An architectural framework for automatic detection of autism using deep convolution networks and genetic algorithm

The brainchild in any medical image processing lied in how accurately the diseases are diagnosed. Especially in the case of neural disorders such as autism spectrum disorder (ASD), accurate detection was still a challenge. Several noninvasive neuroimaging techniques provided experts information about the functionality and anatomical structure of the brain. As autism is a neural disorder, magnetic resonance imaging (MRI) of the brain gave a complex structure and functionality. Many machine learning techniques were proposed to improve the classification and detection accuracy of autism in MRI images. Our work focused mainly on developing the architecture of convolution neural networks (CNN) combining the genetic algorithm. Such artificial intelligence (AI) techniques were very much needed for training as they gave better accuracy compared to traditional statistical methods.

Morphological substrate and clinical manifestations of neurosurgical conditions

Frequent dissociations between morphological substrate and clinical manifestations of the disorder were analyzed. Noninvasive neuroimaging techniques created the opportunity for the life-time verification of incidental findings, which resulted in development of the new area, preventive neurosurgery. Systematization, diagnosis, and criteria for the surgical treatment of incidental findings in neurosurgery are reported. It had been emphasized that while the brain and spinal cord disorder recognition during the preclinical period can only be accomplished based on imaging data, the decision on the management and treatment strategy in apparently healthy individuals has to be clinical-philosophical, and has to be made in view of the subsequent quality of life.

Three-dimensional optical imaging of brain activation during transcranial magnetic stimulation

Repetitive transcranial magnetic stimulation (rTMS) of the brain is an effective clinical treatment for psychiatric disorders. Noninvasive neuroimaging during rTMS allows visualization of cortical brain activations and responses, and it is a potential tool for investigating the neurophysiological response occurring actively during stimulation. In this paper, we present a fast diffuse optical tomography (DOT) approach for three-dimensional brain mapping of hemodynamics during rTMS. Eight healthy subjects were enrolled in the study. These subjects received 10 Hz stimulation with 80%and 100%of resting motor threshold (rMT), respectively, for 4 seconds for each stimulation. Significant hemodynamic activation was observed in all cases with the strongest response when 100%rMT stimulation was applied. This work demonstrates that fast DOT has the potential to become a powerful tool for noninvasive three-dimensional imaging of the brain during rTMS.

Reliability and response assessment of brain responses in fNIRS

Background: Functional near-infrared spectroscopy (fNIRS) is a new noninvasive neuroimaging technology that detects both oxyhemoglobin hemodynamics (HbO) and deoxy-hemoglobin hemodynamics (HbR), but there is no assessment approach that emphasizes the merits of fNIRS. New method: Based on fNIRS, we established an indicator system named the Area-Under-Curve-based Indicator System (AUCIS) to estimate the effect reliability of brain responses. Evaluating the positive and negative responses for HbO and HbR can better explain, to some extent, the comprehensive physiological mechanism of oxygen delivery to and extraction in the brain. Moreover, we also established a reliability coefficient, named AUC’ α, to assess the robustness of within-subject condition effects. Results and Comparison: To validate the AUCIS, we used a simulation-based HRF signal and an open database and compared the performance with other general indicators. The AUCIS showed a greater relative sensitivity and robustness, which can be explained in terms of oxygen delivery and extraction based on the negative and positive responses of HbO and HbR.

In vivo direct imaging of neuronal activity at high temporo-spatial resolution

There has been a longstanding demand for noninvasive neuroimaging methods capable of detecting neuronal activity at both high temporal and spatial resolution. Here, we propose a novel method that enables Direct Imaging of Neuronal Activity for functional MRI (termed DIANA-fMRI) that can dynamically image spiking activity in milliseconds precision, while retaining the original benefit of high spatial resolution of MRI. DIANA-fMRI was demonstrated through in vivo mice brain imaging at 9.4 T applying electrical whisker-pad stimulation, directly imaging the spiking activity as well as capturing its sequential propagation along the thalamocortical pathway, as further confirmed through in vivo spike recording and optogenetics. DIANA-fMRI will open up new avenues in brain science by providing a deeper understanding of the brain's functional organization including neural networks.

