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First-degree relatives of patients diagnosed with schizophrenia (SZ-FDRs) show similar patterns of brain abnormalities and cognitive alterations to patients, albeit with smaller effect sizes. First-degree relatives of patients diagnosed with bipolar disorder (BD-FDRs) show divergent patterns; on average, intracranial volume is larger compared to controls, and findings on cognitive alterations in BD-FDRs are inconsistent. Here, we performed a meta-analysis of global and regional brain measures (cortical and subcortical), current IQ, and educational attainment in 5,795 individuals (1,103 SZ-FDRs, 867 BD-FDRs, 2,190 controls, 942 schizophrenia patients, 693 bipolar patients) from 36 schizophrenia and/or bipolar disorder family cohorts, with standardized methods. Compared to controls, SZ-FDRs showed a pattern of widespread thinner cortex, while BD-FDRs had widespread larger cortical surface area. IQ was lower in SZ-FDRs (d= -0.42,p= 3 x 10(-5)), with weak evidence of IQ reductions among BD-FDRs (d= -0.23,p= .045). Both relative groups had similar educational attainment compared to controls. When adjusting for IQ or educational attainment, the group-effects on brain measures changed, albeit modestly. Changes were in the expected direction, with less pronounced brain abnormalities in SZ-FDRs and more pronounced effects in BD-FDRs. To conclude, SZ-FDRs and BD-FDRs show a differential pattern of structural brain abnormalities. in contrast, both had lower IQ scores and similar school achievements compared to controls. Given that brain differences between SZ-FDRs and BD-FDRs remain after adjusting for IQ or educational attainment, we suggest that differential brain developmental processes underlying predisposition for schizophrenia or bipolar disorder are likely independent of general cognitive impairment., Australian National Health and Medical Research CouncilNational Health and Medical Research Council of Australia [1037196, 1063960, 1066177, 510135, 1176716]; Canadian Institutes of Health ResearchCanadian Institutes of Health Research (CIHR) [103703, 106469, 142255]; Departament de Salut de la Generalitat de CatalunyaGeneralitat de Catalunya [SLT002/16/00331]; Deutsche ForschungsgemeinschaftGerman Research Foundation (DFG) [1617]; Development Service Merit Review Award [I01CX000227]; Dokuz Eylul University Department of Scientific Research [2012.KB.SAG.062]; e:Med program [O1ZX1314B, O1ZX1314G]; Ege University School of Medicine Research Foundation [2009-D-00017]; Fundacio Marato TV3 [091630]; Netherlands Organisation for Health Research and DevelopmentNetherlands Organization for Health Research and Development [10-000-1002]; Generalitat de CatalunyaGeneralitat de Catalunya [2017SGR01271]; German Federal Ministry for Education and ResearchFederal Ministry of Education & Research (BMBF); Medical Research CouncilMedical Research Council UK (MRC) [G0901310]; Ministerstvo Zdravotnictvi Ceske Republiky [NR8786, NT13891]; National Alliance for Research on Schizophrenia and DepressionNARSAD [17319, 20244, 26731]; Swiss National Centre of Competence in Research Robotics [51NF40-185897]; National Institute of Mental HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Mental Health (NIMH) [1S10OD017974-01, P30 NS076408, R01 MH052857, R01 MH080912, R01 MH113619, U01 MH108150, R01 MH085667]; National Institute on AgingUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute on Aging (NIA) [T32AG058507]; National Institutes of HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [P41 EB015922, R01 MH111671, R01 MH116147, R01 MH117601, R01MH121246, R03 MH105808, U54EB020403]; Research Council of NorwayResearch Council of Norway [223273]; Spanish Ministry of Economy and Competitiveness/Instituto de Salud Carlos III [CPII19/00009, PI070066, PI1100683, PI1500467, PI18/00976]; Stanley Medical Research Institute; Swedish Research CouncilSwedish Research Council [K2007-62X-15077-04-1, K2008-62P-20597-01-3, K2010-62X-15078-07-2, K2012-61X-15078-09-3]; Swiss National Science FoundationSwiss National Science Foundation (SNSF) [32003B_156914]; VIDINetherlands Organization for Scientific Research (NWO) [452-11-014, 917-46-370]; Wellcome TrustWellcome Trust [085475/B/08/Z, 085475/Z/08/Z, 064971]; ZonMwNetherlands Organization for Health Research and Development [908-02-123], Australian National Health and Medical Research Council Grants, Grant/Award Numbers: 1037196, 1063960, 1066177, 510135, 1176716; Canadian Institutes of Health Research, Grant/Award Numbers: 103703, 106469, 142255; Departament de Salut de la Generalitat de Catalunya, Grant/Award Number: SLT002/16/00331; Deutsche Forschungsgemeinschaft, Grant/Award Number: 1617; Development Service Merit Review Award, Grant/Award Number: I01CX000227; Dokuz Eylul University Department of Scientific Research Projects Funding, Grant/Award Number: 2012.KB.SAG.062; e:Med program, Grant/Award Numbers: O1ZX1314B, O1ZX1314G; Ege University School of Medicine Research Foundation, Grant/Award Number: 2009-D-00017; Fundacio Marato TV3, Grant/Award Number: 091630; Geestkracht program of the Netherlands Organisation for Health Research and Development, Grant/Award Number: 10-000-1002; Generalitat de Catalunya, Grant/Award Number: 2017SGR01271; German Federal Ministry for Education and Research; Medical Research Council, Grant/Award Number: G0901310; Ministerstvo Zdravotnictvi Ceske Republiky, Grant/Award Numbers: NR8786, NT13891; National Alliance for Research on Schizophrenia and Depression, Grant/Award Numbers: 17319, 20244, 26731; Swiss National Centre of Competence in Research Robotics, Grant/Award Number: 51NF40-185897; National Institute of Mental Health, Grant/Award Numbers: 1S10OD017974-01, P30 NS076408, R01 MH052857, R01 MH080912, R01 MH113619, U01 MH108150, R01 MH085667; National Institute on Aging, Grant/Award Number: T32AG058507; National Institutes of Health, Grant/Award Numbers: P41 EB015922, R01 MH111671, R01 MH116147, R01 MH117601, R01MH121246, R03 MH105808, U54EB020403; Research Council of Norway, Grant/Award Number: 223273; Spanish Ministry of Economy and Competitiveness/Instituto de Salud Carlos III, Grant/Award Numbers: CPII19/00009, PI070066, PI1100683, PI1500467, PI18/00976; Stanley Medical Research Institute; Swedish Research Council, Grant/Award Numbers: K2007-62X-15077-04-1, K2008-62P-20597-01-3, K2010-62X-15078-07-2, K2012-61X-15078-09-3; Swiss National Science Foundation, Grant/Award Number: 32003B_156914; VIDI, Grant/Award Numbers: 452-11-014, 917-46-370; Wellcome Trust, Grant/Award Numbers: 085475/B/08/Z, 085475/Z/08/Z; Wellcome Trust Research Training Fellowship, Grant/Award Number: 064971; ZonMw, Grant/Award Number: 908-02-123

