Data-driven evaluation of functional connectivity metrics

2013 ◽  
Author(s):  
Yingjie Zhang ◽  
Junwei Han ◽  
Xintao Hu ◽  
Lei Guo ◽  
Tianming Liu
Keyword(s):  
Functional Connectivity ◽  
Data Driven

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

2019 ◽  
Vol 29 (9) ◽  
pp. 091101 ◽  
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

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

2020 ◽  
Vol 8 (3) ◽  
pp. 491-505 ◽  
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.

scholarly journals Individual Patterns of Abnormality in Resting-State Functional Connectivity Reveal Two Data-Driven PTSD Subgroups

2020 ◽  
Vol 177 (3) ◽  
pp. 244-253 ◽  
Author(s):  
Adi Maron-Katz ◽  
Yu Zhang ◽  
Manjari Narayan ◽  
Wei Wu ◽  
Russell T. Toll ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Data Driven ◽  
Resting State Functional Connectivity

scholarly journals Select and Cluster: A Method for Finding Functional Networks of Clustered Voxels in fMRI

2016 ◽  
Vol 2016 ◽  
pp. 1-19 ◽  
Author(s):  
Danilo DonGiovanni ◽  
Lucia Maria Vaina
Keyword(s):  
Functional Connectivity ◽  
A Priori ◽  
Data Driven ◽  
Fmri Data ◽  
Support Vector ◽  
Functional Networks ◽  
Compact Groups ◽  
Mutual Correlation ◽  
Pairwise Correlation ◽  
Vector Clustering

Extracting functional connectivity patterns among cortical regions in fMRI datasets is a challenge stimulating the development of effective data-driven or model based techniques. Here, we present a novel data-driven method for the extraction of significantly connected functional ROIs directly from the preprocessed fMRI data without relying on a priori knowledge of the expected activations. This method finds spatially compact groups of voxels which show a homogeneous pattern of significant connectivity with other regions in the brain. The method, called Select and Cluster (S&C), consists of two steps: first, a dimensionality reduction step based on a blind multiresolution pairwise correlation by which the subset of all cortical voxels with significant mutual correlation is selected and the second step in which the selected voxels are grouped into spatially compact and functionally homogeneous ROIs by means of a Support Vector Clustering (SVC) algorithm. The S&C method is described in detail. Its performance assessed on simulated and experimental fMRI data is compared to other methods commonly used in functional connectivity analyses, such as Independent Component Analysis (ICA) or clustering. S&C method simplifies the extraction of functional networks in fMRI by identifying automatically spatially compact groups of voxels (ROIs) involved in whole brain scale activation networks.

scholarly journals Clustering of Multiple Psychiatric Disorders Using Functional Connectivity in the Data-Driven Brain Subnetwork

2021 ◽  
Vol 12 ◽  
Author(s):  
Tomoki Tokuda ◽  
Okito Yamashita ◽  
Yuki Sakai ◽  
Junichiro Yoshimoto
Keyword(s):  
Functional Connectivity ◽  
Psychiatric Disorders ◽  
Autism Spectrum ◽  
Data Driven ◽  
New Paradigm ◽  
Resting State Functional Connectivity ◽  
Major Depressive ◽  
Label Information ◽  
Healthy Control ◽  
Diagnostic Label

Recently, the dimensional approach has attracted much attention, bringing a paradigm shift to a continuum of understanding of different psychiatric disorders. In line with this new paradigm, we examined whether there was common functional connectivity related to various psychiatric disorders in an unsupervised manner without explicitly using diagnostic label information. To this end, we uniquely applied a newly developed network-based multiple clustering method to resting-state functional connectivity data, which allowed us to identify pairs of relevant brain subnetworks and subject cluster solutions accordingly. Thus, we identified four subject clusters, which were characterized as major depressive disorder (MDD), young healthy control (young HC), schizophrenia (SCZ)/bipolar disorder (BD), and autism spectrum disorder (ASD), respectively, with the relevant brain subnetwork represented by the cerebellum-thalamus-pallidum-temporal circuit. The clustering results were validated using independent datasets. This study is the first cross-disorder analysis in the framework of unsupervised learning of functional connectivity based on a data-driven brain subnetwork.

