Non-parametric combination and related permutation tests for neuroimaging

18F-FDG PET hypometabolism patterns reflect clinical heterogeneity in sporadic forms of early-onset Alzheimer's disease

Until now, hypometabolic patterns and their correlations with neuropsychological performance have not been assessed as a function of the various presentations of sporadic early-onset Alzheimer's disease (EOAD). Here, we processed and analyzed the patients' metabolic maps at the vertex and voxel levels by using a nonparametric, permutation method that also regressed out the effects of cortical thickness and gray matter volume, respectively. The hypometabolism patterns in several areas of the brain were significantly correlated with the clinical manifestations. These areas included the paralimbic regions for typical presentations of sporadic EOAD. For atypical presentations, the hypometabolic regions included Broca's and Wernicke's areas and the pulvinar in language forms, bilateral primary and higher processing visual regions (with right predominance) in visuospatial forms, and the bilateral prefrontal cortex in executive forms. Similar hypometabolism patterns were also observed in a correlation analysis of the ¹⁸F-FDG PET data versus domain-specific, neuropsychological test scores. These heterogeneities might reflect different underlying pathophysiological processes in particular clinical presentations of sporadic EOAD and should be taken into account in future longitudinal and therapeutic studies.

Associations between self-reported sleep quality and white matter in community-dwelling older adults: A prospective cohort study

Both sleep disturbances and decline in white matter microstructure are commonly observed in ageing populations, as well as in age‐related psychiatric and neurological illnesses. A relationship between sleep and white matter microstructure may underlie such relationships, but few imaging studies have directly examined this hypothesis. In a study of 448 community‐dwelling members of the Whitehall II Imaging Sub‐Study aged between 60 and 82 years (90 female, mean age 69.2 ± 5.1 years), we used the magnetic resonance imaging technique diffusion tensor imaging to examine the relationship between self‐reported sleep quality and white matter microstructure. Poor sleep quality at the time of the diffusion tensor imaging scan was associated with reduced global fractional anisotropy and increased global axial diffusivity and radial diffusivity values, with small effect sizes. Voxel‐wise analysis showed that widespread frontal‐subcortical tracts, encompassing regions previously reported as altered in insomnia, were affected. Radial diffusivity findings remained significant after additional correction for demographics, general cognition, health, and lifestyle measures. No significant differences in general cognitive function, executive function, memory, or processing speed were detected between good and poor sleep quality groups. The number of times participants reported poor sleep quality over five time‐points spanning a 16‐year period was not associated with white matter measures. In conclusion, these data demonstrate that current sleep quality is linked to white matter microstructure. Small effect sizes may limit the extent to which poor sleep is a promising modifiable factor that may maintain, or even improve, white matter microstructure in ageing. Hum Brain Mapp 38:5465–5473, 2017. © 2017 Wiley Periodicals, Inc.

Predictive value of EEG in postanoxic encephalopathy: A quantitative model-based approach

INTRODUCTION

The majority of comatose patients after cardiac arrest do not regain consciousness due to severe postanoxic encephalopathy. Early and accurate outcome prediction is therefore essential in determining further therapeutic interventions. The electroencephalogram is a standardized and commonly available tool used to estimate prognosis in postanoxic patients. The identification of pathological EEG patterns with poor prognosis relies however primarily on visual EEG scoring by experts. We introduced a model-based approach of EEG analysis (state space model) that allows for an objective and quantitative description of spectral EEG variability.

METHODS

We retrospectively analyzed standard EEG recordings in 83 comatose patients after cardiac arrest between 2005 and 2013 in the intensive care unit of the University Hospital Zürich. Neurological outcome was assessed one month after cardiac arrest using the Cerebral Performance Category. For a dynamic and quantitative EEG analysis, we implemented a model-based approach (state space analysis) to quantify EEG background variability independent from visual scoring of EEG epochs. Spectral variability was compared between groups and correlated with clinical outcome parameters and visual EEG patterns.

RESULTS

Quantitative assessment of spectral EEG variability (state space velocity) revealed significant differences between patients with poor and good outcome after cardiac arrest: Lower mean velocity in temporal electrodes (T4 and T5) was significantly associated with poor prognostic outcome (p<0.005) and correlated with independently identified visual EEG patterns such as generalized periodic discharges (p<0.02). Receiver operating characteristic (ROC) analysis confirmed the predictive value of lower state space velocity for poor clinical outcome after cardiac arrest (AUC 80.8, 70% sensitivity, 15% false positive rate).

