Individual Subject Level Research Articles

S231. THE ROLE OF DOPAMINERGIC AND GLUTAMATERGIC NEUROTRANSMISSION IN DELUSIONAL IDEATION AND SENSORY INFORMATION PROCESSING OF PATIENTS WITH SCHIZOPHRENIA IN COMPARISON TO HEALTHY HUMAN PARTICIPANTS

BackgroundThe primary aim of this study was to generate neurobiological evidence regarding the impact of dopaminergic and glutamatergic neurotransmission on reasoning biases related to delusional ideation in patients with schizophrenia associated with impaired processing of sensory information. The proposed respective roles of these neurotransmitter systems have been encapsulated in the so-called dopamine- and glutamate-hypotheses of schizophrenia. From a behavioural perspective both reduced glutamate and enhanced dopamine levels are currently discussed as critical contributing factors to generate aberrant beliefs (glutamate) during information sampling and to generate confidence or expected precision (dopamine) during action selection. Hence, by modulating levels of glutamate and dopamine in the brain we hypothesized to induce reported impairments of patients with schizophrenia related todelusional ideation.MethodsThe study consisted of three aligned experiments: In the first two experiments a prospective interventional drug study was conducted with n=192 participants employing a randomized, placebo-controlled, double-blinded design on two parallel testing-groups, receiving either dopaminergic or glutamatergic neuromodulators: Experiment I: either 2.5mg haloperidol (D1/D2-receptor antagonist; HAL), 2.5mg bromocriptine (D2-receptor agonist; BRO), or placebo (PLC-1). Experiment II: either 120mg Dextromethorphan (NMDA-receptor antagonist, DXM), 250mg D-Cycloserine (NMDA-receptor agonist, CYC), or placebo (PLC-2). In the third experiment n=45 patients with schizophrenia (SZ) and n=45 healthy control participants (HC) matched for gender, age and IQ were investigated. All experiments employed a computerized (Matlab, Cogent) version of the Beadstask (Huq, Garety et al. 1988). In total participants processes 60 Beadstask trials subdivided into three levels of difficulty: (I) easy trials with a bias of 80–90% for one predominant bead color in a sequence, (II) difficult trials (60–70% bias), and (III) ambiguous trials (no bias, 50% likelihood). Additionally, the task consisted of three parts that were presented in a fixed order: an easy draws-to-decision condition, an easy probability estimates condition, and a difficult draws-to-decision condition.ResultsIn accordance with foregoing studies, SZ patients showed significantly less draws to decision compared to HC (all p≤0.038). Explorative analysis across experimental conditions further revealed no significant differences for participants receiving DXM (NMDA-receptor antagonist) compared SZ patients (all p≥0.090), but obtained less draws to decision in the DXM group than all other groups. Whereas following HAL intervention the number of draws increased significantly compared to any other experimental group (all p≤0.048). Analyzing the probability estimates condition we quantified changes of probability estimates on an individual subject level whenever there was a change of bead color in a sequence (so called disconfirmatory evidence score, DES). In case of easy and difficult trial types we observed significantly higher DES scores in participants with SZ compared to HC (p≤0.003) and again obtained no differences between SZ and DXM (p=0.037).DiscussionOur findings are supportive for a hypothesized relationship between neurotransmitter state alterations of glutamate and dopamine in patients with schizophrenia and the delusional ideation. Future analysis will focus on developing a computational behavioral model of cognitive processing of the Beadstask, implementing our neurobiological findings in order to further disentangle the neurobiological underpinnings of delusional ideation in patients with schizophrenia.

The geometric preference subtype in ASD: identifying a consistent, early-emerging phenomenon through eye tracking

