Time-Varying Directed Interactions in Functional Brain Networks: Modeling and Validation
University of Maryland, College Park（马里兰大学帕克分校）；Georgia Institute of Technology and Emory University（佐治亚理工学院和埃默里大学）；Emory University School of Medicine（埃默里大学医学院）；Georgia Institute of Technology（佐治亚理工学院）；Indiana University School of Medicine（印第安纳大学医学院）；Georgia State University and Georgia Institute of Technology and Emory University（佐治亚州立大学和佐治亚理工学院和埃默里大学）
Nan Xu, Xiaodi Zhang, Wen-Ju Pan, Jeremy L. Smith, Eric H. Schumacher, Jason W. Allen, Vince D. Calhoun, Shella D. Keilholz
Understanding the dynamic nature of brain connectivity is critical for elucidating neural processing, behavior, and brain disorders. Traditional approaches such as sliding-window correlation (SWC) characterize time-varying undirected associations but do not resolve directional interactions, limiting inference about time-resolved information flow in brain networks. We introduce sliding-window prediction correlation (SWpC), which embeds a directional linear time-invariant (LTI) model within each sliding window to estimate time-varying directed functional connectivity (FC). SWpC yields two complementary descriptors of directed interactions: a strength measure (prediction correlation) and a duration measure (window-wise duration of information transfer). Using concurrent local field potential (LFP) and fMRI BOLD recordings from rat somatosensory cortices, we demonstrate stable directionality estimates in both LFP band-limited power and BOLD. Using Human Connectome Project (HCP) motor task fMRI, SWpC detects significant task-evoked changes in directed FC strength and duration and shows higher sensitivity than SWC for identifying task-evoked connectivity differences. Finally, in post-concussion vestibular dysfunction (PCVD), SWpC reveals reproducible vestibular-multisensory brain-state shifts and improves healthy-control vs subacute patient (HC-ST) discrimination using state-derived features. Together, these results show that SWpC provides biologically interpretable, time-resolved directed connectivity patterns across multimodal validation and clinical application settings, supporting both basic and translational neuroscience.
Already two decades passed since the first applications of graph theory to brain neuroimaging. Since that early description, the characterization of the brain as a very large interacting complex network has evolved in several directions. In this brief review we discuss our contributions to this topic and discuss some perspective for future work.
The center stage of neuro-imaging is currently occupied by studies of functional correlations between brain regions. These correlations define the brain functional networks, which are the most frequently used framework to represent and interpret a variety of experimental findings. In previous work we first demonstrated that the relatively stronger BOLD activations contain most of the information relevant to understand functional connectivity and subsequent work confirmed that a large compression of the original signals can be obtained without significant loss of information. In this work we re…
While many neuroscience questions aim to understand the human brain, much current knowledge has been gained using animal models, which replicate genetic, structural, and connectivity aspects of the human brain. While voxel-based analysis (VBA) of preclinical magnetic resonance images is widely-used, a thorough examination of the statistical robustness, stability, and error rates is hindered by high computational demands of processing large arrays, and the many parameters involved. Thus, workflows are often based on intuition or experience, while preclinical validation studies remain scarce. To…
At the inception of human brain mapping, two principles of functional anatomy underwrote most conceptions - and analyses - of distributed brain responses: namely functional segregation and integration. There are currently two main approaches to characterising functional integration. The first is a mechanistic modelling of connectomics in terms of directed effective connectivity that mediates neuronal message passing and dynamics on neuronal circuits. The second phenomenological approach usually characterises undirected functional connectivity (i.e., measurable correlations), in terms of intrin…
Microdialysis is a well-established method for in vivo neurochemical measurements of small molecules, with implanted concentric-design probes offering minimized tissue damage and good temporal and spatial resolution. However, the large majority of measurements do not allow the perfusate to reach equilibrium with the brain, so that inferential methods of sample concentration correction such as zero-net-flux must be used to determine actual brain extracellular fluid glucose concentrations. In order for such methods to be valid, steady-state transfer of the analyte of interest within the brain is…
