scholarly journals Individual coactivation patterns improve the subject identification and their behavior association

2022 ◽  
Author(s):  
Hang Yang ◽  
Xing Yao ◽  
Hong Zhang ◽  
Chun Meng ◽  
Bharat B Biswal
Keyword(s):  
Brain Regions ◽  
Identification Performance ◽  
Identification Rate ◽  
Subject Identification ◽  
Brain States ◽  
The Individual ◽  
Cap Analysis ◽  
Brain Behavior ◽  
Dense Sampling ◽  
Higher Order Networks

Brain states can be characterized by recurring coactivation patterns (CAPs). Traditional CAP analysis is performed at the group-level, while the human brain is individualized and the functional connectome has shown the uniqueness as fingerprint. Whether stable individual CAPs could be obtained from a single fMRI scan and could individual CAPs improve the identification is unclear. An open dataset, the midnight scan club was used in this study to answer these questions. Four CAP states were identified at three distinct levels (group-, subject- and scan-level) separately, and the CAPs were then reconstructed for each scan. Identification rate and differential identifiability were used to evaluate the identification performance. Our results demonstrated that the individual CAPs were unstable when using a single scan. By maintaining high intra-subject similarity and inter-subject differences, subject-level CAPs achieved the best identification performance. Brain regions that contributed to the identifiability were mainly located in higher-order networks (e.g., frontal-parietal network). Besides, head motion reduced the intra-subject similarity, while its impact on identification rate was non-significant. Finally, a pipeline was developed to depict brain-behavior associations in dataset with few samples but dense sampling, and individualized CAP dynamics showed an above-chance level correlation with IQ.

scholarly journals Fundamental Dynamical Modes Underlying Human Brain Synchronization

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Catalina Alvarado-Rojas ◽  
Michel Le Van Quyen
Keyword(s):  
Large Scale ◽  
Brain Regions ◽  
Electrode Placement ◽  
High Dimensional ◽  
Sleep Cycle ◽  
Epileptic Patients ◽  
Brain States ◽  
Intracranial Recordings ◽  
Characteristic Dynamic

Little is known about the long-term dynamics of widely interacting cortical and subcortical networks during the wake-sleep cycle. Using large-scale intracranial recordings of epileptic patients during seizure-free periods, we investigated local- and long-range synchronization between multiple brain regions over several days. For such high-dimensional data, summary information is required for understanding and modelling the underlying dynamics. Here, we suggest that a compact yet useful representation is given by a state space based on the first principal components. Using this representation, we report, with a remarkable similarity across the patients with different locations of electrode placement, that the seemingly complex patterns of brain synchrony during the wake-sleep cycle can be represented by a small number of characteristic dynamic modes. In this space, transitions between behavioral states occur through specific trajectories from one mode to another. These findings suggest that, at a coarse level of temporal resolution, the different brain states are correlated with several dominant synchrony patterns which are successively activated across wake-sleep states.

scholarly journals Increased sensitivity to strong perturbations in a whole-brain model of LSD

2021 ◽  
Author(s):  
Beatrice M. Jobst ◽  
Selen Atasoy ◽  
Adrián Ponce-Alvarez ◽  
Ana Sanjuán ◽  
Leor Roseman ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
External Perturbation ◽  
Brain Regions ◽  
Lysergic Acid ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Whole Brain ◽  
Response Diversity ◽  
Brain States ◽  
The Brain