BackgroundChoroid plexus (ChP) enlargement has been described in first-episode psychosis and psychosis spectrum disorders, but whether ChP enlargement occurs before disease onset is unknown. This study investigated whether ChP volume is enlarged in individuals with clinical high-risk (CHR) for psychosis and whether these changes are related to clinical, cortical, and plasma analyte measures.MethodsClinical and neuroimaging data from the North American Prodrome Longitudinal Study 2 (NAPLS2) was used for analysis. A total of 509 participants (169 controls, 340 CHR) were recruited across 8 sites. Conversion status was determined until 2-years of follow-up, with 36 patients developing psychosis. The lateral ventricle ChP was manually segmented from all available baseline brain scans. A subsample of 84 participants (31 controls, 53 CHR) had plasma analyte and neuroimaging data.ResultsCompared to controls, CHR overall (d=0.22, p=0.017) and converters(d=0.21, p=0.041)demonstrated higher ChP volumes, but not nonconverters. In CHR, greater ChP volume correlated with lower cortical(r=−0.22, pand subcortical gray matter volume(r=− 0.21, p, total white matter volume(r=−0.28,p, and larger lateral ventricle volume(r=0.63,p. In CHR, greater ChP volume, but not lateral ventricle volume, correlated with higher C3(r=0.39, p=0.005)and TSH(r=0.31, p=0.026), and lower MMP7(r=−0.30, p=0.032)and UMOD(r=−0.33, p=0.019).Conclusions and RelevanceCHR and converters to psychosis demonstrated significantly larger ChP volumes compared to controls. ChP enlargement was associated with cortical volume reduction and increased peripheral inflammatory markers. These findings suggest that the ChP may be a key biomarker in psychosis disease onset and conversion.

Mounting evidence has shown disrupted brain network architecture across the psychosis spectrum. However, whether these changes relate to the development of psychosis is unclear. Here, we used graph theoretical analysis to investigate longitudinal changes in resting-state brain networks in samples of 72 subjects at clinical high risk (including 8 cases who converted to full psychosis) and 48 healthy controls drawn from the North American Prodrome Longitudinal Study (NAPLS) consortium. We observed progressive reduction in global efficiency (P = 0.006) and increase in network diversity (P = 0.001) in converters compared with non-converters and controls. More refined analysis separating nodes into nine key brain networks demonstrated that these alterations were primarily driven by progressively diminished local efficiency in the default-mode network (P = 0.004) and progressively enhanced node diversity across all networks (P < 0.05). The change rates of network efficiency and network diversity were significantly correlated (P = 0.003), suggesting these changes may reflect shared underlying neural mechanisms. In addition, change rates of global efficiency and node diversity were significantly correlated with change rate of cortical thinning in the prefrontal cortex in converters (P < 0.03) and could be predicted by visuospatial memory scores at baseline (P < 0.04). These results provide preliminary evidence for longitudinal reconfiguration of resting-state brain networks during psychosis development and suggest that decreased network efficiency, reflecting an increase in path length between nodes, and increased network diversity, reflecting a decrease in the consistency of functional network organization, are implicated in the progression to full psychosis.

Disorganization, presenting as impairment in thought, language and goal-directed behavior, is a core multidimensional syndrome of psychotic disorders. This study examined whether scalable computational measures of spoken language, and smartphone usage pattern, could serve as digital biomarkers of clinical disorganization symptoms.We examined in a longitudinal cohort of adults with a psychotic disorder, the associations between clinical measures of disorganization and computational measures of 1) spoken language derived from monthly, semi-structured, recorded clinical interviews; and 2) smartphone usage pattern derived via passive sensing technologies over the month prior to the interview. The language features included speech quantity, rate, fluency, and semantic regularity. The smartphone features included data missingness and phone usage during sleep time. The clinical measures consisted of the Positive and Negative Symptom Scale (PANSS) conceptual disorganization, difficulty in abstract thinking, and poor attention, items. Mixed linear regression analyses were used to estimate both fixed and random effects.Greater severity of clinical symptoms of conceptual disorganization was associated with greater verbosity and more disfluent speech. Greater severity of conceptual disorganization was also associated with greater missingness of smartphone data, and greater smartphone usage during sleep time. While the observed associations were significant across the group, there was also significant variation between individuals.The findings suggest that digital measures of speech disfluency may serve as scalable markers of conceptual disorganization. The findings warrant further investigation into the use of recorded interviews and passive sensing technologies to assist in the characterization and tracking of psychotic illness.

Progressive grey matter loss has been demonstrated among clinical high-risk (CHR) individuals who convert to psychosis, but it is unknown whether these changes occur prior to psychosis onset. Identifying illness-related neurobiological mechanisms that occur prior to conversion is essential for targeted early intervention. Among participants in the third wave of the North American Prodrome Longitudinal Study (NAPLS3), this report investigated if steeper cortical thinning was observable prior to psychosis onset among CHR individuals who ultimately converted (CHR-C) and assessed the shortest possible time interval in which rates of cortical thinning differ between CHR-C, CHR non-converters (CHR-NC), and health controls (HC). 338 CHR-NC, 42 CHR-C, and 62 HC participants (age 19.3±4.2, 44.8% female, 52.5% racial/ethnic minority) completed up to 5 MRI scans across 8 months. Accelerated thinning among CHR-C compared to CHR-NC and HC was observed in multiple prefrontal, temporal, and parietal cortical regions. CHR-NC also exhibited accelerated cortical thinning compared to HC in several of these areas. Greater percent decrease in cortical thickness was observed among CHR-C compared to other groups across 2.9±1.8 months, on average, in several cortical areas. ROC analyses discriminating CHR-C from CHR-NC by percent thickness change in a left hemisphere region of interest, scanner, age, age2, and sex had an AUC of 0.74, with model predictive power driven primarily by percent thickness change. Findings indicate that accelerated cortical thinning precedes psychosis onset and differentiates CHR-C from CHR-NC and HC across short time intervals. Mechanisms underlying cortical thinning may provide novel treatment targets prior to psychosis onset.

