Psychiatry - Research Publications

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    Brain Morphological Characteristics of Cognitive Subgroups of Schizophrenia-Spectrum Disorders and Bipolar Disorder: A Systematic Review with Narrative Synthesis
    Karantonis, JA ; Carruthers, SP ; Burdick, KE ; Pantelis, C ; Green, M ; Rossell, SL ; Hughes, ME ; Cropley, V ; Van Rheenen, TE (SPRINGER, 2022-02-22)
    Despite a growing body of research, there is yet to be a cohesive synthesis of studies examining differences in brain morphology according to patterns of cognitive function among both schizophrenia-spectrum disorder (SSD) and bipolar disorder (BD) individuals. We aimed to provide a systematic overview of the morphological differences-inclusive of grey and white matter volume, cortical thickness, and cortical surface area-between cognitive subgroups of these disorders and healthy controls, and between cognitive subgroups themselves. An initial search of PubMed and Scopus databases resulted in 1486 articles of which 20 met inclusion criteria and were reviewed in detail. The findings of this review do not provide strong evidence that cognitive subgroups of SSD or BD map to unique patterns of brain morphology. There is preliminary evidence to suggest that reductions in cortical thickness may be more strongly associated with cognitive impairment, whilst volumetric deficits may be largely tied to the presence of disease.
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    The impact of visual dysfunctions in recent-onset psychosis and clinical high-risk state for psychosis.
    Schwarzer, JM ; Meyhoefer, I ; Antonucci, LA ; Kambeitz-Ilankovic, L ; Surmann, M ; Bienek, O ; Romer, G ; Dannlowski, U ; Hahn, T ; Korda, A ; Dwyer, DB ; Ruef, A ; Haas, SS ; Rosen, M ; Lichtenstein, T ; Ruhrmann, S ; Kambeitz, J ; Salokangas, RKR ; Pantelis, C ; Schultze-Lutter, F ; Meisenzahl, E ; Brambilla, P ; Bertolino, A ; Borgwardt, S ; Upthegrove, R ; Koutsouleris, N ; Lencer, R ; PRONIA Consortium, (Springer Science and Business Media LLC, 2022-11)
    Subtle subjective visual dysfunctions (VisDys) are reported by about 50% of patients with schizophrenia and are suggested to predict psychosis states. Deeper insight into VisDys, particularly in early psychosis states, could foster the understanding of basic disease mechanisms mediating susceptibility to psychosis, and thereby inform preventive interventions. We systematically investigated the relationship between VisDys and core clinical measures across three early phase psychiatric conditions. Second, we used a novel multivariate pattern analysis approach to predict VisDys by resting-state functional connectivity within relevant brain systems. VisDys assessed with the Schizophrenia Proneness Instrument (SPI-A), clinical measures, and resting-state fMRI data were examined in recent-onset psychosis (ROP, n = 147), clinical high-risk states of psychosis (CHR, n = 143), recent-onset depression (ROD, n = 151), and healthy controls (HC, n = 280). Our multivariate pattern analysis approach used pairwise functional connectivity within occipital (ON) and frontoparietal (FPN) networks implicated in visual information processing to predict VisDys. VisDys were reported more often in ROP (50.34%), and CHR (55.94%) than in ROD (16.56%), and HC (4.28%). Higher severity of VisDys was associated with less functional remission in both CHR and ROP, and, in CHR specifically, lower quality of life (Qol), higher depressiveness, and more severe impairment of visuospatial constructability. ON functional connectivity predicted presence of VisDys in ROP (balanced accuracy 60.17%, p = 0.0001) and CHR (67.38%, p = 0.029), while in the combined ROP + CHR sample VisDys were predicted by FPN (61.11%, p = 0.006). These large-sample study findings suggest that VisDys are clinically highly relevant not only in ROP but especially in CHR, being closely related to aspects of functional outcome, depressiveness, and Qol. Findings from multivariate pattern analysis support a model of functional integrity within ON and FPN driving the VisDys phenomenon and being implicated in core disease mechanisms of early psychosis states.
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    Publisher Correction: Brain charts for the human lifespan.
