Gray Matter Tissue Research Articles

Highfield imaging of the subgenual anterior cingulate cortex in uni- and bipolar depression.

The subgenual Anterior Cingulate Cortex (sgACC), as a part of the Anterior Cingulate Cortex and the limbic system plays a crucial role in mood regulation. Previous structural and functional brain imaging studies of the sgACC have revealed alterations of Gray Matter (GM) volumes and Blood Oxygenation Level Dependent signals (BOLD) in patients with Major Depressive Disorder (MDD) and Bipolar Disorder (BD), suggesting potential biomarker traits for affective disorders. In this study we investigated the gray matter volume of the sgACC in 3 different patient groups: 40 MDD patients, of which 20 were medicated (MDDm) and 20 were unmedicated (MDDu), and 21 medicated BD patients, and compared them with 23 healthy volunteers. We examined GM volume alteration using high-resolution 7T Magnetic Resonance Imaging (MRI) which produced quantitative maps of the spin-lattice relaxation time (T1). T1 maps provide high contrast between gray and white matter, and at 7 Tesla voxels with submillimeter resolution can be acquired in a reasonable scan time. We developed a semi-automatic segmentation protocol based on refined landmarks derived from previous volumetric studies using quantitative T1 maps as raw input data for automatic tissue segmentation of GM, WM and CSF (cerebrospinal fluid) tissue. The sgACC ROI was then superimposed on these tissue probability maps and traced manually by two independent raters (F.B., M.M.) following our semi-automatic segmentation protocol. Interrater reliability was calculated on a subset of 10 brain scans for each hemisphere, showing an Intra-Class Correlation coefficient (ICC) r = 0.96 for left sgACC and r = 0.84 for right sgACC respectively. In summary, we have developed a reproducible and reliable semi-automatic segmentation protocol to measure gray matter volume in the sgACC. Based on previous findings from meta-analyses on morphometric studies of the sgACC, we hypothesized that patients with MDD would have lower gray matter sgACC volumes compared to healthy subjects. Post-hoc analysis revealed smaller subgenual volumes for the left hemisphere in both the medicated (MDDm) and non-medicated (MDDu) group versus healthy controls (p = .001, p = .008) respectively. For the right hemisphere, the (MDDu) and BD group exhibited significantly lower subgenual volumes than healthy controls (p < .001, p = .004) respectively. To our knowledge, this is the first morphometric MRI study using T1 maps obtained in high-resolution 7 Tesla MRI to compare MDD and BD patients with healthy controls.

Modeling electrical impedance in brain tissue with diffusion tensor imaging for functional neurosurgery applications

Objective.Decades ago, neurosurgeons used electrical impedance measurements in the brain for coarse intraoperative tissue differentiation. Over time, these techniques were largely replaced by more refined imaging and electrophysiological localization. Today, advanced methods of diffusion tensor imaging (DTI) and finite element method (FEM) modeling may permit non-invasive, high-resolution intracerebral impedance prediction. However, expectations for tissue-impedance relationships and experimentally verified parameters for impedance modeling in human brains are lacking. This study seeks to address this need.Approach.We used FEM to simulate high-resolution single- and dual-electrode impedance measurements along linear electrode trajectories through (1) canonical gray and white matter tissue models, and (2) selected anatomic structures within whole-brain patient DTI-based models. We then compared intraoperative impedance measurements taken at known locations along deep brain stimulation (DBS) surgical trajectories with model predictions to evaluate model accuracy and refine model parameters.Main results.In DTI-FEM models, single- and dual-electrode configurations performed similarly. While only dual-electrode configurations were sensitive to white matter fiber orientation, other influences on impedance, such as white matter density, enabled single-electrode impedance measurements to display significant spatial variation even within purely white matter structures. We compared 308 intraoperative single-electrode impedance measurements in five DBS patients to DTI-FEM predictions at one-to-one corresponding locations. After calibration of model coefficients to these data, predicted impedances reliably estimated intraoperative measurements in all patients (R=0.784±0.116,n=5). Through this study, we derived an updated value for the slope coefficient of the DTI conductance model published by Tuchet al,k=0.0649 S⋅smm-3 (originalk=0.844), for use specifically in humans at physiological frequencies.Significance.This is the first study to compare impedance estimates from imaging-based models of human brain tissue to experimental measurements at the same locationsin vivo. Accurate, non-invasive, imaging-based impedance prediction has numerous applications in functional neurosurgery, including tissue mapping, intraoperative electrode localization, and DBS.

Effect of Ethanol on Brain Electrical Tissue Conductivity in Social Drinkers.

