A Framework for Brain Atlases: Lessons from Seizure Dynamics

Revell, Andrew Y; Ab, Silva; Tc, Arnold; Jm, Stein; Rt, Shinohara; Ds, Bassett; Litt, Brian; Davis, Kenneth A.

doi:10.1101/2021.06.11.448063

Cited by 7 publications

(8 citation statements)

References 103 publications

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“…Sample sizes were determined by attempting to gather as many patients as possible with validated outcome scores ≥ 2 years, annotated seizure markings, and high angular resolution diffusion imaging (HARDI). No statistical methods were used to predetermine sample sizes, but our sample sizes are larger than those reported in previous publications using both iEEG and diffusion imaging data 15,16,46…”

Section: Methodsmentioning

confidence: 97%

“…The ROI volume was selected to equate to the volume of an average ROI in another commonly used neuroimaging atlas, the Automated Anatomical Labeling (AAL) atlas [59][60][61] . This soft-boundary 46 , contact-specific atlas was used to because we were studying individual signals from each contact and the tissue they recorded from. Structural networks were generated by computing the number of streamlines passing through each pair of ROIs.…”

Section: Structuralmentioning

confidence: 99%

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White Matter Signals Reflect Information Transmission Between Brain Regions During Seizures

Revell

Mahesh

Armstrong

et al. 2021

Preprint

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White matter supports critical brain functions such as learning and memory, modulates the distribution of action potentials, and acts as a relay of neural communication between different brain regions. Interestingly, neuronal cell bodies exist in deeper white matter tissue, neurotransmitter vesicles are released directly in white matter, and white matter blood-oxygenation level dependent (BOLD) signals are detectable across a range of different tasks -- all appearing to reflect intrinsic electrical signals in white matter. Yet, such signals within white matter have largely been ignored. Here, we elucidate the properties of white matter signals using intracranial EEG. We show that such signals capture the communication between brain regions and differentiate pathophysiologies of epilepsy. In direct contradiction to past assumptions about white matter inactivity, we show that white matter recordings can elucidate brain function and pathophysiology not apparent in gray matter. Broadly, white matter functional recordings acquired through implantable devices may provide a wealth of currently untapped knowledge about the neurobiology of disease.

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Section: Methodsmentioning

confidence: 97%

Section: Structuralmentioning

confidence: 99%

White Matter Signals Reflect Information Transmission Between Brain Regions During Seizures

Revell

Mahesh

Armstrong

et al. 2021

Preprint

Self Cite

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“…Broadly, contact localization followed methodology similar to Revell et al (2021) . All contact localizations were verified by a board-certified neuroradiologist (J.M.S.).…”

Section: Methodsmentioning

confidence: 99%

Neurophysiological Evidence for Cognitive Map Formation during Sequence Learning

Stiso

Lynn

Kahn

et al. 2022

eNeuro

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Humans deftly parse statistics from sequences. Some theories posit that humans learn these statistics by forming cognitive maps, or underlying representations of the latent space which links items in the sequence. Here, an item in the sequence is a node, and the probability of transitioning between two items is an edge. Sequences can then be generated from walks through the latent space, with different spaces giving rise to different sequence statistics. Individual or group differences in sequence learning can be modeled by changing the time scale over which estimates of transition probabilities are built, or in other words, by changing the amount of temporal discounting. Latent space models with temporal discounting bear a resemblance to models of navigation through Euclidean spaces. However, few explicit links have been made between predictions from Euclidean spatial navigation and neural activity during human sequence learning. Here, we use a combination of behavioral modeling and intracranial encephalography (iEEG) recordings to investigate how neural activity might support the formation of space-like cognitive maps through temporal discounting during sequence learning. Specifically, we acquire human reaction times from a sequential reaction time task, to which we fit a model that formulates the amount of temporal discounting as a single free parameter. From the parameter, we calculate each individual’s estimate of the latent space. We find that neural activity reflects these estimates mostly in the temporal lobe, including areas involved in spatial navigation. Similar to spatial navigation, we find that low-dimensional representations of neural activity allow for easy separation of important features, such as modules, in the latent space. Lastly, we take advantage of the high temporal resolution of iEEG data to determine the time scale on which latent spaces are learned. We find that learning typically happens within the first 500 trials, and is modulated by the underlying latent space and the amount of temporal discounting characteristic of each participant. Ultimately, this work provides important links between behavioral models of sequence learning and neural activity during the same behavior, and contextualizes these results within a broader framework of domain general cognitive maps.

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“…Exactly what is the right kind of atlas depends on the research question and the types of inferences researchers want to draw. An illustration of the effects of atlas choice and a framework for choosing between atlases is given by Revell et al (2021) . Selection of appropriate atlases and identification of relevant nodes for chemosensory connectomes requires further methodological study and validation.…”

Section: Challenges To Chemosensory Connectome Neuroimaging Studiesmentioning

confidence: 99%

Future Directions for Chemosensory Connectomes: Best Practices and Specific Challenges

Veldhuizen

Cecchetto

Fjældstad

et al. 2022

Front. Syst. Neurosci.

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Ecological chemosensory stimuli almost always evoke responses in more than one sensory system. Moreover, any sensory processing takes place along a hierarchy of brain regions. So far, the field of chemosensory neuroimaging is dominated by studies that examine the role of brain regions in isolation. However, to completely understand neural processing of chemosensation, we must also examine interactions between regions. In general, the use of connectivity methods has increased in the neuroimaging field, providing important insights to physical sensory processing, such as vision, audition, and touch. A similar trend has been observed in chemosensory neuroimaging, however, these established techniques have largely not been rigorously applied to imaging studies on the chemical senses, leaving network insights overlooked. In this article, we first highlight some recent work in chemosensory connectomics and we summarize different connectomics techniques. Then, we outline specific challenges for chemosensory connectome neuroimaging studies. Finally, we review best practices from the general connectomics and neuroimaging fields. We recommend future studies to develop or use the following methods we perceive as key to improve chemosensory connectomics: (1) optimized study designs, (2) reporting guidelines, (3) consensus on brain parcellations, (4) consortium research, and (5) data sharing.

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A Framework for Brain Atlases: Lessons from Seizure Dynamics

Cited by 7 publications

References 103 publications

White Matter Signals Reflect Information Transmission Between Brain Regions During Seizures

White Matter Signals Reflect Information Transmission Between Brain Regions During Seizures

Neurophysiological Evidence for Cognitive Map Formation during Sequence Learning

Future Directions for Chemosensory Connectomes: Best Practices and Specific Challenges

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