scholarly journals Transient patterns of functional dysconnectivity in youth with psychosis spectrum symptoms

2018 ◽  
Author(s):  
Eva Mennigen ◽  
Dietsje D. Jolles ◽  
Catherine E. Hegarty ◽  
Mohan Gupta ◽  
Maria Jalbrzikowski ◽  
...  
Keyword(s):  
Help Seeking ◽  
Large Scale ◽  
Brain Connectivity ◽  
Network Connectivity ◽  
Resting State Fmri ◽  
Psychotic Symptoms ◽  
Whole Brain ◽  
State Dependent ◽  
Functional Brain Networks ◽  
Window Approach

AbstractPsychosis spectrum disorders are conceptualized as neurodevelopmental disorders accompanied by disruption of large-scale functional brain networks. Both static and dynamic dysconnectivity have been described in patients with schizophrenia and, more recently, in help-seeking individuals at clinical high-risk for psychosis. Less is known, however, about developmental aspects of dynamic functional network connectivity (FNC) associated with psychotic symptoms (PS) in the general population. Here, we investigate resting state fMRI data using established dynamic FNC methods in the Philadelphia Neurodevelopmental Cohort (ages 8-22), including 129 participants experiencing PS and 452 participants without PS (non-PS).Applying a sliding window approach and k-means clustering, 5 dynamic states with distinct whole-brain connectivity patterns were identified. PS-associated dysconnectivity was most prominent in states characterized by synchronization or antagonism of the default mode network (DMN) and cognitive control (CC) domains. Hyperconnectivity between DMN, salience, and CC domains in PS youth only occurred in a state characterized by synchronization of the DMN and CC domains, a state that also becomes less frequent with age. However, dysconnectivity of the sensorimotor and visual systems in PS youth was revealed in other transient states completing the picture of whole-brain dysconnectivity patterns associated with PS.Overall, state-dependent dysconnectivity was observed in PS youth, providing the first evidence that disruptions of dynamic functional connectivity are present across a broader psychosis continuum.

scholarly journals Lynn_etal_MathSkills_CPM_Preprint_V1_05July2021

2021 ◽  
Author(s):  
Andrew Lynn ◽  
Eric D. Wilkey ◽  
Gavin Price
Keyword(s):  
Functional Connectivity ◽  
Predictive Modeling ◽  
Brain Connectivity ◽  
Network Connectivity ◽  
Brain Regions ◽  
Math Skills ◽  
Whole Brain ◽  
Building Models ◽  
State Dependent ◽  
Brain Behavior

The human brain comprises multiple canonical networks, several of which are distributed across frontal, parietal, and temporooccipital regions. Studies report both positive and negative correlations between children’s math skills and the strength of functional connectivity among these regions during math-related tasks and at rest. Yet, it is unclear how the relation between children’s math skills and functional connectivity map onto patterns of distributed whole-brain connectivity, canonical network connectivity, and whether these relations are consistent across different task-states. We used connectome-based predictive modeling to test whether functional connectivity during number comparison and at rest predicts children’s math skills (N=31, Mage=9.21years) using distributed whole-brain connections versus connections among canonical networks. We found that weaker connectivity distributed across the whole brain and weaker connectivity between key math-related brain regions in specific canonical networks predicts better math skills in childhood. The specific connections predicting math skills, and whether they were distributed or mapped onto canonical networks, varied between tasks, suggesting that state-dependent rather than trait-level functional network architectures support children’s math skills. Furthermore, the current predictive modeling approach moves beyond brain-behavior correlations and toward building models of brain connectivity that may eventually aid in predicting future math skills.

scholarly journals Directed brain connectivity identifies widespread functional network changes in Parkinson’s disease

2021 ◽  
Author(s):  
Mite Mijalkov ◽  
Giovanni Volpe ◽  
Joana B. Pereira
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Large Scale ◽  
Neurodegenerative Disorder ◽  
Brain Connectivity ◽  
Brain Regions ◽  
New Method ◽  
Functional Network ◽  
Whole Brain ◽  
Functional Brain Networks

AbstractParkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by topological changes in large-scale functional brain networks. These networks are commonly analysed using undirected correlations between the activation signals of brain regions. However, this approach suffers from an important drawback: it assumes that brain regions get activated at the same time, despite previous evidence showing that brain activation features causality, with signals being typically generated in one region and then propagated to other ones. Thus, in order to address this limitation, in this study we developed a new method to assess whole-brain directed functional connectivity in patients with PD and healthy controls using anti-symmetric delayed correlations, which capture better this underlying causality. To test the potential of this new method, we compared it to standard connectivity analyses based on undirected correlations. Our results show that whole-brain directed connectivity identifies widespread changes in the functional networks of PD patients compared to controls, in contrast to undirected methods. These changes are characterized by increased global efficiency, clustering and transitivity as well as lower modularity. In addition, changes in the directed connectivity patterns in the precuneus, thalamus and superior frontal gyrus were associated with motor, executive and memory deficits in PD patients. Altogether, these findings suggest that directional brain connectivity is more sensitive to functional network changes occurring in PD compared to standard methods. This opens new opportunities for the analysis of brain connectivity and the development of new brain connectivity markers to track PD progression.

Tracking whole-brain connectivity dynamics in the resting-state fMRI with post-facial paralysis synkinesis

2021 ◽  
Author(s):  
Zhen-Zhen Ma ◽  
Jia-Jia Wu ◽  
Xu-Yun Hua ◽  
Mou-Xiong Zheng ◽  
Xiang-Xin Xing ◽  
...  
Keyword(s):  
Facial Paralysis ◽  
Resting State ◽  
Brain Connectivity ◽  
Resting State Fmri ◽  
Whole Brain

scholarly journals Sex differences in brain correlates of STEM anxiety

2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Ariel A. Gonzalez ◽  
Katherine L. Bottenhorn ◽  
Jessica E. Bartley ◽  
Timothy Hayes ◽  
Michael C. Riedel ◽  
...  
Keyword(s):  
Sex Differences ◽  
Academic Performance ◽  
Large Scale ◽  
Math Anxiety ◽  
Brain Connectivity ◽  
Learning Experience ◽  
Network Connectivity ◽  
Male Students ◽  
Stem Learning ◽  
Default Mode

Abstract Anxiety is known to dysregulate the salience, default mode, and central executive networks of the human brain, yet this phenomenon has not been fully explored across the STEM learning experience, where anxiety can impact negatively academic performance. Here, we evaluated anxiety and large-scale brain connectivity in 101 undergraduate physics students. We found sex differences in STEM-related and clinical anxiety, with longitudinal increases in science anxiety observed for both female and male students. Sex-specific relationships between STEM anxiety and brain connectivity emerged, with male students exhibiting distinct inter-network connectivity for STEM and clinical anxiety, and female students demonstrating no significant within-sex correlations. Anxiety was negatively correlated with academic performance in sex-specific ways at both pre- and post-instruction. Moreover, math anxiety in male students mediated the relation between default mode-salience connectivity and course grade. Together, these results reveal complex sex differences in the neural mechanisms driving how anxiety is related to STEM learning.

scholarly journals S198. SELF-DISTURBANCES AND THEIR NEURAL SIGNATURES IN SCHIZOPHRENIA

2020 ◽  
Vol 46 (Supplement_1) ◽  
pp. S114-S114
Author(s):  
Yulia Zaytseva ◽  
Eva Kozakova ◽  
Pavel Mohr ◽  
Filip Spaniel ◽  
Aaron Mishara
Keyword(s):  
Resting State ◽  
Brain Connectivity ◽  
Resting State Fmri ◽  
Psychotic Symptoms ◽  
Healthy Controls ◽  
Healthy Individuals ◽  
Motor Speech ◽  
Cognitive Domains ◽  
Aberrant Salience ◽  
First Rank Symptoms