How the biophysics of electrical conductivity measures relate to brain activity is poorly understood. The sedative, ethanol, reduces metabolic activity but its impact on brain electrical conductivity is unknown. To investigate whether ethanol reduces brain electrical tissue conductivity. Prospective. Fifty-two healthy volunteers (aged 18-37 years, 22 females, 30 males). 3 T, T1-weighted, multi-shot, turbo-field echo (TFE); 3D balanced fast-field echo (bFFE). Brain gray and white matter tissue conductivity measured with phase-based magnetic resonance electrical properties tomography (MREPT) compared before and 20 minutes after ethanol consumption (0.7 g/kg body weight). Differential conductivity whole brain maps were generated for three subgroups: those with strong ( > 0.1 S/m; N = 33), weak (0.02 S/m ≤ ≤ 0.1 S/m; N = 9) conductivity decrease, and no significant response ( < 0.02 S/m, N = 10). Maps were compared in the strong response group where breath alcohol rose between scans, vs. those where it fell. Average breath alcohol levels were compared to the differential conductivity maps using linear regression. T-maps were generated (threshold P < 0.05 and P < 0.001; minimum cluster 48 mm3). Differential conductivity maps were compared with ANOVA. Whole-group analysis showed decreased conductivity that did not survive statistical thresholding. Strong responders (N = 33) showed a consistent pattern of significantly decreased conductivity ( > 0.1 S/m) in frontal/occipital and cerebellar white matter. The weak response group (N = 9) showed a similar pattern of conductivity decrease (0.02 S/m ≤ ≤ 0.1 S/m). There was no significant relationship with breath alcohol levels, alcohol use, age, ethnicity, or sex. The strong responders' regional response was different between ascending (N = 12) or descending (N = 20) alcohol during the scan. Ethanol reduces brain tissue conductivity in a participant-dependent and spatially dependent fashion. 1 TECHNICAL EFFICACY: Stage 2.

Assessing biological self-organization patterns using statistical complexity characteristics: a tool for diffusion tensor imaging analysis.

Diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI) are well-known and powerful imaging techniques for MRI. Although DTI evaluation has evolved continually in recent years, there are still struggles regarding quantitative measurements that can benefit brain areas that are consistently difficult to measure via diffusion-based methods, e.g., gray matter (GM). The present study proposes a new image processing technique based on diffusion distribution evaluation of López-Ruiz, Mancini and Calbet (LMC) complexity called diffusion complexity (DC). The OASIS-3 and TractoInferno open-science databases for healthy individuals were used, and all the codes are provided as open-source materials. The DC map showed relevant signal characterization in brain tissues and structures, achieving contrast-to-noise ratio (CNR) gains of approximately 39% and 93%, respectively, compared to those of the FA and ADC maps. In the special case of GM tissue, the DC map obtains its maximum signal level, showing the possibility of studying cortical and subcortical structures challenging for classical DTI quantitative formalism. The ability to apply the DC technique, which requires the same imaging acquisition for DTI and its potential to provide complementary information to study the brain's GM structures, can be a rich source of information for further neuroscience research and clinical practice.

Improved neurological diagnoses and treatment strategies via automated human brain tissue segmentation from clinical magnetic resonance imaging

ObjectiveSegmentation of medical images is a crucial process in various image analysis applications. Automated segmentation methods excel in accuracy when compared to manual segmentation in the context of medical image analysis. One of the essential phases in the quantitative analysis of the brain is automated brain tissue segmentation using clinically obtained magnetic resonance imaging (MRI) data. It allows for precise quantitative examination of the brain, which aids in diagnosis, identification, and classification of disorders. Consequently, the efficacy of the segmentation approach is crucial to disease diagnosis and treatment planning. MethodsThis study presented a hybrid optimization method for segmenting brain tissue in clinical MRI scans using a fractional Henry horse herd gas optimization-based Shepard convolutional neural network (FrHHGO-based ShCNN). To segment the clinical brain MRI images into white matter (WM), grey matter (GM), and cerebrospinal fluid (CSF) tissues, the proposed framework was evaluated on the Lifespan Human Connectome Projects (HCP) database. The hybrid optimization algorithm, FrHHGO, integrates the fractional Henry gas optimization (FHGO) and horse herd optimization (HHO) algorithms. Training required 30 min, whereas testing and segmentation of brain tissues from an unseen image required an average of 12 s. ResultsCompared to the results obtained with no refinements, the Skull stripping refinement showed significant improvement. As the method included a preprocessing stage, it was flexible enough to enhance image quality, allowing for better results even with low-resolution input. Maximum precision of 93.2%, recall of 91.5%, Dice score of 91.1%, and F1-score of 90.5% were achieved using the proposed FrHHGO-based ShCNN, which was superior to all other approaches. ConclusionThe proposed method may outperform existing state-of-the-art methodologies in qualitative and quantitative measurements across a wide range of medical modalities. It might demonstrate its potential for real-life clinical application.

