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

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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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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Group-level inference of information-based measures for the analyses of cognitive brain networks from neurophysiological data

10.1101/2021.08.14.456339 ◽

2021 ◽

Author(s):

Etienne Combrisson ◽

Michele Allegra ◽

Ruggero Basanisi ◽

Robin A. A. Ince ◽

Bruno L Giordano ◽

...

Keyword(s):

Functional Connectivity ◽

Scientific Practice ◽

Local Level ◽

Random Effect ◽

Brain Networks ◽

Brain Regions ◽

Group Level ◽

Starting Point ◽

Neurophysiological Data ◽

Statistical Approaches

The reproducibility crisis in neuroimaging and in particular in the case of underpowered studies has introduced doubts on our ability to reproduce, replicate and generalize findings. As a response, we have seen the emergence of suggested guidelines and principles for neuroscientists known as Good Scientific Practice for conducting more reliable research. Still, every study remains almost unique in its combination of analytical and statistical approaches. While it is understandable considering the diversity of designs and brain data recording, it also represents a striking point against reproducibility. Here, we propose a non-parametric permutation-based statistical framework, primarily designed for neurophysiological data, in order to perform group-level inferences on non-negative measures of information encompassing metrics from information-theory, machine-learning or measures of distances. The framework supports both fixed- and random-effect models to adapt to inter-individuals and inter-sessions variability. Using numerical simulations, we compared the accuity in ground-truth retrieving of both group models, such as test- and cluster-wise corrections for multiple comparisons. We then reproduced and extended existing results using both spatially uniform MEG and non-uniform intracranial neurophysiological data. We showed how the framework can be used to extract stereotypical task- and behavior-related effects across the population covering scales from the local level of brain regions, inter-areal functional connectivity to measures summarizing network properties. We also present a open-source Python toolbox called Frites that includes all of the methods used here, from functional connectivity estimations to the extraction of cognitive brain networks. Taken together, we believe that this framework deserves careful attention as its robustness and flexibility could be the starting point toward the uniformization of statistical approaches.

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The Functional Segregation and Integration Model: Mixture Model Representations of Consistent and Variable Group-Level Connectivity in fMRI

Neural Computation ◽

10.1162/neco_a_00877 ◽

2016 ◽

Vol 28 (10) ◽

pp. 2250-2290 ◽

Cited By ~ 3

Author(s):

Nathan W. Churchill ◽

Kristoffer Madsen ◽

Morten Mørup

Keyword(s):

Functional Connectivity ◽

Gaussian Mixture ◽

Brain Regions ◽

Model Parameters ◽

Group Level ◽

Integration Model ◽

Connectivity Structure ◽

Functional Relationships ◽

Subject Specific ◽

The Brain

The brain consists of specialized cortical regions that exchange information between each other, reflecting a combination of segregated (local) and integrated (distributed) processes that define brain function. Functional magnetic resonance imaging (fMRI) is widely used to characterize these functional relationships, although it is an ongoing challenge to develop robust, interpretable models for high-dimensional fMRI data. Gaussian mixture models (GMMs) are a powerful tool for parcellating the brain, based on the similarity of voxel time series. However, conventional GMMs have limited parametric flexibility: they only estimate segregated structure and do not model interregional functional connectivity, nor do they account for network variability across voxels or between subjects. To address these issues, this letter develops the functional segregation and integration model (FSIM). This extension of the GMM framework simultaneously estimates spatial clustering and the most consistent group functional connectivity structure. It also explicitly models network variability, based on voxel- and subject-specific network scaling profiles. We compared the FSIM to standard GMM in a predictive cross-validation framework and examined the importance of different model parameters, using both simulated and experimental resting-state data. The reliability of parcellations is not significantly altered by flexibility of the FSIM, whereas voxel- and subject-specific network scaling profiles significantly improve the ability to predict functional connectivity in independent test data. Moreover, the FSIM provides a set of interpretable parameters to characterize both consistent and variable aspects functional connectivity structure. As an example of its utility, we use subject-specific network profiles to identify brain regions where network expression predicts subject age in the experimental data. Thus, the FSIM is effective at summarizing functional connectivity structure in group-level fMRI, with applications in modeling the relationships between network variability and behavioral/demographic variables.

