Brain Decoding Using fMRI Images for Multiple Subjects through Deep Learning

Substantial information related to human cerebral conditions can be decoded through various noninvasive evaluating techniques like fMRI. Exploration of the neuronal activity of the human brain can divulge the thoughts of a person like what the subject is perceiving, thinking, or visualizing. Furthermore, deep learning techniques can be used to decode the multifaceted patterns of the brain in response to external stimuli. Existing techniques are capable of exploring and classifying the thoughts of the human subject acquired by the fMRI imaging data. fMRI images are the volumetric imaging scans which are highly dimensional as well as require a lot of time for training when fed as an input in the deep learning network. However, the hassle for more efficient learning of highly dimensional high-level features in less training time and accurate interpretation of the brain voxels with less misclassification error is needed. In this research, we propose an improved CNN technique where features will be functionally aligned. The optimal features will be selected after dimensionality reduction. The highly dimensional feature vector will be transformed into low dimensional space for dimensionality reduction through autoadjusted weights and combination of best activation functions. Furthermore, we solve the problem of increased training time by using Swish activation function, making it denser and increasing efficiency of the model in less training time. Finally, the experimental results are evaluated and compared with other classifiers which demonstrated the supremacy of the proposed model in terms of accuracy.

Alzheimer's Disease Classification Based on Image Transformation and Features Fusion

It has become an inevitable trend for medical personnel to analyze and diagnose Alzheimer's disease (AD) in different stages by combining functional magnetic resonance imaging (fMRI) and artificial intelligence technologies such as deep learning in the future. In this paper, a classification method was proposed for AD based on two different transformation images of fMRI and improved the 3DPCANet model and canonical correlation analysis (CCA). The main ideas include that, firstly, fMRI images were preprocessed, and subsequently, mean regional homogeneity (mReHo) and mean amplitude of low-frequency amplitude (mALFF) transformation were performed for the preprocessed images. Then, mReHo and mALFF images were extracted features using the improved 3DPCANet, and these two kinds of the extracted features were fused by CCA. Finally, the support vector machine (SVM) was used to classify AD patients with different stages. Experimental results showed that the proposed approach was robust and effective. Classification accuracy for significant memory concern (SMC) vs. mild cognitive impairment (MCI), normal control (NC) vs. AD, and NC vs. SMC, respectively, reached 95.00%, 92.00%, and 91.30%, which adequately proved the feasibility and effectiveness of the proposed method.

Diagnostic Classification of Patients with Dilated Cardiomyopathy Using Ventricular Strain Analysis Algorithm

Dilated cardiomyopathy (DCM) is a cardiomyopathy with left ventricle or double ventricle enlargement and systolic dysfunction. It is an important cause of sudden cardiac death and heart failure and is the leading indication for cardiac transplantation. Major heart diseases like heart muscle damage and valvular problems are diagnosed using cardiac MRI. However, it takes time for cardiologists to measure DCM-related parameters to decide whether patients have this disease. We have presented a method for automatic ventricular segmentation, parameter extraction, and diagnosing DCM. In this paper, left ventricle and right ventricle are segmented by parasternal short-axis cardiac MR image sequence; then, related parameters are extracted in the end-diastole and end-systole of the heart. Machine learning classifiers use extracted parameters as input to predict normal people and patients with DCM, among which Random forest classifier gives the highest accuracy. The results show that the proposed system can be effectively utilized to detect and diagnose DCM automatically. The experimental results suggest the capabilities and advantages of the proposed method to diagnose DCM. A small amount of sample input can generate results comparable to more complex methods.

Dynamic Electrode-to-Image (DETI) mapping reveals the human brain's spatiotemporal code of visual information

A number of neuroimaging techniques have been employed to understand how visual information is transformed along the visual pathway. Although each technique has spatial and temporal limitations, they can each provide important insights into the visual code. While the BOLD signal of fMRI can be quite informative, the visual code is not static and this can be obscured by fMRI’s poor temporal resolution. In this study, we leveraged the high temporal resolution of EEG to develop an encoding technique based on the distribution of responses generated by a population of real-world scenes. This approach maps neural signals to each pixel within a given image and reveals location-specific transformations of the visual code, providing a spatiotemporal signature for the image at each electrode. Our analyses of the mapping results revealed that scenes undergo a series of nonuniform transformations that prioritize different spatial frequencies at different regions of scenes over time. This mapping technique offers a potential avenue for future studies to explore how dynamic feedforward and recurrent processes inform and refine high-level representations of our visual world.

