Alcohol consumption and telomere length: Mendelian randomization clarifies alcohol's effects.

Alcohol's impact on telomere length, a proposed marker of biological aging, is unclear. We performed the largest observational study to date (in n = 245,354 UK Biobank participants) and compared findings with Mendelian randomization (MR) estimates. Two-sample MR used data from 472,174 participants in a recent genome-wide association study (GWAS) of telomere length. Genetic variants were selected on the basis of associations with alcohol consumption (n = 941,280) and alcohol use disorder (AUD) (n = 57,564 cases). Non-linear MR employed UK Biobank individual data. MR analyses suggested a causal relationship between alcohol traits, more strongly for AUD, and telomere length. Higher genetically-predicted AUD (inverse variance-weighted (IVW) ß = -0.06, 95% confidence interval (CI): -0.10 to -0.02, p = 0.001) was associated with shorter telomere length. There was a weaker association with genetically-predicted alcoholic drinks weekly (IVW ß = -0.07, CI: -0.14 to -0.01, p = 0.03). Results were consistent across methods and independent from smoking. Non-linear analyses indicated a potential threshold relationship between alcohol and telomere length. Our findings indicate that alcohol consumption may shorten telomere length. There are implications for age-related diseases.

Toward a multisubject analysis of neural connectivity.

Directed acyclic graphs (DAGs) and associated probability models are widely used to model neural connectivity and communication channels. In many experiments, data are collected from multiple subjects whose connectivities may differ but are likely to share many features. In such circumstances, it is natural to leverage similarity among subjects to improve statistical efficiency. The first exact algorithm for estimation of multiple related DAGs was recently proposed by Oates, Smith, Mukherjee, and Cussens ( 2014 ). In this letter we present examples and discuss implications of the methodology as applied to the analysis of fMRI data from a multisubject experiment. Elicitation of tuning parameters requires care, and we illustrate how this may proceed retrospectively based on technical replicate data. In addition to joint learning of subject-specific connectivity, we allow for heterogeneous collections of subjects and simultaneously estimate relationships between the subjects themselves. This letter aims to highlight the potential for exact estimation in the multisubject setting.

Transcriptomics of cortical gray matter thickness decline during normal aging.

INTRODUCTION: We performed a whole-transcriptome correlation analysis, followed by the pathway enrichment and testing of innate immune response pathway analyses to evaluate the hypothesis that transcriptional activity can predict cortical gray matter thickness (GMT) variability during normal cerebral aging. METHODS: Transcriptome and GMT data were available for 379 individuals (age range=28-85) community-dwelling members of large extended Mexican American families. Collection of transcriptome data preceded that of neuroimaging data by 17 years. Genome-wide gene transcriptome data consisted of 20,413 heritable lymphocytes-based transcripts. GMT measurements were performed from high-resolution (isotropic 800 µm) T1-weighted MRI. Transcriptome-wide and pathway enrichment analysis was used to classify genes correlated with GMT. Transcripts for sixty genes from seven innate immune pathways were tested as specific predictors of GMT variability. RESULTS: Transcripts for eight genes (IGFBP3, LRRN3, CRIP2, SCD, IDS, TCF4, GATA3, and HN1) passed the transcriptome-wide significance threshold. Four orthogonal factors extracted from this set predicted 31.9% of the variability in the whole-brain and between 23.4 and 35% of regional GMT measurements. Pathway enrichment analysis identified six functional categories including cellular proliferation, aggregation, differentiation, viral infection, and metabolism. The integrin signaling pathway was significantly (p<10(-6)) enriched with GMT. Finally, three innate immune pathways (complement signaling, toll-receptors and scavenger and immunoglobulins) were significantly associated with GMT. CONCLUSION: Expression activity for the genes that regulate cellular proliferation, adhesion, differentiation and inflammation can explain a significant proportion of individual variability in cortical GMT. Our findings suggest that normal cerebral aging is the product of a progressive decline in regenerative capacity and increased neuroinflammation.

The danger of systematic bias in group-level FMRI-lag-based causality estimation.

