Mark Yousef

Essay · 9 min read · 35 sources

Sleep as a window into brain health

What a night of sleep can tell us about the brain.

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Part 1 of 3 · Next: AI is the new microscope · The future of brain medicine

My interest in sleep

I started studying neuroscience to learn what the brain could teach us about building better AI systems. At first I was mostly interested in cognition. Working memory, attention, reasoning and executive function seemed like obvious places to borrow from. Later I got curious about affect and mood. Did they have a role to play in AI, or were they legacy routines from evolution that we could do without? The chapters on sleep I usually skipped. After all, the action was in the waking brain, or so I thought.

That changed when I went through bouts of burnout and depression. I couldn't fall asleep, and when I did, I woke several times a night and lay awake. Clinicians call this onset and maintenance insomnia. The worse I slept, the worse my brain worked. Sleep, I realised, was critical for the brain. Duh.

I went back to the chapters I'd skipped, read books like Why We Sleep1 and listened to every Huberman episode and sleep podcast I could find. I got obsessed with my own sleep and tried various supplements, behavioral protocols and sleep gadgets to steer it in the right direction. Fixing my sleep fixed my brain. Since then I've worked with leading sleep scientists and interviewed researchers and clinicians in neurology and psychiatry. Again and again, they pointed me to the link between sleep and various brain disorders.

Along the way I saw the link back to AI. Memory consolidation and dreaming happen during sleep, and maybe AI systems will need to sleep too. But the bigger idea ran the other way. Instead of using the brain to build AI, I could point AI at sleep and use it to understand and improve brain health.

This three-part series asks three questions. What can sleep tell us about brain health? What applications is AI unlocking, and what is holding it back? What does this mean for the future of brain medicine?

I'm not a scientist or a clinician. I'm a founder building in this space, so these are my views, but I try to stay as close to the science as I can.

Sleep and brain health

A clinical sleep study, polysomnography (PSG), records various biosignals through a single night: brain activity, eye movements, muscle tone, leg movements, heart rhythm, breathing and blood oxygen.

It is the standard test for sleep apnea, where the airways keep closing during sleep. Apnea is common. A modelled estimate puts 936 million adults aged 30 to 69 worldwide at the lab threshold, 425 million of them in moderate or severe form2. Simpler home tests now handle many straightforward cases3. Together with a daytime nap test, a sleep study is also how doctors diagnose narcolepsy4. It is also required to confirm REM sleep behaviour disorder (RBD), where people act out their dreams because REM sleep loses its normal muscle paralysis5. It is the main test for other parasomnias and for periodic limb movements too, though not for insomnia, the most common sleep complaint.

Some of these disorders reach far beyond sleep, and RBD is the clearest example. In 1,280 people with isolated RBD followed at 24 specialist centres, about 6% a year went on to parkinsonism or dementia, roughly three in four within 12 years by projection, as Parkinson's disease, Lewy body dementia or multiple system atrophy6. A night in the lab confirms RBD, and that confirmation can come years before the movement or memory problems that lead to the brain disease being diagnosed.

The physicians and researchers I spoke to kept telling me that sleep medicine reaches much further than sleep disorders, and I wanted to see how far. Sleep health has several measurable dimensions7. Building on that, I broke a night into 17 components, from how long you sleep to the fine rhythms in the brain's signal. Then I had AI agents search PubMed, which returned 110,682 articles linking sleep to brain, mental and body conditions, and pull the strongest evidence for each component and condition. Figure 1 shows the result.

