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    Plasma neurofilament light chain protein is not increased in treatment-resistant schizophrenia and first-degree relatives
    Eratne, D ; Janelidze, S ; Malpas, CB ; Loi, S ; Walterfane, M ; Merritt, A ; Diouf, I ; Blennow, K ; Zetterberg, H ; Cilia, B ; Warman, C ; Bousman, C ; Everall, I ; Zalesky, A ; Jayaram, M ; Thomas, N ; Berkovic, SF ; Hansson, O ; Velakoulis, D ; Pantelis, C ; Santillo, A (SAGE PUBLICATIONS LTD, 2022-10)
    OBJECTIVE: Schizophrenia, a complex psychiatric disorder, is often associated with cognitive, neurological and neuroimaging abnormalities. The processes underlying these abnormalities, and whether a subset of people with schizophrenia have a neuroprogressive or neurodegenerative component to schizophrenia, remain largely unknown. Examining fluid biomarkers of diverse types of neuronal damage could increase our understanding of these processes, as well as potentially provide clinically useful biomarkers, for example with assisting with differentiation from progressive neurodegenerative disorders such as Alzheimer and frontotemporal dementias. METHODS: This study measured plasma neurofilament light chain protein (NfL) using ultrasensitive Simoa technology, to investigate the degree of neuronal injury in a well-characterised cohort of people with treatment-resistant schizophrenia on clozapine (n = 82), compared to first-degree relatives (an at-risk group, n = 37), people with schizophrenia not treated with clozapine (n = 13), and age- and sex-matched controls (n = 59). RESULTS: We found no differences in NfL levels between treatment-resistant schizophrenia (mean NfL, M = 6.3 pg/mL, 95% confidence interval: [5.5, 7.2]), first-degree relatives (siblings, M = 6.7 pg/mL, 95% confidence interval: [5.2, 8.2]; parents, M after adjusting for age = 6.7 pg/mL, 95% confidence interval: [4.7, 8.8]), controls (M = 5.8 pg/mL, 95% confidence interval: [5.3, 6.3]) and not treated with clozapine (M = 4.9 pg/mL, 95% confidence interval: [4.0, 5.8]). Exploratory, hypothesis-generating analyses found weak correlations in treatment-resistant schizophrenia, between NfL and clozapine levels (Spearman's r = 0.258, 95% confidence interval: [0.034, 0.457]), dyslipidaemia (r = 0.280, 95% confidence interval: [0.064, 0.470]) and a negative correlation with weight (r = -0.305, 95% confidence interval: [-0.504, -0.076]). CONCLUSION: Treatment-resistant schizophrenia does not appear to be associated with neuronal, particularly axonal degeneration. Further studies are warranted to investigate the utility of NfL to differentiate treatment-resistant schizophrenia from neurodegenerative disorders such as behavioural variant frontotemporal dementia, and to explore NfL in other stages of schizophrenia such as the prodome and first episode.
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    Disruptions in white matter microstructure associated with impaired visual associative memory in schizophrenia-spectrum illness
    Wannan, CMJ ; Bartholomeusz, CF ; Pantelis, C ; Di Biase, MA ; Syeda, WT ; Chakravarty, MM ; Bousman, CA ; Everall, IP ; McGorry, PD ; Zalesky, A ; Cropley, VL (SPRINGER HEIDELBERG, 2022-09-01)
    Episodic memory ability relies on hippocampal-prefrontal connectivity. However, few studies have examined relationships between memory performance and white matter (WM) microstructure in hippocampal-prefrontal pathways in schizophrenia-spectrum disorder (SSDs). Here, we investigated these relationships in individuals with first-episode psychosis (FEP) and chronic schizophrenia-spectrum disorders (SSDs) using tractography analysis designed to interrogate the microstructure of WM tracts in the hippocampal-prefrontal pathway. Measures of WM microstructure (fractional anisotropy [FA], radial diffusivity [RD], and axial diffusivity [AD]) were obtained for 47 individuals with chronic SSDs, 28 FEP individuals, 52 older healthy controls, and 27 younger healthy controls. Tractography analysis was performed between the hippocampus and three targets involved in hippocampal-prefrontal connectivity (thalamus, amygdala, nucleus accumbens). Measures of WM microstructure were then examined in relation to episodic memory performance separately across each group. Both those with FEP and chronic SSDs demonstrated impaired episodic memory performance. However, abnormal WM microstructure was only observed in individuals with chronic SSDs. Abnormal WM microstructure in the hippocampal-thalamic pathway in the right hemisphere was associated with poorer memory performance in individuals with chronic SSDs. These findings suggest that disruptions in WM microstructure in the hippocampal-prefrontal pathway may contribute to memory impairments in individuals with chronic SSDs but not FEP.