The visual information that we sample from our environment undergoes a series of neural modifications, with each modification state (or visual code) consisting of a unique distribution of responses across neurons along the visual pathway. However, current noninvasive neuroimaging techniques provide an account of that code that is coarse with respect to time or space. Here, we present dynamic electrode-to-image (DETI) mapping, an analysis technique that capitalizes on the high temporal resolution of EEG to map neural signals to each pixel within a given image to reveal location-specific modifications of the visual code. The DETI technique reveals maps of features that are associated with the neural signal at each pixel and at each time point. DETI mapping shows that real-world scenes undergo a series of nonuniform modifications over both space and time. Specifically, we find that the visual code varies in a location-specific manner, likely reflecting that neural processing prioritizes different features at different image locations over time. DETI mapping therefore offers a potential avenue for future studies to explore how each modification state informs and refines the conceptual meaning of our visual world.

Topology-preserving smoothing of retinotopic maps

Retinotopic mapping, i.e., the mapping between visual inputs on the retina and neuronal activations in cortical visual areas, is one of the central topics in visual neuroscience. For human observers, the mapping is obtained by analyzing functional magnetic resonance imaging (fMRI) signals of cortical responses to slowly moving visual stimuli on the retina. Although it is well known from neurophysiology that the mapping is topological (i.e., the topology of neighborhood connectivity is preserved) within each visual area, retinotopic maps derived from the state-of-the-art methods are often not topological because of the low signal-to-noise ratio and spatial resolution of fMRI. The violation of topological condition is most severe in cortical regions corresponding to the neighborhood of the fovea (e.g., < 1 degree eccentricity in the Human Connectome Project (HCP) dataset), significantly impeding accurate analysis of retinotopic maps. This study aims to directly model the topological condition and generate topology-preserving and smooth retinotopic maps. Specifically, we adopted the Beltrami coefficient, a metric of quasiconformal mapping, to define the topological condition, developed a mathematical model to quantify topological smoothing as a constrained optimization problem, and elaborated an efficient numerical method to solve the problem. The method was then applied to V1, V2, and V3 simultaneously in the HCP dataset. Experiments with both simulated and real retinotopy data demonstrated that the proposed method could generate topological and smooth retinotopic maps.

Behavioral correlates of cortical semantic representations modeled by word vectors

The quantitative modeling of semantic representations in the brain plays a key role in understanding the neural basis of semantic processing. Previous studies have demonstrated that word vectors, which were originally developed for use in the field of natural language processing, provide a powerful tool for such quantitative modeling. However, whether semantic representations in the brain revealed by the word vector-based models actually capture our perception of semantic information remains unclear, as there has been no study explicitly examining the behavioral correlates of the modeled brain semantic representations. To address this issue, we compared the semantic structure of nouns and adjectives in the brain estimated from word vector-based brain models with that evaluated from human behavior. The brain models were constructed using voxelwise modeling to predict the functional magnetic resonance imaging (fMRI) response to natural movies from semantic contents in each movie scene through a word vector space. The semantic dissimilarity of brain word representations was then evaluated using the brain models. Meanwhile, data on human behavior reflecting the perception of semantic dissimilarity between words were collected in psychological experiments. We found a significant correlation between brain model- and behavior-derived semantic dissimilarities of words. This finding suggests that semantic representations in the brain modeled via word vectors appropriately capture our perception of word meanings.

Multimodal principal component analysis to identify major features of white matter structure and links to reading

The role of white matter in reading has been established by diffusion tensor imaging (DTI), but DTI cannot identify specific microstructural features driving these relationships. Neurite orientation dispersion and density imaging (NODDI), inhomogeneous magnetization transfer (ihMT) and multicomponent driven equilibrium single-pulse observation of T1/T2 (mcDESPOT) can be used to link more specific aspects of white matter microstructure and reading due to their sensitivity to axonal packing and fiber coherence (NODDI) and myelin (ihMT and mcDESPOT). We applied principal component analysis (PCA) to combine DTI, NODDI, ihMT and mcDESPOT measures (10 in total), identify major features of white matter structure, and link these features to both reading and age. Analysis was performed for nine reading-related tracts in 46 neurotypical 6–16 year olds. We identified three principal components (PCs) which explained 79.5% of variance in our dataset. PC1 probed tissue complexity, PC2 described myelin and axonal packing, while PC3 was related to axonal diameter. Mixed effects regression models did not identify any significant relationships between principal components and reading skill. Bayes factor analysis revealed that the absence of relationships was not due to low power. Increasing PC1 in the left arcuate fasciculus with age suggest increases in tissue complexity, while increases of PC2 in the bilateral arcuate, inferior longitudinal, inferior fronto-occipital fasciculi, and splenium suggest increases in myelin and axonal packing with age. Multimodal white matter imaging and PCA provide microstructurally informative, powerful principal components which can be used by future studies of development and cognition. Our findings suggest major features of white matter undergo development during childhood and adolescence, but changes are not linked to reading during this period in our typically-developing sample.

