Differences in Performance of ASD and ADHD Subjects Facing Cognitive Loads in an Innovative Reasoning Experiment

We aim to investigate whether EEG dynamics differ in adults with ASD (Autism Spectrum Disorders) and ADHD (attention-deficit/hyperactivity disorder) compared with healthy subjects during the performance of an innovative cognitive task, Aristotle’s valid and invalid syllogisms, and how these differences correlate with brain regions and behavioral data for each subject. We recorded EEGs from 14 scalp electrodes (channels) in 21 adults with ADHD, 21 with ASD, and 21 healthy, normal subjects. The subjects were exposed in a set of innovative cognitive tasks (inducing varying cognitive loads), Aristotle’s two types of syllogism mentioned above. A set of 39 questions were given to participants related to valid–invalid syllogisms as well as a separate set of questionnaires, in order to collect a number of demographic and behavioral data, with the aim of detecting shared information with values of a feature extracted from EEG, the multiscale entropy (MSE), in the 14 channels (‘brain regions’). MSE, a nonlinear information-theoretic measure of complexity, was computed to extract a feature that quantifies the complexity of the EEG. Behavior-Partial Least Squares Correlation, PLSC, is the method to detect the correlation between two sets of data, brain, and behavioral measures. -PLSC, a variant of PLSC, was applied to build a functional connectivity of the brain regions involved in the reasoning tasks. Graph-theoretic measures were used to quantify the complexity of the functional networks. Based on the results of the analysis described in this work, a mixed 14 × 2 × 3 ANOVA showed significant main effects of group factor and brain region* syllogism factor, as well as a significant brain region* group interaction. There are significant differences between the means of MSE (complexity) values at the 14 channels of the members of the ‘pathological’ groups of participants, i.e., between ASD and ADHD, while the difference in means of MSE between both ASD and ADHD and that of the control group is not significant. In conclusion, the valid–invalid type of syllogism generates significantly different complexity values, MSE, between ASD and ADHD. The complexity of activated brain regions of ASD participants increased significantly when switching from a valid to an invalid syllogism, indicating the need for more resources to ‘face’ the task escalating difficulty in ASD subjects. This increase is not so evident in both ADHD and control. Statistically significant differences were found also in the behavioral response of ASD and ADHD, compared with those of control subjects, based on the principal brain and behavior saliences extracted by PLSC. Specifically, two behavioral measures, the emotional state and the degree of confidence of participants in answering questions in Aristotle’s valid–invalid syllogisms, and one demographic variable, age, statistically and significantly discriminate the three groups’ ASD. The seed-PLC generated functional connectivity networks for ASD, ADHD, and control, were ‘projected’ on the regions of the Default Mode Network (DMN), the ‘reference’ connectivity, of which the structural changes were found significant in distinguishing the three groups. The contribution of this work lies in the examination of the relationship between brain activity and behavioral responses of healthy and ‘pathological’ participants in the case of cognitive reasoning of the type of Aristotle’s valid and invalid syllogisms, using PLSC, a machine learning approach combined with MSE, a nonlinear method of extracting a feature based on EEGs that captures a broad spectrum of EEGs linear and nonlinear characteristics. The results seem promising in adopting this type of reasoning, in the future, after further enhancements and experimental tests, as a supplementary instrument towards examining the differences in brain activity and behavioral responses of ASD and ADHD patients. The application of the combination of these two methods, after further elaboration and testing as new and complementary to the existing ones, may be considered as a tool of analysis in helping detecting more effectively such types of disorders.

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A Novel Unit-Based Personalized Fingerprint Feature Selection Strategy for Dynamic Functional Connectivity Networks

Frontiers in Neuroscience ◽

10.3389/fnins.2021.651574 ◽

2021 ◽

Vol 15 ◽

Author(s):

Feng Zhao ◽

Zhiyuan Chen ◽

Islem Rekik ◽

Peiqiang Liu ◽

Ning Mao ◽

...