BackgroundThe wide range of ability and disability in ASD creates a need for tools that parse the phenotypic heterogeneity into meaningful subtypes. Using eye tracking, our past studies revealed that when presented with social and geometric images, a subset of ASD toddlers preferred viewing geometric images, and these toddlers also had greater symptom severity than ASD toddlers with greater social attention. This study tests whether this “GeoPref test” effect would generalize across different social stimuli.MethodsTwo hundred and twenty-seven toddlers (76 ASD) watched a 90-s video, the Complex Social GeoPref test, of dynamic geometric images paired with social images of children interacting and moving. Proportion of visual fixation time and number of saccades per second to both images were calculated. To allow for cross-paradigm comparisons, a subset of 126 toddlers also participated in the original GeoPref test. Measures of cognitive and social functioning (MSEL, ADOS, VABS) were collected and related to eye tracking data. To examine utility as a diagnostic indicator to detect ASD toddlers, validation statistics (e.g., sensitivity, specificity, ROC, AUC) were calculated for the Complex Social GeoPref test alone and when combined with the original GeoPref test.ResultsASD toddlers spent a significantly greater amount of time viewing geometric images than any other diagnostic group. Fixation patterns from ASD toddlers who participated in both tests revealed a significant correlation, supporting the idea that these tests identify a phenotypically meaningful ASD subgroup. Combined use of both original and Complex Social GeoPref tests identified a subgroup of about 1 in 3 ASD toddlers from the “GeoPref” subtype (sensitivity 35%, specificity 94%, AUC 0.75.) Replicating our previous studies, more time looking at geometric images was associated with significantly greater ADOS symptom severity.ConclusionsRegardless of the complexity of the social images used (low in the original GeoPref test vs high in the new Complex Social GeoPref test), eye tracking of toddlers can accurately identify a specific ASD “GeoPref” subtype with elevated symptom severity. The GeoPref tests are predictive of ASD at the individual subject level and thus potentially useful for various clinical applications (e.g., early identification, prognosis, or development of subtype-specific treatments).

Machine Learning-based Individual Assessment of Cortical Atrophy Pattern in Alzheimer\u2019s Disease Spectrum: Development of the Classifier and Longitudinal Evaluation

To develop a new method for measuring Alzheimer’s disease (AD)-specific similarity of cortical atrophy patterns at the individual-level, we employed an individual-level machine learning algorithm. A total of 869 cognitively normal (CN) individuals and 473 patients with probable AD dementia who underwent high-resolution 3T brain MRI were included. We propose a machine learning-based method for measuring the similarity of an individual subject’s cortical atrophy pattern with that of a representative AD patient cohort. In addition, we validated this similarity measure in two longitudinal cohorts consisting of 79 patients with amnestic-mild cognitive impairment (aMCI) and 27 patients with probable AD dementia. Surface-based morphometry classifier for discriminating AD from CN showed sensitivity and specificity values of 87.1% and 93.3%, respectively. In the longitudinal validation study, aMCI-converts had higher atrophy similarity at both baseline (p < 0.001) and first year visits (p < 0.001) relative to non-converters. Similarly, AD patients with faster decline had higher atrophy similarity than slower decliners at baseline (p = 0.042), first year (p = 0.028), and third year visits (p = 0.027). The AD-specific atrophy similarity measure is a novel approach for the prediction of dementia risk and for the evaluation of AD trajectories on an individual subject level.

Assessing motor, visual and language function using a single 5-minute fMRI paradigm: three birds with one stone.

Clinical functional Magnetic Resonance Imaging (fMRI) requires inferences on localization of major brain functions at the individual subject level. We hypothesized that a single "triple use" task would satisfy sensitivity and reliability requirements for successfully assessing the motor, visual and language domain in this context. This was tested here by the application in a group of healthy adults, assessing sensitivity and reliability at the individual subject level, separately for each domain.Our "triple use" task consisted of 2 conditions (condition 1, assessing motor and visual domain, and condition 2, assessing the language domain), serving mutually as active/control. We included 20 healthy adult subjects. Random effect analyses showed activation in primary motor, visual and language regions, as expected. Less expected regions were activated both for the motor and visual domains. Further, reliability of primary activation patterns was very high across individual subjects, with activation seen in 70-100% of subjects in primary motor, visual, and left-lateralized language regions.These findings suggest the "triple use" task to be reliable at the individual subject's level to assess motor, visual and language domains in the clinical fMRI context. Benefits of such an approach include shortening of acquisition time, simplicity of the task for each domain, and using a visual stimulus. Following establishment of reliability in adults, the task may also be a valuable addition in the pediatric clinical fMRI context, where each of these factors is of high relevance.

