Brain Connectivity : A Journal of Clinical Neurology,Neuroscience,and Neuroimaging

An Age-Specific Atlas for Delineation of White Matter Pathways in Children Aged 6–8 Years (https://doi.org/10.1089/brain.2021.0058)

Diffusion MRI allows a noninvasive assessment of white matter (WM) connectivity. Probabilistic WM atlases allow diffusion metrics to be measured in specific WM pathways and are a critical component in spatial normalization for group analysis. However, due to the developmental changes in WM, it may be suboptimal to use an adult template when assessing data acquired from children.

Arthur Spencer and Jonathan Brooks, along with their colleagues, created the first publicly available probabilistic atlas for 12 major WM tracts by averaging subject-specific fiber bundles from 28 children aged 6–8 years. Using both the newly developed and Johns Hopkins adult atlases, they compared the atlas with subject-specific fiber bundles in two independent validation cohorts, assessing accuracy in terms of volumetric overlap and measured diffusion metrics.

The authors demonstrate that the age-specific atlas gave better overall performance than the adult atlas, achieving higher volumetric overlap with subject-specific fiber tracking and higher correlation of Fractional Anisotropy (FA) measurements with those measured from subject-specific fiber bundles. Estimates of FA values for corticospinal tract, uncinate fasciculus, forceps minor, cingulate gyrus part of the cingulum, and anterior thalamic radiation were all significantly more accurate when estimated with an age-specific atlas. They conclude that an age-specific atlas allows delineation of WM tracts in children without the need for tractography.

The Relationship Between Functional Connectivity and Interoceptive Sensibility (https://doi.org/10.1089/brain.2020.0777)

Interoceptive signals related to changes in heartbeat, respiration, and gastric functioning continuously feedback to the brain. The interpretation of these signals influences several cognitive, affective, and motoric functions. Although numerous studies have delineated the neural substrates of interoceptive accuracy, less is known about the brain areas involved with interoceptive sensibility. In this study, Stephen Smith and Jennifer Kornelsen, along with their colleagues, completed the Multidimensional Assessment of Interoceptive Awareness (MAIA), a self-report measure of interoceptive sensibility, before undergoing a 7-min resting-state functional MRI scan. MAIA scores, as well as scores on its eight subscales, were entered as covariates in subsequent region-of-interest and independent-component analyses.

These analyses yielded three key results. First, interoceptive sensibility was negatively correlated with the FC of visual regions. Second, the cerebellar resting-state network showed positive correlations with two MAIA subscales, suggesting that this structure plays a role in interoceptive functions. Finally, the FC of the insula, a structure critical for interoceptive accuracy, was not correlated with any of the MAIA scores. Their results demonstrate that the brain areas associated with individual differences in interoceptive sensibility show relatively little overlap with those involved with the accurate detection of interoceptive information.

Ageing-Related Modular Architectural Reorganization of the Metabolic Brain Network (https://doi.org/10.1089/brain.2021.0054)

Exploring aging-related reorganization of the brain modular architecture using metabolic brain network could further our understanding about aging-related neuromechanism and neurodegenerations. In this study, Qi Huang and Yihui Guan performed ¹⁸ F-fluorodeoxyglucose (FDG) positron emission tomography (PET) in young and old adult groups, as well as female and male groups. They detected modular architecture and the connector hub nodes to explore the topological role of the brain regions based on the metabolic brain network.

This study revealed that human metabolic brain network was modular and could be clustered into three modules. The modular architecture was reorganized from young to old ages, with regions related to sensorimotor function clustered into the same module, and the number of connector nodes was reduced and most connector nodes were localized in temporo-occipital areas related to visual and auditory functions in old ages. The authors conclude that these findings suggest that aging is associated with reorganized brain modular architecture.

Phase-Synchronized Transcranial Alternating Current Stimulation-Induced Neural Oscillations Modulate Cortico-Cortical Signaling Efficacy (https://doi.org/10.1089/brain.2021.0006)

Synchronized oscillatory brain activity is considered a basis for flexible neuronal network communication. However, the causal role of inter-regional oscillatory phase-relations in modulating signaling efficacy in cortical networks has not been directly demonstrated in humans so far. In this study, Kristoffer Fehér and Yosuke Morishima along with their colleagues evaluated the causal role of transcranial alternating current stimulation (tACS)-induced oscillatory cross-network phase-relations in modulating signaling efficacy across human cortical networks.

They employed concurrent tACS, transcranial magnetic stimulation (TMS), and EEG to measure the modulation of excitability and signaling efficacy across cortical networks during externally induced neural oscillations. Theta oscillatory activity was introduced through tACS in two nodes of the human frontoparietal network (FPN): the dorsolateral prefrontal cortex and the posterior parietal cortex.

They demonstrate that tACS-induced theta oscillations modulate TMS-evoked potentials (TEPs) in a phase-dependent manner, and that the induced oscillatory phase relation across the FPN affects the propagation of phase-dependent TEPs within as well as beyond the FPN. They conclude that the effect of tACS-induced phase-relation across the FPN on signal transmission extends beyond the FPN, and supports a causal role of internodal oscillatory phase-synchrony in routing cortico-cortical information flow.

