Conduction velocity, G-ratio, and extracellular water as microstructural characteristics of autism spectrum disorder

The neuronal differences contributing to the etiology of autism spectrum disorder (ASD) are still not well defined. Previous studies have suggested that myelin and axons are disrupted during development in ASD. By combining structural and diffusion MRI techniques, myelin and axons can be assessed using extracellular water, aggregate g-ratio, and a new approach to calculating axonal conduction velocity termed aggregate conduction velocity, which is related to the capacity of the axon to carry information. In this study, several innovative cellular microstructural methods, as measured from magnetic resonance imaging (MRI), are combined to characterize differences between ASD and typically developing adolescent participants in a large cohort. We first examine the relationship between each metric, including microstructural measurements of axonal and intracellular diffusion and the T1w/T2w ratio. We then demonstrate the sensitivity of these metrics by characterizing differences between ASD and neurotypical participants, finding widespread increases in extracellular water in the cortex and decreases in aggregate g-ratio and aggregate conduction velocity throughout the cortex, subcortex, and white matter skeleton. We finally provide evidence that these microstructural differences are associated with higher scores on the Social Communication Questionnaire (SCQ) a commonly used diagnostic tool to assess ASD. This study is the first to reveal that ASD involves MRI-measurable in vivo differences of myelin and axonal development with implications for neuronal and behavioral function. We also introduce a novel formulation for calculating aggregate conduction velocity, that is highly sensitive to these changes. We conclude that ASD may be characterized by otherwise intact structural connectivity but that functional connectivity may be attenuated by network properties affecting neural transmission speed. This effect may explain the putative reliance on local connectivity in contrast to more distal connectivity observed in ASD.

Biophysical modulation and robustness of itinerant complexity in neuronal networks

Transient synchronization of bursting activity in neuronal networks, which occurs in patterns of metastable itinerant phase relationships between neurons, is a notable feature of network dynamics observed in vivo. However, the mechanisms that contribute to this dynamical complexity in neuronal circuits are not well understood. Local circuits in cortical regions consist of populations of neurons with diverse intrinsic oscillatory features. In this study, we numerically show that the phenomenon of transient synchronization, also referred to as metastability, can emerge in an inhibitory neuronal population when the neurons' intrinsic fast-spiking dynamics are appropriately modulated by slower inputs from an excitatory neuronal population. Using a compact model of a mesoscopic-scale network consisting of excitatory pyramidal and inhibitory fast-spiking neurons, our work demonstrates a relationship between the frequency of pyramidal population oscillations and the features of emergent metastability in the inhibitory population. In addition, we introduce a method to characterize collective transitions in metastable networks. Finally, we discuss potential applications of this study in mechanistically understanding cortical network dynamics.

Conduction Velocity, G-ratio, and Extracellular Water as Microstructural Characteristics of Autism Spectrum Disorder

The neuronal differences contributing to the etiology of autism spectrum disorder (ASD) are still not well defined. Previous studies have suggested that myelin and axons are disrupted during development in ASD. By combining structural and diffusion MRI techniques, myelin and axons can be assessed using extracellular water, aggregate g-ratio, and a novel metric termed aggregate conduction velocity, which is related to the capacity of the axon to carry information. In this study, several innovative cellular microstructural methods, as measured from magnetic resonance imaging (MRI), are combined to characterize differences between ASD and typically developing adolescent participants in a large cohort. We first examine the relationship between each metric, including microstructural measurements of axonal and intracellular diffusion and the T1w/T2w ratio. We then demonstrate the sensitivity of these metrics by characterizing differences between ASD and neurotypical participants, finding widespread increases in extracellular water in the cortex and decreases in aggregate g-ratio and aggregate conduction velocity throughout the cortex, subcortex, and white matter skeleton. We finally provide evidence that these microstructural differences are associated with higher scores on the Social Communication Questionnaire (SCQ) a commonly used diagnostic tool to assess ASD. This study is the first to reveal that ASD involves MRI-measurable in vivo differences of myelin and axonal development with implications for neuronal and behavioral function. We also introduce a novel neuroimaging metric, aggregate conduction velocity, that is highly sensitive to these changes. We conclude that ASD may be characterized by otherwise intact structural connectivity but that functional connectivity may be attenuated by network properties affecting neural transmission speed. This effect may explain the putative reliance on local connectivity in contrast to more distal connectivity observed in ASD.

Increased perivascular space volume in white matter and basal ganglia is associated with cognition in Parkinson's Disease

Perivascular spaces (PVS), fluid-filled compartments surrounding brain vasculature, are an essential component of the glymphatic system responsible for transport of waste and nutrients. Glymphatic system impairment may underlie cognitive deficits in Parkinson's disease (PD). Studies have focused on the role of basal ganglia PVS with cognition in PD, but the role of white matter PVS is unknown. This study examined the relationship of white matter and basal ganglia PVS with domain-specific and global cognition in individuals with PD. Fifty individuals with PD underwent 3T T1w magnetic resonance imaging (MRI) to determine PVS volume fraction, defined as PVS volume normalized to total regional volume, within (i) centrum semiovale, (ii) prefrontal white matter (medial orbitofrontal, rostral middle frontal, superior frontal), and (iii) basal ganglia. A neuropsychological battery included assessment of global cognitive function (Montreal Cognitive Assessment, and global cognitive composite score), and cognitive-specific domains (executive function, memory, visuospatial function, attention, and language). Higher white matter rostral middle frontal PVS was associated with lower scores in both global cognitive and visuospatial function. In the basal ganglia higher PVS was associated with lower scores for memory with a trend towards lower global cognitive composite score. While previous reports have shown that greater amount of PVS in the basal ganglia is associated with decline in global cognition in PD, our findings suggest that increased white matter PVS volume may also underlie changes in cognition.

Physical activity intensity is associated with cognition and functional connectivity in Parkinson's disease

BACKGROUND

Cognitive impairment is common in Parkinson's disease (PD) and often leads to dementia, with no effective treatment. Aging studies suggest that physical activity (PA) intensity has a positive impact on cognition and enhanced functional connectivity may underlie these benefits. However, less is known in PD. This cross-sectional study examined the relationship between PA intensity, cognitive performance, and resting state functional connectivity in PD and whether PA intensity influences the relationship between functional connectivity and cognitive performance.

