An Automatic Method for Generating an Unbiased Intensity Normalizing Factor in Positron Emission Tomography Image Analysis After Stroke

Association between gene expression and functional-metabolic architecture in Parkinson's disease

Gene expression plays a critical role in the pathogenesis of Parkinson's disease (PD). How gene expression profiles are correlated with functional-metabolic architecture remains obscure. We enrolled 34 PD patients and 25 age-and-sex-matched healthy controls for simultaneous 18 F-FDG-PET/functional MRI scanning during resting state. We investigated the functional gradients and the ratio of standard uptake value. Principal component analysis was used to further combine the functional gradients and glucose metabolism into functional-metabolic architecture. Using partial least squares (PLS) regression, we introduced the transcriptomic data from the Allen Institute of Brain Sciences to identify gene expression patterns underlying the affected functional-metabolic architecture in PD. Between-group comparisons revealed significantly higher gradient variation in the visual, somatomotor, dorsal attention, frontoparietal, default mode, and subcortical network (pFDR  < .048) in PD. Increased FDG-uptake was found in the somatomotor and ventral attention network while decreased FDG-uptake was found in the visual network (pFDR  < .008). Spatial correlation analysis showed consistently affected patterns of functional gradients and metabolism (p = 2.47 × 10-8 ). PLS analysis and gene ontological analyses further revealed that genes were mainly enriched for metabolic, catabolic, cellular response to ions, and regulation of DNA transcription and RNA biosynthesis. In conclusion, our study provided genetic pathological mechanism to explain imaging-defined brain functional-metabolic architecture of PD.

Artificial intelligence-based computer-aided diagnosis system supports diagnosis of lymph node metastasis in esophageal squamous cell carcinoma: A multicenter study

Background

This study aimed to develop an artificial intelligence-based computer-aided diagnosis system (AI-CAD) emulating the diagnostic logic of radiologists for lymph node metastasis (LNM) in esophageal squamous cell carcinoma (ESCC) patients, which contributed to clinical treatment decision-making.

Methods

A total of 689 ESCC patients with PET/CT images were enrolled from three hospitals and divided into a training cohort and two external validation cohorts. 452 CT images from three publicly available datasets were also included for pretraining the model. Anatomic information from CT images was first obtained automatically using a U-Net-based multi-organ segmentation model, and metabolic information from PET images was subsequently extracted using a gradient-based approach. AI-CAD was developed in the training cohort and externally validated in two validation cohorts.

Results

The AI-CAD achieved an accuracy of 0.744 for predicting pathological LNM in the external cohort and a good agreement with a human expert in two external validation cohorts (kappa = 0.674 and 0.587, p < 0.001). With the aid of AI-CAD, the human expert's diagnostic performance for LNM was significantly improved (accuracy [95% confidence interval]: 0.712 [0.669-0.758] vs. 0.833 [0.797-0.865], specificity [95% confidence interval]: 0.697 [0.636-0.753] vs. 0.891 [0.851-0.928]; p < 0.001) among patients underwent lymphadenectomy in the external validation cohorts.

Conclusions

The AI-CAD could aid in preoperative diagnosis of LNM in ESCC patients and thereby support clinical treatment decision-making.

Severity-dependent functional connectome and the association with glucose metabolism in the sensorimotor cortex of Parkinson's disease

Functional MRI studies have achieved promising outcomes in revealing abnormal functional connectivity in Parkinson's disease (PD). The primary sensorimotor area (PSMA) received a large amount of attention because it closely correlates with motor deficits. While functional connectivity represents signaling between PSMA and other brain regions, the metabolic mechanism behind PSMA connectivity has rarely been well established. By introducing hybrid PET/MRI scanning, the current study enrolled 33 advanced PD patients during medication-off condition and 25 age-and-sex-matched healthy controls (HCs), aiming to not only identify the abnormal functional connectome pattern of the PSMA, but also to simultaneously investigate how PSMA functional connectome correlates with glucose metabolism. We calculated degree centrality (DC) and the ratio of standard uptake value (SUVr) using resting state fMRI and ¹⁸F-FDG-PET data. A two-sample t-test revealed significantly decreased PSMA DC (P_FWE < 0.014) in PD patients. The PSMA DC also correlated negatively with H-Y stage (P = 0.031). We found a widespread reduction of H-Y stage associated (P-values < 0.041) functional connectivity between PSMA and the visual network, attention network, somatomotor network, limbic network, frontoparietal network as well as the default mode network. The PSMA DC correlated positively with FDG-uptake in the HCs (P = 0.039) but not in the PD patients (P > 0.44). In summary, we identified disease severity-dependent PSMA functional connectome which in addition uncoupled with glucose metabolism in PD patients. The current study highlighted the critical role of simultaneous PET/fMRI in revealing the functional-metabolic mechanism in the PSMA of PD patients.

