• Alpha band
• Anesthesia sensitivity
• Brain functional network
• Electroencephalography (EEG)
• Propofol

• Humans
• Propofol/administration & dosage
• Male
• Female
• Adult
• Brain/drug effects
• Electroencephalography
• Middle Aged
• Anesthetics, Intravenous/administration & dosage
• Machine Learning
• Nerve Net/drug effects
• Anesthesia
• Young Adult

• control theory
• genome-scale metabolic networks
• neurodegenerative diseases
• systems biology

• Systems Biology
• Drug Development
• Genomics
• Interdisciplinary Studies

• BPIN 2020000100357 Sistema General de Regalias
• 20550 Pontificia Universidad Javeriana (Bogotá Colombia)

• Control theory for brain networks
• Descriptive network neuroscience
• Perturbative network neuroscience
• Predictive network neuroscience

• Brain
• Cognition
• Forecasting
• Humans
• Neurosciences

• K99 MH127296 NIMH NIH HHS
• R01 MH112847 NIMH NIH HHS
• R01 MH107235 NIMH NIH HHS
• R01 DC009209 NIDCD NIH HHS
• R21 MH106799 NIMH NIH HHS
• T32 EB020087 NIBIB NIH HHS

• Diffusion maps
• Functional connectivity
• Manifold optimization
• Structural connectome

• epilepsy
• network topology

• Big data analysis
• Irritable bowel syndrome
• Machine learning
• Microbiota
• Microbiota-gut-brain axis
• Neurogastroenterology
• Structural and functional magnetic resonance imaging

• Brain-Gut Axis
• Fecal Microbiota Transplantation
• Gastrointestinal Microbiome
• Humans
• Irritable Bowel Syndrome/therapy
• Microbiota

Very preterm infants (born at less than 32 weeks gestational age) are at high risk for serious motor impairments, including cerebral palsy (CP). The brain network changes that antecede the early development of CP in infants are not well characterized, and a better understanding may suggest new strategies for risk-stratification at term, which could lead to earlier access to therapies. Graph theoretical methods applied to diffusion MRI-derived brain connectomes may help quantify the organization and information transfer capacity of the preterm brain with greater nuance than overt structural or regional microstructural changes. Our aim was to shed light on the pathophysiology of early CP development, before the occurrence of early intervention therapies and other environmental confounders, to help identify the best early biomarkers of CP risk in VPT infants. In a cohort of 395 very preterm infants, we extracted cortical morphometrics and brain volumes from structural MRI and also applied graph theoretical methods to diffusion MRI connectomes, both acquired at term-equivalent age. Metrics from graph network analysis, especially global efficiency, strength values of the major sensorimotor tracts, and local efficiency of the motor nodes and novel non-motor regions were strongly inversely related to early CP diagnosis. These measures remained significantly associated with CP after correction for common risk factors of motor development, suggesting that metrics of brain network efficiency at term may be sensitive biomarkers for early CP detection. We demonstrate for the first time that in VPT infants, early CP diagnosis is anteceded by decreased brain network segregation in numerous nodes, including motor regions commonly-associated with CP and also novel regions that may partially explain the high rate of cognitive impairments concomitant with CP diagnosis. These advanced MRI biomarkers may help identify the highest risk infants by term-equivalent age, facilitating earlier interventions that are informed by early pathophysiological changes.

• Biomarkers Abbreviations: very preterm (VPT)
• Cerebral palsy
• Diffusion MRI
• General Movements Assessment (GMA)
• Hammersmith Infant Neurological Examination (HINE)
• Infant
• Magnetic resonance imaging
• Premature
• White matter
• bronchopulmonary dysplasia (BPD), fractional anisotropy (FA)
• cerebral palsy (CP)
• developing human connectome project (dHCP)
• gestational age (GA)
• global efficiency (Eg(lob))
• local efficiency (E(loc)), clustering coefficient (CC)
• retinopathy of prematurity (ROP)

•

Most preterm infants exhibit regions of high signal intensity on T2 MRI at term.

