Gyral and sulcal connectivity in the human cerebral cortex.

The rapid evolution of image acquisition and data analytic methods has established in vivo whole-brain tractography as a routine technology over the last 20 years. Imaging-based methods provide an additional approach to classic neuroanatomical studies focusing on biomechanical principles of anatomical organization and can in turn overcome the complexity of inter-individual variability associated with histological and tractography studies. In this work we propose a novel, reliable framework for determining brain tracts resolving the anatomical variance of brain regions. We distinguished 4 region types based on anatomical considerations: (i) gyral regions at borders between cortical communities; (ii) gyral regions within communities; (iii) sulcal regions at invariant locations across subjects; and (iv) other sulcal regions. Region types showed strikingly different anatomical and connection properties. Results allowed complementing the current understanding of the brain's communication structure with a model of its anatomical underpinnings.

Heritability of Structural Patterning in the Human Cerebral Cortex.

Genetic influences that govern the spatial patterning of the human cortex and its structural variability are still incompletely known. We analyzed structural MR images in twins, siblings, and pairs of unrelated subjects. A comprehensive set of methods was employed to quantify properties of cortical features at different spatial scales. Measures were used to assess the influence of genetic similarity on structural patterning. Results indicated that: (1) Genetic effects significantly influence all structural features assessed here at all spatial resolutions, albeit at different strengths. (2) While strong genetic effects were found at the whole-brain and hemisphere level, effects were weaker at the regional and vertex level, depending on the measure under study. (3) Besides cortical thickness, sulcal (geodesic) depth was found to be under strong genetic control. The local pattern indicated that two axes along (a) the anterior-posterior direction (insula to parieto-occipital sulcus), and (b) superior-inferior direction (central sulcus to callosal sulcus) presumably determine the segregation of four quadrants in each hemisphere early in development. (4) While strong structural asymmetries were found at the regional level, genetic influences on laterality were relatively minor.

One season of head-to-ball impact exposure alters functional connectivity in a central autonomic network.

Repetitive head impacts represent a risk factor for neurological impairment in team-sport athletes. In the absence of symptoms, a physiological basis for acute injury has not been elucidated. A basic brain function that is disrupted after mild traumatic brain injury is the regulation of homeostasis, instantiated by activity across a specific set of brain regions that comprise a central autonomic network. We sought to relate head-to-ball impact exposure to changes in functional connectivity in a core set of central autonomic regions and then to determine the relation between changes in brain and changes in behavior, specifically cognitive control. Thirteen collegiate men's soccer players and eleven control athletes (golf, cross-country) underwent resting-state fMRI and behavioral testing before and after the season, and a core group of cortical, subcortical, and brainstem regions was selected to represent the central autonomic network. Head-to-ball impacts were recorded for each soccer player. Cognitive control was assessed using a Dot Probe Expectancy task. We observed that head-to-ball impact exposure was associated with diffuse increases in functional connectivity across a core CAN subnetwork. Increased functional connectivity between the left insula and left medial orbitofrontal cortex was associated with diminished proactive cognitive control after the season in those sustaining the greatest number of head-to-ball impacts. These findings encourage measures of autonomic physiology to monitor brain health in contact and collision sport athletes.

Determinants of structural segregation and patterning in the human cortex.

This study aimed at uncovering mechanisms that govern the spatio-temporal patterning of the human cortex and its structural variability, and drawing links between fetal brain development and variability in adult brains. A data-driven analytic approach based on structural MR images revealed the following findings: (1) The cortical surface can be subdivided into 13 independent regions ("communities") based on macroscopic features. (2) Thirty centers of low inter-subject variability were found in major sulci on the cortical surface. Their variability showed a strong positive correlation with the known time points at which they appear in fetal development. Centers forming early induce a higher inter-subject regularity in a larger local vicinity, while those forming later result in smaller regions of higher variability. (3) The layout of sulcal and gyral patterns within a community is governed typically by two centers. Depending on the relative variability of each center, communities can be classified into structural sub-types. (4) Sub-types across ipsi-lateral communities are independent, but associated with the sub-type of the same community on the contra-lateral side. Results shown here integrate well with current knowledge about macroscopic, microscopic, and genetic determinants of brain development.

Network specialization during adolescence: Hippocampal effective connectivity in boys and girls.