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    Brain charts for the human lifespan (vol 604, pg 525, 2022)
    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 Cabez, 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 ; Scholl, 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, E ; Valdes-Sosa, MJ ; Valdes-Sosa, PA ; Valk, SL ; van Amelsvoort, T ; Vandekar, SN ; Vasung, L ; Victoria, LW ; Villeneuve, S ; Villringer, A ; Vertes, 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 ; Bullmore, ET ; Alexander-Bloch, AF (NATURE PORTFOLIO, 2022-10-13)
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    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 Cabez, 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 ; Scholl, 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, E ; Valdes-Sosa, MJ ; Valdes-Sosa, PA ; Valk, SL ; van Amelsvoort, T ; Vandekar, SN ; Vasung, L ; Victoria, LW ; Villeneuve, S ; Villringer, A ; Vertes, 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 ; Bullmore, ET ; Alexander-Bloch, AF (NATURE PORTFOLIO, 2022-04-21)
    Over the past few decades, neuroimaging has become a ubiquitous tool in basic research and clinical studies of the human brain. However, no reference standards currently exist to quantify individual differences in neuroimaging metrics over time, in contrast to growth charts for anthropometric traits such as height and weight1. Here we assemble an interactive open resource to benchmark brain morphology derived from any current or future sample of MRI data ( http://www.brainchart.io/ ). With the goal of basing these reference charts on the largest and most inclusive dataset available, acknowledging limitations due to known biases of MRI studies relative to the diversity of the global population, we aggregated 123,984 MRI scans, across more than 100 primary studies, from 101,457 human participants between 115 days post-conception to 100 years of age. MRI metrics were quantified by centile scores, relative to non-linear trajectories2 of brain structural changes, and rates of change, over the lifespan. Brain charts identified previously unreported neurodevelopmental milestones3, showed high stability of individuals across longitudinal assessments, and demonstrated robustness to technical and methodological differences between primary studies. Centile scores showed increased heritability compared with non-centiled MRI phenotypes, and provided a standardized measure of atypical brain structure that revealed patterns of neuroanatomical variation across neurological and psychiatric disorders. In summary, brain charts are an essential step towards robust quantification of individual variation benchmarked to normative trajectories in multiple, commonly used neuroimaging phenotypes.
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    Cell type-specific manifestations of cortical thickness heterogeneity in schizophrenia
    Di Biase, MA ; Geaghan, MP ; Reay, WR ; Seidlitz, J ; Weickert, CS ; Pebay, A ; Green, MJ ; Quide, Y ; Atkins, JR ; Coleman, MJ ; Bouix, S ; Knyazhanskaya, EE ; Lyall, AE ; Pasternak, O ; Kubicki, M ; Rathi, Y ; Visco, A ; Gaunnac, M ; Lv, J ; Mesholam-Gately, R ; Lewandowski, KE ; Holt, DJ ; Keshavan, MS ; Pantelis, C ; Ongur, D ; Breier, A ; Cairns, MJ ; Shenton, ME ; Zalesky, A (SPRINGERNATURE, 2022-04)
    Brain morphology differs markedly between individuals with schizophrenia, but the cellular and genetic basis of this heterogeneity is poorly understood. Here, we sought to determine whether cortical thickness (CTh) heterogeneity in schizophrenia relates to interregional variation in distinct neural cell types, as inferred from established gene expression data and person-specific genomic variation. This study comprised 1849 participants in total, including a discovery (140 cases and 1267 controls) and a validation cohort (335 cases and 185 controls). To characterize CTh heterogeneity, normative ranges were established for 34 cortical regions and the extent of deviation from these ranges was measured for each individual with schizophrenia. CTh deviations were explained by interregional gene expression levels of five out of seven neural cell types examined: (1) astrocytes; (2) endothelial cells; (3) oligodendrocyte progenitor cells (OPCs); (4) excitatory neurons; and (5) inhibitory neurons. Regional alignment between CTh alterations with cell type transcriptional maps distinguished broad patient subtypes, which were validated against genomic data drawn from the same individuals. In a predominantly neuronal/endothelial subtype (22% of patients), CTh deviations covaried with polygenic risk for schizophrenia (sczPRS) calculated specifically from genes marking neuronal and endothelial cells (r = -0.40, p = 0.010). Whereas, in a predominantly glia/OPC subtype (43% of patients), CTh deviations covaried with sczPRS calculated from glia and OPC-linked genes (r = -0.30, p = 0.028). This multi-scale analysis of genomic, transcriptomic, and brain phenotypic data may indicate that CTh heterogeneity in schizophrenia relates to inter-individual variation in cell-type specific functions. Decomposing heterogeneity in relation to cortical cell types enables prioritization of schizophrenia subsets for future disease modeling efforts.
