Dataset Information

Incomplete hippocampal inversion in schizophrenia: prevalence, severity, and impact on hippocampal structure.

ABSTRACT: Incomplete hippocampal inversion (IHI) is an anatomical variant of the human brain resulting from an arrest in brain development, especially prevalent in the left hemisphere. We hypothesized that IHI is more common in schizophrenia and contributes to the well-known hippocampal structural differences. We studied 199 schizophrenia patients and 161 healthy control participants with 3 T MRI to establish IHI prevalence and the relationship of IHI with hippocampal volume and asymmetry. IHI was more prevalent (left hemisphere: 15% of healthy control participants, 27% of schizophrenia patients; right hemisphere: 4% of healthy control participants, 10% of schizophrenia patients) and more severe in schizophrenia patients compared to healthy control participants. Severe IHI cases were associated with a higher rate of automated segmentation failure. IHI contributed to smaller hippocampal volume and increased R > L volume asymmetry in schizophrenia. The increased prevalence and severity of IHI supports the neurodevelopmental model of schizophrenia. The impact of this developmental variant deserves further exploration in studies of the hippocampus in schizophrenia.

SUBMITTER: Roeske MJ

PROVIDER: S-EPMC8589684 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

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Incomplete hippocampal inversion in schizophrenia: prevalence, severity, and impact on hippocampal structure.

Roeske Maxwell J MJ McHugo Maureen M Vandekar Simon S Blackford Jennifer Urbano JU Woodward Neil D ND Heckers Stephan S

Molecular psychiatry 20210112 9

Incomplete hippocampal inversion (IHI) is an anatomical variant of the human brain resulting from an arrest in brain development, especially prevalent in the left hemisphere. We hypothesized that IHI is more common in schizophrenia and contributes to the well-known hippocampal structural differences. We studied 199 schizophrenia patients and 161 healthy control participants with 3 T MRI to establish IHI prevalence and the relationship of IHI with hippocampal volume and asymmetry. IHI was more pr ...[more]

PMID: 33437006

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Project description:Incomplete Hippocampal Inversion (IHI), sometimes called hippocampal malrotation, is an atypical anatomical pattern of the hippocampus found in about 20% of the general population. IHI can be visually assessed on coronal slices of T1 weighted MR images, using a composite score that combines four anatomical criteria. IHI has been associated with several brain disorders (epilepsy, schizophrenia). However, these studies were based on small samples. Furthermore, the factors (genetic or environmental) that contribute to the genesis of IHI are largely unknown. Large-scale studies are thus needed to further understand IHI and their potential relationships to neurological and psychiatric disorders. However, visual evaluation is long and tedious, justifying the need for an automatic method. In this paper, we propose, for the first time, to automatically rate IHI. We proceed by predicting four anatomical criteria, which are then summed up to form the IHI score, providing the advantage of an interpretable score. We provided an extensive experimental investigation of different machine learning methods and training strategies. We performed automatic rating using a variety of deep learning models ("conv5-FC3", ResNet and "SECNN") as well as a ridge regression. We studied the generalization of our models using different cohorts and performed multi-cohort learning. We relied on a large population of 2,008 participants from the IMAGEN study, 993 and 403 participants from the QTIM and QTAB studies as well as 985 subjects from the UKBiobank. We showed that deep learning models outperformed a ridge regression. We demonstrated that the performances of the "conv5-FC3" network were at least as good as more complex networks while maintaining a low complexity and computation time. We showed that training on a single cohort may lack in variability while training on several cohorts improves generalization (acceptable performances on all tested cohorts including some that are not included in training). The trained models are available at https://github.com/LisaHemforth/AutomaticIHIRating.

Project description:CONTEXT The single-nucleotide polymorphism rs1344706 in the gene ZNF804a has been associated with schizophrenia and with quantitative phenotypic features, including brain structure volume and the core symptoms of schizophrenia. OBJECTIVE To evaluate associations of rs1344706 with brain structure and the core symptoms of schizophrenia. DESIGN Case-control analysis of covariance. SETTING University-based research hospital. PARTICIPANTS Volunteer sample of 335 individuals with schizophrenia spectrum disorders (306 with core schizophrenia) and 198 healthy volunteers. MAIN OUTCOME MEASURES Cerebral cortical gray matter and white matter (WM) volumes (total and frontal, parietal, temporal, and occipital lobes), lateral ventricular cerebrospinal fluid volume, and symptom severity from the Scale for the Assessment of Negative Symptoms and the Scale for the Assessment of Positive Symptoms divided into 3 domains: psychotic, negative, and disorganized. RESULTS The rs1344706 genotype produced significant main effects on total, frontal, and parietal lobe WM volumes (F = 3.98, P = .02; F = 4.95, P = .007; and F = 3.08, P = .05, respectively). In the schizophrenia group, rs1344706 produced significant simple effects on total (F = 3.93, P = .02) and frontal WM volumes (F = 7.16, P < .001) and on psychotic symptom severity (F = 6.07, P = .003); the pattern of effects was concordant with risk allele carriers having larger volumes and more severe symptoms of disease than nonrisk homozygotes. In the healthy volunteer group, risk allele homozygotes had increased total WM volume compared with nonrisk allele carriers (F = 4.61, P = .03), replicating a previously reported association. CONCLUSIONS A growing body of evidence suggests that the risk allele of rs1347706 is associated with a distinctive set of phenotypic features in healthy volunteers and individuals with schizophrenia. Our study supports this assertion by finding that specific genotypes of the polymorphism are associated with brain structure volumes in individuals with schizophrenia and healthy volunteers and with symptom severity in schizophrenia.

Dataset Information

Incomplete hippocampal inversion in schizophrenia: prevalence, severity, and impact on hippocampal structure.

Publications

Incomplete hippocampal inversion in schizophrenia: prevalence, severity, and impact on hippocampal structure.

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