The Impact of Normalization and Segmentation on Resting-State Brain Networks

Reorganization of the functional connectome from rest to a visual perception task in schizophrenia and bipolar disorder

Functional connectome organization is altered in schizophrenia (SZ) and bipolar disorder (BD). However, it remains unclear whether network reorganization during a task relative to rest is also altered in these disorders. This study examined connectome organization in patients with SZ (N = 43) and BD (N = 42) versus healthy controls (HC; N = 39) using fMRI data during a visual object-perception task and at rest. Graph analyses were conducted for the whole-brain network using indices selected a priori: three reflecting network segregation (clustering coefficient, local efficiency, modularity), two reflecting integration (characteristic path length, global efficiency). Group differences were limited to network segregation and were more evident in SZ (clustering coefficient, modularity) than in BD (clustering coefficient) compared to HC. State differences were found across groups for segregation (local efficiency) and integration (characteristic path length). There was no group-by-state interaction for any graph index. In summary, aberrant network organization compared to HC was confirmed, and was more evident in SZ than in BD. Yet, reorganization was largely intact in both disorders. These findings help to constrain models of dysconnection in SZ and BD, suggesting that the extent of functional dysconnectivity in these disorders tends to persist across changes in mental state.

Principles and open questions in functional brain network reconstruction

Graph theory is now becoming a standard tool in system-level neuroscience. However, endowing observed brain anatomy and dynamics with a complex network representation involves often covert theoretical assumptions and methodological choices which affect the way networks are reconstructed from experimental data, and ultimately the resulting network properties and their interpretation. Here, we review some fundamental conceptual underpinnings and technical issues associated with brain network reconstruction, and discuss how their mutual influence concurs in clarifying the organization of brain function.

Localization of epileptic seizure focus by computerized analysis of fMRI recordings

By computerized analysis of cortical activity recorded via fMRI for pediatric epilepsy patients, we implement algorithmic localization of epileptic seizure focus within one of eight cortical lobes. Our innovative machine learning techniques involve intensive analysis of large matrices of mutual information coefficients between pairs of anatomically identified cortical regions. Drastic selection of pairs of regions with biologically significant inter-connectivity provides efficient inputs for our multi-layer perceptron (MLP) classifier. By imposing rigorous parameter parsimony to avoid overfitting, we construct a small-size MLP with very good percentages of successful classification.

Effects of spatial smoothing on group-level differences in functional brain networks

Brain connectivity with functional magnetic resonance imaging (fMRI) is a popular approach for detecting differences between healthy and clinical populations. Before creating a functional brain network, the fMRI time series must undergo several preprocessing steps to control for artifacts and to improve data quality. However, preprocessing may affect the results in an undesirable way. Spatial smoothing, for example, is known to alter functional network structure. Yet, its effects on group-level network differences remain unknown. Here, we investigate the effects of spatial smoothing on the difference between patients and controls for two clinical conditions: autism spectrum disorder and bipolar disorder, considering fMRI data smoothed with Gaussian kernels (0–32 mm). We find that smoothing affects network differences between groups. For weighted networks, incrementing the smoothing kernel makes networks more different. For thresholded networks, larger smoothing kernels lead to more similar networks, although this depends on the network density. Smoothing also alters the effect sizes of the individual link differences. This is independent of the region of interest (ROI) size, but varies with link length. The effects of spatial smoothing are diverse, nontrivial, and difficult to predict. This has important consequences: The choice of smoothing kernel affects the observed network differences.

Spatial smoothing is a preprocessing tool commonly applied to reduce the amount of noise in functional magnetic resonance imaging (fMRI) data. However, smoothing is known to affect the outcomes of functional brain network analysis at the level of individual subjects in undesired ways. Here, we investigate how spatial smoothing affects the observed differences in brain network structure between subject groups. Using fMRI data from two clinical populations and healthy controls, we show that the between-group differences in network structure depend on the amount of spatial smoothing applied during preprocessing in a nontrivial way. The optimal level of spatial smoothing is difficult to define and probably depends on a set of analysis parameters. Therefore, we recommend applying spatial smoothing only after careful consideration.

High-resolution T2-FLAIR and non-contrast CT brain atlas of the elderly

Normative brain atlases are a standard tool for neuroscience research and are, for example, used for spatial normalization of image datasets prior to voxel-based analyses of brain morphology and function. Although many different atlases are publicly available, they are usually biased with respect to an imaging modality and the age distribution. Both effects are well known to negatively impact the accuracy and reliability of the spatial normalization process using non-linear image registration methods. An important and very active neuroscience area that lacks appropriate atlases is lesion-related research in elderly populations (e.g. stroke, multiple sclerosis) for which FLAIR MRI and non-contrast CT are often the clinical imaging modalities of choice. To overcome the lack of atlases for these tasks and modalities, this paper presents high-resolution, age-specific FLAIR and non-contrast CT atlases of the elderly generated using clinical images.

Association between dynamic resting-state functional connectivity and ketamine plasma levels in visual processing networks

Numerous studies demonstrate ketamine’s influence on resting-state functional connectivity (rsFC). Seed-based and static rsFC estimation methods may oversimplify FC. These limitations can be addressed with whole-brain, dynamic rsFC estimation methods. We assessed data from 27 healthy subjects who underwent two 3 T resting-state fMRI scans, once under subanesthetic, intravenous esketamine and once under placebo, in a randomized, cross-over manner. We aimed to isolate only highly robust effects of esketamine on dynamic rsFC by using eight complementary methodologies derived from two dynamic rsFC estimation methods, two functionally defined atlases and two statistical measures. All combinations revealed a negative influence of esketamine on dynamic rsFC within the left visual network and inter-hemispherically between visual networks (p < 0.05, corrected), hereby suggesting that esketamine’s influence on dynamic rsFC is highly stable in visual processing networks. Our findings may be reflective of ketamine’s role as a model for psychosis, a disorder associated with alterations to visual processing and impaired inter-hemispheric connectivity. Ketamine is a highly effective antidepressant and studies have shown changes to sensory processing in depression. Dynamic rsFC in sensory processing networks might be a promising target for future investigations of ketamine’s antidepressant properties. Mechanistically, sensitivity of visual networks for esketamine’s effects may result from their high expression of NMDA-receptors.