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Subject identification using edge-centric functional connectivity

10.1101/2020.09.13.291898 ◽

2020 ◽

Author(s):

Youngheun Jo ◽

Joshua Faskowitz ◽

Farnaz Zamani Esfahlani ◽

Olaf Sporns ◽

Richard F. Betzel

Keyword(s):

Individual Differences ◽

Functional Connectivity ◽

Spatial Scales ◽

Neural Substrates ◽

Brain Regions ◽

Group Level ◽

Complementary Approach ◽

Subject Specific ◽

Subject Identification ◽

Clinical Conditions

Group-level studies do not capture individual differences in network organization, an important prerequisite for understanding neural substrates shaping behavior and for developing interventions in clinical conditions. Recent studies have employed “fingerprinting” analyses on functional connectivity to identify subjects’ idiosyncratic features. Here, we develop a complementary approach based on an edge-centric model of functional connectivity, which focuses on the co-fluctuations of edges. We first show whole-brain edge functional connectivity (eFC) to be a robust substrate that improves identifiability over nodal FC (nFC) across different datasets and parcellations. Next, we characterized subjects’ identifiability at different spatial scales, from single nodes to the level of functional systems and clusters using k-means clustering. Across spatial scales, we find that heteromodal brain regions exhibit consistently greater identifiability than unimodal, sensorimotor, and limbic regions. Lastly, we show that identifiability can be further improved by reconstructing eFC using specific subsets of its principal components. In summary, our results highlight the utility of the edge-centric network model for capturing meaningful subject-specific features and sets the stage for future investigations into individual differences using edge-centric models.

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Altered Functional Connectivity and Sensory Processing in Blepharospasm and Hemifacial Spasm: Coexistence and Difference

Frontiers in Neurology ◽

10.3389/fneur.2021.759869 ◽

2021 ◽

Vol 12 ◽

Author(s):

Ting-Chun Fang ◽

Chun-Ming Chen ◽

Ming-Hong Chang ◽

Chen-Hao Wu ◽

Yi-Jen Guo

Keyword(s):

Functional Connectivity ◽

Sensory Processing ◽

Hemifacial Spasm ◽

Resting State ◽

Large Scale ◽

Detection Threshold ◽

Image Data ◽

Brain Regions ◽

Resting State Functional Connectivity ◽

The Right

Background: Blepharospasm (BSP) and hemifacial spasm (HFS) are both facial hyperkinesia however BSP is thought to be caused by maladaptation in multiple brain regions in contrast to the peripherally induced cause in HFS. Plausible coexisting pathophysiologies between these two distinct diseases have been proposed.Objectives: In this study, we compared brain resting state functional connectivity (rsFC) and quantitative thermal test (QTT) results between patients with BSP, HFS and heathy controls (HCs).Methods: This study enrolled 12 patients with BSP, 11 patients with HFS, and 15 HCs. All subjects received serial neuropsychiatric evaluations, questionnaires determining disease severity and functional impairment, QTT, and resting state functional MRI. Image data were acquired using seed-based analyses using the CONN toolbox.Results: A higher cold detection threshold was found in the BSP and HFS patients compared to the HCs. The BSP and HFS patients had higher rsFC between the anterior cerebellum network and left occipital regions compared to the HCs. In all subjects, impaired cold detection threshold in the QTT of lower extremities had a correlation with higher rsFC between the anterior cerebellar network and left lingual gyrus. Compared to the HCs, increased rsFC in right postcentral gyrus in the BSP patients and decreased rsFC in the right amygdala and frontal orbital cortex in the HFS subjects were revealed when the anterior cerebellar network was used as seed.Conclusions: Dysfunction of sensory processing detected by the QTT is found in the BSP and HSP patients. Altered functional connectivity between the anterior cerebellar network and left occipital region, especially the Brodmann area 19, may indicate the possibility of shared pathophysiology among BSP, HFS, and impaired cold detection threshold. Further large-scale longitudinal study is needed for testing this theory in the future.

