Subtypes of functional connectivity associate robustly with ASD diagnosis

AbstractOur understanding of the changes in functional brain organization in autism is hampered by the extensive heterogeneity that characterizes this neurodevelopmental disorder. Data driven clustering offers a straightforward way to decompose this heterogeneity into subtypes of distinguishable connectivity types and promises an unbiased framework to investigate behavioural symptoms and causative genetic factors. Yet the robustness and generalizability of these imaging subtypes is unknown. Here, we show that unsupervised functional connectivity subtypes are moderately associated with the clinical diagnosis of autism, and that these associations generalize to independent replication data. We found that subtypes identified robust patterns of functional connectivity, but that a discrete assignment of individuals to these subtypes was not supported by the data. Our results support the use of data driven subtyping as a data dimensionality reduction technique, rather than to establish clinical categories.

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The functional brain organization of an individual predicts measures of social abilities in autism spectrum disorder: Predicting symptoms in autism with brain imaging

10.1101/290320 ◽

2018 ◽

Cited By ~ 5

Author(s):

Evelyn MR Lake ◽

Emily S Finn ◽

Stephanie M Noble ◽

Tamara Vanderwal ◽

Xilin Shen ◽

...

Keyword(s):

Autism Spectrum Disorder ◽

Functional Connectivity ◽

Brain Imaging ◽

Data Exchange ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Imaging Data ◽

Brain Organization ◽

Functional Brain ◽

Functional Brain Organization

ABSTRACTAutism Spectrum Disorder (ASD) is associated with multiple complex abnormalities in functional brain connectivity measured with functional magnetic resonance imaging (fMRI). Despite much research in this area, to date, neuroimaging-based models are not able to characterize individuals with ASD with sufficient sensitivity and specificity; this is likely due to the heterogeneity and complexity of this disorder. Here we apply a data-driven subject-level approach, connectome-based predictive modeling, to resting-state fMRI data from a set of individuals from the Autism Brain Imaging Data Exchange. Using leave-one-subject-out and split-half analyses, we define two functional connectivity networks that predict continuous scores on the Social Responsiveness Scale (SRS) and Autism Diagnostic Observation Schedule (ADOS) and confirm that these networks generalize to novel subjects. Notably, these networks were found to share minimal anatomical overlap. Further, our results generalize to individuals for whom SRS/ADOS scores are unavailable, predicting worse scores for ASD than typically developing individuals. In addition, predicted SRS scores for individuals with attention-deficit/hyperactivity disorder (ADHD) from the ADHD-200 Consortium are linked to ADHD symptoms, supporting the hypothesis that the functional brain organization changes relevant to ASD severity share a component associated with attention. Finally, we explore the membership of predictive connections within conventional (atlas-based) functional networks. In summary, our results suggest that an individual’s functional connectivity profile contains information that supports dimensional, non-binary classification in ASD, aligning with the goals of precision medicine and individual-level diagnosis.

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Disrupted stepwise functional brain organization in overweight individuals

Communications Biology ◽

10.1038/s42003-021-02957-7 ◽

2022 ◽

Vol 5 (1) ◽

Author(s):

Hyebin Lee ◽

Junmo Kwon ◽

Jong-eun Lee ◽

Bo-yong Park ◽

Hyunjin Park

Keyword(s):

Functional Connectivity ◽

Eating Behaviors ◽

Brain Regions ◽

Healthy Weight ◽

Connectivity Analysis ◽

Brain Organization ◽

Attention Networks ◽

Functional Hierarchy ◽

Functional Brain Organization ◽

The Brain

AbstractFunctional hierarchy establishes core axes of the brain, and overweight individuals show alterations in the networks anchored on these axes, particularly in those involved in sensory and cognitive control systems. However, quantitative assessments of hierarchical brain organization in overweight individuals are lacking. Capitalizing stepwise functional connectivity analysis, we assess altered functional connectivity in overweight individuals relative to healthy weight controls along the brain hierarchy. Seeding from the brain regions associated with obesity phenotypes, we conduct stepwise connectivity analysis at different step distances and compare functional degrees between the groups. We find strong functional connectivity in the somatomotor and prefrontal cortices in both groups, and both converge to transmodal systems, including frontoparietal and default-mode networks, as the number of steps increased. Conversely, compared with the healthy weight group, overweight individuals show a marked decrease in functional degree in somatosensory and attention networks across the steps, whereas visual and limbic networks show an increasing trend. Associating functional degree with eating behaviors, we observe negative associations between functional degrees in sensory networks and hunger and disinhibition-related behaviors. Our findings suggest that overweight individuals show disrupted functional network organization along the hierarchical axis of the brain and these results provide insights for behavioral associations.

