Reproducibility of R‐fMRI metrics on the impact of different strategies for multiple comparison correction and sample sizes

Concerns regarding reproducibility of resting‐state functional magnetic resonance imaging (R‐fMRI) findings have been raised. Little is known about how to operationally define R‐fMRI reproducibility and to what extent it is affected by multiple comparison correction strategies and sample size. We comprehensively assessed two aspects of reproducibility, test–retest reliability and replicability, on widely used R‐fMRI metrics in both between‐subject contrasts of sex differences and within‐subject comparisons of eyes‐open and eyes‐closed (EOEC) conditions. We noted permutation test with Threshold‐Free Cluster Enhancement (TFCE), a strict multiple comparison correction strategy, reached the best balance between family‐wise error rate (under 5%) and test–retest reliability/replicability (e.g., 0.68 for test–retest reliability and 0.25 for replicability of amplitude of low‐frequency fluctuations (ALFF) for between‐subject sex differences, 0.49 for replicability of ALFF for within‐subject EOEC differences). Although R‐fMRI indices attained moderate reliabilities, they replicated poorly in distinct datasets (replicability < 0.3 for between‐subject sex differences, < 0.5 for within‐subject EOEC differences). By randomly drawing different sample sizes from a single site, we found reliability, sensitivity and positive predictive value (PPV) rose as sample size increased. Small sample sizes (e.g., < 80 [40 per group]) not only minimized power (sensitivity < 2%), but also decreased the likelihood that significant results reflect “true” effects (PPV < 0.26) in sex differences. Our findings have implications for how to select multiple comparison correction strategies and highlight the importance of sufficiently large sample sizes in R‐fMRI studies to enhance reproducibility. Hum Brain Mapp 39:300–318, 2018. © 2017 Wiley Periodicals, Inc.     

Self‐regulation of ventromedial prefrontal cortex activation using real‐time fMRI neurofeedback—Influence of default mode network

The ventromedial prefrontal cortex (vmPFC) is involved in regulation of negative emotion and decision‐making, emotional and behavioral control, and active resilient coping. This pilot study examined the feasibility of training healthy subjects (n = 27) to self‐regulate the vmPFC activity using a real‐time functional magnetic resonance imaging neurofeedback (rtfMRI‐nf). Participants in the experimental group (EG, n = 18) were provided with an ongoing vmPFC hemodynamic activity (rtfMRI‐nf signal represented as variable‐height bar). Individuals were instructed to raise the bar by self‐relevant value‐based thinking. Participants in the control group (CG, n = 9) performed the same task; however, they were provided with computer‐generated sham neurofeedback signal. Results demonstrate that (a) both the CG and the EG show a higher vmPFC fMRI signal at the baseline than during neurofeedback training; (b) no significant positive training effect was seen in the vmPFC across neurofeedback runs; however, the medial prefrontal cortex, middle temporal gyri, inferior frontal gyri, and precuneus showed significant decreasing trends across the training runs only for the EG; (c) the vmPFC rtfMRI‐nf signal associated with the fMRI signal across the default mode network (DMN). These findings suggest that it may be difficult to modulate a single DMN region without affecting other DMN regions. Observed decreased vmPFC activity during the neurofeedback task could be due to interference from the fMRI signal within other DMN network regions, as well as interaction with task‐positive networks. Even though participants in the EG did not show significant positive increase in the vmPFC activity among neurofeedback runs, they were able to learn to accommodate the demand of self‐regulation task to maintain the vmPFC activity with the help of a neurofeedback signal.     

