Effect of visual attention on functional connectivity

In our environment, there are so many things to see, e.g., computer screen, buildings, trees and cars in the street. In this busy scenery, we do not process all the information equally, but rather filter out some information and focus more on certain characteristics in the whole scene. In this process, attention plays an important role, and underlying neural correlate is the matter of interest. We focus on investigating how attention changes the connectivity of the fMRI signal in the human brain. Prior studies examined this question, yet most studies used short trial interval (<20s) in examining the connectivity during attention. The short trial interval excludes the slow fMRI fluctuations (<0.1Hz) that showed segmented connectivity structure in the resting-state studies supported by the neurophysiological observations. In the thesis, we introduce an ultra-long trial (2-3mins) to examine connectivity during task conditions, in attention demanding task. In the first study, we asked whether trial length affects the functional connectivity (FC) strength in general during attention task compared to visually matched condition as control. We observed that the long trial interval (2mins) condition showed nearly twice the FC strength compared to short traditional trials (20s). Moreover, attention reorganized the FC as enhanced positive FC between dorsal attention network (DAN) and visual network (VIS) and decreased negative FC between default mode network (DMN) and DAN/VIS, but reduced positive FC within VIS. Notably, the reorganization is frequency dependent: FC changed relied more on slow frequency (0.004-0.05Hz) for the connection between DAN and VIS and high frequency (0.05-0.2Hz) for decorrelation within VIS. In the second study, we addressed the question whether FC strength relies on visual hierarchical distance in visual processing and attention task. We observed a gradient of connectivity, such that DAN connected strongly with high visual region (e.g., V5/MT) that degrades towards lower visual region (e.g., V1). A reversed effect was observed between DMN and VIS, revealing that DMN connected strongly negatively with high visual region that degrades its negative connectivity strength towards lower visual region. More interestingly, we implemented general linear model to the FC strength that showed attention modulates multiplicatively and addictively the connectivity strength along this visual hierarchy. In the third study, we observed how attention changes the connectivity in different features, e.g., color and motion attention. Here, we used seed-to-whole brain connectivity with regressing out the mean signal from the whole brain. First, we observed that V4 and V5/MT selectively connected to the task positive network, including DAN and visual regions, and negatively connected to the DMN. Then, feature-specific analysis showed that color compared to motion attention, selectively connects the V4 to DAN more than V5/MT to DAN, with selective negative connections between V4 and DMN than V5/MT and DMN. This suggest that feature-based attention led the brain communicate specifically cooperative (positive) way, but also competitive (negative) way. Taken together, attention not only reorganizes the connectivity in frequency dependent way, modulates differentially along the visual hierarchy as well as feature-specific manner. More interestingly, our results showed advantages of using long trial block experiment to detect important network connectivity change during attention. Not only applying frequency dependent analysis, but implementation of the GLM in comparing conditions, as well as, regressing out the mean signal from the whole brain for seed-to-whole brain connectivity analysis. All these methods that is used in the thesis can be extended to examine brain connectivity structure noninvasively, that may show important findings in other cognitive tasks, such as decision making or memory tasks

Spatial parcellations, spectral filtering, and connectivity measures in fMRI: Optimizing for discrimination.

The analysis of Functional Connectivity (FC) is a key technique of fMRI, having been used to distinguish brain states and conditions. While many approaches to calculating FC are available, there have been few assessments of their differences, making it difficult to choose approaches and compare results. Here, we assess the impact of methodological choices on discriminability, using a fully controlled dataset of continuous active states involving basic visual and motor tasks, providing robust localized FC changes. We tested a range of anatomical and functional parcellations, including the AAL atlas, parcellations derived from the Human Connectome Project and Independent Component Analysis (ICA) of many dimensionalities. We measure amplitude, covariance, correlation and regularized partial correlation under different temporal filtering choices. We evaluate features derived from these methods for discriminating states using MVPA. We find that multidimensional parcellations derived from functional data performed similarly, outperforming an anatomical atlas, with correlation and partial correlation (p<0.05, FDR). Partial correlation, with appropriate regularization, outperformed correlation. Amplitude and covariance generally discriminated less well, although gave good results with high-dimensionality ICA. We found that discriminative FC properties are frequency specific; higher frequencies performed surprisingly well under certain configurations of atlas choices and dependency measures, with ICA-based parcellations revealing greater discriminability at high frequencies compared to other parcellations. Methodological choices in FC analyses can have a profound impact on results and can be selected to optimize accuracy, interpretability, and sharing of results. This work contributes to a basis for consistent selection of approaches to estimating and analyzing FC