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A Novel Synchronization-Based Approach for Functional Connectivity Analysis

Complexity ◽

10.1155/2017/7190758 ◽

2017 ◽

Vol 2017 ◽

pp. 1-12 ◽

Cited By ~ 12

Author(s):

Angela Lombardi ◽

Sabina Tangaro ◽

Roberto Bellotti ◽

Alessandro Bertolino ◽

Giuseppe Blasi ◽

...

Keyword(s):

Time Series ◽

Functional Connectivity ◽

Functional Coupling ◽

Healthy Population ◽

Brain Organization ◽

Functional Brain ◽

Novel Approach ◽

Functional Brain Networks ◽

Fmri Time Series ◽

Functional Brain Organization

Complex network analysis has become a gold standard to investigate functional connectivity in the human brain. Popular approaches for quantifying functional coupling between fMRI time series are linear zero-lag correlation methods; however, they might reveal only partial aspects of the functional links between brain areas. In this work, we propose a novel approach for assessing functional coupling between fMRI time series and constructing functional brain networks. A phase space framework is used to map couples of signals exploiting their cross recurrence plots (CRPs) to compare the trajectories of the interacting systems. A synchronization metric is extracted from the CRP to assess the coupling behavior of the time series. Since the functional communities of a healthy population are expected to be highly consistent for the same task, we defined functional networks of task-related fMRI data of a cohort of healthy subjects and applied a modularity algorithm in order to determine the community structures of the networks. The within-group similarity of communities is evaluated to verify whether such new metric is robust enough against noise. The synchronization metric is also compared with Pearson’s correlation coefficient and the detected communities seem to better reflect the functional brain organization during the specific task.

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Age development of cortical rhythmics in the EEG depending on the formation of the child’s personal psychical characteristics

Medical alphabet ◽

10.33667/2078-5631-2019-1-8(383)-42-48 ◽

2019 ◽

Vol 1 (8) ◽

pp. 42-50

Author(s):

A. V. Budkevich ◽

L. B. Ivanov ◽

G. R. Novikova ◽

G. M. Dzhanumova

Keyword(s):

Alpha Rhythm ◽

Age Groups ◽

Healthy Children ◽

Brain Organization ◽

Age Related ◽

Mathematical Processing ◽

Functional Brain Organization ◽

Child's Brain ◽

Attention Function ◽

Apparently Healthy

According to the authors, rationing the age-related EEG parameters in children should be based on personal psychical characteristics. A comparative analysis of personal psychical characteristics and electroencephalographic data was carried out in 300 apparently healthy children aged 3-15 years. According to this principle, two subgroups of conditionally healthy children in each age group were singled out: 1) with an immature attention function and 2) with an increased anxious background that do not reach the pathological level. Registration and analysis of EEG was performed by the Neurokariograf computer complex (MBN, Moscow) using mathematical processing methods.The EEG interpretation was based on the principle of assessing the functional state of a child's brain using a three-component model according to: 1) wakefulness level and its dissociation, 2) severity of signs of the EEG neurotic pattern, 3) directionality of formation of traits of the system-functional brain organization (severity of signs functional hypofrontality).lt was found the presence of EEG signs was indicative of a lower level of wakefulness in children with an immature function of attention in all age groups, compared with the indicators of the average population of group and children with an increased background of anxiety. Children with an increased background of anxiety have a tendency to prevalence and excessive spatial synchronization of the alpha rhythm. ln healthy children, the fact of a decrease in wakefulness and the presence of signs of anxiety in the clinic and in EEG patterns indicates individual personalities and should not be considered as pathology.

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On the Experimental, Numerical and Data-Driven Methods to Study Urban Flows

Energies ◽

10.3390/en14051310 ◽

2021 ◽

Vol 14 (5) ◽

pp. 1310

Author(s):

Pablo Torres ◽

Soledad Le Clainche ◽

Ricardo Vinuesa

Keyword(s):

Physical Phenomenon ◽

Urban Pollution ◽

Pollutant Dispersion ◽

Urban Environments ◽

Machine Learning Algorithms ◽

Data Driven ◽

Thermal Fields ◽

Use Of Data ◽

Sustainability Challenges ◽

New Research

Understanding the flow in urban environments is an increasingly relevant problem due to its significant impact on air quality and thermal effects in cities worldwide. In this review we provide an overview of efforts based on experiments and simulations to gain insight into this complex physical phenomenon. We highlight the relevance of coherent structures in urban flows, which are responsible for the pollutant-dispersion and thermal fields in the city. We also suggest a more widespread use of data-driven methods to characterize flow structures as a way to further understand the dynamics of urban flows, with the aim of tackling the important sustainability challenges associated with them. Artificial intelligence and urban flows should be combined into a new research line, where classical data-driven tools and machine-learning algorithms can shed light on the physical mechanisms associated with urban pollution.

