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Functional near-infrared spectroscopy (fNIRS) is commonly used as a non-invasive tool to measure cerebral neurovascular dynamics. Its potential for diagnostics of various brain disorders has been already demonstrated in many recent studies, including Alzheimer’s disease (AD). fNIRS studies are usually based on comparing hemoglobin measurements at baseline and during a specific task. At present, many proposed methods using fNIRS to diagnose AD involve certain tasks, which may be challenging for the elderly and patients with cognitive decline. Here, we propose a method to characterize AD patients and control in resting state, by applying spectral entropy (SE) analysis on oxyhemoglobin and deoxyhemoglobin, HbO and HbR, respectively, and total hemoglobin (HbT) based on fNIRS signals measured from the left and right sides of the forehead. We applied SE to very low frequency (VLF) (0.008–0.1 Hz), respiratory (0.1–0.6 Hz), and cardiac (0.6–5 Hz) bands to find out which band delivered the optimum result. Next, a t test with 0.05 significant level was performed to compare SE values of AD patients and controls. Results from the VLF band looked promising as SE values from AD patients were always significantly higher than those from controls. In addition, this phenomenon was consistent for both sides of the forehead. However, significant differences in SE values in the respiratory band were found from the left hemisphere only, and in the cardiac band from the right hemisphere only. SE value from the VLF band supports a strong argument that it provides good predictability related to the development of AD. We demonstrated that SE of brain fNIRS signal can be an useful biomarker for Alzheimer’s disease pathology.

Respiratory brain pulsations pertaining to intra-axial hydrodynamic solute transport are markedly altered in focal epilepsy. We used optical flow analysis of ultra-fast functional magnetic resonance imaging (fMRI) data to investigate the velocity characteristics of respiratory brain impulse propagation in patients with focal epilepsy treated with antiseizure medication (ASM) (medicated patients with focal epilepsy; ME, n = 23), drug-naïve patients with at least one seizure (DN, n = 19) and matched healthy control subjects (HC, n = 75). We detected in the two patient groups (ME and DN) several significant alterations in the respiratory brain pulsation propagation velocity, which showed a bidirectional change dominated by a reduction in speed. Furthermore, the respiratory impulses moved more in reversed or incoherent directions in both patient groups vs. the HC group. The speed reductions and directionality changes occurred in specific phases of the respiratory cycle. In conclusion, irrespective of medication status, both patient groups showed incoherent and slower respiratory brain impulses, which may contribute to epileptic brain pathology by hindering brain hydrodynamics.