A Comparative Analysis of Visual Encoding Models Based on Classification and Segmentation Task-Driven CNNs

Nowadays, visual encoding models use convolution neural networks (CNNs) with outstanding performance in computer vision to simulate the process of human information processing. However, the prediction performances of encoding models will have differences based on different networks driven by different tasks. Here, the impact of network tasks on encoding models is studied. Using functional magnetic resonance imaging (fMRI) data, the features of natural visual stimulation are extracted using a segmentation network (FCN32s) and a classification network (VGG16) with different visual tasks but similar network structure. Then, using three sets of features, i.e., segmentation, classification, and fused features, the regularized orthogonal matching pursuit (ROMP) method is used to establish the linear mapping from features to voxel responses. The analysis results indicate that encoding models based on networks performing different tasks can effectively but differently predict stimulus-induced responses measured by fMRI. The prediction accuracy of the encoding model based on VGG is found to be significantly better than that of the model based on FCN in most voxels but similar to that of fused features. The comparative analysis demonstrates that the CNN performing the classification task is more similar to human visual processing than that performing the segmentation task.

Sample size evolution in neuroimaging research: An evaluation of highly-cited studies (1990-2012) and of latest practices (2017-2018) in high-impact journals

We evaluated 1038 of the most cited structural and functional (fMRI) magnetic resonance brain imaging papers (1161 studies) published during 1990-2012 and 270 papers (300 studies) published in top neuroimaging journals in 2017 and 2018. 96% of highly cited experimental fMRI studies had a single group of participants and these studies had median sample size of 12, highly cited clinical fMRI studies (with patient participants) had median sample size of 14.5, and clinical structural MRI studies had median sample size of 50. The sample size of highly cited experimental fMRI studies increased at a rate of 0.74 participant/year and this rate of increase was commensurate with the median sample sizes of neuroimaging studies published in top neuroimaging journals in 2017 (23 participants) and 2018 (24 participants). Only 4 of 131 papers in 2017 and 5 of 142 papers in 2018 had pre-study power calculations, most for single t-tests and correlations. Only 14% of highly cited papers reported the number of excluded participants whereas 49% of papers with their own data in 2017 and 2018 reported excluded participants. Publishers and funders should require pre-study power calculations necessitating the specification of effect sizes. The field should agree on universally required reporting standards. Reporting formats should be standardized so that crucial study parameters could be identified unequivocally.

Interpretable Learning Approaches in Resting-State Functional Connectivity Analysis: The Case of Autism Spectrum Disorder

Deep neural networks have recently been applied to the study of brain disorders such as autism spectrum disorder (ASD) with great success. However, the internal logics of these networks are difficult to interpret, especially with regard to how specific network architecture decisions are made. In this paper, we study an interpretable neural network model as a method to identify ASD participants from functional magnetic resonance imaging (fMRI) data and interpret results of the model in a precise and consistent manner. First, we propose an interpretable fully connected neural network (FCNN) to classify two groups, ASD versus healthy controls (HC), based on input data from resting-state functional connectivity (rsFC) between regions of interests (ROIs). The proposed FCNN model is a piecewise linear neural network (PLNN) which uses piecewise linear function LeakyReLU as its activation function. We experimentally compared the FCNN model against widely used classification models including support vector machine (SVM), random forest, and two new classes of deep neural network models in a large dataset containing 871 subjects from ABIDE I database. The results show the proposed FCNN model achieves the highest classification accuracy. Second, we further propose an interpreting method which could explain the trained model precisely with a precise linear formula for each input sample and decision features which contributed most to the classification of ASD versus HC participants in the model. We also discuss the implications of our proposed approach for fMRI data classification and interpretation.

Non-Invasive Functional-Brain-Imaging with an OPM-based Magnetoencephalography System

A non-invasive functional-brain-imaging system based on optically-pumped-magnetometers (OPM) is presented. The OPM-based magnetoencephalography (MEG) system features 20 OPM channels conforming to the subject's scalp. We have conducted two MEG experiments on three subjects: assessment of somatosensory evoked magnetic field (SEF) and auditory evoked magnetic field (AEF) using our OPM-based MEG system and a commercial MEG system based on superconducting quantum interference devices (SQUIDs). We cross validated the robustness of our system by calculating the distance between the location of the equivalent current dipole (ECD) yielded by our OPM-based MEG system and the ECD location calculated by the commercial SQUID-based MEG system. We achieved sub-centimeter accuracy for both SEF and AEF responses in all three subjects. Due to the proximity (12 mm) of the OPM channels to the scalp, it is anticipated that future OPM-based MEG systems will offer enhanced spatial resolution as they will capture finer spatial features compared to traditional MEG systems employing SQUIDs.