scholarly journals Sex-Related Neurocircuitry Supporting Camouflaging in Adults with Autism: Female Protection Insights

2021 ◽  
Author(s):  
Melissa Walsh ◽  
Broc Pagni ◽  
Leanna Monahan ◽  
Shanna Delaney ◽  
Christopher J Smith ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Cognitive Control ◽  
Emotion Recognition ◽  
Brain Connectivity ◽  
Data Driven ◽  
Sex Bias ◽  
Adults With Autism ◽  
Connectivity Patterns ◽  
Females With Autism

Background: The male preponderance in autism led to the hypothesis that aspects of female biology are protective against autism. Females with autism report engaging in more compensatory behaviors (i.e., camouflaging) to overcome autism-related social differences, which may be a downstream result of protective pathways. No studies have examined sex-related brain pathways supporting camouflaging in females with autism, despite its potential to inform mechanisms underlying the sex bias in autism. Methods: This study included 45 non-intellectually-disabled adults with autism (male/female: 21/24) and 40 neurotypical adults (male/female: 19/21) ages 18-71. We used group multivariate voxel pattern analysis to conduct a data-driven, connectome-wide characterization of "sex-atypical" (sex-by-diagnosis) and "sex-typical" (sex) brain functional connectivity features linked to camouflaging, and validated findings in females with autism multi-modally via structural connectometry. Exploratory associations with cognitive control, memory, emotion recognition, and depression/anxiety examined the adaptive nature of functional connectivity patterns supporting camouflaging in females with autism. Results: We found 1) "sex-atypical" functional connectivity patterns predicting camouflaging in the hypothalamus and precuneus and 2) "sex-typical" patterns in the anterior cingulate and right anterior parahippocampus. Higher hypothalamic functional connectivity with a limbic reward cluster was the strongest predictor of camouflaging in females with autism (a "sex-atypical" pattern), and also predicted better cognitive control/emotion recognition. Structural connectometry validated functional connectivity results with consistent brain pathways/effect patterns implicated across multi-modal findings in females with autism. Conclusion: This data-driven, connectome-wide characterization of "sex-atypical" and "sex-typical" brain connectivity features supporting compensatory social behavior in autism suggests hormones may play a role in the autism sex bias. Furthermore, both "male-typical" and "female-typical" brain connectivity patterns are implicated in camouflaging in females with autism in circuits associated with reward, emotion, and memory processing. "Sex-atypical" results are consistent with the fetal steroidogenic hypothesis, which would result in masculinized brain features in females with autism. However, female genetics/biology may contribute to "female-typical" patterns implicated in camouflaging.

scholarly journals Predictive Pattern Classification Can Distinguish Gender Identity Subtypes from Behavior and Brain Imaging

2020 ◽  
Vol 30 (5) ◽  
pp. 2755-2765 ◽  
Author(s):  
Benjamin Clemens ◽  
Birgit Derntl ◽  
Elke Smith ◽  
Jessica Junger ◽  
Josef Neulen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Functional Connectivity ◽  
Gender Identity ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Subjective Perception ◽  
Questionnaire Data ◽  
Resting State Functional Connectivity ◽  
Gender Dimorphism ◽  
Transgender Persons

Abstract The exact neurobiological underpinnings of gender identity (i.e., the subjective perception of oneself belonging to a certain gender) still remain unknown. Combining both resting-state functional connectivity and behavioral data, we examined gender identity in cisgender and transgender persons using a data-driven machine learning strategy. Intrinsic functional connectivity and questionnaire data were obtained from cisgender (men/women) and transgender (trans men/trans women) individuals. Machine learning algorithms reliably detected gender identity with high prediction accuracy in each of the four groups based on connectivity signatures alone. The four normative gender groups were classified with accuracies ranging from 48% to 62% (exceeding chance level at 25%). These connectivity-based classification accuracies exceeded those obtained from a widely established behavioral instrument for gender identity. Using canonical correlation analyses, functional brain measurements and questionnaire data were then integrated to delineate nine canonical vectors (i.e., brain-gender axes), providing a multilevel window into the conventional sex dichotomy. Our dimensional gender perspective captures four distinguishable brain phenotypes for gender identity, advocating a biologically grounded reconceptualization of gender dimorphism. We hope to pave the way towards objective, data-driven diagnostic markers for gender identity and transgender, taking into account neurobiological and behavioral differences in an integrative modeling approach.

