scholarly journals Measuring Robustness of Brain Networks in Autism Spectrum Disorder with Ricci Curvature

2019 ◽  
Author(s):  
Anish K. Simhal ◽  
Kimberly L.H. Carpenter ◽  
Saad Nadeem ◽  
Joanne Kurtzberg ◽  
Allen Song ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Ricci Curvature ◽  
Diffusion Mri ◽  
Brain Networks ◽  
Autism Spectrum ◽  
Brain Network ◽  
Spectrum Disorder ◽  
Global Changes ◽  
Before And After ◽  
The Brain

ABSTRACTRicci curvature is a method for measuring the robustness of networks. In this work, we use Ricci curvature to measure robustness of brain networks affected by autism spectrum disorder (ASD). Subjects with ASD are given a stem cell infusion and are imaged with diffusion MRI before and after the infusion. By using Ricci curvature to measure changes in robustness, we quantify both local and global changes in the brain networks correlated with the infusion. Our results find changes in regions associated with ASD that were not detected via traditional brain network analysis.

Lateralization of Brain Networks and Clinical Severity in Toddlers with Autism Spectrum Disorder: A HARDI Diffusion MRI Study

2015 ◽  
Vol 9 (3) ◽  
pp. 382-392 ◽  
Author(s):  
Eugenia Conti ◽  
Sara Calderoni ◽  
Anna Gaglianese ◽  
Kerstin Pannek ◽  
Sara Mazzotti ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Diffusion Mri ◽  
Brain Networks ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Clinical Severity ◽  
Mri Study

scholarly journals Dysregulation of temporal dynamics of synchronous neural activity in adolescents on autism spectrum

2019 ◽  
Author(s):  
Evie A. Malaia ◽  
Sungwoo Ahn ◽  
Leonid L Rubchinsky
Keyword(s):  
Autism Spectrum Disorder ◽  
Neural Activity ◽  
Temporal Dynamics ◽  
Phase Locking ◽  
Autism Spectrum ◽  
Brain Network ◽  
Spectrum Disorder ◽  
Multiple Networks ◽  
Increased Sensitivity ◽  
The Brain

AbstractAutism spectrum disorder is increasingly understood to be based on atypical signal transfer among multiple interconnected networks in the brain. Relative temporal patterns of neural activity have been shown to underlie both the altered neurophysiology and the altered behaviors in a variety of neurogenic disorders. We assessed brain network dynamics variability in Autism Spectrum Disorders (ASD) using measures of synchronization (phase-locking) strength, and timing of synchronization and desynchronization of neural activity (desynchronization ratio) across frequency bands of resting state EEG. Our analysis indicated that fronto-parietal synchronization is higher in ASD, but with more short periods of desynchronization. It also indicates that the relationship between the properties of neural synchronization and behavior is different in ASD and typically developing populations. Recent theoretical studies suggest that neural networks with high desynchronization ratio have increased sensitivity to inputs. Our results point to the potential significance of this phenomenon to autistic brain. This sensitivity may disrupt production of an appropriate neural and behavioral responses to external stimuli. Cognitive processes dependent on integration of activity from multiple networks may be, as a result, particularly vulnerable to disruption.Lay SummaryParts of the brain can work together by synchronizing activity of the neurons. We recorded electrical activity of the brain in adolescents with autism spectrum disorder, and then compared the recording to that of their peers without the diagnosis. We found that in participants with autism, there were a lot of very short time periods of non-synchronized activity between frontal and parietal parts of the brain. Mathematical models show that the brain system with this kind of activity is very sensitive to external events.

scholarly journals Evaluating Functional Connectivity Alterations in Autism Spectrum Disorder Using Network-Based Statistics

Diagnostics ◽  
2018 ◽  
Vol 8 (3) ◽  
pp. 51 ◽  
Author(s):  
Aitana Pascual-Belda ◽  
Antonio Díaz-Parra ◽  
David Moratal
Keyword(s):  
Autism Spectrum Disorder ◽  
Brain Connectivity ◽  
Brain Networks ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Typical Development ◽  
Functional Brain ◽  
Functional Brain Networks ◽  
Management Functions ◽  
The Brain