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

Brain Sciences ◽

10.3390/brainsci11111531 ◽

2021 ◽

Vol 11 (11) ◽

pp. 1531

Author(s):

Anastasia Papaioannou ◽

Eva Kalantzi ◽

Christos C. Papageorgiou ◽

Kalliopi Korombili ◽

Anastasia Bokou ◽

...

Keyword(s):

Functional Connectivity ◽

Brain Region ◽

Brain Activity ◽

Behavioral Responses ◽

Brain Regions ◽

Autism Spectrum ◽

Behavioral Data ◽

Multiscale Entropy ◽

Behavioral Measures ◽

And Control

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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Analysis of functional connectivity and oscillatory power using DICS: from raw MEG data to group-level statistics in Python

10.1101/245530 ◽

2018 ◽

Author(s):

Marijn van Vliet ◽

Mia Liljeström ◽

Susanna Aro ◽

Riitta Salmelin ◽

Jan Kujala

Keyword(s):

Functional Connectivity ◽

Spectral Power ◽

Brain Regions ◽

Dynamic Imaging ◽

Oscillatory Activity ◽

Complete Analysis ◽

Cortical Mapping ◽

Group Level ◽

Connectivity Analysis ◽

Level Statistics

AbstractCommunication between brain regions is thought to be facilitated by the synchronization of oscillatory activity. Hence, large-scale functional networks within the brain may be estimated by measuring synchronicity between regions. Neurophysiological recordings, such as magnetoencephalography (MEG) and electroencephalography (EEG), provide a direct measure of oscillatory neural activity with millisecond temporal resolution. In this paper, we describe a full data analysis pipeline for functional connectivity analysis based on dynamic imaging of coherent sources (DICS) of MEG data. DICS is a beamforming technique in the frequency-domain that enables the study of the cortical sources of oscillatory activity and synchronization between brain regions. All the analysis steps, starting from the raw MEG data up to publication-ready group-level statistics and visualization, are discussed in depth, including methodological considerations, rules of thumb and tradeoffs. We start by computing cross-spectral density (CSD) matrices using a wavelet approach in several frequency bands (alpha, theta, beta, gamma). We then provide a way to create comparable source spaces across subjects and discuss the cortical mapping of spectral power. For connectivity analysis, we present a canonical computation of coherence that facilitates a stable estimation of all-to-all connectivity. Finally, we use group-level statistics to limit the network to cortical regions for which significant differences between experimental conditions are detected and produce vertex-and parcel-level visualizations of the different brain networks. Code examples using the MNE-Python package are provided at each step, guiding the reader through a complete analysis of the freely available openfMRI ds000117 “familiar vs. unfamiliar vs. scrambled faces” dataset. The goal is to educate both novice and experienced data analysts with the “tricks of the trade” necessary to successfully perform this type of analysis on their own data.

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Engaging regularly with fiction influences connectivity in cortical areas for language and mentalizing

10.31234/osf.io/e7bqj ◽

2017 ◽

Author(s):

Roel M. Willems ◽

Franziska Hartung

Keyword(s):

Functional Connectivity ◽

Time Course ◽

Language Skills ◽

Brain Regions ◽

Brain Areas ◽

Daily Lives ◽

Cortical Areas ◽

Social Abilities ◽

Behavioral Evidence ◽

Amount Of Reading

Behavioral evidence suggests that engaging with fiction is positively correlated with social abilities. The rationale behind this link is that engaging with fictional narratives offers a ‘training modus’ for mentalizing and empathizing. We investigated the influence of the amount of reading that participants report doing in their daily lives, on connections between brain areas while they listened to literary narratives. Participants (N=57) listened to two literary narratives while brain activation was measured with fMRI. We computed time-course correlations between brain regions, and compared the correlation values from listening to narratives to listening to reversed speech. The between-region correlations were then related to the amount of fiction that participants read in their daily lives. Our results show that amount of fiction reading is related to functional connectivity in areas known to be involved in language and mentalizing. This suggests that reading fiction influences social cognition as well as language skills.

