Dynamic task-specific brain network connectivity in children with severe reading difficulties

Structural neural connectivity correlates with pre-reading abilities in preschool children

Pre-reading abilities are predictive of later reading ability and can be assessed before reading begins. However, the neural correlates of pre-reading abilities in young children are not fully understood. To address this, we examined 246 datasets collected in an accelerated longitudinal design from 81 children aged 2-6 years (age = 4.6 ± 0.98 years, 47 males). Children completed pre-reading assessments (NEPSY-II Phonological Processing and Speeded Naming) and underwent a diffusion magnetic resonance imaging (MRI) scan to assess white matter connectivity. We defined a core neural network of reading and language regions based on prior literature, and structural connections within this network were assessed using graph theory analysis. Linear mixed models accounting for repeated measures were used to test associations between children's pre-reading performance and graph theory measures for the whole bilateral reading network and each hemisphere separately. Phonological Processing scores were positively associated with global efficiency, local efficiency, and clustering coefficient in the bilateral and right hemisphere networks, as well as local efficiency and clustering coefficient in the left hemisphere network. Our findings provide further evidence that structural neural correlates of Phonological Processing emerge in early childhood, before and during early reading instruction.

Dynamic functional brain network connectivity during pseudoword processing relates to children's reading skill

Learning to read requires children to link print (orthography) with its corresponding speech sounds (phonology). Yet, most EEG studies of reading development focus on emerging functional specialization (e.g., developing increasingly refined orthographic representations), rather than directly measuring the functional connectivity that links orthography and phonology in real time. In this proof-of-concept study we relate children's reading skill to both orthographic specialization for print (via the N170, also called the N1, event related potential, ERP) and orthographic-phonological integration (via dynamic/event-related EEG phase synchronization - an index of functional brain network connectivity). Typically developing English speaking children (n = 24; 4-14 years) and control adults (n = 20; 18-35 years) viewed pseudowords, consonants and unfamiliar false fonts during a 1-back memory task while 64-channel EEG was recorded. Orthographic specialization (larger N170 for pseudowords vs. false fonts) became more left-lateralized with age, but not with reading skill. Conversely, children's reading skill correlated with functional brain network connectivity during pseudoword processing that requires orthography-phonology linking. This was seen during two periods of simultaneous low frequency synchronization/high frequency desynchronization of posterior-occipital brain network activity. Specifically, in stronger readers, left posterior-occipital activity showed more delta (1-3Hz) synchronization around 300-500 ms (simultaneous with gamma 30-80 Hz desynchronization) and more gamma desynchronization around 600-1000 ms (simultaneous with theta 3-7Hz synchronization) during pseudoword vs. false font processing. These effects were significant even when controlling for age (moderate - large effect sizes). Dynamic functional brain network connectivity measures the brain's real-time sound-print linking. It may offer an under-explored, yet sensitive, index of the neural plasticity associated with reading development.

Graph Analysis of EEG Functional Connectivity Networks During a Letter-Speech Sound Binding Task in Adult Dyslexics

We performed an EEG graph analysis on data from 31 typical readers (22.27 ± 2.53 y/o) and 24 dyslexics (22.99 ± 2.29 y/o), recorded while they were engaged in an audiovisual task and during resting-state. The task simulates reading acquisition as participants learned new letter-sound mappings via feedback. EEG data was filtered for the delta (0.5–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), and beta (13–30 Hz) bands. We computed the Phase Lag Index (PLI) to provide an estimate of the functional connectivity between all pairs of electrodes per band. Then, networks were constructed using a Minimum Spanning Tree (MST), a unique sub-graph connecting all nodes (electrodes) without loops, aimed at minimizing bias in between groups and conditions comparisons. Both groups showed a comparable accuracy increase during task blocks, indicating that they correctly learned the new associations. The EEG results revealed lower task-specific theta connectivity, and lower theta degree correlation over both rest and task recordings, indicating less network integration in dyslexics compared to typical readers. This pattern suggests a role of theta oscillations in dyslexia and may reflect differences in task engagement between the groups, although robust correlations between MST metrics and performance indices were lacking.

