Schizophrenia Polygenic Risk During Typical Development Reflects Multiscale Cortical Organization.

Background: Schizophrenia is widely recognized as a neurodevelopmental disorder. Abnormal cortical development in otherwise typically developing children and adolescents may be revealed using polygenic risk scores for schizophrenia (PRS-SCZ). Methods: We assessed PRS-SCZ and cortical morphometry in typically developing children and adolescents (3-21 years, 46.8% female) using whole-genome genotyping and T1-weighted magnetic resonance imaging (n = 390) from the PING (Pediatric Imaging, Neurocognition, and Genetics) cohort. We contextualized the findings using 1) age-matched transcriptomics, 2) histologically defined cytoarchitectural types and functionally defined networks, and 3) case-control differences of schizophrenia and other major psychiatric disorders derived from meta-analytic data of 6 ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) working groups, including a total of 12,876 patients and 15,670 control participants. Results: Higher PRS-SCZ was associated with greater cortical thickness, which was most prominent in areas with heightened gene expression of dendrites and synapses. PRS-SCZ-related increases in vertexwise cortical thickness were mainly distributed in association cortical areas, particularly the ventral attention network, while relatively sparing koniocortical type cortex (i.e., primary sensory areas). The large-scale pattern of cortical thickness increases related to PRS-SCZ mirrored the pattern of cortical thinning in schizophrenia and mood-related psychiatric disorders derived from the ENIGMA consortium. Age group models illustrate a possible trajectory from PRS-SCZ-associated cortical thickness increases in early childhood toward thinning in late adolescence, with the latter resembling the adult brain phenotype of schizophrenia. Conclusions: Collectively, combining imaging genetics with multiscale mapping, our work provides novel insight into how genetic risk for schizophrenia affects the cortex early in life.

Evaluation of functional connectivity in subdivisions of the thalamus in schizophrenia.

BACKGROUND: Previous studies in schizophrenia revealed abnormalities in the cortico-cerebellar-thalamo-cortical circuit (CCTCC) pathway, suggesting the necessity for defining thalamic subdivisions in understanding alterations of brain connectivity.AimsTo parcellate the thalamus into several subdivisions using a data-driven method, and to evaluate the role of each subdivision in the alterations of CCTCC functional connectivity in patients with schizophrenia. METHOD: There were 54 patients with schizophrenia and 42 healthy controls included in this study. First, the thalamic structural and functional connections computed, based on diffusion magnetic resonance imaging (MRI, white matter tractography) and resting-state functional MRI, were clustered to parcellate thalamus. Next, functional connectivity of each thalamus subdivision was investigated, and the alterations in thalamic functional connectivity for patients with schizophrenia were inspected. RESULTS: Based on the data-driven parcellation method, six thalamic subdivisions were defined. Loss of connectivity was observed between several thalamic subdivisions (superior-anterior, ventromedial and dorsolateral part of the thalamus) and the sensorimotor system, anterior cingulate cortex and cerebellum in patients with schizophrenia. A gradual pattern of dysconnectivity was observed across the thalamic subdivisions. Additionally, the altered connectivity negatively correlated with symptom scores and duration of illness in individuals with schizophrenia. CONCLUSIONS: The findings of the study revealed a wide range of thalamic functional dysconnectivity in the CCTCC pathway, increasing our understanding of the relationship between the CCTCC pathway and symptoms associated with schizophrenia, and further indicating a potential alteration pattern in the thalamic nuclei in people with schizophrenia.Declaration of interestNone.

Exploring Individual Brain Variability during Development based on Patterns of Maturational Coupling of Cortical Thickness: A Longitudinal MRI Study.

Structural covariance has recently emerged as a tool to study brain connectivity in health and disease. The main assumption behind the phenomenon of structural covariance is that changes in brain structure during development occur in a coordinated fashion. However, no study has yet explored the correlation of structural brain changes within individuals across development. Here, we used longitudinal magnetic resonance imaging scans from 141 normally developing children and adolescents (scanned 3 times) to introduce a novel subject-based maturational coupling approach. For each subject, maturational coupling was defined as similarity in the trajectory of cortical thickness (across the time points) between any two cortical regions. Our approach largely captured features seen in population-based structural covariance, and confirmed strong maturational coupling between homologous and near-neighbor cortical regions. Stronger maturational coupling among several homologous regions was observed for females compared to males, possibly indicating greater interhemispheric connectivity in females. Developmental changes in maturational coupling within the default-mode network (DMN) aligned with developmental changes in structural and functional DMN connectivity. Our findings indicate that patterns of maturational coupling within individuals may provide mechanistic explanation for the phenomenon of structural covariance, and allow investigation of individual brain variability with respect to cognition and disease vulnerability.

Cortical Thickness Abnormalities in Autism Spectrum Disorders Through Late Childhood, Adolescence, and Adulthood: A Large-Scale MRI Study.