    Bethlehem, RAI ; Seidlitz, J ; White, SR ; Vogel, JW ; Anderson, KM ; Adamson, C ; Adler, S ; Alexopoulos, GS ; Anagnostou, E ; Areces-Gonzalez, A ; Astle, DE ; Auyeung, B ; Ayub, M ; Bae, J ; Ball, G ; Baron-Cohen, S ; Beare, R ; Bedford, SA ; Benegal, V ; Beyer, F ; Blangero, J ; Blesa Cábez, M ; Boardman, JP ; Borzage, M ; Bosch-Bayard, JF ; Bourke, N ; Calhoun, VD ; Chakravarty, MM ; Chen, C ; Chertavian, C ; Chetelat, G ; Chong, YS ; Cole, JH ; Corvin, A ; Costantino, M ; Courchesne, E ; Crivello, F ; Cropley, VL ; Crosbie, J ; Crossley, N ; Delarue, M ; Delorme, R ; Desrivieres, S ; Devenyi, GA ; Di Biase, MA ; Dolan, R ; Donald, KA ; Donohoe, G ; Dunlop, K ; Edwards, AD ; Elison, JT ; Ellis, CT ; Elman, JA ; Eyler, L ; Fair, DA ; Feczko, E ; Fletcher, PC ; Fonagy, P ; Franz, CE ; Galan-Garcia, L ; Gholipour, A ; Giedd, J ; Gilmore, JH ; Glahn, DC ; Goodyer, IM ; Grant, PE ; Groenewold, NA ; Gunning, FM ; Gur, RE ; Gur, RC ; Hammill, CF ; Hansson, O ; Hedden, T ; Heinz, A ; Henson, RN ; Heuer, K ; Hoare, J ; Holla, B ; Holmes, AJ ; Holt, R ; Huang, H ; Im, K ; Ipser, J ; Jack, CR ; Jackowski, AP ; Jia, T ; Johnson, KA ; Jones, PB ; Jones, DT ; Kahn, RS ; Karlsson, H ; Karlsson, L ; Kawashima, R ; Kelley, EA ; Kern, S ; Kim, KW ; Kitzbichler, MG ; Kremen, WS ; Lalonde, F ; Landeau, B ; Lee, S ; Lerch, J ; Lewis, JD ; Li, J ; Liao, W ; Liston, C ; Lombardo, MV ; Lv, J ; Lynch, C ; Mallard, TT ; Marcelis, M ; Markello, RD ; Mathias, SR ; Mazoyer, B ; McGuire, P ; Meaney, MJ ; Mechelli, A ; Medic, N ; Misic, B ; Morgan, SE ; Mothersill, D ; Nigg, J ; Ong, MQW ; Ortinau, C ; Ossenkoppele, R ; Ouyang, M ; Palaniyappan, L ; Paly, L ; Pan, PM ; Pantelis, C ; Park, MM ; Paus, T ; Pausova, Z ; Paz-Linares, D ; Pichet Binette, A ; Pierce, K ; Qian, X ; Qiu, J ; Qiu, A ; Raznahan, A ; Rittman, T ; Rodrigue, A ; Rollins, CK ; Romero-Garcia, R ; Ronan, L ; Rosenberg, MD ; Rowitch, DH ; Salum, GA ; Satterthwaite, TD ; Schaare, HL ; Schachar, RJ ; Schultz, AP ; Schumann, G ; Schöll, M ; Sharp, D ; Shinohara, RT ; Skoog, I ; Smyser, CD ; Sperling, RA ; Stein, DJ ; Stolicyn, A ; Suckling, J ; Sullivan, G ; Taki, Y ; Thyreau, B ; Toro, R ; Traut, N ; Tsvetanov, KA ; Turk-Browne, NB ; Tuulari, JJ ; Tzourio, C ; Vachon-Presseau, É ; Valdes-Sosa, MJ ; Valdes-Sosa, PA ; Valk, SL ; van Amelsvoort, T ; Vandekar, SN ; Vasung, L ; Victoria, LW ; Villeneuve, S ; Villringer, A ; Vértes, PE ; Wagstyl, K ; Wang, YS ; Warfield, SK ; Warrier, V ; Westman, E ; Westwater, ML ; Whalley, HC ; Witte, AV ; Yang, N ; Yeo, B ; Yun, H ; Zalesky, A ; Zar, HJ ; Zettergren, A ; Zhou, JH ; Ziauddeen, H ; Zugman, A ; Zuo, XN ; 3R-BRAIN, ; AIBL, ; Alzheimer’s Disease Neuroimaging Initiative, ; Alzheimer’s Disease Repository Without Borders Investigators, ; CALM Team, ; Cam-CAN, ; CCNP, ; COBRE, ; cVEDA, ; ENIGMA Developmental Brain Age Working Group, ; Developing Human Connectome Project, ; FinnBrain, ; Harvard Aging Brain Study, ; IMAGEN, ; KNE96, ; Mayo Clinic Study of Aging, ; NSPN, ; POND, ; PREVENT-AD Research Group, ; VETSA, ; Bullmore, ET ; Alexander-Bloch, AF (Springer Science and Business Media LLC, 2022-10)
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    Exploring Links Between Psychosis and Frontotemporal Dementia Using Multimodal Machine Learning: Dementia Praecox Revisited.
