scholarly journals Dysregulated Spliceosome Gene Expression May Be a Common Process in Brains of Neurological and Psychiatric Disorders

2021 ◽  
Author(s):  
Cuihua Xia ◽  
Rujia Dai ◽  
Jing Yu ◽  
Chunling Zhang ◽  
Ma-li Wong ◽  
...  
Keyword(s):  
Psychiatric Disorders ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Mrna Splicing ◽  
Splicing Factors ◽  
Brain Disorders ◽  
Major Depressive ◽  
Transcriptome Database ◽  
Common Process ◽  
The Brain

Abstract Alternative splicing (AS) contributes to the increased cellular and functional tissue complexity that is substantial in the brain. AS is tightly regulated because it is critical to many biological processes. Defective splicing is observed in several neurological and psychiatric disorders. While exonic mutations usually affect the splicing of an individual RNA, mutations in the splicing factors (components of spliceosome) frequently produce widespread disruption in the processing of many precursor-mRNAs. Thus, we tested the hypotheses that expression changes of spliceosome genes may be a common process and shared splicing pathways may be involved in complex polygenic brain disorders. We searched for expression changes of spliceosome-related genes (SGs) using a transcriptome database of several brain regions in 6 neurological and psychiatric disorders, namely Alzheimer’s disease, and autism spectrum, bipolar and major depressive disorder, Parkinson’s disease, and schizophrenia. Out of 255 SGs detected in brain, 138 showed excessive, significant changes in one or more of these disorders. Dysregulation of 10 SGs was shared in 4 disorders, and they were mostly downregulated. Six associated pathways were over-represented in all 6 disorders, including the major and the minor mRNA splicing pathways and RNA metabolism. Therefore, we found that aberrations in the mRNA splicing process may be a common trajectory to many complex brain disorders involving the spliceosome complex.

scholarly journals The Development of a Practical Artificial Intelligence Tool for Diagnosing and Evaluating Autism Spectrum Disorder: Multicenter Study (Preprint)

2019 ◽  
Author(s):  
Tao Chen ◽  
Ye Chen ◽  
Mengxue Yuan ◽  
Mark Gerstein ◽  
Tingyu Li ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Autism Spectrum Disorder ◽  
Psychiatric Disorders ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Unknown Etiology ◽  
Healthy Controls ◽  
Anatomical Changes ◽  
The Brain

BACKGROUND Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder with an unknown etiology. Early diagnosis and intervention are key to improving outcomes for patients with ASD. Structural magnetic resonance imaging (sMRI) has been widely used in clinics to facilitate the diagnosis of brain diseases such as brain tumors. However, sMRI is less frequently used to investigate neurological and psychiatric disorders, such as ASD, owing to the subtle, if any, anatomical changes of the brain. OBJECTIVE This study aimed to investigate the possibility of identifying structural patterns in the brain of patients with ASD as potential biomarkers in the diagnosis and evaluation of ASD in clinics. METHODS We developed a novel 2-level histogram-based morphometry (HBM) classification framework in which an algorithm based on a 3D version of the histogram of oriented gradients (HOG) was used to extract features from sMRI data. We applied this framework to distinguish patients with ASD from healthy controls using 4 datasets from the second edition of the Autism Brain Imaging Data Exchange, including the ETH Zürich (ETH), NYU Langone Medical Center: Sample 1, Oregon Health and Science University, and Stanford University (SU) sites. We used a stratified 10-fold cross-validation method to evaluate the model performance, and we applied the Naive Bayes approach to identify the predictive ASD-related brain regions based on classification contributions of each HOG feature. RESULTS On the basis of the 3D HOG feature extraction method, our proposed HBM framework achieved an area under the curve (AUC) of >0.75 in each dataset, with the highest AUC of 0.849 in the ETH site. We compared the 3D HOG algorithm with the original 2D HOG algorithm, which showed an accuracy improvement of >4% in each dataset, with the highest improvement of 14% (6/42) in the SU site. A comparison of the 3D HOG algorithm with the scale-invariant feature transform algorithm showed an AUC improvement of >18% in each dataset. Furthermore, we identified ASD-related brain regions based on the sMRI images. Some of these regions (eg, frontal gyrus, temporal gyrus, cingulate gyrus, postcentral gyrus, precuneus, caudate, and hippocampus) are known to be implicated in ASD in prior neuroimaging literature. We also identified less well-known regions that may play unrecognized roles in ASD and be worth further investigation. CONCLUSIONS Our research suggested that it is possible to identify neuroimaging biomarkers that can distinguish patients with ASD from healthy controls based on the more cost-effective sMRI images of the brain. We also demonstrated the potential of applying data-driven artificial intelligence technology in the clinical setting of neurological and psychiatric disorders, which usually harbor subtle anatomical changes in the brain that are often invisible to the human eye.