BrainMental healthHeart, metabolism and survivalStrong evidence (A or B)Weaker (C or D)

The full map, linking every component to every condition, needs a larger screen.

strong evidence (A or B)weaker (C or D)average of all 26
Brain activity
Deep sleep
Spindles
EEG slowing
REM amount
REM latency
REM density
REM paralysis
REM without atonia
Breathing
Apnea events
Oxygen drops
Continuity
Time to fall asleep
Waking in the night
Arousals
Duration
Short sleep
Long sleep
Timing
Regularity
Circadian rhythm
Movement
Limb movements
A night of sleep7 parts, 17 componentsConditions26, in three familiesBrain activityDeep sleep 18Spindles 8EEG slowing 6REM amount 16REM latency 11REM density 6REM paralysisREM without atonia 4BreathingApnea events 24Oxygen drops 7ContinuityTime to fall asleep 9Waking in the night 16Arousals 9DurationShort sleep 25Long sleep 8TimingRegularity 13Circadian rhythm 18MovementLimb movements 8BRAINDementiaAlzheimer'sMild cognitive impairmentParkinson'sLewy body dementiaStrokeEpilepsyMultiple sclerosisMigraineBrain injuryMENTAL HEALTHDepressionBipolar disorderAnxietyPTSDSchizophreniaADHDAutismAlcohol useHEART, METABOLISM AND SURVIVALHigh blood pressureCardiovascular diseaseAtrial fibrillationType 2 diabetesObesityKidney diseaseDeath, any causeInflammationA night of sleep7 parts, 17 componentsConditions26, in three familiesBRAIN ACTIVITYDeep sleep 18Spindles 8EEG slowing 6REM amount 16REM latency 11REM density 6REM PARALYSISREM without atonia 4BREATHINGApnea events 24Oxygen drops 7CONTINUITYTime to fall asleep 9Waking in the night 16Arousals 9DURATIONShort sleep 25Long sleep 8TIMINGRegularity 13Circadian rhythm 18MOVEMENTLimb movements 8BRAINDementiaAlzheimer'sMild cognitiveimpairmentParkinson'sLewy bodydementiaStrokeEpilepsyMultiplesclerosisMigraineBrain injuryMENTAL HEALTHDepressionBipolar disorderAnxietyPTSDSchizophreniaADHDAutismAlcohol useHEART, METABOLISMAND SURVIVALHigh bloodpressureCardiovasculardiseaseAtrialfibrillationType 2 diabetesObesityKidney diseaseDeath, any causeInflammation

Sleep fingerprint

Depression

A depressed night takes longer to start and breaks up more often. That broken sleep is shared with most psychiatric conditions. REM that arrives early and carries more eye movements leans toward depression, but it is not unique to it and shrinks in the best-controlled studies.

Both waysWhy

Long-term studies find that people with insomnia are more than twice as likely to become depressed later, and genetic studies point both ways, with insomnia-linked genes raising depression risk and depression-linked genes raising insomnia risk, though one genetic study found neither. The strongest evidence comes from trials. Treating insomnia with CBT-I cut new depression roughly in half in adults over 60 (12% against 26% over three years) and by about 40% in young people (10% against 18% over a year), but each result rests on a single trial, and only about half of the older adults completed three-year follow-up.