Wavelet‐based regularity analysis reveals recurrent spatiotemporal behavior in resting‐state fMRI

One of the major findings from multimodal neuroimaging studies in the past decade is that the human brain is anatomically and functionally organized into large‐scale networks. In resting state fMRI (rs‐fMRI), spatial patterns emerge when temporal correlations between various brain regions are tallied, evidencing networks of ongoing intercortical cooperation. However, the dynamic structure governing the brain's spontaneous activity is far less understood due to the short and noisy nature of the rs‐fMRI signal. Here, we develop a wavelet‐based regularity analysis based on noise estimation capabilities of the wavelet transform to measure recurrent temporal pattern stability within the rs‐fMRI signal across multiple temporal scales. The method consists of performing a stationary wavelet transform to preserve signal structure, followed by construction of “lagged” subsequences to adjust for correlated features, and finally the calculation of sample entropy across wavelet scales based on an “objective” estimate of noise level at each scale. We found that the brain's default mode network (DMN) areas manifest a higher level of irregularity in rs‐fMRI time series than rest of the brain. In 25 aged subjects with mild cognitive impairment and 25 matched healthy controls, wavelet‐based regularity analysis showed improved sensitivity in detecting changes in the regularity of rs‐fMRI signals between the two groups within the DMN and executive control networks, compared with standard multiscale entropy analysis. Wavelet‐based regularity analysis based on noise estimation capabilities of the wavelet transform is a promising technique to characterize the dynamic structure of rs‐fMRI as well as other biological signals. Hum Brain Mapp 36:3603–3620, 2015. © 2015 Wiley Periodicals, Inc.     

Changes in resting‐state functionally connected parietofrontal networks after videogame practice

Neuroimaging studies provide evidence for organized intrinsic activity under task‐free conditions. This activity serves functionally relevant brain systems supporting cognition. Here, we analyze changes in resting‐state functional connectivity after videogame practice applying a test–retest design. Twenty young females were selected from a group of 100 participants tested on four standardized cognitive ability tests. The practice and control groups were carefully matched on their ability scores. The practice group played during two sessions per week across 4 weeks (16 h total) under strict supervision in the laboratory, showing systematic performance improvements in the game. A group independent component analysis (GICA) applying multisession temporal concatenation on test–retest resting‐state fMRI, jointly with a dual‐regression approach, was computed. Supporting the main hypothesis, the key finding reveals an increased correlated activity during rest in certain predefined resting state networks (albeit using uncorrected statistics) attributable to practice with the cognitively demanding tasks of the videogame. Observed changes were mainly concentrated on parietofrontal networks involved in heterogeneous cognitive functions. Hum Brain Mapp 34:3143–3157, 2013. © 2012 Wiley Periodicals, Inc.     

Longitudinal reliability of tract‐based spatial statistics in diffusion tensor imaging

Relatively little is known about reliability of longitudinal diffusion‐tensor imaging (DTI) measurements despite growing interest in using DTI to track change in white matter structure. The purpose of this study is to quantify within‐ and between session scan‐rescan reliability of DTI‐derived measures that are commonly used to describe the characteristics of neural white matter in the context of neural plasticity research. DTI data were acquired from 16 cognitively healthy older adults (mean age 68.4). We used the Tract‐Based Spatial Statistics (TBSS) approach implemented in FSL, evaluating how different DTI preprocessing choices affect reliability indices. Test‐Retest reliability, quantified as ICC averaged across the voxels of the TBSS skeleton, ranged from 0.524 to 0.798 depending on the specific DTI‐derived measure and the applied preprocessing steps. The two main preprocessing steps that we found to improve TBSS reliability were (a) the use of a common individual template and (b) smoothing DTI data using a 1‐voxel median filter. Overall our data indicate that small choices in the preprocessing pipeline have a significant effect on test‐retest reliability, therefore influencing the power to detect change within a longitudinal study. Furthermore, differences in the data processing pipeline limit the comparability of results across studies. Hum Brain Mapp 35:4544–4555, 2014. © 2014 Wiley Periodicals, Inc.     

Spatial source phase: A new feature for identifying spatial differences based on complex‐valued resting‐state fMRI data