Peripersonal Space: An Index of Multisensory Body–Environment Interactions in Real, Virtual, and Mixed Realities

Human-environment interactions normally occur in the physical milieu, and thus by medium of the body and within the space immediately adjacent to and surrounding the body; the peri-personal space (PPS). However, human interactions increasingly occur with or within virtual environments, and hence novel approaches and metrics must be developed to index human-environment interactions in virtual reality (VR). Here we present a multisensory task that measures the spatial extent of human PPS in real, virtual, and augmented realities. We validated it in a mixed reality ecosystem in which real environment and virtual objects are blended together in order to administer and control visual, auditory, and tactile stimuli in ecologically valid conditions. Within this mixed-reality environment, participants are asked to respond as fast as possible to tactile stimuli on their body, while task-irrelevant visual or audio-visual stimuli approach their body. Results demonstrate that, in analogy with observations derived from monkey electrophysiology and in real environmental surroundings, tactile detection is enhanced when visual or auditory stimuli are close to the body, and not when far from it. We then calculate the location where this multisensory facilitation occurs as a proxy of the boundary of PPS. We observe that mapping of PPS via audio-visual, as opposed to visual alone, looming stimuli results in sigmoidal fits – allowing for the bifurcation between near and far space – with greater goodness of fit. In sum, our approach is able to capture the boundaries of PPS on a spatial continuum, at the individual-subject level, and within a fully controlled and previously laboratory-validated setup, while maintaining the richness and ecological validity of real-life events. The task can therefore be applied to study the properties of peri-personal space in humans and to index the features governing human-environment interactions in virtual or mixed reality. We propose PPS as an ecologically valid and neurophysiologically established metric in the study of the impact of VR and related technologies on society and individuals.

Distinct Patterns of Temporal and Directional Connectivity among Intrinsic Networks in the Human Brain.

To determine the spatiotemporal relationships among intrinsic networks of the human brain, we recruited seven neurosurgical patients (four males and three females) who were implanted with intracranial depth electrodes. We first identified canonical resting-state networks at the individual subject level using an iterative matching procedure on each subject's resting-state fMRI data. We then introduced single electrical pulses to fMRI pre-identified nodes of the default network (DN), frontoparietal network (FPN), and salience network (SN) while recording evoked responses in other recording sites within the same networks. We found bidirectional signal flow across the three networks, albeit with distinct patterns of evoked responses within different time windows. We used a data-driven clustering approach to show that stimulation of the FPN and SN evoked a rapid (<70 ms) response that was predominantly higher within the SN sites, whereas stimulation of the DN led to sustained responses in later time windows (85-200 ms). Stimulations in the medial temporal lobe components of the DN evoked relatively late effects (>130 ms) in other nodes of the DN, as well as FPN and SN. Our results provide temporal information about the patterns of signal flow between intrinsic networks that provide insights into the spatiotemporal dynamics that are likely to constrain the architecture of the brain networks supporting human cognition and behavior.SIGNIFICANCE STATEMENT Despite great progress in the functional neuroimaging of the human brain, we still do not know the precise set of rules that define the patterns of temporal organization between large-scale networks of the brain. In this study, we stimulated and then recorded electrical evoked potentials within and between three large-scale networks of the brain, the default network (DN), frontoparietal network (FPN), and salience network (SN), in seven subjects undergoing invasive neurosurgery. Using a data-driven clustering approach, we observed distinct temporal and directional patterns between the three networks, with FPN and SN activity predominant in early windows and DN stimulation affecting the network in later windows. These results provide important temporal information about the interactions between brain networks supporting human cognition and behavior.

A Novel Feature-Map Based ICA Model for Identifying the Individual, Intra/Inter-Group Brain Networks across Multiple fMRI Datasets.

Independent component analysis (ICA) has been widely used in functional magnetic resonance imaging (fMRI) data analysis to evaluate functional connectivity of the brain; however, there are still some limitations on ICA simultaneously handling neuroimaging datasets with diverse acquisition parameters, e.g., different repetition time, different scanner, etc. Therefore, it is difficult for the traditional ICA framework to effectively handle ever-increasingly big neuroimaging datasets. In this research, a novel feature-map based ICA framework (FMICA) was proposed to address the aforementioned deficiencies, which aimed at exploring brain functional networks (BFNs) at different scales, e.g., the first level (individual subject level), second level (intragroup level of subjects within a certain dataset) and third level (intergroup level of subjects across different datasets), based only on the feature maps extracted from the fMRI datasets. The FMICA was presented as a hierarchical framework, which effectively made ICA and constrained ICA as a whole to identify the BFNs from the feature maps. The simulated and real experimental results demonstrated that FMICA had the excellent ability to identify the intergroup BFNs and to characterize subject-specific and group-specific difference of BFNs from the independent component feature maps, which sharply reduced the size of fMRI datasets. Compared with traditional ICAs, FMICA as a more generalized framework could efficiently and simultaneously identify the variant BFNs at the subject-specific, intragroup, intragroup-specific and intergroup levels, implying that FMICA was able to handle big neuroimaging datasets in neuroscience research.

