Striatal Acetylcholine-Dopamine Imbalance in Parkinson Disease:In Vivo Neuroimaging Study with Dual-Tracer PET and Dopaminergic PET-Informed Correlational Tractography

Previous studies of animal models of Parkinson disease (PD) suggest an imbalance between striatal acetylcholine and dopamine, although other studies have questioned this. To our knowledge, there are no previous in vivo neuroimaging studies examining striatal acetylcholine-dopamine imbalance in PD patients. Using cholinergic and dopaminergic PET (F-18-fluoroethoxybenzovesamicol [F-18-FEOBV] and C-11-dihydrotetrabenazine [C-11-DTBZ], respectively) and correlational tractography, our aim was to investigate the acetylcholine-dopamine interaction at 2 levels of dopaminergic loss in PD subjects: integrity loss of the nigrostriatal dopaminergic white matter tract and loss at the presynaptic-terminal level. Methods: The study involved 45 subjects with mild to moderate PD (36 men, 9 women; mean age, 66.3 +/- 6.3 y, disease duration, 5.8 +/- 3.6 y; Hoehn and Yahr stage, 2.2 +/- 0.6) and 15 control subjects (9 men, 6 women; mean age, 69.1 +/- 8.6 y). PET imaging was performed using standard protocols. We first estimated the integrity of the dopaminergic nigrostriatal white matter tracts in PD subjects by incorporating molecular information from striatal C-11-DTBZ PET into the fiber tracking process using correlational tractography (based on quantitative anisotropy [QA], a measure of tract integrity). Subsequently, we used voxel-based correlation to test the association of the mean QA of the nigrostriatal tract of each cerebral hemisphere with the striatal F-18-FEOBV distribution volume ratio (DVR) in PD subjects. The same analysis was performed for C-11-DTBZ DVR in 12 striatal subregions (presynaptic-terminal level). Results: Unlike C-11-DTBZ DVR in striatal subregions, the mean QA of the nigrostriatal tract of the most affected hemisphere showed a negative correlation with a striatal cluster of F-18-FEOBV DVR in PD subjects (corrected P = 0.039). We also found that the mean F-18-FEOBV DVR within this cluster was higher in the PD group than in the control group (P = 0.01). Cross-validation analyses confirmed these findings. We also found an increase in bradykinesia ratings associated with increased acetylcholine-dopamine imbalance in the most affected hemisphere (r = 0.41, P = 0.006). Conclusion: Our results provide evidence for the existence of striatal acetylcholine-dopamine imbalance in early PD and may provide an avenue for testing in vivo effects of therapeutic strategies aimed at restoring striatal acetylcholine-dopamine balance in PD

Dopaminergic Nigrostriatal Connectivity in Early Parkinson Disease:In Vivo Neuroimaging Study of C-11-DTBZ PET Combined with Correlational Tractography

Previous histopathologic and animal studies have shown axonal impairment and loss of connectivity of the nigrostriatal pathway in Parkinson disease (PD). However, there are conflicting reports from in vivo human studies. C-11-dihydrotetrabenazine (C-11-DTBZ) is a vesicular monoamine type 2 transporter PET ligand that allows assessment of nigrostriatal presynaptic dopaminergic terminal integrity. Correlational tractography based on diffusion MRI can incorporate ligand-specific information provided by C-11-DTBZ PET into the fiber-tracking process. The purpose of this study was to assess the in vivo association between the integrity of the nigrostriatal tract (defined by correlational tractography) and the degree of striatal dopaminergic denervation based on C-11-DTBZ PET. Methods: The study involved 30 subjects with mild to moderate PD (23 men and 7 women; mean age, 66 +/- 6.2 y; disease duration, 6.4 +/- 4.0 y; Hoehn and Yahr stage, 2.1 +/- 0.6; Movement Disorder Society [MDS]-revised Unified Parkinson Disease Rating Scale [UPDRS] [I-III] total score, 43.4 +/- 17.8) and 30 control subjects (18 men and 12 women; mean age, 62 +/- 10.3 y). C-11-DTBZ PET was performed using standard synthesis and acquisition protocols. Correlational tractography was performed to assess quantitative anisotropy (QA; a measure of tract integrity) of white matter fibers correlating with information derived from striatal C-11-DTBZ data using the DS! Studio toolbox. Scans were realigned according to least and most clinically affected cerebral hemispheres. Results: Nigrostriatal tracts were identified in both hemispheres of PD patients. Higher mean QA values along the identified tracts were significantly associated with higher striatal C-11-DTBZ distribution volume ratios (least affected: r = 0.57, P = 0.001; most affected: r = 0.44, P = 0.02). Lower mean QA values of the identified tract in the LA hemisphere associated with increased severity of bradykinesia sub-score derived from MDS-UPDRS part III (r = 0.42; P = 0.02). Cross-validation revealed the generalizability of these results. Conclusion: These findings suggest that impaired integrity of dopaminergic nigrostriatal nerve terminals is associated with nigrostriatal axonal dysfunction in mild to moderate PD. Assessment of nigrostriatal tract integrity may be suitable as a biomarker of earlyor even prodromal-stage PD