Modern Magnetic Resonance Imaging Modalities to Advance Neuroimaging in Astronauts.

INTRODUCTION: The rapid development of the space industry requires a deeper understanding of spaceflight's impact on the brain. MRI research reports brain volume changes following spaceflight in astronauts, potentially affecting cognition. Recently, we have demonstrated that this evidence of volumetric changes, as measured by typical T1-weighted sequences (e.g., magnetization-prepared rapid gradient echo sequence; MPRAGE), is error-prone due to the microgravity-related redistribution of cerebrospinal fluid in the brain. More modern neuroimaging methods, particularly dual-echo MPRAGE (DEMPRAGE) and magnetization-prepared rapid gradient echo sequence utilizing two inversion pulses (MP2RAGE), have been suggested to be resilient to this error. Here, we tested if these imaging modalities offered consistent segmentation performance improvements in some commonly employed neuroimaging software packages.METHODS: We conducted manual gray matter tissue segmentation in traditional T1w MRI images to utilize for comparison. Automated tissue segmentation was performed for traditional T1w imaging, as well as on DEMPRAGE and MP2RAGE images from the same subjects. Statistical analysis involved a comparison of total gray matter volumes for each modality, and the extent of tissue segmentation agreement was assessed using a test of similarity (Dice coefficient).RESULTS: Neither DEMPRAGE nor MP2RAGE exhibited consistent segmentation performance across all toolboxes tested.DISCUSSION: This research indicates that customized data collection and processing methods are necessary for reliable and valid structural MRI segmentation in astronauts, as current methods provide erroneous classification and hence inaccurate claims of neuroplastic brain changes in the astronaut population.Berger L, Burles F, Jaswal T, Williams R, Iaria G. Modern magnetic resonance imaging modalities to advance neuroimaging in astronauts. Aerosp Med Hum Perform. 2024; 95(5):245-253.

Treatment with RNase alleviates brain injury but not neuroinflammation in neonatal hypoxia/ischemia.

There is a need for new treatments to reduce brain injuries derived from neonatal hypoxia/ischemia. The only viable option used in the clinic today in infants born at term is therapeutic hypothermia, which has a limited efficacy. Treatments with exogenous RNase have shown great promise in a range of different adult animal models including stroke, ischemia/reperfusion injury, or experimental heart transplantation, often by conferring vascular protective and anti-inflammatory effects. However, any neuroprotective function of RNase treatment in the neonate remains unknown. Using a well-established model of neonatal hypoxic/ischemic brain injury, we evaluated the influence of RNase treatment on RNase activity, gray and white matter tissue loss, blood-brain barrier function, as well as levels and expression of inflammatory cytokines in the brain up to 6 h after the injury using multiplex immunoassay and RT-PCR. Intraperitoneal treatment with RNase increased RNase activity in both plasma and cerebropinal fluids. The RNase treatment resulted in a reduction of brain tissue loss but did not affect the blood-brain barrier function and had only a minor modulatory effect on the inflammatory response. It is concluded that RNase treatment may be promising as a neuroprotective regimen, whereas the mechanistic effects of this treatment appear to be different in the neonate compared to the adult and need further investigation.

Are there any differences at gray matter sites between severe obstructive sleep apnea patients and healthy controls?

ObjectivesObstructive sleep apnea (OSA) is a disease that may cause many medical conditions. Neurocognitive disorders may be triggered by OSA. In recent studies, selectively decreased gray matter tissue was observed in patients with OSA. We aimed to determine if there was a substantial difference in patients with extreme OSA by comparing the microstructural changes in different gray matter sub-areas with healthy controls using diffusion-weighted imaging methods. MethodsWe studied 15 diagnosed severe OSA subjects before any treatment and 32 healthy control subjects. High resolution Magnetic Resonance Imaging (MRI) T1 and T2-weighted scans were visually examined to assess any major brain lesions. ResultsThere were no statistically significant differences of age and gender between the groups.The left and right globus pallidus, putamen and thalamus values did not differ significantly between OSA and control subjects. Right putamen values was negatively correlated with Apnea Hypopnea Index (AHI), supine AHI and non-REM AHI in OSA subjects, but no correlations appeared with left putamen values. The other gray matter parameters did not show any correlations with PSG parameters. AHI, Supine AHI, Non-Supine AHI, REM and NON-REM AHI values was not show any correlation with Right and Left Putamen volume sizes. ConclusionsWe made a morphological comparison of various gray matter areas of OSA patients and healthy volunteers in our study. We observed a significant decrease in right putamen gray matter volumes in patients with higher AHI values. Decreased cognitive functions are found in patients with OSA. In order to demonstrate this cognitive loss in patients with morphologically there is a need for further prospective studies with larger sample sizes.