CONCLUSION

Model-based quantitative EEG analysis (state space analysis) provides a novel, complementary marker for prognosis in postanoxic encephalopathy.

Brain functional connectivity alterations in a rat model of excessive alcohol drinking: A resting-state network analysis

Alcohol use disorders (AUD) are a major public health concern. Understanding the brain network alterations is of the utmost importance to diagnose and develop treatment strategies. Employing resting-state functional magnetic resonance imaging, we have performed a longitudinal study in a rat model of chronic excessive alcohol consumption, to identify functional alterations in brain networks triggered by alcohol drinking. Two time points were considered: 1) before alcohol consumption (control condition) and 2) after 30 days of alcohol drinking (alcohol condition). We first identified nine resting-state networks with group independent component analysis. Afterwards, dual regression was applied to obtain subject-specific time courses and spatial maps. L2-regularized partial correlation analysis between pairs of networks showed that functional connectivity (FC) between the retrosplenial-visual and striatal networks decreases due to alcohol consumption, whereas FC between the prefrontal-cingulate and striatal networks increases. Analysis of subject-specific spatial maps revealed FC decreases within networks after alcohol drinking, including the striatal, motor-parietal, prefrontal-cingulate, retrosplenial-visual and left motor-parietal networks. Overall, our results unveil a generalized decrease in brain FC induced by alcohol drinking in genetically predisposed animals, even after a relatively short period of exposure (1 month). The only exception to this hypo-connectivity state is the functional association between the striatal and prefrontal-cingulate networks, which increases after drinking, supporting evidence in human alcoholics.

Associations between Mobility, Cognition, and Brain Structure in Healthy Older Adults

Mobility limitations lead to a cascade of adverse events in old age, yet the neural and cognitive correlates of mobility performance in older adults remain poorly understood. In a sample of 387 adults (mean age 69.0 ± 5.1 years), we tested the relationship between mobility measures, cognitive assessments, and MRI markers of brain structure. Mobility was assessed in 2007-2009, using gait, balance and chair-stands tests. In 2012-2015, cognitive testing assessed executive function, memory and processing-speed; gray matter volumes (GMV) were examined using voxel-based morphometry, and white matter microstructure was assessed using tract-based spatial statistics of fractional anisotropy, axial diffusivity (AD), and radial diffusivity (RD). All mobility measures were positively associated with processing-speed. Faster walking speed was also correlated with higher executive function, while memory was not associated with any mobility measure. Increased GMV within the cerebellum, basal ganglia, post-central gyrus, and superior parietal lobe was associated with better mobility. In addition, better performance on the chair-stands test was correlated with decreased RD and AD. Overall, our results indicate that, even in non-clinical populations, mobility measures can be sensitive to sub-clinical variance in cognition and brain structures.

Altered eigenvector centrality is related to local resting-state network functional connectivity in patients with longstanding type 1 diabetes mellitus

INTRODUCTION

Longstanding type 1 diabetes (T1DM) is associated with microangiopathy and poorer cognition. In the brain, T1DM is related to increased functional resting-state network (RSN) connectivity in patients without, which was decreased in patients with clinically evident microangiopathy. Subcortical structure seems affected in both patient groups. How these localized alterations affect the hierarchy of the functional network in T1DM is unknown. Eigenvector centrality mapping (ECM) and degree centrality are graph theoretical methods that allow determining the relative importance (ECM) and connectedness (degree centrality) of regions within the whole-brain network hierarchy.

METHODS

Therefore, ECM and degree centrality of resting-state functional MRI-scans were compared between 51 patients with, 53 patients without proliferative retinopathy, and 49 controls, and associated with RSN connectivity, subcortical gray matter volume, and cognition.