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Feed-forward artificial neural network provides data-driven inference of functional connectivity

Chaos An Interdisciplinary Journal of Nonlinear Science ◽

10.1063/1.5117263 ◽

2019 ◽

Vol 29 (9) ◽

pp. 091101 ◽

Cited By ~ 9

Author(s):

Nikita Frolov ◽

Vladimir Maksimenko ◽

Annika Lüttjohann ◽

Alexey Koronovskii ◽

Alexander Hramov

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Functional Connectivity ◽

Data Driven ◽

Feed Forward ◽

Artificial Neural

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Mapping Sensorimotor Cortex With Slow Cortical Potential Resting-State Networks While Awake and Under Anesthesia

Neurosurgery ◽

10.1227/neu.0b013e318258e5d1 ◽

2012 ◽

Vol 71 (2) ◽

pp. 305-316 ◽

Cited By ~ 24

Author(s):

Jonathan D. Breshears ◽

Charles M. Gaona ◽

Jarod L. Roland ◽

Mohit Sharma ◽

David T. Bundy ◽

...

Keyword(s):

Brain Mapping ◽

Sensorimotor Cortex ◽

Resting State ◽

Intractable Epilepsy ◽

Cortical Potential ◽

Data Driven ◽

Resting State Networks ◽

Seed Selection ◽

Brain Organization ◽

Slow Cortical Potential

Abstract BACKGROUND: The emerging insight into resting-state cortical networks has been important in our understanding of the fundamental architecture of brain organization. These networks, which were originally identified with functional magnetic resonance imaging, are also seen in the correlation topography of the infraslow rhythms of local field potentials. Because of the fundamental nature of these networks and their independence from task-related activations, we posit that, in addition to their neuroscientific relevance, these slow cortical potential networks could play an important role in clinical brain mapping. OBJECTIVE: To assess whether these networks would be useful in identifying eloquent cortex such as sensorimotor cortex in patients both awake and under anesthesia. METHODS: This study included 9 subjects undergoing surgical treatment for intractable epilepsy. Slow cortical potentials were recorded from the cortical surface in patients while awake and under propofol anesthesia. To test brain-mapping utility, slow cortical potential networks were identified with data-driven (seed-independent) and anatomy-driven (seed-based) approaches. With electrocortical stimulation used as the gold standard for comparison, the sensitivity and specificity of these networks for identifying sensorimotor cortex were calculated. RESULTS: Networks identified with a data-driven approach in patients under anesthesia and awake were 90% and 93% sensitive and 58% and 55% specific for sensorimotor cortex, respectively. Networks identified with systematic seed selection in patients under anesthesia and awake were 78% and 83% sensitive and 67% and 60% specific, respectively. CONCLUSION: Resting-state networks may be useful for tailoring stimulation mapping and could provide a means of identifying eloquent regions in patients while under anesthesia.

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Functional Brain Connectivity during Multiple Motor Imagery Tasks in Spinal Cord Injury

Neural Plasticity ◽

10.1155/2018/9354207 ◽

2018 ◽

Vol 2018 ◽

pp. 1-20 ◽

Cited By ~ 2

Author(s):

Alkinoos Athanasiou ◽

Nikos Terzopoulos ◽

Niki Pandria ◽

Ioannis Xygonakis ◽

Nicolas Foroglou ◽

...

Keyword(s):

Spinal Cord ◽

Spinal Cord Injury ◽

Functional Connectivity ◽

Motor Imagery ◽

Healthy Subjects ◽

Brain Connectivity ◽

Descending Pathways ◽

Brain Organization ◽

Sensorimotor Network ◽

Cord Injury

Reciprocal communication of the central and peripheral nervous systems is compromised during spinal cord injury due to neurotrauma of ascending and descending pathways. Changes in brain organization after spinal cord injury have been associated with differences in prognosis. Changes in functional connectivity may also serve as injury biomarkers. Most studies on functional connectivity have focused on chronic complete injury or resting-state condition. In our study, ten right-handed patients with incomplete spinal cord injury and ten age- and gender-matched healthy controls performed multiple visual motor imagery tasks of upper extremities and walking under high-resolution electroencephalography recording. Directed transfer function was used to study connectivity at the cortical source space between sensorimotor nodes. Chronic disruption of reciprocal communication in incomplete injury could result in permanent significant decrease of connectivity in a subset of the sensorimotor network, regardless of positive or negative neurological outcome. Cingulate motor areas consistently contributed the larger outflow (right) and received the higher inflow (left) among all nodes, across all motor imagery categories, in both groups. Injured subjects had higher outflow from left cingulate than healthy subjects and higher inflow in right cingulate than healthy subjects. Alpha networks were less dense, showing less integration and more segregation than beta networks. Spinal cord injury patients showed signs of increased local processing as adaptive mechanism. This trial is registered with NCT02443558.

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