AbstractLysergic acid diethylamide (LSD) is a potent psychedelic drug, which has seen a revival in clinical and pharmacological research within recent years. Human neuroimaging studies have shown fundamental changes in brain-wide functional connectivity and an expansion of dynamical brain states, thus raising the question about a mechanistic explanation of the dynamics underlying these alterations. Here, we applied a novel perturbational approach based on a whole-brain computational model, which opens up the possibility to externally perturb different brain regions in silico and investigate differences in dynamical stability of different brain states, i.e. the dynamical response of a certain brain region to an external perturbation. After adjusting the whole-brain model parameters to reflect the dynamics of functional magnetic resonance imaging (fMRI) BOLD signals recorded under the influence of LSD or placebo, perturbations of different brain areas were simulated by either promoting or disrupting synchronization in the regarding brain region. After perturbation offset, we quantified the recovery characteristics of the brain area to its basal dynamical state with the Perturbational Integration Latency Index (PILI) and used this measure to distinguish between the two brain states. We found significant changes in dynamical complexity with consistently higher PILI values after LSD intake on a global level, which indicates a shift of the brain’s global working point further away from a stable equilibrium as compared to normal conditions. On a local level, we found that the largest differences were measured within the limbic network, the visual network and the default mode network. Additionally, we found a higher variability of PILI values across different brain regions after LSD intake, indicating higher response diversity under LSD after an external perturbation. Our results provide important new insights into the brain-wide dynamical changes underlying the psychedelic state - here provoked by LSD intake - and underline possible future clinical applications of psychedelic drugs in particular psychiatric disorders.HighlightsNovel offline perturbational method applied on functional magnetic resonance imaging (fMRI) data under the effect of lysergic acid diethylamide (LSD)Shift of brain’s global working point to more complex dynamics after LSD intakeConsistently longer recovery time after model perturbation under LSD influenceStrongest effects in resting state networks relevant for psychedelic experienceHigher response diversity across brain regions under LSD influence after an external in silico perturbation

scholarly journals Identification and Research of Adhd and Healthy Controls using Fmri

2019 ◽  
Vol 8 (6S) ◽  
pp. 370-372
Keyword(s):  
Brain Regions ◽  
Healthy Controls ◽  
Hyperactivity Disorder ◽  
Network Activation ◽  
Healthy Control ◽  
Temporal And Spatial ◽  
The Individual ◽  
The Brain ◽  
Coronal View

Analyzing the brain regions for different activations corresponding to the activation input for an experimental setup of task functional MRI or a resting state functional Magnetic Resonance Imaging(fMRI) for a diagnosed or healthy control is a challenging issue as the processing data is voluminous 4D data with nearly 1,51,552 voxels for a single volume of 261 scans fMRI. The data considered for analysis consists of 10 healthy controls and 10 Attention Deficit Hyperactivity Disorder(ADHD) fMRI. The workflow starts with preprocessing the individual scan for realignment, coregistration and Normalisation to Montreal Neurological Institute (MNI) space. Single site scan visit consists of 64x64x37 voxels. Seventy independent components are obtained from processed data by data reduction, Independent Component Analysis (ICA) calculation, Back reconstruction and Component Calibration. ICA performs satisfactorily well on temporal and spatial localization. Visual medial network activation is pronounced in ADHD Controls than in healthy people. Sagittal, Axial and Coronal view of ADHD controls is obtained as component number 42.The analysis is further used for the automatic classification of healthy controls and ADHD people.

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

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.

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 Quantifying mechanisms in neurodegenerative diseases (NDDs) using candidate mechanism perturbation amplitude (CMPA) algorithm

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Reagon Karki ◽  
Alpha Tom Kodamullil ◽  
Charles Tapley Hoyt ◽  
Martin Hofmann-Apitius
Keyword(s):  
Biological Networks ◽  
Molecular Mechanisms ◽  
Biological Network ◽  
Expression Patterns ◽  
Cell Types ◽  
Brain Regions ◽  
Heat Diffusion ◽  
Disease Etiology ◽  
The Individual ◽  
Biological Entities

Abstract Background Literature derived knowledge assemblies have been used as an effective way of representing biological phenomenon and understanding disease etiology in systems biology. These include canonical pathway databases such as KEGG, Reactome and WikiPathways and disease specific network inventories such as causal biological networks database, PD map and NeuroMMSig. The represented knowledge in these resources delineates qualitative information focusing mainly on the causal relationships between biological entities. Genes, the major constituents of knowledge representations, tend to express differentially in different conditions such as cell types, brain regions and disease stages. A classical approach of interpreting a knowledge assembly is to explore gene expression patterns of the individual genes. However, an approach that enables quantification of the overall impact of differentially expressed genes in the corresponding network is still lacking. Results Using the concept of heat diffusion, we have devised an algorithm that is able to calculate the magnitude of regulation of a biological network using expression datasets. We have demonstrated that molecular mechanisms specific to Alzheimer (AD) and Parkinson Disease (PD) regulate with different intensities across spatial and temporal resolutions. Our approach depicts that the mitochondrial dysfunction in PD is severe in cortex and advanced stages of PD patients. Similarly, we have shown that the intensity of aggregation of neurofibrillary tangles (NFTs) in AD increases as the disease progresses. This finding is in concordance with previous studies that explain the burden of NFTs in stages of AD. Conclusions This study is one of the first attempts that enable quantification of mechanisms represented as biological networks. We have been able to quantify the magnitude of regulation of a biological network and illustrate that the magnitudes are different across spatial and temporal resolution.

