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.     

Challenges in measuring individual differences in functional connectivity using fMRI: The case of healthy aging

Many studies report individual differences in functional connectivity, such as those related to age. However, estimates of connectivity from fMRI are confounded by other factors, such as vascular health, head motion and changes in the location of functional regions. Here, we investigate the impact of these confounds, and pre‐processing strategies that can mitigate them, using data from the Cambridge Centre for Ageing & Neuroscience ( www.cam-can.com ). This dataset contained two sessions of resting‐state fMRI from 214 adults aged 18–88. Functional connectivity between all regions was strongly related to vascular health, most likely reflecting respiratory and cardiac signals. These variations in mean connectivity limit the validity of between‐participant comparisons of connectivity estimates, and were best mitigated by regression of mean connectivity over participants. We also showed that high‐pass filtering, instead of band‐pass filtering, produced stronger and more reliable age‐effects. Head motion was correlated with gray‐matter volume in selected brain regions, and with various cognitive measures, suggesting that it has a biological (trait) component, and warning against regressing out motion over participants. Finally, we showed that the location of functional regions was more variable in older adults, which was alleviated by smoothing the data, or using a multivariate measure of connectivity. These results demonstrate that analysis choices have a dramatic impact on connectivity differences between individuals, ultimately affecting the associations found between connectivity and cognition. It is important that fMRI connectivity studies address these issues, and we suggest a number of ways to optimize analysis choices. Hum Brain Mapp 38:4125–4156, 2017. © 2017 Wiley Periodicals, Inc.     

Network component analysis reveals developmental trajectories of structural connectivity and specific alterations in autism spectrum disorder

The structural organization of the brain can be characterized as a hierarchical ensemble of segregated modules linked by densely interconnected hub regions that facilitate distributed functional interactions. Disturbances to this network may be an important marker of abnormal development. Recently, several neurodevelopmental disorders, including autism spectrum disorder (ASD), have been framed as disorders of connectivity but the full nature and timing of these disturbances remain unclear. In this study, we use non‐negative matrix factorization, a data‐driven, multivariate approach, to model the structural network architecture of the brain as a set of superposed subnetworks, or network components. In an openly available dataset of 196 subjects scanned between 5 and 85 years we identify a set of robust and reliable subnetworks that develop in tandem with age and reflect both anatomically local and long‐range, network hub connections. In a second experiment, we compare network components in a cohort of 51 high‐functioning ASD adolescents to a group of age‐matched controls. We identify a specific subnetwork representing an increase in local connection strength in the cingulate cortex in ASD (t = 3.44, P < 0.001). This work highlights possible long‐term implications of alterations to the developmental trajectories of specific cortical subnetworks. Hum Brain Mapp 38:4169–4184, 2017. © 2017 Wiley Periodicals, Inc.     

Functional hypergraph uncovers novel covariant structures over neurodevelopment

Brain development during adolescence is marked by substantial changes in brain structure and function, leading to a stable network topology in adulthood. However, most prior work has examined the data through the lens of brain areas connected to one another in large‐scale functional networks. Here, we apply a recently developed hypergraph approach that treats network connections (edges) rather than brain regions as the unit of interest, allowing us to describe functional network topology from a fundamentally different perspective. Capitalizing on a sample of 780 youth imaged as part of the Philadelphia Neurodevelopmental Cohort, this hypergraph representation of resting‐state functional MRI data reveals three distinct classes of subnetworks (hyperedges): clusters, bridges, and stars, which respectively represent homogeneously connected, bipartite, and focal architectures. Cluster hyperedges show a strong resemblance to previously‐described functional modules of the brain including somatomotor, visual, default mode, and salience systems. In contrast, star hyperedges represent highly localized subnetworks centered on a small set of regions, and are distributed across the entire cortex. Finally, bridge hyperedges link clusters and stars in a core–periphery organization. Notably, developmental changes within hyperedges are ordered in a similar core–periphery fashion, with the greatest developmental effects occurring in networked hyperedges within the functional core. Taken together, these results reveal a novel decomposition of the network organization of human brain, and further provide a new perspective on the role of local structures that emerge across neurodevelopment. Hum Brain Mapp 38:3823–3835, 2017. © 2017 Wiley Periodicals, Inc.     