    Koutsouleris, N ; Pantelis, C ; Velakoulis, D ; McGuire, P ; Dwyer, DB ; Urquijo-Castro, M-F ; Paul, R ; Dong, S ; Popovic, D ; Oeztuerk, O ; Kambeitz, J ; Salokangas, RKR ; Hietala, J ; Bertolino, A ; Brambilla, P ; Upthegrove, R ; Wood, SJ ; Lencer, R ; Borgwardt, S ; Maj, C ; Nöthen, M ; Degenhardt, F ; Polyakova, M ; Mueller, K ; Villringer, A ; Danek, A ; Fassbender, K ; Fliessbach, K ; Jahn, H ; Kornhuber, J ; Landwehrmeyer, B ; Anderl-Straub, S ; Prudlo, J ; Synofzik, M ; Wiltfang, J ; Riedl, L ; Diehl-Schmid, J ; Otto, M ; Meisenzahl, E ; Falkai, P ; Schroeter, ML ; International FTD-Genetics Consortium (IFGC), the German Frontotemporal Lobar Degeneration (FTLD) Consortium, and the PRONIA Consortium, (American Medical Association (AMA), 2022-09-01)
    IMPORTANCE: The behavioral and cognitive symptoms of severe psychotic disorders overlap with those seen in dementia. However, shared brain alterations remain disputed, and their relevance for patients in at-risk disease stages has not been explored so far. OBJECTIVE: To use machine learning to compare the expression of structural magnetic resonance imaging (MRI) patterns of behavioral-variant frontotemporal dementia (bvFTD), Alzheimer disease (AD), and schizophrenia; estimate predictability in patients with bvFTD and schizophrenia based on sociodemographic, clinical, and biological data; and examine prognostic value, genetic underpinnings, and progression in patients with clinical high-risk (CHR) states for psychosis or recent-onset depression (ROD). DESIGN, SETTING, AND PARTICIPANTS: This study included 1870 individuals from 5 cohorts, including (1) patients with bvFTD (n = 108), established AD (n = 44), mild cognitive impairment or early-stage AD (n = 96), schizophrenia (n = 157), or major depression (n = 102) to derive and compare diagnostic patterns and (2) patients with CHR (n = 160) or ROD (n = 161) to test patterns' prognostic relevance and progression. Healthy individuals (n = 1042) were used for age-related and cohort-related data calibration. Data were collected from January 1996 to July 2019 and analyzed between April 2020 and April 2022. MAIN OUTCOMES AND MEASURES: Case assignments based on diagnostic patterns; sociodemographic, clinical, and biological data; 2-year functional outcomes and genetic separability of patients with CHR and ROD with high vs low pattern expression; and pattern progression from baseline to follow-up MRI scans in patients with nonrecovery vs preserved recovery. RESULTS: Of 1870 included patients, 902 (48.2%) were female, and the mean (SD) age was 38.0 (19.3) years. The bvFTD pattern comprising prefrontal, insular, and limbic volume reductions was more expressed in patients with schizophrenia (65 of 157 [41.2%]) and major depression (22 of 102 [21.6%]) than the temporo-limbic AD patterns (28 of 157 [17.8%] and 3 of 102 [2.9%], respectively). bvFTD expression was predicted by high body mass index, psychomotor slowing, affective disinhibition, and paranoid ideation (R2 = 0.11). The schizophrenia pattern was expressed in 92 of 108 patients (85.5%) with bvFTD and was linked to the C9orf72 variant, oligoclonal banding in the cerebrospinal fluid, cognitive impairment, and younger age (R2 = 0.29). bvFTD and schizophrenia pattern expressions forecasted 2-year psychosocial impairments in patients with CHR and were predicted by polygenic risk scores for frontotemporal dementia, AD, and schizophrenia. Findings were not associated with AD or accelerated brain aging. Finally, 1-year bvFTD/schizophrenia pattern progression distinguished patients with nonrecovery from those with preserved recovery. CONCLUSIONS AND RELEVANCE: Neurobiological links may exist between bvFTD and psychosis focusing on prefrontal and salience system alterations. Further transdiagnostic investigations are needed to identify shared pathophysiological processes underlying the neuroanatomical interface between the 2 disease spectra.