Adolescence is a complex period of concurrent mental and physical development that facilitates adult functioning at multiple levels. Despite the growing number of neuroimaging studies of cognitive development in adolescence focusing on regional activation patterns, there remains a paucity of information about the functional interactions across these participating regions that are critical for cognitive functioning, including memory. The current study used structural equation modeling (SEM) to determine how interactions among brain regions critical for memory change over the course of adolescence. We obtained functional MRI in 77 individuals aged 8-16 years old, divided into younger (ages 8-10) and older (ages > 11) cohorts, using an incidental encoding memory task to activate hippocampus formation and associated brain networks, as well as behavioral data on memory function. SEM was performed on the imaging data for four groups (younger girls, younger boys, older girls, and older boys) that were subsequently compared using a stacked model approach. Significant differences were seen between the models for these groups. Younger boys had a predominantly posterior distribution of connections originating in primary visual regions and terminating on multi-modal processing regions. In older boys, there was a relatively greater anterior connection distribution, with increased effective connectivity within association and multi-modal processing regions. Connection patterns in younger girls were similar to those of older boys, with a generally anterior-posterior distributed network among sensory, multi-modal, and limbic regions. In contrast, connections in older girls were widely distributed but relatively weaker. Memory performance increased with age, without a significant difference between the sexes. These findings suggest a progressive reorganization among brain regions, with a commensurate increase in efficiency of cognitive functioning, from younger to older individuals in both girls and boys, providing insight into the age- and gender-specific processes at play during this critical transition period.

Mapping complementary features of cross-species structural connectivity to construct realistic "Virtual Brains".

Modern systems neuroscience increasingly leans on large-scale multi-lab neuroinformatics initiatives to provide necessary capacity for biologically realistic modeling of primate whole-brain activity. Here, we present a framework to assemble primate brain's biologically plausible anatomical backbone for such modeling initiatives. In this framework, structural connectivity is determined by adding complementary information from invasive macaque axonal tract tracing and non-invasive human diffusion tensor imaging. Both modalities are combined by means of available interspecies registration tools and a newly developed Bayesian probabilistic modeling approach to extract common connectivity evidence. We demonstrate how this novel framework is embedded in the whole-brain simulation platform called The Virtual Brain (TVB). Hum Brain Mapp 38:2080-2093, 2017. © 2017 Wiley Periodicals, Inc.

Computational modeling of resting-state activity demonstrates markers of normalcy in children with prenatal or perinatal stroke.

Children who sustain a prenatal or perinatal brain injury in the form of a stroke develop remarkably normal cognitive functions in certain areas, with a particular strength in language skills. A dominant explanation for this is that brain regions from the contralesional hemisphere "take over" their functions, whereas the damaged areas and other ipsilesional regions play much less of a role. However, it is difficult to tease apart whether changes in neural activity after early brain injury are due to damage caused by the lesion or by processes related to postinjury reorganization. We sought to differentiate between these two causes by investigating the functional connectivity (FC) of brain areas during the resting state in human children with early brain injury using a computational model. We simulated a large-scale network consisting of realistic models of local brain areas coupled through anatomical connectivity information of healthy and injured participants. We then compared the resulting simulated FC values of healthy and injured participants with the empirical ones. We found that the empirical connectivity values, especially of the damaged areas, correlated better with simulated values of a healthy brain than those of an injured brain. This result indicates that the structural damage caused by an early brain injury is unlikely to have an adverse and sustained impact on the functional connections, albeit during the resting state, of damaged areas. Therefore, these areas could continue to play a role in the development of near-normal function in certain domains such as language in these children.

Utility of EEG measures of brain function in patients with acute stroke.

EEG has been used to study acute stroke for decades; however, because of several limitations EEG-based measures rarely inform clinical decision-making in this setting. Recent advances in EEG hardware, recording electrodes, and EEG software could overcome these limitations. The present study examined how well dense-array (256 electrodes) EEG, acquired with a saline-lead net and analyzed with whole brain partial least squares (PLS) modeling, captured extent of acute stroke behavioral deficits and varied in relation to acute brain injury. In 24 patients admitted for acute ischemic stroke, 3 min of resting-state EEG was acquired at bedside, including in the ER and ICU. Traditional quantitative EEG measures (power in a specific lead, in any frequency band) showed a modest association with behavioral deficits [NIH Stroke Scale (NIHSS) score] in bivariate models. However, PLS models of delta or beta power across whole brain correlated strongly with NIHSS score (R(2) = 0.85-0.90) and remained robust when further analyzed with cross-validation models (R(2) = 0.72-0.73). Larger infarct volume was associated with higher delta power, bilaterally; the contralesional findings were not attributable to mass effect, indicating that EEG captures significant information about acute stroke effects not available from MRI. We conclude that 1) dense-array EEG data are feasible as a bedside measure of brain function in patients with acute stroke; 2) high-dimension EEG data are strongly correlated with acute stroke behavioral deficits and are superior to traditional single-lead metrics in this regard; and 3) EEG captures significant information about acute stroke injury not available from structural brain imaging.

