Faculty Bibliography

Chronic pain alters cortical and subcortical plasticity, causing enhanced sensory and affective responses to peripheral nociceptive inputs. Previous studies have shown that ketamine had the potential to inhibit abnormally amplified affective responses of single neurons by suppressing hyperactivity in the anterior cingulate cortex (ACC). However, the mechanism of this enduring effect has yet to be understood at the network level. In this study, we recorded local field potentials from the ACC of freely moving rats. Animals were injected with complete Freund's adjuvant (CFA) to induce persistent inflammatory pain. Mechanical stimulations were administered to the hind paw before and after CFA administration. We found a significant increase in the high-gamma band (60-100 Hz) power in response to evoked pain after CFA treatment. Ketamine, however, reduced the high-gamma band power in response to evoked pain in CFA-treated rats. In addition, ketamine had a sustained effect on the high-gamma band power lasting up to five days after a single dose administration. These results demonstrate that ketamine has the potential to alter maladaptive neural responses in the ACC induced by chronic pain.

Head direction (HD) cells form a fundamental component in the brain's spatial navigation system and are intricately linked to spatial memory and cognition. Although HD cells have been shown to act as an internal neuronal compass in various cortical and subcortical regions, the neural substrate of HD cells is incompletely understood. It is reported that HD cells in the somatosensory cortex comprise regular-spiking (RS, putative excitatory) and fast-spiking (FS, putative inhibitory) neurons. Surprisingly, somatosensory FS HD cells fire in bursts and display much sharper head-directionality than RS HD cells. These FS HD cells are nonconjunctive, rarely theta rhythmic, sparsely connected and enriched in layer 5. Moreover, sharply tuned FS HD cells, in contrast with RS HD cells, maintain stable tuning in darkness; FS HD cells' coexistence with RS HD cells and angular head velocity (AHV) cells in a layer-specific fashion through the somatosensory cortex presents a previously unreported configuration of spatial representation in the neocortex. Together, these findings challenge the notion that FS interneurons are weakly tuned to sensory stimuli, and offer a local circuit organization relevant to the generation and transmission of HD signaling in the brain.

Prefrontal cortical neurons play essential roles in performing rule-dependent tasks and working memory-based decision making. Motivated by PFC recordings of task-performing mice, we developed an excitatory"“inhibitory spiking recurrent neural network (SRNN) to perform a rule-dependent two-alternative forced choice (2AFC) task. We imposed several important biological constraints onto the SRNN and adapted spike frequency adaptation (SFA) and SuperSpike gradient methods to train the SRNN efficiently. The trained SRNN produced emergent rule-specific tunings in single-unit representations, showing rule-dependent population dynamics that resembled experimentally observed data. Under various test conditions, we manipulated the SRNN parameters or configuration in computer simulations, and we investigated the impacts of rule-coding error, delay duration, recurrent weight connectivity and sparsity, and excitation/inhibition (E/I) balance on both task performance and neural representations. Overall, our modeling study provides a computational framework to understand neuronal representations at a fine timescale during working memory and cognitive control and provides new experimentally testable hypotheses in future experiments.

Objective/UNASSIGNED:Sudden unexpected death in epilepsy (SUDEP) is the leading cause of epilepsy-related mortality. Although lots of effort has been made in identifying clinical risk factors for SUDEP in the literature, there are few validated methods to predict individual SUDEP risk. Prolonged postictal EEG suppression (PGES) is a potential SUDEP biomarker, but its occurrence is infrequent and requires epilepsy monitoring unit admission. We use machine learning methods to examine SUDEP risk using interictal EEG and ECG recordings from SUDEP cases and matched living epilepsy controls. Methods/UNASSIGNED:This multicenter, retrospective, cohort study examined interictal EEG and ECG recordings from 30 SUDEP cases and 58 age-matched living epilepsy patient controls. We trained machine learning models with interictal EEG and ECG features to predict the retrospective SUDEP risk for each patient. We assessed cross-validated classification accuracy and the area under the receiver operating characteristic (AUC) curve. Results/UNASSIGNED:The logistic regression (LR) classifier produced the overall best performance, outperforming the support vector machine (SVM), random forest (RF), and convolutional neural network (CNN). Among the 30 patients with SUDEP [14 females; mean age (SD), 31 (8.47) years] and 58 living epilepsy controls [26 females (43%); mean age (SD) 31 (8.5) years], the LR model achieved the median AUC of 0.77 [interquartile range (IQR), 0.73-0.80] in five-fold cross-validation using interictal alpha and low gamma power ratio of the EEG and heart rate variability (HRV) features extracted from the ECG. The LR model achieved the mean AUC of 0.79 in leave-one-center-out prediction. Conclusions/UNASSIGNED:Our results support that machine learning-driven models may quantify SUDEP risk for epilepsy patients, future refinements in our model may help predict individualized SUDEP risk and help clinicians correlate predictive scores with the clinical data. Low-cost and noninvasive interictal biomarkers of SUDEP risk may help clinicians to identify high-risk patients and initiate preventive strategies.

