Clinical Comparison of 99mTc Exametazime and 123I Ioflupane SPECT in Patients with Chronic Mild Traumatic Brain Injury

Newberg, Andrew B.; Serruya, Mijail; Gepty, Andrew; Intenzo, Charles; Lewis, Todd; Amen, Daniel; Russell, David; Wintering, Nancy

doi:10.1371/journal.pone.0087009

Cited by 16 publications

(12 citation statements)

References 46 publications

(56 reference statements)

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“…The default mode network (DMN), consisting of the inferior orbital frontal cortex, anterior cingulate gyrus, posterior cingulate gyrus, hippocampus, precuneus, superior parietal lobe, and angular gyrus (Buckner et al 2008 ), shows hyperperfusion in patients diagnosed with PTSD and hypoperfusion in patients diagnosed with TBI (Erickson et al 2014 ; Dubroff and Newberg 2008 ; Newberg and Alavi 2003 ; Newberg et al 2014 ; Liu et al 2013 ). By further elucidating the correlation between increased perfusion in DMN structures in PTSD and decreased perfusion in TBI, clinicians may be able to more reliably diagnose PTSD from TBI.…”

Section: Introductionmentioning

confidence: 99%

Functional neuroimaging with default mode network regions distinguishes PTSD from TBI in a military veteran population

Raji

Willeumier

Taylor

et al. 2015

Brain Imaging and Behavior

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PTSD and TBI are two common conditions in veteran populations that can be difficult to distinguish clinically. The default mode network (DMN) is abnormal in a multitude of neurological and psychiatric disorders. We hypothesize that brain perfusion SPECT can be applied to diagnostically separate PTSD from TBI reliably in a veteran cohort using DMN regions. A group of 196 veterans (36 with PTSD, 115 with TBI, 45 with PTSD/TBI) were selected from a large multi-site population cohort of individuals with psychiatric disease. Inclusion criteria were peacetime or wartime veterans regardless of branch of service and included those for whom the traumatic brain injury was not service related. SPECT imaging was performed on this group both at rest and during a concentration task. These measures, as well as the baseline-concentration difference, were then inputted from DMN regions into separate binary logistic regression models controlling for age, gender, race, clinic site, co-morbid psychiatric diseases, TBI severity, whether or not the TBI was service related, and branch of armed service. Predicted probabilities were then inputted into a receiver operating characteristic analysis to compute sensitivity, specificity, and accuracy. Compared to PSTD, persons with TBI were older, male, and had higher rates of bipolar and major depressive disorder (p < 0.05). Baseline quantitative regions with SPECT separated PTSD from TBI in the veterans with 92 % sensitivity, 85 % specificity, and 94 % accuracy. With concentration scans, there was 85 % sensitivity, 83 % specificity and 89 % accuracy. Baseline-concentration (the difference metric between the two scans) scans were 85 % sensitivity, 80 % specificity, and 87 % accuracy. In separating TBI from PTSD/TBI visual readings of baseline scans had 85 % sensitivity, 81 % specificity, and 83 % accuracy. Concentration scans had 80 % sensitivity, 65 % specificity, and 79 % accuracy. Baseline-concentration scans had 82 % sensitivity, 69 % specificity, and 81 % accuracy. For separating PTSD from PTSD/TBI baseline scans had 87 % sensitivity, 83 % specificity, and 92 % accuracy. Concentration scans had 91 % sensitivity, 76 % specificity, and 88 % accuracy. Baseline-concentration scans had 84 % sensitivity, 64 % specificity, and 85 % accuracy. This study demonstrates the ability to separate PTSD and TBI from each other in a veteran population using functional neuroimaging.

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Section: Introductionmentioning

confidence: 99%

Functional neuroimaging with default mode network regions distinguishes PTSD from TBI in a military veteran population

Raji

Willeumier

Taylor

et al. 2015

Brain Imaging and Behavior

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“…Specially, the increased CBF in the right ITG was found to be negatively correlated with the attention scores in acute mTBI patients, which may explain the deficits of some cognitive function in mTBI. Contrarily, Newberg et al identified significantly lower CBF levels in the right temporal lobes of mTBI patients using SPECT [36]. In addition, one study found that children with mTBI with persistent posttraumatic symptoms had more severe medial temporal lobe hypoperfusion than those without persistent posttraumatic symptoms [37].…”

Section: Discussionmentioning

confidence: 99%

Cerebral Blood Flow and Its Connectivity Deficits in Mild Traumatic Brain Injury at the Acute Stage

