Connectomics signatures of prenatal cocaine exposure affected adolescent brains

Neuroimaging in infants with prenatal opioid exposure: Current evidence, recent developments and targets for future research

Prenatal opioid exposure (POE) has shown to be a risk factor for adverse long-term cognitive and behavioral outcomes in offspring. However, the neural mechanisms of these outcomes remain poorly understood. While preclinical and human studies suggest that these outcomes may be due to opioid-mediated changes in the fetal and early postnatal brain, other maternal, social, and environmental factors are also shown to play a role. Recent neuroimaging studies reveal brain alterations in children with POE. Early neuroimaging and novel methodology could provide an in vivo mechanistic understanding of opioid mediated alterations in developing brain. However, this is an area of ongoing research. In this review we explore recent imaging developments in POE, with emphasis on the neonatal and infant brain, and highlight some of the challenges of imaging the developing brain in this population. We also highlight evidence from animal models and imaging in older children and youth to understand areas where future research may be targeted in infants with POE.

Assessing Fine-Granularity Structural and Functional Connectivity in Children With Attention Deficit Hyperactivity Disorder

Attention deficit hyperactivity disorder (ADHD) was considered to be a disorder with high heterogeneity, as various abnormalities were found across widespread brain regions in recent neuroimaging studies. However, remarkable individual variability of cortical structure and function may have partially contributed to these discrepant findings. In this work, we applied the Dense Individualized and Common Connectivity-Based Cortical Landmarks (DICCCOL) method to identify fine-granularity corresponding functional cortical regions across different subjects based on the shape of a white matter fiber bundle and measured functional connectivities between these cortical regions. Fiber bundle pattern and functional connectivity were compared between ADHD patients and normal controls in two independent samples. Interestingly, four neighboring DICCCOLs located close to the left parietooccipital area consistently exhibited discrepant fiber bundles in both datasets. The left precentral gyrus (DICCCOL 175, BA 6) and the right anterior cingulate gyrus (DICCCOL 321, BA 32) had the highest connection number among 78 pairs of abnormal functional connectivities with good cross-sample consistency. Furthermore, abnormal functional connectivities were significantly correlated with ADHD symptoms. Our studies revealed novel fine-granularity structural and functional alterations in ADHD.

Longitudinal changes of amygdala functional connectivity in adolescents prenatally exposed to cocaine

BACKGROUND

Prenatal cocaine exposure (PCE) is associated with arousal dysregulation, but interactions between exposure and age are rarely investigated directly with longitudinal study designs. Our previous study had examined task-elicited emotional arousal and noted persistently high amygdala activations in the development of adolescents with PCE. However, while externally imposed emotional arousal could be considered a "state" effect depending on specific task stimuli, it is still unclear whether similar developmental alterations extend to intrinsic functional connectivity (FC), reflecting more of a "trait" effect.

METHODS

We used a longitudinal design and analyzed resting-state functional magnetic resonance imaging data acquired twice from 25 adolescents with PCE and 16 non-exposed controls. Both groups were each scanned first at the mean age of 14.3 and then again at 16.6 years. Seeding in bilateral amygdalae and comparing the 2^nd scan with the 1^st, we examined the interaction effect between PCE and age on FCs in the emotional network.

RESULTS

Compared with the younger age, we observed a generally decreased FC in the emotional network of the control group at the older age, but these FCs were generally increased at the older age in this same network of the PCE group. Additionally, this interaction effect of exposure by age in the right fusiform was positively correlated with the emotional interference imposed by external task stimuli.

CONCLUSIONS

These results provided additional data directly characterizing developmental changes in the emotional network of adolescents with PCE, complementing and extending the notion of a PCE-associated long-term teratogenic effect on arousal regulation.

Exploring early human brain development with structural and physiological neuroimaging