METHODS

96 individuals with mild-moderate PD completed a comprehensive neuropsychological battery. Intensity of PA was objectively captured over a seven-day period using a wearable device (ActiGraph). Time spent in light and moderate intensity PA was determined based on standardized actigraphy cut points. Resting-state fMRI was assessed in a subset of 50 individuals to examine brain-wide functional connectivity.

RESULTS

Moderate intensity PA (MIPA), but not light PA, was associated with better global cognition, visuospatial function, memory, and executive function. Individuals who met the WHO recommendation of ≥150 min/week of MIPA demonstrated better global cognition, executive function, and visuospatial function. Resting-state functional connectivity associated with MIPA included a combination of brainstem, hippocampus, and regions in the frontal, cingulate, and parietal cortices, which showed higher connectivity across the brain in those achieving the WHO MIPA recommendation. Meeting this recommendation positively moderated the associations between identified functional connectivity and global cognition, visuospatial function, and language.

CONCLUSION

Encouraging MIPA, particularly the WHO recommendation of ≥150 min of MIPA/week, may represent an important prescription for PD cognition.

Integrative Models of Brain Structure and Dynamics: Concepts, Challenges, and Methods

The anatomical architecture of the brain constrains the dynamics of interactions between various regions. On a microscopic scale, neural plasticity regulates the connections between individual neurons. This microstructural adaptation facilitates coordinated dynamics of populations of neurons (mesoscopic scale) and brain regions (macroscopic scale). However, the mechanisms acting on multiple timescales that govern the reciprocal relationship between neural network structure and its intrinsic dynamics are not well understood. Studies empirically investigating such relationships on the whole-brain level rely on macroscopic measurements of structural and functional connectivity estimated from various neuroimaging modalities such as Diffusion-weighted Magnetic Resonance Imaging (dMRI), Electroencephalography (EEG), Magnetoencephalography (MEG), and functional Magnetic Resonance Imaging (fMRI). dMRI measures the anisotropy of water diffusion along axonal fibers, from which structural connections are estimated. EEG and MEG signals measure electrical activity and magnetic fields induced by the electrical activity, respectively, from various brain regions with a high temporal resolution (but limited spatial coverage), whereas fMRI measures regional activations indirectly via blood oxygen level-dependent (BOLD) signals with a high spatial resolution (but limited temporal resolution). There are several studies in the neuroimaging literature reporting statistical associations between macroscopic structural and functional connectivity. On the other hand, models of large-scale oscillatory dynamics conditioned on network structure (such as the one estimated from dMRI connectivity) provide a platform to probe into the structure-dynamics relationship at the mesoscopic level. Such investigations promise to uncover the theoretical underpinnings of the interplay between network structure and dynamics and could be complementary to the macroscopic level inquiries. In this article, we review theoretical and empirical studies that attempt to elucidate the coupling between brain structure and dynamics. Special attention is given to various clinically relevant dimensions of brain connectivity such as the topological features and neural synchronization, and their applicability for a given modality, spatial or temporal scale of analysis is discussed. Our review provides a summary of the progress made along this line of research and identifies challenges and promising future directions for multi-modal neuroimaging analyses.

Bridging the Brain and Data Sciences

Brain scientists are now capable of collecting more data in a single experiment than researchers a generation ago might have collected over an entire career. Indeed, the brain itself seems to thirst for more and more data. Such digital information not only comprises individual studies but is also increasingly shared and made openly available for secondary, confirmatory, and/or combined analyses. Numerous web resources now exist containing data across spatiotemporal scales. Data processing workflow technologies running via cloud-enabled computing infrastructures allow for large-scale processing. Such a move toward greater openness is fundamentally changing how brain science results are communicated and linked to available raw data and processed results. Ethical, professional, and motivational issues challenge the whole-scale commitment to data-driven neuroscience. Nevertheless, fueled by government investments into primary brain data collection coupled with increased sharing and community pressure challenging the dominant publishing model, large-scale brain and data science is here to stay.

Mapping the rest of the human connectome: Atlasing the spinal cord and peripheral nervous system

The emergence of diffusion, structural, and functional neuroimaging methods has enabled major multi-site efforts to map the human connectome, which has heretofore been defined as containing all neural connections in the central nervous system (CNS). However, these efforts are not structured to examine the richness and complexity of the peripheral nervous system (PNS), which arguably forms the (neglected) rest of the connectome. Despite increasing interest in an atlas of the spinal cord (SC) and PNS which is simultaneously stereotactic, interactive, electronically dissectible, scalable, population-based and deformable, little attention has thus far been devoted to this task of critical importance. Nevertheless, the atlasing of these complete neural structures is essential for neurosurgical planning, neurological localization, and for mapping those components of the human connectome located outside of the CNS. Here we recommend a modification to the definition of the human connectome to include the SC and PNS, and argue for the creation of an inclusive atlas to complement current efforts to map the brain's human connectome, to enhance clinical education, and to assist progress in neuroscience research. In addition to providing a critical overview of existing neuroimaging techniques, image processing methodologies and algorithmic advances which can be combined for the creation of a full connectome atlas, we outline a blueprint for ultimately mapping the entire human nervous system and, thereby, for filling a critical gap in our scientific knowledge of neural connectivity.

Do Biological Sex and Early Developmental Milestones Predict the Age of First Concerns and Eventual Diagnosis in Autism Spectrum Disorder?