scholarly journals The Development of a Practical Artificial Intelligence Tool for Diagnosing and Evaluating Autism Spectrum Disorder: Multicenter Study

10.2196/15767 ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. e15767 ◽  
Author(s):  
Tao Chen ◽  
Ye Chen ◽  
Mengxue Yuan ◽  
Mark Gerstein ◽  
Tingyu Li ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Autism Spectrum Disorder ◽  
Psychiatric Disorders ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Unknown Etiology ◽  
Healthy Controls ◽  
Anatomical Changes ◽  
The Brain

Background Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder with an unknown etiology. Early diagnosis and intervention are key to improving outcomes for patients with ASD. Structural magnetic resonance imaging (sMRI) has been widely used in clinics to facilitate the diagnosis of brain diseases such as brain tumors. However, sMRI is less frequently used to investigate neurological and psychiatric disorders, such as ASD, owing to the subtle, if any, anatomical changes of the brain. Objective This study aimed to investigate the possibility of identifying structural patterns in the brain of patients with ASD as potential biomarkers in the diagnosis and evaluation of ASD in clinics. Methods We developed a novel 2-level histogram-based morphometry (HBM) classification framework in which an algorithm based on a 3D version of the histogram of oriented gradients (HOG) was used to extract features from sMRI data. We applied this framework to distinguish patients with ASD from healthy controls using 4 datasets from the second edition of the Autism Brain Imaging Data Exchange, including the ETH Zürich (ETH), NYU Langone Medical Center: Sample 1, Oregon Health and Science University, and Stanford University (SU) sites. We used a stratified 10-fold cross-validation method to evaluate the model performance, and we applied the Naive Bayes approach to identify the predictive ASD-related brain regions based on classification contributions of each HOG feature. Results On the basis of the 3D HOG feature extraction method, our proposed HBM framework achieved an area under the curve (AUC) of >0.75 in each dataset, with the highest AUC of 0.849 in the ETH site. We compared the 3D HOG algorithm with the original 2D HOG algorithm, which showed an accuracy improvement of >4% in each dataset, with the highest improvement of 14% (6/42) in the SU site. A comparison of the 3D HOG algorithm with the scale-invariant feature transform algorithm showed an AUC improvement of >18% in each dataset. Furthermore, we identified ASD-related brain regions based on the sMRI images. Some of these regions (eg, frontal gyrus, temporal gyrus, cingulate gyrus, postcentral gyrus, precuneus, caudate, and hippocampus) are known to be implicated in ASD in prior neuroimaging literature. We also identified less well-known regions that may play unrecognized roles in ASD and be worth further investigation. Conclusions Our research suggested that it is possible to identify neuroimaging biomarkers that can distinguish patients with ASD from healthy controls based on the more cost-effective sMRI images of the brain. We also demonstrated the potential of applying data-driven artificial intelligence technology in the clinical setting of neurological and psychiatric disorders, which usually harbor subtle anatomical changes in the brain that are often invisible to the human eye.

scholarly journals Mimicry is associated with procedural learning, not social bonding, neural systems in autism

2020 ◽  
Author(s):  
Bahar Tunçgenç ◽  
Carolyn Koch ◽  
Amira Herstic ◽  
Inge-Marie Eigsti ◽  
Stewart Mostofsky
Keyword(s):  
Social Bonding ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Skill Learning ◽  
Autism Spectrum Conditions ◽  
Neural Systems ◽  
Procedural Skill ◽  
Communication Difficulties ◽  
Learning Functions ◽  
The Brain

