Exploration of salient risk factors involved in mild cognitive impairment.

Mild cognitive impairment (MCI) is a prevalent and complex condition among older adults that often progresses into Alzheimer's disease (AD). Although MCI affects individuals differently, there are specific indicators of risk commonly associated with the development of MCI. The present study explored the prevalence of seven established MCI risk categories within a large sample of older adults with and without MCI. We explored trends across the different diagnostic groups and extracted the most salient risk factors related to MCI using partial least squares. Neuropsychological risk categories showed the largest differences across groups, with the cognitively unimpaired groups outperforming the MCI groups on all measures. Apolipoprotein E4 (ApoE4) carriers were significantly more common among the more severe MCI group, whereas ApoE4 non-carriers were more common in the healthy controls. Participants with subjective and objective cognitive impairment were trending towards AD-like cerebral spinal fluid (CSF) biomarker levels. Increased age, being male and having fewer years of education were identified as important risk factors of MCI. Higher CSF tau levels were correlated with ApoE4 carrier status, age and a decrease in the ability to carry out daily activities across all diagnostic groups. Amyloid beta1-42 CSF concentration was positively correlated with cognitive and memory performance and non-ApoE4 carrier status regardless of diagnostic status. Unlike previous research, poor cardiovascular health or being female had no relation to MCI. Altogether, the results highlighted risk factors that were specific to persons with MCI, findings that will inform future research in healthy aging, MCI and AD.

Complexity Matching: Brain Signals Mirror Environment Information Patterns during Music Listening and Reward.

Understanding how the human brain integrates information from the environment with intrinsic brain signals to produce individual perspectives is an essential element of understanding the human mind. Brain signal complexity, measured with multiscale entropy, has been employed as a measure of information processing in the brain, and we propose that it can also be used to measure the information available from a stimulus. We can directly assess the correspondence between brain signal complexity and stimulus complexity as an indication of how well the brain reflects the content of the environment in an analysis that we term "complexity matching." Music is an ideal stimulus because it is a multidimensional signal with a rich temporal evolution and because of its emotion- and reward-inducing potential. When participants focused on acoustic features of music, we found that EEG complexity was lower and more closely resembled the musical complexity compared to an emotional task that asked them to monitor how the music made them feel. Music-derived reward scores on the Barcelona Music Reward Questionnaire correlated with less complexity matching but higher EEG complexity. Compared with perceptual-level processing, emotional and reward responses are associated with additional internal information processes above and beyond those linked to the external stimulus. In other words, the brain adds something when judging the emotional valence of music.

Exploring the limits of network topology estimation using diffusion-based tractography and tracer studies in the macaque cortex.

Reconstructing the anatomical pathways of the brain to study the human connectome has become an important endeavour for understanding brain function and dynamics. Reconstruction of the cortico-cortical connectivity matrix in vivo often relies on noninvasive diffusion-weighted imaging (DWI) techniques but the extent to which they can accurately represent the topological characteristics of structural connectomes remains unknown. We addressed this question by constructing connectomes using DWI data collected from macaque monkeys in vivo and with data from published invasive tracer studies. We found the strength of fiber tracts was well estimated from DWI and topological properties like degree and modularity were captured by tractography-based connectomes. Rich-club/core-periphery type architecture could also be detected but the classification of hubs using betweenness centrality, participation coefficient and core-periphery identification techniques was inaccurate. Our findings indicate that certain aspects of cortical topology can be faithfully represented in noninvasively-obtained connectomes while other network analytic measures warrant cautionary interpretations.

A multi-scale layer-resolved spiking network model of resting-state dynamics in macaque visual cortical areas.