Despite advances in early detection, the average age of autism spectrum disorder (ASD) diagnosis exceeds 4 years and is often later in females. In typical development, biological sex predicts inter-individual variation across multiple developmental milestones, with females often exhibiting earlier progression. The goal of this study was to examine sex differences in caregiver-reported developmental milestones (first word, phrase, walking) and their contribution to timing of initial concerns expressed by caregivers and eventual age of diagnosis. 195 (105 males) children and adolescents aged 8 to 17 years with a clinical diagnosis of ASD were recruited to the study (mean IQ = 99.76). While developmental milestones did not predict timing of diagnosis or age parents first expressed concerns, females had earlier first words and phrases than males. There was a marginal difference in the age of diagnosis, with females receiving their diagnosis 1 year later than males. Despite sex differences in developmental milestones and diagnostic variables, IQ was the most significant predictor in the timing of initial concerns and eventual diagnosis, suggesting children with lower IQ, regardless of sex, are identified and diagnosed earlier. Overall, biological sex and developmental milestones did not account for a large proportion of variance for the eventual age of ASD diagnosis, suggesting other factors (such as IQ and the timing of initial concerns) are potentially more influential. LAY SUMMARY: In this study, a later age of diagnosis in females having ASD was confirmed; however, biological sex was not the stronger predictor of age of diagnosis. Parents reported that females learned language more quickly than males, and parents noted their first concerns when females were older than males. In this sample, the strongest predictor of age of diagnosis was the age of first concerns.

Neuroimaging of traumatic brain injury in military personnel: An overview

BACKGROUND

The incidence of blunt-force traumatic brain injury (TBI) is especially prevalent in the military, where the emergency care admission rate has been reported to be 24.6-41.8 per 10,000 soldier-years. Given substantial advancements in modern neuroimaging techniques over the past decade in terms of structural, functional, and connectomic approaches, this mode of exploration can be viewed as best suited for understanding the underlying pathology and for providing proper intervention at effective time-points.

APPROACH

Here we survey neuroimaging studies of mild-to-severe TBI in military veterans with the intent to aid the field in the creation of a roadmap for clinicians and researchers whose aim is to understand TBI progression.

DISCUSSION

Recent advancements on the quantification of neurocognitive dysfunction, cellular dysfunction, intracranial pressure, cerebral blood flow, inflammation, post-traumatic neuropathophysiology, on blood serum biomarkers and on their correlation to neuroimaging findings are reviewed to hypothesize how they can be used in conjunction with one another. This may allow clinicians and scientists to comprehensively study TBI in military service members, leading to new treatment strategies for both currently-serving as well as veteran personnel, and to improve the study of TBI more broadly.

Democratizing data science through data science training

The biomedical sciences have experienced an explosion of data which promises to overwhelm many current practitioners. Without easy access to data science training resources, biomedical researchers may find themselves unable to wrangle their own datasets. In 2014, to address the challenges posed such a data onslaught, the National Institutes of Health (NIH) launched the Big Data to Knowledge (BD2K) initiative. To this end, the BD2K Training Coordinating Center (TCC; bigdatau.org) was funded to facilitate both in-person and online learning, and open up the concepts of data science to the widest possible audience. Here, we describe the activities of the BD2K TCC and its focus on the construction of the Educational Resource Discovery Index (ERuDIte), which identifies, collects, describes, and organizes online data science materials from BD2K awardees, open online courses, and videos from scientific lectures and tutorials. ERuDIte now indexes over 9,500 resources. Given the richness of online training materials and the constant evolution of biomedical data science, computational methods applying information retrieval, natural language processing, and machine learning techniques are required - in effect, using data science to inform training in data science. In so doing, the TCC seeks to democratize novel insights and discoveries brought forth via large-scale data science training.

Corrigendum: Resting-State Functional Connectivity in Autism Spectrum Disorders: A Review

[This corrects the article on p. 205 in vol. 7, PMID: 28101064.].

Clinical neuroimaging in the preterm infant: Diagnosis and prognosis

Perinatal care advances emerging over the past twenty years have helped to diminish the mortality and severe neurological morbidity of extremely and very preterm neonates (e.g., cystic Periventricular Leukomalacia [c-PVL] and Germinal Matrix Hemorrhage - Intraventricular Hemorrhage [GMH-IVH grade 3-4/4]; 22 to < 32 weeks of gestational age, GA). However, motor and/or cognitive disabilities associated with mild-to-moderate white and gray matter injury are frequently present in this population (e.g., non-cystic Periventricular Leukomalacia [non-cystic PVL], neuronal-axonal injury and GMH-IVH grade 1-2/4). Brain research studies using magnetic resonance imaging (MRI) report that 50% to 80% of extremely and very preterm neonates have diffuse white matter abnormalities (WMA) which correspond to only the minimum grade of severity. Nevertheless, mild-to-moderate diffuse WMA has also been associated with significant affectations of motor and cognitive activities. Due to increased neonatal survival and the intrinsic characteristics of diffuse WMA, there is a growing need to study the brain of the premature infant using non-invasive neuroimaging techniques sensitive to microscopic and/or diffuse lesions. This emerging need has led the scientific community to try to bridge the gap between concepts or ideas from different methodologies and approaches; for instance, neuropathology, neuroimaging and clinical findings. This is evident from the combination of intense pre-clinical and clinicopathologic research along with neonatal neurology and quantitative neuroimaging research. In the following review, we explore literature relating the most frequently observed neuropathological patterns with the recent neuroimaging findings in preterm newborns and infants with perinatal brain injury. Specifically, we focus our discussions on the use of neuroimaging to aid diagnosis, measure morphometric brain damage, and track long-term neurodevelopmental outcomes.

Mild cognitive impairment and structural brain abnormalities in a sexagenarian with a history of childhood traumatic brain injury

In this report, we present a case study involving an older, female patient with a history of pediatric traumatic brain injury (TBI). Magnetic resonance imaging and diffusion tensor imaging volumes were acquired from the volunteer in question, her brain volumetrics and morphometrics were extracted, and these were then systematically compared against corresponding metrics obtained from a large sample of older healthy control (HC) subjects as well as from subjects in various stages of mild cognitive impairment (MCI) and Alzheimer disease (AD). Our analyses find the patient's brain morphometry and connectivity most similar to those of patients classified as having early-onset MCI, in contrast to HC, late MCI, and AD samples. Our examination will be of particular interest to those interested in assessing the clinical course in older patients having suffered TBI earlier in life, in contradistinction to those who experience incidents of head injury during aging.

