Structural brain changes in schizophrenia at different stages of the illness: A selective review of longitudinal magnetic resonance imaging studies

Dietsche, Bruno; Kircher, Tilo; Falkenberg, Irina

doi:10.1177/0004867417699473

Cited by 174 publications

(134 citation statements)

References 67 publications

(104 reference statements)

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“…Structural MRI findings in schizophrenia have been recently reviewed [220]. There is enough information to propose that the condition is associated with a continuing development of gray matter aberrations, chiefly throughout the first stages of the disease.…”

Section: Imagingmentioning

confidence: 99%

The Reeler Mouse: A Translational Model of Human Neurological Conditions, or Simply a Good Tool for Better Understanding Neurodevelopment?

Lossi

Castagna

Granato

et al. 2019

JCM

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The first description of the Reeler mutation in mouse dates to more than fifty years ago, and later, its causative gene (reln) was discovered in mouse, and its human orthologue (RELN) was demonstrated to be causative of lissencephaly 2 (LIS2) and about 20% of the cases of autosomal-dominant lateral temporal epilepsy (ADLTE). In both human and mice, the gene encodes for a glycoprotein referred to as reelin (Reln) that plays a primary function in neuronal migration during development and synaptic stabilization in adulthood. Besides LIS2 and ADLTE, RELN and/or other genes coding for the proteins of the Reln intracellular cascade have been associated substantially to other conditions such as spinocerebellar ataxia type 7 and 37, VLDLR-associated cerebellar hypoplasia, PAFAH1B1-associated lissencephaly, autism, and schizophrenia. According to their modalities of inheritances and with significant differences among each other, these neuropsychiatric disorders can be modeled in the homozygous (reln −/− ) or heterozygous (reln +/− ) Reeler mouse. The worth of these mice as translational models is discussed, with focus on their construct and face validity. Description of face validity, i.e., the resemblance of phenotypes between the two species, centers onto the histological, neurochemical, and functional observations in the cerebral cortex, hippocampus, and cerebellum of Reeler mice and their human counterparts. mutations of the RELN gene were linked to a form of lissencephaly with cerebellar hypoplasia (LCH) [9], with associated findings suggested that RELN was linked to some neuropsychiatric conditions [10], and RELN was demonstrated to be reduced in the cerebellum of autistic patients after Western blotting and immunodetection [11].Determining a good translational mouse model for a neuropsychiatric condition needs construct, predictive, and face validity [12]. Rigorously, construct validity only relates to transgenic mice, but, in a broader definition, it also comprehends the syndromic models and the spontaneous DNA mutations linked to the phenotype under study. In other words, this factor defines the similarity of the disease between the mouse and the human disorder in terms of the causal gene(s) as e.g., deducted from gene association and linkage analysis. As mentioned above, LCH is a human monogenic condition caused by a mutation in RELN. Therefore, the Reeler mouse fully meets the criterion of construct validity for the condition. There is also evidence for genetics to be associated with the etiology of several neuropsychiatric conditions, such as autism and schizophrenia, but, as the result of their multidimensional clinical symptoms, causal gene(s), if any, persist to be undiscovered [13]. Nonetheless, there are numerous genes associated with the human autistic pathology after analysis of Mendelian disorders (syndromes), rare mutations, or association studies; see e.g., [14].Predictive validity, i.e., the similarity of the response to cures in humans and mice is difficult to establish, in the nonexistence of a re...

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Section: Imagingmentioning

confidence: 99%

The Reeler Mouse: A Translational Model of Human Neurological Conditions, or Simply a Good Tool for Better Understanding Neurodevelopment?