Deep sleepSpindlesEEG slowingREM amountREM latencyREM densityREM without atoniaApnea eventsOxygen dropsTime to fall asleepWaking in the nightArousalsShort sleepLong sleepRegularityCircadian rhythmLimb movements
DepressionAverage of all 26Longer spoke: stronger evidence
Key findings for Depression 12 papers
  • AWhole night Pooling long-term studies, people with insomnia were more than twice as likely to develop depression later. Summary RR 2.31 (95% CI 1.90-2.81); umbrella review of 25 meta-analyses of prospective cohorts (63 summary estimates, 29 outcomes); observational, outcomes often symptom scales, residual confounding by subthreshold symptoms possible.Wu 2023 · Public Health · doi
  • BWhole night In 708 young people aged 15 to 25 with insomnia and mild depressive symptoms, six weeks of app-based CBT-I lowered new major depression over the next year from 18% to 10%, compared with app-based health education. HR 0.58 (95% CI 0.38-0.87); assessor-blind RCT, n=708 Chinese youth (mean age 22.1), insomnia disorder plus subclinical depression; new MDD by telephone clinical interview over 12 months; NNT 10.9; insomnia mediated the effect.Chen 2025 · PLoS Med · doi
  • BWhole night In 291 adults over 60 with insomnia and no major depression in the past year, CBT-I halved new or returning major depression over three years compared with sleep education (12% against 26%). HR 0.51 (95% CI 0.29-0.88); single-site assessor-blinded RCT vs active sleep education therapy, n=291 (mean age 70.1); DSM-5 interview; follow-up extended from 24 to 36 months by protocol change, 52-57% completed 36 months, more dropout in the CBT-I arm.Irwin 2022 · JAMA Psychiatry · doi
  • BWhole night Genetic studies suggest the arrow runs both ways: gene variants linked to insomnia raised the odds of major depression by about 30%, and variants linked to depression raised the odds of insomnia by a similar amount. IVW OR 1.31 (95% CI 1.25-1.37) insomnia to MDD; OR 1.37 (1.14-1.64) MDD to insomnia; bidirectional two-sample MR, PGC and UK Biobank summary data. Gao 2019 (Eur Psychiatry, PMID 31234011) found no significant effect in either direction for MDD; pleiotropy and sample overlap cannot be excluded.Sun 2022 · Epidemiol Psychiatr Sci · doi
Show 8 moreShow fewer
  • BWaking in the night Across 53 studies with wrist sensors, people with depression spent more time awake after first falling asleep and slept less efficiently than healthy controls, a small to moderate difference. WASO SMD 0.37 (95% CI 0.22-0.52); sleep efficiency SMD -0.41 (-0.56 to -0.25); actigraphy case-control meta-analysis, 4,000 depressed (MDD or depressive symptoms) vs 7,115 controls; heterogeneous devices and samples. On PSG, Baglioni 2016 (PMID 27416139) found major depression altered on 10 of 11 variables, continuity included.Ho 2024 · J Affect Disord · doi
  • BTime to fall asleep An umbrella review of overnight-recording meta-analyses across 27 brain and mental disorders rated the evidence for longer time to fall asleep in major depression as highly suggestive, the second-highest of five levels, and wrist-sensor studies show the same small delay. Credibility class highly suggestive for increased sleep latency and reduced sleep efficiency in MDD (REM latency only suggestive); umbrella review of 27 meta-analyses, 465 PSG case-control studies; pooled values in full text. Actigraphy: Ho 2024 (PMID 38851435) sleep latency SMD 0.23 (0.12-0.33), 53 studies.Zhang 2022 · Psychol Med · doi
  • BArousals On wrist sensors, people with depression woke up more often during the night than healthy controls, a moderate difference. Nocturnal awakenings SMD 0.58 (95% CI 0.29-0.88); actigraphy meta-analysis, 53 studies, 4,000 depressed vs 7,115 controls; actigraphic awakenings are movement-defined wake bouts, not EEG arousals.Ho 2024 · J Affect Disord · doi
  • BCircadian rhythm In a genetic study drawing on nearly 700,000 people, gene variants linked to being a morning person, scaled to a one-hour earlier sleep midpoint, went with 23% lower odds of major depression. MR IVW OR 0.77 (95% CI 0.63-0.94) per 1-h earlier sleep midpoint; stringent PGC-defined MDD OR 0.73 (0.54-1.00); UK Biobank hospital codes OR 0.64 (0.39-1.06), ns; up to 340 chronotype loci (UK Biobank and 23andMe, n=697,828), scaled by accelerometer midpoint in 85,502; MDD 170,756 cases vs 329,443 controls, European ancestry; Egger intercept null. Observational: Ho 2024 (PMID 38851435) actigraphic rhythm amplitude SMD -0.33 (-0.57 to -0.09).Daghlas 2021 · JAMA Psychiatry · doi
  • BRegularity On wrist sensors, people with depression have slightly less regular day-to-day rest and activity than controls, and in UK Biobank, regular sleepers had 38% lower risk of a new depression diagnosis over 7.5 years than irregular sleepers. Interdaily stability SMD -0.17 (95% CI -0.28 to -0.05), actigraphy meta-analysis, 53 studies. Li 2025 (Psychol Med, PMID 40814280): sleep regularity index 71 or more vs 51 or less, HR 0.62 (0.52-0.73), n=79,666, 1,646 incident cases, single 7-day baseline; residual confounding possible.Ho 2024 · J Affect Disord · doi
  • BShort sleep Pooling seven long-term studies, adults who reported short sleep had about 31% higher risk of later depression than normal sleepers, and long sleepers about 42% higher. Short sleep RR 1.31 (95% CI 1.04-1.64), I² 0%; long sleep RR 1.42 (1.04-1.92), I² 0%; meta-analysis of 7 prospective studies, 25,271 participants for short sleep; self-reported duration; observational.Zhai 2015 · Depress Anxiety · doi
  • CWhole night In 3,247 middle-aged and older adults with one night of home sleep recording, only a larger share of light N1 sleep predicted new depressive symptoms over about five years; REM and deep sleep did not. OR 1.06 per percentage point of N1 (95% CI 1.02-1.10); prospective cohort (Sleep Heart Health Study), single unattended in-home PSG, 225 incident cases of self-reported depressive symptoms over mean 5.3 years; exact onset timing missing.Jiang 2024 · J Affect Disord · doi
  • CREM latency In drug-free depressed adults without other disorders, REM sleep starts earlier and carries more eye movements than in healthy people, but a 2024 meta-analysis of depressed adults found both differences lost significance once six poorly matched studies were dropped. SMD (pooled values in full text); 43 articles, 46 REM latency and 22 REM density comparisons, random effects, no publication bias. Contested: Ricciardiello 2024 (Sleep Med, PMID 39515262, 15 studies) found the REM latency and density effects nullified after removing 6 poorly selected case-control studies; Baglioni 2016 (PMID 27416139) found REM latency and REM duration no longer altered in comorbidity-free MDD, although REM density stayed raised. Antidepressants suppress REM and lengthen REM latency, so medicated samples show the opposite.Arıkan 2023 · Sleep Med Rev · doi
Figure 1. How strongly studies link 17 components of a night of sleep to 26 conditions: strong evidence is graded A or B, weaker C or D. Numbers count the conditions each component touches, which also reflects how much it has been studied. Choose a condition to see its fingerprint and key papers. How I built the map is in the note below.
How I built this map

