Functional Evidence for Memory Stabilization in Sensorimotor Adaptation: A 24-h Resting-State fMRI Study.

Adaptation learning is crucial to maintain precise motor control in face of environmental perturbations. Although much progress has been made in understanding the psychophysics and neurophysiology of sensorimotor adaptation (SA), the time course of memory consolidation remains elusive. The lack of a reproducible gradient of memory resistance using protocols of retrograde interference has even led to the proposal that memories produced through SA do not consolidate. Here, we pursued an alternative approach using resting-state fMRI to track changes in functional connectivity (FC) induced by learning. Given that consolidation leads to long-term memory, we hypothesized that a change in FC that predicted long-term memory but not short-term memory would provide indirect evidence for memory stabilization. Six scans were acquired before, 15 min, 1, 3, 5.5, and 24 h after training on a center-out task under veridical or distorted visual feedback. The experimental group showed an increment in FC of a network including motor, premotor, posterior parietal cortex, cerebellum, and putamen that peaked at 5.5 h. Crucially, the strengthening of this network correlated positively with long-term retention but negatively with short-term retention. Our work provides evidence, suggesting that adaptation memories stabilize within a 6-h window, and points to different mechanisms subserving short- and long-term memory.

Aging Effects on Whole-Brain Functional Connectivity in Adults Free of Cognitive and Psychiatric Disorders.

Aging is associated with decreased resting-state functional connectivity (RSFC) within the default mode network (DMN), but most functional imaging studies have restricted the analysis to specific brain regions or networks, a strategy not appropriate to describe system-wide changes. Moreover, few investigations have employed operational psychiatric interviewing procedures to select participants; this is an important limitation since mental disorders are prevalent and underdiagnosed and can be associated with RSFC abnormalities. In this study, resting-state fMRI was acquired from 59 adults free of cognitive and psychiatric disorders according to standardized criteria and based on extensive neuropsychological and clinical assessments. We tested for associations between age and whole-brain RSFC using Partial Least Squares, a multivariate technique. We found that normal aging is not only characterized by decreased RSFC within the DMN but also by ubiquitous increases in internetwork positive correlations and focal internetwork losses of anticorrelations (involving mainly connections between the DMN and the attentional networks). Our results reinforce the notion that the aging brain undergoes a dedifferentiation processes with loss of functional diversity. These findings advance the characterization of healthy aging effects on RSFC and highlight the importance of adopting a broad, system-wide perspective to analyze brain connectivity.

Dynamic functional connectivity shapes individual differences in associative learning.

Current neuroscientific research has shown that the brain reconfigures its functional interactions at multiple timescales. Here, we sought to link transient changes in functional brain networks to individual differences in behavioral and cognitive performance by using an active learning paradigm. Participants learned associations between pairs of unrelated visual stimuli by using feedback. Interindividual behavioral variability was quantified with a learning rate measure. By using a multivariate statistical framework (partial least squares), we identified patterns of network organization across multiple temporal scales (within a trial, millisecond; across a learning session, minute) and linked these to the rate of change in behavioral performance (fast and slow). Results indicated that posterior network connectivity was present early in the trial for fast, and later in the trial for slow performers. In contrast, connectivity in an associative memory network (frontal, striatal, and medial temporal regions) occurred later in the trial for fast, and earlier for slow performers. Time-dependent changes in the posterior network were correlated with visual/spatial scores obtained from independent neuropsychological assessments, with fast learners performing better on visual/spatial subtests. No relationship was found between functional connectivity dynamics in the memory network and visual/spatial test scores indicative of cognitive skill. By using a comprehensive set of measures (behavioral, cognitive, and neurophysiological), we report that individual variations in learning-related performance change are supported by differences in cognitive ability and time-sensitive connectivity in functional neural networks. Hum Brain Mapp 37:3911-3928, 2016. © 2016 Wiley Periodicals, Inc.

An automated pipeline for constructing personalized virtual brains from multimodal neuroimaging data.