scholarly journals Stress and the dynamic genome: Steroids, epigenetics, and the transposome

2014 ◽  
Vol 112 (22) ◽  
pp. 6828-6833 ◽  
Author(s):  
Richard G. Hunter ◽  
Khatuna Gagnidze ◽  
Bruce S. McEwen ◽  
Donald W. Pfaff
Keyword(s):  
Brain Function ◽  
Acute Stress ◽  
Brain Regions ◽  
Epigenetic Mechanism ◽  
Epigenetic Mechanisms ◽  
Individual Organism ◽  
Genomic Plasticity ◽  
Genomic Stress ◽  
Dynamic Genome ◽  
The Individual

Stress plays a substantial role in shaping behavior and brain function, often with lasting effects. How these lasting effects occur in the context of a fixed postmitotic neuronal genome has been an enduring question for the field. Synaptic plasticity and neurogenesis have provided some of the answers to this question, and more recently epigenetic mechanisms have come to the fore. The exploration of epigenetic mechanisms recently led us to discover that a single acute stress can regulate the expression of retrotransposons in the rat hippocampus via an epigenetic mechanism. We propose that this response may represent a genomic stress response aimed at maintaining genomic and transcriptional stability in vulnerable brain regions such as the hippocampus. This finding and those of other researchers have made clear that retrotransposons and the genomic plasticity they permit play a significant role in brain function during stress and disease. These observations also raise the possibility that the transposome might have adaptive functions at the level of both evolution and the individual organism.

scholarly journals Language, Culture and the Neurobiology of Pain: A Theoretical Exploration

1989 ◽  
Vol 2 (4) ◽  
pp. 235-260 ◽  
Author(s):  
Horacio Fabrega
Keyword(s):  
Important Influence ◽  
Paradigm Case ◽  
Language And Culture ◽  
Noxious Stimuli ◽  
The Individual ◽  
Brain Behavior

Language and culture, as conceptualized in traditional anthropology, may have an important influence on pain and brain-behavior relations. The paradigm case for the influence of language and culture on perception and cognition is stipulated in the Sapir–Whorf hypothesis which has been applied to phenomena “external” to the individual. In this paper, the paradigm is applied to information the person retrieves from “inside” his body; namely, “noxious” stimuli which get registered in consciousness as pain.

Roles of default-mode network and supplementary motor area in human vigilance performance: evidence from real-time fMRI

2013 ◽  
Vol 109 (5) ◽  
pp. 1250-1258 ◽  
Author(s):  
Oliver Hinds ◽  
Todd W. Thompson ◽  
Satrajit Ghosh ◽  
Julie J. Yoo ◽  
Susan Whitfield-Gabrieli ◽  
...  
Keyword(s):  
Real Time ◽  
Default Mode Network ◽  
Human Performance ◽  
Supplementary Motor Area ◽  
Brain Regions ◽  
Motor Area ◽  
Neural Systems ◽  
Brain State ◽  
Default Mode ◽  
Brain States

We used real-time functional magnetic resonance imaging (fMRI) to determine which regions of the human brain have a role in vigilance as measured by reaction time (RT) to variably timed stimuli. We first identified brain regions where activation before stimulus presentation predicted RT. Slower RT was preceded by greater activation in the default-mode network, including lateral parietal, precuneus, and medial prefrontal cortices; faster RT was preceded by greater activation in the supplementary motor area (SMA). We examined the roles of these brain regions in vigilance by triggering trials based on brain states defined by blood oxygenation level-dependent activation measured using real-time fMRI. When activation of relevant neural systems indicated either a good brain state (increased activation of SMA) or a bad brain state (increased activation of lateral parietal cortex and precuneus) for performance, a target was presented and RT was measured. RTs on trials triggered by a good brain state were significantly faster than RTs on trials triggered by a bad brain state. Thus human performance was controlled by monitoring brain states that indicated high or low vigilance. These findings identify neural systems that have a role in vigilance and provide direct evidence that the default-mode network has a role in human performance. The ability to control and enhance human behavior based on brain state may have broad implications.

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