Early brain development, from the embryonic period to infancy, is characterized by rapid structural and functional changes. These changes can be studied using structural and physiological neuroimaging methods. In order to optimally acquire and accurately interpret this data, concepts from adult neuroimaging cannot be directly transferred. Instead, one must have a basic understanding of fetal and neonatal structural and physiological brain development, and the important modulators of this process. Here, we first review the major developmental milestones of transient cerebral structures and structural connectivity (axonal connectivity) followed by a summary of the contributions from ex vivo and in vivo MRI. Next, we discuss the basic biology of neuronal circuitry development (synaptic connectivity, i.e. ensemble of direct chemical and electrical connections between neurons), physiology of neurovascular coupling, baseline metabolic needs of the fetus and the infant, and functional connectivity (defined as statistical dependence of low-frequency spontaneous fluctuations seen with functional magnetic resonance imaging (fMRI)). The complementary roles of magnetic resonance imaging (MRI), electroencephalography (EEG), magnetoencephalography (MEG), and near-infrared spectroscopy (NIRS) are discussed. We include a section on modulators of brain development where we focus on the placenta and emerging placental MRI approaches. In each section we discuss key technical limitations of the imaging modalities and some of the limitations arising due to the biology of the system. Although neuroimaging approaches have contributed significantly to our understanding of early brain development, there is much yet to be done and a dire need for technical innovations and scientific discoveries to realize the future potential of early fetal and infant interventions to avert long term disease.

Joint representation of connectome-scale structural and functional profiles for identification of consistent cortical landmarks in macaque brain

Discovery and representation of common structural and functional cortical architectures has been a significant yet challenging problem for years. Due to the remarkable variability of structural and functional cortical architectures in human brain, it is challenging to jointly represent a common cortical architecture which can comprehensively encode both structure and function characteristics. In order to better understand this challenge and considering that macaque monkey brain has much less variability in structure and function compared with human brain, in this paper, we propose a novel computational framework to apply our DICCCOL (Dense Individualized and Common Connectivity-based Cortical Landmarks) and HAFNI (Holistic Atlases of Functional Networks and Interactions) frameworks on macaque brains, in order to jointly represent structural and functional connectome-scale profiles for identification of a set of consistent and common cortical landmarks across different macaque brains based on multimodal DTI and resting state fMRI (rsfMRI) data. Experimental results demonstrate that 100 consistent and common cortical landmarks are successfully identified via the proposed framework, each of which has reasonably accurate anatomical, structural fiber connection pattern, and functional correspondences across different macaque brains. This set of 100 landmarks offer novel insights into the structural and functional cortical architectures in macaque brains.

An examination of the association between prenatal cocaine exposure and brain activation measures of arousal and attention in young adults: An fMRI study using the Attention Network Task

Prenatal drug exposure, including cocaine, alcohol, marijuana, and tobacco, is associated with deficits in behavioral regulation and attention. Using fMRI, the objective of this study was to characterize the association between prenatal cocaine exposure (PCE) and the underlying neural substrates associated with behavioral outcomes of attention. Forty-seven young adults were recruited for this study from the ongoing Maternal Health Practices and Child Development (MHPCD) Project, a longitudinal study of the effects of PCE on growth, behavior, and cognitive function. Three groups were compared: 1) prenatal exposure to cocaine, alcohol, marijuana, and tobacco (CAMT, n = 15), 2) prenatal exposure to alcohol, marijuana, and tobacco (AMT, n = 17), and 3) no prenatal exposure to drugs (Controls, n = 15). Subjects were frequency matched on gender, race, handedness, and 15-year IQ. This study used the theoretical model proposed by Posner and Peterson (1990), which posits three dissociable components of attention: alerting, orienting, and executive attention. Subjects completed a functional MRI (fMRI) scan while performing the Attention Network Task, a validated neuroimaging measure of the 3-network model of attention. Behavioral and fMRI data revealed no associations between PCE and task accuracy, speed of processing, or activation in key brain regions associated with each of the attention networks. The results of this study show that any subtle differences in brain function associated with PCE are not detectable using the ANT task and fMRI. These results should be interpreted in the context of other studies that have found associations between PCE and arousal with emotionally arousing stimuli, compared to this study that found no associations using emotionally neutral stimuli.

Atomic connectomics signatures for characterization and differentiation of mild cognitive impairment

In recent years, functional connectomics signatures have been shown to be a very valuable tool in characterizing and differentiating brain disorders from normal controls. However, if the functional connectivity alterations in a brain disease are localized within sub-networks of a connectome, then accurate identification of such disease-specific sub-networks is critical and this capability entails both fine-granularity definition of connectome nodes and effective clustering of connectome nodes into disease-specific and non-disease-specific sub-networks. In this work, we adopted the recently developed DICCCOL (dense individualized and common connectivity-based cortical landmarks) system as a fine-granularity high-resolution connectome construction method to deal with the first issue, and employed an effective variant of non-negative matrix factorization (NMF) method to pinpoint disease-specific sub-networks, which we called atomic connectomics signatures in this work. We have implemented and applied this novel framework to two mild cognitive impairment (MCI) datasets from two different research centers, and our experimental results demonstrated that the derived atomic connectomics signatures can effectively characterize and differentiate MCI patients from their normal controls. In general, our work contributed a novel computational framework for deriving descriptive and distinctive atomic connectomics signatures in brain disorders.

