Cumulative effect of anatomical risk factors for schizophrenia: an MRI study

Leonard, Christiana M.; Kuldau, John M.; Breier, Joshua I.; Zuffante, Paula; Gautier, Erin R.; Heron, D.; Lavery, Elise M.; Packing, Jennifer; Williams, Sharyl; Debose, Cheryl A.

doi:10.1016/s0006-3223(99)00052-9

Cited by 87 publications

(40 citation statements)

References 40 publications

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“…Similar situations prevail for behavioural disorders such as dyslexia and autism. In a recent paper, Leonard et al (1999), employed a novel approach for using MRI to identify patients with schizophrenia. Rather than utilizing a single measure (e.g.…”

Section: (B) Probabilisticmentioning

confidence: 99%

“…However, the groups were not balanced for ethnic background or intelligence quotient (IQ)öand in fact, the control group had signi¢cantly higher verbal and performance IQs than the schizophrenic patients. Using their derived discriminant function, Leonard et al (1999) were able to classify 79% of the controls and 76% of the schizophrenic patients correctly into the proper diagnostic category. Furthermore, they found that these 10 anatomical measures accounted for a signi¢cant part of the variance in measured full scale IQ in the schizophrenics but not in the normal controls.…”

Section: (B) Probabilisticmentioning

confidence: 99%

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A probabilistic atlas and reference system for the human brain: International Consortium for Brain Mapping (ICBM)

Mazziotta

Toga²,

Evans

et al. 2001

Phil. Trans. R. Soc. Lond. B

2,325

1,753

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Motivated by the vast amount of information that is rapidly accumulating about the human brain in digital form, we embarked upon a program in 1992 to develop a four-dimensional probabilistic atlas and reference system for the human brain. Through an International Consortium for Brain Mapping (ICBM) a dataset is being collected that includes 7000 subjects between the ages of eighteen and ninety years and including 342 mono-and dizygotic twins. Data on each subject includes detailed demographic, clinical, behavioural and imaging information. DNA has been collected for genotyping from 5800 subjects. A component of the programme uses post-mortem tissue to determine the probabilistic distribution of microscopic cyto-and chemoarchitectural regions in the human brain. This, combined with macroscopic information about structure and function derived from subjects in vivo, provides the ¢rst large scale opportunity to gain meaningful insights into the concordance or discordance in micro-and macroscopic structure and function. The philosophy, strategy, algorithm development, data acquisition techniques and validation methods are described in this report along with database structures. Examples of results are described for the normal adult human brain as well as examples in patients with Alzheimer's disease and multiple sclerosis. The ability to quantify the variance of the human brain as a function of age in a large population of subjects for whom data is also available about their genetic composition and behaviour will allow for the ¢rst assessment of cerebral genotype^phenotype^behavioural correlations in humans to take place in a population this large. This approach and its application should provide new insights and opportunities for investigators interested in basic neuroscience, clinical diagnostics and the evaluation of neuropsychiatric disorders in patients.

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Section: (B) Probabilisticmentioning

confidence: 99%

Section: (B) Probabilisticmentioning

confidence: 99%

A probabilistic atlas and reference system for the human brain: International Consortium for Brain Mapping (ICBM)

Mazziotta

Toga²,

Evans

et al. 2001

Phil. Trans. R. Soc. Lond. B

2,325

1,753

View full text Add to dashboard Cite

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“…It seems worth investigating to which extent this PS paradigm resembles the various other alterations due to PS discussed here, and to what extent these alterations can also be seen in human depression. In other words, it is worth investigating this PS paradigm in a 'neural systems' approach [127] because the risk of developing neuropsychiatric disorders most probably depends on the total amount of neural alterations in the brain, rather than on anomalies in a single structure or circuit [128].…”

Section: Possible Impact Of Alterations Of the Hippocampal Morphologymentioning

confidence: 99%

Prenatal Programming of Depression and Schizophrenia?

Dafotakis¹,

Vehoff²,

Korr³

et al. 2002

Neuroembryol Aging

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In recent years the concept has been explored that the pathologies of depression and schizophrenia originate during intrauterine life. Several animal models have been developed to investigate the etiopathogenesis of both depression and schizophrenia. However, most of these models have focussed only on single aspects of the corresponding pathology, whereas integrated models using a ‘neural systems’ approach are missing. Over recent last years, our laboratory has developed two novel animal models related to depression and schizophrenia based on such a neural systems approach, using prenatal stress (PS) and prenatal low-dose X-irradiation (PLDI) as damaging agents. This paper provides a comprehensive overview on the cellular and molecular mechanisms mediating the damaging influence of PS and PLDI on the developing brain, and discuss these novel animal models in the context of the relevant literature. Both models provide rationales for future prophylactic interventions in perinatal and adult life, developed on the basis of a better understanding of the underlying mechanisms.

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“…The most commonly used classification methods have been the linear discriminant analysis (LDA) [6]- [10], support vector machines (SVM) [11]- [13] or the k-nearest neighbour algorithm [14]- [16]. Due to the large amount of features obtained from 3-D medical images, the classification is often preceded by data reduction performed by principal component analysis [17], independent component analysis [18], selection of regions of interests (ROI) [6]- [8], and other data reduction methods. So far, only few studies have presented complex pipelines for data reduction and classification, such as the COMPARE method [19], which combines deformation-based morphometry with machine learning methods (watershed segmentation algorithm and support vector machine-recursive feature elimination technique).…”

Section: Introductionmentioning

confidence: 99%

Brain Image Classification Based on Automated Morphometry and Penalised Linear Discriminant Analysis with Resampling

Janoušová

Schwarz

Montana

et al. 2015

Annals of Computer Science and Information Systems

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Abstract-This paper presents a new data-driven classification pipeline for discriminating two groups of individuals based on the medical images of their brain. The algorithm combines deformation-based morphometry and penalised linear discriminant analysis with resampling. The method is based on sparse representation of the original brain images using deformation logarithms reflecting the differences in the brain in comparison to the normal template anatomy. The sparse data enables efficient data reduction and classification via the penalised linear discriminant analysis with resampling. The classification accuracy obtained in an experiment with magnetic resonance brain images of first episode schizophrenia patients and healthy controls is comparable to the related state-of-the-art studies.

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Cumulative effect of anatomical risk factors for schizophrenia: an MRI study

Cited by 87 publications

References 40 publications

A probabilistic atlas and reference system for the human brain: International Consortium for Brain Mapping (ICBM)

A probabilistic atlas and reference system for the human brain: International Consortium for Brain Mapping (ICBM)

Prenatal Programming of Depression and Schizophrenia?

Brain Image Classification Based on Automated Morphometry and Penalised Linear Discriminant Analysis with Resampling

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