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    Affinity scores: An individual-centric fingerprinting framework for neuropsychiatric disorders
    Wannan, CMJ ; Pantelis, C ; Merritt, AH ; Tonge, B ; Syeda, WT (SPRINGERNATURE, 2022-08-09)
    Population-centric frameworks of biomarker identification for psychiatric disorders focus primarily on comparing averages between groups and assume that diagnostic groups are (1) mutually-exclusive, and (2) homogeneous. There is a paucity of individual-centric approaches capable of identifying individual-specific 'fingerprints' across multiple domains. To address this, we propose a novel framework, combining a range of biopsychosocial markers, including brain structure, cognition, and clinical markers, into higher-level 'fingerprints', capable of capturing intra-illness heterogeneity and inter-illness overlap. A multivariate framework was implemented to identify individualised patterns of brain structure, cognition and clinical markers based on affinity to other participants in the database. First, individual-level affinity scores defined each participant's "neighbourhood" across each measure based on variable-specific hop sizes. Next, diagnostic verification and classification algorithms were implemented based on multivariate affinity score profiles. To perform affinity-based classification, data were divided into training and test samples, and 5-fold nested cross-validation was performed on the training data. Affinity-based classification was compared to weighted K-nearest neighbours (KNN) classification. The framework was applied to the Australian Schizophrenia Research Bank (ASRB) dataset, which included data from individuals with chronic and treatment resistant schizophrenia and healthy controls. Individualised affinity scores provided a 'fingerprint' of brain structure, cognition, and clinical markers, which described the affinity of an individual to the representative groups in the dataset. Diagnostic verification capability was moderate to high depending on the choice of multivariate affinity metric. Affinity score-based classification achieved a high degree of accuracy in the training, nested cross-validation and prediction steps, and outperformed KNN classification in the training and test datasets. Affinity scores demonstrate utility in two keys ways: (1) Early and accurate diagnosis of neuropsychiatric disorders, whereby an individual can be grouped within a diagnostic category/ies that best matches their fingerprint, and (2) identification of biopsychosocial factors that most strongly characterise individuals/disorders, and which may be most amenable to intervention.