Postdose 3 G1 serum neutralizing antibody as correlate of protection for pentavalent rotavirus vaccine

ABSTRACT Although clinical trials of the pentavalent rotavirus vaccine (RotaTeq®, RV5) have demonstrated efficacy against RV gastroenteritis (RGE) in low and high-income settings, a clear correlate of protection or a measure of immune response that could predict efficacy has yet to be identified. This is the first time that immunogenicity data with both serum neutralized antibody (SNA) titers and anti-RV IgA titers from several clinical efficacy trials were pooled to provide a unique context for evaluating the correlation between immunogenicity and RGE risk or efficacy of RV5. The correlation between immunogenicity and RGE risk is evaluated with data at the individual subject level. The analyses show that higher Postdose 3 (PD3) G1 SNA titers are associated with lower odds of contracting any RGE. The correlation between immunogenicity and efficacy is assessed using aggregated population level data, which shows higher efficacy associated with higher PD3 G1 SNA geometric mean titer (GMT) ratio (between RV5 and placebo) and PD3 serum anti-RV IgA GMT ratio. Among high-income countries, efficacy plateaus over the range of PD3 G1 SNA GMT ratios and PD3 serum anti-RV IgA GMT ratios. From both individual- and population-level analyses, PD3 G1 SNA titers correlated most closely with the RGE risk or efficacy for RV5.

Reduced functional connectivity between ventromedial prefrontal cortex and insula relates to longer corrected QT interval in HIV+ and HIV− individuals

ObjectiveProlongation of the QT interval, i.e., measure of the time between the start of the Q wave and the end of the T wave, is a precursor to fatal cardiac arrhythmias commonly observed in individuals infected with the Human Immunodeficiency Virus (HIV), and is related to dysregulation of the autonomic nervous system. We investigated the relationship between QT interval length and resting state functional connectivity (rsFC) of the ventromedial prefrontal cortex (VMPFC), a core region of the brain that is involved with cardio-autonomic regulation. MethodEighteen HIV+ men on antiretroviral therapy and with no history of heart disease were compared with 26 HIV-negative control subjects who had similar demographic and cardio-metabolic characteristics. A seed-based rsFC analysis of the right and left VMPFC was performed at the individual subject level, and 2nd-level analyses were conducted to identify the following: group differences in connectivity, brain regions correlating with corrected (QTc) interval length before and after controlling for those group differences, and regions where seed-based rsFC correlates with CD4 count and QTc interval within HIV+ individuals. ResultsHIV-negative adults showed greater rsFC between the VMPFC seed regions and several default mode network structures. Across groups greater rsFC with the left anterior insula was associated with shorter QTc intervals, whereas right posterior insula connectivity with the VMPFC correlated with greater QTc intervals. HIV patients with lower CD4 counts and higher QTc intervals showed greater rsFC between the right VMPFC and the right posterior insula and dorsal cingulate gyrus. ConclusionsThis study demonstrates that QTc interval lengths are associated with distinct patterns of VMPFC rsFC with posterior and anterior insula. In HIV patients, longer QTc interval and lower CD4 count corresponded to weaker VMPFC connectivity with the dorsal striatrum. SignificanceA forebrain control mechanism may be implicated in the suppression of cardiovagal influence that confers risk for ventricular arrhythmias and sudden cardiac death in HIV+ individuals.

The role of variability in the property listing task.

It is generally believed that concepts can be characterized by their properties (or features). When investigating concepts encoded in language, researchers often ask subjects to produce lists of properties that describe them (i.e., the Property Listing Task, PLT). These lists are accumulated to produce Conceptual Property Norms (CPNs). CPNs contain frequency distributions of properties for individual concepts. It is widely believed that these distributions represent the underlying semantic structure of those concepts. Here, instead of focusing on the underlying semantic structure, we aim at characterizing the PLT. An often disregarded aspect of the PLT is that individuals show intersubject variability (i.e., they produce only partially overlapping lists). In our study we use a mathematical analysis of this intersubject variability to guide our inquiry. To this end, we resort to a set of publicly available norms that contain information about the specific properties that were informed at the individual subject level. Our results suggest that when an individual is performing the PLT, he or she generates a list of properties that is a mixture of general and distinctive properties, such that there is a non-linear tendency to produce more general than distinctive properties. Furthermore, the low generality properties are precisely those that tend not to be repeated across lists, accounting in this manner for part of the intersubject variability. In consequence, any manipulation that may affect the mixture of general and distinctive properties in lists is bound to change intersubject variability. We discuss why these results are important for researchers using the PLT.

