scholarly journals How tasks change whole-brain functional organization to reveal brain-phenotype relationships

2019 ◽  
Author(s):  
Abigail S. Greene ◽  
Siyuan Gao ◽  
Stephanie Noble ◽  
Dustin Scheinost ◽  
R. Todd Constable
Keyword(s):  
Predictive Modeling ◽  
Functional Organization ◽  
Prediction Performance ◽  
Fmri Data ◽  
Bold Signal ◽  
Psychophysiological Interaction ◽  
Induced Changes ◽  
Complex Human Traits ◽  
The Brain ◽  
Task Activation

AbstractFunctional connectivity (FC) calculated from task fMRI data better reveals brain-phenotype relationships than rest-based FC, but how tasks have this effect is unknown. In over 700 individuals performing 7 tasks, we use psychophysiological interaction (PPI) and predictive modeling analyses to demonstrate that task-induced changes in FC successfully predict phenotype, and these changes are not simply driven by task activation. Activation, however, is useful for prediction only if the in-scanner task is related to the predicted phenotype. Given this evidence that tasks change patterns of FC independent of activation to amplify brain-phenotype relationships, we develop and apply an inter-subject PPI analysis to further characterize these predictive FC changes. We find that task-induced consistency of FC patterns across individuals is useful for prediction—to a point; these results suggest that tasks improve FC-based prediction performance by de-noising the BOLD signal, revealing meaningful individual differences in brain functional organization. Together, these findings demonstrate that, when it comes to the effects of in-scanner tasks on the brain, focal activation is only the tip of the iceberg, and they offer a framework to best leverage both task activation and FC to reveal the neural bases of complex human traits, symptoms, and behaviors.

scholarly journals Functional parcellation of human brain using localized topo-connectivity mapping

2021 ◽  
Author(s):  
Yu Zhao ◽  
Yurui Gao ◽  
Muwei Li ◽  
Adam W. Anderson ◽  
Zhaohua Ding ◽  
...  
Keyword(s):  
Human Brain ◽  
Functional Organization ◽  
Statistical Tests ◽  
Simulated Data ◽  
Fmri Data ◽  
Anatomical Structures ◽  
Connectivity Mapping ◽  
Subcortical Region ◽  
Human Connectome Project ◽  
The Brain

<p>The analysis of connectivity between parcellated regions of cortex provides insights into the functional architecture of the brain at a systems level. However, there has been less progress in the derivation of functional structures from voxel-wise analyses at finer scales. We propose a novel method, called localized topo-connectivity mapping with singular-value-decomposition-informed filtering (or filtered LTM), to identify and characterize voxel-wise functional structures in the human brain using resting-state fMRI data. Here we describe its mathematical background and provide a proof-of-concept using simulated data that allow an intuitive interpretation of the results of filtered LTM. The algorithm has also been applied to 7T fMRI data as part of the Human Connectome Project to generate group-average LTM images. Functional structures revealed by this approach agree moderately well with anatomical structures identified by T<sub>1</sub>-weighted images and fractional anisotropy maps derived from diffusion MRI. Moreover, the LTM images also reveal subtle functional variations that are not apparent in the anatomical structures. To assess the performance of LTM images, the subcortical region and occipital white matter were separately parcellated. Statistical tests were performed to demonstrate that the synchronies of fMRI signals in LTM-informed parcellations are significantly larger than those of random parcellations. Overall, the filtered LTM approach can serve as a tool to investigate the functional organization of the brain at the scale of individual voxels as measured in fMRI.</p>

scholarly journals Functional parcellation of human brain using localized topo-connectivity mapping

2021 ◽  
Author(s):  
Yu Zhao ◽  
Yurui Gao ◽  
Muwei Li ◽  
Adam W. Anderson ◽  
Zhaohua Ding ◽  
...  
Keyword(s):  
Human Brain ◽  
Functional Organization ◽  
Statistical Tests ◽  
Simulated Data ◽  
Fmri Data ◽  
Anatomical Structures ◽  
Connectivity Mapping ◽  
Subcortical Region ◽  
Human Connectome Project ◽  
The Brain

