Correlation between resting state fMRI total neuronal activity and PET metabolism in healthy controls and patients with disorders of consciousness

INTRODUCTION

The mildly invasive 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) is a well-established imaging technique to measure 'resting state' cerebral metabolism. This technique made it possible to assess changes in metabolic activity in clinical applications, such as the study of severe brain injury and disorders of consciousness.

OBJECTIVE

We assessed the possibility of creating functional MRI activity maps, which could estimate the relative levels of activity in FDG-PET cerebral metabolic maps. If no metabolic absolute measures can be extracted, our approach may still be of clinical use in centers without access to FDG-PET. It also overcomes the problem of recognizing individual networks of independent component selection in functional magnetic resonance imaging (fMRI) resting state analysis.

METHODS

We extracted resting state fMRI functional connectivity maps using independent component analysis and combined only components of neuronal origin. To assess neuronality of components a classification based on support vector machine (SVM) was used. We compared the generated maps with the FDG-PET maps in 16 healthy controls, 11 vegetative state/unresponsive wakefulness syndrome patients and four locked-in patients.

RESULTS

The results show a significant similarity with ρ = 0.75 ± 0.05 for healthy controls and ρ = 0.58 ± 0.09 for vegetative state/unresponsive wakefulness syndrome patients between the FDG-PET and the fMRI based maps. FDG-PET, fMRI neuronal maps, and the conjunction analysis show decreases in frontoparietal and medial regions in vegetative patients with respect to controls. Subsequent analysis in locked-in syndrome patients produced also consistent maps with healthy controls.

CONCLUSIONS

The constructed resting state fMRI functional connectivity map points toward the possibility for fMRI resting state to estimate relative levels of activity in a metabolic map.

Decoding intracranial EEG data with multiple kernel learning method

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Multiple Kernel Learning (MKL) can explore multiple dimensions simultaneously.

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MKL method is sparse and performs feature selection during modeling of the data.

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We propose to use the MKL method to analyze electrophysiology data (EEG or MEG).

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We provide a prototype example of how MKL method can apply to ECoG data.

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We show multiple dimensions of ECoG signal can contribute to numerical processing.

Background

Machine learning models have been successfully applied to neuroimaging data to make predictions about behavioral and cognitive states of interest. While these multivariate methods have greatly advanced the field of neuroimaging, their application to electrophysiological data has been less common especially in the analysis of human intracranial electroencephalography (iEEG, also known as electrocorticography or ECoG) data, which contains a rich spectrum of signals recorded from a relatively high number of recording sites.

New method

In the present work, we introduce a novel approach to determine the contribution of different bandwidths of EEG signal in different recording sites across different experimental conditions using the Multiple Kernel Learning (MKL) method.

Comparison with existing method

To validate and compare the usefulness of our approach, we applied this method to an ECoG dataset that was previously analysed and published with univariate methods.

Results

Our findings proved the usefulness of the MKL method in detecting changes in the power of various frequency bands during a given task and selecting automatically the most contributory signal in the most contributory site(s) of recording.

Conclusions

With a single computation, the contribution of each frequency band in each recording site in the estimated multivariate model can be highlighted, which then allows formulation of hypotheses that can be tested a posteriori with univariate methods if needed.

Cerebral functional connectivity periodically (de)synchronizes with anatomical constraints

This paper studies the link between resting-state functional connectivity (FC), measured by the correlations of fMRI BOLD time courses, and structural connectivity (SC), estimated through fiber tractography. Instead of a static analysis based on the correlation between SC and FC averaged over the entire fMRI time series, we propose a dynamic analysis, based on the time evolution of the correlation between SC and a suitably windowed FC. Assessing the statistical significance of the time series against random phase permutations, our data show a pronounced peak of significance for time window widths around 20-30 TR (40-60 s). Using the appropriate window width, we show that FC patterns oscillate between phases of high modularity, primarily shaped by anatomy, and phases of low modularity, primarily shaped by inter-network connectivity. Building upon recent results in dynamic FC, this emphasizes the potential role of SC as a transitory architecture between different highly connected resting-state FC patterns. Finally, we show that the regions contributing the most to these whole-brain level fluctuations of FC on the supporting anatomical architecture belong to the default mode and the executive control networks suggesting that they could be capturing consciousness-related processes such as mind wandering.

