Brain Connectivity

Aging affects the brain at the anatomical and functional levels, resulting in a decline in motor and cognitive performance. Functional magnetic resonance imaging (fMRI) studies documented lower connectivity within brain networks and higher connectivity between them, for older as compared with young adults. However, it is still unclear whether the reduced segregation between networks, as observed with fMRI, has neurophysiological underpinnings.

We collected high-density electroencephalography (hdEEG) data in 24 young and 24 older adults at rest. Bimanual coordination performance was also measured in the same participants, using a computerized test. Using the hdEEG data, we reconstructed oscillatory power and functional connectivity for six large-scale brain networks, in delta, theta, alpha, beta and gamma frequency bands. We evaluated age-related differences in network power and connectivity between young and older participants, and their possible relationships with bimanual coordination performance.

We observed that the level of network segregation generally decreased with age, in line with fMRI findings. However, there was a relatively strong dependence on the frequency band and the brain network being considered. EEG connectivity in the sensorimotor network predicted motor performance differences across older individuals, particularly when neural oscillations in the beta frequency band were considered.

Our study provides electrophysiological evidence in support of the “de-differentiation hypothesis” for the aging brain, and for the existence of a clear link between the strength of EEG connectivity at rest and motor performance.

Using advanced analyses of high-density electroencephalographic data, we could show how band-limited network connectivity differs between young and older adults, and how these differences can predict motor performance. The analyses revealed a complex pattern of connectivity–behavior associations, which not only depended on the network considered, but also on the specific neural oscillations. This may provide novel targets for age-related interventions inducing neural entrainment, based for instance on transcranial alternating current stimulation.

Major depressive disorder (MDD) is a highly prevalent and disabling disease. Currently, patients’ treatment choices depend on their clinical symptoms observed by clinicians, which are subjective. Rich evidence suggests that different functional networks’ dysfunctions correspond to different intervention preferences. In this study, we aimed to develop a prediction model based on data-driven subgroups to provide treatment recommendations.

All 630 participants enrolled from four sites underwent functional magnetic resonances imaging at baseline. In the discovery data set (n = 228), we first identified MDD subgroups by the hierarchical clustering method using the canonical variates of resting-state functional connectivity (FC) through canonical correlation analyses. The demographic symptom improvement and FC were compared among subgroups. The preference intervention for each subgroup was also determined. Next, we predicted the individual treatment strategy. Specifically, a patient was assigned into predefined subgroups based on FC similarities and then his/her treatment strategy was determined by the subgroups’ preferred interventions.

Three subgroups with specific treatment recommendations were emerged, including (1) a selective serotonin reuptake inhibitors-oriented subgroup with early improvements in working and activities, (2) a stimulation-oriented subgroup with more alleviation in suicide, and (3) a selective serotonin noradrenaline reuptake inhibitors-oriented subgroup with more alleviation in hypochondriasis. Through cross-dataset testing, respectively, conducted on three testing data sets, results showed an overall accuracy of 72.83%.

Our works revealed the correspondences between subgroups and their treatment preferences and predicted individual treatment strategy based on such correspondences. Our model has the potential to support psychiatrists in early clinical decision making for better treatment outcomes.

This study proposes a novel framework to provide treatment recommendations by integrating resting-state functional connectivity and advanced machine learning technique in a large data set. Our data-driven approach is able to objectively and automatically cluster patients into different subgroups and recommends the optimal treatment strategies based on specific brain circuits and clinical symptoms. Our results have the potential to support psychiatrists in early clinical decision making for better treatment outcomes.

Postconcussion syndrome (PCS) or persistent symptoms of concussion refers to a constellation of symptoms that persist for weeks and months after a concussion. To better capture the heterogeneity of the symptoms of patients with PCS, we aimed to separate patients into clinical subtypes based on brain connectivity changes.

Subject-specific structural and functional connectomes were created based on diffusion weighted and resting state functional magnetic resonance imaging, respectively. Following an informed dimensionality reduction, a Gaussian mixture model was used on patient-specific structural and functional connectivity matrices to find potential patient clusters. For validation, the resulting patient subtypes were compared in terms of cognitive, neuropsychiatric, and postconcussive symptom differences.

Multimodal analyses of brain connectivity were predictive of behavioral outcomes. Our modeling revealed two patient subtypes: mild and severe. The severe subgroup showed significantly higher levels of depression, anxiety, aggression, and a greater number of symptoms than the mild patient subgroup.

This study suggests that structural and functional connectivity changes together can help us better understand the symptom severity and neuropsychiatric profiles of patients with PCS. This work allows us to move toward precision medicine in concussions and provides a novel machine learning approach that can be applicable to other heterogeneous conditions.

Structural and functional brain connectivity of patients with postconcussion syndrome (PCS) are predictive of their behavioral outcomes. Taking a data-driven machine learning approach, we revealed two distinct clinical subtypes of patients: those with mild versus severe symptoms. Patients in the severe subtype reported higher levels of depression, anxiety, aggressive attitudes, and severe symptoms, whereas mild patients' symptom and brain connectivity profiles resemble healthy controls. This study does not only report clinical subtypes of PCS but also suggests that multimodal neuroimaging analysis of resting state and diffusion-weighted imaging together is more powerful than unimodal analyses in predicting neuropsychiatric symptoms and symptom severity of patients.