In a recent prospective longitudinal neuroimaging study, clinical high-risk (CHR) individuals who later developed full-blown psychosis showed an accelerated rate of gray matter thinning in superior and medial prefrontal cortex (PFC) and expansion of the ventricular system after applying a stringent correction for multiple comparisons. Although cortical and subcortical volume loss and enlarged ventricles are well characterized structural brain abnormalities among patients with schizophrenia, no prior study has evaluated whether these progressive changes of neuroanatomical indicators are linked in time prior to onset of psychosis. Therefore, we investigated the relationship between the changes in cortical gray matter thickness and ventricular volume using the longitudinal neuroimaging data from the North American Prodrome Longitudinal Study at the whole-brain level. The results showed that ventricular expansion is linked in time to progressive reduction of gray matter, rather than to structural changes in proximal subcortical regions, in a broadly distributed set of cortical regions among CHR youth, including superior, medial, lateral, and inferior PFC, superior temporal gyrus, and parietal cortices. In contrast, healthy controls did not show the same pattern of associations. The main findings were further replicated using a third assessment wave of MRI scans in a subset of study participants who were followed for an additional year. These findings suggest that the gray matter regions exhibiting aberrant rates of thinning in relation to psychosis risk are not limited to the PFC regions that survived the statistical threshold in our primary study, but also extend to other cortical regions previously implicated in schizophrenia.

WOS: 000485217300010, PubMed ID: 31443932, BACKGROUND: Schizophrenia and bipolar disorder share genetic liability, and some structural brain abnormalities are common to both conditions. First-degree relatives of patients with schizophrenia (FDRs-SZ) show similar brain abnormalities to patients, albeit with smaller effect sizes. Imaging findings in first-degree relatives of patients with bipolar disorder (FDRs-BD) have been inconsistent in the past, but recent studies report regionally greater volumes compared with control subjects. METHODS: We performed a meta-analysis of global and subcortical brain measures of 6008 individuals (1228 FDRs-SZ, 852 FDRs-BD, 2246 control subjects, 1016 patients with schizophrenia, 666 patients with bipolar disorder) from 34 schizophrenia and/or bipolar disorder family cohorts with standardized methods. Analyses were repeated with a correction for intracranial volume (ICV) and for the presence of any psychopathology in the relatives and control subjects. RESULTS: FDRs-BD had significantly larger ICV (d = +10.16, q < .05 corrected), whereas FDRs-SZ showed smaller thalamic volumes than control subjects (d = -0.12, q < .05 corrected). ICV explained the enlargements in the brain measures in FDRs-BD. In FDRs-SZ, after correction for ICV, total brain, cortical gray matter, cerebral white matter, cerebellar gray and white matter, and thalamus volumes were significantly smaller; the cortex was thinner (d < -0.09, q < .05 corrected); and third ventricle was larger (d = +0.15, q < .05 corrected). The findings were not explained by psychopathology in the relatives or control subjects. CONCLUSIONS: Despite shared genetic liability, FDRs-SZ and FDRs-BD show a differential pattern of structural brain abnormalities, specifically a divergent effect in ICV. This may imply that the neurodevelopmental trajectories leading to brain anomalies in schizophrenia or bipolar disorder are distinct., Research Council of NorwayResearch Council of Norway [223273]; National Institutes of Health (NIH)United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [R01 MH117601, R01 MH116147, R01 MH111671, P41 EB015922, 5T32MH073526, U54EB020403, R03 MH105808]; National Institute on Aging (NIA)United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute on Aging (NIA) [T32AG058507]; Canadian Institutes of Health ResearchCanadian Institutes of Health Research (CIHR) [103703, 106469, 341717]; National Centre for Mental Health; 2010 National Alliance for Research on Schizophrenia and Depression (NARSAD) Young Investigator Award [17319]; Dokuz Eylul University Department of Scientific Research Projects FundingDokuz Eylul University [2012.KB.SAG.062]; National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley National Health Service Foundation Trust and King's College London; Ege University School of Medicine Research Foundation [2009-D-00017]; Medical Research CouncilMedical Research Council UK (MRC) [G0901310]; Geestkracht program of the Netherlands Organisation for Health Research and Development [10-000-1002]; National Institute of Mental HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Mental Health (NIMH) [R01 MH 085667]; Nova Scotia Health Research Foundation; Dalhousie Clinical Research Scholarship; 2007 Brain and Behavior Research Foundation Young Investigator Award; Ministry of Health of the Czech RepublicMinistry of Health, Czech Republic [NR8786, NT13891]; Swedish Research CouncilSwedish Research Council [K2007-62X-15077-04-1, K2008-62P-20597-01-3, K2010-62X-15078-07-2, K2012-61X-15078-09-3]; Stockholm County CouncilStockholm County Council; Karolinska InstitutetKarolinska Institutet; Knut and Alice Wallenberg FoundationKnut & Alice Wallenberg Foundation; HUBIN project; Spanish Ministry of Economy and Competitiveness/Instituto de Salud Carlos III [PI070066, PI1100683, PI1500467]; Fundacio Marato TV3 [091630]; ERDF Funds from the European Commission A Way of Making Europe"); Brain and Behaviour Research Foundation (NARSAD Young Investigator Award); Alicia Koplowitz Foundation; Stanley Medical Research Institute; NARSADNARSAD [20244]; Wellcome TrustWellcome Trust [064971, 085475/B/08/Z, 085475/Z/08/Z]; NARSAD Young Investigator AwardNARSAD; European CommunityEuropean Community (EC); NIMH Intramural Research ProgramUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Mental Health (NIMH); NIHR Biomedical Research Centre at University College London Hospital; British Medical Association Margaret Temple Fellowship 2016; German Federal Ministry for Education and ResearchFederal Ministry of Education & Research (BMBF) [O1ZX1314B, O1ZX1314G]; Deutsche ForschungsgemeinschaftGerman Research Foundation (DFG) [1617]; NIMHUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Mental Health (NIMH) [R01 MH116147, R01 MH113619]; NIHUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [U01 