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    Neuroanatomical heterogeneity and homogeneity in individuals at clinical high risk for psychosis
    Baldwin, H ; Radua, J ; Antoniades, M ; Haas, SS ; Frangou, S ; Agartz, I ; Allen, P ; Andreassen, OA ; Atkinson, K ; Bachman, P ; Baeza, I ; Bartholomeusz, CF ; Chee, MWL ; Colibazzi, T ; Cooper, RE ; Corcoran, CM ; Cropley, VL ; Ebdrup, BH ; Fortea, A ; Glenthoj, LB ; Hamilton, HK ; Haut, KM ; Hayes, RA ; He, Y ; Heekeren, K ; Kaess, M ; Kasai, K ; Katagiri, N ; Kim, M ; Kindler, J ; Klaunig, MJ ; Koike, S ; Koppel, A ; Kristensen, TD ; Bin Kwak, Y ; Kwon, JS ; Lawrie, SM ; Lebedeva, I ; Lee, J ; Lin, A ; Loewy, RL ; Mathalon, DH ; Michel, C ; Mizrahi, R ; Moller, P ; Nelson, B ; Nemoto, T ; Nordholm, D ; Omelchenko, MA ; Pantelis, C ; Raghava, JM ; Rossberg, J ; Roessler, W ; Salisbury, DF ; Sasabayashi, D ; Schall, U ; Smigielski, L ; Sugranyes, G ; Suzuki, M ; Takahashi, T ; Tamnes, CK ; Tang, J ; Theodoridou, A ; Thomopoulos, S ; Tomyshev, AS ; Uhlhaas, PJ ; Vaernes, TG ; van Amelsvoort, TAMJ ; Van Erp, TGM ; Waltz, JA ; Westlye, LT ; Wood, SJ ; Zhou, JH ; McGuire, P ; Thompson, PM ; Jalbrzikowski, M ; Hernaus, D ; Fusar-Poli, P (SPRINGERNATURE, 2022-07-26)
    Individuals at Clinical High Risk for Psychosis (CHR-P) demonstrate heterogeneity in clinical profiles and outcome features. However, the extent of neuroanatomical heterogeneity in the CHR-P state is largely undetermined. We aimed to quantify the neuroanatomical heterogeneity in structural magnetic resonance imaging measures of cortical surface area (SA), cortical thickness (CT), subcortical volume (SV), and intracranial volume (ICV) in CHR-P individuals compared with healthy controls (HC), and in relation to subsequent transition to a first episode of psychosis. The ENIGMA CHR-P consortium applied a harmonised analysis to neuroimaging data across 29 international sites, including 1579 CHR-P individuals and 1243 HC, offering the largest pooled CHR-P neuroimaging dataset to date. Regional heterogeneity was indexed with the Variability Ratio (VR) and Coefficient of Variation (CV) ratio applied at the group level. Personalised estimates of heterogeneity of SA, CT and SV brain profiles were indexed with the novel Person-Based Similarity Index (PBSI), with two complementary applications. First, to assess the extent of within-diagnosis similarity or divergence of neuroanatomical profiles between individuals. Second, using a normative modelling approach, to assess the 'normativeness' of neuroanatomical profiles in individuals at CHR-P. CHR-P individuals demonstrated no greater regional heterogeneity after applying FDR corrections. However, PBSI scores indicated significantly greater neuroanatomical divergence in global SA, CT and SV profiles in CHR-P individuals compared with HC. Normative PBSI analysis identified 11 CHR-P individuals (0.70%) with marked deviation (>1.5 SD) in SA, 118 (7.47%) in CT and 161 (10.20%) in SV. Psychosis transition was not significantly associated with any measure of heterogeneity. Overall, our examination of neuroanatomical heterogeneity within the CHR-P state indicated greater divergence in neuroanatomical profiles at an individual level, irrespective of psychosis conversion. Further large-scale investigations are required of those who demonstrate marked deviation.
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    The Relationship Between Grey Matter Volume and Clinical and Functional Outcomes in People at Clinical High Risk for Psychosis.
    Tognin, S ; Richter, A ; Kempton, MJ ; Modinos, G ; Antoniades, M ; Azis, M ; Allen, P ; Bossong, MG ; Perez, J ; Pantelis, C ; Nelson, B ; Amminger, P ; Riecher-Rössler, A ; Barrantes-Vidal, N ; Krebs, M-O ; Glenthøj, B ; Ruhrmann, S ; Sachs, G ; Rutten, BPF ; de Haan, L ; van der Gaag, M ; EU-GEI High Risk Study Group, ; Valmaggia, LR ; McGuire, P (Oxford University Press (OUP), 2022-01)
    OBJECTIVE: To examine the association between baseline alterations in grey matter volume (GMV) and clinical and functional outcomes in people at clinical high risk (CHR) for psychosis. METHODS: 265 CHR individuals and 92 healthy controls were recruited as part of a prospective multi-center study. After a baseline assessment using magnetic resonance imaging (MRI), participants were followed for at least two years to determine clinical and functional outcomes, including transition to psychosis (according to the Comprehensive Assessment of an At Risk Mental State, CAARMS), level of functioning (according to the Global Assessment of Functioning), and symptomatic remission (according to the CAARMS). GMV was measured in selected cortical and subcortical regions of interest (ROI) based on previous studies (ie orbitofrontal gyrus, cingulate gyrus, gyrus rectus, inferior temporal gyrus, parahippocampal gyrus, striatum, and hippocampus). Using voxel-based morphometry, we analysed the relationship between GMV and clinical and functional outcomes. RESULTS: Within the CHR sample, a poor functional outcome (GAF < 65) was associated with relatively lower GMV in the right striatum at baseline (P < .047 after Family Wise Error correction). There were no significant associations between baseline GMV and either subsequent remission or transition to psychosis. CONCLUSIONS: In CHR individuals, lower striatal GMV was associated with a poor level of overall functioning at follow-up. This finding was not related to effects of antipsychotic or antidepressant medication. The failure to replicate previous associations between GMV and later psychosis onset, despite studying a relatively large sample, is consistent with the findings of recent large-scale multi-center studies.
