Risk of attention deficit hyperactivity disorder diagnosis following multiple exposures to general anesthesia in the paediatric population: A systematic review and meta-analysis.

Objectives: The risk of attention deficit hyperactivity disorder (ADHD) following multiple exposures to anesthesia has been debated. Our objective was to systematically review the literature to examine the association between multiple exposures to general anesthesia before age 5 and subsequent diagnosis of ADHD. Methods: A systematic search of EMBASE, PubMed, and SCOPUS was performed using key search terms in February 2022. We included studies that: were published after 1980, included only otherwise healthy children who experienced two or more exposures to general anesthetic before age 5, diagnosed ADHD by a medical professional before age 19 years after exposure to general anesthetic, were cross-sectional, case-control, or cohort study, and were published in English. The results (expressed as hazard ratios [HR] and associated 95% confidence intervals [CI]) were pooled using meta-analytic techniques. Studies which did not present their results as HR and 95% CI were analyzed separately. GRADE was used to determine the certainty of the findings. PRISMA guidelines were followed at each stage of the review. Results: Eight studies (196,749 children) were included. Five reported HR and 95% CI and were subsequently pooled for meta-analysis. Multiple exposures to anesthesia were associated with diagnosis of ADHD before the 19th year of life (HR: 1.71; 95% CI: 1.59, 1.84). Two of the three studies not used in the meta-analysis also found an increased risk of ADHD diagnosis following multiple anesthetic exposures. Conclusions: There was an association between multiple early exposures to general anesthesia and later diagnosis of ADHD.

Blood Flow and Glucose Metabolism Dissociation in the Putamen Is Predictive of Levodopa Induced Dyskinesia in Parkinson's Disease Patients.

Background: The forefront treatment of Parkinson's disease (PD) is Levodopa. When patients are treated with Levodopa cerebral blood flow is increased while cerebral metabolic rate is decreased in key subcortical regions including the putamen. This phenomenon is especially pronounced in patients with Levodopa-induced dyskinesia (LID). Method: To study the effect of clinically-determined anti-parkinsonian medications, 10 PD patients (5 with LID and 5 without LID) have been scanned with FDG-PET (a probe for glucose metabolism) and perfusion MRI (a probe for cerebral blood flow) both when they are ON and OFF medications. Patients additionally underwent resting state fMRI to detect changes in dopamine-mediated cortico-striatal connectivity. The degree of blood flow-glucose metabolism dissociation was quantified by comparing the FDG-PET and perfusion MRI data. Results: A significant interaction effect (imaging modality × medication; blood flow-glucose metabolism dissociation) has been found in the putamen (p = 0.023). Post-hoc analysis revealed that anti-parkinsonian medication consistently normalized the pathologically hyper-metabolic state of the putamen while mixed effects were observed in cerebral blood flow changes. This dissociation was especially predominant in patients with LID compared to those without. Unlike the prior study, this differentiation was not observed when cortico-striatal functional connectivity was assessed. Conclusion: We confirmed striatal neurovascular dissociation between FDG-PET and perfusion MRI in response to clinically determined anti-parkinsonian medication. We further proposed a novel analytical method to quantify the degree of dissociation in the putamen using only the ON condition scans, Putamen-to-thalamus Hyper-perfusion/hypo-metabolism Index (PHI), which may have the potential to be used as a biomarker for LID (correctly classifying 8 out 10 patients). For wider use of PHI, a larger validation study is warranted.

Machine learning identified an Alzheimer's disease-related FDG-PET pattern which is also expressed in Lewy body dementia and Parkinson's disease dementia.

Utilizing the publicly available neuroimaging database enabled by Alzheimer's disease Neuroimaging Initiative (ADNI; http://adni.loni.usc.edu/ ), we have compared the performance of automated classification algorithms that differentiate AD vs. normal subjects using Positron Emission Tomography (PET) with fluorodeoxyglucose (FDG). General linear model, scaled subprofile modeling and support vector machines were examined. Among the tested classification methods, support vector machine with Iterative Single Data Algorithm produced the best performance, i.e., sensitivity (0.84) × specificity (0.95), by 10-fold cross-validation. We have applied the same classification algorithm to four different datasets from ADNI, Health Science Centre (Winnipeg, Canada), Dong-A University Hospital (Busan, S. Korea) and Asan Medical Centre (Seoul, S. Korea). Our data analyses confirmed that the support vector machine with Iterative Single Data Algorithm showed the best performance in prediction of future development of AD from the prodromal stage (mild cognitive impairment), and that it was also sensitive to other types of dementia such as Parkinson's Disease Dementia and Dementia with Lewy Bodies, and that perfusion imaging using single photon emission computed tomography may achieve a similar accuracy to that of FDG-PET.

Distinct brain metabolic patterns separately associated with cognition, motor function, and aging in Parkinson's disease dementia.

We explored whether patients with Parkinson's disease dementia (PDD) show a distinct spatial metabolic pattern that characterizes cognitive deficits in addition to motor dysfunction. Eighteen patients with PDD underwent 3 separate positron emission tomography sessions with [18F]fluorodeoxyglucose (for glucose metabolism), fluorinated N-3-fluoropropyl-2-beta-carboxymethoxy-3-beta-(4-iodophenyl) nortropane (for dopamine transporter density) and Pittsburgh compound-B (for beta-amyloid load). We confirmed in PDD versus normal controls, overall hypometabolism in the posterior and prefrontal brain regions accompanied with hypermetabolism in subcortical structures and the cerebellar vermis. A multivariate network analysis then revealed 3 metabolic patterns that are separately associated with cognitive performance (p = 0.042), age (p = 0.042), and motor symptom severity (p = 0.039). The age-related pattern's association with aging was replicated in healthy controls (p = 0.047) and patients with Alzheimer's disease (p = 0.002). The cognition-related pattern's association with cognitive performance was observed, with a trend-level of correlation, in patients with dementia with Lewy bodies (p = 0.084) but not in patients with Alzheimer's disease (p = 0.974). We found no association with fluorinated N-3-fluoropropyl-2-beta-carboxymethoxy-3-beta-(4-iodophenyl) nortropane and Pittsburgh compound-B positron emission tomography with patients' cognitive performance.