Large amounts of multimodal neuroimaging data are acquired every year worldwide. In order to extract high-dimensional information for computational neuroscience applications standardized data fusion and efficient reduction into integrative data structures are required. Such self-consistent multimodal data sets can be used for computational brain modeling to constrain models with individual measurable features of the brain, such as done with The Virtual Brain (TVB). TVB is a simulation platform that uses empirical structural and functional data to build full brain models of individual humans. For convenient model construction, we developed a processing pipeline for structural, functional and diffusion-weighted magnetic resonance imaging (MRI) and optionally electroencephalography (EEG) data. The pipeline combines several state-of-the-art neuroinformatics tools to generate subject-specific cortical and subcortical parcellations, surface-tessellations, structural and functional connectomes, lead field matrices, electrical source activity estimates and region-wise aggregated blood oxygen level dependent (BOLD) functional MRI (fMRI) time-series. The output files of the pipeline can be directly uploaded to TVB to create and simulate individualized large-scale network models that incorporate intra- and intercortical interaction on the basis of cortical surface triangulations and white matter tractograpy. We detail the pitfalls of the individual processing streams and discuss ways of validation. With the pipeline we also introduce novel ways of estimating the transmission strengths of fiber tracts in whole-brain structural connectivity (SC) networks and compare the outcomes of different tractography or parcellation approaches. We tested the functionality of the pipeline on 50 multimodal data sets. In order to quantify the robustness of the connectome extraction part of the pipeline we computed several metrics that quantify its rescan reliability and compared them to other tractography approaches. Together with the pipeline we present several principles to guide future efforts to standardize brain model construction. The code of the pipeline and the fully processed data sets are made available to the public via The Virtual Brain website (thevirtualbrain.org) and via github (https://github.com/BrainModes/TVB-empirical-data-pipeline). Furthermore, the pipeline can be directly used with High Performance Computing (HPC) resources on the Neuroscience Gateway Portal (http://www.nsgportal.org) through a convenient web-interface.

Comparing the stability and reproducibility of brain-behavior relationships found using canonical correlation analysis and partial least squares within the ABCD sample.

Canonical correlation analysis (CCA) and partial least squares correlation (PLS) detect linear associations between two data matrices by computing latent variables (LVs) having maximal correlation (CCA) or covariance (PLS). This study compared the similarity and generalizability of CCA- and PLS-derived brain-behavior relationships. Data were accessed from the baseline Adolescent Brain Cognitive Development (ABCD) dataset (N > 9,000, 9-11 years). The brain matrix consisted of cortical thickness estimates from the Desikan-Killiany atlas. Two phenotypic scales were examined separately as the behavioral matrix; the Child Behavioral Checklist (CBCL) subscale scores and NIH Toolbox performance scores. Resampling methods were used to assess significance and generalizability of LVs. LV1 for the CBCL brain relationships was found to be significant, yet not consistently stable or reproducible, across CCA and PLS models (singular value: CCA = .13, PLS = .39, p < .001). LV1 for the NIH brain relationships showed similar relationships between CCA and PLS and was found to be stable and reproducible (singular value: CCA = .21, PLS = .43, p < .001). The current study suggests that stability and reproducibility of brain-behavior relationships identified by CCA and PLS are influenced by the statistical characteristics of the phenotypic measure used when applied to a large population-based pediatric sample.

Clinical neuroscience research is going through a translational crisis largely due to the challenges of producing meaningful and generalizable results. Two critical limitations within clinical neuroscience research are the use of univariate statistics and between-study methodological variation. Univariate statistics may not be sensitive enough to detect complex relationships between several variables, and methodological variation poses challenges to the generalizability of the results. We compared two widely used multivariate statistical approaches, canonical correlations analysis (CCA) and partial least squares correlation (PLS), to determine the generalizability and stability of their solutions. We show that the properties of the measures inputted into the analysis likely play a more substantial role in the generalizability and stability of results compared to the specific approach applied (i.e., CCA or PLS).

Visual dominance and multisensory integration changes with age.