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    Different Frequency of Heschl's Gyrus Duplication Patterns in Neuropsychiatric Disorders: An MRI Study in Bipolar and Major Depressive Disorders
    Takahashi, T ; Sasabayashi, D ; Yucel, M ; Whittle, S ; Lorenzetti, V ; Walterfang, M ; Suzuki, M ; Pantelis, C ; Malhi, GS ; Allen, NB (FRONTIERS MEDIA SA, 2022-06-13)
    An increased prevalence of duplicated Heschl's gyrus (HG) has been repeatedly demonstrated in various stages of schizophrenia as a potential neurodevelopmental marker, but it remains unknown whether other neuropsychiatric disorders also exhibit this macroscopic brain feature. The present magnetic resonance imaging study aimed to examine the disease specificity of the established finding of altered HG patterns in schizophrenia by examining independent cohorts of bipolar disorder (BD) and major depressive disorder (MDD). Twenty-six BD patients had a significantly higher prevalence of HG duplication bilaterally compared to 24 age- and sex-matched controls, while their clinical characteristics (e.g., onset age, number of episodes, and medication) did not relate to HG patterns. No significant difference was found for the HG patterns between 56 MDD patients and 33 age- and sex-matched controls, but the patients with a single HG were characterized by more severe depressive/anxiety symptoms compared to those with a duplicated HG. Thus, in keeping with previous findings, the present study suggests that neurodevelopmental pathology associated with gyral formation of the HG during the late gestation period partly overlaps between schizophrenia and BD, but that HG patterns may make a somewhat distinct contribution to the phenomenology of MDD.
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    Add-On MEmaNtine to Dopamine Antagonism to Improve Negative Symptoms at First Psychosis- the AMEND Trial Protocol
    Sandstrom, KO ; Baltzersen, OB ; Marsman, A ; Lemvigh, CK ; Boer, VO ; Bojesen, KB ; Nielsen, MO ; Lundell, H ; Sulaiman, DK ; Sorensen, ME ; Fagerlund, B ; Lahti, AC ; Syeda, WT ; Pantelis, C ; Petersen, ET ; Glenthoj, BY ; Siebner, HR ; Ebdrup, BH (FRONTIERS MEDIA SA, 2022-05-20)
    BACKGROUND: Antipsychotic drugs are primarily efficacious in treating positive symptoms by blocking the dopamine D2 receptor, but they fail to substantially improve negative symptoms and cognitive deficits. The limited efficacy may be attributed to the fact that the pathophysiology of psychosis involves multiple neurotransmitter systems. In patients with chronic schizophrenia, memantine, a non-competitive glutamatergic NMDA receptor antagonist, shows promise for ameliorating negative symptoms and improving cognition. Yet, it is unknown how memantine modulates glutamate levels, and memantine has not been investigated in patients with first-episode psychosis. AIMS: This investigator-initiated double-blinded randomized controlled trial is designed to (1) test the clinical effects on negative symptoms of add-on memantine to antipsychotic medication, and (2) neurobiologically characterize the responders to add-on memantine. MATERIALS AND EQUIPMENT: Antipsychotic-naïve patients with first-episode psychosis will be randomized to 12 weeks treatment with [amisulpride + memantine] or [amisulpride + placebo]. We aim for a minimum of 18 patients in each treatment arm to complete the trial. Brain mapping will be performed before and after 12 weeks focusing on glutamate and neuromelanin in predefined regions. Regional glutamate levels will be probed with proton magnetic resonance spectroscopy (MRS), while neuromelanin signal will be mapped with neuromelanin-sensitive magnetic resonance imaging (MRI). We will also perform structural and diffusion weighted, whole-brain