MRI uncovers disrupted hippocampal microstructure that underlies memory impairments after early-life adversity.

Memory and related cognitive functions are progressively impaired in a subgroup of individuals experiencing childhood adversity and stress. However, it is not possible to identify vulnerable individuals early, a crucial step for intervention. In this study, high-resolution magnetic resonance imaging (MRI) and intra-hippocampal diffusion tensor imaging (DTI) were employed to examine for structural signatures of cognitive adolescent vulnerabilities in a rodent model of early-life adversity. These methods were complemented by neuroanatomical and functional assessments of hippocampal network integrity during adolescence, adulthood and middle-age. The high-resolution MRI identified selective loss of dorsal hippocampal volume, and intra-hippocampal DTI uncovered disruption of dendritic structure, consistent with disrupted local connectivity, already during late adolescence in adversity-experiencing rats. Memory deteriorated over time, and stunting of hippocampal dendritic trees was apparent on neuroanatomical analyses. Thus, disrupted hippocampal neuronal structure and connectivity, associated with cognitive impairments, are detectable via non-invasive imaging modalities in rats experiencing early-life adversity. These high-resolution imaging approaches may constitute promising tools for prediction and assessment of at-risk individuals in the clinic. © 2016 Wiley Periodicals, Inc.

A new neuroinformatics approach to personalized medicine in neurology: The Virtual Brain.

PURPOSE OF REVIEW: An exciting advance in the field of neuroimaging is the acquisition and processing of very large data sets (so called 'big data'), permitting large-scale inferences that foster a greater understanding of brain function in health and disease. Yet what we are clearly lacking are quantitative integrative tools to translate this understanding to the individual level to lay the basis for personalized medicine. RECENT FINDINGS: Here we address this challenge through a review on how the relatively new field of neuroinformatics modeling has the capacity to track brain network function at different levels of inquiry, from microscopic to macroscopic and from the localized to the distributed. In this context, we introduce a new and unique multiscale approach, The Virtual Brain (TVB), that effectively models individualized brain activity, linking large-scale (macroscopic) brain dynamics with biophysical parameters at the microscopic level. We also show how TVB modeling provides unique biological interpretable data in epilepsy and stroke. SUMMARY: These results establish the basis for a deliberate integration of computational biology and neuroscience into clinical approaches for elucidating cellular mechanisms of disease. In the future, this can provide the means to create a collection of disease-specific models that can be applied on the individual level to personalize therapeutic interventions. VIDEO ABSTRACT.

Interhemispheric functional connectivity following prenatal or perinatal brain injury predicts receptive language outcome.

Early brain injury alters both structural and functional connectivity between the cerebral hemispheres. Despite increasing knowledge on the individual hemispheric contributions to recovery from such injury, we know very little about how their interactions affect this process. In the present study, we related interhemispheric structural and functional connectivity to receptive language outcome following early left hemisphere stroke. We used functional magnetic resonance imaging to study 14 people with neonatal brain injury, and 25 age-matched controls during passive story comprehension. With respect to structural connectivity, we found that increased volume of the corpus callosum predicted good receptive language outcome, but that this is not specific to people with injury. In contrast, we found that increased posterior superior temporal gyrus interhemispheric functional connectivity during story comprehension predicted better receptive language performance in people with early brain injury, but worse performance in typical controls. This suggests that interhemispheric functional connectivity is one potential compensatory mechanism following early injury. Further, this pattern of results suggests refinement of the prevailing notion that better language outcome following early left hemisphere injury relies on the contribution of the contralesional hemisphere (i.e., the "right-hemisphere-take-over" theory). This pattern of results was also regionally specific; connectivity of the angular gyrus predicted poorer performance in both groups, independent of brain injury. These results present a complex picture of recovery, and in some cases, such recovery relies on increased cooperation between the injured hemisphere and homologous regions in the contralesional hemisphere, but in other cases, the opposite appears to hold.

Prevention of post-stroke generalized anxiety disorder, using escitalopram or problem-solving therapy.

This study examined the efficacy of antidepressant treatment for preventing the onset of generalized anxiety disorder (GAD) among patients with recent stroke. Of 799 patients assessed, 176 were randomized, and 149 patients without evidence of GAD at the initial visit were included in this double-blind treatment with escitalopram (N=47) or placebo (N=49) or non-blinded problem-solving therapy (PST; 12 total sessions; N=53). Participants given placebo over 12 months were 4.95 times more likely to develop GAD than patients given escitalopram and 4.00 times more likely to develop GAD than patients given PST. Although these results should be considered preliminary, the authors found that both escitalopram and PST were effective in preventing new onset of post-stroke GAD.