Envisioning the future of 'big data' biomedicine

Through the increasing availability of more efficient data collection procedures, biomedical scientists are now confronting ever larger sets of data, often finding themselves struggling to process and interpret what they have gathered. This, while still more data continues to accumulate. This torrent of biomedical information necessitates creative thinking about how the data are being generated, how they might be best managed, analyzed, and eventually how they can be transformed into further scientific understanding for improving patient care. Recognizing this as a major challenge, the National Institutes of Health (NIH) has spearheaded the "Big Data to Knowledge" (BD2K) program - the agency's most ambitious biomedical informatics effort ever undertaken to date. In this commentary, we describe how the NIH has taken on "big data" science head-on, how a consortium of leading research centers are developing the means for handling large-scale data, and how such activities are being marshalled for the training of a new generation of biomedical data scientists. All in all, the NIH BD2K program seeks to position data science at the heart of 21st Century biomedical research.

Resting-State Functional Connectivity in Autism Spectrum Disorders: A Review

Ongoing debate exists within the resting-state functional MRI (fMRI) literature over how intrinsic connectivity is altered in the autistic brain, with reports of general over-connectivity, under-connectivity, and/or a combination of both. Classifying autism using brain connectivity is complicated by the heterogeneous nature of the condition, allowing for the possibility of widely variable connectivity patterns among individuals with the disorder. Further differences in reported results may be attributable to the age and sex of participants included, designs of the resting-state scan, and to the analysis technique used to evaluate the data. This review systematically examines the resting-state fMRI autism literature to date and compares studies in an attempt to draw overall conclusions that are presently challenging. We also propose future direction for rs-fMRI use to categorize individuals with autism spectrum disorder, serve as a possible diagnostic tool, and best utilize data-sharing initiatives.

Multimodal Imaging of Neurometabolic Pathology due to Traumatic Brain Injury

The impact of traumatic brain injury (TBI) involves a combination of complex biochemical processes beginning with the initial insult and lasting for days, months and even years post-trauma. These changes range from neuronal integrity losses to neurotransmitter imbalance and metabolite dysregulation, leading to the release of pro- or anti-apoptotic factors which mediate cell survival or death. Such dynamic processes affecting the brain's neurochemistry can be monitored using a variety of neuroimaging techniques, whose combined use can be particularly useful for understanding patient-specific clinical trajectories. Here, we describe how TBI changes the metabolism of essential neurochemical compounds, summarize how neuroimaging approaches facilitate the study of such alterations, and highlight promising ways in which neuroimaging can be used to investigate post-TBI changes in neurometabolism.

Interacting with the National Database for Autism Research (NDAR) via the LONI Pipeline workflow environment

Under the umbrella of the National Database for Clinical Trials (NDCT) related to mental illnesses, the National Database for Autism Research (NDAR) seeks to gather, curate, and make openly available neuroimaging data from NIH-funded studies of autism spectrum disorder (ASD). NDAR has recently made its database accessible through the LONI Pipeline workflow design and execution environment to enable large-scale analyses of cortical architecture and function via local, cluster, or "cloud"-based computing resources. This presents a unique opportunity to overcome many of the customary limitations to fostering biomedical neuroimaging as a science of discovery. Providing open access to primary neuroimaging data, workflow methods, and high-performance computing will increase uniformity in data collection protocols, encourage greater reliability of published data, results replication, and broaden the range of researchers now able to perform larger studies than ever before. To illustrate the use of NDAR and LONI Pipeline for performing several commonly performed neuroimaging processing steps and analyses, this paper presents example workflows useful for ASD neuroimaging researchers seeking to begin using this valuable combination of online data and computational resources. We discuss the utility of such database and workflow processing interactivity as a motivation for the sharing of additional primary data in ASD research and elsewhere.

Scale-Dependent Variability and Quantitative Regimes in Graph-Theoretic Representations of Human Cortical Networks

Studying brain connectivity is important due to potential differences in brain circuitry between health and disease. One drawback of graph-theoretic approaches to this is that their results are dependent on the spatial scale at which brain circuitry is examined and explicitly on how vertices and edges are defined in network models. To investigate this, magnetic resonance and diffusion tensor images were acquired from 136 healthy adults, and each subject's cortex was parceled into as many as 50,000 regions. Regions were represented as nodes in a reconstructed network representation, and interregional connectivity was inferred via deterministic tractography. Network model behavior was explored as a function of nodal number and connectivity weighing. Three distinct regimes of quantitative behavior assumed by network models as a function of spatial scale are identified, and their existence may be modulated by the spatial folding scale of the cortex. The maximum number of network nodes used to model human brain circuitry in this study (∼50,000) is larger than in previous macroscale neuroimaging studies. Results suggest that network model properties vary appreciably as a function of vertex assignment convention and edge weighing scheme and that graph-theoretic analysis results should not be compared across spatial scales without appropriate understanding of how spatial scale and model topology modulate network model properties. These findings have implications for comparing macro- to mesoscale studies of brain network models and understanding how choosing network-theoretic parameters affects the interpretation of brain connectivity studies.

Functional neuroimaging of traumatic brain injury: advances and clinical utility

Functional deficits due to traumatic brain injury (TBI) can have significant and enduring consequences upon patients' life quality and expectancy. Although functional neuroimaging is essential for understanding TBI pathophysiology, an insufficient amount of effort has been dedicated to the task of translating functional neuroimaging findings into information with clinical utility. The purpose of this review is to summarize the use of functional neuroimaging techniques - especially functional magnetic resonance imaging, diffusion tensor imaging, positron emission tomography, magnetic resonance spectroscopy, and electroencephalography - for advancing current knowledge of TBI-related brain dysfunction and for improving the rehabilitation of TBI patients. We focus on seven core areas of functional deficits, namely consciousness, motor function, attention, memory, higher cognition, personality, and affect, and, for each of these, we summarize recent findings from neuroimaging studies which have provided substantial insight into brain function changes due to TBI. Recommendations are also provided to aid in setting the direction of future neuroimaging research and for understanding brain function changes after TBI.

