5,987 research outputs found
The neural basis of unwanted thoughts during resting state.
Human beings are constantly engaged in thought. Sometimes thoughts occur repetitively and can become distressing. Up to now the neural bases of these intrusive or unwanted thoughts is largely unexplored. To study the neural correlates of unwanted thoughts, we acquired resting-state fMRI data of 41 female healthy subjects and assessed the self-reported amount of unwanted thoughts during measurement. We analyzed local connectivity by means of regional homogeneity (ReHo) and functional connectivity of a seed region. More unwanted thoughts (state) were associated with lower ReHo in right dorsolateral prefrontal cortex (DLPFC) and higher ReHo in left striatum (putamen). Additional seed-based analysis revealed higher functional connectivity of the left striatum with left inferior frontal gyrus (IFG) in participants reporting more unwanted thoughts. The state-dependent higher connectivty in left striatum was positively correlated with rumination assessed with a dedicated questionnaire focussing on trait aspects. Unwanted thoughts are associated with activity in the fronto-striatal brain circuitry. The reduction of local connectivity in DLPFC could reflect deficiencies in thought suppression processes, whereas the hightened activity in left striatum could imply an imbalance of gating mechanisms housed in basal ganglia. Its functional connectivity to left IFG is discussed as the result of thought-related speech processes
Local synchronization of resting-state dynamics encodes Gray's trait Anxiety
The Behavioral Inhibition System (BIS) as defined within the Reinforcement Sensitivity Theory (RST) modulates reactions to stimuli indicating aversive events. Grayās trait Anxiety determines the extent to which stimuli activate the BIS. While studies have identified the amygdala-septo-hippocampal circuit as the key-neural substrate of this system in recent years and measures of resting-state dynamics such as randomness and local synchronization of spontaneous BOLD fluctuations have recently been linked to personality traits, the relation between resting-state dynamics and the BIS remains unexplored. In the present study, we thus examined the local synchronization of spontaneous fMRI BOLD fluctuations as measured by Regional Homogeneity (ReHo) in the hippocampus and the amygdala in twenty-seven healthy subjects. Correlation analyses showed that Grayās trait Anxiety was significantly associated with mean ReHo in both the amygdala and the hippocampus. Specifically, Grayās trait Anxiety explained 23% and 17% of resting-state ReHo variance in the left amygdala and the left hippocampus, respectively. In summary, we found individual differences in Grayās trait Anxiety to be associated with ReHo in areas previously associated with BIS functioning. Specifically, higher ReHo in resting-state neural dynamics corresponded to lower sensitivity to punishment scores both in the amygdala and the hippocampus. These findings corroborate and extend recent findings relating resting-state dynamics and personality while providing first evidence linking properties of resting-state fluctuations to Grayās BIS
Fluctuations between high- and low-modularity topology in time-resolved functional connectivity
Modularity is an important topological attribute for functional brain
networks. Recent studies have reported that modularity of functional networks
varies not only across individuals being related to demographics and cognitive
performance, but also within individuals co-occurring with fluctuations in
network properties of functional connectivity, estimated over short time
intervals. However, characteristics of these time-resolved functional networks
during periods of high and low modularity have remained largely unexplored. In
this study we investigate spatiotemporal properties of time-resolved networks
in the high and low modularity periods during rest, with a particular focus on
their spatial connectivity patterns, temporal homogeneity and test-retest
reliability. We show that spatial connectivity patterns of time-resolved
networks in the high and low modularity periods are represented by increased
and decreased dissociation of the default mode network module from
task-positive network modules, respectively. We also find that the instances of
time-resolved functional connectivity sampled from within the high (low)
modularity period are relatively homogeneous (heterogeneous) over time,
indicating that during the low modularity period the default mode network
interacts with other networks in a variable manner. We confirmed that the
occurrence of the high and low modularity periods varies across individuals
with moderate inter-session test-retest reliability and that it is correlated
with previously-reported individual differences in the modularity of functional
connectivity estimated over longer timescales. Our findings illustrate how
time-resolved functional networks are spatiotemporally organized during periods
of high and low modularity, allowing one to trace individual differences in
long-timescale modularity to the variable occurrence of network configurations
at shorter timescales.Comment: Reorganized the paper; to appear in NeuroImage; arXiv abstract
shortened to fit within character limit
Individual classification of ADHD patients by integrating multiscale neuroimaging markers and advanced pattern recognition techniques
Accurate classification or prediction of the brain state across individual subject, i.e., healthy, or with brain disorders, is generally a more difficult task than merely finding group differences. The former must be approached with highly informative and sensitive biomarkers as well as effective pattern classification/feature selection approaches. In this paper, we propose a systematic methodology to discriminate attention deficit hyperactivity disorder (ADHD) patients from healthy controls on the individual level. Multiple neuroimaging markers that are proved to be sensitive features are identified, which include multiscale characteristics extracted from blood oxygenation level dependent (BOLD) signals, such as regional homogeneity (ReHo) and amplitude of low-frequency fluctuations. Functional connectivity derived from Pearson, partial, and spatial correlation is also utilized to reflect the abnormal patterns of functional integration, or, dysconnectivity syndromes in the brain. These neuroimaging markers are calculated on either voxel or regional level. Advanced feature selection approach is then designed, including a brain-wise association study (BWAS). Using identified features and proper feature integration, a support vector machine (SVM) classifier can achieve a cross-validated classification accuracy of 76.15% across individuals from a large dataset consisting of 141 healthy controls and 98 ADHD patients, with the sensitivity being 63.27% and the specificity being 85.11%. Our results show that the most discriminative features for classification are primarily associated with the frontal and cerebellar regions. The proposed methodology is expected to improve clinical diagnosis and evaluation of treatment for ADHD patient, and to have wider applications in diagnosis of general neuropsychiatric disorders