The dissertation consists of three peer-reviewed publications and is related to the basic research of autism spectrum disorder (ASD), especially the assessment of changes in brain function using functional magnetic resonance imaging (fMRI). The purpose was to discover possible differences in cued and spontaneous brain activity in autistic child and adolescent participants compared to typically developing controls. We used blood oxygen level dependent (BOLD) fMRI imaging of the brain, with which the participants were examined at rest and while looking at facial expressions. The resting state (RS) fMRI data artifacts were reduced, and brain networks were identified using independent component analysis. In addition, the RS was analyzed 1) over the entire measurement period using the regional homogeneity (ReHo) method, which measures local connectivity, and 2) based on the states of different brain networks grouped into shorter periods using the co-activation patterns (CAP) method. Statistically significant differences between groups were found in RS, more clearly with the CAP method. Also, significant differences in brain activity were found between the groups regarding the observation of facial expressions. The dissertation increases the understanding of changes in brain networks related to the autism spectrum, strengthening and supplementing previous research results. Based on our results, analyses of brain networks grouped into similar activation phases of shorter duration are worth further development. The new information can help develop earlier and more accurate imaging diagnostics, tentatively recognizing possible intervention target brain networks and evaluating therapeutic effects., Tiivistelmä Väitöskirja koostuu kolmesta vertaisarvioidusta julkaisusta ja liittyy autismikirjon kehityshäiriön perustutkimukseen, erityisesti aivotoiminnan muutosten arviointiin toiminnallisen magneettikuvauksen (functional MRI, fMRI) avulla. Tutkimuksen tarkoituksena oli selvittää stimuloidun ja spontaanin aivotoiminnan mahdollisia eroavaisuuksia lasten ja nuorten autismikirjossa neurotyypillisiin verrokkeihin nähden. Tutkimusmenetelmänä käytettiin veren happipitoisuudesta riippuvaista aivojen fMRI-kuvausta, jolla osallistujia tutkittiin levossa sekä heidän katsellessaan kasvojen ilmeitä. Itsenäisten komponenttien analyysilla (ICA) vähennettiin lepotilan fMRI-datan häiriöitä ja tunnistettiin aivoverkostoja. Lisäksi lepotilaa analysoitiin 1) koko mittausjakson ajalta signaalien alueellista homogeenisuutta ts. aivojen paikallista kytkennällisyyttä mittaavalla regional homogeneity (ReHo) -menetelmällä ja 2) eri aivoverkostojen tilojen perusteella lyhyemmiksi ajanjaksoiksi ns. yhtäaikaisten aktivaatioiden kuvioihin (co-activation patterns; CAP) ryhmiteltyinä. Näissä löydettiin tilastollisesti merkittäviä ryhmien välisiä eroja, selkeämmin CAP-menetelmällä. Myös kasvojen ilmeiden tarkkailuun liittyen havaittiin tilastollisesti merkittäviä aivotoiminnan eroja ryhmien välillä. Väitöskirja lisää ymmärrystä autismikirjoon liittyvistä aivoverkostojen muutoksista vahvistaen ja täydentäen aiempia tutkimustuloksia. Sen perusteella samankaltaisiin lyhempikestoisiin aktivaatiovaiheisiin ryhmiteltyjen aivoverkostojen analyyseja kannattaa kehittää. Uusi tieto voi auttaa varhaisemman ja tarkemman kuvantamisdiagnostiikan kehittämisessä, tarvittaessa oikeisiin aivoverkostoihin kohdennetuissa interventioissa ja niiden vaikutusten arvioinnissa ja seurannassa.

Functional magnetic resonance imaging (fMRI) is a common medical device to diagnose Alzheimer’s disease (AD), but it is not for all subjects due to its cost and other issues. We investigated the potential of functional near-infrared spectroscopy (fNIRS) to separate AD patients from controls as a pre-screening prior to more thorough examination using fMRI. For this purpose, two-channel fNIRS device with 690 nm and 830 nm, sampled at 10 Hz, was placed on the forehead with 3 cm distance between light source and detector to provide resting state fNIRS signals from both sides of pre-frontal cortex. We applied fractional amplitude of physiological fluctuation (fAPF), modified from fractional amplitude of low frequency fluctuation (fALFF), to oxy-, deoxy-, and total-hemoglobin in very low frequency (0.008‐0.1 Hz), respiratory (0.1‐0.6 Hz), and cardiac (0.6‐5 Hz) bands. A t-test at 0.05 significance level was used to evaluate if the fAPF score from AD patients and healthy controls is significantly different. We found that fAPF score of total hemoglobin from both side at cardiac band showed its potential to distinguish AD patients from healthy controls. This finding was in-line with the recent finding that heart failure may co-occur in AD patients with the prevalence of one third of cases.

We review theoretical and numerical models of the glymphatic system, which circulates cerebrospinal fluid and interstitial fluid around the brain, facilitating solute transport. Models enable hypothesis development and predictions of transport, with clinical applications including drug delivery, stroke, cardiac arrest, and neurodegenerative disorders like Alzheimer’s disease. We sort existing models into broad categories by anatomical function: Perivascular flow, transport in brain parenchyma, interfaces to perivascular spaces, efflux routes, and links to neuronal activity. Needs and opportunities for future work are highlighted wherever possible; new models, expanded models, and novel experiments to inform models could all have tremendous value for advancing the field.