A High Speed Networked Signal Processing Platform for Multi-element Radio Telescopes

A new architecture is presented for a Networked Signal Processing System (NSPS) suitable for handling the real-time signal processing of multi-element radio telescopes. In this system, a multi-element radio telescope is viewed as an application of a multi-sensor, data fusion problem which can be decomposed into a general set of computing and network components for which a practical and scalable architecture is enabled by current technology. The need for such a system arose in the context of an ongoing program for reconfiguring the Ooty Radio Telescope (ORT) as a programmable 264-element array, which will enable several new observing capabilities for large scale surveys on this mature telescope. For this application, it is necessary to manage, route and combine large volumes of data whose real-time collation requires large I/O bandwidths to be sustained. Since these are general requirements of many multi-sensor fusion applications, we first describe the basic architecture of the NSPS in terms of a Fusion Tree before elaborating on its application for the ORT. The paper addresses issues relating to high speed distributed data acquisition, Field Programmable Gate Array (FPGA) based peer-to-peer networks supporting significant on-the fly processing while routing, and providing a last mile interface to a typical commodity network like Gigabit Ethernet. The system is fundamentally a pair of two co-operative networks, among which one is part of a commodity high performance computer cluster and the other is based on Commercial-Off The-Shelf (COTS) technology with support from software/firmware components in the public domain.Comment: 19 pages, 4 eps figures, To be published in Experimental Astronomy (Springer

The Dynamic Brain: From Spiking Neurons to Neural Masses and Cortical Fields

The cortex is a complex system, characterized by its dynamics and architecture, which underlie many functions such as action, perception, learning, language, and cognition. Its structural architecture has been studied for more than a hundred years; however, its dynamics have been addressed much less thoroughly. In this paper, we review and integrate, in a unifying framework, a variety of computational approaches that have been used to characterize the dynamics of the cortex, as evidenced at different levels of measurement. Computational models at different space–time scales help us understand the fundamental mechanisms that underpin neural processes and relate these processes to neuroscience data. Modeling at the single neuron level is necessary because this is the level at which information is exchanged between the computing elements of the brain; the neurons. Mesoscopic models tell us how neural elements interact to yield emergent behavior at the level of microcolumns and cortical columns. Macroscopic models can inform us about whole brain dynamics and interactions between large-scale neural systems such as cortical regions, the thalamus, and brain stem. Each level of description relates uniquely to neuroscience data, from single-unit recordings, through local field potentials to functional magnetic resonance imaging (fMRI), electroencephalogram (EEG), and magnetoencephalogram (MEG). Models of the cortex can establish which types of large-scale neuronal networks can perform computations and characterize their emergent properties. Mean-field and related formulations of dynamics also play an essential and complementary role as forward models that can be inverted given empirical data. This makes dynamic models critical in integrating theory and experiments. We argue that elaborating principled and informed models is a prerequisite for grounding empirical neuroscience in a cogent theoretical framework, commensurate with the achievements in the physical sciences

Reduced coupling between offline neural replay events and default mode network activation in schizophrenia

