Regional, circuit, and network heterogeneity of brain abnormalities in psychiatric disorders

Segal, Ashlea; Parkes, Linden; Aquino, Kevin; Kia, Seyed Mostafa; Wolfers, Thomas; Franke, Barbara; Hoogman, Martine; Beckmann, Christian F.; Andreassen, Ole A.; Zalesky, Andrew; Harrison, Ben J; Davey, Christopher G.; Soriano‐Mas, Carles; Cardoner, Narcı́s; Tiego, Jeggan; Yücel, Murat; Braganza, Leah; Suo, Chao; Berk, Michael; Cotton, Sue; Bellgrove, Mark A.; Marquand, André F.; Fornito, Alex

doi:10.1101/2022.03.07.22271986

Cited by 26 publications

(62 citation statements)

References 132 publications

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“…Future studies should examine how altered connectivity in patients impacts our findings. Moreover, our findings are dependent on group-level summary metrics of brain volume and may not be representative of volume changes at the individual patient level, which show substantial heterogeneity [67][68][69] . Subsequent work could look at whether using individual-level measures of brain volume and connectivity can improve model predictions.…”

Section: Limitations and Conclusionmentioning

confidence: 87%

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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Section: Limitations and Conclusionmentioning

confidence: 87%

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

Chopra

Oldham

Segal

et al. 2022

Preprint

Self Cite

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“…Additionally, focusing on a particular diagnostic category assumes homogeneity of symptoms and mechanisms (i.e., the homogeneity assumption) 94 , but it is well known that people with the same diagnosis may exhibit little to no overlap in symptoms (i.e., the heterogeneity problem) 95 . Recent neuroimaging studies have attempted to characterize such within-diagnosis heterogeneity at a neural level [219][220][221][222][223][224] . Co-morbidity between putatively distinct disorders (i.e., the comorbidity problem) 76 , and issues of arbitrary clinical cut-offs and subthreshold symptomatology 44,45 are well-documented limitations of current psychiatric taxonomies 225,226 .…”

Section: Box 2 -Limitations Of Traditional Approaches To Psychiatric ...mentioning

confidence: 99%

Precision behavioral phenotyping as a strategy for uncovering the biological correlates of psychopathology

Tiego¹,

Martin²,

DeYoung³

et al. 2022

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Our capacity to measure diverse aspects of human biology in vivo has developed rapidly in the past decades, but the rate at which these techniques have generated translatable insights into the biology of psychopathological conditions has lagged far behind. This slow progress is partly due to the poor sensitivity, specificity, and replicability of many findings in the literature, which have in turn been attributed to small effect sizes, small sample sizes, and low statistical power. A commonly proposed solution is to rely on large, consortia-sized samples to facilitate discovery of replicable findings. Here, we argue that increasing sample sizes will have limited impact unless a more fundamental issue is addressed: the precision with which target behavioral phenotypes are measured. We discuss key problems and outline several ways forward, largely centered on the use of appropriate statistical models with deep, transdiagnostic assessment of hierarchically organized and homogeneous psychopathology dimensions across the full range of the severity spectrum. We provide worked examples to demonstrate key problems and potential solutions. Arguably, a precision phenotyping approach can enhance the discovery and replicability of associations between biology and behavior, thereby facilitating insights into the pathophysiological mechanisms underlying psychiatric disorders.

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“…While ASD is primarily characterized by impairments in social communication skills and by repetitive behaviors, a SZ diagnosis consists of positive (e.g., hallucinations, delusions) and negative (e.g., social withdrawal) symptoms. The heterogeneity of both diagnostic categories (Benkarim et al, 2022; Segal et al, 2022) and their phenotypic overlap (Kästner et al, 2015) can hinder accurate diagnosis. More precisely, ASD and SZ co-occur in approximately 4% of cases (Lai et al, 2019), and share both social (Oliver et al, 2020) and sensory-motor deficits (Du et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, a recent meta-analysis also showed that comorbidity of ASD and SZ has a 4% prevalence (Lai et al, 2019). However, given the substantial heterogeneity in both SZ and ASD (e.g., Segal et al, 2022), it is difficult to ascertain to which extent and in which brain areas or networks there is overlap between the two diagnoses.…”

Section: Introductionmentioning

confidence: 99%

Using the Excitation/Inhibition Ratio to Optimize the Classification of Autism and Schizophrenia

Uscătescu

Hyatt

Dunn³

et al. 2022

Preprint

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The excitation/inhibition (E/I) ratio has been shown to be elevated in both autism spectrum disorder (ASD) and schizophrenia (SZ), relative to neurotypical controls. However, the degree of E/I imbalance overlap and differentiation between SZ and ASD is not known. Our main objectives were therefore (1) to quantify group differences in the E/I ratio between controls, ASD and SZ, and (2) to assess the potential of the E/I ratio for differential diagnosis. We collected resting state fMRI (rsfMRI) and phenotypic data from 55 controls, 30 ASD, and 39 SZ, ages 18 to 35 (IQ>80). For each participant, we computed the Hurst exponent (H), an indicator of the E/I ratio, for the timecourses of 53 independent components covering the entire brain. Next, using Optimal Classification Trees (OCT), we ran a classification analysis on the two clinical groups using five incremental feature sets (i.e., models): (1) Positive and Negative Syndrome Scale (PANSS) and the Autism Diagnostic Observation Schedule (ADOS) only; (2) PANSS, ADOS, Bermond–Vorst Alexithymia Questionnaire (BVAQ), Empathy Quotient (EQ), and IQ; (3) H only; (4) H, PANSS and ADOS; (5) H, PANSS, ADOS, BVAQ, EQ and IQ. We observed decreased H (i.e., increase in E/I ratio) in ASD and SZ compared to controls, and in SZ compared to ASD in the Cerebellar, Sensorimotor, Visual and Cognitive Control networks. The OCT classification showed a consistent increase in discrimination accuracy across models between ASD and SZ, suggesting that the E/I ratio in combination with phenotypic measures can contribute to differential diagnosis in adults.

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Regional, circuit, and network heterogeneity of brain abnormalities in psychiatric disorders

Cited by 26 publications

References 132 publications

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

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

Precision behavioral phenotyping as a strategy for uncovering the biological correlates of psychopathology

Using the Excitation/Inhibition Ratio to Optimize the Classification of Autism and Schizophrenia

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