Tracking Positive and Negative Symptom Improvement in First-Episode Schizophrenia Treated with Risperidone Using Individual-Level Functional Connectivity (http://doi.org/10.1089/brain.2021.0061)

In this study, Yun-Shuang Fan and Huafu Chen along with their colleagues aimed to track dimension-specific changes in psychotic symptoms after risperidone treatment using individual-level functional connectivity (FC). They used cortical parcellation approach that accounts for individual heterogeneity in cortical functional anatomy to localize functional regions in a longitudinal cohort, consisting of 42 drug-naive first-episodes schizophrenia (FES) patients at baseline and after 8 weeks of risperidone treatment. FC was calculated in individually specified brain regions and used to predict the baseline severity and improvement of positive and negative symptoms in FES.

The authors found that distinct sets of individual-specific FC were separately associated with the positive and negative symptom burden at baseline, which could be used to track the corresponding symptom resolution in FES patients after risperidone treatment. Between-network connections of the FPN contributed the most to predicting the positive symptom domain. A combination of between-network connections of the default mode network, FPN, and within-network connections of the FPN contributed markedly to the prediction model of negative symptom.

Brain Networks and Cognitive Impairment in Parkinson's Disease (http://doi.org/10.1089/brain.2020.0985)

PD phenotype is not limited to motor impairment but, rather, a wide range of nonmotor disturbances can occur, cognitive impairment being one of the commonest. However, how the large-scale organization of brain activity differs in cognitively impaired patients, as opposed to cognitively preserved ones, remains poorly understood. Rosaria Rucco and Pierpaolo Sorrentino along with their colleagues investigated the relations between both FC and brain networks with cognitive decline, in patients with PD.

Starting from source-reconstructed resting-state MEG data, they applied the phase linearity measurement (PLM) to estimate FC, globally and between brain areas, in PD patients with and without cognitive impairment (respectively, PD-CI and PD-NC), as compared with healthy subjects (HS). In addition, using graph analysis, they characterized the alterations in brain network topology and evaluated the relationship between network topology to the FC and to the cognitive performance.

They found reduced global and nodal PLM in several temporal (fusiform gyrus, Heschl's gyrus, and inferior temporal gyrus), parietal (postcentral gyrus), and occipital (lingual gyrus) areas within the left hemisphere, in the gamma band, in PD-CI patients, as compared with PD-NC and HS. With regard to the global topological features, PD-CI patients, as compared with HS and PD-NC patients, showed differences in multifrequencies bands (delta, alpha, and gamma) in the Leaf fraction, Tree hierarchy (both higher in PD-CI) and Diameter (lower in PD-CI). They conclude that large-scale rearrangements occur selectively in cognitively compromised PD patients and correlated to cognitive impairment.

Ensemble Learning for Multiple Sclerosis Disability Estimation Using Brain Structural Connectivity (http://doi.org/10.1089/brain.2020.1003)

Recently, machine learning (ML) techniques have reached a high level of performance in brain disease diagnosis and/or prognosis, but the decision process of a trained ML system is typically nontransparent. Berardino Barile and Dominique Sappey-Marinier along with their colleagues used brain structural connectivity data, a fully automatic ensemble learning model, augmented with an interpretable model, which is proposed for the estimation of Multiple Sclerosis (MS) patients' disability, measured by the Expanded Disability Status Scale (EDSS). An ensemble of four boosting-based models (GBM, XGBoost, CatBoost, and LightBoost) organized following a stacking generalization scheme, was developed using diffusion tensor imaging (DTI)-based structural connectivity data. In addition, an interpretable model based on conditional logistic regression was developed to explain the best performances in terms of WM links for three classes of EDSS (low, medium, and high).

The ensemble model reached excellent level of performance (root mean squared error of 0.92 ± 0.28) compared with single-based models and provided a better EDSS estimation using DTI-based structural connectivity data compared with conventional MRI measures associated with patient data (age, gender, and disease duration). The authors conclude that the combination of advanced ML models and sensitive techniques such as DTI-based structural connectivity demonstrated to be useful for the estimation of MS patients' disability and to point out the most important brain WM networks involved in disability.

Cross-Frequency Coupling in Childhood Absence Epilepsy (https://doi.org/10.1089/brain.2021.0119)

The purpose of this study was to investigate within-frequency and cross-frequency coupling during human absence seizures, to identify key regions (hubs) within the absence network that contribute to propagation and maintenance. In this study, Jeffrey Tenney and Darren Kadis along with their colleagues evaluated children with new onset and untreated childhood absence epilepsy who had more than 60 typical absence seizures during both EEG-fMRI and MEG recordings. A multilayer network approach was used to investigate within-frequency and cross-frequency coupling for canonical frequency bands. A rigorous null-modeling approach was used to determine connections outside the noise floor.

The authors conclude that there was a strong coupling between beta and gamma frequencies, within the left frontal cortex, and between left frontal and right parietal regions. There was also strong connectivity between left frontal and right parietal nodes within the gamma band. Multilayer versatility analysis identified a cluster of network hubs in the left frontal region. They conclude that cortical regions commonly identified as being critical for absence seizure generation (frontal cortex and precuneus) have strong cross-frequency and within-frequency coupling between beta and gamma bands.

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.