Supplemental Digital Content is Available in the Text. Purpose:Object naming requires visual decoding, conceptualization, semantic categorization, and phonological encoding, all within 400 to 600 ms of stimulus presentation and before a word is spoken. In this study, we sought to predict semantic categories of naming responses based on prearticulatory brain activity recorded with scalp EEG in healthy individuals.Methods:We assessed 19 healthy individuals who completed a naming task while undergoing EEG. The naming task consisted of 120 drawings of animate/inanimate objects or abstract drawings. We applied a one-dimensional, two-layer, neural network to predict the semantic categories of naming responses based on prearticulatory brain activity.Results:Classifications of animate, inanimate, and abstract responses had an average accuracy of 80%, sensitivity of 72%, and specificity of 87% across participants. Across participants, time points with the highest average weights were between 470 and 490 milliseconds after stimulus presentation, and electrodes with the highest weights were located over the left and right frontal brain areas.Conclusions:Scalp EEG can be successfully used in predicting naming responses through prearticulatory brain activity. Interparticipant variability in feature weights suggests that individualized models are necessary for highest accuracy. Our findings may inform future applications of EEG in reconstructing speech for individuals with and without speech impairments.

Temporal lobe epilepsy is associated with MRI findings reflecting underlying mesial temporal sclerosis. Identifying these MRI features is critical for the diagnosis and management of temporal lobe epilepsy. To date, this process relies on visual assessment by highly trained human experts (e.g. neuroradiologists, epileptologists). Artificial intelligence is increasingly recognized as a promising aid in the radiological evaluation of neurological diseases, yet its applications in temporal lobe epilepsy have been limited. Here, we applied a convolutional neural network to assess the classification accuracy of temporal lobe epilepsy based on structural MRI. We demonstrate that convoluted neural networks can achieve high accuracy in the identification of unilateral temporal lobe epilepsy cases even when the MRI had been originally interpreted as normal by experts. We show that accuracy can be potentiated by employing smoothed grey matter maps and a direct acyclic graphs approach. We further discuss the foundations for the development of computer-aided tools to assist with the diagnosis of epilepsy.

Objectives.People with refractory epilepsy are overwhelmed by the uncertainty of their next seizures. Accurate prediction of future seizures could greatly improve the quality of life for these patients. New evidence suggests that seizure occurrences can have cyclical patterns for some patients. Even though these cyclicalities are not intuitive, they can be identified by machine learning (ML), to identify patients with predictable vs unpredictable seizure patterns.Approach.Self-reported seizure logs of 153 patients from the Human Epilepsy Project with more than three reported seizures (totaling 8337 seizures) were used to obtain inter-seizure interval time-series for training and evaluation of the forecasting models. Two classes of prediction methods were studied: (1) statistical approaches using Bayesian fusion of population-wise and individual-wise seizure patterns; and (2) ML-based algorithms including least squares, least absolute shrinkage and selection operator, support vector machine (SVM) regression, and long short-term memory regression. Leave-one-person-out cross-validation was used for training and evaluation, by training on seizure diaries of all except one subject and testing on the left-out subject.Main results.The leading forecasting models were the SVM regression and a statistical model that combined the median of population-wise seizure time-intervals with a test subject's prior seizure intervals. SVM was able to forecast 50%, 70%, 81%, 84%, and 87% of seizures of unseen subjects within 0, 1, 2, 3 to 4 d of mean absolute forecasting error, respectively. The subject-wise performances show that patients with more frequent seizures were generally better predicted.Significance.ML models can leverage non-random patterns within self-reported seizure diaries to forecast future seizures. While diary-based seizure forecasting alone is only one of many aspects of clinical care of patients with epilepsy, studying the level of predictability across seizures and patients paves the path towards a better understanding of predictable vs unpredictable seizures on individualized and population-wise bases.

