Towards a consensus regarding global signal regression for resting state functional connectivity MRI

The number of resting state functional connectivity MRI studies continues to expand at a rapid rate along with the options for data processing. Of the processing options, few have generated as much controversy as global signal regression and the subsequent observation of negative correlations (anti-correlations). This debate has motivated new processing strategies and advancement in the field, but has also generated significant confusion and contradictory guidelines. In this article, we work towards a consensus regarding global signal regression. We highlight several points of agreement including the fact that there is not a single “right” way to process resting state data that reveals the “true” nature of the brain. Although further work is needed, different processing approaches likely reveal complementary insights about the brain's functional organisation

Using resting-state DMN effective connectivity to characterize the neurofunctional architecture of empathy

Neuroimaging studies in social neuroscience have largely relied on functional connectivity (FC) methods to characterize the functional integration between different brain regions. However, these methods have limited utility in social-cognitive studies that aim to understand the directed information flow among brain areas that underlies complex psychological processes. In this study we combined functional and effective connectivity approaches to characterize the functional integration within the Default Mode Network (DMN) and its role in self-perceived empathy. Forty-two participants underwent a resting state fMRI scan and completed a questionnaire of dyadic empathy. Independent Component Analysis (ICA) showed that higher empathy scores were associated with an increased contribution of the medial prefrontal cortex (mPFC) to the DMN spatial mode. Dynamic causal modelling (DCM) combined with Canonical Variance Analysis (CVA) revealed that this association was mediated indirectly by the posterior cingulate cortex (PCC) via the right inferior parietal lobule (IPL). More specifically, in participants with higher scores in empathy, the PCC had a greater effect on bilateral IPL and the right IPL had a greater influence on mPFC. These results highlight the importance of using analytic approaches that address directed and hierarchical connectivity within networks, when studying complex psychological phenomena, such as empathy.- This study was funded by BIAL Foundation (Grant number 87/12); by the Portuguese Foundation for Science and Technology and the Portuguese Ministry of Education and Science through national funds and co-financed by FEDER through COMPETE2020 under the PT2020 Partnership Agreement (POCI-01-0145-FEDER-007653); by the postdoctoral scholarship UMINHO/BPD/18/2017 and by the Portuguese Foundation for Science Doctoral scholarship (PD/BD/105963/2014). This work was conducted at Psychology Research Centre (UID/PSI/01662/2013), University of Minho

The Value of Instability: An Investigation of Intra-Subject Variability in Brain Activity Among Obese Adolescent Girls

BACKGROUND: The present study investigated the value of intra-subject variability (ISV) as a metric for revealing differences in cognition and brain activation associated with an obese versus lean body mass. METHODS: Ninety-six adolescents with a lean body mass (BMI %-ile = 5-85), and 92 adolescents with an obese body mass (BMI %-ile \u3e=95), performed two tasks (Stroop and Go/NoGo) challenging response inhibition skills. The standard deviations and averages of their reaction time and P300 electroencephalographic responses to task stimuli were computed across trials. RESULTS: During the Go/NoGo task, the reaction times of subjects with an obese body mass were more variable than those of their lean body mass peers. Accompanying the greater ISV in reaction times was a group difference in P300 amplitude ISV in the opposite direction across both tasks. The effect sizes associated with these group differences in ISV were marginally greater than the effect sizes for the comparisons of the group means. CONCLUSIONS: ISV may be superior to the mean as a tool for differentiating groups without significant cognitive impairment. The co-occurrence of reduced ISV in P300 amplitude and elevated ISV in reaction time may indicate a constraint among obese adolescent girls in the range of information processing strategies and neural networks that can compete to optimize response output. It remains to be determined if this decrement in neural plasticity has implications for their problem solving skills as well as their response to weight management interventions

A Novel Joint Brain Network Analysis Using Longitudinal Alzheimer's Disease Data.

There is well-documented evidence of brain network differences between individuals with Alzheimer's disease (AD) and healthy controls (HC). To date, imaging studies investigating brain networks in these populations have typically been cross-sectional, and the reproducibility of such findings is somewhat unclear. In a novel study, we use the longitudinal ADNI data on the whole brain to jointly compute the brain network at baseline and one-year using a state of the art approach that pools information across both time points to yield distinct visit-specific networks for the AD and HC cohorts, resulting in more accurate inferences. We perform a multiscale comparison of the AD and HC networks in terms of global network metrics as well as at the more granular level of resting state networks defined under a whole brain parcellation. Our analysis illustrates a decrease in small-worldedness in the AD group at both the time points and also identifies more local network features and hub nodes that are disrupted due to the progression of AD. We also obtain high reproducibility of the HC network across visits. On the other hand, a separate estimation of the networks at each visit using standard graphical approaches reveals fewer meaningful differences and lower reproducibility

Intra- and Inter-Scanner Reliability of Voxel-Wise Whole-Brain Analytic Metrics for Resting State fMRI