Fighting Sleep at Night: Brain Correlates and Vulnerability to Sleep Loss

OBJECTIVE

Even though wakefulness at night leads to profound performance deterioration and is regularly experienced by shift workers, its cerebral correlates remain virtually unexplored.

METHODS

We assessed brain activity in young healthy adults during a vigilant attention task under high and low sleep pressure during night-time, coinciding with strongest circadian sleep drive. We examined sleep-loss-related attentional vulnerability by considering a PERIOD3 polymorphism presumably impacting on sleep homeostasis.

RESULTS

Our results link higher sleep-loss-related attentional vulnerability to cortical and subcortical deactivation patterns during slow reaction times (i.e., suboptimal vigilant attention). Concomitantly, thalamic regions were progressively less recruited with time-on-task and functionally less connected to task-related and arousal-promoting brain regions in those volunteers showing higher attentional instability in their behavior. The data further suggest that the latter is linked to shifts into a task-inactive default-mode network in between task-relevant stimulus occurrence.

INTERPRETATION

We provide a multifaceted view on cerebral correlates of sleep loss at night and propose that genetic predisposition entails differential cerebral coping mechanisms, potentially compromising adequate performance during night work.

Forensic ancestry analysis with two capillary electrophoresis ancestry informative marker (AIM) panels: Results of a collaborative EDNAP exercise

There is increasing interest in forensic ancestry tests, which are part of a growing number of DNA analyses that can enhance routine profiling by obtaining additional genetic information about unidentified DNA donors. Nearly all ancestry tests use single nucleotide polymorphisms (SNPs), but these currently rely on SNaPshot single base extension chemistry that can fail to detect mixed DNA. Insertion-deletion polymorphism (Indel) tests have been developed using dye-labeled primers that allow direct capillary electrophoresis detection of PCR products (PCR-to-CE). PCR-to-CE maintains the direct relationship between input DNA and signal strength as each marker is detected with a single dye, so mixed DNA is more reliably detected. We report the results of a collaborative inter-laboratory exercise of 19 participants (15 from the EDNAP European DNA Profiling group) that assessed a 34-plex SNP test using SNaPshot and a 46-plex Indel test using PCR-to-CE. Laboratories were asked to type five samples with different ancestries and detect an additional mixed DNA sample. Statistical inference of ancestry was made by participants using the Snipper online Bayes analysis portal plus an optional PCA module that analyzes the genotype data alongside calculation of Bayes likelihood ratios. Exercise results indicated consistent genotyping performance from both tests, reaching a particularly high level of reliability for the Indel test. SNP genotyping gave 93.5% concordance (compared to the organizing laboratory's data) that rose to 97.3% excluding one laboratory with a large number of miscalled genotypes. Indel genotyping gave a higher concordance rate of 99.8% and a reduced no-call rate compared to SNP analysis. All participants detected the mixture from their Indel peak height data and successfully assigned the correct ancestry to the other samples using Snipper, with the exception of one laboratory with SNP miscalls that incorrectly assigned ancestry of two samples and did not obtain informative likelihood ratios for a third. Therefore, successful ancestry assignments were achieved by participants in 92 of 95 Snipper analyses. This exercise demonstrates that ancestry inference tests based on binary marker sets can be readily adopted by laboratories that already have well-established CE regimes in place. The Indel test proved to be easy to use and allowed all exercise participants to detect the DNA mixture as well as achieving complete and concordant profiles in nearly all cases. Lastly, two participants successfully ran parallel next-generation sequencing analyses (each using different systems) and achieved high levels of genotyping concordance using the exercise PCR primer mixes unmodified.