Keyword(s):

Feature Selection ◽

Functional Connectivity ◽

Evaluation Method ◽

Brain Regions ◽

Autism Spectrum ◽

Superior Performance ◽

Selection Strategy ◽

Dynamic Functional Connectivity ◽

Discriminative Feature ◽

Fingerprint Feature

The sliding-window-based dynamic functional connectivity networks (SW-D-FCN) derive from resting-state functional Magnetic Resonance Imaging has become an increasingly useful tool in the diagnosis of various neurodegenerative diseases. However, it is still challenging to learn how to extract and select the most discriminative features from SW-D-FCN. Conventionally, existing methods opt to select a single discriminative feature set or concatenate a few more from the SW-D-FCN. However, such reductionist strategies may fail to fully capture the personalized discriminative characteristics contained in each functional connectivity (FC) sequence of the SW-D-FCN. To address this issue, we propose a unit-based personalized fingerprint feature selection (UPFFS) strategy to better capture the most discriminative feature associated with a target disease for each unit. Specifically, we regard the FC sequence between any pair of brain regions of interest (ROIs) is regarded as a unit. For each unit, the most discriminative feature is identified by a specific feature evaluation method and all the most discriminative features are then concatenated together as a feature set for the subsequent classification task. In such a way, the personalized fingerprint feature derived from each FC sequence can be fully mined and utilized in classification decision. To illustrate the effectiveness of the proposed strategy, we conduct experiments to distinguish subjects diagnosed with autism spectrum disorder from normal controls. Experimental results show that the proposed strategy can select relevant discriminative features and achieve superior performance to benchmark methods.

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Neural Connectivity Subtypes Predict Discrete Attentional-Bias Profiles Among Heterogeneous Anxiety Patients

Clinical Psychological Science ◽

10.1177/2167702620906149 ◽

2020 ◽

Vol 8 (3) ◽

pp. 491-505 ◽

Cited By ~ 2

Author(s):

Rebecca B. Price ◽

Adriene M. Beltz ◽

Mary L. Woody ◽

Logan Cummings ◽

Danielle Gilchrist ◽

...

Keyword(s):

Functional Connectivity ◽

Selective Attention ◽

Attentional Bias ◽

Sensory Processing ◽

Brain Regions ◽

Data Driven ◽

Neural Connectivity ◽

Group Level ◽

Behavioral Measures ◽

Substantial Heterogeneity

On average, anxious patients show altered attention to threat—including early vigilance toward threat and later avoidance of threat—accompanied by altered functional connectivity across brain regions. However, substantial heterogeneity within clinical, neural, and attentional features of anxiety is overlooked in typical group-level comparisons. We used a well-validated method for data-driven parsing of neural connectivity to reveal connectivity-based subgroups among 60 adults with transdiagnostic anxiety. Subgroups were externally compared on attentional patterns derived from independent behavioral measures. Two subgroups emerged. Subgroup A (68% of patients) showed stronger executive network influences on sensory processing regions and a paradigmatic “vigilance–avoidance” pattern on external behavioral measures. Subgroup B was defined by a larger number of limbic influences on sensory regions and exhibited a more atypical and inconsistent attentional profile. Neural connectivity-based categorization revealed an atypical, limbic-driven pattern of connectivity in a subset of anxious patients that generalized to atypical patterns of selective attention.

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Neurocognitive Aging and Functional Connectivity Using Functional Magnetic Resonance Imaging

Oxford Research Encyclopedia of Psychology ◽

10.1093/acrefore/9780190236557.013.385 ◽

2018 ◽

Author(s):

Hana Burianová

Keyword(s):

Functional Connectivity ◽

Cognitive Processes ◽

Large Scale ◽

Healthy Aging ◽

Brain Activity ◽

Brain Regions ◽

Effective Strategies ◽

Cognitive Improvement ◽

Age Related ◽

Neurocognitive Aging

Determining the mechanisms that underlie neurocognitive aging, such as compensation or dedifferentiation, and facilitating the development of effective strategies for cognitive improvement is essential due to the steadily rising aging population. One approach to study the characteristics of healthy aging comprises the assessment of functional connectivity, delineating markers of age-related neurocognitive plasticity. Functional connectivity paradigms characterize complex one-to-many (or many-to-many) structure–function relations, as higher-level cognitive processes are mediated by the interaction among a number of functionally related neural areas rather than localized to discrete brain regions. Task-related or resting-state interregional correlations of brain activity have been used as reliable indices of functional connectivity, delineating age-related alterations in a number of large-scale brain networks, which subserve attention, working memory, episodic retrieval, and task-switching. Together with behavioral and regional activation studies, connectivity studies and modeling approaches have contributed to our understanding of the mechanisms of age-related reorganization of distributed functional networks; specifically, reduced neural specificity (dedifferentiation) and associated impairment in inhibitory control and compensatory neural recruitment.