Broad Learning Enhanced 1H-MRS for Early Diagnosis of Neuropsychiatric Systemic Lupus Erythematosus

In this paper, we explore the potential of using the multivoxel proton magnetic resonance spectroscopy (¹H-MRS) to diagnose neuropsychiatric systemic lupus erythematosus (NPSLE) with the assistance of a support vector machine broad learning system (BL-SVM). We retrospectively analysed 23 confirmed patients and 16 healthy controls, who underwent a 3.0 T magnetic resonance imaging (MRI) sequence with multivoxel ¹H-MRS in our hospitals. One hundred and seventeen metabolic features were extracted from the multivoxel ¹H-MRS image. Thirty-three metabolic features selected by the Mann-Whitney U test were considered to have a statistically significant difference (p < 0.05). However, the best accuracy achieved by conventional statistical methods using these 33 metabolic features was only 77%. We turned to develop a support vector machine broad learning system (BL-SVM) to quantitatively analyse the metabolic features from ¹H-MRS. Although not all the individual features manifested statistics significantly, the BL-SVM could still learn to distinguish the NPSLE from the healthy controls. The area under the receiver operating characteristic curve (AUC), the sensitivity, and the specificity of our BL-SVM in predicting NPSLE were 95%, 95.8%, and 93%, respectively, by 3-fold cross-validation. We consequently conclude that the proposed system effectively and efficiently working on limited and noisy samples may brighten a noinvasive in vivo instrument for early diagnosis of NPSLE.

Investigation of an emotion perception test using functional magnetic resonance imaging

BACKGROUND AND OBJECTIVE

Patients with mood disorders are known to have an emotion recognition deficit in facial emotion processing. Emotion perception involves two systems of cognitive and affective processes associated with brain activation in the fusiform gyrus and prefrontal cortices. To overcome the limitations of existing emotion perception tests, we designed an emotion perception index to assess the individuals' mood status.

METHODS

We selected 66 emotional faces (22 pleasant, 22 unpleasant, and 22 neutral) for the emotion perception test and recruited 40 healthy participants to verify the test. The participants completed a demographic data questionnaire and were administered the Beck Depressive Inventory (BDI). They were also scanned to assess the brain functional connectivity (FC) between seeds of the fusiform gyrus and other brain regions using resting-state functional magnetic resonance imaging (rs-fMRI). After rs-fMRI scanning, the participants were administered the emotion perception test on a computer.

RESULTS

In response to 108 questions regarding emotional face differentiation, the study group showed an average correct-answer rate of 90.7 ± 6.4% and a mean reaction time of 1.4 ± 0.4 s. We created an emotion perception index from the calculation of correct rate, number of correct responses, and reaction time in response to 108 questions; the mean of the emotion perception index in the study group was 3.8 ± 0.2. The emotion perception index was positively correlated with the BDI scores (r = 0.4, p = 0.01); further, it was positively correlated with the FC from the fusiform gyrus to the left superior frontal gyrus (FDRq < 0.01), left medial frontal gyrus (FDRq < 0.01), left frontal precentral gyrus (FDRq = 0.02), left insula (FDRq < 0.01), and left occipital cuneus (FDRq = 0.01). The FC from the fusiform gyrus to the left insula was positively correlated with the BDI scores (r = 0.59, p < 0.001).

CONCLUSIONS

The emotion perception index designed in this study may correctly indicate the mood status of individuals. In addition, the emotion perception test was associated with brain FC from the fusiform gyrus to the frontal and insular cortices.

Group-representative functional network estimation from multi-subject fMRI data via MRF-based image segmentation

BACKGROUND AND OBJECTIVE

There has been growing interest in using functional connectivity patterns, determined from fMRI data to characterize groups of individuals exhibiting common traits. However, the present challenge lies in efficient and accurate identification of distinct patterns observed consistently across multiple subjects. Existing approaches either impose strong assumptions, require aligning images before processing, or require data-intensive machine learning algorithms with manually labeled training datasets. In this paper, we propose a more principled and flexible approach to address this.

METHODS

Our approach redefines the problem of estimating the group-representative functional network as an image segmentation problem. After employing an improved clustering-based ICA scheme to pre-process the dataset of individual functional network images, we use a maximum a posteriori-Markov random field (MAP-MRF) framework to solve the image segmentation problem. In this framework, we propose a probabilistic model of the individual pixels of the fMRI data, with the model involving a latent group-representative functional network image. Given an observed dataset, we apply a novel and efficient variational Bayes algorithm to recover the associated latent group image. Our methodology seeks to overcome limitations in more traditional schemes by exploiting spatial relationships underlying the connectivity maps and accounting for uncertainty in the estimation process.