Neuroimaging and genetics in aging and age-related disease

Neuroimaging, genetics, and phenomic explorations in the study of brain aging seek to characterize typical patterns of morphometric, function, and connectomics and how these change over the lifespan. With a detailed but multifaceted knowledge of these patterns, neuroscientists and clinicians can better recognize the processes at play when age-related brain changes vary from these expectations. Employing a range of neuroimaging methods, genome-wide as well as focused gene targeting, and 'big data' computation, progress is occurring toward gaining such understanding. We are excited to present these articles as a special issue of Brain Imaging and Behavior which grew out of from our New Horizons in Human Brain Imaging meeting on the theme of the neuroimaging of brain aging, held in March 2013.

Epileptogenic focus localization in treatment-resistant post-traumatic epilepsy

Pharmacologically intractable post-traumatic epilepsy (PTE) is a major clinical challenge for patients with penetrating traumatic brain injury, where the risk for this condition remains very high even decades after injury. Although over 20 anti-epileptic drugs (AED) are in common use today, approximately one-third of epilepsy patients have drug-refractory seizures and even more have AED-related adverse effects which compromise life quality. Simultaneously, there have been repeated recommendations by radiologists and neuroimaging experts to incorporate localization based on electroencephalography (EEG) into the process of clinical decision making regarding PTE patients. Nevertheless, thus far, little progress has been accomplished towards the use of EEG as a reliable tool for locating epileptogenic foci prior to surgical resection. In this review, we discuss the epidemiology of pharmacologically resistant PTE, address the need for effective anti-epileptogenic treatments, and highlight recent progress in the development of noninvasive methods for the accurate localization of PTE foci for the purpose of neurosurgical intervention. These trends indicate the current emergence of promising methodologies for the noninvasive study of post-traumatic epileptogenesis and for the improved neurosurgical planning of epileptic foci resection.

The DTI connectivity of the human claustrum

The origin, structure, and function of the claustrum, as well as its role in neural computation, have remained a mystery since its discovery in the 17th century. Assessing the in vivo connectivity of the claustrum may bring forth useful insights with relevance to model the overall functionality of the claustrum itself. Using structural and diffusion tensor neuroimaging in N = 100 healthy subjects, we found that the claustrum has the highest connectivity in the brain by regional volume. Network theoretical analyses revealed that (a) the claustrum is a primary contributor to global brain network architecture, and that (b) significant connectivity dependencies exist between the claustrum, frontal lobe, and cingulate regions. These results illustrate that the claustrum is ideally located within the human central nervous system (CNS) connectome to serve as the putative "gate keeper" of neural information for consciousness awareness. Our findings support and underscore prior theoretical contributions about the involvement of the claustrum in higher cognitive function and its relevance in devastating neurological disease.

Reviewing the ketamine model for schizophrenia

The observation that antagonists of the N-methyl-D-aspartate receptor (NMDAR), such as phencyclidine (PCP) and ketamine, transiently induce symptoms of acute schizophrenia had led to a paradigm shift from dopaminergic to glutamatergic dysfunction in pharmacological models of schizophrenia. The glutamate hypothesis can explain negative and cognitive symptoms of schizophrenia better than the dopamine hypothesis, and has the potential to explain dopamine dysfunction itself. The pharmacological and psychomimetic effects of ketamine, which is safer for human subjects than phencyclidine, are herein reviewed. Ketamine binds to a variety of receptors, but principally acts at the NMDAR, and convergent genetic and molecular evidence point to NMDAR hypofunction in schizophrenia. Furthermore, NMDAR hypofunction can explain connectional and oscillatory abnormalities in schizophrenia in terms of both weakened excitation of inhibitory γ-aminobutyric acidergic (GABAergic) interneurons that synchronize cortical networks and disinhibition of principal cells. Individuals with prenatal NMDAR aberrations might experience the onset of schizophrenia towards the completion of synaptic pruning in adolescence, when network connectivity drops below a critical value. We conclude that ketamine challenge is useful for studying the positive, negative, and cognitive symptoms, dopaminergic and GABAergic dysfunction, age of onset, functional dysconnectivity, and abnormal cortical oscillations observed in acute schizophrenia.

DTI tractography and white matter fiber tract characteristics in euthymic bipolar I patients and healthy control subjects

With the introduction of diffusion tensor imaging (DTI), structural differences in white matter (WM) architecture between psychiatric populations and healthy controls can be systematically observed and measured. In particular, DTI-tractography can be used to assess WM characteristics over the entire extent of WM tracts and aggregated fiber bundles. Using 64-direction DTI scanning in 27 participants with bipolar disorder (BD) and 26 age-and-gender-matched healthy control subjects, we compared relative length, density, and fractional anisotrophy (FA) of WM tracts involved in emotion regulation or theorized to be important neural components in BD neuropathology. We interactively isolated 22 known white matter tracts using region-of-interest placement (TrackVis software program) and then computed relative tract length, density, and integrity. BD subjects demonstrated significantly shorter WM tracts in the genu, body and splenium of the corpus callosum compared to healthy controls. Additionally, bipolar subjects exhibited reduced fiber density in the genu and body of the corpus callosum, and in the inferior longitudinal fasciculus bilaterally. In the left uncinate fasciculus, however, BD subjects exhibited significantly greater fiber density than healthy controls. There were no significant differences between groups in WM tract FA for those tracts that began and ended in the brain. The significance of differences in tract length and fiber density in BD is discussed.

Graphical neuroimaging informatics: application to Alzheimer's disease

The Informatics Visualization for Neuroimaging (INVIZIAN) framework allows one to graphically display image and meta-data information from sizeable collections of neuroimaging data as a whole using a dynamic and compelling user interface. Users can fluidly interact with an entire collection of cortical surfaces using only their mouse. In addition, users can cluster and group brains according in multiple ways for subsequent comparison using graphical data mining tools. In this article, we illustrate the utility of INVIZIAN for simultaneous exploration and mining a large collection of extracted cortical surface data arising in clinical neuroimaging studies of patients with Alzheimer's Disease, mild cognitive impairment, as well as healthy control subjects. Alzheimer's Disease is particularly interesting due to the wide-spread effects on cortical architecture and alterations of volume in specific brain areas associated with memory. We demonstrate INVIZIAN's ability to render multiple brain surfaces from multiple diagnostic groups of subjects, showcase the interactivity of the system, and showcase how INVIZIAN can be employed to generate hypotheses about the collection of data which would be suitable for direct access to the underlying raw data and subsequent formal statistical analysis. Specifically, we use INVIZIAN show how cortical thickness and hippocampal volume differences between group are evident even in the absence of more formal hypothesis testing. In the context of neurological diseases linked to brain aging such as AD, INVIZIAN provides a unique means for considering the entirety of whole brain datasets, look for interesting relationships among them, and thereby derive new ideas for further research and study.

