Population imaging of neural activity in awake behaving mice.

A longstanding goal in neuroscience has been to image membrane voltage across a population of individual neurons in an awake, behaving mammal. Here we describe a genetically encoded fluorescent voltage indicator, SomArchon, which exhibits millisecond response times and is compatible with optogenetic control, and which increases the sensitivity, signal-to-noise ratio, and number of neurons observable several-fold over previously published fully genetically encoded reagents1-8. Under conventional one-photon microscopy, SomArchon enables the routine population analysis of around 13 neurons at once, in multiple brain regions (cortex, hippocampus, and striatum) of head-fixed, awake, behaving mice. Using SomArchon, we detected both positive and negative responses of striatal neurons during movement, as previously reported by electrophysiology but not easily detected using modern calcium imaging techniques9-11, highlighting the power of voltage imaging to reveal bidirectional modulation. We also examined how spikes relate to the subthreshold theta oscillations of individual hippocampal neurons, with SomArchon showing that the spikes of individual neurons are more phase-locked to their own subthreshold theta oscillations than to local field potential theta oscillations. Thus, SomArchon reports both spikes and subthreshold voltage dynamics in awake, behaving mice.

Adversarial Learning for MRI Reconstruction and Classification of Cognitively Impaired Individuals.

Game theory-inspired deep learning using a generative adversarial network provides an environment to competitively interact and accomplish a goal. In the context of medical imaging, most work has focused on achieving single tasks such as improving image resolution, segmenting images, and correcting motion artifacts. We developed a dual-objective adversarial learning framework that simultaneously 1) reconstructs higher quality brain magnetic resonance images (MRIs) that 2) retain disease-specific imaging features critical for predicting progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD). We obtained 3-Tesla, T1-weighted brain MRIs of participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI, N=342) and the National Alzheimer's Coordinating Center (NACC, N = 190) datasets. We simulated MRIs with missing data by removing 50% of sagittal slices from the original scans (i.e., diced scans). The generator was trained to reconstruct brain MRIs using the diced scans as input. We introduced a classifier into the GAN architecture to discriminate between stable (i.e., sMCI) and progressive MCI (i.e., pMCI) based on the generated images to facilitate encoding of disease-related information during reconstruction. The framework was trained using ADNI data and externally validated on NACC data. In the NACC cohort, generated images had better image quality than the diced scans (Structural similarity (SSIM) index: 0.553 ± 0.116 versus 0.348 ± 0.108). Furthermore, a classifier utilizing the generated images distinguished pMCI from sMCI more accurately than with the diced scans (F1-score: 0.634 ± 0.019 versus 0.573 ± 0.028). Competitive deep learning has potential to facilitate disease-oriented image reconstruction in those at risk of developing Alzheimer's disease.

Large Language Models in Neurology Research and Future Practice.

Recent advancements in generative artificial intelligence, particularly using large language models (LLMs), are gaining increased public attention. We provide a perspective on the potential of LLMs to analyze enormous amounts of data from medical records and gain insights on specific topics in neurology. In addition, we explore use cases for LLMs, such as early diagnosis, supporting patient and caregivers, and acting as an assistant for clinicians. We point to the potential ethical and technical challenges raised by LLMs, such as concerns about privacy and data security, potential biases in the data for model training, and the need for careful validation of results. Researchers must consider these challenges and take steps to address them to ensure that their work is conducted in a safe and responsible manner. Despite these challenges, LLMs offer promising opportunities for improving care and treatment of various neurologic disorders.

Deep learning for risk-based stratification of cognitively impaired individuals.

Quantifying the risk of progression to Alzheimer's disease (AD) could help identify persons who could benefit from early interventions. We used data from the Alzheimer's Disease Neuroimaging Initiative (ADNI, n = 544, discovery cohort) and the National Alzheimer's Coordinating Center (NACC, n = 508, validation cohort), subdividing individuals with mild cognitive impairment (MCI) into risk groups based on cerebrospinal fluid amyloid-ß levels and identifying differential gray matter patterns. We then created models that fused neural networks with survival analysis, trained using non-parcellated T1-weighted brain MRIs from ADNI data, to predict the trajectories of MCI to AD conversion within the NACC cohort (integrated Brier score: 0.192 [discovery], and 0.108 [validation]). Using modern interpretability techniques, we verified that regions important for model prediction are classically associated with AD. We confirmed AD diagnosis labels using postmortem data. We conclude that our framework provides a strategy for risk-based stratification of individuals with MCI and for identifying regions key for disease prognosis.

Deep learning for subtyping the Alzheimer's disease spectrum.

In a recent article from Cell Reports Medicine, Kwak et al. generate novel insights about subtyping cognitively impaired individuals based on structural imaging. Quantifying heterogeneity in Alzheimer's disease via subtyping could help us harness new disease-modifying therapies and improve patient care by providing a more targeted approach.

Multimodal Microvascular Mapping for Head and Neck, Skull Base Research and Education: An Anatomical Donor Study.