P149 Reproducibility of the long intracortical inhibition curve in healthy subjects

Introduction Over the last decades, transcranial magnetic stimulation (TMS) has been used to assess cortical excitability in a variety of neuropsychiatric conditions, e.g. epilepsy, dementia and depression. To detect changes in excitability in longitudinal studies, it is important to validate the repeatability of TMS outcomes for the same subject between different sessions. Previous studies in healthy subjects found mixed degrees of reproducibility, ranging from poor to good agreement Badawy et al., 2012 , Du et al., 2014 . Especially, the long intracortical inhibition (LICI) curve showed a large variability across sessions (Du et al., 2014) . Objective To evaluate the reproducibility of the LICI curve in healthy subjects using paired pulse TMS. Subjects & methods Twenty-seven healthy subjects were studied two times ( ± 1 week apart) with paired pulse TMS combined with electromyography. Both motor hot spots (abductor digiti minimi) were stimulated with 50 paired pulses (intensity 120% of resting motor threshold) at interstimulus intervals (ISIs): 100, 150, 200, 250 and 300 ms. In nineteen subjects a figure-of-eight coil was positioned and held in place manually during both sessions, while in eight subjects a robotic arm was used. The concordance correlation coefficient (rho-c) was used to estimate the agreement between repeated sessions (Lin, 1989) . Results The average LICI curve among all subjects showed a close agreement between TMS sessions for manual (rho-c = 0.88) and robot-guided (rho-c = 0.95) positioning. However, the reproducibility decreased when individual LICI curves were compared; manual (rho-c = 0.66) and robot-guided (rho-c = 0.86). There was a large variation in reproducibility between subjects, ranging from poor to good levels of agreement. Additionally, moderate levels of agreement and a large variation was found for individual ISIs: manual (range rho-c: 0.42–0.55) versus robot-guided (range rho-c: 0.62–0.89). Conclusion Reproducibility of LICI curves was highly variable between healthy subjects. Good levels of agreement were found for analysis on group levels, but reproducibility decreased for the level of individual subjects or ISIs. Overall, reproducibility of the LICI curve was higher for robot-guided than for manual coil positioning.

Catching Audiovisual Interactions With a First-Person Fisherman Video Game.

The human brain is excellent at integrating information from different sources across multiple sensory modalities. To examine one particularly important form of multisensory interaction, we manipulated the temporal correlation between visual and auditory stimuli in a first-person fisherman video game. Subjects saw rapidly swimming fish whose size oscillated, either at 6 or 8 Hz. Subjects categorized each fish according to its rate of size oscillation, while trying to ignore a concurrent broadband sound seemingly emitted by the fish. In three experiments, categorization was faster and more accurate when the rate at which a fish oscillated in size matched the rate at which the accompanying, task-irrelevant sound was amplitude modulated. Control conditions showed that the difference between responses to matched and mismatched audiovisual signals reflected a performance gain in the matched condition, rather than a cost from the mismatched condition. The performance advantage with matched audiovisual signals was remarkably robust over changes in task demands between experiments. Performance with matched or unmatched audiovisual signals improved over successive trials at about the same rate, emblematic of perceptual learning in which visual oscillation rate becomes more discriminable with experience. Finally, analysis at the level of individual subjects' performance pointed to differences in the rates at which subjects can extract information from audiovisual stimuli.

EEG and fMRI agree: Mental arithmetic is the easiest form of imagery to detect.

fMRI and EEG during mental imagery provide alternative methods of detecting awareness in patients with disorders of consciousness (DOC) without reliance on behaviour. Because using fMRI in patients with DOC is difficult, studies increasingly employ EEG. However, there has been no verification that these modalities provide converging information at the individual subject level. The present study examined simultaneous EEG and fMRI in healthy volunteers during six mental imagery tasks to determine whether one mental imagery task generates more robust activation across subjects; whether activation can be predicted from familiarity with the imagined activity; and whether EEG and fMRI converge upon the same conclusions about individual imagery performance. Mental arithmetic generated the most robust activation in the majority of subjects for both EEG and fMRI, and level of activation could not be predicted from familiarity, with either modality. We conclude that overall, EEG and fMRI agree regarding individual mental imagery performance.

