Lateral differences in the default mode network in healthy controls and patients with schizophrenia

Neuropharmacological computational analysis of longitudinal electroencephalograms in clozapine-treated patients with schizophrenia using hierarchical dynamic causal modeling

The hierarchical characteristics of the brain are prominent in the pharmacological treatment of psychiatric diseases, primarily targeting cellular receptors that extend upward to intrinsic connectivity within a region, interregional connectivity, and, consequently, clinical observations such as an electroencephalogram (EEG). To understand the long-term effects of neuropharmacological intervention on neurobiological properties at different hierarchical levels, we explored long-term changes in neurobiological parameters of an N-methyl-D-aspartate canonical microcircuit model (CMM-NMDA) in the default mode network (DMN) and auditory hallucination network (AHN) using dynamic causal modeling of longitudinal EEG in clozapine-treated patients with schizophrenia. The neurobiological properties of the CMM-NMDA model associated with symptom improvement in schizophrenia were found across hierarchical levels, from a reduced membrane capacity of the deep pyramidal cell and intrinsic connectivity with the inhibitory population in DMN and intrinsic and extrinsic connectivity in AHN. The medication duration mainly affects the intrinsic connectivity and NMDA time constant in DMN. Virtual perturbation analysis specified the contribution of each parameter to the cross-spectral density (CSD) of the EEG, particularly intrinsic connectivity and membrane capacitances for CSD frequency shifts and progression. It further reveals that excitatory and inhibitory connectivity complements frequency-specific CSD changes, notably the alpha frequency band in DMN. Positive and negative synergistic interactions exist between neurobiological properties primarily within the same region in patients treated with clozapine. The current study shows how computational neuropharmacology helps explore the multiscale link between neurobiological properties and clinical observations and understand the long-term mechanism of neuropharmacological intervention reflected in clinical EEG.

Characterizing different cognitive and neurobiological profiles in a community sample of children using a non-parametric approach: An fMRI study

Executive Functions (EF) is an umbrella term for a set of mental processes geared towards goal-directed behavior supporting academic skills such as reading abilities. One of the brain's functional networks implicated in EF is the Default Mode Network (DMN). The current study uses measures of inhibitory control, a main sub-function of EF, to create cognitive and neurobiological "inhibitory control profiles" and relate them to reading abilities in a large sample (N = 5055) of adolescents aged 9-10 from the Adolescent Brain Cognitive Development (ABCD) study. Using a Latent Profile Analysis (LPA) approach, data related to inhibitory control was divided into four inhibition classes. For each class, functional connectivity within the DMN was calculated from resting-state data, using a non-parametric algorithm for detecting group similarities. These inhibitory control profiles were then related to reading abilities. The four inhibitory control groups showed significantly different reading abilities, with neurobiologically different DMN segregation profiles for each class versus controls. The current study demonstrates that a community sample of children is not entirely homogeneous and is composed of different subgroups that can be differentiated both behaviorally/cognitively and neurobiologically, by focusing on inhibitory control and the DMN. Educational implications relating these results to reading abilities are noted.

Laterality Hotspots in the Striatum

Striatal loci are connected to both the ipsilateral and contralateral frontal cortex. Normative quantitation of the dissimilarity between striatal loci's hemispheric connection profiles and its spatial variance across the striatum, and assessment of how interindividual differences relate to function, stands to further the understanding of the role of corticostriatal circuits in lateralized functions and the role of abnormal corticostriatal laterality in neurodevelopmental and other neuropsychiatric disorders. A resting-state functional connectivity fingerprinting approach (n = 261) identified "laterality hotspots"-loci whose profiles of connectivity with ipsilateral and contralateral frontal cortex were disproportionately dissimilar-in the right rostral ventral putamen, left rostral central caudate, and bilateral caudal ventral caudate. Findings were replicated in an independent sample and were robust to both preprocessing choices and the choice of cortical atlas used for parcellation definitions. Across subjects, greater rightward connectional laterality at the right ventral putamen hotspot and greater leftward connectional laterality at the left rostral caudate hotspot were associated with higher performance on tasks engaging lateralized functions (i.e., response inhibition and language, respectively). In sum, we find robust and reproducible evidence for striatal loci with disproportionately lateralized connectivity profiles where interindividual differences in laterality magnitude are associated with behavioral capacities on lateralized functions.

Lateralization of Resting-State Networks in Children: Association with Age, Sex, Handedness, Intelligence Quotient, and Behavior

Introduction: Lateralization in brain function has been associated with age and sex in previous work; however, there has been less focus on lateralization of functional networks during development. Aim: We aim to examine laterality in typical development; a clearer understanding of how and to what extent functional brain networks are lateralized in typical development may eventually prove to hold predictive information in psychopathology. Material and Methods: In this study, we examine the lateralization of resting-state networks assessed with a group-independent component analysis using resting-state functional magnetic resonance imaging from a large cohort consisting of 774 children, ages 6-10 years. This is an extension of our previous work on normal aging in adults, where we now assess whether there are similar patterns in children. Results: Unlike the results from our study of healthy aging in adults, which showed a decrease in laterality with increasing age, in this study we found both decreases and increases in lateralization in multiple networks with development. For example, auditory and sensorimotor regions had greater bilateral connectivity with development, whereas regions including the dorsolateral frontal cortex (Brodmann area left 9 and left 46) showed an increase in left lateralization with development. Conclusion: Our findings support a complex, nonlinear association between laterality and age in school-age children, a time when brain function and structure are developing rapidly. We also found brain networks in which laterality was significantly associated with sex, handedness, and intelligence quotient, but we did not find any significant association with behavioral scores. Impact statement Lateralization in brain function has been associated with age and sex in several previous studies; however, there has been less focus on lateralization of functional networks during development. A clearer understanding of how and to what extent functional brain networks are lateralized in typical development may eventually prove to hold predictive information in psychopathology. In this study, we examine the lateralization of resting-state networks assessed with a group-independent component analysis using resting-state functional magnetic resonance imaging from a large cohort consisting of 774 children, ages 6-10 years.

A Latent Gaussian Copula Model for Mixed Data Analysis in Brain Imaging Genetics

Recent advances in imaging genetics make it possible to combine different types of data including medical images like functional magnetic resonance imaging (fMRI) and genetic data like single nucleotide polymorphisms (SNPs) for comprehensive diagnosis of mental disorders. Understanding complex interactions among these heterogeneous data may give rise to a new perspective, while at the same time demand statistical models for their integration. Various graphical models have been proposed for the study of interaction or association networks with continuous, binary, and count data as well as the mixture of them. However, limited efforts have been made for the multinomial case, for instance, SNP data. Our goal is therefore to fill the void by developing a graphical model for the integration of fMRI image and SNP data, which can provide deeper understanding of the unknown neurogenetic mechanism. In this article, we propose a latent Gaussian copula model for mixed data containing multinomial components. We assume that the discrete variable is obtained by discretizing a latent (unobserved) continuous variable and then create a semi-rank based estimator of the graph structure. The simulation results demonstrate that the proposed latent correlation has more steady and accurate performance than several existing methods in detecting graph structure. When applying to a real schizophrenia data consisting of SNP array and fMRI image collected by the Mind Clinical Imaging Consortium (MCIC), the proposed method reveals a set of distinct SNP-brain associations, which are verified to be biologically significant. The proposed model is statistically promising in handling mixed types of data including multinomial components, which can find widespread applications. To promote reproducible research, the R code is available at https://github.com/Aiying0512/LGCM.

