Faculty Bibliography

OBJECTIVE:In people with chronic kidney disease (CKD), hyperphosphatemia is a risk factor for mortality. Though unproven, dietary phosphorus control is considered essential in CKD. Although dietary and serum phosphorus are correlated, phosphorus from plant foods rich in phytate is less bioavailable than from animal and processed foods. Yogurt, valued for its low phosphorus and high protein, may be detrimental in CKD due to animal protein content. Plant-based yogurts (PBYs) might offer similar benefits without the downsides of animal protein, but little is known about their phosphorus content. DESIGN AND METHODS/METHODS:Protein contents and phosphorus additives were gathered from nutrition labels of several PBYs, including almond, cashew, oat, coconut, and soy substrates. Phosphorus content was measured via emission spectrometry by Eurofins (Madison, WI), and the phosphorus-to-protein ratio (PPR) was calculated for each PBY. RESULTS:Phosphorus content was highest in Silk Soy Strawberry, Silk Almond Strawberry, and Siggi's Coconut Mixed Berries, while it was lowest in So Delicious Coconut Strawberry, Oatly Oat Strawberry, Forager Cashew Strawberry, and Kite Hill Almond Strawberry. Ingredient labels revealed that Silk Soy Strawberry, Silk Almond Strawberry, and Oatly Oat Strawberry contained phosphorus additives, and Siggi's Coconut Mixed Berries contained pea protein additives. Though from the same substrate class, So Delicious Coconut Strawberry and Siggi's Coconut Mixed Berries showed significant differences in phosphorus and protein contents. All seven PBYs had higher PPR ratios than dairy yogurts like Stonyfield Organic Oikos Strawberry, Chobani Nonfat Strawberry, and Yoplait Greek Strawberry. CONCLUSION/CONCLUSIONS:Low-PPR foods are important for CKD patients. Siggi's Coconut Mixed Berries had the lowest PPR, making it potentially the most desirable for CKD patients. However, there is high variability in PPR among PBYs with the same substrate; therefore, Delicious Coconut Strawberry had the highest PPR, highlighting the importance of product selection for patients with CKD.

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.

Recent advances in machine learning have led to revolutionary breakthroughs in computer games, image and natural language understanding, and scientific discovery. Foundation models and large-scale language models (LLMs) have recently achieved human-like intelligence thanks to BigData. With the help of self-supervised learning (SSL) and transfer learning, these models may potentially reshape the landscapes of neuroscience research and make a significant impact on the future. Here we present a mini-review on recent advances in foundation models and generative AI models as well as their applications in neuroscience, including natural language and speech, semantic memory, brain-machine interfaces (BMIs), and data augmentation. We argue that this paradigm-shift framework will open new avenues for many neuroscience research directions and discuss the accompanying challenges and opportunities.

Transcranial alternating current stimulation (ctACS) has the potential to be an appealing, non-invasive treatment option for psychiatric and neurological disorders. However, its potential has been limited by significant knowledge gaps in the details and mechanisms of how ctACS affects cerebellar output on single cell and population levels. We investigated this issue by making single-unit recordings of Purkinje cells (PC) and lateral cerebellar nuclear (Lat CN) cells in response to ctACS in anesthetized adult female Sprague-Dawley rats. The ctACS electrode was positioned directly on the skull above crus 1, either ipsilaterally just medial to the recording site or contralaterally. The return electrode was placed under the skin of the shoulder ipsilateral to the recorded cell. In response to ctACS at frequencies ranging from 0.5 to 80 Hz, PC and CN activity was modulated in a frequency-dependent manner. PC and CN entrainment strength increased with stimulation frequency. Moreover, a unimodal response was seen for most PCs across all frequencies, whereas most CN cells transitioned to bimodal patterns as stimulus frequency increased. The phase of the local minima CN cells, and its change with frequency, was consistent with CN cells being driven synaptically by PC activity. Furthermore, the nearer ctACS location to the recording site, the stronger the entrainment, suggesting that ctACS electrode placement could be used to target specific cerebellar output channels. In sum, the results show that transcranial stimulation of the cerebellar cortex can modulate cerebellar output, which has potential implications for its use in treating neurological and psychiatric disorders.

Identifying directed information flow or Granger causality between multivariate time series is important for a wide range of applications in science and engineering. However, traditional data-driven approaches are insufficient to assess Granger causality between multimodal data with distinct temporal resolution. Here we propose a new analysis approach to address this challenge and present quantitative characterizations and statistical assessment on frequency-dependent directed information flow ("generalized spectral causality"). We validate our approach with intensive computer simulations on bivariate and trivariate systems for various conditions.

