Hemispheric lateralization of topological organization in structural brain networks

Development of the Fetal Brain Corticocortical Structural Network during the Second-to-Third Trimester Based on Diffusion MRI

During the second-to-third trimester, the neuronal pathways of the fetal brain experience rapid development, resulting in the complex architecture of the interwired network at birth. While diffusion MRI-based tractography has been employed to study the prenatal development of structural connectivity network (SCN) in preterm neonatal and postmortem fetal brains, the in utero development of SCN in the normal fetal brain remains largely unknown. In this study, we utilized in utero dMRI data from human fetuses of both sexes between 26 and 38 gestational weeks to investigate the developmental trajectories of the fetal brain SCN, focusing on intrahemispheric connections. Our analysis revealed significant increases in global efficiency, mean local efficiency, and clustering coefficient, along with significant decrease in shortest path length, while small-worldness persisted during the studied period, revealing balanced network integration and segregation. Widespread short-ranged connectivity strengthened significantly. The nodal strength developed in a posterior-to-anterior and medial-to-lateral order, reflecting a spatiotemporal gradient in cortical network connectivity development. Moreover, we observed distinct lateralization patterns in the fetal brain SCN. Globally, there was a leftward lateralization in network efficiency, clustering coefficient, and small-worldness. The regional lateralization patterns in most language, motor, and visual-related areas were consistent with prior knowledge, except for Wernicke's area, indicating lateralized brain wiring is an innate property of the human brain starting from the fetal period. Our findings provided a comprehensive view of the development of the fetal brain SCN and its lateralization, as a normative template that may be used to characterize atypical development.

Penalized canonical correlation analysis reveals a relationship between temperament traits and brain oscillations during mind wandering

INTRODUCTION

There has been a growing interest in studying brain activity under naturalistic conditions. However, the relationship between individual differences in ongoing brain activity and psychological characteristics is not well understood. We investigated this connection, focusing on the association between oscillatory activity in the brain and individually characteristic dispositional traits. Given the variability of unconstrained resting states among individuals, we devised a paradigm that could harmonize the state of mind across all participants.

METHODS

We constructed task contrasts that included focused attention (FA), self-centered future planning, and rumination on anxious thoughts triggered by visual imagery. Magnetoencephalography was recorded from 28 participants under these 3 conditions for a duration of 16 min. The oscillatory power in the alpha and beta bands was converted into spatial contrast maps, representing the difference in brain oscillation power between the two conditions. We performed permutation cluster tests on these spatial contrast maps. Additionally, we applied penalized canonical correlation analysis (CCA) to study the relationship between brain oscillation patterns and behavioral traits.

RESULTS

The data revealed that the FA condition, as compared to the other conditions, was associated with higher alpha and beta power in the temporal areas of the left hemisphere and lower alpha and beta power in the parietal areas of the right hemisphere. Interestingly, the penalized CCA indicated that behavioral inhibition was positively correlated, whereas anxiety was negatively correlated, with a pattern of high oscillatory power in the bilateral precuneus and low power in the bilateral temporal regions. This unique association was found in the anxious-thoughts condition when contrasted with the focused-attention condition.

CONCLUSION

Our findings suggest individual temperament traits significantly affect brain engagement in naturalistic conditions. This research underscores the importance of considering individual traits in neuroscience and offers an effective method for analyzing brain activity and psychological differences.

Microstructural alterations in the locus coeruleus-entorhinal cortex pathway in Alzheimer's disease and frontotemporal dementia

INTRODUCTION

We investigated in vivo the microstructural integrity of the pathway connecting the locus coeruleus to the transentorhinal cortex (LC-TEC) in patients with Alzheimer's disease (AD) and frontotemporal dementia (FTD).

METHODS

Diffusion-weighted MRI scans were collected for 21 AD, 20 behavioral variants of FTD (bvFTD), and 20 controls. Fractional anisotropy (FA), mean, axial, and radial diffusivities (MD, AxD, RD) were computed in the LC-TEC pathway using a normative atlas. Atrophy was assessed using cortical thickness and correlated with microstructural measures.

RESULTS

We found (i) higher RD in AD than controls; (ii) higher MD, RD, and AxD, and lower FA in bvFTD than controls and AD; and (iii) a negative association between LC-TEC MD, RD, and AxD, and entorhinal cortex (EC) thickness in bvFTD (all p < 0.050).

DISCUSSION

LC-TEC microstructural alterations are more pronounced in bvFTD than AD, possibly reflecting neurodegeneration secondary to EC atrophy.

Highlights

Microstructural integrity of LC-TEC pathway is understudied in AD and bvFTD.LC-TEC microstructural alterations are present in both AD and bvFTD.Greater LC-TEC microstructural alterations in bvFTD than AD.LC-TEC microstructural alterations in bvFTD are associated to EC neurodegeneration.

Structural brain network lateralization across childhood and adolescence

Developmental lateralization of brain function is imperative for behavioral specialization, yet few studies have investigated differences between hemispheres in structural connectivity patterns, especially over the course of development. The present study compares the lateralization of structural connectivity patterns, or topology, across children, adolescents, and young adults. We applied a graph theory approach to quantify key topological metrics in each hemisphere including efficiency of information transfer between regions (global efficiency), clustering of connections between regions (clustering coefficient [CC]), presence of hub-nodes (betweenness centrality [BC]), and connectivity between nodes of high and low complexity (hierarchical complexity [HC]) and investigated changes in these metrics during development. Further, we investigated BC and CC in seven functionally defined networks. Our cross-sectional study consisted of 211 participants between the ages of 6 and 21 years with 93% being right-handed and 51% female. Global efficiency, HC, and CC demonstrated a leftward lateralization, compared to a rightward lateralization of BC. The sensorimotor, default mode, salience, and language networks showed a leftward asymmetry of CC. BC was only lateralized in the salience (right lateralized) and dorsal attention (left lateralized) networks. Only a small number of metrics were associated with age, suggesting that topological organization may stay relatively constant throughout school-age development, despite known underlying changes in white matter properties. Unlike many other imaging biomarkers of brain development, our study suggests topological lateralization is consistent across age, highlighting potential nonlinear mechanisms underlying developmental specialization.

Individual differences in procedural learning are associated with fiber specific white matter microstructure of the superior cerebellar peduncles in healthy adults

Functional neuroimaging has consistently implicated the fronto-basal ganglia-cerebellar circuit in procedural learning-defined as the incidental acquisition of sequence information through repetition. Limited work has probed the role of white matter fiber pathways that connect the regions in this network, such as the superior cerebellar peduncles (SCP) and the striatal premotor tracts (STPMT), in explaining individual differences in procedural learning. High angular diffusion weighted imaging was acquired from 20 healthy adults aged 18-45 years. Fixel-based analysis was performed to extract specific measures of white matter microstructure (fiber density; FD) and macrostructure (fiber cross-section; FC), from the SCP and STPMT. These fixel metrics were correlated with performance on the serial reaction time (SRT) task, and sensitivity to the sequence was indexed by the difference in reaction time between the final block of sequence trials and the randomized block (namely, the 'rebound effect'). Analyses revealed a significant positive relationship between FD and the rebound effect in segments of both the left and right SCP (p_FWE < .05). That is, increased FD in these tracts was associated with greater sensitivity to the sequence on the SRT task. No significant associations were detected between fixel metrics in the STPMT and the rebound effect. Our results support the likely role of white matter organization in the basal ganglia-cerebellar circuit in explaining individual differences in procedural learning.