Mapping the human connectome

Knowledge of the properties of white matter fiber tracts isa crucial and necessary step toward a precise understanding of the functional architecture of the living human brain. Previously, this knowledge was severely limited, as it was difficult to visualize these structures or measure their functions in vivo. The HCP has recently generated considerable interest because of its potential to explore connectivity and its relationship with genetics and behavior. For neuroscientists and the lay public alike, the ability to assess, measure, and explore this wealth of layered information concerning how the brain is wired is a much sought after prize.The navigation of the human connectome and the discovery of how it is affected through genetics, and in a range of neurological and psychiatric diseases, have far reaching implications. From a range of ongoing connectomics related activities, the systematic characterization of brain connectedness and the resulting functional aspects of such connectivity will not only realize the work of Ramón y Cajal and others, but will also greatly expand our understanding of the brain, the mind, and what it is to be truly human. The similarities and differences that mark normal diversity will help us to understand variation among people and set the stage to chart genetic influences on typical brain development and decline during aging. What is more, an understanding of how brains might become disordered will shed light on autism, schizophrenia, Alzheimer’s, and other diseases that exact a tremendous and terrible social and economic toll.

Mapping connectivity damage in the case of Phineas Gage

White matter (WM) mapping of the human brain using neuroimaging techniques has gained considerable interest in the neuroscience community. Using diffusion weighted (DWI) and magnetic resonance imaging (MRI), WM fiber pathways between brain regions may be systematically assessed to make inferences concerning their role in normal brain function, influence on behavior, as well as concerning the consequences of network-level brain damage. In this paper, we investigate the detailed connectomics in a noted example of severe traumatic brain injury (TBI) which has proved important to and controversial in the history of neuroscience. We model the WM damage in the notable case of Phineas P. Gage, in whom a "tamping iron" was accidentally shot through his skull and brain, resulting in profound behavioral changes. The specific effects of this injury on Mr. Gage's WM connectivity have not previously been considered in detail. Using computed tomography (CT) image data of the Gage skull in conjunction with modern anatomical MRI and diffusion imaging data obtained in contemporary right handed male subjects (aged 25-36), we computationally simulate the passage of the iron through the skull on the basis of reported and observed skull fiducial landmarks and assess the extent of cortical gray matter (GM) and WM damage. Specifically, we find that while considerable damage was, indeed, localized to the left frontal cortex, the impact on measures of network connectedness between directly affected and other brain areas was profound, widespread, and a probable contributor to both the reported acute as well as long-term behavioral changes. Yet, while significantly affecting several likely network hubs, damage to Mr. Gage's WM network may not have been more severe than expected from that of a similarly sized "average" brain lesion. These results provide new insight into the remarkable brain injury experienced by this noteworthy patient.

Source cancellation profiles of electroencephalography and magnetoencephalography

Recorded electric potentials and magnetic fields due to cortical electrical activity have spatial spread even if their underlying brain sources are focal. Consequently, as a result of source cancellation, loss in signal amplitude and reduction in the effective signal-to-noise ratio can be expected when distributed sources are active simultaneously. Here we investigate the cancellation effects of EEG and MEG through the use of an anatomically correct forward model based on structural MRI acquired from 7 healthy adults. A boundary element model (BEM) with four compartments (brain, cerebrospinal fluid, skull and scalp) and highly accurate cortical meshes (~300,000 vertices) were generated. Distributed source activations were simulated using contiguous patches of active dipoles. To investigate cancellation effects in both EEG and MEG, quantitative indices were defined (source enhancement, cortical orientation disparity) and computed for varying values of the patch radius as well as for automatically parcellated gyri and sulci. Results were calculated for each cortical location, averaged over all subjects using a probabilistic atlas, and quantitatively compared between MEG and EEG. As expected, MEG sensors were found to be maximally sensitive to signals due to sources tangential to the scalp, and minimally sensitive to radial sources. Compared to EEG, however, MEG was found to be much more sensitive to signals generated antero-medially, notably in the anterior cingulate gyrus. Given that sources of activation cancel each other according to the orientation disparity of the cortex, this study provides useful methods and results for quantifying the effect of source orientation disparity upon source cancellation.

Study factors influencing ventricular enlargement in schizophrenia: a 20 year follow-up meta-analysis

A meta-analysis was performed on studies employing the ventricular-brain ratio to compare schizophrenic subjects to that of normal controls. This was a follow-up to a similar meta-analysis published in 1992 in which study-, in addition to clinical-, factors were found to contribute significantly to the reported difference between patients with schizophrenia and controls. Seventy-two (N=72) total studies were identified from the peer reviewed literature, 39 from the original meta-analysis, and 33 additional studies published since which met strict criteria for inclusion and analysis - thus representing ~30 years of schizophrenia ventricular enlargement research. Sample characteristics from schizophrenics and controls were coded for use as predictor variables against within sample VBR values as well as for between sample VBR differences. Additionally, a number of factors concerning how the studies were conducted and reported were also coded. Obtained data was subjected to unweighted univariate as well as multiple regression analyses. In particular, results indicated significant differences between schizophrenics and controls in ventricular size but also the influence of the diagnostic criteria used to define schizophrenia on the magnitude of the reported VBR. This suggests that differing factors of the diagnostic criteria may be sensitive to ventricular enlargement and might be worthy of further examination. Interestingly, we observed an inverse relationship between VBR difference and the number of co-authors on the study. This latter finding suggests that larger research groups report smaller VBR differences and may be more conservative or exacting in their research methodology. Analyses weighted by sample size provided identical conclusions. The effects of study factors such as these are helpful for understanding the variation in the size of the reported differences in VBR between patients and controls as well as for understanding the evolution of research on complex clinical syndromes employing neuroimaging morphometrics.

