scholarly journals Deep Multimodal Learning for the Diagnosis of Autism Spectrum Disorder

2020 ◽  
Vol 6 (6) ◽  
pp. 47
Author(s):  
Michelle Tang ◽  
Pulkit Kumar ◽  
Hao Chen ◽  
Abhinav Shrivastava
Keyword(s):  
Autism Spectrum ◽  
Disease Classification ◽  
Multimodal Learning ◽  
Training Strategy ◽  
Current State ◽  
Comprehensive Picture ◽  
Overall Performance ◽  
Multimodal Training ◽  
The Brain ◽  
Joint Representation

Recent medical imaging technologies, specifically functional magnetic resonance imaging (fMRI), have advanced the diagnosis of neurological and neurodevelopmental disorders by allowing scientists and physicians to observe the activity within and between different regions of the brain. Deep learning methods have frequently been implemented to analyze images produced by such technologies and perform disease classification tasks; however, current state-of-the-art approaches do not take advantage of all the information offered by fMRI scans. In this paper, we propose a deep multimodal model that learns a joint representation from two types of connectomic data offered by fMRI scans. Incorporating two functional imaging modalities in an automated end-to-end autism diagnosis system will offer a more comprehensive picture of the neural activity, and thus allow for more accurate diagnoses. Our multimodal training strategy achieves a classification accuracy of 74% and a recall of 95%, as well as an F1 score of 0.805, and its overall performance is superior to using only one type of functional data.

From many to one: Meditation and the plasticity of the predictive mind

2020 ◽  
Author(s):  
Ruben Laukkonen ◽  
Heleen A Slagter
Keyword(s):  
Cognitive Enhancement ◽  
The Self ◽  
Predictive Processing ◽  
Focused Attention ◽  
Conceptual Processing ◽  
Current State ◽  
Contemplative Science ◽  
Open Monitoring ◽  
Living Organisms ◽  
The Brain

How profoundly can humans change their own minds? In this paper we offer a unifying account of meditation under the predictive processing view of living organisms. We start from relatively simple axioms. First, the brain is an organ that serves to predict based on past experience, both phylogenetic and ontogenetic. Second, meditation serves to bring one closer to the here and now by disengaging from anticipatory processes. We propose that practicing meditation therefore gradually reduces predictive processing, in particular counterfactual cognition—the tendency to construct abstract and temporally deep representations—until all conceptual processing falls away. Our Many- to-One account also places three main styles of meditation (focused attention, open monitoring, and non-dual meditation) on a single continuum, where each technique progressively relinquishes increasingly engrained habits of prediction, including the self. This deconstruction can also make the above processes available to introspection, permitting certain insights into one’s mind. Our review suggests that our framework is consistent with the current state of empirical and (neuro)phenomenological evidence in contemplative science, and is ultimately illuminating about the plasticity of the predictive mind. It also serves to highlight that contemplative science can fruitfully go beyond cognitive enhancement, attention, and emotion regulation, to its more traditional goal of removing past conditioning and creating conditions for potentially profound insights. Experimental rigor, neurophenomenology, and no-report paradigms combined with neuroimaging are needed to further our understanding of how different styles of meditation affect predictive processing and the self, and the plasticity of the predictive mind more generally.

Possibility that the Onset of Autism Spectrum Disorder is Induced by Failure of the Glutamine-Glutamate Cycle

2020 ◽  
Vol 14 (2) ◽  
pp. 170-174
Author(s):  
Koichi Kawada ◽  
Nobuyuki Kuramoto ◽  
Seisuke Mimori
Keyword(s):  
Autism Spectrum Disorder ◽  
Endoplasmic Reticulum ◽  
Environmental Factors ◽  
Endoplasmic Reticulum Stress ◽  
Synapse Formation ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Neurodevelopmental Disease ◽  
Number Of Patients ◽  
The Brain

: Autism spectrum disorder (ASD) is a neurodevelopmental disease, and the number of patients has increased rapidly in recent years. The causes of ASD involve both genetic and environmental factors, but the details of causation have not yet been fully elucidated. Many reports have investigated genetic factors related to synapse formation, and alcohol and tobacco have been reported as environmental factors. This review focuses on endoplasmic reticulum stress and amino acid cycle abnormalities (particularly glutamine and glutamate) induced by many environmental factors. In the ASD model, since endoplasmic reticulum stress is high in the brain from before birth, it is clear that endoplasmic reticulum stress is involved in the development of ASD. On the other hand, one report states that excessive excitation of neurons is caused by the onset of ASD. The glutamine-glutamate cycle is performed between neurons and glial cells and controls the concentration of glutamate and GABA in the brain. These neurotransmitters are also known to control synapse formation and are important in constructing neural circuits. Theanine is a derivative of glutamine and a natural component of green tea. Theanine inhibits glutamine uptake in the glutamine-glutamate cycle via slc38a1 without affecting glutamate; therefore, we believe that theanine may prevent the onset of ASD by changing the balance of glutamine and glutamate in the brain.