MH097435, R01 MH084803, R01 EB020062, R01 MH080912, R01 MH052857]; National Science FoundationNational Science Foundation (NSF) [1636893, 1734853]; Australian National Health and Medical Research CouncilNational Health and Medical Research Council of Australia [510135, 1037196, 1063960, 1066177]; ZonMwNetherlands Organization for Health Research and Development [908-02-123]; VIDINetherlands Organization for Scientific Research (NWO) [452-11-014, 917-46-370]; Janssen PharmaceuticaJohnson & Johnson USAJanssen Biotech Inc; Otsuka PharmaceuticalOtsuka Pharmaceutical; Bipolar Disorder Research Network, The researchers and studies included in this article were supported by the Research Council of Norway (Grant No. 223273), National Institutes of Health (NIH) (Grant No. R01 MH117601 [to NJ], Grant Nos. R01 MH116147, R01 MH111671, and P41 EB015922 [to PMT], Grant Nos. 5T32MH073526 and U54EB020403 [to CRKC], and Grant No. R03 MH105808 [to CEB and SCF]) and National Institute on Aging (NIA) (Grant No. T32AG058507 [to CRKC]).; C-SFS: This work was supported by Canadian Institutes of Health Research.; Cardiff: This work was supported by the National Centre for Mental Health, Bipolar Disorder Research Network, 2010 National Alliance for Research on Schizophrenia and Depression (NARSAD) Young Investigator Award (Grant No. 17319).; DEU: This work was supported by Dokuz Eylul University Department of Scientific Research Projects Funding (Grant No. 2012. KB. SAG. 062). This report represents independent research funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley National Health Service Foundation Trust and King's College London. The views expressed are those of the authors and not necessarily those of the National Health Service, NIHR, or Department of Health.; EGEU: This work was supported by the Ege University School of Medicine Research Foundation (Grant No. 2009-D-00017).; EHRS: The Edinburgh High Risk Study was supported by the Medical Research Council.; GROUP: The infrastructure for the GROUP study was supported by the Geestkracht program of the Netherlands Organisation for Health Research and Development (Grant No. 10-000-1002).; ENBD_UT/BPO_FLB: This work was supported by the National Institute of Mental Health (Grant No. R01 MH 085667).; HHR/PHHR: This work was supported by the Canadian Institutes of Health Research (Grant Nos. 103703, 106469, and 341717), Nova Scotia Health Research Foundation, Dalhousie Clinical Research Scholarship (to TH), 2007 Brain and Behavior Research Foundation Young Investigator Award (to TH), and Ministry of Health of the Czech Republic (Grant Nos. NR8786 and NT13891).; HUBIN: This work was supported by the Swedish Research Council (Grant Nos. K2007-62X-15077-04-1, K2008-62P-20597-01-3, K2010-62X-15078-07-2, K2012-61X-15078-09-3), regional agreement on medical training and clinical research between Stockholm County Council and the Karolinska Institutet, Knut and Alice Wallenberg Foundation, and HUBIN project.; IDIBAPS: This work was supported by the Spanish Ministry of Economy and Competitiveness/Instituto de Salud Carlos III (Grant Nos. PI070066, PI1100683, and PI1500467) and Fundacio Marato TV3 (Grant No. 091630), co-financed by ERDF Funds from the European Commission A Way of Making Europe"), Brain and Behaviour Research Foundation (NARSAD Young Investigator Award), and Alicia Koplowitz Foundation.; IoP-BD: The Maudsley Bipolar Twin Study was supported by the Stanley Medical Research Institute and NARSAD.; IoP-SZ: This work was supported by a Wellcome Trust Research Training Fellowship (Grant No. 064971 to MMP), NARSAD Young Investigator Award (to TT), and European Community's Sixth Framework Programme through a Marie Curie Training Network called the European Twin Study Network on Schizophrenia.; Lieber Institute for Brain Development (LIBD): This work was supported by the NIMH Intramural Research Program (to DRW's laboratory). LIBD is a nonprofit research institute located in Baltimore, MD. The work performed at LIBD was performed in accordance with an NIMH material transfer agreement with LIBD.; MFS: The Maudsley Family Study cohort collection was supported by the Wellcome Trust (Grant Nos. 085475/B/08/Z and 085475/Z/08/Z), NIHR Biomedical Research Centre at University College London Hospital, Medical Research Council (Grant No. G0901310), and British Medical Association Margaret Temple Fellowship 2016.; MooDS: This work was supported by the German Federal Ministry for Education and Research grants NGFNplus MooDS (Systematic Investigation of the Molecular Causes of Major Mood Disorders and Schizophrenia) and Integrated Network IntegraMent (Integrated Understanding of Causes and Mechanisms in Mental Disorders) under the auspices of the e: Med program (Grant Nos. O1ZX1314B and O1ZX1314G) and Deutsche Forschungsgemeinschaft (Grant No. 1617 [to AH]).; MSSM: This work was supported by NIMH (Grant Nos. R01 MH116147 and R01 MH113619).; NU: This work was supported by NIH (Grant Nos. U01 MH097435, R01 MH084803, and R01 EB020062) and National Science Foundation (Grant Nos. 1636893 and 1734853).; OLIN: This work was supported by NIH (Grant No. R01 MH080912).; STAR: This work was supported by NIH (Grant No. R01 MH052857).; SydneyBipolarGroup: The Australian cohort collection was supported by the Australian National Health and Medical Research Council Program Grants (Grant No. 510135 [to PBM] and Grant No. 1037196 [to PBM and PRS]) and Project Grants (Grant No. 1063960 [to JMF and PRS] and Grant No. 1066177 [to JMF]).; UMCU: This work was supported by NARSAD (Grant No. 20244 [to MHJH]), ZonMw (Grant No. 908-02-123 [to HEHP]), VIDI (Grant No. 452-11-014 [to NEMvH] and Grant No. 917-46-370 [to HEHP]), and Stanley Medical Research Institute.; CliNG: We thank Anna Fanelli, Kathrin Jakob, and Maria Keil for help with data acquisition.; All authors have contributed to and approved the contents of this manuscript.; GS has received research and travel support from Janssen Pharmaceutica and Otsuka Pharmaceutical and honoraria from Adamed Pharma. NY has been an investigator in clinical studies conducted together with Janssen-Cilag, Corcept Therapeutics, and COMPASS Pathways in the last 3 years. AM-L has received consultant fees from Boehringer Ingelheim, BrainsWay, Elsevier, Lundbeck International Neuroscience Foundation, and Science Advances. CRKC has received partial research support from Biogen, Inc. (Boston, MA) for work unrelated to the topic of this manuscript. The remaining authors report no biomedical financial interests or potential conflicts of interest.

