Budhachandra S. Khundrakpam

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.

Anatomical Substrates of the Alerting, Orienting and Executive Control Components of Attention: Focus on the Posterior Parietal Lobe

Both neuropsychological and functional neuroimaging studies have identified that the posterior parietal lobe (PPL) is critical for the attention function. However, the unique role of distinct parietal cortical subregions and their underlying white matter (WM) remains in question. In this study, we collected both magnetic resonance imaging and diffusion tensor imaging (DTI) data in normal participants, and evaluated their attention performance using attention network test (ANT), which could isolate three different attention components: alerting, orienting and executive control. Cortical thickness, surface area and DTI parameters were extracted from predefined PPL subregions and correlated with behavioural performance. Tract-based spatial statistics (TBSS) was used for the voxel-wise statistical analysis. Results indicated structure-behaviour relationships on multiple levels. First, a link between the cortical thickness and WM integrity of the right inferior parietal regions and orienting performance was observed. Specifically, probabilistic tractography demonstrated that the integrity of WM connectivity between the bilateral inferior parietal lobules mediated the orienting performance. Second, the scores of executive control were significantly associated with the WM diffusion metrics of the right supramarginal gyrus. Finally, TBSS analysis revealed that alerting performance was significant correlated with the fractional anisotropy of local WM connecting the right thalamus and supplementary motor area. We conclude that distinct areas and features within PPL are associated with different components of attention. These findings could yield a more complete understanding of the nature of the PPL contribution to visuospatial attention.

Cortical Thickness, Cortico-Amygdalar Networks, and Externalizing Behaviors in Healthy Children

BACKGROUND Fronto-amygdalar networks are implicated in childhood psychiatric disorders characterized by high rates of externalizing (aggressive, noncompliant, oppositional) behavior. Although externalizing behaviors are distributed continuously across clinical and nonclinical samples, little is known about how brain variations may confer risk for problematic behavior. Here, we studied cortical thickness, amygdala volume, and cortico-amygdalar network correlates of externalizing behavior in a large sample of healthy children. METHODS Two hundred ninety-seven healthy children (6-18 years; mean = 12 ± 3 years), with 517 magnetic resonance imaging scans, from the National Institutes of Health Magnetic Resonance Imaging Study of Normal Brain Development, were studied. Relationships between externalizing behaviors (measured with the Child Behavior Checklist) and cortical thickness, amygdala volume, and cortico-amygdalar structural networks were examined using first-order linear mixed-effects models, after controlling for age, sex, scanner, and total brain volume. Results significant at p ≤ .05, following multiple comparison correction, are reported. RESULTS Left orbitofrontal, right retrosplenial cingulate, and medial temporal cortex thickness were negatively correlated with externalizing behaviors. Although amygdala volume alone was not correlated with externalizing behaviors, an orbitofrontal cortex-amygdala network predicted rates of externalizing behavior. Children with lower levels of externalizing behaviors exhibited positive correlations between orbitofrontal cortex and amygdala structure, while these regions were not correlated in children with higher levels of externalizing behavior. CONCLUSIONS Our findings identify key cortical nodes in frontal, cingulate, and temporal cortex associated with externalizing behaviors in children; and indicate that orbitofrontal-amygdala network properties may influence externalizing behaviors, along a continuum and across healthy and clinical samples.

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.

Cortical Structural Connectivity Alterations in Primary Insomnia: Insights from MRI-Based Morphometric Correlation Analysis

The etiology and maintenance of insomnia are proposed to be associated with increased cognitive and physiological arousal caused by acute stressors and associated cognitive rumination. A core feature of such hyperarousal theory of insomnia involves increased sensory processing that interferes with the onset and maintenance of sleep. In this work, we collected structural magnetic resonance imaging data from 35 patients with primary insomnia and 35 normal sleepers and applied structural covariance analysis to investigate whether insomnia is associated with disruptions in structural brain networks centered at the sensory regions (primary visual, primary auditory, and olfactory cortex). As expected, insomnia patients showed increased structural covariance in cortical thickness between sensory and motor regions. We also observed trends of increased covariance between sensory regions and the default-mode network, and the salience network regions, and trends of decreased covariance between sensory regions and the frontoparietal working memory network regions, in insomnia patients. The observed changes in structural covariance tended to correlated with poor sleep quality. Our findings support previous functional neuroimaging studies and provide novel insights into variations in brain network configuration that may be involved in the pathophysiology of insomnia.

Imaging structural covariance in the development of intelligence

ABSTRACT 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. HIGHLIGHTSStructural covariance was done for different levels of verbal and non‐verbal intelligence.Distinct group differences in structural covariance were observed for verbal intelligence.Greater involvement of left‐hemispheric language‐related regions for high VIQ group.

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

Abstract 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.

Predicting symptom severity in autism spectrum disorder based on cortical thickness measures in agglomerative data

ABSTRACT Machine learning approaches have been widely used for the identification of neuropathology from neuroimaging data. However, these approaches require large samples and suffer from the challenges associated with multi‐site, multi‐protocol data. We propose a novel approach to address these challenges, and demonstrate its usefulness with the Autism Brain Imaging Data Exchange (ABIDE) database. We predict symptom severity based on cortical thickness measurements from 156 individuals with autism spectrum disorder (ASD) from four different sites. The proposed approach consists of two main stages: a domain adaptation stage using partial least squares regression to maximize the consistency of imaging data across sites; and a learning stage combining support vector regression for regional prediction of severity with elastic‐net penalized linear regression for integrating regional predictions into a whole‐brain severity prediction. The proposed method performed markedly better than simpler alternatives, better with multi‐site than single‐site data, and resulted in a considerably higher cross‐validated correlation score than has previously been reported in the literature for multi‐site data. This demonstration of the utility of the proposed approach for detecting structural brain abnormalities in ASD from the multi‐site, multi‐protocol ABIDE dataset indicates the potential of designing machine learning methods to meet the challenges of agglomerative data. HIGHLIGHTSA machine learning method to deal with challenges of agglomerative data is proposed.We demonstrate the method by predicting the symptom severity in ASD based on MRI.We use the imaging data from the multi‐site, multi‐protocol ABIDE database.The proposed method performed better than simpler alternatives.It also benefited from the multi‐site data.

Structural insights into aberrant cortical morphometry and network organization in psychogenic erectile dysfunction

Functional neuroimaging studies have revealed abnormal brain dynamics of male sexual arousal (SA) in psychogenic erectile dysfunction (pED). However, the neuroanatomical correlates of pED are still unclear. In this work, we obtained cortical thickness (CTh) measurements from structural magnetic resonance images of 40 pED patients and 39 healthy control subjects. Abnormalities in CTh related to pED were explored using a scale space search based brain morphometric analysis. Organizations of brain structural covariance networks were analyzed as well. Compared with healthy men, pED patients showed significantly decreased CTh in widespread cortical regions, most of which were previously reported to show abnormal dynamics of male SA in pED, such as the medial prefrontal, orbitofrontal, cingulate, inferotemporal, and insular cortices. CTh reductions in these areas were found to be significantly correlated with male sexual functioning degradation. Moreover, pED patients showed decreased interregional CTh correlations from the right lateral orbitofrontal cortex to the right supramarginal gyrus and the left angular cortex, implying disassociations between the cognitive, motivational, and inhibitory networks of male SA in pED. This work provides structural insights on the complex phenomenon of psychogenic sexual dysfunction in men, and suggests a specific vulnerability factor, possibly as an extra “organic” factor, that may play an important role in pED. Hum Brain Mapp 36:4469–4482, 2015.

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.