Clinical performance of a multiparametric MRI-based post concussive syndrome index.

Introduction: Diffusion Tensor Imaging (DTI) has revealed measurable changes in the brains of patients with persistent post-concussive syndrome (PCS). Because of inconsistent results in univariate DTI metrics among patients with mild traumatic brain injury (mTBI), there is currently no single objective and reliable MRI index for clinical decision-making in patients with PCS. Purpose: This study aimed to evaluate the performance of a newly developed PCS Index (PCSI) derived from machine learning of multiparametric magnetic resonance imaging (MRI) data to classify and differentiate subjects with mTBI and PCS history from those without a history of mTBI. Materials and methods: Data were retrospectively extracted from 139 patients aged between 18 and 60 years with PCS who underwent MRI examinations at 2 weeks to 1-year post-mTBI, as well as from 336 subjects without a history of head trauma. The performance of the PCS Index was assessed by comparing 69 patients with a clinical diagnosis of PCS with 264 control subjects. The PCSI values for patients with PCS were compared based on the mechanism of injury, time interval from injury to MRI examination, sex, history of prior concussion, loss of consciousness, and reported symptoms. Results: Injured patients had a mean PCSI value of 0.57, compared to the control group, which had a mean PCSI value of 0.12 (p = 8.42e-23) with accuracy of 88%, sensitivity of 64%, and specificity of 95%, respectively. No statistically significant differences were found in the PCSI values when comparing the mechanism of injury, sex, or loss of consciousness. Conclusion: The PCSI for individuals aged between 18 and 60 years was able to accurately identify patients with post-concussive injuries from 2 weeks to 1-year post-mTBI and differentiate them from the controls. The results of this study suggest that multiparametric MRI-based PCSI has great potential as an objective clinical tool to support the diagnosis, treatment, and follow-up care of patients with post-concussive syndrome. Further research is required to investigate the replicability of this method using other types of clinical MRI scanners.

Acetylcholinesterase inhibitors to enhance recovery from traumatic brain injury: a comprehensive review and case series.

OBJECTIVE: Acetylcholinesterase inhibitors (AChEIs) are used off-label, in both adult and pediatric patients, to help further neuro-recovery after traumatic brain injury (TBI). Evidence is limited and piecemeal. This review describes how TBI affects the cholinergic system and consolidates evidence supporting or refuting the use of AChEIs following TBI. METHODS: NCBI MEDLINE search included all articles published through March 2021 on AChEI use in acute and post-acute adult TBI rehabilitation (treatment began <90 days or ≥90 days since injury, respectively), and in pediatric TBI rehabilitation. Further, we checked ClinicalTrials.gov for ongoing trials using AChEIs for TBI rehabilitation in the United States. RESULTS: 27 original articles from NCBI Medline, published through March 2021, were included. The use of AChEIs following TBI in acute and post-acute rehabilitation settings, in both adult and pediatric patients, along with medication side effects, is discussed. CONCLUSIONS: Most studies showed benefits with only moderate effect sizes because of small sample sizes. Reported side effects are minimal and stop soon after AChEIs is discontinued. Conclusions are limited by paucity of research; but fortunately, a large randomized controlled trial is ongoing, and more are needed to truly determine the efficacy of AChEIs in helping with recovery from TBI.

Goals of Care Conversations and Subsequent Advance Care Planning Outcomes for People with Dementia.

BACKGROUND: Advance care planning has been shown to improve end of life decision-making for people with dementia. However, the impact of goals of care conversations between people with dementia and their caregivers has not been characterized. OBJECTIVE: In this study, we evaluate the association between goals of care conversations and advance care planning outcomes. METHODS: Retrospective advance care planning measures were collected via a questionnaire administered to 166 caregivers after the death of the person with dementia for whom they provided care. RESULTS: At time of death, the majority of decedents with dementia had advance directives, health care agents, and previous goals of care conversations with their caregiver. Goals of care conversations were significantly associated with the perceived usefulness of advance directives, the perceived adherence to advance directives, and decedent dying at their desired place of death, but not with disagreements around end-of-life care. CONCLUSION: Our findings suggest that goals of care conversations are an important component of advance care planning. These findings support the development of interventions that facilitate such conversations between people with dementia and their caregivers.

Time to treatment initiation and outcomes in high-grade glioma patients in rehabilitation: a retrospective cohort study.