Artificial intelligence has recently gained popularity across different medical fields to aid in the detection of diseases based on pathology samples or medical imaging findings. Brain magnetic resonance imaging (MRI) is a key assessment tool for patients with temporal lobe epilepsy (TLE). The role of machine learning and artificial intelligence to increase detection of brain abnormalities in TLE remains inconclusive. We used support vector machine (SV) and deep learning (DL) models based on region of interest (ROI-based) structural (n = 336) and diffusion (n = 863) brain MRI data from patients with TLE with ("lesional") and without ("non-lesional") radiographic features suggestive of underlying hippocampal sclerosis from the multinational (multi-center) ENIGMA-Epilepsy consortium. Our data showed that models to identify TLE performed better or similar (68-75%) compared to models to lateralize the side of TLE (56-73%, except structural-based) based on diffusion data with the opposite pattern seen for structural data (67-75% to diagnose vs. 83% to lateralize). In other aspects, structural and diffusion-based models showed similar classification accuracies. Our classification models for patients with hippocampal sclerosis were more accurate (68-76%) than models that stratified non-lesional patients (53-62%). Overall, SV and DL models performed similarly with several instances in which SV mildly outperformed DL. We discuss the relative performance of these models with ROI-level data and the implications for future applications of machine learning and artificial intelligence in epilepsy care.

High degree nodes in the white matter (WM) connectome are important in neuropsychiatric disease and cognitive function and are significantly related to hubs in the functional connectome.

Epilepsy is a common and serious neurological disorder, with many different constituent conditions characterized by their electro clinical, imaging, and genetic features. MRI has been fundamental in advancing our understanding of brain processes in the epilepsies. Smaller‐scale studies have identified many interesting imaging phenomena, with implications both for understanding pathophysiology and improving clinical care. Through the infrastructure and concepts now well‐established by the ENIGMA Consortium, ENIGMA‐Epilepsy was established to strengthen epilepsy neuroscience by greatly increasing sample sizes, leveraging ideas and methods established in other ENIGMA projects, and generating a body of collaborating scientists and clinicians to drive forward robust research. Here we review published, current, and future projects, that include structural MRI, diffusion tensor imaging (DTI), and resting state functional MRI (rsfMRI), and that employ advanced methods including structural covariance, and event‐based modeling analysis. We explore age of onset‐ and duration‐related features, as well as phenomena‐specific work focusing on particular epilepsy syndromes or phenotypes, multimodal analyses focused on understanding the biology of disease progression, and deep learning approaches. We encourage groups who may be interested in participating to make contact to further grow and develop ENIGMA‐Epilepsy.

Objective Medial temporal lobe epilepsy (TLE) is the most common form of medication‐resistant focal epilepsy in adults. Despite removal of medial temporal structures, more than one‐third of patients continue to have disabling seizures postoperatively. Seizure refractoriness implies that extramedial regions are capable of influencing the brain network and generating seizures. We tested whether abnormalities of structural network integration could be associated with surgical outcomes. Methods Presurgical magnetic resonance images from 121 patients with drug‐resistant TLE across 3 independent epilepsy centers were used to train feed‐forward neural network models based on tissue volume or graph‐theory measures from whole‐brain diffusion tensor imaging structural connectomes. An independent dataset of 47 patients with TLE from 3 other epilepsy centers was used to assess the predictive values of each model and regional anatomical contributions toward surgical treatment results. Results The receiver operating characteristic area under the curve based on regional betweenness centrality was 0.88, significantly higher than a random model or models based on gray matter volumes, degree, strength, and clustering coefficient. Nodes most strongly contributing to the predictive models involved the bilateral parahippocampal gyri, as well as the superior temporal gyri. Interpretation Network integration in the medial and lateral temporal regions was related to surgical outcomes. Patients with abnormally integrated structural network nodes were less likely to achieve seizure freedom. These findings are in line with previous observations related to network abnormalities in TLE and expand on the notion of underlying aberrant plasticity. Our findings provide additional information on the mechanisms of surgical refractoriness. ANN NEUROL 2020;88:970–983

Structural neural network architecture patterns in the human brain could be related to individual differences in phenotype, behavior, genetic determinants, and clinical outcomes from neuropsychiatric disorders. Recent studies have indicated that a personalized neural (brain) fingerprint can be identified from structural brain connectomes. However, the accuracy, reproducibility and translational potential of personalized fingerprints in terms of cognition is not yet fully determined. In this study, we introduce a dynamic connectome modeling approach to identify a critical set of white matter subnetworks that can be used as a personalized fingerprint. Several individual variable assessments were performed that demonstrate the accuracy and practicality of personalized fingerprint, specifically predicting the identity and IQ of middle age adults, and the developmental quotient in toddlers. Our findings suggest the fingerprint found by our dynamic modeling approach is sufficient for differentiation between individuals, and is also capable of predicting general intellectual ability across human development.