AbstractMimicry facilitates social bonding throughout the lifespan. Mimicry impairments in autism spectrum conditions (ASC) are widely reported, including differentiation of the brain networks associated with its social bonding and learning functions. This study examined associations between volumes of brain regions associated with social bonding versus procedural skill learning, and mimicry of gestures during a naturalistic interaction in ASC and neurotypical (NT) children. Consistent with predictions, results revealed reduced mimicry in ASC relative to the NT children. Mimicry frequency was negatively associated with autism symptom severity. Mimicry was predicted predominantly by the volume of procedural skill learning regions in ASC, and by bonding regions in NT. Further, bonding regions contributed significantly less to mimicry in ASC than in NT, while the contribution of learning regions was not different across groups. These findings suggest that associating mimicry with skill learning, rather than social bonding, may partially explain observed communication difficulties in ASC.

scholarly journals Graph Ricci Curvatures Reveal Disease-related Changes in Autism Spectrum Disorder

2021 ◽  
Author(s):  
Pavithra Elumalai ◽  
Yasharth Yadav ◽  
Nitin Williams ◽  
Emil Saucan ◽  
Jürgen Jost ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Resting State ◽  
Neurodevelopmental Disorders ◽  
Data Exchange ◽  
Meta Analysis ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
The Brain

Autism Spectrum Disorder (ASD) is a set of neurodevelopmental disorders that pose a significant global health burden. Measures from graph theory have been used to characterise ASD-related changes in resting-state fMRI functional connectivity networks (FCNs), but recently developed geometry-inspired measures have not been applied so far. In this study, we applied geometry-inspired graph Ricci curvatures to investigate ASD-related changes in resting-state fMRI FCNs. To do this, we applied Forman-Ricci and Ollivier-Ricci curvatures to compare networks of ASD and healthy controls (N = 1112) from the Autism Brain Imaging Data Exchange I (ABIDE-I) dataset. We performed these comparisons at the brain-wide level as well as at the level of individual brain regions, and further, determined the behavioral relevance of region-specific differences with Neurosynth meta-analysis decoding. We found brain-wide ASD-related differences for both Forman-Ricci and Ollivier-Ricci curvatures. For Forman-Ricci curvature, these differences were distributed across 83 of the 200 brain regions studied, and concentrated within the Default Mode, Somatomotor and Ventral Attention Network. Meta-analysis decoding identified the brain regions showing curvature differences as involved in social cognition, memory, language and movement. Notably, comparison with results from previous non-invasive stimulation (TMS/tDCS) experiments revealed that the set of brain regions showing curvature differences overlapped with the set of brain regions whose stimulation resulted in positive cognitive or behavioural outcomes in ASD patients. These results underscore the utility of geometry-inspired graph Ricci curvatures in characterising disease-related changes in ASD, and possibly, other neurodevelopmental disorders.

Executive Functions in Adolescents with Autism Spectrum Disorder

2017 ◽  
pp. 67-90
Author(s):  
Yael Dai ◽  
Inge-Marie Eigsti
Keyword(s):  
Autism Spectrum Disorder ◽  
Working Memory ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Clinical Implications ◽  
Future Directions ◽  
Adolescents With Autism ◽  
Memory Flexibility ◽  
The Brain

This chapter reviews strengths and weaknesses in executive function (EF) domains, including inhibition, working memory, flexibility, fluency, and planning, in adolescents (age 13–19) with autism spectrum disorder (ASD). Given the dramatic developmental changes in the brain regions that support EF during the period of adolescence, it is critical to evaluate which EF abilities show a distinct profile during this period. As this chapter will demonstrate, youth with ASD show deficits across all domains of EF, particularly in complex tasks that include arbitrary instructions. We describe the fundamental measures for assessing skills in each domain and discuss limitations and future directions for research, as well as clinical implications of these findings for working with youth with ASD.

scholarly journals Analysis of shared heritability in common disorders of the brain

