Graph Lasso-Based Test for Evaluating Functional Brain Connectivity in Sickle Cell Disease

Electroacupuncture modulates abnormal brain connectivity after ischemia reperfusion injury in rats: A graph theory-based approach

BACKGROUND

Electroacupuncture (EA) has been shown to facilitate brain plasticity-related functional recovery following ischemic stroke. The functional magnetic resonance imaging technique can be used to determine the range and mode of brain activation. After stroke, EA has been shown to alter brain connectivity, whereas EA's effect on brain network topology properties remains unclear. An evaluation of EA's effects on global and nodal topological properties in rats with ischemia reperfusion was conducted in this study.

METHODS AND RESULTS

There were three groups of adult male Sprague-Dawley rats: sham-operated group (sham group), middle cerebral artery occlusion/reperfusion (MCAO/R) group, and MCAO/R plus EA (MCAO/R + EA) group. The differences in global and nodal topological properties, including shortest path length, global efficiency, local efficiency, small-worldness index, betweenness centrality (BC), and degree centrality (DC) were estimated. Graphical network analyses revealed that, as compared with the sham group, the MCAO/R group demonstrated a decrease in BC value in the right ventral hippocampus and increased BC in the right substantia nigra, accompanied by increased DC in the left nucleus accumbens shell (AcbSh). The BC was increased in the right hippocampus ventral and decreased in the right substantia nigra after EA intervention, and MCAO/R + EA resulted in a decreased DC in left AcbSh compared to MCAO/R.

CONCLUSION

The results of this study provide a potential basis for EA to promote cognitive and motor function recovery after ischemic stroke.

The Development of Neuroimaging Biomarkers for Cognitive Decline in Sickle Cell Disease

Sickle cell disease (SCD) is complicated by neurologic complications including vasculopathy, hemorrhagic or ischemic overt stroke, silent cerebral infarcts and cognitive dysfunction. Patients with SCD, even in the absence of vasculopathy or stroke, have experience cognitive dysfunction that progresses with age. Transcranial Doppler ultrasound and structural brain MRI are currently used for primary and secondary stroke prevention, but laboratory or imaging biomarkers do not currently exist that are specific to the risk of cognitive dysfunction in patients with SCD. Recent investigations have used advanced MR sequences assessing cerebral hemodynamics, white matter microstructure and functional connectivity to better understand the pathophysiology of cognitive decline in SCD, with the long-term goal of developing neuroimaging biomarkers to be used in risk prediction algorithms and to assess the efficacy of treatment options for patients with SCD.

MRI detection of brain abnormality in sickle cell disease

Introduction: Over the past decades, neuroimaging studies have clarified that a significant proportion of patients with sickle cell disease (SCD) have functionally significant brain abnormalities. Clinically, structural magnetic resonance imaging (MRI) sequences (T2, FLAIR, diffusion-weighted imaging) have been used by radiologists to diagnose chronic and acute cerebral infarction (both overt and clinically silent), while magnetic resonance angiography and venography have been used to diagnose arteriopathy and venous thrombosis. In research settings, imaging scientists are increasingly applying quantitative techniques to shine further light on underlying mechanisms.Areas covered: From a June 2020 PubMed search of 'magnetic' or 'MRI' and 'sickle' over the previous 5 years, we selected manuscripts on T1-based morphometric analysis, diffusion tensor imaging, arterial spin labeling, T2-oximetry, quantitative susceptibility, and connectivity.Expert Opinion: Quantitative MRI techniques are identifying structural and hemodynamic biomarkers associated with risk of neurological and neurocognitive complications. A growing body of evidence suggests that these biomarkers are sensitive to change with treatments, such as blood transfusion and hydroxyurea, indicating that they may hold promise as endpoints in future randomized clinical trials of novel approaches including hemoglobin F upregulation, reduction of polymerization, and gene therapy. With further validation, such techniques may eventually also improve neurological and neurocognitive risk stratification in this vulnerable population.

Silent cerebral infarcts in patients with sickle cell disease: a systematic review and meta-analysis

BACKGROUND AND PURPOSE

Silent cerebral infarcts (SCIs) are the most common neurological complication in children and adults with sickle cell disease (SCD). In this systematic review, we provide an overview of studies that have detected SCIs in patients with SCD by cerebral magnetic resonance imaging (MRI). We focus on the frequency of SCIs, the risk factors involved in their development and their clinical consequences.

METHODS

The databases of Embase, MEDLINE ALL via Ovid, Web of Science Core Collection, Cochrane Central Register of Trials via Wiley and Google Scholar were searched from inception to June 1, 2019.