<p>The analysis of connectivity between parcellated regions of cortex provides insights into the functional architecture of the brain at a systems level. However, there has been less progress in the derivation of functional structures from voxel-wise analyses at finer scales. We propose a novel method, called localized topo-connectivity mapping with singular-value-decomposition-informed filtering (or filtered LTM), to identify and characterize voxel-wise functional structures in the human brain using resting-state fMRI data. Here we describe its mathematical background and provide a proof-of-concept using simulated data that allow an intuitive interpretation of the results of filtered LTM. The algorithm has also been applied to 7T fMRI data as part of the Human Connectome Project to generate group-average LTM images. Functional structures revealed by this approach agree moderately well with anatomical structures identified by T<sub>1</sub>-weighted images and fractional anisotropy maps derived from diffusion MRI. Moreover, the LTM images also reveal subtle functional variations that are not apparent in the anatomical structures. To assess the performance of LTM images, the subcortical region and occipital white matter were separately parcellated. Statistical tests were performed to demonstrate that the synchronies of fMRI signals in LTM-informed parcellations are significantly larger than those of random parcellations. Overall, the filtered LTM approach can serve as a tool to investigate the functional organization of the brain at the scale of individual voxels as measured in fMRI.</p>

scholarly journals Unraveling reproducible dynamic states of individual brain functional parcellation

2020 ◽  
pp. 1-28 ◽  
Author(s):  
Amal Boukhdhir ◽  
Yu Zhang ◽  
Max Mignotte ◽  
Pierre Bellec
Keyword(s):  
Functional Organization ◽  
Time Windows ◽  
Brain Networks ◽  
Anterior Cingulate ◽  
Fmri Data ◽  
Network Analyses ◽  
Stability Maps ◽  
The Rich ◽  
The Brain ◽  
Dynamic States

Data-driven parcellations are widely used for exploring the functional organization of the brain, and also for reducing the high dimensionality of fMRI data. Despite the flurry of methods proposed in the literature, functional brain parcellations are not highly reproducible at the level of individual subjects, even with very long acquisitions. Some brain areas are also more difficult to parcellate than others, with association heteromodal cortices being the most challenging. An important limitation of classical parcellations is that they are static, that is, they neglect dynamic reconfigurations of brain networks. In this paper, we proposed a new method to identify dynamic states of parcellations, which we hypothesized would improve reproducibility over static parcellation approaches. For a series of seed voxels in the brain, we applied a cluster analysis to regroup short (3 min) time windows into “states” with highly similar seed parcels. We splitted individual time series of the Midnight scan club sample into two independent sets of 2.5 hr (test and retest). We found that average within-state parcellations, called stability maps, were highly reproducible (over 0.9 test-retest spatial correlation in many instances) and subject specific (fingerprinting accuracy over 70% on average) between test and retest. Consistent with our hypothesis, seeds in heteromodal cortices (posterior and anterior cingulate) showed a richer repertoire of states than unimodal (visual) cortex. Taken together, our results indicate that static functional parcellations are incorrectly averaging well-defined and distinct dynamic states of brain parcellations. This work calls to revisit previous methods based on static parcellations, which includes the majority of published network analyses of fMRI data. Our method may, thus, impact how researchers model the rich interactions between brain networks in health and disease.

scholarly journals Unraveling reproducible dynamic states of individual brain functional parcellation

2020 ◽  
Author(s):  
Amal Boukhdhir ◽  
Yu Zhang ◽  
Max Mignotte ◽  
Pierre Bellec
Keyword(s):  
Functional Organization ◽  
Time Windows ◽  
Brain Networks ◽  
Anterior Cingulate ◽  
Fmri Data ◽  
Network Analyses ◽  
Stability Maps ◽  
The Rich ◽  
The Brain ◽  
Dynamic States

AbstractData-driven parcellations are widely used for exploring the functional organization of the brain, and also for reducing the high dimensionality of fMRI data. Despite the flurry of methods proposed in the literature, functional brain parcellations are not highly reproducible at the level of individual subjects, even with very long acquisitions. Some brain areas are also more difficult to parcellate than others, with association heteromodal cortices being the most challenging. An important limitation of classical parcellations is that they are static, i.e. they neglect dynamic reconfigurations of brain networks. In this paper, we proposed a new method to identify dynamic states of parcellations, which we hypothesized would improve reproducibility over static parcellation approaches. For a series of seed voxels in the brain, we applied a cluster analysis to regroup short (3 minutes) time windows into “states” with highly similar seed parcels. We splitted individual time series of the Midnight scan club sample into two independent sets of 2.5 hours (test and retest). We found that average within-state parcellations, called stability maps, were highly reproducible (over .9 test-retest spatial correlation in many instances) and subject specific (fingerprinting accuracy over 70% on average) between test and retest. Consistent with our hypothesis, seeds in heteromodal cortices (posterior and anterior cingulate) showed a richer repertoire of states than unimodal (visual) cortex. Taken together, our results indicate that static functional parcellations are incorrectly averaging well-defined and distinct dynamic states of brain parcellations. This work calls to revisit previous methods based on static parcellations, which includes the majority of published network analyses of fMRI data. Our method may, thus, impact how researchers model the rich interactions between brain networks in health and disease.