The physiological underpinnings of the necessity of sleep remain uncertain. Recent evidence suggests that sleep increases the convection of cerebrospinal fluid (CSF) and promotes the export of interstitial solutes, thus providing a framework to explain why all vertebrate species require sleep. Cardiovascular, respiratory and vasomotor brain pulsations have each been shown to drive CSF flow along perivascular spaces, yet it is unknown how such pulsations may change during sleep in humans. To investigate these pulsation phenomena in relation to sleep, we simultaneously recorded fast fMRI, magnetic resonance encephalography (MREG), and electroencephalography (EEG) signals in a group of healthy volunteers. We quantified sleep-related changes in the signal frequency distributions by spectral entropy analysis and calculated the strength of the physiological (vasomotor, respiratory, and cardiac) brain pulsations by power sum analysis in 15 subjects (age 26.5 ± 4.2 years, 6 females). Finally, we identified spatial similarities between EEG slow oscillation (0.2–2 Hz) power and MREG pulsations. Compared with wakefulness, nonrapid eye movement (NREM) sleep was characterized by reduced spectral entropy and increased brain pulsation intensity. These effects were most pronounced in posterior brain areas for very low-frequency (≤0.1 Hz) vasomotor pulsations but were also evident brain-wide for respiratory pulsations, and to a lesser extent for cardiac brain pulsations. There was increased EEG slow oscillation power in brain regions spatially overlapping with those showing sleep-related MREG pulsation changes. We suggest that reduced spectral entropy and enhanced pulsation intensity are characteristic of NREM sleep. With our findings of increased power of slow oscillation, the present results support the proposition that sleep promotes fluid transport in human brain.

Respiratory brain pulsations have recently been shown to drive electrophysiological brain activity in patients with epilepsy. Furthermore, functional neuroimaging indicates that respiratory brain pulsations have increased variability and amplitude in patients with epilepsy compared to healthy individuals. To determine whether the respiratory drive is altered in epilepsy, we compared respiratory brain pulsation synchronicity between healthy controls and patients. Whole brain fast functional magnetic resonance imaging was performed on 40 medicated patients with focal epilepsy, 20 drug-naïve patients and 102 healthy controls. Cerebrospinal fluid associated respiratory pulsations were used to generate individual whole brain respiratory synchronization maps, which were compared between groups. Finally, we analyzed the seizure frequency effect and diagnostic accuracy of the respiratory synchronization defect in epilepsy. Respiratory brain pulsations related to the verified fourth ventricle pulsations were significantly more synchronous in patients in frontal, periventricular and mid-temporal regions, while the seizure frequency correlated positively with synchronicity. The respiratory brain synchronicity had a good diagnostic accuracy (ROCAUC = 0.75) in discriminating controls from medicated patients. The elevated respiratory brain synchronicity in focal epilepsy suggests altered physiological effect of cerebrospinal fluid pulsations possibly linked to regional brain water dynamics involved with interictal brain physiology.

Background: Narcolepsy is a chronic neurological disease characterized by daytime sleep attacks, cataplexy, and fragmented sleep. The disease is hypothesized to arise from destruction or dysfunction of hypothalamic hypocretin-producing cells that innervate wake-promoting systems including the ascending arousal network (AAN), which regulates arousal via release of neurotransmitters like noradrenalin. Brain pulsations are thought to drive intracranial cerebrospinal fluid flow linked to brain metabolite transfer that sustains homeostasis. This flow increases in sleep and is suppressed by noradrenalin in the awake state. Here we tested the hypothesis that narcolepsy is associated with altered brain pulsations, and if these pulsations can differentiate narcolepsy type 1 from healthy controls. Methods: In this case-control study, 23 patients with narcolepsy type 1 (NT1) were imaged with ultrafast fMRI (MREG) along with 23 age- and sex-matched healthy controls (HC). The physiological brain pulsations were quantified as the frequency-wise signal variance. Clinical relevance of the pulsations was investigated with correlation and receiving operating characteristic analysis. Results: We find that variance and fractional variance in the very low frequency (MREGvlf) band are greater in NT1 compared to HC, while cardiac (MREGcard) and respiratory band variances are lower. Interestingly, these pulsations differences are prominent in the AAN region. We further find that fractional variance in MREGvlf shows promise as an effective bi-classification metric (AUC = 81.4%/78.5%), and that disease severity measured with narcolepsy severity score correlates with MREGcard variance (R = −0.48, p = 0.0249). Conclusions: We suggest that our novel results reflect impaired CSF dynamics that may be linked to altered glymphatic circulation in narcolepsy type 1.