RESULTS

We validate our approach using synthetic, simulated and real data. First, we generate datasets from the proposed forward model with subject-specific binary masking and measurement noise, as well as from a variant of the model without measurement noise. We use both datasets to evaluate our model, along with two algorithms: coordinate-ascent algorithm and variational Bayes algorithm. We conclude that our proposed model with variational Bayes outperforms other competitors, even under model-misspecification. Using variational Bayes offers a significant improvement in performance, with almost no additional computational overhead. We next test our approach on simulated fMRI data. We show our approach is robust to initialization and can recover a solution close to the ground truth. Finally, we apply our proposed methodology along with baselines to a real dataset of fMRI recordings of individuals from two groups, a control group and a group suffering from depression, with recordings made while individuals were subjected to musical stimuli. Our methodology is able to identify group differences that are less clear under competing methods.

CONCLUSIONS

Our model-based approach demonstrates the advantage of probabilistic models and modern algorithms that account for uncertainty in accurate identification of group-representative connectivity maps. The variational Bayes methodology yields highly accurate results without increasing the computational load compared to traditional methods. In addition, it is robust to model misspecification, and increases the ability to avoid local optima in the solution.

Identifying nonlinear dynamical systems via generative recurrent neural networks with applications to fMRI

A major tenet in theoretical neuroscience is that cognitive and behavioral processes are ultimately implemented in terms of the neural system dynamics. Accordingly, a major aim for the analysis of neurophysiological measurements should lie in the identification of the computational dynamics underlying task processing. Here we advance a state space model (SSM) based on generative piecewise-linear recurrent neural networks (PLRNN) to assess dynamics from neuroimaging data. In contrast to many other nonlinear time series models which have been proposed for reconstructing latent dynamics, our model is easily interpretable in neural terms, amenable to systematic dynamical systems analysis of the resulting set of equations, and can straightforwardly be transformed into an equivalent continuous-time dynamical system. The major contributions of this paper are the introduction of a new observation model suitable for functional magnetic resonance imaging (fMRI) coupled to the latent PLRNN, an efficient stepwise training procedure that forces the latent model to capture the 'true' underlying dynamics rather than just fitting (or predicting) the observations, and of an empirical measure based on the Kullback-Leibler divergence to evaluate from empirical time series how well this goal of approximating the underlying dynamics has been achieved. We validate and illustrate the power of our approach on simulated 'ground-truth' dynamical systems as well as on experimental fMRI time series, and demonstrate that the learnt dynamics harbors task-related nonlinear structure that a linear dynamical model fails to capture. Given that fMRI is one of the most common techniques for measuring brain activity non-invasively in human subjects, this approach may provide a novel step toward analyzing aberrant (nonlinear) dynamics for clinical assessment or neuroscientific research.

Heritability estimates on resting state fMRI data using ENIGMA analysis pipeline

Big data initiatives such as the Enhancing NeuroImaging Genetics through Meta-Analysis consortium (ENIGMA), combine data collected by independent studies worldwide to achieve more generalizable estimates of effect sizes and more reliable and reproducible outcomes. Such efforts require harmonized image analyses protocols to extract phenotypes consistently. This harmonization is particularly challenging for resting state fMRI due to the wide variability of acquisition protocols and scanner platforms; this leads to site-to-site variance in quality, resolution and temporal signal-to-noise ratio (tSNR). An effective harmonization should provide optimal measures for data of different qualities. We developed a multi-site rsfMRI analysis pipeline to allow research groups around the world to process rsfMRI scans in a harmonized way, to extract consistent and quantitative measurements of connectivity and to perform coordinated statistical tests. We used the single-modality ENIGMA rsfMRI preprocessing pipeline based on modelfree Marchenko-Pastur PCA based denoising to verify and replicate resting state network heritability estimates. We analyzed two independent cohorts, GOBS (Genetics of Brain Structure) and HCP (the Human Connectome Project), which collected data using conventional and connectomics oriented fMRI protocols, respectively. We used seed-based connectivity and dual-regression approaches to show that the rsfMRI signal is consistently heritable across twenty major functional network measures. Heritability values of 20-40% were observed across both cohorts.

Scanning the horizon: towards transparent and reproducible neuroimaging research

Functional neuroimaging techniques have transformed our ability to probe the neurobiological basis of behaviour and are increasingly being applied by the wider neuroscience community. However, concerns have recently been raised that the conclusions that are drawn from some human neuroimaging studies are either spurious or not generalizable. Problems such as low statistical power, flexibility in data analysis, software errors and a lack of direct replication apply to many fields, but perhaps particularly to functional MRI. Here, we discuss these problems, outline current and suggested best practices, and describe how we think the field should evolve to produce the most meaningful and reliable answers to neuroscientific questions.

Neural effects of social environmental stress - an activation likelihood estimation meta-analysis

BACKGROUND

Social environmental stress, including childhood abuse and deprivation, is associated with increased rates of psychiatric disorders such as schizophrenia and depression. However, the neural mechanisms mediating risk are not completely understood. Functional magnetic resonance imaging (MRI) studies have reported effects of social environmental stress on a variety of brain regions, but interpretation of results is complicated by the variety of environmental risk factors examined and different methods employed.

