SUMMARY In order to link neural activity with cognitive function, information is needed about both the temporal dynamics and the content of neural codes. Traditionally, recording single neurons in animals has been the primary means of obtaining high temporal resolution as well as precise information about neural tuning properties such as selectivity for different sensory features. Recent fMRI studies in humans have been able to measure feature selectivity within specific sub-regions of sensory cortex (e.g., orientation selectivity in primary visual cortex, or V1) [1, 2]. However, investigating the neural mechanisms that support cognitive processing – which often occur rapidly on a sub-second scale – using a temporally insensitive method such as fMRI severely limits the types of inferences that can be drawn. Here, we describe a new method for tracking the rapid temporal evolution of feature-selective information processing with scalp recordings of EEG. We generate orientation-selective response profiles based on the spatially distributed pattern of steady-state visual evoked potential (SSVEP) responses to flickering visual stimuli. Using this approach, we report a multiplicative attentional modulation of these feature-selective response profiles with a temporal resolution of 24ms–120 ms, which is far faster than that achieved using fMRI. Finally, we show that behavioral performance on a discrimination task can be predicted based on the amplitude of these temporally precise feature-selective response profiles. This method thus provides a high temporal resolution metric that can be used to track the influence of cognitive manipulations on feature-selective information processing in human cortex.

Objective. Steady-State Visual Evoked Potentials (SSVEPs) are neural oscillations from the parietal and occipital regions of the brain that are evoked from flickering visual stimuli. SSVEPs are robust signals measurable in the electroencephalogram (EEG) and are commonly used in brain-computer interfaces (BCIs). However, methods for high-accuracy decoding of SSVEPs usually require hand-crafted approaches that leverage domain-specific knowledge of the stimulus signals, such as specific temporal frequencies in the visual stimuli and their relative spatial arrangement. When this knowledge is unavailable, such as when SSVEP signals are acquired asynchronously, such approaches tend to fail. Approach. In this paper, we show how a compact convolutional neural network (Compact-CNN), which only requires raw EEG signals for automatic feature extraction, can be used to decode signals from a 12-class SSVEP dataset without the need for user-specific calibration. Main results. The Compact-CNN demonstrates across subject mean accuracy of approximately 80 %, out-performing current state-of-the-art, handcrafted approaches using canonical correlation analysis (CCA) and Combined-CCA. Furthermore, the Compact-CNN approach can reveal the underlying feature representation, revealing that the deep learner extracts additional phase-and amplitude-related features associated with the structure of the dataset. Significance. We discuss how our Compact-CNN shows promise for BCI applications that allow users to freely gaze/attend to any stimulus at any time (e.g., asynchronous BCI) as well as provides a method for analyzing SSVEP signals in a way that might augment our understanding about the basic processing in the visual cortex.Evoked potentials are robust signals in the electroencephalogram (EEG) induced by sensory stimuli, and they have been used to study normal and abnormal function of the sensory cortex [1]. The most well-studied of these are Steady-State Visual Evoked Potentials (SSVEPs), which are neural oscillations in the visual cortex that are evoked from stimuli that temporally flicker in a narrow frequency band [2,3]. SSVEPs likely arise from a reorganization of spontaneous intrinsic brain oscillations in response to a stimulus [4]. Paradigms leveraging SSVEP responses have been used to investigate the organization of the visual system [5,6], identify biomarkers of disease and sensory function [7][8][9], and probe visual perception [10,11].The robustness of SSVEP has enabled its use as a control signal for brain computer interfaces (BCIs) that enable low-bandwith communication for individuals with catastrophic loss of motor functions, bypassing neuro-muscular pathways and establishing a communication link directly to the brain [12,13]. In a typical SSVEP BCI, a patient/subject is presented with a grid of squares on a computer monitor, where each square contains semantic information such as a letter, number, character, or action. Superimposed on these squares are visual flicker frequencies that uniquely "tag" each square, thus mapp...

Over the last several decades, spatial attention has been shown to influence the activity of neurons in visual cortex in various ways. These conflicting observations have inspired competing models to account for the influence of attention on perception and behavior. Here, we used electroencephalography (EEG) to assess steady-state visual evoked potentials (SSVEP) in human subjects and showed that highly focused spatial attention primarily enhanced neural responses to high-contrast stimuli (response gain), whereas distributed attention primarily enhanced responses to medium-contrast stimuli (contrast gain). Together, these data suggest that different patterns of neural modulation do not reflect fundamentally different neural mechanisms, but instead reflect changes in the spatial extent of attention.

The human brain can be represented as a graph in which neural units such as cells or small volumes of tissue are heterogeneously connected to one another through structural or functional links. Brain graphs are parsimonious representations of neural systems that have begun to offer fundamental insights into healthy human cognition, as well as its alteration in disease. A critical open question in network neuroscience lies in how neural units cluster into densely interconnected groups that can provide the coordinated activity that is characteristic of perception, action, and adaptive behaviors. Tools that have proven particularly useful for addressing this question are community detection approaches, which can identify communities or modules: groups of neural units that are densely interconnected with other units in their own group but sparsely interconnected with units in other groups. In this paper, we describe a common community detection algorithm known as modularity maximization, and we detail its applications to brain graphs constructed from neuroimaging data. We pay particular attention to important algorithmic considerations, especially in recent extensions of these techniques to graphs that evolve in time. After recounting a few fundamental insights that these techniques have provided into brain function, we highlight potential avenues of methodological advancements for future studies seeking to better characterize the patterns of coordinated activity in the brain that accompany human behavior. This tutorial provides a naive reader with an introduction to theoretical considerations pertinent to the generation of brain graphs, an understanding of modularity maximization for community detection, a resource of statistical measures that can be used to characterize community structure, and an appreciation of the usefulness of these approaches in uncovering behaviorally-relevant network dynamics in neuroimaging data.