Primary central nervous system lymphoma (PCNSL) is an aggressive brain disease where lymphocytes invade along perivascular spaces of arteries and veins. The invasion markedly changes (peri)vascular structures but its effect on physiological brain pulsations has not been previously studied. Using physiological magnetic resonance encephalography (MREGBOLD) scanning, this study aims to quantify the extent to which (peri)vascular PCNSL involvement alters the stability of physiological brain pulsations mediated by cerebral vasculature. Clinical implications and relevance were explored. In this study, 21 PCNSL patients (median 67y; 38% females) and 30 healthy age-matched controls (median 63y; 73% females) were scanned for MREGBOLD signal during 2018–2021. Motion effects were removed. Voxel-by-voxel Coefficient of Variation (CV) maps of MREGBOLD signal was calculated to examine the stability of physiological brain pulsations. Group-level differences in CV were examined using nonparametric covariate-adjusted tests. Subject-level CV alterations were examined against control population Z-score maps wherein clusters of increased CV values were detected. Spatial distributions of clusters and findings from routine clinical neuroimaging were compared [contrast-enhanced, diffusion-weighted, fluid-attenuated inversion recovery (FLAIR) data]. Whole-brain mean CV was linked to short-term mortality with 100% sensitivity and 100% specificity, as all deceased patients revealed higher values (n = 5, median 0.055) than surviving patients (n = 16, median 0.028) (p < .0001). After adjusting for medication, head motion, and age, patients revealed higher CV values (group median 0.035) than healthy controls (group median 0.024) around arterial territories (p ≤ .001). Abnormal clusters (median 1.10 × 10₅mm₃) extended spatially beyond FLAIR lesions (median 0.62 × 10₅mm₃) with differences in volumes (p = .0055).

The physiological pulsations that drive tissue fluid homeostasis are not well characterized during brain activation. Therefore, we used fast magnetic resonance encephalography (MREG) fMRI to measure full band (0–5 Hz) blood oxygen level-dependent (BOLDFB) signals during a dynamic visual task in 23 subjects. This revealed brain activity in the very low frequency (BOLDVLF) as well as in cardiac and respiratory bands. The cardiovascular hemodynamic envelope (CHe) signal correlated significantly with the visual BOLDVLF response, considered as an independent signal source in the V1-V2 visual cortices. The CHe preceded the canonical BOLDVLF response by an average of 1.3 (± 2.2) s. Physiologically, the observed CHe signal could mark increased regional cardiovascular pulsatility following vasodilation.

Accumulation of amyloid-β is a key neuropathological feature in brain of Alzheimer’s disease patients. Alterations in cerebral haemodynamics, such as arterial impulse propagation driving the (peri)vascular CSF flux, predict future Alzheimer’s disease progression. We now present a non-invasive method to quantify the three-dimensional propagation of cardiovascular impulses in human brain using ultrafast 10 Hz magnetic resonance encephalography. This technique revealed spatio-temporal abnormalities in impulse propagation in Alzheimer’s disease. The arrival latency and propagation speed both differed in patients with Alzheimer’s disease. Our mapping of arterial territories revealed Alzheimer’s disease-specific modifications, including reversed impulse propagation around the hippocampi and in parietal cortical areas. The findings imply that pervasive abnormality in (peri)vascular CSF impulse propagation compromises vascular impulse propagation and subsequently glymphatic brain clearance of amyloid-β in Alzheimer’s disease.