I had AI research agents count the PubMed articles for each pairing of a sleep component and a condition, and pull the strongest evidence for each: umbrella reviews, meta-analyses, genetic studies and randomized trials. I checked each number against the paper, confirmed each DOI resolves, read the key papers and graded each pairing from A (strongest) to D (weakest). The grades are my judgement, not a formal assessment, and null results are under-represented.

The evidence base

110,682articles
2,716meta-analyses
651genetic studies
275graded findings
70strong links
2convincing
8,645 papers a year by 2025, up from 756 in 2000
Every sleep signature: 26 conditions by 17 components
18Deep sleep8Spindles6EEG slowing16REM amount11REM latency6REM density4REM without atonia24Apnea events7Oxygen drops9Time to fall asleep16Waking in the night9Arousals25Short sleep8Long sleep13Regularity18Circadian rhythm8Limb movementsLewy body dementiaInflammationMultiple sclerosisMigraineADHDAutismMild cognitive impairmentParkinson'sEpilepsyPTSDAlzheimer'sDepressionBipolar disorderSchizophreniaAnxietyBrain injuryStrokeHigh blood pressureKidney diseaseCardiovascular diseaseType 2 diabetesDeath, any causeAtrial fibrillationAlcohol useDementiaObesity

Counts. On 5 October 2026 an AI research agent counted PubMed articles for every pair of seven broad sleep measures (such as short sleep, insomnia and apnea) and 26 conditions: 110,682 distinct articles. These are search hits, not study quality, and one paper can count in several pairs.