Application of glutamate weighted CEST in brain imaging of nicotine dependent participants in vivo at 7T.

With nicotine dependence being a significant healthcare issue worldwide there is a growing interest in developing novel therapies and diagnostic aids to assist in treating nicotine addiction. Glutamate (Glu) plays an important role in cognitive function regulation in a wide range of conditions including traumatic brain injury, aging, and addiction. Chemical exchange saturation transfer (CEST) imaging via ultra-high field MRI can image the exchange of certain saturated labile protons with the surrounding bulk water pool, making the technique a novel tool to investigate glutamate in the context of addiction. The aim of this work was to apply glutamate weighted CEST (GluCEST) imaging to study the dorsal anterior cingulate cortex (dACC) in a small population of smokers and non-smokers to determine its effectiveness as a biomarker of nicotine use. 2D GluCEST images were acquired on 20 healthy participants: 10 smokers (ages 29-50) and 10 non-smokers (ages 25-69), using a 7T MRI system. T1-weighted images were used to segment the GluCEST images into white and gray matter tissue and further into seven gray matter regions. Wilcoxon rank-sum tests were performed, comparing mean GluCEST contrast between smokers and non-smokers across brain regions. GluCEST levels were similar between smokers and non-smokers; however, there was a moderate negative age dependence (R2 = 0.531) in smokers within the cingulate gyrus. Feasibility of GluCEST imaging was demonstrated for in vivo investigation of smokers and non-smokers to assess glutamate contrast differences as a potential biomarker with a moderate negative age correlation in the cingulate gyrus suggesting reward network involvement.

Regional AT-8 reactive tau species correlate with intracellular Aβ levels in cases of low AD neuropathologic change

The amyloid cascade hypothesis states that Aβ aggregates induce pathological changes in tau, leading to neurofibrillary tangles (NFTs) and cell death. A caveat with this hypothesis is the spatio-temporal divide between plaques and NFTs. This has been addressed by the inclusion of soluble Aβ and tau species in the revised amyloid cascade hypothesis. Nevertheless, despite the potential for non-plaque Aβ to contribute to tau pathology, few studies have examined relative correlative strengths between total Aβ, plaque Aβ and intracellular Aβ with tau pathology within a single tissue cohort. Employing frozen and fixed frontal cortex grey and white matter tissue from non-AD controls (Con; n = 39) and Alzheimer’s disease (AD) cases (n = 21), biochemical and immunohistochemical (IHC) measures of Aβ and AT-8 phosphorylated tau were assessed. Biochemical native-state dot blots from crude tissue lysates demonstrated robust correlations between total Aβ and AT-8 tau, when considered as a combined cohort (Con and AD) and when as Con and AD cases, separately. In contrast, no associations between Aβ plaques and AT-8 were reported when using IHC measurements in either Con or AD cases. However, when intracellular Aβ was measured via the Aβ specific antibody MOAB-2, a correlative relationship with AT-8 tau was reported in non-AD controls but not in AD cases. Collectively the data suggests that accumulating intracellular Aβ may influence AT-8 pathology, early in AD-related neuropathological change. Despite the lower levels of phospho-tau and Aβ in controls, the robust correlative relationships observed suggest a physiological association of Aβ production and tau phosphorylation, which may be modified during disease. This study is supportive of a revised amyloid cascade hypothesis and demonstrates regional associative relationships between tau pathology and intracellular Aβ, but not extracellular Aβ plaques.

Medial positioning of the hippocampus and hippocampal fissure volume in developmental topographical disorientation.