Spatial source phase, the phase information of spatial maps extracted from functional magnetic resonance imaging (fMRI) data by data‐driven methods such as independent component analysis (ICA), has rarely been studied. While the observed phase has been shown to convey unique brain information, the role of spatial source phase in representing the intrinsic activity of the brain is yet not clear. This study explores the spatial source phase for identifying spatial differences between patients with schizophrenia (SZs) and healthy controls (HCs) using complex‐valued resting‐state fMRI data from 82 individuals. ICA is first applied to preprocess fMRI data, and post‐ICA phase de‐ambiguity and denoising are then performed. The ability of spatial source phase to characterize spatial differences is examined by the homogeneity of variance test (voxel‐wise F‐test) with false discovery rate correction. Resampling techniques are performed to ensure that the observations are significant and reliable. We focus on two components of interest widely used in analyzing SZs, including the default mode network (DMN) and auditory cortex. Results show that the spatial source phase exhibits more significant variance changes and higher sensitivity to the spatial differences between SZs and HCs in the anterior areas of DMN and the left auditory cortex, compared to the magnitude of spatial activations. Our findings show that the spatial source phase can potentially serve as a new brain imaging biomarker and provide a novel perspective on differences in SZs compared to HCs, consistent with but extending previous work showing increased variability in patient data.     

A comparison of multiband and multiband multiecho gradient-echo EPI for task fMRI at 3 T

A multiband (MB) echo-planar imaging (EPI) sequence is compared to a multiband multiecho (MBME) EPI protocol to investigate differences in sensitivity for task functional magnetic resonance imaging (fMRI) at 3 T. Multiecho sampling improves sensitivity in areas where single-echo-EPI suffers from dropouts. However, It requires in-plane acceleration to reduce the echo train length, limiting the slice acceleration factor and the temporal and spatial resolution Data were acquired for both protocols in two sessions 24 h apart using an adapted color-word interference Stroop task. Besides protocol comparison statistically, we performed test–retest reliability across sessions for different protocols and denoising methods. We evaluated the sensitivity of two different echo-combination strategies for MBME-EPI. We examined the performance of three different data denoising approaches: “Standard,” “AROMA,” and “FIX” for MB and MBME, and assessed whether a specific method is preferable. We consider using an appropriate autoregressive model order within the general linear model framework to correct TR differences between the protocols. The comparison between protocols and denoising methods showed at group level significantly higher mean z-scores and the number of active voxels for MBME in the motor, subcortical and medial frontal cortices. When comparing different echo combinations, our results suggest that a contrast-to-noise ratio weighted echo combination improves sensitivity in MBME compared to simple echo-summation. This study indicates that MBME can be a preferred protocol in task fMRI at spatial resolution (≥2 mm), primarily in medial prefrontal and subcortical areas.     

Dissociated patterns of anti‐correlations with dorsal and ventral default‐mode networks at rest

Previous studies of resting state functional connectivity have demonstrated that the default‐mode network (DMN) is negatively correlated with a set of brain regions commonly activated during goal‐directed tasks. However, the location and extent of anti‐correlations are inconsistent across different studies, which has been posited to result largely from differences in whether or not global signal regression (GSR) was applied as a pre‐processing step. Notably, coordinates of seed regions‐of‐interest defined within the posterior cingulate cortex (PCC)/precuneus, an area often employed to study functional connectivity of the DMN, have been inconsistent across studies. Taken together with recent observations that the DMN contains functionally heterogeneous subdivisions, it is presently unclear whether these seeds map to different DMN subnetworks, whose patterns of anti‐correlation may differ. If so, then seed location may be a non‐negligible factor that, in addition to differences in preprocessing steps, contributes to the inconsistencies reported among published studies regarding DMN correlations/anti‐correlations. In this study, they examined anti‐correlations of different subnetworks within the DMN during rest using both seed‐based and point process analyses, and discovered that: (1) the ventral branch of the DMN (vDMN) yielded significantly weaker anti‐correlations than that associated with the dorsal branch of the DMN (dDMN); (2) vDMN anti‐correlations introduced by GSR were distinct from dDMN anti‐correlations; (3) PCC/precuneus seeds employed by earlier studies mapped to different DMN subnetworks, which may explain some of the inconsistency (in addition to preprocessing steps) in the reported DMN anti‐correlations. Hum Brain Mapp 38:2454–2465, 2017. © 2017 Wiley Periodicals, Inc.     

