Faculty Bibliography

Dialectical thinking represents a cognitive style emphasizing change, contradiction, and holism. Cross-cultural studies reveal a stark contrast of dialectical thinking between East Asian and Western cultures, highlighting East Asians' superior ability to embrace contradictions and foresee transformation, fostering psychological resilience through emotional complexity and tolerance for contradictions. Despite its importance, the neural basis of dialectical thinking remains underexplored. This review synthesizes current neuroscientific findings and introduces the dialectical-integration network (DIN) hypothesis, which identifies key brain regions such as the dorsal anterior cingulate cortex (dACC), medial prefrontal cortex (mPFC), dorsal lateral prefrontal cortex (DLPFC), nucleus accumbens, basal ganglia, and amygdala. These regions, along with networks like the default mode network (DMN) and frontoparietal network (FPN), facilitate holistic reasoning, conflict resolution, and sensory-emotional integration. The psychological benefits of dialectical thinking include enhanced cognitive flexibility, reduced emotional extremes, and improved conflict resolution. This review emphasizes the need for cross-cultural and neuroscientific research to explore the principle of change, a core aspect of dialectical cognition. By bridging cultural psychology and cognitive neuroscience, this work offers theoretical and methodological insights into culturally shaped cognitive styles, with practical applications in education, mental health, and intercultural communication. The DIN model provides a framework for future research on dynamic neural interactions supporting dialectical thinking.

Animals need daily intakes of three macronutrients: sugar, protein, and fat. Under fasted conditions, however, animals prioritize sugar as a primary source of energy. They must detect ingested sugar-specifically D-glucose-and quickly report its presence to the brain. Hypothalamic neurons that can respond to the caloric content in the gut regardless of the identity of macronutrient have been identified, but until now, the existence of neurons that can encode the specific macronutrients remained unknown. We found that a subset of corticotropin-releasing factor (CRF)-expressing neurons in the hypothalamic paraventricular nucleus (CRF^PVN) respond specifically to D-glucose in the gut, separately from other macronutrients or sugars. CRF^PVN neuronal activity is essential for fasted mice to develop a preference for D-glucose. These responses of CRF^PVN neurons to intestinal D-glucose require a specific spinal gut-brain pathway including the dorsal lateral parabrachial nuclei. These findings reveal the neural circuit that encodes the identity of D-glucose.

Obstructive sleep apnea (OSA) exerts pathogenic effects through a combination of sleep fragmentation (SF) and intermittent hypoxia (IH). The mechanisms through which sleep disruption impacts memory might arise by investigating disruption of specific sleep stages and, when such disruption occurs through OSA, by evaluating the individual contributions of SF and IH. Given region-specific EEG slow activity during non-REM sleep has been associated with overnight declarative, motor and spatial memory formation, we investigated the effects of disrupting slow wave sleep (SWS) on a virtual maze navigation task. Thirty three participants (24 male, 56 years old [range 28-68 years] with OSA (baseline AHI4%>20/hour) who were habitually well-treated and adherent to CPAP completed 3 timed trials on a 3D spatial maze before and after polysomnographically (PSG) recorded sleep. We restricted CPAP withdrawal to SWS through real-time monitoring of the PSG under three conditions: 1) stable-SWS on therapeutic CPAP, 2) SWS-CPAP withdrawal containing SF and IH, and 3) SWS-CPAP withdrawal with supplemental oxygen containing SF with reduced IH. SWS-specific CPAP withdrawal (with or without supplemental oxygen) did not significantly impact EEG slow oscillation or spatial navigational memory, despite effectively reducing %SWS and SWS bout length. Greater regional EEG slow oscillation (0.6-1Hz), but not delta (1-4Hz) activity, was associated with improvements in overnight memory during stable SWS in the CPAP condition. These observations suggest that slow oscillations may be important for overnight memory processing, and sleep disruptions of sufficient magnitude to reduce slow oscillations may be required to capture demonstrable change in spatial navigation performance.

We examine the neurobiology of intuition, a term often inconsistently defined in scientific literature. While researchers generally agree that intuition represents "an experienced-based process resulting in a spontaneous tendency toward a hunch or hypothesis," we establish a firmer neurobiological foundation by framing intuition evolutionarily as a pathfinding mechanism emerging from the brain's optimization of its relationship with the environment. Our review synthesizes empirical findings on intuition's neurobiological basis, including relevant brain networks and their relationship to cognitive states like insight. We propose that unsolved problems dynamically alter attractor landscapes, guiding future intuitions. We investigate "opportunistic assimilation" through nonlinear neurodynamics and identify hippocampal sharp wave ripples as potential neural correlates of intuition, citing their role in creativity, choice, action planning, and abstract thinking. Finally, we explore intuition through two complementary perspectives: the free energy principle, which models brains as minimizing uncertainty through predictive hierarchical coding, and metastable coordination dynamics, describing the brain's simultaneous tendencies toward regional cooperation and functional autonomy. Together, these principles provide a comprehensive neurodynamical account of intuition's neurophenomenology.