The functional brain network has gained increased attention in the neuroscience community because of its ability to reveal the underlying architecture of human brain. In general, majority work of functional network connectivity is built based on the correlations between discrete-time-series signals that link only two different brain regions. However, these simple region-to-region connectivity models do not capture complex connectivity patterns between three or more brain regions that form a connectivity subnetwork, or subnetwork for short. To overcome this current limitation, a hypergraph learning-based method is proposed to identify subnetwork differences between two different cohorts. To achieve our goal, a hypergraph is constructed, where each vertex represents a subject and also a hyperedge encodes a subnetwork with similar functional connectivity patterns between different subjects. Unlike previous learning-based methods, our approach is designed to jointly optimize the weights for all hyperedges such that the learned representation is in consensus with the distribution of phenotype data, i.e. clinical labels. In order to suppress the spurious subnetwork biomarkers, we further enforce a sparsity constraint on the hyperedge weights, where a larger hyperedge weight indicates the subnetwork with the capability of identifying the disorder condition. We apply our hypergraph learning-based method to identify subnetwork biomarkers in Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD). A comprehensive quantitative and qualitative analysis is performed, and the results show that our approach can correctly classify ASD and ADHD subjects from normal controls with 87.65 and 65.08% accuracies, respectively.

Non-fluent speech is one of the most common impairments in post-stroke aphasia. The rehabilitation of non-fluent speech in aphasia is particularly challenging as patients are rarely able to produce and practice fluent speech production. Speech entrainment is a behavioural technique that enables patients with non-fluent aphasia to speak fluently. However, its mechanisms are not well understood and the level of improved fluency with speech entrainment varies among individuals with non-fluent aphasia. In this study, we evaluated the behavioural and neuroanatomical factors associated with better speech fluency with the aid of speech entrainment during the training phase of speech entrainment. We used a lesion-symptom mapping approach to define the relationship between chronic stroke location on MRI and the number of different words per second produced during speech entrainment versus picture description spontaneous speech. The behavioural variable of interest was the speech entrainment/picture description ratio, which, if ≥1, indicated an increase in speech output during speech entrainment compared to picture description. We used machine learning (shallow neural network) to assess the statistical significance and out-of-sample predictive accuracy of the neuroanatomical model, and its regional contributors. We observed that better assisted speech (higher speech entrainment/picture description ratio) was achieved by individuals who had preservation of the posterior middle temporal gyrus, inferior fronto-occipital fasciculus and uncinate fasciculus, while exhibiting lesions in areas typically associated with non-fluent aphasia, such as the superior longitudinal fasciculus, precentral, inferior frontal, supramarginal and insular cortices. Our findings suggest that individuals with dorsal stream damage but preservation of ventral stream structures are more likely to achieve more fluent speech with the aid of speech entrainment compared to spontaneous speech. This observation provides insight into the mechanisms of non-fluent speech in aphasia and has potential implications for future research using speech entrainment for rehabilitation of non-fluent aphasia.

The original version of the chapter “GRAND: Unbiased Connectome Atlas of Brain Network by Groupwise Graph Shrinkage and Network Diffusion”, starting on p.127 was revised. An acknowledgment has been added. The original chapter was corrected.