Simultaneous PET/fMRI revealed increased motor area input to subthalamic nucleus in Parkinson's disease

Invasive electrophysiological recordings in patients with Parkinson's disease (PD) are extremely difficult for cross-sectional comparisons with healthy controls. Noninvasive approaches for identifying information flow between the motor area and the subthalamic nucleus (STN) are critical for evaluation of treatment strategy. We aimed to investigate the direction of the cortical-STN hyperdirect pathway using simultaneous 18F-FDG-PET/functional magnetic resonance imaging (fMRI). Data were acquired during resting state on 34 PD patients and 25 controls. The ratio of standard uptake value for PET images and the STN functional connectivity (FC) maps for fMRI data were generated. The metabolic connectivity mapping (MCM) approach that combines PET and fMRI data was used to evaluate the direction of the connectivity. Results showed that PD patients exhibited both increased FDG uptake and STN-FC in the sensorimotor area (PFDR < 0.05). MCM analysis showed higher cortical-STN MCM value in the PD group (F = 6.63, P = 0.013) in the left precentral gyrus. There was a high spatial overlap between the increased glucose metabolism and increased STN-FC in the sensorimotor area in PD. The MCM approach further revealed an exaggerated cortical input to the STN in PD, supporting the precentral gyrus as a target for treatment such as the repetitive transcranial magnetic stimulation.

Feature-based Quality Assessment of Middle Cerebral Artery Occlusion Using 18F-Fluorodeoxyglucose Positron Emission Tomography

In animal experiments, ischemic stroke is usually induced through middle cerebral artery occlusion (MCAO), and quality assessment of this procedure is crucial. However, an accurate assessment method based on 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) is still lacking. The difficulty lies in the inconsistent preprocessing pipeline, biased intensity normalization, or unclear spatiotemporal uptake of FDG. Here, we propose an image feature-based protocol to assess the quality of the procedure using a 3D scale-invariant feature transform and support vector machine. This feature-based protocol provides a convenient, accurate, and reliable tool to assess the quality of the MCAO procedure in FDG PET studies. Compared with existing approaches, the proposed protocol is fully quantitative, objective, automatic, and bypasses the intensity normalization step. An online interface was constructed to check images and obtain assessment results.

Modulation effect of substantia nigra iron deposition and functional connectivity on putamen glucose metabolism in Parkinson's disease

Neurodegeneration of the substantia nigra affects putamen activity in Parkinson's disease (PD), yet in vivo evidence of how the substantia nigra modulates putamen glucose metabolism in humans is missing. We aimed to investigate how substantia nigra modulates the putamen glucose metabolism using a cross-sectional design. Resting-state fMRI, susceptibility-weighted imaging, and [18 F]-fluorodeoxyglucose-PET (FDG-PET) data were acquired. Forty-two PD patients and 25 healthy controls (HCs) were recruited for simultaneous PET/MRI scanning. The main measurements of the current study were R 2 * images representing iron deposition (28 PD and 25 HCs), standardized uptake value ratio (SUVr) images representing FDG-uptake (33 PD and 25 HCs), and resting state functional connectivity maps from resting state fMRI (34 PD and 25 HCs). An interaction term based on the general linear model was used to investigate the joint modulation effect of nigral iron deposition and nigral-putamen functional connectivity on putamen FDG-uptake. Compared with HCs, we found increased iron deposition in the substantia nigra (p = .007), increased FDG-uptake in the putamen (left: PFWE  < 0.001; right: PFWE  < 0.001), and decreased functional connectivity between the substantia nigra and the anterior putamen (left PFWE  < 0.001, right: PFWE  = 0.007). We then identified significant interaction effect of nigral iron deposition and nigral-putamen connectivity on FDG-uptake in the putamen (p = .004). The current study demonstrated joint modulation effect of the substantia nigra iron deposition and nigral-putamen functional connectivity on putamen glucose metabolic distribution, thereby revealing in vivo pathological mechanism of nigrostriatal neurodegeneration of PD.

Voxel-Based Analysis of [18F]-FDG Brain PET in Rats Using Data-Driven Normalization

Introduction: [18F]-FDG PET is a widely used imaging modality that visualizes cellular glucose uptake and provides functional information on the metabolic state of different tissues in vivo. Various quantification methods can be used to evaluate glucose metabolism in the brain, including the cerebral metabolic rate of glucose (CMR_glc) and standard uptake values (SUVs). Especially in the brain, these (semi-)quantitative measures can be affected by several physiological factors, such as blood glucose level, age, gender, and stress. Next to this inter- and intra-subject variability, the use of different PET acquisition protocols across studies has created a need for the standardization and harmonization of brain PET evaluation. In this study we present a framework for statistical voxel-based analysis of glucose uptake in the rat brain using histogram-based intensity normalization.