Age plays a crucial role in the performance of schizophrenia vs. controls (SZ-HC) neuroimaging-based machine learning (ML) models as the accuracy of identifying first-episode psychosis from controls is poor compared to chronic patients. Resolving whether this finding reflects longitudinal progression in a disorder-specific brain pattern or a systematic but non-disorder-specific deviation from a normal brain aging (BA) trajectory in schizophrenia would help the clinical translation of diagnostic ML models. We trained two ML models on structural MRI data: an SZ-HC model based on 70 schizophrenia patients and 74 controls and a BA model (based on 561 healthy individuals, age range = 66 years). We then investigated the two models' predictions in the naturalistic longitudinal Northern Finland Birth Cohort 1966 (NFBC1966) following 29 schizophrenia and 61 controls for nine years. The SZ-HC model's schizophrenia-specificity was further assessed by utilizing independent validation (62 schizophrenia, 95 controls) and depression samples (203 depression, 203 controls). We found better performance at the NFBC1966 follow-up (sensitivity = 75.9%, specificity = 83.6%) compared to the baseline (sensitivity = 58.6%, specificity = 86.9%). This finding resulted from progression in disorder-specific pattern expression in schizophrenia and was not explained by concomitant acceleration of brain aging. The disorder-specific pattern's progression reflected longitudinal changes in cognition, outcomes, and local brain changes, while BA captured treatment-related and global brain alterations. The SZ-HC model was also generalizable to independent schizophrenia validation samples but classified depression as control subjects. Our research underlines the importance of taking account of longitudinal progression in a disorder-specific pattern in schizophrenia when developing ML classifiers for different age groups.

Neurological brain diseases induce increasing costs in health care around the world. Epilepsies are one of the most common neurological diseases globally. While seizure-freedom is achieved in a majority of patients with proper treatment, epilepsy can still be refractory to antiepileptic medication and can cause impaired quality of life and premature death compared to the general population. In clinical diagnostic work-up, the unpredictable and temporary nature of epileptic activity in the brain with several different specified and still unknown etiologies can make the precise localization of the epileptic foci difficult. A new pathophysiological theory behind epilepsies focuses on neuron-glia interactions and an impeccably functioning blood–brain barrier supporting the homeostasis for unhindered brain functionality. Cerebrospinal fluid is driven by brain pulsations via Aquaporin-4 in the brain and plays a critical role in supporting the water channels balance. Recently developed fast functional neuroimaging methods can be used to study whether this homeostasis is disturbed in patients with focal-onset epilepsy. Additionally, the fast functional MRI sequence, ultra-fast magnetic resonance encephalography (MREG), offers a method for differentiating distinct frequency brain pulsations. Previous evidence with intracranial electroencephalography has shown that respiration directly affects epileptic activity and brain function altogether. Thus, respiratory brain pulsations measured by MREG were a focus of particular interest in comparisons between patients with focal-onset epilepsy and healthy controls in this study, totaling 40and 102 subjects, respectively, gathered during 2012–2020 in Oulu, Finland and Freiburg, Germany. Additionally, we introduce data from 22 patients with new-onset seizure gathered in Oulu, Finland, allowing the exclusion of the potential effect of antiepileptic medication as a cause of the observed changes in observable brain pulsations. In, Tiivistelmä Neurologiset aivosairaudet aiheuttavat alati kasvavia kustannuksia terveydenhuollolle eri puolilla maailmaa. Epilepsiat ovat yksi yleisimmistä neurologisista sairauksista maailmanlaajuisesti. Vaikka suurimmalla osalla potilaista saavutetaan kohtauksettomuus, epilepsia voi kuitenkin olla vaikea, jolloin asianmukaisesta lääkehoidostahuolimatta potilaalle aiheutuu merkittävää elämänlaadun alentumista ja kohonnut riskiennenaikaiselle kuolemalle verrattuna muuhun väestöön. Kliinisessä diagnostiikassa aivojen epileptisen toiminnan ennalta-arvaamattomuus ja kohtauksellisuus useiden tunnettujen ja tuntemattomien syiden vuoksi voi aiheuttaa haasteita epileptisen toiminnanpaikallistamiseen aivokudoksessa. Uusi patofysiologinen teoria epilepsian taustalla keskittyy hermosolujen ja sen tukikudoksen, eli neuronien ja glian vuorovaikutukseen ja veriaivoesteen toimintaan aivojen homeostaasin säilyttämiseksi. Aivo-selkäydinnesteen liikettä ajavat aivoissa tapahtuvat pulsaatiot, jotka Akvaporiini-4 kanavien välityksellä ylläpitävät aivojenvesikanavien tasapainoa. Uudella toiminnallisella neurokuvantamismenetelmällä voidaan tutkia, ovatko homeostaasia ylläpitävät aivojen pulsaatiot häiriintyneet paikallis-alkuisessa epilepsiassa. Lisäksi käytetty ultranopea magneettiresonanssienkefalogrammi (MREG), tarjoaa tutkimusmenetelmän eri taajuuksilla tapahtuvien aivopulsaatioidenerottamiseksi toisistaan. Aikaisemmin on osoitettu kallonsisäisillä aivosähkökäyrämittauksilla, että hengityksellä on suora vaikutus epileptiseen aivotoimintaan ja sen aktiivisuuteen. Tämän vuoksi MREG:lla mitatut aivojen hengityspulsaatiot olivat tutkimuksessa erityisenkiinnostuksen kohteena vertailtaessa paikallis-alkuista epilepsiaa sairastavia potilaita ja terveitä kontrolleja, joita kerättiin vuosien 2012–2020 aikana vastaavasti yhteensä 40 ja 102 kappaletta Oulussa ja Freiburgissa Saksassa. Lisäksi esittelemme Oulussa kerätyn 22 tuoreen kohtauspotilaan aineiston, joka mahdollisti lääkityksen vaikutuk