Directionality is a fundamental feature of network connections. Most structural brain networks are intrinsically directed because of the nature of chemical synapses, which comprise most neuronal connections. Due to limitations of non-invasive imaging techniques, the directionality of connections between structurally connected regions of the human brain cannot be confirmed. Hence, connections are represented as undirected, and it is still unknown how this lack of directionality affects brain network topology. Using six directed brain networks from different species and parcellations (cat, mouse…
We describe the experimental procedures for a dataset that we have made publicly available at https://doi.org/10.5281/zenodo.2649006 in mat and csv formats. This dataset contains electroencephalographic (EEG) recordings of 25 subjects testing the Brain Invaders (Congedo, 2011), a visual P300 Brain-Computer Interface inspired by the famous vintage video game Space Invaders (Taito, Tokyo, Japan). The visual P300 is an event-related potential elicited by a visual stimulation, peaking 240-600 ms after stimulus onset. EEG data were recorded by 16 electrodes in an experiment that took place in the G…
Tulane University（杜兰大学）；University of Nebraska Medical Center（内布拉斯加大学医学中心）；Mind Research Network（心智研究网络）；Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)（三机构神经影像与数据科学转化研究中心（TReNDS））；Georgia State University（佐治亚州立大学）；Georgia Institute of Technology（佐治亚理工学院）；Emory University（埃默里大学）
Emotion perception is essential to affective and cognitive development which involves distributed brain circuits. The ability of emotion identification begins in infancy and continues to develop throughout childhood and adolescence. Understanding the development of brain's emotion circuitry may help us explain the emotional changes observed during adolescence. Our previous study delineated the trajectory of brain functional connectivity (FC) from late childhood to early adulthood during emotion identification tasks. In this work, we endeavour to deepen our understanding from association to cau…
Recent experimental results on spike avalanches measured in the urethane-anesthetized rat cortex have revealed scaling relations that indicate a phase transition at a specific level of cortical firing rate variability. The scaling relations point to critical exponents whose values differ from those of a branching process, which has been the canonical model employed to understand brain criticality. This suggested that a different model, with a different phase transition, might be required to explain the data. Here we show that this is not necessarily the case. By employing two different models …
Comprehending the interplay between spatial and temporal characteristics of neural dynamics can contribute to our understanding of information processing in the human brain. Graph neural networks (GNNs) provide a new possibility to interpret graph structured signals like those observed in complex brain networks. In our study we compare different spatio-temporal GNN architectures and study their ability to model neural activity distributions obtained in functional MRI (fMRI) studies. We evaluate the performance of the GNN models on a variety of scenarios in MRI studies and also compare it to a …
Brain networks are adaptively rewired continually, adjusting their topology to bring about functionality and efficiency in sensory, motor and cognitive tasks. In model neural network architectures, adaptive rewiring generates complex, brain-like topologies. Present models, however, cannot account for the emergence of complex directed connectivity structures. We tested a biologically plausible model of adaptive rewiring in directed networks, based on two algorithms widely used in distributed computing: advection and consensus. When both are used in combination as rewiring criteria, adaptive rew…
Interconnectivity, fault tolerance, and dynamic evolution of the circuitry are long sought-after objectives of bio-inspired engineering. Here, we propose dendritic transistors composed of organic semiconductors as building blocks for neuromorphic computing. These devices, owning to their voltage-triggered growth and resemblance to neural structures, respond to action potentials to achieve complex brain-like features, such as Pavlovian learning, pattern recognition, and spike-timing-dependent plasticity. The dynamic formation of the connections is reminiscent of a biological learning mechanism …
Targeted electrical stimulation of the brain perturbs neural networks and modulates their rhythmic activity both at the site of stimulation and at remote brain regions. Understanding, or even predicting, this neuromodulatory effect is crucial for any therapeutic use of brain stimulation. To this end, we analyzed the stimulation responses in 131 stimulation sessions across 66 patients with focal epilepsy recorded through intracranial EEG (iEEG). We considered functional and structural connectivity features as predictors of the response at every iEEG contact. Taking advantage of multiple recordi…
This paper explores the discrete Dynamic Causal Modeling (DDCM) and its relationship with Directed Information (DI). We prove the conditional equivalence between DDCM and DI in characterizing the causal relationship between two brain regions. The theoretical results are demonstrated using fMRI data obtained under both resting state and stimulus based state. Our numerical analysis is consistent with that reported in previous study.
