- This table displays the calculated N1 amplitude pre- and post-sleep for each of the eleven depressed subjects. The overnight change in amplitudeis also calculated, as well as the mean, standard deviation, and p and t values. Neither of thesevalues displays significance. Orange values represent points that would have been outliers if we

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had used 2 standard deviation.Subject PreSleep_Dep PostSleep_Dep Overnight Change_Dep (-)B120NW -14.33230972 -14.10698605 -0.225323677B130AU -5.161910057 -4.627454758 -0.534455299B135LK -12.75158405 -10.73036098 -2.021223068B142SJ -10.33222389 -10.49464703 0.162423134B155DK -10.76493454 -10.20313072 -0.561803818B157KK -7.837329388 -9.646419525 1.809090137B158OT -3.775166035 -2.669214249 -1.105951786B159DW -12.51911163 -9.562823296 -2.956288338B162TF -3.664168119 -1.449521661 -2.214646459B165LWB -4.053227901 -2.920983791 -1.13224411B167KD -19.56045914 -22.02588844 2.465429306

M= -9.52294768 -8.948857318 -0.574090362STDEV= 5.14412514 5.969076452 1.63214393

2.5 STDEV hi 3.337365169 5.973833812 3.5062694642.5 STDEV lo -22.38326053 -23.87154845 -4.654450187

t tests Pre vs. Post_Dep Dep vs. Con_Change (-) p = 0.270442982 0.100490536

t (10) = 1.17 t (10) = 1.72

Table 4: P2 Amplitude of Control Subjects- This table displays the calculated P2 amplitude pre- and post-sleep for each of the control subjects. The change in amplitude is also calculated,as well as the mean, standard deviation, and p and t values. Neither display significance.

Subject PreSleep_Con PostSleep_Con Overnight Change_Con (-)C114EM 17.38280296 12.19486237 5.187940598C100JM 11.19477463 9.32272625 1.872048378C78CP 12.5847683 12.86163235 -0.276864052C106AA 13.41247368 8.046705246 5.365768433C107MF 6.703750134 10.97121716 -4.267467022C85TR 11.4428339 12.11953163 -0.676697731C89AM 15.17484379 19.70868874 -4.533844948C86EL 3.58420229 0.788653195 2.795549095C74MD 20.91690254 19.41464615 1.502256393C97SL 19.53075218 15.55759048 3.973161697C73EM 5.623976707 -0.298680782 5.92265749

M= 12.50473465 10.97159753 1.533137121STDEV= 5.615164264 6.457745933 3.655030955

2.5 STDEV hi 26.54264531 27.11596236 10.670714512.5 STDEV lo -1.533176013 -5.172767307 -7.604440267

t testsPre vs.Post_Con

p = 0.194341931t (10) = 1.39

Table 5: P2 Amplitude of Depressed Subjects- This table displays the calculated P2 amplitude pre- and post-sleep for each of the eleven depressed subjects. The overnight change in amplitudeis also calculated, as well as the mean, standard deviation, and p and t values. Neither of thesevalues displays significance. Orange values represent points that would have been outliers if we

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had used 2 standard deviation.

Subject PreSleep_Dep PostSleep_Dep Overnight Change_Dep (-)B120NW 16.20042992 18.2090416 -2.008611679B130AU 7.014438152 5.73831749 1.276120663B135LK 17.52749062 18.25637245 -0.728881836B142SJ 5.958817482 3.869665146 2.089152336B155DK 8.310996056 9.973079681 -1.662083626B157KK 19.56665802 11.81066227 7.75599575B158OT 9.665380478 8.617565155 1.047815323B159DW 4.24742794 6.423761845 -2.176333904B162TF 4.596893787 7.049726009 -2.452832222B165LWB 3.874248028 7.852319241 -3.978071213B167KD 25.20217896 12.05887413 13.14330482

M= 11.1059054 9.987216819 1.118688583STDEV= 7.295904755 4.76269499 5.100323146

2.5 STDEV hi 29.34566729 21.89395429 13.869496452.5 STDEV lo -7.133856484 -1.919520655 -11.63211928

t tests Pre vs. Post_Dep Dep vs. Con_Change (-) p = 0.483629022 0.828820626

t (10) = 0.73 t (10) = 0.22

Table 6: Latency : This table displays the latency p and t values from paired and un-paired t -tests for both N1 and P2. There is no significant change in latency between both the control anddepressed subject groups.

N1: Pre vs. Post_Dep N1: Dep vs. Con_Change (-) N1: Pre vs. Post_Con p = 0.540961841 0.808208796 0.605066482

t tests t (10) = 0.63 t (10) = 0.25 t (10) = 0.53 p = P2: Pre vs. Post_Dep P2: Dep vs. Con_Change (-) P2: Pre vs. Post_Con

t tests 0.260915529 0.684849502 0.20882465t (10) = 1.19 t (10) = 0.41 t (10) = 1.34

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Figure 1: Sleep Stage Histogram: This is an example of a sleep histogram for a healthy control,in which time (beginning at 11pm) is plotted in accordance with each specific stage of sleep. Thesix stages of sleep are represented: waking, REM, S1, S2, S3, and S4. Stage 3 is consideredslow-wave sleep and is when slow-wave activity occurs. As shown in the figure, multiple sleepcycles occur in one night, as well as short periods of wakefulness (Lawrence, 2010).

Figure 2: Sleep Stage Waveforms : This displays an example of EEG recordings during eachstage of sleep. Stage 3, slow-wave sleep, is characterized by waveforms of higher amplitudes andlower frequencies when compared to the proceeding stages (Pastorius, 2010).

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Figure 3: EEG Waveform: This is an example graph of what the averaged segments from theAEP recordings will look like, with N1 and P2 labeled. The dotted line shows where the stimuluswas presented. The red line represents the post-sleep data. A decline in N1 and P2 amplitudesafter sleep is what is expected to occur (Lightfoot, 2010).

Figure 4: Overnight Change in AEP- Raw Amplitude: This graphdisplays the overnight change in AEP as the raw amplitude calculated fromthe group averages. It shows data collected from one channel in the frontallobe, the fronto-central channel. This graph expresses our averaged resultsfrom 11 subjects, in which the controls show a greater decline in N1 amplitude overnightwhen compared to the depressed, which matches our hypothesis and may suggest "abnormal"

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homeostatic sleep regulation.

Figure 5: Overnight Change in AEP- GMFP: This graph is an example of displaying theovernight change in AEP as the global mean field power, which places both negative and

positive amplitudes on the same positive scale. It also considers all 185 channels rather than just the fronto-central channel. Displaying data in a GMFP would be useful in displayingdata in future studies.

biology 152 independent research project

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