Age plays a crucial role in the performance of schizophrenia vs. controls (SZ-HC) neuroimaging-based machine learning (ML) models as the accuracy of identifying first-episode psychosis from controls is poor compared to chronic patients. Resolving whether this finding reflects longitudinal progression in a disorder-specific brain pattern or a systematic but non-disorder-specific deviation from a normal brain aging (BA) trajectory in schizophrenia would help the clinical translation of diagnostic ML models. We trained two ML models on structural MRI data: an SZ-HC model based on 70 schizophrenia patients and 74 controls and a BA model (based on 561 healthy individuals, age range = 66 years). We then investigated the two models’ predictions in the naturalistic longitudinal Northern Finland Birth Cohort 1966 (NFBC1966) following 29 schizophrenia and 61 controls for nine years. The SZ-HC model’s schizophrenia-specificity was further assessed by utilizing independent validation (62 schizophrenia, 95 controls) and depression samples (203 depression, 203 controls). We found better performance at the NFBC1966 follow-up (sensitivity = 75.9%, specificity = 83.6%) compared to the baseline (sensitivity = 58.6%, specificity = 86.9%). This finding resulted from progression in disorder-specific pattern expression in schizophrenia and was not explained by concomitant acceleration of brain aging. The disorder-specific pattern’s progression reflected longitudinal changes in cognition, outcomes, and local brain changes, while BA captured treatment-related and global brain alterations. The SZ-HC model was also generalizable to independent schizophrenia validation samples but classified depression as control subjects. Our research underlines the importance of taking account of longitudinal progression in a disorder-specific pattern in schizophrenia when developing ML classifiers for different age groups.