Systemic Review on Transcranial Electrical Stimulation Parameters and EEG/fNIRS Features for Brain Diseases

BackgroundBrain disorders are gradually becoming the leading cause of death worldwide. However, the lack of knowledge of brain disease’s underlying mechanisms and ineffective neuropharmacological therapy have led to further exploration of optimal treatments and brain monitoring techniques.ObjectiveThis study aims to review the current state of brain disorders, which utilize transcranial electrical stimulation (tES) and daily usable noninvasive neuroimaging techniques. Furthermore, the second goal of this study is to highlight available gaps and provide a comprehensive guideline for further investigation.MethodA systematic search was conducted of the PubMed and Web of Science databases from January 2000 to October 2020 using relevant keywords. Electroencephalography (EEG) and functional near-infrared spectroscopy were selected as noninvasive neuroimaging modalities. Nine brain disorders were investigated in this study, including Alzheimer’s disease, depression, autism spectrum disorder, attention-deficit hyperactivity disorder, epilepsy, Parkinson’s disease, stroke, schizophrenia, and traumatic brain injury.ResultsSixty-seven studies (1,385 participants) were included for quantitative analysis. Most of the articles (82.6%) employed transcranial direct current stimulation as an intervention method with modulation parameters of 1 mA intensity (47.2%) for 16–20 min (69.0%) duration of stimulation in a single session (36.8%). The frontal cortex (46.4%) and the cerebral cortex (47.8%) were used as a neuroimaging modality, with the power spectrum (45.7%) commonly extracted as a quantitative EEG feature.ConclusionAn appropriate stimulation protocol applying tES as a therapy could be an effective treatment for cognitive and neurological brain disorders. However, the optimal tES criteria have not been defined; they vary across persons and disease types. Therefore, future work needs to investigate a closed-loop tES with monitoring by neuroimaging techniques to achieve personalized therapy for brain disorders.

NeuroModulation Modeling (NMM): Inferring the form of neuromodulation from fMRI tuning functions

Many cognitive neuroscience theories assume that changes in behavior arise from changes in the tuning properties of neurons (e.g., Dosher & Lu 1998, Ling, Liu, & Carrasco 2009). However, direct tests of these theories with electrophysiology are rarely feasible with humans. Non-invasive functional magnetic resonance imaging (fMRI) produces voxel tuning, but each voxel aggregates hundreds of thousands of neurons, and voxel tuning modulation is a complex mixture of the underlying neural responses. We developed a pair of statistical tools to address this problem, which we refer to as NeuroModulation Modeling (NMM). NMM advances fMRI analysis methods, inferring the response of neural subpopulations by leveraging modulations at the voxel-level to differentiate between different forms of neuromodulation. One tool uses hierarchical Bayesian modeling and model comparison while the other tool uses a non-parametric slope analysis. We tested the validity of NMM by applying it to fMRI data collected from participants viewing orientation stimuli at high- and low-contrast, which is known from electrophysiology to cause multiplicative scaling of neural tuning (e.g., Sclar & Freeman 1982). In seeming contradiction to ground truth, increasing contrast appeared to cause an additive shift in orientation tuning of voxel-level fMRI data. However, NMM indicated multiplicative gain rather than an additive shift, in line with single-cell electrophysiology. Beyond orientation, this approach could be applied to determine the form of neuromodulation in any fMRI experiment, provided that the experiment tests multiple points along a stimulus dimension to which neurons are tuned (e.g., direction of motion, isoluminant hue, pitch, etc.).Significance StatementThe spatial resolution afforded by noninvasive neuroimaging in humans continues to improve, but the best available resolution is insufficient for testing theories in cognitive neuroscience; many theories are specified at the level of individual neurons, but magnetic resonance imaging aggregates over hundreds of thousands of neurons. With limited resolution, it is unclear how to test assumptions and predictions of these theories in humans. To bridge this gap, we developed a modeling framework that allows researchers to infer a key property of the neural code -- how stimulus features and cognitive states modulate neural tuning – given only noninvasive neuroimaging data. The framework is broadly applicable to constrain and test theories that link changes in behavior to changes in neural tuning.