Enhanced subject-specific resting-state network detection and extraction with fast fMRI.

Resting-state networks have become an important tool for the study of brain function. An ultra-fast imaging technique that allows to measure brain function, called Magnetic Resonance Encephalography (MREG), achieves an order of magnitude higher temporal resolution than standard echo-planar imaging (EPI). This new sequence helps to correct physiological artifacts and improves the sensitivity of the fMRI analysis. In this study, EPI is compared with MREG in terms of capability to extract resting-state networks. Healthy controls underwent two consecutive resting-state scans, one with EPI and the other with MREG. Subject-level independent component analyses (ICA) were performed separately for each of the two datasets. Using Stanford FIND atlas parcels as network templates, the presence of ICA maps corresponding to each network was quantified in each subject. The number of detected individual networks was significantly higher in the MREG data set than for EPI. Moreover, using short time segments of MREG data, such as 50 seconds, one can still detect and track consistent networks. Fast fMRI thus results in an increased capability to extract distinct functional regions at the individual subject level for the same scan times, and also allow the extraction of consistent networks within shorter time intervals than when using EPI, which is notably relevant for the analysis of dynamic functional connectivity fluctuations. Hum Brain Mapp 38:817-830, 2017. © 2016 Wiley Periodicals, Inc.

Defining thalamic nuclei and topographic connectivity gradients in vivo

The thalamus consists of multiple nuclei that have been previously defined by their chemoarchitectual and cytoarchitectual properties ex vivo. These form discrete, functionally specialized, territories with topographically arranged graduated patterns of connectivity. However, previous in vivo thalamic parcellation with MRI has been hindered by substantial inter-individual variability or discrepancies between MRI derived segmentations and histological sections.Here, we use the Euclidean distance to characterize probabilistic tractography distributions derived from diffusion MRI. We generate 12 feature maps by performing voxel-wise parameterization of the distance histograms (6 feature maps) and the distribution of three-dimensional distance transition gradients generated by applying a Sobel kernel to the distance metrics. We use these 12 feature maps to delineate individual thalamic nuclei, then extract the tractography profiles for each and calculate the voxel-wise tractography gradients. Within each thalamic nucleus, the tractography gradients were topographically arranged as distinct non-overlapping cortical networks with transitory overlapping mid-zones.This work significantly advances quantitative segmentation of the thalamus in vivo using 3T MRI. At an individual subject level, the thalamic segmentations consistently achieve a close relationship with a priori histological atlas information, and resolve in vivo topographic gradients within each thalamic nucleus for the first time. Additionally, these techniques allow individual thalamic nuclei to be closely aligned across large populations and generate measures of inter-individual variability that can be used to study both basic function and pathological processes in vivo.

Assessment of diversification in the regional economy (Case study of subjects of the Siberian Federal District)

The article deals with the problem of assessing economic diversification at the level of individual federal subjects. Diversification is one of the strategic goals for development of Siberia. Assessing the progress in furthering this goal requires its consideration at the level of measurable indicators. In world practice, measurement and evaluation of the level of diversification of the regional economy are based on a variety of indicators reflecting individual characteristics of diversification processes. This paper presents a comparative analysis of diversification in subjects of the Siberian Federal District over the period of 2008–2013. In assessing diversification, we used the Herfindahl–Hirschman indices (gross value added and number of employees by economic activity, the structure of shipped products in manufacturing, the export commodity structure) and the proportion of manufacturing, share of single-industry towns resident in the regional population, and the number of small businesses (enterprises) per 10000 people. Discrepancies in the estimates of various indicators and contradictory trends are shown in the development of diversification processes in individual subjects of the Siberian Federal District. Correlation analysis showed no strong relationship between diversification and the growth rate of the regional economy or the existence of a strong link between economic diversification and its stability. The regions are grouped according to the level of their stability.