Abstract Background The self-disturbances (SDs) concept is considered to be part of the Schneider’s first rank symptoms, i.e., thought-withdrawal, thought-insertion, thought-broadcasting, somatic-passivity experiences, mental/motor automatisms, disrupted unitary self-experience (Mishara et al., 2014). SDs were originally described by W. Mayer-Gross (1920), who observed them in psychotic patients. Methods We classified Mayer-Gross’ findings on SDs into the following categories: experience is new/compelling (aberrant salience), reduced access/importance of autobiographical past, cognitions/emotions occur independently from self’s volition, foreign agents have power over self and developed an SDs scale based on these categories and cognitive domains (perception, motor, speech, thinking etc.). Scale is applied as a measure of the frequency of the experiences. In our current study on phenomenology and neurobiology of psychotic symptoms, we administered the scale to a study group of patients with schizophrenia (N=84) and healthy volunteers (N=170). Further, the resting state fMRI was performed and the group was divided into two subgroups with (N=13) and without self-disturbances (N=10) and in healthy individuals (N=39). Results We found substantial differences in the frequency of self-disturbances in patients with schizophrenia compared to healthy controls (total score differences, Z=-5.83, p< 0.001). On a neural level, patients with self-disturbances experienced a decreased functional brain connectivity of the default mode and salience networks as compared to the patients without self-disturbances and healthy controls. The differences were mainly explained by the factor ‘’foreign agents’’ and the novelty of the experience. Discussion The scale identifies self-disturbances in schizophrenia and confirms self-related processing in patients with schizophrenia to be associated with altered activation in the cortical midline structures. Supported by the grant projects MH CR AZV 17-32957A and MEYS NPU4NUDZ: LO1611.

scholarly journals Attention Networks and the Intrinsic Network Structure of the Human Brain

2021 ◽  
Author(s):  
Sebastian Markett ◽  
David Nothdurfter ◽  
Antonia Focsa ◽  
Martin Reuter ◽  
Philippe Jawinski
Keyword(s):  
Network Structure ◽  
Network Theory ◽  
Large Scale ◽  
Brain Connectivity ◽  
Resting State Fmri ◽  
Relevant Information ◽  
Attention Network ◽  
Attention Networks ◽  
Large Scale Networks ◽  
The Brain

Attention network theory states that attention is not a unified construct but consists of three independent systems that are supported by separable distributed networks: an alerting network to deploy attentional resources in anticipation of upcoming events, an orienting network to direct attention to a cued location, and a control network to select relevant information at the expense of concurrently available information. Ample behavioral and neuroimaging evidence supports the dissociation of the three attention domains. The strong assumption that each attentional system is realized through a separable network, however, raises the question how these networks relate to the intrinsic network structure of the brain. Our understanding of brain networks has advanced majorly in the past years due to the increasing focus on brain connectivity. It is well established that the brain is intrinsically organized into several large-scale networks whose modular structure persists across task states. Existing proposals on how the presumed attention networks relate to intrinsic networks rely mostly on anecdotal and partly contradictory arguments. We addressed this issue by mapping different attention networks with highest spatial precision at the level of cifti-grayordinates. Resulting group maps were compared to the group-level topology of 23 intrinsic networks which we reconstructed from the same participants' resting state fMRI data. We found that all attention domains recruited multiple and partly overlapping intrinsic networks and converged in the dorsal fronto-parietal and midcingulo-insular network. While we observed a preference of each attentional domain for its own set of intrinsic networks, implicated networks did not match well to those proposed in the literature. Our results indicate a necessary refinement of the attention network theory.

scholarly journals Post-stroke fatigue is linked to resting state posterior hypoactivity and prefrontal hyperactivity

2021 ◽  
Author(s):  
Georgia Mary Cotter ◽  
Mohamed Salah Khlif ◽  
Laura Bird ◽  
Mark E Howard ◽  
Amy Brodtmann ◽  
...  
Keyword(s):  
Ischaemic Stroke ◽  
Resting State ◽  
Large Scale ◽  
Brain Activity ◽  
Low Frequency ◽  
Resting State Fmri ◽  
Stroke Severity ◽  
Stroke Survivors ◽  
Whole Brain ◽  
Post Stroke