Objective This study was aimed to develop a method combining computed tomography (CT) and fluorescence imaging, allowing identification of microvasculature in anatomical donors and facilitating translational research and education. Methods We investigated homogeneity and radiopacity of 30 different mixtures including radiopaque substances povidone-iodine (Betadine), barium sulfate (BaSO 4 ), and bismuth subsalicylate (Pepto-Bismol) varying in suspension and dilution with agar, latex, or gelatin. Three candidate mixtures were selected for testing the extent of perfusion in renal vasculature to establish methodology. From these candidate mixtures, two were selected for mixture with fluorescein and infusion into cadavers based on their ability to perfuse renal vasculature. The extent to which these two candidate mixtures combined with fluorescein were able to perfuse vasculature in a cadaver head was used to determine which mixture was superior. Results BaSO 4 and bismuth subsalicylate-based mixtures demonstrated superior opacity in vials. In terms of solidifying agents, gelatin-based mixtures demonstrated increased friability and lower melting points compared with the other agents, so only latex and agar-based mixtures were used moving forward past the vial stage. Combinations of BaSO 4 and latex and BaSO 4 and 3% agar were found to perfuse kidneys superiorly to the mixture containing bismuth subsalicylate. Finally, in cadaver heads, the mixture containing BaSO 4 , agar, and fluorescein was found to perfuse the smallest vasculature. Conclusion A final combination of BaSO 4 , 3% agar, and fluorescein proves to be a powerful and novel combination enabling CT imaging, fluorescence imaging, and dissection of vasculature. This paves the way for future translational research and education.

Testicular torsion: epidemiological risk factors for orchiectomy in pediatric and adult patients.

Testicular torsion is a known cause of morbidity in pediatric patients, but the burden in the adult population is poorly understood. We sought to determine the incidence of testicular torsion and risk factors for orchiectomy in a population encompassing all ages. A cohort analysis of 1625 males undergoing surgery for torsion was performed using the 2011 and 2012 Healthcare Cost and Utilization Project Nationwide Emergency Departments Sample. Patient and hospital factors were examined for association with orchiectomy vs. testicular salvage. The estimated yearly incidence of testicular torsion was 5.9 per 100,000 males ages 1-17 years and 1.3 per 100,000 males ≥18 years. Among those undergoing surgical intervention, orchiectomy was performed in 33.6%. The risk of orchiectomy was highest in patients 1-11 years of age and patients over 50 years of age (46.0% and 69.7% of patients, respectively). Orchiectomy was also associated with public insurance (Medicaid/Medicare) or self-pay as primary payer. While testicular torsion is less common in the adult population, the rate of orchiectomy is high. Those with disadvantaged payer status are also at increased risk for testicular loss.

Opposing roles for striatonigral and striatopallidal neurons in dorsolateral striatum in consolidating new instrumental actions.

Comparatively little is known about how new instrumental actions are encoded in the brain. Using whole-brain c-Fos mapping, we show that neural activity is increased in the anterior dorsolateral striatum (aDLS) of mice that successfully learn a new lever-press response to earn food rewards. Post-learning chemogenetic inhibition of aDLS disrupts consolidation of the new instrumental response. Similarly, post-learning infusion of the protein synthesis inhibitor anisomycin into the aDLS disrupts consolidation of the new response. Activity of D1 receptor-expressing medium spiny neurons (D1-MSNs) increases and D2-MSNs activity decreases in the aDLS during consolidation. Chemogenetic inhibition of D1-MSNs in aDLS disrupts the consolidation process whereas D2-MSN inhibition strengthens consolidation but blocks the expression of previously learned habit-like responses. These findings suggest that D1-MSNs in the aDLS encode new instrumental actions whereas D2-MSNs oppose this new learning and instead promote expression of habitual actions.

Precision Calcium Imaging of Dense Neural Populations via a Cell-Body-Targeted Calcium Indicator.

Methods for one-photon fluorescent imaging of calcium dynamics can capture the activity of hundreds of neurons across large fields of view at a low equipment complexity and cost. In contrast to two-photon methods, however, one-photon methods suffer from higher levels of crosstalk from neuropil, resulting in a decreased signal-to-noise ratio and artifactual correlations of neural activity. We address this problem by engineering cell-body-targeted variants of the fluorescent calcium indicators GCaMP6f and GCaMP7f. We screened fusions of GCaMP to natural, as well as artificial, peptides and identified fusions that localized GCaMP to within 50 µm of the cell body of neurons in mice and larval zebrafish. One-photon imaging of soma-targeted GCaMP in dense neural circuits reported fewer artifactual spikes from neuropil, an increased signal-to-noise ratio, and decreased artifactual correlation across neurons. Thus, soma-targeting of fluorescent calcium indicators facilitates usage of simple, powerful, one-photon methods for imaging neural calcium dynamics.

Unique contributions of parvalbumin and cholinergic interneurons in organizing striatal networks during movement.

Striatal parvalbumin (PV) and cholinergic interneurons (CHIs) are poised to play major roles in behavior by coordinating the networks of medium spiny cells that relay motor output. However, the small numbers and scattered distribution of these cells have hindered direct assessment of their contribution to activity in networks of medium spiny neurons (MSNs) during behavior. Here, we build on recent improvements in single-cell calcium imaging combined with optogenetics to test the capacity of PVs and CHIs to affect MSN activity and behavior in mice engaged in voluntary locomotion. We find that PVs and CHIs have unique effects on MSN activity and dissociable roles in supporting movement. PV cells facilitate movement by refining the activation of MSN networks responsible for movement execution. CHIs, in contrast, synchronize activity within MSN networks to signal the end of a movement bout. These results provide new insights into the striatal network activity that supports movement.