The Contribution of Common Surgically Implanted Hardware to Functional MR Imaging Artifacts

Functional networks in prolonged disorders of consciousness

Prolonged disorders of consciousness (DoC) are characterized by extended disruptions of brain activities that sustain wakefulness and awareness and are caused by various etiologies. During the past decades, neuroimaging has been a practical method of investigation in basic and clinical research to identify how brain properties interact in different levels of consciousness. Resting-state functional connectivity within and between canonical cortical networks correlates with consciousness by a calculation of the associated temporal blood oxygen level-dependent (BOLD) signal process during functional MRI (fMRI) and reveals the brain function of patients with prolonged DoC. There are certain brain networks including the default mode, dorsal attention, executive control, salience, auditory, visual, and sensorimotor networks that have been reported to be altered in low-level states of consciousness under either pathological or physiological states. Analysis of brain network connections based on functional imaging contributes to more accurate judgments of consciousness level and prognosis at the brain level. In this review, neurobehavioral evaluation of prolonged DoC and the functional connectivity within brain networks based on resting-state fMRI were reviewed to provide reference values for clinical diagnosis and prognostic evaluation.

Graph Theoretical Analysis of Brain Network Characteristics in Brain Tumor Patients: A Systematic Review

Graph theory is a branch of mathematics that allows for the characterization of complex networks, and has rapidly grown in popularity in network neuroscience in recent years. Researchers have begun to use graph theory to describe the brain networks of individuals with brain tumors to shed light on disrupted networks. This systematic review summarizes the current literature on graph theoretical analysis of magnetic resonance imaging data in the brain tumor population with particular attention paid to treatment effects and other clinical factors. Included papers were published through June 24^th, 2020. Searches were conducted on Pubmed, PsycInfo, and Web of Science using the search terms (graph theory OR graph analysis) AND (brain tumor OR brain tumour OR brain neoplasm) AND (MRI OR EEG OR MEG). Studies were eligible for inclusion if they: evaluated participants with a primary brain tumor, used graph theoretical analyses on structural or functional MRI data, MEG, or EEG, were in English, and were an empirical research study. Seventeen papers met criteria for inclusion. Results suggest alterations in network properties are often found in people with brain tumors, although the directions of differences are inconsistent and few studies reported effect sizes. The most consistent finding suggests increased network segregation. Changes are most prominent with more intense treatment, in hub regions, and with factors such as faster tumor growth. The use of graph theory to study brain tumor patients is in its infancy, though some conclusions can be drawn. Future studies should focus on treatment factors, changes over time, and correlations with functional outcomes to better identify those in need of early intervention.

Clinical Use of Cerebrovascular Compliance Imaging to Evaluate Revascularization in Patients With Moyamoya

BACKGROUND

Surgical revascularization is often performed in patients with moyamoya, however routine tools for efficacy evaluation are underdeveloped. The gold standard is digital subtraction angiography (DSA); however, DSA requires ionizing radiation and procedural risk, and therefore is suboptimal for routine surveillance of parenchymal health.

OBJECTIVE

To determine whether parenchymal vascular compliance measures, obtained noninvasively using magnetic resonance imaging (MRI), provide surrogates to revascularization success by comparing measures with DSA before and after surgical revascularization.

METHODS

Twenty surgical hemispheres with DSA and MRI performed before and after revascularization were evaluated. Cerebrovascular reactivity (CVR)-weighted images were acquired using hypercapnic 3-Tesla gradient echo blood oxygenation level-dependent MRI. Standard and novel analysis algorithms were applied (i) to quantify relative CVR (rCVRRAW), and decompose this response into (ii) relative maximum CVR (rCVRMAX) and (iii) a surrogate measure of the time for parenchyma to respond maximally to the stimulus, CVRDELAY. Measures between time points in patients with good and poor surgical outcomes based on DSA-visualized neoangiogenesis were contrasted (signed-rank test; significance: 2-sided P < .050).

RESULTS

rCVRRAW increases (P = .010) and CVRDELAY decreases (P = .001) were observed pre- vs post-revascularization in hemispheres with DSA-confirmed collateral formation; no difference was found pre- vs post-revascularization in hemispheres with poor revascularization. No significant change in rCVRMAX post-revascularization was observed in either group, or between any of the MRI measures, in the nonsurgical hemisphere.

CONCLUSION

Improvement in parenchymal compliance measures post-revascularization, primarily attributed to reductions in microvascular response time, is concurrent with collateral formation visualized on DSA, and may be useful for longitudinal monitoring of surgical outcomes.

Challenges and Pitfalls Associated with Diagnostic and Prognostic Applications of Functional Neuroimaging in Disorders of Consciousness

The diagnostic assessment of patients with disorder of consciousness is currently based on clinical testing at the bedside and prone to a high error rate in the assessment of the degree of conscious awareness. Investigation of more objective assessment strategies, such as the use of functional magnetic resonance imaging (fMRI) to detect conscious awareness, are becoming increasingly popular in the research community. However, inherent challenges to the use of fMRI threaten its validity as a diagnostic tool and will need to be resolved prior to its integration into the clinical setting. These challenges, which range from the heterogeneity of the patient sample to factors influencing data acquisition and biases in interpretation strategies, are discussed below. Recommendations aimed at mitigating some of the limitations are provided.