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1. An information network flow approach for measuring functional connectivity and predicting behavior

   https://doi.org/10.1002/brb3.1346  

   Kumar, Sreejan ; Yoo, Kwangsun ; Rosenberg, Monica D. ; Scheinost, Dustin ; Constable, R. Todd ; Zhang, Sheng ; Li, Chiang‐Shan R. ; Chun, Marvin M. ( July 2019 , Brain and Behavior)

   Connectome‐based predictive modeling (CPM) is a recently developed machine‐learning‐based framework to predict individual differences in behavior from functional brain connectivity (FC). In these models, FC was operationalized as Pearson's correlation between brain regions’ fMRI time courses. However, Pearson's correlation is limited since it only captures linear relationships. We developed a more generalized metric of FC based on information flow. This measure represents FC by abstracting the brain as a flow network of nodes that send bits of information to each other, where bits are quantified through an information theory statistic called transfer entropy.

   With a sample of individuals performing a sustained attention task and resting during functional magnetic resonance imaging (fMRI) (n = 25), we use the CPM framework to build machine‐learning models that predict attention from FC patterns measured with information flow. Models trained onn − 1 participants’ task‐based patterns were applied to an unseen individual's resting‐state pattern to predict task performance. For further validation, we applied our model to two independent datasets that included resting‐state fMRI data and a measure of attention (Attention Network Task performance [n = 41] and stop‐signal task performance [n = 72]).

   Our model significantly predicted individual differences in attention task performance across three different datasets.

   Information flow may be a useful complement to Pearson's correlation as a measure of FC because of its advantages for nonlinear analysis and network structure characterization.

    

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2. Attention differentially modulates multiunit activity in the lateral geniculate nucleus and V1 of macaque monkeys

   https://doi.org/10.1002/cne.25168  

   Shah, Shraddha ; Mancarella, Marc ; Hembrook‐Short, Jacqueline R. ; Mock, Vanessa L. ; Briggs, Farran ( May 2021 , Journal of Comparative Neurology)

   Attention promotes the selection of behaviorally relevant sensory signals from the barrage of sensory information available. Visual attention modulates the gain of neuronal activity in all visual brain areas examined, although magnitudes of gain modulations vary across areas. For example, attention gain magnitudes in the dorsal lateral geniculate nucleus (LGN) and primary visual cortex (V1) vary tremendously across fMRI measurements in humans and electrophysiological recordings in behaving monkeys. We sought to determine whether these discrepancies are due simply to differences in species or measurement, or more nuanced properties unique to each visual brain area. We also explored whether robust and consistent attention effects, comparable to those measured in humans with fMRI, are observable in the LGN or V1 of monkeys. We measured attentional modulation of multiunit activity in the LGN and V1 of macaque monkeys engaged in a contrast change detection task requiring shifts in covert visual spatial attention. Rigorous analyses of LGN and V1 multiunit activity revealed robust and consistent attentional facilitation throughout V1, with magnitudes comparable to those observed with fMRI. Interestingly, attentional modulation in the LGN was consistently negligible. These findings demonstrate that discrepancies in attention effects are not simply due to species or measurement differences. We also examined whether attention effects correlated with the feature selectivity of recorded multiunits. Distinct relationships suggest that attentional modulation of multiunit activity depends upon the unique structure and function of visual brain areas.

    

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3. Improvements in task performance after practice are associated with scale-free dynamics of brain activity

   https://doi.org/10.1162/netn_a_00319  

   Kardan, Omid ; Stier, Andrew J. ; Layden, Elliot A. ; Choe, Kyoung Whan ; Lyu, Muxuan ; Zhang, Xihan ; Beilock, Sian L. ; Rosenberg, Monica D. ; Berman, Marc G. ( October 2023 , Network Neuroscience)

   Although practicing a task generally benefits later performance on that same task, there are individual differences in practice effects. One avenue to model such differences comes from research showing that brain networks extract functional advantages from operating in the vicinity of criticality, a state in which brain network activity is more scale-free. We hypothesized that higher scale-free signal from fMRI data, measured with the Hurst exponent (H), indicates closer proximity to critical states. We tested whether individuals with higher H during repeated task performance would show greater practice effects. In Study 1, participants performed a dual-n-back task (DNB) twice during MRI (n = 56). In Study 2, we used two runs of n-back task (NBK) data from the Human Connectome Project sample (n = 599). In Study 3, participants performed a word completion task (CAST) across six runs (n = 44). In all three studies, multivariate analysis was used to test whether higher H was related to greater practice-related performance improvement. Supporting our hypothesis, we found patterns of higher H that reliably correlated with greater performance improvement across participants in all three studies. However, the predictive brain regions were distinct, suggesting that the specific spatial H↑ patterns are not task-general.