MRI. MRS and MRI will be performed at an ultra-high field strength (7 Tesla). Alongside, participants undergo clinical and neuropsychological assessments. Twenty matched healthy controls will undergo similar baseline- and 12-week examinations, but without receiving treatment. OUTCOME MEASURES: The primary endpoint is negative symptom severity. Secondary outcomes comprise: (i) clinical endpoints related to cognition, psychotic symptoms, side effects, and (ii) neurobiological endpoints related to regional glutamate- and neuromelanin levels, and structural brain changes. ANTICIPATED RESULTS: We hypothesize that add-on memantine to amisulpride will be superior to amisulpride monotherapy in reducing negative symptoms, and that this effect will correlate with thalamic glutamate levels. Moreover, we anticipate that add-on memantine will restore regional white matter integrity and improve cognitive functioning. PERSPECTIVES: By combining two licensed, off-patent drugs, AMEND aims to optimize treatment of psychosis while investigating the memantine response. Alongside, AMEND will provide neurobiological insights to effects of dual receptor modulation, which may enable future stratification of patients with first-episode psychosis before initial antipsychotic treatment. CLINICAL TRIAL REGISTRATION: [ClinicalTrials.gov], identifier [NCT04789915].
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    Cortico-cognition coupling in treatment resistant schizophrenia
    Syeda, WT ; Wannan, CMJ ; Merritt, AH ; Raghava, JM ; Jayaram, M ; Velakoulis, D ; Kristensen, TD ; Soldatos, RF ; Tonissen, S ; Thomas, N ; Ambrosen, KS ; Sorensen, ME ; Fagerlund, B ; Rostrup, E ; Glenthoj, BY ; Skafidas, E ; Bousman, CA ; Johnston, LA ; Everall, I ; Ebdrup, BH ; Pantelis, C (ELSEVIER SCI LTD, 2022-01-01)
    BACKGROUND: Brain structural alterations and cognitive dysfunction are independent predictors for poor clinical outcome in schizophrenia, and the associations between these domains remains unclear. We employed a novel, multiblock partial least squares correlation (MB-PLS-C) technique and investigated multivariate cortico-cognitive patterns in patients with treatment-resistant schizophrenia (TRS) and matched healthy controls (HC). METHOD: Forty-one TRS patients (age 38.5 ± 9.1, 30 males (M)), and 45 HC (age 40.2 ± 10.6, 29 M) underwent 3T structural MRI. Volumes of 68 brain regions and seven variables from CANTAB covering memory and executive domains were included. Univariate group differences were assessed, followed by the MB-PLS-C analyses to identify group-specific multivariate patterns of cortico-cognitive coupling. Supplementary three-group analyses, which included 23 non-affected first-degree relatives (NAR), were also conducted. RESULTS: Univariate tests demonstrated that TRS patients showed impairments in all seven cognitive tasks and volume reductions in 12 cortical regions following Bonferroni correction. The MB-PLS-C analyses revealed two significant latent variables (LVs) explaining > 90% of the sum-of-squares variance. LV1 explained 78.86% of the sum-of-squares variance, describing a shared, widespread structure-cognitive pattern relevant to both TRS patients and HCs. In contrast, LV2 (13.47% of sum-of-squares variance explained) appeared specific to TRS and comprised a differential cortico-cognitive pattern including frontal and temporal lobes as well as paired associates learning (PAL) and intra-extra dimensional set shifting (IED). Three-group analyses also identified two significant LVs, with NARs more closely resembling healthy controls than TRS patients. CONCLUSIONS: MB-PLS-C analyses identified multivariate brain structural-cognitive patterns in the latent space that may provide a TRS signature.