scholarly journals Dissociable Cerebellar-Prefrontal Networks Underlying Executive Function: Evidence from the Human Connectome Project

2018 ◽  
Author(s):  
Joseph M. Orr ◽  
Trevor B. Jackson ◽  
Michael J. Imburgio ◽  
Jessica A. Bernard
Keyword(s):  
Executive Function ◽  
Functional Data ◽  
Resting State ◽  
Functional Organization ◽  
Preliminary Evidence ◽  
Functional Connections ◽  
Human Connectome Project ◽  
Crus I ◽  
Resting State Connectivity

AbstractTo date, investigations of executive function (EF) have focused on the prefrontal cortex (PFC), and prominent theories of EF are framed with respect to this brain region. Multiple theories describe a hierarchical functional organization for the lateral PFC. However, recent evidence has indicated that the cerebellum (CB) also plays a role in EF. Posterior CB regions (Crus I & II) show structural and functional connections with the PFC, and CB networks are associated with individual differences in EF in healthy adults. However, it is unclear whether the cerebellum shows a similar functional gradient as does the PFC. Here, we investigated high-resolution resting-state data from 225 participants in the Human Connectome Project. We compared resting-state connectivity from posterior cerebellar ROIs, and examined functional data from several tasks that activate the lateral PFC. Demonstrating preliminary evidence for parallel PFC and CB gradients, Crus I was functionally connected with rostrolateral PFC, Crus II with middle and ventral PFC, and Lobule VI with posterior PFC. Contrary to previous work, the activation of the task thought to activate rostrolateral PFC resembled the connectivity maps of Crus II, not Crus I; similarly, the activation of the task thought to activate middle PFC resembled the connectivity maps of Crus I, not Crus II. Nevertheless, there was evidence for dissociable CB-PFC networks. Further work is necessary to understand the functional role of these networks.

scholarly journals Frequency-dependent circuits anchored in the dorsal and ventral left anterior insula

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Yifeng Wang ◽  
Qijun Zou ◽  
Yujia Ao ◽  
Yang Liu ◽  
Yujie Ouyang ◽  
...  
Keyword(s):  
Functional Organization ◽  
Functional Integration ◽  
Anterior Insula ◽  
Frequency Effect ◽  
Frequency Dependent ◽  
Functional Connections ◽  
Human Connectome Project ◽  
Cognitive Neurosciences ◽  
The Right

Abstract The hub role of the right anterior insula (AI) has been emphasized in cognitive neurosciences and been demonstrated to be frequency-dependently organized. However, the functional organization of left AI (LAI) has not been systematically investigated. Here we used 100 unrelated datasets from the Human Connectome Project to study the frequency-dependent organization of LAI along slow 6 to slow 1 bands. The broadband functional connectivity of LAI was similar to previous findings. In slow 6-slow 3 bands, both dorsal and ventral seeds in LAI were correlated to the salience network (SN) and language network (LN) and anti-correlated to the default mode network (DMN). However, these seeds were only correlated to the LAI in slow 2-slow 1 bands. These findings indicate that broadband and narrow band functional connections reflect different functional organizations of the LAI. Furthermore, the dorsal seed had a stronger connection with the LN and anti-correlation with DMN while the ventral seed had a stronger connection within the SN in slow 6-slow 3 bands. In slow 2-slow 1 bands, both seeds had stronger connections with themselves. These observations indicate distinctive functional organizations for the two parts of LAI. Significant frequency effect and frequency by seed interaction were also found, suggesting different frequency characteristics of these two seeds. The functional integration and functional segregation of LDAI and LVAI were further supported by their cognitive associations. The frequency- and seed-dependent functional organizations of LAI may enlighten future clinical and cognitive investigations.

