scholarly journals Resting-state fMRI Connectivity between Semantic and Phonologic Regions of Interest May Inform Language Targets in Aphasia

2020 ◽  
Author(s):  
Amy E. Ramage ◽  
Semra Aytur ◽  
Kirrie J. Ballard
Keyword(s):  
Functional Connectivity ◽  
Left Hemisphere ◽  
Resting State ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Regions Of Interest ◽  
Healthy Controls ◽  
Language Performance ◽  
Data Set ◽  
Language Functions

PurposeBrain imaging has provided puzzle pieces in the understanding of language. In neurologically healthy populations, structure of certain brain regions is associated with particular language functions (e.g., semantics, phonology). In studies on focal brain damage, certain brain regions or connections are considered sufficient or necessary for a given language function. However, few of these account for the effects of lesioned tissue on the functional dynamics of the brain for language processing. Here, functional connectivity amongst semantic-phonologic regions of interest (ROIs) is assessed to fill a gap in our understanding about the neural substrates of impaired language and whether connectivity strength can predict language performance on a clinical tool in individuals with aphasia.MethodClinical assessment of language, using the Western Aphasia Battery-Revised (WAB-R), and resting-state fMRI data were obtained for 30 individuals with chronic aphasia secondary to left hemisphere stroke and 18 age-matched healthy controls. Functional connectivity (FC) between bilateral ROIs was contrasted by group and used to predict WAB-R scores.ResultsNetwork coherence was observed in healthy controls and participants with stroke. The left-right premotor cortex connection was stronger in healthy controls, as reported by New et al. (2015) in the same data set. FC of (1) bilateral connections between temporal regions, in the left hemisphere and bilaterally, predicted lexical semantic processing for Auditory Comprehension and (2) ipsilateral connections between temporal and frontal regions in both hemispheres predicted access to semantic-phonologic representations and processing for verbal production.ConclusionsNetwork connectivity of brain regions associated with semantic-phonologic processing is predictive of language performance in post-stroke aphasia. The most predictive connections involved right hemisphere ROIs – particularly those for which structural adaptions are known to associate with recovered word retrieval performance. Predictions may be made, based on these findings, about which connections have potential as targets for neuroplastic functional changes with intervention in aphasia.

scholarly journals Resting-State Functional Magnetic Resonance Imaging Connectivity Between Semantic and Phonological Regions of Interest May Inform Language Targets in Aphasia

2020 ◽  
Vol 63 (9) ◽  
pp. 3051-3067
Author(s):  
Amy E. Ramage ◽  
Semra Aytur ◽  
Kirrie J. Ballard
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Functional Magnetic Resonance Imaging ◽  
Resting State ◽  
Brain Regions ◽  
Regions Of Interest ◽  
Healthy Controls ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Language Performance

Purpose Brain imaging has provided puzzle pieces in the understanding of language. In neurologically healthy populations, the structure of certain brain regions is associated with particular language functions (e.g., semantics, phonology). In studies on focal brain damage, certain brain regions or connections are considered sufficient or necessary for a given language function. However, few of these account for the effects of lesioned tissue on the “functional” dynamics of the brain for language processing. Here, functional connectivity (FC) among semantic–phonological regions of interest (ROIs) is assessed to fill a gap in our understanding about the neural substrates of impaired language and whether connectivity strength can predict language performance on a clinical tool in individuals with aphasia. Method Clinical assessment of language, using the Western Aphasia Battery–Revised, and resting-state functional magnetic resonance imaging data were obtained for 30 individuals with chronic aphasia secondary to left-hemisphere stroke and 18 age-matched healthy controls. FC between bilateral ROIs was contrasted by group and used to predict Western Aphasia Battery–Revised scores. Results Network coherence was observed in healthy controls and participants with stroke. The left–right premotor cortex connection was stronger in healthy controls, as reported by New et al. (2015) in the same data set. FC of (a) connections between temporal regions, in the left hemisphere and bilaterally, predicted lexical–semantic processing for auditory comprehension and (b) ipsilateral connections between temporal and frontal regions in both hemispheres predicted access to semantic–phonological representations and processing for verbal production. Conclusions Network connectivity of brain regions associated with semantic–phonological processing is predictive of language performance in poststroke aphasia. The most predictive connections involved right-hemisphere ROIs—particularly those for which structural adaptions are known to associate with recovered word retrieval performance. Predictions may be made, based on these findings, about which connections have potential as targets for neuroplastic functional changes with intervention in aphasia. Supplemental Material https://doi.org/10.23641/asha.12735785

scholarly journals Altered Intra- and Interregional Synchronization in Resting-State Cerebral Networks Associated with Chronic Tinnitus