The graded map in Figure 1. AI research agents then searched PubMed for evidence linking each of the 17 components to each condition, preferring meta-analyses of sleep-study (PSG) comparisons, then prospective cohorts, then landmark single studies. Every number was taken from an abstract or full text, and every DOI was checked in PubMed. On 10 October a second round re-checked every finding against its PubMed record, dropped those that did not hold up and regraded others. I read the key papers. 275 findings support 206 links, and 17 null results were recorded but are not drawn.

Grades. A: a large, consistent meta-analysis, replicated or with genetic or trial support. B: a meta-analysis with a clear, significant pooled effect. C: a small or mixed meta-analysis, or a few good studies. D: single small studies or mixed results. The grades are my judgement, not a formal GRADE assessment.

Limits. Most findings are associations, and many come from case-control comparisons, questionnaires or wrist actigraphy rather than one night of EEG. Null results are under-represented. Papers per year on sleep and dementia or cognition went from 113 in 2000 to 1,929 in 2025, and on sleep and depression from 360 to 5,558.

Papers behind Figure 1

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Reading the map

Two things stand out to me. The first is a shared core. Almost every condition is linked to the same few disturbances. Short sleep shows up in 25 of the 26 conditions, apnea in 24, changes in deep (slow-wave) sleep and circadian rhythm in 18 each, and waking in the night in 16. Part of the reason is that these are the most studied measurements, and the links vary a lot in strength. The core works a bit like a fever for the brain. It tells you something is wrong, but not what.

The second is that each condition has its own fingerprint. A meta-analysis of sleep studies across mental disorders found that no single measure was altered in only one disorder, yet no two disorders shared the same profile8. The specific signs mostly sit in the finer detail of the recording. Schizophrenia, for example, shows far fewer sleep spindles, the brief bursts of activity that mark stage 2 sleep9.

This is what got me excited. If each condition leaves its own fingerprint, one night could be a readout for many conditions at once. These profiles are group averages, though, and whether a model can read them in one person's night is the question the next part turns to.

Cause or signal

A fingerprint tells you something is there, but not whether sleep drives the disease or just reflects it. If it drives it, fixing sleep could help. If it only reflects it, sleep is still a window worth watching. Three kinds of evidence separate the two: cohorts show what predicts what, genetic studies (Mendelian randomization) use inherited variants as a natural experiment, and trials test whether fixing sleep changes the outcome. Depression and neurodegeneration tell two different stories.

Depression: sleep as part of the cause. All three point the same way. Insomnia roughly doubles the risk of later depression10, and genetic studies find a smaller effect in both directions11. Treating insomnia with cognitive behavioural therapy for insomnia (CBT-I), a short course that retrains sleep habits and thoughts about sleep, halved new or recurring depression in a single-site trial of 291 older adults with insomnia disorder, from 26% to 12% over three years12, though not every prevention trial has worked13. During my depression I couldn't tell whether bad sleep was dragging my mood down or the other way round. The research says both, and that matches what I went through. Once my sleep improved, my mood lifted, and a better mood made it easier to sleep.

Neurodegeneration: sleep as an early warning. RBD is the sharpest case. Acting out dreams doesn't cause Parkinson's, but it is one of the disease's earliest signs6. With dementia it helps to ask two separate questions: whether sleep predicts it, and whether it causes it. It predicts it, if you read the night closely. When five cohorts pooled single-night sleep studies, none of the standard sleep scores predicted dementia14, but read as a brain age, largely the same recordings did: each 10 years of extra brain age carried a 39% higher risk15. Whether sleep causes it is less clear. Genetic studies mostly find no causal effect on Alzheimer's16,17. One night of disrupted deep sleep does raise one form of amyloid-β, the protein that builds up in Alzheimer's, the next morning18, but the disease also disturbs sleep for years before diagnosis19. For now, sleep looks more like a warning light than a cause, and a warning light is still worth having.