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Neural features of sustained emotional information processing in autism spectrum disorder

Autism ◽

10.1177/1362361320903137 ◽

2020 ◽

Vol 24 (4) ◽

pp. 941-953 ◽

Cited By ~ 1

Author(s):

Carla A Mazefsky ◽

Amanda Collier ◽

Josh Golt ◽

Greg J Siegle

Keyword(s):

Autism Spectrum Disorder ◽

Emotion Dysregulation ◽

Emotional Processing ◽

Brain Activity ◽

A Priori ◽

Brain Regions ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Typically Developing ◽

Autism Spectrum Disorder Group

Emotion dysregulation is common in autism spectrum disorder; a better understanding of the underlying neural mechanisms could inform treatment development. The tendency toward repetitive cognition in autism spectrum disorder may also increase susceptibility to perseverate on distressing stimuli, which may then increase emotion dysregulation. Therefore, this study investigated the mechanisms of sustained processing of negative information in brain activity using functional magnetic resonance imaging. We used an event-related task that alternated between emotional processing of personally relevant negative words, neutral words, and a non-emotional task. A priori criteria were developed to define heightened and sustained emotional processing, and feature conjunction analysis was conducted to identify all regions satisfying these criteria. Participants included 25 adolescents with autism spectrum disorder and 23 IQ-, age-, and gender-matched typically developing controls. Regions satisfying all a priori criteria included areas in the salience network and the prefrontal dorsolateral cortex, which are areas implicated in emotion regulation outside of autism spectrum disorder. Collectively, activity in the identified regions accounted for a significant amount of variance in emotion dysregulation in the autism spectrum disorder group. Overall, these results may provide a potential neural mechanism to explain emotion dysregulation in autism spectrum disorder, which is a significant risk factor for poor mental health. Lay abstract Many individuals with autism spectrum disorder struggle with emotions that are intense and interfering, which is referred to as emotion dysregulation. Prior research has established that individuals with autism may be more likely than individuals who are not autistic to have repetitive thoughts. It is possible that persistent thoughts about negative or distressing stimuli may contribute to emotion dysregulation in autism spectrum disorder. This study aimed to identify areas of the brain with evidence of persistent processing of negative information via functional magnetic resonance neuroimaging. We used a task that alternated between emotional processing of personally relevant negative words, neutral words, and a non-emotional task. Criteria were developed to define heightened and persistent emotional processing, and analyses were conducted to identify all brain regions satisfying these criteria. Participants included 25 adolescents with autism spectrum disorder and 23 typically developing adolescents who were similar to the autism spectrum disorder group in IQ, age, and gender ratios. Brain regions identified as having greater and continued processing following negative stimuli in the autism spectrum disorder group as compared with the typically developing group included the salience network and the prefrontal dorsolateral cortex. These areas have been previously implicated in emotion dysregulation outside of autism spectrum disorder. Collectively, brain activity in the identified regions was associated with parent-reported emotion dysregulation in the autism spectrum disorder group. These results help to identify a potential process in the brain associated with emotion dysregulation in autism spectrum disorder. This information may be useful for the development of treatments to decrease emotion dysregulation in autism spectrum disorder.

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Altered Intra- and Interregional Synchronization in Resting-State Cerebral Networks Associated with Chronic Tinnitus

Neural Plasticity ◽

10.1155/2015/475382 ◽

2015 ◽

Vol 2015 ◽

pp. 1-11 ◽

Cited By ~ 20

Author(s):

Yu-Chen Chen ◽

Jian Zhang ◽

Xiao-Wei Li ◽

Wenqing Xia ◽

Xu Feng ◽

...

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Brain Activity ◽

Inferior Frontal Gyrus ◽

Resting State Fmri ◽

Brain Regions ◽

Chronic Tinnitus ◽

Healthy Controls ◽

Tinnitus Distress ◽

The Right

Objective. Subjective tinnitus is hypothesized to arise from aberrant neural activity; however, its neural bases are poorly understood. To identify aberrant neural networks involved in chronic tinnitus, we compared the resting-state functional magnetic resonance imaging (fMRI) patterns of tinnitus patients and healthy controls.Materials and Methods. Resting-state fMRI measurements were obtained from a group of chronic tinnitus patients (n=29) with normal hearing and well-matched healthy controls (n=30). Regional homogeneity (ReHo) analysis and functional connectivity analysis were used to identify abnormal brain activity; these abnormalities were compared to tinnitus distress.Results. Relative to healthy controls, tinnitus patients had significant greater ReHo values in several brain regions including the bilateral anterior insula (AI), left inferior frontal gyrus, and right supramarginal gyrus. Furthermore, the left AI showed enhanced functional connectivity with the left middle frontal gyrus (MFG), while the right AI had enhanced functional connectivity with the right MFG; these measures were positively correlated with Tinnitus Handicap Questionnaires (r=0.459,P=0.012andr=0.479,P=0.009, resp.).Conclusions. Chronic tinnitus patients showed abnormal intra- and interregional synchronization in several resting-state cerebral networks; these abnormalities were correlated with clinical tinnitus distress. These results suggest that tinnitus distress is exacerbated by attention networks that focus on internally generated phantom sounds.