Cortical activity has distinct features across scales, from the spiking statistics of individual cells to global resting-state networks. We here describe the first full-density multi-area spiking network model of cortex, using macaque visual cortex as a test system. The model represents each area by a microcircuit with area-specific architecture and features layer- and population-resolved connectivity between areas. Simulations reveal a structured asynchronous irregular ground state. In a metastable regime, the network reproduces spiking statistics from electrophysiological recordings and cortico-cortical interaction patterns in fMRI functional connectivity under resting-state conditions. Stable inter-area propagation is supported by cortico-cortical synapses that are moderately strong onto excitatory neurons and stronger onto inhibitory neurons. Causal interactions depend on both cortical structure and the dynamical state of populations. Activity propagates mainly in the feedback direction, similar to experimental results associated with visual imagery and sleep. The model unifies local and large-scale accounts of cortex, and clarifies how the detailed connectivity of cortex shapes its dynamics on multiple scales. Based on our simulations, we hypothesize that in the spontaneous condition the brain operates in a metastable regime where cortico-cortical projections target excitatory and inhibitory populations in a balanced manner that produces substantial inter-area interactions while maintaining global stability.

Visual Sampling Predicts Hippocampal Activity.

Eye movements serve to accumulate information from the visual world, contributing to the formation of coherent memory representations that support cognition and behavior. The hippocampus and the oculomotor network are well connected anatomically through an extensive set of polysynaptic pathways. However, the extent to which visual sampling behavior is related to functional responses in the hippocampus during encoding has not been studied directly in human neuroimaging. In the current study, participants engaged in a face processing task while brain responses were recorded with fMRI and eye movements were monitored simultaneously. The number of gaze fixations that a participant made on a given trial was correlated significantly with hippocampal activation such that more fixations were associated with stronger hippocampal activation. Similar results were also found in the fusiform face area, a face-selective perceptual processing region. Notably, the number of fixations was associated with stronger hippocampal activation when the presented faces were novel, but not when the faces were repeated. Increases in fixations during viewing of novel faces also led to larger repetition-related suppression in the hippocampus, indicating that this fixation-hippocampal relationship may reflect the ongoing development of lasting representations. Together, these results provide novel empirical support for the idea that visual exploration and hippocampal binding processes are inherently linked. SIGNIFICANCE STATEMENT: The hippocampal and oculomotor networks have each been studied extensively for their roles in the binding of information and gaze function, respectively. Despite the evidence that individuals with amnesia whose damage includes the hippocampus show alterations in their eye movement patterns and recent findings that the two systems are anatomically connected, it has not been demonstrated whether visual exploration is related to hippocampal activity in neurologically intact adults. In this combined fMRI-eye-tracking study, we show how hippocampal responses scale with the number of gaze fixations made during viewing of novel, but not repeated, faces. These findings provide new evidence suggesting that the hippocampus plays an important role in the binding of information, as sampled by gaze fixations, during visual exploration.

Stable long-range interhemispheric coordination is supported by direct anatomical projections.

The functional interaction between the brain's two hemispheres includes a unique set of connections between corresponding regions in opposite hemispheres (i.e., homotopic regions) that are consistently reported to be exceptionally strong compared with other interhemispheric (i.e., heterotopic) connections. The strength of homotopic functional connectivity (FC) is thought to be mediated by the regions' shared functional roles and their structural connectivity. Recently, homotopic FC was reported to be stable over time despite the presence of dynamic FC across both intrahemispheric and heterotopic connections. Here we build on this work by considering whether homotopic FC is also stable across conditions. We additionally test the hypothesis that strong and stable homotopic FC is supported by the underlying structural connectivity. Consistent with previous findings, interhemispheric FC between homotopic regions were significantly stronger in both humans and macaques. Across conditions, homotopic FC was most resistant to change and therefore was more stable than heterotopic or intrahemispheric connections. Across time, homotopic FC had significantly greater temporal stability than other types of connections. Temporal stability of homotopic FC was facilitated by direct anatomical projections. Importantly, temporal stability varied with the change in conductive properties of callosal axons along the anterior-posterior axis. Taken together, these findings suggest a notable role for the corpus callosum in maintaining stable functional communication between hemispheres.

Network structure shapes spontaneous functional connectivity dynamics.

