Characteristic core voxels in normal individuals revealed by hyperbolic disc embedding and k-core percolation on resting state fMRI

Hyperbolic disc embedding and k-core percolation reveal the core structure of the functional connectivity on resting-state fMRI (rsfMRI). Inter-voxel relations were visualized on embedded hyperbolic discs, and their core composition was traced using k-core percolation. Using rsfMRI data of 180 normal adults from the Human Connectome Project database, scale-free intervoxel connectivity represented by IC-voxels composition, while visualized on hyperbolic discs using 𝕊1/ℍ2 model, showed the expected change of the largest component decreasing its size on k-core percolation eventually yielding the core structures of individuals. This kmax-core voxels-ICs composition revealed such stereotypes of individuals as visual network dominant, default mode network dominant, and distributed patterns. Characteristic core structures of resting-state brain connectivity of normal subjects disclosed the distributed or asymmetric contribution of voxels to the kmax-core, which suggests the hierarchical dominance of certain IC subnetworks characteristic to subgroups of individuals at rest.

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Predicting Individual Traits from Unperformed Tasks

10.1101/2021.10.12.464045 ◽

2021 ◽

Author(s):

Shachar Gal ◽

Niv Tik ◽

Michal Bernstein-Eliav ◽

Ido Tavor

Keyword(s):

Individual Differences ◽

Resting State ◽

Cognitive Abilities ◽

Functional Organization ◽

Brain Connectivity ◽

Brain Activation ◽

Resting State Fmri ◽

Human Connectome Project ◽

Connectivity Patterns ◽

Intelligence Scores

Relating individual differences in cognitive traits to brain functional organization is a long-lasting challenge for the neuroscience community. Individual intelligence scores were previously predicted from whole-brain connectivity patterns, extracted from functional magnetic resonance imaging (fMRI) data acquired at rest. Recently, it was shown that task-induced brain activation maps outperform these resting-state connectivity patterns in predicting individual intelligence, suggesting that a cognitively demanding environment improves prediction of cognitive abilities. Here, we use data from the Human Connectome Project to predict task-induced brain activation maps from resting-state fMRI, and proceed to use these predicted activity maps to further predict individual differences in a variety of traits. While models based on original task activation maps remain the most accurate, models based on predicted maps significantly outperformed those based on the resting-state connectome. Thus, we provide a promising approach for the evaluation of measures of human behavior from brain activation maps, that could be used without having participants actually perform the tasks.

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

Brain Research Bulletin ◽

10.1016/j.brainresbull.2021.04.025 ◽

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

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Rest-task Modulation of fMRI-derived Global Signal Topography is Mediated by Transient Co-activation Patterns

10.1101/798819 ◽

2019 ◽

Cited By ~ 1

Author(s):

Jianfeng Zhang ◽

Zirui Huang ◽

Shankar Tumati ◽

Georg Northoff

Keyword(s):

Resting State ◽

Resting State Fmri ◽

Peak Period ◽

Physiological Noise ◽

Activation Patterns ◽

Global Signal ◽

Co Activation ◽

Human Connectome Project ◽

Partial Dissociation ◽

Signal Correlation

AbstractRecent resting-state fMRI studies have revealed that the global signal (GS) exhibits a non-uniform spatial distribution across the gray matter. Whether this topography is informative remains largely unknown. We therefore tested rest-task modulation of global signal topography by analyzing static global signal correlation and dynamic co-activation patterns in a large sample of fMRI dataset (n=837) from the Human Connectome Project. The GS topography in the resting-state and in seven different tasks was first measured by correlating the global signal with the local timeseries (GSCORR). In the resting state, high GSCORR was observed mainly in the primary sensory and motor regions, while low GSCORR was seen in the association brain areas. This pattern changed during the seven tasks, with mainly decreased GSCORR in sensorimotor cortex. Importantly, this rest-task modulation of GSCORR could be traced to transient co-activation patterns at the peak period of global signal (GS-peak). By comparing the topography of GSCORR and respiration effects, we observed that the topography of respiration mimicked the topography of global signal in the resting-state whereas both differed during the task states; due to such partial dissociation, we assume that GSCORR could not be equated with a respiration effect. Finally, rest-task modulation of GS topography could not be exclusively explained by other sources of physiological noise. Together, we here demonstrate the informative nature of global signal topography by showing its rest-task modulation, the underlying dynamic co-activation patterns, and its partial dissociation from respiration effects during task states.

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Resting-state brain activity can predict target-independent aptitude in fMRI-neurofeedback training

10.1101/2021.02.08.430334 ◽

2021 ◽

Author(s):

Takashi Nakano ◽

Masahiro Takamura ◽

Haruki Nishimura ◽

Maro Machizawa ◽

Naho Ichikawa ◽

...

Keyword(s):

Resting State ◽

Brain Connectivity ◽

Brain Activity ◽

Resting State Fmri ◽

Brain Regions ◽

Posterior Cingulate Cortex ◽

Fmri Data ◽

Independent Test ◽

The Individual ◽

The Brain

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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Sex Classification by Resting State Brain Connectivity

Cerebral Cortex ◽

10.1093/cercor/bhz129 ◽

2019 ◽

Vol 30 (2) ◽

pp. 824-835 ◽

Cited By ~ 12

Author(s):

Susanne Weis ◽

Kaustubh R Patil ◽

Felix Hoffstaedter ◽

Alessandra Nostro ◽

B T Thomas Yeo ◽

...

Keyword(s):

Resting State ◽

Brain Connectivity ◽

Brain Regions ◽

Brain Organization ◽

Functional Brain ◽

Clear Cut ◽

Human Connectome Project ◽

Machine Learning Approach ◽

Imaging Research ◽

Functional Brain Organization

Abstract A large amount of brain imaging research has focused on group studies delineating differences between males and females with respect to both cognitive performance as well as structural and functional brain organization. To supplement existing findings, the present study employed a machine learning approach to assess how accurately participants’ sex can be classified based on spatially specific resting state (RS) brain connectivity, using 2 samples from the Human Connectome Project (n1 = 434, n2 = 310) and 1 fully independent sample from the 1000BRAINS study (n = 941). The classifier, which was trained on 1 sample and tested on the other 2, was able to reliably classify sex, both within sample and across independent samples, differing both with respect to imaging parameters and sample characteristics. Brain regions displaying highest sex classification accuracies were mainly located along the cingulate cortex, medial and lateral frontal cortex, temporoparietal regions, insula, and precuneus. These areas were stable across samples and match well with previously described sex differences in functional brain organization. While our data show a clear link between sex and regionally specific brain connectivity, they do not support a clear-cut dimorphism in functional brain organization that is driven by sex alone.

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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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