scholarly journals Evaluation of functional connectivity in subdivisions of the thalamus in schizophrenia

2019 ◽  
Vol 214 (5) ◽  
pp. 288-296 ◽  
Author(s):  
Jinnan Gong ◽  
Cheng Luo ◽  
Xiangkui Li ◽  
Sisi Jiang ◽  
Budhachandra S. Khundrakpam ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Brain Connectivity ◽  
Anterior Cingulate ◽  
Data Driven ◽  
Thalamic Nuclei ◽  
Functional Connections ◽  
Duration Of Illness ◽  
Wide Range ◽  
Magnetic Resonance Imaging Mri ◽  
White Matter Tractography

BackgroundPrevious studies in schizophrenia revealed abnormalities in the cortico-cerebellar-thalamo-cortical circuit (CCTCC) pathway, suggesting the necessity for defining thalamic subdivisions in understanding alterations of brain connectivity.AimsTo parcellate the thalamus into several subdivisions using a data-driven method, and to evaluate the role of each subdivision in the alterations of CCTCC functional connectivity in patients with schizophrenia.MethodThere were 54 patients with schizophrenia and 42 healthy controls included in this study. First, the thalamic structural and functional connections computed, based on diffusion magnetic resonance imaging (MRI, white matter tractography) and resting-state functional MRI, were clustered to parcellate thalamus. Next, functional connectivity of each thalamus subdivision was investigated, and the alterations in thalamic functional connectivity for patients with schizophrenia were inspected.ResultsBased on the data-driven parcellation method, six thalamic subdivisions were defined. Loss of connectivity was observed between several thalamic subdivisions (superior-anterior, ventromedial and dorsolateral part of the thalamus) and the sensorimotor system, anterior cingulate cortex and cerebellum in patients with schizophrenia. A gradual pattern of dysconnectivity was observed across the thalamic subdivisions. Additionally, the altered connectivity negatively correlated with symptom scores and duration of illness in individuals with schizophrenia.ConclusionsThe findings of the study revealed a wide range of thalamic functional dysconnectivity in the CCTCC pathway, increasing our understanding of the relationship between the CCTCC pathway and symptoms associated with schizophrenia, and further indicating a potential alteration pattern in the thalamic nuclei in people with schizophrenia.Declaration of interestNone.

Interaction of the Salience Network, Ventral Attention Network, Dorsal Attention Network and Default Mode Network in Neonates and Early Development of the Bottom-up Attention System.

2021 ◽  
Author(s):  
Valeria Onofrj ◽  
Antonio Maria Chiarelli ◽  
Richard Wise ◽  
Cesare Colosimo ◽  
Massimo Caulo
Keyword(s):  
Functional Connectivity ◽  
Default Mode Network ◽  
Mediation Analysis ◽  
Data Driven ◽  
Salience Network ◽  
Bottom Up ◽  
Attention Network ◽  
Default Mode ◽  
Dorsal Attention Network

Abstract The Salience Network (SN), Ventral Attention Network (VAN), Dorsal Attention Network (DAN) and Default Mode Network (DMN) have shown significant interactions and overlapping functions in bottom-up and top-down mechanisms of attention. In the present study we tested if the SN, VAN, DAN and DMN connectivity can infer the gestational age (GA) at birth in a study group of 88 healthy neonates with GA at birth ranging from 28 to 40 weeks. We also ascertained whether the connectivity within each of the SN, VAN, DAN and DMN is able to infer the average functional connectivity of the others. The ability to infer GA at birth or another network's connectivity was evaluated using a multi-variate data-driven framework. A mediation analysis was performed in order to estimate the transmittance of change of a network’s functional connectivity (FC) over another mediated by the GA.The VAN, DAN and the DMN infer the GA at birth (p<0.05). The SN, DMN and VAN were able to infer the average connectivity over the other networks (p<0.05). Mediation analysis between VAN’s and DAN’s inference on GA found reciprocal transmittance of change of VAN’s and DAN’s connectivity (p<0.05). Our findings suggest that the VAN has a prominent role in the bottom-up salience detection in early infancy and that the role of the VAN and the SN may overlap in the bottom-up control of attention.

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