scholarly journals FOXP1 syndrome: a review of the literature and practice parameters for medical assessment and monitoring

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Reymundo Lozano ◽  
Catherine Gbekie ◽  
Paige M. Siper ◽  
Shubhika Srivastava ◽  
Jeffrey M. Saland ◽  
...  
Keyword(s):  
Neurodevelopmental Disorder ◽  
Autism Spectrum ◽  
Medical Assessment ◽  
Forkhead Box ◽  
Organ Systems ◽  
Practice Parameters ◽  
Assessment And Monitoring ◽  
Multidisciplinary Group ◽  
The Brain

AbstractFOXP1 syndrome is a neurodevelopmental disorder caused by mutations or deletions that disrupt the forkhead box protein 1 (FOXP1) gene, which encodes a transcription factor important for the early development of many organ systems, including the brain. Numerous clinical studies have elucidated the role of FOXP1 in neurodevelopment and have characterized a phenotype. FOXP1 syndrome is associated with intellectual disability, language deficits, autism spectrum disorder, hypotonia, and congenital anomalies, including mild dysmorphic features, and brain, cardiac, and urogenital abnormalities. Here, we present a review of human studies summarizing the clinical features of individuals with FOXP1 syndrome and enlist a multidisciplinary group of clinicians (pediatrics, genetics, psychiatry, neurology, cardiology, endocrinology, nephrology, and psychology) to provide recommendations for the assessment of FOXP1 syndrome.

scholarly journals Ensemble-Based Out-of-Distribution Detection

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 567
Author(s):  
Donghun Yang ◽  
Kien Mai Mai Ngoc ◽  
Iksoo Shin ◽  
Kyong-Ha Lee ◽  
Myunggwon Hwang
Keyword(s):  
Detection Method ◽  
State Of The Art ◽  
Metric Learning ◽  
Feature Space ◽  
Confidence Score ◽  
Distance Metric Learning ◽  
Current State ◽  
Overall Performance ◽  
Deep Learning Model

To design an efficient deep learning model that can be used in the real-world, it is important to detect out-of-distribution (OOD) data well. Various studies have been conducted to solve the OOD problem. The current state-of-the-art approach uses a confidence score based on the Mahalanobis distance in a feature space. Although it outperformed the previous approaches, the results were sensitive to the quality of the trained model and the dataset complexity. Herein, we propose a novel OOD detection method that can train more efficient feature space for OOD detection. The proposed method uses an ensemble of the features trained using the softmax-based classifier and the network based on distance metric learning (DML). Through the complementary interaction of these two networks, the trained feature space has a more clumped distribution and can fit well on the Gaussian distribution by class. Therefore, OOD data can be efficiently detected by setting a threshold in the trained feature space. To evaluate the proposed method, we applied our method to various combinations of image datasets. The results show that the overall performance of the proposed approach is superior to those of other methods, including the state-of-the-art approach, on any combination of datasets.

025 Sleep Disruption on an Orbital Shaker alters Glutamate in Prairie Vole Prefrontal Cortex

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A11-A12
Author(s):  
Carolyn Jones ◽  
Randall Olson ◽  
Alex Chau ◽  
Peyton Wickham ◽  
Ryan Leriche ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Prefrontal Cortex ◽  
Early Life ◽  
Autism Spectrum ◽  
Prairie Vole ◽  
Sleep Disruption ◽  
Glutamatergic Synapses ◽  
The Brain ◽  
Orbital Shaker

