Functional MRI in Pediatric Neurodevelopmental and Behavioral Disorders

2018 ◽  
pp. 140-157
Author(s):  
John Vijay Sagar Kommu ◽  
Sowmyashree Mayur Kaku
Keyword(s):  
Behavioral Disorders ◽  
Language Processing ◽  
Sentence Comprehension ◽  
Inferior Frontal Gyrus ◽  
Affective Response ◽  
Resting State Fmri ◽  
Autism Spectrum ◽  
Gaze Aversion ◽  
Control Networks ◽  
Neurobiological Substrates

This chapter addresses functional magnetic resonance imaging (fMRI) of brain in children with neurodevelopmental and behavioral disorders. Common challenges of pediatric fMRI studies are related to acquisition and processing. In children with disruptive behavior disorders, deficits in affective response, empathy, and decision-making have been reported. Resting-state fMRI studies in attention-deficit hyperactivity disorder (ADHD) have shown altered activity in default mode and cognitive control networks. Task-based fMRI studies in ADHD have implicated frontoparietal cognitive and attentional networks. The role of stimulants in restoring the altered brain function has been examined using fMRI studies. In children with autism spectrum disorder, fMRI studies using face-processing tasks, theory-of-mind tasks, imitation, and language processing (e.g., sentence comprehension), as well as studies of gaze aversion, interest in social faces, and faces with emotions have implicated cerebellum, amygdala, hippocampus, insula, fusiform gyrus, superior temporal sulcus, planum temporale, inferior frontal gyrus, basal ganglia, thalamus, cingulate cortex, corpus callosum, and brainstem. In addition, fMRI has been a valuable research tool for understanding neurobiological substrates in children with psychiatric disorders (e.g., psychosis, posttraumatic stress disorder, and anxiety disorders).

Cross-linguistic influence on brain activation during second language processing: An fMRI study

2007 ◽  
Vol 10 (2) ◽  
pp. 175-187 ◽  
Author(s):  
HYEONJEONG JEONG ◽  
MOTOAKI SUGIURA ◽  
YUKO SASSA ◽  
SATORU YOKOYAMA ◽  
KAORU HORIE ◽  
...  
Keyword(s):  
Second Language ◽  
Language Processing ◽  
Sentence Comprehension ◽  
Inferior Frontal Gyrus ◽  
First Language ◽  
Superior Temporal Gyrus ◽  
Cortical Activation ◽  
Linguistic Distance ◽  
Anterior Portion ◽  
Chinese Group

The goal of this study was to examine the effect of the linguistic distance between a first language (L1) and a second language (L2) on neural activity during second language relative to first language processing. We compared different L1–L2 pairs in which different linguistic features characterize linguistic distance. Chinese and Korean native speakers were instructed to perform sentence comprehension tasks in two L2s (English and Japanese) and their respective L1s. Activation while understanding English sentences relative to understanding sentences in L1 was greater for the Korean group than the Chinese group in the left inferior frontal gyrus, bilateral posterior superior temporal gyri, and right cerebellum. Activation while understanding Japanese sentences relative to understanding sentences in L1 was greater for the Chinese group than the Korean group in the anterior portion of the left superior temporal gyrus. The results demonstrated that the location of the L2–L1 processing-induced cortical activation varies between different L1–L2 pairs.

When Hearing Does Not Mean Understanding: On the Neural Processing of Syntactically Complex Sentences by Listeners With Hearing Loss

2021 ◽  
pp. 1-13
Author(s):  
Margreet Vogelzang ◽  
Christiane M. Thiel ◽  
Stephanie Rosemann ◽  
Jochem W. Rieger ◽  
Esther Ruigendijk
Keyword(s):  
Hearing Loss ◽  
Sentence Processing ◽  
Secondary Task ◽  
Sentence Comprehension ◽  
Inferior Frontal Gyrus ◽  
Normal Hearing ◽  
Cognitive Demands ◽  
Sentence Complexity ◽  
Complex Sentences ◽  
And Task