A new era for executive function research: On the transition from centralized to distributed executive functioning

"Executive functions" (EFs) is an umbrella term for higher cognitive control functions such as working memory, inhibition, and cognitive flexibility. One of the most challenging problems in this field of research has been to explain how the wide range of cognitive processes subsumed as EFs are controlled without an all-powerful but ill-defined central executive in the brain. Efforts to localize control mechanisms in circumscribed brain regions have not led to a breakthrough in understanding how the brain controls and regulates itself. We propose to re-conceptualize EFs as emergent consequences of highly distributed brain processes that communicate with a pool of highly connected hub regions, thus precluding the need for a central executive. We further discuss how graph-theory driven analysis of brain networks offers a unique lens on this problem by providing a reference frame to study brain connectivity in EFs in a holistic way and helps to refine our understanding of the mechanisms underlying EFs by providing new, testable hypotheses and resolves empirical and theoretical inconsistencies in the EF literature.

Weaker Cerebellocortical Connectivity Within Sensorimotor and Executive Networks in Schizophrenia Compared to Healthy Controls: Relationships with Processing Speed

Background: The cognitive dysmetria theory of schizophrenia proposes that communication between the cerebellum and cerebral cortex is disrupted by structural and functional abnormalities, resulting in psychotic symptoms and cognitive deficits. Methods: Using publicly available data, resting-state functional connectivity (rsFC) was calculated from 20 hemispheric cerebellar lobules as seed regions of interest to the rest of the brain. Group differences in rsFC between individuals with schizophrenia (SZ) and healthy controls (HCs) were computed, and relationships between rsFC and symptom severity and cognitive functioning were explored. Results: HCs demonstrated stronger connectivity than SZ between several cerebellar lobules and cortical regions, most robustly between motor-related cerebellar lobules (V and VIIIa/b) and temporal and parietal cortices. In addition, seven of nine lobules in which reduced cerebellocortical connectivity was observed showed diagnosis × processing speed interactions; HC showed a positive relationship between connectivity and processing speed, whereas SZ did not show this relationship. Other cognitive domains and symptom severity did not show relationships with connectivity. Conclusions: These findings partially support the cognitive dysmetria theory, and suggest that disrupted cerebellocortical connectivity is associated with slowed processing speed in schizophrenia. Impact statement We show in this work that in chronic schizophrenia, there is weaker functional connectivity between previously unstudied inferior posterior cerebellar lobules and cortical association areas. These findings align and extend previous work showing abnormal connectivity of anterior cerebellar lobules. Further, we present a novel finding that these connectivity deficits are differentially associated with processing speed in the schizophrenia versus healthy control groups. Findings provide further evidence for cerebellocortical dysconnectivity and processing speed deficits as biomarkers of schizophrenia, which may have implications for downstream effects on higher order cognitive functions, in line with the cognitive dysmetria theory.

Asymmetric tumor-related alterations of network-specific intrinsic functional connectivity in glioma patients

Resting-state functional MRI (rs-fMRI) allows mapping temporally coherent brain networks, and intra- and inter-network alterations have been described in different diseases. This prospective study investigated hemispheric resting-state functional connectivity (RSFC) differences in the default-mode network (DMN) and fronto-parietal network (FPN) between patients with left- and right-hemispheric gliomas (LH PAT, RH PAT), addressing asymmetry effects the tumor might have on network-specific intrinsic functional connectivity under consideration of the prognostically relevant isocitrate-dehydrogenase (IDH) mutation status. Twenty-seven patients (16 LH PAT, 12 IDH-wildtype) and 27 healthy controls underwent anatomical and rs-fMRI as well as neuropsychological assessment. Independent component analyses were performed to identify the DMN and FPN. Hemispheric DMN- and FPN-RSFC were computed, compared across groups, and correlated with cognitive performance. Patient groups did not differ in tumor volume, grade or location. RH PAT showed higher contra-tumoral DMN-RSFC than controls and LH PAT. With regard to the FPN, contra-tumoral RSFC was increased in both patient groups as compared to controls. Higher contra-tumoral RSFC was associated with worse cognitive performance in patients, which, however, seemed to apply mainly to IDH-wildtype patients. The benefit of RSFC alterations for cognitive performance varied depending on the affected hemisphere, cognitive demand, and seemed to be altered by IDH-mutation status. At the time of study initiation, a clinical trial registration was not mandatory at our faculty, but it can be applied for if requested.

Lateralization of attention in adults with ADHD: Evidence of pseudoneglect

Background.

We investigated whether adults with attention-deficit/hyperactivity disorder (ADHD) show pseudoneglect—preferential allocation of attention to the left visual field (LVF) and a resulting slowing of mean reaction times (MRTs) in the right visual field (RVF), characteristic of neurotypical (NT) individuals —and whether lateralization of attention is modulated by presentation speed and incentives.

Method.

Fast Task, a four-choice reaction-time task where stimuli were presented in LVF or RVF, was used to investigate differences in MRT and reaction time variability (RTV) in adults with ADHD (n = 43) and NT adults (n = 46) between a slow/no-incentive and fast/incentive condition. In the lateralization analyses, pseudoneglect was assessed based on MRT, which was calculated separately for the LVF and RVF for each condition and each study participant.

Results.

Adults with ADHD had overall slower MRT and increased RTV relative to NT. MRT and RTV improved under the fast/incentive condition. Both groups showed RVF-slowing with no between-group or between-conditions differences in RVF-slowing.

Conclusion.

Adults with ADHD exhibited pseudoneglect, a NT pattern of lateralization of attention, which was not attenuated by presentation speed and incentives.

Polygenic effects of schizophrenia on hippocampal grey matter volume and hippocampus-medial prefrontal cortex functional connectivity

BACKGROUND

Schizophrenia is a complex mental disorder with high heritability and polygenic inheritance. Multimodal neuroimaging studies have also indicated that abnormalities of brain structure and function are a plausible neurobiological characterisation of schizophrenia. However, the polygenic effects of schizophrenia on these imaging endophenotypes have not yet been fully elucidated.

AIMS

To investigate the effects of polygenic risk for schizophrenia on the brain grey matter volume and functional connectivity, which are disrupted in schizophrenia.

METHOD

Genomic and neuroimaging data from a large sample of Han Chinese patients with schizophrenia (N = 509) and healthy controls (N = 502) were included in this study. We examined grey matter volume and functional connectivity via structural and functional magnetic resonance imaging, respectively. Using the data from a recent meta-analysis of a genome-wide association study that comprised a large number of Chinese people, we calculated a polygenic risk score (PGRS) for each participant.

RESULTS

The imaging genetic analysis revealed that the individual PGRS showed a significantly negative correlation with the hippocampal grey matter volume and hippocampus-medial prefrontal cortex functional connectivity, both of which were lower in the people with schizophrenia than in the controls. We also found that the observed neuroimaging measures showed weak but similar changes in unaffected first-degree relatives of patients with schizophrenia.

CONCLUSIONS

These findings suggested that genetically influenced brain grey matter volume and functional connectivity may provide important clues for understanding the pathological mechanisms of schizophrenia and for the early diagnosis of schizophrenia.

Discordant attributes of structural and functional brain connectivity in a two-layer multiplex network

Several studies have suggested that functional connectivity (FC) is constrained by the underlying structural connectivity (SC) and mutually correlated. However, not many studies have focused on differences in the network organization of SC and FC, and on how these differences may inform us about their mutual interaction. To explore this issue, we adopt a multi-layer framework, with SC and FC, constructed using Magnetic Resonance Imaging (MRI) data from the Human Connectome Project, forming a two-layer multiplex network. In particular, we examine node strength assortativity within and between the SC and FC layer. We find that, in general, SC is organized assortatively, indicating brain regions are on average connected to other brain regions with similar node strengths. On the other hand, FC shows disassortative mixing. This discrepancy is apparent also among individual resting-state networks within SC and FC. In addition, these patterns show lateralization, with disassortative mixing within FC subnetworks mainly driven from the left hemisphere. We discuss our findings in the context of robustness to structural failure, and we suggest that discordant and lateralized patterns of associativity in SC and FC may provide clues to understand laterality of some neurological dysfunctions and recovery.