Rapid advances in generative artificial intelligence (AI) and deep representation learning have revolutionized numerous engineering applications in signal processing, computer vision, speech recognition and translation, and natural language processing due to amazingly powerful representation power (e.g., [1,2]). Generative AI-empowered tools, such as ChatGPT and Sora, have fundamentally changed the landscape of human-computer communications research. One emerging application along this line is to link the brain to the computer (i.e., brain-computer interface or BCI) and to develop paradigm-shift brain-to-content technologies. This BCI system upgrade (i.e., BCI 2.0) is empowered by generative AI and deep learning ("new engine") and large amounts of data ("gas"). In this article, we will revisit the old song sung in a new tune, highlight some state-of-the-art progresses, and briefly discuss the future outlook.

Identifying directed spectral information flow between multivariate time series is important for many applications in finance, climate, geophysics and neuroscience. Spectral Granger causality (SGC) is a prediction-based measure characterizing directed information flow at specific oscillatory frequencies. However, traditional vector autoregressive (VAR) approaches are insufficient to assess SGC when time series have mixed frequencies (MF) or are coupled by nonlinearity. Here we propose a time-frequency canonical correlation analysis approach ("MF-TFCCA") to assess the strength and driving frequency of spectral information flow. We validate the approach with extensive computer simulations on MF time series under various interaction conditions and further assess statistical significance of the estimate with surrogate data. In various benchmark comparisons, MF-TFCCA consistently outperforms the traditional parametric MF-VAR model in both computational efficiency and detection accuracy, and recovers the dominant driving frequencies. We further apply MF-TFCCA to real-life finance, climate and neuroscience data. Our analysis framework provides an exploratory and computationally efficient nonparametric approach to quantify directed information flow between MF time series in the presence of complex and nonlinear interactions.

High-speed wide-field fluorescence microscopy has the potential to capture biological processes with exceptional spatiotemporal resolution. However, conventional cameras suffer from low signal-to-noise ratio at high frame rates, limiting their ability to detect faint fluorescent events. Here, we introduce an image sensor where each pixel has individually programmable sampling speed and phase, so that pixels can be arranged to simultaneously sample at high speed with a high signal-to-noise ratio. In high-speed voltage imaging experiments, our image sensor significantly increases the output signal-to-noise ratio compared to a low-noise scientific CMOS camera (~2-3 folds). This signal-to-noise ratio gain enables the detection of weak neuronal action potentials and subthreshold activities missed by the standard scientific CMOS cameras. Our camera with flexible pixel exposure configurations offers versatile sampling strategies to improve signal quality in various experimental conditions.

Gonadal hormones act throughout the brain ¹ , and neuropsychiatric disorders vary in symptom severity over the reproductive cycle, pregnancy, and perimenopause ^2-4 . Yet how hormones influence cognitive processes is unclear. Exogenous 17 β -estradiol modulates dopamine signaling in the nucleus accumbens core (NAcc) ^5,6 , which instantiates reward prediction errors (RPEs) for reinforcement learning ^7-16 . Here we show that endogenous 17 β -estradiol enhances RPEs and sensitivity to previous rewards by reducing dopamine reuptake proteins in the NAcc. Rats performed a task with different reward states; they adjusted how quickly they initiated trials across states, balancing effort against expected rewards. NAcc dopamine reflected RPEs that predicted and causally influenced initiation times. Elevated endogenous 17 β -estradiol increased sensitivity to reward states by enhancing dopaminergic RPEs in the NAcc. Proteomics revealed reduced dopamine transporter expression. Finally, knockdown of midbrain estrogen receptors suppressed reinforcement learning. 17 β -estradiol therefore controls RPEs via dopamine reuptake, mechanistically revealing how hormones influence neural dynamics for motivation and learning.

Models trained with self-supervised learning objectives have recently matched or surpassed models trained with traditional supervised object recognition in their ability to predict neural responses of object-selective neurons in the primate visual system. A self-supervised learning objective is arguably a more biologically plausible organizing principle, as the optimization does not require a large number of labeled examples. However, typical self-supervised objectives may result in network representations that are overly invariant to changes in the input. Here, we show that a representation with structured variability to input transformations is better aligned with known features of visual perception and neural computation. We introduce a novel framework for converting standard invariant SSL losses into "contrastive-equivariant" versions that encourage preservation of input transformations without supervised access to the transformation parameters. We demonstrate that our proposed method systematically increases the ability of models to predict responses in macaque inferior temporal cortex. Our results demonstrate the promise of incorporating known features of neural computation into task-optimization for building better models of visual cortex.