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Sex-dependent and sex-independent brain resting-state functional connectivity in children with autism spectrum disorder

10.1101/038026 ◽

2016 ◽

Cited By ~ 3

Author(s):

Xin Di ◽

Bharat B Biswal

Keyword(s):

Autism Spectrum Disorder ◽

Functional Connectivity ◽

Resting State ◽

Data Exchange ◽

Brain Regions ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Imaging Data ◽

Resting State Functional Connectivity ◽

Main Effects

Background: Males are more likely to suffer from autism spectrum disorder (ASD) than females. As to whether females with ASD have similar brain alterations remain an open question. The current study aimed to examine sex-dependent as well as sex-independent alterations in resting-state functional connectivity in individuals with ASD compared with typically developing (TD) individuals. Method: Resting-state functional MRI data were acquired from the Autism Brain Imaging Data Exchange (ABIDE). Subjects between 6 to 20 years of age were included for analysis. After matching the intelligence quotient between groups for each dataset, and removing subjects due to excessive head motion, the resulting effective sample contained 28 females with ASD, 49 TD females, 129 males with ASD, and 141 TD males, with a two (diagnosis) by two (sex) design. Functional connectivity among 153 regions of interest (ROIs) comprising the whole brain was computed. Two by two analysis of variance was used to identify connectivity that showed diagnosis by sex interaction or main effects of diagnosis. Results: The main effects of diagnosis were found mainly between visual cortex and other brain regions, indicating sex-independent connectivity alterations. We also observed two connections whose connectivity showed diagnosis by sex interaction between the precuneus and medial cerebellum as well as the precunes and dorsal frontal cortex. While males with ASD showed higher connectivity in these connections compared with TD males, females with ASD had lower connectivity than their counterparts. Conclusions: Both sex-dependent and sex-independent functional connectivity alterations are present in ASD.

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Reproducible functional connectivity alterations are associated with autism spectrum disorder

10.1101/303115 ◽

2018 ◽

Author(s):

Štefan Holiga ◽

Joerg F. Hipp ◽

Christopher H. Chatham ◽

Pilar Garces ◽

Will Spooren ◽

...

Keyword(s):

Autism Spectrum Disorder ◽

Functional Connectivity ◽

Large Scale ◽

Clinical Symptoms ◽

Brain Activity ◽

Network Connectivity ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Replication Procedure ◽

Medication Status

AbstractDespite the high clinical burden little is known about pathophysiology underlying autism spectrum disorder (ASD). Recent resting state functional magnetic resonance imaging (rs-fMRI) studies have found atypical synchronization of brain activity in ASD. However, no consensus has been reached on the nature and clinical relevance of these alterations. Here we address these questions in the most comprehensive, large-scale effort to date comprising evaluation of four large ASD cohorts. We followed a strict exploration and replication procedure to identify core rs-fMRI functional connectivity (degree centrality) alterations associated with ASD as compared to typically developing (TD) controls (ASD: N=841, TD: N=984). We then tested for associations of these imaging phenotypes with clinical and demographic factors such as age, sex, medication status and clinical symptom severity. We find reproducible patterns of ASD-associated functional hyper- and hypo-connectivity with hypo-connectivity being primarily restricted to sensory-motor regions and hyper-connectivity hubs being predominately located in prefrontal and parietal cortices. We establish shifts in between-network connectivity from outside to within the identified regions as a key driver of these abnormalities. The magnitude of these alterations is linked to core ASD symptoms related to communication and social interaction and is not affected by age, sex or medication status. The identified brain functional alterations provide a reproducible pathophysiological phenotype underlying the diagnosis of ASD reconciling previous divergent findings. The large effect sizes in standardized cohorts and the link to clinical symptoms emphasize the importance of the identified imaging alterations as potential treatment and stratification biomarkers for ASD.

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Longitudinal Spatial Patterns of Intrinsic Brain Activity and Functional Connectivity in Upper Limb Amputees Patients

10.21203/rs.2.21498/v1 ◽

2020 ◽

Author(s):

bingbo bao ◽

xuyun hua ◽

haifeng wei ◽

pengbo luo ◽

hongyi zhu ◽

...