The structural organization of the brain constrains the range of interactions between different regions and shapes ongoing information processing. Therefore, it is expected that large-scale dynamic functional connectivity (FC) patterns, a surrogate measure of coordination between brain regions, will be closely tied to the fiber pathways that form the underlying structural network. Here, we empirically examined the influence of network structure on FC dynamics by comparing resting-state FC (rsFC) obtained using BOLD-fMRI in macaques (Macaca fascicularis) to structural connectivity derived from macaque axonal tract tracing studies. Consistent with predictions from simulation studies, the correspondence between rsFC and structural connectivity increased as the sample duration increased. Regions with reciprocal structural connections showed the most stable rsFC across time. The data suggest that the transient nature of FC is in part dependent on direct underlying structural connections, but also that dynamic coordination can occur via polysynaptic pathways. Temporal stability was found to be dependent on structural topology, with functional connections within the rich-club core exhibiting the greatest stability over time. We discuss these findings in light of highly variable functional hubs. The results further elucidate how large-scale dynamic functional coordination exists within a fixed structural architecture.

An Anatomical Interface between Memory and Oculomotor Systems.

Visual behavior is guided by memories from prior experience and knowledge of the visual scene. The hippocampal system (HC), in particular, has been implicated in the guidance of saccades: Amnesic patients, following damage to the HC, exhibit selective deficits in their gaze patterns. However, the neural circuitry by which mnemonic representations influence the oculomotor system remains unknown. We used a data-driven, network-based approach on directed anatomical connectivity from the macaque brain to reveal an extensive set of polysnaptic pathways spanning the extrastriate, posterior parietal and prefrontal cortices that potentially mediate the exchange of information between the memory and visuo-oculomotor systems. We additionally show how the potential for directed information flow from the hippocampus to oculomotor control areas is exceptionally high. In particular, the dorsolateral pFC and FEF-regions known to be responsible for the cognitive control of saccades-are topologically well positioned to receive information from the hippocampus. Together with neuropsychological evidence of altered gaze patterns following damage to the hippocampus, our findings suggest that a reconsideration of hippocampal involvement in oculomotor guidance is needed.

Structural architecture supports functional organization in the human aging brain at a regionwise and network level.

Functional interactions in the brain are constrained by the underlying anatomical architecture, and structural and functional networks share network features such as modularity. Accordingly, age-related changes of structural connectivity (SC) may be paralleled by changes in functional connectivity (FC). We provide a detailed qualitative and quantitative characterization of the SC-FC coupling in human aging as inferred from resting-state blood oxygen-level dependent functional magnetic resonance imaging and diffusion-weighted imaging in a sample of 47 adults with an age range of 18-82. We revealed that SC and FC decrease with age across most parts of the brain and there is a distinct age-dependency of regionwise SC-FC coupling and network-level SC-FC relations. A specific pattern of SC-FC coupling predicts age more reliably than does regionwise SC or FC alone (r = 0.73, 95% CI = [0.7093, 0.8522]). Hence, our data propose that regionwise SC-FC coupling can be used to characterize brain changes in aging. Hum Brain Mapp 37:2645-2661, 2016. © 2016 Wiley Periodicals, Inc.

Predictive saccade target selection in superior colliculus during visual search.

Searching for a visual object naturally involves sequences of gaze fixations, during which the current foveal image is analyzed and the next object to inspect is selected as a saccade target. Fixation durations during such sequences are short, suggesting that saccades may be concurrently processed. Therefore, the selection of the next saccade target may occur before the current saccade target is acquired. To test this hypothesis, we trained four female rhesus monkeys (Macaca mulatta) to perform a multiple-fixation visual conjunction search task. We simultaneously recorded the activity of sensorimotor neurons in the midbrain superior colliculus (SC) in two monkeys. In this task, monkeys made multiple fixations before foveating the target. Fixation durations were significantly shorter than the latency of the initial responses to the search display, with approximately one-quarter being shorter than the shortest response latencies. The time at which SC sensorimotor activity discriminated the target from distracters occurred significantly earlier for the selection of subsequent fixations than for the selection of the first fixation. Target selection during subsequent fixations occurred even before the visual afferent delay in more than half of the neuronal sample, suggesting that the process of selection can encompass at least two future saccade targets. This predictive selection was present even when differences in saccade latencies were taken into account. Altogether, these findings demonstrate how neural representations on the visual salience map are processed in parallel, thus facilitating visual search.