Purpose Adults with mild-to-moderate age-related hearing loss typically exhibit issues with speech understanding, but their processing of syntactically complex sentences is not well understood. We test the hypothesis that listeners with hearing loss' difficulties with comprehension and processing of syntactically complex sentences are due to the processing of degraded input interfering with the successful processing of complex sentences. Method We performed a neuroimaging study with a sentence comprehension task, varying sentence complexity (through subject–object order and verb–arguments order) and cognitive demands (presence or absence of a secondary task) within subjects. Groups of older subjects with hearing loss ( n = 20) and age-matched normal-hearing controls ( n = 20) were tested. Results The comprehension data show effects of syntactic complexity and hearing ability, with normal-hearing controls outperforming listeners with hearing loss, seemingly more so on syntactically complex sentences. The secondary task did not influence off-line comprehension. The imaging data show effects of group, sentence complexity, and task, with listeners with hearing loss showing decreased activation in typical speech processing areas, such as the inferior frontal gyrus and superior temporal gyrus. No interactions between group, sentence complexity, and task were found in the neuroimaging data. Conclusions The results suggest that listeners with hearing loss process speech differently from their normal-hearing peers, possibly due to the increased demands of processing degraded auditory input. Increased cognitive demands by means of a secondary visual shape processing task influence neural sentence processing, but no evidence was found that it does so in a different way for listeners with hearing loss and normal-hearing listeners.

Influence of Primary Auditory Cortex in the Characterization of Autism Spectrum in Young Adults using Brain Connectivity Parameters and Deep Belief Networks: An fMRI Study

2020 ◽  
Vol 16 (9) ◽  
pp. 1059-1073
Author(s):  
Vidhusha Srinivasan ◽  
N. Udayakumar ◽  
Kavitha Anandan
Keyword(s):  
Functional Connectivity ◽  
Auditory Cortex ◽  
Language Processing ◽  
Brain Connectivity ◽  
Primary Auditory Cortex ◽  
Autism Spectrum ◽  
Belief Networks ◽  
Deep Belief Networks ◽  
High Functioning ◽  
Low Functioning

Background: The spectrum of autism encompasses High Functioning Autism (HFA) and Low Functioning Autism (LFA). Brain mapping studies have revealed that autism individuals have overlaps in brain behavioural characteristics. Generally, high functioning individuals are known to exhibit higher intelligence and better language processing abilities. However, specific mechanisms associated with their functional capabilities are still under research. Objective: This work addresses the overlapping phenomenon present in autism spectrum through functional connectivity patterns along with brain connectivity parameters and distinguishes the classes using deep belief networks. Methods: The task-based functional Magnetic Resonance Images (fMRI) of both high and low functioning autistic groups were acquired from ABIDE database, for 58 low functioning against 43 high functioning individuals while they were involved in a defined language processing task. The language processing regions of the brain, along with Default Mode Network (DMN) have been considered for the analysis. The functional connectivity maps have been plotted through graph theory procedures. Brain connectivity parameters such as Granger Causality (GC) and Phase Slope Index (PSI) have been calculated for the individual groups. These parameters have been fed to Deep Belief Networks (DBN) to classify the subjects under consideration as either LFA or HFA. Results: Results showed increased functional connectivity in high functioning subjects. It was found that the additional interaction of the Primary Auditory Cortex lying in the temporal lobe, with other regions of interest complimented their enhanced connectivity. Results were validated using DBN measuring the classification accuracy of 85.85% for high functioning and 81.71% for the low functioning group. Conclusion: Since it is known that autism involves enhanced, but imbalanced components of intelligence, the reason behind the supremacy of high functioning group in language processing and region responsible for enhanced connectivity has been recognized. Therefore, this work that suggests the effect of Primary Auditory Cortex in characterizing the dominance of language processing in high functioning young adults seems to be highly significant in discriminating different groups in autism spectrum.

scholarly journals A Deep Learning Approach to Predict Autism Spectrum Disorder Using Multisite Resting-State fMRI

2021 ◽  
Vol 11 (8) ◽  
pp. 3636
Author(s):  
Faria Zarin Subah ◽  
Kaushik Deb ◽  
Pranab Kumar Dhar ◽  
Takeshi Koshiba
Keyword(s):  
Autism Spectrum Disorder ◽  
Resting State ◽  
State Of The Art ◽  
Resting State Fmri ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Bootstrap Analysis ◽  
Proposed Model ◽  
Art Methods ◽  
The Mean