Connectome-scale assessment of structural and functional connectivity in mild traumatic brain injury at the acute stage

Mild traumatic brain injury (mTBI) accounts for over one million emergency visits each year in the United States. The large-scale structural and functional network connectivity changes of mTBI are still unknown. This study was designed to determine the connectome-scale brain network connectivity changes in mTBI at both structural and functional levels. 40 mTBI patients at the acute stage and 50 healthy controls were recruited. A novel approach called Dense Individualized and Common Connectivity-based Cortical Landmarks (DICCCOLs) was applied for connectome-scale analysis of both diffusion tensor imaging and resting state functional MRI data. Among 358 networks identified on DICCCOL analysis, 41 networks were identified as structurally discrepant between patient and control groups. The involved major white matter tracts include the corpus callosum, and superior and inferior longitudinal fasciculi. Functional connectivity analysis identified 60 connectomic signatures that differentiate patients from controls with 93.75% sensitivity and 100% specificity. Analysis of functional domains showed decreased intra-network connectivity within the emotion network and among emotion-cognition interactions, and increased interactions among action-emotion and action-cognition as well as within perception networks. This work suggests that mTBI may result in changes of structural and functional connectivity on a connectome scale at the acute stage.

Bayesian Inference for Functional Dynamics Exploring in fMRI Data

This paper aims to review state-of-the-art Bayesian-inference-based methods applied to functional magnetic resonance imaging (fMRI) data. Particularly, we focus on one specific long-standing challenge in the computational modeling of fMRI datasets: how to effectively explore typical functional interactions from fMRI time series and the corresponding boundaries of temporal segments. Bayesian inference is a method of statistical inference which has been shown to be a powerful tool to encode dependence relationships among the variables with uncertainty. Here we provide an introduction to a group of Bayesian-inference-based methods for fMRI data analysis, which were designed to detect magnitude or functional connectivity change points and to infer their functional interaction patterns based on corresponding temporal boundaries. We also provide a comparison of three popular Bayesian models, that is, Bayesian Magnitude Change Point Model (BMCPM), Bayesian Connectivity Change Point Model (BCCPM), and Dynamic Bayesian Variable Partition Model (DBVPM), and give a summary of their applications. We envision that more delicate Bayesian inference models will be emerging and play increasingly important roles in modeling brain functions in the years to come.

Longitudinal changes of amygdala and default mode activation in adolescents prenatally exposed to cocaine

Prenatal cocaine exposure (PCE) is associated with long-term and negative effect on arousal regulation. Recent neuroimaging studies have examined brain mechanisms related to arousal dysregulation with cross-sectional experimental designs; but longitudinal changes in the brain, reflecting group differences in neurodevelopment, have never been directly examined. To directly assess the interaction of PCE and neurodevelopment, the present study used a longitudinal design to analyze functional magnetic resonance imaging (fMRI) data collected from 33 adolescents (21 with PCE and 12 non-exposed controls) while they performed the same working memory task with emotional distracters at two points in time. The mean age of participants was 14.3 years at time_1 and 16.7 years at time_2. With confounding factors statistically controlled, the fMRI data revealed significant exposure-by-time interaction in the activations of the amygdala and default mode network (DMN). For the control adolescents, brain activations associated with emotional arousal (amygdala) and cognitive effort (DMN) were both reduced at time_2 as compared to that at time_1. However, these activation reductions were not observed in the PCE group, indicating persistently high levels of emotional arousal and cognitive effort. In addition, correlations between longitudinal changes in the brain and in behavior have shown that adolescents with persistently high emotional arousal were more likely in need of high cognitive effort; and their cognitive performance was more likely to be affected by distractive challenges. The present results complement and extend previous findings from cross-sectional studies with further evidence supporting the view of PCE associated long-term teratogenic effects on arousal regulation.