Hemianopia after occipital stroke is believed to be mainly due to local damage at or near the lesion site. However, magnetic resonance imaging studies suggest functional connectivity network (FCN) reorganization also in distant brain regions. Because it is unclear whether reorganization is adaptive or maladaptive, compensating for, or aggravating vision loss, we characterized FCNs electrophysiologically to explore local and global brain plasticity and correlated FCN reorganization with visual performance.

Resting-state electroencephalography (EEG) was recorded in chronic, unilateral stroke patients and healthy age-matched controls (n = 24 each). This study was approved by the local ethics committee. The correlation of oscillating EEG activity was calculated with the imaginary part of coherence between pairs of regions of interest, and FCN graph theory metrics (degree, strength, clustering coefficient) were correlated with stimulus detection and reaction time.

Stroke brains showed altered FCNs in the alpha- and low beta-band in numerous occipital, temporal brain structures. On a global level, FCN had a less efficient network organization whereas on the local level node networks were reorganized especially in the intact hemisphere. Here, the occipital network was 58% more rigid (with a more “regular” network structure) whereas the temporal network was 32% more efficient (showing greater “small-worldness”), both of which correlated with worse or better visual processing, respectively.

Occipital stroke is associated with both local and global FCN reorganization, but this can be both adaptive and maladaptive. We propose that the more “regular” FCN structure in the intact visual cortex indicates maladaptive plasticity, where less processing efficacy with reduced signal/noise ratio may cause the perceptual deficits in the intact visual field (VF). In contrast, reorganization in intact temporal brain regions is presumably adaptive, possibly supporting enhanced peripheral movement perception.

The functional connectivity network (FCN) after occipital stroke changes toward a more “regular” pattern. It is maladaptive in the intact occipital region, possibly leading to creating perceptual deficits causing spatiotemporal visual impairments in the “intact” but crowded visual field. The FCN can also be “adaptive,” enabling temporal gyrus structures to compensate for the loss of vision.

Subject head motion is an ongoing challenge in functional magnetic resonance imaging, particularly in the estimation of functional connectivity. Infants (1-month old) scanned during nonsedated sleep often have occasional but large movements of several millimeters separated by periods with relatively little movement. This results in residual signal changes even after image realignment and can distort estimates of functional connectivity. A new motion correction technique, JumpCor, is introduced to reduce the effects of this motion and compared to other existing techniques.

Different approaches for reducing residual motion artifacts after image realignment were compared both in actual and simulated data: JumpCor, regressing out the estimated subject motion, and regressing out the average white matter, cerebrospinal fluid (CSF), and global signals and their temporal derivatives.

Motion-related signal changes resulting from infrequent large motion were significantly reduced both by regressing out the estimated motion parameters and by JumpCor. Furthermore, JumpCor significantly reduced artifacts and improved the quality of functional connectivity estimates when combined with typical preprocessing approaches.

Motion-related signal changes resulting from occasional large motion can be effectively corrected using JumpCor and to a certain extent also by regressing out the estimated motion. This technique should reduce the data loss in studies where participants exhibit this type of motion, such as sleeping infants.

Functional magnetic resonance imaging scans from 1-month-old infants scanned during nonsedated sleep often show occasional but large head motion. Data from subjects that move this much are commonly discarded in functional connectivity studies. However, this study introduces a new motion correction technique, JumpCor, that can effectively reduce this type of motion artifact and compares this to other existing motion correction techniques. The ability to retain valuable subject data despite large motion will be highly important for the study of brain connectivity, particularly in neurodevelopmental studies where this type of motion is common.

Graph metrics of structural brain networks demonstrate to be a powerful tool for investigating brain topology at a large scale. However, the variability of the results related to applying different magnetic resonance acquisition schemes and tractography reconstruction techniques is not fully characterized.

The present work aims to evaluate the influence of different combinations of diffusion acquisition schemes (single and multishell), diffusion models (tensor and spherical deconvolution), and tractography reconstruction approaches (deterministic and probabilistic) on the reproducibility of graph metrics derived from structural connectome on test/retest (TRT) data released by the Human Connectome Project. From each implemented experimental setup, both global and local graph metrics were evaluated and their reproducibility was estimated by the intraclass correlation coefficient (ICC). Moreover, the percentage relative standard deviation (pRSD) from the ICC values of local graph metrics was calculated to quantify how much the reproducibility varied across nodes within each experimental setup.

The presented results show that different combinations of diffusion acquisition schemes, diffusion models, and tractography algorithms can strongly affect the reproducibility of global and local graph metrics. The combination of constrained spherical deconvolution (CSD) and deterministic tractography gave generally high reproducibility (ICCs >0.75) and lowest pRSD for the considered graph metrics, meanwhile probabilistic CSD with a high b-value returned the highest reproducibility. Notably, the introduction of streamline selection filters on CSD can substantially affect the reproducibility.

This work demonstrates that the TRT reproducibility of graph metrics is generally high but can vary substantially with different combinations of acquisition and reconstruction schemes.

This work demonstrates the influence of different diffusion acquisition schemes, diffusion models, and tractography reconstruction approaches on the reproducibility of graph metrics derived from structural connectome. The presented findings impact on the choice of both acquisition protocol and processing pipeline for topological analyses to produce reproducible measurements for brain network studies.