As the multi-center studies with resting-state functional magnetic resonance imaging (RS-fMRI) have been more and more applied to neuropsychiatric studies, both intra- and inter-scanner reliability of RS-fMRI are becoming increasingly important. The amplitude of low frequency fluctuation (ALFF), regional homogeneity (ReHo), and degree centrality (DC) are 3 main RS-fMRI metrics in a way of voxel-wise whole-brain (VWWB) analysis. Although the intra-scanner reliability (i.e., test-retest reliability) of these metrics has been widely investigated, few studies has investigated their inter-scanner reliability. In the current study, 21 healthy young subjects were enrolled and scanned with blood oxygenation level dependent (BOLD) RS-fMRI in 3 visits (V1 – V3), with V1 and V2 scanned on a GE MR750 scanner and V3 on a Siemens Prisma. RS-fMRI data were collected under two conditions, eyes open (EO) and eyes closed (EC), each lasting 8 minutes. We firstly evaluated the intra- and inter-scanner reliability of ALFF, ReHo, and DC. Secondly, we measured systematic difference between two scanning visits of the same scanner as well as between two scanners. Thirdly, to account for the potential difference of intra- and inter-scanner local magnetic field inhomogeneity, we measured the difference of relative BOLD signal intensity to the mean BOLD signal intensity of the whole brain between each pair of visits. Last, we used percent amplitude of fluctuation (PerAF) to correct the difference induced by relative BOLD signal intensity. The inter-scanner reliability was much worse than intra-scanner reliability; Among the VWWB metrics, DC showed the worst (both for intra-scanner and inter-scanner comparisons). PerAF showed similar intra-scanner reliability with ALFF and the best reliability among all the 4 metrics. PerAF reduced the influence of BOLD signal intensity and hence increase the inter-scanner reliability of ALFF. For multi-center studies, inter-scanner reliability should be taken into account

Sex differences and menstrual cycle effects in cognitive and sensory resting state networks

It has not yet been established if resting state (RS) connectivity reflects stable characteristics of the brain, or if it is modulated by the psychological and/or physiological state of the participant. Based on research demonstrating sex hormonal effects in task-related brain activity, the present study aimed to investigate corresponding differences in RS networks. RS functional Magnetic Resonance Imaging (RS fMRI) was conducted in women during three different menstrual cycle phases, while men underwent three repeated RS fMRI testing sessions. Independent component analysis was used to identify the default mode network (DMN) and an auditory RS network. For the DMN, RS connectivity was stable across testing sessions in men, but varied across the menstrual cycle in women. For the auditory network (AN), retest reliable sex difference was found. Although RS activity in the DMN has been interpreted as trait characteristic of functional brain organization, these findings suggest that RS activity in networks involving frontal areas might be less stable than in sensory-based networks and can dynamically fluctuate. This also implies that some of the previously reported effects of sex hormones on task-related activity might to some extent be mediated by cycle-related fluctuations in RS activity, especially when frontal areas are involve

A parsimonious statistical method to detect groupwise differentially expressed functional connectivity networks

Group‐level functional connectivity analyses often aim to detect the altered connectivity patterns between subgroups with different clinical or psychological experimental conditions, for example, comparing cases and healthy controls. We present a new statistical method to detect differentially expressed connectivity networks with significantly improved power and lower false‐positive rates. The goal of our method was to capture most differentially expressed connections within networks of constrained numbers of brain regions (by the rule of parsimony). By virtue of parsimony, the false‐positive individual connectivity edges within a network are effectively reduced, whereas the informative (differentially expressed) edges are allowed to borrow strength from each other to increase the overall power of the network. We develop a test statistic for each network in light of combinatorics graph theory, and provide p‐values for the networks (in the weak sense) by using permutation test with multiple‐testing adjustment. We validate and compare this new approach with existing methods, including false discovery rate and network‐based statistic, via simulation studies and a resting‐state functional magnetic resonance imaging case–control study. The results indicate that our method can identify differentially expressed connectivity networks, whereas existing methods are limited. Hum Brain Mapp 36:5196–5206, 2015. © 2015 Wiley Periodicals, Inc.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/136357/1/hbm23007_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136357/2/hbm23007.pd

Segregation of functional networks is associated with cognitive resilience in Alzheimer's disease

Cognitive resilience is an important modulating factor of cognitive decline in Alzheimer's disease, but the functional brain mechanisms that support cognitive resilience remain elusive. Given previous findings in normal aging, we tested the hypothesis that higher segregation of the brain's connectome into distinct functional networks represents a functional mechanism underlying cognitive resilience in Alzheimer's disease. Using resting-state functional MRI, we assessed both resting-state-fMRI global system segregation, i.e. the balance of between-network to within-network connectivity, and the alternate index of modularity Q as predictors of cognitive resilience. We performed all analyses in two independent samples for validation: First, we included 108 individuals with autosomal dominantly inherited Alzheimer's disease and 71 non-carrier controls. Second, we included 156 amyloid-PET positive subjects across the spectrum of sporadic Alzheimer's disease as well as 184 amyloid-negative controls. In the autosomal dominant Alzheimer's disease sample, disease severity was assessed by estimated years from symptom onset. In the sporadic Alzheimer's sample, disease stage was assessed by temporal-lobe tau-PET (i.e. composite across Braak stage I & III regions). In both samples, we tested whether the effect of disease severity on cognition was attenuated at higher levels of functional network segregation. For autosomal dominant Alzheimer's disease, we found higher fMRI-assessed system segregation to be associated with an attenuated effect of estimated years from symptom onset on global cognition (p = 0.007). Similarly, for sporadic Alzheimer's disease patients, higher fMRI-assessed system segregation was associated with less decrement in global cognition (p = 0.001) and episodic memory (p = 0.004) per unit increase of temporal lobe tau-PET. Confirmatory analyses using the alternate index of modularity Q revealed consistent results. In conclusion, higher segregation of functional connections into distinct large-scale networks supports cognitive resilience in Alzheimer's disease