Developmental topographical disorientation (DTD) refers to the lifelong inability to orient by means of cognitive maps in familiar surroundings despite otherwise well-preserved general cognitive functions, and the absence of any acquired brain injury or neurological condition. While reduced functional connectivity between the hippocampus and other brain regions has been reported in DTD individuals, no structural differences in gray matter tissue for the whole brain neither for the hippocampus were detected. Considering that the human hippocampus is the main structure associated with cognitive map-based navigation, here, we investigated differences in morphological and morphometric hippocampal features between individuals affected by DTD (N = 20) and healthy controls (N = 238). Specifically, we focused on a developmental anomaly of the hippocampus that is characterized by the incomplete infolding of hippocampal subfields during fetal development, giving the hippocampus a more round or pyramidal shape, called incomplete hippocampal inversion (IHI). We rated IHI according to standard criteria and extracted hippocampal subfield volumes after FreeSurfer's automatic segmentation. We observed similar IHI prevalence in the group of individuals with DTD with respect to the control population. Neither differences in whole hippocampal nor major hippocampal subfield volumes have been observed between groups. However, when assessing the IHI independent criteria, we observed that the hippocampus in the DTD group is more medially positioned comparing to the control group. In addition, we observed bigger hippocampal fissure volume for the DTD comparing to the control group. Both of these findings were stronger for the right hippocampus comparing to the left. Our results provide new insights regarding the hippocampal morphology of individuals affected by DTD, highlighting the role of structural anomalies during early prenatal development in line with the developmental nature of the spatial disorientation deficit.

Investigations on correlations between changes of optical tissue properties and NMR relaxation times

BackgroundAccurate light dosimetry is a complex remaining challenge in interstitial photodynamic therapy (iPDT) for malignant gliomas. The light dosimetry should ideally be based on the tissue morphology and the individual optical tissue properties of each tissue type in the target region. First investigations are reported on using NMR information to estimate changes of individual optical tissue properties. MethodsPorcine brain tissue and optical tissue phantoms were investigated. To the porcine brain, supplements were added to simulate an edema or high blood content. The tissue phantoms were based on agar, Lipoveneous, ink, blood and gadobutrol (Gd-based MRI contrast agent). The concentrations of phantom ingredients and tissue additives are varied to compare concentration-dependent effects on optical and NMR properties. A 3-tesla whole-body MRI system was used to determine T1 and T2 relaxation times. Optical tissue properties, i.e., the spectrally resolved absorption and reduced scattering coefficient, were obtained using a single integrating sphere setup. The observed changes of NMR and optical properties were compared to each other. ResultsBy adjusting the NMR relaxation times and optical tissue properties of the tissue phantoms to literature values, recipes for human brain tumor, white matter and grey matter tissue phantoms were obtained that mimic these brain tissues simultaneously in both properties. For porcine brain tissue, it was observed that with increasing water concentration in the tissue, both NMR-relaxation times increased, while µa decreased and µs’ increased at 635 nm. The addition of blood to porcine brain samples showed a constant T1, while T2 shortened and the absorption coefficient at 635 nm increased. ConclusionsIn this investigation, by changing sample contents, notable changes of both NMR relaxation times and optical tissue properties have been observed and their relations examined. The developed dual NMR/optical tissue phantoms can be used in iPDT research, clinical training and demonstrations.

Multi-Dimensional Clustering Based on Restricted Distance-Dependent Mixture Dirichlet Process for Diffusion Tensor Imaging

Brain imaging research poses challenges due to the intricate structure of the brain and the absence of clearly discernible features in the images. In this study, we propose a technique for analyzing brain image data identifying crucial regions relevant to patients’ conditions, specifically focusing on Diffusion Tensor Imaging data. Our method utilizes the Bayesian Dirichlet process prior incorporating generalized linear models, that enhances clustering performance while it benefits from the flexibility of accommodating varying numbers of clusters. Our approach improves the performance of identifying potential classes utilizing locational information by considering the proximity between locations as clustering constraints. We apply our technique to a dataset from Transforming Research and Clinical Knowledge in Traumatic Brain Injury study, aiming to identify important regions in the brain’s gray matter, white matter, and overall brain tissue that differentiate between young and old age groups. Additionally, we explore a link between our discoveries and the existing outcomes in the field of brain network research.

Changes in microstructural similarity of hippocampal subfield circuits in pathological cognitive aging.

The hippocampal networks support multiple cognitive functions and may have biological roles and functions in pathological cognitive aging (PCA) and its associated diseases, which have not been explored. In the current study, a total of 116 older adults with 39 normal controls (NC) (mean age: 52.3 ± 13.64years; 16 females), 39 mild cognitive impairment (MCI) (mean age: 68.15 ± 9.28years, 14 females), and 38 dementia (mean age: 73.82 ± 8.06years, 8 females) were included. The within-hippocampal subfields and the cortico-hippocampal circuits were assessed via a micro-structural similarity network approach using T1w/T2w ratio and regional gray matter tissue probability maps, respectively. An analysis of covariance was conducted to identify between-group differences in structural similarities among hippocampal subfields. The partial correlation analyses were performed to associate changes in micro-structural similarities with cognitive performance in the three groups, controlling the effect of age, sex, education, and cerebralsmall-vesseldisease. Compared with the NC, an altered T1w/T2w ratio similarity between left CA3 and left subiculum was observed in the mild cognitive impairment (MCI) and dementia. The left CA3 was the most impaired region correlated with deteriorated cognitive performance. Using these regions as seeds for GM similarity comparisons between hippocampal subfields and cortical regions, group differences were observed primarily between the left subiculum and several cortical regions. By utilizing T1w/T2w ratio as a proxy measure for myelin content, our data suggest that the imbalanced synaptic weights within hippocampal CA3 provide a substrate to explain the abnormal firing characteristics of hippocampal neurons in PCA. Furthermore, our work depicts specific brain structural characteristics of normal and pathological cognitive aging and suggests a potential mechanism for cognitive aging heterogeneity.