METHOD

We examined brain regions consistently showing differences in blood oxygen level-dependent (BOLD) response in individuals exposed to higher levels of environmental stress by performing a coordinate-based meta-analysis on 54 functional MRI studies using activation likelihood estimation (ALE), including an overall sample of 3044 participants. We performed separate ALE analyses on studies examining adults (mean age ⩾18 years) and children/adolescents (mean age <18 years) and a contrast analysis comparing the two types of study.

RESULTS

Across both adult and children/adolescent studies, ALE meta-analysis revealed several clusters in which differences in BOLD response were associated with social environmental stress across multiple studies. These clusters incorporated several brain regions, among which the right amygdala was most frequently implicated.

CONCLUSIONS

These findings suggest that a variety of social environmental stressors is associated with differences in the BOLD response of specific brain regions such as the right amygdala in both children/adolescents and adults. What remains unknown is whether these environmental stressors have differential effects on treatment response in these brain regions.

Simple method to improve spatial resolution for in vivo two-photon fluorescence imaging

There is a growing effort to image single neurons in vivo, and observe their individual contribution to the brain's functional organization. This effort generally relies on two-photon imaging to explore the structure and activity of cortical columns extending beneath the brain's surface. The need to protect living tissue, however, demands the introduction of coverslips and similar objects that can modify the optics of the imaging beam. This paper develops three-dimensional (3D) analytical and numerical models to characterize and correct for the resulting degradation of image quality. We have illustrated the use of these models by describing a simple, practical technique to reduce the effect of spherical aberration for in vivo two-photon fluorescence experiments.

Making Large-Scale Networks from fMRI Data

Pairwise correlations are currently a popular way to estimate a large-scale network (> 1000 nodes) from functional magnetic resonance imaging data. However, this approach generally results in a poor representation of the true underlying network. The reason is that pairwise correlations cannot distinguish between direct and indirect connectivity. As a result, pairwise correlation networks can lead to fallacious conclusions; for example, one may conclude that a network is a small-world when it is not. In a simulation study and an application to resting-state fMRI data, we compare the performance of pairwise correlations in large-scale networks (2000 nodes) against three other methods that are designed to filter out indirect connections. Recovery methods are evaluated in four simulated network topologies (small world or not, scale-free or not) in scenarios where the number of observations is very small compared to the number of nodes. Simulations clearly show that pairwise correlation networks are fragmented into separate unconnected components with excessive connectedness within components. This often leads to erroneous estimates of network metrics, like small-world structures or low betweenness centrality, and produces too many low-degree nodes. We conclude that using partial correlations, informed by a sparseness penalty, results in more accurate networks and corresponding metrics than pairwise correlation networks. However, even with these methods, the presence of hubs in the generating network can be problematic if the number of observations is too small. Additionally, we show for resting-state fMRI that partial correlations are more robust than correlations to different parcellation sets and to different lengths of time-series.

GPU-based parallel group ICA for functional magnetic resonance data

The goal of our study is to develop a fast parallel implementation of group independent component analysis (ICA) for functional magnetic resonance imaging (fMRI) data using graphics processing units (GPU). Though ICA has become a standard method to identify brain functional connectivity of the fMRI data, it is computationally intensive, especially has a huge cost for the group data analysis. GPU with higher parallel computation power and lower cost are used for general purpose computing, which could contribute to fMRI data analysis significantly. In this study, a parallel group ICA (PGICA) on GPU, mainly consisting of GPU-based PCA using SVD and Infomax-ICA, is presented. In comparison to the serial group ICA, the proposed method demonstrated both significant speedup with 6-11 times and comparable accuracy of functional networks in our experiments. This proposed method is expected to perform the real-time post-processing for fMRI data analysis.

Dysregulation between emotion and theory of mind networks in borderline personality disorder

Individuals with borderline personality disorder (BPD) commonly display deficits in emotion regulation, but findings in the area of social cognitive (e.g., theory of mind, ToM) capacities have been heterogeneous. The aims of the current study were to investigate differences between patients with BPD and controls in functional connectivity (1) between the emotion and ToM network and (2) in the default mode network (DMN). Functional magnetic resonance imaging was used to investigate 19 healthy controls and 17 patients with BPD at rest and during ToM processing. Functional coupling was analysed. Significantly decreased functional connectivity was found for patients compared with controls between anterior cingulate cortex and three brain areas involved in ToM processes: the left superior temporal lobe, right supramarginal/inferior parietal lobes, and right middle cingulate cortex. Increased functional connectivity was found in patients compared with controls between the precuneus as the DMN seed and the left inferior frontal lobe, left precentral/middle frontal, and left middle occipital/superior parietal lobes during rest. Reduced functional coupling between the emotional and the ToM network during ToM processing is in line with emotion-regulation dysfunctions in BPD. The increased connectivity between precuneus and frontal regions during rest might be related to extensive processing of internal thoughts and self-referential information in BPD.

