Brain Connectivity : A Journal of Clinical Neurology and Neuroscience

Altered White Matter Tracts in the Somatosensory, Salience, Motor, and Default Mode Networks in 7-Year-Old Children Living with Human Immunodeficiency Virus: A Tractographic Analysis (https://doi.org/10.1089/brain.2020.0948)

Despite early initiation of combination antiretroviral therapy, children with perinatally acquired HIV (CPHIV) continue to demonstrate white matter alterations. Children perinatally HIV-exposed, but uninfected (CHEU) similarly show differences in white matter integrity compared with children who are HIV-unexposed and uninfected (CHUU).

In this study, Joanah Madzime and Marcin Jankiewicz, along with their colleagues, have hypothesized that reduced structural connectivity in CPHIV within the default mode network (DMN), visual, ventral default mode network (vDMN), somatosensory, salience, auditory, motor, executive, basal ganglia, and posterior default mode network (pDMN). They also hypothesized that CHEU will have increased structural connectivity compared with CHUU in the vDMN, somatosensory, pDMN, dorsal attention, salience, auditory, motor, and basal ganglia.

The authors recruited 7-year-old CPHIV and age-matched CHEU. They used diffusion tensor imaging (DTI)-based tractography to investigate white matter connections that link gray matter regions within resting state functional networks. They found altered white matter integrity in the somatosensory, salience, default mode, and motor networks of CPHIV compared with CHEU. The superior temporal cortex, superior frontal cortex, and putamen were affected in all four networks and have also been reported to demonstrate morphological alterations in the same cohort. In CHEU, white matter integrity was higher in the visual network, pDMN, and motor network compared with CHUU. They conclude that altered white matter integrity may influence gray matter morphology and functional network alterations.

White Matter Integrity in a Rat Model of Epileptogenesis: Structural Connectomics and Fixel-Based Analysis (https://doi.org/10.1089/brain.2021.0026)

Large-scale brain networks are affected during the development of epilepsy, and these networks can be investigated using diffusion magnetic resonance imaging (dMRI). The most commonly used model to analyze dMRI is DTI. However, DTI metrics are not specific to microstructure or pathology and the DTI model does not take into account crossing fibers, which may lead to erroneous results. To overcome these limitations, Emma Christiaen and Christian Vanhove, along with their colleagues, used a more advanced model based on multishell multitissue constrained spherical deconvolution to perform tractography with more precise fiber orientation estimates and to assess changes in intra-axonal volume using fixel-based analysis.

In this study, dMRI were acquired before and at several time-points after induction of status epilepticus in the intraperitoneal kainic acid (IPKA) rat model of temporal lobe epilepsy (TLE). Tractography was performed and fixel metrics were calculated in several white matter tracts. The tractogram was analyzed using graph theory.

Global degree, global, and local efficiency were decreased in IPKA animals compared with controls during epileptogenesis. Nodal degree was decreased in the limbic system and default-mode network, mainly during early epileptogenesis. Furthermore, fiber density and fiber-density-and-cross-section were decreased in several white matter tracts.

Connectivity Patterns in the Core Resting-State Networks and Their Influence on Cognition (https://doi.org/10.1089/brain.2020.0943)

To understand the role of three prominent resting-state networks (rsNWs) (default mode network [DMN], salience network [SN], and central executive network [CEN]), in cognition, Tanja Veselinović and Irene Neuner, along with their colleagues, investigated patterns of different network properties (resting-state activity [RSA] and short- and long-range FC) in these three core rsNWs, as well as the dynamics of age-associated changes and their relation to cognitive performance in a sample of healthy controls covering a large age-span.

Using a whole-network-based approach, three measures were calculated from the functional magnetic resonance imaging (fMRI) data: amplitude of low-frequency fluctuations (ALFFs), regional homogeneity (ReHo), and degree of centrality (DC). The cognitive test battery covered the following domains: memory, executive functioning, processing speed, attention, and visual perception.

For all three fMRI measures (ALFF, ReHo, and DC), the highest values of spontaneous brain activity (ALFF), short- and long-range connectivity (ReHo and DC) were observed in the DMN and the lowest in the SN. Significant age-associated decrease was observed in the DMN for ALFF and DC, and in the SN for ALFF and ReHo. Significant negative partial correlations were observed for working memory and ALFF in all three networks, as well as for additional cognitive parameters and ALFF in CEN.

The authors conclude that higher RSA in the three core rsNWs may have an unfavorable effect on cognition. Conversely, the pattern of network properties in healthy subjects included low RSA and FC in the SN.

Machine Learning Evidence for Sex Differences Consistently Influences Resting-State Functional Magnetic Resonance Imaging Fluctuations Across Multiple Independently Acquired Data Sets (https://doi.org/10.1089/brain.2020.0878)

It has been suggested that sex differences might be embedded in the blood-oxygen-level-dependent properties such as the ALFF and the fraction of ALFF (fALFF). In this study, Zoubi Al and Jerzy Bodurka, along with their colleagues, comprehensively investigated sex differences using a reliable and explainable machine learning (ML) pipeline. Five independent cohorts of resting-state fMRI (rs-fMRI) with more than 5500 samples were used to assess sex classification performance and map the spatial distribution of the important brain regions.