A Novel Noninvasive Approach Based on SPECT and EEG for the Location of the Epileptogenic Zone in Pharmacoresistant Non-Lesional Epilepsy

Background and objectives: The aim of this study is to propose a methodology that combines non-invasive functional modalities electroencephalography (EEG) and single photon emission computed tomography (SPECT) to estimate the location of the epileptogenic zone (EZ) for the presurgical evaluation of patients with drug-resistant non-lesional epilepsy. Materials and Methods: This methodology consists of: (i) Estimation of ictal EEG source imaging (ESI); (ii) application of the subtraction of ictal and interictal SPECT co-registered with MRI (SISCOM) methodology; and (iii) estimation of ESI but using the output of the SISCOM as a priori information for the estimation of the sources. The methodology was implemented in a case series as an example of the application of this novel approach for the presurgical evaluation. A gold standard and a coincidence analysis based on measures of sensitivity and specificity were used as a preliminary assessment of the proposed methodology to localize EZ. Results: In patients with good postoperative evolution, the estimated EZ presented a spatial coincidence with the resection site represented by high values of sensitivity and specificity. For the patient with poor postoperative evolution, the methodology showed a partial incoherence between the estimated EZ and the resection site. In cases of multifocal epilepsy, the method proposed spatially extensive epileptogenic zones. Conclusions: The results of the case series provide preliminary evidence of the methodology's potential to epileptogenic zone localization in non-lesion drug-resistant epilepsy. The novelty of the article consists in estimating the sources of ictal EEG using SISCOM result as a prior for the inverse solution. Future studies are necessary in order to validate the described methodology. The results constitute a starting point for further studies in order to support the clinical reliability of the proposed methodology and advocate for their implementation in the presurgical evaluation of patients with intractable non-lesional epilepsy.</p

Topography of Cholinergic Changes in Dementia With Lewy Bodies and Key Neural Network Hubs

Objectives: The authors investigated the topography of cholinergic vulnerability in patients with dementia with Lewy bodies (DLB) using positron emission tomography (PET) imaging with the vesicular acetylcholine transporter (VAChT) [F-18]- fluoroethoxybenzovesamicol ([F-18]- FEOBV) radioligand. Methods: Five elderly participants with DLB (mean age, 77.8 years [SD=4.2]) and 21 elderly healthy control subjects (mean age, 73.62 years [SD=8.37]) underwent clinical assessment and [F-18]-FEOBV PET. Results: Compared with the healthy control group, reduced VAChT binding in patients with DLB demonstrated non-diffuse regionally distinct and prominent reductions in bilateral opercula and anterior cingulate to mid-cingulate cortices, bilateral insula, right (more than left) lateral geniculate nuclei, pulvinar, right proximal optic radiation, bilateral anterior and superior thalami, and posterior hippocampal fimbria and fornices. Conclusions: The topography of cholinergic vulnerability in DLB comprises key neural hubs involved in tonic alertness (cingulo-opercular), saliency (insula), visual attention (visual thalamus), and spatial navigation (fimbria/fornix) networks. The distinct denervation pattern suggests an important cholinergic role in specific clinical disease-defining features, such as cognitive fluctuations, visuoperceptual abnormalities causing visual hallucinations, visuospatial changes, and loss of balance caused by DLB

Cerebral topography of vesicular cholinergic transporter changes in neurologically intact adults:A [18F]FEOBV PET study