Poor reading is characterized by a more connected network with wrong hubs

Using graph theory, we examined topological organization of the language network in Chinese children with poor reading during an auditory rhyming task and a visual spelling task, compared to reading-matched controls and age-matched controls. First, poor readers (PR) showed reduced clustering coefficient in the left inferior frontal gyrus (IFG) and higher nodal efficiency in the bilateral superior temporal gyri (STG) during the visual task, indicating a less functionally specialized cluster around the left IFG and stronger functional links between bilateral STGs and other regions. Furthermore, PR adopted additional right-hemispheric hubs in both tasks, which may explain increased global efficiency across both tasks and lower normalized characteristic shortest path length in the visual task for the PR. These results underscore deficits in the left IFG during visual word processing and conform previous findings about compensation in the right hemisphere in children with poor reading.

Maternal depression is associated with decreased functional connectivity within semantics and phonology networks in preschool children

INTRODUCTION

Maternal depression is characterized by a lack of emotional responsiveness and engagement with their child, which may lead to the child's decreased cognitive, and language outcomes all related to the child's future reading outcomes. The relations between maternal depression and functional connectivity in neural circuits supporting language in the child was explored.

METHODS

Eleven 4-year-old girls completed language abilities assessment and resting-state functional magnetic resonance imaging scan. Their mothers completed the Beck's Depression Inventory (BDI) to examine maternal depression when the child was 12 months old and at the age of 4. Functional connections within the child's resting-state phonology, semantics, language networks were correlated with maternal BDI scores at the age of 4 years.

RESULTS

Higher maternal depression was associated with the child's decreased within the semantic and phonological networks connectivity during rest. Higher maternal depression at 4 years moderated the relationship between early depression scores and functional connectivity within the phonological network.

CONCLUSIONS

Maternal depression in the first year of life is related to functional connections of phonological processing and enhanced by current maternal depression levels. We conclude that after a mother gives birth, resources should be provided to minimize depressive symptoms and interventions should be applied to support their child's language development for future reading acquisition.

Large-scale network topology reveals brain functional abnormality in Chinese dyslexic children

It has been revealed that dyslexic children learning alphabetic languages are characterized by aberrant topological organization of brain networks. However, little is known about the functional organization and the reconfiguration pattern of brain networks in Chinese dyslexic children. Using graph theoretical analysis and functional magnetic resonance images (fMRI), we examined this issue specifically from the perspective of functional integration and segregation. We first compared large-scale topological organizations between dyslexic children and typically developing children during a Chinese phonological rhyming task, and found that dyslexic children showed increased local efficiency and clustering coefficient compared with typically developing children, which were negatively correlated with task performance. Furthermore, dyslexic children and typically developing children could be accurately distinguished at the individual-subject level based on the nodal local efficiency or clustering coefficient. Second, we studied the group difference of network reconfiguration and found that dyslexic children showed more difficulty when shifting from the resting state to the phonological task. Our results suggest an over-segregated brain functional organization and deficits in brain network reconfiguration in Chinese dyslexic children, which helps to advance our knowledge on the neural mechanisms underlying dyslexia.

Altered electroencephalographic networks in developmental dyslexia after remedial training: a prospective case-control study

Electroencephalographic studies using graph theoretic analysis have found aberrations in functional connectivity in children with developmental dyslexia. However, how the training with visual tasks can change the functional connectivity of the semantic network in developmental dyslexia is still unclear. We looked for differences in local and global topological properties of functional networks between 21 healthy controls and 22 dyslexic children (8-9 years old) before and after training with visual tasks in this prospective case-control study. The minimum spanning tree method was used to construct the subjects' brain networks in multiple electroencephalographic frequency ranges during a visual word/pseudoword discrimination task. We found group differences in the theta, alpha, beta and gamma bands for four graph measures suggesting a more integrated network topology in dyslexics before the training compared to controls. After training, the network topology of dyslexic children had become more segregated and similar to that of the controls. In the θ, α and β1-frequency bands, compared to the controls, the pre-training dyslexics exhibited a reduced degree and betweenness centrality of the left anterior temporal and parietal regions. The simultaneous appearance in the left hemisphere of hubs in temporal and parietal (α, β1), temporal and superior frontal cortex (θ, α), parietal and occipitotemporal cortices (β1), identified in the networks of normally developing children was not present in the brain networks of dyslexics. After training, the hub distribution for dyslexics in the theta and beta1 bands had become similar to that of the controls. In summary, our findings point to a less efficient network configuration in dyslexics compared to a more optimal global organization in the controls. This is the first study to investigate the topological organization of functional brain networks of Bulgarian dyslexic children. Approval for the study was obtained from the Ethics Committee of the Institute of Neurobiology and the Institute for Population and Human Studies, Bulgarian Academy of Sciences (approval No. 02-41/12.07.2019) on March 28, 2017, and the State Logopedic Center and the Ministry of Education and Science (approval No. 09-69/14.03.2017) on July 12, 2019.