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The effect of congenital blindness on resting-state functional connectivity revisited

Scientific Reports ◽

10.1038/s41598-021-91976-9 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Maria J. S. Guerreiro ◽

Madita Linke ◽

Sunitha Lingareddy ◽

Ramesh Kekunnaya ◽

Brigitte Röder

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Visual Experience ◽

Brain Regions ◽

State Condition ◽

Resting State Functional Connectivity ◽

Congenital Blindness ◽

Eyes Closed ◽

Congenitally Blind ◽

Eyes Open

AbstractLower resting-state functional connectivity (RSFC) between ‘visual’ and non-‘visual’ neural circuits has been reported as a hallmark of congenital blindness. In sighted individuals, RSFC between visual and non-visual brain regions has been shown to increase during rest with eyes closed relative to rest with eyes open. To determine the role of visual experience on the modulation of RSFC by resting state condition—as well as to evaluate the effect of resting state condition on group differences in RSFC—, we compared RSFC between visual and somatosensory/auditory regions in congenitally blind individuals (n = 9) and sighted participants (n = 9) during eyes open and eyes closed conditions. In the sighted group, we replicated the increase of RSFC between visual and non-visual areas during rest with eyes closed relative to rest with eyes open. This was not the case in the congenitally blind group, resulting in a lower RSFC between ‘visual’ and non-‘visual’ circuits relative to sighted controls only in the eyes closed condition. These results indicate that visual experience is necessary for the modulation of RSFC by resting state condition and highlight the importance of considering whether sighted controls should be tested with eyes open or closed in studies of functional brain reorganization as a consequence of blindness.

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Cochlear activity in silent cue-target intervals shows a theta-rhythmic pattern and is correlated to attentional alpha and theta modulations

BMC Biology ◽

10.1186/s12915-021-00992-8 ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Moritz Herbert Albrecht Köhler ◽

Gianpaolo Demarchi ◽

Nathan Weisz

Keyword(s):

Selective Attention ◽

Brain Regions ◽

Attentional Focus ◽

Theta Activity ◽

Superior Olivary Complex ◽

Rhythmic Pattern ◽

Auditory Input ◽

Auditory Selective Attention ◽

Beta Power ◽

Cortical Regions

AbstractBackgroundA long-standing debate concerns where in the processing hierarchy of the central nervous system (CNS) selective attention takes effect. In the auditory system, cochlear processes can be influenced via direct and mediated (by the inferior colliculus) projections from the auditory cortex to the superior olivary complex (SOC). Studies illustrating attentional modulations of cochlear responses have so far been limited to sound-evoked responses. The aim of the present study is to investigate intermodal (audiovisual) selective attention in humans simultaneously at the cortical and cochlear level during a stimulus-free cue-target interval.ResultsWe found that cochlear activity in the silent cue-target intervals was modulated by a theta-rhythmic pattern (~ 6 Hz). While this pattern was present independently of attentional focus, cochlear theta activity was clearly enhanced when attending to the upcoming auditory input. On a cortical level, classical posterior alpha and beta power enhancements were found during auditory selective attention. Interestingly, participants with a stronger release of inhibition in auditory brain regions show a stronger attentional modulation of cochlear theta activity.ConclusionsThese results hint at a putative theta-rhythmic sampling of auditory input at the cochlear level. Furthermore, our results point to an interindividual variable engagement of efferent pathways in an attentional context that are linked to processes within and beyond processes in auditory cortical regions.

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