Identification of optimal structural connectivity using functional connectivity and neural modeling.

The complex network dynamics that arise from the interaction of the brain's structural and functional architectures give rise to mental function. Theoretical models demonstrate that the structure-function relation is maximal when the global network dynamics operate at a critical point of state transition. In the present work, we used a dynamic mean-field neural model to fit empirical structural connectivity (SC) and functional connectivity (FC) data acquired in humans and macaques and developed a new iterative-fitting algorithm to optimize the SC matrix based on the FC matrix. A dramatic improvement of the fitting of the matrices was obtained with the addition of a small number of anatomical links, particularly cross-hemispheric connections, and reweighting of existing connections. We suggest that the notion of a critical working point, where the structure-function interplay is maximal, may provide a new way to link behavior and cognition, and a new perspective to understand recovery of function in clinical conditions.

A working memory account of refixations in visual search.

We tested the hypothesis that active exploration of the visual environment is mediated not only by visual attention but also by visual working memory (VWM) by examining performance in both a visual search and a change detection task. Subjects rarely fixated previously examined distracters during visual search, suggesting that they successfully retained those items. Change detection accuracy decreased with increasing set size, suggesting that subjects had a limited VWM capacity. Crucially, performance in the change detection task predicted visual search efficiency: Higher VWM capacity was associated with faster and more accurate responses as well as lower probabilities of refixation. We found no temporal delay for return saccades, suggesting that active vision is primarily mediated by VWM rather than by a separate attentional disengagement mechanism commonly associated with the inhibition-of-return (IOR) effect. Taken together with evidence that visual attention, VWM, and the oculomotor system involve overlapping neural networks, these data suggest that there exists a general capacity for cognitive processing.

Information processing architecture of functionally defined clusters in the macaque cortex.

Computational and empirical neuroimaging studies have suggested that the anatomical connections between brain regions primarily constrain their functional interactions. Given that the large-scale organization of functional networks is determined by the temporal relationships between brain regions, the structural limitations may extend to the global characteristics of functional networks. Here, we explored the extent to which the functional network community structure is determined by the underlying anatomical architecture. We directly compared macaque (Macaca fascicularis) functional connectivity (FC) assessed using spontaneous blood oxygen level-dependent functional magnetic resonance imaging (BOLD-fMRI) to directed anatomical connectivity derived from macaque axonal tract tracing studies. Consistent with previous reports, FC increased with increasing strength of anatomical connection, and FC was also present between regions that had no direct anatomical connection. We observed moderate similarity between the FC of each region and its anatomical connectivity. Notably, anatomical connectivity patterns, as described by structural motifs, were different within and across functional modules: partitioning of the functional network was supported by dense bidirectional anatomical connections within clusters and unidirectional connections between clusters. Together, our data directly demonstrate that the FC patterns observed in resting-state BOLD-fMRI are dictated by the underlying neuroanatomical architecture. Importantly, we show how this architecture contributes to the global organizational principles of both functional specialization and integration.

Aging changes the interactions between the oculomotor and memory systems.

The use of multi-modal approaches, particularly in conjunction with multivariate analytic techniques, can enrich models of cognition, brain function, and how they change with age. Recently, multivariate approaches have been applied to the study of eye movements in a manner akin to that of neural activity (i.e., pattern similarity). Here, we review the literature regarding multi-modal and/or multivariate approaches, with specific reference to the use of eyetracking to characterize age-related changes in memory. By applying multi-modal and multivariate approaches to the study of aging, research has shown that aging is characterized by moment-to-moment alterations in the amount and pattern of visual exploration, and by extension, alterations in the activity and function of the hippocampus and broader medial temporal lobe (MTL). These methodological advances suggest that age-related declines in the integrity of the memory system has consequences for oculomotor behavior in the moment, in a reciprocal fashion. Age-related changes in hippocampal and MTL structure and function may lead to an increase in, and change in the patterns of, visual exploration in an effort to upregulate the encoding of information. However, such visual exploration patterns may be non-optimal and actually reduce the amount and/or type of incoming information that is bound into a lasting memory representation. This research indicates that age-related cognitive impairments are considerably broader in scope than previously realized.