Reading-Network in Developmental Dyslexia before and after Visual Training

Electroencephalographic studies using graph-theoretic analysis have found aberrations in functional connectivity in dyslexics. How visual nonverbal training (VT) can change the functional connectivity of the reading network in developmental dyslexia is still unclear. We studied differences in the local and global topological properties of functional reading networks between controls and dyslexic children before and after VT. The minimum spanning tree method was used to construct the reading networks in multiple electroencephalogram (EEG) frequency bands. Compared to controls, pre-training dyslexics had a higher leaf fraction, tree hierarchy, kappa, and smaller diameter (θ—γ-frequency bands), and therefore, they had a less segregated neural network than controls. After training, the reading-network metrics of dyslexics became similar to controls. In β1 and γ-frequency bands, pre-training dyslexics exhibited a reduced degree and betweenness centrality of hubs in superior, middle, and inferior frontal areas in both brain hemispheres compared to the controls. Dyslexics relied on the left anterior temporal (β1, γ1) and dorsolateral prefrontal cortex (γ1), while in the right hemisphere, they relied on the occipitotemporal, parietal, (β1), motor (β2, γ1), and somatosensory cortices (γ1). After training, hubs appeared in both hemispheres at the middle occipital (β), parietal (β1), somatosensory (γ1), and dorsolateral prefrontal cortices (γ2), while in the left hemisphere, they appeared at the middle temporal, motor (β1), intermediate (γ2), and inferior frontal cortices (γ1, β2). Language-related brain regions were more active after visual training. They contribute to an understanding of lexical and sublexical representation. The same role has areas important for articulatory processes of reading.

Functional connectivity of the reading network is associated with prenatal polybrominated diphenyl ether concentrations in a community sample of 5 year-old children: A preliminary study

Genetic factors explain 60 percent of variance in reading disorder. Exposure to neurotoxicants, including polybrominated diphenyl ethers (PBDEs), may be overlooked risk factors for reading problems. We used resting-state functional magnetic resonance imaging (rs-fMRI) to examine associations between prenatal PBDE concentrations and functional connectivity of a reading-related network (RN) in a community sample of 5-year-old children (N = 33). Maternal serum PBDE concentrations (∑PBDE) were measured at 12.2 ± 2.8 weeks gestation (mean ± SD). The RN was defined by 12 regions identified in prior task-based fMRI meta-analyses; global efficiency (GE) was used to measure network integration. Linear regression evaluated associations between ∑PBDE, word reading, and GE of the RN and the default mode network (DMN); the latter to establish specificity of findings. Weighted quantile sum regression analyses evaluated the contributions of specific PBDE congeners to observed associations. Greater RN efficiency was associated with better word reading in these novice readers. Children with higher ∑PBDE showed reduced GE of the RN; ∑PBDE was not associated with DMN efficiency, demonstrating specificity of our results. Consistent with prior findings, ∑PBDE was not associated word reading at 5-years-old. Altered efficiency and integration of the RN may underlie associations between ∑PBDE concentrations and reading problems observed previously in older children.

Electrophysiological Correlates of Response Time Variability During a Sustained Attention Task

Individuals with Attention Deficit Hyperactivity Disorder (ADHD) tend to perform cognitive tasks with greater Response Time Variability (RTV). Greater RTV in ADHD may be due to inefficient functional connectivity of the brain during information processing. This study aimed to investigate the relationship between brain connectivity, RTV, and levels of ADHD symptoms. Twenty-eight children aged 9–12 years and 49 adolescents aged 15–18 years performed the Sustained Attention to Response Task (SART) while EEG was recorded. The participants’ levels of ADHD symptoms were measured using self- and parent-rated questionnaires. The ex-Gaussian analysis and The Fast Fourier Transform were used to measure multiple aspects of RTV. Functional connectivity between 64 electrodes was computed during task performance, and global efficiency and modularity were calculated, reflecting integration and segregation of the brain, respectively. There was a positive association between multiple RTV measures and the level of ADHD symptoms, where participants with higher levels of ADHD symptoms showed greater RTV, except for sigma from the ex-Gaussian analysis. More efficient brain network activity, measured by global efficiency, was associated with reduced RTV. Children showed greater RTV and less efficient brain network activity compared with the adolescents. These findings support the view that stable responses are achieved with more integrated (and efficient) brain connectivity.