Aims: To investigate wait time (WT) for chemoradiation and survival in post-op high-grade glioma (HGG) patients admitted to inpatient rehabilitation compared with those discharged home. Materials & methods: A total of 291 HGG patients (14.4% grade III and 84.9% grade IV) were included in this retrospective cohort study. Patients were grouped by disposition following surgery. Results: Median length of stay was longer in acute inpatient rehabilitation facility (AIRF) patients (10d) compared with patients discharged home (3d). AIRF admission was associated with higher odds of excessive treatment delay. Median survival for AIRF patients less than for patients discharged home (42.9 vs 72.71 weeks). WT was not associated with survival even after adjusting for prognostic factors. Conclusion: HGG patients discharged to rehabilitation facilities have longer length of stay, longer WT and shorter survival compared with patients discharged home.

Amantadine and memantine: a comprehensive review for acquired brain injury.

This comprehensive review discusses clinical studies of patients following brain injuries (traumatic, acquired, or stroke), who have been treated with amantadine or memantine. Both amantadine and memantine are commonly used in the acute rehabilitation setting following brain injuries, despite their lack of FDA-approval for neuro-recovery. Given the broad utilization of such agents, there is a need to review the evidence supporting this common off-label prescribing. The purpose of this review is to describe the mechanisms of action for memantine and amantadine, as well as to complete a comprehensive review of the clinical uses of these agents. We included 119 original, clinical research articles from NCBI Medline, published before 2019. We focused on the domains of neuroplasticity, functional recovery, motor recovery, arousal, fatigue, insomnia, behavior, agitation, and cognition. Most of the existing research supporting the use of amantadine and memantine in recovery from brain injuries was done in very small populations, limiting the significance of conclusions. While most studies are positive; small effect sizes are usually reported, or populations are subject to bias. Furthermore, evidence is so limited that this review includes research regarding both acute and chronic acquired brain injury populations. Fortunately, reported short-term side effects generally are modest, and stop soon after amantadine/memantine is discontinued. However, responses are inconsistent, and the phenotype of responders remains elusive.

A Novel Multimodal MRI Analysis for Alzheimer's Disease Based on Convolutional Neural Network.

Recent years, Alzheimer's disease (AD) has become a significant threat to human health while the accurate screening and diagnosis of AD remain a tough problem. Multimodal Magnetic resonance imaging (MRI) can help to identify the variation of brain function and structure in a non-invasive way. Deep learning, especially the convolutional neural networks (CNN), can be utilized to automatically detect appropriate features for classification, which is well adapted for computer-aided AD screening and identification. This paper proposed a multimodal MRI analytical method based on CNN, which is also suitable for single type MRI data analysis. First, the human brain network connectivity matrix were extracted from multimodal MRI data, used as the input data for CNN. Then a novel CNN framework was proposed to process the network matrix and classify AD, amnestic mild cognitive impairment (aMCI) patients and normal controls (NC). The advantage of this method lies in that we combined multimodal MRI information through CNN convolution kernel, and achieved a higher classification accuracy. In our experiments, the comprehensive classification accuracy of AD, aMCI patients and NC was as high as 92.06% when using multimodal MRI data as input, which is effective enough to provide a reference for multimodal MRI data analysis.

The Correlation Analysis Between DTI Network Parameters and AVLT Scale Scores of Alzheimer's Disease.

Neuroimaging and neuropsychology are employed to investigate the pathological features and clinical characteristics of Alzheimer's disease (AD) in order to find a method for the precise treatment. Diffusion tensor imaging (DTI) provides a non-intrusive examination of cranial nerve diseases which can help us observe the microstructure of neuron fibers. Building the brain network provides a chance to reveal the significance of specific brain region and the relevance among different regions. In this study, we propose a completely novel method to analyze AD. First whole brain network is built on the basis of a novel segmentation atlas, and global graph theoretical parameters are calculated to evaluate the characteristic of whole brain. Then graph theoretical parameters of specific brain regions are extracted based on whole brain network. Finally neuropsychology scale are employed and we analyze the correlation between graph theoretical parameters of specific regions and scale scores. Our results illustrate the connection between neuroimaging data and neuropsychological scores, and provide a reasonable explanation for the potential connection between clinical performance and physiological brain lesions of AD patients.

Multivariate Analysis of White Matter Structural Networks of Alzheimer's Disease.

The connectome-wide association studies exploring association between brain connectome and disease phenotypes have suffered from massive number of comparisons. In this paper, we propose to apply a multivariate distance-based analytic framework on brain white matter (WM) structural networks invaded by Alzheimer's disease (AD). Eighty-three subjects including patients with AD, amnestic mild cognitive impairment (aMCI) and healthy subjects were scanned with dMRI. By constructing WM structural network for each individual, we used both multivariate and traditional univariate statistical models to complimentarily analyze network pattern and fiber strength changes due to AD. WM connections linked with several brain structures were found significantly changed between AD group and normal controls. No significant findings were observed between aMCI group and normal controls. Our results demonstrate the sensitivity of the combined connectome-based analytic framework in detecting abnormalities of structural brain network.