Regional Brain Fusion: Graph Convolutional Network for Alzheimer's Disease Prediction and Analysis

Alzheimer's disease (AD) has raised extensive concern in healthcare and academia as one of the most prevalent health threats to the elderly. Due to the irreversible nature of AD, early and accurate diagnoses are significant for effective prevention and treatment. However, diverse clinical symptoms and limited neuroimaging accuracy make diagnoses challenging. In this article, we built a brain network for each subject, which assembles several commonly used neuroimaging data simply and reasonably, including structural magnetic resonance imaging (MRI), diffusion-weighted imaging (DWI), and amyloid positron emission tomography (PET). Based on some existing research results, we applied statistical methods to analyze (i) the distinct affinity of AD burden on each brain region, (ii) the topological lateralization between left and right hemispheric sub-networks, and (iii) the asymmetry of the AD attacks on the left and right hemispheres. In the light of advances in graph convolutional networks for graph classifications and summarized characteristics of brain networks and AD pathologies, we proposed a regional brain fusion-graph convolutional network (RBF-GCN), which is constructed with an RBF framework mainly, including three sub-modules, namely, hemispheric network generation module, multichannel GCN module, and feature fusion module. In the multichannel GCN module, the improved GCN by our proposed adaptive native node attribute (ANNA) unit embeds within each channel independently. We not only fully verified the effectiveness of the RBF framework and ANNA unit but also achieved competitive results in multiple sets of AD stages' classification tasks using hundreds of experiments over the ADNI clinical dataset.

Brain Functional Connectivity Asymmetry: Left Hemisphere Is More Modular

Graph-theoretical approaches are increasingly used to study the brain and may enhance our understanding of its asymmetries. In this paper, we hypothesize that the structure of the left hemisphere is, on average, more modular. To this end, we analyzed resting-state functional magnetic resonance imaging data of 90 healthy subjects. We computed functional connectivity by Pearson’s correlation coefficient, turned the matrix into an unweighted graph by keeping a certain percentage of the strongest connections, and quantified modularity separately for the subgraph formed by each hemisphere. Our results show that the left hemisphere is more modular. The result is consistent across a range of binarization thresholds, regardless of whether the two hemispheres are thresholded together or separately. This illustrates that graph-theoretical analysis can provide a robust characterization of lateralization of brain functional connectivity.

Network analysis shows decreased ipsilesional structural connectivity in glioma patients

Gliomas that infiltrate networks and systems, such as the motor system, often lead to substantial functional impairment in multiple systems. Network-based statistics (NBS) allow to assess local network differences and graph theoretical analyses enable investigation of global and local network properties. Here, we used network measures to characterize glioma-related decreases in structural connectivity by comparing the ipsi- with the contralesional hemispheres of patients and correlated findings with neurological assessment. We found that lesion location resulted in differential impairment of both short and long connectivity patterns. Network analysis showed reduced global and local efficiency in the ipsilesional hemisphere compared to the contralesional hemispheric networks, which reflect the impairment of information transfer across different regions of a network.

Tumors and their location distinctly alter both local and global brain connectivity within the ipsilesional hemisphere of glioma patients.

Is it left or is it right? A classification approach for investigating hemispheric differences in low and high dimensionality

Hemispheric asymmetries, i.e., differences between the two halves of the brain, have extensively been studied with respect to both structure and function. Commonly employed pairwise comparisons between left and right are suitable for finding differences between the hemispheres, but they come with several caveats when assessing multiple asymmetries. What is more, they are not designed for identifying the characterizing features of each hemisphere. Here, we present a novel data-driven framework-based on machine learning-based classification-for identifying the characterizing features that underlie hemispheric differences. Using voxel-based morphometry data from two different samples (n = 226, n = 216), we separated the hemispheres along the midline and used two different pipelines: First, for investigating global differences, we embedded the hemispheres into a two-dimensional space and applied a classifier to assess if the hemispheres are distinguishable in their low-dimensional representation. Second, to investigate which voxels show systematic hemispheric differences, we employed two classification approaches promoting feature selection in high dimensions. The two hemispheres were accurately classifiable in both their low-dimensional (accuracies: dataset 1 = 0.838; dataset 2 = 0.850) and high-dimensional (accuracies: dataset 1 = 0.966; dataset 2 = 0.959) representations. In low dimensions, classification of the right hemisphere showed higher precision (dataset 1 = 0.862; dataset 2 = 0.894) compared to the left hemisphere (dataset 1 = 0.818; dataset 2 = 0.816). A feature selection algorithm in the high-dimensional analysis identified voxels that most contribute to accurate classification. In addition, the map of contributing voxels showed a better overlap with moderate to highly lateralized voxels, whereas conventional t test with threshold-free cluster enhancement best resembled the LQ map at lower thresholds. Both the low- and high-dimensional classifiers were capable of identifying the hemispheres in subsamples of the datasets, such as males, females, right-handed, or non-right-handed participants. Our study indicates that hemisphere classification is capable of identifying the hemisphere in their low- and high-dimensional representation as well as delineating brain asymmetries. The concept of hemisphere classifiability thus allows a change in perspective, from asking what differs between the hemispheres towards focusing on the features needed to identify the left and right hemispheres. Taking this perspective on hemispheric differences may contribute to our understanding of what makes each hemisphere special.

Effect of Electro-Acupuncture on Lateralization of the Human Swallowing Motor Cortex Excitability by Navigation-Transcranial Magnetic Stimulation-Electromyography

Background

The use of transcranial magnetic stimulation combined with electromyography for the functional evaluation of the cerebral cortex in both clinical and non-clinical populations is becoming increasingly common. Numerous studies have shown that electro-acupuncture (EA) can regulate cerebral cortical excitability. However, the effect of EA on the lateralization of the human swallowing motor cortex excitability is not yet fully understood.

Objective

The aim of this study was to assess whether lateralization is present in the swallowing motor cortex of healthy subjects, and to investigate the impact of EA at Lianquan (CV23) and Fengfu (GV16) on lateralization.

Methods

Forty subjects were randomized 1:1 into the EA group and the sham-EA group. The bilateral swallowing motor cortices was located by a neuroimaging navigation system. Then, the resting motor threshold (RMT) and motor evoked potential (MEP) of the mylohyoid of healthy subjects were recorded while applying combined transcranial magnetic stimulation and electromyography before and after EA or sham-EA.

Results

First, the RMT and MEP latency of the contralateral mylohyoid innervated by the right swallowing cortex (71.50 ± 1.67%, 8.30 ± 0.06 ms) were lower than those innervated by the left (79.38 ± 1.27%, 8.40 ± 0.06 ms). Second, EA at CV23 and GV16 reduced the bilateral RMT and enhanced the bilateral MEP latency and amplitude (P = 0.005, P < 0.001; P = 0.002, P = 0.001; P = 0.002, P = 0.009), while sham-EA did not (P > 0.05). Third, EA had an effect on the RMT and MEP latency in terms of lateralization changes, but this was not significant (P = 0.067, P = 0.156).

Conclusion

The right swallowing motor cortex of healthy subjects is more excitable than that of the left at resting state. Thus, we found that lateralization is present in the swallowing motor cortex of healthy people, which might indicate a hemispheric dominance of swallowing predominates in the right swallowing motor cortex. In addition, EA at CV23 and GV16 can instantly promote the excitability of the bilateral swallowing motor cortices. But there was no significant difference in EA stimulation in terms of lateralization.

Altered Brain Functional Asymmetry in Patients With Major Depressive Disorder Related to Gastrointestinal Symptoms

Objective

Disrupted brain functional asymmetry has been reported in major depressive disorder (MDD). The comorbidity may be a crucial factor to this functional asymmetry. It is quite common that gastrointestinal (GI) symptoms are comorbid with MDD, but limited evidence focuses on the effect of GI comorbidity on the neuropathology of MDD from a functional lateralization perspective.

Methods

Resting-state functional magnetic resonance imaging was obtained in 28 healthy controls (HCs), 35 MDD patients with GI symptoms (GI-MDD patients), and 17 patients with MDD without GI symptoms (nGI-MDD patients). The parameter of asymmetry (PAS) was used to analyze the imaging data and evaluate the changes of functional asymmetry.

Results

The GI-MDD patients showed increased PAS scores in the left inferior frontal gyrus (IFG) and superior medial prefrontal cortex (MPFC) and decreased PAS scores in the right postcentral gyrus in comparison with nGI-MDD patients. The PAS scores of the left IFG and left superior MPFC were correlated with the severity of GI problems and could be applied to distinguish GI-MDD patients from nGI-MDD patients with an accuracy, a sensitivity, and a specificity of 92.31, 100, and 76.47%, respectively. Furthermore, GI-MDD and nGI-MDD patients both displayed increased PAS scores in the PCC/precuneus.