Resting-State Connectivity Biomarkers of Cognitive Performance and Social Function in Individuals With Schizophrenia Spectrum Disorder and Healthy Control Subjects

BACKGROUND

Deficits in neurocognition and social cognition are drivers of reduced functioning in schizophrenia spectrum disorders, with potentially shared neurobiological underpinnings. Many studies have sought to identify brain-based biomarkers of these clinical variables using a priori dichotomies (e.g., good vs. poor cognition, deficit vs. nondeficit syndrome).

METHODS

We evaluated a fully data-driven approach to do the same by building and validating a brain connectivity-based biomarker of social cognitive and neurocognitive performance in a sample using resting-state and task-based functional magnetic resonance imaging (n = 74 healthy control participants, n = 114 persons with schizophrenia spectrum disorder, 188 total). We used canonical correlation analysis followed by clustering to identify a functional connectivity signature of normal and poor social cognitive and neurocognitive performance.

RESULTS

Persons with poor social cognitive and neurocognitive performance were differentiated from those with normal performance by greater resting-state connectivity in the mirror neuron and mentalizing systems. We validated our findings by showing that poor performers also scored lower on functional outcome measures not included in the original analysis and by demonstrating neuroanatomical differences between the normal and poorly performing groups. We used a support vector machine classifier to demonstrate that functional connectivity alone is enough to distinguish normal and poorly performing participants, and we replicated our findings in an independent sample (n = 75).

CONCLUSIONS

A brief functional magnetic resonance imaging scan may ultimately be useful in future studies aimed at characterizing long-term illness trajectories and treatments that target specific brain circuitry in those with impaired cognition and function.

Spatio-Temporal Dynamics of Intrinsic Networks in Functional Magnetic Imaging Data Using Recurrent Neural Networks

We introduce a novel recurrent neural network (RNN) approach to account for temporal dynamics and dependencies in brain networks observed via functional magnetic resonance imaging (fMRI). Our approach directly parameterizes temporal dynamics through recurrent connections, which can be used to formulate blind source separation with a conditional (rather than marginal) independence assumption, which we call RNN-ICA. This formulation enables us to visualize the temporal dynamics of both first order (activity) and second order (directed connectivity) information in brain networks that are widely studied in a static sense, but not well-characterized dynamically. RNN-ICA predicts dynamics directly from the recurrent states of the RNN in both task and resting state fMRI. Our results show both task-related and group-differentiating directed connectivity.

Variability and reliability of effective connectivity within the core default mode network: A multi-site longitudinal spectral DCM study

Dynamic causal modelling (DCM) for resting state fMRI – namely spectral DCM – is a recently developed and widely adopted method for inferring effective connectivity in intrinsic brain networks. Most applications of spectral DCM have focused on group-averaged connectivity within large-scale intrinsic brain networks; however, the consistency of subject- and session-specific estimates of effective connectivity has not been evaluated. Establishing reliability (within subjects) is crucial for its clinical use; e.g., as a neurophysiological phenotype of disease progression. Effective connectivity during rest is likely to vary due to changes in cognitive, and physiological states. Quantifying these variations may help understand functional brain architectures – and inform clinical applications. In the present study, we investigated the consistency of effective connectivity within and between subjects, as well as potential sources of variability (e.g., hemispheric asymmetry). We also addressed the effects on consistency of standard data processing procedures. DCM analyses were applied to four longitudinal resting state fMRI datasets. Our sample comprised 17 subjects with 589 resting state fMRI sessions in total. These data allowed us to quantify the robustness of connectivity estimates for each subject, and to generalise our conclusions beyond specific data features. We found that subjects showed systematic and reliable patterns of hemispheric asymmetry. When asymmetry was taken into account, subjects showed very similar connectivity patterns. We also found that various processing procedures (e.g. global signal regression and ROI size) had little effect on inference and the reliability of connectivity estimates for the majority of subjects. Finally, Bayesian model reduction significantly increased the consistency of connectivity patterns.

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Across datasets, subjects’ effective connectivity patterns in the core default mode network showed hemispheric asymmetry.

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Differences in hemispheric asymmetry was found to be a major source of between-subject variability.

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In contrast, most subjects showed reliable within-subject hemispheric asymmetry.

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Differences in preprocessing methods had little effect on connectivity estimates.

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Bayesian model reduction increased the within- and between-subject consistency of connectivity patterns.

Left lateralized cerebral glucose metabolism declines in amyloid-β positive persons with mild cognitive impairment

Background

Previous publications indicate that Alzheimer's Disease (AD) related cortical atrophy may develop in asymmetric patterns, with accentuation of the left hemisphere. Since fluorodeoxyglucose positron emission tomography (FDG PET) measurements of the regional cerebral metabolic rate of glucose (rCMRgl) provide a sensitive and specific marker of neurodegenerative disease progression, we sought to investigate the longitudinal pattern of rCMRgl in amyloid-positive persons with mild cognitive impairment (MCI) and dementia, hypothesizing asymmetric declines of cerebral glucose metabolism.

Methods

Using florbetapir PET and cerebrospinal fluid (CSF) measures to define amyloid-β (Aβ) positivity, 40 Aβ negative (Aβ-) cognitively unimpaired controls (CU; 76 ± 5y), 76 Aβ positive (Aβ+) persons with MCI (76 ± 7y) and 51 Aβ + persons with probable AD dementia (75 ± 7y) from the AD Neuroimaging Initiative (ADNI) were included in this study with baseline and 2-year follow-up FDG PET scans. The degree of lateralization of longitudinal rCMRgl declines in subjects with Aβ + MCI and AD in comparison with Aβ- CU were statistically quantified via bootstrapped lateralization indices [(LI); range − 1 (right) to 1 (left)].

Results

Compared to Aβ- CU, Aβ + MCI patients showed marked left hemispheric lateralization (LI: 0.78). In contrast, modest right hemispheric lateralization (LI: −0.33) of rCMRgl declines was found in Aβ + persons with probable AD dementia. Additional comparisons of Aβ + groups (i.e. MCI and probable AD dementia) consequently indicated right hemispheric lateralization (LI: −0.79) of stronger rCMRgl declines in dementia stages of AD. For all comparisons, voxel-based analyses confirmed significant (pFWE<0.05) declines of rCMRgl within AD-typical brain regions. Analyses of cognitive data yielded predominant decline of memory functions in both MCI and dementia stages of AD.

Conclusions

These data indicate that in early stages, AD may be characterized by a more lateralized pattern of left hemispheric rCMRgl declines. However, metabolic differences between hemispheres appear to diminish with further progression of the disease.

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Lateralized cerebral glucose metabolism declines in Alzheimer's Disease.

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Early stages show strong left-hemispheric lateralization.

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Advanced stages show weak right-hemispheric lateralization.

