Resting state functional magnetic resonance imaging (fMRI) can identify network alterations that occur in complex psychiatric diseases and behaviors, but its interpretation is difficult because the neural basis of the infraslow BOLD fluctuations is poorly understood. Previous results link dynamic activity during the resting state to both infraslow frequencies in local field potentials (LFP) ( < 1 Hz) and band-limited power in higher frequency LFP ( > 1 Hz). To investigate the relationship between these frequencies, LFPs were recorded from rats under two anesthetics: isoflurane and dexmedetomidine. Signal phases were calculated from low-frequency LFP and compared to signal amplitudes from high-frequency LFP to determine if modulation existed between the two frequency bands (phase-amplitude coupling). Isoflurane showed significant, consistent phase-amplitude coupling at nearly all pairs of frequencies, likely due to the burst-suppression pattern of activity that it induces. However, no consistent phase-amplitude coupling was observed in rats that were anesthetized with dexmedetomidine. fMRI-LFP correlations under isoflurane using high frequency LFP were reduced when the low frequency LFP's influence was accounted for, but not vice-versa, or in any condition under dexmede tomidine. The lack of consistent phase-amplitude coupling under dexmedetomidine and lack of shared variance between high frequency and low frequency LFP as it relates to fMRI suggests that high and low frequency neural electrical signals may contribute differently, possibly even independently, to resting state fMRI. This finding suggests that researchers take care in interpreting the neural basis of resting state fMRI, as multiple dynamic factors in the underlying electrophysiology could be driving any particular observation.
Individuals with attention-deficit/hyperactivity disorder have disrupted functional connectivity in the default mode and task positive networks. Traditional fMRI analysis techniques that focus on ‘static’ changes in functional connectivity have been successful in identifying differences between healthy controls and individuals with ADHD. However, such analyses are unable to explain the mechanisms behind the functional connectivity differences observed. Here, we study dynamic changes in functional connectivity in individuals with ADHD through investigation of quasi-periodic patterns (QPPs). QPPs are reliably recurring low-frequency spatiotemporal patterns in the brain linked to infra-slow electrical activity. They have been shown to contribute to functional connectivity observed through static analysis techniques. We find that QPPs contribute to functional connectivity specifically in regions that are disrupted in individuals with ADHD. Individuals with ADHD also show differences in the spatiotemporal pattern observed within the QPPs. This difference results in a weaker contribution of QPPs to functional connectivity in the default mode and task positive networks. We conclude that quasi-periodic patterns provide insight into the mechanisms behind functional connectivity differences seen in individuals with ADHD. This allows for a better understanding of the etiology of the disorder and development of effective treatments.
The BOLD signal reflects hemodynamic events within the brain, which in turn are driven by metabolic changes and neural activity. However, the link between BOLD changes and neural activity is indirect and can be influenced by a number of non-neuronal processes. Motion and physiological cycles have long been known to affect the BOLD signal and are present in both humans and animal models. Differences in physiological baseline can also contribute to intra- and inter-subject variability. The use of anesthesia, common in animal studies, alters neural activity, vascular tone, and neurovascular coupling. Most intriguing, perhaps, are the contributions from other processes that do not appear to be neural in origin but which may provide information about other aspects of neurophysiology. This review discusses different types of noise and non-neuronal contributors to the BOLD signal, sources of variability for animal studies, and insights to be gained from animal models.
Most neuroimaging studies of resting state networks in amnesic mild cognitive impairment (aMCI) have concentrated on functional connectivity (FC) based on instantaneous correlation in a single network. The purpose of the current study was to investigate effective connectivity in aMCI patients based on Granger causality of four important networks at resting state derived from functional magnetic resonance imaging data - default mode network (DMN), hippocampal cortical memory network (HCMN), dorsal attention network (DAN) and fronto-parietal control network (FPCN). Structural and functional MRI data were collected from 16 aMCI patients and 16 age, gender-matched healthy controls. Correlation-purged Granger causality analysis was used, taking gray matter atrophy as covariates, to compare the group difference between aMCI patients and healthy controls. We found that the causal connectivity between networks in aMCI patients was significantly altered with both increases and decreases in the aMCI group as compared to healthy controls. Some alterations were significantly correlated with the disease severity as measured by mini-mental state examination (MMSE), and California verbal learning test (CVLT) scores. When the whole-brain signal averaged over the entire brain was used as a nuisance covariate, the within-group maps were significantly altered while the between-group difference maps did not. These results suggest that the alterations in causal influences may be one of the possible underlying substrates of cognitive impairments in aMCI. The present study extends and complements previous FC studies and demonstrates the coexistence of causal disconnection and compensation in aMCI patients, and thus might provide insights into biological mechanism of the disease.
Introduction Memory deficits characterize Alzheimer's dementia and the clinical precursor stage known as mild cognitive impairment. Nonpharmacologic interventions hold promise for enhancing functioning in these patients, potentially delaying functional impairment that denotes transition to dementia. Previous findings revealed that mnemonic strategy training (MST) enhances long-term retention of trained stimuli and is accompanied by increased blood oxygen level–dependent signal in the lateral frontal and parietal cortices as well as in the hippocampus. The present study was designed to enhance MST generalization, and the range of patients who benefit, via concurrent delivery of transcranial direct current stimulation (tDCS). Methods This protocol describes a prospective, randomized controlled, four-arm, double-blind study targeting memory deficits in those with mild cognitive impairment. Once randomized, participants complete five consecutive daily sessions in which they receive either active or sham high definition tDCS over the left lateral prefrontal cortex, a region known to be important for successful memory encoding and that has been engaged by MST. High definition tDCS (active or sham) will be combined with either MST or autobiographical memory recall (comparable to reminiscence therapy). Participants undergo memory testing using ecologically relevant measures and functional magnetic resonance imaging before and after these treatment sessions as well as at a 3-month follow-up. Primary outcome measures include face-name and object-location association tasks. Secondary outcome measures include self-report of memory abilities as well as a spatial navigation task (near transfer) and prose memory (medication instructions; far transfer). Changes in functional magnetic resonance imaging will be evaluated during both task performance and the resting-state using activation and connectivity analyses. Discussion The results will provide important information about the efficacy of cognitive and neuromodulatory techniques as well as the synergistic interaction between these promising approaches. Exploratory results will examine patient characteristics that affect treatment efficacy, thereby identifying those most appropriate for intervention.