Conclusions

This study revealed the influence of concomitant GI symptoms on functional asymmetry in MDD patients. Increased PAS scores of the left IFG and superior MPFC might represent an unbalanced regulation of brain over GI function and had the potential to be regarded as distinctive features related to functional GI symptoms in MDD.

Decreased Functional Connectivities of Low-Degree Level Rich Club Organization and Caudate in Post-stroke Cognitive Impairment Based on Resting-State fMRI and Radiomics Features

Background

Stroke is an important cause of cognitive impairment. Rich club organization, a highly interconnected network brain core region, is closely related to cognition. We hypothesized that the disturbance of rich club organization exists in patients with post-stroke cognitive impairment (PSCI).

Methods

We collected data on resting-state functional magnetic resonance imaging (rs-fMRI) with 21 healthy controls (HC), 16 hemorrhagic stroke (hPSCI), and 21 infarct stroke (iPSCI). 3D shape features and first-order statistics of stroke lesions were extracted using 3D slicer software. Additionally, we assessed cognitive function using the Montreal Cognitive Assessment (MoCA) and Mini-Mental State Examination (MMSE).

Results

Normalized rich club coefficients were higher in hPSCI and iPSCI than HC at low-degree k-levels (k = 1–8 in iPSCI, k = 2–8 in hPSCI). Feeder and local connections were significantly decreased in PSCI patients versus HC, mainly distributed in salience network (SN), default-mode network (DMN), cerebellum network (CN), and orbitofrontal cortex (ORB), especially involving the right and left caudate with changed nodal efficiency. The feeder and local connections of significantly between-group difference were positively related to MMSE and MoCA scores, primarily distributed in the sensorimotor network (SMN) and visual network (VN) in hPSCI, SN, and DMN in iPSCI. Additionally, decreased local connections and low-degree ϕnorm(k) were correlated to 3D shape features and first-order statistics of stroke lesions.

Conclusion

This study reveals the disrupted low-degree level rich club organization and relatively preserved functional core network in PSCI patients. Decreased feeder and local connections in cognition-related networks (DMN, SN, CN, and ORB), particularly involving the caudate nucleus, may offer insight into pathological mechanism of PSCI patients. The shape and signal features of stroke lesions may provide an essential clue for the damage of functional connectivity and the whole brain networks.

Subjective cognitive decline-related worries modulate the relationship between global amyloid load and gray matter volume in preclinical Alzheimer's disease

Subjective cognitive decline (SCD)-related worries are indicative of an increased risk for developing Alzheimer's disease (AD) dementia. However, the influence of SCD-related worries on the relationship between amyloid and gray matter (GM) atrophy remains unknown. A total of 93 SCD participants underwent ¹⁸F-florbetapir PET and T1-weighted MRI scans. SCD individuals were classified into amyloid-positive or amyloid-negative groups based on global amyloid uptake. Three-step statistical analyses were performed: (1) partial correlation analysis was conducted to determine whether global amyloid relates to GM volume in amyloid-positive and amyloid-negative groups; (2) linear regression analysis was conducted to determine whether the interaction term (worries × global amyloid) predicts GM volume; and (3) post hoc subgroup linear regression analysis was conducted to determine the association between amyloid and GM volume in the subgroups with and without worries. Age, sex, education and total intracranial volume were adjusted in all models. We found a negative relationship between global amyloid load and GM volume in the right hemisphere (r = 0.441, p = 0.012) and right temporal cortex (r = 0.506, p = 0.003) in the amyloid-positive group. Moreover, in the amyloid-positive group, a significant worries × amyloid interaction effect on GM volume was found in the bilateral hemisphere (right: p_interaction=0.037; left: p_interaction=0.036), left temporal cortex (p_interaction=0.044) and bilateral frontal cortex (right: p_interaction=0.010; left: p_interaction=0.011). Subsequent post hoc analysis revealed a significant amyloid-GM association only in the subgroup with worries but not in the subgroup without worries. In preclinical AD cases, SCD-related worries may occur as a symptom in those cases where amyloid affects GM to a greater extent and may thus represent a high-risk population for future cognitive decline.

Deciphering the Interregional and Interhemisphere Proteome of the Human Brain in the Context of the Human Proteome Project

This study, which performs an extensive mass spectrometry-based analysis of 19 brain regions from both left and right hemispheres, presents the first draft of the human brain interhemispheric proteome. This high-resolution proteomics data provides comprehensive coverage of 3300 experimentally measured (nonhypothetical) proteins across multiple regions, allowing the characterization of protein-centric interhemispheric differences and synapse biology, and portrays the regional mapping of specific regions for brain disorder biomarkers. In the context of the Human Proteome Project (HPP), the interhemispheric proteome data reveal specific markers like chimerin 2 (CHN2) in the cerebellar vermis, olfactory marker protein (OMP) in the olfactory bulb, and ankyrin repeat domain 63 (ANKRD63) in basal ganglia, in line with regional brain transcriptomes mapped in the Human Protein Atlas (HPA). In addition, an in silico analysis pipeline was used to predict the structure and function of the uncharacterized uPE1 protein ANKRD63, and parallel reaction monitoring (PRM) was applied to validate its region-specific expression. Finally, we have built the Interhemispheric Brain Proteome Map (IBPM) Portal (www.brainprot.org) to stimulate the scientific community's interest in the brain molecular landscape and accelerate and support research in neuroproteomics. Data are available via ProteomeXchange with identifier PXD019936.

Diffusion MRI of the infant brain reveals unique asymmetry patterns during the first-half-year of development

The human brain demonstrates anatomical and functional lateralization/asymmetry between the left and right hemispheres, and such asymmetry is known to start from the early age of life. However, how the asymmetry changes with brain development during infancy remained unknown. In this study, we aimed to systematically investigate the spatiotemporal pattern of brain asymmetry in healthy preterm-born infants during the first-half-year of development, using high angular resolution diffusion MRI. Sixty-five healthy preterm-born infants (gestational age between 25.3-36.6 weeks) were scanned with postmenstrual age (PMA) ranging from term-equivalent age (TEA) to 6-months. At the regional level, we performed a region-of-interest-based analysis by segmenting the brain into 63 symmetrical pairs of regions, based on which the laterality index was assessed and correlated with PMA. At the voxel level, we performed a fixel-based analysis of each fiber component between the native and left-right flipped data, separately in TEA-1 month, 1-3 months, and 3-6 months groups. The infant brains demonstrated extensive regions with structural asymmetry during their first half-of-year of life. A distinct central-peripheral asymmetry pattern was observed in mean diffusivity, namely, leftward lateralization in the neocortex and rightward asymmetry in the deep brain regions. Besides, the posterior brain demonstrated a higher lateralization index compared with the anterior brain in all metrics, which is congruent with the brain developmental pattern from caudal to rostral. Regionally, language processing regions showed a rightward asymmetry, while visuospatial processing regions exhibited leftward lateralization in fractional anisotropy, fibre density, and fibre cross-section measurements, and most white matter regions were lateralized to the left in these measurements. The laterality index of several regions (12 out 63) demonstrated significant developmental changes in mean diffusivity. At the fixel level, the fiber cross-section of inferior fronto-occipital fasciculus showed significant leftward asymmetry and the extent of asymmetry increased with PMA. In summary, the results revealed unique spatiotemporal patterns of macro- and micro-structural asymmetry in early life, which dynamically changed with age. These findings may contribute to the understanding of brain development during infancy.

Abnormal Rich Club Organization in Hemispheric White Matter Networks of ADHD

Objective: Brain network studies have revealed abnormal topology asymmetry of white matter (WM) in ADHD. Recently, rich club organization was proposed to be a key feature of brain network topology. However, abnormalities in the rich club organization of hemispheric WM networks in ADHD remain unclear. Method: Forty ADHD patients and 51 normal controls participated in this study. Structural networks were reconstructed based on diffusion tensor imaging (DTI) and analyzed with graph theory. Results: The two groups exhibited different patterns of asymmetry in connectivity measures of rich club connections. ADHD patients showed more feeder connections than normal controls. Reduced rightward asymmetry was observed in connectivity measures of local connections involving several peripheral regions of the ADHD patients. In addition, abnormal regional asymmetry scores were associated with ADHD symptoms. Conclusion: The topological changes in rich club organization provide a novel insight into the alteration of WM connections in ADHD.