Objects comprise of visual and auditory signatures that arrive through distinct sensory channels. Exposure to cross-modal events sets up expectations about what a given object most likely "sounds" like, and vice versa, thereby facilitating detection and recognition. Whereas episodic and working memory functions decline with age, the extent to which multisensory integration processes change with age remains an open question. In the present study, we examined whether multisensory integration processes play a compensatory role in normal aging. Magnetoencephalography recordings of semantically-related cross-modal and unimodal auditory and visual stimuli captured the spatiotemporal dynamics of multisensory responses in young and older adults. Whereas sensory-specific regions showed increased activity in response to cross-modal compared to unimodal stimuli 100 ms after stimulus onset in both age groups, posterior parietal and medial prefrontal regions responded preferentially to cross-modal stimuli between 150 and 300 ms in the older group only. Additionally, faster detection of cross-modal stimuli correlated with increased activity in inferior parietal and medial prefrontal regions 100 ms after stimulus onset in older compared to younger adults. Age-related differences in visual dominance were also observed with older adults exhibiting significantly larger multisensory facilitation effects relative to the auditory modality. Using structural equation modeling, we showed that age-related increases in parietal and medial prefrontal source activity predicted faster detection of cross-modal stimuli. Furthermore, the relationship between performance and source activity was mediated by age-related reductions in gray matter volume in those regions. Thus, we propose that multisensory integration processes change with age such that posterior parietal and medial prefrontal activity underlies the integrated response in older adults.

ICA-based artifact correction improves spatial localization of adaptive spatial filters in MEG.

Beamformers are one of the most common inverse models currently used in the estimation of source activity from magnetoencephelography (MEG) data. They rely on a minimization of total power while constraining the gain in the voxel of interest, resulting in the suppression of background noise. Nonetheless, in cases where background noise is strong compared to the source of interest, or when many sources are present, the ability of the beamformer to detect and accurately localize weak sources is reduced. In visual paradigms, two main background sources can substantially impact an accurate estimation of weaker sources. Ocular artifacts are orders of magnitude higher than neural sources making it difficult for the beamformer to effectively suppress them. Primary visual activations also result in strong signals that can impede localization of weak sources. In this paper, we systematically evaluated how neural (visual) and non-neural (eye, heart) sources affect the localization accuracy of frontal and medial temporal sources in visual tasks. These sources are of tremendous interest in learning and memory studies as well as in clinical settings (Alzheimer's/epilepsy) and are typically difficult to localize robustly in MEG. Empirical data from two tasks - active learning and control - were used to evaluate our analysis techniques. Global field power calculations showed multiple time periods where active learning was significantly different from response selection with dominant sources converging to the eyes. Extensive leakage of eye activity into frontal and visual that evoked responses into parietal cortices was also observed. Contributions from ocular activity to the reconstructed time series were indiscernible from task-based recruitment of frontal sources in the original data. Removing artifacts (eye movements, cardiac, and muscular) by means of independent component analysis (ICA) led to a significant improvement in detection and localization of frontal and medial temporal sources. We verified our results by using simulations of sources placed in frontal and medial temporal regions with various types of background noise (eye, heart, and visual). We report that the detection and localization accuracy of frontal and medial temporal sources with beamformer techniques is highly dependent on the magnitude and location of background sources and that removing artifacts can substantially improve the beamformer's performance.

Comparing the stability and reproducibility of brain-behaviour relationships found using Canonical Correlation Analysis and Partial Least Squares within the ABCD Sample.