Beyond Performance Scores: Directed Functional Connectivity as a Brain-Based Biomarker for Motor Skill Learning and Retention
Rensselaer Polytechnic Institute（伦斯勒理工学院）；University at Buffalo（布法罗大学）；University at Buffalo Jacobs School of Medicine and Biomedical Sciences（布法罗大学雅各布斯医学与生物医学科学学院）；Uniformed Services University of the Health Sciences（uniformed services health sciences university）；U.S. Army Futures Command, Combat Capabilities Development Command Soldier Center STTC（美国陆军未来司令部作战能力发展司令部士兵中心STTC）
The traditional methods for detecting autism spectrum disorder (ASD) are expensive, subjective, and time-consuming, often taking years for a diagnosis, with many children growing well into adolescence and even adulthood before finally confirming the disorder. Recently, graph-based learning techniques have demonstrated impressive results on resting-state functional magnetic resonance imaging (rs-fMRI) data from the Autism Brain Imaging Data Exchange (ABIDE). We introduce IMAGIN, a multI-granular, Multi-Atlas spatio-temporal attention Graph Isomorphism Network, which we use to learn graph repres…
Conventional neuroimaging analyses have revealed the computational specificity of localized brain regions, exploiting the power of the subtraction technique in fMRI and event-related potential analyses in EEG. Moving beyond this convention, many researchers have begun exploring network-based neurodynamics and coordination between brain regions as a function of behavioral parameters or environmental statistics; however, most approaches average evoked activity across the experimental session to study task-dependent networks. Here, we examined on-going oscillatory activity and use a methodology t…
Contemporary neuroimaging methods can shed light on the basis of human neural and cognitive specializations, with important implications for neuroscience and medicine. Different MRI acquisitions provide different brain networks at the macroscale; whilst diffusion-weighted MRI (dMRI) provides a structural connectivity (SC) coincident with the bundles of parallel fibers between brain areas, functional MRI (fMRI) accounts for the variations in the blood-oxygenation-level-dependent T2* signal, providing functional connectivity (FC).Understanding the precise relation between FC and SC, that is, bet…
Fluid intelligence (Gf) has been defined as the ability to reason and solve previously unseen problems. Links to Gf have been found in magnetic resonance imaging (MRI) sequences such as functional MRI and diffusion tensor imaging. As part of the Adolescent Brain Cognitive Development Neurocognitive Prediction Challenge 2019, we sought to predict Gf in children aged 9-10 from T1-weighted (T1W) MRIs. The data included atlas-aligned volumetric T1W images, atlas-defined segmented regions, age, and sex for 3739 subjects used for training and internal validation and 415 subjects used for external va…
Background: Positron Emission Tomography (PET) with 18F-fluorodeoxyglucose (18F-FDG) reveals metabolic abnormalities in Parkinson's disease (PD) at a systemic level. Previous metabolic connectome studies derived from groups of patients have failed to identify the individual neurophysiological details. We aim to establish an individual metabolic connectome method to characterize the aberrant connectivity patterns and topological alterations of the individual-level brain metabolic connectome and their diagnostic value in PD. Methods: The 18F-FDG PET data of 49 PD patients and 49 healthy controls…
Our brains are viewed as interconnected distributed systems. The connections between distant areas in the brain are significantly delayed. How to obtained now in such networks with delayed interconnections? We aim to show that delayed communication and interconnectedness of the brain impose an interaction with the environment, assuming that such an access to now, which we label t-present, is of use for this system. It is conjectured that for any sensory, motor or cognitive functions to work efficiently an updated sort of time origin is required, and we claim that it is uniquely given by a dire…
Kharkevich Institute for Information Transmission Problems（哈尔克维奇信息传输问题研究所）；University of Southern California（南加州大学）；Skolkovo Institute of Science and Technology（斯科尔科沃科学技术研究所）
In this work, we study the extent to which structural connectomes and topological derivative measures are unique to individual changes within human brains. To do so, we classify structural connectome pairs from two large longitudinal datasets as either belonging to the same individual or not. Our data is comprised of 227 individuals from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and 226 from the Parkinson's Progression Markers Initiative (PPMI). We achieve 0.99 area under the ROC curve score for features which represent either weights or network structure of the connectomes (node …
Objective The electrical characteristics of the EEG signals can be used for seizure detection. Statistical independence between different brain regions is measured by functional brain connectivity (FBC). Specific directional effects can't consider by FBC and thus effective brain connectivity (EBC) is used to measure causal intervention between one neuronal region and the rest of the neuronal regions. Our main purpose is to provide a reliable automatic seizure detection approach. Methods In this study, three new methods are provided. Deep modular neural network (DMNN) is developed based on a co…