Large-scale Neuroanatomical Visualization Using a Manifold Embedding Approach

We present a unified framework for data processing, mining and interactive visualization of large-scale neuroanatomical databases. The input data is assumed to lie in a specific atlas space, or simply exist as a separate collection. Users can specify their own atlas for comparative analyses. The original data exist as MRI images in standard formats. It is uploaded to a remote server and processed offline by a parallelized pipeline workflow. This workflow transforms the data to represent it as both volumetric and triangular mesh cortical surfaces. We use multiresolution representations to scale complexity to data storage availability as well as graphical processing performance. Our workflow implements predefined metrics for clustering and classification, and data projection schemes to aid in visualization. Additionally the system provides a visual query interface for performing selection requests based on user-defined search criteria.

The generation of tetrahedral mesh models for neuroanatomical MRI

In this article, we describe a detailed method for automatically generating tetrahedral meshes from 3D images having multiple region labels. An adaptively sized tetrahedral mesh modeling approach is described that is capable of producing meshes conforming precisely to the voxelized regions in the image. Efficient tetrahedral mesh improvement is then performed minimizing an energy function containing three terms: a smoothing term to remove the voxelization, a fidelity term to maintain continuity with the image data, and a novel elasticity term to prevent the tetrahedra from becoming flattened or inverted as the mesh deforms while allowing the voxelization to be removed entirely. The meshing algorithm is applied to structural MR image data that has been automatically segmented into 56 neuroanatomical sub-divisions as well as on two other examples. The resulting tetrahedral representation has several desirable properties such as tetrahedra with dihedral angles away from 0 and 180 degrees, smoothness, and a high resolution. Tetrahedral modeling via the approach described here has applications in modeling brain structure in normal as well as diseased brain in human and non-human data and facilitates examination of 3D object deformations resulting from neurological illness (e.g. Alzheimer's disease), development, and/or aging.

TETRAHEDRAL MESH GENERATION FOR MEDICAL IMAGES WITH MULTIPLE REGIONS USING ACTIVE SURFACES

In this paper, we present a method for automatically generating tetrahedral meshes from 3D images with multiple region labels. The first step consists of constructing an adaptively sized tetrahedral mesh that conforms exactly to the voxelized regions in the image. Active surfaces (active contours in 2D) are then employed to smooth the region boundaries and remove the voxelization. Specifically, an energy with three terms is minimized: a smoothing term to remove the voxelization, a fidelity term to keep the mesh from moving too far away from the image data, and an elasticity term to keep the tetrahedra from becoming flattened or inverted as the mesh deforms. The algorithm for tetrahedral mesh generation is applied to an MRI image that has been automatically segmented using an existing method. The resulting mesh has a number of desirable properties such as tetrahedra with all dihedral angles away from 0 and 180 degrees, smoothness, and a high level of detail for the number of tetrahedra used.

Interactive exploration of neuroanatomical meta-spaces

Large-archives of neuroimaging data present many opportunities for re-analysis and mining that can lead to new findings of use in basic research or in the characterization of clinical syndromes. However, interaction with such archives tends to be driven textually, based on subject or image volume meta-data, not the actual neuroanatomical morphology itself, for which the imaging was performed to measure. What is needed is a content-driven approach for examining not only the image content itself but to explore brains that are anatomically similar, and identifying patterns embedded within entire sets of neuroimaging data. With the aim of visual navigation of large- scale neurodatabases, we introduce the concept of brain meta-spaces. The meta-space encodes pair-wise dissimilarities between all individuals in a population and shows the relationships between brains as a navigable framework for exploration. We employ multidimensional scaling (MDS) to implement meta-space processing for a new coordinate system that distributes all data points (brain surfaces) in a common frame-of-reference, with anatomically similar brain data located near each other. To navigate within this derived meta-space, we have developed a fully interactive 3D visualization environment that allows users to examine hundreds of brains simultaneously, visualize clusters of brains with similar characteristics, zoom in on particular instances, and examine the surface topology of an individual brain's surface in detail. The visualization environment not only displays the dissimilarities between brains, but also renders complete surface representations of individual brain structures, allowing an instant 3D view of the anatomies, as well as their differences. The data processing is implemented in a grid-based setting using the LONI Pipeline workflow environment. Additionally users can specify a range of baseline brain atlas spaces as the underlying scale for comparative analyses. The novelty in our approach lies in the user ability to simultaneously view and interact with many brains at once but doing so in a vast meta-space that encodes (dis) similarity in morphometry. We believe that the concept of brain meta-spaces has important implications for the future of how users interact with large-scale archives of primary neuroimaging data.

Is it time to re-prioritize neuroimaging databases and digital repositories?

The development of in vivo brain imaging has lead to the collection of large quantities of digital information. In any individual research article, several tens of gigabytes-worth of data may be represented-collected across normal and patient samples. With the ease of collecting such data, there is increased desire for brain imaging datasets to be openly shared through sophisticated databases. However, very often the raw and pre-processed versions of these data are not available to researchers outside of the team that collected them. A range of neuroimaging databasing approaches has streamlined the transmission, storage, and dissemination of data from such brain imaging studies. Though early sociological and technical concerns have been addressed, they have not been ameliorated altogether for many in the field. In this article, we review the progress made in neuroimaging databases, their role in data sharing, data management, potential for the construction of brain atlases, recording data provenance, and value for re-analysis, new publication, and training. We feature the LONI IDA as an example of an archive being used as a source for brain atlas workflow construction, list several instances of other successful uses of image databases, and comment on archive sustainability. Finally, we suggest that, given these developments, now is the time for the neuroimaging community to re-prioritize large-scale databases as a valuable component of brain imaging science.