GENETIC ANALYSIS OF STRUCTURAL BRAIN CONNECTIVITY USING DICCCOL MODELS OF DIFFUSION MRI IN 522 TWINS

Genetic and environmental factors affect white matter connectivity in the normal brain, and they also influence diseases in which brain connectivity is altered. Little is known about genetic influences on brain connectivity, despite wide variations in the brain's neural pathways. Here we applied the "DICCCOL" framework to analyze structural connectivity, in 261 twin pairs (522 participants, mean age: 21.8 y ± 2.7SD). We encoded connectivity patterns by projecting the white matter (WM) bundles of all "DICCCOLs" as a tracemap (TM). Next we fitted an A/C/E structural equation model to estimate additive genetic (A), common environmental (C), and unique environmental/error (E) components of the observed variations in brain connectivity. We found 44 "heritable DICCCOLs" whose connectivity was genetically influenced (a²>1%); half of them showed significant heritability (a²>20%). Our analysis of genetic influences on WM structural connectivity suggests high heritability for some WM projection patterns, yielding new targets for genome-wide association studies.

Predictive models of resting state networks for assessment of altered functional connectivity in mild cognitive impairment

Due to the difficulties in establishing correspondences between functional regions across individuals and populations, systematic elucidation of functional connectivity alterations in mild cognitive impairment (MCI) in comparison with normal controls (NC) is still a challenging problem. In this paper, we assessed the functional connectivity alterations in MCI via novel, alternative predictive models of resting state networks (RSNs) learned from multimodal resting state fMRI (R-fMRI) and diffusion tensor imaging (DTI) data. First, ICA-clustering was used to construct RSNs from R-fMRI data in NC group. Second, since the RSNs in MCI are already altered and can hardly be constructed directly from R-fMRI data, structural landmarks derived from DTI data were employed as the predictive models of RSNs for MCI. Third, given that the landmarks are structurally consistent and correspondent across NC and MCI, functional connectivities in MCI were assessed based on the predicted RSNs and compared with those in NC. Experimental results demonstrated that the predictive models of RSNs based on multimodal R-fMRI and DTI data systematically and comprehensively revealed widespread functional connectivity alterations in MCI in comparison with NC.

Anatomy-guided Dense Individualized and Common Connectivity-based Cortical Landmarks (A-DICCCOL)

Establishment of structural and functional correspondences of human brain that can be quantitatively encoded and reproduced across different subjects and populations is one of the key issues in brain mapping. As an attempt to address this challenge, our recently developed Dense Individualized and Common Connectivity-based Cortical Landmarks (DICCCOL) system reported 358 connectional landmarks, each of which possesses consistent DTI-derived white matter fiber connection pattern that is reproducible in over 240 healthy brains. However, the DICCCOL system can be substantially improved by integrating anatomical and morphological information during landmark initialization and optimization procedures. In this paper, we present a novel anatomy-guided landmark discovery framework that defines and optimizes landmarks via integrating rich anatomical, morphological, and fiber connectional information for landmark initialization, group-wise optimization and prediction, which are formulated and solved as an energy minimization problem. The framework finally determined 555 consistent connectional landmarks. Validation studies demonstrated that the 555 landmarks are reproducible, predictable, and exhibited reasonably accurate anatomical, connectional, and functional correspondences across individuals and populations and thus are named anatomy-guided DICCCOL or A-DICCCOL. This A-DICCCOL system represents common cortical architectures with anatomical, connectional, and functional correspondences across different subjects and would potentially provide opportunities for various applications in brain science.

Prenatal drug exposure to illicit drugs alters working memory-related brain activity and underlying network properties in adolescence

The persistence of effects of prenatal drug exposure (PDE) on brain functioning during adolescence is poorly understood. We explored neural activation to a visuospatial working memory (VSWM) versus a control task using functional magnetic resonance imaging (fMRI) in adolescents with PDE and a community comparison group (CC) of non-exposed adolescents. We applied graph theory metrics to resting state data using a network of nodes derived from the VSWM task activation map to further explore connectivity underlying WM functioning. Participants (ages 12-15 years) included 47 adolescents (27 PDE and 20 CC). All analyses controlled for potentially confounding differences in birth characteristics and postnatal environment. Significant group by task differences in brain activation emerged in the left middle frontal gyrus (BA 6) with the CC group, but not the PDE group, activating this region during VSWM. The PDE group deactivated the culmen, whereas the CC group activated it during the VSWM task. The CC group demonstrated a significant relation between reaction time and culmen activation, not present in the PDE group. The network analysis underlying VSWM performance showed that PDE group had lower global efficiency than the CC group and a trend level reduction in local efficiency. The network node corresponding to the BA 6 group by task interaction showed reduced nodal efficiency and fewer direct connections to other nodes in the network. These results suggest that adolescence reveals altered neural functioning related to response planning that may reflect less efficient network functioning in youth with PDE.