    

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4. Switching between External and Internal Attention in Hippocampal Networks

   https://doi.org/10.1523/JNEUROSCI.0029-23.2023  

   Poskanzer, Craig ; Aly, Mariam ( August 2023 , The Journal of Neuroscience)

   Everyday experience requires processing external signals from the world around us and internal information retrieved from memory. To do both, the brain must fluctuate between states that are optimized for external versus internal attention. Here, we focus on the hippocampus as a region that may serve at the interface between these forms of attention and ask how it switches between prioritizing sensory signals from the external world versus internal signals related to memories and thoughts. Pharmacological, computational, and animal studies have identified input from the cholinergic basal forebrain as important for biasing the hippocampus toward processing external information, whereas complementary research suggests the dorsal attention network (DAN) may aid in allocating attentional resources toward accessing internal information. We therefore tested the hypothesis that the basal forebrain and DAN drive the hippocampus toward external and internal attention, respectively. We used data from 29 human participants (17 female) who completed two attention tasks during fMRI. One task (memory-guided) required proportionally more internal attention, and proportionally less external attention, than the other (explicitly instructed). We discovered that background functional connectivity between the basal forebrain and hippocampus was stronger during the explicitly instructed versus memory-guided task. In contrast, DAN–hippocampus background connectivity was stronger during the memory-guided versus explicitly instructed task. Finally, the strength of DAN–hippocampus background connectivity was correlated with performance on the memory-guided but not explicitly instructed task. Together, these results provide evidence that the basal forebrain and DAN may modulate the hippocampus to switch between external and internal attention.

   SIGNIFICANCE STATEMENTHow does the brain balance the need to pay attention to internal thoughts and external sensations? We focused on the human hippocampus, a region that may serve at the interface between internal and external attention, and asked how its functional connectivity varies based on attentional states. The hippocampus was more strongly coupled with the cholinergic basal forebrain when attentional states were guided by the external world rather than retrieved memories. This pattern flipped for functional connectivity between the hippocampus and dorsal attention network, which was higher for attention tasks that were guided by memory rather than external cues. Together, these findings show that distinct networks in the brain may modulate the hippocampus to switch between external and internal attention.

    

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5. Optimal decoding of neural dynamics occurs at mesoscale spatial and temporal resolutions

   https://doi.org/10.3389/fncel.2024.1287123  

   Samiei, Toktam ; Zou, Zhuowen ; Imani, Mohsen ; Nozari, Erfan ( February 2024 , Frontiers in Cellular Neuroscience)

   Jonathan R. Whitlock (Ed.)

   Understanding the neural code has been one of the central aims of neuroscience research for decades. Spikes are commonly referred to as the units of information transfer, but multi-unit activity (MUA) recordings are routinely analyzed in aggregate forms such as binned spike counts, peri-stimulus time histograms, firing rates, or population codes. Various forms of averaging also occur in the brain, from the spatial averaging of spikes within dendritic trees to their temporal averaging through synaptic dynamics. However, how these forms of averaging are related to each other or to the spatial and temporal units of information representation within the neural code has remained poorly understood.

   In this work we developed NeuroPixelHD, a symbolic hyperdimensional model of MUA, and used it to decode the spatial location and identity of static images shown ton= 9 mice in the Allen Institute Visual Coding—NeuroPixels dataset from large-scale MUA recordings. We parametrically varied the spatial and temporal resolutions of the MUA data provided to the model, and compared its resulting decoding accuracy.

   For almost all subjects, we found 125ms temporal resolution to maximize decoding accuracy for both the spatial location of Gabor patches (81 classes for patches presented over a 9×9 grid) as well as the identity of natural images (118 classes corresponding to 118 images) across the whole brain. This optimal temporal resolution nevertheless varied greatly between different regions, followed a sensory-associate hierarchy, and was significantly modulated by the central frequency of theta-band oscillations across different regions. Spatially, the optimal resolution was at either of two mesoscale levels for almost all mice: the area level, where the spiking activity of all neurons within each brain area are combined, and the population level, where neuronal spikes within each area are combined across fast spiking (putatively inhibitory) and regular spiking (putatively excitatory) neurons, respectively. We also observed an expected interplay between optimal spatial and temporal resolutions, whereby increasing the amount of averaging across one dimension (space or time) decreases the amount of averaging that is optimal across the other dimension, and vice versa.

   Our findings corroborate existing empirical practices of spatiotemporal binning and averaging in MUA data analysis, and provide a rigorous computational framework for optimizing the level of such aggregations. Our findings can also synthesize these empirical practices with existing knowledge of the various sources of biological averaging in the brain into a new theory of neural information processing in which theunit of informationvaries dynamically based on neuronal signal and noise correlations across space and time.

    

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