Several magnetic resonance imaging studies have reported reductions in hippocampal volume in patients with psychosis. It is unclear whether structural abnormalities predate illness onset. We conducted a detailed, systematic literature search for studies reporting hippocampal volume in subjects with clinical high-risk, compared to healthy controls. The overall sample size comprised 1429 subjects. Meta-analysis revealed no difference for left, but a small, albeit significant, difference for right hippocampal volume, such that clinical high-risk patients had slightly smaller hippocampal volume than healthy controls (g=0.24, p=0.0418). Meta-regression indicated a moderating effect of manual tracing approach, due to one outlying site. The small difference on the right side did not remain significant (g=0.14, 95%CI=[-0.03-0.32], p=0.11) after removal of this outlier. This meta-analysis suggests that there is no reduction in hippocampal volume before transition to psychosis and hippocampal volume cannot be used as a biomarker in clinical high-risk individuals.

In a recent machine learning study classifying “brain age” based on cross-sectional neuroanatomical data, clinical high-risk (CHR) individuals were observed to show deviation from the normal neuromaturational pattern, which in turn was predictive of greater risk of conversion to psychosis and a pattern of stably poor functional outcome. These effects were unique to cases who were between 12 and 17 years of age when their prodromal and psychotic symptoms began, suggesting that neuroanatomical deviance observable at the point of ascertainment of a CHR syndrome marks risk for an early onset form of psychosis. In the present study, we sought to clarify the pattern of neuroanatomical deviance linked to this “early onset” form of psychosis and whether this deviance is associated with poorer premorbid functioning. T1 MRI scans from 378 CHR individuals and 190 healthy controls (HC) from the North American Prodrome Longitudinal Study (NAPLS2) were analyzed. Widespread smaller cortical volume was observed among CHR individuals compared with HC at baseline evaluation, particularly among the younger group (i.e., those who were 12 to 17 years of age). Moreover, the younger CHR individuals who converted or presented worsened clinical symptoms at follow-up (within 2 years) exhibited smaller surface area in rostral anterior cingulate, lateral and medial prefrontal regions, and parahippocampal gyrus relative to the younger CHR individuals who remitted or presented a stable pattern of prodromal symptoms at follow-up. In turn, poorer premorbid functioning in childhood was associated with smaller surface area in medial orbitofrontal, lateral frontal, rostral anterior cingulate, precuneus, and temporal regions. Together with our prior report, these results are consistent with the view that neuroanatomical deviance manifesting in early adolescence marks vulnerability to a form of psychosis presenting with poor premorbid adjustment, an earlier age of onset (generally prior to the age of 18 years), and poor long-term outcome. Keywords: Magnetic resonance imaging, Clinical high risk, Psychosis, Brain development, Premorbid functioning, Schizophrenia

Memory deficits are a hallmark of psychotic disorders such as schizophrenia. However, whether the neural dysfunction underlying these deficits is present before the onset of illness and potentially predicts conversion to psychosis is unclear. In this study, we investigated brain functional alterations during memory processing in a sample of 155 individuals at clinical high risk (including 18 subjects who later converted to full psychosis) and 108 healthy controls drawn from the second phase of the North American Prodrome Longitudinal Study (NAPLS-2). All participants underwent functional magnetic resonance imaging with a paired-associate memory paradigm at the point of recruitment and were clinically followed up for approximately 2 years. We found that at baseline, subjects at high risk showed significantly higher activation during memory retrieval in the prefrontal, parietal, and bilateral temporal cortices (PFWE < .035). This effect was more pronounced in converters than nonconverters and was particularly manifested in unmedicated subjects (P < .001). The hyperactivation was significantly correlated with retrieval reaction time during scan in converters (P = .009) but not in nonconverters and controls, suggesting an exaggerated retrieval effort. These findings suggest that hyperactivation during memory retrieval may mark processes associated with conversion to psychosis, and such measures have potential as biomarkers for psychosis prediction.

In a recent study, a neuroanatomical-based age prediction model observed neuromaturational deviance among clinical high-risk individuals who developed psychosis. Here we aimed to investigate whether incorporating “brain age gap” (discrepancy between neuroanatomical-based predicted age and chronological age) to the North American Prodromal Longitudinal Study risk calculator would enhance prediction of psychosis conversion. The effect of brain age gap was significant (HR = 1.21, P = 0.047), but its predictive variance was found to overlap entirely with age at ascertainment, consistent with the view that greater brain-age gap and earlier age at onset of prodromal symptoms are correlated indicators of insidious-onset forms of psychosis.

Background Both early (pre- and perinatal) and late (adolescent) neurodevelopmental disturbances are hypothesized to contribute to the pathophysiology of schizophrenia. Disturbances originating earlier in life (e.g., resulting from the interplay of genetic factors and obstetric complications) would be expected to affect brain integrity from birth onwards and could therefore help to explain cases with subtle deficits in premorbid functioning during childhood and earlier ages at onset of full psychosis (i.e., early to mid-teens). In contrast, disturbances that emerge during late adolescence and early adulthood (e.g., via abnormal neuromaturational events and/or environmental factors) could help to explain cases with normal premorbid psychological health and a more acute onset of psychotic symptoms and functional impairment in the late teens and early twenties. However, it is yet unclear whether neuroanatomical data among individuals at clinical high risk (CHR) for psychosis can be modeled to detect early versus late neurodevelopmental influences that is predictive of future psychosis onset. Therefore, in this study, we investigated whether the timing of the appearance or course of the deviation from normal brain maturation, as determined using a machine learning algorithm trained on structural MRI data to estimate age, is potentially relevant to the early versus late neurodevelopmental framework among CHR individuals. Methods A neuroanatomical-based age prediction model was trained using a supervised machine learning technique with T1 MRI scans from 953 typically developing healthy controls (HC) from the Pediatric Imaging, Neurocognition, and Genetics study (PING) study. The trained model was then applied to 109 HCs and 275 CHR, including 39 converters (CHR-C), from the North American Prodrome Longitudinal Study (NAPLS2) and 14 cases of first episode psychosis patients (FE) for external validation and clinical application. Discrepancy between neuroanatomical-based estimated age and chronological age was computed for each individual (i.e., brain age gap) and compared across clinical groups. Results The PING-derived model for estimating age accurately predicted NAPLS HC subjects’ chronological ages, explaining 51% of the variance (P < 0.001) in chronological age, with a mean absolute error of 1.41 years, providing evidence of independent external validation. CHR subjects and FE adolescents showed a significantly greater overestimated gap between model-predicted age and chronological age compared with HC (Ps < 0.01). This effect was significantly moderated by chronological age, with neuroanatomical-based estimated age systematically overestimating CHR cases aged 12–17 years, but not among those aged 18–21 years. In the ROC analysis, brain age gap was a significant predictor of conversion to psychosis with an area under the curve of 0.63 (P < 0.05) among younger adolescents. In addition, increased deviation of brain age gap predicted pattern of stably low functioning over time (P < 0.05) among CHR individuals. In contrast, previously reported evidence of an accelerated reduction in cortical thickness among CHR-C was found to apply only to those cases who were 18 years or older. Discussion These results are consistent with the view that both early and later neurodevelopmental disturbances contribute to the onset and course of schizophrenia, with the two sets of influences having differing implications for the intercepts and trajectories in structural brain parameters as a function of age. The results also suggest that baseline neuroanatomical measures are likely to be useful in prediction of psychosis especially (or only) among CHR cases who are below 18 years of age at the time of ascertainment.

A recent prospective longitudinal neuroimaging study of 274 prodromal risk syndrome subjects revealed that those who later developed full-blown psychotic symptoms exhibited accelerated gray matter loss and third ventricle expansion around the time of onset of psychosis. Previous studies also indicate that higher levels of unusual thought content during prodromal states are a significant predictor of psychosis in clinically high-risk youth (CHR). However, the relationship between clinical symptoms and changes in neuroanatomical structure has not been previously examined in the North American Prodrome Longitudinal Study (NAPLS) sample at the atlas level. In this report, we investigated whether symptom severity as measured by the Scale of Prodromal Symptoms (SOPS) predicted the accelerated gray matter decline in 274 CHR cases, including 35 who converted to psychosis. Higher levels of unusual thought content (pre-delusional) symptoms at baseline were associated with a steeper rate of gray matter loss in the prefrontal cortex bilaterally among converters. In contrast, there was no association found among non-converters. Steeper gray matter loss seems to be unique to those (CHR) individuals with higher levels of sub-psychotic pre-delusional symptoms that acutely worsen in the ramp-up to full-blown psychosis, and as such may reflect pathophysiological processes driving emergence of psychosis.

