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 (Aβ) 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.

In silico exploration of mouse brain dynamics by focal stimulation reflects the organization of functional networks and sensory processing.

Resting-state functional networks such as the default mode network (DMN) dominate spontaneous brain dynamics. To date, the mechanisms linking brain structure and brain dynamics and functions in cognition, perception, and action remain unknown, mainly due to the uncontrolled and erratic nature of the resting state. Here we used a stimulation paradigm to probe the brain's resting behavior, providing insights on state-space stability and multiplicity of network trajectories after stimulation.

Linking Molecular Pathways and Large-Scale Computational Modeling to Assess Candidate Disease Mechanisms and Pharmacodynamics in Alzheimer's Disease.

While the prevalence of neurodegenerative diseases associated with dementia such as Alzheimer's disease (AD) increases, our knowledge on the underlying mechanisms, outcome predictors, or therapeutic targets is limited. In this work, we demonstrate how computational multi-scale brain modeling links phenomena of different scales and therefore identifies potential disease mechanisms leading the way to improved diagnostics and treatment. The Virtual Brain (TVB; thevirtualbrain.

Time rescaling reproduces EEG behavior during transition from propofol anesthesia-induced unconsciousness to consciousness.

General anesthesia (GA) is a reversible manipulation of consciousness whose mechanism is mysterious at the level of neural networks leaving space for several competing hypotheses. We recorded electrocorticography (ECoG) signals in patients who underwent intracranial monitoring during awake surgery for the treatment of cerebral tumors in functional areas of the brain. Therefore, we recorded the transition from unconsciousness to consciousness directly on the brain surface.

Fast-Slow Bursters in the Unfolding of a High Codimension Singularity and the Ultra-slow Transitions of Classes.

Bursting is a phenomenon found in a variety of physical and biological systems. For example, in neuroscience, bursting is believed to play a key role in the way information is transferred in the nervous system. In this work, we propose a model that, appropriately tuned, can display several types of bursting behaviors.

Ebbinghaus figures that deceive the eye do not necessarily deceive the hand.

In support of the visual stream dissociation hypothesis, which states that distinct visual streams serve vision-for-perception and vision-for-action, visual size illusions were reported over 20 years ago to 'deceive the eye but not the hand'. Ever since, inconclusive results and contradictory interpretations have accumulated. Therefore, we investigated the effects of the Ebbinghaus figure on repetitive aiming movements with distinct dynamics.

Selective Activation of Resting-State Networks following Focal Stimulation in a Connectome-Based Network Model of the Human Brain.

When the brain is stimulated, for example, by sensory inputs or goal-oriented tasks, the brain initially responds with activities in specific areas. The subsequent pattern formation of functional networks is constrained by the structural connectivity (SC) of the brain. The extent to which information is processed over short- or long-range SC is unclear.

Heterogeneity of time delays determines synchronization of coupled oscillators.

Network couplings of oscillatory large-scale systems, such as the brain, have a space-time structure composed of connection strengths and signal transmission delays. We provide a theoretical framework, which allows treating the spatial distribution of time delays with regard to synchronization, by decomposing it into patterns and therefore reducing the stability analysis into the tractable problem of a finite set of delay-coupled differential equations. We analyze delay-structured networks of phase oscillators and we find that, depending on the heterogeneity of the delays, the oscillators group in phase-shifted, anti-phase, steady, and non-stationary clusters, and analytically compute their stability boundaries.

How do parcellation size and short-range connectivity affect dynamics in large-scale brain network models?

Recent efforts to model human brain activity on the scale of the whole brain rest on connectivity estimates of large-scale networks derived from diffusion magnetic resonance imaging (dMRI). This type of connectivity describes white matter fiber tracts. The number of short-range cortico-cortical white-matter connections is, however, underrepresented in such large-scale brain models.

Transcranial direct current stimulation changes resting state functional connectivity: A large-scale brain network modeling study.

Transcranial direct current stimulation (tDCS) is a noninvasive technique for affecting brain dynamics with promising application in the clinical therapy of neurological and psychiatric disorders such as Parkinson's disease, Alzheimer's disease, depression, and schizophrenia. Resting state dynamics increasingly play a role in the assessment of connectivity-based pathologies such as Alzheimer's and schizophrenia. We systematically applied tDCS in a large-scale network model of 74 cerebral areas, investigating the spatiotemporal changes in dynamic states as a function of structural connectivity changes.

Mathematical framework for large-scale brain network modeling in The Virtual Brain.

In this article, we describe the mathematical framework of the computational model at the core of the tool The Virtual Brain (TVB), designed to simulate collective whole brain dynamics by virtualizing brain structure and function, allowing simultaneous outputs of a number of experimental modalities such as electro- and magnetoencephalography (EEG, MEG) and functional Magnetic Resonance Imaging (fMRI). The implementation allows for a systematic exploration and manipulation of every underlying component of a large-scale brain network model (BNM), such as the neural mass model governing the local dynamics or the structural connectivity constraining the space time structure of the network couplings. Here, a consistent notation for the generalized BNM is given, so that in this form the equations represent a direct link between the mathematical description of BNMs and the components of the numerical implementation in TVB.

Functional connectivity dynamics: modeling the switching behavior of the resting state.

Functional connectivity (FC) sheds light on the interactions between different brain regions. Besides basic research, it is clinically relevant for applications in Alzheimer's disease, schizophrenia, presurgical planning, epilepsy, and traumatic brain injury. Simulations of whole-brain mean-field computational models with realistic connectivity determined by tractography studies enable us to reproduce with accuracy aspects of average FC in the resting state.