RESULTS

In all patients versus controls, ECM and degree centrality were lower in the bilateral thalamus and the dorsal striatum, with lowest values in patients without proliferative retinopathy (P_FWE  < 0.05). Increased ECM in this group versus patients with proliferative retinopathy was seen in the bilateral lateral occipital cortex, and in the right cuneus and occipital fusiform gyrus versus controls (P_FWE  < 0.05). In all patients, ECM and degree centrality were related to altered visual, sensorimotor, and auditory and language RSN connectivity (P_FWE  < 0.05), but not to subcortical gray matter volume or cognition (P_FDR  > 0.05).

CONCLUSION

The findings suggested reorganization of the hierarchy of the cortical connectivity network in patients without proliferative retinopathy, which is lost with disease progression. Centrality seems sensitive to capture early T1DM-related functional connectivity alterations, but not disease progression. Hum Brain Mapp 38:3623-3636, 2017. © 2017 Wiley Periodicals, Inc.

Diminished Posterior Precuneus Connectivity with the Default Mode Network Differentiates Normal Aging from Alzheimer's Disease

Both normal aging and Alzheimer's disease (AD) have been associated with a reduction in functional brain connectivity. It is unknown how connectivity patterns due to aging and AD compare. Here, we investigate functional brain connectivity in 12 young adults (mean age 22.8 ± 2.8), 12 older adults (mean age 73.1 ± 5.2) and 12 AD patients (mean age 74.0 ± 5.2; mean MMSE 22.3 ± 2.5). Participants were scanned during 6 different sessions with resting state functional magnetic resonance imaging (RS-fMRI), resulting in 72 scans per group. Voxelwise connectivity with 10 functional networks was compared between groups (p < 0.05, corrected). Normal aging was characterized by widespread decreases in connectivity with multiple brain networks, whereas AD only affected connectivity between the default mode network (DMN) and precuneus. The preponderance of effects was associated with regional gray matter volume. Our findings indicate that aging has a major effect on functional brain interactions throughout the entire brain, whereas AD is distinguished by additional diminished posterior DMN-precuneus coherence.

Neural mechanisms underlying visual attention to health warnings on branded and plain cigarette packs

AIMS

To (1) test if activation in brain regions related to reward (nucleus accumbens) and emotion (amygdala) differ when branded and plain packs of cigarettes are viewed, (2) test whether these activation patterns differ by smoking status and (3) examine whether activation patterns differ as a function of visual attention to health warning labels on cigarette packs.

DESIGN

Cross-sectional observational study combining functional magnetic resonance imaging (fMRI) with eye-tracking. Non-smokers, weekly smokers and daily smokers performed a memory task on branded and plain cigarette packs with pictorial health warnings presented in an event-related design.

SETTING

Clinical Research and Imaging Centre, University of Bristol, UK.

PARTICIPANTS

Non-smokers, weekly smokers and daily smokers (n = 72) were tested. After exclusions, data from 19 non-smokers, 19 weekly smokers and 20 daily smokers were analysed.

MEASUREMENTS

Brain activity was assessed in whole brain analyses and in pre-specified masked analyses in the amygdala and nucleus accumbens. On-line eye-tracking during scanning recorded visual attention to health warnings.

FINDINGS

There was no evidence for a main effect of pack type or smoking status in either the nucleus accumbens or amygdala, and this was unchanged when taking account of visual attention to health warnings. However, there was evidence for an interaction, such that we observed increased activation in the right amygdala when viewing branded as compared with plain packs among weekly smokers (P = 0.003). When taking into account visual attention to health warnings, we observed higher levels of activation in the visual cortex in response to plain packaging compared with branded packaging of cigarettes (P = 0.020).

CONCLUSIONS

Based on functional magnetic resonance imaging and eye-tracking data, health warnings appear to be more salient on 'plain' cigarette packs than branded packs.