In a recent report of the North American Prodrome Longitudinal Study (NAPLS), clinical high-risk individuals who converted to psychosis showed a steeper rate of cortical gray matter reduction compared with non-converters and healthy controls, and the rate of cortical thinning was correlated with levels of proinflammatory cytokines at baseline. These findings suggest a critical role for microglia, the resident macrophages in the brain, in perturbations of cortical maturation processes associated with onset of psychosis. Elucidating gene expression pathways promoting microglial action prior to disease onset would inform potential preventative intervention targets. Here we used a forward stepwise regression algorithm to build a classifier of baseline microRNA expression in peripheral leukocytes associated with annualized rate of cortical thinning in a subsample of the NAPLS cohort (N=74). Our cortical thinning classifier included nine microRNAs, p=3.63 × 10-08, R2=0.358, permutation-based p=0.039, the gene targets of which were enriched for intracellular signaling pathways that are important to coordinating inflammatory responses within immune cells (p

Recent work suggests that genes encoding complement proteins that are active in the innate immune system may confer risk for schizophrenia by disrupting typical synaptic pruning in late adolescence. Alterations in the complement pathway may contribute to aberrant cortical thinning in schizophrenia prodromes and reduced prefrontal cortical thickness in chronic schizophrenia patients; however, this theory needs to be translated to humans. We conducted a series of analyses in a sample of adult Swedish twins enriched for schizophrenia (N=129) to assess the plausibility of a relationship between complement gene expression and cortical thickness that could go awry in the etiology of schizophrenia. First, we identified that peripheral mRNA expression levels of two complement genes (C5, SERPING1) made unique contributions to the variance in superior frontal cortical thickness among all participants. Vertex-wise maps of the association between gene expression levels and thickness across the cortex suggested that this relationship was especially strong with SERPING1 in the superior frontal region, consistent with the pattern of disruption in cortical thickness observed in schizophrenia. Additional analyses identified that these genes are expressed in the human superior frontal cortex, that heritable genetic factors influence SERPING1 gene expression levels, and that these associations are observed regardless of case status. These findings provide initial evidence linking the complement system with cortical thinning in humans, a process potentially involved in the pathogenesis of schizophrenia.

Background: Genes involved in the complement pathway of the innate immune system have been implicated in risk for schizophrenia. Emerging evidence suggests that this pathway is involved in synaptic pruning, a phenomenon that likely influences cortical thinning and may underlie reduction of cortical thickness in prefrontal regions in schizophrenia. However, a link between complement genes and cortical thickness has not yet been established in humans.

Background: In a recent report of the North American Prodrome Longitudinal Study (NAPLS), clinical high-risk individuals who converted to psychosis showed a steeper rate of cortical gray matter reduction compared with nonconverters and healthy controls, and the rate of cortical thinning was correlated with levels of proinflammatory cytokines at baseline. These findings suggest a critical role for microglia, the resident macrophages in the brain, in perturbations of cortical maturation processes associated with onset of psychosis. Elucidating gene expression pathways promoting microglial action prior to disease onset would inform potential preventative intervention targets. Methods: We used a forward stepwise regression algorithm to build a classifier of baseline microRNA expression in peripheral leukocytes associated with annualized rate of cortical thinning in a subsample of the NAPLS cohort (N=74). Results: Our cortical thinning classifier included 9 microRNAs, P=3.63 × 108, R2=0.358, permutation-based P=.039, the gene targets of which were enriched for intracellular signaling pathways that are important to coordinating inflammatory responses within immune cells (P < .05, Benjamini-Hochberg corrected). The classifier was also related to proinflammatory cytokine levels in serum (P=.038). Furthermore, miRNAs that predicted conversion status were found to do so in a manner partially mediated by rate of cortical thinning (point estimate=0.078 [95% CIs: 0.003, 0.168], P=.03). Conclusion: Many of the miRNAs identified here have been previously implicated in brain development, synaptic plasticity, immune function, and/or schizophrenia, showing some convergence across studies and methodologies. Altered intracellular signaling within the immune system may interact with cortical maturation in individuals at high risk for schizophrenia promoting disease onset.

Background: In a recent prospective longitudinal neuroimaging study, clinical high-risk (CHR) individuals who later developed full-blown psychosis showed an accelerated rate of gray matter thinning in superior and medial prefrontal cortex (PFC) and expansion of the ventricular system after applying a stringent correction for multiple comparisons. Although cortical and subcortical volume loss and enlarged ventricles are well characterized structural brain abnormalities among patients with schizophrenia, no prior study has evaluated whether these progressive changes of neuroanatomical indicators are linked in time prior to onset of psychosis. Therefore, we investigated the relationship between the changes in cortical gray matter thickness and ventricular volume using the longitudinal neuroimaging data from the North American Prodrome Longitudinal Study (NAPLS) at the whole-brain level. Methods: MRI structural data were acquired at baseline and 12-month follow-up, and follow-up scans for those who developed fully psychotic symptoms were assessed at the point of conversion. In total, 37 CHR cases who converted to psychosis, 230 CHR cases who did not convert (nonconverters), and 132 healthy comparison subjects had usable baseline and second time point scans. Imaging measures were first transformed to annualized rates of percent change (ARCH) in each cortical vertex. Interval is the time between BL and FU scans in years. Relationships between ARCH of total ventricle volume and ARCH of cortical gray matter values were tested vertex-wise using the general linear model. Among the subjects with BL and 12-FU data available, 125 CHR cases and 66 controls were followed to an additional third time point for a 24-month MRI assessment. For the purpose of testing the replicability of our main hypotheses, neuroanatomical ARCH measures between the 12 and 24month follow-ups were also computed with a parallel set of statistical tests as described earlier. Results: The results showed that ventricular expansion is linked in time to progressive reduction of gray matter, rather than to structural changes in proximal subcortical regions, in a broadly distributed set of cortical regions among CHR youth, including superior, medial, lateral, and inferior PFC, superior temporal gyrus, and parietal cortices. In contrast, the healthy controls did not show the same pattern of associations. The main findings were further replicated using a third assessment wave of MRI scans in a subset of study participants who were followed for an additional year. Conclusion: In summary, expansion of the ventricular spaces is linked in time with an accelerated rate of widespread cortical thinning prior to psychosis onset. The cortical regions experiencing altered maturation during the psychosis prodrome may be more widespread than the regionally specific clusters that have been identified in previous case–control studies

