Neuroprogression across the Early Course of Psychosis

Lewandowski, Kathryn Eve; Bouix, Sylvain; Öngür, Döst; Shenton, Martha E.

doi:10.20900/jpbs.20200002

Cited by 17 publications

(21 citation statements)

References 131 publications

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“…This case-control study used data from 4 independent data sets sampling different stages of psychotic illness: the STAGES (Staged Treatment and Acceptability Guidelines in Early Psychosis Study) clinical trial (first-episode psychosis), Human Connectome Project Early Psychosis (early psychosis), BrainGluSchi (schizophrenia), and COBRE (schizophrenia). Representative structural and functional connectomes were derived using an independent control sample.…”

Section: Methodsmentioning

confidence: 99%

“…All participants gave written informed consent after having the study fully explained to them . The remaining 3 publicly available data sets were approved by their respective committees, which ensured appropriate informed consent procedures . This study aligns with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.…”

Section: Methodsmentioning

confidence: 99%

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Network-Based Spreading of Gray Matter Changes Across Different Stages of Psychosis

Chopra,

Segal,

Oldham

et al. 2023

JAMA Psychiatry

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ImportancePsychotic illness is associated with anatomically distributed gray matter reductions that can worsen with illness progression, but the mechanisms underlying the specific spatial patterning of these changes is unknown.ObjectiveTo test the hypothesis that brain network architecture constrains cross-sectional and longitudinal gray matter alterations across different stages of psychotic illness and to identify whether certain brain regions act as putative epicenters from which volume loss spreads.Design, Settings, and ParticipantsThis case-control study included 534 individuals from 4 cohorts, spanning early and late stages of psychotic illness. Early-stage cohorts included patients with antipsychotic-naive first-episode psychosis (n = 59) and a group of patients receiving medications within 3 years of psychosis onset (n = 121). Late-stage cohorts comprised 2 independent samples of people with established schizophrenia (n = 136). Each patient group had a corresponding matched control group (n = 218). A sample of healthy adults (n = 356) was used to derive representative structural and functional brain networks for modeling of network-based spreading processes. In Longitudinal illness-related and antipsychotic-related gray matter changes over 3 and 12 months were examined using a triple-blind randomized placebo-control magnetic resonance imaging study of the antipsychotic-naive patients. All data were collected between April 29, 2008, and January 15, 2020, and analyses were performed between March 1, 2021, and January 14, 2023.Main Outcomes and MeasuresCoordinated deformation models were used to estimate the extent of gray matter volume (GMV) change in each of 332 parcellated areas by the volume changes observed in areas to which they were structurally or functionally coupled. To identify putative epicenters of volume loss, a network diffusion model was used to simulate the spread of pathology from different seed regions. Correlations between estimated and empirical spatial patterns of GMV alterations were used to quantify model performance.ResultsOf 534 included individuals, 354 (66.3%) were men, and the mean (SD) age was 28.4 (7.4) years. In both early and late stages of illness, spatial patterns of cross-sectional volume differences between patients and controls were more accurately estimated by coordinated deformation models constrained by structural, rather than functional, network architecture (r range, &gt;0.46 to &lt;0.57; P &lt; .01). The same model also robustly estimated longitudinal volume changes related to illness (r ≥ 0.52; P &lt; .001) and antipsychotic exposure (r ≥ 0.50; P &lt; .004). Network diffusion modeling consistently identified, across all 4 data sets, the anterior hippocampus as a putative epicenter of pathological spread in psychosis. Epicenters of longitudinal GMV loss were apparent in posterior cortex early in the illness and shifted to the prefrontal cortex with illness progression.Conclusion and RelevanceThese findings highlight a central role for white matter fibers as conduits for the spread of pathology across different stages of psychotic illness, mirroring findings reported in neurodegenerative conditions. The structural connectome thus represents a fundamental constraint on brain changes in psychosis, regardless of whether these changes are caused by illness or medication. Moreover, the anterior hippocampus represents a putative epicenter of early brain pathology from which dysfunction may spread to affect connected areas.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Network-Based Spreading of Gray Matter Changes Across Different Stages of Psychosis

Chopra,

Segal,

Oldham

et al. 2023

JAMA Psychiatry

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“…This study used data from four independent datasets sampling different stages of the psychotic-illness continuum: the STAGES clinical trial 13,33 (first episode psychosis; FEP), Human Connectome Project Early Psychosis 34 (early psychosis; EP), BrainGluSchi 35 (schizophrenia; SCZ-BGS), and COBRE 36 (schizophrenia; SCZ-COBRE). Hereafter, these cohorts will be referred to as FEP, EP, SCZ-BGS and SCZ-COBRE, respectively.…”

Section: Methodsmentioning

confidence: 99%

Network-based spreading of grey matter changes across different stages of psychosis