Functional cerebral asymmetry analyses reveal how the control system implements its flexibility

The control system in human brain generally exerts the goal-directed regulation on a variety of mental processes. To deal with different control demands, these brain areas of the control system, especially the dorsolateral prefrontal cortex (DLPFC), may be flexibly recruited across different tasks. However, few studies have investigated how the flexibility of the control system is realized during cognitive control. Present study employed functional magnetic resonance imaging to examine the brain responses during two domain distinct conflict tasks (verbal color-word Stroop and visuospatial arrow flanker). The voxel-wise asymmetries in both functional activity and psychophysiological interaction (PPI) between these two tasks were compared. The results showed that the brain areas of control system were consistently activated in these two tasks. When considering functional cerebral asymmetries, the left DLPFC was dominantly activated during the Stroop task, while more symmetric DLPFC activation was found during the flanker task. The left DLPFC rather than the right DLPFC showed greater positive interaction with the visual areas V1 and V2 during the Stroop interference, but interactions of both the left and right DLPFC with the right visual area V5/MT were positively enhanced during the flanker interference. These results suggest that the flexible cognitive control is achieved by the control system's task-specific activity and its top-down interaction with domain-specific brain areas, in implementing flexible representation and modulation of control demands.

Aberrant Brain Connectivity in Schizophrenia Detected via a Fast Gaussian Graphical Model

Schizophrenia (SZ) is a chronic and severe mental disorder that affects how a person thinks, feels, and behaves. It has been proposed that this disorder is related to disrupted brain connectivity, which has been verified by many studies. With the development of functional magnetic resonance imaging (fMRI), further exploration of brain connectivity was made possible. Region-based networks are commonly used for mapping brain connectivity. However, they fail to illustrate the connectivity within regions of interest (ROIs) and lose precise location information. Voxel-based networks provide higher precision, but are difficult to construct and interpret due to the high dimensionality of the data. In this paper, we adopt a novel high-dimensional Gaussian graphical model - ψ-learning method, which can help ease computational burden and provide more accurate inference for the underlying networks. This method has been proven to be an equivalent measure of the partial correlation coefficient and, thus, is flexible for network comparison through statistical tests. The fMRI data we used were collected by the mind clinical imaging consortium using an auditory task in which there are 92 SZ patients and 116 healthy controls. We compared the networks at three different scales by using global measurements, community structure, and edge-wise comparisons within the networks. Our results reveal, at the highest voxel resolution, sets of distinct aberrant patterns for the SZ patients, and more precise local structures are provided within ROIs for further investigation.

Decreased hemispheric connectivity and decreased intra- and inter- hemisphere asymmetry of resting state functional network connectivity in schizophrenia

Many studies have shown that schizophrenia patients have aberrant functional network connectivity (FNC) among brain regions, suggesting schizophrenia manifests with significantly diminished (in majority of the cases) connectivity. Schizophrenia is also associated with a lack of hemispheric lateralization. Hoptman et al. (2012) reported lower inter-hemispheric connectivity in schizophrenia patients compared to controls using voxel-mirrored homotopic connectivity. In this study, we merge these two points of views together using a group independent component analysis (gICA)-based approach to generate hemisphere-specific timecourses and calculate intra-hemisphere and inter-hemisphere FNC on a resting state fMRI dataset consisting of age- and gender-balanced 151 schizophrenia patients and 163 healthy controls. We analyzed the group differences between patients and healthy controls in each type of FNC measures along with age and gender effects. The results reveal that FNC in schizophrenia patients shows less hemispheric asymmetry compared to that of the healthy controls. We also found a decrease in connectivity in all FNC types such as intra-left (L_FNC), intra-right (R_FNC) and inter-hemisphere (Inter_FNC) in the schizophrenia patients relative to healthy controls, but general patterns of connectivity were preserved in patients. Analyses of age and gender effects yielded results similar to those reported in whole brain FNC studies.

Study of Resting-State Functional Connectivity Networks Using EEG Electrodes Position As Seed

Electroencephalography (EEG) is the standard diagnosis method for a wide variety of diseases such as epilepsy, sleep disorders, encephalopathies, and coma, among others. Resting-state functional magnetic resonance (rs-fMRI) is currently a technique used in research in both healthy individuals as well as patients. EEG and fMRI are procedures used to obtain direct and indirect measurements of brain neural activity: EEG measures the electrical activity of the brain using electrodes placed on the scalp, and fMRI detects the changes in blood oxygenation that occur in response to neural activity. EEG has a high temporal resolution and low spatial resolution, while fMRI has high spatial resolution and low temporal resolution. Thus, the combination of EEG with rs-fMRI using different methods could be very useful for research and clinical applications. In this article, we describe and show the results of a new methodology for processing rs-fMRI using seeds positioned according to the 10-10 EEG standard. We analyze the functional connectivity and adjacency matrices obtained using 65 seeds based on 10-10 EEG scheme and 21 seeds based on 10-20 EEG. Connectivity networks are created using each 10-20 EEG seeds and are analyzed by comparisons to the seven networks that have been found in recent studies. The proposed method captures high correlation between contralateral seeds, ipsilateral and contralateral occipital seeds, and some in the frontal lobe.

Asymmetrical Electroencephalographic Change of Human Brain During Sleep Onset Period

OBJECTIVE

Human cerebral hemisphere is known to function asymmetrically with daytime left hemisphere superiority in most right-handed persons. It may have relevance to the localization of specific function of the brain. This study attempted to reveal whether the functional cerebral asymmetry in the wakeful state is still maintained throughout the sleep onset period.

METHODS

Thirty-channel EEG was recorded in 61 healthy subjects. The EEG power spectra of each of the seven frequencies were compared between the two kinds of 30-second states; the wakeful stage and the late-sleep stage 1.

RESULTS

The asymmetrical indices of sleep stage 1 at several fronto-central leads were decreased in the delta, theta, alpha-2, and all beta bands. Conversely, at parts of parieto-occipital leads showed an increase in the indices of the theta, alphas, beta-1, and beta-2 bands. Any fronto-central leads did not show an increase in the index, and no parieto-occipital leads showed a decrease.

CONCLUSION

During the sleep onset period, power spectral asymmetry of the brain showed a different pattern from the wakeful stage. This asymmetrical pattern of EEG powers may suggest a reversal of the left hemispheric dominance during sleep.

Increased spatial granularity of left brain activation and unique age/gender signatures: a 4D frequency domain approach to cerebral lateralization at rest

Cerebral lateralization is a well-studied topic. However, most of the research to date in functional magnetic resonance imaging (fMRI) has been carried out on hemodynamic fluctuations of voxels, networks, or regions of interest (ROIs). For example, cerebral differences can be revealed by comparing the temporal activation of an ROI in one hemisphere with the corresponding homotopic region in the other hemisphere. While this approach can reveal significant information about cerebral organization, it does not provide information about the full spatiotemporal organization of the hemispheres. The cerebral differences revealed in literature suggest that hemispheres have different spatiotemporal organization in the resting state. In this study, we evaluate cerebral lateralization in the 4D spatiotemporal frequency domain to compare the hemispheres in the context of general activation patterns at different spatial and temporal scales. We use a gender-balanced resting fMRI dataset comprising over 600 healthy subjects ranging in age from 12 to 71, that have previously been studied with a network specific voxel-wise and global analysis of lateralization (Agcaoglu, et al. NeuroImage, 2014). Our analysis elucidates significant differences in the spatiotemporal organization of brain activity between hemispheres, and generally more spatiotemporal fluctuation in the left hemisphere especially in the high spatial frequency bands, and more power in the right hemisphere in the low and middle spatial frequencies. Importantly, the identified effects are not visible in the context of a typical assessment of voxelwise, regional, or even global laterality, thus our study highlights the value of 4D spatiotemporal frequency domain analyses as a complementary and powerful tool for studying brain function.

