Test-retest analysis of a non-invasive method of quantifying [C-11]-PBR28 binding in Alzheimer's disease

Purpose: In order to maximise the utility of [11C]-PBR28 for use in longitudinal studies and clinical trials in Alzheimer’s disease (AD), there is a need to develop non-invasive metrics of tracer binding that do not require arterial cannulation. Recent work has suggested that standardised uptake value (SUV)-based methods may be sensitive to changes in translocator protein (TSPO) levels associated with neurodegeneration. However, the test-retest reliability of these approaches in AD over a time period relevant for clinical trials is unknown. In this study, the test-retest reliability of three SUV-based metrics was assessed in AD patients over 12 weeks. Methods: Five patients with mild AD and the high-affinity binding TSPO genotype underwent two [11C]-PBR28 PET scans approximately 12 weeks apart. The test-retest reliability (TRR) of the unadjusted SUV, SUV relative to cerebellar grey matter (SUVRC) and SUV normalised to whole brain activity (SUVRWB) in nine cortical and limbic regions of interest was assessed using the absolute variability and the intraclass correlation coefficient. Results: Of the three measures, SUVRWB performed best overall, showing low absolute variability (mean −0.13 %, SD 2.47 %) and high reliability (mean ICC = 0.83). Unadjusted SUV also performed well, with high reliability (ICC = 0.94) but also high variability (mean −1.24 %, SD 7.28 %). By comparison, the SUVRC showed higher variability (mean −3.98 %, SD 7.07 %) and low reliability (ICC = 0.65). Conclusions: In this AD sample, we found that SUV-derived metrics of [11C]-PBR28 binding showed high stability over 12 weeks. These results compare favourably with studies reporting TRR of absolute quantification of [11C]-PBR28. Pending further validation of SUV-based measures of [11C]-PBR28, semi-quantitative methods of [11C]-PBR28 analysis may prove useful in longitudinal studies of AD

Mean expression of the X-chromosome is associated with neuronal density

peer reviewedBackground: Neurodegenerative diseases are characterized by key features such as loss of neurons, astrocytosis, and microglial activation/proliferation. These changes cause differences in the density of cell types between control and disease subjects, confounding results from gene expression studies. Chromosome X (ChrX) is known to be specifically important in the brain. We hypothesized the existence of a chromosomal signature of gene expression associated with the X-chromosome for neurological conditions not normally associated with that chromosome. The hypothesis was investigated using publicly available microarray datasets from studies on Parkinson's disease, Alzheimer's disease, and Huntington's disease. Data were analyzed using Chromowave, an analytical tool for detecting spatially extended expression changes along chromosomes. To examine associations with neuronal density and astrocytosis, the expression of cell specific reporter genes was extracted. The association between these genes and the expression patterns extracted by Chromowave was then analyzed. Further analyses of the X:Autosome ratios for laser dissected neurons, microglia cultures and whole tissue were performed to detect cell specific differences. Results: We observed an extended pattern of low expression of ChrX consistent in all the neurodegenerative disease brain datasets. There was a strong correlation between mean ChrX expression and the pattern extracted from the autosomal genes representing neurons, but not with mean autosomal expression. No chromosomal patterns associated with the neuron specific genes were found on other chromosomes. The chromosomal expression pattern was not present in datasets from blood cells. The X:Autosome expression ratio was also higher in neuronal cells than in tissues with a mix of cell types. Conclusions: The results suggest that neurological disorders show as a reduction in mean expression of many genes along ChrX. The most likely explanation for this finding relates to the documented general up-regulation of ChrX in brain tissue which, this work suggests, occurs primarily in neurons. If validated, this cell specific ChrX expression warrants further research as understanding the biological reasons and mechanisms for this expression, may help to elucidate a connection with the development of neurodegenerative disorders

Executive Functions and Prefrontal Cortex: A Matter of Persistence?