Predicting MEG resting-state functional connectivity from microstructural information

Understanding how human brain microstructure influences functional connectivity is an important endeavor. In this work, magnetic resonance imaging data from 90 healthy participants were used to calculate structural connectivity matrices using the streamline count, fractional anisotropy, radial diffusivity, and a myelin measure (derived from multicomponent relaxometry) to assign connection strength. Unweighted binarized structural connectivity matrices were also constructed. Magnetoencephalography resting-state data from those participants were used to calculate functional connectivity matrices, via correlations of the Hilbert envelopes of beamformer time series in the delta, theta, alpha, and beta frequency bands. Nonnegative matrix factorization was performed to identify the components of the functional connectivity. Shortest path length and search-information analyses of the structural connectomes were used to predict functional connectivity patterns for each participant. The microstructure-informed algorithms predicted the components of the functional connectivity more accurately than they predicted the total functional connectivity. This provides a methodology to understand functional mechanisms better. The shortest path length algorithm exhibited the highest prediction accuracy. Of the weights of the structural connectivity matrices, the streamline count and the myelin measure gave the most accurate predictions, while the fractional anisotropy performed poorly. Overall, different structural metrics paint very different pictures of the structural connectome and its relationship to functional connectivity.

Left-Right Side-Specific Neuropeptide Mechanism Mediates Contralateral Responses to a Unilateral Brain Injury

Neuropeptides are implicated in control of lateralized processes in the brain. A unilateral brain injury (UBI) causes the contralesional sensorimotor deficits. To examine whether opioid neuropeptides mediate UBI induced asymmetric processes we compared effects of opioid antagonists on the contralesional and ipsilesional hindlimb responses to the left-sided and right-sided injury in rats. UBI induced hindlimb postural asymmetry (HL-PA) with the contralesional hindlimb flexion, and activated contralesional withdrawal reflex of extensor digitorum longus (EDL) evoked by electrical stimulation and recorded with EMG technique. No effects on the interossei (Int) and peroneaus longus (PL) were evident. The general opioid antagonist naloxone blocked postural effects, did not change EDL asymmetry while uncovered cryptic asymmetry in the PL and Int reflexes induced by UBI. Thus, the spinal opioid system may either mediate or counteract the injury effects. Strikingly, effects of selective opioid antagonists were the injury side-specific. The μ-antagonist β-funaltrexamine (FNA) and κ-antagonist nor-binaltorphimine (BNI) reduced postural asymmetry after the right but not left UBI. In contrast, the δ-antagonist naltrindole (NTI) inhibited HL-PA after the left but not right-side brain injury. The opioid gene expression and opioid peptides were lateralized in the lumbar spinal cord, and coordination between expression of the opioid and neuroplasticity-related genes was impaired by UBI that together may underlie the side-specific effects of the antagonists. We suggest that mirror-symmetric neural circuits that mediate effects of left and right brain injury on the contralesional hindlimbs are differentially controlled by the lateralized opioid system.

Abnormalities in hemispheric lateralization of intra- and inter-hemispheric white matter connections in schizophrenia

Hemispheric lateralization is a prominent feature of the human brain and is grounded into intra- and inter-hemispheric white matter (WM) connections. However, disruptions in hemispheric lateralization involving both intra- and inter-hemispheric WM connections in schizophrenia is still unclear. Hence, a quantitative measure of the hemispheric lateralization of intra- and inter-hemispheric WM connections could provide new insights into schizophrenia. This work performed diffusion tensor imaging on 50 patients and 58 matched healthy controls. Using graph theory, the global and nodal efficiencies were computed for both intra- and inter-hemispheric networks. We found that patients with schizophrenia showed significantly decrease in both global and nodal efficiency of hemispheric networks relative to healthy controls. Specially, deficits in intra-hemispheric integration and inter-hemispheric communication were revealed in frontal and temporal regions for schizophrenia. We also found disrupted hemispheric asymmetries in brain regions associated with emotion, memory, and visual processes for schizophrenia. Moreover, abnormal hemispheric asymmetry of nodal efficiency was significantly correlated with the symptom of the patients. Our finding indicated that the hemispheric WM lateralization of intra- and inter-hemispheric connections could serve as a potential imaging biomarker for schizophrenia.

Gender-Related and Hemispheric Effects in Cortical Thickness-Based Hemispheric Brain Morphological Network

Objective

The current study examined gender-related differences in hemispheric asymmetries of graph metrics, calculated from a cortical thickness-based brain structural covariance network named hemispheric morphological network.

Methods

Using the T1-weighted magnetic resonance imaging scans of 285 participants (150 females, 135 males) retrieved from the Human Connectome Project (HCP), hemispheric morphological networks were constructed per participant. In these hemispheric morphologic networks, the degree of similarity between two different brain regions in terms of the distributed patterns of cortical thickness values (the Jensen–Shannon divergence) was defined as weight of network edge that connects two different brain regions. After the calculation and summation of global and local graph metrics (across the network sparsity levels K = 0.10‐0.36), asymmetry indexes of these graph metrics were derived.

Results

Hemispheric morphological networks satisfied small-worldness and global efficiency for the network sparsity ranges of K = 0.10–0.36. Between-group comparisons (female versus male) of asymmetry indexes revealed opposite directionality of asymmetries (leftward versus rightward) for global metrics of normalized clustering coefficient, normalized characteristic path length, and global efficiency (all p < 0.05). For the local graph metrics, larger rightward asymmetries of cingulate-superior parietal gyri for nodal efficiency in male compared to female, larger leftward asymmetry of temporal pole for degree centrality in female compared to male, and opposite directionality of interhemispheric asymmetry of rectal gyrus for degree centrality between female (rightward) and male (leftward) were shown (all p < 0.05).

Conclusion

Patterns of interhemispheric asymmetries for cingulate, superior parietal gyrus, temporal pole, and rectal gyrus are different between male and female for the similarities of the cortical thickness distribution with other brain regions. Accordingly, possible effect of gender-by-hemispheric interaction has to be considered in future studies of brain morphology and brain structural covariance networks.

Disrupted Rich Club Organization of Hemispheric White Matter Networks in Bipolar Disorder

Neuroimaging studies suggest disrupted connections of the brain white matter (WM) network in bipolar disorder (BD). A group of highly interconnected high-density structures, termed the 'rich club,' represents an important network for brain functioning. Recent works have revealed abnormal rich club organization in brain networks in BD. However, little is known regarding changes in the rich club organization of the hemispheric WM network in BD. Forty-nine BD patients and fifty-five age- and sex-matched normal controls (NCs) underwent diffusion tensor imaging (DTI). Graph theory approaches were applied to quantify group-specific rich club organization and nodal degree of hemispheric WM networks. We demonstrated that rich club organization of hemispheric WM networks in BD was disrupted, with disrupted feeder and local connections among hub and peripheral regions located in the default mode network (DMN) and the control execution network (CEN). In addition, BD patients showed abnormal asymmetry in the feeder and local connections, involving the hub and peripheral regions associated with emotion regulation and visuospatial functions. Moreover, the clinical symptoms of BD showed a significant correlation with the aberrant asymmetry in the regional degree of peripheral regions. These findings reveal that BD is closely associated with disrupted feeder and local connections but no alteration in rich-club connections in the rich club organization of hemispheric WM networks and provide novel insight into the changes of brain functions in BD.

Brain Lateralization: A Comparative Perspective

Comparative studies on brain asymmetry date back to the 19th century but then largely disappeared due to the assumption that lateralization is uniquely human. Since the reemergence of this field in the 1970s, we learned that left-right differences of brain and behavior exist throughout the animal kingdom and pay off in terms of sensory, cognitive, and motor efficiency. Ontogenetically, lateralization starts in many species with asymmetrical expression patterns of genes within the Nodal cascade that set up the scene for later complex interactions of genetic, environmental, and epigenetic factors. These take effect during different time points of ontogeny and create asymmetries of neural networks in diverse species. As a result, depending on task demands, left- or right-hemispheric loops of feedforward or feedback projections are then activated and can temporarily dominate a neural process. In addition, asymmetries of commissural transfer can shape lateralized processes in each hemisphere. It is still unclear if interhemispheric interactions depend on an inhibition/excitation dichotomy or instead adjust the contralateral temporal neural structure to delay the other hemisphere or synchronize with it during joint action. As outlined in our review, novel animal models and approaches could be established in the last decades, and they already produced a substantial increase of knowledge. Since there is practically no realm of human perception, cognition, emotion, or action that is not affected by our lateralized neural organization, insights from these comparative studies are crucial to understand the functions and pathologies of our asymmetric brain.