Anatomical and functional connectivity in the default mode network of post‐traumatic stress disorder patients after civilian and military‐related trauma

Posttraumatic stress disorder (PTSD) is characterized by unwanted intrusive thoughts and hyperarousal at rest. As these core symptoms reflect disturbance in resting‐state mechanisms, we investigated the functional and anatomical involvement of the default mode network (DMN) in this disorder. The relation between symptomatology and trauma characteristics was considered. Twenty PTSD patients and 20 matched trauma‐exposed controls that were exposed to a similar traumatic event were recruited for this study. In each group, 10 patients were exposed to military trauma, and 10 to civilian trauma. PTSD, anxiety, and depression symptom severity were assessed. DMN maps were identified in resting‐state scans using independent component analysis. Regions of interest (medial prefrontal, precuneus, and bilateral inferior parietal) were defined and average z‐scores were extracted for use in the statistical analysis. The medial prefrontal and the precuneus regions were used for cingulum tractography whose integrity was measured and compared between groups. Similar functional and anatomical connectivity patterns were identified in the DMN of PTSD patients and trauma‐exposed controls. In the PTSD group, functional and anatomical connectivity parameters were strongly correlated with clinical measures, and there was evidence of coupling between the anatomical and functional properties. Type of trauma and time from trauma were found to modulate connectivity patterns. To conclude, anatomical and functional connectivity patterns are related to PTSD symptoms and trauma characteristics influence connectivity beyond clinical symptoms. Hum Brain Mapp 37:589–599, 2016. © 2015 Wiley Periodicals, Inc .     

Regression‐based machine‐learning approaches to predict task activation using resting‐state fMRI

Resting‐state fMRI has shown the ability to predict task activation on an individual basis by using a general linear model (GLM) to map resting‐state network features to activation z‐scores. The question remains whether the relatively simplistic GLM is the best approach to accomplish this prediction. In this study, several regression‐based machine‐learning approaches were compared, including GLMs, feed‐forward neural networks, and random forest bootstrap aggregation (bagging). Resting‐state and task data from 350 Human Connectome Project subjects were analyzed. First, the effect of the number of training subjects on the prediction accuracy was evaluated. In addition, the prediction accuracy and Dice coefficient were compared across models. Prediction accuracy increased with the training number up to 200 subjects; however, an elbow in the prediction curve occurred around 30–40 training subjects. All models performed well with correlation matrices, which displayed correlation between actual and predicted task activation for all subjects, exhibiting a strong diagonal trend for all tasks. Overall, the neural network and random forest bagging techniques outperformed the GLM. These approaches, however, require additional computing power and processing time. These results show that, while the GLM performs well, resting‐state fMRI prediction of task activation could benefit from more complex machine learning approaches.     

Translation and validation of the 15‐item Myasthenia Gravis Quality of life scale in Dutch

Introduction: The 15‐item Myasthenia Gravis Quality of Life (MG‐QOL15) scale has been developed to assess the health‐related quality of life of patients with myasthenia gravis (MG). The aim of this study was to translate the original English version into Dutch and to test the test–retest reliability and construct validity. Methods: Fifty patients with MG were included. Test–retest reliability and internal consistency were assessed using the intraclass correlation coefficient (ICC) and the Cronbach α. Construct validity was assessed by testing 5 predefined hypotheses. Results: A good test–retest reliability was confirmed with an ICC of 0.866. The Cronbach α was 0.93. The predefined hypotheses were confirmed in 80% of cases, which points to good construct validity. Discussion: The Dutch MG‐QOL15 has good test–retest reliability and good construct validity. It can be used for research in a Dutch‐speaking population. It is also suitable for monitoring individual patients in clinical practice. Muscle Nerve 57 : 206–211, 2018     

Significance of longitudinal changes in the default‐mode network for cognitive recovery after stroke

