What Causes Primary Psychotic Disorders Like Schizophrenia?

Author(s):  
Beth Broussard ◽  
Michael T. Compton

It is believed that a combination of certain genes and a number of early life risk factors probably leads to subtle abnormalities in brain development, which set the stage for the later development of psychosis. It is unlikely that only one gene can cause psychosis. Instead, a number of genes each play a small role in a person’s risk for developing psychosis. The abnormal brain development that happens early in life among people who later develop psychosis is very subtle and usually remains undetected and quiet. School difficulties and social problems may develop during childhood and adolescence. In addition to the subtle abnormalities in early brain development, additional subtle abnormalities may also happen during the important period of adolescent brain maturation. Research suggests that adolescent drug use, especially marijuana use, increases one’s risk of developing psychosis.

2001 ◽  
Vol 35 (3) ◽  
pp. 272-281 ◽  
Author(s):  
Judith L. Rapoport ◽  
Xavier F. Castellanos ◽  
Nitin Gogate ◽  
Kristin Janson ◽  
Shawn Kohler ◽  
...  

Objective: The availability of non-invasive brain imaging permits the study of normal and abnormal brain development in childhood and adolescence. This paper summarizes current knowledge of brain abnormalities of two conditions, attention deficit hyperactivity disorder (ADHD) and childhood onset schizophrenia (COS), and illustrates how such findings are bringing clinical and preclinical perspectives closer together. Method: A selected review is presented of the pattern and temporal characteristics of anatomic brain magnetic resonance imaging (MRI) studies in ADHD and COS. These results are discussed in terms of candidate mechanisms suggested by studies in developmental neuroscience. Results: There are consistent, diagnostically specific patterns of brain abnormality for ADHD and COS. Attention deficit hyperactivity disorder is characterized by a slightly smaller (4%) total brain volume (both white and grey matter), less-consistent abnormalities of the basal ganglia and a striking (15%) decrease in posterior inferior cerebellar vermal volume. These changes do not progress with age. In contrast, patients with COS have smaller brain volume due to a 10% decrease in cortical grey volume. Moreover, in COS there is a progressive loss of regional grey volume particularly in frontal and temporal regions during adolescence. Conclusions: In ADHD, the developmental pattern suggests an early non-progressive ‘lesion’ involving neurotrophic factors controlling overall brain growth and selected dopamine circuits. In contrast, in COS, which shows progressive grey matter loss, various candidate processes influencing later synaptic and dendritic pruning are suggested by human post-mortem and developmental animal studies.


2008 ◽  
Vol 8 (4) ◽  
pp. 334-342 ◽  
Author(s):  
Elizabeth H. Ruder ◽  
Joanne F. Dorgan ◽  
Sibylle Kranz ◽  
Penny M. Kris-Etherton ◽  
Terryl J. Hartman

1993 ◽  
Vol 83 (2) ◽  
pp. 185-189 ◽  
Author(s):  
W A Vega ◽  
R S Zimmerman ◽  
G J Warheit ◽  
E Apospori ◽  
A G Gil

2020 ◽  
Author(s):  
Gareth Ball ◽  
Claire E Kelly ◽  
Richard Beare ◽  
Marc L Seal

AbstractTypical brain development follows a protracted trajectory throughout childhood and adolescence. Deviations from typical growth trajectories have been implicated in neurodevelopmental and psychiatric disorders. Recently, the use of machine learning algorithms to model age as a function of structural or functional brain properties has been used to examine advanced or delayed brain maturation in healthy and clinical populations. Termed ‘brain age’, this approach often relies on complex, nonlinear models that can be difficult to interpret. In this study, we use model explanation methods to examine the cortical features that contribute to brain age modelling on an individual basis.In a large cohort of n=768 typically-developing children (aged 3-21 years), we build models of brain development using three different machine learning approaches. We employ SHAP, a model-agnostic technique to estimate sample-specific feature importance, to identify regional cortical metrics that explain errors in brain age prediction. We find that, on average, brain age prediction and the cortical features that explain model predictions are consistent across model types and reflect previously reported patterns of regional brain development. However, while several regions are found to contribute to brain age prediction, we find little spatial correspondence between individual estimates of feature importance, even when matched for age, sex and brain age prediction error. We also find no association between brain age error and cognitive performance in this typically-developing sample.Overall, this study shows that, while brain age estimates based on cortical development are relatively robust and consistent across model types and preprocessing strategies, significant between-subject variation exists in the features that explain erroneous brain age predictions on an individual level.