Connectivity of the anterior insula differentiates participants with first-episode schizophrenia spectrum disorders from controls: a machine-learning study

BACKGROUND

Early diagnosis of schizophrenia could improve the outcomes and limit the negative effects of untreated illness. Although participants with schizophrenia show aberrant functional connectivity in brain networks, these between-group differences have a limited diagnostic utility. Novel methods of magnetic resonance imaging (MRI) analyses, such as machine learning (ML), may help bring neuroimaging from the bench to the bedside. Here, we used ML to differentiate participants with a first episode of schizophrenia-spectrum disorder (FES) from healthy controls based on resting-state functional connectivity (rsFC).

METHOD

We acquired resting-state functional MRI data from 63 patients with FES who were individually matched by age and sex to 63 healthy controls. We applied linear kernel support vector machines (SVM) to rsFC within the default mode network, the salience network and the central executive network.

RESULTS

The SVM applied to the rsFC within the salience network distinguished the FES from the control participants with an accuracy of 73.0% (p = 0.001), specificity of 71.4% and sensitivity of 74.6%. The classification accuracy was not significantly affected by medication dose, or by the presence of psychotic symptoms. The functional connectivity within the default mode or the central executive networks did not yield classification accuracies above chance level.

CONCLUSIONS

Seed-based functional connectivity maps can be utilized for diagnostic classification, even early in the course of schizophrenia. The classification was probably based on trait rather than state markers, as symptoms or medications were not significantly associated with classification accuracy. Our results support the role of the anterior insula/salience network in the pathophysiology of FES.

Hand preference is selectively related to common and internal carotid arterial asymmetry

This study documents relationships between handedness and carotid arterial asymmetries. The article is divided into two sections, considering first geometric (n = 195) and then haemodynamic (n = 228) asymmetries. In the geometric study, diameters, lengths, and angles of the common carotid arteries in left and right-handed participants were measured using computed tomography angiography scans. Resistance to blood flow was calculated according to Poiseuille's formula. In the haemodynamic study, peak systolic and end-diastolic velocity, vessel diameter, and volume flow rate of the common, internal, and external carotid arteries were measured in left and right-handed participants, using Doppler ultrasonography. The findings reveal for the first time that the extracranial arteries supplying the cerebral hemispheres are asymmetrical in a direction that increases blood flow to the hemisphere dominant for handedness. Significant handedness interactions were identified in arterial length, diameter, resistance to blood flow, velocity and flow volume rate (p < .001). Arterial resistance and volume flow rates significantly predicted hand preference and proficiency. Our findings reveal a vascular correlate of handedness, but causality cannot be determined from this study alone. These asymmetries appear to be independent of aortic arch anomalies, suggesting a top-down, possibly demand-driven, pattern of development.

Sulcal pattern, extension, and morphology of the precuneus in adult humans

The precuneus represents a relevant cortical component of the parietal lobes. It is involved in visuospatial integration, imagery and simulation, self-awareness, and it is a main node of the Default Mode Network. Its morphology is extremely variable among adult humans, and it has been hypothesized to have undergone major morphological changes in the evolution of Homo sapiens. Recent studies have evidenced a marked variation also associated with its sulcal patterns. The present survey contributes to add further information on this topic, investigating the extension of its main folds, their geometrical influence on the lateral parietal areas, and the relationships with the sulcal schemes. The subparietal sulcus, on average, extends 14mm in its anterior and middle regions and 11mm in its posterior area. The precuneal area extends 36mm above this sulcus. The subparietal sulcus is generally wider on the right hemisphere. Males have larger values than females, but differences are not significant. Sulcal pattern is not correlated with the size of the subparietal sulcus extension. There is a lack of consistent correspondence between hemispheres in the sulcal patterns, pointing further towards a notable individual variability and random asymmetries. The vertical extension of the precuneus influences the height and proportions of the upper parietal profile, but the lateral parietal outline is not sensitive to precuneal variation. There is no correlation between external cortical shape and the size of the subparietal sulcus. Morphological analyses of the precuneus must be integrated with studies on histological factors involved in its variability and, ultimately, with analyses on possible relationships with functional factors.

Abnormalities in large scale functional networks in unmedicated patients with schizophrenia and effects of risperidone

Objective

To describe abnormalities in large scale functional networks in unmedicated patients with schizophrenia and to examine effects of risperidone on networks.

Material and methods

34 unmedicated patients with schizophrenia and 34 matched healthy controls were enrolled in this longitudinal study. We collected resting state functional MRI data with a 3T scanner at baseline and six weeks after they were started on risperidone. In addition, a group of 19 healthy controls were scanned twice six weeks apart. Four large scale networks, the dorsal attention network, executive control network, salience network, and default mode network were identified with seed based functional connectivity analyses. Group differences in connectivity, as well as changes in connectivity over time, were assessed on the group's participant level functional connectivity maps.

Results

In unmedicated patients with schizophrenia we found resting state connectivity to be increased in the dorsal attention network, executive control network, and salience network relative to control participants, but not the default mode network. Dysconnectivity was attenuated after six weeks of treatment only in the dorsal attention network. Baseline connectivity in this network was also related to clinical response at six weeks of treatment with risperidone.

Conclusions

Our results demonstrate abnormalities in large scale functional networks in patients with schizophrenia that are modulated by risperidone only to a certain extent, underscoring the dire need for development of novel antipsychotic medications that have the ability to alleviate symptoms through attenuation of dysconnectivity.

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We found widespread functional dysconnectivity in unmedicated patients with schizophrenia.

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Large scale functional networks appear differentially affected in the disorder.

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Attenuation of dysconnectivity with risperidone is seen only to a limited extent.

Evidence of a dissociation pattern in default mode subnetwork functional connectivity in schizophrenia

The default mode network (DMN) is suggested to play a pivotal role in schizophrenia; however, the dissociation pattern of functional connectivity of DMN subsystems remains uncharacterized in this disease. In this study, resting-state fMRI data were acquired from 55 schizophrenic patients and 53 matched healthy controls. DMN connectivity was estimated from time courses of independent components. The lateral DMN exhibited decreased connectivity with the unimodal sensorimotor cortex but increased connectivity with the heteromodal association areas in schizophrenics. The increased connectivity between the lateral DMN and right control network was significantly correlated with negative and anergia factor scores in the schizophrenic patients. The anterior and posterior DMNs exhibited increased and decreased connectivity with the right control and lateral visual networks, respectively, in schizophrenics. The altered DMN connectivity may underlie the hallucinations, delusions, thought disturbances, and negative symptoms involved in schizophrenia. Furthermore, DMN connectivity patterns could be used to differentiate patients from controls with 76.9% accuracy. These findings may shed new light on the distinct role of DMN subsystems in schizophrenia, thereby furthering our understanding of the pathophysiology of schizophrenia. Elucidating key disease-related DMN subsystems is critical for identifying treatment targets and aiding in the clinical diagnosis and development of treatment strategies.

Three approaches to investigating functional compromise to the default mode network after traumatic axonal injury

The default mode network (DMN) is a reliably elicited functional neural network with potential clinical implications. Its discriminant and prognostic utility following traumatic axonal injury (TAI) have not been previously investigated. The present study used three approaches to analyze DMN functional connectedness, including a whole-brain analysis [A1], network-specific analysis [A2], and between-node (edge) analysis [A3]. The purpose was to identify the utility of each method in distinguishing between healthy and brain-injured individuals, and determine whether observed differences have clinical significance. Resting-state fMRI was acquired from 25 patients with TAI and 17 healthy controls. Patients were scanned 6-11 months post-injury, and functional and neurocognitive outcomes were assessed the same day. Using all three approaches, TAI subjects revealed significantly weaker functional connectivity (FC) than controls, and binary logistic regressions demonstrated all three approaches have discriminant value. Clinical outcomes were not correlated with FC using any approach. Results suggest that compromise to the functional connectedness of the DMN after TAI can be identified using resting-state FC; however, the degree of functional compromise to this network, as measured in this study, may not have clinical implications in chronic TAI.