Shang

et al. 2020

Neural Plasticity

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Objective. The influence of cognitive impairment after mild traumatic brain injury (mTBI) on cerebral vascular perfusion has been widely concerned, yet the resting-state cerebral blood flow (CBF) connectivity alterations based on arterial spin labeling (ASL) in mild traumatic brain injury (mTBI) remain unclear. This study investigated region CBF and CBF connectivity features in acute mTBI patients, as well as the associations between CBF changes and cognitive impairment. Materials and Methods. Forty-five acute mTBI patients and 42 health controls underwent pseudocontinuous arterial spin labeling (pCASL) perfusion magnetic resonance imaging (MRI). The alterations in regional CBF and relationship between the CBF changes and cognitive impairment were detected. The ASL-CBF connectivity of the brain regions with regional CBF significant differences was also compared between two groups. Neuropsychological tests covered seven cognitive domains. Associations between the CBF changes and cognitive impairment were further investigated. Results. Compared with the healthy controls, the acute mTBI patients exhibited increased CBF in the bilateral inferior temporal gyrus (ITG) and decreased CBF in the right middle frontal gyrus (MFG), the bilateral superior frontal gyrus (SFG), and the right cerebellum posterior lobe (CPL). In the mTBI patients, significant correlations were identified between the CBF changes and cognitive impairment. Importantly, the acute mTBI patients exhibited CBF disconnections between the right CPL and right fusiform gyrus (FG) as well as bilateral ITG, between the left SFG and left middle occipital gyrus (MOG), and between the right SFG and right FG as well as right parahippocampal gyrus. Conclusion. Our results suggest that acute mTBI patients exhibit both regional CBF abnormalities and CBF connectivity deficits, which may underlie the cognitive impairment of the acute mTBI patients.

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“…Among these were six healthy patients, 102 patients with mild PD (HYS Stage 1 to Stage 3), and 94 patients with severe PD (HYS Stage 4 and Stage 5) [5,17,18]. A three-dimensional method was used to estimate the features, including skewness, kurtosis, Cyhelsky’s skewness coefficient, Pearson’s median skewness, DAT activity volume, and DAT activity maximum, in the images of activity distribution and striatal activity volume [14,19,20]. Subsequently, the reports were randomly allocated into two groups, each containing 101 reports.…”

Section: Materials and Methodologymentioning

confidence: 99%

Feasible Classified Models for Parkinson Disease from 99mTc-TRODAT-1 SPECT Imaging

Hsu

Lin

Chen

et al. 2019

Sensors

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The neuroimaging techniques such as dopaminergic imaging using Single Photon Emission Computed Tomography (SPECT) with 99mTc-TRODAT-1 have been employed to detect the stages of Parkinson’s disease (PD). In this retrospective study, a total of 202 99mTc-TRODAT-1 SPECT imaging were collected. All of the PD patient cases were separated into mild (HYS Stage 1 to Stage 3) and severe (HYS Stage 4 and Stage 5) PD, according to the Hoehn and Yahr Scale (HYS) standard. A three-dimensional method was used to estimate six features of activity distribution and striatal activity volume in the images. These features were skewness, kurtosis, Cyhelsky’s skewness coefficient, Pearson’s median skewness, dopamine transporter activity volume, and dopamine transporter activity maximum. Finally, the data were modeled using logistic regression (LR) and support vector machine (SVM) for PD classification. The results showed that SVM classifier method produced a higher accuracy than LR. The sensitivity, specificity, PPV, NPV, accuracy, and AUC with SVM method were 0.82, 1.00, 0.84, 0.67, 0.83, and 0.85, respectively. Additionally, the Kappa value was shown to reach 0.68. This claimed that the SVM-based model could provide further reference for PD stage classification in medical diagnosis. In the future, more healthy cases will be expected to clarify the false positive rate in this classification model.

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Clinical Comparison of 99mTc Exametazime and 123I Ioflupane SPECT in Patients with Chronic Mild Traumatic Brain Injury

Cited by 16 publications

References 46 publications

Functional neuroimaging with default mode network regions distinguishes PTSD from TBI in a military veteran population

Functional neuroimaging with default mode network regions distinguishes PTSD from TBI in a military veteran population

Cerebral Blood Flow and Its Connectivity Deficits in Mild Traumatic Brain Injury at the Acute Stage

Feasible Classified Models for Parkinson Disease from 99mTc-TRODAT-1 SPECT Imaging

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