Background and Purpose. Fatigue is associated with poor functional outcomes and increased mortality following stroke. Survivors identify fatigue as one of their key unmet needs. Despite the growing body of research into post-stroke fatigue, the specific neural mechanisms remain largely unknown. Methods. This observational study included 63 stroke survivors (22 women; age 30-89 years; mean 67.5 years) from the Cognition And Neocortical Volume After Stroke (CANVAS) study, a cohort study examining cognition, mood, and brain volume in stroke survivors following ischaemic stroke. Participants underwent brain imaging 3 months post-stroke, including a 7-minute resting state fMRI echoplanar sequence. We calculated the fractional amplitude of low-frequency fluctuations, a measure of resting state brain activity at the whole-brain level. Results. Forty-five participants reported experiencing post-stroke fatigue as measured by an item on the Patient Health Questionnaire-9. A generalised linear regression model analysis with age, sex, and stroke severity covariates was conducted to compare resting state brain activity in the 0.01-0.08 Hz range, as well as its subcomponents - slow-5 (0.01-0.027 Hz), and slow-4 (0.027-0.073 Hz) frequency bands between fatigued and non-fatigued participants. We found no significant associations between post-stroke fatigue and ischaemic stroke lesion location or stroke volume. However, in the overall 0.01-0.08 Hz band, participants with post-stroke fatigue demonstrated significantly lower resting-state activity in the calcarine cortex (p<0.001, cluster-corrected pFDR=0.009, k=63) and lingual gyrus (p<0.001, cluster-corrected pFDR=0.025, k=42) and significantly higher activity in the medial prefrontal cortex (p<0.001, cluster-corrected pFDR=0.03, k=45), attributed to slow-4 and slow-5 oscillations, respectively. Conclusions. Post-stroke fatigue is associated with posterior hypoactivity and prefrontal hyperactivity, reflecting dysfunction within large-scale brain systems such as fronto-striatal-thalamic and frontal-occipital networks. These systems in turn might reflect a relationship between post-stroke fatigue and abnormalities in executive and visual functioning. This first whole-brain resting-state study provides new targets for further investigation of post-stroke fatigue beyond the lesion approach.

scholarly journals Sex differences in brain correlates of STEM anxiety

2019 ◽  
Author(s):  
Ariel A. Gonzalez ◽  
Katherine L. Bottenhorn ◽  
Jessica E. Bartley ◽  
Timothy Hayes ◽  
Michael C. Riedel ◽  
...  
Keyword(s):  
Sex Differences ◽  
Academic Performance ◽  
Large Scale ◽  
Math Anxiety ◽  
Brain Connectivity ◽  
Learning Experience ◽  
Network Connectivity ◽  
Male Students ◽  
Stem Learning ◽  
Default Mode

ABSTRACTAnxiety is known to dysregulate the salience, default mode, and central executive networks of the human brain, yet this phenomenon has not been fully explored across the STEM learning experience, where anxiety can impact negatively academic performance. Here, we evaluated anxiety and large-scale brain connectivity in 101 undergraduate physics students. We found sex differences in STEM-related but not clinical anxiety, with longitudinal increases in science anxiety observed for both female and male students. Sex-specific impacts of STEM anxiety on brain connectivity emerged, with male students exhibiting distinct inter-network connectivity for STEM and clinical anxiety and female students demonstrating no significant within-sex correlations. Anxiety was negatively correlated with academic performance in sex-specific ways at both pre-and post-instruction. Moreover, math anxiety in male students mediated the relation between default mode-salience connectivity and course grade. Together, these results reveal complex sex differences in the neural mechanisms driving how anxiety impacts STEM learning.

scholarly journals Individual Variation in Brain Network Topology Predicts Emotional Intelligence