Five rs-fMRI samples were used to extract ALFF and fALFF features from predefined brain parcellations and were then fed into an unbiased and explainable ML pipeline with a wide range of methods. The pipeline comprehensively assessed unbiased performance for within-sample and across-sample validation. In addition, the parcellation effect, classifiers selection, scanning length, spatial distribution, reproducibility, and feature importance were analyzed and evaluated.

The results demonstrated high sex classification accuracies from healthy adults (area under the curve >0.89) while degrading for nonhealthy subjects. Sex classification showed moderate to good intraclass correlation coefficient based on parcellation. Linear classifiers outperform nonlinear classifiers. Sex differences could be detected even with a short rs-fMRI scan (e.g., 2 min).

This suggests that sex differences should be considered seriously in any study design, analysis, or interpretation. Features that discriminate males and females were found to be distributed across several different brain regions, suggesting a complex mosaic for sex differences in rs-fMRI.

Magnetoencephalography Imaging Reveals Abnormal Information Flow in Temporal Lobe Epilepsy (https://doi.org/10.1089/brain.2020.0989)

Widespread network disruption has been hypothesized to be an important predictor of outcomes in patients with refractory TLE. Most studies examining functional network disruption in epilepsy have largely focused on the symmetric bidirectional metrics of the strength of network connections. However, a more complete description of network dysfunction impacts in epilepsy requires an investigation of the potentially more sensitive directional metrics of information flow.

In this study, Kiwamu Kudo and Srikantan Nagarajan describe a whole-brain MEG-imaging approach to examine resting-state directional information flow networks, quantified by phase-transfer entropy, in patients with TLE compared with healthy controls.

They demonstrate that deficits of information flow were specific to alpha-band frequencies. In alpha-band, while healthy controls exhibit a clear posterior-to-anterior directionality of information flow, in patients with TLE, this pattern of regional information outflow and inflow was significantly altered in frontal and occipital regions. The changes in information flow within the alpha-band in selected brain regions were correlated with interictal spike frequency and duration of epilepsy.

The authors conclude that impaired information flow is an important dimension of network dysfunction associated with the pathophysiological mechanisms of TLE.

Structural Connectivity Patterns of Side Effects Induced by Subthalamic Deep Brain Stimulation for Parkinson's Disease (https://doi.org/10.1089/brain.2021.0051)

Deep brain stimulation (DBS) targeting the subthalamic nucleus is an effective treatment for advanced Parkinson's disease but may induce adverse effects. This study investigated the relationship between structural connectivity patterns of DBS electrodes and stimulation-induced side effects.

In this study, Quirin Strotzer and Anton Beer, along with their colleagues, evaluated patients with Parkinson's disease treated with bilateral subthalamic DBS. Overall, 168 electrode contacts were categorized as inducing or noninducing depending on their capability for inducing side effects such as motor effects, paresthesia, dysarthria, oculomotor effects, hyperkinesia, and other complications as assessed during the initial programming session. Furthermore, the connectivity of each contact with target regions was evaluated by probabilistic tractography based on diffusion weighted imaging. Finally, stimulation sites and structural connectivity patterns of inducing and noninducing contacts were compared.

Inducing contacts differed across the various side effects and from those mitigating Parkinson's symptoms. Although contacts showed a largely overlapping spatial distribution within the subthalamic region, they could be distinguished by their connectivity patterns. In particular, inducing contacts were more likely connected with supplementary motor areas (hypkinesia and dysarthria), frontal cortex (oculomotor), fibers of the internal capsule (paresthesia), and the basal ganglia-thalamo-cortical circuitry (dysarthria).

The authors conclude that the side effects induced by DBS seem to be associated with distinct connectivity patterns. Cerebellar connections are hardly associated with side effects, although they seem relevant for mitigating motor symptoms in Parkinson's disease. A symptom-specific connectivity-based approach for target planning in DBS may enhance treatment outcomes and reduce adverse effects.

Changes in Brain Functional and Effective Connectivity After Treatment for Breast Cancer and Implications for Intervention Targets (https://doi.org/10.1089/brain.2021.0049)

Patients with breast cancer frequently report cognitive impairment both during and after completion of therapy. Evidence suggests that cancer-related cognitive impairments are related to widespread neural network dysfunction. Disruption of the network may play a key role in the development of cognitive impairment.

In this study, Nicholas Phillips and Shelli Kesler, along with their colleagues, compared neuroimaging and neurocognitive data from newly diagnosed primary breast cancer patients (mean age = 48, standard deviation [SD] = 8.9 years) and healthy female controls (mean age = 50, SD = 10 years) before treatment and 1 year after treatment completion. DMN functional and effective connectivity measures were obtained using graph theory and Bayesian network analysis methods, respectively.

Compared with healthy females, the breast cancer group displayed higher global efficiency and path length post-treatment (p < 0.03, corrected). Breast cancer survivors showed significantly lower performance on measures of verbal memory, attention, and verbal fluency (p < 0.05) at both time-points. Local DMN brain network organization, as measured by edge betweenness centralities, was significantly altered in the breast cancer group compared with controls at both time-points (p < 0.0001, corrected) with several connections showing a significant group-by-time effect (p < 0.003, corrected). Significant correlations were seen between hormone blockade therapy, radiation therapy, chemotherapy cycles, memory and verbal fluency test, and edge betweenness centralities.

Finally, I would like to thank all the researchers and all the staff at Mary Ann Liebert, Inc., publishers, editors, and reviewers of Brain Connectivity who are dedicated to advance research and improve our lives in every corner of the world.