New trends on brain-computer interface (BCI) design are aiming to combine this technology with immersive virtual reality in order to provide a sense of realism to its users. In this study, we propose an experimental BCI to control an immersive telepresence system using motor imagery (MI). The system is immersive in the sense that the users can control the movement of a NAO humanoid robot in a first person perspective (1PP), i.e., as if the movement of the robot was his/her own. We analyze functional brain connectivity between 1PP and 3PP during the control of our BCI using graph theory propert…
York University（约克大学）；University of Alberta（阿尔伯塔大学）
Recent studies suggest that artificial neural networks (ANNs) that match the spectral properties of the mammalian visual cortex -- namely, the $\sim 1/n$ eigenspectrum of the covariance matrix of neural activities -- achieve higher object recognition performance and robustness to adversarial attacks than those that do not. To our knowledge, however, no previous work systematically explored how modifying the ANN's spectral properties affects performance. To fill this gap, we performed a systematic search over spectral regularizers, forcing the ANN's eigenspectrum to follow $1/n^α$ power laws wi…
Artificial intelligence in deep brain stimulation for movement disorders: a systematic review and technology readiness assessment
Military University Hospital of Sfax（萨法军事大学医院）；Institut du Neurone（神经元研究所）；Edinburgh Medical School, University of Edinburgh（爱丁堡大学爱丁堡医学院）；UCL Queen Square Institute of Neurology（伦敦大学学院皇后广场神经科学研究所）；Lausanne University Hospital (CHUV)（洛桑大学医院）；Ecole Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）；University Hospital Lausanne（洛桑大学医院）；University of Lausanne（洛桑大学）；CHU of Grenoble（格勒诺布尔大学医院）；Humanitas University（希曼纳大学）；IRCCS Humanitas Research Hospital（IRCCS希曼纳研究医院）；Umeå University（于默奥大学）；Hannover Medical School（汉诺威医学院）；Charité- Universitätsmedizin Berlin（柏林夏里特医科大学）；University College London（伦敦大学学院）；Evelina London Children’s Hospital（伊芙琳娜伦敦儿童医院）；King’s College London（伦敦国王学院）；Neuro-X Institute, Ecole Polytechnique Fédérale de Lausanne（洛桑联邦理工学院神经X研究所）；Grenoble Alpes University（格勒诺布尔阿尔卑斯大学）；Grenoble Institut of Neurosciences（格勒诺布尔神经科学研究所）；Julius Maximilians University Hospital Würzburg（维尔茨堡朱利叶斯马克西米利安大学医院）
Vanderbilt University（范德堡大学）；Laboratory of Behavioral Neuroscience, National Institute on Aging（行为神经科学实验室，美国国家老龄化研究所）
Whole brain segmentation on a structural magnetic resonance imaging (MRI) is essential in non-invasive investigation for neuroanatomy. Historically, multi-atlas segmentation (MAS) has been regarded as the de facto standard method for whole brain segmentation. Recently, deep neural network approaches have been applied to whole brain segmentation by learning random patches or 2D slices. Yet, few previous efforts have been made on detailed whole brain segmentation using 3D networks due to the following challenges: (1) fitting entire whole brain volume into 3D networks is restricted by the current…
Databases of directed- and weighted- connectivity for mouse, macaque and marmoset monkeys, have recently become available and begun to be used to build dynamical models. A hierarchical organization can be defined based on laminar patterns of cortical connections, possibly improved by thalamocortical projections. A large-scale model of the macaque cortex endowed with a laminar structure accounts for layer-dependent and frequency-modulated interplays between bottom-up and top-down processes. Signal propagation in a version of the model with spiking neurons displays a threshold of stimulus amplit…
Inferring Active Neural Circuits Using Diffusion Scores
Yale University（耶鲁大学）；University of Tübingen（蒂宾根大学）；IE University（IE大学）；University of Massachusetts Chan Medical School（马萨诸塞大学陈医学院）；Wu Tsai Institute（吴氏研究所）
We introduce a novel recurrent neural network (RNN) approach to account for temporal dynamics and dependencies in brain networks observed via functional magnetic resonance imaging (fMRI). Our approach directly parameterizes temporal dynamics through recurrent connections, which can be used to formulate blind source separation with a conditional (rather than marginal) independence assumption, which we call RNN-ICA. This formulation enables us to visualize the temporal dynamics of both first order (activity) and second order (directed connectivity) information in brain networks that are widely s…
Knowing brain connectivity is of great importance both in basic research and for clinical applications. We are proposing a method to infer directed connectivity from zero-lag covariances of neuronal activity recorded at multiple sites. This allows us to identify causal relations that are reflected in neuronal population activity. To derive our strategy, we assume a generic linear model of interacting continuous variables, the components of which represent the activity of local neuronal populations. The suggested method for inferring connectivity from recorded signals exploits the fact that the…