Chopra

Oldham

Segal

et al. 2022

Preprint

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Background: Different regions of the brain's grey matter are connected by a complex structural network of white matter fibres which are responsible for the propagation of action potentials and the transport of trophic and other molecules. In neurodegenerative disease, these connections constrain the way in which grey matter volume loss progresses. Here, we investigated whether connectome architecture also shapes the spatial pattern of longitudinal grey matter volume changes attributable to illness and antipsychotic medication in first episode psychosis (FEP). Methods: We conducted a triple-blind randomised placebo-control MRI study where 62 young adults with first episode psychosis received either an atypical antipsychotic or placebo over 6-months. A healthy control group was also recruited. Anatomical MRI scans were acquired at baseline, 3-months and 12-months. Deformation-based morphometry was used to estimate illness-related and antipsychotic-related grey matter volume changes over time. Representative functional and structural brain connectivity patterns were derived from an independent healthy control group using resting-state functional MRI and diffusion-weighted imaging. We used neighbourhood deformation models to predict the extent of brain change in a given area by the changes observed in areas to which it is either structurally connected or functionally coupled. Results: At baseline, we found that empirical illness-related regional volume differences were strongly correlated with predicted differences using a model constrained by structural connectivity weights (ρ = .541; p < .001). At 3-months and 12-months, we also found a strong correlation between longitudinal regional illness-related (ρ > .516; p < .001) and antipsychotic-related volume change (ρ > .591; p < .001) with volumetric changes in structurally connected areas. These correlations were significantly greater than those observed across various null models accounting for lower-order spatial and network properties of the data. Associations between empirical and predicted volume change estimates were much lower for models that only considered binary structural connectivity (all ρ < .376), or which were constrained by inter-regional functional coupling (all ρ < .436). Finally, we found that potential epicentres of volume change emerged posteriorly early in the illness and shifted to the prefrontal cortex by later illness stages. Conclusion: Psychosis- and antipsychotic-related grey matter volume changes are strongly shaped by anatomical brain connectivity. This result is consistent with findings in other neurological disorders and implies that such connections may constrain pathological processes causing brain dysfunction in FEP.

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“…Additionally, schizophrenia is associated with structural abnormalities in brain regions that support these cognitive functions. These consist of reductions in the anterior cingulate, prefrontal and temporal cortical regions and in the volume of the hippocampus, amygdala, thalamus, and insula (3)(4)(5)(6). Longitudinal studies suggest more rapid progressive decreases in brain tissue in patients with schizophrenia than in healthy individuals, primarily in frontal and temporal areas (7,8) raising the possibility that processes related to schizophrenia may contribute to the premature development of age-related brain changes (i.e., accelerated aging).…”

Section: Introductionmentioning

confidence: 99%

Accelerated Global and Local Brain Aging Differentiate Cognitively Impaired From Cognitively Spared Patients With Schizophrenia

Haas

Sanford

et al. 2022

Front. Psychiatry

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BackgroundAccelerated aging has been proposed as a mechanism underlying the clinical and cognitive presentation of schizophrenia. The current study extends the field by examining both global and regional patterns of brain aging in schizophrenia, as inferred from brain structural data, and their association with cognitive and psychotic symptoms.MethodsGlobal and local brain-age-gap-estimates (G-brainAGE and L-brainAGE) were computed using a U-Net Model from T1-weighted structural neuroimaging data from 84 patients (aged 16–35 years) with early-stage schizophrenia (illness duration <5 years) and 1,169 healthy individuals (aged 16–37 years). Multidomain cognitive data from the patient sample were submitted to Heterogeneity through Discriminative Analysis (HYDRA) to identify cognitive clusters.ResultsHYDRA classified patients into a cognitively impaired cluster (n = 69) and a cognitively spared cluster (n = 15). Compared to healthy individuals, G-brainAGE was significantly higher in the cognitively impaired cluster (+11.08 years) who also showed widespread elevation in L-brainAGE, with the highest deviance observed in frontal and temporal regions. The cognitively spared cluster showed a moderate increase in G-brainAGE (+8.94 years), and higher L-brainAGE localized in the anterior cingulate cortex. Psychotic symptom severity in both clusters showed a positive but non-significant association with G-brainAGE.DiscussionAccelerated aging in schizophrenia can be detected at the early disease stages and appears more closely associated with cognitive dysfunction rather than clinical symptoms. Future studies replicating our findings in multi-site cohorts with larger numbers of participants are warranted.

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Neuroprogression across the Early Course of Psychosis

Cited by 17 publications

References 131 publications

Network-Based Spreading of Gray Matter Changes Across Different Stages of Psychosis

Network-Based Spreading of Gray Matter Changes Across Different Stages of Psychosis

Network-based spreading of grey matter changes across different stages of psychosis

Accelerated Global and Local Brain Aging Differentiate Cognitively Impaired From Cognitively Spared Patients With Schizophrenia

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