Prevention of poststroke apathy using escitalopram or problem-solving therapy.

OBJECTIVE: Apathy occurs frequently following stroke and prior studies have demonstrated the negative effect of apathy on recovery from stroke. This study was a secondary analysis examining the efficacy of escitalopram, problem-solving therapy (PST), or placebo administered for 1 year to prevent the onset of apathy among patients with recent stroke. METHODS: Patients within 3 months of an index stroke who did not meet DSM-IV diagnostic criteria for major or minor depression and who did not have a serious comorbid physical illness were enrolled. Patients were recruited from three sites: University of Iowa, University of Chicago, and Burke Rehabilitation Hospital. One hundred fifty-four patients without evidence of apathy at initial evaluation were included in the randomized controlled trial using escitalopram (10 mg patients ≤65 years; 5 mg patients >65 years) (N = 51) or placebo (N = 47) or non-blinded PST (12 total sessions) (N = 56) over 1 year. At 3, 6, 9, and 12 months, patients were assessed for diagnosis and severity of apathy using the Apathy Scale. RESULTS: Using a Cox proportional hazards model of time to onset of apathy, participants given placebo were 3.47 times more likely to develop apathy than patients given escitalopram and 1.84 times more likely to develop apathy than patients given PST after controlling for age, sex, cognitive impairment, and diabetes mellitus status (adjusted hazard ratio: 3.47, 95% CI: 1.79-6.73 [escitalopram group]; adjusted hazard ratio: 1.84, 95% CI: 1.21-2.80 [PST group]). CONCLUSION: Escitalopram or PST was significantly more effective in preventing new onset of apathy following stroke compared with placebo.

Analyzing the cortical fine structure as revealed by ex-vivo anatomical MRI.

The human cortex has a rich fiber structure as revealed by myelin-staining of histological slices. Myelin also contributes to the image contrast in Magnetic Resonance Imaging (MRI). Recent advances in Magnetic Resonance (MR) scanner and imaging technology allowed the acquisition of an ex-vivo data set at an isotropic resolution of 100 µm. This study focused on a computational analysis of this data set with the aim of bridging between histological knowledge and MRI-based results. This work highlights: (1) the design and implementation of a processing chain that extracts intracortical features from a high-resolution MR image; (2) a demonstration of the correspondence between MRI-based cortical intensity profiles and the myelo-architectonic layering of the cortex; (3) the characterization and classification of four basic myelo-architectonic profile types; (4) the distinction of cortical regions based on myelo-architectonic features; and (5) the segmentation of cortical modules in the entorhinal cortex.

State-switching and high-order spatiotemporal organization of dynamic functional connectivity are disrupted by Alzheimer's disease.

Spontaneous activity during the resting state, tracked by BOLD fMRI imaging, or shortly rsfMRI, gives rise to brain-wide dynamic patterns of interregional correlations, whose structured flexibility relates to cognitive performance. Here, we analyze resting-state dynamic functional connectivity (dFC) in a cohort of older adults, including amnesic mild cognitive impairment (aMCI, N = 34) and Alzheimer's disease (AD, N = 13) patients, as well as normal control (NC, N = 16) and cognitively "supernormal" controls (SNC, N = 10) subjects. Using complementary state-based and state-free approaches, we find that resting-state fluctuations of different functional links are not independent but are constrained by high-order correlations between triplets or quadruplets of functionally connected regions. When contrasting patients with healthy subjects, we find that dFC between cingulate and other limbic regions is increasingly bursty and intermittent when ranking the four groups from SNC to NC, aMCI and AD. Furthermore, regions affected at early stages of AD pathology are less involved in higher order interactions in patient than in control groups, while pairwise interactions are not significantly reduced. Our analyses thus suggest that the spatiotemporal complexity of dFC organization is precociously degraded in AD and provides a richer window into the underlying neurobiology than time-averaged FC connections.

Seeking the Amygdala: Novel Use of Diffusion Tensor Imaging to Delineate the Basolateral Amygdala.