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 Affect and the Brain's Functional Organization: A Resting-State Connectivity Approach

PLoS ONE ◽  
2013 ◽  
Vol 8 (7) ◽  
pp. e68015 ◽  
Author(s):  
Christiane S. Rohr ◽  
Hadas Okon-Singer ◽  
R. Cameron Craddock ◽  
Arno Villringer ◽  
Daniel S. Margulies
Keyword(s):  
Resting State ◽  
Functional Organization ◽  
Resting State Connectivity

scholarly journals Brain connectivity at rest predicts individual differences in normative activity during movie watching

2021 ◽  
Author(s):  
David C Gruskin ◽  
Gaurav H Patel
Keyword(s):  
Spatial Distribution ◽  
Individual Differences ◽  
Resting State ◽  
Functional Organization ◽  
Brain Connectivity ◽  
Brain Activity ◽  
Sensory Information ◽  
Resting State Functional Connectivity ◽  
Similar Region ◽  
Human Connectome Project

When multiple individuals are exposed to the same sensory event, some are bound to have less typical experiences than others. These atypical experiences are underpinned by atypical stimulus-evoked brain activity, the extent of which is often indexed by intersubject correlation (ISC). Previous research has attributed individual differences in ISC to variation in trait-like behavioral phenotypes. Here, we extend this line of work by showing that an individual's degree and spatial distribution of ISC are closely related to their brain's intrinsic functional architecture. Using resting state and movie watching fMRI data from 176 Human Connectome Project participants, we reveal that resting state functional connectivity (RSFC) profiles can be used to predict cortex-wide ISC with considerable accuracy. Similar region-level analyses demonstrate that the amount of ISC a brain region exhibits during movie watching is associated with its connectivity to others at rest, and that the nature of these connectivity-activity relationships varies as a function of the region's role in sensory information processing. Finally, we show that an individual's unique spatial distribution of ISC, independent of its magnitude, is also related to their RSFC profile. These findings suggest that the brain's ability to process complex sensory information is tightly linked to its baseline functional organization and motivate a more comprehensive understanding of individual responses to naturalistic stimuli.

scholarly journals Correction: Affect and the Brain's Functional Organization: A Resting-State Connectivity Approach

PLoS ONE ◽  
2013 ◽  
Vol 8 (12) ◽  
Author(s):  
Christiane S. Rohr ◽  
Hadas Okon-Singer ◽  
R. Cameron Craddock ◽  
Arno Villringer ◽  
Daniel S. Margulies
Keyword(s):  
Resting State ◽  
Functional Organization ◽  
Resting State Connectivity

scholarly journals Typicality of Functional Connectivity robustly captures motion artifacts in rs-fMRI across datasets, atlases and preprocessing pipelines

2020 ◽  
Author(s):  
Jakub Kopal ◽  
Anna Pidnebesna ◽  
David Tomeček ◽  
Jaroslav Tintěra ◽  
Jaroslav Hlinka
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Resting State Fmri ◽  
Motion Artifacts ◽  
Connectivity Pattern ◽  
Head Motion ◽  
Connectivity Matrix ◽  
Normal Brain ◽  
Functional Connections ◽  
Human Connectome Project

AbstractFunctional connectivity analysis of resting state fMRI data has recently become one of the most common approaches to characterizing individual brain function. It has been widely suggested that the functional connectivity matrix, calculated by correlating signals from regions of interest, is a useful approximate representation of the brain’s connectivity, potentially providing behaviorally or clinically relevant markers. However, functional connectivity estimates are known to be detrimentally affected by various artifacts, including those due to in-scanner head motion. Treatment of such artifacts poses a standing challenge because of their high variability. Moreover, as individual functional connections generally covary only very weakly with head motion estimates, motion influence is difficult to quantify robustly, and prone to be neglected in practice. Although the use of individual estimates of head motion, or group-level correlation of motion and functional connectivity has been suggested, a sufficiently sensitive measure of individual functional connectivity quality has not yet been established. We propose a new intuitive summary index, the Typicality of Functional Connectivity, to capture deviations from normal brain functional connectivity pattern. Based on results of resting state fMRI for 245 healthy subjects we show that this measure is significantly correlated with individual head motion metrics. The results were further robustly reproduced across atlas granularity and preprocessing options, as well as other datasets including 1081 subjects from the Human Connectome Project. The Typicality of Functional Connectivity provides individual proxy measure of motion effect on functional connectivity and is more sensitive to inter-individual variation of motion than individual functional connections. In principle it should be sensitive also to other types of artifacts, processing errors and possibly also brain pathology, allowing wide use in data quality screening and quantification in functional connectivity studies as well as methodological investigations.

scholarly journals Boost in Test-Retest Reliability in Resting State fMRI with Predictive Modeling