2018 ◽  
Author(s):  
Ling George ◽  
Lee Ivy ◽  
Guimond Synthia ◽  
Lutz Olivia ◽  
Tandon Neeraj ◽  
...  
Keyword(s):  
Emotional Intelligence ◽  
Social Cognition ◽  
Network Topology ◽  
Resting State ◽  
Large Scale ◽  
Psychotic Disorders ◽  
Brain Connectivity ◽  
Distance Matrix ◽  
Resting State Fmri ◽  
Brain Network

AbstractBackgroundSocial cognitive ability is a significant determinant of functional outcome and deficits in social cognition are a disabling symptom of psychotic disorders. The neurobiological underpinnings of social cognition are not well understood, hampering our ability to ameliorate these deficits.ObjectiveUsing ‘resting-state’ fMRI (functional magnetic resonance imaging) and a trans-diagnostic, data-driven analytic strategy, we sought to identify the brain network basis of emotional intelligence, a key domain of social cognition.MethodsStudy participants included 60 participants with a diagnosis of schizophrenia or schizoaffective disorder and 46 healthy comparison participants. All participants underwent a resting-state fMRI scan. Emotional Intelligence was measured using the Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT). A connectome-wide analysis of brain connectivity examined how each individual brain voxel’s connectivity correlated with emotional intelligence using multivariate distance matrix regression (MDMR).ResultsWe identified a region in the left superior parietal lobule (SPL) where individual network topology predicted emotional intelligence. Specifically, the association of this region with the Default Mode Network predicted higher emotional intelligence and association with the Dorsal Attention Network predicted lower emotional intelligence. This correlation was observed in both schizophrenia and healthy comparison participants.ConclusionPrevious studies have demonstrated individual variance in brain network topology but the cognitive or behavioral relevance of these differences was undetermined. We observe that the left SPL, a region of high individual variance at the cytoarchitectonic level, also demonstrates individual variance in its association with large scale brain networks and that network topology predicts emotional intelligence.

scholarly journals Widespread attenuating changes in brain connectivity associated with the general factor of psychopathology in 9- and 10-year olds

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chandra Sripada ◽  
Mike Angstadt ◽  
Aman Taxali ◽  
Daniel Kessler ◽  
Tristan Greathouse ◽  
...  
Keyword(s):  
Large Scale ◽  
Brain Connectivity ◽  
Network Connectivity ◽  
Quadrant Analysis ◽  
Default Network ◽  
General Factor ◽  
Multiple Control ◽  
Control Networks ◽  
Large Scale Networks ◽  
P Factor

AbstractConvergent research identifies a general factor (“P factor”) that confers transdiagnostic risk for psychopathology. Large-scale networks are key organizational units of the human brain. However, studies of altered network connectivity patterns associated with the P factor are limited, especially in early adolescence when most mental disorders are first emerging. We studied 11,875 9- and 10-year olds from the Adolescent Brain and Cognitive Development (ABCD) study, of whom 6593 had high-quality resting-state scans. Network contingency analysis was used to identify altered interconnections associated with the P factor among 16 large-scale networks. These connectivity changes were then further characterized with quadrant analysis that quantified the directionality of P factor effects in relation to neurotypical patterns of positive versus negative connectivity across connections. The results showed that the P factor was associated with altered connectivity across 28 network cells (i.e., sets of connections linking pairs of networks); pPERMUTATION values < 0.05 FDR-corrected for multiple comparisons. Higher P factor scores were associated with hypoconnectivity within default network and hyperconnectivity between default network and multiple control networks. Among connections within these 28 significant cells, the P factor was predominantly associated with “attenuating” effects (67%; pPERMUTATION < 0.0002), i.e., reduced connectivity at neurotypically positive connections and increased connectivity at neurotypically negative connections. These results demonstrate that the general factor of psychopathology produces attenuating changes across multiple networks including default network, involved in spontaneous responses, and control networks involved in cognitive control. Moreover, they clarify mechanisms of transdiagnostic risk for psychopathology and invite further research into developmental causes of distributed attenuated connectivity.

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