Lateralization of resting state networks and relationship to age and gender

Brain lateralization is a widely studied topic, however there has been little work focused on lateralization of intrinsic networks (regions showing similar patterns of covariation among voxels) in the resting brain. In this study, we evaluate resting state network lateralization in an age and gender-balanced functional magnetic resonance imaging (fMRI) dataset comprising over 600 healthy subjects ranging in age from 12 to 71. After establishing sample-wide network lateralization properties, we continue with an investigation of age and gender effects on network lateralization. All data was gathered on the same scanner and preprocessed using an automated pipeline (Scott et al., 2011). Networks were extracted via group independent component analysis (gICA) (Calhoun et al., 2001). Twenty-eight resting state networks discussed in previous (Allen et al., 2011) work were re-analyzed with a focus on lateralization. We calculated homotopic voxelwise measures of laterality in addition to a global lateralization measure, called the laterality cofactor, for each network. As expected, many of the intrinsic brain networks were lateralized. For example, the visual network was strongly right lateralized, auditory network and default mode networks were mostly left lateralized. Attentional and frontal networks included nodes that were left lateralized and other nodes that were right lateralized. Age was strongly related to lateralization in multiple regions including sensorimotor network regions precentral gyrus, postcentral gyrus and supramarginal gyrus; and visual network regions lingual gyrus; attentional network regions inferior parietal lobule, superior parietal lobule and middle temporal gyrus; and frontal network regions including the inferior frontal gyrus. Gender showed significant effects mainly in two regions, including visual and frontal networks. For example, the inferior frontal gyrus was more right lateralized in males. Significant effects of age were found in sensorimotor and visual networks on the global measure. In summary, we report a large-sample of lateralization study that finds intrinsic functional brain networks to be highly lateralized, with regions that are strongly related to gender and age locally, and with age a strong factor in lateralization, and gender exhibiting a trend-level effect on global measures of laterality.

Deep learning for neuroimaging: a validation study

Deep learning methods have recently made notable advances in the tasks of classification and representation learning. These tasks are important for brain imaging and neuroscience discovery, making the methods attractive for porting to a neuroimager's toolbox. Success of these methods is, in part, explained by the flexibility of deep learning models. However, this flexibility makes the process of porting to new areas a difficult parameter optimization problem. In this work we demonstrate our results (and feasible parameter ranges) in application of deep learning methods to structural and functional brain imaging data. These methods include deep belief networks and their building block the restricted Boltzmann machine. We also describe a novel constraint-based approach to visualizing high dimensional data. We use it to analyze the effect of parameter choices on data transformations. Our results show that deep learning methods are able to learn physiologically important representations and detect latent relations in neuroimaging data.

Functional connectivity of the hippocampus in temporal lobe epilepsy: feasibility of a task-regressed seed-based approach

OBJECTIVE

Resting-state functional connectivity (FC) has revealed marked network dysfunction in patients with temporal lobe epilepsy (TLE) compared to healthy controls. However, the nature and the location of these changes have not been fully elucidated nor confirmed by other methodologies. We assessed the presence of hippocampal FC changes in TLE based on the low frequency residuals of task-related functional magnetic resonance imaging data after the removal of task-related activation [i.e., task-regressed functional connectivity MRI (fcMRI)].

METHOD

We employed a novel, task-regressed approach to quantify hippocampal FC, and compare hippocampal FC in 17 patients with unilateral TLE (9 left) with 17 healthy controls.

RESULTS

Our results suggest widespread FC reductions in the mesial cortex associated with the default mode network (DMN), and some local FC increases in the lateral portions of the right hemisphere. We found more pronounced FC decreases in the left hemisphere than in the right, and these FC decreases were greatest in patients with left TLE. Moreover, the FC reductions observed between the hippocampus and posterior cingulate, inferior parietal, paracentral regions are in agreement with previous resting state studies.

CONCLUSIONS

Consistent with the existing literature, FC reductions in TLE appear widespread with prominent reductions in the medial portion of the DMN. Our data expand the literature by demonstrating that reductions in FC may be greatest in the left hemisphere and in patients with left TLE. Overall, our findings suggest that task-regressed FC is a viable alternative to resting state and that future studies may extract similar information on network connectivity from already existing datasets.

Restricted Boltzmann machines for neuroimaging: an application in identifying intrinsic networks

Matrix factorization models are the current dominant approach for resolving meaningful data-driven features in neuroimaging data. Among them, independent component analysis (ICA) is arguably the most widely used for identifying functional networks, and its success has led to a number of versatile extensions to group and multimodal data. However there are indications that ICA may have reached a limit in flexibility and representational capacity, as the majority of such extensions are case-driven, custom-made solutions that are still contained within the class of mixture models. In this work, we seek out a principled and naturally extensible approach and consider a probabilistic model known as a restricted Boltzmann machine (RBM). An RBM separates linear factors from functional brain imaging data by fitting a probability distribution model to the data. Importantly, the solution can be used as a building block for more complex (deep) models, making it naturally suitable for hierarchical and multimodal extensions that are not easily captured when using linear factorizations alone. We investigate the capability of RBMs to identify intrinsic networks and compare its performance to that of well-known linear mixture models, in particular ICA. Using synthetic and real task fMRI data, we show that RBMs can be used to identify networks and their temporal activations with accuracy that is equal or greater than that of factorization models. The demonstrated effectiveness of RBMs supports its use as a building block for deeper models, a significant prospect for future neuroimaging research.

Detecting volumetric changes in fMRI connectivity networks in schizophrenia patients

There is a growing interest in identifying neuroimaging-based biomarkers for schizophrenia. Previous studies have shown both functional and structural brain abnormalities in schizophrenia patients. One main category of these findings consists of volumetric abnormalities in brain structure in different cortical and subcortical structures in patients' brain. However there has been little work investigating changes in the brain's functional volumes. Nor has there been work studying differences in brain networks as opposed to single regions. In this study, we investigated the volumes of functional networks as potential biomarkers. Independent component analysis was used to decompose fMRI images into maximally independent spatial maps and corresponding time-courses. Volume of functional networks was computed from subject-specific back reconstructed spatial maps. The results show that different nodes of the default-mode network exhibit volumetric abnormalities in schizophrenia patients. Interestingly these networks are larger in patients compared to controls.

Attention shifts the language network reflecting paradigm presentation

OBJECTIVES

Functional magnetic resonance imaging (fMRI) is a reliable and non-invasive method with which to localize language function in pre-surgical planning. In clinical practice, visual stimulus presentation is often difficult or impossible, due to the patient's restricted language or attention abilities. Therefore, our aim was to investigate modality-specific differences in visual and auditory stimulus presentation.

METHODS

Ten healthy subjects participated in an fMRI study comprising two experiments with visual and auditory stimulus presentation. In both experiments, two language paradigms (one for language comprehension and one for language production) used in clinical practice were investigated. In addition to standard data analysis by the means of the general linear model (GLM), independent component analysis (ICA) was performed to achieve more detailed information on language processing networks.