Abstract Introduction Glutamate concentrations in the cortex fluctuate with the sleep wake cycle in both rodents and humans. Altered glutamatergic signaling, as well as the early life onset of sleep disturbances have been implicated in neurodevelopmental disorders such as autism spectrum disorder. In order to study how sleep modulates glutamate activity in brain regions relevant to social behavior and development, we disrupted sleep in the socially monogamous prairie vole (Microtus ochrogaster) rodent species and quantified markers of glutamate neurotransmission within the prefrontal cortex, an area of the brain responsible for advanced cognition and complex social behaviors. Methods Male and female prairie voles were sleep disrupted using an orbital shaker to deliver automated gentle cage agitation at continuous intervals. Sleep was measured using EEG/EMG signals and paired with real time glutamate concentrations in the prefrontal cortex using an amperometric glutamate biosensor. This same method of sleep disruption was applied early in development (postnatal days 14–21) and the long term effects on brain development were quantified by examining glutamatergic synapses in adulthood. Results Consistent with previous research in rats, glutamate concentration in the prefrontal cortex increased during periods of wake in the prairie vole. Sleep disruption using the orbital shaker method resulted in brief cortical arousals and reduced time in REM sleep. When applied during development, early life sleep disruption resulted in long-term changes in both pre- and post-synaptic components of glutamatergic synapses in the prairie vole prefrontal cortex including increased density of immature spines. Conclusion In the prairie vole rodent model, sleep disruption on an orbital shaker produces a sleep, behavioral, and neurological phenotype that mirrors aspects of autism spectrum disorder including altered features of excitatory neurotransmission within the prefrontal cortex. Studies using this method of sleep disruption combined with real time biosensors for excitatory neurotransmitters will enhance our understanding of modifiable risk factors, such as sleep, that contribute to the altered development of glutamatergic synapses in the brain and their relationship to social behavior. Support (if any) NSF #1926818, VA CDA #IK2 BX002712, Portland VA Research Foundation, NIH NHLBI 5T32HL083808-10, VA Merit Review #I01BX001643

scholarly journals LSD1 enzyme inhibitor TAK-418 unlocks aberrant epigenetic machinery and improves autism symptoms in neurodevelopmental disorder models

2021 ◽  
Vol 7 (11) ◽  
pp. eaba1187
Author(s):  
Rina Baba ◽  
Satoru Matsuda ◽  
Yuuichi Arakawa ◽  
Ryuji Yamada ◽  
Noriko Suzuki ◽  
...  
Keyword(s):  
Gene Expression ◽  
Enzyme Activity ◽  
Neurodevelopmental Disorders ◽  
Neurodevelopmental Disorder ◽  
Specific Inhibitor ◽  
Autism Spectrum ◽  
Poly I:C ◽  
Control Of Gene Expression ◽  
Epigenetic Machinery ◽  
The Brain

Persistent epigenetic dysregulation may underlie the pathophysiology of neurodevelopmental disorders, such as autism spectrum disorder (ASD). Here, we show that the inhibition of lysine-specific demethylase 1 (LSD1) enzyme activity normalizes aberrant epigenetic control of gene expression in neurodevelopmental disorders. Maternal exposure to valproate or poly I:C caused sustained dysregulation of gene expression in the brain and ASD-like social and cognitive deficits after birth in rodents. Unexpectedly, a specific inhibitor of LSD1 enzyme activity, 5-((1R,2R)-2-((cyclopropylmethyl)amino)cyclopropyl)-N-(tetrahydro-2H-pyran-4-yl)thiophene-3-carboxamide hydrochloride (TAK-418), almost completely normalized the dysregulated gene expression in the brain and ameliorated some ASD-like behaviors in these models. The genes modulated by TAK-418 were almost completely different across the models and their ages. These results suggest that LSD1 enzyme activity may stabilize the aberrant epigenetic machinery in neurodevelopmental disorders, and the inhibition of LSD1 enzyme activity may be the master key to recover gene expression homeostasis. TAK-418 may benefit patients with neurodevelopmental disorders.

scholarly journals Genetic Approaches Using Zebrafish to Study the Microbiota–Gut–Brain Axis in Neurological Disorders

Cells ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 566
Author(s):  
Jae-Geun Lee ◽  
Hyun-Ju Cho ◽  
Yun-Mi Jeong ◽  
Jeong-Soo Lee
Keyword(s):  
Neurological Disorders ◽  
Genetic Model ◽  
Molecular Genetic ◽  
Autism Spectrum ◽  
Behavioral Abnormalities ◽  
Bidirectional Signaling ◽  
Intestinal Dysbiosis ◽  
The Central Nervous System ◽  
The Brain

The microbiota–gut–brain axis (MGBA) is a bidirectional signaling pathway mediating the interaction of the microbiota, the intestine, and the central nervous system. While the MGBA plays a pivotal role in normal development and physiology of the nervous and gastrointestinal system of the host, its dysfunction has been strongly implicated in neurological disorders, where intestinal dysbiosis and derived metabolites cause barrier permeability defects and elicit local inflammation of the gastrointestinal tract, concomitant with increased pro-inflammatory cytokines, mobilization and infiltration of immune cells into the brain, and the dysregulated activation of the vagus nerve, culminating in neuroinflammation and neuronal dysfunction of the brain and behavioral abnormalities. In this topical review, we summarize recent findings in human and animal models regarding the roles of the MGBA in physiological and neuropathological conditions, and discuss the molecular, genetic, and neurobehavioral characteristics of zebrafish as an animal model to study the MGBA. The exploitation of zebrafish as an amenable genetic model combined with in vivo imaging capabilities and gnotobiotic approaches at the whole organism level may reveal novel mechanistic insights into microbiota–gut–brain interactions, especially in the context of neurological disorders such as autism spectrum disorder and Alzheimer’s disease.