The ENIGMA Toolbox: Cross-disorder integration and multiscale neural contextualization of multisite neuroimaging datasets

Among ‘big data’ initiatives, the ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) Consortium—a worldwide alliance of over 2,000 scientists diversified into over 50 Working Groups—has yielded some of the largest studies of the healthy and diseased brain. Integration of multisite datasets to assess transdiagnostic similarities and differences and to contextualize findings with respect to neural organization, however, have been limited. Here, we introduce the ENIGMA Toolbox, a Python/Matlab ecosystem for (i) accessing ENIGMA datasets, allowing for cross-disorder analysis, (ii) visualizing data on brain surfaces, and (iii) contextualizing findings at the microscale (postmortem cytoarchitecture and gene expression) and macroscale (structural and functional connectomes). Our Toolbox equips scientists with tutorials to explore molecular, histological, and network correlates of noninvasive neuroimaging markers of brain disorders. Moreover, our Toolbox bridges the gap between standardized data processing protocols and analytic workflows and facilitates cross-consortia initiatives. The Toolbox is documented and openly available at http://enigma-toolbox.readthedocs.io.Abstract Figure

Abstract WP84: MRI Based Scores Outperform CTA Based Scores in Predicting Outcome After Basilar Artery Occlusion

Introduction: Clinical outcomes in patients with acute basilar occlusion (BAO) vary widely; several prognostic scores based on noninvasive imaging have been proposed. We aimed to compare the predictive value of several noninvasive neuroimaging scores in patients with BAO. Methods: We performed a single-center retrospective cohort study including all patients with acute BAO from 2015-2019. Using available clinical radiographic data, we calculated the following scores based on computed tomography (CT) and CT angiogram: Goyal posterior communicating artery score, posterior circulation collateral score, Basilar Artery on Computed Tomography Angiography (BATMAN) score, pc-ASPECTS score, and pons-midbrain index. We also calculated the following scores based on diffusion-weighted (DWI) magnetic resonance imaging (MRI): Bern DWI score, MRI pc-ASPECTS, and pons-midbrain index on DWI. We then used logistic regression with area under the ROC curve analysis to determine the accuracy of each score in predicting favorable 3-month outcome (modified Rankin Scale 0-2). Results: Of 39 patients in our cohort, 92.3% underwent endovascular treatment (n=36) and 35.8% (n=14) had a favorable 3-month outcome. The Bern DWI score (AUC 0.790, 95% CI 0.619-0.960), pc-ASPECTS on MRI (AUC 0.880, 95% CI 0.601-0.958), and pons-midbrain index on MRI (AUC 0.764, 95% CI 0.594-0.934) accurately predicted 3-month outcome when analyzed as raw scores (Figure 1).: Conclusion: MRI scores more strongly predict outcome in BAO as compared to CTA collateral scores. Larger prospective studies are needed to confirm our findings.

Noninvasive neuroimaging enhances continuous neural tracking for robotic device control

Brain-computer interfaces (BCIs) using signals acquired with intracortical implants have achieved successful high-dimensional robotic device control useful for completing daily tasks. However, the substantial amount of medical and surgical expertise required to correctly implant and operate these systems greatly limits their use beyond a few clinical cases. A noninvasive counterpart requiring less intervention that can provide high-quality control would profoundly improve the integration of BCIs into the clinical and home setting. Here, we present and validate a noninvasive framework using electroencephalography (EEG) to achieve the neural control of a robotic device for continuous random target tracking. This framework addresses and improves upon both the “brain” and “computer” components by increasing, respectively, user engagement through a continuous pursuit task and associated training paradigm and the spatial resolution of noninvasive neural data through EEG source imaging. In all, our unique framework enhanced BCI learning by nearly 60% for traditional center-out tasks and by more than 500% in the more realistic continuous pursuit task. We further demonstrated an additional enhancement in BCI control of almost 10% by using online noninvasive neuroimaging. Last, this framework was deployed in a physical task, demonstrating a near-seamless transition from the control of an unconstrained virtual cursor to the real-time control of a robotic arm. Such combined advances in the quality of neural decoding and the practical utility of noninvasive robotic arm control will have major implications for the eventual development and implementation of neurorobotics by means of noninvasive BCI.