Brain Network Measures for Groups of Nodes: Application to Normal Aging and Alzheimer's Disease

Background: The nodal brain network measures (e.g., centrality measures) are defined for a single node and the global network measures (e.g., global efficiency) are defined for the whole brain in the literature. But a meaningful group of nodes will be benefited from a formulation that applies to a group of nodes rather than a single node or the whole brain. The question such as "which brain lobe is more structurally central in the older-adult brain?" could be answered to some extent by the application of a centrality measure that applied to the group of nodes from each lobe. In the brain asymmetric studies, path-based global measures were applied to the left and right hemispherical networks separately, considering only intrahemispheric edges. However, for a valid comparison, such global measures should include the interhemispheric edges as well. This problem can be solved by considering both hemispherical nodes as two groups in one network. Methods: Novel definitions for group nodes network measures are presented in this study, to solve a number of such group-context problems in the brain networks analysis. We apply the group measures to the structural connectomes of older adults and Alzheimer's disease (AD) subjects based on the brain lobes and hemispherical groups to demonstrate the effectiveness of the proposed measures. Results: The temporal and parietal lobes are the most central lobes in older adults and AD, but the strength of these lobes has been heavily affected in AD. However, the rewiring of the AD brain preserves the paths for communication between other regions through these lobes. Leftward efficiency revealed in older adults and the asymmetry disappeared in the rewired AD. Conclusion: We prove that the concepts of group network measures have the potential to solve a number of such group-context problems in the brain networks analysis and the group network measures change the way of analyzing brain networks.

Structural brain networks and functional motor outcome after stroke-a prospective cohort study

The time course of topological reorganization that occurs in the structural connectome after an ischaemic stroke is currently not well understood. We aimed to determine the evolution of structural brain networks in stroke patients with motor deficits and relate changes in their global topology to residual symptom burden and functional impairment. In this prospective cohort study, ischaemic stroke patients with supratentorial infarcts and motor symptoms were assessed longitudinally by advanced diffusion MRI and detailed clinical testing of upper extremity motor function at four time points from the acute to the chronic stage. For each time point, structural connectomes were reconstructed, and whole-hemisphere global network topology was quantified in terms of integration and segregation parameters. Using non-linear joint mixed-effects regression modelling, network evolution was related to lesion volume and clinical outcome. Thirty patients were included for analysis. Graph-theoretical analysis demonstrated that, over time, brain networks became less integrated and more segregated with decreasing global efficiency and increasing modularity. Changes occurred in both stroke and intact hemispheres and, in the latter, were positively associated with lesion volume. Greater change in topology was associated with larger residual symptom burden and greater motor impairment 1, 3 and 12 months after stroke. After ischaemic stroke, brain networks underwent characteristic changes in both ipsi- and contralesional hemispheres. Topological network changes reflect the severity of damage to the structural network and are associated with functional outcome beyond the impact of lesion volume.

Towards Differential Connectomics with NeuroVIISAS

The comparison of connectomes is an essential step to identify changes in structural and functional neuronal networks. However, the connectomes themselves as well as the comparisons of connectomes could be manifold. In most applications, comparisons of connectomes are applied to specific sets of data. In many studies collections of scripts are applied optimized for certain species (non-generic approaches) or diseases (control versus disease group connectomes). These collections of scripts have a limited functionality which do not support functional and topographic mappings of connectomes (hemispherical asymmetries, peripheral nervous system). The platform-independent and generic neuroVIISAS framework is built to circumvent limitations that come with variants of nomenclatures, connectivity lists and connectional hierarchies as well as restrictions to structural connectome analyses. A new analytical module is introduced into the framework to compare different types of connectomes and different representations of the same connectome within a unique software environment. As an example a differential analysis of the partial connectome of the laboratory rat that is based on virus tract tracing with the same regions of non-virus tract tracing has been performed. A relatively large connectional coherence between the two different techniques was found. However, some detected connections are described by virus tract-tracing only.

Habitual tea drinking modulates brain efficiency: evidence from brain connectivity evaluation

The majority of tea studies have relied on neuropsychological measures, and much fewer on neuroimaging measures, especially for interregional connections. To date, there has been no exploration of the effect of tea on system-level brain networks. We recruited healthy older participants to two groups according to their history of tea drinking frequency and investigated both functional and structural networks to reveal the role of tea drinking on brain organization. The results showed that tea drinking gave rise to the more efficient structural organization, but had no significant beneficial effect on the global functional organization. The suppression of hemispheric asymmetry in the structural connectivity network was observed as a result of tea drinking. We did not observe any significant effects of tea drinking on the hemispheric asymmetry of the functional connectivity network. In addition, functional connectivity strength within the default mode network (DMN) was greater for the tea-drinking group, and coexistence of increasing and decreasing connective strengths was observed in the structural connectivity of the DMN. Our study offers the first evidence of the positive contribution of tea drinking to brain structure and suggests a protective effect on age-related decline in brain organisation.

Optimization of graph construction can significantly increase the power of structural brain network studies

Structural brain networks derived from diffusion magnetic resonance imaging data have been used extensively to describe the human brain, and graph theory has allowed quantification of their network properties. Schemes used to construct the graphs that represent the structural brain networks differ in the metrics they use as edge weights and the algorithms they use to define the network topologies. In this work, twenty graph construction schemes were considered. The schemes use the number of streamlines, the fractional anisotropy, the mean diffusivity or other attributes of the tracts to define the edge weights, and either an absolute threshold or a data-driven algorithm to define the graph topology. The test-retest data of the Human Connectome Project were used to compare the reproducibility of the graphs and their various attributes (edges, topologies, graph theoretical metrics) derived through those schemes, for diffusion images acquired with three different diffusion weightings. The impact of the scheme on the statistical power of the study and on the number of participants required to detect a difference between populations or an effect of an intervention was also calculated. The reproducibility of the graphs and their attributes depended heavily on the graph construction scheme. Graph reproducibility was higher for schemes that used thresholding to define the graph topology, while data-driven schemes performed better at topology reproducibility (mean similarities of 0.962 and 0.984 respectively, for graphs derived from diffusion images with b=2000 s/mm²). Additionally, schemes that used thresholding resulted in better reproducibility for local graph theoretical metrics (intra-class correlation coefficients (ICC) of the order of 0.8), compared to data-driven schemes. Thresholded and data-driven schemes resulted in high (0.86 or higher) ICCs only for schemes that use exclusively the number of streamlines to construct the graphs. Crucially, the number of participants required to detect a difference between populations or an effect of an intervention could change by a factor of two or more depending on the scheme used, affecting the power of studies to reveal the effects of interest.

Functional near-infrared spectroscopy evidence for the development of topological asymmetry between hemispheric brain networks from childhood to adulthood

Cerebral asymmetry is considered an important marker of the successful development of the human brain. Recent studies have demonstrated topological asymmetries between structurally hemispheric networks in the human brain. However, it remains largely unknown whether and how the functionally topological asymmetries evolve from childhood to adulthood, a critical period that constitutes the primary peak of human brain and cognitive development. Here, we adopted resting-state functional near-infrared spectroscopy imaging data to construct hemispheric functional networks and then applied graph theory analysis to quantify the topological characteristics of the hemispheric networks. We found that the adult group exhibited consistent leftward hemispheric asymmetries in both global and local network efficiency, and the degree of leftward asymmetry in local network efficiency was significantly increased with development from childhood to adulthood. At the nodal level, the degree of leftward asymmetry in nodal efficiency, mainly involving the frontal, parietal-occipital junction, and occipital regions, increased with development. These developmental patterns of topological asymmetries suggest that the protracted maturation of functional segregation in the left hemisphere could underlie language development from childhood to adulthood and provide insight into the development of human brain functional networks.