Decay in Entorhinal White Matter/Gray Matter contrast in Alzheimer’s Disease Patients is reduced by 40Hz sensory stimulation

AbstractBackgroundPrevious research has shown that white matter (WM) and gray matter (GM) tissues exhibit regionally selective pathological changes in Alzheimer’s disease (AD). The contrast between WM and GM signal intensities (WM/GM contrast) measured by magnetic resonance imaging (MRI) has previously been proposed as a method for quantifying these changes. It has been shown that WM/GM contrast can provide sensitive, independent, and distinct information about pathologies compared with traditional MRI measures. Studies have also shown, including entorhinal WM/GM contrast to traditional MRI measures resulted in better discrimination between AD and controls. We previously demonstrated that 40Hz auditory‐visual stimulation therapy reduced WM atrophy and myelin loss in AD patients. The present study quantified the effects of the therapy on the WM/GM contrast in the entorhinal region.MethodThis study used neuroimaging data from Overture, a phase I/II randomized, placebo‐controlled feasibility study (NCT03556280) in patients on the AD spectrum. Treatment group received 40Hz gamma sensory stimulation daily at home for six months, whereas placebo group received sham stimulation. Structural MRI was obtained at baseline, month 3 and month 6 visits using 1.5 Tesla scanner. FreeSurfer was used to process T1 MRI including entorhinal parcellation and cortical reconstructions for 38 participants (66% Treatment, 34% Placebo). For each vertex, WM signal intensity (WMI) is sampled at 1mm below, and GM signal intensity (GMI) is sampled at 30% of cortical thickness above the WM/GM border. WM/GM contrast was calculated as 100*(WMI–GMI)/(0.5*(WMI+GMI)) %. Bayesian linear mixed effects modeling was used to model changes in WM/GM contrast in the entorhinal region.ResultFollowing 6 months of treatment, we observed that the treatment group demonstrated 0.06±0.99% decrease and the placebo group demonstrated 1.26±1.04% decrease in WM/GM contrast (p&lt;0.022).ConclusionWe found that six months of 40Hz gamma sensory therapy reduced the decline in WM/GM contrast in the entorhinal region. Since WM/GM contrast is associated with AD severity, and the entorhinal region is of particular interest in the early stages of AD, early intervention of the therapy may result in enhanced tissue preservation and reduced neurological damage.

An automated pipeline for Centiloid quantification of amyloid‐β using multiple 11C‐PiB‐PET and 18F‐PET tracers