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    White Matter Alterations Between Brain Network Hubs Underlie Processing Speed Impairment in Patients With Schizophrenia.
    Klauser, P ; Cropley, VL ; Baumann, PS ; Lv, J ; Steullet, P ; Dwir, D ; Alemán-Gómez, Y ; Bach Cuadra, M ; Cuenod, M ; Do, KQ ; Conus, P ; Pantelis, C ; Fornito, A ; Van Rheenen, TE ; Zalesky, A (Oxford University Press (OUP), 2021-01)
    Processing speed (PS) impairment is one of the most severe and common cognitive deficits in schizophrenia. Previous studies have reported correlations between PS and white matter diffusion properties, including fractional anisotropy (FA), in several fiber bundles in schizophrenia, suggesting that white matter alterations could underpin decreased PS. In schizophrenia, white matter alterations are most prevalent within inter-hub connections of the rich club. However, the spatial and topological characteristics of this association between PS and FA have not been investigated in patients. In this context, we tested whether structural connections comprising the rich club network would underlie PS impairment in 298 patients with schizophrenia or schizoaffective disorder and 190 healthy controls from the Australian Schizophrenia Research Bank. PS, measured using the digit symbol coding task, was largely (Cohen's d = 1.33) and significantly (P < .001) reduced in the patient group when compared with healthy controls. Significant associations between PS and FA were widespread in the patient group, involving all cerebral lobes. FA was not associated with other cognitive measures of phonological fluency and verbal working memory in patients, suggesting specificity to PS. A topological analysis revealed that despite being spatially widespread, associations between PS and FA were over-represented among connections forming the rich club network. These findings highlight the need to consider brain network topology when investigating high-order cognitive functions that may be spatially distributed among several brain regions. They also reinforce the evidence that brain hubs and their interconnections may be particularly vulnerable parts of the brain in schizophrenia.
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    O2.3. ABNORMAL BRAIN AGING IN YOUTH WITH SUBCLINICAL PSYCHOSIS AND OBSESSIVE-COMPULSIVE SYMPTOMS
    Cropley, V ; Tian, Y ; Fernando, K ; Mansour, S ; Pantelis, C ; Cocchi, L ; Zalesky, A (Oxford University Press (OUP), 2020-05-18)
    Abstract Background Psychiatric symptoms in childhood and adolescence have been associated with both delayed and accelerated patterns of grey matter development. This suggests that deviation in brain structure from a normative range of variation for a given age might be important in the emergence of psychopathology. Distinct from chronological age, brain age refers to the age of an individual that is inferred from a normative model of brain structure for individuals of the same age and sex. We predicted brain age from a common set of grey matter features and examined whether the difference between an individual’s chronological and brain age was associated with the severity of psychopathology in children and adolescents. Methods Participants included 1313 youths (49.8% male) aged 8–21 who underwent structural imaging as part of the Philadelphia Neurodevelopmental Cohort. Independent Component Analysis was used to obtain 7 psychopathology dimensions representing Conduct, Anxiety, Obsessive-Compulsive, Attention, Depression, Bipolar, and Psychosis symptoms and an overall measure of severity (General Psychopathology). Using 10-fold cross-validation, support vector machine regression was trained in 402 typically developing youth to predict individual age based on a feature space comprising 111 grey matter regions. This yielded a brain age prediction for each individual. Brain age gap was calculated for each individual by subtracting chronological age from predicted brain age. The general linear model was used to test for an association between brain age gap and each of the 8 dimensions of psychopathology in a test sample of 911 youth. The regional specificity and spatial pattern of brain age gap was also investigated. Error control across the 8 models was achieved with a false discovery rate of 5%. Results Brain age gap was significantly associated with dimensions characterizing obsessive-compulsive (t=2.5, p=0.01), psychosis (t=3.16, p=0.0016) and general psychopathology (t=4.08, p&lt;0.0001). For all three dimensions, brain age gap was positively associated with symptom severity, indicating that individuals with a brain that was predicted to be ‘older’ than expectations set by youth of the same chronological age and sex tended to have higher symptom scores. Findings were confirmed with a categorical approach, whereby higher brain age gap was observed in youth with a lifetime endorsement of psychosis (t=2.35, p=0.02) and obsessive-compulsive (t=2.35, p=0.021) symptoms, in comparison to typically developing individuals. Supplementary analyses revealed that frontal grey matter was the most important feature mediating the association between brain age gap and psychosis symptoms, whereas subcortical volumes were most important for the association between brain age gap and obsessive-compulsive and general symptoms. Discussion We found that the brain was ‘older’ in youth experiencing higher subclinical symptoms of psychosis, obsession-compulsion, and general psychopathology, compared to normally developing youth of the same chronological age. Our results suggest that deviations in normative brain age patterns in youth may contribute to the manifestation of specific psychiatric symptoms of subclinical severity that cut across psychopathology dimensions.