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Yu-Chen Chen ◽  
Jian Zhang ◽  
Xiao-Wei Li ◽  
Wenqing Xia ◽  
Xu Feng ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Brain Activity ◽  
Inferior Frontal Gyrus ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Chronic Tinnitus ◽  
Healthy Controls ◽  
Tinnitus Distress ◽  
The Right

Objective. Subjective tinnitus is hypothesized to arise from aberrant neural activity; however, its neural bases are poorly understood. To identify aberrant neural networks involved in chronic tinnitus, we compared the resting-state functional magnetic resonance imaging (fMRI) patterns of tinnitus patients and healthy controls.Materials and Methods. Resting-state fMRI measurements were obtained from a group of chronic tinnitus patients (n=29) with normal hearing and well-matched healthy controls (n=30). Regional homogeneity (ReHo) analysis and functional connectivity analysis were used to identify abnormal brain activity; these abnormalities were compared to tinnitus distress.Results. Relative to healthy controls, tinnitus patients had significant greater ReHo values in several brain regions including the bilateral anterior insula (AI), left inferior frontal gyrus, and right supramarginal gyrus. Furthermore, the left AI showed enhanced functional connectivity with the left middle frontal gyrus (MFG), while the right AI had enhanced functional connectivity with the right MFG; these measures were positively correlated with Tinnitus Handicap Questionnaires (r=0.459,P=0.012andr=0.479,P=0.009, resp.).Conclusions. Chronic tinnitus patients showed abnormal intra- and interregional synchronization in several resting-state cerebral networks; these abnormalities were correlated with clinical tinnitus distress. These results suggest that tinnitus distress is exacerbated by attention networks that focus on internally generated phantom sounds.

scholarly journals Insulin Resistance-Associated Interhemispheric Functional Connectivity Alterations in T2DM: A Resting-State fMRI Study

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Wenqing Xia ◽  
Shaohua Wang ◽  
Andrea M. Spaeth ◽  
Hengyi Rao ◽  
Pin Wang ◽  
...  
Keyword(s):  
Insulin Resistance ◽  
Functional Connectivity ◽  
Resting State ◽  
Correlation Coefficients ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Middle Temporal Gyrus ◽  
Model Assessment ◽  
Healthy Controls ◽  
Resting State Functional Connectivity

We aim to investigate whether decreased interhemispheric functional connectivity exists in patients with type 2 diabetes mellitus (T2DM) by using resting-state functional magnetic resonance imaging (rs-fMRI). In addition, we sought to determine whether interhemispheric functional connectivity deficits associated with cognition and insulin resistance (IR) among T2DM patients. We compared the interhemispheric resting state functional connectivity of 32 T2DM patients and 30 healthy controls using rs-fMRI. Partial correlation coefficients were used to detect the relationship between rs-fMRI information and cognitive or clinical data. Compared with healthy controls, T2DM patients showed bidirectional alteration of functional connectivity in several brain regions. Functional connectivity values in the middle temporal gyrus (MTG) and in the superior frontal gyrus were inversely correlated with Trail Making Test-B score of patients. Notably, insulin resistance (log homeostasis model assessment-IR) negatively correlated with functional connectivity in the MTG of patients. In conclusion, T2DM patients exhibit abnormal interhemispheric functional connectivity in several default mode network regions, particularly in the MTG, and such alteration is associated with IR. Alterations in interhemispheric functional connectivity might contribute to cognitive dysfunction in T2DM patients.

scholarly journals Effects of long-term cocaine self-administration on brain resting-state functional connectivity in nonhuman primates

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Stephen J. Kohut ◽  
Dionyssios Mintzopoulos ◽  
Brian D. Kangas ◽  
Hannah Shields ◽  
Kelly Brown ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Cognitive Processing ◽  
Resting State ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Anterior Cingulate ◽  
Resting State Functional Connectivity ◽  
Self Administration ◽  
Chronic Cocaine