AbstractBackgroundQuantitative measures of amyloid‐ß (Aß) pathology using positron emission tomography (PET) imaging are sensitive to identify pathological changes early in Alzheimer’s disease (AD). The Centiloid scale aims to standardize these in vivo amyloid quantifications to a 100‐point scale, where an average value of zero signifies high certainty of amyloid negativity and 100 identifies average typical AD Aß‐pathology load (Klunk et al., 2015). The current study developed and validated a single and fully automated Centiloid quantification pipeline for multiple amyloid PET compounds.MethodQyScore’s® fully automated pipeline was validated on 11C‐PiB‐PET and 18F‐PET images from the Centiloid project (https://www.gaain.org/centiloid‐project): 34 young controls [age = 31.5 ± 6.3 years] and 47 AD patients [age = 67.5 ± 10.5 years; CDR = 0.5–1]. 18F tracers included Florbetapir (FBP, N = 46), Forbetaben (FBB, N = 35), Flutemetamol (FTM, N = 74) and NAV4694 (NAV, N = 55). PET/MR image pairs were both co‐registered and normalized in the MNI template space. The fully automated segmentation from QyScore®, a CE‐marked and FDA‐cleared neuroimaging medical device, parcellated the masks of the grey matter tissue (target) and of the cerebellum (reference) region (Figure 1). The standardized uptake value ratio (SUVr) was computed as the ratio of the mean signal in both regions. Correlations of 11C‐PiB‐PET and 18F‐PET SUVr values with published SUVr data were computed. Further, correlations between 18F‐PET SUVr and paired 11C‐PiB‐PET SUVr were computed. Correlation coefficients (R2) &gt; 0.7 were required to consider the Centiloid calibration valid.ResultQyScore’s® fully automated quantitative pipeline produced SUVr values well within the bounds defined by the Centiloid method (SUVr_AD‐100 = 2.08 +/‐ 0.2; SUVr_YC‐0 = 1.01+/‐ 0.05; R2 = 0.99; slope = 1.00; intercept = ‐0.44). 11C‐PiB SUVr correlation coefficients with published values were above 0.99 (Figure 2). Correlation coefficients of 18F‐PET SUVr with 11C‐PiB‐PET SUVr were respectively 0.91, 0.95, 0.96, 0.99 (Figure 3). Equations for converting to Centiloid were respectively: • Centiloid = 177.79 FBP_SUVr ‐ 183.56 • Centiloid = 153.08 FBB_SUVr – 152.93 • Centiloid = 122.39 FTM_SUVr – 120.97 • Centiloid = 90.20 NAV_SUVr – 91.61ConclusionWe demonstrate the feasibility and reliability of a fully automated amyloid PET pipeline for multiple amyloid‐PET compounds (11C‐PiB and 18F) suitable for implementation in clinical trials.

An automated pipeline for Centiloid quantification of amyloid‐β using multiple 11C‐PiB‐PET and 18F‐PET tracers

AbstractBackgroundQuantitative measures of amyloid‐β (Aβ) pathology using positron emission tomography (PET) imaging are sensitive to identify pathological changes early in Alzheimer’s disease (AD). The Centiloid scale aims to standardize these in vivo amyloid quantifications to a 100‐point scale, where an average value of zero signifies high certainty of amyloid negativity and 100 identifies average typical AD Aβ‐pathology load (Klunk et al., 2015). The current study developed and validated a single and fully automated Centiloid quantification pipeline for multiple amyloid PET compounds.MethodQyScore’s® fully automated pipeline was validated on 11C‐PiB‐PET and 18F‐PET images from the Centiloid project (https://www.gaain.org/centiloid‐project): 34 young controls [age = 31.5 ± 6.3 years] and 47 AD patients [age = 67.5 ± 10.5 years; CDR = 0.5–1]. 18F tracers included Florbetapir (FBP, N = 46), Forbetaben (FBB, N = 35), Flutemetamol (FTM, N = 74) and NAV4694 (NAV, N = 55). PET/MR image pairs were both co‐registered and normalized in the MNI template space. The fully automated segmentation from QyScore®, a CE‐marked and FDA‐cleared neuroimaging medical device, parcellated the masks of the grey matter tissue (target) and of the cerebellum (reference) region (Figure 1). The standardized uptake value ratio (SUVr) was computed as the ratio of the mean signal in both regions. Correlations of 11C‐PiB‐PET and 18F‐PET SUVr values with published SUVr data were computed. Further, correlations between 18F‐PET SUVr and paired 11C‐PiB‐PET SUVr were computed. Correlation coefficients (R2) &gt; 0.7 were required to consider the Centiloid calibration valid.ResultQyScore’s® fully automated quantitative pipeline produced SUVr values well within the bounds defined by the Centiloid method (SUVr_AD‐100 = 2.08 +/‐ 0.2; SUVr_YC‐0 = 1.01+/‐ 0.05; R2 = 0.99; slope = 1.00; intercept = ‐0.44). 11C‐PiB SUVr correlation coefficients with published values were above 0.99 (Figure 2). Correlation coefficients of 18F‐PET SUVr with 11C‐PiB‐PET SUVr were respectively 0.91, 0.95, 0.96, 0.99 (Figure 3).Equations for converting to Centiloid were respectively o Centiloid = 177.79 FBP_SUVr ‐ 183.56 o Centiloid = 153.08 FBB_SUVr – 152.93 o Centiloid = 122.39 FTM_SUVr – 120.97 o Centiloid = 90.20 NAV_SUVr – 91.61ConclusionWe demonstrate the feasibility and reliability of a fully automated amyloid PET pipeline for multiple amyloid‐PET compounds (11C‐PiB and 18F) suitable for implementation in clinical trials.