In this paper, we present an EEG study of two music improvisation experiments. Professional musicians with high level of improvisation skills were asked to perform music either according to notes (composed music) or in improvisation. Each piece of music was performed in two different modes: strict mode and "let-go" mode. Synchronized EEG data was measured from both musicians and listeners. We used one of the most reliable causality measures: conditional mutual information from mixed embedding (MIME), to analyze directed correlations between different EEG channels, which was combined with netwo…
Circadian and other physiological rhythms play a key role in both normal homeostasis and disease processes. Such is the case of circadian and infradian seizure patterns observed in epilepsy. In this paper we explore a new implantable stimulator that implements chronotherapy as a feedforward input to supplement both open-loop and closed-loop methods. This integrated algorithm allows for stimulation to be adjusted to the ultradian, circadian and infradian patterns observed in patients through slowly-varying temporal adjustments of stimulation and algorithm sub-components, while also enabling ada…
Neural network oscillations are a fundamental mechanism for cognition, perception and consciousness. Consequently, perturbations of network activity play an important role in the pathophysiology of brain disorders. When structural information from non-invasive brain imaging is merged with mathematical modeling, then generative brain network models constitute in-silico platforms for the exploration of causal mechanisms of brain function and clinical hypothesis testing. We here demonstrate along the example of drug-resistant epilepsy that patient-specific virtual brain models derived from diffus…
High-fidelity and Network-based Spatio-temporal Mathematical Models of Alzheimer's Disease Progression and their Validation Against PET-SUVR Imaging Data
Dynamic directed functional connectivity as a neural biomarker for objective motor skill assessment
Rensselaer Polytechnic Institute（伦斯勒理工学院）；University at Buffalo（布法罗大学）；Uniformed Services University of the Health Sciences（卫生科学 uniformed 大学）；U.S. Army Futures Command, Combat Capabilities Development Command Soldier Center STTC（美国陆军未来司令部作战能力发展司令部士兵中心 STTC）；University of Birmingham（伯明翰大学）；Florida A&M University-Florida State University（佛罗里达农工大学-佛罗里达州立大学）
Brain function relies on a precisely coordinated and dynamic balance between the functional integration and segregation of distinct neural systems. Characterizing the way in which neural systems reconfigure their interactions to give rise to distinct but hidden brain states remains an open challenge. In this paper, we propose a Bayesian model-based characterization of latent brain states and showcase a novel method based on posterior predictive discrepancy using the latent block model to detect transitions between latent brain states in blood oxygen level-dependent (BOLD) time series. The set …
Recent machine learning models have shown that including attention as a component results in improved model accuracy and interpretability, despite the concept of attention in these approaches only loosely approximating the brain's attention mechanism. Here we extend this work by building a more brain-inspired deep network model of the primate ATTention Network (ATTNet) that learns to shift its attention so as to maximize the reward. Using deep reinforcement learning, ATTNet learned to shift its attention to the visual features of a target category in the context of a search task. ATTNet's dors…
The brain is a complex organ characterized by heterogeneous patterns of structural connections supporting unparalleled feats of cognition and a wide range of behaviors. New noninvasive imaging techniques now allow these patterns to be carefully and comprehensively mapped in individual humans and animals. Yet, it remains a fundamental challenge to understand how the brain's structural wiring supports cognitive processes, with major implications for the personalized treatment of mental health disorders. Here, we review recent efforts to meet this challenge that draw on intuitions, models, and th…
Segmenting deep brain structures from magnetic resonance images is important for patient diagnosis, surgical planning, and research. Most current state-of-the-art solutions follow a segmentation-by-registration approach, where subject MRIs are mapped to a template with well-defined segmentations. However, registration-based pipelines are time-consuming, thus, limiting their clinical use. This paper uses deep learning to provide a robust and efficient deep brain segmentation solution. The method consists of a pre-processing step to conform all MRI images to the same orientation, followed by a c…
The use of time- and frequency-based features has proven effective in the process of classifying mental tasks in Brain Computer Interfaces (BCIs). Still, most of those methods provide little insight about the underlying brain activity and functions. Thus, a better understanding of the mechanisms and dynamics of brain activity, is necessary in order to obtain useful and informative features for BCIs. In the present study, the objective is to investigate the differences in functional connectivity of two motor imagery tasks, through a partial directed coherence (PDC) analysis, which is a frequenc…
