Patterns of regional gray matter loss at different stages of schizophrenia: A multisite, cross-sectional VBM study in first-episode and chronic illness

Torres, Ulysses S.; Duran, Fábio L.S.; Schaufelberger, Maristela S.; Crippa, José Alexandre S; Louzã, Mário Rodrigues; Sallet, Paulo Clemente; Kanegusuku, Caroline Y.O.; Elkis, Hélio; Gattaz, Wagner F.; Bassitt, Débora Pastore; Zuardi, Antônio Waldo; Hallak, Jaime Eduardo Cecílio; Leite, Cláudia C.; Castro, Cláudio Campi de; Santos, Antônio Carlos dos; Murray, Robin M.; Busatto, Geraldo F.

doi:10.1016/j.nicl.2016.06.002

Cited by 110 publications

(75 citation statements)

References 98 publications

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“…On the other hand, we found a regional increase of grey matter in schizophrenia patients compared to healthy controls in the putamen, caudate and pallidum. These local increases of GM in schizophrenia were also frequently reported in previous studies .…”

Section: Discussionsupporting

confidence: 88%

“…We indeed found that classification of patients with schizophrenia relied on reduced grey matter compared to healthy controls in the cingulate gyrus, precentral and postcentral gyrus, temporal pole, hippocampus, amygdala and thalamus. These regional deficits of grey matter in schizophrenia patients have been consistently reported in univariate studies .…”

Section: Discussionsupporting

confidence: 72%

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Identifying a neuroanatomical signature of schizophrenia, reproducible across sites and stages, using machine learning with structured sparsity

Pierrefeu

Löfstedt

Laidi

et al. 2018

Acta Psychiatr Scand

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Identifying a neuroanatomical signature of schizophrenia, reproducible across sites and stages, using machine learning with structured sparsity Objective: Structural MRI (sMRI) increasingly offers insight into abnormalities inherent to schizophrenia. Previous machine learning applications suggest that individual classification is feasible and reliable and, however, is focused on the predictive performance of the clinical status in cross-sectional designs, which has limited biological perspectives. Moreover, most studies depend on relatively small cohorts or single recruiting site. Finally, no study controlled for disease stage or medication's effect. These elements cast doubt on previous findings' reproducibility. Method: We propose a machine learning algorithm that provides an interpretable brain signature. Using large datasets collected from 4 sites (276 schizophrenia patients, 330 controls), we assessed cross-site prediction reproducibility and associated predictive signature. For the first time, we evaluated the predictive signature regarding medication and illness duration using an independent dataset of first-episode patients. Results: Machine learning classifiers based on neuroanatomical features yield significant intersite prediction accuracies (72%) together with an excellent predictive signature stability. This signature provides a neural score significantly correlated with symptom severity and the extent of cognitive impairments. Moreover, this signature demonstrates its efficiency on first-episode psychosis patients (73% accuracy). Conclusion: These results highlight the existence of a common neuroanatomical signature for schizophrenia, shared by a majority of patients even from an early stage of the disorder. Significant outcomes• Significant intersite prediction accuracy of clinical diagnosis based on sMRI.• Identification of a robust and interpretable structural brain signature of schizophrenia.• The predictive signature generalizes to the detection of patients at the early stage of the disorder. Limitations• At this stage, this does not imply that such predictive models are able to distinguish patients with various psychiatric conditions. • A minority of patients do not present such brain abnormalities, which directly questions the need for a disorder stratification.

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Section: Discussionsupporting

confidence: 88%

Section: Discussionsupporting

confidence: 72%

Identifying a neuroanatomical signature of schizophrenia, reproducible across sites and stages, using machine learning with structured sparsity

Pierrefeu

Löfstedt

Laidi

et al. 2018

Acta Psychiatr Scand

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“…We did not observe any significant relationship between ARHS severity and alterations in gFCD, although such a relationship has been reported by others (Lefort-Besnard et al 2018;Torres et al 2016;Torres et al 2016;Wei et al 2016). It is possible that brain alterations as measured by gFCD may only offer a qualitative index, not a quantitative one, of AVH.…”

Section: Discussioncontrasting

confidence: 67%

Without insight accompanied with deteriorated brain functional alterations in healthy individuals with auditory verbal hallucinations: a pilot study