Hemisphere and Gender Differences in the Rich-Club Organization of Structural Networks

Structural and functional differences in brain hemispheric asymmetry have been well documented between female and male adults. However, potential differences in the connectivity patterns of the rich-club organization of hemispheric structural networks in females and males remain to be determined. In this study, diffusion tensor imaging was used to construct hemispheric structural networks in healthy subjects, and graph theoretical analysis approaches were applied to quantify hemisphere and gender differences in rich-club organization. The results showed that rich-club organization was consistently observed in both hemispheres of female and male adults. Moreover, a reduced level of connectivity was found in the left hemisphere. Notably, rightward asymmetries were mainly observed in feeder and local connections among one hub region and peripheral regions, many of which are implicated in visual processing and spatial attention functions. Additionally, significant gender differences were revealed in the rich-club, feeder, and local connections in rich-club organization. These gender-related hub and peripheral regions are involved in emotional, sensory, and cognitive control functions. The topological changes in rich-club organization provide novel insight into the hemisphere and gender effects on white matter connections and underlie a potential network mechanism of hemisphere- and gender-based differences in visual processing, spatial attention and cognitive control.

Connectivity of the ventral visual cortex is necessary for object recognition in patients

The functional profiles of regions in the ventral occipital-temporal cortex (VTC), a critical region for object visual recognition, are associated with the VTC connectivity patterns to nonvisual regions relevant to the corresponding object domain. However, whether and how whole-brain connections affect recognition behavior remains untested. We directly examined the necessity of VTC connectivity in object recognition behavior by testing 82 patients whose lesion spared relevant VTC regions but affected various white matter (WM) tracts and other regions. In these patients, we extracted the whole-brain anatomical connections of two VTC domain-selective (large manmade objects and animals) clusters with probabilistic tractography, and examined whether such connectivity pattern can predict recognition performance of the corresponding domains with support vector regression (SVR) analysis. We found that the whole-brain anatomical connectivity of large manmade object-specific cluster successfully predicted patients' large object recognition performance but not animal recognition or control tasks, even after we excluded connections with early visual regions. The contributing connections to large object recognition included tracts between VTC-large object cluster and distributed regions both within and beyond the visual cortex (e.g., putamen, superior, and middle temporal gyrus). These results provide causal evidence that the VTC whole-brain anatomical connectivity is necessary for at least certain domains of object recognition behavior.

Aberrant development of the asymmetry between hemispheric brain white matter networks in autism spectrum disorder

Atypical brain asymmetry/lateralization has long been hypothesized for autism spectrum disorder (ASD), and this model has been repeatedly supported by various neuroimaging studies. Recently, hemispheric network topologies have been found to be asymmetric, thereby providing a new avenue for investigating brain asymmetries under various conditions. To date, however, how network topological asymmetries are altered in ASD remains largely unexplored. To clarify this, the present study included ASD individuals from the newly released Autism Brain Imaging Data Exchange II database (58 right-handed male ASD individuals aged 5 to 26 years and 70 age- and IQ-matched typically developing (TD) individuals). Diffusion and structural magnetic resonance imaging were used to construct hemispheric white matter networks, and graph-theory approaches were applied to quantify topological efficiencies for hemispheric networks. Statistical analyses revealed a decreased rightward asymmetry of network efficiencies with increasing age in the TD group, but not in the ASD group. More specifically, the TD group did not exhibit an age-related increase in network efficiency in the right hemisphere, but the ASD group did. For the left hemisphere, no difference between the groups was observed for the developmental trajectory of network efficiencies. Intriguingly, within the ASD group, more severe restricted and repetitive behavior in ASD was found to be correlated with less rightward asymmetry of network local efficiency. These findings provide suggestive evidence of atypical network topological asymmetries and offer important insights into the abnormal development of ASD brains.

Asymmetry of Hemispheric Network Topology Reveals Dissociable Processes between Functional and Structural Brain Connectome in Community-Living Elders

Human brain is structurally and functionally asymmetrical and the asymmetries of brain phenotypes have been shown to change in normal aging. Recent advances in graph theoretical analysis have showed topological lateralization between hemispheric networks in the human brain throughout the lifespan. Nevertheless, apparent discrepancies of hemispheric asymmetry were reported between the structural and functional brain networks, indicating the potentially complex asymmetry patterns between structural and functional networks in aging population. In this study, using multimodal neuroimaging (resting-state fMRI and structural diffusion tensor imaging), we investigated the characteristics of hemispheric network topology in 76 (male/female = 15/61, age = 70.08 ± 5.30 years) community-dwelling older adults. Hemispheric functional and structural brain networks were obtained for each participant. Graph theoretical approaches were then employed to estimate the hemispheric topological properties. We found that the optimal small-world properties were preserved in both structural and functional hemispheric networks in older adults. Moreover, a leftward asymmetry in both global and local levels were observed in structural brain networks in comparison with a symmetric pattern in functional brain network, suggesting a dissociable process of hemispheric asymmetry between structural and functional connectome in healthy older adults. Finally, the scores of hemispheric asymmetry in both structural and functional networks were associated with behavioral performance in various cognitive domains. Taken together, these findings provide new insights into the lateralized nature of multimodal brain connectivity, highlight the potentially complex relationship between structural and functional brain network alterations, and augment our understanding of asymmetric structural and functional specializations in normal aging.

Gray matter asymmetries in aging and neurodegeneration: A review and meta-analysis

Inter-hemispheric asymmetries are a common phenomenon of the human brain. Some evidence suggests that neurodegeneration related to aging and disease may preferentially affect the left-usually language- and motor-dominant-hemisphere. Here, we used activation likelihood estimation meta-analysis to assess gray matter (GM) loss and its lateralization in healthy aging and in neurodegeneration, namely, mild cognitive impairment (MCI), Alzheimer's dementia (AD), Parkinson's disease (PD), and Huntington's disease (HD). This meta-analysis, comprising 159 voxel-based morphometry publications (enrolling 4,469 patients and 4,307 controls), revealed that GM decline appeared to be asymmetric at trend levels but provided no evidence for increased left-hemisphere vulnerability. Regions with asymmetric GM decline were located in areas primarily affected by neurodegeneration. In HD, the left putamen showed converging evidence for more pronounced atrophy, while no consistent pattern was found in PD. In MCI, the right hippocampus was more atrophic than its left counterpart, a pattern that reversed in AD. The stability of these findings was confirmed using permutation tests. However, due to the lenient threshold used in the asymmetry analysis, further work is needed to confirm our results and to provide a better understanding of the functional role of GM asymmetries, for instance in the context of cognitive reserve and compensation. Hum Brain Mapp 38:5890-5904, 2017. © 2017 Wiley Periodicals, Inc.

The Abnormality of Topological Asymmetry between Hemispheric Brain White Matter Networks in Alzheimer's Disease and Mild Cognitive Impairment

A large number of morphology-based studies have previously reported a variety of regional abnormalities in hemispheric asymmetry in Alzheimer’s disease (AD). Recently, neuroimaging studies have revealed changes in the topological organization of the structural network in AD. However, little is known about the alterations in topological asymmetries. In the present study, we used diffusion tensor image tractography to construct the hemispheric brain white matter networks of 25 AD patients, 95 mild cognitive impairment (MCI) patients, and 48 normal control (NC) subjects. Graph theoretical approaches were then employed to estimate hemispheric topological properties. Rightward asymmetry in both global and local network efficiencies were observed between the two hemispheres only in AD patients. The brain regions/nodes exhibiting increased rightward asymmetry in both AD and MCI patients were primarily located in the parahippocampal gyrus and cuneus. The observed rightward asymmetry was attributed to changes in the topological properties of the left hemisphere in AD patients. Finally, we found that the abnormal hemispheric asymmetries of brain network properties were significantly correlated with memory performance (Rey’s Auditory Verbal Learning Test). Our findings provide new insights into the lateralized nature of hemispheric disconnectivity and highlight the potential for using hemispheric asymmetry of brain network measures as biomarkers for AD.