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    S187. EXPLORING NEURODEVELOPMENTAL AND FAMILIAL ORIGINS OF NEUROLOGICAL SOFT SIGNS IN SCHIZOPHRENIA
    Cooper, R ; Van Rheenen, T ; Zalesky, A ; Wannan, C ; Wang, Y ; Bousman, C ; Everall, I ; Pantelis, C ; Cropley, V (Oxford University Press (OUP), 2020-05-18)
    Abstract Background The neurodevelopmental hypothesis is the most widely regarded framework for understanding the development of schizophrenia. One of the most commonly cited pieces of evidence for this theory is the presence of neurological soft signs (NSS) in individuals prior to the onset of psychosis. Increased NSS is also reported in unaffected individuals with a family history of schizophrenia, suggesting that NSS may also have a familial component. Although much research has implicated reduced grey matter volume (GMV) in association with these signs, a subcomponent of volume, known as gyrification, has been poorly researched. Given that gyrification develops predominantly in prenatal life it may be particularly susceptible to a neurodevelopmental abnormality. The aims of this study were to investigate the neurodevelopmental and familial underpinnings of NSS in schizophrenia. Specifically, we examined the brain structural correlates, at both the level of GMV and gyrification, of NSS in individuals with schizophrenia, their unaffected relatives and healthy controls. We aimed to determine whether gyrification better predicted NSS severity than GMV, and whether the relationship between brain structure and NSS were present in a step-wise manner across the diagnostic groups. Methods The sample consisted of individuals with schizophrenia (N=66), their unaffected relatives (N=27) and healthy controls (N=53). NSS was assessed with the Neurological Evaluation Scale (NES), and GMV and gyrification were extracted from MRI using the FreeSurfer imaging suite. A series of analysis of covariance were used to compare NES scores and brain measures between the groups. Separate linear regression analyses were used to assess whether whole-brain GMV and gyrification predicted NES above a covariate-only model. Moderation analyses were used to assess whether the relationship between NES and brain structure were different between the diagnostic groups. Error control was achieved with a false discovery rate of 5%. Results NES was significantly higher in schizophrenia patients than relatives (p&lt;.0001), who were in turn significantly higher than controls (p=.034). With the groups combined, lower GMV (p&lt;.0001), as well as lower gyrification (p=.004), predicted higher NES above a covariate-only model. GMV predicted greater variance in NSS in comparison to gyrification, explaining an additional 20.3% of the variance in NES, in comparison to the additional 5.5% of variance in NES explained by gyrification. Diagnostic group moderated the association between GMV and NES (p=.019), but not between gyrification and NES (p=.245). Follow-up tests revealed that lower GMV was associated with higher NES in schizophrenia (t=-4.5, p&lt;.0001) and relatives (t=-2.5, p=.015) but not controls (t=-1.9, p=.055). Discussion Our findings indicate that NSS is heritable, being present in patients with established schizophrenia, and to a lesser extent, in unaffected relatives. Consistent with previous research, we revealed that GMV predicted NSS severity, suggesting that abnormalities in volume may underlie these signs. We additionally found that gyrification predicted, although to a lesser extent than volume, NSS severity, providing some support for schizophrenia being of possible neurodevelopmental origin. Evidence for an association between volume and NSS in relatives, whom are not confounded by illness-related factors such as medication and symptom severity, indicates a familial contribution to the neural underpinnings of NSS. Together, our study suggests that there may be various aetiological pathways underlying soft signs across the schizophrenia diathesis, some that may be of familial or neurodevelopmental origin.
