Association between age of cannabis initiation and gray matter covariance networks in recent onset psychosis

AbstractCannabis use during adolescence is associated with an increased risk of developing psychosis. According to a current hypothesis, this results from detrimental effects of early cannabis use on brain maturation during this vulnerable period. However, studies investigating the interaction between early cannabis use and brain structural alterations hitherto reported inconclusive findings. We investigated effects of age of cannabis initiation on psychosis using data from the multicentric Personalized Prognostic Tools for Early Psychosis Management (PRONIA) and the Cannabis Induced Psychosis (CIP) studies, yielding a total sample of 102 clinically-relevant cannabis users with recent onset psychosis. GM covariance underlies shared maturational processes. Therefore, we performed source-based morphometry analysis with spatial constraints on structural brain networks showing significant alterations in schizophrenia in a previous multisite study, thus testing associations of these networks with the age of cannabis initiation and with confounding factors. Earlier cannabis initiation was associated with more severe positive symptoms in our cohort. Greater gray matter volume (GMV) in the previously identified cerebellar schizophrenia-related network had a significant association with early cannabis use, independent of several possibly confounding factors. Moreover, GMV in the cerebellar network was associated with lower volume in another network previously associated with schizophrenia, comprising the insula, superior temporal, and inferior frontal gyrus. These findings are in line with previous investigations in healthy cannabis users, and suggest that early initiation of cannabis perturbs the developmental trajectory of certain structural brain networks in a manner imparting risk for psychosis later in life.

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M1. INVESTIGATING THE RELATIONSHIP BETWEEN CHILDHOOD TRAUMA AND PSYCHIATRIC DISEASE USING MACHINE LEARNING TECHNIQUES

Schizophrenia Bulletin ◽

10.1093/schbul/sbaa030.313 ◽

2020 ◽

Vol 46 (Supplement_1) ◽

pp. S133-S133

Author(s):

Theresa Haidl ◽

Dennis Hedderich ◽

Marlene Rosen ◽

Thorsten Lichtenstein ◽

Nathalie Kaiser ◽

...

Keyword(s):

Childhood Trauma ◽

Machine Learning Techniques ◽

Brain Maturation ◽

Patient Specific ◽

Psychiatric Disease ◽

Support Vector ◽

Recent Onset ◽

Increased Risk ◽

Patient Groups ◽

Structural Brain

Abstract Background Childhood trauma (CT) is associated with an increased risk for psychiatric disorders like major depression and psychosis. However, the pathophysiological relationship between CT, psychiatric disease and structural brain alterations is still unknown. Methods PRONIA (‘Personalized Prognostic Tools for Early Psychosis Mangement’) is a prospective collaboration project funded by the European Union under the 7th Framework Programme (grant agreement n° 602152). Considering a broad set of variables (sMRI, rsMRI, DTI, psychopathological, life event related and sociobiographic data, neurocognition, genomics and other blood derived parameters) as well as advanced statistical methods, PRONIA aims at developing an innovative multivariate prognostic tool enabling an individualized prediction of illness trajectories and outcome. Seven clinical centers in five European countries and in Australia participate in the evaluation of three clinical groups (subjects clinically at high risk of developing a psychosis (CHR), patients with a recent onset psychosis (ROP) and patients with a recent onset depression (ROD)) as well as healthy controls (HC). To investigate the high-dimensional patterns of CT experience, measured by the childhood trauma questionnaire (CTQ), in HC and our three patient groups (PAT) (n=643), we used a Support Vector Machine (SVM). Furthermore, we tested whether patient-specific CT exposure is associated with structural brain changes by VBM analyses. Results We found that patients and HC could be separated very well by their CTQ pattern, whereas the different patient groups showed no specific CTQ pattern. Furthermore, an association with extensive grey matter changes suggests an impact on brain maturation which may put individuals at increased risk for mental disease. Discussion We have demonstrated in this large multi-center cohort that adverse experiences in childhood contribute transdiagnostically to the riskr for developing a psychiatric disease. The observed association between CTQ scores and structural changes suggests an impact of adverse childhood experiences on brain development. Resulting alterations may add to a neurobiological vulnerability for depression and psychosis. A role of both features for other mental disorders could be assumed and warrants further investigation.

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The non-specific nature of mental health and structural brain outcomes following childhood trauma

Psychological Medicine ◽

10.1017/s0033291721002439 ◽

2021 ◽

pp. 1-10

Author(s):

Theresa K. Haidl ◽

Dennis M. Hedderich ◽

Marlene Rosen ◽

Nathalie Kaiser ◽

Mauro Seves ◽

...