Importance Altered neurodevelopmental trajectories are thought to reflect heterogeneity in the pathophysiologic characteristics of schizophrenia, but whether neural indicators of these trajectories are associated with future psychosis is unclear. Objective To investigate distinct neuroanatomical markers that can differentiate aberrant neurodevelopmental trajectories among clinically high-risk (CHR) individuals. Design, Setting, and Participants In this prospective longitudinal multicenter study, a neuroanatomical-based age prediction model was developed using a supervised machine learning technique with T1-weighted magnetic resonance imaging scans of 953 healthy controls 3 to 21 years of age from the Pediatric Imaging, Neurocognition, and Genetics (PING) study and then applied to scans of 275 CHR individuals (including 39 who developed psychosis) and 109 healthy controls 12 to 21 years of age from the North American Prodrome Longitudinal Study 2 (NAPLS 2) for external validation and clinical application. Scans from NAPLS 2 were collected from January 15, 2010, to April 30, 2012. Main Outcomes and Measures Discrepancy between neuroanatomical-based predicted age (hereafter referred to as brain age) and chronological age. Results The PING-derived model (460 females and 493 males; age range, 3-21 years) accurately estimated the chronological ages of the 109 healthy controls in the NAPLS 2 (43 females and 66 males; age range, 12-21 years), providing evidence of independent external validation. The 275 CHR individuals in the NAPLS 2 (111 females and 164 males; age range, 12-21 years) showed a significantly greater mean (SD) gap between model-predicted age and chronological age (0.64 [2.16] years) compared with healthy controls (P = .008). This outcome was significantly moderated by chronological age, with brain age systematically overestimating the ages of CHR individuals who developed psychosis at ages 12 to 17 years but not the brain ages of those aged 18 to 21 years. Greater brain age deviation was associated with a higher risk for developing psychosis (F = 3.70;P = .01) and a pattern of stably poor functioning over time, but only among younger CHR adolescents. Previously reported evidence of accelerated reduction in cortical thickness among CHR individuals who developed psychosis was found to apply only to those who were 18 years of age or older. Conclusions and Relevance These results are consistent with the view that neuroanatomical markers of schizophrenia may help to explain some of the heterogeneity of this disorder, particularly with respect to early vs later age of onset of psychosis, with younger and older individuals having differing intercepts and trajectories in structural brain parameters as a function of age. The results also suggest that baseline neuroanatomical measures are likely to be useful in estimating onset of psychosis, especially (or only) among CHR individuals with an earlier age of onset of prodromal symptoms.

Administration of dendritic cells (DCs) combined with oncolytic adenovirus (Ad) expressing antitumor cytokines induces a potent antitumor effect and antitumor immunity by ameliorating the immunosuppressive tumor microenvironment. However, this combination therapy has significant limitations due to rapid dissemination and inactivation of the therapeutics at the tumor site, necessitating multiple injections of both therapeutics. In this study, we investigated injectable and biodegradable gelatin-based hydrogel as a carrier for oncolytic Ad co-expressing interleukin (IL)-12 and granulocyte-macrophage colony-stimulating factor (GM-CSF) (oAd) and DCs to elicit a potent antitumor effect with single administration. Gel matrix enabled sustained release of both oAd and DCs while preserving their biological activity over a considerable time period, leading to efficient retention of both therapeutics in tumor tissue. Further, tumors treated with oAd- and DC-loaded gel (oAd+DC/gel) showed a significantly greater expression level of IL-12, GM-CSF, and interferon-γ (IFN-γ) than either single treatment (oAd or DC) or oAd in combination with DC (oAd+DC), resulting in efficient activation of both endogenous and exogenous DCs, migration of DCs to draining lymph nodes, and tumor infiltration of CD4+ and CD8+ T cells. Moreover, oAd+DC/gel resulted in a significantly higher number of tumor-specific IFN-γ-secreting immune cells compared with oAd+DC. Lastly, oAd+DC/gel significantly attenuated tumor-mediated thymic atrophy, which is associated with immunosuppression in the tumor microenvironment, compared with oAd+DC. Taken together, these results demonstrate that gelatin gel-mediated co-delivery of oncolytic Ad and DCs might be a promising strategy to efficiently retain both therapeutics in tumor tissue and induce a potent antitumor immune response for an extended time period via a single administration. Citation Format: Eonju Oh, Jung-Eun Oh, JinWoo Hong, YoonHo Chung, Yunki Lee, Ki Dong Park, Sung Wan Kim, Chae-Ok Yun. Sustained co-delivery of dendritic cells and oncolytic adenovirus co-expressing IL-12 and GM-CSF using biodegradable polymer matrix for cancer immunotherapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 4640.

© 2015 Elsevier Inc. The main objective of the multi-site Pediatric Imaging, Neurocognition, and Genetics (PING) study was to create a large repository of standardized measurements of behavioral and imaging phenotypes accompanied by whole genome genotyping acquired from typically-developing children varying widely in age (3 to 20 years). This cross-sectional study produced sharable data from 1493 children, and these data have been described in several publications focusing on brain and cognitive development. Researchers may gain access to these data by applying for an account on the PING portal and filing a data use agreement. Here we describe the recruiting and screening of the children and give a brief overview of the assessments performed, the imaging methods applied, the genetic data produced, and the numbers of cases for whom different data types are available. We also cite sources of more detailed information about the methods and data. Finally we describe the procedures for accessing the data and for using the PING data exploration portal.

© 2015, Springer-Verlag Berlin Heidelberg. Anxiety is a risk factor for many adverse neuropsychiatric and socioeconomic outcomes, and has been linked to functional and structural changes in the ventromedial prefrontal cortex (VMPFC). However, the nature of these differences, as well as how they develop in children and adolescents, remains poorly understood. More effective interventions to minimize the negative consequences of anxiety require better understanding of its neurobiology in children. Recent research suggests that structural imaging studies may benefit from clearly delineating between cortical surface area and thickness when examining these associations, as these distinct cortical phenotypes are influenced by different cellular mechanisms and genetic factors. The present study examined relationships between cortical surface area and thickness of the VMPFC and a self-report measure of anxiety (SCARED-R) in 287 youths aged 7–20 years from the Pediatric Imaging, Neurocognition, and Genetics (PING) study. Age and gender interactions were examined for significant associations in order to test for developmental differences. Cortical surface area and thickness were also examined simultaneously to determine whether they contribute independently to the prediction of anxiety. Anxiety was negatively associated with relative cortical surface area of the VMPFC as well as with global cortical thickness, but these associations diminished with age. The two cortical phenotypes contributed additively to the prediction of anxiety. These findings suggest that higher anxiety in children may be characterized by both delayed expansion of the VMPFC and an altered trajectory of global cortical thinning. Further longitudinal studies will be needed to confirm these findings.

