Interacting ventral temporal gradients of timescales and functional connectivity and their relationships to visual behavior.

Cortical gradients in endogenous and stimulus-evoked neurodynamic timescales, and long-range cortical interactions, provide organizational constraints to the brain and influence neural populations' roles in cognition. It is unclear how these functional gradients interrelate and which influence behavior. Here, intracranial recordings from 4,090 electrode contacts in 35 individuals map gradients of neural timescales and functional connectivity to assess their interactions along category-selective ventral temporal cortex. Endogenous and stimulus-evoked information processing timescales were not significantly correlated with one another suggesting that local neural timescales are context dependent and may arise through distinct neurophysiological mechanisms. Endogenous neural timescales correlated with functional connectivity even after removing the effects of shared anatomical gradients. Neural timescales and functional connectivity correlated with how strongly a population's activity predicted behavior in a simple visual task. These results suggest both interrelated and distinct neurophysiological processes give rise to different functional connectivity and neural timescale gradients, which together influence behavior.

Deep brain stimulation for parkinson's disease induces spontaneous cortical hypersynchrony in extended motor and cognitive networks.

The mechanism of action of deep brain stimulation (DBS) to the basal ganglia for Parkinson's disease remains unclear. Studies have shown that DBS decreases pathological beta hypersynchrony between the basal ganglia and motor cortex. However, little is known about DBS's effects on long range corticocortical synchronization. Here, we use machine learning combined with graph theory to compare resting-state cortical connectivity between the off and on-stimulation states and to healthy controls. We found that turning DBS on increased high beta and gamma band synchrony (26 to 50 Hz) in a cortical circuit spanning the motor, occipitoparietal, middle temporal, and prefrontal cortices. The synchrony in this network was greater in DBS on relative to both DBS off and controls, with no significant difference between DBS off and controls. Turning DBS on also increased network efficiency and strength and subnetwork modularity relative to both DBS off and controls in the beta and gamma band. Thus, unlike DBS's subcortical normalization of pathological basal ganglia activity, it introduces greater synchrony relative to healthy controls in cortical circuitry that includes both motor and non-motor systems. This increased high beta/gamma synchronization may reflect compensatory mechanisms related to DBS's clinical benefits, as well as undesirable non-motor side effects.

Gaze dynamics are sensitive to target orienting for working memory encoding in virtual reality.

Numerous studies have demonstrated that visuospatial attention is a requirement for successful working memory encoding. It is unknown, however, whether this established relationship manifests in consistent gaze dynamics as people orient their visuospatial attention toward an encoding target when searching for information in naturalistic environments. To test this hypothesis, participants' eye movements were recorded while they searched for and encoded objects in a virtual apartment (Experiment 1). We decomposed gaze into 61 features that capture gaze dynamics and a trained sliding window logistic regression model that has potential for use in real-time systems to predict when participants found target objects for working memory encoding. A model trained on group data successfully predicted when people oriented to a target for encoding for the trained task (Experiment 1) and for a novel task (Experiment 2), where a new set of participants found objects and encoded an associated nonword in a cluttered virtual kitchen. Six of these features were predictive of target orienting for encoding, even during the novel task, including decreased distances between subsequent fixation/saccade events, increased fixation probabilities, and slower saccade decelerations before encoding. This suggests that as people orient toward a target to encode new information at the end of search, they decrease task-irrelevant, exploratory sampling behaviors. This behavior was common across the two studies. Together, this research demonstrates how gaze dynamics can be used to capture target orienting for working memory encoding and has implications for real-world use in technology and special populations.

Multiple adjoining word- and face-selective regions in ventral temporal cortex exhibit distinct dynamics.

The map of category-selectivity in human ventral temporal cortex (VTC) provides organizational constraints to models of object recognition. One important principle is lateral-medial response biases to stimuli that are typically viewed in the center or periphery of the visual field. However, little is known about the relative temporal dynamics and location of regions that respond preferentially to stimulus classes that are centrally viewed, like the face- and word-processing networks. Here, word- and face-selective regions within VTC were mapped using intracranial recordings from 36 patients. Partially overlapping, but also anatomically dissociable patches of face- and word-selectivity were found in VTC. In addition to canonical word-selective regions along the left posterior occipitotemporal sulcus, selectivity was also located medial and anterior to face-selective regions on the fusiform gyrus at the group level and within individual male and female subjects. These regions were replicated using 7 Tesla fMRI in healthy subjects. Left hemisphere word-selective regions preceded right hemisphere responses by 125 ms, potentially reflecting the left hemisphere bias for language; with no hemispheric difference in face-selective response latency. Word-selective regions along the posterior fusiform responded first, then spread medially and laterally, then anteriorally. Face-selective responses were first seen in posterior fusiform regions bilaterally, then proceeded anteriorally from there. For both words and faces, the relative delay between regions was longer than would be predicted by purely feedforward models of visual processing. The distinct time-courses of responses across these regions, and between hemispheres, suggest a complex and dynamic functional circuit supports face and word perception.SIGNIFICANCE STATEMENT:Representations of visual objects in the human brain have been shown to be organized by several principles, including whether those objects tend to be viewed centrally or peripherally in the visual field. However, it remains unclear how regions that process objects that are viewed centrally, like words and faces, are organized relative to one another. Here, invasive and non-invasive neuroimaging suggests there is a mosaic of regions in ventral temporal cortex that respond selectively to either words or faces. These regions display differences in the strength and timing of their responses, both within and between brain hemispheres, suggesting they play different roles in perception. These results illuminate extended, bilateral, and dynamic brain pathways that support face perception and reading.

