The relationship of genetic susceptibilities for psychosis with physiological fluctuation in functional MRI data

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.Peer reviewe

Mental Well-being of Healthcare Workers in 2 Hospital Districts During the First Wave Ofthe Covid-19 Pandemic in Finland a Cross-sectional Study

Peer reviewe

Mielenterveyshäiriöiden riskitekijät ja taudinkulku Pohjois-Suomen vuoden 1966 syntymäkohortissa

Vertaisarvioitu.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.Peer reviewe

Structural and functional alterations in the brain gray matter among first-degree relatives of schizophrenia patients : A multimodal meta-analysis of 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. (C) 2019 Elsevier B.V. All rights reserved.Peer reviewe

Mielenterveyshäiriöiden riskitekijät ja taudinkulku Pohjois-Suomen vuoden 1966 syntymäkohortissa

Vertaisarvioitu.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.Peer reviewe

A machine learning approach to predict resilience and sickness absence in the healthcare workforce during the COVID-19 pandemic

During the COVID-19 pandemic, healthcare workers (HCWs) have faced unprecedented workloads and personal health risks leading to mental disorders and surges in sickness absence. Previous work has shown that interindividual differences in psychological resilience might explain why only some individuals are vulnerable to these consequences. However, no prognostic tools to predict individual HCW resilience during the pandemic have been developed. We deployed machine learning (ML) to predict psychological resilience during the pandemic. The models were trained in HCWs of the largest Finnish hospital, Helsinki University Hospital (HUS, N = 487), with a six-month follow-up, and prognostic generalizability was evaluated in two independent HCW validation samples (Social and Health Services in Kymenlaakso: Kymsote, N = 77 and the City of Helsinki, N = 322) with similar follow-ups never used for training the models. Using the most predictive items to predict future psychological resilience resulted in a balanced accuracy (BAC) of 72.7-74.3% in the HUS sample. Similar performances (BAC = 67-77%) were observed in the two independent validation samples. The models' predictions translated to a high probability of sickness absence during the pandemic. Our results provide the first evidence that ML techniques could be harnessed for the early detection of COVID-19-related distress among HCWs, thereby providing an avenue for potential targeted interventions.Peer reviewe

The progression of disorder-specific brain pattern expression in schizophrenia over 9 years.

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

Patterns of risk—Using machine learning and structural neuroimaging to identify pedophilic offenders

BackgroundChild sexual abuse (CSA) has become a focal point for lawmakers, law enforcement, and mental health professionals. With high prevalence rates around the world and far-reaching, often chronic, individual, and societal implications, CSA and its leading risk factor, pedophilia, have been well investigated. This has led to a wide range of clinical tools and actuarial instruments for diagnosis and risk assessment regarding CSA. However, the neurobiological underpinnings of pedosexual behavior, specifically regarding hands-on pedophilic offenders (PO), remain elusive. Such biomarkers for PO individuals could potentially improve the early detection of high-risk PO individuals and enhance efforts to prevent future CSA.AimTo use machine learning and MRI data to identify PO individuals.MethodsFrom a single-center male cohort of 14 PO individuals and 15 matched healthy control (HC) individuals, we acquired diffusion tensor imaging data (anisotropy, diffusivity, and fiber tracking) in literature-based regions of interest (prefrontal cortex, anterior cingulate cortex, amygdala, and corpus callosum). We trained a linear support vector machine to discriminate between PO and HC individuals using these WM microstructure data. Post hoc, we investigated the PO model decision scores with respect to sociodemographic (age, education, and IQ) and forensic characteristics (psychopathy, sexual deviance, and future risk of sexual violence) in the PO subpopulation. We assessed model specificity in an external cohort of 53 HC individuals.ResultsThe classifier discriminated PO from HC individuals with a balanced accuracy of 75.5% (sensitivity = 64.3%, specificity = 86.7%, P5000 = 0.018) and an out-of-sample specificity to correctly identify HC individuals of 94.3%. The predictive brain pattern contained bilateral fractional anisotropy in the anterior cingulate cortex, diffusivity in the left amygdala, and structural prefrontal cortex-amygdala connectivity in both hemispheres. This brain pattern was associated with the number of previous child victims, the current stance on sexuality, and the professionally assessed risk of future sexual violent reoffending.ConclusionAberrant white matter microstructure in the prefronto-temporo-limbic circuit could be a potential neurobiological correlate for PO individuals at high-risk of reoffending with CSA. Although preliminary and exploratory at this point, our findings highlight the general potential of MRI-based biomarkers and particularly WM microstructure patterns for future CSA risk assessment and preventive efforts

Maternal prepregnancy body mass index and offspring white matter microstructure: results from three birth cohorts

Prepregnancy maternal obesity is a global health problem and has been associated with offspring metabolic and mental ill-health. However, there is a knowledge gap in understanding potential neurobiological factors related to these associations. This study explored the relation between maternal prepregnancy body mass index (BMI) and offspring brain white matter microstructure at the age of 6, 10, and 26 years in three independent cohorts. Maternal BMI was associated with higher FA and lower MD in multiple brain tracts in offspring aged 10 and 26 years, but not at 6 years of age. Future studies should examine whether our observations can be replicated and explore the potential causal nature of the findings.This work was supported by the European Union’s Horizon 2020 research and innovation program [grant agreement no. 633595 DynaHEALTH] and no. 733206 LifeCycle], the Netherlands Organization for Health Research and Development [ZONMW Vici project 016.VICI.170.200]. The PREOBE cohort was funded by Spanish Ministry of Innovation and Science. Junta de Andalucía: Excellence Projects (P06-CTS-02341) and Spanish Ministry of Economy and Competitiveness (BFU2012-40254-C03-01). The first phase of the Generation R Study is made possible by financial support from the Erasmus Medical Centre, the Erasmus University, and the Netherlands Organization for Health Research and Development (ZonMW, grant ZonMW Geestkracht 10.000.1003). The Northern Finland Birth Cohort 1986 is funded by University of Oulu, University Hospital of Oulu, Academy of Finland (EGEA), Sigrid Juselius Foundation, European Commission (EURO-BLCS, Framework 5 award QLG1-CT-2000-01643), NIH/NIMH (5R01MH63706:02

Early adversity, psychosis risk and brain response to faces

Abstract 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