Although a considerable number of patients suffer from cognitive impairments after stroke, the neural mechanism of cognitive recovery has not yet been clarified. Repeated resting‐state functional magnetic resonance imaging (fMRI) was used in this study to examine longitudinal changes in the default‐mode network (DMN) during the 6 months after stroke, and to investigate the relationship between DMN changes and cognitive recovery. Out of 24 initially recruited right‐hemispheric stroke patients, 11 (eight males, mean age 55.7 years) successfully completed the repeated fMRI protocol. Patients underwent three fMRI sessions at 1, 3 and 6 months after stroke. Their DMNs were analysed and compared with those of 11 age‐matched healthy subjects (nine males, mean age 56.2 years). Correlations between DMN connectivity and improvement of the cognitive performance scores were also assessed. The stroke patients were found to demonstrate markedly decreased DMN connectivity of the posterior cingulate cortex, precuneus, medial frontal gyrus and inferior parietal lobes at 1 month after stroke. At 3 months after stroke, the DMN connectivity of these brain areas was almost restored, suggesting that the period is critical for neural reorganization. The DMN connectivity of the dorsolateral prefrontal cortex in the contralesional hemisphere showed a significant correlation with cognitive function recovery in stroke patients, and should be considered a compensatory process for overcoming cognitive impairment due to brain lesion. This is the first longitudinal study to demonstrate the changes in DMN during recovery after stroke and the key regions influencing cognitive recovery.     

A comparison of denoising pipelines in high temporal resolution task‐based functional magnetic resonance imaging data

It has been known for decades that head motion/other artifacts affect the blood oxygen level‐dependent signal. Recent recommendations predominantly focus on denoising resting state data, which may not apply to task data due to the different statistical relationships that exist between signal and noise sources. Several blind‐source denoising strategies (FIX and AROMA) and more standard motion parameter (MP) regression (0, 12, or 24 parameters) analyses were therefore compared across four sets of event‐related functional magnetic resonance imaging (erfMRI) and block‐design (bdfMRI) datasets collected with multiband 32‐ (repetition time [TR] = 460 ms) or older 12‐channel (TR = 2,000 ms) head coils. The amount of motion varied across coil designs and task types. Quality control plots indicated small to moderate relationships between head motion estimates and percent signal change in both signal and noise regions. Blind‐source denoising strategies eliminated signal as well as noise relative to MP24 regression; however, the undesired effects on signal depended both on algorithm (FIX > AROMA) and design (bdfMRI > erfMRI). Moreover, in contrast to previous results, there were minimal differences between MP12/24 and MP0 pipelines in both erfMRI and bdfMRI designs. MP12/24 pipelines were detrimental for a task with both longer block length (30 ± 5 s) and higher correlations between head MPs and design matrix. In summary, current results suggest that there does not appear to be a single denoising approach that is appropriate for all fMRI designs. However, even nonaggressive blind‐source denoising approaches appear to remove signal as well as noise from task‐related data at individual subject and group levels.     

Distortion correction for diffusion‐weighted MRI tractography and fMRI in the temporal lobes

Single shot echo‐planar imaging (EPI) sequences are currently the most commonly used sequences for diffusion‐weighted imaging (DWI) and functional magnetic resonance imaging (fMRI) as they allow relatively high signal to noise with rapid acquisition time. A major drawback of EPI is the substantial geometric distortion and signal loss that can occur due to magnetic field inhomogeneities close to air‐tissue boundaries. If DWI‐based tractography and fMRI are to be applied to these regions, then the distortions must be accurately corrected to achieve meaningful results. We describe robust acquisition and processing methods for correcting such distortions in spin echo (SE) EPI using a variant of the reversed direction k space traversal method with a number of novel additions. We demonstrate that dual direction k space traversal with maintained diffusion‐encoding gradient strength and direction results in correction of the great majority of eddy current‐associated distortions in DWI, in addition to those created by variations in magnetic susceptibility. We also provide examples to demonstrate that the presence of severe distortions cannot be ignored if meaningful tractography results are desired. The distortion correction routine was applied to SE‐EPI fMRI acquisitions and allowed detection of activation in the temporal lobe that had been previously found using PET but not conventional fMRI. Hum Brain Mapp, 2010. © 2010 Wiley‐Liss, Inc.     