Continuous decoding of cognitive load from electroencephalography reveals task-general and task-specific correlates.

OBJECTIVE: Algorithms to detect changes in cognitive load using non-invasive biosensors (e.g. electroencephalography (EEG)) have the potential to improve human-computer interactions by adapting systems to an individual's current information processing capacity, which may enhance performance and mitigate costly errors. However, for algorithms to provide maximal utility, they must be able to detect load across a variety of tasks and contexts. The current study aimed to build models that capture task-general EEG correlates of cognitive load, which would allow for load detection across variable task contexts. APPROACH: Sliding-window support vector machines (SVM) were trained to predict periods of high versus low cognitive load across three cognitively and perceptually distinct tasks: n-back, mental arithmetic, and multi-object tracking. To determine how well these SVMs could generalize to novel tasks, they were trained on data from two of the three tasks and evaluated on the held-out task. Additionally, to better understand task-general and task-specific correlates of cognitive load, a set of models were trained on subsets of EEG frequency features. MAIN RESULTS: Models achieved reliable performance in classifying periods of high versus low cognitive load both within and across tasks, demonstrating their generalizability. Furthermore, continuous model outputs correlated with subtle differences in self-reported mental effort and they captured predicted changes in load within individual trials of each task. Additionally, alpha or beta frequency features achieved reliable within- and cross-task performance, suggesting that activity in these frequency bands capture task-general signatures of cognitive load. In contrast, delta and theta frequency features performed considerably worse than the full cross-task models, suggesting that delta and theta activity may be reflective of task-specific differences across cognitive load conditions. SIGNIFICANCE: EEG data contains task-general signatures of cognitive load. Sliding-window SVMs can capture these signatures and continuously detect load across multiple task contexts.

Quantitatively validating the efficacy of artifact suppression techniques to study the cortical consequences of deep brain stimulation with magnetoencephalography.

Deep brain stimulation (DBS) is an established and effective treatment for several movement disorders and is being developed to treat a host of neuropsychiatric disorders including epilepsy, chronic pain, obsessive compulsive disorder, and depression. However, the neural mechanisms through which DBS produces therapeutic benefits, and in some cases unwanted side effects, in these disorders are only partially understood. Non-invasive neuroimaging techniques that can assess the neural effects of active stimulation are important for advancing our understanding of the neural basis of DBS therapy. Magnetoencephalography (MEG) is a safe, passive imaging modality with relatively high spatiotemporal resolution, which makes it a potentially powerful method for examining the cortical network effects of DBS. However, the degree to which magnetic artifacts produced by stimulation and the associated hardware can be suppressed from MEG data, and the comparability between signals measured during DBS-on and DBS-off conditions, have not been fully quantified. The present study used machine learning methods in conjunction with a visual perception task, which should be relatively unaffected by DBS, to quantify how well neural data can be salvaged from artifact contamination introduced by DBS and how comparable DBS-on and DBS-off data are after artifact removal. Machine learning also allowed us to determine whether the spatiotemporal pattern of neural activity recorded during stimulation are comparable to those recorded when stimulation is off. The spatiotemporal patterns of visually evoked neural fields could be accurately classified in all 8 patients with DBS implants during both DBS-on and DBS-off conditions and performed comparably across those two conditions. Further, the classification accuracy for classifiers trained on the spatiotemporal patterns evoked during DBS-on trials and applied to DBS-off trials, and vice versa, were similar to that of the classifiers trained and tested on either trial type, demonstrating the comparability of these patterns across conditions. Together, these results demonstrate the ability of MEG preprocessing techniques, like temporal signal space separation, to salvage neural data from recordings contaminated with DBS artifacts and validate MEG as a powerful tool to study the cortical consequences of DBS.

Very high density EEG elucidates spatiotemporal aspects of early visual processing.

Standard human EEG systems based on spatial Nyquist estimates suggest that 20-30 mm electrode spacing suffices to capture neural signals on the scalp, but recent studies posit that increasing sensor density can provide higher resolution neural information. Here, we compared "super-Nyquist" density EEG ("SND") with Nyquist density ("ND") arrays for assessing the spatiotemporal aspects of early visual processing. EEG was measured from 128 electrodes arranged over occipitotemporal brain regions (14 mm spacing) while participants viewed flickering checkerboard stimuli. Analyses compared SND with ND-equivalent subsets of the same electrodes. Frequency-tagged stimuli were classified more accurately with SND than ND arrays in both the time and the frequency domains. Representational similarity analysis revealed that a computational model of V1 correlated more highly with the SND than the ND array. Overall, SND EEG captured more neural information from visual cortex, arguing for increased development of this approach in basic and translational neuroscience.