Autism spectrum disorder (ASD) is a complex and degenerative neuro-developmental disorder. Most of the existing methods utilize functional magnetic resonance imaging (fMRI) to detect ASD with a very limited dataset which provides high accuracy but results in poor generalization. To overcome this limitation and to enhance the performance of the automated autism diagnosis model, in this paper, we propose an ASD detection model using functional connectivity features of resting-state fMRI data. Our proposed model utilizes two commonly used brain atlases, Craddock 200 (CC200) and Automated Anatomical Labelling (AAL), and two rarely used atlases Bootstrap Analysis of Stable Clusters (BASC) and Power. A deep neural network (DNN) classifier is used to perform the classification task. Simulation results indicate that the proposed model outperforms state-of-the-art methods in terms of accuracy. The mean accuracy of the proposed model was 88%, whereas the mean accuracy of the state-of-the-art methods ranged from 67% to 85%. The sensitivity, F1-score, and area under receiver operating characteristic curve (AUC) score of the proposed model were 90%, 87%, and 96%, respectively. Comparative analysis on various scoring strategies show the superiority of BASC atlas over other aforementioned atlases in classifying ASD and control.

scholarly journals Imbalanced social-communicative and restricted repetitive behavior subtypes of autism spectrum disorder exhibit different neural circuitry

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Natasha Bertelsen ◽  
◽  
Isotta Landi ◽  
Richard A. I. Bethlehem ◽  
Jakob Seidlitz ◽  
...  
Keyword(s):  
Repetitive Behavior ◽  
Resting State Fmri ◽  
Neural Circuitry ◽  
Autism Spectrum ◽  
Repetitive Behaviors ◽  
Typically Developing ◽  
Out Of Sample ◽  
Genetic Mechanisms ◽  
Social Communicative ◽  
The Eu

AbstractSocial-communication (SC) and restricted repetitive behaviors (RRB) are autism diagnostic symptom domains. SC and RRB severity can markedly differ within and between individuals and may be underpinned by different neural circuitry and genetic mechanisms. Modeling SC-RRB balance could help identify how neural circuitry and genetic mechanisms map onto such phenotypic heterogeneity. Here, we developed a phenotypic stratification model that makes highly accurate (97–99%) out-of-sample SC = RRB, SC > RRB, and RRB > SC subtype predictions. Applying this model to resting state fMRI data from the EU-AIMS LEAP dataset (n = 509), we find that while the phenotypic subtypes share many commonalities in terms of intrinsic functional connectivity, they also show replicable differences within some networks compared to a typically-developing group (TD). Specifically, the somatomotor network is hypoconnected with perisylvian circuitry in SC > RRB and visual association circuitry in SC = RRB. The SC = RRB subtype show hyperconnectivity between medial motor and anterior salience circuitry. Genes that are highly expressed within these networks show a differential enrichment pattern with known autism-associated genes, indicating that such circuits are affected by differing autism-associated genomic mechanisms. These results suggest that SC-RRB imbalance subtypes share many commonalities, but also express subtle differences in functional neural circuitry and the genomic underpinnings behind such circuitry.

EXPRESS: Selective adaptation in sentence comprehension: Evidence from event-related brain potentials

2020 ◽  
pp. 174702182098462
Author(s):  
Masataka Yano ◽  
Shugo Suwazono ◽  
Hiroshi Arao ◽  
Daichi Yasunaga ◽  
Hiroaki Oishi
Keyword(s):  
Language Processing ◽  
Sentence Comprehension ◽  
Processing System ◽  
Selective Adaptation ◽  
Event Related Potential ◽  
Equal Probability ◽  
N400 Effect ◽  
Mixed Effect ◽  
Processing Cost ◽  
Low Probability

The present study conducted two event-related potential experiments to investigate whether readers adapt their expectations to morphosyntactically (Experiment 1) or semantically (Experiment 2) anomalous sentences when they are repeatedly exposed to them. To address this issue, we manipulated the probability of morphosyntactically/semantically grammatical and anomalous sentence occurrence through experiments. For the low probability block, anomalous sentences were presented less frequently than grammatical sentences (with a ratio of 1 to 4), while they were presented as frequently as grammatical sentences in the equal probability block. Experiment 1 revealed a smaller P600 effect for morphosyntactic violations in the equal probability block than in the low probability block. Linear mixed-effect models were used to examine how the size of the P600 effect changed as the experiment went along. The results showed that the smaller P600 effect of the equal probability block resulted from an amplitude’s decline in morphosyntactically violated sentences over the course of the experiment, suggesting an adaptation to morphosyntactic violations. In Experiment 2, semantically anomalous sentences elicited a larger N400 effect than their semantically natural counterparts regardless of probability manipulation. No evidence was found in favor of adaptation to semantic violations in that the processing cost of semantic violations did not decrease over the course of the experiment. Therefore, the present study demonstrated a dynamic aspect of language-processing system. We will discuss why the language-processing system shows a selective adaptation to morphosyntactic violations.

