Putting the "dynamic" back into dynamic functional connectivity.
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ABSTRACT:
SUBMITTER: Heitmann S
PROVIDER: S-EPMC6130444 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Failed back surgery syndrome (FBSS), a chronic neuropathic pain condition, is a common indication for spinal cord stimulation (SCS). However, the mechanisms of SCS, especially its effects on supraspinal/brain functional connectivity, are still not fully understood. Resting state functional magnetic resonance imaging (rsfMRI) studies have shown characteristics in patients with chronic low back pain (cLBP). In this case study, we performed rsfMRI scanning (3.0 T) on an FBSS patient, who presented with chronic low back and leg pain following her previous lumbar microdiscectomy and had undergone permanent SCS. Appropriate MRI safety measures were undertaken to scan this subject. Seed-based functional connectivity (FC) was performed on the rsfMRI data acquired from the FBSS subject, and then compared to a group of 17 healthy controls. Seeds were identified by an atlas of resting state networks (RSNs), which is composed of 32 regions grouped into 8 networks. Sliding-window method and k-means clustering were used in dynamic FC analysis, which resulted in 4 brain states for each group. Our results demonstrated the safety and feasibility of 3T MRI scanning in a patient with implanted SCS system. Compared to the brain states of healthy controls, the FBSS subject presented very different FC patterns in less frequent brain states. The mean dwell time of brain states showed distinct distributions: the FBSS subject seemed to prefer a single state over the others. Although future studies with large sample sizes are needed to make statistical conclusions, our findings demonstrated the promising application of dynamic FC to provide more granularity with FC changes associated with different brain states in chronic pain.
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Project description:The mechanism of brain reorganization in pain chronification is unknown. In a longitudinal brain imaging study, subacute back pain (SBP) patients were followed over the course of 1 year. When pain persisted (SBPp, in contrast to recovering SBP and healthy controls), brain gray matter density decreased. Initially greater functional connectivity of nucleus accumbens with prefrontal cortex predicted pain persistence, implying that corticostriatal circuitry is causally involved in the transition from acute to chronic pain.
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Project description:The brain must dynamically integrate, coordinate, and respond to internal and external stimuli across multiple time scales. Non-invasive measurements of brain activity with fMRI have greatly advanced our understanding of the large-scale functional organization supporting these fundamental features of brain function. Conclusions from previous resting-state fMRI investigations were based upon static descriptions of functional connectivity (FC), and only recently studies have begun to capitalize on the wealth of information contained within the temporal features of spontaneous BOLD FC. Emerging evidence suggests that dynamic FC metrics may index changes in macroscopic neural activity patterns underlying critical aspects of cognition and behavior, though limitations with regard to analysis and interpretation remain. Here, we review recent findings, methodological considerations, neural and behavioral correlates, and future directions in the emerging field of dynamic FC investigations.
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Project description:There 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 and 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?s 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, the default mode network has consistent influence on the cerebellar, the limbic and the auditory/temporal networks. We also show 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.
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Project description:Several methods have been developed to measure dynamic functional connectivity (dFC) in fMRI data. These methods are often based on a sliding-window analysis, which aims to capture how the brain's functional organization varies over the course of a scan. The aim of many studies is to compare dFC across groups, such as younger versus older people. However, spurious group differences in measured dFC may be caused by other sources of heterogeneity between people. For example, the shape of the haemodynamic response function (HRF) and levels of measurement noise have been found to vary with age. We use a generic simulation framework for fMRI data to investigate the effect of such heterogeneity on estimates of dFC. Our findings show that, despite no differences in true dFC, individual differences in measured dFC can result from other (non-dynamic) features of the data, such as differences in neural autocorrelation, HRF shape, connectivity strength and measurement noise. We also find that common dFC methods such as k-means and multilayer modularity approaches can detect spurious group differences in dynamic connectivity due to inappropriate setting of their hyperparameters. fMRI studies therefore need to consider alternative sources of heterogeneity across individuals before concluding differences in dFC.
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Project description:AimsAlzheimer's disease (AD) is a progressive neurodegenerative disorder. Previous studies have demonstrated abnormalities in functional connectivity (FC) of AD under the assumption that FC is stationary during scanning. However, studies on the FC dynamics of AD, which may provide more insightful perspectives in understanding the neural mechanisms of AD, remain largely unknown.MethodsCombining the sliding-window approach and the k-means algorithm, we identified three reoccurring dynamic FC states from resting-state fMRI data of 26 AD and 26 healthy controls. The between-group differences both in FC states and in regional temporal variability were calculated, followed by a correlation analysis of these differences with cognitive performances of AD patients.ResultsWe identified three reoccurring FC states and found abnormal FC mainly in the frontal and temporal cortices. The temporal properties of FC states were changed in AD as characterized by decreased dwell time in State I and increased dwell time in State II. Besides, we found decreased regional temporal variability mainly in the somatomotor, temporal and parietal regions. Disrupted dynamic FC was significantly correlated with cognitive performances of AD patients.ConclusionOur findings suggest abnormal dynamic FC in AD patients, which provides novel insights for understanding the pathophysiological mechanisms of AD.
