scholarly journals Individual-Specific fMRI-Subspaces Improve Functional Connectivity Prediction of Behavior

2019 ◽  
Author(s):  
Rajan Kashyap ◽  
Ru Kong ◽  
Sagarika Bhattacharjee ◽  
Jingwei Li ◽  
Juan Zhou ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Posterior Cingulate Cortex ◽  
List Type ◽  
Resting State Functional Connectivity ◽  
Behavioral Measures ◽  
Specific Effects ◽  
Human Connectome Project ◽  
Common Group ◽  
Behavioral Prediction

AbstractThere is significant interest in using resting-state functional connectivity (RSFC) to predict human behavior. Good behavioral prediction should in theory require RSFC to be sufficiently distinct across participants; if RSFC were the same across participants, then behavioral prediction would obviously be poor. Therefore, we hypothesize that removing common resting-state functional magnetic resonance imaging (rs-fMRI) signals that are shared across participants would improve behavioral prediction. Here, we considered 803 participants from the human connectome project (HCP) with four rs-fMRI runs. We applied the common and orthogonal basis extraction (COBE) technique to decompose each HCP run into two subspaces: a common (group-level) subspace shared across all participants and a subject-specific subspace. We found that the first common COBE component of the first HCP run was localized to the visual cortex and was unique to the run. On the other hand, the second common COBE component of the first HCP run and the first common COBE component of the remaining HCP runs were highly similar and localized to regions within the default network, including the posterior cingulate cortex and precuneus. Overall, this suggests the presence of run-specific (state-specific) effects that were shared across participants. By removing the first and second common COBE components from the first HCP run, and the first common COBE component from the remaining HCP runs, the resulting RSFC improves behavioral prediction by an average of 11.7% across 58 behavioral measures spanning cognition, emotion and personality.HighlightsWe decomposed rs-fMRI signals into common subspace & individual-specific subspaceCommon subspace is shared across all Human Connectome Project (HCP) participantsCommon subspaces are different across runs, suggesting state-specific effectsIndividual-specific subspaces are unique to individualsRemoval of common subspace signals improve behavioral prediction by 11.7%

scholarly journals Neural basis underlying the trait of attachment anxiety and avoidance revealed by the amplitude of low-frequency fluctuations and resting-state functional connectivity

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Min Deng ◽  
Xing Zhang ◽  
Xiaoyan Bi ◽  
Chunhai Gao
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Low Frequency ◽  
Cingulate Cortex ◽  
Posterior Cingulate Cortex ◽  
Attachment Anxiety ◽  
Frequency Fluctuation ◽  
Resting State Functional Connectivity ◽  
Attachment Avoidance ◽  
Posterior Cingulate

Abstract Background Attachment theory demonstrates that early attachment experience shapes internal working models with mental representations of self and close relationships, which affects personality traits and interpersonal relationships in adulthood. Although research has focused on brain structural and functional underpinnings to disentangle attachment styles in healthy individuals, little is known about the spontaneous brain activity associated with self-reported attachment anxiety and avoidance during the resting state. Methods One hundred and nineteen individuals participated in the study, completing the Experience in Close Relationship scale immediately after an 8-min fMRI scanning. We used the resting-state functional magnetic resonance imaging (rs-fMRI) signal of the amplitude of low-frequency fluctuation and resting-state functional connectivity to identify attachment-related regions and networks. Results Consequently, attachment anxiety is closely associated with the amplitude of low-frequency fluctuations in the right posterior cingulate cortex, over-estimating emotional intensity and exaggerating outcomes. Moreover, the functional connectivity between the posterior cingulate cortex and fusiform gyrus increases detection ability for potential threat or separation information, facilitating behavior motivation. The attachment avoidance is positively correlated with the amplitude of low-frequency fluctuation in the bilateral lingual gyrus and right postcentral and negatively correlated with the bilateral orbital frontal cortex and inferior temporal gyrus. Functional connection with attachment avoidance contains critical nodes in the medial temporal lobe memory system, frontal-parietal network, social cognition, and default mode network necessary to deactivate the attachment system and inhibit attachment-related behavior. Conclusion and implications These findings clarify the amplitude of low-frequency fluctuation and resting-state functional connectivity neural signature of attachment style, associated with attachment strategies in attachment anxiety and attachment avoidance individuals. These findings may improve our understanding of the pathophysiology of the attachment-related disorder.

scholarly journals Brain Connectivity Tracks Effects of Chemotherapy Separately from Behavioral Measures

2018 ◽  
Author(s):  
Omid Kardan ◽  
Mary K. Askren ◽  
Misook Jung ◽  
Scott Peltier ◽  
Bratislav Misic ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Cognitive Performance ◽  
Resting State ◽  
Brain Connectivity ◽  
Brain Regions ◽  
Self Report ◽  
Breast Cancer Patients ◽  
Resting State Functional Connectivity ◽  
Behavioral Measures ◽  
Cognitive Health