Schippers, Renken and Keysers (NeuroImage, 2011) present a simulation of multi-subject lag-based causality estimation. We fully agree that single-subject evaluations (e.g., Smith et al., 2011) need to be revisited in the context of multi-subject studies, and Schippers' paper is a good example, including detailed multi-level simulation and cross-subject statistical modelling. The authors conclude that "the average chance to find a significant Granger causality effect when no actual influence is present in the data stays well below the p-level imposed on the second level statistics" and that "when the analyses reveal a significant directed influence, this direction was accurate in the vast majority of the cases". Unfortunately, we believe that the general meaning that may be taken from these statements is not supported by the paper's results, as there may in reality be a systematic (group-average) difference in haemodynamic delay between two brain areas. While many statements in the paper (e.g., the final two sentences) do refer to this problem, we fear that the overriding message that many readers may take from the paper could cause misunderstanding.

Cluster-based computational methods for mass univariate analyses of event-related brain potentials/fields: A simulation study.

BACKGROUND: In recent years, analyses of event related potentials/fields have moved from the selection of a few components and peaks to a mass-univariate approach in which the whole data space is analyzed. Such extensive testing increases the number of false positives and correction for multiple comparisons is needed. METHOD: Here we review all cluster-based correction for multiple comparison methods (cluster-height, cluster-size, cluster-mass, and threshold free cluster enhancement - TFCE), in conjunction with two computational approaches (permutation and bootstrap). RESULTS: Data driven Monte-Carlo simulations comparing two conditions within subjects (two sample Student's t-test) showed that, on average, all cluster-based methods using permutation or bootstrap alike control well the family-wise error rate (FWER), with a few caveats. CONCLUSIONS: (i) A minimum of 800 iterations are necessary to obtain stable results; (ii) below 50 trials, bootstrap methods are too conservative; (iii) for low critical family-wise error rates (e.g. p=1%), permutations can be too liberal; (iv) TFCE controls best the type 1 error rate with an attenuated extent parameter (i.e. power<1).

Positron emission tomographic imaging of serotonin activation effects on prefrontal cortex in healthy volunteers.

Serotonergic system abnormalities have been implicated in major depression, suicide, violence, alcoholism, and other psychopathologies. The prolactin response to fenfluramine has been widely used as a neuroendocrine probe to study brain serotonin responsivity. We have extended this methodology by using the positron emission tomography (PET) 18F-fluorodeoxyglucose (18FDG) method to examine the fenfluramine-induced changes in regional cerebral glucose metabolism (rCMRglu), an indicator of changes in regional neuronal activity. We report results on 16 healthy controls, each of whom underwent two PET studies. One group of six subjects had a placebo on day 1 and a single 60 mg oral dose of fenfluramine on day 2. The second group, of 10 subjects, was tested on two consecutive occasions without drug or placebo. Data were analyzed for significant rCMRglu changes on day 2 vs day 1 using the statistical parametric mapping method (p < 0.01). Subjects who did not receive drugs showed no statistically significant areas of rCMRglu increase or decrease on day 2 versus day 1. In contrast, the group that received fenfluramine showed significant fenfluramine-induced responses. Areas of rCMRglu increases involved mainly the left prefrontal and left temperoparietal cortex. Within the prefrontal cortex, two major areas of rCMRglu increase included, first, an area centered on the anterior cingulate and, second, an area in the lateral prefrontal cortex involving principally the inferior, middle, and superior frontal gyri. Some decreases in rCMRglu were observed, principally in the right hemisphere. This PET-fenfluramine paradigm is a potentially useful method for studying abnormalities of serotonin function in the prefrontal cortex.

Comparative evaluation of MR-based partial-volume correction schemes for PET.