Assessing connectivity related injury burden in diffuse traumatic brain injury

Many of the clinical and behavioral manifestations of traumatic brain injury (TBI) are thought to arise from disruption to the structural network of the brain due to diffuse axonal injury (DAI). However, a principled way of summarizing diffuse connectivity alterations to quantify injury burden is lacking. In this study, we developed a connectome injury score, Disruption Index of the Structural Connectome (DISC), which summarizes the cumulative effects of TBI‐induced connectivity abnormalities across the entire brain. Forty patients with moderate‐to‐severe TBI examined at 3 months postinjury and 35 uninjured healthy controls underwent magnetic resonance imaging with diffusion tensor imaging, and completed behavioral assessment including global clinical outcome measures and neuropsychological tests. TBI patients were selected to maximize the likelihood of DAI in the absence of large focal brain lesions. We found that hub‐like regions, with high betweenness centrality, were most likely to be impaired as a result of diffuse TBI. Clustering of participants revealed a subgroup of TBI patients with similar connectivity abnormality profiles who exhibited relatively poor cognitive performance. Among TBI patients, DISC was significantly correlated with post‐traumatic amnesia, verbal learning, executive function, and processing speed. Our experiments jointly demonstrated that assessing structural connectivity alterations may be useful in development of patient‐oriented diagnostic and prognostic tools. Hum Brain Mapp 38:2913–2922, 2017. © 2017 Wiley Periodicals, Inc.     

Reorganization of brain connectivity in obesity

Global brain connectivity (GBC) identifies regions of the brain, termed “hubs,” which are densely connected and metabolically costly, and have a wide influence on brain function. Since obesity is associated with central and peripheral metabolic dysfunction we sought to determine if GBC is altered in obesity. Two independent fMRI data sets were subjected to GBC analyses. The first data set was acquired while participants (n = 15 healthy weight and 15 obese) tasted milkshake and the second with participants at rest (n = 33 healthy weight and 28 obese). In the resting state and during milkshake consumption GBC is consistently decreased in the ventromedial and ventrolateral prefrontal cortex, insula and caudate nucleus, and increased in brain regions belonging to the dorsal attention network including premotor areas, superior parietal lobule, and visual cortex. During milkshake consumption, but not at rest, additional decreases in GBC are observed in feeding‐related circuitry including the insula, amygdala, anterior hippocampus, hypothalamus, midbrain, brainstem and somatomotor cortex. Additionally, GBC differences were not accounted for by age. These results demonstrate that obesity is associated with decreased GBC in prefrontal and feeding circuits and increased GBC in the dorsal attention network. We therefore conclude that global brain organization is altered in obesity to favor networks important for external orientation over those monitoring homeostatic state and guiding feeding decisions. Furthermore, since prefrontal decreases are also observed at rest in obese individuals future work should evaluate whether these changes are associated with neurocognitive impairments frequently observed in obesity and diabetes. Hum Brain Mapp 38:1403–1420, 2017. © 2016 Wiley Periodicals, Inc.     

Connectivity strength‐weighted sparse group representation‐based brain network construction for MCI classification

Brain functional network analysis has shown great potential in understanding brain functions and also in identifying biomarkers for brain diseases, such as Alzheimer's disease (AD) and its early stage, mild cognitive impairment (MCI). In these applications, accurate construction of biologically meaningful brain network is critical. Sparse learning has been widely used for brain network construction; however, its l₁‐norm penalty simply penalizes each edge of a brain network equally, without considering the original connectivity strength which is one of the most important inherent linkwise characters. Besides, based on the similarity of the linkwise connectivity, brain network shows prominent group structure (i.e., a set of edges sharing similar attributes). In this article, we propose a novel brain functional network modeling framework with a “connectivity strength‐weighted sparse group constraint.” In particular, the network modeling can be optimized by considering both raw connectivity strength and its group structure, without losing the merit of sparsity. Our proposed method is applied to MCI classification, a challenging task for early AD diagnosis. Experimental results based on the resting‐state functional MRI, from 50 MCI patients and 49 healthy controls, show that our proposed method is more effective (i.e., achieving a significantly higher classification accuracy, 84.8%) than other competing methods (e.g., sparse representation, accuracy = 65.6%). Post hoc inspection of the informative features further shows more biologically meaningful brain functional connectivities obtained by our proposed method. Hum Brain Mapp 38:2370–2383, 2017. © 2017 Wiley Periodicals, Inc.     