Introduction: Canonical Correlation Analysis (CCA) and Partial Least Squares Correlation (PLS) detect associations between two data matrices based on computing a linear combination between the two matrices (called latent variables; LVs). These LVs maximize correlation (CCA) and covariance (PLS). These different maximization criteria may render one approach more stable and reproducible than the other when working with brain and behavioural data at the population-level. This study compared the LVs which emerged from CCA and PLS analyses of brain-behaviour relationships from the Adolescent Brain Cognitive Development (ABCD) dataset and examined their stability and reproducibility. Methods: Structural T1-weighted imaging and behavioural data were accessed from the baseline Adolescent Brain Cognitive Development dataset (N > 9000, ages = 9-11 years). The brain matrix consisted of cortical thickness estimates in different cortical regions. The behavioural matrix consisted of 11 subscale scores from the parent-reported Child Behavioral Checklist (CBCL) or 7 cognitive performance measures from the NIH Toolbox. CCA and PLS models were separately applied to the brain-CBCL analysis and brain-cognition analysis. A permutation test was used to assess whether identified LVs were statistically significant. A series of resampling statistical methods were used to assess stability and reproducibility of the LVs. Results: When examining the relationship between cortical thickness and CBCL scores, the first LV was found to be significant across both CCA and PLS models (singular value: CCA = .13, PLS = .39, p < .001). LV1 from the CCA model found that covariation of CBCL scores was linked to covariation of cortical thickness. LV1 from the PLS model identified decreased cortical thickness linked to lower CBCL scores. There was limited evidence of stability or reproducibility of LV1 for both CCA and PLS. When examining the relationship between cortical thickness and cognitive performance, there were 6 significant LVs for both CCA and PLS (p < .01). The first LV showed similar relationships between CCA and PLS and was found to be stable and reproducible (singular value: CCA = .21, PLS = .43, p < .001). Conclusion: CCA and PLS identify different brain-behaviour relationships with limited stability and reproducibility when examining the relationship between cortical thickness and parent-reported behavioural measures. However, both methods identified relatively similar brain-behaviour relationships that were stable and reproducible when examining the relationship between cortical thickness and cognitive performance. The results of the current study suggest that stability and reproducibility of brain-behaviour relationships identified by CCA and PLS are influenced by characteristics of the analyzed sample and the included behavioural measurements when applied to a large pediatric dataset.

Tracing the route to path analysis in neuroimaging.

This article provides a personal perspective of the adoption of path analysis (structural equation modeling) to neuroimaging. The paper covers the motivation stemming from the need to merge functional measures with neuroanatomy and early innovations in its application. The use of path analysis as a means to test directional hypotheses about networks is presented along with the development of the complementary method, partial least squares. A method is useful when it provides insights that were previously inaccessible, and reflecting this, the paper concludes with a synopsis of the theoretical developments that arose for the routine use of methods like path analysis.

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.

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.

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.

Modality-dependent "what" and "where" preparatory processes in auditory and visual systems.

The present study examined the modality specificity and spatio-temporal dynamics of "what" and "where" preparatory processes in anticipation of auditory and visual targets using ERPs and a cue-target paradigm. Participants were presented with an auditory (Experiment 1) or a visual (Experiment 2) cue that signaled them to attend to the identity or location of an upcoming auditory or visual target. In both experiments, participants responded faster to the location compared to the identity conditions. Multivariate spatio-temporal partial least square (ST-PLS) analysis of the scalp-recorded data revealed supramodal "where" preparatory processes between 300-600 msec and 600-1200 msec at central and posterior parietal electrode sites in anticipation of both auditory and visual targets. Furthermore, preparation for pitch processing was captured at modality-specific temporal regions between 300 and 700 msec, and preparation for shape processing was detected at occipital electrode sites between 700 and 1150 msec. The spatio-temporal patterns noted above were replicated when a visual cue signaled the upcoming response (Experiment 2). Pitch or shape preparation exhibited modality-dependent spatio-temporal patterns, whereas preparation for target localization was associated with larger amplitude deflections at multimodal, centro-parietal sites preceding both auditory and visual targets. Using a novel paradigm, the study supports the notion of a division of labor in the auditory and visual pathways following both auditory and visual cues that signal identity or location response preparation to upcoming auditory or visual targets.

Partial Least Squares (PLS) methods for neuroimaging: a tutorial and review.

Partial Least Squares (PLS) methods are particularly suited to the analysis of relationships between measures of brain activity and of behavior or experimental design. In neuroimaging, PLS refers to two related methods: (1) symmetric PLS or Partial Least Squares Correlation (PLSC), and (2) asymmetric PLS or Partial Least Squares Regression (PLSR). The most popular (by far) version of PLS for neuroimaging is PLSC. It exists in several varieties based on the type of data that are related to brain activity: behavior PLSC analyzes the relationship between brain activity and behavioral data, task PLSC analyzes how brain activity relates to pre-defined categories or experimental design, seed PLSC analyzes the pattern of connectivity between brain regions, and multi-block or multi-table PLSC integrates one or more of these varieties in a common analysis. PLSR, in contrast to PLSC, is a predictive technique which, typically, predicts behavior (or design) from brain activity. For both PLS methods, statistical inferences are implemented using cross-validation techniques to identify significant patterns of voxel activation. This paper presents both PLS methods and illustrates them with small numerical examples and typical applications in neuroimaging.