Recommended from our members
Connectivity analysis with structural equation modelling: an example of the effects of voxel selection
Structural equation modelling (SEM) of neuroimaging data is commonly applied to a network of distributed brain regions. We applied SEM to an fMRI dataset to identify condition-specific effects in a simple experiment composed of visual stimulation and baseline conditions. The visual network was composed of three well-defined anatomical regions (V1, V2, and V5) and three path connections (V1 ā V2, V1 ā V5, and V2 ā V5). This network was used to test four hypotheses: (1) whether the condition-specific effects for all three connections vary according to the data selected for modelling; (2) whether the āsummaryā measures that are often used are indeed appropriate; (3) whether measures taken from the voxel timecourse can reliably predict the condition-specific effects for each one of the three path connections, and (4) whether all voxels within an anatomical region yield equivalent SEM outcomes. There was some variability in the significance of the condition-specific effects across randomly selected voxels within regions. However, the SEM outcome from the āsummaryā measures was comparable to the most frequent pattern of condition-specific effects. Magnitude, delay, spread, and goodness-of-fit measures taken from a gamma fit to the voxel time courses predicted reliably the significance of the SEM condition-specific effects for each connection. This result enabled us to identify spatially coherent regions at the boundaries of V2 that displayed different condition-specific effects from those seen in the majority of the voxels. Although the generality of these results awaits further investigation, this example highlights a number of important issues for SEM
Disrupted functional brain network organization in patients with obstructive sleep apnea.
IntroductionObstructive sleep apnea (OSA) subjects show impaired autonomic, affective, executive, sensorimotor, and cognitive functions. Brain injury in OSA subjects appears in multiple sites regulating these functions, but the integrity of functional networks within the regulatory sites remains unclear. Our aim was to examine the functional interactions and the complex network organization of these interactions across the whole brain in OSA, using regional functional connectivity (FC) and brain network topological properties.MethodsWe collected resting-state functional magnetic resonance imaging (MRI) data, using a 3.0-Tesla MRI scanner, from 69 newly diagnosed, treatment-naĆÆve, moderate-to-severe OSA (age, 48.3 Ā± 9.2 years; body mass index, 31 Ā± 6.2 kg/m(2); apnea-hypopnea index (AHI), 35.6 Ā± 23.3 events/h) and 82 control subjects (47.6 Ā± 9.1 years; body mass index, 25.1 Ā± 3.5 kg/m(2)). Data were analyzed to examine FC in OSA over controls as interregional correlations and brain network topological properties.ResultsObstructive sleep apnea subjects showed significantly altered FC in the cerebellar, frontal, parietal, temporal, occipital, limbic, and basal ganglia regions (FDR, P < 0.05). Entire functional brain networks in OSA subjects showed significantly less efficient integration, and their regional topological properties of functional integration and specialization characteristics also showed declined trends in areas showing altered FC, an outcome which would interfere with brain network organization (P < 0.05; 10,000 permutations). Brain sites with abnormal topological properties in OSA showed significant relationships with AHI scores.ConclusionsOur findings suggest that the dysfunction extends to resting conditions, and the altered FC and impaired network organization may underlie the impaired responses in autonomic, cognitive, and sensorimotor functions. The outcomes likely result from the prominent structural changes in both axons and nuclear structures, which occur in the condition
Acute modulation of brain connectivity in Parkinson disease after automatic mechanical peripheral stimulation: A pilot study
The present study shows the results of a double-blind sham-controlled pilot trial to test whether measurable stimulus-specific functional connectivity changes exist after Automatic Mechanical Peripheral Stimulation (AMPS) in patients with idiopathic Parkinson Disease.Eleven patients (6 women and 5 men) with idiopathic Parkinson Disease underwent brain fMRI immediately before and after sham or effective AMPS. Resting state Functional Connectivity (RSFC) was assessed using the seed-ROI based analysis. Seed ROIs were positioned on basal ganglia, on primary sensory-motor cortices, on the supplementary motor areas and on the cerebellum. Individual differences for pre- and post-effective AMPS and pre- and post-sham condition were obtained and first entered in respective one-sample t-test analyses, to evaluate the mean effect of condition.Effective AMPS, but not sham stimulation, induced increase of RSFC of the sensory motor cortex, nucleus striatum and cerebellum. Secondly, individual differences for both conditions were entered into paired group t-test analysis to rule out sub-threshold effects of sham stimulation, which showed stronger connectivity of the striatum nucleus with the right lateral occipital cortex and the cuneal cortex (max Z score 3.12) and with the right anterior temporal lobe (max Z score 3.42) and of the cerebellum with the right lateral occipital cortex and the right cerebellar cortex (max Z score 3.79).Our results suggest that effective AMPS acutely increases RSFC of brain regions involved in visuo-spatial and sensory-motor integration.This study provides Class II evidence that automatic mechanical peripheral stimulation is effective in modulating brain functional connectivity of patients with Parkinson Disease at rest.Clinical Trials.gov NCT01815281
- ā¦