Spatio-temporal Principles of Infra-slow Brain Activity

In the study of systems where basic laws have eluded us, as is largely the case in neuroscience, the simplest approach to progress might be to ask: what are the biggest, most noticeable things the system does when left alone? Without any perturbations or fine dissections, can regularities be found in the basic operations of the system as a whole? In the case of the brain, it turns out that there is an amazing amount of activity even in the absence of explicit environmental inputs or outputs. We call this spontaneous, or resting state, brain activity. Prior work has shown that spontaneous brain activity is dominated by very low frequencies: the biggest changes in brain activity happen relatively slowly, over 10’s-100’s of seconds. Moreover, this very slow activity of the brain is quite metabolically expensive. The brain accounts for 2% of body mass in an adult, but requires 20% of basal metabolic expenditure. Remarkably, the energy required to sustain brain function is nearly constant whether one is engaged in a demanding mental task or simply out to lunch. Furthermore, work over the past three decades has established that the spontaneous activities of the brain are not random, but instead organized into specific patterns, most often characterized by correlations within large brain systems. Yet, how do these correlations arise, and does spontaneous activity support slow signaling within and between neural systems? In this thesis, we approach these questions by providing a comprehensive analysis of the temporal structure of very low frequency spontaneous activity. Specifically, we focus on the direction of travel in low frequency activity, measured using resting state fMRI in humans, but also using electrophysiological techniques in humans and mice, and optical calcium imaging in mice. Our temporal analyses reveal heretofore unknown regularities in the way slow signals move through the brain. We further find that very low frequency activity behaves differently than faster frequencies, that it travels through distinct layers of the cortex, and that its travel patterns give rise to correlations within networks. We also demonstrate that the travel patterns of very low frequency activity are highly dependent on the state of the brain, especially the difference between wake and sleep states. Taken together, the findings in this thesis offer a glimpse into the principles that govern brain activity

On the existence of a generalized non-specific task-dependent network

In this paper we suggest the existence of a generalized task-related cortical network that is up-regulated whenever the task to be performed requires the allocation of generalized non-specific cognitive resources, independent of the specifics of the task to be performed. We have labeled this general purpose network, the extrinsic mode network (EMN) as complementary to the default mode network (DMN), such that the EMN is down-regulated during periods of task-absence, when the DMN is up-regulated, and vice versa. We conceptualize the EMN as a cortical network for extrinsic neuronal activity, similar to the DMN as being a cortical network for intrinsic neuronal activity. The EMN has essentially a fronto-temporo-parietal spatial distribution, including the inferior and middle frontal gyri, inferior parietal lobule, supplementary motor area, inferior temporal gyrus. We hypothesize that this network is always active regardless of the cognitive task being performed. We further suggest that failure of network up- and down-regulation dynamics may provide neuronal underpinnings for cognitive impairments seen in many mental disorders, such as, e.g., schizophrenia. We start by describing a common observation in functional imaging, the close overlap in fronto-parietal activations in healthy individuals to tasks that denote very different cognitive processes. We now suggest that this is because the brain utilizes the EMN network as a generalized response to tasks that exceeds a cognitive demand threshold and/or requires the processing of novel information. We further discuss how the EMN is related to the DMN, and how a network for extrinsic activity is related to a network for intrinsic activity. Finally, we discuss whether the EMN and DMN networks interact in a common single brain system, rather than being two separate and independent brain systems

Programming the Adapteva Epiphany 64-core Network-on-chip Coprocessor

In the construction of exascale computing systems energy efficiency and power consumption are two of the major challenges. Low-power high performance embedded systems are of increasing interest as building blocks for large scale high- performance systems. However, extracting maximum performance out of such systems presents many challenges. Various aspects from the hardware architecture to the programming models used need to be explored. The Epiphany architecture integrates low-power RISC cores on a 2D mesh network and promises up to 70 GFLOPS/Watt of processing efficiency. However, with just 32 KB of memory per eCore for storing both data and code, and only low level inter-core communication support, programming the Epiphany system presents several challenges. In this paper we evaluate the performance of the Epiphany system for a variety of basic compute and communication operations. Guided by this data we explore strategies for implementing scientific applications on memory constrained low-powered devices such as the Epiphany. With future systems expected to house thousands of cores in a single chip, the merits of such architectures as a path to exascale is compared to other competing systems.Comment: 14 pages, submitted to IJHPCA Journal special editio

Targeted neurostimulation reverses a spatiotemporal biomarker of treatment-resistant depression