UNLABELLED: Because of limitations of spatial resolution, quantitative PET measurements of cerebral blood flow, glucose metabolism and neuroreceptor binding are influenced by partial-volume averaging among neighboring tissues with differing tracer concentrations. METHODS: Two MR-based approaches to partial-volume correction of PET images were compared using simulations and a multicompartment phantom. The two-compartment method corrects PET data for the diluting effects of cerebrospinal fluid (CSF) spaces. The more complex three-compartment method also accounts for the effect of partial-volume averaging between gray and white matter. The effects of the most significant sources of error on MR-based partial-volume correction, including misregistration, resolution mismatch, segmentation errors and white matter heterogeneity, were evaluated. We also examined the relative usefulness of both approaches in PET studies of aging and neurodegenerative disease. RESULTS: Although the three-compartment method was highly accurate (with 100% gray matter recovery achieved in simulations), it was also more sensitive to all errors tested, particularly image segmentation and PET-MR registration. CONCLUSION: Based on these data, we conclude that the two-compartment approach is better suited for comparative PET studies, whereas the three-compartment algorithm is capable of greater accuracy for absolute quantitative measures.

Post-ECT increases in MRI regional T2 relaxation times and their relationship to cognitive side effects: a pilot study.

This pilot study examined the hypothesis that magnetic resonance imaging T2 relaxation times of specific brain regions increase after electroconvulsive therapy (ECT) and that these increases are related to the cognitive side effects of ECT. Six depressed patients undergoing unilateral ECT were studied. The results demonstrate significant post-ECT T2 increases in the right and left thalamus, and suggest a correlation between regional T2 increase and anterograde memory impairment following ECT. These findings are consistent with a post-ECT increase in brain water content (perhaps secondary to a breakdown of the blood-brain barrier) and suggest that this process may be related to the memory impairment following ECT.

Changes in forebrain function from waking to REM sleep in depression: preliminary analyses of [18F]FDG PET studies.

Based on recent functional brain imaging studies of healthy human REM sleep, we hypothesized that alterations in REM sleep in mood disorder patients reflect a functional dysregulation within limbic and paralimbic forebrain structures during that sleep state. Six unipolar depressed subjects and eight healthy subjects underwent separate [18F]2-fluoro-2-deoxy-D-glucose ([18F]FDG) PET scans during waking and during their first REM period of sleep. Statistical parametric mapping contrasts were performed to detect changes in relative regional cerebral glucose metabolism (rCMRglu) from waking to REM sleep in each group as well as interactions in patterns of change between groups. Clinical and EEG sleep comparisons from an undisturbed night of sleep were also performed. In contrast to healthy control subjects, depressed patients did not show increases in rCMRglu in anterior paralimbic structures in REM sleep compared to waking. Depressed subjects showed greater increases from waking to REM sleep in rCMRglu in the tectal area and a series of left hemispheric areas including sensorimotor cortex, inferior temporal cortex, uncal gyrus-amygdala, and subicular complex than did the control subjects. These observations indicate that changes in limbic and paralimbic function from waking to REM sleep differ significantly from normal in depressed patients.

Statistical limitations in functional neuroimaging. I. Non-inferential methods and statistical models.

Functional neuroimaging (FNI) provides experimental access to the intact living brain making it possible to study higher cognitive functions in humans. In this review and in a companion paper in this issue, we discuss some common methods used to analyse FNI data. The emphasis in both papers is on assumptions and limitations of the methods reviewed. There are several methods available to analyse FNI data indicating that none is optimal for all purposes. In order to make optimal use of the methods available it is important to know the limits of applicability. For the interpretation of FNI results it is also important to take into account the assumptions, approximations and inherent limitations of the methods used. This paper gives a brief overview over some non-inferential descriptive methods and common statistical models used in FNI. Issues relating to the complex problem of model selection are discussed. In general, proper model selection is a necessary prerequisite for the validity of the subsequent statistical inference. The non-inferential section describes methods that, combined with inspection of parameter estimates and other simple measures, can aid in the process of model selection and verification of assumptions. The section on statistical models covers approaches to global normalization and some aspects of univariate, multivariate, and Bayesian models. Finally, approaches to functional connectivity and effective connectivity are discussed. In the companion paper we review issues related to signal detection and statistical inference.

Statistical limitations in functional neuroimaging. II. Signal detection and statistical inference.