Adaptive testing for multiple traits in a proportional odds model with applications to detect SNP-brain network associations

There has been increasing interest in developing more powerful and flexible statistical tests to detect genetic associations with multiple traits, as arising from neuroimaging genetic studies. Most of existing methods treat a single trait or multiple traits as response while treating an SNP as a predictor coded under an additive inheritance mode. In this paper, we follow an earlier approach in treating an SNP as an ordinal response while treating traits as predictors in a proportional odds model (POM). In this way, it is not only easier to handle mixed types of traits, e.g., some quantitative and some binary, but it is also potentially more robust to the commonly adopted additive inheritance mode. More importantly, we develop an adaptive test in a POM so that it can maintain high power across many possible situations. Compared to the existing methods treating multiple traits as responses, e.g., in a generalized estimating equation (GEE) approach, the proposed method can be applied to a high dimensional setting where the number of phenotypes (p) can be larger than the sample size (n), in addition to a usual small P setting. The promising performance of the proposed method was demonstrated with applications to the Alzheimer's Disease Neuroimaging Initiative (ADNI) data, in which either structural MRI driven phenotypes or resting-state functional MRI (rs-fMRI) derived brain functional connectivity measures were used as phenotypes. The applications led to the identification of several top SNPs of biological interest. Furthermore, simulation studies showed competitive performance of the new method, especially for p>n.

Investigating resting-state functional connectivity in the cervical spinal cord at 3T

The study of spontaneous fluctuations in the blood-oxygen-level-dependent (BOLD) signal has recently been extended from the brain to the spinal cord. Two ultra-high field functional magnetic resonance imaging (fMRI) studies in humans have provided evidence for reproducible resting-state connectivity between the dorsal horns as well as between the ventral horns, and a study in non-human primates has shown that these resting-state signals are impacted by spinal cord injury. As these studies were carried out at ultra-high field strengths using region-of-interest (ROI) based analyses, we investigated whether such resting-state signals could also be observed at the clinically more prevalent field strength of 3 T. In a reanalysis of a sample of 20 healthy human participants who underwent a resting-state fMRI acquisition of the cervical spinal cord, we were able to observe significant dorsal horn connectivity as well as ventral horn connectivity, but no consistent effects for connectivity between dorsal and ventral horns, thus replicating the human 7 T results. These effects were not only observable when averaging along the acquired length of the spinal cord, but also when we examined each of the acquired spinal segments separately, which showed similar patterns of connectivity. Finally, we investigated the robustness of these resting-state signals against variations in the analysis pipeline by varying the type of ROI creation, temporal filtering, nuisance regression and connectivity metric. We observed that – apart from the effects of band-pass filtering – ventral horn connectivity showed excellent robustness, whereas dorsal horn connectivity showed moderate robustness. Together, our results provide evidence that spinal cord resting-state connectivity is a robust and spatially consistent phenomenon that could be a valuable tool for investigating the effects of pathology, disease progression, and treatment response in neurological conditions with a spinal component, such as spinal cord injury.

omicsNPC: Applying the Non-Parametric Combination Methodology to the Integrative Analysis of Heterogeneous Omics Data

Background

The advance of omics technologies has made possible to measure several data modalities on a system of interest. In this work, we illustrate how the Non-Parametric Combination methodology, namely NPC, can be used for simultaneously assessing the association of different molecular quantities with an outcome of interest. We argue that NPC methods have several potential applications in integrating heterogeneous omics technologies, as for example identifying genes whose methylation and transcriptional levels are jointly deregulated, or finding proteins whose abundance shows the same trends of the expression of their encoding genes.

Results

We implemented the NPC methodology within “omicsNPC”, an R function specifically tailored for the characteristics of omics data. We compare omicsNPC against a range of alternative methods on simulated as well as on real data. Comparisons on simulated data point out that omicsNPC produces unbiased / calibrated p-values and performs equally or significantly better than the other methods included in the study; furthermore, the analysis of real data show that omicsNPC (a) exhibits higher statistical power than other methods, (b) it is easily applicable in a number of different scenarios, and (c) its results have improved biological interpretability.

Conclusions

The omicsNPC function competitively behaves in all comparisons conducted in this study. Taking into account that the method (i) requires minimal assumptions, (ii) it can be used on different studies designs and (iii) it captures the dependences among heterogeneous data modalities, omicsNPC provides a flexible and statistically powerful solution for the integrative analysis of different omics data.

