Faculty Bibliography

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.

Immediate escape and gradual habituation are both crucial for animal survival in response to repeated threat exposures. In this issue of Neuron, Liu et al. identified key neural circuits supporting each of these two responsive patterns to repeated visual threats.¹.

Widespread hyperalgesia, characterized by pain sensitivity beyond the primary pain site, is a common yet under-characterized feature across chronic pain conditions, including chronic pancreatitis (CP). In this exploratory study, we identified a candidate neural biosignature of widespread hyperalgesia using high-density electroencephalography (EEG) in patients with chronic low back pain (cLBP). Specifically, stimulus-evoked delta, theta, and alpha oscillatory activity in the bilateral medial orbitofrontal cortex (mOFC) differentiated cLBP patients with widespread hyperalgesia from healthy controls. To examine cross-condition generalizability and advance predictive biomarker development for CP, we applied this mOFC-derived EEG biosignature to an independent cohort of patients with CP. The biosignature distinguished CP patients with widespread hyperalgesia and predicted individual treatment responses to peripherally targeted endoscopic therapy. These preliminary findings provide early support for a shared cortical signature of central sensitization across pain conditions and offer translational potential for developing EEG-based predictive tools for treatment response in CP.