Executive function is thought to originates from the dynamics of frontal cortical networks. We examined the dynamic properties of the blood oxygen level dependent time-series measured with functional MRI (fMRI) within the prefrontal cortex (PFC) to test the hypothesis that temporally persistent neural activity underlies performance in three tasks of executive function. A numerical estimate of signal persistence, the Hurst exponent, postulated to represent the coherent firing of cortical networks, was determined and correlated with task performance. Increasing persistence in the lateral PFC was shown to correlate with improved performance during an n-back task. Conversely, we observed a correlation between persistence and increasing commission error – indicating a failure to inhibit a prepotent response – during a Go/No-Go task. We propose that persistence within the PFC reflects dynamic network formation and these findings underline the importance of frequency analysis of fMRI time-series in the study of executive functions

Parental depression and offspring psychopathology: A Children of Twins study

Background Associations between parental depression and offspring affective and disruptive disorders are well documented. Few genetically informed studies have explored the processes underlying intergenerational associations. Method A semi-structured interview assessing DSM-III-R psychiatric disorders was administered to twins (n=1296) from the Australian Twin Register (ATR), their spouses (n=1046) and offspring (n=2555). We used the Children of Twins (CoT) design to delineate the extent to which intergenerational associations were consistent with a causal influence or due to genetic confounds. Results In between-family analyses, parental depression was associated significantly with offspring depression [hazard ratio (HR) 1.52, 95% confidence interval (CI) 1.20–1.93] and conduct disorder (CD; HR 2.27, CI 1.31–3.93). Survival analysis indicated that the intergenerational transmission of depression is consistent with a causal (environmental) inference, with a significant intergenerational association in offspring of discordant monozygotic (MZ) twin pairs (HR 1.39, CI 1.00–1.94). Logistic regression analysis suggested that the parental depression–offspring CD association was due to shared genetic liability in the parents and offspring. No intergenerational association was found when comparing the offspring of discordant MZ twins [odds ratio (OR) 1.41, CI 0.63–3.14], but offspring of discordant dizygotic (DZ) twins differed in their rates of CD (OR 2.53, CI 0.95–6.76). All findings remained after controlling for several measured covariates, including history of depression and CD in the twins' spouses. Conclusions The mechanisms underlying associations between parental depression and offspring psychopathology seem to differ depending on the outcome. The results are consistent with a causal environmental role of parental depression in offspring depression whereas common genetic factors account for the association of parental depression and offspring CD

An automated machine learning approach to predict brain age from cortical anatomical measures

The use of machine learning (ML) algorithms has significantly increased in neuroscience. However, from the vast extent of possible ML algorithms, which one is the optimal model to predict the target variable? What are the hyperparameters for such a model? Given the plethora of possible answers to these questions, in the last years, automated ML (autoML) has been gaining attention. Here, we apply an autoML library called Tree-based Pipeline Optimisation Tool (TPOT) which uses a tree-based representation of ML pipelines and conducts a genetic programming-based approach to find the model and its hyperparameters that more closely predicts the subject's true age. To explore autoML and evaluate its efficacy within neuroimaging data sets, we chose a problem that has been the focus of previous extensive study: brain age prediction. Without any prior knowledge, TPOT was able to scan through the model space and create pipelines that outperformed the state-of-the-art accuracy for Freesurfer-based models using only thickness and volume information for anatomical structure. In particular, we compared the performance of TPOT (mean absolute error [MAE]: 4.612 ± .124 years) and a relevance vector regression (MAE 5.474 ± .140 years). TPOT also suggested interesting combinations of models that do not match the current most used models for brain prediction but generalise well to unseen data. AutoML showed promising results as a data-driven approach to find optimal models for neuroimaging applications

PET imaging of putative microglial activation in individuals at ultra-high risk for psychosis, recently diagnosed and chronically ill with schizophrenia