The field of functional neuroimaging (FNI) methodology has developed into a mature but evolving area of knowledge and its applications have been extensive. A general problem in the analysis of FNI data is finding a signal embedded in noise. This is sometimes called signal detection. Signal detection theory focuses in general on issues relating to the optimization of conditions for separating the signal from noise. When methods from probability theory and mathematical statistics are directly applied in this procedure it is also called statistical inference. In this paper we briefly discuss some aspects of signal detection theory relevant to FNI and, in addition, some common approaches to statistical inference used in FNI. Low-pass filtering in relation to functional-anatomical variability and some effects of filtering on signal detection of interest to FNI are discussed. Also, some general aspects of hypothesis testing and statistical inference are discussed. This includes the need for characterizing the signal in data when the null hypothesis is rejected, the problem of multiple comparisons that is central to FNI data analysis, omnibus tests and some issues related to statistical power in the context of FNI. In turn, random field, scale space, non-parametric and Monte Carlo approaches are reviewed, representing the most common approaches to statistical inference used in FNI. Complementary to these issues an overview and discussion of non-inferential descriptive methods, common statistical models and the problem of model selection is given in a companion paper. In general, model selection is an important prelude to subsequent statistical inference. The emphasis in both papers is on the assumptions and inherent limitations of the methods presented. Most of the methods described here generally serve their purposes well when the inherent assumptions and limitations are taken into account. Significant differences in results between different methods are most apparent in extreme parameter ranges, for example at low effective degrees of freedom or at small spatial autocorrelation. In such situations or in situations when assumptions and approximations are seriously violated it is of central importance to choose the most suitable method in order to obtain valid results.

Increased ventral striatal monoaminergic innervation in Tourette syndrome.

BACKGROUND: Excessive striatal dopaminergic innervation is suggested to underlie Tourette syndrome (TS). Prior imaging and postmortem studies yield conflicting data. METHODS: The authors used PET with the type 2 vesicular monoamine transporter ligand [(11)C]dihydrotetrabenazine (DTBZ) to quantify striatal monoaminergic innervation in patients with TS (n = 19) and control subjects (n = 27). Compartmental modeling was used to determine blood to brain ligand transport (K(1)) and tissue to plasma distribution volume (a measure of ligand binding) during continuous infusion of DTBZ. TS data were compared with control data using predefined regions of interest and on a voxel by voxel basis. RESULTS: There were no significant differences in ligand binding or ligand transport between patients with TS and control subjects in the dorsal striatum. With voxel by voxel analysis, there was increased DTBZ binding in the right ventral striatum. CONCLUSIONS: Previously reported differences between patients with TS and control subjects in dorsal striatal dopamine terminal markers may reflect medication-induced regulation of terminal marker expression or be the result of intrinsic differences in striatal dopaminergic synaptic function. Increased right ventral striatal DTBZ binding suggests that abnormal ventral striatal dopaminergic innervation may underlie tics.

Comparison of functional magnetic resonance imaging with positron emission tomography and magnetoencephalography to identify the motor cortex in a patient with an arteriovenous malformation.

Alterations in gyral contour made it difficult to identify the motor cortex thought to be near an arteriovenous malformation (AVM) in a 24-year-old man considered for stereotactic radiosurgery. Functional imaging in three modalities was performed preoperatively to compare the reliability of localization using functional magnetic resonance imaging (fMRI) on a conventional scanner with positron emission tomography (PET) and magnetoencephalography (MEG). Similar tasks were used for each imaging modality in an attempt to activate and identify the sensory and motor cortex. Data from all three modalities converged for the sensory task, and fMRI and PET data converged for the motor task. The right hemisphere motor strip was localized adjacent and anterior to the AVM. These data were used in planning the radiosurgery isodose configuration to the AVM in order to reduce the irradiation of motor cortex parenchyma. A postoperative fMRI study was also performed using newer techniques to reduce head motion artifact and to improve signal-to-noise ratio. The data confirmed the conclusions derived from the preoperative evaluations. This study demonstrates how conventional MRI scanners can be used for functional studies of use in surgical planning.