scholarly journals Predicting Sex from Resting-State fMRI Across Multiple Independent Acquired Datasets

2020 ◽  
Author(s):  
Obada Al Zoubi ◽  
Masaya Misaki ◽  
Aki Tsuchiyagaito ◽  
Vadim Zotev ◽  
Evan White ◽  
...  
Keyword(s):  
Sex Differences ◽  
Resting State ◽  
Functional Organization ◽  
Intraclass Correlation ◽  
Low Frequency ◽  
Resting State Fmri ◽  
Bold Signal ◽  
Brain Response ◽  
Human Connectome Project ◽  
The Brain

AbstractSex is an important biological variable often used in analyzing and describing the functional organization of the brain during cognitive and behavioral tasks. Several prior studies have shown that blood-oxygen-level-dependent (BOLD) functional MRI (fMRI) functional connectivity (FC) can be used to differentiate sex among individuals. Herein, we demonstrate that sex can be further classified with high accuracy using the intrinsic BOLD signal fluctuations from resting-state fMRI (rs-fMRI). We adopted the amplitude of low-frequency fluctuation (ALFF), and the fraction of ALFF (fALFF) features from the automated anatomical atlas (AAL) and Power’s functional atlas as an input to different machine learning (ML) methods. Using datasets from five independently acquired subject cohorts and with eight fMRI scanning sessions, we comprehensively assessed unbiased performance using nested-cross validation for within-sample and across sample accuracies. The results demonstrated high prediction accuracies for the Human Connectome Project (HCP) dataset (area under cure (AUC) > 0.89). The yielded accuracies suggest that sex difference is embodied and well-pronounced in the low-frequency BOLD signal fluctuation. The performance degrades with the heterogeneity of the cohort and suggests that other factors,.e.g. psychiatric disorders and demographics influences the BOLD signal and may interact with the classification of sex. In addition, the results revealed high learning generalizability with the HCP scan, but not across different datasets. The intraclass correlation coefficient (ICC) across HCP scans showed moderate-to-good reliability based on atlas selection (ICC = 0.65 [0.63-0.67] and ICC= 0.78 [0.76-0.80].). We also assessed the effect of scan duration on the predictability of sex and showed that sex differences could be detected even with a short rs-fMRI scan (e.g., 2 minutes). Moreover, we provided statistical maps of the brain regions differentially recruited by or predicting sex using Shapely values and determined an overlap with previous reports of brain response due to sex differences. Altogether, our analysis suggests that sex differences are well-pronounced in rs-fMRI and should be considered seriously in any study design, analysis, or interpretation.

scholarly journals The unbalanced reorganization of weaker functional connections induces the altered brain network topology in schizophrenia

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rossana Mastrandrea ◽  
Fabrizio Piras ◽  
Andrea Gabrielli ◽  
Nerisa Banaj ◽  
Guido Caldarelli ◽  
...  
Keyword(s):  
Resting State ◽  
Functional Organization ◽  
Functional Integration ◽  
Brain Network ◽  
Modular Organization ◽  
Node Degree ◽  
Imaging Data ◽  
Brain Disorder ◽  
Functional Connections ◽  
The Brain

AbstractNetwork neuroscience shed some light on the functional and structural modifications occurring to the brain associated with the phenomenology of schizophrenia. In particular, resting-state functional networks have helped our understanding of the illness by highlighting the global and local alterations within the cerebral organization. We investigated the robustness of the brain functional architecture in 44 medicated schizophrenic patients and 40 healthy comparators through an advanced network analysis of resting-state functional magnetic resonance imaging data. The networks in patients showed more resistance to disconnection than in healthy controls, with an evident discrepancy between the two groups in the node degree distribution computed along a percolation process. Despite a substantial similarity of the basal functional organization between the two groups, the expected hierarchy of healthy brains' modular organization is crumbled in schizophrenia, showing a peculiar arrangement of the functional connections, characterized by several topologically equivalent backbones. Thus, the manifold nature of the functional organization’s basal scheme, together with its altered hierarchical modularity, may be crucial in the pathogenesis of schizophrenia. This result fits the disconnection hypothesis that describes schizophrenia as a brain disorder characterized by an abnormal functional integration among brain regions.