Between 50 and 80% of very preterm infants (<32 weeks gestational age) exhibit increased white matter signal intensity on T2-weighted MRI at term-equivalent age, known as diffuse white matter abnormality (DWMA). A few studies have linked DWMA with microstructural abnormalities, but the exact relationship remains poorly understood. We related DWMA extent to graph theory measures of network efficiency at term in a representative cohort of 343 very preterm infants. We performed anatomic and diffusion MRI at term and quantified DWMA volume with our novel, semi-automated algorithm. From diffusion-weighted structural connectomes, we calculated the graph theory metrics local efficiency and clustering coefficient, which measure the ability of groups of nodes to perform specialized processing, and global efficiency, which assesses the ability of brain regions to efficiently combine information. We computed partial correlations between these measures and DWMA volume, adjusted for confounders. Increasing DWMA volume was associated with decreased global efficiency of the entire very preterm brain and decreased local efficiency and clustering coefficient in a variety of regions supporting cognitive, linguistic, and motor function. We show that DWMA is associated with widespread decreased brain network efficiency, suggesting that it is pathologic and likely has adverse developmental consequences.

• Diffuse white matter abnormality
• Graph theory
• Very preterm
• diffusion MRI

• Alzheimer’s disease
• diffusion tensor imaging
• graph theory method
• structural network analysis
• subcortical ischemic vascular dementia

• C. elegans model
• computational modeling
• connectome analysis
• graph theory
• networks (circuits)

Source localization and functional brain network modeling are methods of identifying critical regions during cognitive tasks. The first activity estimates the relative differences of the signal amplitudes in regions of interest (ROI) and the second activity measures the statistical dependence among signal fluctuations. We hypothesized that the source amplitude–functional connectivity relationship decouples or reverses in persons having brain impairments. Five Broca’s aphasics with five matched cognitively healthy controls underwent overt picture-naming magnetoencephalography scans. The gamma-band (30–45 Hz) phase-locking values were calculated as connections among the ROIs. We calculated the partial correlation coefficients between the amplitudes and network measures and detected four node types, including hothubs with high amplitude and high connectivity, coldhubs with high connectivity but lower amplitude, non-hub hotspots, and non-hub coldspots. The results indicate that the high-amplitude regions are not necessarily highly connected hubs. Furthermore, the Broca aphasics utilized different hothub sets for the naming task. Both groups had dark functional networks composed of coldhubs. Thus, source amplitude–functional connectivity relationships could help reveal functional reorganizations in patients. The amplitude–connectivity combination provides a new perspective for pathological studies of the brain’s dark functional networks.

• Aphasia
• Functional brain networks
• Functional connectivity
• Magnetoencephalography
• Network hubs
• Phase-locking value
• Source amplitude

• Brain networks
• Community detection algorithms
• Empirical mode decomposition
• Random walkers
• k-modes clustering

• Adult
• Brain/diagnostic imaging
• Brain/physiology
• Brain Mapping/methods
• Electroencephalography/methods
• Female
• Humans
• Image Processing, Computer-Assisted
• Magnetic Resonance Imaging/methods
• Male
• Signal Processing, Computer-Assisted
• Transcranial Magnetic Stimulation/methods
• Young Adult

Reestablishing cerebral connectivity is a critical part of restoring neuronal network integrity and brain function after trauma, stroke, and neurodegenerative diseases. Creating transplantable axon tracts in the laboratory is an unexplored strategy for overcoming the common barriers limiting axon regeneration in vivo, including growth-inhibiting factors and the limited outgrowth capacity of mature neurons in the brain. We describe the generation, phenotype, and connectivity of constrained three-dimensional human axon tracts derived from brain organoids. These centimeter-long constructs are encased in an agarose shell that permits physical manipulation and are composed of discrete cellular regions spanned by axon tracts, mirroring the separation of cerebral gray and white matter. Features of cerebral cortex also are emulated, as evidenced by the presence of neurons with different cortical layer phenotypes. This engineered neural tissue represents a first step toward potentially reconstructing brain circuits by physically replacing neuronal populations and long-range axon tracts in the brain.