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Project description:Temporal networks have become increasingly pervasive in many real-world applications, including the functional connectivity analysis of spatially separated regions of the brain. A major challenge in analysis of such networks is the identification of noise confounds, which introduce temporal ties that are nonessential, or links that are formed by chance due to local properties of the nodes. Several approaches have been suggested in the past for static networks or temporal networks with binary weights for extracting significant ties whose likelihood cannot be reduced to the local properties of the nodes. In this work, we propose a data-driven procedure to reveal the irreducible ties in dynamic functional connectivity of resting-state fMRI data with continuous weights. This framework includes a null model that estimates the latent characteristics of the distributions of temporal links through optimization, followed by a statistical test to filter the links whose formation can be reduced to the activities and local properties of their interacting nodes. We demonstrate the benefits of this approach by applying it to a resting-state fMRI dataset, and provide further discussion on various aspects and advantages of it.
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Project description:TP53-mutant blood cancers remain a major clinical challenge. BH3-mimetic drugs inhibit BCL-2 pro-survival proteins to promote cancer cell apoptosis. Despite acting downstream of TP53, functional TP53 is required for maximal cancer cell killing by BH3-mimetics through an unknown mechanism. Here, we report TP53 can be activated following BH3-mimetic induced mitochondrial outer membrane permeabilization, which leads to induction of BH3-only proteins, thereby potentiating the pro-apoptotic signal. TP53-deficient lymphomas lack this feed-forward loop, providing opportunities for survival and disease relapse after BH3-mimetic treatment. The therapeutic barrier imposed by defects in TP53 could be overcome by direct activation of the cGAS/STING pathway, which promotes apoptosis of blood cancer cells through TP53-independent BH3-only protein upregulation. Combining clinically relevant STING agonists with BH3-mimetics efficiently killed TP53-mutant mouse B lymphoma, human NK/T lymphoma and acute myeloid leukemia cells. This represents a promising therapy regime that can be fast-tracked to tackle TP53-mutant blood cancers in the clinic.
2024-04-25 | GSE261373 | GEO
Project description:PURPOSE:Low back pain (LBP) is a common ailment in most developed countries. Because most cases of LBP are known as 'non-specific', it has been challenging to develop experimental pain models of LBP which reproduce patients' clinical pain. In addition, previous models have limited applicability in a steady-pain-state neuroimaging environment. Thus, this study aims to devise a low back pain model with a simple methodology to induce experimental LBP, which has similar pain properties to patients' clinical pain, and to apply the model in a steady-pain-state neuroimaging study. METHODS:Our low back extension (LBE) pain model was tested on 217 LBP patients outside the magnetic resonance imaging (MRI) scanner to determine the reproducibility of endogenous pain and the similarity to their own clinical pain (STUDY1), and applied in a steady-pain-state functional MRI study (47 LBP patients and 23 healthy controls) to determine its applicability (induced head motions and brain functional connectivity changes; STUDY2). RESULTS:By the LBE pain model, 68.2% of the LBP patients reported increased LBP with high similarity of sensations to their own clinical pain (STUDY1), and the head motions were statistically similar to and correlated with those in resting state (STUDY2). Furthermore, the LBE model altered brain functional connectivity by decreasing the default-mode and the sensorimotor networks, and increasing the salience network, which was significantly associated with the intensity of the induced pain. Conversely, the healthy controls showed increased somatosensory network (but not of the cognitive pain processing). CONCLUSION:Our investigations suggest that our LBE pain model, which increased LBP with high similarity to the LBP patients' own pain sensation and induced patient-specific brain responses with acceptable head motion, could be applied to neuroimaging studies investigating brain responses to different levels of endogenous LBP.
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Project description:SignificanceQuantitative oblique back-illumination microscopy (qOBM) is a recently developed label-free imaging technique that enables 3D quantitative phase imaging of thick scattering samples with epi-illumination. Here, we propose dynamic qOBM to achieve functional imaging based on subcellular dynamics, potentially indicative of metabolic activity. We show the potential utility of this novel technique by imaging adherent mesenchymal stromal cells (MSCs) grown in bioreactors, which can help address important unmet needs in cell manufacturing for therapeutics.AimWe aim to develop dynamic qOBM and demonstrate its potential for functional imaging based on cellular and subcellular dynamics.ApproachTo obtain functional images with dynamic qOBM, a sample is imaged over a period of time and its temporal signals are analyzed. The dynamic signals display an exponential frequency response that can be analyzed with phasor analysis. Functional images of the dynamic signatures are obtained by mapping the frequency dynamic response to phasor space and color-coding clustered signals.ResultsFunctional imaging with dynamic qOBM provides unique information related to subcellular activity. The functional qOBM images of MSCs not only improve conspicuity of cells in complex environments (e.g., porous micro-carriers) but also reveal two distinct cell populations with different dynamic behavior.ConclusionsIn this work we present a label-free, fast, and scalable functional imaging approach to study and intuitively display cellular and subcellular dynamics. We further show the potential utility of this novel technique to help monitor adherent MSCs grown in bioreactors, which can help achieve quality-by-design of cell products, a significant unmet need in the field of cell therapeutics. This approach also has great potential for dynamic studies of other thick samples, such as organoids.
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