Near-infrared spectroscopy (fNIRS) measures concentrations of oxygenated (HbO) and deoxygenated (HbR) hemoglobin in the brain. Recently, we demonstrated its potential also for measuring concentrations of cerebral water (cH₂O). We performed fNIRS measurements during rest to study fluctuations in concentrations of cH₂O, HbO and HbR in 33 well-rested healthy control subjects (HC) and 18 acutely sleep-deprived HC. Resting-state fNIRS signal was filtered in full-band, cardiac, respiratory, low-, and very-low-frequency bands. The sum of HbO and HbR constitutes the regional cerebral blood volume (CBV). CBV and cH₂O concentrations were analyzed via temporal correlation and phase synchrony. Fluctuation in concentrations of cH₂O and CBV was strongly anti-correlated across all frequency bands in both frontal and parietal cortices. Fluctuation in concentrations of cH₂O and CBV showed neither a completely synchronous nor a random phase relationship in both frontal and parietal cortices. Acutely sleep-deprived subjects did not show significant differences in temporal correlation or phase synchrony between fluctuations in cH₂O and CBV concentrations compared with well-rested HC. The reciprocal interrelation between fluctuations in CBV and cH₂O concentrations is consistent with the Munro–Kellie doctrine of constant intracranial volume. This coupling may constitute a functional mechanism underlying glymphatic circulation, which persists despite acutely disturbed sleep patterns.

This video-based study examines the pragmatic non-verbal comprehension skills and corresponding neural-level findings in young Finnish autistic adults, and controls. Items from the Assessment Battery of Communication (ABaCo) were chosen to evaluate the comprehension of non-verbal communication. Inter-subject correlation (ISC) analysis of the functional magnetic resonance imaging data was used to reveal the synchrony of brain activation across participants during the viewing of pragmatically complex scenes of ABaCo videos. The results showed a significant difference between the ISC maps of the autistic and control groups in tasks involving the comprehension of non-verbal communication, thereby revealing several brain regions where correlation of brain activity was greater within the control group. The results suggest a possible weaker modulation of brain states in response to the pragmatic non-verbal communicative situations in autistic participants. Although there was no difference between the groups in behavioural responses to ABaCo items, there was more variability in the accuracy of the responses in the autistic group. Furthermore, mean answering and reaction times correlated with the severity of autistic traits. The results indicate that even if young autistic adults may have learned to use compensatory resources in their communicative-pragmatic comprehension, pragmatic processing in naturalistic situations still requires additional effort.