•

Transplantable 3D axon tracts are tissue engineered from human brain organoids

• Biological Sciences
• Neuroscience
• Tissue Engineering

• complex networks
• deep brain stimulation
• functional brain activity
• learning
• network control
• neurofeedback
• robustness
• time scales

Brain stimulation can modulate the activity of neural circuits impaired by Alzheimer’s disease (AD), having promising clinical benefit. However, all individuals with the same condition currently receive identical brain stimulation, with limited theoretical basis for this generic approach. In this study, we introduce a control theory framework for obtaining exogenous signals that revert pathological electroencephalographic activity in AD at a minimal energetic cost, while reflecting patients’ biological variability. We used anatomical networks obtained from diffusion magnetic resonance images acquired by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) as mediators for the interaction between Duffing oscillators. The nonlinear nature of the brain dynamics is preserved, given that we extend the so-called state-dependent Riccati equation control to reflect the stimulation objective in the high-dimensional neural system. By considering nonlinearities in our model, we identified regions for which control inputs fail to correct abnormal activity. There are changes to the way stimulated regions are ranked in terms of the energetic cost of controlling the entire network, from a linear to a nonlinear approach. We also found that limbic system and basal ganglia structures constitute the top target locations for stimulation in AD. Patients with highly integrated anatomical networks–namely, networks having low average shortest path length, high global efficiency–are the most suitable candidates for the propagation of stimuli and consequent success on the control task. Other diseases associated with alterations in brain dynamics and the self-control mechanisms of the brain can be addressed through our framework.

This work aims to close the knowledge gap between theory and experiment in brain stimulation. Previous modeling approaches for stimulation have overlooked the nonlinear dynamical nature of the brain and failed to shed light on efficient mechanisms for the exogenous control of the brain. Amid the current efforts for developing personalized medicine, we introduce a framework for producing tailored stimulation signals, based on individual neuroimaging data and innovative modeling. This is the first time, to our knowledge, that brain stimulation for the most common cause of dementia, Alzheimer’s disease, is theoretically addressed. Our approach leads to the identification of potential target regions and subjects to successfully respond to brain stimulation therapies and yields various disease-reverting signals. Although focused on Alzheimer’s in this study, our methodology could be applied to other clinical conditions characterized by abnormalities in brain dynamics, like epilepsy and Parkinson’s, the treatment of which can benefit from the use of optimal control strategies.

• Algorithms
• Alzheimer Disease/diagnostic imaging
• Brain/diagnostic imaging
• Brain/physiopathology
• Computer Simulation
• Diffusion Magnetic Resonance Imaging/methods
• Electroencephalography/methods
• Humans
• Image Processing, Computer-Assisted
• Neuroimaging/methods
• Nonlinear Dynamics
• Signal Processing, Computer-Assisted

• U01 AG024904 NIA NIH HHS
• U01 AG024904 NIH HHS

• brain graph
• dynamic
• multi-modal
• nodes

• P20 GM103472 NIGMS NIH HHS
• R01 DA040487 NIDA NIH HHS
• R37 MH043775 NIMH NIH HHS
• R01 EB006841 NIBIB NIH HHS
• R01 EB000840 NIBIB NIH HHS
• P20 RR021938 NCRR NIH HHS
• R01 EB020407 NIBIB NIH HHS
• R01 EB005846 NIBIB NIH HHS
• R01 MH094524 NIMH NIH HHS

• R01 MH107235 NIMH NIH HHS
• R01 DC009209 NIDCD NIH HHS
• R21 MH106799 NIMH NIH HHS
• R01 HD086888 NICHD NIH HHS
• R01 MH109520 NIMH NIH HHS
• T32 EB020087 NIBIB NIH HHS
• R01 MH107703 NIMH NIH HHS

• communication
• multiscale
• networks
• neuroimaging
• neuroscience
• social networks

• Brain/physiology
• Cognition/physiology
• Humans
• Internet
• Models, Neurological
• Nerve Net/physiology
• Neural Pathways/physiology
• Neurosciences
• Social Support

• DP2 DA035156 NIDA NIH HHS

• cognition
• control theory
• graph theory
• network neuroscience
• neurofeedback

• Brain/physiology
• Cognition/physiology
• Connectome/methods
• Humans
• Nerve Net/physiology
• Neural Pathways/physiology
• Neurofeedback/physiology

• R01 DC009209 NIDCD NIH HHS
• R01 NS099348 NINDS NIH HHS
• R01 MH107235 NIMH NIH HHS
• R21 MH106799 NIMH NIH HHS
• R01 HD086888 NICHD NIH HHS
• R01 MH109520 NIMH NIH HHS
• R01 MH107703 NIMH NIH HHS