Visual Improvement after Intra-Arterial Thrombolysis for Central Retinal Artery Occlusion Does Not Correlate with Time to Treatment

Background: Intra-arterial thrombolysis (IAT) for the treatment of acute central retinal artery occlusion (CRAO) has demonstrated variable results for improving visual acuity and remains controversial. Despite limited evidence, time from symptom onset to thrombolysis is believed to be an important factor in predicting visual improvement after IAT. Methods: A comprehensive review of the literature was conducted and individual subject level data were extracted from relevant studies. From these, a secondary analysis was performed. Initial and final logarithm of the minimum angle of resolution (logMAR) scores were either abstracted directly from relevant studies or converted from provided Snellen chart scores. Change in logMAR scores was used to determine overall treatment efficacy. Results: Data on 118 patients undergoing IAT from five studies were evaluated. Median logMAR improvement in visual acuity was -0.400 (p < 0.001). There was no significant association between logMAR change and time to treatment when time (hours) was described as a continuous variable or described categorically [0-4, 4-8, 8-12, 12+ h; or 0-6, 6-12, 12+ h]. Conclusion: The visual improvement observed in this series had no relationship to the time from symptom onset to treatment with IAT. This suggests that patients may have the possibility for improvement even with delayed presentation to the neurointerventionalist. Other factors, such as completeness of retinal occlusion, may be more important than time to treatment. Additional studies to determine optimal patient selection criteria for the endovascular treatment of acute CRAO are needed.

Task-free MRI predicts individual differences in brain activity during task performance.

When asked to perform the same task, different individuals exhibit markedly different patterns of brain activity. This variability is often attributed to volatile factors, such as task strategy or compliance. We propose that individual differences in brain responses are, to a large degree, inherent to the brain and can be predicted from task-independent measurements collected at rest. Using a large set of task conditions, spanning several behavioral domains, we train a simple model that relates task-independent measurements to task activity and evaluate the model by predicting task activation maps for unseen subjects using magnetic resonance imaging. Our model can accurately predict individual differences in brain activity and highlights a coupling between brain connectivity and function that can be captured at the level of individual subjects.

Factors affecting characterization and localization of interindividual differences in functional connectivity using MRI.

Much recent attention has been paid to quantifying anatomic and functional neuroimaging on the individual subject level. For optimal individual subject characterization, specific acquisition and analysis features need to be identified that maximize interindividual variability while concomitantly minimizing intra-subject variability. We delineate the effect of various acquisition parameters (length of acquisition, sampling frequency) and analysis methods (time course extraction, region of interest parcellation, and thresholding of connectivity-derived network graphs) on characterizing individual subject differentiation. We utilize a non-parametric statistical metric that quantifies the degree to which a parameter set allows this individual subject differentiation by both maximizing interindividual variance and minimizing intra-individual variance. We apply this metric to analysis of four publicly available test-retest resting-state fMRI (rs-fMRI) data sets. We find that for the question of maximizing individual differentiation, (i) for increasing sampling, there is a relative tradeoff between increased sampling frequency and increased acquisition time; (ii) for the sizes of the interrogated data sets, only 3-4 min of acquisition time was sufficient to maximally differentiate each subject with an algorithm that utilized no a priori information regarding subject identification; and (iii) brain regions that most contribute to this individual subject characterization lie in the default mode, attention, and executive control networks. These findings may guide optimal rs-fMRI experiment design and may elucidate the neural bases for subject-to-subject differences. Hum Brain Mapp 37:1986-1997, 2016. © 2016 Wiley Periodicals, Inc.

Disintegration of sensorimotor brain networks in schizophrenia

A large body of literature reported widespread structural and functional abnormalities throughout the brain in schizophrenia spectrum disorders (SZ). Corresponding with the typical symptomatology in SZ where sensory dysfunctions contribute to the core social and cognitive impairment, converging evidence suggests a disturbed interplay between higher-order (cognitive) and lower-order (sensory) regions. This talk will discuss the results of several recent studies, investigating brain connectivity in SZ using functional magnetic resonance imaging data from large samples. Within-network sensorimotor as well as sensorimotor-thalamic aberrations in SZ robustly appear among the core findings across studies, both during performance of cognitive tasks and during rest. We utilized machine learning to distinguish SZ from healthy controls based on connectivity profiles. When classifying on sensorimotor connections alone, not only can we reach accuracies largely above chance but also, these accuracies are as good as when incorporating whole brain connectivity in the classification. Whereas the overall accuracy levels in distinguishing SZ from controls are not useful in a clinical context, these results underline the robustness of the sensorimotor findings on the individual subject level. Targeting the sensory and perceptual domains may thus be key for future research to get a better understanding of the heterogeneity of clinical manifestations in severe mental disorders and to map clinical symptoms to imaging phenotypes.Disclosure of interestThe authors have not supplied their declaration of competing interest.