Schizophrenia is characterized by an abnormal resting state and default mode network brain activity. However, despite intense study, the mechanisms linking default mode network dynamics to neural computation remain elusive. During rest, sequential hippocampal reactivations, known as 'replay', are played out within default mode network activation windows, highlighting a potential role of replay-default mode network coupling in memory consolidation and model-based mental simulation. Here, we test a hypothesis of reduced replay-default mode network coupling in schizophrenia, using magnetoencephalography and a non-spatial sequence learning task designed to elicit off-task (i.e. resting state) neural replay. Participants with a diagnosis of schizophrenia (n = 28, mean age 28.2 years, range 20-40, 6 females, 13 not taking antipsychotic medication) and non-clinical control participants (n = 29, mean age 28.1 years, range 18-45, 6 females, matched at group level for age, intelligence quotient, gender, years in education and working memory) underwent a magnetoencephalography scan both during task completion and during a post-task resting state session. We used neural decoding to infer the time course of default mode network activation (time-delay embedding hidden Markov model) and spontaneous neural replay (temporally delayed linear modelling) in resting state magnetoencephalography data. Using multiple regression, we then quantified the extent to which default mode network activation was uniquely predicted by replay events that recapitulated the learned task sequences (i.e. 'task-relevant' replay-default mode network coupling). In control participants, replay-default mode network coupling was augmented following sequence learning, an augmentation that was specific for replay of task-relevant (i.e. learned) state transitions. This task-relevant replay-default mode network coupling effect was significantly reduced in schizophrenia (t(52) = 3.93, P = 0.018). Task-relevant replay-default mode network coupling predicted memory maintenance of learned sequences (ρ(52) = 0.31, P = 0.02). Importantly, reduced task-relevant replay-default mode network coupling in schizophrenia was not explained by differential replay or altered default mode network dynamics between groups nor by reference to antipsychotic exposure. Finally, task-relevant replay-default mode network coupling during rest correlated with stimulus-evoked default mode network modulation as measured in a separate task session. In the context of a proposed functional role of replay-default mode network coupling, our findings shed light on the functional significance of default mode network abnormalities in schizophrenia and provide for a consilience between task-based and resting state default mode network findings in this disorder

Medial Geniculate Projections to Auditory and Visual Cortex in Hearing and Deaf Cats

Following early-onset deafness, studies have demonstrated crossmodal plasticity, throughout “deaf” auditory cortex. Crossmodally reorganized auditory cortex shows increased dendritic spine density, which suggests increased numbers of axon terminals. I examined projections from the medial geniculate body (MGB) of hearing and early-deaf cats in order to reveal the distribution of synaptic boutons in the cortex, originating from MGB neurons. Anterograde fluorescent dextran tracers were deposited bilaterally in the MGB in order to label cortical axon terminals. Deafness resulted in axon terminal increases in visual cortex, and conservation of auditory axon terminal distribution. Visual areas PLLS and area 18 received increased projections from deaf MGB. Distributions of thalamocortical axon terminals in crossmodally reorganized deaf auditory areas PAF, DZ and fAES were stable. These findings indicate a need for studies of corticocortical connectivity, to find an anatomical basis for crossmodal reorganization

Dynamic reorganization of the cortico-basal ganglia-thalamo-cortical network during task learning

Adaptive behavior is coordinated by neuronal networks that are distributed across multiple brain regions such as in the cortico-basal ganglia-thalamo-cortical (CBGTC) network. Here, we ask how cross-regional interactions within such mesoscale circuits reorganize when an animal learns a new task. We apply multi-fiber photometry to chronically record simultaneous activity in 12 or 48 brain regions of mice trained in a tactile discrimination task. With improving task performance, most regions shift their peak activity from the time of reward-related action to the reward-predicting stimulus. By estimating cross-regional interactions using transfer entropy, we reveal that functional networks encompassing basal ganglia, thalamus, neocortex, and hippocampus grow and stabilize upon learning, especially at stimulus presentation time. The internal globus pallidus, ventromedial thalamus, and several regions in the frontal cortex emerge as salient hub regions. Our results highlight the learning-related dynamic reorganization that brain networks undergo when task-appropriate mesoscale network dynamics are established for goal-oriented behavior