Personalized Connectome-Based Modeling in Patients with Semi-Acute Phase TBI: Relationship to Acute Neuroimaging and 6 Month Follow-Up.

Following traumatic brain injury (TBI), cognitive impairments manifest through interactions between microscopic and macroscopic changes. On the microscale, a neurometabolic cascade alters neurotransmission, while on the macroscale diffuse axonal injury impacts the integrity of long-range connections. Large-scale brain network modeling allows us to make predictions across these spatial scales by integrating neuroimaging data with biophysically based models to investigate how microscale changes invisible to conventional neuroimaging influence large-scale brain dynamics. To this end, we analyzed structural and functional neuroimaging data from a well characterized sample of 44 adult TBI patients recruited from a regional trauma center, scanned at 1-2 weeks postinjury, and with follow-up behavioral outcome assessed 6 months later. Thirty-six age-matched healthy adults served as comparison participants. Using The Virtual Brain, we fit simulations of whole-brain resting-state functional MRI to the empirical static and dynamic functional connectivity of each participant. Multivariate partial least squares (PLS) analysis showed that patients with acute traumatic intracranial lesions had lower cortical regional inhibitory connection strengths than comparison participants, while patients without acute lesions did not differ from the comparison group. Further multivariate PLS analyses found correlations between lower semiacute regional inhibitory connection strengths and more symptoms and lower cognitive performance at a 6 month follow-up. Critically, patients without acute lesions drove this relationship, suggesting clinical relevance of regional inhibitory connection strengths even when traumatic intracranial lesions were not present. Our results suggest that large-scale connectome-based models may be sensitive to pathophysiological changes in semi-acute phase TBI patients and predictive of their chronic outcomes.

A Robust Modular Automated Neuroimaging Pipeline for Model Inputs to TheVirtualBrain.

TheVirtualBrain, an open-source platform for large-scale network modeling, can be personalized to an individual using a wide range of neuroimaging modalities. With the growing number and scale of neuroimaging data sharing initiatives of both healthy and clinical populations comes an opportunity to create large and heterogeneous sets of dynamic network models to better understand individual differences in network dynamics and their impact on brain health. Here we present TheVirtualBrain-UK Biobank pipeline, a robust, automated and open-source brain image processing solution to address the expanding scope of TheVirtualBrain project. Our pipeline generates connectome-based modeling inputs compatible for use with TheVirtualBrain. We leverage the existing multimodal MRI processing pipeline from the UK Biobank made for use with a variety of brain imaging modalities. We add various features and changes to the original UK Biobank implementation specifically for informing large-scale network models, including user-defined parcellations for the construction of matching whole-brain functional and structural connectomes. Changes also include detailed reports for quality control of all modalities, a streamlined installation process, modular software packaging, updated software versions, and support for various publicly available datasets. The pipeline has been tested on various datasets from both healthy and clinical populations and is robust to the morphological changes observed in aging and dementia. In this paper, we describe these and other pipeline additions and modifications in detail, as well as how this pipeline fits into the TheVirtualBrain ecosystem.

Integrating psychosocial variables and societal diversity in epidemic models for predicting COVID-19 transmission dynamics.