Methods: [18F]-FDG PET images of 28 normal rat brains were coregistered and voxel-wisely averaged. Ratio images were generated by voxel-wisely dividing each of these images with the group average. The most prevalent value in the ratio image was used as normalization factor. The normalized PET images were voxel-wisely averaged to generate a normal rat brain atlas. The variability of voxel intensities across the normalized PET images was compared to images that were either normalized by whole brain normalization, or not normalized.

To illustrate the added value of this normal rat brain atlas, 9 animals with a striatal hemorrhagic lesion and 9 control animals were intravenously injected with [18F]-FDG and the PET images of these animals were voxel-wisely compared to the normal atlas by group- and individual analyses.

Results: The average coefficient of variation of the voxel intensities in the brain across normal [18F]-FDG PET images was 6.7% for the histogram-based normalized images, 11.6% for whole brain normalized images, and 31.2% when no normalization was applied. Statistical voxel-based analysis, using the normal template, indicated regions of significantly decreased glucose uptake at the site of the ICH lesion in the ICH animals, but not in control animals.

Conclusion: In summary, histogram-based intensity normalization of [18F]-FDG uptake in the brain is a suitable data-driven approach for standardized voxel-based comparison of brain PET images.

Intensity normalization methods in brain FDG-PET quantification

BACKGROUND

The lack of standardization of intensity normalization methods and its unknown effect on the quantification output is recognized as a major drawback for the harmonization of brain FDG-PET quantification protocols. The aim of this work is the ground truth-based evaluation of different intensity normalization methods on brain FDG-PET quantification output.

METHODS

Realistic FDG-PET images were generated using Monte Carlo simulation from activity and attenuation maps directly derived from 25 healthy subjects (adding theoretical relative hypometabolisms on 6 regions of interest and for 5 hypometabolism levels). Single-subject statistical parametric mapping (SPM) was applied to compare each simulated FDG-PET image with a healthy database after intensity normalization based on reference regions methods such as the brain stem (RRBS), cerebellum (RRC) and the temporal lobe contralateral to the lesion (RRTL), and data-driven methods, such as proportional scaling (PS), histogram-based method (HN) and iterative versions of both methods (iPS and iHN). The performance of these methods was evaluated in terms of the recovery of the introduced theoretical hypometabolic pattern and the appearance of unspecific hypometabolic and hypermetabolic findings.

RESULTS

Detected hypometabolic patterns had significantly lower volumes than the introduced hypometabolisms for all intensity normalization methods particularly for slighter reductions in metabolism . Among the intensity normalization methods, RRC and HN provided the largest recovered hypometabolic volumes, while the RRBS showed the smallest recovery. In general, data-driven methods overcame reference regions and among them, the iterative methods overcame the non-iterative ones. Unspecific hypermetabolic volumes were similar for all methods, with the exception of PS, where it became a major limitation (up to 250 cm³) for extended and intense hypometabolism. On the other hand, unspecific hypometabolism was similar far all methods, and usually solved with appropriate clustering.

CONCLUSIONS

Our findings showed that the inappropriate use of intensity normalization methods can provide remarkable bias in the detected hypometabolism and it represents a serious concern in terms of false positives. Based on our findings, we recommend the use of histogram-based intensity normalization methods. Reference region methods performance was equivalent to data-driven methods only when the selected reference region is large and stable.

A population stereotaxic positron emission tomography brain template for the macaque and its application to ischemic model

PURPOSE

Positron emission tomography (PET) is a non-invasive imaging tool for the evaluation of brain function and neuronal activity in normal and diseased conditions with high sensitivity. The macaque monkey serves as a valuable model system in the field of translational medicine, for its phylogenetic proximity to man. To translation of non-human primate neuro-PET studies, an effective and objective data analysis platform for neuro-PET studies is needed.

MATERIALS AND METHODS

A set of stereotaxic templates of macaque brain, namely the Institute of High Energy Physics & Jinan University Macaque Template (HJT), was constructed by iteratively registration and averaging, based on 30 healthy rhesus monkeys. A brain atlas image was created in HJT space by combining sub-anatomical regions and defining new 88 bilateral functional regions, in which a unique integer was assigned for each sub-anatomical region.

RESULTS

The HJT comprised a structural MRI T1 weighted image (T1WI) template image, a functional FDG-PET template image, intracranial tissue segmentations accompanied with a digital macaque brain atlas image. It is compatible with various commercially available software tools, such as SPM and PMOD. Data analysis was performed on a stroke model compared with a group of healthy controls to demonstrate the usage of HJT.