Fusing DTI and fMRI data: a survey of methods and applications

The relationship between brain structure and function has been one of the centers of research in neuroimaging for decades. In recent years, diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) techniques have been widely available and popular in cognitive and clinical neurosciences for examining the brain's white matter (WM) micro-structures and gray matter (GM) functions, respectively. Given the intrinsic integration of WM/GM and the complementary information embedded in DTI/fMRI data, it is natural and well-justified to combine these two neuroimaging modalities together to investigate brain structure and function and their relationships simultaneously. In the past decade, there have been remarkable achievements of DTI/fMRI fusion methods and applications in neuroimaging and human brain mapping community. This survey paper aims to review recent advancements on methodologies and applications in incorporating multimodal DTI and fMRI data, and offer our perspectives on future research directions. We envision that effective fusion of DTI/fMRI techniques will play increasingly important roles in neuroimaging and brain sciences in the years to come.

Survey of encoding and decoding of visual stimulus via FMRI: an image analysis perspective

A variety of exciting scientific achievements have been made in the last few decades in brain encoding and decoding via functional magnetic resonance imaging (fMRI). This trend continues to rise in recent years, as evidenced by the increasing number of published papers in this topic and several published survey papers addressing different aspects of research issues. Essentially, these survey articles were mainly from cognitive neuroscience and neuroimaging perspectives, although computational challenges were briefly discussed. To complement existing survey articles, this paper focuses on the survey of the variety of image analysis methodologies, such as neuroimage registration, fMRI signal analysis, ROI (regions of interest) selection, machine learning algorithms, reproducibility analysis, structural and functional connectivity, and natural image analysis, which were employed in previous brain encoding/decoding research works. This paper also provides discussions of potential limitations of those image analysis methodologies and possible future improvements. It is hoped that extensive discussions of image analysis issues could contribute to the advancements of the increasingly important brain encoding/decoding field.

Characterizing and Differentiating Brain State Dynamics via Hidden Markov Models

Functional connectivity measured from resting state fMRI (R-fMRI) data has been widely used to examine the brain's functional activities and has been recently used to characterize and differentiate brain conditions. However, the dynamical transition patterns of the brain's functional states have been less explored. In this work, we propose a novel computational framework to quantitatively characterize the brain state dynamics via hidden Markov models (HMMs) learned from the observations of temporally dynamic functional connectomics, denoted as functional connectome states. The framework has been applied to the R-fMRI dataset including 44 post-traumatic stress disorder (PTSD) patients and 51 normal control (NC) subjects. Experimental results show that both PTSD and NC brains were undergoing remarkable changes in resting state and mainly transiting amongst a few brain states. Interestingly, further prediction with the best-matched HMM demonstrates that PTSD would enter into, but could not disengage from, a negative mood state. Importantly, 84% of PTSD patients and 86% of NC subjects are successfully classified via multiple HMMs using majority voting.

Imaging brain plasticity after trauma

The brain is highly plastic after stroke or epilepsy; however, there is a paucity of brain plasticity investigation after traumatic brain injury (TBI). This mini review summarizes the most recent evidence of brain plasticity in human TBI patients from the perspective of advanced magnetic resonance imaging. Similar to other forms of acquired brain injury, TBI patients also demonstrated both structural reorganization as well as functional compensation by the recruitment of other brain regions. However, the large scale brain network alterations after TBI are still unknown, and the field is still short of proper means on how to guide the choice of TBI rehabilitation or treatment plan to promote brain plasticity. The authors also point out the new direction of brain plasticity investigation.

Group-wise FMRI activation detection on DICCCOL landmarks

Group-wise activation detection in task-based fMRI has been widely used because of its robustness to noises and its capacity to deal with variability of individual brains. However, current group-wise fMRI activation detection methods typically rely on the co-registration of individual brains' fMRI images, which has difficulty in dealing with the remarkable anatomic variation of different brains. As a consequence, the resulted misalignments could significantly degrade the required inter-subject correspondences, thus substantially reducing the sensitivity and specificity of group-wise fMRI activation detection. To deal with these challenges, this paper presents a novel approach to detecting group-wise fMRI activation on our recently developed and validated Dense Individualized and Common Connectivity-based Cortical Landmarks (DICCCOL). The basic idea here is that the first-level general linear model (GLM) analysis is first performed on the fMRI signal of each corresponding DICCCOL landmark in individual brain's own space, and then the estimated effect sizes of the same landmark from a group of subjects are statistically assessed with the mixed-effect model at the group level. Finally, the consistently activated DICCCOL landmarks are determined and declared in a group-wise fashion in response to external block-based stimuli. Our experimental results have demonstrated that the proposed approach can detect meaningful activations.