Neuroimaging studies in autism spectrum disorders (ASDs) have provided inconsistent evidence of cortical abnormality. This is probably due to the small sample sizes used in most studies, and important differences in sample characteristics, particularly age, as well as to the heterogeneity of the disorder. To address these issues, we assessed abnormalities in ASD within the Autism Brain Imaging Data Exchange data set, which comprises data from approximately 1100 individuals (~6-55 years). A subset of these data that met stringent quality control and inclusion criteria (560 male subjects; 266 ASD; age = 6-35 years) were used to compute age-specific differences in cortical thickness in ASD and the relationship of any such differences to symptom severity of ASD. Our results show widespread increased cortical thickness in ASD, primarily left lateralized, from 6 years onwards, with differences diminishing during adulthood. The severity of symptoms related to social affect and communication correlated with these cortical abnormalities. These results are consistent with the conjecture that developmental patterns of cortical thickness abnormalities reflect delayed cortical maturation and highlight the dynamic nature of morphological abnormalities in ASD.

Imaging structural covariance in the development of intelligence.

Verbal and non-verbal intelligence in children is highly correlated, and thus, it has been difficult to differentiate their neural substrates. Nevertheless, recent studies have shown that verbal and non-verbal intelligence can be dissociated and focal cortical regions corresponding to each have been demonstrated. However, the pattern of structural covariance corresponding to verbal and non-verbal intelligence remains unexplored. In this study, we used 586 longitudinal anatomical MRI scans of subjects aged 6-18 years, who had concurrent intelligence quotient (IQ) testing on the Wechsler Abbreviated Scale of Intelligence. Structural covariance networks (SCNs) were constructed using interregional correlations in cortical thickness for low-IQ (Performance IQ=100±8, Verbal IQ=100±7) and high-IQ (PIQ=121±8, VIQ=120±9) groups. From low- to high-VIQ group, we observed constrained patterns of anatomical coupling among cortical regions, complemented by observations of higher global efficiency and modularity, and lower local efficiency in high-VIQ group, suggesting a shift towards a more optimal topological organization. Analysis of nodal topological properties (regional efficiency and participation coefficient) revealed greater involvement of left-hemispheric language related regions including inferior frontal and superior temporal gyri for high-VIQ group. From low- to high-PIQ group, we did not observe significant differences in anatomical coupling patterns, global and nodal topological properties. Our findings indicate that people with higher verbal intelligence have structural brain differences from people with lower verbal intelligence - not only in localized cortical regions, but also in the patterns of anatomical coupling among widely distributed cortical regions, possibly resulting to a system-level reorganization that might lead to a more efficient organization in high-VIQ group.

Attention Performance Measured by Attention Network Test Is Correlated with Global and Regional Efficiency of Structural Brain Networks.

Functional neuroimaging studies have indicated the involvement of separate brain areas in three distinct attention systems: alerting, orienting, and executive control (EC). However, the structural correlates underlying attention remains unexplored. Here, we utilized graph theory to examine the neuroanatomical substrates of the three attention systems measured by attention network test (ANT) in 65 healthy subjects. White matter connectivity, assessed with diffusion tensor imaging deterministic tractography was modeled as a structural network comprising 90 nodes defined by the automated anatomical labeling (AAL) template. Linear regression analyses were conducted to explore the relationship between topological parameters and the three attentional effects. We found a significant positive correlation between EC function and global efficiency of the whole brain network. At the regional level, node-specific correlations were discovered between regional efficiency and all three ANT components, including dorsolateral superior frontal gyrus, thalamus and parahippocampal gyrus for EC, thalamus and inferior parietal gyrus for alerting, and paracentral lobule and inferior occipital gyrus for orienting. Our findings highlight the fundamental architecture of interregional structural connectivity involved in attention and could provide new insights into the anatomical basis underlying human behavior.

Brain connectivity in normally developing children and adolescents.

The developing human brain undergoes an astonishing sequence of events that continuously shape the structural and functional brain connectivity. Distinct regional variations in the timelines of maturational events (synaptogenesis and synaptic pruning) occurring at the synaptic level are reflected in brain measures at macroscopic resolution (cortical thickness and gray matter density). Interestingly, the observed brain changes coincide with cognitive milestones suggesting that the changing scaffold of brain circuits may subserve cognitive development. Recent advances in connectivity analysis propelled by graph theory have allowed, on one hand, the investigation of maturational changes in global organization of structural and functional brain networks; and on the other hand, the exploration of specific networks within the context of global brain networks. An emerging picture from several connectivity studies is a system-level rewiring that constantly refines the connectivity of the developing brain.

Disruption of structural covariance networks for language in autism is modulated by verbal ability.