CONCLUSION

We have constructed a stereotaxic template set of macaque brain named HJT, which standardizes macaque neuroimaging data analysis, supports novel radiotracer development and facilitates translational neuro-disorders research.

Dynamic Changes of Functional Neuronal Activities Between the Auditory Pathway and Limbic Systems Contribute to Noise-Induced Tinnitus with a Normal Audiogram

Tinnitus is thought to be triggered by aberrant neural activity in the central auditory pathway and is often accompanied by comorbidities of emotional distress and anxiety, which imply maladaptive functional connectivity to limbic structures, such as the amygdala and hippocampus. Tinnitus patients with normal audiograms can also have accompanying anxiety and depression, clinically. To test the role of functional connectivity between the central auditory pathway and limbic structures in patients with tinnitus with normal audiograms, we developed a murine noise-induced tinnitus model with a temporary threshold shift (TTS). Tinnitus mice exhibited reduced auditory brainstem response wave I amplitude, and an enhanced wave IV amplitude and wave IV/I amplitude ratio, as compared with control and non-tinnitus mice. Resting-state functional magnetic resonance imaging (fMRI) was used to identify abnormal connectivity of the amygdala and hippocampus and to determine the relationship with tinnitus characteristics. We found increased fMRI responses with amplitude of low-frequency fluctuation (ALFF) in the auditory cortex and decreased ALFF in the amygdala and hippocampus at day 1, but decreased ALFF in the auditory cortex and increased ALFF in the amygdala at day 28 post-noise exposure in tinnitus mice. Decreased functional connectivity between auditory brain regions and limbic structures was demonstrated at day 28 in tinnitus mice. Therefore, aberrant neural activities in tinnitus mice with TTS involved not only the central auditory pathway, but also limbic structures, and there was maladaptive functional connectivity between the central auditory pathway and limbic structures, such as the amygdala and hippocampus.

A stereotaxic MRI template set of mouse brain with fine sub-anatomical delineations: Application to MEMRI studies of 5XFAD mice

PURPOSE

Manganese-enhanced magnetic resonance imaging (MEMRI) can help us trace the active neurons and neuronal pathway in transgenic mouse AD model. 5XFAD has been widespread accepted as a valuable model system for studying brain dysfunction progresses in the courses of AD. To further understand the development of AD at early stages, an effective and objective data analysis platform for MEMRI studies should be constructed.

MATERIALS AND METHODS

A set of stereotaxic templates of mouse brain in Paxinos and Franklin space, "the Institute of High Energy Physics Mouse Template", or IMT for short, was constructed by iteratively registration and averaging. An atlas image was reconstructed from the Paxinos and Franklin atlas figures and each sub-anatomical segmentation was assigning a unique integer. An analysis SPM plug-in toolbox was further created, that automates and standardizes the time-consuming processes of brain extraction, tissue segmentation, and statistical analysis for MEMRI scans.

RESULTS

The IMT comprised a T2WI template image, a MEMRI template image, intracranial tissue segmentations, and accompany with a digital mouse brain atlas image, in which 707 sub-anatomical brain regions are delineated. Data analyses were performed on groups of developing 5XFAD mice to demonstrate the usage of IMT, and the results shows that abnormal neuronal activity occurs at early stage in 5XFAD mice.

CONCLUSION

We have constructed a stereotaxic template set of mouse brain named IMT with fine delineations of sub-anatomical structures, which is compatible with SPM. It will give a widely range of researchers a standardized coordinate system for localization of any mouse brain related data.

Abnormal Metabolic Connectivity in Rats at the Acute Stage of Ischemic Stroke

Stroke at the acute stage is a major cause of disability in adults, and is associated with dysfunction of brain networks. However, the mechanisms underlying changes in brain connectivity in stroke are far from fully elucidated. In the present study, we investigated brain metabolism and metabolic connectivity in a rat ischemic stroke model of middle cerebral artery occlusion (MCAO) at the acute stage using 18F-fluorodeoxyglucose positron emission tomography. Voxel-wise analysis showed decreased metabolism mainly in the ipsilesional hemisphere, and increased metabolism mainly in the contralesional cerebellum. We used further metabolic connectivity analysis to explore the brain metabolic network in MCAO. Compared to sham controls, rats with MCAO showed most significantly reduced nodal and local efficiency in the ipsilesional striatum. In addition, the MCAO group showed decreased metabolic central connection of the ipsilesional striatum with the ipsilesional cerebellum, ipsilesional hippocampus, and bilateral hypothalamus. Taken together, the present study demonstrated abnormal metabolic connectivity in rats at the acute stage of ischemic stroke, which might provide insight into clinical research.