Cognitive ability is an important predictor of mental health outcomes that is influenced by neurodevelopment. Evidence suggests that the foundational wiring of the human brain is in place by birth, and that the white matter (WM) connectome supports developing brain function. It is unknown, however, how the WM connectome at birth supports emergent cognition. In this study, a deep learning model was trained using cross-validation to classify full-term infants (n = 75) as scoring above or below the median at age 2 using WM connectomes generated from diffusion weighted magnetic resonance images at birth. Results from this model were used to predict individual cognitive scores. We additionally identified WM connections important for classification. The model was also evaluated in a separate set of preterm infants (n = 37) scanned at term-age equivalent. Findings revealed that WM connectomes at birth predicted 2-year cognitive score group with high accuracy in both full-term (89.5%) and preterm (83.8%) infants. Scores predicted by the model were strongly correlated with actual scores (r = 0.98 for full-term and r = 0.96 for preterm). Connections within the frontal lobe, and between the frontal lobe and other brain areas were found to be important for classification. This work suggests that WM connectomes at birth can accurately predict a child's 2-year cognitive group and individual score in full-term and preterm infants. The WM connectome at birth appears to be a useful neuroimaging biomarker of subsequent cognitive development that deserves further study.

ObjectiveWe evaluated whether deep learning applied to whole-brain presurgical structural connectomes could be used to predict postoperative seizure outcome more accurately than inference from clinical variables in patients with mesial temporal lobe epilepsy (TLE). MethodsFifty patients with unilateral TLE were classified either as having persistent disabling seizures (SZ) or becoming seizure-free (SZF) at least 1year after epilepsy surgery. Their presurgical structural connectomes were reconstructed from whole-brain diffusion tensor imaging. A deep network was trained based on connectome data to classify seizure outcome using 5-fold cross-validation. ResultsClassification accuracy of our trained neural network showed positive predictive value (PPV; seizure freedom) of 887% and mean negative predictive value (NPV; seizure refractoriness) of 79 +/- 8%. Conversely, a classification model based on clinical variables alone yielded <50% accuracy. The specific features that contributed to high accuracy classification of the neural network were located not only in the ipsilateral temporal and extratemporal regions, but also in the contralateral hemisphere. SignificanceDeep learning demonstrated to be a powerful statistical approach capable of isolating abnormal individualized patterns from complex datasets to provide a highly accurate prediction of seizure outcomes after surgery. Features involved in this predictive model were both ipsilateral and contralateral to the clinical foci and spanned across limbic and extralimbic networks.

The internet revolution has made information acquisition easy and cheap so that it has been producing massive Web/social data in our real life.The emergence of big social media has lead researchers to study the possibility of their exploitation in order to identify hidden knowledge.However, a huge number of issues appear in obtained big social data [23,24,26,28].First, there are incomplete social data due to all kinds of reasons, such as security and private information.Second, the structure of social data is different, including structured data (e.g., social Web data), semi-structured data (e.g., XML data) and unstructured data (e.g., social networks).Third, the Web data are often highdimensional.However, current computer techniques can only deal with structured, complete and moderate-sized-dimensional data.Moreover, current computer technologies can only mine the basic structure and are not capable of mining their natural complex structure (or deep structure).Hence, there is a huge gap between existing technologies and the real requirements of actual big social data.In this case, deep mining of big social data (such as data preprocessing, deep pattern discovery, pattern fusion, and outlier/noise detection) stands as an interesting promise to relief such a gap [4,8,22,25,27] .In [7], Komarasamy et al. proposed a multi-phase scheduling method to deal with parallel jobs in hierarchical model.The proposed method includes job preprocessing, prioritization,

3D reconstruction of the fiber connectivity of the rat brain at microscopic scale enables gaining detailed insight about the complex structural organization of the brain. We introduce a new method for registration and 3D reconstruction of highand ultra-high resolution ( 64 μm and 1.3 μm pixel size) histological images of a Wistar rat brain acquired by 3D polarized light imaging (3D-PLI). Our method exploits multi-scale and multi-modal 3D-PLI data up to cellular resolution. We propose a new feature transform-based similarity measure and a weighted regularization scheme for accurate and robust non-rigid registration. To transform the 1.3 μm ultra-high resolution data to the reference blockface images a feature-based registration method followed by a non-rigid registration is proposed. Our approach has been successfully applied to 278 histological sections of a rat brain and the performance has been quantitatively evaluated using manually placed landmarks by an expert.