Cognitive control related network analysis: A novel way to measure neuron fiber connection of Alzheimer's disease.

Effective measurement of cognitive impairment caused by Alzheimer's disease (AD) will provide a chance for early medical intervention and delay the disease onset. Diffusion tensor imaging (DTI) provides a non-intrusive examination of cranial nerve diseases which can help us observe the microstructure of neuron fibers. Cognitive control network (CCN) consists of the brain regions that highly related to human self-control. In this study, hub-and-spoke model which was widely used in transportation and sociology area had been employed to analyze the relationship of CCN and other regions under its control, cognitive control related network (CCRN) was built by applying this model. Local and global graph theoretical parameters were calculated and went through statistical analysis. Significant difference had been found in the scale of local as well as global which may represent the impairment of cognitive control ability. This result may provide a potential bio-marker for the loss of connection caused by Alzheimer's disease.

Structural changes of cingulate cortex in post stroke depression.

Disruption of neural connections among regions regulating mood and cognition rather than cerebrovascular lesions may contribute to post-stroke depression (PSD). In this paper, we hypothesized that structural abnormalities in white matter structure like cingulate cortex induced by focal infarcts would play a role in mood regulation or depression after stroke onset. Various DTI coefficients including FA, RD and ADC with multiple signal distribution measurements were collected and statistically analyzed. The results identify significant differences in volume and multiple diffusion indices of DTI intensity distribution in cingulate cortex between PSD patients and the normal control. It indicates the neuronal loss secondary to demyelination in cingulate cortex due to stroke. Additionally, the volume loss of cingulate cortex in PSD patients observed in the results may further demonstrate the reduction of glial cells in cingulate cortex, as the axonal number/size changed little.

A fast atlas pre-selection procedure for multi-atlas based brain segmentation.

Multi-atlas based MR image segmentation has been recognized as a quantitative analysis approach for brain. For such purpose, atlas databases keep increasing to include various anatomical characteristics of human brain. Atlas pre-selection becomes a necessary step for efficient and accurate automated segmentation of human brain images. In this study, we proposed a method of atlas pre-selection for target image segmentation on the MriCloud platform, which is a state-of-the-art multi-atlas based segmentation tool. In the MRIcloud pipeline, segmentation of lateral ventricle (LV) label is generated as an additional input in the segmentation pipeline. Under this circumstance, similarity of the LV label between target image and atlases was adopted as the atlas ranking scheme. Dice overlap coefficient was calculated and taken as the quantitative measure for atlas ranking. Segmentation results based on the proposed method were compared with that based on atlas pre-selection by mutual information (MI) between images. The final segmentation results showed a comparable accuracy of the proposed method with that from MI based atlas pre-selection. However, the computation load for the atlas pre-selection was speeded up by about 20 times compared to MI based pre-selection. The proposed method provides a promising assistance for quantitative analysis of brain images.

A simulation study on marrow fat effect on biomechanics of vertebra bone.

Trabecular bone and bone marrow are main components of cancellous bone. Most mechanical studies for bone mainly focus on hard tissues, while if bone marrow contributes to bone biomechanics is not clear yet. This study was proposed to investigate marrow fat effect on trabecular bone biomechanics by simulation. Finite element (FE) bone models were established based on quantitative CT images at L3 lumbar spine, from which trabecular structures with and without marrow fat were investigated respectively. Auni-static compressive test was applied on the proposed models until to the appearance of fracture. Simulation results showed that trabecular models filled with marrow fat had about 3%-9% less maximum stress in volume than models with only trabeculae. However, its average stress in volume was about 9%-56% larger than those with only trabeculae. The strain energy density of the bone model with marrow fat showed a more uniformed distribution. As a conclusion, marrow fat has contributions to the bone mechanics. It can balance the stress distribution of the bone tissue, which may reduce bone deformation under a compressive loading. The mixture of trabecular structure and marrow fat would be against higher compress load before the failure point.

DWI-based neural fingerprinting technology: a preliminary study on stroke analysis.

Stroke is a common neural disorder in neurology clinics. Magnetic resonance imaging (MRI) has become an important tool to assess the neural physiological changes under stroke, such as diffusion weighted imaging (DWI) and diffusion tensor imaging (DTI). Quantitative analysis of MRI images would help medical doctors to localize the stroke area in the diagnosis in terms of structural information and physiological characterization. However, current quantitative approaches can only provide localization of the disorder rather than measure physiological variation of subtypes of ischemic stroke. In the current study, we hypothesize that each kind of neural disorder would have its unique physiological characteristics, which could be reflected by DWI images on different gradients. Based on this hypothesis, a DWI-based neural fingerprinting technology was proposed to classify subtypes of ischemic stroke. The neural fingerprint was constructed by the signal intensity of the region of interest (ROI) on the DWI images under different gradients. The fingerprint derived from the manually drawn ROI could classify the subtypes with accuracy 100%. However, the classification accuracy was worse when using semiautomatic and automatic method in ROI segmentation. The preliminary results showed promising potential of DWI-based neural fingerprinting technology in stroke subtype classification. Further studies will be carried out for enhancing the fingerprinting accuracy and its application in other clinical practices.

