Shrinkage prediction of seed-voxel brain connectivity using resting state fMRI

AbstractNeurofeedback (NF) aptitude, which refers to an individual’s ability to change its brain activity through NF training, has been reported to vary significantly from person to person. The prediction of individual NF aptitudes is critical in clinical NF applications. In the present study, we extracted the resting-state functional brain connectivity (FC) markers of NF aptitude independent of NF-targeting brain regions. We combined the data in fMRI-NF studies targeting four different brain regions at two independent sites (obtained from 59 healthy adults and six patients with major depressive disorder) to collect the resting-state fMRI data associated with aptitude scores in subsequent fMRI-NF training. We then trained the regression models to predict the individual NF aptitude scores from the resting-state fMRI data using a discovery dataset from one site and identified six resting-state FCs that predicted NF aptitude. Next we validated the prediction model using independent test data from another site. The result showed that the posterior cingulate cortex was the functional hub among the brain regions and formed predictive resting-state FCs, suggesting NF aptitude may be involved in the attentional mode-orientation modulation system’s characteristics in task-free resting-state brain activity.

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Network analysis of functional brain connectivity in borderline personality disorder using resting-state fMRI

NeuroImage Clinical ◽

10.1016/j.nicl.2016.02.006 ◽

2016 ◽

Vol 11 ◽

pp. 302-315 ◽

Cited By ~ 32

Author(s):

Tingting Xu ◽

Kathryn R. Cullen ◽

Bryon Mueller ◽

Mindy W. Schreiner ◽

Kelvin O. Lim ◽

...

Keyword(s):

Borderline Personality Disorder ◽

Personality Disorder ◽

Network Analysis ◽

Resting State ◽

Brain Connectivity ◽

Resting State Fmri ◽

Borderline Personality ◽

Functional Brain

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Influences of Hunger, Satiety and Oral Glucose on Functional Brain Connectivity: A Multimethod Resting-State fMRI Study

Neuroscience ◽

10.1016/j.neuroscience.2018.04.029 ◽

2018 ◽

Vol 382 ◽

pp. 80-92 ◽

Cited By ~ 15

Author(s):

Arkan Al-Zubaidi ◽

Marcus Heldmann ◽

Alfred Mertins ◽

Kamila Jauch-Chara ◽

Thomas F. Münte

Keyword(s):

Resting State ◽

Brain Connectivity ◽

Resting State Fmri ◽

Oral Glucose ◽

Functional Brain ◽

Fmri Study

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S198. SELF-DISTURBANCES AND THEIR NEURAL SIGNATURES IN SCHIZOPHRENIA

Schizophrenia Bulletin ◽

10.1093/schbul/sbaa031.264 ◽

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.

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Functional brain connectivity in resting-state fMRI using phase and magnitude data

Journal of Neuroscience Methods ◽

10.1016/j.jneumeth.2017.10.016 ◽

2018 ◽

Vol 293 ◽

pp. 299-309 ◽

Cited By ~ 12

Author(s):

Zikuan Chen ◽

Arvind Caprihan ◽

Eswar Damaraju ◽

Srinivas Rachakonda ◽

Vince Calhoun

Keyword(s):

Resting State ◽

Brain Connectivity ◽

Resting State Fmri ◽

Functional Brain

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Disrupted Functional Brain Connectivity in Partial Epilepsy: A Resting-State fMRI Study

PLoS ONE ◽

10.1371/journal.pone.0028196 ◽

2012 ◽

Vol 7 (1) ◽

pp. e28196 ◽

Cited By ~ 63

Author(s):

Cheng Luo ◽

Chuan Qiu ◽

Zhiwei Guo ◽

Jiajia Fang ◽

Qifu Li ◽

...

Keyword(s):

Resting State ◽

Brain Connectivity ◽

Resting State Fmri ◽

Partial Epilepsy ◽

Functional Brain ◽

Fmri Study

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Concurrent tACS-fMRI reveals causal influence of power synchronized neural activity on resting state fMRI connectivity

10.1101/122820 ◽

2017 ◽

Cited By ~ 1

Author(s):

Marc Bächinger ◽

Valerio Zerbi ◽

Marius Moisa ◽

Rafael Polania ◽

Quanying Liu ◽

...