2020 ◽  
Vol 49 (2) ◽  
pp. 208-217 ◽  
Author(s):  
Yaxi Li ◽  
Qian-Li Xue ◽  
Michelle C Odden ◽  
Xi Chen ◽  
Chenkai Wu

Abstract Background exposures in childhood and adolescence may impact the development of diseases and symptoms in late life. However, evidence from low- and middle-income countries is scarce. In this cross-sectional study, we examined the association of early life risk factors with frailty amongst older adults using a large, nationally representative cohort of community-dwelling Chinese sample. Methods we included 6,806 participants aged $\ge$60 years from the China Health and Retirement Longitudinal Study. We measured 13 risk factors in childhood or adolescence through self-reports, encompassing six dimensions (education, family economic status, nutritional status, domestic violence, neighbourhood and health). We used multinomial regression models to examine the association between risk factors and frailty. We further calculated the absolute risk difference for the statistically significant factors. Results persons with higher personal and paternal education attainment, better childhood neighbourhood quality and better childhood health status had lower risk of being frail in old age. Severe starvation in childhood was associated with higher risk of prefrailty. The risk differences of being frail were 5.6% lower for persons with a high school or above education, 1.5% lower for those whose fathers were literate, 4.8% lower for the highest neighbourhood quality and 2.9% higher for worse childhood health status compared to their counterparts. Conclusions unfavorable socioeconomic status and worse health condition in childhood and adolescence may increase the risk of late-life frailty amongst Chinese older adults.


2020 ◽  
Vol 46 (Supplement_1) ◽  
pp. S262-S263
Author(s):  
Agnes Steixner-Kumar ◽  
Jan Seidel ◽  
Vinicius Daguano Gastaldi ◽  
Martin Begemann ◽  
Hannelore Ehrenreich

Abstract Background Drug (ab)use and substance use disorders are frequently observed in patients with psychiatric illness, but the underlying causes remain widely unknown. A number of environmental risk factors have been proposed to affect the use of one or multiple drugs in the general population and adolescents. Whereas most previous studies focused on the influence of single risk factors on the use of one or a few selected drugs, the effect of accumulated environmental risk in early life on multiple drug use remains to be studied. Similarly, evidence on genetic susceptibility to the (ab)use of a single drug, e.g. nicotine, alcohol, cocaine, is abundant, while the role of genetic predisposition for multiple drug use - in particular during early life - is yet to be explored. Thus, the current work aims to study the role of environmental as well as genetic risk factors for multiple drug abuse (‘polytoxicomania’) in a large sample of schizophrenic/schizoaffective patients. Methods Information from ~2000 schizophrenia/schizoaffective patients on (preadult) multiple drug use (> 2 drugs) and environmental risk factors was extracted from the Göttingen Research Association for Schizophrenia (GRAS) data collection – currently the largest data base of deeply phenotyped patients with schizophrenia/schizoaffective disorder or other neuropsychiatric diseases. In addition, genetic data from these patients and 2111 healthy blood donors were used in a novel genetic approach that employs multiple genome-wide association studies (GWAS) to identify genetic associations with preadult multiple drug use. Genotyping was performed on a semi-custom Axiom MyDesign Genotyping Array (Affymetrix, Santa Clara, CA, USA), based on a CEU (Caucasian residents of European ancestry from UT, USA) marker backbone. Results The accumulation of environmental risk factors, i.e. sexual abuse, physical abuse, migration, urbanicity, together with alcohol and cannabis consumption as secondary risk factors, in early life (< 18 years) were strongly associated with lifetime multiple drug use (p = 3.48 x 10^-44, extreme group comparison odds ratio (OR) = 31.8). When the sample was split into preadult and adult multiple drug users, there was a remarkable association of the number of preadult environmental risk factors with preadult multiple drug use (p = 1.12 x 10^-25, OR = 243.6). Furthermore, preadult environmental risk accumulation strongly predicted onset of multiple drug use in adulthood (> 18 years; p = 6.27 x 10^-18, OR = 19.4). The application of the novel genetic approach yielded 35 single-nucleotide variants (SNPs) that potentially confer susceptibility to preadult multiple drug use. Out of these, 14 were located in gene-coding regions. Interestingly, 9 of these genes are implicated in neuronal development/function or metabolite transport/transformation. Additional gene-based analyses identified another 4 genes relevant for metabolite transport/transformation as well as 4 genes that play a role in hypoxia signaling. Discussion The present results show that an accumulation of environmental risk factors during early life (< 18 years) is a strong predictor of multiple drug use during adolescence and later life. These findings suggest that exposure to accumulated environmental risk during early life is not only associated with violent aggression – as previously reported by our lab – but is also an important predictor of multiple drug use. Moreover, we present first evidence of a genetic susceptibility to preadult multiple drug use, which will benefit from future replication in suitable samples of patients with mental illness or the general population.


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