Fine-granularity functional interaction signatures for characterization of brain conditions

In the human brain, functional activity occurs at multiple spatial scales. Current studies on functional brain networks and their alterations in brain diseases via resting-state functional magnetic resonance imaging (rs-fMRI) are generally either at local scale (regionally confined analysis and inter-regional functional connectivity analysis) or at global scale (graph theoretic analysis). In contrast, inferring functional interaction at fine-granularity sub-network scale has not been adequately explored yet. Here our hypothesis is that functional interaction measured at fine-granularity sub-network scale can provide new insight into the neural mechanisms of neurological and psychological conditions, thus offering complementary information for healthy and diseased population classification. In this paper, we derived fine-granularity functional interaction (FGFI) signatures in subjects with Mild Cognitive Impairment (MCI) and Schizophrenia by diffusion tensor imaging (DTI) and rs-fMRI, and used patient-control classification experiments to evaluate the distinctiveness of the derived FGFI features. Our experimental results have shown that the FGFI features alone can achieve comparable classification performance compared with the commonly used inter-regional connectivity features. However, the classification performance can be substantially improved when FGFI features and inter-regional connectivity features are integrated, suggesting the complementary information achieved from the FGFI signatures.

Brain structural and functional changes in adolescents with psychiatric disorders

During adolescence, hormonal and neurodevelopmental changes geared to ensuring reproduction and achieving independence are very likely mediated by the growth of neural processes, remodeling of synaptic connections, increased myelination in prefrontal areas and maturation of connecting subcortical areas. These processes, greatly accelerated in adolescence, follow an asynchronous pattern in different brain areas. Neuroimaging research using functional and structural magnetic resonance imaging has produced most of the insights regarding brain structural and functional neuropathology in adolescent psychiatric disorders. In schizophrenia, first episodes during adolescence are linked to greater-than-normal losses in gray matter density and white matter integrity and show a divergence of maturational trajectories from normative neural development in a progression similar to that of adult-onset schizophrenia. Anxiety and mood disorders in adolescence have been linked to abnormally increased activity in the amygdala and ventral prefrontal cortical areas, although some data suggest that neural abnormalities in the amygdala and anxiety maybe particularly more frequent in adolescents than in adults. Alcohol misuse in adolescence results in reduced integrity in the white matter and reduced gray matter density that, given the high intensity of adolescent synaptic and myelin remodeling, may result in persistent and profound changes in circuits supporting memory and emotional and appetitive control. The interaction of persistent changes due to prenatal exposure with the contemporaneous expression of genetic factors and disturbing environmental exposure may be an important factor in the appearance of psychiatric disorders in adolescence. Further progress in understanding adolescent psychopathology will require postmortem research of molecular and cellular determinants in the adolescent brain.

Inferring functional interaction and transition patterns via dynamic Bayesian variable partition models

Multivariate connectivity and functional dynamics have been of wide interest in the neuroimaging field, and a variety of methods have been developed to study functional interactions and dynamics. In contrast, the temporal dynamic transitions of multivariate functional interactions among brain networks, in particular, in resting state, have been much less explored. This article presents a novel dynamic Bayesian variable partition model (DBVPM) that simultaneously considers and models multivariate functional interactions and their dynamics via a unified Bayesian framework. The basic idea is to detect the temporal boundaries of piecewise quasi-stable functional interaction patterns, which are then modeled by representative signature patterns and whose temporal transitions are characterized by finite-state transition machines. Results on both simulated and experimental datasets demonstrated the effectiveness and accuracy of the DBVPM in dividing temporally transiting functional interaction patterns. The application of DBVPM on a post-traumatic stress disorder (PTSD) dataset revealed substantially different multivariate functional interaction signatures and temporal transitions in the default mode and emotion networks of PTSD patients, in comparison with those in healthy controls. This result demonstrated the utility of DBVPM in elucidating salient features that cannot be revealed by static pair-wise functional connectivity analysis.

