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Tiivistelmä Psykoosilääkkeet lievittävät skitsofrenian psykoosioireita ja vähentävät relapsiriskiä. Psykoosilääkityksen pitkäaikaiskäytön hyöty-haittasuhde skitsofrenian hoidossa on osin tuntematon. Etenkin suurten psykoosilääkeannosten pitkäaikaiskäyttö voi altistaa metabolisille, neurologisille, kognitiivisille ja psykologisille haitoille. Psykoosilääkehoidon haittoja voidaan minimoida systemaattisella lääkehoidon ohjauksella, jossa lääkehoito suunnitellaan ja toteutetaan moniammatillisessa yhteistyössä potilaan ja hänen läheistensä kanssa. Lääkehoidon ohjaus voi auttaa käyttämään pienintä vaikuttavaa hoitoannosta sekä parantaa hoitoon sitoutumista ja hoidon tuloksellisuutta. Skitsofrenian psykoosilääkehoidon ohjauskäytäntöjen toteutus edellyttää eri ammattiryhmien kouluttamista, psykoosilääkehoidosta vastaavaa tiimiä ja organisaatiorajat ylittävää moniammatillista työpari- ja verkostotyötä.

Background: Although the link between early adversity (EA) and later-life psychiatric disorders is well established, it has yet to be elucidated whether EA is related to distortions in the processing of different facial expressions. We conducted a meta-analysis to investigate whether exposure to EA relates to distortions in responses to different facial emotions at three levels: 1) event-related potentials of the P100 and N170, 2) amygdala functional magnetic resonance imaging responses, and 3) accuracy rate or reaction time in behavioral data. Methods: The systematic literature search (PubMed and Web of Science) up to April 2020 resulted in 29 behavioral studies (n = 8555), 32 functional magnetic resonance imaging studies (n = 2771), and 3 electroencephalography studies (n = 197) for random-effect meta-analyses. Results: EA was related to heightened bilateral amygdala reactivity to sad faces (but not other facial emotions). Exposure to EA was related to faster reaction time but a normal accuracy rate in response to angry and sad faces. In response to fearful and happy faces, EA was related to a lower accuracy rate only in individuals with recent EA exposure. This effect was more pronounced in individuals with exposure to EA before (vs. after) the age of 3 years. These findings were independent of psychiatric diagnoses. Because of the low number of eligible electroencephalography studies, no conclusions could be reached regarding the effect of EA on the event-related potentials. Conclusions: EA relates to alterations in behavioral and neurophysiological processing of facial emotions. Our study stresses the importance of assessing age at exposure and time since EA because these factors mediate some EA-related perturbations.

Tiivistelmä Kohortti- ja rekisteritutkimuksilla on merkittävä rooli mielenterveyshäiriöiden riskitekijöiden, ennusteen ja hoidon selvittämisessä. Pohjois-Suomen syntymäkohortissa 1966 (Kohortti 66) on tutkittu vuodesta 1990 alkaen mielenterveyshäiriöitä, erityisesti skitsofreniaa ja myös masennusta. Tulosten mukaan skitsofreniaa ennustavat useat varhaiset raskauteen, synnytykseen ja kehitykseen liittyvät tekijät. Skitsofreniaan liittyy muutoksia aivojen rakenteessa ja kognitiivisessa suorituskyvyssä sekä somaattisen terveyden ongelmia. Ennuste on usein epätyydyttävä. Masennuksen osalta on löydetty riskitekijöitä lapsuudesta aikuisuuteen sekä suurentunut somaattisen oheissairastavuuden riski. Kohortissa 66 on tutkittu myös muun muassa persoonallisuushäiriöitä, somatisaatio-oireita, alkoholinkäyttöä ja temperamenttipiirteitä. Tutkimustulokset ovat lisänneet tietoa psykiatristen häiriöiden riskitekijöistä, taudinkulusta ja hoidosta.

Background/Objective: The risk-benefit ratio of antipsychotics in schizophrenia depends primarily on their effect on brain chemistry. An important factor influencing the efficacy of prescribed drugs is medication management, which can be defined as an ongoing process to manage and monitor the recommended use of antipsychotics to facilitate their cost-effective, adherent, and acceptable use. Materials: We reviewed narratively relevant literature that examined the medication management of antipsychotics in schizophrenia based on a search of PubMed, Scopus, Web of Science, PsycARTICLES, and Cochrane in May 2020. We also included controlled interventional studies with a follow-up period of at least 2 years. Results: Based on the previous literature, there is no unified approach for optimal medication management, but multiple useful strategies are presented for individual patients, prescribers, and organizations. Conclusions: Systematic medication management may improve the risk-benefit balance of antipsychotics by achieving the lowest effective dose, minimizing adverse effects, and improving adherence. There is a need for well-designed naturalistic studies and clinical trials to optimize management in schizophrenia.