Major depressive disorder (MDD) is widely hypothesized to result from disordered communication across brain-wide networks. Yet, prior resting-state-functional MRI (rs-fMRI) studies of MDD have studied zero-lag temporal synchrony (functional connectivity) in brain activity absent directional information. We utilize the recent discovery of stereotyped brain-wide directed signaling patterns in humans to investigate the relationship between directed rs-fMRI activity, MDD, and treatment response to FDA-approved neurostimulation paradigm termed Stanford neuromodulation therapy (SNT). We find that SNT over the left dorsolateral prefrontal cortex (DLPFC) induces directed signaling shifts in the left DLPFC and bilateral anterior cingulate cortex (ACC). Directional signaling shifts in the ACC, but not the DLPFC, predict improvement in depression symptoms, and moreover, pretreatment ACC signaling predicts both depression severity and the likelihood of SNT treatment response. Taken together, our findings suggest that ACC-based directed signaling patterns in rs-fMRI are a potential biomarker of MDD

Partial covariance based functional connectivity computation using Ledoit-Wolf covariance regularization

Highlights •We use the well characterized matrix regularization technique described by Ledoit and Wolf to calculate high dimensional partial correlations in fMRI data. •Using this approach we demonstrate that partial correlations reveal RSN structure suggesting that RSNs are defined by widely and uniquely shared variance. •Partial correlation functional connectivity is sensitive to changes in brain state indicating that they contain functional information. Functional connectivity refers to shared signals among brain regions and is typically assessed in a task free state. Functional connectivity commonly is quantified between signal pairs using Pearson correlation. However, resting-state fMRI is a multivariate process exhibiting a complicated covariance structure. Partial covariance assesses the unique variance shared between two brain regions excluding any widely shared variance, hence is appropriate for the analysis of multivariate fMRI datasets. However, calculation of partial covariance requires inversion of the covariance matrix, which, in most functional connectivity studies, is not invertible owing to rank deficiency. Here we apply Ledoit–Wolf shrinkage (L2 regularization) to invert the high dimensional BOLD covariance matrix. We investigate the network organization and brain-state dependence of partial covariance-based functional connectivity. Although RSNs are conventionally defined in terms of shared variance, removal of widely shared variance, surprisingly, improved the separation of RSNs in a spring embedded graphical model. This result suggests that pair-wise unique shared variance plays a heretofore unrecognized role in RSN covariance organization. In addition, application of partial correlation to fMRI data acquired in the eyes open vs. eyes closed states revealed focal changes in uniquely shared variance between the thalamus and visual cortices. This result suggests that partial correlation of resting state BOLD time series reflect functional processes in addition to structural connectivity

On the role of the corpus callosum in interhemispheric functional connectivity in humans

Resting state functional connectivity is defined in terms of temporal correlations between physiologic signals, most commonly studied using functional magnetic resonance imaging. Major features of functional connectivity correspond to structural (axonal) connectivity. However, this relation is not one-to-one. Interhemispheric functional connectivity in relation to the corpus callosum presents a case in point. Specifically, several reports have documented nearly intact interhemispheric functional connectivity in individuals in whom the corpus callosum (the major commissure between the hemispheres) never develops. To investigate this question, we assessed functional connectivity before and after surgical section of the corpus callosum in 22 patients with medically refractory epilepsy. Section of the corpus callosum markedly reduced interhemispheric functional connectivity. This effect was more profound in multimodal associative areas in the frontal and parietal lobe than primary regions of sensorimotor and visual function. Moreover, no evidence of recovery was observed in a limited sample in which multiyear, longitudinal follow-up was obtained. Comparison of partial vs. complete callosotomy revealed several effects implying the existence of polysynaptic functional connectivity between remote brain regions. Thus, our results demonstrate that callosal as well as extracallosal anatomical connections play a role in the maintenance of interhemispheric functional connectivity

A comparison of machine learning classifiers for pediatric epilepsy using resting-state functional MRI latency data

Epilepsy affects 1 in 150 children under the age of 10 and is the most common chronic pediatric neurological condition; poor seizure control can irreversibly disrupt normal brain development. The present study compared the ability of different machine learning algorithms trained with resting-state functional MRI (rfMRI) latency data to detect epilepsy. Preoperative rfMRI and anatomical MRI scans were obtained for 63 patients with epilepsy and 259 healthy controls. The normal distribution of latency z-scores from the epilepsy and healthy control cohorts were analyzed for overlap in 36 seed regions. In these seed regions, overlap between the study cohorts ranged from 0.44-0.58. Machine learning features were extracted from latency z-score maps using principal component analysis. Extreme Gradient Boosting (XGBoost), Support Vector Machines (SVM), and Random Forest algorithms were trained with these features. Area under the receiver operating characteristics curve (AUC), accuracy, sensitivity, specificity and F1-scores were used to evaluate model performance. The XGBoost model outperformed all other models with a test AUC of 0.79, accuracy of 74%, specificity of 73%, and a sensitivity of 77%. The Random Forest model performed comparably to XGBoost across multiple metrics, but it had a test sensitivity of 31%. The SVM model did not perform \u3e70% in any of the test metrics. The XGBoost model had the highest sensitivity and accuracy for the detection of epilepsy. Development of machine learning algorithms trained with rfMRI latency data could provide an adjunctive method for the diagnosis and evaluation of epilepsy with the goal of enabling timely and appropriate care for patients