RESULTS

GLM analysis revealed modality-specific brain activation for both language paradigms for the contrast visual > auditory in the area of the intraparietal sulcus and the hippocampus, two areas related to attention and working memory. Using group ICA, a language network was detected for both paradigms independent of stimulus presentation modality. The investigation of language lateralization revealed no significant variations. Visually presented stimuli further activated an attention-shift network, which could not be identified for the auditory presented language.

CONCLUSION

The results of this study indicate that the visually presented language stimuli additionally activate an attention-shift network. These findings will provide important information for pre-surgical planning in order to preserve reading abilities after brain surgery, significantly improving surgical outcomes. Our findings suggest that the presentation modality for language paradigms should be adapted on behalf of individual indication.

The default modes of reading: modulation of posterior cingulate and medial prefrontal cortex connectivity associated with comprehension and task focus while reading

Reading is a fundamental human capacity and yet it can easily be derailed by the simple act of mind-wandering. A large-scale brain network, referred to as the default mode network (DMN), has been shown to be involved in both mind-wandering and reading, raising the question as to how the same neural system could be implicated in processes with both costs and benefits to narrative comprehension. Resting-state functional magnetic resonance imaging (rs-fMRI) was used to explore whether the intrinsic functional connectivity of the two key midline hubs of the DMN—the posterior cingulate cortex (PCC) and anterior medial prefrontal cortex (aMPFC)—was predictive of individual differences in reading comprehension and task focus recorded outside of the scanner. Worse comprehension was associated with greater functional connectivity between the PCC and a region of the ventral striatum. Better comprehension was associated with greater functional connectivity with a region of the right insula. By contrast reports of increasing task focus were associated with functional connectivity from the aMPFC to clusters in the PCC, the left parietal and temporal cortex, and the cerebellum. Our results suggest that the DMN has both costs (such as poor comprehension) and benefits to reading (such as an on-task focus) because its midline core can couple its activity with other regions to form distinct functional communities that allow seemingly opposing mental states to occur. This flexible coupling allows the DMN to participate in cognitive states that complement the act of reading as well as others that do not.

Default mode, dorsal attention and auditory resting state networks exhibit differential functional connectivity in tinnitus and hearing loss

We investigated auditory, dorsal attention, and default mode networks in adults with tinnitus and hearing loss in a resting state functional connectivity study. Data were obtained using continuous functional magnetic resonance imaging (fMRI) while the participants were at “rest” and were not performing any task. Participants belonged to one of three groups: middle-aged adults with tinnitus and mild-to-moderate high frequency hearing loss (TIN), age-matched controls with normal hearing and no tinnitus (NH), and a second control group with mild-to-moderate high frequency hearing loss without tinnitus (HL). After standard preprocessing, (a) a group independent component analysis (ICA) using 30 components and (b) a seeding-based connectivity analysis were conducted. In the group ICA, the default mode network was the only network to display visual differences between subject groups. In the seeding analysis, we found increased connectivity between the left parahippocampus and the auditory resting state network in the TIN group when compared to NH controls. Similarly, there was also an increased correlation between the right parahippocampus and the dorsal attention network when compared to HL controls. Other group differences in this attention network included decreased correlations between the seed regions and the right supramarginal gyrus in TIN patients when compared to HL controls. In the default mode network, there was a strong decrease in correlation between the seed regions and the precuneus when compared to both control groups. The findings of this study identify specific alterations in the connectivity of the default mode, dorsal attention, and auditory resting state networks due to tinnitus. The results suggest that therapies for tinnitus that mitigate the increased connectivity of limbic regions with auditory and attention resting state networks and the decreased coherence of the default mode network could be effective at reducing tinnitus-related distress.

Spatiotemporal Segregation of Neural Response to Auditory Stimulation: An fMRI Study Using Independent Component Analysis and Frequency-Domain Analysis

Although auditory processing has been widely studied with conventional parametric methods, there have been a limited number of independent component analysis (ICA) applications in this area. The purpose of this study was to examine spatiotemporal behavior of brain networks in response to passive auditory stimulation using ICA. Continuous broadband noise was presented binaurally to 19 subjects with normal hearing. ICA was performed to segregate spatial networks, which were subsequently classified according to their temporal relation to the stimulus using power spectrum analysis. Classification of separated networks resulted in 3 stimulus-activated, 9 stimulus-deactivated, 2 stimulus-neutral (stimulus-dependent but not correlated with the stimulation timing), and 2 stimulus-unrelated (fluctuations that did not follow the stimulus cycles) components. As a result of such classification, spatiotemporal subdivisions were observed in a number of cortical structures, namely auditory, cingulate, and sensorimotor cortices, where parts of the same cortical network responded to the stimulus with different temporal patterns. The majority of the classified networks seemed to comprise subparts of the known resting-state networks (RSNs); however, they displayed different temporal behavior in response to the auditory stimulus, indicating stimulus-dependent temporal segregation of RSNs. Only one of nine deactivated networks coincided with the “classic” default-mode network, suggesting the existence of a stimulus-dependent default-mode network, different from that commonly accepted.

Training-mediated leftward asymmetries during music processing: a cross-sectional and longitudinal fMRI analysis

Practicing a musical instrument has a profound impact on the structure and function of the human brain. The present fMRI study explored how relative hemispheric asymmetries in task-related activity during music processing (same/different discrimination) are shaped by musical training (quantified as cumulative hours of instrument practice), using both a large (N=84) cross-sectional data set of children and adults, and a smaller (N=20) two time-point longitudinal data set of children tracked over 3 to 5 years. The cross-sectional analysis revealed a significant leftward asymmetry in task-related activation, with peaks in Heschl's gyrus and supramarginal gyrus (SMG). The SMG peak was further characterized by a leftward asymmetry in the partial correlation strength with subjects' cumulative hours of practice, controlling for subjects' age and task performance. This SMG peak was found to exhibit a similar pattern of response in the longitudinal data set (in this case, with subjects' cumulative hours of practice over the course of the study), controlling for age, scan interval, and amount of instrument practice prior to the first scan. This study presents novel insights into the ways musical instrument training shapes task-related asymmetries in neural activity during music processing.

Functional Heterogeneity within the Default Network during Semantic Processing and Speech Production

This fMRI study investigated the functional heterogeneity of the core nodes of the default mode network (DMN) during language processing. The core nodes of the DMN were defined as task-induced deactivations over multiple tasks in 94 healthy subjects. We used a factorial design that manipulated different tasks (semantic matching or speech production) and stimuli (familiar words and objects or unfamiliar stimuli), alternating with periods of fixation/rest. Our findings revealed several consistent effects in the DMN, namely less deactivations in the left inferior parietal lobule during semantic than perceptual matching in parallel with greater deactivations during semantic matching in anterior subdivisions of the posterior cingulate cortex (PCC) and the ventromedial prefrontal cortex (MPFC). This suggests that, when the brain is engaged in effortful semantic tasks, a part of the DMN in the left angular gyrus was less deactivated as five other nodes of the DMN were more deactivated. These five DMN areas, where deactivation was greater for semantic than perceptual matching, were further differentiated because deactivation was greater in (i) posterior ventral MPFC for speech production relative to semantic matching, (ii) posterior precuneus and PCC for perceptual processing relative to speech production, and (iii) right inferior parietal cortex for pictures of objects relative to written words during both naming and semantic decisions. Our results thus highlight that task difficulty alone cannot fully explain the functional variability in task-induced deactivations. Together these results emphasize that core nodes within the DMN are functionally heterogeneous and differentially sensitive to the type of language processing.