Reorganization of Large-Scale Functional Networks During Low-Frequency Electrical Stimulation of the Cortical Surface

We investigated the functional network reorganization caused by low-frequency electrical stimulation (LFES) of human brain cortical surface. Intracranial EEG data from subdural grid positions were analyzed in 16 pre-surgery epileptic patients. LFES was performed by injecting current pulses (10[Formula: see text]mA, 0.2[Formula: see text]ms pulse width, 0.5[Formula: see text]Hz, 25 trials) into all adjacent electrode contacts. Dynamic functional connectivity analysis was carried out on two frequency bands (low: 1–4[Formula: see text]Hz; high: 10–40[Formula: see text]Hz) to investigate the early, high frequency and late, low frequency responses elicited by the stimulation. The centralization increased in early compared to late responses, suggesting a more prominent role of direct neural links between primarily activated areas and distant brain regions. Injecting the current into the seizure onset zone (SOZ) evoked a more integrated functional topology during the early (N1) period of the response, whereas during the late (N2) period — regardless of the stimulation site — the connectedness of the SOZ was elevated compared to the non-SOZ tissue. The abnormal behavior of the epileptic sub-network during both part of the responses supports the idea of the pathogenic role of impaired inhibition and excitation mechanisms in epilepsy.

EEG Resting State Functional Connectivity in Adult Dyslexics Using Phase Lag Index and Graph Analysis

Developmental dyslexia may involve deficits in functional connectivity across widespread brain networks that enable fluent reading. We investigated the large-scale organization of electroencephalography (EEG) functional networks at rest in 28 dyslexics and 36 typically reading adults. For each frequency band (delta, theta alpha and beta), we assessed functional connectivity strength with the phase lag index (PLI). Network topology was examined using minimum spanning tree (MST) graphs derived from the functional connectivity matrices. We found significant group differences in the alpha band (8-13 Hz). The graph analysis indicated more interconnected nodes, in dyslexics compared to typical readers. The graph metrics were significantly correlated with age in dyslexics but not in typical readers, which may indicate more heterogeneity in maturation of brain networks in dyslexics. The present findings support the involvement of alpha oscillations in higher cognition and the sensitivity of graph metrics to characterize functional networks in adult dyslexia. Finally, the current results extend our previous findings on children.

Contributions of Letter-Speech Sound Learning and Visual Print Tuning to Reading Improvement: Evidence from Brain Potential and Dyslexia Training Studies

We use a neurocognitive perspective to discuss the contribution of learning letter-speech sound (L-SS) associations and visual specialization in the initial phases of reading in dyslexic children. We review findings from associative learning studies on related cognitive skills important for establishing and consolidating L-SS associations. Then we review brain potential studies, including our own, that yielded two markers associated with reading fluency. Here we show that the marker related to visual specialization (N170) predicts word and pseudoword reading fluency in children who received additional practice in the processing of morphological word structure. Conversely, L-SS integration (indexed by mismatch negativity (MMN)) may only remain important when direct orthography to semantic conversion is not possible, such as in pseudoword reading. In addition, the correlation between these two markers supports the notion that multisensory integration facilitates visual specialization. Finally, we review the role of implicit learning and executive functions in audiovisual learning in dyslexia. Implications for remedial research are discussed and suggestions for future studies are presented.

Toward Developmental Connectomics of the Human Brain

Imaging connectomics based on graph theory has become an effective and unique methodological framework for studying structural and functional connectivity patterns of the developing brain. Normal brain development is characterized by continuous and significant network evolution throughout infancy, childhood, and adolescence, following specific maturational patterns. Disruption of these normal changes is associated with neuropsychiatric developmental disorders, such as autism spectrum disorders or attention-deficit hyperactivity disorder. In this review, we focused on the recent progresses regarding typical and atypical development of human brain networks from birth to early adulthood, using a connectomic approach. Specifically, by the time of birth, structural networks already exhibit adult-like organization, with global efficient small-world and modular structures, as well as hub regions and rich-clubs acting as communication backbones. During development, the structure networks are fine-tuned, with increased global integration and robustness and decreased local segregation, as well as the strengthening of the hubs. In parallel, functional networks undergo more dramatic changes during maturation, with both increased integration and segregation during development, as brain hubs shift from primary regions to high order functioning regions, and the organization of modules transitions from a local anatomical emphasis to a more distributed architecture. These findings suggest that structural networks develop earlier than functional networks; meanwhile functional networks demonstrate more dramatic maturational changes with the evolution of structural networks serving as the anatomical backbone. In this review, we also highlighted topologically disorganized characteristics in structural and functional brain networks in several major developmental neuropsychiatric disorders (e.g., autism spectrum disorders, attention-deficit hyperactivity disorder and developmental dyslexia). Collectively, we showed that delineation of the brain network from a connectomics perspective offers a unique and refreshing view of both normal development and neuropsychiatric disorders.