Altered resting‐state connectivity during interictal generalized spike‐wave discharges in drug‐naïve childhood absence epilepsy

Purpose: To investigate the intrinsic brain connections at the time of interictal generalized spike‐wave discharges (GSWDs) to understand their mechanism of effect on brain function in untreated childhood absence epilepsy (CAE). Methods: The EEG‐functional MRI (fMRI) was used to measure the resting state functional connectivity during interictal GSWDs in drug‐naïve CAE, and three different brain networks—the default mode network (DMN), cognitive control network (CCN), and affective network (AN)—were investigated. Results: Cross‐correlation functional connectivity analysis with priori seed revealed decreased functional connectivity within each of these three networks in the CAE patients during interictal GSWDS. It included precuneus‐dorsolateral prefrontal cortex (DLPFC), dorsomedial prefrontal cortex (DMPFC), and inferior parietal lobule in the DMN; DLPFC‐inferior frontal junction (IFJ), and pre‐supplementary motor area (pre‐SMA) subregions connectivity disruption in CCN; ACC‐ventrolateral prefrontal cortex (VLPFC) and DMPFC in AN; There were also some regions, primarily the parahippcampus, paracentral in AN, and the left frontal mid orb in the CCN, which showed increased connectivity. Conclusions: The current findings demonstrate significant alterations of resting‐state networks in drug naïve CAE subjects during interictal GSWDs and interictal GSWDs can cause dysfunction in specific networks important for psychosocial function. Impairment of these networks may cause deficits both during and between seizures. Our study may contribute to the understanding of neuro‐pathophysiological mechanism of psychosocial function impairments in patients with CAE. Hum Brain Mapp, 2013. © 2012 Wiley Periodicals, Inc.     

Multiple‐region directed functional connectivity based on phase delays

Network analysis is increasingly advancing the field of neuroimaging. Neural networks are generally constructed from pairwise interactions with an assumption of linear relations between them. Here, a high‐order statistical framework to calculate directed functional connectivity among multiple regions, using wavelet analysis and spectral coherence has been presented. The mathematical expression for 4 regions was derived and used to characterize a quartet of regions as a linear , combined (nonlinear), or disconnected network. Phase delays between regions were used to obtain network's temporal hierarchy and directionality. The validity of the mathematical derivation along with the effects of coupling strength and noise on its outcomes were studied by computer simulations of the Kuramoto model. The simulations demonstrated correct directionality for a large range of coupling strength and low sensitivity to Gaussian noise compared with pairwise coherences. The analysis was applied to resting‐state fMRI data of 40 healthy young subjects to characterize the ventral visual system, motor system and default mode network (DMN). It was shown that the ventral visual system was predominantly composed of linear networks while the motor system and the DMN were composed of combined (nonlinear) networks. The ventral visual system exhibits its known temporal hierarchy, the motor system exhibits center ? out hierarchy and the DMN has dorsal ? ventral and anterior ? posterior organizations. The analysis can be applied in different disciplines such as seismology, or economy and in a variety of brain data including stimulus‐driven fMRI, electrophysiology, EEG, and MEG, thus open new horizons in brain research. Hum Brain Mapp 38:1374–1386, 2016 . © 2016 Wiley Periodicals, Inc.     

Task and task‐free FMRI reproducibility comparison for motor network identification

Test‐retest reliability of individual functional magnetic resonance imaging (fMRI) results is of importance in clinical practice and longitudinal experiments. While several studies have investigated reliability of task‐induced motor network activation, less is known about the reliability of the task‐free motor network. Here, we investigate the reproducibility of task‐free fMRI, and compare it to motor task activity. Sixteen healthy subjects participated in this study with a test‐retest interval of seven weeks. The task‐free motor network was assessed with a univariate, seed‐voxel‐based correlation analysis. Reproducibility was tested by means of intraclass correlation (ICC) values and ratio of overlap. Higher ICC values and a better overlap were found for task fMRI as compared to task‐free fMRI. Furthermore, ratio of overlap improved for task fMRI at higher thresholds, while it decreased for task‐free fMRI, suggesting a less focal spatial pattern of the motor network during resting state. However, for both techniques the most active voxels were located in the primary motor cortex. This indicates that, just like task fMRI, task‐free fMRI can properly identify critical brain areas for motor task performance. Although both fMRI techniques are able to detect the motor network, resting‐state fMRI is less reliable than task fMRI. Hum Brain Mapp 35:340–352, 2014. © 2012 Wiley Periodicals, Inc.     