Based in part on post-mortem observations of reductions of dendritic branching and spine and synapse density in schizophrenia (Glantz & Lewis, 2000), many schizophrenia researchers now believe that disruptions in cellular connectivity are in some manner involved in the pathophysiology of the disorder. Broadly, reduced cellular connectivity may manifest as reduced neuropil and axonal integrity (e.g., myelination) and altered functional connectivity between local and distal brain regions. Such reductions in connectivity are likely to be present at least in part in some cases from birth, representing a life-long biological vulnerability, but may progress beyond a threshold critical for expression of psychotic symptoms as a function of normal neuromaturational events (i.e., synaptic pruning) during adolescence (McGlashan & Hoffman, 2000). In other cases, reductions in connectivity may emerge during adolescence due to aberrant neurodevelopmental processes (i.e., abnormal pruning (McGlashan & Hoffman, 2000)) and/or environmental insults (e.g., elevated cortisol leading to dendritic atrophy). The contributions of early (pre- and perinatal) and later (adolescent) brain developmental processes to psychosis risk are not mutually exclusive, and both sets of processes may be operative in some cases (Cannon et al., 2003). Although the theoretical framework just described has existed in some form or another for nearly 30 years, until recently there has been very little in the way of direct empirical tests bearing on the question of whether a progressive change in brain structure and/or function is associated with the emergence of psychosis. Nearly all of the hundreds of neuroimaging studies of schizophrenia to date have utilized patients with established illness. Given that antipsychotic drugs and other factors associated with disease chronicity may account for any differential changes in brain structure and function observed in such patients, there has been lingering doubt about the potential role of these abnormalities in the pathophysiology of the disorder. Recently, it has become possible to track changes in brain structure and function prospectively in individuals with a heightened risk for developing psychosis, termed clinical high-risk (CHR) or prodromal risk syndrome patients. Such cases are considered to be at risk because they have experienced a recent onset of sub-psychotic symptoms; however, only some actually progress to a full-blown psychotic illness such as schizophrenia in the near future. A recent meta-analysis that included 27 studies, with a total sample of over 2500 clinical high-risk patients, revealed that risk for transition to psychosis was about 30% after two years of follow-up, with a decelerating rate of conversion over this period (Fusar-Poli et al., 2012). These observations help to establish the CHR syndrome as the single best predictor of future psychosis, 3-fold higher than for the next best predictor, family history of schizophrenia. Notably, the criteria are shown to be sensitive to conversion to full illness within a fairly circumscribed temporal window, 2 to 3 years from initial ascertainment. The prodromal risk paradigm has thus been validated as an approach that can help address questions of temporal sequencing of brain abnormalities in relation to symptom progression, while minimizing the confounding influences of medication effects and chronicity. This article reviews recent work bearing on the question of the timing of onset and course of structural brain changes in association with the onset and course of psychosis. These studies make use of magnetic resonance imaging-based techniques, (MRI) and diffusion tensor imaging (DTI), that can be safely and reliably applied repeatedly in the same individuals, allowing for assessment of the dynamically changing brain throughout life (Brown et al., 2012; Cannon et al., 2013). We focus primarily on longitudinal studies of CHR samples, but set the stage for those studies by first considering patterns of brain abnormalities detected in patients with established illness. We conclude with a consideration of potential mechanisms underlying progressive tissue changes during the prodromal phase of schizophrenia and implications for prevention.

Background: Individuals at clinical high risk (CHR) who progress to fully psychotic symptoms have been observed to show a steeper rate of cortical gray matter reduction compared with individuals without symptomatic progression and with healthy control subjects. Whether such changes reflect processes associated with the pathophysiology of schizophrenia or exposure to antipsychotic drugs is unknown. Methods: In this multisite study, 274 CHR cases, including 35 individuals who converted to psychosis, and 135 healthy comparison subjects were scanned with magnetic resonance imaging at baseline, 12-month follow-up, or the point of conversion for the subjects who developed fully psychotic symptoms. Results: In a traveling subjects substudy, excellent reliability was observed for measures of cortical thickness and subcortical volumes. Controlling for multiple comparisons throughout the brain, CHR subjects who converted to psychosis showed a steeper rate of gray matter loss in the right superior frontal, middle frontal, and medial orbitofrontal cortical regions as well as a greater rate of expansion of the third ventricle compared with CHR subjects who did not convert to psychosis and healthy control subjects. Differential tissue loss was present in subjects who had not received antipsychotic medications during the interscan interval and was predicted by baseline levels of an aggregate measure of proinflammatory cytokines in plasma. Conclusions: These findings demonstrate that the brain changes are not explained by exposure to antipsychotic drugs but likely play a role in psychosis pathophysiology. Given that the cortical changes were more pronounced in subjects with briefer durations of prodromal symptoms, contributing factors may predominantly play a role in acute-onset forms of psychosis. © 2014 Society of Biological Psychiatry.

Self-regulation refers to the ability to control behavior, cognition, and emotions, and self-regulation failure is related to a range of neuropsychiatric problems. It is poorly understood how structural maturation of the brain brings about the gradual improvement in self-regulation during childhood. In a large-scale multicenter effort, 735 children (4–21 y) underwent structural MRI for quantification of cortical thickness and surface area and diffusion tensor imaging for quantification of the quality of major fiber connections. Brain development was related to a standardized measure of cognitive control (the flanker task from the National Institutes of Health Toolbox), a critical component of self-regulation. Ability to inhibit responses and impose cognitive control increased rapidly during preteen years. Surface area of the anterior cingulate cortex accounted for a significant proportion of the variance in cognitive performance. This finding is intriguing, because characteristics of the anterior cingulum are shown to be related to impulse, attention, and executive problems in neurodevelopmental disorders, indicating a neural foundation for self-regulation abilities along a continuum from normality to pathology. The relationship was strongest in the younger children. Properties of large-fiber connections added to the picture by explaining additional variance in cognitive control. Although cognitive control was related to surface area of the anterior cingulate independently of basic processes of mental speed, the relationship between white matter quality and cognitive control could be fully accounted for by speed. The results underscore the need for integration of different aspects of brain maturation to understand the foundations of cognitive development.

Structural MRI allows unparalleled in vivo study of the anatomy of the developing human brain. For more than two decades [1], MRI research has revealed many new aspects of this multifaceted maturation process, significantly augmenting scientific knowledge gathered from postmortem studies. Postnatal brain development is notably protracted and involves considerable changes in cerebral cortical [2-4], subcortical [5], and cerebellar [6, 7] structures, as well as significant architectural changes in white matter fiber tracts [8-11] (see [12]). Although much work has described isolated features of neuroanatomical development, it remains a critical challenge to characterize the multidimensional nature of brain anatomy, capturing different phases of development among individuals. Capitalizing on key advances in multisite, multimodal MRI, and using cross-validated nonlinear modeling, we demonstrate that developmental brain phase can be assessed with much greater precision than has been possible using other biological measures, accounting for more than 92% of the variance in age. Further, our composite metric of morphology, diffusivity, and signal intensity shows that the average difference in phase among children of the same age is only about 1 year, revealing for the first time a latent phenotype in the human brain for which maturation timing is tightly controlled. Video Abstract: © 2012 Elsevier Ltd. All rights reserved.