Science ◽  
2018 ◽  
Vol 360 (6395) ◽  
pp. eaap8757 ◽  
Author(s):  
◽  
Verneri Anttila ◽  
Brendan Bulik-Sullivan ◽  
Hilary K. Finucane ◽  
Raymond K. Walters ◽  
...  
Keyword(s):  
Psychiatric Disorders ◽  
Neurological Disorders ◽  
Statistical Power ◽  
Association Studies ◽  
Genetic Correlations ◽  
Genome Wide Association Studies ◽  
Brain Disorders ◽  
Cognitive Measures ◽  
Genome Wide ◽  
The Brain

Disorders of the brain can exhibit considerable epidemiological comorbidity and often share symptoms, provoking debate about their etiologic overlap. We quantified the genetic sharing of 25 brain disorders from genome-wide association studies of 265,218 patients and 784,643 control participants and assessed their relationship to 17 phenotypes from 1,191,588 individuals. Psychiatric disorders share common variant risk, whereas neurological disorders appear more distinct from one another and from the psychiatric disorders. We also identified significant sharing between disorders and a number of brain phenotypes, including cognitive measures. Further, we conducted simulations to explore how statistical power, diagnostic misclassification, and phenotypic heterogeneity affect genetic correlations. These results highlight the importance of common genetic variation as a risk factor for brain disorders and the value of heritability-based methods in understanding their etiology.

scholarly journals Clustering of Multiple Psychiatric Disorders Using Functional Connectivity in the Data-Driven Brain Subnetwork

2021 ◽  
Vol 12 ◽  
Author(s):  
Tomoki Tokuda ◽  
Okito Yamashita ◽  
Yuki Sakai ◽  
Junichiro Yoshimoto
Keyword(s):  
Functional Connectivity ◽  
Psychiatric Disorders ◽  
Autism Spectrum ◽  
Data Driven ◽  
New Paradigm ◽  
Resting State Functional Connectivity ◽  
Major Depressive ◽  
Label Information ◽  
Healthy Control ◽  
Diagnostic Label

Recently, the dimensional approach has attracted much attention, bringing a paradigm shift to a continuum of understanding of different psychiatric disorders. In line with this new paradigm, we examined whether there was common functional connectivity related to various psychiatric disorders in an unsupervised manner without explicitly using diagnostic label information. To this end, we uniquely applied a newly developed network-based multiple clustering method to resting-state functional connectivity data, which allowed us to identify pairs of relevant brain subnetworks and subject cluster solutions accordingly. Thus, we identified four subject clusters, which were characterized as major depressive disorder (MDD), young healthy control (young HC), schizophrenia (SCZ)/bipolar disorder (BD), and autism spectrum disorder (ASD), respectively, with the relevant brain subnetwork represented by the cerebellum-thalamus-pallidum-temporal circuit. The clustering results were validated using independent datasets. This study is the first cross-disorder analysis in the framework of unsupervised learning of functional connectivity based on a data-driven brain subnetwork.

scholarly journals Regulation of neuronal mRNA splicing and Tau isoform ratio by ATXN3 through deubiquitylation of splicing factors

2019 ◽  
Author(s):  
Andreia Neves-Carvalho ◽  
Sara Duarte-Silva ◽  
Joana Silva ◽  
Bruno Almeida ◽  
Sasja Heetveld ◽  
...  
Keyword(s):  
Neurodegenerative Disorders ◽  
Rna Metabolism ◽  
Mrna Splicing ◽  
Splicing Factors ◽  
Loss Of Function ◽  
Relevant Role ◽  
The Brain ◽  
Precise Role ◽  
Exon 10

ABSTRACTThe ubiquitylation/deubiquitylation balance in cells is maintained by Deubiquitylating enzymes, including ATXN3. The precise role of this protein, mutated in SCA3, remains elusive, as few substrates for its deubiquitylating activity were identified. Therefore, we characterized the ubiquitome of neuronal cells lacking ATXN3, and found altered polyubiquitylation in a large proportion of proteins involved in RNA metabolism, including splicing factors. Using transcriptomic analysis and reporter minigenes we confirmed that splicing was globally altered in these cells. Among the targets with altered splicing was SRSF7 (9G8), a key regulator of MAPT (Tau) exon 10 splicing. Loss-of-function of ATXN3 led to a deregulation of MAPT exon 10 splicing resulting in a decreased 4R/3R-Tau ratio. Similar alterations were found in the brain of a SCA3 mouse and humans, pointing to a relevant role of this mechanism in SCA3, and establishing a previously unsuspected link between two key proteins involved in different neurodegenerative disorders.