Glioblastoma has poor prognosis with inevitable local recurrence despite aggressive treatment with surgery and chemoradiation. Radiation therapy (RT) is typically guided by contrast-enhanced T1-weighted magnetic resonance imaging (MRI) for defining the high-dose target and T2-weighted fluid-attenuation inversion recovery MRI for defining the moderate-dose target. There is an urgent need for improved imaging methods to better delineate tumors for focal RT. Spectroscopic MRI (sMRI) is a quantitative imaging technique that enables whole-brain analysis of endogenous metabolite levels, such as the ratio of choline-to-N-acetylaspartate. Previous work has shown that choline-to-N-acetylaspartate ratio accurately identifies tissue with high tumor burden beyond what is seen on standard imaging and can predict regions of metabolic abnormality that are at high risk for recurrence. To facilitate efficient clinical implementation of sMRI for RT planning, we developed the Brain Imaging Collaboration Suite (BrICS; https://brainimaging.emory.edu/brics-demo), a cloud platform that integrates sMRI with standard imaging and enables team members from multiple departments and institutions to work together in delineating RT targets. BrICS is being used in a multisite pilot study to assess feasibility and safety of dose-escalated RT based on metabolic abnormalities in patients with glioblastoma (Clinicaltrials.gov NCT03137888). The workflow of analyzing sMRI volumes and preparing RT plans is described. The pipeline achieved rapid turnaround time by enabling team members to perform their delegated tasks independently in BrICS when their clinical schedules allowed. To date, 18 patients have been treated using targets created in BrICS and no severe toxicities have been observed.
Histone deacetylases regulate a wide variety of cellular functions and have been implicated in redifferentiation of various tumors. Histone deacetylase inhibitors (HDACi) are potential pharmacologic agents to improve outcomes for patients with gliomas. We assessed the therapeutic efficacy of belinostat (PXD-101), an HDACi with blood-brain barrier permeability. Belinostat was first tested in an orthotopic rat glioma model to assess in vivo tumoricidal effect. Our results showed that belinostat was effective in reducing tumor volume in the orthotopic rat glioma model in a dose-dependent manner. We also tested the antidepression activity of belinostat in 2 animal models of depression and found it to be effective. Furthermore, we confirmed that myo-inositol levels improved by belinostat treatment in vitro. In a human pilot study, it was observed that belinostat in combination with chemoradiation may delay initial recurrence of disease. Excitingly, belinostat significantly improved depressive symptoms in patients with glioblastoma compared with control subjects. Finally, spectroscopic magnetic resonance imaging of 2 patient cases from this pilot study are presented to indicate how spectroscopic magnetic resonance imaging can be used to monitor metabolite response and assess treatment effect on whole brain. This study highlights the potential of belinostat to be a synergistic therapeutic agent in the treatment of gliomas.
Purpose: To apply cross-correlation delay (XCD) analysis to myocardial phase contrast magnetic resonance (PCMR) tissue velocity data and to compare XCD to three established "time-to-peak" dyssynchrony parameters. Materials and Methods: Myocardial tissue velocity was acquired using PCMR in 10 healthy volunteers (negative controls) and 10 heart failure patients who met criteria for cardiac resynchronization therapy (positive controls). All dyssynchrony parameters were computed from PCMR velocity curves. Sensitivity, specificity, and receiver operator curve (ROC) analysis for separating positive and negative controls were computed for each dyssynchrony parameter. Results: XCD had higher sensitivity (90%) and specificity (100%) for discriminating between normal and patient groups than any of the time-to-peak dyssynchrony parameters. ROC analysis showed that XCD was the best parameter for separating the positive and negative control groups. Conclusion:XCD is superior to time-to-peak dyssynchrony parameters for discriminating between subjects with and without dyssynchrony and may aid in the selection of patients for cardiac resynchronization therapy.
There has been an increasing interest in studying cardiac fibers in order to improve the current knowledge regarding the mechanical and physiological properties of the heart during heart failure (HF), particularly early HF. Having a thorough understanding of the changes in cardiac fiber orientation may provide new insight into the mechanisms behind the progression of left ventricular (LV) remodeling and HF. We conducted a systematic review on various technologies for imaging cardiac fibers and its link to HF. This review covers literature reports from 1900 to 2017. PubMed and Google Scholar databases were searched using the keywords “cardiac fiber” and “heart failure” or “myofiber” and “heart failure.” This review highlights imaging methodologies, including magnetic resonance diffusion tensor imaging (MR-DTI), ultrasound, and other imaging technologies as well as their potential applications in basic and translational research on the development and progression of HF. MR-DTI and ultrasound have been most useful and significant in evaluating cardiac fibers and HF. New imaging technologies that have the ability to measure cardiac fiber orientations and identify structural and functional information of the heart will advance basic research and clinical diagnoses of HF.