AbstractSeveral studies in cancer research have suggested that cognitive dysfunction following chemotherapy, referred to in lay terms as “chemobrain”, is a serious problem. At present, the changes in integrative brain function that underlie such dysfunction remains poorly understood. Recent developments in neuroimaging suggest that patterns of functional connectivity can provide a broadly applicable neuromarker of cognitive performance and other psychometric measures. The current study used multivariate analysis methods to identify patterns of disruption in resting state functional connectivity of the brain due to chemotherapy and the degree to which the disruptions can be linked to behavioral measures of distress and cognitive performance. Sixty two women (22 healthy control, 18 patients treated with adjuvant chemotherapy, and 22 treated without chemotherapy) were evaluated with neurocognitive measures followed by self-report questionnaires and open eyes resting-state fMRI scanning at three time points: diagnosis (M0, pre-adjuvant treatment), at least 1 month (M1), and 7 months (M7) after treatment. The results indicated deficits in cognitive health of breast cancer patients immediately after chemotherapy that improved over time. This psychological trajectory was paralleled by a disruption and later recovery of resting-state functional connectivity, mostly in the parietal and frontal brain regions. The functional connectivity alteration pattern seems to be a separable treatment symptom from the decreased cognitive health. More targeted support for patients should be developed to ameliorate these multi-faceted side effects of chemotherapy treatment on neural functioning and cognitive health.

Fatigue in multiple sclerosis: The contribution of resting-state functional connectivity reorganization

2017 ◽  
Vol 24 (13) ◽  
pp. 1696-1705 ◽  
Author(s):  
Alvino Bisecco ◽  
Federica Di Nardo ◽  
Renato Docimo ◽  
Giuseppina Caiazzo ◽  
Alessandro d’Ambrosio ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Regression Analysis ◽  
Functional Connectivity ◽  
Motor Cortex ◽  
Resting State ◽  
Primary Motor Cortex ◽  
Linear Regression Analysis ◽  
Posterior Cingulate Cortex ◽  
Resting State Functional Connectivity ◽  
Sensorimotor Network

Objectives: To investigate resting-state functional connectivity (RS-FC) of the default-mode network (DMN) and of sensorimotor network (SMN) network in relapsing remitting (RR) multiple sclerosis (MS) patients with fatigue (F) and without fatigue(NF). Methods: In all, 59 RRMS patients and 29 healthy controls (HC) underwent magnetic resonance imaging (MRI) protocol including resting-state fMRI (RS-fMRI). Functional connectivity of the DMN and SMN was evaluated by independent component analysis (ICA). A linear regression analysis was performed to explore whether fatigue was mainly driven by changes observed in the DMN or in the SMN. Regional gray matter atrophy was assessed by voxel-based morphometry (VBM). Results: Compared to HC, F-MS patients showed a stronger RS-FC in the posterior cingulate cortex (PCC) and a reduced RS-FC in the anterior cingulated cortex (ACC) of the DMN. F-MS patients, compared to NF-MS patients, revealed (1) an increased RS-FC in the PCC and a reduced RS-FC in the ACC of the DMN and (2) an increased RS-FC in the primary motor cortex and in the supplementary motor cortex of the SMN. The regression analysis suggested that fatigue is mainly driven by RS-FC changes of the DMN. Conclusions: Fatigue in RRMS is mainly associated to a functional rearrangement of non-motor RS networks.

scholarly journals Advancing motion denoising of multiband resting state functional connectivity fMRI data

2019 ◽  
Author(s):  
John C. Williams ◽  
Philip N. Tubiolo ◽  
Jacob R. Luceno ◽  
Jared X. Van Snellenberg
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Quantitative Methods ◽  
Resting State Functional Connectivity ◽  
Quantitative Metrics ◽  
Human Connectome Project ◽  
Optimal Censoring ◽  
Temporal And Spatial ◽  
Control Functional ◽  
Subject Motion

AbstractMultiband-accelerated fMRI provides dramatically improved temporal and spatial resolution of resting state functional connectivity (RSFC) studies of the human brain, but poses unique challenges for denoising of subject motion induced data artifacts, a major confound in RSFC research. We comprehensively evaluated existing and novel approaches to volume censoring-based motion denoising in the Human Connectome Project dataset. We show that assumptions underlying common metrics for evaluating motion denoising pipelines, especially those based on quality control-functional connectivity (QC-FC) correlations and differences between high- and low-motion participants, are problematic, making these criteria inappropriate for quantifying pipeline performance. We further develop two new quantitative metrics that are free from these issues and demonstrate their use as benchmarks for comparing volume censoring methods. Finally, we develop rigorous, quantitative methods for determining optimal censoring thresholds and provide straightforward recommendations and code for all investigators to apply this optimized approach to their own RSFC datasets.

scholarly journals General Functional Connectivity: shared features of resting-state and task fMRI drive reliable and heritable individual differences in functional brain networks