RESULTS

The search yielded 651 results of which 69 studies met the eligibility criteria. The prevalence of SCIs in patients with SCD ranges from 5.6 to 80.6% with most studies reported in the 20 to 50% range. The pooled prevalence of SCIs in HbSS and HbSβ⁰ SCD patients is 29.5%. SCIs occur more often in patients with the HbSS and HbSβ⁰ genotype in comparison with other SCD genotypes, as SCIs are found in 9.2% of HbSC and HbSβ⁺ patients. Control subjects showed a mean pooled prevalence of SCIs of 9.8%. Data from included studies showed a statistically significant association between increasing mean age of the study population and mean SCI prevalence. Thirty-three studies examined the risk factors for SCIs. The majority of the risk factors show no clear association with prevalence, since more or less equal numbers of studies give evidence for and against the causal association.

CONCLUSIONS

This systematic review and meta-analysis shows SCIs are common in patients with SCD. No clear risk factors for their development were identified. Larger, prospective and controlled clinical, neuropsychological and neuroimaging studies are needed to understand how SCD and SCIs affect cognition.

Functional Connectivity Decreases with Metabolic Stress in Sickle Cell Disease

OBJECTIVE

Children with sickle cell disease (SCD) experience cognitive deficits even when unaffected by stroke. Using functional connectivity magnetic resonance imaging (MRI) as a potential biomarker of cognitive function, we tested our hypothesis that children with SCD would have decreased functional connectivity, and that children experiencing the greatest metabolic stress, indicated by elevated oxygen extraction fraction, would have the lowest connectivity.

METHODS

We prospectively obtained brain MRIs and cognitive testing in healthy controls and children with SCD.

RESULTS

We analyzed data from 60 participants (20 controls and 40 with sickle cell disease). There was no difference in global cognition or cognitive subdomains between cohorts. However, we found decreased functional connectivity within the sensory-motor, lateral sensory-motor, auditory, salience, and subcortical networks in participants with SCD compared with controls. Further, as white matter oxygen extraction fraction increased, connectivity within the visual (p = 0.008, parameter estimate = -0.760 [95% CI = -1.297, -0.224]), default mode (p = 0.012, parameter estimate = -0.417 [95% CI = -0.731, -0.104]), and cingulo-opercular (p = 0.009, parameter estimate = -0.883 [95% CI = -1.517, -0.250]) networks decreased.

INTERPRETATION

We conclude that there is diminished functional connectivity within these anatomically contiguous networks in children with SCD compared with controls, even when differences are not seen with cognitive testing. Increased white matter oxygen extraction fraction was associated with decreased connectivity in select networks. These data suggest that elevated oxygen extraction fraction and disrupted functional connectivity are potentially presymptomatic neuroimaging biomarkers for cognitive decline in SCD. ANN NEUROL 2020;88:995-1008.

Use of Mobile Health Apps and Wearable Technology to Assess Changes and Predict Pain During Treatment of Acute Pain in Sickle Cell Disease: Feasibility Study

BACKGROUND

Sickle cell disease (SCD) is an inherited red blood cell disorder affecting millions worldwide, and it results in many potential medical complications throughout the life course. The hallmark of SCD is pain. Many patients experience daily chronic pain as well as intermittent, unpredictable acute vaso-occlusive painful episodes called pain crises. These pain crises often require acute medical care through the day hospital or emergency department. Following presentation, a number of these patients are subsequently admitted with continued efforts of treatment focused on palliative pain control and hydration for management. Mitigating pain crises is challenging for both the patients and their providers, given the perceived unpredictability and subjective nature of pain.

OBJECTIVE

The objective of this study was to show the feasibility of using objective, physiologic measurements obtained from a wearable device during an acute pain crisis to predict patient-reported pain scores (in an app and to nursing staff) using machine learning techniques.

METHODS

For this feasibility study, we enrolled 27 adult patients presenting to the day hospital with acute pain. At the beginning of pain treatment, each participant was given a wearable device (Microsoft Band 2) that collected physiologic measurements. Pain scores from our mobile app, Technology Resources to Understand Pain Assessment in Patients with Pain, and those obtained by nursing staff were both used with wearable signals to complete time stamp matching and feature extraction and selection. Following this, we constructed regression and classification machine learning algorithms to build between-subject pain prediction models.

RESULTS

Patients were monitored for an average of 3.79 (SD 2.23) hours, with an average of 5826 (SD 2667) objective data values per patient. As expected, we found that pain scores and heart rate decreased for most patients during the course of their stay. Using the wearable sensor data and pain scores, we were able to create a regression model to predict subjective pain scores with a root mean square error of 1.430 and correlation between observations and predictions of 0.706. Furthermore, we verified the hypothesis that the regression model outperformed the classification model by comparing the performances of the support vector machines (SVM) and the SVM for regression.