Network science is enhancing our understanding of how the human brain works at a systems level. A complete population-wise mapping of region-to-region connections, called connectome atlas, is the key to gaining a more full understanding for network-related brain disorders and for discovering biomarkers for early diagnosis. Since a brain network is commonly encoded in an adjacency matrix, it is difficult to apply the state-of-the-art atlas construction approaches by normalizing and averaging the individual adjacency matrices into a common space. In this paper, we propose a novel data-driven approach to construct an unbiased connectome atlas to capture both shared and complementary network topologies across individual brain networks, offering insight into the full spectrum of brain connectivity. Specifically, we employ a hypergraph to model the manifold of a population of brain networks. In this hypergraph, each node represents the individual participant’s brain network, and the edge weight captures the distance between two participants’ brain networks. The construction of a connectome atlas can be achieved using a hierarchical process of graph shrinkage toward the latent common space where the network topologies of all individual brain networks gradually become similar to each other. During the graph shrinkage, the adjacency matrix of each brain network is transformed to the common space by a series of diffusion matrices which exchange the connectome information with respect to the adjacency matrices on the neighboring hypergraph nodes such that the most representative characteristics of network topology are eventually propagated to the final connectome atlas. We have validated our connectome atlas construction method on the simulated brain network data and DTI data of 111 twin pairs in determining the genetic contribution of the structural connectivity.

Identifying white matter connectivity patterns in the human brain derived from neuroimaging data is an important area of research in computational medicine. Recently, machine learning techniques typically use region-to-region or hub-base connectivity features to understand how the brain is organized, and then use this information to predict the clinical outcome. Unfortunately, computational models that are trained with these types of features are very localized to a particular region in the brain, i.e. one particular brain region or two connected brain regions, and may not provide the level of information needed to understand more complex relationships that span multiple connected brain regions. To overcome this limitation a new subnetwork feature is introduced that combine region-to-region and hub-based delay information using the shortest path algorithm. The proposed feature is then used to construct a deep learning model to recognize the identity of 20 different subjects. The results show person identification models trained with our feature are approximately 30% and 50% more accurate than models trained only using hub-based features and region-to-region features, respectively. Lastly, a connectome fingerprint is identified using a neural network backtrack approach that selects the subnetwork features that are responsible for classification performance.

The underlying neural mechanisms of autism spectrum disorders (ASD) remains unclear. Most of the previous studies based on connectomics to discriminate ASD from typically developing (TD) subjects focused either on global graph metrics or specific discriminant connections. In this paper we investigate whether there is a correlation between local and global features, and whether the characterization that discriminates ASD from TD subjects is primarily given by widespread network differences, or the difference lies in specific local connections which are just captured by global metrics. Namely, whether miswiring of brain connections related to ASD is localized or diffuse. The presented results suggest that the widespread hypothesis is more likely.

Statistical shape modeling has been widely used in medical-imaging applications, where the shape and possible shape variation of an anatomic structure are derived from a set of shape instances of the structure. Shape correspondence, which aims to accurately identify corresponding landmarks across the given set of shape instances, is one of the main challenges in constructing a statistical shape model. In this chapter, we will discuss several important research topics in shape correspondence, including 1) pairwise shape correspondence, 2) groupwise shape correspondence, and 3) shape-correspondence performance evaluation. For simplicity, we focus on 2D shape correspondence in this chapter, with a brief discussion of the extensions to 3D shape correspondence at the end. Experiments on sample structures are presented to evaluate the performance of the proposed methods.

Synopsis Most brain network connectivity models consider correlations between discrete-time series signals that only connect two brain regions. Here we propose a method to explore subnetwork biomarkers that are significantly distinguishable between two clinical cohorts. We construct a hypergraph by exhaustively inspecting all possible sub-networks for all subjects. The objective function of hypergraph learning is to jointly optimize the weights for all hyperedges. We deploy our method to find high order childhood autism biomarkers from rs-fMRI images. Promising results have been obtained from comprehensive evaluation on the discriminative power in diagnosis of Autism. propose a novel learning-based method to discover high order sub-network connectome biomarkers that can be used to distinguish two clinical cohorts.