We examined putative microglial activation as a function of illness course in schizophrenia. Microglial activity was quantified using [11C](R)-(1-[2-chrorophynyl]-N-methyl-N-[1-methylpropyl]-3 isoquinoline carboxamide (11C-(R)-PK11195) positron emission tomography (PET) in: (i) 10 individuals at ultra-high risk (UHR) of psychosis; (ii) 18 patients recently diagnosed with schizophrenia; (iii) 15 patients chronically ill with schizophrenia; and, (iv) 27 age-matched healthy controls. Regional-binding potential (BPND) was calculated using the simplified reference-tissue model with four alternative reference inputs. The UHR, recent-onset and chronic patient groups were compared to age-matched healthy control groups to examine between-group BPND differences in 6 regions: dorsal frontal, orbital frontal, anterior cingulate, medial temporal, thalamus and insula. Correlation analysis tested for BPND associations with gray matter volume, peripheral cytokines and clinical variables. The null hypothesis of equality in BPND between patients (UHR, recent-onset and chronic) and respective healthy control groups (younger and older) was not rejected for any group comparison or region. Across all subjects, BPND was positively correlated to age in the thalamus (r=0.43, P=0.008, false discovery rate). No correlations with regional gray matter, peripheral cytokine levels or clinical symptoms were detected. We therefore found no evidence of microglial activation in groups of individuals at high risk, recently diagnosed or chronically ill with schizophrenia. While the possibility of 11C-(R)-PK11195-binding differences in certain patient subgroups remains, the patient cohorts in our study, who also displayed normal peripheral cytokine profiles, do not substantiate the assumption of microglial activation in schizophrenia as a regular and defining feature, as measured by 11C-(R)-PK11195 BPND.M A Di Biase, A Zalesky, G O'keefe, L Laskaris, B T Baune, C S Weickert, J Olver, P D McGorry, G P Amminger, B Nelson, A M Scott, I Hickie, R Banati, F Turkheimer, M Yaqub, I P Everall, C Pantelis and V Crople

Null hypothesis significance testing: a short tutorial

Although thoroughly criticized, null hypothesis significance testing (NHST) remains the statistical method of choice used to provide evidence for an effect, in biological, biomedical and social sciences. In this short tutorial, I first summarize the concepts behind the method, distinguishing test of significance (Fisher) and test of acceptance (Newman-Pearson) and point to common interpretation errors regarding the p-value. I then present the related concepts of confidence intervals and again point to common interpretation errors. Finally, I discuss what should be reported in which context. The goal is to clarify concepts to avoid interpretation errors and propose reporting practices.</ns4:p

The translocator protein (TSPO) is prodromal to mitophagy loss in neurotoxicity

Dysfunctional mitochondria characterise Parkinson's Disease (PD). Uncovering etiological molecules, which harm the homeostasis of mitochondria in response to pathological cues, is therefore pivotal to inform early diagnosis and therapy in the condition, especially in its idiopathic forms. This study proposes the 18 kDa Translocator Protein (TSPO) to be one of those. Both in vitro and in vivo data show that neurotoxins, which phenotypically mimic PD, increase TSPO to enhance cellular redox-stress, susceptibility to dopamine-induced cell death, and repression of ubiquitin-dependent mitophagy. TSPO amplifies the extracellular signal-regulated protein kinase 1 and 2 (ERK1/2) signalling, forming positive feedback, which represses the transcription factor EB (TFEB) and the controlled production of lysosomes. Finally, genetic variances in the transcriptome confirm that TSPO is required to alter the autophagy-lysosomal pathway during neurotoxicity

Simultaneous genetic analysis of means and covariance structure: Pearson-Lawley selection rules

The object of this paper is to indicate that the Pearson-Lawley selection rules form a plausible general theory for the simultaneous genetic analysis of means and covariance structure. Models are presented based on phenotypic selection and latent selection. Previously presented quantitative genetic models to decompose means and covariance structure simultaneously are reconsidered as instances of latent selection. The selection rules are very useful in the context of behavior genetic modeling because they lead to testable models and a conceptual framework for explaining variation between and within groups by the same genetic and environmental factors. © 1994 Plenum Publishing Corporation