Dyslexic children lack word selectivity gradients in occipito-temporal and inferior frontal cortex

fMRI studies using a region-of-interest approach have revealed that the ventral portion of the left occipito-temporal cortex, which is specialized for orthographic processing of visually presented words (and includes the so-called "visual word form area", VWFA), is characterized by a posterior-to-anterior gradient of increasing selectivity for words in typically reading adults, adolescents, and children (e.g. Brem et al., 2006, 2009). Similarly, the left inferior frontal cortex (IFC) has been shown to exhibit a medial-to-lateral gradient of print selectivity in typically reading adults (Vinckier et al., 2007). Functional brain imaging studies of dyslexia have reported relative underactivity in left hemisphere occipito-temporal and inferior frontal regions using whole-brain analyses during word processing tasks. Hence, the question arises whether gradient sensitivities in these regions are altered in dyslexia. Indeed, a region-of-interest analysis revealed the gradient-specific functional specialization in the occipito-temporal cortex to be disrupted in dyslexic children (van der Mark et al., 2009). Building on these studies, we here (1) investigate if a word-selective gradient exists in the inferior frontal cortex in addition to the occipito-temporal cortex in normally reading children, (2) compare typically reading with dyslexic children, and (3) examine functional connections between these regions in both groups. We replicated the previously reported anterior-to-posterior gradient of increasing selectivity for words in the left occipito-temporal cortex in typically reading children, and its absence in the dyslexic children. Our novel finding is the detection of a pattern of increasing selectivity for words along the medial-to-lateral axis of the left inferior frontal cortex in typically reading children and evidence of functional connectivity between the most lateral aspect of this area and the anterior aspects of the occipito-temporal cortex. We report absence of an IFC gradient and connectivity between the lateral aspect of the IFC and the anterior occipito-temporal cortex in the dyslexic children. Together, our results provide insights into the source of the anomalies reported in previous studies of dyslexia and add to the growing evidence of an orthographic role of IFC in reading.

Detect AD Patients by Using EEG Coherence Analysis

The purpose of this study is to discriminate mild Alzheimer's disease (AD) patients from the normal aging. The EEG coherence was applied to analyze the data from auditory oddball paradigm to discriminate the differences of corticocortical connections between mild AD patients and healthy subjects. The results showed that the lower values of coherence were performed in mild AD patients than in the normal aging subjects, especially in theta band. The implications and suggestions are shown in this study.

Neurobiological underpinnings of math and reading learning disabilities

The primary goal of this review is to highlight current research and theories describing the neurobiological basis of math (MD), reading (RD), and comorbid math and reading disability (MD+RD). We first describe the unique brain and cognitive processes involved in acquisition of math and reading skills, emphasizing similarities and differences in each domain. Next we review functional imaging studies of MD and RD in children, integrating relevant theories from experimental psychology and cognitive neuroscience to characterize the functional neuroanatomy of cognitive dysfunction in MD and RD. We then review recent research on the anatomical correlates of MD and RD. Converging evidence from morphometry and tractography studies are presented to highlight distinct patterns of white matter pathways which are disrupted in MD and RD. Finally, we examine how the intersection of MD and RD provides a unique opportunity to clarify the unique and shared brain systems which adversely impact learning and skill acquisition in MD and RD, and point out important areas for future work on comorbid learning disabilities.