Learning Morse Code Alters Microstructural Properties in the Inferior Longitudinal Fasciculus: A DTI Study

Learning relies on neuroplasticity, which has mainly been studied in gray matter (GM). However, there is mounting evidence indicating a critical role of white matter changes involved in learning processes. One of the most important learning processes in human development is language acquisition. However, due to the length of this learning process, it has been notoriously difficult to investigate the underlying neuroplastic changes. Here, we report a novel learning paradigm to assess the role of white matter plasticity for language acquisition. By acoustically presenting Morse Code (MC) using an in house developed audio book as a model for language-type learning, we generated a well-controlled learning environment that allows for the detection of subtle white matter changes related to language type learning in a much shorter time frame than usual language acquisition. In total 12 letters of the MC alphabet were learned within six learning session, which allowed study participants to perform a word recognition MC decoding task. In this study, we found that learning MC was associated with significant microstructural changes in the left inferior longitudinal fasciculus (ILF). The fractional anisotropy (FA) of this associative fiber bundle connecting the occipital and posterior temporal cortex with the temporal pole as well as the hippocampus and amygdala was increased. Furthermore, white matter plasticity was associated with task performance of MC decoding, indicating that the structural changes were related to learning efficiency. In conclusion, our findings demonstrate an important role of white matter neuroplasticity for acquiring a new language skill.

Anatomical integration and rich-club connectivity in euthymic bipolar disorder

BACKGROUND

Although repeatedly associated with white matter microstructural alterations, bipolar disorder (BD) has been relatively unexplored using complex network analysis. This method combines structural and diffusion magnetic resonance imaging (MRI) to model the brain as a network and evaluate its topological properties. A group of highly interconnected high-density structures, termed the 'rich-club', represents an important network for integration of brain functioning. This study aimed to assess structural and rich-club connectivity properties in BD through graph theory analyses.

METHOD

We obtained structural and diffusion MRI scans from 42 euthymic patients with BD type I and 43 age- and gender-matched healthy volunteers. Weighted fractional anisotropy connections mapped between cortical and subcortical structures defined the neuroanatomical networks. Next, we examined between-group differences in features of graph properties and sub-networks.

RESULTS

Patients exhibited significantly reduced clustering coefficient and global efficiency, compared with controls globally and regionally in frontal and occipital regions. Additionally, patients displayed weaker sub-network connectivity in distributed regions. Rich-club analysis revealed subtly reduced density in patients, which did not withstand multiple comparison correction. However, hub identification in most participants indicated differentially affected rich-club membership in the BD group, with two hubs absent when compared with controls, namely the superior frontal gyrus and thalamus.

CONCLUSIONS

This graph theory analysis presents a thorough investigation of topological features of connectivity in euthymic BD. Abnormalities of global and local measures and network components provide further neuroanatomically specific evidence for distributed dysconnectivity as a trait feature of BD.

Developmental Changes in Topological Asymmetry Between Hemispheric Brain White Matter Networks from Adolescence to Young Adulthood

Human brain asymmetries have been well described. Intriguingly, a number of asymmetries in brain phenotypes have been shown to change throughout the lifespan. Recent studies have revealed topological asymmetries between hemispheric white matter networks in the human brain. However, it remains unknown whether and how these topological asymmetries evolve from adolescence to young adulthood, a critical period that constitutes the second peak of human brain and cognitive development. To address this question, the present study included a large cohort of healthy adolescents and young adults. Diffusion and structural magnetic resonance imaging were acquired to construct hemispheric white matter networks, and graph-theory was applied to quantify topological parameters of the hemispheric networks. In both adolescents and young adults, rightward asymmetry in both global and local network efficiencies was consistently observed between the 2 hemispheres, but the degree of the asymmetry was significantly decreased in young adults. At the nodal level, the young adults exhibited less rightward asymmetry of nodal efficiency mainly around the parasylvian area, posterior tempo-parietal cortex, and fusiform gyrus. These developmental patterns of network asymmetry provide novel insight into the human brain structural development from adolescence to young adulthood and also likely relate to the maturation of language and social cognition that takes place during this period.

Hemispheric Differences in White Matter Microstructure between Two Profiles of Children with High Intelligence Quotient vs. Controls: A Tract-Based Spatial Statistics Study

Objectives: The main goal of this study was to investigate and compare the neural substrate of two children's profiles of high intelligence quotient (HIQ). Methods: Two groups of HIQ children were included with either a homogeneous (Hom-HIQ: n = 20) or a heterogeneous IQ profile (Het-HIQ: n = 24) as defined by a significant difference between verbal comprehension index and perceptual reasoning index. Diffusion tensor imaging was used to assess white matter (WM) microstructure while tract-based spatial statistics (TBSS) analysis was performed to detect and localize WM regional differences in fractional anisotropy (FA), mean diffusivity, axial (AD), and radial diffusivities. Quantitative measurements were performed on 48 regions and 21 fiber-bundles of WM. Results: Hom-HIQ children presented higher FA than Het-HIQ children in widespread WM regions including central structures, and associative intra-hemispheric WM fasciculi. AD was also greater in numerous WM regions of Total-HIQ, Hom-HIQ, and Het-HIQ groups when compared to the Control group. Hom-HIQ and Het-HIQ groups also differed by their hemispheric lateralization in AD differences compared to Controls. Het-HIQ and Hom-HIQ groups showed a lateralization ratio (left/right) of 1.38 and 0.78, respectively. Conclusions: These findings suggest that both inter- and intra-hemispheric WM integrity are enhanced in HIQ children and that neural substrate differs between Hom-HIQ and Het-HIQ. The left hemispheric lateralization of Het-HIQ children is concordant with their higher verbal index while the relative right hemispheric lateralization of Hom-HIQ children is concordant with their global brain processing and adaptation capacities as evidenced by their homogeneous IQ.

Anatomical dysconnectivity in bipolar disorder compared with schizophrenia: A selective review of structural network analyses using diffusion MRI

BACKGROUND

The dysconnectivity hypothesis suggests that psychotic illnesses arise not from regionally specific focal pathophysiology, but rather from impaired neuroanatomical integration across networks of brain regions. Decreased white matter organization has been hypothesized to be a feature of psychotic illnesses in general, which is supported by meta-analyses of DTI studies in bipolar disorder and schizophrenia. Although many diffusion MRI studies investigate bipolar disorder and schizophrenia alone, relatively few studies directly compare structural features in these psychotic illnesses. Recently, the application of graph theory analyses to DTI data has supported the dysconnectivity hypothesis in bipolar disorder and schizophrenia, employing topological properties to assess neuroanatomical dysconnectivity.

METHODS

This selective review evaluates white matter alterations using Diffusion Tensor Imaging (DTI) in bipolar disorder and schizophrenia, with a focus upon direct comparison DTI studies in both psychotic illnesses. We then expand in more detail on the development of network analyses and the application of these techniques in bipolar disorder and schizophrenia.

RESULTS

Converging evidence indicates that frontal connectivity alterations are common to both disorders, with prominent fronto-temporal deficits identified in schizophrenia and inter-hemispheric and limbic alterations reported in bipolar disorder.

LIMITATIONS

In bipolar disorder, most connectome reports use cortical maps alone, which given the importance of the limbic system in emotional regulation may limit the scope of network approaches in mood disorders.

CONCLUSIONS

Further direct connectivity comparisons between these psychotic illnesses may assist in unravelling the neuroanatomical deviations underpinning the overlapping features of psychosis and cognitive impairment, and the more diagnostically distinctive features of affective disturbance in bipolar disorder and deficit syndrome in schizophrenia.

Laterality of Brain Activation for Risk Factors of Addiction

Background:

Laterality of brain activation is reported for tests of risk factors of addiction - impulsivity and craving - but authors rarely address the potential significance of those asymmetries.

Objective:

The purpose of this study is to demonstrate this laterality and discuss its relevance to cognitive and neurophysiological asymmetries associated with drug abuse vulnerability in order to provide new insights for future research in drug abuse.

Method:

From published reports, brain areas of activation for two tests of response inhibition or craving for drugs of abuse were compiled from fMRI activation peaks and were tabulated for eight sections (octants) in each hemisphere. Percent asymmetries were calculated (R-L/R+L) across studies for each area.