CONCLUSIONS

The Microsoft Band 2 allowed easy collection of objective, physiologic markers during an acute pain crisis in adults with SCD. Features can be extracted from these data signals and matched with pain scores. Machine learning models can then use these features to feasibly predict patient pain scores.

Anemia predicts lower white matter volume and cognitive performance in sickle and non-sickle cell anemia syndrome

Severe chronic anemia is an independent predictor of overt stroke, white matter damage, and cognitive dysfunction in the elderly. Severe anemia also predisposes to white matter strokes in young children, independent of the anemia subtype. We previously demonstrated symmetrically decreased white matter (WM) volumes in patients with sickle cell disease (SCD). In the current study, we investigated whether patients with non-sickle anemia also have lower WM volumes and cognitive dysfunction. Magnetic Resonance Imaging was performed on 52 clinically asymptomatic SCD patients (age = 21.4 ± 7.7; F = 27, M = 25; hemoglobin = 9.6 ± 1.6 g/dL), 26 non-sickle anemic patients (age = 23.9 ± 7.9; F = 14, M = 12; hemoglobin = 10.8 ± 2.5 g/dL) and 40 control subjects (age = 27.7 ± 11.3; F = 28, M = 12; hemoglobin = 13.4 ± 1.3 g/dL). Voxel-wise changes in WM brain volumes were compared to hemoglobin levels to identify brain regions that are vulnerable to anemia. White matter volume was diffusely lower in deep, watershed areas proportionally to anemia severity. After controlling for age, sex, and hemoglobin level, brain volumes were independent of disease. WM volume loss was associated with lower Full Scale Intelligence Quotient (FSIQ; P = .0048; r² = .18) and an abnormal burden of silent cerebral infarctions (P = .029) in males, but not in females. Hemoglobin count and cognitive measures were similar between subjects with and without white-matter hyperintensities. The spatial distribution of volume loss suggests chronic hypoxic cerebrovascular injury, despite compensatory hyperemia. Neurocognitive consequences of WM volume changes and silent cerebral infarction were strongly sexually dimorphic. Understanding the possible neurological consequences of chronic anemia may help inform our current clinical practices.

MultiLink Analysis: Brain Network Comparison via Sparse Connectivity Analysis

The analysis of the brain from a connectivity perspective is revealing novel insights into brain structure and function. Discovery is, however, hindered by the lack of prior knowledge used to make hypotheses. Additionally, exploratory data analysis is made complex by the high dimensionality of data. Indeed, to assess the effect of pathological states on brain networks, neuroscientists are often required to evaluate experimental effects in case-control studies, with hundreds of thousands of connections. In this paper, we propose an approach to identify the multivariate relationships in brain connections that characterize two distinct groups, hence permitting the investigators to immediately discover the subnetworks that contain information about the differences between experimental groups. In particular, we are interested in data discovery related to connectomics, where the connections that characterize differences between two groups of subjects are found. Nevertheless, those connections do not necessarily maximize the accuracy in classification since this does not guarantee reliable interpretation of specific differences between groups. In practice, our method exploits recent machine learning techniques employing sparsity to deal with weighted networks describing the whole-brain macro connectivity. We evaluated our technique on functional and structural connectomes from human and murine brain data. In our experiments, we automatically identified disease-relevant connections in datasets with supervised and unsupervised anatomy-driven parcellation approaches and by using high-dimensional datasets.

Graph theory analysis reveals how sickle cell disease impacts neural networks of patients with more severe disease

Sickle cell disease (SCD) is a hereditary blood disorder associated with many life-threatening comorbidities including cerebral stroke and chronic pain. The long-term effects of this disease may therefore affect the global brain network which is not clearly understood. We performed graph theory analysis of functional networks using non-invasive fMRI and high resolution EEG on thirty-one SCD patients and sixteen healthy controls. Resting state data were analyzed to determine differences between controls and patients with less severe and more severe sickle cell related pain. fMRI results showed that patients with higher pain severity had lower clustering coefficients and local efficiency. The neural network of the more severe patient group behaved like a random network when performing a targeted attack network analysis. EEG results showed the beta1 band had similar results to fMRI resting state data. Our data show that SCD affects the brain on a global level and that graph theory analysis can differentiate between patients with different levels of pain severity.

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Graph theory used to study global impact of long term sickle cell disease on brain.

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EEG and fMRI results compared to study spatial and temporal impact of disease.

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More severe patients have less clustering and efficiency.

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Small world values correlated with past hospitalizations, linking results to pain.