Reliability correction for functional connectivity: Theory and implementation

Network properties can be estimated using functional connectivity MRI (fcMRI). However, regional variation of the fMRI signal causes systematic biases in network estimates including correlation attenuation in regions of low measurement reliability. Here we computed the spatial distribution of fcMRI reliability using longitudinal fcMRI datasets and demonstrated how pre‐estimated reliability maps can correct for correlation attenuation. As a test case of reliability‐based attenuation correction we estimated properties of the default network, where reliability was significantly lower than average in the medial temporal lobe and higher in the posterior medial cortex, heterogeneity that impacts estimation of the network. Accounting for this bias using attenuation correction revealed that the medial temporal lobe's contribution to the default network is typically underestimated. To render this approach useful to a greater number of datasets, we demonstrate that test‐retest reliability maps derived from repeated runs within a single scanning session can be used as a surrogate for multi‐session reliability mapping. Using data segments with different scan lengths between 1 and 30 min, we found that test‐retest reliability of connectivity estimates increases with scan length while the spatial distribution of reliability is relatively stable even at short scan lengths. Finally, analyses of tertiary data revealed that reliability distribution is influenced by age, neuropsychiatric status and scanner type, suggesting that reliability correction may be especially important when studying between‐group differences. Collectively, these results illustrate that reliability‐based attenuation correction is an easily implemented strategy that mitigates certain features of fMRI signal nonuniformity. Hum Brain Mapp, 2015. © 2015 Wiley Periodicals, Inc .     

Myotonic Dystrophy Health Index: Initial evaluation of a disease‐specific outcome measure

Introduction: In preparation for clinical trials we examine the validity, reliability, and patient understanding of the Myotonic Dystrophy Health Index (MDHI). Methods: Initially we partnered with 278 myotonic dystrophy type‐1 (DM1) patients and identified the most relevant questions for the MDHI. Next, we used factor analysis, patient interviews, and test–retest reliability assessments to refine and evaluate the instrument. Lastly, we determined the capability of the MDHI to differentiate between known groups of DM1 participants. Results: Questions in the final MDHI represent 17 areas of DM1 health. The internal consistency was acceptable in all subscales. The MDHI had a high test–retest reliability (ICC = 0.95) and differentiated between DM1 patient groups with different disease severities. Conclusions: Initial evaluation of the MDHI provides evidence that it is valid and reliable as an outcome measure for assessing patient‐reported health. These results suggest that important aspects of DM1 health may be measured effectively using the MDHI. Muscle Nerve 49 : 906–914, 2014     

Default‐mode network functional connectivity is closely related to metabolic activity

Over the last decade, the brain's default‐mode network (DMN) and its function has attracted a lot of attention in the field of neuroscience. However, the exact underlying mechanisms of DMN functional connectivity, or more specifically, the blood‐oxygen level‐dependent (BOLD) signal, are still incompletely understood. In the present study, we combined 2‐deoxy‐2‐[¹⁸F]fluoroglucose positron emission tomography (FDG‐PET), proton magnetic resonance spectroscopy (¹H‐MRS), and resting‐state functional magnetic resonance imaging (rs‐fMRI) to investigate more directly the association between local glucose consumption, local glutamatergic neurotransmission and DMN functional connectivity during rest. The results of the correlation analyzes using the dorsal posterior cingulate cortex (dPCC) as seed region showed spatial similarities between fluctuations in FDG‐uptake and fluctuations in BOLD signal. More specifically, in both modalities the same DMN areas in the inferior parietal lobe, angular gyrus, precuneus, middle, and medial frontal gyrus were positively correlated with the dPCC. Furthermore, we could demonstrate that local glucose consumption in the medial frontal gyrus, PCC and left angular gyrus was associated with functional connectivity within the DMN. We did not, however, find a relationship between glutamatergic neurotransmission and functional connectivity. In line with very recent findings, our results lend further support for a close association between local metabolic activity and functional connectivity and provide further insights towards a better understanding of the underlying mechanism of the BOLD signal. Hum Brain Mapp 36:2027–2038, 2015. © 2015 Wiley Periodicals, Inc.