Functional MRI at the crossroads

Since the observation of the blood oxygenation level dependent (BOLD) effect on measured MR signal in the brain, functional magnetic resonance imaging (fMRI) has rapidly become the tool of choice for exploring brain function in cognitive neuroscience. Although fMRI is an exciting and powerful means to examining the brain in vivo, the field has sometimes permitted itself to believe that patterns of BOLD activity reveal more than it is possible to measure given the method's spatial and temporal sampling, while concurrently not fully exploring the amount of information it provides. In this article, we examine some of the constraints on the kinds of inferences that can be supported by fMRI. We critique the concept of reverse inference that is often employed to claim some cognitive function must be present given activity in a specific region. We review the consideration of functional and effective connectivity that remain infrequently applied in cognitive neuroimaging, highlighting recent thinking on the ways in which functional imaging can be used to characterize inter-regional communication. Recent advances in neuroimaging that make it possible to assess anatomical connectivity using diffusion tensor imaging (DTI) and we discuss how these may inform interpretation of fMRI results. Descriptions of fMRI studies in the media, in some instances, serve to misrepresent fMRI's capabilities. We comment on how researchers need to faithfully represent fMRI's promise and limitations in dealing with the media. Finally, as we stand at the crossroads of fMRI research, where one pathway leads toward a rigorous understanding of cognitive operations using fMRI and another leads us to a predictable collection of observations absent of clear insight, we offer our impressions of a fruitful path for future functional imaging research.

Individual Variability in Brain Activity: A Nuisance or an Opportunity?

Functional imaging research has been heavily influenced by results based on population-level inference. However, group average results may belie the unique patterns of activity present in the individual that ordinarily are considered random noise. Recent advances in the evolution of MRI hardware have led to significant improvements in the stability and reproducibility of blood oxygen level dependent (BOLD) measurements. These enhancements provide a unique opportunity for closer examination of individual patterns of brain activity. Three objectives can be accomplished by considering brain scans at the individual level; (1) Mapping functional anatomy at a fine grained analysis; (2) Determining if an individual scan is normative with respect to a reference population; and (3) Understanding the sources of intersubject variability in brain activity. In this review, we detail these objectives, briefly discuss their histories and present recent trends in the analyses of individual variability. Finally, we emphasize the unique opportunities and challenges for understanding individual differences through international collaboration among Pacific Rim investigators.

New Horizons for the Next Era of Human Brain Imaging, Cognitive, and Behavioral Research: Pacific Rim Interactivity

Beginning in the 1990's, substantial advances have been made in the ability to image the living human brain. Functional MRI, PET, and other modalities have been developed to provide a rich means for assessing brain function and structure across spatial and temporal dimensions. Such methods are now the preferred means to examine the brain in vivo, with several thousand articles now appearing in the literature each year. The next era of human brain imaging is upon us now as technological developments reach a level where data can be processed quickly and combined with other biological information to provide fundamentally new applications and insights. This new era will involve and require the collaborative participation of leading research groups from around the world to share information and expertise for understanding observed effects and synthesizing these into new knowledge. One particular community that is gaining in its prominence in the field is that of the Pacific Rim, whose collective research efforts present an important corpus of research effort into brain structure and function. The Pacific Rim represents an important collection of researchers interested in the greater sharing of ideas. In this special issue of Brain Imaging and Behavior, we focus on emerging areas of research that utilize brain imaging methodology, and discuss how current developments are driving the expansion of functional imaging research. Moreover, we focus on the robust interaction of researchers from around the Pacific Rim whose collaborations are significantly shaping the future of brain imaging.

Domain-specific data sharing in neuroscience: what do we have to learn from each other?

Molecular biology and genomics have made notable strides in the sharing of primary data and resources. In other domains of neuroscience research, however, there has been resistance to adopting formalized strategies for data exchange, archiving, and availability. In this article, we discuss how neuroscience domains might follow the lead of molecular biology on what has been successful and what has failed in active data sharing. This considers not only the technical challenges but also the sociological concerns in making it possible. Though, not a pain-free process, with increased data availability, scientists from multiple fields can enjoy greater opportunity for novel discoveries about the brain in health and disease.

Mapping the human brain: new insights from FMRI data sharing

The sharing of primary data in the field of neuroscience has received considerable scrutiny from scientific societies and from science journals. Many see this as value added for science publishing that can enhance and inform secondary examination of data and results. Still others worry that data sharing is an undue burden for researchers with little long term value to science. But examples of how data sharing can be done successfully do exist. The fMRI Data Center, established at Dartmouth College in 2000 and now based at the University of California Santa Barbara, has worked to facilitate the open sharing of neuroimaging data from peer-reviewed papers to foster progress in cognitive science. The fMRI study on the representation of objects in the human occipital and temporal cortex, published in 2000 in the Journal of Cognitive Neuroscience (JOCN), marked the first deposition in the new database. Despite initial concerns about fMRI data sharing, this data set was frequently downloaded. We describe the original results of distributed brain activation patterns elicited by faces and objects in the human visual system, and overview several secondary analyses by independent investigators. A philosopher tested Husserl's temporal components of consciousness, whereas other brain imagers deployed new analytic tools, from Dynamic Causal Modeling, which estimates the neural interactions between cortical regions, to a novel method for constructing reproducibility maps. These re-analyses revealed new findings not reported in the original study, provided new perspectives on visual perception, generated new predictions, and resulted in new collaborations and publications in high profile journals.

Individual variability in brain activations associated with episodic retrieval: A role for large-scale databases

The localization of brain functions using neuroimaging techniques is commonly dependent on statistical analyses of groups of subjects in order to identify sites of activation, particularly in studies of episodic memory. Exclusive reliance on group analysis may be to the detriment of understanding the true underlying cognitive nature of brain activations. In this overview, we found that the patterns of brain activity associated with episodic retrieval are very distinct for individual subjects from the patterns of brain activity at the group level. These differences appear to go beyond the relatively small variations due to cyctoarchitectonic differences or spatial normalization. We review evidence that individual patterns of brain activity vary widely across subjects and are reliable over time despite extensive variability. We suggest that varied but reliable individual patterns of significant brain activity may be indicative of different cognitive strategies used to produce a recognition response. We argue that individual analyses in conjunction with group analyses are likely to be critical in fully understanding the relationship between retrieval processes and underlying neural systems.