The amygdaloid complex, including the basolateral nucleus (BLA), contributes crucially to emotional and cognitive brain functions, and is a major target of research in both humans and rodents. However, delineating structural amygdala plasticity in both normal and disease-related contexts using neuroimaging has been hampered by the difficulty of unequivocally identifying the boundaries of the BLA. This challenge is a result of the poor contrast between BLA and the surrounding gray matter, including other amygdala nuclei. Here, we describe a novel diffusion tensor imaging (DTI) approach to enhance contrast, enabling the optimal identification of BLA in the rodent brain from magnetic resonance (MR) images. We employed this methodology together with a slice-shifting approach to accurately measure BLA volumes. We then validated the results by direct comparison to both histological and cellular-identity (parvalbumin)-based conventional techniques for defining BLA in the same brains used for MRI. We also confirmed BLA connectivity targets using DTI-based tractography. The novel approach enables the accurate and reliable delineation of BLA. Because this nucleus is involved in and changed by developmental, degenerative and adaptive processes, the instruments provided here should be highly useful to a broad range of neuroimaging studies. Finally, the principles used here are readily applicable to numerous brain regions and across species.

Gesture in the developing brain.

Speakers convey meaning not only through words, but also through gestures. Although children are exposed to co-speech gestures from birth, we do not know how the developing brain comes to connect meaning conveyed in gesture with speech. We used functional magnetic resonance imaging (fMRI) to address this question and scanned 8- to 11-year-old children and adults listening to stories accompanied by hand movements, either meaningful co-speech gestures or meaningless self-adaptors. When listening to stories accompanied by both types of hand movement, both children and adults recruited inferior frontal, inferior parietal, and posterior temporal brain regions known to be involved in processing language not accompanied by hand movements. There were, however, age-related differences in activity in posterior superior temporal sulcus (STSp), inferior frontal gyrus, pars triangularis (IFGTr), and posterior middle temporal gyrus (MTGp) regions previously implicated in processing gesture. Both children and adults showed sensitivity to the meaning of hand movements in IFGTr and MTGp, but in different ways. Finally, we found that hand movement meaning modulates interactions between STSp and other posterior temporal and inferior parietal regions for adults, but not for children. These results shed light on the developing neural substrate for understanding meaning contributed by co-speech gesture.

The mirror neuron system and treatment of stroke.

Mirror neurons discharge during the execution of ecological goal-directed manual and oral actions, as well as during the observation of the same actions done by other individuals. These neurons were first identified in the ventral premotor cortex (PMv; area F5) and later on in the inferior parietal lobule (areas PF and PFG) of monkey brain, constituting a "mirror neuron" system. Several pieces of experimental data suggest that a mirror neuron system devoted to hand, mouth, and foot actions might also be present in humans. In the present paper, we review the experimental evidence on the role of the mirror neuron system in action understanding and imitation, both in hand motor function and speech. Based on the features of the mirror neuron system and its role in action understanding and imitation, we discuss the use of action observation and imitation as an approach for systematic training in the rehabilitation of patients with motor impairment of the upper limb and aphasia following stroke. We present the results of some preliminary studies to test this concept, and a discussion of network models as a measure of neurobiological change.

Brain simulation augments machine-learning-based classification of dementia.

Introduction: Computational brain network modeling using The Virtual Brain (TVB) simulation platform acts synergistically with machine learning (ML) and multi-modal neuroimaging to reveal mechanisms and improve diagnostics in Alzheimer's disease (AD). Methods: We enhance large-scale whole-brain simulation in TVB with a cause-and-effect model linking local amyloid beta (Aß) positron emission tomography (PET) with altered excitability. We use PET and magnetic resonance imaging (MRI) data from 33 participants of the Alzheimer's Disease Neuroimaging Initiative (ADNI3) combined with frequency compositions of TVB-simulated local field potentials (LFP) for ML classification. Results: The combination of empirical neuroimaging features and simulated LFPs significantly outperformed the classification accuracy of empirical data alone by about 10% (weighted F1-score empirical 64.34% vs. combined 74.28%). Informative features showed high biological plausibility regarding the AD-typical spatial distribution. Discussion: The cause-and-effect implementation of local hyperexcitation caused by Aß can improve the ML-driven classification of AD and demonstrates TVB's ability to decode information in empirical data using connectivity-based brain simulation.

Increased frequency of first-episode poststroke depression after discontinuation of escitalopram.

BACKGROUND AND PURPOSE: The purpose of this study was to compare escitalopram, problem-solving therapy, and placebo to prevent poststroke depression during 6 months after discontinuation of treatment. METHODS: We examined for depression 33 patients assigned to placebo, 34 to escitalopram, and 41 to problem-solving therapy. RESULTS: After controlling for age, gender, prior mood disorder, and severity of stroke, new-onset major depression and Hamilton Depression scores were significantly higher 6 months after escitalopram was discontinued compared with the problem-solving therapy or placebo groups. CONCLUSIONS: Discontinuation of escitalopram may increase poststroke depressive symptoms.