Keyword(s):

Mental Health ◽

Risk Factor ◽

Depressive Symptoms ◽

Childhood Trauma ◽

Brain Structure ◽

Mental Health Disorders ◽

Recent Onset ◽

Increased Risk ◽

Health Disorders ◽

Structural Brain

Abstract Background Childhood trauma (CT) is associated with an increased risk of mental health disorders; however, it is unknown whether this represents a diagnosis-specific risk factor for specific psychopathology mediated by structural brain changes. Our aim was to explore whether (i) a predictive CT pattern for transdiagnostic psychopathology exists, and whether (ii) CT can differentiate between distinct diagnosis-dependent psychopathology. Furthermore, we aimed to identify the association between CT, psychopathology and brain structure. Methods We used multivariate pattern analysis in data from 643 participants of the Personalised Prognostic Tools for Early Psychosis Management study (PRONIA), including healthy controls (HC), recent onset psychosis (ROP), recent onset depression (ROD), and patients clinically at high-risk for psychosis (CHR). Participants completed structured interviews and self-report measures including the Childhood Trauma Questionnaire, SCID diagnostic interview, BDI-II, PANSS, Schizophrenia Proneness Instrument, Structured Interview for Prodromal Symptoms and structural MRI, analyzed by voxel-based morphometry. Results (i) Patients and HC could be distinguished by their CT pattern with a reasonable precision [balanced accuracy of 71.2% (sensitivity = 72.1%, specificity = 70.4%, p ≤ 0.001]. (ii) Subdomains ‘emotional neglect’ and ‘emotional abuse’ were most predictive for CHR and ROP, while in ROD ‘physical abuse’ and ‘sexual abuse’ were most important. The CT pattern was significantly associated with the severity of depressive symptoms in ROD, ROP, and CHR, as well as with the PANSS total and negative domain scores in the CHR patients. No associations between group-separating CT patterns and brain structure were found. Conclusions These results indicate that CT poses a transdiagnostic risk factor for mental health disorders, possibly related to depressive symptoms. While differences in the quality of CT exposure exist, diagnostic differentiation was not possible suggesting a multi-factorial pathogenesis.

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Altered voxel-wise gray matter structural brain networks in schizophrenia: Association with brain genetic expression pattern

Brain Imaging and Behavior ◽

10.1007/s11682-018-9880-6 ◽

2018 ◽

Vol 13 (2) ◽

pp. 493-502 ◽

Cited By ~ 21

Author(s):

Feng Liu ◽

Hongjun Tian ◽

Jie Li ◽

Shen Li ◽

Chuanjun Zhuo

Keyword(s):

Expression Pattern ◽

Gray Matter ◽

Brain Networks ◽

Genetic Expression ◽

Structural Brain

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IC-P-106: Alzheimer's disease-specific changes in cerebral gray matter revealed using a novel voxel-wise approach for constructing structural brain networks

Alzheimer s & Dementia ◽

10.1016/j.jalz.2012.05.140 ◽

2012 ◽

Vol 8 (4S_Part_2) ◽

pp. P60-P60

Author(s):

Christopher Whitlow ◽

Joseph Maldjian

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Gray Matter ◽

Brain Networks ◽

Disease Specific ◽

Structural Brain

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Aberrant topographical organization in gray matter structural network in late life depression: a graph theoretical analysis

International Psychogeriatrics ◽

10.1017/s104161021300149x ◽

2013 ◽

Vol 25 (12) ◽

pp. 1929-1940 ◽

Cited By ~ 27

Author(s):

Hyun Kook Lim ◽

Won Sang Jung ◽

Howard J Aizenstein

Keyword(s):

Gray Matter ◽

Gray Matter Volume ◽

Brain Networks ◽

Small World ◽

Late Life ◽

Clustering Coefficient ◽

Cingulate Gyrus ◽

Late Life Depression ◽

Matter Volume ◽

Structural Brain

ABSTRACTBackground:Although previous studies on late life depression (LLD) have shown morphological abnormalities in frontal–striatal–temporal areas, alterations in coordinated patterns of structural brain networks in LLD are still poorly understood. The aim of this study was to investigate differences in gray matter structural brain network between LLD and healthy controls.Methods:We used gray matter volume measurement from magnetic resonance imaging to investigate large-scale structural brain networks in 37 LLD patients and 40 normal controls. Brain networks were constructed by thresholding gray matter volume correlation matrices of 90 regions and analyzed using graph theoretical approaches.Results:Although both LLD and control groups showed a small-world organization of group networks, there were no differences in the clustering coefficient, the path length, and the small-world index across a wide range of network density. Compared with controls, LLD patients showed decreased nodal betweenness in the medial orbitofrontal and angular gyrus regions. In addition, LLD patients showed hub regions in superior temporal gyrus and middle cingulate gyrus, and putamen. On the other hand, the control group showed hub regions in the medial orbitofrontal gyrus, middle cingulate gyrus, and cuneus.Conclusion:Our findings suggest that the gray matter structural networks are not globally but regionally altered in LLD patients. This multivariate structural analysis using graph theory might provide a more appropriate paradigm for understanding complicated neurobiological mechanism of LLD.