AbstractLong-term cocaine use is associated with a variety of neural and behavioral deficits that impact daily function. This study was conducted to examine the effects of chronic cocaine self-administration on resting-state functional connectivity of the dorsal anterior cingulate (dACC) and putamen—two brain regions involved in cognitive function and motoric behavior—identified in a whole brain analysis. Six adult male squirrel monkeys self-administered cocaine (0.32 mg/kg/inj) over 140 sessions. Six additional monkeys that had not received any drug treatment for ~1.5 years served as drug-free controls. Resting-state fMRI imaging sessions at 9.4 Tesla were conducted under isoflurane anesthesia. Functional connectivity maps were derived using seed regions placed in the left dACC or putamen. Results show that cocaine maintained robust self-administration with an average total intake of 367 mg/kg (range: 299–424 mg/kg). In the cocaine group, functional connectivity between the dACC seed and regions primarily involved in motoric behavior was weaker, whereas connectivity between the dACC seed and areas implicated in reward and cognitive processing was stronger. In the putamen seed, weaker widespread connectivity was found between the putamen and other motor regions as well as with prefrontal areas that regulate higher-order executive function; stronger connectivity was found with reward-related regions. dACC connectivity was associated with total cocaine intake. These data indicate that functional connectivity between regions involved in motor, reward, and cognitive processing differed between subjects with recent histories of cocaine self-administration and controls; in dACC, connectivity appears to be related to cumulative cocaine dosage during chronic exposure.

High-Order Functional Connectivity Network for Differential Diagnosis of Depression

2019 ◽  
Vol 9 (6) ◽  
pp. 1095-1102
Author(s):  
Jian Yang ◽  
Xu Mao ◽  
Ning Liu ◽  
Ning Zhong
Keyword(s):  
Time Series ◽  
Differential Diagnosis ◽  
Functional Connectivity ◽  
Resting State ◽  
Correlation Time ◽  
Time Window ◽  
Resting State Fmri ◽  
High Order ◽  
Healthy Controls ◽  
Resting State Functional Connectivity

Resting-state functional connectivity (FC) changes dynamically and major depressive disorder (MDD) has abnormality in functional connectivity networks (FCNs), but few existing resting-state fMRI study on MDD utilizes the dynamics, especially for identifying depressive individuals from healthy controls. In this paper, we propose a methodological procedure for differential diagnosis of depression, called HN3D, which is based on high-order functional connectivity networks (HFCN). Firstly, HN3D extracts time series by independent component analysis, and partitions them into overlapped short series by sliding time window. Secondly, it constructs a FCN for each time window and concatenates correlation matrices of all FCNs to generate correlation time series. Then, correlation time series are grouped into different clusters and high-order correlations for HFCN is calculated based on their means. Finally, graph based features of HFCNs are extracted and selected for a linear discriminative classifier. Tested on 21 healthy controls and 20 MDD patients, HN3D achieved its best 100% classification accuracy, which is much higher than results based on stationary FCNs. In addition, most discriminative components of HN3D locate in default mode network and visual network, which are consistent with existing stationary-based results on depression. Though HN3D needs to be studied further, it is helpful for the differential diagnosis of depression and might have potentiality in identifying relevant biomarkers.

scholarly journals Dynamic Expression of Brain Functional Systems Disclosed by Fine-Scale Analysis of Edge Time Series

2020 ◽  
Author(s):  
Olaf Sporns ◽  
Joshua Faskowitz ◽  
Andreia Sofia Teixera ◽  
Richard F. Betzel
Keyword(s):  
Time Series ◽  
Functional Connectivity ◽  
Resting State ◽  
Pearson Correlation ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Statistical Dependence ◽  
Fine Scale ◽  
Time Step ◽  
Functional Systems

AbstractFunctional connectivity (FC) describes the statistical dependence between brain regions in resting-state fMRI studies and is usually estimated as the Pearson correlation of time courses. Clustering reveals densely coupled sets of regions constituting a set of resting-state networks or functional systems. These systems manifest most clearly when FC is sampled over longer epochs lasting many minutes but appear to fluctuate on shorter time scales. Here, we propose a new approach to track these temporal fluctuations. Un-wrapping FC signal correlations yields pairwise co-fluctuation time series, one for each node pair/edge, and reveals fine-scale dynamics across the network. Co-fluctuations partition the network, at each time step, into exactly two communities. Sampled over time, the overlay of these bipartitions, a binary decomposition of the original time series, very closely approximates functional connectivity. Bipartitions exhibit characteristic spatiotemporal patterns that are reproducible across participants and imaging sessions and disclose fine-scale profiles of the time-varying levels of expression of functional systems. Our findings document that functional systems appear transiently and intermittently, and that FC results from the overlay of many variable instances of system expression. Potential applications of this decomposition of functional connectivity into a set of binary patterns are discussed.

scholarly journals Resting-state functional connectivity of social brain regions predicts motivated dishonesty

2021 ◽  
Author(s):  
Luoyao Pang ◽  
Huidi Li ◽  
Quanying Liu ◽  
Yue-jia Luo ◽  
Dean Mobbs ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Region Of Interest ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Resting State Functional Connectivity ◽  
Social Brain ◽  
Moral Regulation ◽  
Interpretable Model ◽  
Reward Valuation