EEG source connectivity analysis: from dense array recordings to brain networks

The recent past years have seen a noticeable increase of interest for electroencephalography (EEG) to analyze functional connectivity through brain sources reconstructed from scalp signals. Although considerable advances have been done both on the recording and analysis of EEG signals, a number of methodological questions are still open regarding the optimal way to process the data in order to identify brain networks. In this paper, we analyze the impact of three factors that intervene in this processing: i) the number of scalp electrodes, ii) the combination between the algorithm used to solve the EEG inverse problem and the algorithm used to measure the functional connectivity and iii) the frequency bands retained to estimate the functional connectivity among neocortical sources. Using High-Resolution (hr) EEG recordings in healthy volunteers, we evaluated these factors on evoked responses during picture recognition and naming task. The main reason for selection this task is that a solid literature background is available about involved brain networks (ground truth). From this a priori information, we propose a performance criterion based on the number of connections identified in the regions of interest (ROI) that belong to potentially activated networks. Our results show that the three studied factors have a dramatic impact on the final result (the identified network in the source space) as strong discrepancies were evidenced depending on the methods used. They also suggest that the combination of weighted Minimum Norm Estimator (wMNE) and the Phase Synchronization (PS) methods applied on High-Resolution EEG in beta/gamma bands provides the best performance in term of topological distance between the identified network and the expected network in the above-mentioned cognitive task.

jSIPRO - analysis tool for magnetic resonance spectroscopic imaging

Magnetic resonance spectroscopic imaging (MRSI) involves a huge number of spectra to be processed and analyzed. Several tools enabling MRSI data processing have been developed and widely used. However, the processing programs primarily focus on sophisticated spectra processing and offer limited support for the analysis of the calculated spectroscopic maps. In this paper the jSIPRO (java Spectroscopic Imaging PROcessing) program is presented, which is a java-based graphical interface enabling post-processing, viewing, analysis and result reporting of MRSI data. Interactive graphical processing as well as protocol controlled batch processing are available in jSIPRO. jSIPRO does not contain a built-in fitting program. Instead, it makes use of fitting programs from third parties and manages the data flows. Currently, automatic spectra processing using LCModel, TARQUIN and jMRUI programs are supported. Concentration and error values, fitted spectra, metabolite images and various parametric maps can be viewed for each calculated dataset. Metabolite images can be exported in the DICOM format either for archiving purposes or for the use in neurosurgery navigation systems.

Functional activity maps based on significance measures and Independent Component Analysis

The use of functional imaging has been proven very helpful for the process of diagnosis of neurodegenerative diseases, such as Alzheimer's Disease (AD). In many cases, the analysis of these images is performed by manual reorientation and visual interpretation. Therefore, new statistical techniques to perform a more quantitative analysis are needed. In this work, a new statistical approximation to the analysis of functional images, based on significance measures and Independent Component Analysis (ICA) is presented. After the images preprocessing, voxels that allow better separation of the two classes are extracted, using significance measures such as the Mann-Whitney-Wilcoxon U-Test (MWW) and Relative Entropy (RE). After this feature selection step, the voxels vector is modelled by means of ICA, extracting a few independent components which will be used as an input to the classifier. Naive Bayes and Support Vector Machine (SVM) classifiers are used in this work. The proposed system has been applied to two different databases. A 96-subjects Single Photon Emission Computed Tomography (SPECT) database from the "Virgen de las Nieves" Hospital in Granada, Spain, and a 196-subjects Positron Emission Tomography (PET) database from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Values of accuracy up to 96.9% and 91.3% for SPECT and PET databases are achieved by the proposed system, which has yielded many benefits over methods proposed on recent works.

A miniaturized platform for laser speckle contrast imaging

Imaging the brain in animal models enables scientists to unravel new biological insights. Despite critical advancements in recent years, most laboratory imaging techniques comprise of bulky bench top apparatus that require the imaged animals to be anesthetized and immobilized. Thus, animals are imaged in their non-native state severely restricting the scope of behavioral experiments. To address this gap, we report a miniaturized microscope that can be mounted on a rat's head for imaging in awake and unrestrained conditions. The microscope uses laser speckle contrast imaging (LSCI), a high resolution yet wide field imaging modality for imaging blood vessels and perfusion. Design details of both the image formation and acquisition modules are presented. A Monte Carlo simulation was used to estimate the depth of tissue penetration achievable by the imaging system while the produced speckle Airy disc patterns were simulated using Fresnel's diffraction theory. The microscope system weighs only 7 g and occupies less than 5 cm³ and was successfully used to generate proof of concept LSCI images of rat brain vasculature. We validated the utility of the head-mountable system in an awake rat brain model by confirming no impairment to the rat's native behavior.

