Pain-free resting-state functional brain connectivity predicts individual pain sensitivity.
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ABSTRACT:
SUBMITTER: Spisak T
PROVIDER: S-EPMC6954277 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Anatomical connectivity can constrain both a neural circuit's function and its underlying computation. This principle has been demonstrated for many small, defined neural circuits. For example, connectome reconstructions have informed models for direction selectivity in the vertebrate retina1,2 as well as the Drosophila visual system.3 In these cases, the circuit in question is relatively compact, well-defined, and has known functions. However, how the connectome constrains global properties of large-scale networks, across multiple brain regions or the entire brain, is incompletely understood. As the availability of partial or complete connectomes expands to more systems and species4-8 it becomes critical to understand how this detailed anatomical information can inform our understanding of large-scale circuit function.9,10 Here, we use data from the Drosophila connectome4 in conjunction with whole-brain in vivo imaging11 to relate structural and functional connectivity in the central brain. We find a strong relationship between resting-state functional correlations and direct region-to-region structural connectivity. We find that the relationship between structure and function varies across the brain, with some regions displaying a tight correspondence between structural and functional connectivity whereas others, including the mushroom body, are more strongly dependent on indirect connections. Throughout this work, we observe features of structural and functional networks in Drosophila that are strikingly similar to those seen in mammalian cortex, including in the human brain. Given the vast anatomical and functional differences between Drosophila and mammalian nervous systems, these observations suggest general principles that govern brain structure, function, and the relationship between the two.
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Project description:BackgroundDeep brain stimulation of the subthalamic nucleus is a widely used adjunctive therapy for motor symptoms of Parkinson's disease, but with variable motor response. Predicting motor response remains difficult, and novel approaches may improve surgical outcomes as well as the understanding of pathophysiological mechanisms. The objective of this study was to determine whether preoperative resting-state functional connectivity MRI predicts motor response from deep brain stimulation of the subthalamic nucleus.MethodsWe collected preoperative resting-state functional MRI from 70 participants undergoing subthalamic nucleus deep brain stimulation. For this cohort, we analyzed the strength of STN functional connectivity with seeds determined by stimulation-induced (ON/OFF) 15 O H2 O PET regional cerebral blood flow differences in a partially overlapping group (n = 42). We correlated STN-seed functional connectivity strength with postoperative motor outcomes and applied linear regression to predict motor outcomes.ResultsPreoperative functional connectivity between the left subthalamic nucleus and the ipsilateral internal globus pallidus correlated with postsurgical motor outcomes (r = -0.39, P = 0.0007), with stronger preoperative functional connectivity relating to greater improvement. Left pallidal-subthalamic nucleus connectivity also predicted motor response to DBS after controlling for covariates.DiscussionPreoperative pallidal-subthalamic nucleus resting-state functional connectivity predicts motor benefit from deep brain stimulation, although this should be validated prospectively before clinical application. These observations suggest that integrity of pallidal-subthalamic nucleus circuits may be critical to motor benefits from deep brain stimulation. © 2020 International Parkinson and Movement Disorder Society.
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Project description:MRI-based resting-state functional connectivity (rsFC) has been shown to predict response to pharmacological and non-pharmacological treatments for chronic pain, but not yet for motor cortex transcranial magnetic stimulation (M1-rTMS). Twenty-seven fibromyalgia syndrome (FMS) patients participated in this double-blind, crossover, and sham-controlled study. Ten daily treatments of 10 Hz M1-rTMS were given over 2 weeks. Before treatment series, patients underwent resting-state fMRI and clinical pain evaluation. Significant pain reduction occurred following active, but not sham, M1-rTMS. The following rsFC patterns predicted reductions in clinical pain intensity after the active treatment: weaker rsFC of the default-mode network with the middle frontal gyrus (r = 0.76, p < 0.001), the executive control network with the rostro-medial prefrontal cortex (r = 0.80, p < 0.001), the thalamus with the middle frontal gyrus (r = 0.82, p < 0.001), and the pregenual anterior cingulate cortex with the inferior parietal lobule (r = 0.79, p < 0.001); and stronger rsFC of the anterior insula with the angular gyrus (r = − 0.81, p < 0.001). The above regions process the attentional and emotional aspects of pain intensity; serve as components of the resting-state networks; are modulated by rTMS; and are altered in FMS. Therefore, we suggest that in FMS, the weaker pre-existing interplay between pain-related brain regions and networks, the larger the pain relief resulting from M1-rTMS.
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Project description:Personality neuroscience aims to find associations between brain measures and personality traits. Findings to date have been severely limited by a number of factors, including small sample size and omission of out-of-sample prediction. We capitalized on the recent availability of a large database, together with the emergence of specific criteria for best practices in neuroimaging studies of individual differences. We analyzed resting-state functional magnetic resonance imaging data from 884 young healthy adults in the Human Connectome Project (HCP) database. We attempted to predict personality traits from the "Big Five", as assessed with the NEO-FFI test, using individual functional connectivity matrices. After regressing out potential confounds (such as age, sex, handedness and fluid intelligence), we used a cross-validated framework, together with test-retest replication (across two sessions of resting-state fMRI for each subject), to quantify how well the neuroimaging data could predict each of the five personality factors. We tested three different (published) denoising strategies for the fMRI data, two inter-subject alignment and brain parcellation schemes, and three different linear models for prediction. As measurement noise is known to moderate statistical relationships, we performed final prediction analyses using average connectivity across both imaging sessions (1 h of data), with the analysis pipeline that yielded the highest predictability overall. Across all results (test/retest; 3 denoising strategies; 2 alignment schemes; 3 models), Openness to experience emerged as the only reliably predicted personality factor. Using the full hour of resting-state data and the best pipeline, we could predict Openness to experience (NEOFAC_O: r=0.24, R2=0.024) almost as well as we could predict the score on a 24-item intelligence test (PMAT24_A_CR: r=0.26, R2=0.044). Other factors (Extraversion, Neuroticism, Agreeableness and Conscientiousness) yielded weaker predictions across results that were not statistically significant under permutation testing. We also derived two superordinate personality factors ("α" and "β") from a principal components analysis of the NEO-FFI factor scores, thereby reducing noise and enhancing the precision of these measures of personality. We could account for 5% of the variance in the β superordinate factor (r=0.27, R2=0.050), which loads highly on Openness to experience. We conclude with a discussion of the potential for predicting personality from neuroimaging data and make specific recommendations for the field.