The co-occurrence of multisensory facilitation and cross-modal conflict in the human brain.

Perceptual objects often comprise a visual and auditory signature that arrives simultaneously through distinct sensory channels, and cross-modal features are linked by virtue of being attributed to a specific object. Continued exposure to cross-modal events sets up expectations about what a given object most likely "sounds" like, and vice versa, thereby facilitating object detection and recognition. The binding of familiar auditory and visual signatures is referred to as semantic, multisensory integration. Whereas integration of semantically related cross-modal features is behaviorally advantageous, situations of sensory dominance of one modality at the expense of another impair performance. In the present study, magnetoencephalography recordings of semantically related cross-modal and unimodal stimuli captured the spatiotemporal patterns underlying multisensory processing at multiple stages. At early stages, 100 ms after stimulus onset, posterior parietal brain regions responded preferentially to cross-modal stimuli irrespective of task instructions or the degree of semantic relatedness between the auditory and visual components. As participants were required to classify cross-modal stimuli into semantic categories, activity in superior temporal and posterior cingulate cortices increased between 200 and 400 ms. As task instructions changed to incorporate cross-modal conflict, a process whereby auditory and visual components of cross-modal stimuli were compared to estimate their degree of congruence, multisensory processes were captured in parahippocampal, dorsomedial, and orbitofrontal cortices 100 and 400 ms after stimulus onset. Our results suggest that multisensory facilitation is associated with posterior parietal activity as early as 100 ms after stimulus onset. However, as participants are required to evaluate cross-modal stimuli based on their semantic category or their degree of congruence, multisensory processes extend in cingulate, temporal, and prefrontal cortices.

Distinct functional networks associated with improvement of affective symptoms and cognitive function during citalopram treatment in geriatric depression.

Variability in the affective and cognitive symptom response to antidepressant treatment has been observed in geriatric depression. The underlying neural circuitry is poorly understood. This study evaluated the cerebral glucose metabolic effects of citalopram treatment and applied multivariate, functional connectivity analyses to identify brain networks associated with improvements in affective symptoms and cognitive function. Sixteen geriatric depressed patients underwent resting positron emission tomography (PET) studies of cerebral glucose metabolism and assessment of affective symptoms and cognitive function before and after 8 weeks of selective serotonin reuptake inhibitor treatment (citalopram). Voxel-wise analyses of the normalized glucose metabolic data showed decreased cerebral metabolism during citalopram treatment in the anterior cingulate gyrus, middle temporal gyrus, precuneus, amygdala, and parahippocampal gyrus. Increased metabolism was observed in the putamen, occipital cortex, and cerebellum. Functional connectivity analyses revealed two networks which were uniquely associated with improvement of affective symptoms and cognitive function during treatment. A subcortical-limbic-frontal network was associated with improvement in affect (depression and anxiety), while a medial temporal-parietal-frontal network was associated with improvement in cognition (immediate verbal learning/memory and verbal fluency). The regions that comprise the cognitive network overlap with the regions that are affected in Alzheimer's dementia. Thus, alterations in specific brain networks associated with improvement of affective symptoms and cognitive function are observed during citalopram treatment in geriatric depression.

The interplay of cue modality and response latency in brain areas supporting crossmodal motor preparation: an event-related fMRI study.