Analysis of brain metabolism by proton magnetic resonance spectroscopy (1H-MRS) in attention-deficit/hyperactivity disorder suggests a generalized differential ontogenic pattern from controls

Attention-deficit/hyperactivity disorder (ADHD) is the most common behavioral disorder of childhood. Preliminary studies with proton magnetic resonance spectroscopy ((1)H-MRS) of the brain have reported differences in brain metabolite concentration-to-Cr ratios between individuals with ADHD and unaffected controls in several frontal brain regions including anterior cingulate cortex. Using multivoxel (1)H-MRS, we compared 14 individuals affected with ADHD to 20 individuals without ADHD from the same genetic isolate. After controlling by sex, age, and multiple testing, we found significant differences at the right posterior cingulate of the Glx/Cr ratio density distribution function between ADHD cases and controls (P < 0.05). Furthermore, we found several interactions of metabolite concentration-to-Cr ratio, age, and ADHD status: Ins/Cr and Glx/Cr ratios at the left posterior cingulate, and NAA/Cr at the splenius, right posterior cingulate, and at the left posterior cingulate. We also found a differential metabolite ratio interaction between ADHD cases and controls for Ins/Cr and NAA/Cr at the right striatum. These results show that: (1) NAA/Cr, Glx/Cr, and Ins/Cr ratios, as reported in other studies, exhibit significant differences between ADHD cases and controls; (2) differences of these metabolite ratios between ADHD cases and controls evolve in specific and recognizable patterns throughout age, a finding that replicates previous results obtained by structural MRI, where is demonstrated that brain ontogeny follows a different program in ADHD cases and controls; (3) Ins/Cr and NAA/Cr ratios, at the right striatum, interact in a differential way between ADHD cases and controls. As a whole, these results replicate previous 1H-MRS findings and add new intriguing differential metabolic and ontogeny patterns between ADHD cases and controls that warrant further pursue.

Sample size estimation for comparing parameters using dynamic causal modeling

Functional magnetic resonance imaging (fMRI) has proved to be useful for analyzing the effects of illness and pharmacological agents on brain activation. Many fMRI studies now incorporate effective connectivity analyses on data to assess the networks recruited during task performance. The assessment of the sample size that is necessary for carrying out such calculations would be useful if these techniques are to be confidently applied. Here, we present a method of estimating the sample size that is required for a study to have sufficient power. Our approach uses Bayesian Model Selection to find a best fitting model and then uses a bootstrapping technique to provide an estimate of the parameter variance. As illustrative examples, we apply this technique to two different tasks and show that for our data, ~20 volunteers per group is sufficient. Due to variability between task, volunteers, scanner, and acquisition parameters, this would need to be evaluated on individual datasets. This approach will be a useful guide for Dynamic Causal Modeling studies.

Opiate-induced dopamine release is modulated by severity of alcohol dependence: an [(18)F]fallypride positron emission tomography study

BACKGROUND

Preclinical data implicate the reinforcing effects of alcohol to be mediated by interaction between the opioid and dopamine systems of the brain. Specifically, alcohol-induced release of β-endorphins stimulates μ-opioid receptors (MORs), which is believed to cause dopamine release in the brain reward system. Individual differences in opioid or dopamine neurotransmission have been suggested to be responsible for enhanced liability to abuse alcohol. In the present study, a single dose of the MOR agonist remifentanil was administered in detoxified alcohol-dependent patients and healthy control subjects to mimic the β-endorphin-releasing properties of ethanol and to assess the effects of direct MOR stimulation on dopamine release in the mesolimbic reward system.

METHODS

Availability of D(2/3) receptors was assessed before and after single-dose administration of the MOR agonist remifentanil in 11 detoxified alcohol-dependent patients and 11 healthy control subjects with positron emission tomography with the radiotracer [(18)F]fallypride. Severity of dependence as assessed with the Alcohol Use Disorders Identification Test was compared with remifentanil-induced percentage change in [(18)F]fallypride binding (Δ%BP(ND)).

RESULTS

The [(18)F]fallypride binding potentials (BP(ND)s) were significantly reduced in the ventral striatum, dorsal putamen, and amygdala after remifentanil application in both patients and control subjects. In the patient group, ventral striatum Δ%BP(ND) was correlated with the Alcohol Use Disorders Identification Test score.

CONCLUSIONS

The data provide evidence for a MOR-mediated interaction between the opioid and the dopamine system, supporting the assumption that one way by which alcohol unfolds its rewarding effects is via a MOR-(γ-aminobutyric acid)-dopamine pathway. No difference in dopamine release was found between patients and control subjects, but evidence for a patient-specific association between sensitivity to MOR stimulation and severity of alcohol dependence was found.