Connectome-scale assessments of structural and functional connectivity in MCI

Mild cognitive impairment (MCI) has received increasing attention not only because of its potential as a precursor for Alzheimer's disease but also as a predictor of conversion to other neurodegenerative diseases. Although MCI has been defined clinically, accurate and efficient diagnosis is still challenging. Although neuroimaging techniques hold promise, compared to commonly used biomarkers including amyloid plaques, tau protein levels and brain tissue atrophy, neuroimaging biomarkers are less well validated. In this article, we propose a connectomes-scale assessment of structural and functional connectivity in MCI via two independent multimodal DTI/fMRI datasets. We first used DTI-derived structural profiles to explore and tailor the most common and consistent landmarks, then applied them in a whole-brain functional connectivity analysis. The next step fused the results from two independent datasets together and resulted in a set of functional connectomes with the most differentiation power, hence named as "connectome signatures." Our results indicate that these "connectome signatures" have significantly high MCI-vs-controls classification accuracy, at more than 95%. Interestingly, through functional meta-analysis, we found that the majority of "connectome signatures" are mainly derived from the interactions among different functional networks, for example, cognition-perception and cognition-action domains, rather than from within a single network. Our work provides support for using functional "connectome signatures" as neuroimaging biomarkers of MCI.

Characterization of task-free and task-performance brain states via functional connectome patterns

Both resting state fMRI (R-fMRI) and task-based fMRI (T-fMRI) have been widely used to study the functional activities of the human brain during task-free and task-performance periods, respectively. However, due to the difficulty in strictly controlling the participating subject's mental status and their cognitive behaviors during R-fMRI/T-fMRI scans, it has been challenging to ascertain whether or not an R-fMRI/T-fMRI scan truly reflects the participant's functional brain states during task-free/task-performance periods. This paper presents a novel computational approach to characterizing and differentiating the brain's functional status into task-free or task-performance states, by which the functional brain activities can be effectively understood and differentiated. Briefly, the brain's functional state is represented by a whole-brain quasi-stable connectome pattern (WQCP) of R-fMRI or T-fMRI data based on 358 consistent cortical landmarks across individuals, and then an effective sparse representation method was applied to learn the atomic connectome patterns (ACPs) of both task-free and task-performance states. Experimental results demonstrated that the learned ACPs for R-fMRI and T-fMRI datasets are substantially different, as expected. A certain portion of ACPs from R-fMRI and T-fMRI data were overlapped, suggesting some subjects with overlapping ACPs were not in the expected task-free/task-performance brain states. Besides, potential outliers in the T-fMRI dataset were further investigated via functional activation detections in different groups, and our results revealed unexpected task-performances of some subjects. This work offers novel insights into the functional architectures of the brain.

A linear model for characterization of synchronization frequencies of neural networks

The synchronization frequency of neural networks and its dynamics have important roles in deciphering the working mechanisms of the brain. It has been widely recognized that the properties of functional network synchronization and its dynamics are jointly determined by network topology, network connection strength, i.e., the connection strength of different edges in the network, and external input signals, among other factors. However, mathematical and computational characterization of the relationships between network synchronization frequency and these three important factors are still lacking. This paper presents a novel computational simulation framework to quantitatively characterize the relationships between neural network synchronization frequency and network attributes and input signals. Specifically, we constructed a series of neural networks including simulated small-world networks, real functional working memory network derived from functional magnetic resonance imaging, and real large-scale structural brain networks derived from diffusion tensor imaging, and performed synchronization simulations on these networks via the Izhikevich neuron spiking model. Our experiments demonstrate that both of the network synchronization strength and synchronization frequency change according to the combination of input signal frequency and network self-synchronization frequency. In particular, our extensive experiments show that the network synchronization frequency can be represented via a linear combination of the network self-synchronization frequency and the input signal frequency. This finding could be attributed to an intrinsically-preserved principle in different types of neural systems, offering novel insights into the working mechanism of neural systems.

Meta-analysis of functional roles of DICCCOLs

DICCCOL (Dense Individualized and Common Connectivity-based Cortical Landmarks) is a recently published system composed of 358 cortical landmarks that possess consistent correspondences across individuals and populations. Meanwhile, each DICCCOL landmark is localized in an individual brain's unique morphological profile, and therefore the DICCCOL system offers a universal and individualized brain reference and localization framework. However, in current 358 diffusion tensor imaging (DTI)-derived DICCCOLs, only 95 of them have been functionally annotated via task-based or resting-state fMRI datasets and the functional roles of other DICCCOLs are unknown yet. This work aims to take the advantage of existing literature fMRI studies (1110 publications) reported and aggregated in the BrainMap database to examine the possible functional roles of 358 DICCCOLs via meta-analysis. Our experimental results demonstrate that a majority of 358 DICCCOLs can be functionally annotated by the BrainMap database, and many DICCCOLs have rich and diverse functional roles in multiple behavior domains. This study provides novel insights into the functional regularity and diversity of 358 DICCCOLs, and offers a starting point for future elucidation of fine-grained functional roles of cortical landmarks.