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    Brain morphology is differentially impacted by peripheral cytokines in schizophrenia-spectrum disorder
    Laskaris, L ; Mancuso, S ; Shannon Weickert, C ; Zalesky, A ; Chana, G ; Wannan, C ; Bousman, C ; Baune, BT ; McGorry, P ; Pantelis, C ; Cropley, VL (Elsevier, 2021)
    Deficits in brain morphology are one of the most widely replicated neuropathological features in schizophrenia-spectrum disorder (SSD), although their biological underpinnings remain unclear. Despite the existence of hypotheses by which peripheral inflammation may impact brain structure, few studies have examined this relationship in SSD. This study aimed to establish the relationship between peripheral markers of inflammation and brain morphology and determine whether such relationships differed across healthy controls and individuals with first episode psychosis (FEP) and chronic schizophrenia. A panel of 13 pro- and anti-inflammatory cytokines were quantified from serum in 175 participants [n = 84 Healthy Controls (HC), n = 40 FEP, n = 51 Chronic SCZ]. We first performed a series of permutation tests to identify the cytokines most consistently associated with brain structural regions. Using moderation analysis, we then determined the extent to which individual variation in select cytokines, and their interaction with diagnostic status, predicted variation in brain structure. We found significant interactions between cytokine level and diagnosis on brain structure. Diagnostic status significantly moderated the relationship of IFNγ, IL4, IL5 and IL13 with frontal thickness, and of IFNγ and IL5 and total cortical volume. Specifically, frontal thickness was positively associated with IFNγ, IL4, IL5 and IL13 cytokine levels in the healthy control group, whereas pro-inflammatory cytokines IFNγ and IL5 were associated with lower total cortical volume in the FEP group. Our findings suggest that while there were no relationships detected in chronic schizophrenia, the relationship between peripheral inflammatory markers and select brain regions are differentially impacted in FEP and healthy controls. Longitudinal investigations are required to determine whether the relationship between brain structure and peripheral inflammation changes over time.
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    Large-Scale Evidence for an Association Between Peripheral Inflammation and White Matter Free Water in Schizophrenia and Healthy Individuals
    Di Biase, MA ; Zalesky, A ; Cetin-Karayumak, S ; Rathi, Y ; Lv, J ; Boerrigter, D ; North, H ; Tooney, P ; Pantelis, C ; Pasternak, O ; Shannon Weickert, C ; Cropley, VL (Oxford University Press (OUP), 2021-03-01)
    INTRODUCTION: Clarifying the role of neuroinflammation in schizophrenia is subject to its detection in the living brain. Free-water (FW) imaging is an in vivo diffusion-weighted magnetic resonance imaging (dMRI) technique that measures water molecules freely diffusing in the brain and is hypothesized to detect inflammatory processes. Here, we aimed to establish a link between peripheral markers of inflammation and FW in brain white matter. METHODS: All data were obtained from the Australian Schizophrenia Research Bank (ASRB) across 5 Australian states and territories. We first tested for the presence of peripheral cytokine deregulation in schizophrenia, using a large sample (N = 1143) comprising the ASRB. We next determined the extent to which individual variation in 8 circulating pro-/anti-inflammatory cytokines related to FW in brain white matter, imaged in a subset (n = 308) of patients and controls. RESULTS: Patients with schizophrenia showed reduced interleukin-2 (IL-2) (t = -3.56, P = .0004) and IL-12(p70) (t = -2.84, P = .005) and increased IL-6 (t = 3.56, P = .0004), IL-8 (t = 3.8, P = .0002), and TNFα (t = 4.30, P < .0001). Higher proinflammatory signaling of IL-6 (t = 3.4, P = .0007) and TNFα (t = 2.7, P = .0007) was associated with higher FW levels in white matter. The reciprocal increases in serum cytokines and FW were spatially widespread in patients encompassing most major fibers; conversely, in controls, the relationship was confined to the anterior corpus callosum and thalamic radiations. No relationships were observed with alternative dMRI measures, including the fractional anisotropy and tissue-related FA. CONCLUSIONS: We report widespread deregulation of cytokines in schizophrenia and identify inflammation as a putative mechanism underlying increases in brain FW levels.