Lossi

Castagna

Granato

et al. 2019

JCM

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“…While current evidences, as reviewed for various stages of psychosis (i.e., high-risk status, 9,10,22,23,27,[38][39][40] first episode, and chronic stages 1,5,6,41,42 ), including longitudinal assessment, 11,[43][44][45] suggest both neurodevelopmental pathology and dynamic brain changes underlying emergence of overt psychosis, this article further selectively reviews recent neuroimaging findings of brain morphologic changes during the course of psychosis, including our own data, for better understanding of the underlying neurobiology of psychotic disorders. We also refer to the possible clinical applicability of neuroimaging data to a future diagnostic system for schizophrenia and related disorders, and early intervention for psychosis.…”

mentioning

confidence: 99%

Brain morphologic changes in early stages of psychosis: Implications for clinical application and early intervention

Takahashi

Suzuki

2018

Psychiatry Clin Neurosci

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To date, a large number of magnetic resonance imaging (MRI) studies have been conducted in schizophrenia, which generally demonstrate gray matter reduction, predominantly in the frontal and temporo-limbic regions, as well as gross brain abnormalities (e.g., a deviated sulcogyral pattern). Although the causes as well as timing and course of these findings remain elusive, these morphologic changes (especially gross brain abnormalities and medial temporal lobe atrophy) are likely present at illness onset, possibly reflecting early neurodevelopmental abnormalities. In addition, longitudinal MRI studies suggest that patients with schizophrenia and related psychoses also have progressive gray matter reduction during the transition period from prodrome to overt psychosis, as well as initial periods after psychosis onset, while such changes may become almost stable in the chronic stage. These active brain changes during the early phases seem to be relevant to the development of clinical symptoms in a region-specific manner (e.g., superior temporal gyrus atrophy and positive psychotic symptoms), but may be at least partly ameliorated by antipsychotic medication. Recently, increasing evidence from MRI findings in individuals at risk for developing psychosis has suggested that those who subsequently develop psychosis have baseline brain changes, which could be at least partly predictive of later transition into psychosis. In this article, we selectively review previous MRI findings during the course of psychosis and also refer to the possible clinical applicability of these neuroimaging research findings, especially in the diagnosis of schizophrenia and early intervention for psychosis.

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“…Schizophrenia is a chronic and debilitating mental disorder with a considerable disease burden (Rossler, Salize, van Os, & Riecher‐Rossler, ). Following on from previous postmortem studies (Bogerts, ), there have been efforts to identify anatomical alterations in brains with schizophrenia, particularly in light of advancements in brain imaging techniques (Dietsche, Kircher, & Falkenberg, ; Haijma et al, ; Shepherd, Laurens, Matheson, Carr, & Green, ; van Erp et al, ). Although there have been some significant findings to date on the anatomical alterations in the brains of schizophrenia patients, the pathophysiology of this disorder has not yet been comprehensively explained by these findings.…”

Section: Introductionmentioning

confidence: 99%

Diagnosing schizophrenia with network analysis and a machine learning method

Joo

Shon

et al. 2020

Int J Methods Psych Res

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Objective Schizophrenia is a chronic and debilitating neuropsychiatric disorder. It has been suggested that impaired brain connectivity underlies the pathophysiology of schizophrenia. Network analysis has thus recently emerged in the field of schizophrenia research. Methods We investigated 48 schizophrenia patients and 24 healthy controls using network analysis and a machine learning method. A number of global and nodal network properties were estimated from graphs that were reconstructed using probabilistic brain tractography. These network properties were then compared between groups and used for machine learning to classify schizophrenia patients and healthy controls. Results In classifying schizophrenia patients and healthy controls via network properties, the support vector machine, random forest, naïve Bayes, and gradient boosting machine learning models showed an encouraging level of performance. The overall connectivity was revealed as the most significant contributing feature to this classification among the global network properties. Among the nodal network properties, although the relative importance of each region of interest was not identical, there were still some patterns. Conclusion In conclusion, the possibility exists to classify schizophrenia patients and healthy controls using network properties, and we have found that there is a provisional pattern of involved brain regions among patients with schizophrenia.

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Structural brain changes in schizophrenia at different stages of the illness: A selective review of longitudinal magnetic resonance imaging studies

Cited by 174 publications

References 67 publications

The Reeler Mouse: A Translational Model of Human Neurological Conditions, or Simply a Good Tool for Better Understanding Neurodevelopment?

The Reeler Mouse: A Translational Model of Human Neurological Conditions, or Simply a Good Tool for Better Understanding Neurodevelopment?

Brain morphologic changes in early stages of psychosis: Implications for clinical application and early intervention

Diagnosing schizophrenia with network analysis and a machine learning method

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