scholarly journals Sex Differences in Functional Connectivity Between Resting State Brain Networks in Autism Spectrum Disorder

2021 ◽  
Author(s):  
Vânia Tavares ◽  
Luís Afonso Fernandes ◽  
Marília Antunes ◽  
Hugo Ferreira ◽  
Diana Prata
Keyword(s):  
Autism Spectrum Disorder ◽  
Sex Differences ◽  
Resting State ◽  
Brain Connectivity ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Control Networks ◽  
Highly Sensitive ◽  
Female Specific ◽  
Main Effects

AbstractFunctional brain connectivity (FBC) has previously been examined in autism spectrum disorder (ASD) between-resting-state networks (RSNs) using a highly sensitive and reproducible hypothesis-free approach. However, results have been inconsistent and sex differences have only recently been taken into consideration using this approach. We estimated main effects of diagnosis and sex and a diagnosis by sex interaction on between-RSNs FBC in 83 ASD (40 females/43 males) and 85 typically developing controls (TC; 43 females/42 males). We found increased connectivity between the default mode (DM) and (a) the executive control networks in ASD (vs. TC); (b) the cerebellum networks in males (vs. females); and (c) female-specific altered connectivity involving visual, language and basal ganglia (BG) networks in ASD—in suggestive compatibility with ASD cognitive and neuroscientific theories.

scholarly journals Natural language processing (NLP) tools in extracting biomedical concepts from research articles: a case study on autism spectrum disorder

2020 ◽  
Vol 20 (S11) ◽  
Author(s):  
Jacqueline Peng ◽  
Mengge Zhao ◽  
James Havrilla ◽  
Cong Liu ◽  
Chunhua Weng ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Clinical Manifestations ◽  
Predictive Performance ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Research Articles

Abstract Background Natural language processing (NLP) tools can facilitate the extraction of biomedical concepts from unstructured free texts, such as research articles or clinical notes. The NLP software tools CLAMP, cTAKES, and MetaMap are among the most widely used tools to extract biomedical concept entities. However, their performance in extracting disease-specific terminology from literature has not been compared extensively, especially for complex neuropsychiatric disorders with a diverse set of phenotypic and clinical manifestations. Methods We comparatively evaluated these NLP tools using autism spectrum disorder (ASD) as a case study. We collected 827 ASD-related terms based on previous literature as the benchmark list for performance evaluation. Then, we applied CLAMP, cTAKES, and MetaMap on 544 full-text articles and 20,408 abstracts from PubMed to extract ASD-related terms. We evaluated the predictive performance using precision, recall, and F1 score. Results We found that CLAMP has the best performance in terms of F1 score followed by cTAKES and then MetaMap. Our results show that CLAMP has much higher precision than cTAKES and MetaMap, while cTAKES and MetaMap have higher recall than CLAMP. Conclusion The analysis protocols used in this study can be applied to other neuropsychiatric or neurodevelopmental disorders that lack well-defined terminology sets to describe their phenotypic presentations.

scholarly journals Implicit Semantic Processing of Linguistic and Non-linguistic Stimuli in Adults with Autism Spectrum Disorder

2021 ◽  
Author(s):  
Emme O’Rourke ◽  
Emily L. Coderre
Keyword(s):  
Autism Spectrum Disorder ◽  
Language Processing ◽  
Semantic Processing ◽  
Autistic Traits ◽  
Autism Spectrum ◽  
Event Related Potential ◽  
Spectrum Disorder ◽  
Adults With Autism ◽  
Priming Task ◽  
Adults With Asd

AbstractWhile many individuals with autism spectrum disorder (ASD) experience difficulties with language processing, non-linguistic semantic processing may be intact. We examined neural responses to an implicit semantic priming task by comparing N400 responses—an event-related potential related to semantic processing—in response to semantically related or unrelated pairs of words or pictures. Adults with ASD showed larger N400 responses than typically developing adults for pictures, but no group differences occurred for words. However, we also observed complex modulations of N400 amplitude by age and by level of autistic traits. These results offer important implications for how groups are delineated and compared in autism research.

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