Background: The prognosis for patients (pts) with relapsed T-ALL and T-LL is dismal. The primary goal of T-ALL/T-LL treatment is to prevent relapse. In the phase 3 Children's Oncology Group (COG) clinical trial AALL1231 (NCT02112916), children, adolescents and young adults (age 1-30 years) with T-ALL and T-LL were treated with a modified augmented BFM (aBFM) backbone that used dexamethasone as the only corticosteroid and included two (rather than one) doses of pegaspargase during induction and delayed intensification. Pts were stratified as standard (SR), intermediate (IR), or very high risk (VHR), primarily based on disease response: morphology, minimal residual disease (MRD) performed by multiparameter flow cytometry at a central reference laboratory) at end of induction and consolidation (T-ALL), and radiographic response for T-LL. Pts were randomized 1:1 to receive/not receive bortezomib during induction and delayed intensification (1.3mg/m ² x 4 doses per block). VHR T-ALL pts were defined as having day 29 M3 marrow (>25% blasts) or end of consolidation (EOC) MRD >0.1%. 10-15% of T-ALL pts were predicted to be VHR based on COG AALL0434. Pts with induction failure (M3 marrow by morphology) or EOC MRD >0.1% were expected to have 4-yr event-free survival (EFS) of ~66+/-16%. Following consolidation, VHR pts received 3 BFM-based intensification blocks in lieu of interim maintenance (IM). Detectable MRD following the intensification blocks was considered an event and these pts were removed from protocol therapy. VHR ALL pts who had undetectable MRD continued protocol therapy, received delayed intensification, an IM phase with Capizzi escalating methotrexate plus pegaspargase, and maintenance. A secondary aim of AALL1231 was to compare survival in VHR T-ALL pts with EOC MRD >= 0.1% but undetectable MRD after intensification of chemotherapy with those who continued to have detectable MRD and were eligible for other treatment strategies, including hematopoietic stem cell transplant (HSCT). This study also analyzed outcomes for pts with M3 marrow at the end of induction.
Result(s): AALL1231 accrued 847 pts (824 eligible and evaluable) of 1400 anticipated from 2014 until early closure. The 3-year EFS for the bortezomib randomization for the SR and IR groups has been reported previously (Teachey, et. al ASH 2020). Because only 2 of 209 T-LL pts were VHR; this report focuses on the outcomes of the 5.2% (32/615) of T-ALL pts who were VHR. In total, 25 VHR T-ALL pts were EOC MRD >0.1%, and 18 of these had MRD sent at the end of HR intensification. Of the 8 pts who became MRD undetectable and continued protocol therapy, only 2 survived (3-year overall survival [OS] 25+15.3%). In contrast, 10 pts who had detectable MRD were taken off protocol and underwent HSCT. Of these 10, only one relapsed (3-year OS 90+12.7%). The 3-year OS for the 10 pts who were M3 at Day 29 was 60.0+/-17.0%. As there were not enough pts to assess the impact of EOC MRD on pts who were M3 at Day 29, we assessed the impact of EOC MRD on outcomes in M2 (5-25% blasts at Day 29; n = 24) and M3 pts, which defines induction failure in other cooperative groups. M2+M3 T-ALL who were EOC MRD <0.1% (n = 15) had 3-year OS of 86.7+/-10.0% vs 45.5+/-15.0% for those with EOC MRD >0.1% (n = 12) pts.
Conclusion(s): T-ALL pts treated on AALL1231 who are EOC MRD >=0.1% with undetectable MRD after 3 BFM-based intensification blocks had a very poor outcome when treated with standard cytotoxic chemotherapy. In contrast, while patient numbers are small, those pts that remained MRD-positive after 3 intensification blocks and underwent HSCT had an excellent outcome. These data not only impact the recommended treatment for T-ALL pts who are induction and consolidation failures, but also support the importance of the graft-versus-leukemia (GVL) effect in refractory T-ALL. Disclosures: Hayashi: Magenta Therapeutics: Consultancy. August: Jazz: Membership on an entity's Board of Directors or advisory committees. Hermiston: Sobi: Consultancy; Novartis: Consultancy. Bollard: Cabeletta Bio: Membership on an entity's Board of Directors or advisory committees; Catamaran Bio and Mana Therapeutics: Other: member and cofounder; SOBI: Other: DSMB. Loh: MediSix therapeutics: Membership on an entity's Board of Directors or advisory committees. Raetz: Pfizer: Research Funding; Celgene: Other: DSMB member. Teachey: BEAM Therapeutics: Consultancy, Research Funding; NeoImmune Tech: Research Funding; Sobi: Consultancy; Janssen: Consultancy.
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Spatiotemporal patterns of large-scale spiking and field potentials of the rodent hippocampus encode spatial representations during maze runs, immobility, and sleep. Here, we show that multisite hippocampal field potential amplitude at ultra-high-frequency band (FPA_uhf), a generalized form of multiunit activity, provides not only a fast and reliable reconstruction of the rodent's position when awake, but also a readout of replay content during sharp-wave ripples. This FPA_uhf feature may serve as a robust real-time decoding strategy from large-scale recordings in closed-loop experiments. Furthermore, we develop unsupervised learning approaches to extract low-dimensional spatiotemporal FPA_uhf features during run and ripple periods and to infer latent dynamical structures from lower-rank FPA_uhf features. We also develop an optical flow-based method to identify propagating spatiotemporal LFP patterns from multisite array recordings, which can be used as a decoding application. Finally, we develop a prospective decoding strategy to predict an animal's future decision in goal-directed navigation.