Mapping complementary features of cross‐species structural connectivity to construct realistic “Virtual Brains”

Modern systems neuroscience increasingly leans on large‐scale multi‐lab neuroinformatics initiatives to provide necessary capacity for biologically realistic modeling of primate whole‐brain activity. Here, we present a framework to assemble primate brain's biologically plausible anatomical backbone for such modeling initiatives. In this framework, structural connectivity is determined by adding complementary information from invasive macaque axonal tract tracing and non‐invasive human diffusion tensor imaging. Both modalities are combined by means of available interspecies registration tools and a newly developed Bayesian probabilistic modeling approach to extract common connectivity evidence. We demonstrate how this novel framework is embedded in the whole‐brain simulation platform called The Virtual Brain (TVB). Hum Brain Mapp 38:2080–2093, 2017. © 2017 Wiley Periodicals, Inc.     

Overweight is associated with lower resting state functional connectivity in females after eliminating genetic effects: A twin study

Obesity is related to altered functional connectivity of resting state brain networks that are involved in reward and motivation. It is unknown to what extent these associations reflect genetic confounding and whether the obesity‐related connectivity changes are associated with differences in dietary intake. In this study, resting state functional MRI was performed after an overnight fast in 16 female monozygotic twin pairs (aged 48.8 ± 9.8 years) with a mean BMI discordance of 3.96 ± 2.1 kg/m² (range 0.7–8.2). Functional connectivity of the salience, basal ganglia, default mode and anterior cingulate–orbitofrontal cortex networks was examined by independent component analysis. Dietary intake was assessed using 3‐day 24‐hour recalls. Results revealed that within the basal ganglia network, heavier versus leaner co‐twins have decreased functional connectivity strength in bilateral putamen (P < 0.05, FWE‐corrected). There were no differences in connectivity in the other networks examined. In the overall group, lower functional connectivity strength in the left putamen was correlated with higher intake of total fat (P < 0.01). It was concluded that, after eliminating genetic effects, overweight is associated with lower resting state functional connectivity in bilateral putamen in the basal ganglia network. The association between lower putamen connectivity and higher fat intake suggests an important role of the putamen in appetitive mechanisms. The cross‐sectional nature of our study cannot discriminate cause and consequence, but the findings are compatible with an effect of lower putamen connectivity on increased BMI and associated higher fat intake. Hum Brain Mapp 38:5069–5081, 2017. © 2017 Wiley Periodicals, Inc.     

Detecting large‐scale networks in the human brain using high‐density electroencephalography

High‐density electroencephalography (hdEEG) is an emerging brain imaging technique that can be used to investigate fast dynamics of electrical activity in the healthy and the diseased human brain. Its applications are however currently limited by a number of methodological issues, among which the difficulty in obtaining accurate source localizations. In particular, these issues have so far prevented EEG studies from reporting brain networks similar to those previously detected by functional magnetic resonance imaging (fMRI). Here, we report for the first time a robust detection of brain networks from resting state (256‐channel) hdEEG recordings. Specifically, we obtained 14 networks previously described in fMRI studies by means of realistic 12‐layer head models and exact low‐resolution brain electromagnetic tomography (eLORETA) source localization, together with independent component analysis (ICA) for functional connectivity analysis. Our analyses revealed three important methodological aspects. First, brain network reconstruction can be improved by performing source localization using the gray matter as source space, instead of the whole brain. Second, conducting EEG connectivity analyses in individual space rather than on concatenated datasets may be preferable, as it permits to incorporate realistic information on head modeling and electrode positioning. Third, the use of a wide frequency band leads to an unbiased and generally accurate reconstruction of several network maps, whereas filtering data in a narrow frequency band may enhance the detection of specific networks and penalize that of others. We hope that our methodological work will contribute to rise of hdEEG as a powerful tool for brain research. Hum Brain Mapp 38:4631–4643, 2017. © 2017 Wiley Periodicals, Inc.