Functional network architecture of reading-related regions across development

Reading requires coordinated neural processing across a large number of brain regions. Studying relationships between reading-related regions informs the specificity of information processing performed in each region. Here, regions of interest were defined from a meta-analysis of reading studies, including a developmental study. Relationships between regions were defined as temporal correlations in spontaneous fMRI signal; i.e., resting state functional connectivity MRI (RSFC). Graph theory based network analysis defined the community structure of the "reading-related" regions. Regions sorted into previously defined communities, such as the fronto-parietal and cingulo-opercular control networks, and the default mode network. This structure was similar in children, and no apparent "reading" community was defined in any age group. These results argue against regions, or sets of regions, being specific or preferential for reading, instead indicating that regions used in reading are also used in a number of other tasks.

Altered small-world topology of structural brain networks in infants with intrauterine growth restriction and its association with later neurodevelopmental outcome

Intrauterine growth restriction (IUGR) due to placental insufficiency affects 5-10% of all pregnancies and it is associated with a wide range of short- and long-term neurodevelopmental disorders. Prediction of neurodevelopmental outcomes in IUGR is among the clinical challenges of modern fetal medicine and pediatrics. In recent years several studies have used magnetic resonance imaging (MRI) to demonstrate differences in brain structure in IUGR subjects, but the ability to use MRI for individual predictive purposes in IUGR is limited. Recent research suggests that MRI in vivo access to brain connectivity might have the potential to help understanding cognitive and neurodevelopment processes. Specifically, MRI based connectomics is an emerging approach to extract information from MRI data that exhaustively maps inter-regional connectivity within the brain to build a graph model of its neural circuitry known as brain network. In the present study we used diffusion MRI based connectomics to obtain structural brain networks of a prospective cohort of one year old infants (32 controls and 24 IUGR) and analyze the existence of quantifiable brain reorganization of white matter circuitry in IUGR group by means of global and regional graph theory features of brain networks. Based on global and regional analyses of the brain network topology we demonstrated brain reorganization in IUGR infants at one year of age. Specifically, IUGR infants presented decreased global and local weighted efficiency, and a pattern of altered regional graph theory features. By means of binomial logistic regression, we also demonstrated that connectivity measures were associated with abnormal performance in later neurodevelopmental outcome as measured by Bayley Scale for Infant and Toddler Development, Third edition (BSID-III) at two years of age. These findings show the potential of diffusion MRI based connectomics and graph theory based network characteristics for estimating differences in the architecture of neural circuitry and developing imaging biomarkers of poor neurodevelopment outcome in infants with prenatal diseases.

Probing the brain in autism using FMRI and diffusion tensor imaging

Newly emerging theories suggest that the brain does not function as a cohesive unit in autism, and this discordance is reflected in the behavioral symptoms displayed by individuals with autism. While structural neuroimaging findings have provided some insights into brain abnormalities in autism, the consistency of such findings is questionable. Functional neuroimaging, on the other hand, has been more fruitful in this regard because autism is a disorder of dynamic processing and allows examination of communication between cortical networks, which appears to be where the underlying problem occurs in autism. Functional connectivity is defined as the temporal correlation of spatially separate neurological events. Findings from a number of recent fMRI studies have supported the idea that there is weaker coordination between different parts of the brain that should be working together to accomplish complex social or language problems. One of the mysteries of autism is the coexistence of deficits in several domains along with relatively intact, sometimes enhanced, abilities. Such complex manifestation of autism calls for a global and comprehensive examination of the disorder at the neural level. A compelling recent account of the brain functioning in autism, the cortical underconnectivity theory, provides an integrating framework for the neurobiological bases of autism. The cortical underconnectivity theory of autism suggests that any language, social, or psychological function that is dependent on the integration of multiple brain regions is susceptible to disruption as the processing demand increases. In autism, the underfunctioning of integrative circuitry in the brain may cause widespread underconnectivity. In other words, people with autism may interpret information in a piecemeal fashion at the expense of the whole. Since cortical underconnectivity among brain regions, especially the frontal cortex and more posterior areas, has now been relatively well established, we can begin to further understand brain connectivity as a critical component of autism symptomatology. A logical next step in this direction is to examine the anatomical connections that may mediate the functional connections mentioned above. Diffusion Tensor Imaging (DTI) is a relatively novel neuroimaging technique that helps probe the diffusion of water in the brain to infer the integrity of white matter fibers. In this technique, water diffusion in the brain is examined in several directions using diffusion gradients. While functional connectivity provides information about the synchronization of brain activation across different brain areas during a task or during rest, DTI helps in understanding the underlying axonal organization which may facilitate the cross-talk among brain areas. This paper will describe these techniques as valuable tools in understanding the brain in autism and the challenges involved in this line of research.