The study of resting-state functional brain networks is a powerful tool to understand the neurological bases of a variety of disorders such as Autism Spectrum Disorder (ASD). In this work, we have studied the differences in functional brain connectivity between a group of 74 ASD subjects and a group of 82 typical-development (TD) subjects using functional magnetic resonance imaging (fMRI). We have used a network approach whereby the brain is divided into discrete regions or nodes that interact with each other through connections or edges. Functional brain networks were estimated using the Pearson’s correlation coefficient and compared by means of the Network-Based Statistic (NBS) method. The obtained results reveal a combination of both overconnectivity and underconnectivity, with the presence of networks in which the connectivity levels differ significantly between ASD and TD groups. The alterations mainly affect the temporal and frontal lobe, as well as the limbic system, especially those regions related with social interaction and emotion management functions. These results are concordant with the clinical profile of the disorder and can contribute to the elucidation of its neurological basis, encouraging the development of new clinical approaches.

scholarly journals Measuring robustness of brain networks in autism spectrum disorder with Ricci curvature

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Anish K. Simhal ◽  
Kimberly L. H. Carpenter ◽  
Saad Nadeem ◽  
Joanne Kurtzberg ◽  
Allen Song ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Ricci Curvature ◽  
Brain Networks ◽  
Autism Spectrum ◽  
Spectrum Disorder

scholarly journals Art of Music, as Harmony of the Spheres and Autism Spectrum Disorder

2016 ◽  
Author(s):  
Bharathi Geetha ◽  
Thangaraj Sugunadevi ◽  
Babu Srija ◽  
Nagarajan Laleethambika ◽  
Vellingiri Balachandar
Keyword(s):  
Autism Spectrum Disorder ◽  
Brain Area ◽  
Autism Spectrum ◽  
Brain Network ◽  
Spectrum Disorder ◽  
Motor Speech ◽  
Personalized Care ◽  
Communication Capacity ◽  
The Brain

Music has the innate potential to reach all parts of the brain, stimulates certain brain areas which are not achievable through other modalities. Music Therapy (MT) is being used for more than a century to treat individuals who needs personalized care. MT optimizes motor, speech and language responsibilities of the brain and improves cognitive performance. Pervasive developmentdisorder (PDD) is a multifaceted, neuro developmental disorder and autism spectrum disorder (ASD) comes under PDD, which is defined by deficiencies in three principal spheres: social connection with others, communicative and normal movement skills. The conventional imaging studies illustrate reduced brain area connectivity in people with ASD, involving selected parts of the brain cortex. People with ASD express much interest in musical activities which engages the brain network areas and improves communication and social skills.The main objective of this review is to analyze the potential role of MT in treating the neurological conditions, particularly ASD. Evidence based studies have reported the extensive therapeutic application of music on various part of the brain in a nonverbal child with autism through hearing or making music.Hence we hypothesized that MT intervention can improve the communication capacity in people with ASD, than customary neurorestoration therapy alone.

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 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.

scholarly journals Multisite Autism Spectrum Disorder Classification Using Convolutional Neural Network Classifier and Individual Morphological Brain Networks

2021 ◽  
Vol 14 ◽  
Author(s):  
Jingjing Gao ◽  
Mingren Chen ◽  
Yuanyuan Li ◽  
Yachun Gao ◽  
Yanling Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Autism Spectrum Disorder ◽  
Convolutional Neural Network ◽  
Structural Mri ◽  
Autism Spectrum ◽  
Brain Network ◽  
Spectrum Disorder ◽  
Structural Covariance ◽  
Almost All ◽  
New Framework

Autism spectrum disorder (ASD) is a range of neurodevelopmental disorders with behavioral and cognitive impairment and brings huge burdens to the patients’ families and the society. To accurately identify patients with ASD from typical controls is important for early detection and early intervention. However, almost all the current existing classification methods for ASD based on structural MRI (sMRI) mainly utilize the independent local morphological features and do not consider the covariance patterns of these features between regions. In this study, by combining the convolutional neural network (CNN) and individual structural covariance network, we proposed a new framework to classify ASD patients with sMRI data from the ABIDE consortium. Moreover, gradient-weighted class activation mapping (Grad-CAM) was applied to characterize the weight of features contributing to the classification. The experimental results showed that our proposed method outperforms the currently used methods for classifying ASD patients with the ABIDE data and achieves a high classification accuracy of 71.8% across different sites. Furthermore, the discriminative features were found to be mainly located in the prefrontal cortex and cerebellum, which may be the early biomarkers for the diagnosis of ASD. Our study demonstrated that CNN is an effective tool to build the framework for the diagnosis of ASD with individual structural covariance brain network.

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