Acetylcholine plays a major role in brain cognitive and motor functions with regional cholinergic terminal loss common in several neurodegenerative disorders. We describe age-related declines of regional cholinergic neuron terminal density in vivo using the positron emission tomography (PET) ligand [18F](-)5-Fluoroethoxybenzovesamicol ([18F] FEOBV), a vesamicol analogue selectively binding to the vesicular acetylcholine transporter (VAChT). A total of 42 subjects without clinical evidence of neurologic disease (mean 50.55 [range 20-80] years, 24 Male/18 Female) underwent [18F]FEOBV brain PET imaging. We used SPM based voxel-wise statistical analysis to perform whole brain voxel-based parametric analysis (family-wise error corrected, FWE) and to also extract the most significant clusters of regions correlating with aging with gender as nuisance variable. Age-related VAChT binding reductions were found in primary sensorimotor cortex, visual cortex, caudate nucleus, anterior to mid-cingulum, bilateral insula, para-hippocampus, hippocampus, anterior temporal lobes/amygdala, dorsomedial thalamus, metathalamus, and cerebellum (gender and FWE-corrected, P &lt; 0.05). These findings show a specific topographic pattern of regional vulnerability of cholinergic nerve terminals across multiple cholinergic systems accompanying aging.</p

Effects of Interventions on Cerebral Perfusion in the Alzheimer's Disease Spectrum:A Systematic Review

Cerebral perfusion dysfunctions are seen in the early stages of Alzheimer’s disease (AD). We systematically reviewed the literature to investigate the effect of pharmacological and non-pharmacological interventions on cerebral hemodynamics in randomized controlled trials involving AD patients or Mild Cognitive Impairment (MCI) due to AD. Studies involving other dementia types were excluded. Data was searched in April 2021 on MEDLINE, Embase, and Web of Science. Risk of bias was assessed using Cochrane Risk of Bias Tool. A metasynthesis was performed separating results from MCI and AD studies. 31 studies were included and involved 310 MCI and 792 CE patients. The MCI studies (n = 8) included physical, cognitive, dietary, and pharmacological interventions. The AD studies (n = 23) included pharmacological, physical interventions, and phytotherapy. Cerebral perfusion was assessed with PET, ASL, Doppler, fNIRS, DSC-MRI, Xe-CT, and SPECT. Randomization and allocation concealment methods and subject characteristics such as AD-onset, education, and ethnicity were missing in several papers. Positive effects on hemodynamics were seen in 75 % of the MCI studies, and 52 % of the AD studies. Inserting cerebral perfusion outcome measures, together with established AD biomarkers, is fundamental to target all disease mechanisms and understand the role of cerebral perfusion in AD

The effect of lesion filling on brain network analysis in multiple sclerosis using structural magnetic resonance imaging

BACKGROUND: Graph theoretical network analysis with structural magnetic resonance imaging (MRI) of multiple sclerosis (MS) patients can be used to assess subtle changes in brain networks. However, the presence of multiple focal brain lesions might impair the accuracy of automatic tissue segmentation methods, and hamper the performance of graph theoretical network analysis. Applying "lesion filling" by substituting the voxel intensities of a lesion with the voxel intensities of nearby voxels, thus creating an image devoid of lesions, might improve segmentation and graph theoretical network analysis. This study aims to determine if brain networks are different between MS subtypes and healthy controls (HC) and if the assessment of these differences is affected by lesion filling. METHODS: The study included 49 MS patients and 19 HC that underwent a T1w, and T2w-FLAIR MRI scan. Graph theoretical network analysis was performed from grey matter fractions extracted from the original T1w-images and T1w-images after lesion filling. RESULTS: Artefacts in lesion-filled T1w images correlated positively with total lesion volume (r = 0.84, p < 0.001) and had a major impact on grey matter segmentation accuracy. Differences in sensitivity for network alterations were observed between original T1w data and after application of lesion filling: graph theoretical network analysis obtained from lesion-filled T1w images produced more differences in network organization in MS patients. CONCLUSION: Lesion filling might reduce variability across subjects resulting in an increased detection rate of network alterations in MS, but also induces significant artefacts, and therefore should be applied cautiously especially in individuals with higher lesions loads