The prefrontal cortex (PFC) regulates a wide range of sensory experiences. Chronic pain is known to impair normal neural response, leading to enhanced aversion. However, it remains unknown how nociceptive responses in the cortex are processed at the population level and whether such processes are disrupted by chronic pain. Using in vivo endoscopic calcium imaging, we identify increased population activity in response to noxious stimuli and stable patterns of functional connectivity among neurons in the prelimbic (PL) PFC from freely behaving rats. Inflammatory pain disrupts functional connectivity of PFC neurons and reduces the overall nociceptive response. Interestingly, ketamine, a well-known neuromodulator, restores the functional connectivity among PL-PFC neurons in the inflammatory pain model to produce anti-aversive effects. These results suggest a dynamic resource allocation mechanism in the prefrontal representations of pain and indicate that population activity in the PFC critically regulates pain and serves as an important therapeutic target.

Pain is a dynamic, complex and multidimensional experience. The identification of pain from brain activity as neural readout may effectively provide a neural code for pain, and further provide useful information for pain diagnosis and treatment. Advances in neuroimaging and large-scale electrophysiology have enabled us to examine neural activity with improved spatial and temporal resolution, providing opportunities to decode pain in humans and freely behaving animals. This topical review provides a systematical overview of state-of-the-art methods for decoding pain from brain signals, with special emphasis on electrophysiological and neuroimaging modalities. We show how pain decoding analyses can help pain diagnosis and discovery of neurobiomarkers for chronic pain. Finally, we discuss the challenges in the research field and point to several important future research directions.

Recurrent neural networks (RNNs) have been widely used to model sequential neural dynamics ("neural sequences") of cortical circuits in cognitive and motor tasks. Efforts to incorporate biological constraints and Dale's principle will help elucidate the neural representations and mechanisms of underlying circuits. We trained an excitatory-inhibitory RNN to learn neural sequences in a supervised manner and studied the representations and dynamic attractors of the trained network. The trained RNN was robust to trigger the sequence in response to various input signals and interpolated a time-warped input for sequence representation. Interestingly, a learned sequence can repeat periodically when the RNN evolved beyond the duration of a single sequence. The eigenspectrum of the learned recurrent connectivity matrix with growing or damping modes, together with the RNN's nonlinearity, were adequate to generate a limit cycle attractor. We further examined the stability of dynamic attractors while training the RNN to learn two sequences. Together, our results provide a general framework for understanding neural sequence representation in the excitatory-inhibitory RNN.

The prefrontal cortex (PFC) plays a prominent role in performing flexible cognitive functions and working memory, yet the underlying computational principle remains poorly understood. Here, we trained a rate-based recurrent neural network (RNN) to explore how the context rules are encoded, maintained across seconds-long mnemonic delay, and subsequently used in a context-dependent decision-making task. The trained networks replicated key experimentally observed features in the PFC of rodent and monkey experiments, such as mixed selectivity, neuronal sequential activity, and rotation dynamics. To uncover the high-dimensional neural dynamical system, we further proposed a geometric framework to quantify and visualize population coding and sensory integration in a temporally defined manner. We employed dynamic epoch-wise principal component analysis (PCA) to define multiple task-specific subspaces and task-related axes, and computed the angles between task-related axes and these subspaces. In low-dimensional neural representations, the trained RNN first encoded the context cues in a cue-specific subspace, and then maintained the cue information with a stable low-activity state persisting during the delay epoch, and further formed line attractors for sensor integration through low-dimensional neural trajectories to guide decision-making. We demonstrated via intensive computer simulations that the geometric manifolds encoding the context information were robust to varying degrees of weight perturbation in both space and time. Overall, our analysis framework provides clear geometric interpretations and quantification of information coding, maintenance, and integration, yielding new insight into the computational mechanisms of context-dependent computation.