Zhuo

Feng

Lin³

et al. 2019

Brain Imaging and Behavior

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Few studies have reported on brain functional differences between healthy individuals with auditory verbal hallucinations (Hi-AVH) with and without insight, so we designed a study to address this knowledge gap. We enrolled 12 Hi-AVH with insight, 15 Hi-AVH without insight, and 15 AVH-free controls (Healthy controls). Global functional connectivity density (gFCD) mapping was used to estimate brain networks. We found that the most common alterations in both Hi-AVH groups were increased gFCD in superior parietal lobule and superior temporal gyrus. We also found that distinct brain functional patterns of Hi-AVH without insight comprised lower gFCD in the frontal lobe oculomotor area, dorsolateral prefrontal cortex, supramarginal gyrus, primary auditory cortex, sensorimotor cortex, ventral anterior, and posterior cingulate Our pilot findings support the hypothesis that abnormal reciprocal action in the circuits for processing perception, memory, language, and attentional control may be pathological features of auditory verbal hallucinations.

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“…Patients in a first episode of psychosis (FEP) differ from their chronic psychosis counterparts in various aspects, including symptom patterns [87], cognitive impairments [88], neuroanatomical and functional changes [89,90], and response to antipsychotic treatment as well [90,91]. FEP also shows a specific immune-inflammatory profile.…”

Section: Irs and Cirs In Patients At High Risk For Psychosis And Firsmentioning

confidence: 99%

The Role of Aberrations in the Immune-inflammatory Reflex System (IRS) and the Compensatory Immune-regulatory Reflex System (CIRS) in Different Phenotypes of Schizophrenia: The IRS-CIRS Theory of Schizophrenia

Roomruangwong¹,

Noto²,

Kanchanatawan³

et al. 2018

Preprint

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In this paper we propose a novel theoretical framework, which was previously developed for major depression and bipolar disorder, namely the compensatory immune-regulatory reflex system (CIRS), as applied to the neuro-immune pathophysiology of schizophrenia and its phenotypes, including first episode psychosis (FEP), acute relapses, chronic and treatment resistant schizophrenia (TRS), comorbid depression, and deficit schizophrenia. These schizophrenia phenotypes and manifestations are accompanied by increased production of positive acute phase proteins, including haptoglobin and α2-macroglobulin, complement factors, and macrophagic M1 (IL-1β, IL-6 and TNF-α), T helper (Th)-1 (interferon-γ and IL-2R), Th-2 (IL-4, IL-5), Th-17 (IL-17) and T regulatory (Treg; IL-10 and transforming growth factor (TGF)-β1) cytokines, cytokine-induced activation of the tryptophan catabolite (TRYCAT) pathway as well as chemokines, including CCL-11 (eotaxin), CCL-2, CCL-3 and CXCL-8. While the immune profiles in the different schizophrenia phenotypes indicate activation of the immune-inflammatory response system (IRS), there are simultaneous signs of CIRS activation, including increased levels of the IL-1 receptor antagonist (sIL-1RA), sIL-2R and tumor necrosis factor-a receptors, Th-2 and Treg phenotypes with increased IL-4 and IL-10 production, and increased levels of TRYCATs and haptoglobin, α2-macroglobulin and other acute phase reactants, which have immune-regulatory and anti-inflammatory effects. Signs of activated IRS and CIRS pathways are also detected in TRS, chronic and deficit schizophrenia indicating that these conditions are accompanied by a new homeostatic setpoint between upregulated IRS and CIRS components. In FEP, increased baseline CIRS activity is a protective factor which may predict favorable clinical outcomes. Moreover, impairments in the CIRS are associated with deficit schizophrenia and greater impairments in semantic and episodic memory. It is concluded that CIRS plays a key role in the pathophysiology of schizophrenia by negatively regulating the primary IRS and contributing to recovery from the acute phase of illness. Components of the CIRS may offer promising therapeutic targets for schizophrenia.

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Patterns of regional gray matter loss at different stages of schizophrenia: A multisite, cross-sectional VBM study in first-episode and chronic illness

Cited by 110 publications

References 98 publications

Identifying a neuroanatomical signature of schizophrenia, reproducible across sites and stages, using machine learning with structured sparsity

Identifying a neuroanatomical signature of schizophrenia, reproducible across sites and stages, using machine learning with structured sparsity

Without insight accompanied with deteriorated brain functional alterations in healthy individuals with auditory verbal hallucinations: a pilot study

The Role of Aberrations in the Immune-inflammatory Reflex System (IRS) and the Compensatory Immune-regulatory Reflex System (CIRS) in Different Phenotypes of Schizophrenia: The IRS-CIRS Theory of Schizophrenia

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