Default mode network in concussed individuals in response to the YMCA physical stress test

We hypothesize that the evolution of mild traumatic brain injury (mTBI) may be related to differential effects of a concussive blow on the functional integrity of the brain default mode network (DMN) at rest and/or in response to physical stress. Accordingly, in this resting-state functional magnetic resonance imaging (fMRI) study, we examined 14 subjects 10±2 days post-sports-related mTBI and 15 age-matched normal volunteers (NVs) to investigate the possibility that the integrity of the DMN is disrupted at the resting state and/or following the physical stress test. First, all mTBI subjects were asymptomatic based upon clinical evaluation and neuropsychological (NP) assessments prior to the MRI session. Second, the functional integrity within the DMN, a main resting-state network, remained resilient to a single concussive blow. Specifically, the major regions of interest (ROIs) constituting the DMN (e.g., the posterior cingulate cortex [PCC]/precuneus area, the medial prefrontal cortex [MPFC], and left and right lateral parietal cortices [LLP and RLP]) and the connectivity within these four ROIs was similar between NVs and mTBI subjects prior to the YMCA physical stress test. However, the YMCA physical stress test disrupted the DMN, significantly reducing the magnitude of the connection between the PCC and left lateral parietal ROI, and PCC and right lateral parietal ROI, as well as between the PCC and MPFC in mTBI subjects. Thus while the DMN remained resilient to a single mTBI without exertion at 10 days post-injury, it was altered in response to limited physical stress. This may explain some clinical features of mTBI and provide some insight into its mechanism. This important finding should be considered by clinical practitioners when making decisions regarding return-to-play and clearing mTBI athletes for sports participation.

A framework for interpreting functional networks in schizophrenia

Some promising genetic correlates of schizophrenia have emerged in recent years but none explain more than a small fraction of cases. The challenge of our time is to characterize the neuronal networks underlying schizophrenia and other neuropsychiatric illnesses. Early models of schizophrenia have been limited by the ability to readily evaluate large-scale networks in living patients. With the development of resting state and advanced structural magnetic resonance imaging, it has become possible to do this. While we are at an early stage, a number of models of intrinsic brain networks have been developed to account for schizophrenia and other neuropsychiatric disorders. This paper reviews the recent voxel-based morphometry (VBM), diffusion tensor imaging (DTI), and resting functional magnetic resonance imaging literature in light of the proposed networks underlying these disorders. It is suggested that there is support for recently proposed models that suggest a pivotal role for the salience network. However, the interactions of this network with the default mode network and executive control networks are not sufficient to explain schizophrenic symptoms or distinguish them from other neuropsychiatric disorders. Alternatively, it is proposed that schizophrenia arises from a uniquely human brain network associated with directed effort including the dorsal anterior and posterior cingulate cortex (PCC), auditory cortex, and hippocampus while mood disorders arise from a different brain network associated with emotional encoding including the ventral anterior cingulate cortex (ACC), orbital frontal cortex, and amygdala. Both interact with the dorsolateral prefrontal cortex and a representation network including the frontal and temporal poles and the fronto-insular cortex, allowing the representation of the thoughts, feelings, and actions of self and others across time.

Cerebral Asymmetry: A Quantitative, Multifactorial, and Plastic Brain Phenotype

The longitudinal fissure separates the human brain into two hemispheres that remain connected through the corpus callosum. The left and the right halves of the brain resemble each other, and almost every structure present in one side has an equivalent structure in the other. Despite this exceptional correspondence, the two hemispheres also display important anatomical differences and there is marked lateralization of certain cognitive and motor functions such as language and handedness. However, the mechanisms that underlie the establishment of these hemispheric specializations, as well as their physiological and behavioral implications, remain largely unknown. Thanks to recent advances in neuroimaging, a series of studies documenting variation in symmetry and asymmetry as a function of age, gender, brain region, and pathological state, have been published in the past decade. Here, we review evidence of normal and atypical cerebral asymmetry, and the factors that influence it at the macrostructural level. Given the prominent role that cerebral asymmetry plays in the organization of the brain, and its possible implication in neurodevelopmental and psychiatric conditions, further research in this area is anticipated.

Hemispheric asymmetries of functional connectivity and grey matter volume in the default mode network

Resting state networks such as the default mode network have been widely reported. Although a plethora of information on its functional relevance has been generated, little is known about lateralization or hemisphere asymmetry within the DMN. We used high-resolution resting state fMRI and T1 3D data to investigate such asymmetries in two groups of healthy subjects, one right-handed and one left-handed. Independent component analysis and the dual regression approach were carried out to identify functional asymmetries, while voxel-based morphometry was used to identify structural asymmetries in grey matter volume within the DMN. Greater leftward functional connectivity was observed in the posterior cingulate gyrus (PCG) for both groups. Leftward functional asymmetry was observed in the thalamus and rightward functional asymmetries were observed in the middle frontal and middle/superior temporal gyrus in the right-handed group. Rightward asymmetries in grey matter volume were observed in the posterior portion of the PCG for both groups. The right-handed group exhibited leftward structural asymmetries in the anterior portion of the PCG and in the middle frontal and posterior portion of the middle temporal gyrus, while rightward asymmetries were observed in the posterior portion of the PCG and anterior portions of temporal regions. These results suggest that functional connectivity and grey matter volume are not equally distributed between hemispheres within the DMN, and that functional asymmetries are not always reflected or determined by structural asymmetries.

Altered topological properties of functional network connectivity in schizophrenia during resting state: a small-world brain network study

Aberrant topological properties of small-world human brain networks in patients with schizophrenia (SZ) have been documented in previous neuroimaging studies. Aberrant functional network connectivity (FNC, temporal relationships among independent component time courses) has also been found in SZ by a previous resting state functional magnetic resonance imaging (fMRI) study. However, no study has yet determined if topological properties of FNC are also altered in SZ. In this study, small-world network metrics of FNC during the resting state were examined in both healthy controls (HCs) and SZ subjects. FMRI data were obtained from 19 HCs and 19 SZ. Brain images were decomposed into independent components (ICs) by group independent component analysis (ICA). FNC maps were constructed via a partial correlation analysis of ICA time courses. A set of undirected graphs were built by thresholding the FNC maps and the small-world network metrics of these maps were evaluated. Our results demonstrated significantly altered topological properties of FNC in SZ relative to controls. In addition, topological measures of many ICs involving frontal, parietal, occipital and cerebellar areas were altered in SZ relative to controls. Specifically, topological measures of whole network and specific components in SZ were correlated with scores on the negative symptom scale of the Positive and Negative Symptom Scale (PANSS). These findings suggest that aberrant architecture of small-world brain topology in SZ consists of ICA temporally coherent brain networks.

Altered small-world brain networks in temporal lobe in patients with schizophrenia performing an auditory oddball task

The functional architecture of the human brain has been extensively described in terms of complex networks characterized by efficient small-world features. Recent functional magnetic resonance imaging (fMRI) studies have found altered small-world topological properties of brain functional networks in patients with schizophrenia (SZ) during the resting state. However, little is known about the small-world properties of brain networks in the context of a task. In this study, we investigated the topological properties of human brain functional networks derived from fMRI during an auditory oddball (AOD) task. Data were obtained from 20 healthy controls and 20 SZ; A left and a right task-related network which consisted of the top activated voxels in temporal lobe of each hemisphere were analyzed separately. All voxels were detected by group independent component analysis. Connectivity of the left and right task-related networks were estimated by partial correlation analysis and thresholded to construct a set of undirected graphs. The small-worldness values were decreased in both hemispheres in SZ. In addition, SZ showed longer shortest path length and lower global efficiency only in the left task-related networks. These results suggested small-world attributes are altered during the AOD task-related networks in SZ which provided further evidences for brain dysfunction of connectivity in SZ.