Systematic approximations of neural fields through networks of neural masses in the virtual brain.

Full brain network models comprise a large-scale connectivity (the connectome) and neural mass models as the network's nodes. Neural mass models absorb implicitly a variety of properties in their constant parameters to achieve a reduction in complexity. In situations, where the local network connectivity undergoes major changes, such as in development or epilepsy, it becomes crucial to model local connectivity explicitly.

Bottom up modeling of the connectome: linking structure and function in the resting brain and their changes in aging.

With the increasing availability of advanced imaging technologies, we are entering a new era of neuroscience. Detailed descriptions of the complex brain network enable us to map out a structural connectome, characterize it with graph theoretical methods, and compare it to the functional networks with increasing detail. To link these two aspects and understand how dynamics and structure interact to form functional brain networks in task and in the resting state, we use theoretical models.

Modeling brain resonance phenomena using a neural mass model.

Stimulation with rhythmic light flicker (photic driving) plays an important role in the diagnosis of schizophrenia, mood disorder, migraine, and epilepsy. In particular, the adjustment of spontaneous brain rhythms to the stimulus frequency (entrainment) is used to assess the functional flexibility of the brain. We aim to gain deeper understanding of the mechanisms underlying this technique and to predict the effects of stimulus frequency and intensity.

Bifurcation analysis of neural mass models: Impact of extrinsic inputs and dendritic time constants.

Neural mass models (NMMs) explain dynamics of neuronal populations and were designed to strike a balance between mathematical simplicity and biological plausibility. They are currently widely used as generative models for noninvasive electrophysiological brain measurements; that is, magneto- and electroencephalography (M/EEG). Here, we systematically describe the oscillatory regimes which a NMM of a single cortical source with extrinsic input from other cortical and subcortical areas to each subpopulation can explain.

Phase coupling between different motor areas during tongue-movement imagery.

Motor imagery can be accompanied by an enhancement of brain oscillations (event-related synchronization, ERS) within specific frequency bands. To characterize the neuronal couplings involved during these prominent power changes, we have chosen a certain coupling measure that bears directly on the issue of transient cortical connections. Specifically, we applied for the first time the phase-locking value to investigate the phase coupling of sensorimotor rhythms in different motor areas during tongue-movement imagery.

Rapid palatal expansion. Part 3: strains developed during active and retention phases.

Patterns of strain accumulation/dissipation during the active and retention phases of rapid palatal expansion treatment were studied in a preliminary animal model (5 cats) followed by clinical study (14 patients). Two uni-axial strain gauges were bonded to the arms of a hyrax screw. The strain gauges were wired intraorally to a common male connector and protected against salivary assault.

Rapid palatal expansion. Part 2: Dentoskeletal changes in cats with patent versus synostosed midpalatal suture.

Intercanine expansion (C-C) following rapid palatal expansion is made up of sutural displacement (Sd-Sd), tooth tip (Tt-Tt), tooth displacement (Td-Td), and alveolar process tipping and bending (At+b-At+b). The involvement of these four components was studied on 10 rapid palatal expansion treated and two control cats during an active phase (25 days), a retention phase (60 days), and a relapse phase (60 days). The midpalatal suture was analyzed for linear measurements, radiopaque versus radiolucent zones and optical density from occlusal radiographs.

Rapid palatal expansion: Part 1. Mineralization pattern of the midpalatal suture in cats.

The mineralization pattern of the midpalatal suture after rapid palatal expansion was investigated in 10 treated and 2 control cats, in light of the tendency of RPE to relapse. The rapid palatal expansion treatment consisted of active (25 days), retention (60 days), and relapse (60 days) phases. Standardized occlusal radiographs were taken periodically and analyzed for suture width, suture optical density in anterior vs.

Chronic childhood spinal muscular atrophy in Germany (West-Thüringen)--an epidemiological study.

This study presents the most extensive epidemiological data on chronic forms of spinal muscular atrophy in childhood (CSMA) in West-Thüringen in Germany. The incidence of CSMA was calculated to be 1 in 9,420 live births. The prevalence was 1.

Epidemiological data on Werdnig-Hoffmann disease in Germany (West-Thüringen).

This study contains the largest body of epidemiological data on Werdnig-Hoffmann disease (acute infantile spinal muscular atrophy; ASMA) in West-Thüringen in Germany. The incidence of ASMA was calculated to be 1 in 10,202 live births. The prevalence was 1 in 595,362 of the general population (as of 31 December 1987).

Population data on acute infantile and chronic childhood spinal muscular atrophy in Warsaw.

This study provides epidemiological data on acute infantile (ASMA) and chronic childhood spinal (CSMA) muscular atrophy in Warsaw for the period 1976-1985. All calculations are based on the assumption that ASMA and CSMA result from mutations at two different gene loci. The incidence of ASMA and CSMA was 1 in 19474 live births with a corresponding gene and carrier frequency of 714 x 10(-5) and 1 in 70, respectively.

Segregation analysis of 1885 DMD families: significant departure from the expected proportion of sporadic cases.

The proportion of sporadic cases of Duchenne muscular dystrophy has been estimated by classical segregation analysis in a pooled sample of 1885 sibships from 7 different countries. A significant departure from the theoretical expectations based on mutation-selection equilibrium is observed (segregation frequency = 0.439 +/- 0.