One night, or many

So why isn't a sleep study as routine as a blood panel at your GP? Most people never get one. In a US cohort of working adults studied in the 1990s, 82% of men and 93% of women with moderate to severe apnea had never been diagnosed20, and apnea is the condition sleep clinics are built for.

Even the few people who do get a sleep study get a single night, in a bed that isn't theirs. Across 53 studies of 1,422 healthy people, sleep was shorter and more broken on the first night in a lab21. When 66 patients had one sleep study in hospital and one at home, they slept 47 minutes longer at home, fell asleep 17 minutes faster and spent more time in REM sleep22. A night in the lab can reveal a lot, but it is a snapshot taken in an unfamiliar place.

But nights at home vary too. When 94 men wore an EEG headband for five weeknights, the time each spent in deep sleep varied by 44% to 58% from one night to the next23.

No two nights are the same, just as no two days are. Researchers split any repeated measurement into three parts24. The trait is the level your nights keep returning to, the part that makes your sleep yours. A state is a departure from that level that passes: one bad night, a stressful month, a new medicine, a depressive episode that lifts25. A change that lasts becomes part of the trait. Noise is error in the measurement itself, such as two experts scoring the same night differently26. One night mixes all three. Over many nights, short states and noise average out, leaving the trait and any state that lasts (Figure 2a).

a Three simulated people, four weeks of total sleep

One nightAverage of the past seven nights
4h6h8hOne nightPerson A starts an interventionday 1day 7day 14day 21day 28Person APerson BPerson Cnight to nighttraitstate

b How alike two nights are (1 = identical)

c How far brain age moves, in years

Figure 2. a: Simulated. Dots are single nights, lines the seven-night average. The spreads match 49 older adults recorded at home27, and person A's 40-minute rise matches trazodone across 11 trials28. b: Agreement between two lab nights (intraclass correlation) in 107 older adults; brain-rhythm bars span three scalp sites29. c: Spread (one standard deviation) of brain age between nights in 86 patients with normal EEGs30.

The traits appear strongest in the brain's rhythms. Sleep spindles and slow waves come back almost unchanged from one night to the next, while total sleep barely repeats (Figure 2b)29. The pattern is so personal that in 40 twins it was 96% heritable31.

States show up as a change against that baseline, and some only show in the brain. Across the menstrual cycle, a ring picked up the change in skin temperature but not in sleep32, while the sleep EEG showed spindle power rising and falling33. A new medicine is a state too. In Figure 2a, its 40 extra minutes of sleep are lost among single nights and stand out in the weekly average28.

Averaging helps a lot. A brain age read from the sleep EEG moved by several years between single nights, and averaging four nights cut that spread by more than half (Figure 2c)30.

Watches and rings give you many nights, but they only read movement and heart rate34, so they can't see the spindles and slow waves where the traits are strongest. EEG headbands now record them at home, and their sleep staging agrees with a full sleep study almost as well as two human scorers agree with each other35,26.

I've slept with most of these, more than 1,000 nights with watches, rings and EEG headbands, and my own nights show the same layers. They wobble from day to day and week to week, and I think I've started to see a state that comes with the seasons. I live in Copenhagen, where summer nights barely get dark and winter days are short. I'm prone to seasonal affective disorder, the low mood that comes with winter, and I wonder how much of it runs through changes in my sleep and circadian rhythm. And if I protect my sleep through the winter, will the low mood stay away? I'll share the data in a later post.

In part 2, we look at how AI is unlocking new applications in sleep and what is holding it back. In part 3, we look at where the technologies are heading and what it could mean for the future of brain medicine.

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