Down syndrome (DS), stemming from the triplication of human chromosome 21, results in intellectual disability, with early mid-life onset of Alzheimer's disease (AD) pathology. Early interventions to reduce cognitive impairments and neuropathology are lacking. One modality, maternal choline supplementation (MCS), has shown beneficial effects on behavior and gene expression in neurodevelopmental and neurodegenerative disorders, including trisomic mice. Loss of basal forebrain cholinergic neurons (BFCNs) and other DS/AD relevant hallmarks were observed in a well-established trisomic model (Ts65Dn, Ts). MCS attenuates these endophenotypes with beneficial behavioral effects in trisomic offspring. We postulate MCS ameliorates dysregulated cellular mechanisms within vulnerable BFCNs, with attenuation driven by novel gene expression. Here, choline acetyltransferase immunohistochemical labeling identified BFCNs in the medial septal/ventral diagonal band nuclei of the basal forebrain in Ts and normal disomic (2N) offspring at ~11 months of age from dams exposed to MCS or normal choline during the perinatal period. BFCNs (~500 per mouse) were microisolated and processed for RNA-sequencing. Bioinformatic assessment elucidated differentially expressed genes (DEGs) and pathway alterations in the context of genotype (Ts, 2N) and maternal diet (MCS, normal choline). MCS attenuated select dysregulated DEGs and relevant pathways in aged BFCNs. Trisomic MCS-responsive improvements included pathways such as cognitive impairment and nicotinamide adenine dinucleotide signaling, among others, indicative of increased behavioral and bioenergetic fitness. Although MCS does not eliminate the DS/AD phenotype, early choline delivery provides long-lasting benefits to aged trisomic BFCNs, indicating that MCS prolongs neuronal health in the context of DS/AD.

Cochlear implants are neuroprosthetic devices that restore hearing and speech comprehension to profoundly deaf humans, and represent an exemplar application of biomedical engineering and research to clinical conditions. However, the utility of these devices in many subjects is limited, largely due to lack of information about how neural circuits respond to implant stimulation. Recently we showed that deafened rats can use cochlear implants to recognize sounds, and that this training refined the responses of single neurons in the primary auditory cortex. Here we asked how local populations of cortical neurons represent acute implant stimuli, using electrode arrays we developed for cortical surface recordings for micro-electrocorticography (μECoG), a form of intracranial electroencephalography (iEEG). We found that there was a limited tonotopic organization across recording sites, relative to a clearer tonotopic spatial representation in normal-hearing rats. Single-trial iEEG responses to acoustic inputs were more reliable than responses to cochlear implant stimulation, although stimulus identity could be successfully decoded in both cases. However, the spatio-temporal response profiles to acoustic vs cochlear implant stimulation were substantially different. Decoders trained on acoustic responses showed essentially zero information transfer when tested on electrical stimulation responses in the same animals after deafening and cochlear implant stimulation. Thus while acute cochlear implant stimulation might activate the auditory cortex in a cochleotopic manner, the dynamics of network activity are quite distinct, suggesting that pitch percepts from acoustic and electrical stimulation are fundamentally different.

Electroencephalography (EEG) connectomes offer powerful tools for studying brain connectivity and advancing our understanding of brain function and dysfunction in both healthy and pathological conditions. Celebrating the 100th anniversary of EEG discovery, this Perspective explores the frontiers of EEG-based brain connectivity in basic and translational neuroscience research. We review new concepts, emerging analysis frameworks and significant advances in harnessing EEG connectomes. We suggest that leveraging machine learning approaches may offer promising paths to maximize the strengths of EEG connectomes. We also discuss how combined EEG connectome and neuromodulation provide a personalized and adaptive closed-loop paradigm to promote neuroplasticity and treat dysfunctional brains. We further address the limitations and challenges of the current methodology and touch on important issues regarding research rigour and clinical viability for translational impact.

Genetic variation within intron 3 of the CACNA1C calcium channel gene is associated with schizophrenia and bipolar disorder, but analysis of the causal variants and their effect is complicated by a nearby variable-number tandem repeat (VNTR). Here, we used 155 long-read genome assemblies from 78 diverse individuals to delineate the structure and population variability of the CACNA1C intron 3 VNTR. We categorized VNTR sequences into 7 Types of structural alleles using sequence differences among repeat units. Only 12 repeat units at the 5' end of the VNTR were shared across most Types, but several Types were related through a series of large and small duplications. The most diverged Types were rare and present only in individuals with African ancestry, but the multiallelic structural polymorphism Variable Region 2 was present across populations at different frequencies, consistent with expansion of the VNTR preceding the emergence of early hominins. VR2 was in complete linkage disequilibrium with fine-mapped schizophrenia variants (SNPs) from genome-wide association studies (GWAS). This risk haplotype was associated with decreased CACNA1C gene expression in brain tissues profiled by the GTEx project. Our work suggests that sequence variation within a human-specific VNTR affects gene expression, and provides a detailed characterization of new alleles at a flagship neuropsychiatric locus.

The mammalian forebrain is the seat of higher cognition with architectural parallels to modern machine learning systems. Specifically, the cortex resembles recurrent neural networks (RNNs) while the thalamus resembles feedforward neural networks (FNNs). How such architectural features endow the forebrain with its learning capacity, is unknown. Here we take inspiration from empirical thalamocortical discovery and develop a multiplicative coupling mechanism between RNN-FNN architectures that collectively enhance their computational strengths and learning. The multiplicative interaction imposes a Hebbian-weight amplification onto synaptic-neuronal coupling, enabling context-dependent gating and rapid switching. We demonstrate that multiplicative feedback-driven synaptic plasticity achieves 2-100 folds of speed improvement in supervised, reinforcement and unsupervised learning settings, boosting memory capacity, model robustness and generalization of RNNs. We further demonstrate the efficacy and biological plausibility of multiplicative gating in modeling multiregional circuits, including a prefrontal cortex-mediodorsal thalamus network for context-dependent decision making, a cortico-thalamic-cortical network for working memory and attention, and an entorhinal cortex-hippocampus network for visuospatial navigation and sequence replay. Taken together, our results demonstrate the profound insights into neuroscience-inspired computation that enable multi-plastic attractor dynamics and computation in recurrent neural circuits.