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Variations in Brain Morphometry Among Healthy Preschoolers Born Very Preterm

10.1101/734277 ◽

2019 ◽

Cited By ~ 1

Author(s):

Holly M. Hasler ◽

Timothy T. Brown ◽

Natacha Akshoomoff

Keyword(s):

Preterm Birth ◽

Surface Area ◽

Cortical Thickness ◽

Gray Matter ◽

Gestational Age ◽

Brain Maturation ◽

Healthy Children ◽

Very Preterm ◽

Increased Risk ◽

Sulcal Depth

AbstractBackgroundPreterm birth is associated with an increased risk of neonatal brain injury, which can lead to alterations in brain maturation. Advances in neonatal care have dramatically reduced the incidence of the most significant medical consequences of preterm birth. Relatively healthy preterm infants remain at increased risk for subtle injuries that impact future neurodevelopmental and functioning.AimsTo investigate the gray matter morphometry measures of cortical thickness, surface area, and sulcal depth in the brain using magnetic resonance imaging at 5 years of age in healthy children born very preterm.Study designCohort studySubjectsParticipants were 52 children born very preterm (VPT; less than 33 weeks gestational age) and 37 children born full term.Outcome measuresCortical segmentation and calculation of morphometry measures were completed using FreeSurfer version 5.3.0 and compared between groups using voxel-wise, surface-based analyses.ResultsThe VPT group had a significantly thinner cortex in temporal and parietal regions as well as thicker gray matter in the occipital and inferior frontal regions. Reduced surface area was found in the fusiform area in the VPT group. Sulcal depth was also lower in the VPT group within the posterior parietal and inferior temporal regions and greater sulcal depth was found in the middle temporal and medial parietal regions. Results in some of these regions were correlated with gestational age at birth in the VPT group.ConclusionsThe most widespread differences between the VPT and FT groups were found in cortical thickness. These findings may represent a combination of delayed maturation and permanent alterations caused by the perinatal processes associated with very preterm birth.

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Cannabis Use in Youth is Associated with Limited Alterations in Brain Structure

10.1101/443911 ◽

2018 ◽

Cited By ~ 1

Author(s):

J. Cobb Scott ◽

Adon F. G. Rosen ◽

Tyler M. Moore ◽

David R. Roalf ◽

Theodore D. Satterthwaite ◽

...

Keyword(s):

Cortical Thickness ◽

Gray Matter ◽

Brain Structure ◽

Matter Density ◽

Cannabis Use ◽

Significant Group ◽

Small Magnitude ◽

Gray Matter Density ◽

Structural Neuroimaging ◽

Structural Brain

ABSTRACTFrequent cannabis use during adolescence has been associated with alterations in brain structure. However, studies have featured relatively inconsistent results, predominantly from small samples, and few studies have examined less frequent users to shed light on potential brain structure differences across levels of cannabis use. In this study, high-resolution T1-weighted MRIs were obtained from 781 youth aged 14-21 years who were studied as part of the Philadelphia Neurodevelopmental Cohort. This sample included 147 cannabis users (109 Occasional [≤1-2 times per week] and 38 Frequent [≥ 3 times per week] Users) and 634 cannabis Non-Users. Several structural neuroimaging measures were examined in whole brain analyses, including gray and white matter volumes, cortical thickness, and gray matter density. Established procedures for stringent quality control were conducted, and two automated neuroimaging software processing packages were used to ensure robustness of results. There were no significant differences by cannabis group in global or regional brain volumes, cortical thickness, or gray matter density, and no significant group by age interactions were found. Follow-up analyses indicated that values of structural neuroimaging measures by cannabis group were similar across regions, and any differences among groups were likely of a small magnitude. In sum, structural brain metrics were similar among adolescent and young adult cannabis users and non-users. Our data converge with prior large-scale studies suggesting small or limited associations between cannabis use and structural brain measures in youth. Detailed studies of vulnerability to structural brain alterations and longitudinal studies examining long-term risk are indicated.

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Topological segregation of functional networks increases in developing brains

10.1101/378562 ◽

2018 ◽

Cited By ~ 2

Author(s):

Wei He ◽

Paul F. Sowman ◽

Jon Brock ◽

Andrew C. Etchell ◽

Cornelis J. Stam ◽

...

Keyword(s):

Network Topology ◽

Minimum Spanning Tree ◽

Brain Networks ◽

Developmental Trajectory ◽

Brain Maturation ◽

Functional Networks ◽

Functional Brain ◽

Functional Brain Networks ◽

Reconstructed Time Series ◽

Cortical Regions

AbstractA growing literature conceptualises human brain development from a network perspective, but it remains unknown how functional brain networks are refined during the preschool years. The extant literature diverges in its characterisation of functional network development, with little agreement between haemodynamic- and electrophysiology-based measures. In children aged from 4 to 12 years, as well as adults, age appropriate magnetoencephalography was used to estimate unbiased network topology, using minimum spanning tree (MST) constructed from phase synchrony between beamformer-reconstructed time-series. During childhood, network topology becomes increasingly segregated, while cortical regions decrease in centrality. We propose a heuristic MST model, in which a clear developmental trajectory for the emergence of complex brain networks is delineated. Our results resolve topological reorganisation of functional networks across temporal and special scales in youth and fill a gap in the literature regarding neurophysiological mechanisms of functional brain maturation during the preschool years.

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