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.

Previously, schizophrenia is found to be related to the variability of the functional magnetic resonance imaging (fMRI) signal in the white matter. However, evidence about the relationship between genetic vulnerabilities and physiological fluctuation in the brain is lacking. We investigated whether familial risk for psychosis (FR) and polygenic risk score for schizophrenia (PRS) are linked with physiological fluctuation in fMRI data. We used data from the Oulu Brain and Mind study (n = 140−149, aged 20−24 years) that is a substudy of the Northern Finland Birth Cohort 1986. The participants underwent a resting-state fMRI scan. Coefficient of variation (CV) of blood oxygen level dependent (BOLD) signal (CVBOLD) was used as a proxy of physiological fluctuation in the brain. Familial risk was defined to be present if at least one parent had been diagnosed with psychosis previously. PRS was computed based on the results of the prior GWAS by the Schizophrenia Working Group. FR or PRS were not associated with CVBOLD in cerebrospinal fluid, white matter, or grey matter. The findings did not provide evidence for the previous suggestions that genetic vulnerabilities for schizophrenia become apparent in alterations of the variation of the BOLD signal in the brain.

Biomarkers sensitive to prodromal or early pathophysiological changes in Alzheimer’s disease (AD) symptoms could improve disease detection and enable timely interventions. Changes in brain hemodynamics may be associated with the main clinical AD symptoms. To test this possibility, we measured the variability of blood oxygen level-dependent (BOLD) signal in individuals from three independent datasets (totaling 80 AD patients and 90 controls). We detected a replicable increase in brain BOLD signal variability in the AD populations, which constituted a robust biomarker for clearly differentiating AD cases from controls. Fast BOLD scans showed that the elevated BOLD signal variability in AD arises mainly from cardiovascular brain pulsations. Manifesting in abnormal cerebral perfusion and cerebrospinal fluid convection, present observation presents a mechanism explaining earlier observations of impaired glymphatic clearance associated with AD in humans.

Background: Physiological brain pulsations have been shown to play a critical role in maintaining interstitial homeostasis in the glymphatic brain clearance mechanism. We investigated whether psychotic symptomatology is related to the physiological variation of the human brain using fMRI. Methods: The participants (N = 277) were from the Northern Finland Birth Cohort 1986. Psychotic symptoms were evaluated with the Positive Symptoms Scale of the Structured Interview for Prodromal Syndromes (SIPS). We used the coefficient of variation of BOLD signal (CVBOLD) as a proxy for physiological brain pulsatility. The CVBOLD-analyses were controlled for motion, age, sex, and educational level. The results were also compared with fMRI and voxel-based morphometry (VBM) meta-analyses of schizophrenia patients (data from the Brainmap database). Results: At the global level, participants with psychotic-like symptoms had higher CVBOLD in cerebrospinal fluid (CSF) and white matter (WM), when compared to participants with no psychotic symptoms. Voxel-wise analyses revealed that CVBOLD was increased, especially in periventricular white matter, basal ganglia, cerebellum and parts of the cortical structures. Those brain regions, which included alterations of physiological fluctuation in symptomatic psychosis risk, overlapped <6% with the regions that were found to be affected in the meta-analyses of previous fMRI and VBM studies in schizophrenia patients. Motion did not vary as a function of SIPS. Conclusions: Psychotic-like symptoms were associated with elevated CVBOLD in a variety of brain regions. The CVBOLD findings may produce new information about cerebral physiological fluctuations that have been out of reach in previous fMRI and VBM studies.