2019 ◽  
Author(s):  
Aman Taxali ◽  
Mike Angstadt ◽  
Saige Rutherford ◽  
Chandra Sripada
Keyword(s):  
Predictive Modeling ◽  
Predictive Models ◽  
Resting State ◽  
Imaging Features ◽  
Retest Reliability ◽  
Modeling Methods ◽  
Aggregate Information ◽  
Human Connectome Project ◽  
Test Retest Reliability ◽  
Resting State Connectivity

AbstractRecent studies found low test-retest reliability in fMRI, raising serious concerns among researchers, but these studies mostly focused on reliability of individual fMRI features (e.g., individual connections in resting state connectivity maps). Meanwhile, neuroimaging researchers increasingly employ multivariate predictive models that aggregate information across a large number of features to predict outcomes of interest, but the test-retest reliability of predicted outcomes of these models has not previously been systematically studied. Here we apply ten predictive modeling methods to resting state connectivity maps from the Human Connectome Project dataset to predict 61 outcome variables. Compared to mean reliability of individual resting state connections, we find mean reliability of the predicted outcomes of predictive models is substantially higher for all ten modeling methods assessed. Moreover, improvement was consistently observed across all scanning and processing choices (i.e., scan lengths, censoring thresholds, volume-versus surface-based processing). For the most reliable methods, reliability of predicted outcomes was mostly, though not exclusively, in the “good” range (above 0.60).Finally, we identified three mechanisms that help to explain why predicted outcomes of predictive models have higher reliability than individual imaging features. We conclude that researchers can potentially achieve higher test-retest reliability by making greater use of predictive models.

scholarly journals 0724 Altered Brain Network Organization in Patients With Obstructive Sleep Apnea

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A275-A276
Author(s):  
H Park ◽  
J Cha ◽  
H Kim ◽  
E Joo
Keyword(s):  
Obstructive Sleep Apnea ◽  
Executive Function ◽  
Sleep Apnea ◽  
Resting State ◽  
Verbal Fluency ◽  
Association Cortex ◽  
Apnea Hypopnea Index ◽  
Significant Group ◽  
Functional Connections ◽  
Obstructive Sleep

Abstract Introduction Previous functional MRI (fMRI) studies have reported altered brain networks in patients with obstructive sleep apnea (OSA), but the extent of such abnormal connectivity was inconsistent across studies. Moreover, despite the important role of the cerebellum in respiration and OSA, connections of the cerebellum to the cerebral cortex have been rarely assessed. Here, we investigated functional network changes in cerebral and cerebellar cortices of OSA patients. Methods Resting-state fMRI, polysomnography and neuropsychological (NP) tests data were acquired from 74 treatment naïve OSA patients (age: 45.8±10.7 years, apnea-hypopnea index: 46.4±18.5 /h) and 33 normal controls (39.6±9.3 years). Connectivity matrices were extracted by computing correlation coefficients from various ROIs, and Fisher r-to-z transformations. ROIs consisted of 234 regions matched to 17 functional networks, including 200 parcels of the cortex, and 34 parcels of the cerebellum. Between-group connectivity with age as a covariate was analyzed, and threshold for FDR correction was set at q<0.05. In the functional connections that showed the significant group differences, linear regression was conducted to examine the association between connectivity and composite score of NP tests in OSA patients. Results OSA subjects showed decreased attention, executive function, verbal fluency and verbal memory compared to controls. Resting-state functional connectivity was increased between regions involved in the default mode network (DMN), including left medial prefrontal, ventrolateral prefrontal and lateral temporal cortices. In OSA, the connectivity changes between these DMN areas negatively correlated with attention/executive function and verbal fluency. Multiple cerebellar regions showed reduces in connectivity with cerebral cortical areas including frontal eye field, temporoparietal junction, temporo-occipital gyrus, and parieto-occipital association cortex. Conclusion OSA affects mainly the DMN and cerebello-cerebral pathway. The disruption of function in these two networks are known to relate to sleep deprivation and respiratory abnormality. The abnormal DMN found in OSA patients further related to their cognitive impairment. Support This research was supported by Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Science, ICT & Future Planning, Republic of Korea (2017R1A2B4003120) and by Samsung Biomedical Research Institute grant (OTC1190671)

scholarly journals Predicting Individual Traits from Unperformed Tasks

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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