How Stress Physically Re-shapes the Brain: Impact on Brain Cell Shapes, Numbers and Connections in Psychiatric Disorders

2019 ◽  
Author(s):  
Dominic Kaul ◽  
Sibylle Schwab ◽  
Naguib Mechawar ◽  
Natalie Matosin
Keyword(s):  
Psychiatric Disorders ◽  
Molecular Mechanisms ◽  
Brain Regions ◽  
Brain Cell ◽  
Intervention Strategies ◽  
Traumatic Experiences ◽  
Brain Pathology ◽  
Cell Shapes ◽  
Brain Circuitry ◽  
The Brain

Exposure to stressful or traumatic experiences is one of the most robust risk factors for severe psychiatric disorders and has been shown to reshape entire brain regions, especially those involved in processing the stress response. This is likely underpinned by alterations to brain cell shapes, numbers and their connections, thus changing brain circuitry to enable coping with the current and future stress. In this review, we present a model for how stress re-shapes the brain, consolidating evidence of morphometric changes and the cellular and molecular mechanisms that underlie them. We illustrate how the temporal effects of stress can cause persistent remodelling of brain cells, highlighting that an individual's stress history is important for understanding psychiatric disorder risk and development. Understanding how stress re-shapes the brain is a critical step for understanding stress as a risk factor for brain pathology, and to develop appropriate biomarkers, treatments and intervention strategies.

scholarly journals Correlations Among mRNA Expression Levels of ATP7A, Serum Ceruloplasmin Levels, and Neuronal Metabolism in Unmedicated Major Depressive Disorder

2020 ◽  
Vol 23 (10) ◽  
pp. 642-652 ◽  
Author(s):  
Xuanjun Liu ◽  
Shuming Zhong ◽  
Lan Yan ◽  
Hui Zhao ◽  
Ying Wang ◽  
...  
Keyword(s):  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Mrna Expression ◽  
Brain Regions ◽  
Regions Of Interest ◽  
Major Depressive ◽  
Expression Levels ◽  
Neuronal Metabolism ◽  
The Brain ◽  
Mrna Expression Levels

Abstract Background Previous studies have found that elevated copper levels induce oxidation, which correlates with the occurrence of major depressive disorder (MDD). However, the mechanism of abnormal cerebral metabolism of MDD patients remains ambiguous. The main function of the enzyme ATPase copper-transporting alpha (ATP7A) is to transport copper across the membrane to retain copper homeostasis, which is closely associated with the onset of mental disorders and cognitive impairment. However, less is known regarding the association of ATP7A expression in MDD patients. Methods A total of 31 MDD patients and 21 healthy controls were recruited in the present study. Proton magnetic resonance spectroscopy was used to assess the concentration levels of N-acetylaspartate, choline (Cho), and creatine (Cr) in brain regions of interest, including prefrontal white matter (PWM), anterior cingulate cortex (ACC), thalamus, lentiform nucleus, and cerebellum. The mRNA expression levels of ATP7A were measured using polymerase chain reaction (SYBR Green method). The correlations between mRNA expression levels of ATP7A and/or ceruloplasmin levels and neuronal biochemical metabolite ratio in the brain regions of interest were evaluated. Results The decline in the mRNA expression levels of ATP7A and the increase in ceruloplasmin levels exhibited a significant correlation in MDD patients. In addition, negative correlations were noted between the decline in mRNA expression levels of ATP7A and the increased Cho/Cr ratios of the left PWM, right PWM, and right ACC in MDD patients. A positive correlation between elevated ceruloplasmin levels and increased Cho/Cr ratio of the left PWM was noted in MDD patients. Conclusions The findings suggested that the decline in the mRNA expression levels of ATP7A and the elevated ceruloplasmin levels induced oxidation that led to the disturbance of neuronal metabolism in the brain, which played important roles in the pathophysiology of MDD. The decline in the mRNA expression levels of ATP7A and the elevated ceruloplasmin levels affected neuronal membrane metabolic impairment in the left PWM, right PWM, and right ACC of MDD patients.

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