Predicting cortical ROIs via joint modeling of anatomical and connectional profiles

Localization of cortical regions of interests (ROIs) in structural neuroimaging data such as diffusion tensor imaging (DTI) and T1-weighted MRI images has significant importance in basic and clinical neurosciences. However, this problem is considerably challenging due to the lack of quantitative mapping between brain structure and function, which relies on the availability of multimodal training data including benchmark task-based functional MRI (fMRI) images and effective machine learning algorithms. This paper presents a novel joint modeling approach that learns predictive models of ROIs from concurrent task-based fMRI, DTI, and T1-weighted MRI datasets. In particular, the effective generalized multiple kernel learning (GMKL) algorithm and ROI coordinate principal component analysis (PCA) model are employed to infer the intrinsic relationships between anatomical T1-weighted MRI/connectional DTI features and task-based fMRI-derived functional ROIs. Then, these predictive models of cortical ROIs are evaluated by cross-validation studies, independent datasets, and reproducibility studies. Experimental results are promising. We envision that these predictive models can be potentially applied in many scenarios that have only DTI and/or T1-weighted MRI data, but without task-based fMRI data.

Dynamic functional connectomics signatures for characterization and differentiation of PTSD patients

Functional connectomes (FCs) have been recently shown to be powerful in characterizing brain conditions. However, many previous studies assumed temporal stationarity of FCs, while their temporal dynamics are rarely explored. Here, based on the structural connectomes constructed from diffusion tensor imaging data, FCs are derived from resting-state fMRI (R-fMRI) data and are then temporally divided into quasi-stable segments via a sliding time window approach. After integrating and pooling over a large number of those temporally quasi-stable FC segments from 44 post-traumatic stress disorder (PTSD) patients and 51 healthy controls, common FC (CFC) patterns are derived via effective dictionary learning and sparse coding algorithms. It is found that there are 16 CFC patterns that are reproducible across healthy controls, and interestingly, two additional CFC patterns with altered connectivity patterns [termed signature FC (SFC) here] exist dominantly in PTSD subjects. These two SFC patterns alone can successfully differentiate 80% of PTSD subjects from healthy controls with only 2% false positive. Furthermore, the temporal transition dynamics of CFC patterns in PTSD subjects are substantially different from those in healthy controls. These results have been replicated in separate testing datasets, suggesting that dynamic functional connectomics signatures can effectively characterize and differentiate PTSD patients.

Adolescents with prenatal cocaine exposure show subtle alterations in striatal surface morphology and frontal cortical volumes

Background

Published structural neuroimaging studies of prenatal cocaine exposure (PCE) in humans have yielded somewhat inconsistent results, with several studies reporting no significant differences in brain structure between exposed subjects and controls. Here, we sought to clarify some of these discrepancies by applying methodologies that allow for the detection of subtle alterations in brain structure.

Methods

We applied surface-based anatomical modeling methods to magnetic resonance imaging (MRI) data to examine regional changes in the shape and volume of the caudate and putamen in adolescents with prenatal cocaine exposure (n = 40, including 28 exposed participants and 12 unexposed controls, age range 14 to 16 years). We also sought to determine whether changes in regional brain volumes in frontal and subcortical regions occurred in adolescents with PCE compared to control participants.

Results

The overall volumes of the caudate and putamen did not significantly differ between PCE participants and controls. However, we found significant (P <0.05, uncorrected) effects of levels of prenatal exposure to cocaine on regional patterns of striatal morphology. Higher levels of prenatal cocaine exposure were associated with expansion of certain striatal subregions and with contraction in others. Volumetric analyses revealed no significant changes in the volume of any subcortical region of interest, but there were subtle group differences in the volumes of some frontal cortical regions, in particular reduced volumes of caudal middle frontal cortices and left lateral orbitofrontal cortex in exposed participants compared to controls.

Conclusions

Prenatal cocaine exposure may lead to subtle and regionally specific patterns of regional dysmorphology in the striatum and volumetric changes in the frontal lobes. The localized and bidirectional nature of effects may explain in part the contradictions in the existing literature.