Motivated dishonesty is a typical social behavior varying from person to person. Resting-state fMRI (rsfMRI) is capable of identifying unique patterns from functional connectivity (FC) between brain networks. To identify the relevant neural patterns and build an interpretable model to predict dishonesty, we scanned 8-min rsfMRI before an information-passing task. In the task, we employed monetary rewards to induce dishonesty. We applied both connectome-based predictive modeling (CPM) and region-of-interest (ROI) analysis to examine the association between FC and dishonesty. CPM indicated that the stronger FC between fronto-parietal and default mode networks can predict a higher dishonesty rate. The ROIs were set in the regions involving four cognitive processes (self-reference, cognitive control, reward valuation, and moral regulation). The ROI analyses showed that a stronger FC between these regions and the prefrontal cortex can predict a higher dishonesty rate. Our study offers an integrated model to predict dishonesty with rsfMRI, and the results suggest that the frequent motivated dishonest behavior may require a higher engagement of social brain regions.

scholarly journals Directed functional connectivity using dynamic graphical models

2017 ◽  
Author(s):  
Simon Schwab ◽  
Ruth Harbord ◽  
Valerio Zerbi ◽  
Lloyd Elliott ◽  
Soroosh Afyouni ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Graphical Models ◽  
Resting State ◽  
Graphical Model ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Reciprocal Relationship ◽  
Validity And Reliability ◽  
Varying Coefficients ◽  
Time Varying Coefficients

AbstractThere are a growing number of neuroimaging methods that model spatio-temporal patterns of brain activity to allow more meaningful characterizations of brain networks. This paper proposes dynamic graphical models (DGMs) for dynamic, directed functional connectivity. DGMs are a multivariate graphical model with time-varying coefficients that describe instantaneous directed relationships between nodes. A further benefit of DGMs is that networks may contain loops and that large networks can be estimated. We use network simulations, human resting-state fMRI (N = 500) to investigate the validity and reliability of the estimated networks. We simulate systematic lags of the hemodynamic response at different brain regions to investigate how these lags potentially bias directionality estimates. In the presence of such lag confounds (0.4–0.8 seconds offset between connected nodes), our method has a sensitivity of 72%–77% to detect the true direction. Stronger lag confounds have reduced sensitivity, but do not increase false positives (i.e., directionality estimates of the opposite direction). In human resting-state fMRI, we find the DMN has consistent influence on the cerebellar, the limbic and the auditory/temporal network, as well a consistent reciprocal relationship between the visual medial and visual lateral network. Finally, we apply the method in a small mouse fMRI sample and discover a highly plausible relationship between areas in the hippocampus feeding into the cingulate cortex. We provide a computationally efficient implementation of DGM as a free software package for R.

scholarly journals Sex-specific differences in resting-state functional connectivity of large-scale networks in postconcussion syndrome

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Reema Shafi ◽  
Adrian P. Crawley ◽  
Maria Carmela Tartaglia ◽  
Charles H. Tator ◽  
Robin E. Green ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Large Scale ◽  
Brain Regions ◽  
Healthy Controls ◽  
Postconcussion Syndrome ◽  
Resting State Functional Connectivity ◽  
Post Injury ◽  
Network Nodes ◽  
Specific Impact

AbstractConcussions are associated with a range of cognitive, neuropsychological and behavioral sequelae that, at times, persist beyond typical recovery times and are referred to as postconcussion syndrome (PCS). There is growing support that concussion can disrupt network-based connectivity post-injury. To date, a significant knowledge gap remains regarding the sex-specific impact of concussion on resting state functional connectivity (rs-FC). The aims of this study were to (1) investigate the injury-based rs-FC differences across three large-scale neural networks and (2) explore the sex-specific impact of injury on network-based connectivity. MRI data was collected from a sample of 80 concussed participants who fulfilled the criteria for postconcussion syndrome and 31 control participants who did not have any history of concussion. Connectivity maps between network nodes and brain regions were used to assess connectivity using the Functional Connectivity (CONN) toolbox. Network based statistics showed that concussed participants were significantly different from healthy controls across both salience and fronto-parietal network nodes. More specifically, distinct subnetwork components were identified in the concussed sample, with hyperconnected frontal nodes and hypoconnected posterior nodes across both the salience and fronto-parietal networks, when compared to the healthy controls. Node-to-region analyses showed sex-specific differences across association cortices, however, driven by distinct networks. Sex-specific network-based alterations in rs-FC post concussion need to be examined to better understand the underlying mechanisms and associations to clinical outcomes.

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