Results:

For impulsivity, most activation peaks favored the right hemisphere. Overall, the percent difference was 32% (Χ² = 16.026; p < 0.0001) with the greater asymmetry for anterior peaks (46.8%; Χ² = 17.329; p < 0.0001). The asymmetries for cue-induced craving were opposite, favoring the left hemisphere by 6.7% (Χ² = 4.028; p < 0.05). The consistency of left asymmetry was found for almost all drugs. For nicotine, studies where subjects were not allowed to smoke (deprived) prior to measurement had the same left hemisphere activation but those who smoked (satiated) before the fMRI measure showed right asymmetry.

Conclusion:

Brain activation studies demonstrate different left/right hemispheric contributions for impulsivity versus craving - factors related to addiction. Failure to take laterality into consideration is a missed opportunity in designing studies and gaining insight into the etiology of drug abuse and pathways for treatment.

Left hemisphere structural connectivity abnormality in pediatric hydrocephalus patients following surgery

Neuroimaging research in surgically treated pediatric hydrocephalus patients remains challenging due to the artifact caused by programmable shunt. Our previous study has demonstrated significant alterations in the whole brain white matter structural connectivity based on diffusion tensor imaging (DTI) and graph theoretical analysis in children with hydrocephalus prior to surgery or in surgically treated children without programmable shunts. This study seeks to investigate the impact of brain injury on the topological features in the left hemisphere, contratelateral to the shunt placement, which will avoid the influence of shunt artifacts and makes further group comparisons feasible for children with programmable shunt valves. Three groups of children (34 in the control group, 12 in the 3-month post-surgery group, and 24 in the 12-month post-surgery group, age between 1 and 18 years) were included in the study. The structural connectivity data processing and analysis were performed based on DTI and graph theoretical analysis. Specific procedures were revised to include only left brain imaging data in normalization, parcellation, and fiber counting from DTI tractography. Our results showed that, when compared to controls, children with hydrocephalus in both the 3-month and 12-month post-surgery groups had significantly lower normalized clustering coefficient, lower small-worldness, and higher global efficiency (all p < 0.05, corrected). At a regional level, both patient groups showed significant alteration in one or more regional connectivity measures in a series of brain regions in the left hemisphere (8 and 10 regions in the 3-month post-surgery and the 12-month post-surgery group, respectively, all p < 0.05, corrected). No significant correlation was found between any of the global or regional measures and the contemporaneous neuropsychological outcomes [the General Adaptive Composite (GAC) from the Adaptive Behavior Assessment System, Second Edition (ABAS-II)]. However, one global network measure (global efficiency) and two regional network measures in the insula (local efficiency and between centrality) tested at 3-month post-surgery were found to correlate with GAC score tested at 12-month post-surgery with statistical significance (all p < 0.05, corrected). Our data showed that the structural connectivity analysis based on DTI and graph theory was sensitive in detecting both global and regional network abnormality when the analysis was conducted in the left hemisphere only. This approach provides a new avenue enabling the application of advanced neuroimaging analysis methods in quantifying brain damage in children with hydrocephalus surgically treated with programmable shunts.

•

We studied the structural connectivity of left hemisphere brain network in children with hydrocephalus post-surgery

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Children with hydrocephalus post-surgery had significantly abnormal structural connectivity in the left hemisphere based on graph analysis

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Significant correlation was found between graph measures at 3-months post-surgery and developmental outcome at 12-month post-surgery

Intrahemispheric white matter asymmetries: the missing link between brain structure and functional lateralization?

Hemispheric asymmetries are a central principle of nervous system architecture and shape the functional organization of most cognitive systems. Structural gray matter asymmetries and callosal interactions have been identified as contributing neural factors but always fell short to constitute a full explanans. Meanwhile, recent advances in in vivo white matter tractography have unrevealed the asymmetrical organization of many intrahemispheric white matter pathways, which might serve as the missing link to explain the substrate of functional lateralization. By taking into account callosal interactions, gray matter asymmetries and asymmetrical interhemispheric pathways, we opt for a new triadic model that has the potential to explain many observations which cannot be elucidated within the current frameworks of lateralized cognition.

D-BRAIN: Anatomically Accurate Simulated Diffusion MRI Brain Data

Diffusion Weighted (DW) MRI allows for the non-invasive study of water diffusion inside living tissues. As such, it is useful for the investigation of human brain white matter (WM) connectivity in vivo through fiber tractography (FT) algorithms. Many DW-MRI tailored restoration techniques and FT algorithms have been developed. However, it is not clear how accurately these methods reproduce the WM bundle characteristics in real-world conditions, such as in the presence of noise, partial volume effect, and a limited spatial and angular resolution. The difficulty lies in the lack of a realistic brain phantom on the one hand, and a sufficiently accurate way of modeling the acquisition-related degradation on the other. This paper proposes a software phantom that approximates a human brain to a high degree of realism and that can incorporate complex brain-like structural features. We refer to it as a Diffusion BRAIN (D-BRAIN) phantom. Also, we propose an accurate model of a (DW) MRI acquisition protocol to allow for validation of methods in realistic conditions with data imperfections. The phantom model simulates anatomical and diffusion properties for multiple brain tissue components, and can serve as a ground-truth to evaluate FT algorithms, among others. The simulation of the acquisition process allows one to include noise, partial volume effects, and limited spatial and angular resolution in the images. In this way, the effect of image artifacts on, for instance, fiber tractography can be investigated with great detail. The proposed framework enables reliable and quantitative evaluation of DW-MR image processing and FT algorithms at the level of large-scale WM structures. The effect of noise levels and other data characteristics on cortico-cortical connectivity and tractography-based grey matter parcellation can be investigated as well.

Abnormal wiring of the connectome in adults with high-functioning autism spectrum disorder

Background

Recent brain imaging findings suggest that there are widely distributed abnormalities affecting the brain connectivity in individuals with autism spectrum disorder (ASD). Using graph theoretical analysis, it is possible to investigate both global and local properties of brain’s wiring diagram, i.e., the connectome.

Methods

We acquired diffusion-weighted magnetic resonance imaging data from 14 adult males with high-functioning ASD and 19 age-, gender-, and IQ-matched controls. As with diffusion tensor imaging-based tractography, it is not possible to detect complex (e.g., crossing) fiber configurations, present in 60–90 % of white matter voxels; we performed constrained spherical deconvolution-based whole brain tractography. Unweighted and weighted structural brain networks were then reconstructed from these tractography data and analyzed with graph theoretical measures.

Results

In subjects with ASD, global efficiency was significantly decreased both in the unweighted and the weighted networks, normalized characteristic path length was significantly increased in the unweighted networks, and strength was significantly decreased in the weighted networks. In the local analyses, betweenness centrality of the right caudate was significantly increased in the weighted networks, and the strength of the right superior temporal pole was significantly decreased in the unweighted networks in subjects with ASD.

Conclusions

Our findings provide new insights into understanding ASD by showing that the integration of structural brain networks is decreased and that there are abnormalities in the connectivity of the right caudate and right superior temporal pole in subjects with ASD.

Electronic supplementary material

The online version of this article (doi:10.1186/s13229-015-0058-4) contains supplementary material, which is available to authorized users.

PARKINSON'S DISEASE PATIENTS WITH DOMINANT HEMIBODY AFFECTED BY THE DISEASE RELY MORE ON VISION TO MAINTAIN UPRIGHT POSTURAL CONTROL

This study assesses the association between disease onset side (dominant or non-dominant) and vision on postural control of Parkinson's disease patients. Patient volunteers composed two groups, according to the onset side affected: Dominant group (n=9; M age=66.1 yr., SD=7.2; 6 women, 3 men) and Non-dominant group (n=9; M age=67.4 yr., SD=6.4; 6 women, 3 men). The groups' postural control was assessed by posturography during quiet upright stance in two conditions, Eyes open and Eyes closed. Two-way analyses of variance (ANOVAs; group×condition) with repeated measures for the second factor assessed the differences associated with affected hemibody and vision on postural control. Analyses indicated that patients with the dominant side affected also presented significantly greater variation in center of pressure than those with the non-dominant side affected, mainly in the Eyes closed condition. The results demonstrate a higher reliance on vision in the dominant side, possibly to compensate somatosensory system impairments. These results also highlight the importance of analyzing the hemibody affected by the disease when postural control is assessed in this population.