An amazing amount of activity is continuously occurring in the brain in multiple temporal and spatial scales even in the absence of explicit environmental outputs or inputs; this is called the resting-state, or spontaneous brain activity. It is now widely known that spontaneous brain activity, measured using resting state functional magnetic resonance imaging (fMRI) of the blood oxygen level dependent (BOLD) signal, is dominated by very low frequencies (VLFs; less than 0.1 Hz). Spatial correlations within VLF spontaneous brain activity result in what is widely referred to as functional connectivity, and the associated functionally connected regions are known as resting-state networks (RSNs). Conventional functional connectivity analyses such as seed-based analysis and independent component analysis (ICA), have revealed that spontaneous activity vary in different tasks and in some diseases, but also in a resting state in healthy subjects. However, conventional functional connectivity analyses have not addressed the temporal dimension of brain communication, that is, the propagation of information flow between brain regions. By studying temporal lags in the brain, it has recently been established that spontaneous BOLD fluctuations consist of reproducible patterns of whole brain activity propagation and these patterns are markedly altered as a function of brain state, whether pathological or physiological. In this thesis, we utilised fast magnetic resonance encephalography (MREG) imaging data and provided a comprehensive analysis approach, dynamic lag analysis (DLA), to study probabilistic patterns of information flow between brain regions. Our temporal analyses revealed new patterns in the way slow signals propagate between functional brain regions, and suggested that information flow is aberrant in autism spectrum disorder (ASD) and type 1 narcolepsy with cataplexy compared with neurotypical individuals. Our findings offer a glimpse into the principles that, Tiivistelmä Aivot ovat monimutkainen järjestelmä, jossa on käynnissä jatkuvasti valtava määrä aktiivisuutta monissa avaruudellisissa ja ajallisissa ulottuvuuksissa. Tätä toimintaa kutsutaan aivojen spontaaniksi tai lepotila-aktiivisuudeksi. Toiminnallisen magneettikuvauksen (TMK) tutkimusten kautta tiedämme nykyään, että aivotoiminnan spontaania aktiivisuutta dominoivat erittäin hitaat vaihtelut. Spontaanin aivotoiminnan erittäin hitaiden vaihteluiden avaruudellisia korrelaatioita kutsutaan yleisesti toiminnalliseksi liittyvyydeksi, ja näitä toiminnallisesti liittyneitä alueita aivojen lepotilahermoverkostoiksi. Perinteiset toiminnallisen liitettävyyden analyysien kautta tiedämme nykyään, että spontaani aivotoiminta huojuu tietyissä tehtävissä, jossain sairauksissa, mutta myös terveillä koehenkilöillä. Perinteiset analyysit eivät kuitenkaan ole keskittyneet aivotoiminnan ajallisiin vaihteluihin eli kuinka informaatio aivoalueiden välillä ajallisesti etenee. Aivan hiljattain on aivotoiminnan ajallisen kytkeytymisen tutkimuksista saatu selville, että veren happipitoisuudesta riippuvan (engl. blood oxygen level dependent, BOLD) signaalin spontaaneissa vaihteluissa on tunnistettavissa toistettavia ajallisia kytkeytymisiä. Ne vaihtelevat riippuen aivotoiminnan fysiologisesta tilasta tai patologisista prosesseista. Tässä väitöskirjatutkimuksessa kehitimme uuden analyysimenetelmän, dynaamisen viiveanalyysin (engl. dynamic lag analysis, DLA), ja hyödynsimme nopeaa magneettiresonanssienkefalogrammi (MREG) kuvantamista tutkiaksemme informaation kulkua ihmisillä eri toiminnallisten aivoalueiden välillä. Ajallisen analyysimenetelmän avulla löysimme uusia mekanismeja aivotoiminnan hitaiden vaihteluiden ajallisessa välittymisessä aivoalueiden välillä. Havaitsimme lisäksi, että aivotoiminnan ajallinen kytkeytyminen on poikkeava autismin kirjon oireyhtymässä ja tyypin 1 narkolepsiassa verrattuna terveisiin koehenkilöihin. Tuloksemme tarjoavat pilkahduksen mielenkiintoista uutta tie

Objective: This review article gives an account of the development of the MR-encephalography (MREG) method, which started as a mere ‘Gedankenexperiment’ in 2005 and gradually developed into a method for ultrafast measurement of physiological activities in the brain. After going through different approaches covering k-space with radial, rosette, and concentric shell trajectories we have settled on a stack-of-spiral trajectory, which allows full brain coverage with (nominal) 3 mm isotropic resolution in 100 ms. The very high acceleration factor is facilitated by the near-isotropic k-space coverage, which allows high acceleration in all three spatial dimensions. Methods: The methodological section covers the basic sequence design as well as recent advances in image reconstruction including the targeted reconstruction, which allows real-time feedback applications, and—most recently—the time-domain principal component reconstruction (tPCR), which applies a principal component analysis of the acquired time domain data as a sparsifying transformation to improve reconstruction speed as well as quality. Applications: Although the BOLD-response is rather slow, the high speed acquisition of MREG allows separation of BOLD-effects from cardiac and breathing related pulsatility. The increased sensitivity enables direct detection of the dynamic variability of resting state networks as well as localization of single interictal events in epilepsy patients. A separate and highly intriguing application is aimed at the investigation of the glymphatic system by assessment of the spatiotemporal patterns of cardiac and breathing related pulsatility. Discussion: MREG has been developed to push the speed limits of fMRI. Compared to multiband-EPI this allows considerably faster acquisition at the cost of reduced image quality and spatial resolution.