Normalization enhances brain network features that predict individual intelligence in children with epilepsy

BACKGROUND AND PURPOSE

Architecture of the cerebral network has been shown to associate with IQ in children with epilepsy. However, subject-level prediction on this basis, a crucial step toward harnessing network analyses for the benefit of children with epilepsy, has yet to be achieved. We compared two network normalization strategies in terms of their ability to optimize subject-level inferences on the relationship between brain network architecture and brain function.

MATERIALS AND METHODS

Patients with epilepsy and resting state fMRI were retrospectively identified. Brain network nodes were defined by anatomic parcellation, first in patient space (nodes defined for each patient) and again in template space (same nodes for all patients). Whole-brain weighted graphs were constructed according to pair-wise correlation of BOLD-signal time courses between nodes. The following metrics were then calculated: clustering coefficient, transitivity, modularity, path length, and global efficiency. Metrics computed on graphs in patient space were normalized to the same metric computed on a random network of identical size. A machine learning algorithm was used to predict patient IQ given access to only the network metrics.

RESULTS

Twenty-seven patients (8-18 years) comprised the final study group. All brain networks demonstrated expected small world properties. Accounting for intrinsic population heterogeneity had a significant effect on prediction accuracy. Specifically, transformation of all patients into a common standard space as well as normalization of metrics to those computed on a random network both substantially outperformed the use of non-normalized metrics.

CONCLUSION

Normalization contributed significantly to accurate subject-level prediction of cognitive function in children with epilepsy. These findings support the potential for quantitative network approaches to contribute clinically meaningful information in children with neurological disorders.

Altered functional connectivity of the default mode network in Williams syndrome: a multimodal approach

Resting state brain networks are implicated in a variety of relevant brain functions. Importantly, abnormal patterns of functional connectivity (FC) have been reported in several neurodevelopmental disorders. In particular, the Default Mode Network (DMN) has been found to be associated with social cognition. We hypothesize that the DMN may be altered in Williams syndrome (WS), a neurodevelopmental genetic disorder characterized by an unique cognitive and behavioral phenotype. In this study, we assessed the architecture of the DMN using fMRI in WS patients and typically developing matched controls (sex and age) in terms of FC and volumetry of the DMN. Moreover, we complemented the analysis with a functional connectome approach. After excluding participants due to movement artifacts (n = 3), seven participants with WS and their respective matched controls were included in the analyses. A decreased FC between the DMN regions was observed in the WS group when compared with the typically developing group. Specifically, we found a decreased FC in a posterior hub of the DMN including the precuneus, calcarine and the posterior cingulate of the left hemisphere. The functional connectome approach showed a focalized and global increased FC connectome in the WS group. The reduced FC of the posterior hub of the DMN in the WS group is consistent with immaturity of the brain FC patterns and may be associated with the singularity of their visual spatial phenotype.

A Hitchhiker's Guide to Functional Magnetic Resonance Imaging

Functional Magnetic Resonance Imaging (fMRI) studies have become increasingly popular both with clinicians and researchers as they are capable of providing unique insights into brain functions. However, multiple technical considerations (ranging from specifics of paradigm design to imaging artifacts, complex protocol definition, and multitude of processing and methods of analysis, as well as intrinsic methodological limitations) must be considered and addressed in order to optimize fMRI analysis and to arrive at the most accurate and grounded interpretation of the data. In practice, the researcher/clinician must choose, from many available options, the most suitable software tool for each stage of the fMRI analysis pipeline. Herein we provide a straightforward guide designed to address, for each of the major stages, the techniques, and tools involved in the process. We have developed this guide both to help those new to the technique to overcome the most critical difficulties in its use, as well as to serve as a resource for the neuroimaging community.

Individual Differences in Dynamic Functional Brain Connectivity across the Human Lifespan

Individual differences in brain functional networks may be related to complex personal identifiers, including health, age, and ability. Dynamic network theory has been used to identify properties of dynamic brain function from fMRI data, but the majority of analyses and findings remain at the level of the group. Here, we apply hypergraph analysis, a method from dynamic network theory, to quantify individual differences in brain functional dynamics. Using a summary metric derived from the hypergraph formalism-hypergraph cardinality-we investigate individual variations in two separate, complementary data sets. The first data set ("multi-task") consists of 77 individuals engaging in four consecutive cognitive tasks. We observe that hypergraph cardinality exhibits variation across individuals while remaining consistent within individuals between tasks; moreover, the analysis of one of the memory tasks revealed a marginally significant correspondence between hypergraph cardinality and age. This finding motivated a similar analysis of the second data set ("age-memory"), in which 95 individuals, aged 18-75, performed a memory task with a similar structure to the multi-task memory task. With the increased age range in the age-memory data set, the correlation between hypergraph cardinality and age correspondence becomes significant. We discuss these results in the context of the well-known finding linking age with network structure, and suggest that hypergraph analysis should serve as a useful tool in furthering our understanding of the dynamic network structure of the brain.