Objective: We conducted a multimodal coordinate-based meta-analysis (CBMA) to investigate structural and functional brain alterations in first-degree relatives of schizophrenia patients (FRs). Methods: We conducted a systematic literature search from electronic databases to find studies that examined differences between FRs and healthy controls using whole-brain functional magnetic resonance imaging (fMRI) or voxel-based morphometry (VBM). A CBMA of 30 fMRI (754 FRs; 959 controls) and 11 VBM (885 FRs; 775 controls) datasets were conducted using the anisotropic effect-size version of signed differential mapping. Further, we conducted separate meta-analyses about functional alterations in different cognitive tasks: social cognition, executive functioning, working memory, and inhibitory control. Results: FRs showed higher fMRI activation in the right frontal gyrus during cognitive tasks than healthy controls. In VBM studies, there were no differences in gray matter density between FRs and healthy controls. Furthermore, multi-modal meta-analysis obtained no differences between FRs and healthy controls. By utilizing the BrainMap database, we showed that the brain region which showed functional alterations in FRs (i) overlapped only slightly with the brain regions that were affected in the meta-analysis of schizophrenia patients and (ii) correlated positively with the brain regions that exhibited increased activity during cognitive tasks in healthy individuals. Conclusions: Based on this meta-analysis, FRs may exhibit only minor functional alterations in the brain during cognitive tasks, and the alterations are much more restricted and only slightly overlapping with the regions that are affected in schizophrenia patients. The familial risk did not relate to structural alterations in the gray matter.

Schizophrenia and other psychotic disorders are severe and disabling mental disorders that break out during early adulthood, often when a person is in his/her early 20s. Furthermore, functional decline in many cognitive areas, including the ability to communicate in social interactions and impaired facial expression recognition, is typical to patients with schizophrenia. Understanding the risk factors of psychosis is essential as these disorders may be more amenable to treatment in their early stages. However, recognition of those at the highest risk of psychosis is challenging as no definitive biomarkers are available. Functional MRI is a promising tool that can potentially identify neural signals relating to the individual’s risk of psychosis onset. Psychotic disorders are etiologically heterogeneous disorders — both environmental and genetic factors have been linked to the onset of psychotic disorders. The most influential risk factor for a psychotic disorder is familial risk with genetic loading. The present study examines whether familial risk of psychosis (FR), the polygenic risk score for schizophrenia (PRS) and early adversity associate with brain response to faces. We used fMRI to measure blood oxygen level dependent (BOLD) response to faces. Our study showed that FR associated with deviant prefrontal cortex BOLD responses. In addition, we detected that interregional BOLD signal and grey matter volume varied as a function of PRS; the lowest functional and structural covariance was detected in individuals with high PRS. We also detected that early adversities associated with brain response to faces and that this association varied as a function of glucocorticoid receptor gene expression. Our findings indicate that the above risk factors of psychosis associate with brain response to faces., Tiivistelmä Skitsofrenia ja muut psykoosisairaudet ovat vakavia mielenterveyden häiriöitä, jotka puhkeavat usein nuorella aikuisiällä. Eräs tyypillinen piirre psykoosisairauksille on vaikeus tunnistaa muiden ihmisten kasvonilmeitä. Psykoosisairauksien riskitekijöiden ymmärtäminen on tärkeää, sillä hoito tehoaa parhaiten sairastumisen alkuvaiheessa. Suurimmassa psykoosivaarassa olevien henkilöiden tunnistaminen on kuitenkin haastavaa, sillä luotettavia tautiin liittyviä biomarkkereita ei ole saatavilla. Toiminnallinen magneettikuvaus (fMRI) on lupaava työkalu, jolla saattaa olla tulevaisuudessa käyttöarvoa psykoosivaaraan liittyvien aivomuutosten tunnistamisessa. Etiologialtaan psyykoosisairaudet ovat heterogeenisiä: sekä ympäristö että perinnölliset tekijät vaikuttavat yksilön sairastumisriskiin. Voimakkain riskitekijä on suvullinen psykoosialttius. Tässä osajulkaisuväitöskirjassa tutkitaan suvullisen psykoosialttiuden, skitsofrenian polygeenisen riskipisteen (PRS) sekä varhaisten vastoinkäymisten yhteyttä aivojen kasvonilmeitä tulkitsevaan järjestelmään. Tutkimuksessa on hyödynnetty fMRI-kuvausta kasvonilmestimuluksen aikana. Tutkimuksessamme suvullinen psykoosialttius oli yhteydessä etuotsalohkon fMRI-signaalimuutoksiin. Tämän lisäksi havaitsimme, että kasvonilmejärjestelmän fMRI-signaalin ja harmaan aineen kovarianssi oli yhteydessä PRS:ään: matalin aivoalueiden välinen korrelaatio havaittiin henkilöillä, joiden PRS oli korkea. Havaitsimme myös, että varhaiset vastoinkäymiset ovat yhteydessä kasvonilmeiden aikaansaamiin aivovasteisiin. Tämä assosiaatio oli myös yhteydessä glukokortikoidireseptorin geenin ilmentymiseen. Väitöskirjan löydökset viittaavat siihen, että edellä mainitut psykoosin riskitekijät ovat yhteydessä kasvonilmeitä tulkitsevaan järjestelmään.