A blood pressure monitoring method for stroke management.

Blood pressure is one important risk factor for stroke prognosis. Therefore, continuous monitoring of blood pressure is crucial for preventing and predicting stroke. However, current blood pressure devices are mainly air-cuff based, which only can provide measurements intermittently. This study proposed a new blood pressure estimation method based on the pulse transit time to realize continuous monitoring. The proposed method integrated a linear model with a compensation algorithm. A calibration method was further developed to guarantee that the model was personalized for individuals. Variation and variability of pulse transit time were introduced to construct the compensation algorithm in the model. The proposed method was validated by the data collected from 30 healthy subjects, aged from 23 to 25 years old. By comparing the estimated value to the measurement from an oscillometry, the result showed that the mean error of the estimated blood pressure was -0.2 ± 2.4 mmHg and 0.5 ± 3.9 mmHg for systolic and diastolic blood pressure, respectively. In addition, the estimation performance of the proposed model is better than the linear model, especially for the diastolic blood pressure. The results indicate that the proposed method has promising potential to realize continuous blood pressure measurement.

Muscle-based pharmacokinetic modeling of marrow perfusion for osteoporotic bone in females.

The pharmacokinetic model has been widely used in tissue perfusion analysis, such as bone marrow perfusion. In the modeling process, the arterial input function is important to guarantee the reliability of the fitting result. However, the arterial input function is variable and hard to control, which makes it difficult to compare results across different studies. The purpose of this study was to establish a muscle-based pharmacokinetic model for bone marrow perfusion without using arterial input function. Erector spinae muscle around the vertebral body was selected as the reference region. The study was carried out in elderly females with different bone mineral densities (normal, osteopenia, and osteoporosis). Quantitative parameters were extracted from the pharmacokinetic model. Parameter K (trans,BM) (contrast agent extravasation rate constants for blood perfusion of the bone marrow) showed a significant reduction in subjects with lower bone mineral density, which is consistent with previous studies. However, muscle perfusion parameters remained unchanged among different groups. The results indicated that the muscle-based model was stable for bone marrow perfusion modeling. Additionally, nonsignificant change in muscle parameters indicated that the diminished perfusion is only a local rather than a systematic change in the bone marrow for osteoporosis.

QRS detection by lifting scheme constructing multi-resolution morphological decomposition.

QRS complex detecting algorithm is core of ECG auto-diagnosis method and deeply influences cardiac cycle division for signal compression. However, ECG signals collected by noninvasive surface electrodes areusually mixed with several kinds of interference, and its waveform variation is the main reason for the hard realization of ECG processing. This paper proposes a QRS complex detecting algorithm based on multi-resolution mathematical morphological decomposition. This algorithm possesses superiorities in R peak detection of both mathematical morphological method and multi-resolution decomposition. Moreover, a lifting constructing method with Maximizationupdating operator is adopted to further improve the algorithm performance. And an efficient R peak search-back algorithm is employed to reduce the false positives (FP) and false negatives (FN). The proposed algorithm provides a good performance applying to MIT-BIH Arrhythmia Database, and achieves over 99% detection rate, sensitivity and positive predictivity, respectively, and calculation burden is low. Therefore, the proposed method is appropriate for portable medical devices in Telemedicine system.

A preliminary study of DTI Fingerprinting on stroke analysis.

DTI (Diffusion Tensor Imaging) is a well-known MRI (Magnetic Resonance Imaging) technique which provides useful structural information about human brain. However, the quantitative measurement to physiological variation of subtypes of ischemic stroke is not available. An automatically quantitative method for DTI analysis will enhance the DTI application in clinics. In this study, we proposed a DTI Fingerprinting technology to quantitatively analyze white matter tissue, which was applied in stroke classification. The TBSS (Tract Based Spatial Statistics) method was employed to generate mask automatically. To evaluate the clustering performance of the automatic method, lesion ROI (Region of Interest) is manually drawn on the DWI images as a reference. The results from the DTI Fingerprinting were compared with those obtained from the reference ROIs. It indicates that the DTI Fingerprinting could identify different states of ischemic stroke and has promising potential to provide a more comprehensive measure of the DTI data. Further development should be carried out to improve DTI Fingerprinting technology in clinics.