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Project description:Language deficits following brain tumors should consider the dynamic interactions between different tumor growth kinetics and functional network reorganization. We measured the resting-state functional connectivity of 126 patients with left cerebral gliomas involving language network areas, including 77 patients with low-grade gliomas (LGG) and 49 patients with high-grade gliomas (HGG). Functional network mapping for language was performed by construction of a multivariate machine learning-based prediction model of individual aphasia quotient (AQ), a summary score that indicates overall severity of language impairment. We found that the AQ scores for HGG patients were significantly lower than those of LGG patients. The prediction accuracy of HGG patients (R2 = 0.27, permutation P = 0.007) was much higher than that of LGG patients (R2 = 0.09, permutation P = 0.032). The rsFC regions predictive of LGG's AQ involved the bilateral frontal, temporal, and parietal lobes, subcortical regions, and bilateral cerebro-cerebellar connections, mainly in regions belonging to the canonical language network. The functional network of language processing for HGG patients showed strong dependence on connections of the left cerebro-cerebellar connections, limbic system, and the temporal, occipital, and prefrontal lobes. Together, our findings suggested that individual language processing of glioma patients links large-scale, bilateral, cortico-subcortical, and cerebro-cerebellar functional networks with different network reorganizational mechanisms underlying the different levels of language impairments in LGG and HGG patients.
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Project description:Chronic pain symptoms often change over time, even in individuals who have had symptoms for years. Studying biological factors that predict trends in symptom change in chronic pain may uncover novel pathophysiological mechanisms and potential therapeutic targets. In this study, we investigated whether brain functional connectivity measures obtained from resting-state functional magnetic resonance imaging at baseline can predict longitudinal symptom change (3, 6, and 12 months after scan) in urologic chronic pelvic pain syndrome. We studied 52 individuals with urologic chronic pelvic pain syndrome (34 women, 18 men) who had baseline neuroimaging followed by symptom tracking every 2 weeks for 1 year as part of the Multidisciplinary Approach to the Study of Chronic Pelvic Pain (MAPP) Research Network study. We found that brain functional connectivity can make a significant prediction of short-term (3 month) pain reduction with 73.1% accuracy (69.2% sensitivity and 75.0% precision). In addition, we found that the brain regions with greatest contribution to the classification were preferentially aligned with the left frontoparietal network. Resting-state functional magnetic resonance imaging measures seemed to be less informative about 6- or 12-month symptom change. Our study provides the first evidence that future trends in symptom change in patients in a state of chronic pain may be linked to functional connectivity within specific brain networks.
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Project description:Motor imagery (MI) relies on the mental simulation of an action without any overt motor execution (ME), and can facilitate motor learning and enhance the effect of rehabilitation in patients with neurological conditions. While functional magnetic resonance imaging (fMRI) during MI and ME reveals shared cortical representations, the role and functional relevance of the resting-state functional connectivity (RSFC) of brain regions involved in MI is yet unknown. Here, we performed resting-state fMRI followed by fMRI during ME and MI with the dominant hand. We used a behavioral chronometry test to measure ME and MI movement duration and compute an index of performance (IP). Then, we analyzed the voxel-matched correlation between the individual MI parameter estimates and seed-based RSFC maps in the MI network to measure the correspondence between RSFC and MI fMRI activation. We found that inter-individual differences in intrinsic connectivity in the MI network predicted several clusters of activation. Taken together, present findings provide first evidence that RSFC within the MI network is predictive of the activation of MI brain regions, including those associated with behavioral performance, thus suggesting a role for RSFC in obtaining a deeper understanding of neural substrates of MI and of MI ability. Hum Brain Mapp 37:3847-3857, 2016. © 2016 Wiley Periodicals, Inc.
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Project description:The personality dimensions of neuroticism and extraversion are strongly associated with emotional experience and affective disorders. Previous studies reported functional magnetic resonance imaging (fMRI) activity correlates of these traits, but no study has used brain-based measures to predict them. Here, using a fully cross-validated approach, we predict novel individuals' neuroticism and extraversion from functional connectivity (FC) data observed as they simply rested during fMRI scanning. We applied a data-driven technique, connectome-based predictive modeling (CPM), to resting-state FC data and neuroticism and extraversion scores (self-reported NEO Five Factor Inventory) from 114 participants of the Nathan Kline Institute Rockland sample. After dividing the whole brain into 268 nodes using a predefined functional atlas, we defined each individual's FC matrix as the set of correlations between the activity timecourses of every pair of nodes. CPM identified networks consisting of functional connections correlated with neuroticism and extraversion scores, and used strength in these networks to predict a left-out individual's scores. CPM predicted neuroticism and extraversion in novel individuals, demonstrating that patterns in resting-state FC reveal trait-level measures of personality. CPM also revealed predictive networks that exhibit some anatomical patterns consistent with past studies and potential new brain areas of interest in personality.
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