During the current COVID-19 pandemic, governments must make decisions based on a variety of information including estimations of infection spread, health care capacity, economic and psychosocial considerations. The disparate validity of current short-term forecasts of these factors is a major challenge to governments. By causally linking an established epidemiological spread model with dynamically evolving psychosocial variables, using Bayesian inference we estimate the strength and direction of these interactions for German and Danish data of disease spread, human mobility, and psychosocial factors based on the serial cross-sectional COVID-19 Snapshot Monitoring (COSMO; N = 16,981). We demonstrate that the strength of cumulative influence of psychosocial variables on infection rates is of a similar magnitude as the influence of physical distancing. We further show that the efficacy of political interventions to contain the disease strongly depends on societal diversity, in particular group-specific sensitivity to affective risk perception. As a consequence, the model may assist in quantifying the effect and timing of interventions, forecasting future scenarios, and differentiating the impact on diverse groups as a function of their societal organization. Importantly, the careful handling of societal factors, including support to the more vulnerable groups, adds another direct instrument to the battery of political interventions fighting epidemic spread.

Beneficial effects of the NMDA antagonist ketamine on decision processes in visual search.

The ability of sensory-motor circuits to integrate sensory evidence over time is thought to underlie the process of decision-making in perceptual discrimination. Recent work has suggested that the NMDA receptor contributes to mediating neural activity integration. To test this hypothesis, we trained three female rhesus monkeys (Macaca mulatta) to perform a visual search task, in which they had to make a saccadic eye movement to the location of a target stimulus presented among distracter stimuli of lower luminance. We manipulated NMDA-receptor function by administering an intramuscular injection of the noncompetitive NMDA antagonist ketamine and assessed visual search performance before and after manipulation. Ketamine was found to lengthen response latency in a dose-dependent fashion. Surprisingly, it was also observed that response accuracy was significantly improved when lower doses were administered. These findings suggest that NMDA receptors play a crucial role in the process of decision-making in perceptual discrimination. They also further support the idea that multiple neural representations compete with one another through mutual inhibition, which may explain the speed-accuracy trade-off rule that shapes discrimination behavior: lengthening integration time helps resolve small differences between choice alternatives, thereby improving accuracy.

Investigating the role of the superior colliculus in active vision with the visual search paradigm.

We review here both the evidence that the functional visuomotor organization of the optic tectum is conserved in the primate superior colliculus (SC) and the evidence for the linking proposition that SC discriminating activity instantiates saccade target selection. We also present new data in response to questions that arose from recent SC visual search studies. First, we observed that SC discriminating activity predicts saccade initiation when monkeys perform an unconstrained search for a target defined by either a single visual feature or a conjunction of two features. Quantitative differences between the results in these two search tasks suggest, however, that SC discriminating activity does not only reflect saccade programming. This finding concurs with visual search studies conducted in posterior parietal cortex and the idea that, during natural active vision, visual attention is shifted concomitantly with saccade programming. Second, the analysis of a large neuronal sample recorded during feature search revealed that visual neurons in the superficial layers do possess discriminating activity. In addition, the hypotheses that there are distinct types of SC neurons in the deeper layers and that they are differently involved in saccade target selection were not substantiated. Third, we found that the discriminating quality of single-neuron activity substantially surpasses the ability of the monkeys to discriminate the target from distracters, raising the possibility that saccade target selection is a noisy process. We discuss these new findings in light of the visual search literature and the view that the SC is a visual salience map for orienting eye movements.

Signal complexity indicators of health status in clinical EEG.

Brain signal variability changes across the lifespan in both health and disease, likely reflecting changes in information processing capacity related to development, aging and neurological disorders. While signal complexity, and multiscale entropy (MSE) in particular, has been proposed as a biomarker for neurological disorders, most observations of altered signal complexity have come from studies comparing patients with few to no comorbidities against healthy controls. In this study, we examined whether MSE of brain signals was distinguishable across patient groups in a large and heterogeneous set of clinical-EEG data. Using a multivariate analysis, we found unique timescale-dependent differences in MSE across various neurological disorders. We also found MSE to differentiate individuals with non-brain comorbidities, suggesting that MSE is sensitive to brain signal changes brought about by metabolic and other non-brain disorders. Such changes were not detectable in the spectral power density of brain signals. Our findings suggest that brain signal complexity may offer complementary information to spectral power about an individual's health status and is a promising avenue for clinical biomarker development.