The genetics of skin, hair, and eye color variation and its relevance to forensic pigmentation predictive tests

This review examines the potential application of single nucleotide polymorphism (SNP)-based predictive tests for skin, hair, and eye color to forensic analysis in support of police investigations lacking DNA database matches or eyewitness testimony. Brief descriptions of the biology of melanogenesis and the main genes involved are presented in order to understand the basis of common pigmentation variation in humans. We outline the most recently developed forensically sensitive multiplex tests that can be applied to investigative analyses. The review also describes the biology of the SNPs with the closest associations to, and therefore the best predictors for, common variation in eye, hair, and skin pigmentation. Because pigmentation pathways are complex in their patterns, many of the better-studied human albinism traits provide insight into how pigmentation SNPs interact, control, or modify gene expression and show varying degrees of association with the key genes identified to date. These aspects of SNP action are discussed in an overview of each of the functional groups of pigmentation genes.

Cerebral responses and role of the prefrontal cortex in conditioned pain modulation: an fMRI study in healthy subjects

The mechanisms underlying conditioned pain modulation (CPM) are multifaceted. We searched for a link between individual differences in prefrontal cortex activity during multi-trial heterotopic noxious cold conditioning and modulation of the cerebral response to phasic heat pain. In 24 healthy female subjects, we conditioned laser heat stimuli to the left hand by applying alternatively ice-cold or lukewarm compresses to the right foot. We compared pain ratings with cerebral fMRI BOLD responses. We also analyzed the relation between CPM and BOLD changes produced by the heterotopic cold conditioning itself, as well as the impact of anxiety and habituation of cold-pain ratings. Specific cerebral activation was identified in precuneus and left posterior insula/SII, respectively, during early and sustained phases of cold application. During cold conditioning, laser pain decreased (n=7), increased (n=10) or stayed unchanged (n=7). At the individual level, the psychophysical effect was directly proportional to the cold-induced modulation of the laser-induced BOLD response in left posterior insula/SII. The latter correlated with the BOLD response recorded 80s earlier during the initial 10-s phase of cold application in anterior cingulate, orbitofrontal and lateral prefrontal cortices. High anxiety and habituation of cold pain were associated with greater laser heat-induced pain during heterotopic cold stimulation. The habituation was also linked to the early cold-induced orbitofrontal responses. We conclude that individual differences in conditioned pain modulation are related to different levels of prefrontal cortical activation by the early part of the conditioning stimulus, possibly due to different levels in trait anxiety.

Calculated globulin (CG) as a screening test for antibody deficiency

Calculated globulin (total protein - albumin) is usually tested as part of a liver function test profile in both primary and secondary care and determines the serum globulin concentration, of which immunoglobulins are a major component. The main use hitherto of calculated globulin is to detect paraproteins when the level is high. This study investigated the potential to use low levels of calculated globulin to detect antibody deficiency. Serum samples with calculated globulin cut-off < 18 g/l based on results of a pilot study were collected from nine hospitals in Wales over a 12-month period. Anonymized request information was obtained and the samples tested for immunoglobulin levels, serum electrophoresis and, if appropriate, immunofixation. A method comparison for albumin measurement using bromocresol green and bromocresol purple was undertaken. Eighty-nine per cent (737 of 826) samples had an immunoglobulin (Ig)G level of < 6 g/l using the bromocresol green methodology with a cut-off of < 18 g/l, and 56% (459) had an IgG of < 4 g/l. Patients with both secondary and primary antibody deficiency were discovered and serum electrophoresis and immunofixation showed that 1·2% (10) had previously undetected small paraproteins associated with immune-paresis. Using bromocresol purple, 74% of samples had an IgG of < 6 g/l using a cut-off of < 23 g/l. Screening using calculated globulin with defined cut-off values detects both primary and secondary antibody deficiency and new paraproteins associated with immune-paresis. It is cheap, widely available and under-utilized. Antibody-deficient patients have been discovered using information from calculated globulin values, shortening diagnostic delay and time to treatment with immunoglobulin replacement therapy.