The presence of widespread speech and language deficits is a core feature of autism spectrum disorders (ASD). These impairments have often been attributed to altered connections between brain regions. Recent developments in anatomical correlation-based approaches to map structural covariance offer an effective way of studying such connections in vivo. In this study, we employed such a structural covariance network (SCN)-based approach to investigate the integrity of anatomical networks in fronto-temporal brain regions of twenty children with ASD compared to an age and gender-matched control group of twenty-two children. Our findings reflected large-scale disruption of inter and intrahemispheric covariance in left frontal SCNs in the ASD group compared to controls, but no differences in right fronto-temporal SCNs. Interhemispheric covariance in left-seeded networks was further found to be modulated by verbal ability of the participants irrespective of autism diagnosis, suggesting that language function might be related to the strength of interhemispheric structural covariance between frontal regions. Additionally, regional cortical thickening was observed in right frontal and left posterior regions, which was predicted by decreasing symptom severity and increasing verbal ability in ASD. These findings unify reports of regional differences in cortical morphology in ASD. They also suggest that reduced left hemisphere asymmetry and increased frontal growth may not only reflect neurodevelopmental aberrations but also compensatory mechanisms.

Prediction of brain maturity based on cortical thickness at different spatial resolutions.

Several studies using magnetic resonance imaging (MRI) scans have shown developmental trajectories of cortical thickness. Cognitive milestones happen concurrently with these structural changes, and a delay in such changes has been implicated in developmental disorders such as attention-deficit/hyperactivity disorder (ADHD). Accurate estimation of individuals' brain maturity, therefore, is critical in establishing a baseline for normal brain development against which neurodevelopmental disorders can be assessed. In this study, cortical thickness derived from structural magnetic resonance imaging (MRI) scans of a large longitudinal dataset of normally growing children and adolescents (n=308), were used to build a highly accurate predictive model for estimating chronological age (cross-validated correlation up to R=0.84). Unlike previous studies which used kernelized approach in building prediction models, we used an elastic net penalized linear regression model capable of producing a spatially sparse, yet accurate predictive model of chronological age. Upon investigating different scales of cortical parcellation from 78 to 10,240 brain parcels, we observed that the accuracy in estimated age improved with increased spatial scale of brain parcellation, with the best estimations obtained for spatial resolutions consisting of 2560 and 10,240 brain parcels. The top predictors of brain maturity were found in highly localized sensorimotor and association areas. The results of our study demonstrate that cortical thickness can be used to estimate individuals' brain maturity with high accuracy, and the estimated ages relate to functional and behavioural measures, underscoring the relevance and scope of the study in the understanding of biological maturity.

Developmental changes in organization of structural brain networks.

Recent findings from developmental neuroimaging studies suggest that the enhancement of cognitive processes during development may be the result of a fine-tuning of the structural and functional organization of brain with maturation. However, the details regarding the developmental trajectory of large-scale structural brain networks are not yet understood. Here, we used graph theory to examine developmental changes in the organization of structural brain networks in 203 normally growing children and adolescents. Structural brain networks were constructed using interregional correlations in cortical thickness for 4 age groups (early childhood: 4.8-8.4 year; late childhood: 8.5-11.3 year; early adolescence: 11.4-14.7 year; late adolescence: 14.8-18.3 year). Late childhood showed prominent changes in topological properties, specifically a significant reduction in local efficiency, modularity, and increased global efficiency, suggesting a shift of topological organization toward a more random configuration. An increase in number and span of distribution of connector hubs was found in this age group. Finally, inter-regional connectivity analysis and graph-theoretic measures indicated early maturation of primary sensorimotor regions and protracted development of higher order association and paralimbic regions. Our finding reveals a time window of plasticity occurring during late childhood which may accommodate crucial changes during puberty and the new developmental tasks that an adolescent faces.

Thermal conduction tensor imaging and energy flow analysis of brain: a feasibility study using MRI.

The imaging of the distribution of thermal conductivity tensor at various points in a tissue is an essential need when accurate knowledge of heat energy flow in tissue is required for diagnostic and therapeutic management in oncology, neurology, and interventional radiology. Conventionally, tissue thermal conductivity is assumed as scalar, which induces errors in obtaining proper heat flow distribution. Using statistical thermodynamics principles, we present a method for constructing thermal conductivity tensor image of a tissue or an organ, using an MRI scanner. We elucidate the necessary tensorial cross-property relationship between different transport processes and confirm the same by experimental data. Using the proposed method, we perform a preliminary study of the procedure of thermal conductivity tensor imaging of the human brain as a case study. The methodology is quantitatively elucidated by measurement of tissue anisotropy distribution, tensor eigenvalues, and path tracking, along with three illustrative examples showing that transport properties estimated by the proposed thermal conductivity approach closely corroborates, with over 90% accuracy, to the experimentally measured values of the transport parameters which have been independently experimentally measured directly. By combining diffusion and perfusion tensor imaging approaches using mobility-encoding and spin-labelling methodologies respectively, we delineate the possible applications of this novel imaging modality to clinical problems of energy flow mapping involving biological heat transfer equations, such as planning of hyperthermic treatment to brain tumors, and electrode localization for deep brain stimulation in Parkinson's disease.