Deep learning in neuroimaging of epilepsy

In recent years, artificial intelligence, particularly deep learning (DL), has demonstrated utility in diverse areas of medicine. DL uses neural networks to automatically learn features from the raw data while this is not possible with conventional machine learning. It is helpful for the assessment of patients with epilepsy and whilst most published studies have been aimed at the automatic detection and prediction of seizures from electroencephalographic records, there is a growing number of investigations that use neuroimaging modalities (structural and functional magnetic resonance imaging, diffusion-weighted imaging and positron emission tomography) as input data. We review the application of DL to neuroimaging (sMRI, fMRI, DWI and PET) of focal epilepsy, specifically presurgical evaluation of drug-refractory epilepsy. First, a brief theoretical overview of artificial neural networks and deep learning is presented. Next, we review applications of deep learning to neuroimaging of epilepsy: diagnosis and lateralization, automated detection of lesion, presurgical evaluation and prediction of postsurgical outcome. Finally, the limitations, challenges and possible future directions in the application of these methods in the study of epilepsies are discussed. This approach could become an essential tool in clinical practice, particularly in the evaluation of images considered negative by visual inspection, in individualized treatments, and in the approach to epilepsy as a network disorder. However, greater multicenter collaboration is required to achieve the collection of sufficient data with the required quality together with the open access availability of the developed codes and tools.</p

FDG-PET for Prediction of AD Dementia in Mild Cognitive Impairment. A Review of the State of the Art with Particular Emphasis on the Comparison with Other Neuroimaging Modalities (MRI and Perfusion SPECT)

This review article aims at providing a state-of-the-art review of the role of fluorodeoxyglucose (FDG) positron emission tomography (PET) imaging (FDG-PET) in the prediction of Alzheimer's dementia in subjects suffering mild cognitive impairment (MCI), with a particular focus on the predictive power of FDG-PET compared to structural magnetic resonance imaging (sMRI). We also address perfusion single photon emission computed tomography (SPECT) as a less costly and more accessible alternative to FDG-PET. A search in PubMed was performed, taking into consideration relevant scientific articles published in English within the last five years and limited to human studies. This recent literature confirms the effectiveness of FDG-PET and sMRI for prediction of AD dementia in MCI. However, there are discordant results regarding which image modality is superior. This could be explained by the high variability of metrics used to evaluate both imaging modalities and/or by sampling/population issues such as age, disease severity and conversion time. FDG-PET seems to outperform sMRI in rapidly converting early-onset MCI individuals, whereas sMRI may outperform FDG-PET in late-onset MCI subjects, in which case FDG PET might only provide a complementary role. Although FDG-PET performs better than perfusion SPECT, current evidence confirms perfusion SPECT as a valid alternative when FDG-PET is not available. Finally, possible future directions in the field are discussed

Machine learning in the integration of simple variables for identifying patients with myocardial ischemia

Background A significant number of variables are obtained when characterizing patients suspected with myocardial ischemia or at risk of MACE. Guidelines typically use a handful of them to support further workup or therapeutic decisions. However, it is likely that the numerous available predictors maintain intrinsic complex interrelations. Machine learning (ML) offers the possibility to elucidate complex patterns within data to optimize individual patient classification. We evaluated the feasibility and performance of ML in utilizing simple accessible clinical and functional variables for the identification of patients with ischemia or an elevated risk of MACE as determined through quantitative PET myocardial perfusion reserve (MPR). Methods 1,234 patients referred to Nitrogen-13 ammonia PET were analyzed. Demographic (4), clinical (8), and functional variables (9) were retrieved and input into a cross-validated ML workflow consisting of feature selection and modeling. Two PET-defined outcome variables were operationalized: (1) any myocardial ischemia (regional MPR <2.0) and (2) an elevated risk of MACE (global MPR <2.0). ROC curves were used to evaluate ML performance. Results 16 features were included for boosted ensemble ML. ML achieved an AUC of 0.72 and 0.71 in identifying patients with myocardial ischemia and with an elevated risk of MACE, respectively. ML performance was superior to logistic regression when the latter used the ESC guidelines risk models variables for both PET-defined labels (P <.001 and P = .01, respectively). Conclusions ML is feasible and applicable in the evaluation and utilization of simple and accessible predictors for the identification of patients who will present myocardial ischemia and an elevated risk of MACE in quantitative PET imaging