scholarly journals The Modular Organization of Pain Brain Networks: An fMRI Graph Analysis Informed by Intracranial EEG

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Camille Fauchon ◽  
David Meunier ◽  
Isabelle Faillenot ◽  
Florence B Pomares ◽  
Hélène Bastuji ◽  
...  
Keyword(s):  
Information Integration ◽  
Functional Organization ◽  
Brain Connectivity ◽  
Brain Networks ◽  
Modular Organization ◽  
Noxious Heat ◽  
Intracranial Eeg ◽  
Brain Connectome ◽  
Posterior Parietal ◽  
The Brain

Abstract Intracranial EEG (iEEG) studies have suggested that the conscious perception of pain builds up from successive contributions of brain networks in less than 1 s. However, the functional organization of cortico-subcortical connections at the multisecond time scale, and its accordance with iEEG models, remains unknown. Here, we used graph theory with modular analysis of fMRI data from 60 healthy participants experiencing noxious heat stimuli, of whom 36 also received audio stimulation. Brain connectivity during pain was organized in four modules matching those identified through iEEG, namely: 1) sensorimotor (SM), 2) medial fronto-cingulo-parietal (default mode-like), 3) posterior parietal-latero-frontal (central executive-like), and 4) amygdalo-hippocampal (limbic). Intrinsic overlaps existed between the pain and audio conditions in high-order areas, but also pain-specific higher small-worldness and connectivity within the sensorimotor module. Neocortical modules were interrelated via “connector hubs” in dorsolateral frontal, posterior parietal, and anterior insular cortices, the antero-insular connector being most predominant during pain. These findings provide a mechanistic picture of the brain networks architecture and support fractal-like similarities between the micro-and macrotemporal dynamics associated with pain. The anterior insula appears to play an essential role in information integration, possibly by determining priorities for the processing of information and subsequent entrance into other points of the brain connectome.

Changes in the Cortex Neurons in Single and Fractional Gamma Radiation

2021 ◽  
Vol 66 (4) ◽  
pp. 18-24
Author(s):  
I. Ushakov ◽  
Vladimir Fyodorov
Keyword(s):  
Nervous System ◽  
Cerebral Cortex ◽  
Radiation Exposure ◽  
Comparative Assessment ◽  
Male Rats ◽  
Fractionated Irradiation ◽  
Radiation Induced ◽  
Post Radiation ◽  
Induced Changes ◽  
The Brain

Purpose: Comparative assessment of radiation-induced changes in neurons of the cerebral cortex after a single and fractionated exposure to ionizing radiation in doses of 0.1 – 1.0 Gy. Material and methods. The study was carried out in compliance with the rules of bioethics on 180 white outbred male rats at the age of 4 months. by the beginning of the experiment, exposed to a single or fractionated exposure to γ-quanta of 60Co in total doses of 0.1; 0.2; 0.5 and 1.0 Gy. Neuromorphological and histochemical methods were used to assess morphometric and tinctorial parameters of nerve cells, as well as changes in the content of protein and nucleic acids in neurons in the early and late periods of the post-radiation period. Using one-way analysis of variance, a comparative assessment of neuromorphological indicators under various modes of radiation exposure is given. Results: In the control and irradiated animals throughout their life, undulating changes in the indicators of the state of the neurons of the brain occur with a gradual decrease by the end of the experiment. Despite a number of features of the dynamics of neuromorphological parameters, these irradiation regimes do not cause functionally significant changes in the neurons of the cortex. However, in some periods of the post-radiation period, the changes under the studied irradiation regimes were multidirectional and did not always correspond to age control. Significant differences in the response of neurons to these modes of radiation exposure in the sensory and motor areas of the cerebral cortex have not been established. Conclusion: No functionally significant radiation-induced changes in neurons were found either with single or fractionated irradiation. At the same time, different modes of irradiation in general caused the same type of changes in neurons. However, in some periods of observation, changes in neuromorphological parameters under the studied irradiation regimes were not unidirectional and differed from age control, which indicates a possible risk of disturbances in the functioning of the nervous system against the background of other harmful and dangerous factors.

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