The human brain is a complex dynamical system, and how cognition emerges from spatiotemporal patterns of regional brain activity remains an open question. As different regions dynamically interact to perform cognitive tasks, variable patterns of partial synchrony can be observed, forming chimera states. We propose that the spatial patterning of these states plays a fundamental role in the cognitive organization of the brain and present a cognitively informed, chimera-based framework to explore how large-scale brain architecture affects brain dynamics and function. Using personalized brain network models, we systematically study how regional brain stimulation produces different patterns of synchronization across predefined cognitive systems. We analyze these emergent patterns within our framework to understand the impact of subject-specific and region-specific structural variability on brain dynamics. Our results suggest a classification of cognitive systems into four groups with differing levels of subject and regional variability that reflect their different functional roles.

Social ties are crucial for humans. Disruption of ties through social exclusion has a marked effect on our thoughts and feelings; however, such effects can be tempered by broader social network resources. Here, we use fMRI data acquired from 80 male adolescents to investigate how social exclusion modulates functional connectivity within and across brain networks involved in social pain and understanding the mental states of others (i.e., mentalizing). Furthermore, using objectively logged friendship network data, we examine how individual variability in brain reactivity to social exclusion relates to the density of participants' friendship networks, an important aspect of social network structure. We find increased connectivity within a set of regions previously identified as a mentalizing system during exclusion relative to inclusion. These results are consistent across the regions of interest as well as a whole-brain analysis. Next, examining how social network characteristics are associated with task-based connectivity dynamics, we find that participants who showed greater changes in connectivity within the mentalizing system when socially excluded by peers had less dense friendship networks. This work provides insight to understand how distributed brain systems respond to social and emotional challenges and how such brain dynamics might vary based on broader social network characteristics.social exclusion | mentalizing | fMRI | functional connectivity | social networks

Studies of biological motion have identified specialized neural machinery for the perception of human actions. Our experiments examine behavioral and neural responses to novel, articulating and non-human 'biological motion'. We find that non-human actions are seen as animate, but do not convey body structure when viewed as point-lights. Non-human animations fail to engage the human STSp, and neural responses in pITG, ITS and FFA/FBA are reduced only for the point-light versions. Our results suggest that STSp is specialized for human motion and ventral temporal regions support general, dynamic shape perception. We also identify a region in ventral temporal cortex 'selective' for non-human animations, which we suggest processes novel, dynamic objects.

Background Although left ventricular hypertrophy (LVH) is frequently associated with impaired coronary vasodilator reserve, it is uncertain whether this leads to myocardial ischemia under physiological conditions. The goal of the present study was to determine whether swine with moderate LVH exhibit metabolic evidence of ischemia when myocardial oxygen requirements are increased. Methods and Results Myocardial metabolism was evaluated in an open-chest anesthetized preparation at baseline and during dobutamine infusion in 13 adolescent pigs with moderate LVH induced by supravalvular aortic banding and 12 age-matched control pigs. Transmural myocardial blood flow was quantified with radioactive microspheres; the ratio of phosphocreatine to ATP (PCr/ATP) in the anterior LV free wall was measured by 31 P–nuclear magnetic resonance; and anterior wall lactate release was quantified from the arterial-coronary venous difference in 14 C- or 13 C-labeled lactate. In a subset of 5 animals from each group, the metabolic fate of exogenous glucose was determined from the transmyocardial difference in 6- 14 C-glucose and its metabolites 14 C-lactate and 14 CO 2 . Coronary reserve, as assessed by the ratio of blood flow during adenosine infusion to baseline blood flow, was significantly lower in the LVH pigs compared with controls (3.5±0.4 versus 5.5±0.4 mL/g · min, P <.05); however, transmural myocardial blood flow was similar in both groups of pigs, both at baseline and with dobutamine stimulation, probably reflecting the higher coronary perfusion pressure in the LVH pigs. At baseline, PCr/ATP tended to be lower in the LVH pigs ( P =.09) but decreased similarly with dobutamine infusion in both groups. Isotopically measured anterior wall lactate release did not differ between the groups at baseline, nor did the increase in lactate release differ during dobutamine stimulation. The uptake of glucose, lactate, and free fatty acids did not differ between the groups in the basal state. However, during dobutamine stimulation, glucose uptake was greater in the LVH group (0.84±0.09 μmol/g · min versus 0.59±0.08 μmol/g · min, P <.05). In a subset of animals, 14 C-glucose was used to assess glucose oxidation. These data showed that the LVH animals had a greater rate of glucose oxidation (0.60±0.10 versus 0.28±0.08 μmol/g · min, P <.05) and a greater rate of glucose conversion to lactate (0.20±0.04 versus 0.09±0.02 μmol/g · min, P <.05) compared with the control pigs. Conclusions These results suggest that despite their reduced coronary vasodilator reserve and the absence of a greater rise in myocardial blood flow to compensate for a substantially higher LV double product, pigs with this model of moderate LVH do not exhibit a greater susceptibility to myocardial ischemia during dobutamine stress. However, LVH pigs exhibit significantly greater use of exogenous glucose during dobutamine stress, as evidenced by increases in both glucose oxidation and anaerobic glycolysis.