2018 ◽  
Author(s):  
Maxwell L. Elliott ◽  
Annchen R. Knodt ◽  
Megan Cooke ◽  
M. Justin Kim ◽  
Tracy R. Melzer ◽  
...  
Keyword(s):  
Individual Differences ◽  
Functional Connectivity ◽  
Resting State ◽  
Biomarker Discovery ◽  
Resting State Functional Connectivity ◽  
Heritable Variation ◽  
State Data ◽  
Human Connectome Project ◽  
Intrinsic Connectivity ◽  
And Task

AbstractIntrinsic connectivity, measured using resting-state fMRI, has emerged as a fundamental tool in the study of the human brain. However, due to practical limitations, many studies do not collect enough resting-state data to generate reliable measures of intrinsic connectivity necessary for studying individual differences. Here we present general functional connectivity (GFC) as a method for leveraging shared features across resting-state and task fMRI and demonstrate in the Human Connectome Project and the Dunedin Study that GFC offers better test-retest reliability than intrinsic connectivity estimated from the same amount of resting-state data alone. Furthermore, at equivalent scan lengths, GFC displays higher heritability on average than resting-state functional connectivity. We also show that predictions of cognitive ability from GFC generalize across datasets, performing as well or better than resting-state or task data alone. Collectively, our work suggests that GFC can improve the reliability of intrinsic connectivity estimates in existing datasets and, subsequently, the opportunity to identify meaningful correlates of individual differences in behavior. Given that task and resting-state data are often collected together, many researchers can immediately derive more reliable measures of intrinsic connectivity through the adoption of GFC rather than solely using resting-state data. Moreover, by better capturing heritable variation in intrinsic connectivity, GFC represents a novel endophenotype with broad applications in clinical neuroscience and biomarker discovery.

scholarly journals Altered resting-state functional connectivity in emotion-processing brain regions in adults who were born very preterm

2016 ◽  
Vol 46 (14) ◽  
pp. 3025-3039 ◽  
Author(s):  
C. Papini ◽  
T. P. White ◽  
A. Montagna ◽  
P. J. Brittain ◽  
S. Froudist-Walsh ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Emotion Processing ◽  
Recognition Task ◽  
Cingulate Cortex ◽  
Posterior Cingulate Cortex ◽  
Resting State Functional Connectivity ◽  
Dynamic Stimuli ◽  
Posterior Cingulate ◽  
Very Preterm

BackgroundVery preterm birth (VPT; <32 weeks of gestation) has been associated with impairments in emotion regulation, social competence and communicative skills. However, the neuroanatomical mechanisms underlying such impairments have not been systematically studied. Here we investigated the functional integrity of the amygdala connectivity network in relation to the ability to recognize emotions from facial expressions in VPT adults.MethodThirty-six VPT-born adults and 38 age-matched controls were scanned at rest in a 3-T MRI scanner. Resting-state functional connectivity (rs-fc) was assessed with SPM8. A seed-based analysis focusing on three amygdalar subregions (centro-medial/latero-basal/superficial) was performed. Participants’ ability to recognize emotions was assessed using dynamic stimuli of human faces expressing six emotions at different intensities with the Emotion Recognition Task (ERT).ResultsVPT individuals compared to controls showed reduced rs-fc between the superficial subregion of the left amygdala, and the right posterior cingulate cortex (p = 0.017) and the left precuneus (p = 0.002). The VPT group further showed elevated rs-fc between the left superficial amygdala and the superior temporal sulcus (p = 0.008). Performance on the ERT showed that the VPT group was less able than controls to recognize anger at low levels of intensity. Anger scores were significantly associated with rs-fc between the superficial amygdala and the posterior cingulate cortex in controls but not in VPT individuals.ConclusionsThese findings suggest that alterations in rs-fc between the amygdala, parietal and temporal cortices could represent the mechanism linking VPT birth and deficits in emotion processing.

scholarly journals Different Resting-State Functional Connectivity Alterations in Smokers and Nonsmokers with Internet Gaming Addiction

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Xue Chen ◽  
Yao Wang ◽  
Yan Zhou ◽  
Yawen Sun ◽  
Weina Ding ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Correlation Method ◽  
Posterior Cingulate Cortex ◽  
Gaming Addiction ◽  
Resting State Functional Connectivity ◽  
Functional Changes ◽  
Internet Gaming ◽  
Internet Gaming Addiction ◽  
The Right

This study investigated changes in resting-state functional connectivity (rsFC) of posterior cingulate cortex (PCC) in smokers and nonsmokers with Internet gaming addiction (IGA). Twenty-nine smokers with IGA, 22 nonsmokers with IGA, and 30 healthy controls (HC group) underwent a resting-state fMRI scan. PCC connectivity was determined in all subjects by investigating synchronized low-frequency fMRI signal fluctuations using a temporal correlation method. Compared with the nonsmokers with IGA, the smokers with IGA exhibited decreased rsFC with PCC in the right rectus gyrus. Left middle frontal gyrus exhibited increased rsFC. The PCC connectivity with the right rectus gyrus was found to be negatively correlated with the CIAS scores in the smokers with IGA before correction. Our results suggested that smokers with IGA had functional changes in brain areas related to motivation and executive function compared with the nonsmokers with IGA.

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