Crossmodal (auditory, visual) motor facilitation can be defined as a cue in one sensory modality eliciting speeded responses to targets in a different sensory modality. We used event-related functional magnetic resonance imaging (fMRI) to isolate brain activity underlying crossmodal motor preparation. Our predictions were that interactions between input modality and processes underlying response selection would be indexed by distinct spatiotemporal brain dynamics. A crossmodal response selection task was designed in which a central, nonspatial cue indicated the response rule (compatible or incompatible) to a lateralized target. Cues and targets appeared in auditory and visual modalities and were separated by a lengthy delay period in which cue-related brain activity could be dissociated. We found faster reaction times to auditory compared with visual cues. Next, we correlated brain activity with behavioural performance using multivariate spatiotemporal partial least squares. We identified a distinct, significant brain-behaviour pattern in which faster reaction times to auditory cues were correlated with higher blood oxygenation level-dependent percent signal change in medial visual, frontoparietal (inferior parietal lobule, superior frontal gyrus and premotor cortex) and subcortical (thalamus and cerebellum) areas. For visual cues, quicker responses were linked to greater activity in the same frontoparietal and subcortical but not medial visual areas. Our results show that both modality-dependent and modality-independent brain areas with different brain-behaviour relationships are implicated in crossmodal motor preparation.

Maturation of EEG power spectra in early adolescence: a longitudinal study.

This study investigated the fine-grained development of the EEG power spectra in early adolescence, and the extent to which it is reflected in changes in peak frequency. It also sought to determine whether sex differences in the EEG power spectra reflect differential patterns of maturation. A group of 56 adolescents were tested at age 10 years and then at two further time-points approximately 18 months apart. The EEG was recorded during both eyes-closed and eyes-open conditions and Fourier transformed to provide estimates of absolute and relative spectral power at 0.5 Hz intervals from 0.5 to 40 Hz. The peak alpha frequency for each individual at each time-point was also determined for relative spectral power. Partial Least Squares (PLS) analysis was used to determine the combination of electrodes and frequencies that showed developmental change, or differed between the sexes. As a function of age, absolute delta and theta frequencies power decreased, and relative alpha2 and beta frequencies increased, replicating the standard findings of a decrease in lower, and increase in higher, frequencies with age. A small but significant increase in peak alpha frequency with age was detected. Moreover PLS analysis performed with individual alpha frequencies aligned to 10 Hz suggested that the age-related increase seen in alpha2 relative power was driven by changes in the peak frequency. Although males demonstrated higher alpha power than females, there were no sex differences in peak frequency, suggesting that there may be more to sex differences in EEG power than simply different rates of maturation between the two sexes.

Bridging Scales in Alzheimer's Disease: Biological Framework for Brain Simulation With The Virtual Brain.

Despite the acceleration of knowledge and data accumulation in neuroscience over the last years, the highly prevalent neurodegenerative disease of AD remains a growing problem. Alzheimer's Disease (AD) is the most common cause of dementia and represents the most prevalent neurodegenerative disease. For AD, disease-modifying treatments are presently lacking, and the understanding of disease mechanisms continues to be incomplete. In the present review, we discuss candidate contributing factors leading to AD, and evaluate novel computational brain simulation methods to further disentangle their potential roles. We first present an overview of existing computational models for AD that aim to provide a mechanistic understanding of the disease. Next, we outline the potential to link molecular aspects of neurodegeneration in AD with large-scale brain network modeling using The Virtual Brain (www.thevirtualbrain.org), an open-source, multiscale, whole-brain simulation neuroinformatics platform. Finally, we discuss how this methodological approach may contribute to the understanding, improved diagnostics, and treatment optimization of AD.

Learning related activation of somatosensory cortex by an auditory stimulus recorded with magnetoencephalography.

Advances in non-invasive neuroimaging technology now provide a means of directly observing learning within the brain. Classical conditioning serves as an ideal starting point for examining the dynamic expression of learning within the human brain, since this paradigm is well characterized using multiple levels of analysis in a broad range of species. We used MEG to expand the characterization of conditioned responses (CR) recorded from the human brain with a simultaneous examination of their spatial, temporal and spectral properties. We paired an auditory conditioned stimulus (CS+) with a somatosensory unconditioned stimulus (US). We found that when the US was randomly omitted, presentations of CS+ alone, elicited greater desynchronization of beta-band activity in contralateral somatosensory cortex compared to presentations of an auditory stimulus that was never paired with the US (CS-), and compared the CS+ following a non-reinforced extinction session. This differentiation was largest between 150 and 350ms following US omission. We show that cross-modal CRs in the primary sensorimotor system are predominantly characterized by modulation of ongoing cortical oscillations.