Keyword(s):

Functional Connectivity ◽

Spatial Patterns ◽

Brain Plasticity ◽

Brain Activity ◽

Low Frequency ◽

Resting State Fmri ◽

Brain Regions ◽

Brain Network ◽

Intrinsic Connectivity ◽

Intrinsic Brain Activity

Abstract Background: Amputation in adults is a serious condition and most patients were associated with the remapping of representations in motor and sensory brain network. Methods: The present study includes 8 healthy volunteers and 16 patients with amputation. We use resting-state fMRI to investigate the local and extent brain plasticity in patients suffering from amputation simultaneously. Both the amplitude of low-frequency fluctuations (ALFF) and degree centrality (DC) were used for the assessment of neuroplasticity in central level. Results: We described changes in spatial patterns of intrinsic brain activity and functional connectivity in amputees in the present study and we found that not only the sensory and motor cortex, but also the related brain regions involved in the functional plasticity after upper extremity deafferentation. Conclusion: Our findings showed local and extensive cortical changes in the sensorimotor and cognitive-related brain regions, which may imply the dysfunction in not only sensory and motor function, but also sensorimotor integration and motor plan. The activation and intrinsic connectivity in the brain changed a lot showed correlation with the deafferentation status.

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An Autism Spectrum Disorder Adaptive Identification Based on the Elimination of Brain Connections: A Proof of Long-Range Underconnectivity

10.21203/rs.3.rs-982098/v1 ◽

2021 ◽

Author(s):

Fatima zahra Benabdallah ◽

Ahmed Drissi El Maliani ◽

Dounia Lotfi ◽

Rachid Jennane ◽

Mohammed El hassouni

Keyword(s):

Autism Spectrum Disorder ◽

Functional Connectivity ◽

Long Range ◽

Data Exchange ◽

Brain Connectivity ◽

Brain Regions ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Imaging Data ◽

Brain Imaging Data

Abstract Autism spectrum disorder (ASD) is theoretically characterized by alterations in functional connectivity between brain regions. Many works presented approaches to determine informative patterns that help to predict autism from typical development. However, most of the proposed pipelines are not specifically designed for the autism problem, i.e they do not corroborate with autism theories about functional connectivity. In this paper, we propose a framework that takes into account the properties of local connectivity and long range under-connectivity in the autistic brain. The originality of the proposed approach is to adopt elimination as a technique in order to well emerge the autistic brain connectivity alterations, and show how they contribute to differentiate ASD from controls. Experimental results conducted on the large multi-site Autism Brain Imaging Data Exchange (ABIDE) show that our approach provides accurate prediction up to 70% and succeeds to prove the existence of deficits in the long-range connectivity in the ASD subjects brains.

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Brain Connectivity Tracks Effects of Chemotherapy Separately from Behavioral Measures

10.1101/352690 ◽

2018 ◽

Author(s):

Omid Kardan ◽

Mary K. Askren ◽

Misook Jung ◽

Scott Peltier ◽

Bratislav Misic ◽

...

Keyword(s):

Functional Connectivity ◽

Cognitive Performance ◽

Resting State ◽

Brain Connectivity ◽

Brain Regions ◽

Self Report ◽

Breast Cancer Patients ◽

Resting State Functional Connectivity ◽

Behavioral Measures ◽

Cognitive Health

AbstractSeveral studies in cancer research have suggested that cognitive dysfunction following chemotherapy, referred to in lay terms as “chemobrain”, is a serious problem. At present, the changes in integrative brain function that underlie such dysfunction remains poorly understood. Recent developments in neuroimaging suggest that patterns of functional connectivity can provide a broadly applicable neuromarker of cognitive performance and other psychometric measures. The current study used multivariate analysis methods to identify patterns of disruption in resting state functional connectivity of the brain due to chemotherapy and the degree to which the disruptions can be linked to behavioral measures of distress and cognitive performance. Sixty two women (22 healthy control, 18 patients treated with adjuvant chemotherapy, and 22 treated without chemotherapy) were evaluated with neurocognitive measures followed by self-report questionnaires and open eyes resting-state fMRI scanning at three time points: diagnosis (M0, pre-adjuvant treatment), at least 1 month (M1), and 7 months (M7) after treatment. The results indicated deficits in cognitive health of breast cancer patients immediately after chemotherapy that improved over time. This psychological trajectory was paralleled by a disruption and later recovery of resting-state functional connectivity, mostly in the parietal and frontal brain regions. The functional connectivity alteration pattern seems to be a separable treatment symptom from the decreased cognitive health. More targeted support for patients should be developed to ameliorate these multi-faceted side effects of chemotherapy treatment on neural functioning and cognitive health.

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