Viscoelastic response of gray matter and white matter brain tissues under creep and relaxation

Accurate measurement of the mechanical properties of brain tissue is of paramount importance for understanding its mechanics-biology relationship. Most published studies on brain viscoelasticity have been conducted using a single relaxation test, without validating the validity of linear viscoelasticity, which is insufficient to establish an accurate constitutive equation for brain tissue. We obtained the creep and relaxation profiles of fresh adult porcine white matter (N = 120) and gray matter (N = 56) under finite step-and-hold uniaxial compression, using a mechanical testing machine, with 16.67 mm/s loading rate and 80 s hold time. These curves were employed to determine viscoelastic properties and demonstrated an excellent fit with a concise power-law function. The average initial modulus for gray matter (GM) was 6.619 kPa, higher than that for white matter under transverse loading (WM-2D) at 5.579 kPa (p < 0.01), yet lower than that for white matter under axial loading (WM-1D) at 6.759 kPa (p = 0.0121). Notably, WM-2D exhibited the highest degree of fluidity (β = 0.216). Our findings reveal that gray matter behaves as a linear viscoelastic material with power-law creep compliance and relaxation modulus. Conversely, the creep and relaxation behavior of white matter deviates from the verification relationship derived from linear viscoelastic theory, indicating its nonlinearity. This fact underscores the inaccuracy of assuming a linear constitutive relationship to characterize the viscoelastic properties of white matter. By combining the power-law function with the experimentally obtained creep compliance and relaxation modulus, we offer a unique approach to determining the viscoelastic characteristics of brain tissue.

Dielectric Spectroscopy Shows a Permittivity Contrast between Meningioma Tissue and Brain White and Gray Matter-A Potential Physical Biomarker for Meningioma Discrimination.

The effectiveness of surgical resection of meningioma, the most common primary CNS tumor, depends on the capability to intraoperatively discriminate between the meningioma tissue and the surrounding brain white and gray matter tissues. Aiming to find a potential biomarker based on tissue permittivity, dielectric spectroscopy of meningioma, white matter, and gray matter ex vivo tissues was performed using the open-ended coaxial probe method in the microwave frequency range from 0.5 to 18 GHz. The averages and the 95% confidence intervals of the measured permittivity for each tissue were compared. The results showed the absence of overlap between the 95% confidence intervals for meningioma tissue and for brain white and gray matter, indicating a significant difference in average permittivity (p ≤ 0.05) throughout almost the entire measured frequency range, with the most pronounced contrast found between 2 GHz and 5 GHz. The discovered contrast is relevant as a potential physical biomarker to discriminate meningioma tissue from the surrounding brain tissues by means of permittivity measurement, e.g., for intraoperative meningioma margin assessment. The permittivity models for each tissue, developed in this study as its byproducts, will allow more accurate electromagnetic modeling of brain tumor and healthy tissues, facilitating the development of new microwave-based medical devices and tools.

Multimodal magnetic resonance imaging after experimental moderate and severe traumatic brain injury: A longitudinal correlative assessment of structural and cerebral blood flow changes.

Traumatic brain injury (TBI) is a worldwide problem that results in death or disability for millions of people every year. Progressive neurological complications and long-term impairment can significantly disrupt quality of life. We demonstrated the feasibility of multiple magnetic resonance imaging (MRI) modalities to investigate and predict aberrant changes and progressive atrophy of gray and white matter tissue at several acute and chronic time points after moderate and severe parasagittal fluid percussion TBI. T2-weighted imaging, diffusion tensor imaging (DTI), and perfusion weighted imaging (PWI) were performed. Adult Sprague-Dawley rats were imaged sequentially on days 3, 14, and 1, 4, 6, 8, and 12 months following surgery. TBI caused dynamic white and gray matter alterations with significant differences in DTI values and injury-induced alterations in cerebral blood flow (CBF) as measured by PWI. Regional abnormalities after TBI were observed in T2-weighted images that showed hyperintense cortical lesions and significant cerebral atrophy in these hyperintense areas 1 year after TBI. Temporal DTI values indicated significant injury-induced changes in anisotropy in major white matter tracts, the corpus callosum and external capsule, and in gray matter, the hippocampus and cortex, at both early and chronic time points. These alterations were primarily injury-severity dependent with severe TBI exhibiting a greater degree of change relative to uninjured controls. PWI evaluating CBF revealed sustained global reductions in the cortex and in the hippocampus at most time points in an injury-independent manner. We next sought to investigate prognostic correlations across MRI metrics, timepoints, and cerebral pathology, and found that diffusion abnormalities and reductions in CBF significantly correlated with specific vulnerable structures at multiple time points, as well as with the degree of cerebral atrophy observed 1 year after TBI. This study further supports using DTI and PWI as a means of prognostic imaging for progressive structural changes after TBI and emphasizes the progressive nature of TBI damage.