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Justin M. Fine

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

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YNICL Journal 2022 Journal Article

Discerning Seizure-Onset v. Propagation Zone: Pre-and-Post-Operative Resting-State fMRI Directionality and Boerwinkle Neuroplasticity Index

Varina L. Boerwinkle
Bethany L. Sussman
Sarah N. Wyckoff
Iliana Manjón
Justin M. Fine
P. David Adelson

The goal of this study was to determine resting state fMRI (rs-fMRI) effective connectivity (RSEC) capacity, agnostic of epileptogenic events, in distinguishing seizure onset zones (SOZ) from propagation zones (pZ). Consecutive patients (2.1-18.2 years old), with epilepsy and hypothalamic hamartoma, pre-operative rs-fMRI-directed surgery, post-operative imaging, and Engel class I outcomes were collected. Cross-spectral dynamic causal modelling (DCM) was used to estimate RSEC between the ablated rs-fMRI-SOZ to its region of highest connectivity outside the HH, defined as the propagation zone (pZ). Pre-operatively, RSEC from the SOZ and PZ was expected to be positive (excitatory), and pZ to SOZ negative (inhibitory), and post-operatively to be either diminished or non-existent. Sensitivity, accuracy, positive predictive value were determined for node-to-node connections. A Parametric Empirical Bayes (PEB) group analysis on pre-operative data was performed to identify group effects and effects of Engel class outcome and age. Pre-operative RSEC strength was also evaluated for correlation with percent seizure frequency improvement, sex, and region of interest size. Of the SOZ's RSEC, only 3.6% had no connection of significance to the pZ when patient models were individually reduced. Among remaining, 96% were in expected (excitatory signal found from SOZ → pZ and inhibitory signal found from pZ → SOZ) versus 3.6% reversed polarities. Both pre-operative polarity signals were equivalently as expected, with one false signal direction out of 26 each (3.7% total). Sensitivity of 95%, specificity 73%, accuracy of 88%, negative predictive value 88%, and positive predictive value of 88% in identifying and differentiating the SOZ and pZ. Groupwise PEB analysis confirmed SOZ → pZ EC was excitatory, and pZ → SOZ EC was inhibitory. Patients with better outcomes (Engel Ia vs. Ib) showed stronger inhibitory signal (pZ → SOZ). Age was negatively associated with absolute RSEC bidirectionally but had no relationship with Directionality SOZ identification performance. In an additional hierarchical PEB analysis identifying changes from pre-to-post surgery, SOZ → pZ modulation became less excitatory and pZ → SOZ modulation became less inhibitory. This study demonstrates the accuracy of Directionality to identify the origin of excitatory and inhibitory signal between the surgically confirmed SOZ and the region of hypothesized propagation zone in children with DRE due to a HH. Thus, this method validation study in a homogenous DRE population may have potential in narrowing the SOZ-candidates for epileptogenicity in other DRE populations and utility in other neurological disorders.

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YNIMG Journal 2021 Journal Article

Neural correlates of visual attention during risky decision evidence integration

John R. Purcell
Andrew Jahn
Justin M. Fine
Joshua W. Brown

Value-based decision-making is presumed to involve a dynamic integration process that supports assessing the potential outcomes of different choice options. Decision frameworks assume the value of a decision rests on both the desirability and risk surrounding an outcome. Previous work has highlighted neural representations of risk in the human brain, and their relation to decision choice. Key neural regions including the insula and anterior cingulate cortex (ACC) have been implicated in encoding the effects of risk on decision outcomes, including approach and avoidance. Yet, it remains unknown whether these regions are involved in the dynamic integration processes that precede and drive choice, and their relationship with ongoing attention. Here, we used concurrent fMRI and eye-tracking to discern neural activation related to visual attention preceding choice between sure-thing (i.e. safe) and risky gamble options. We found activation in both dorsal ACC (dACC) and posterior insula (PI) scaled in opposite directions with the difference in attention to risky rewards relative to risky losses. PI activation also differentiated foveations on both risky options (rewards and losses) relative to a sure-thing option. These findings point to ACC involvement in ongoing evaluation of risky but higher value options. The role of PI in risky outcomes points to a more general evaluative role in the decision-making that compares both safe and risky outcomes, irrespective of potential for gains or losses.

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YNIMG Journal 2017 Journal Article

Neural oscillations reflect latent learning states underlying dual-context sensorimotor adaptation

Justin M. Fine
Dalton Moore
Marco Santello

Recent studies have suggested that individuals can form multiple motor memories when simultaneously adapting to multiple, but oppositely-oriented perturbations. These findings predict that individuals detect the change in learning context, allowing the selective initialization and update of motor memories. However, previous electrophysiological studies of sensorimotor adaptation have not identified a neural mechanism supporting the detection of a context switch and adaptation to separate contexts. Here, we tested the hypothesis that such a mechanism is identifiable through neural oscillations measured through EEG. Human participants learned to manipulate an object in two opposite contexts (mass distribution). This task was designed based on previous work showing that people can adapt to both contexts. We found that sensorimotor α and β, and medial frontal θ frequency bands all exhibited different response patterns with respect to the error in each context. To determine whether any frequency's responses to error were distinctly related to a switch in context, we predicted single-trial EEG data from a computational learning model that can adapt to multiple contexts simultaneously based on a switching mechanism. This analysis revealed that only medial frontal θ was predicted by a component of the model state that adapts to errors based on a context switch. In contrast, α and β were predicted by a model state that was updated from performance errors independent of the context. These findings provide novel evidence showing that sensorimotor and medial frontal oscillations are predicted by different adaptation processes, and that changes in medial frontal activity may indicate the formation of motor memories by responding to changes in learning context.

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