Keyword(s):

Resting State ◽

Right Hemisphere ◽

Brain Connectivity ◽

Resting State Fmri ◽

Causal Link ◽

Resting State Networks ◽

Novel Approach ◽

Electrophysiological Signals ◽

Power Fluctuations ◽

Transcranial Alternating Current Stimulation

AbstractResting state fMRI (rs-fMRI) is commonly used to study the brain’s intrinsic neural coupling, which reveals specific spatiotemporal patterns in the form of resting state networks (RSN). It has been hypothesized that slow rs-fMRI oscillations (<0.1 Hz) are driven by underlying electrophysiological rhythms that typically occur at much faster timescales (>5 Hz); however, causal evidence for this relationship is currently lacking. Here we measured rs-fMRI in humans while applying transcranial alternating current stimulation (tACS) to entrain brain rhythms in left and right sensorimotor cortices.The two driving tACS signals were tailored to the individual’s alpha rhythm (8-12 Hz) and fluctuated in amplitude according to a 1 Hz power envelope. We entrained the left versus right hemisphere in accordance to two different coupling modes where either alpha oscillations were synchronized between hemispheres (phase-synchronized tACS) or the slower oscillating power envelopes (power-synchronized tACS).Power-synchronized tACS significantly increased rs-fMRI connectivity within the stimulated RSN compared to phase-synchronized or no tACS. This effect outlasted the stimulation period and tended to be more effective in individuals who exhibited a naturally weak interhemispheric coupling. Using this novel approach, our data provide causal evidence that synchronized power fluctuations contribute to the formation of fMRI-based RSNs. Moreover, our findings demonstrate that the brain’s intrinsic coupling at rest can be selectively modulated by choosing appropriate tACS signals, which could lead to new interventions for patients with altered rs-fMRI connectivity.Significance StatementResting state fMRI has become an important tool to estimate brain connectivity. However, relatively little is known about how slow hemodynamic oscillations measured with fMRI relate to electrophysiological processes.It was suggested that slowly fluctuating power envelopes of electrophysiological signals synchronize across brain areas and that the topography of this activity is spatially correlated to resting state networks derived from rs-fMRI. Here we take a novel approach to address this problem and establish a causal link between the power fluctuations of electrophysiological signals and rs-fMRI via a new neuromodulation paradigm, which exploits these power-synchronization mechanisms.These novel mechanistic insights bridge different scientific domains and are of broad interest to researchers in the fields of Medical Imaging, Neuroscience, Physiology and Psychology.

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Individual Variation in Brain Network Topology Predicts Emotional Intelligence

10.1101/275768 ◽

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.

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Template-based prediction of vigilance fluctuations in resting-state fMRI

10.1101/234179 ◽

2017 ◽

Cited By ~ 1

Author(s):

Maryam Falahpour ◽

Catie Chang ◽

Chi Wah Wong ◽

Thomas T. Liu

Keyword(s):

Resting State ◽

Brain Connectivity ◽

Resting State Fmri ◽

Fmri Data ◽

Eyes Closed ◽

Vigilance State ◽

Global Signal ◽

Significant Difference ◽

Eyes Open ◽

Positive Correlations

AbstractChanges in vigilance or alertness during a typical resting state fMRI scan are inevitable and have been found to affect measures of functional brain connectivity. Since it is not often feasible to monitor vigilance with EEG during fMRI scans, it would be of great value to have methods for estimating vigilance levels from fMRI data alone. A recent study, conducted in macaque monkeys, proposed a template-based approach for fMRI-based estimation of vigilance fluctuations. Here, we use simultaneously acquired EEG/fMRI data to investigate whether the same template-based approach can be employed to estimate vigilance fluctuations of awake humans across different resting-state conditions. We first demonstrate that the spatial pattern of correlations between EEG-defined vigilance and fMRI in our data is consistent with the previous literature. Notably, however, we observed a significant difference between the eyes-closed (EC) and eyes-open (EO) conditions finding stronger negative correlations with vigilance in regions forming the default mode network and higher positive correlations in thalamus and insula in the EC condition when compared to the EO condition. Taking these correlation maps as “templates” for vigilance estimation, we found that the template-based approach produced fMRI-based vigilance estimates that were significantly correlated with EEG-based vigilance measures, indicating its generalizability from macaques to humans. We also demonstrate that the performance of this method was related to the overall amount of variability in a subject’s vigilance state, and that the template-based approach outperformed the use of the global signal as a vigilance estimator. In addition, we show that the template-based approach can be used to estimate the variability across scans in the amplitude of the vigilance fluctuations. We discuss the benefits and tradeoffs of using the template-based approach in future fMRI studies.

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