Differentiation of Healthy Individuals from Those with Autism Spectrum Disorders using Information Graph of Complementary Opposites

2020 ◽  
Keyword(s):  
Children With Autism ◽  
Autism Spectrum ◽  
Brain Signals ◽  
Three Stages ◽  
The Mean ◽  
Spectrum Disorders ◽  
Pediatric Psychiatry ◽  
The Brain ◽  
Optimal Feature ◽  
Information Graph

This study aimed to examine the brain signals of children with Autism Spectrum Disorder (ASD) and use a method according to the concept of complementary opposites to obtain the prominent features or a pattern of EEG signal that represents the biological characteristic of such children. In this study, 20 children with the mean±SD age of 8±5 years were divided into two groups of normal control (NC) and ASD. The diagnosis and approval of individuals in both groups were conducted by two experts in the field of pediatric psychiatry and neurology. The recording protocol was designed with the most accuracy; therefore, the brain signals were recorded with the least noise in the awake state of the individuals in both groups. Moreover, the recording was conducted in three stages from two channels (C3-C4) of EEG ( referred to as the central part of the brain) which were symmetrical in function. In this study, the Mandala method was adopted based on the concept of complementary opposites to investigate the features extracted from Mandala pattern topology and obtain new features and pseudo-patterns for the screening and early diagnosis of ASD. The optimal feature here was based on different stages of processing and statistical analysis of Pattern Detection Capability (PDC). The PDC is a biomarker derived from the Mandala pattern for differentiating the NC from ASD groups.

Non-directive play therapy with autistic adolescents: a qualitative study of therapists’ interactional practices

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Eliza Maciejewska
Keyword(s):  
Autism Spectrum Disorder ◽  
Conversation Analysis ◽  
Play Therapy ◽  
Autism Spectrum ◽  
Current State ◽  
Local Functions ◽  
Play Therapists ◽  
Practical Implications ◽  
Detailed Picture

Abstract This case study identifies and examines interactional practices of non-directive play therapists during their therapeutic sessions with autistic adolescents. The study involved two therapists and two adolescents (siblings) on the autism spectrum. The video-recorded sessions took place at participants’ home and were conducted in Polish. Employing insights and tools from discourse-analytic approaches, in particular conversation analysis (CA), the findings show how clients and therapists are both involved in co-constructing therapeutic interactions by orienting to each other’s utterances. CA is presented in this article as a useful tool for recognizing and describing the therapists’ interactional contributions and their local functions. The therapeutic practices identified in the analysis (talk-in-practice) – e.g. mirroring, meaning expansion, recast and scaffolding – are further juxtaposed with theories concerning interactional practices in non-directive therapies (talk-in-theory) in order to provide a more detailed picture of these practices as well as complete them. The findings from this study expand the current state of knowledge of non-directive play therapies of autism spectrum disorder (ASD) and carry practical implications for specialists involved in ASD treatment.

The role of motivation in talent selection and ­development in competitive sport

2021 ◽  
Keyword(s):  
Measurement Instrument ◽  
Psychological Characteristics ◽  
Advantages And Disadvantages ◽  
Current State ◽  
Talent Selection ◽  
Comprehensive Picture ◽  
Performance Development ◽  
The Right

The early and long-term development of promising young athletes is a decisive factor in being internationally competitive in top-level sports. Among the multitude of talent criteria suggested in the literature, motivation plays a prominent role in the area of psychological characteristics. It is recognised in practice and research as a relevant criterion for performance development across all sports. This article provides an overview of the current state of talent research in the field of motivation. First, the most common theories of motivation in competitive sports are described, then different measurement methods and their advantages and disadvantages as well as the predictive value of motivation for athletic performance are discussed. Finally, implications for practice are suggested. It can be summarised that motivation in sport is conceptualised and operationalised in different ways and that the decision for the right measurement instrument depends on the goal of the assessment. To get a comprehensive picture of an athlete’s motivational status, it is useful to assess several aspects of motivation through different methods.

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