Time related effects on functional brain connectivity after serotonergic and cholinergic neuromodulation

Psychopharmacological research, if properly designed, may offer insight into both timing and area of effect, increasing our understanding of the brain's neurotransmitter systems. For that purpose, the acute influence of the selective serotonin reuptake inhibitor citalopram (30 mg) and the acetylcholinesterase inhibitor galantamine (8 mg) was repeatedly measured in 12 healthy young volunteers with resting state functional magnetic resonance imaging (RS‐fMRI). Eighteen RS‐fMRI scans were acquired per subject during this randomized, double blind, placebo‐controlled, crossover study. Within‐group comparisons of voxelwise functional connectivity with 10 functional networks were examined (P < 0.05, FWE‐corrected) using a non‐parametric multivariate approach with cerebrospinal fluid, white matter, heart rate, and baseline measurements as covariates. Although both compounds did not change cognitive performance on several tests, significant effects were found on connectivity with multiple resting state networks. Serotonergic stimulation primarily reduced connectivity with the sensorimotor network and structures that are related to self‐referential mechanisms, whereas galantamine affected networks and regions that are more involved in learning, memory, and visual perception and processing. These results are consistent with the serotonergic and cholinergic trajectories and their functional relevance. In addition, this study demonstrates the power of using repeated measures after drug administration, which offers the chance to explore both combined and time specific effects. Hum Brain Mapp 38:308–325, 2017. © 2016 Wiley Periodicals, Inc.

Faster permutation inference in brain imaging

Permutation tests are increasingly being used as a reliable method for inference in neuroimaging analysis. However, they are computationally intensive. For small, non-imaging datasets, recomputing a model thousands of times is seldom a problem, but for large, complex models this can be prohibitively slow, even with the availability of inexpensive computing power. Here we exploit properties of statistics used with the general linear model (GLM) and their distributions to obtain accelerations irrespective of generic software or hardware improvements. We compare the following approaches: (i) performing a small number of permutations; (ii) estimating the p-value as a parameter of a negative binomial distribution; (iii) fitting a generalised Pareto distribution to the tail of the permutation distribution; (iv) computing p-values based on the expected moments of the permutation distribution, approximated from a gamma distribution; (v) direct fitting of a gamma distribution to the empirical permutation distribution; and (vi) permuting a reduced number of voxels, with completion of the remainder using low rank matrix theory. Using synthetic data we assessed the different methods in terms of their error rates, power, agreement with a reference result, and the risk of taking a different decision regarding the rejection of the null hypotheses (known as the resampling risk). We also conducted a re-analysis of a voxel-based morphometry study as a real-data example. All methods yielded exact error rates. Likewise, power was similar across methods. Resampling risk was higher for methods (i), (iii) and (v). For comparable resampling risks, the method in which no permutations are done (iv) was the absolute fastest. All methods produced visually similar maps for the real data, with stronger effects being detected in the family-wise error rate corrected maps by (iii) and (v), and generally similar to the results seen in the reference set. Overall, for uncorrected p-values, method (iv) was found the best as long as symmetric errors can be assumed. In all other settings, including for familywise error corrected p-values, we recommend the tail approximation (iii). The methods considered are freely available in the tool PALM - Permutation Analysis of Linear Models.

Ipsilesional anodal tDCS enhances the functional benefits of rehabilitation in patients after stroke

Anodal transcranial direct current stimulation (tDCS) can boost the effects of motor training and facilitate plasticity in the healthy human brain. Motor rehabilitation depends on learning and plasticity, and motor learning can occur after stroke. We tested whether brain stimulation using anodal tDCS added to motor training could improve rehabilitation outcomes in patients after stroke. We performed a randomized, controlled trial in 24 patients at least 6 months after a first unilateral stroke not directly involving the primary motor cortex. Patients received either anodal tDCS (n= 11) or sham treatment (n= 13) paired with daily motor training for 9 days. We observed improvements that persisted for at least 3 months post-intervention after anodal tDCS compared to sham treatment on the Action Research Arm Test (ARAT) and Wolf Motor Function Test (WMFT) but not on the Upper Extremity Fugl-Meyer (UEFM) score. Functional magnetic resonance imaging (MRI) showed increased activity during movement of the affected hand in the ipsilesional motor and premotor cortex in the anodal tDCS group compared to the sham treatment group. Structural MRI revealed intervention-related increases in gray matter volume in cortical areas, including ipsilesional motor and premotor cortex after anodal tDCS but not sham treatment. The addition of ipsilesional anodal tDCS to a 9-day motor training program improved long-term clinical outcomes relative to sham treatment in patients after stroke.